dfv-model-building.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "16a5867c",
   "metadata": {},
   "source": [
    "# 0. Self Introduction  \n",
    "Deep Facial Verification using Siamese Neural Network\n",
    "------------------------------------------------------------------------------------------------------------------------\n",
    "#### ***Final project for partial fulfilment of course Data Science at SCDE of Jawaharlal Nehru Technological University Hyderabad.***\n",
    "________________________________________________________________________________________________________________________\n",
    "\n",
    "\n",
    "Project by: ***Narra SuryaKoushik Reddy***\n",
    "\n",
    "E-Mail:  ***narrasuryakoushikreddy@gmail.com***\n",
    "\n",
    "Project Report: Yet to compleate\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26ab6a3a",
   "metadata": {},
   "source": [
    "# 1. Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9125a797",
   "metadata": {},
   "source": [
    "## 1.1 Installing Dependencies'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56d65327",
   "metadata": {},
   "source": [
    "only install this dependencies if you don't have them ***(I have already installed them in the beginning)***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4a46c34e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install tensorflow "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "80f1fede",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install tensorflow-gpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9e3ef244",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install opencv-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cb48b081",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6fed4b2",
   "metadata": {},
   "source": [
    "## 1.2 Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f939e144",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-26 09:49:02.203549: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2023-05-26 09:49:02.205020: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-05-26 09:49:02.233988: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-05-26 09:49:02.234521: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-05-26 09:49:02.745136: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "# import standard dependencies \n",
    "import cv2\n",
    "import os\n",
    "import random #optional but i imported it just in case\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# importhing tensorflow dependencies \n",
    "# tensorflow functional API is lot more flexible for hard DL models like SNN\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Layer, Conv2D, Dense, MaxPooling2D, Input, Flatten \n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "139ad710",
   "metadata": {},
   "source": [
    "## 1.3 Set GPU Growth"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d1361b2",
   "metadata": {},
   "source": [
    "**Using my GPU to avoid Out Of Memory (OOM) errors by setting GPU Memory Consumption Growth**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3caee061",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Koushik Reddy\\AppData\\Local\\Temp\\ipykernel_2668\\138082233.py:1: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.config.list_physical_devices('GPU')` instead.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.test.is_gpu_available() # just making sure Tensorflow is detecting my GPU if not we need to setup manually(pip install tensorflow-gpu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "54d986d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-26 09:49:08.980592: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-05-26 09:49:08.981369: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1956] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n"
     ]
    }
   ],
   "source": [
    "# setting up GPU\n",
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "for gpu in gpus:\n",
    "    tf.config.experimental.set_memory_growth(gpu, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c411ff3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d7038b60",
   "metadata": {},
   "source": [
    "## 1.4 Create Folder Structures\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6c5f512c",
   "metadata": {},
   "outputs": [],
   "source": [
    "POS_PATH = os.path.join('data', 'positive')\n",
    "NEG_PATH = os.path.join('data', 'negative')\n",
    "ANC_PATH = os.path.join('data', 'anchor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e6dd7b78",
   "metadata": {},
   "outputs": [],
   "source": [
    "# making the directories \n",
    "os.makedirs(POS_PATH)\n",
    "os.makedirs(NEG_PATH)\n",
    "os.makedirs(ANC_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "428acd18",
   "metadata": {},
   "source": [
    "# 2 Collect Positives and Anchors "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11ee165e",
   "metadata": {},
   "source": [
    "## 2.1 Untar Labelled Faces in the Wild Dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1cb4e17",
   "metadata": {},
   "source": [
    "***This data set is downloaded from the inter net and the link for that is in the Resources and References section***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e1551a91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncompressing TAR GZ Labelled faces in the Wild Dataset\n",
    "!tar -xf lfw.tgz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "864d9fe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# moving LFW images to the following repository data/negative\n",
    "\n",
    "for directory in os.listdir('lfw'):\n",
    "    for file in os.listdir(os.path.join('lfw', directory)):\n",
    "        EX_PATH = os.path.join('lfw', directory, file)\n",
    "        NEW_PATH = os.path.join(NEG_PATH, file)\n",
    "        os.replace(EX_PATH, NEW_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "218f8a17",
   "metadata": {},
   "source": [
    "## 2.2 Collecting Positive and Anchor Samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "150582fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import UUID library to generate unique image names\n",
    "import uuid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bfbd98c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data/anchor/7989c336-fb7c-11ed-b171-90e8685c3a0d.jpg'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.path.join(ANC_PATH, '{}.jpg'.format(uuid.uuid1())) # checking if the UUID is working"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8dae4012",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
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      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
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      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
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      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
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      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
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      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
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      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
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      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
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      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
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      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
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      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
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      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
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      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
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      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
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      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
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      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
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      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
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      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# establishing a connection to the webcam\n",
    "cap = cv2.VideoCapture(0) # i'm using inbuilt webcam (laptop cam) so it's '0'\n",
    "while cap.isOpened():\n",
    "    ret, frame = cap.read()\n",
    "    \n",
    "    #cutting down the frame to 250x250 pixels\n",
    "    frame = frame[120:120+250, 200:200+250, :]\n",
    "    \n",
    "    # collectin anchors \n",
    "    if cv2.waitKey(1) & 0XFF == ord('a'): # if we click 'a' it will collect a anchor\n",
    "        imgname = os.path.join(ANC_PATH, '{}.jpg'.format(uuid.uuid1()))\n",
    "        cv2.imwrite(imgname, frame) # write out anchor image\n",
    "    \n",
    "    # collection positives\n",
    "    if cv2.waitKey(1) & 0XFF == ord('p'): # if we click 'p' it will collect a positive\n",
    "        imgname = os.path.join(POS_PATH, '{}.jpg'.format(uuid.uuid1()))\n",
    "        cv2.imwrite(imgname, frame) # write out positive image\n",
    "    \n",
    "    # showing image back to screen\n",
    "    cv2.imshow('capturing image', frame)\n",
    "    \n",
    "    # beaking greacefully (if we click 'q' the it should close the webcam window)\n",
    "    if cv2.waitKey(1) & 0XFF == ord('q'):\n",
    "        break\n",
    "\n",
    "        \n",
    "# release the webcam (if something went wrong we can reset)\n",
    "cap.release()\n",
    "\n",
    "# close the image show window/frame\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c23ef16f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff82393f190>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3XZR/KdUhPZSy5FX2z+xirPoyUwNdy9eA7dPp/QYp6MC0MrnefJrCdffeAKBqkj49elsAtfI67Py2ThnFE8cQVm5nUo/I1v2wCW6tV2NXnYBSB1rfI7Pxs+r5rtrSfl4aD21F2Tgq9EMlkafGMgFV1X3UjKMtazu/4lEHZBVcB7QcCGLort9Euq/gtCwB8zzD+0EkBXVIQARjwTiM0E238zwjxgExMqbpjHkOWOYFD3cPOBzOOB7OWOaIGAGOnAEz16DxeHXOZXDUexak5mlBCKJWPJ1OGJZBVHazz89zOGOeZpync1Y9Amj2LloHIJG+xjmAeECYI/zgQcQYhx0yqKq0SREUixRl+XB1QOsJIi1zoV8ur1aVMi8+tFFQxZKtn1HpzDKFnXeKOjf/s13whmpu9RrVHpG2vkyG6TYefWTBNi+cTllfIVmwXNX7SkHrvQepKqWOdWbC1lzxtYmBLDkQtkXbC1W5ehXYuqWdC4aGElrGjTvffgqpnbRXpqdsfBeLdA71AncAxE068gKgt5HaFp7A8+Lit7BZS5AJmgAYYOL6Cb0emcFhqSVk5+DdDjEEhBhwPp/wxRdvcDyecDwckSNdZPtDBPmAjz/6CN57kE5cdiAISJyOAkxfvHkrUtR5Nm1USU3aEWPE6XTCOI64ubkRiWwYMpgsy4JlWaroEvubHZZlwfl8zqpA5xwQB3F95rIXkYhw+2rEMAydWGtG4cwOcXFYmPEQHnEeJwzDgN1uB45HOCdqwpubGzhPYB8QA2MYGfvBl1HiRFO3sGFT1dU+pP29odR67jyn5ouC06bqsIcuNXNR37+mQh2JsLpsyzF9wN2H02NfB0Ux5bYewl8S+N5zkNLO5/wLqIdAVAdUqRds2iKquu2S0q/n1KlPoJ+elHFjQDfXZ5Wjzf+K2bCpRrCbmxuJjdyaBW28kVRyscZZhgJQ0S+IH4WozKy3XpEKU26sYyTed7LBdctrMxRmtvWIrNpR2w9rLEobGVXtF0NFC3NN0wshBByPRyCp/ZZlweFwwOl0xsPdAY+PjzhPE86nCcsyIwTx0iteXYwXL15gl5wOVCW33wvInM8Tzqcz7t4+IIS4Uiu3tqXdTvLZ7/fG2240bS3zfaWSZoBjxLIELLO4jvuUt0pkzMigZYHKAplzTlzQGwBdFpHWVIpblgXD4OEGB9p7eCL46HNldEsppXrb2rpGcqjkJyvRkLFubYBazvkijZb8qq0oebk131fvVRXrpA5taIEQdpLSxrMMjp1notEncHOv6jdLVbbAhdOABHPDMpcNE5gBvFXdb6/PNr3XIFWmHxXWa+vZJxwbKhVdntRfXeatQzQpgTD3t8DrCS2A1dqxjUfUcP4X06p5T3A91F5uVRtAVkdy551mbYgtzUEjYrTVNV7rKd9GxGyrbvecsW8yLC/UAKUKCTNOpMDFhTimf3S/UAxyb54XvH37TqJAhIBpmnE4POJ0mnC4P+E8TaLuSy7iov5bqrJPhxnjOOboERrua7fbYU57oM7nKYOTBQfrJDEMA4ZB1HVqB1JwsaBhgS5HbanUmRF5p0Ei8uQoCSJcAZWuq9aL1dZtSaGT7HWt2ziOol7EDAIwDz55KQLkdGw6jIVkJIzOam07UWURCeG1buPPFZuyFHc5zqetWb0imjpf0nddJDdbNy+AV7eCW+DQgmkHsLboxdbG3hYAfzklqcvpqwmvbtXPl9P2gzFFG6A8TnbibgHUhUlZFcudhvLGdfPOZjJLzH3JWVWpF5pOJEus0heu1XxFlRXKD0jcuk1mhJrLxJ3FI/msr3BxDCHAO5dAJyJyIeQxSUnzPCMm+9HxeMQf//Ef493dO0zTlBlMggPxmKQPAQcFErURWaI3zzMOhwMA4O7uDgDw6tWrLHVU9Y2xcoZQgr/b7XB7e4v9fo8XL14UmxIzTqcTTqcTpklUb5pOp1MBECXG3mP0SGUXiY/JIQSqwLJnY9XgtFrHeZ4rEHHOYRgGHA6HLP09Pj7CDw63j1L3YfAYdx7eOzjnV8Csmg6Rseu+zOunXb/VtInmnnG8WhH8a9eABcAWCNaMavIF36jb15wqrcKWH99GBXphkZCyqyQve+PrTb84IJXFShTmojI6PjULCLU7+NaO+Avvt1WyRuzqiWtUAL08y7OR0WkT1wxQK6Y/mVqpqPMICwHOPymz3Ch9l8C3J7nmNZqez0yawne9kAv3awNdUjO2G82rrrcLyUpQGtJHJk6MRR0XEngcj0e8e/cOd+/ucDqdwFEIvHjgTeAY4d0AZp+C5kpSgn1xMzeQQUbv22gPvaT77fRdJfaqapumKbuNz/OMx8fHtBk44ng8ZumVQ5GiQorZZwHBlq8R0y1wtu7u+ryW1dZZ76l05b2vIrHP04ywAOMoIPXy5Ut4z+JcoaBJyEDvXJGS1s5LlFSF+Wo97nnubXGjymhpGdiYa9z8EgDqUASoPk7WVwR5cRQKi3rCESrm6mKU9aaa2jxKanfqPLT63m9Dnf/GOiarZdA0VFSkhJLSta5rebu4Nv0CgFRD8FWsrDh4zoOXfzd5tNGbs4qg+3wvm20ipB58PW1B3pir0oPhxlqpK12GeXiTaSPjBZgX6WqyXTlTqteawpTTtvhR3WverfZ/GaJiuE2upEDbr+mKMiS2H0wZRavT4fDMddknIm7WuptfiJtIScu84Hw84927d3h4eMDnX7zB48MDpvMEsMPpdBLX8iENLlMS1JL9rAGYLcBp9y1Zgt9+2ndaTz0l3ApSClTTNOF4FMcNVf3lfCpXcBYFwhNqcQugFhxae5dKVe19raMCVQghS2XTJOM3DB7DIPbKYRxFshoGOE9wXoG82L/sulBmo4TcymhkGoJ8TZ9Z17OmF1ktjUtj2f9hPV8rqTKpWKKOZcVotuvzSu3KxXTts6bPerxy49W6fqhlyXWDvEoRfHVV3nOQMlz3xVQ4Ipk3He7EEneyjssaxXudI4HNhDMTWZ8h+7tTT1ULAcmxoxWtTd7VhoZ1VpsNAgqQ6ALIRP7ya3VZ5vlKBZDyTCGAtp1GgLUzgyUG+l1jM2+PbSHYdVl2w20OdtoyKiiAyKybbQNAC2wcjLt3b/D27TscDxM++/RTvHv3Du/ePQhxJAdPe8xzlMgM51IvYCn7gky9LgFUTwqwYKDXrNSi6kOrOtS81MvveDxmj75pmsDMlRNFWw8AoLgGw1Zqat3ce27v7fVeWVqnSqUZI+7evM3PfvzNj3H74hYvXr3ARx99jP3NKHu5EmCRI2Bpy0lzgGOS+q1EpP1sA/42zNzqB63AxRSz/r5KXJVTLpPYZRkISzQengpUXD371Jql/E+Peeu8sPVAWs913TeK7wBVHV7OZJsZZ93Hd90W4vcbpOyBhOUiur3ZDnrbjdR/L5M9K4Fz+ucCx1nncOmWAFBm0joSxFdyDaXkCbeRBTOnzZTmYLvqgeaa7QeTqeaDlZ3Cgk3E9sS8xCU+zYzU3l8M7urLkSWm3Nkc5S+pLZcl4NNPf5Klp89+8han0xnzFLAfX2QuHUD2ngNa5mQNOM9JPTuU5qXEv7UHafBXBTC1P1l1o0o2PVvSytOvAaQWfHrSkf3dk/5693VZshGTZeOv/DyfzljmBY8Pj5hPM/zoMYwe3/mV78imcqf2OVm/g08kjSO8c4lgXqkxWM0xS6i3RIpEnDedKpQ57K9Bjb7ive+OOUwIrW3V37Xt6zye1fXNtW5lO0WVAUwPLOYhv1E37ctfCpBCnzEH9+eMZdj1Ui2f19n0soYhyDGawKQ9onglcTICTlfyIt7MabUAq58b0teKg0yhjjaA2r5XuDW0nbL93mb9nnrXSH+9wcuvK+fYi6dHVf41sCaAYkZcAs7ThNPphM8+/TzZoU44Hs7J9Zvg3SAOFFyrqHoE/xpV3yVvsR4IbAGXeuypg4J+X+YgbUSSOJK69fn7Bvtt6LmzZxDDBgOe6heCHOGRmXYqcznqeVYkbuohBtDi8PDwAPJOVH5uwLjzGEaH29tbOJL9dCVYg+xlSznmsq5OXbrS6xQkJnOLqejkyfUzgPXYbKUgaj4vVfSKRDWNsSujZGW/Nx2RFxjVK7KhKUnf3amrSuWX579N7zdIAVgT4qcWYC2fb+2TqvM3nHE6rjzEBXGeceNvsEVAnyIwpT56TzerWvn9kiThNq43JfS4ZtUpm6Ok9bHOaVmX14IdAhL7Xtl70gCfXYPV+uuoF0ydgBIytudCrvvghIFoiKkKT9qtKc/IIZ9Eez5P+OKLL/D555/jD//fH4rhnwFEDwefQ0Gpp980TdltXI3/bb16btnPTT3VX3tPQep0OmWg8t5LoFn4ND0p9SGl0E12bsJ8pyqsUiutAcWrT1WO1bPM4BCqTKk5YiKGgHma6nGiWoIkIngn0oVPqr139/e5zZ9/9gbf+Ogb+Pjjj7D/7gu4gUDk0lhLviHEHEtQbT+279p5+3Sy4/jE84ls9Ib+ohS7Wi/PDSRwIQD2Chgcsm2IFmSmVSr1RDkq+a4vISv89BzvJ5jfJ9J7DVLkIsg1XOtT3jCNUT4Ft7n8igmRzySc9+AJ5MdKNKaOJNKTkL++9GWJXyJWSUxnsCxw1GFir4C/zD3JUQw9ttHkxHXuvfxFaOKELJdrkFV8CgaW6CkKKqtuMZCl9PM84XQ84nA44Ed//BM8PD7i8HjA4/05q4m8LyffqscaUR0Dz25ebaWoawDq4v6bBEItSLVed+0G26L/V4cQ0/cWrJuis5TTJAWmEn+wPF9JfaQedUXdF3VTbnrOOSdRMFrmxjWehck+HBX4gKq/D48HLElifPnyBW5fvMDtzU0+9t4P/utZgNSizZUZZlHyS67Tqx0nnkqbKqeNvK+pr1lQT54MwfgyUXs0vdcglVMlHPEmE5C9c1pX5rygOyoZvc7KHyggyZHcdar13za31jj+vLTmWp7M6qqiVHTSSdzKUJ2ObARXbgvjzltdj76qd0ymCUWK3NSteavi45aIGJVGDtqbbE4ijQGHxxMeHh5wf3ePt2/vcDwecT6f8zEVkkXxTlMg0qQ2qdZ+0wOmp6TqLTvWNflZaarK26iJW+2Ler6t6C/qucVcXPJ1z1fPccJ+F1zQ76aepi7ioFQKdOQqkLLY0ls7KsnN84LHx8e0Z+2EX/3VX8UweAAOnimZSjg3KkveGiQ4O5yYWdzqsbrTVpGezQsbQPYEUPbte1+dtd12YlprfrgCxHZCXFVamUg5+3r0qxL4OgYOeM9BKjLl6NpX0/9qATJAeqQ1GYmpfSVmIrcluF6Sx57PzJlJlCOcN4DY1SNoaZfBJT9OQrydK60qijoz4ToF1W66vNlI7du2DkSGV3BccZucVBOt52Sb4sZ9YcBFFSqnY6QjuGPE+TQljz7Gm8/e4c3bt3j79q24lk8Tpnmu2ihRyNdEMoSAm5sbORDQSFFtZIUvm2w4IZtnV9VpwLMFuwL2euKwzCW1l3BcE/+2tyU4rs9SjE2tJFXIdQdYO+3MzhOuSE1tO3oOGNaJ4/HxEW/evEGMER999BGAvezWGajug8LXiHoWnBwttG8IJZQPkNHJEt+eUwGlbRN57a2pRFeFvtEn26n3dAtsaR1TuadMg/wwBze2h8JmHw1TfxvOqEKZ+jCVanhaIO+COPBkLM6U3muQCunvywuSXP1rgUC6tZlIXSKs/34NnE/F4azz482nr53qpbauyIhV7tsuGqYeebXX+T5rybErwMQWsaqHLmahgnGqVMOpiG2KY8S7t4+YznLURAgLNGjqjz/5BA8PDzgcDjifNfKCLOnWntSzNx0Oh0wsNdTRNcCUPduo7G9q3cvbWHht2lIvXigVqsJkRGhcRCm2ATauJR11qdd6bqkgpZQ12bRjtOVgcEnSXElqzfMavJaI8OMf/xg3Nze4ubnBd77zHYzjKOdfYTCMRMjEWqxVNuKJtfPaozc2CGo3IkPbC5eiPHSklspksTWfuqgAdRJpMzU+glWOeZyjQ972kQMgU/kOZSS36VM3FXG4ub7BTHfSew1SFUGtuPR+B24t5rJ4LxPKaj3b6BRm6LfoBZustugY2ZvNGHL9T35BiMAWkW8y3xK+2ncNF1Yq8NSk7JEn/TTcaAVoVqw13J0FPwsO1RdKxDSpl0iORAfLkRcxihR0Pp3wxRdf4PB4xOHwmLhH6YyHhwecz+e8qVSIWCnoKVWblWCedsDpv9+6HveIcG/PUa9uW0S8TfVcNxODzOhwkr0cgchnB4ReO3plZTWaPFDVr5eHdZrogV8vKZBrzEMiwul0ylHdh2HAzc1NDhNV3uN29rW1r9YxM0s/cHqr2rN4ad2Vud6W12Uua0RvQMKm3nq14yDvVsdz5OFeO4uRc4isjj8todDfut6ouWfrZN/lIoxtNeFKnHuvQaqkjiSzNXcYqGxShmZuTjda/xT7Vs/jauPdlH8xaF+oHxLwmEmhJLw9dh3gdDRJ2sC6UX3LGRd9cB/cypXCf3XjX9iThM2/K7ucgspmQWlTo9Y/S2rNuGaiaH5q/YmwG4cUwWBBDIzT8Yg3b9/ghz/6Y7x7e4d3794BKLYkdRJYOwpw7qMtwJDyt13NezYUm5+VpqzKTL0E2zLbEENb6TrpyoKGtb/ZOaqzzW3uq+r1gz7XOli0ANVrn5UmewCcvgkp5BLs9+bmJuep0TVijDifz3j58iVevnyZjrWX9SoRKqyaEo1tztYzgRQDSCrkQvA3uL7VZQY21mY3qZ3r6hfKeyXYtKvWVx49o3GQr07CeVVqamRGtRqnKs+maKVrVFOhPJbK4beM9xXpvQYpz2LOAHAdKucNZwbt9WXS+212Gxk/YwLJ+FzDHRYgsGrw9RP6DFc428/tmtQ25tLvjYZzijix2u8R8aRClmXxXwLOtja14wQDLC7Yakf60Q9/grfv3uHzzz8HB2C/3+Hjjz/C6ZhCBZ3nhphT7vgtgLomtWqwLfUWgBxXj7kONdS++5TTRZv3pTqrjaabT8PdXisVamgjTTFGUbOZzc5t/SxI2+tEJUCtVYmmIU4EVCUuCTx7Pp+rsmw+Ki2/eiUnBLfPRWY4sg1f9926HxTRCXkj+M9FKo5d0g49X80ye2spcZomPB8Rn1uzXHyZZ78UkhQbuYK3dpZvbF6sHu1MzOtQb53hame5coe20K0JUUQdMpPKcr5yuxDzwmCyYQVrV/ItVV+/Nf0H2Z4z1RrnueESq+jO10Co4RyNhGplw4oLjzHvb8pnSAEgEoA6Ho/4yU8+wf39PQ7HIwZ3I6q5WLjv1slByiic81OqvkvJShVb4GPL7nrmddJqT9KXqNt2nVExPK3jQq+c1tvRSjeqxrTSon2u/d0ClpbrnHDvTElJaaVnyJhrXVrA07qEEPDDH/4QH3/8MV68eIGXL19m9WXRhJW19BymID2F/rp5Yp1Xj1mNQbs2Nt5rnrH+DtJR5cj4p+1cdv49t/11Wr1DaGiLVJSvzPu9Bim2QlHV3iJWPmvZFmqHYuy0hdgizPVV6KA0abBWk1yTRJdvvWdqyS9r7oxYrqJ2LjcvHAuaNRel1yw4rtpa9AQFHJtHOL9n2XAun13iSfV3TpwfF8Oveh+1tg/dFyScsFFJkkhTb9+8wx/98R/ifJYNt9E7xMBVDDsFiTbv50pQT0k+NuirdYbQ5NKxIFalVnnLddSG19bnq6angLDdwJyPNEl/up/pko1Y32/BXD/V3gROBx3CbtYWYLGbi229dI4sy4If//jH+P73v4/vfOc76dTiHRyJlyN3iIjM5rYvzXrM665tVLepT6cs7VgGD0Dl8pA+VbXGti6dVEl4llb97CS/PJ7WxJLaea0C9L0GqVra6D9R+uHCYOY8arfK6ytSgKDmxmysuucp31qyXy+fbiXQl/8aLvjJ0vtcHLMSh0s5MMTbyYJVBLxKYQ6Iz/HFLEQsuyYDlW1hN+wQ0vETn3zyI9zdvcNnn32GuDh47BBm4PT4UIFPD5haqeDrSsycT8c9n8+ZaLeAZtVdwJfjYC+np/NrnSNsPW26ZI/Ss6q2guNupVYS0jSbLQHelzxL/sXbUK+r9GTte69fv8b5fBb1LzO+8Y1vFPXfIHY3BS1ty08/XRF4wAIoG5vYVeoyo5bM6WcHUDY5Q4fLTrrr0vsNUka+fbLJqq6y9HMlYpe8ykJpCHO1iM0jqu0DjKHQZF9x3JerWr2Hzly0XJxRFazy1VA4ieNitMSQDKiaelLqmQS+yrBtphZJbbeaRUXNvaKZYO24VcYS6iametRqIbVdnKcJb9++xZs3b/DwcJ8M59Lu2ADQFlg9bccxtbpyAHuSQvv+lirtqXK2CH/t2LG6m8aAYN2MVRVNVPKo7EGduvVS7z2t5zW2Nrs3zOZnWr3qu97YWGlOk3MO0zRlVaQ6XLx48QI37gaOkEKepQNK0WhTbPzM7llRXw7UCFa4MdqHNt9KfWf6sM0wZ3YVil2RLoy59bapyl/XR48g4fwCZ1XtU+m9BqlrUtIUiLjZEEgZysKRVUQTlRzRvIU0kddqAilTozuX53UtXSQ8F680d3sqxvaR9Fxta8mVrEAe6Z5Ob6l6WaTMfUkjt4dKBA4FRa1En6DaRZ9eaiYt5R6mrPqLDHEHjpQP9Ts8PuLTTz/FmzdvcDodcT7PAA+p7Hoj7NZfLz33+tazl0DuEvH+KvYAORK+Q6gUoIzERmQBCtX1tl6tLalXzzo0Uz+1fWLHwh6CaMto1aVrsCMQFSmqVTXO85wjw8/zjNvbWxCROHk48faEizLrnM9rQuh9qJhAM2rX+U20w1CpvKi+n+2/DcGqvJKLZsFKTJlybdZpzQgWsrbx0sYwKnMjKtP0INf9kbOmxZCEBFhXrqNfXJCqROVa+2lIhLm6pSFdA0X//d5Imqns6PJEWN2xv7bcj43qjPvPFHDiimD3E4EgxB3REHiC2VHfeSdHrLCAx6VaTKDNsP3XJEKMATGGvKkUFLGEGT/5RM56+uyzz3B4TCGN2CUniZ5zRJGsfhrqvV5SwtuW/1XSl1NHGU6dCCAHcpyJjebbSitqN7Pu+tom6y3Xc1W/VP8tpqf1CNTybGqlJBHGI2ZAtiCwHKqodcqHI6Z0OByyKnFZFuz3e4zjiBevdhhGDwKwRDkozDv5TSRH2YewmJpc015bd9e5vnFSbe94j25x1HxXaNiaYxeY3ytTpOQhCa7MXH7Lk5dhJNCkGbmy6F8wkFrLPpVZ6tI7hC6hFwlMj3wuUkOrutqg3+XrSgW4xZ60+WXZpimL8gsCr+uI6C3srrytjAqj2lgOe2igqRdrWYaDttxlVD6uYaWQAI8caGUIriW8wpUBoCAS2uhS2ClC5IDz4YjT+YwvvvgCd3d3eHw8YDpPiCFK+CNQlZ9+9jbfbqUvAyI9ibH1NFN7Sc+J4to89b1KakqpBsBOG4Tayhw2rtdE9fyw5Wt9JW8IEwIh2luOHVvtap1GbL3bdl+S3NflJFcjSqGVGIhB5iqRuFlbb0uVwtVV/ebmRrz+PLCLO+x3hdjaMSSKKHvLJG4LE5nZ3HwjlXrq9StL+MIcy6qeNSPcaEBN3lQWcqYRTfkb0+3SXv1SBSNus4HBSsW4LoSUPuW1rbTwOobmvQapLcMu7DjlS3VEiUL6DdHcCnGSAEYWpimkRwsu0jYzmZ54ZH0e8NY7bP5VoLLlGECBEAbnHEKU0PxpGVfZrYgcM2BjvJkNtPkYBuYUtFOrWupAaZFweic/lMX/AmwixBkOExHDsINLqow4BTw8PuD+/h6fffYpHu9POB0nzPMicfry4lzbnJ5S7+mzl9Kl+5e82FqQsvcuOShcIvaWUPccQupx6GbSzbNVo1lvSiV+BMjJuFdITm07bL1boLVj9NQGZhu/Twl0lqqIRKomaeb5fM6OEhZ4NTqFbgh2ziEGYHA7wCcKke1bUi97jhhV9ikCbFvJ1fe0P3SNtsQfwEpXVl6q+rPuS0MD8vInqNRWtnJs92fF95qUgzMDsPH6DKtaKrsx1cqa1H65LmafpvcapJ6XSicpMKn8IT8sJ9B5t4svfc7haQ6h79VTv3VNhAHrSdeK/Ki5LEKSnAIC20myBkPxpGNxyTGEaVVOtBKLWTzMZu+y7eVYpLPuqqjdUpdlBkNcmedpwjJPIkG9+QLv3r0TG9TjgmWOCFGCyWqynl09x4mfZlJGQMuzqrJrI0fY1DofWPuM3r9GQlTCWhPZcq0XPHYdI9ACDLrgc03qAbNVi7bX27apBFTykT8iArnE7HNACGKXur29reIsZmYtBJzPZ0zThMfHR3zzm9/Eq1evwMzYv3AgB8RUDlI/jeMofFuM4BiTiU/jAF7fB2Wd1kwi3PPnSH63O/zaPw7K+F2dZXaOMHTieYJQU8UiHlyb3m+Q4ho8am+9vphcOkmfe6qQZiQyA2QXGaG/We5S5usRtrxWuXAhj66MbrngFLU9cVtkukuYPiPKpBTBsEbZYnsnk7txL06HzHHpmE6X5R5riKiVAgtEsZGCCIQYYjJ2LzgeT3i8P+LwcMJ0SgAVYjo9Fysw+rJS1HOlrS1JQZ+3m4ifyq/20CvA1EpOvbSpXUipBSh9p7UnaZ1tXEMA+XytMq/64NQCypY0BQhDoeX0XPR7IFyprXN+hGjqFiiATNRvu39qGIbVPq4YI+7u7nA6nXA4HPDtX/0GdvsRg/c5ugUNlJmNeVngyYutyrt8OrDphUyG1l3UkzyUeLuuJjCvFkZeo6LksZkrANktAGYurrPNJVvNxgqFKGVk5CeqHrY0r6z4zOOmioj/pEi98crtKO83SOXO0SM0rARh0dosmMbj6doNZXWZ1TxJlHzr4K/rU0V4CKg2E66FjuZLy53IhHDU4HVbLyNtsX6v5uZTQGjr3dNP6MR2zfMGEKjRUjAjQrczEzgywhIxTTMe7h/xeH/A4eGMeQpYFgkmq2dAtUBzDfBcC07Xpp7nWk9CaJ/pSRbP9fh7zv2e2k2TqiXFdhOz1OQ9rwDO1rdXlpUE2/Zo32h4KGC9x+opBsPmGwhwqDdG2311cgbVjP1+nx00tD0xRjw+PuajP2hY8PLlLW5ubzEOYwY2OdJe8mEPeHU+UZprRY5cx27VsV7Y6bXOXCjPW8qv1K/JrlpjhTaI2WMtyfRWbvVmw9v3Z1kz7xMFi4YWFFu3xut8Or3nIAUA2xtwixWKmkFYc3k/rbo9J1Xcp7uiTqRuxgAhHeZnbyfJMave1IBaWKVVda9VV1i7B2UAulRne0Jscb+gdKeCVwOsBIAj4/7ugM8/+wL/7J/+UxwPUzq8cG1wt/X7aav1bLLEunWKuKYe14DLc9Rpl8qwINA7JmSeZxwOR4RFI0cQhrEQ6a1kY+61klkbLglAOmBy7URyyQHlqWtallVhtvEUb25unsyfiPCjH/4Qwygbsb/97e/gxYsXeP36FfY3A5x32O09hKkLYIqITBA7FME95c36laemMuRU/14xh/rd1qUw9+uKUK1x4dYc8FSy+cUG5S49u53ee5AqQ0TNdR0YM/ESbJX9NzV3+pQax5ZXi7hP1e5yyguEajsDqCm/N6ZJ8uDmoaKJFJkky+bcyI5U9892nWuOueozWstx5dnVlaopPS4NrFy7AwLjeDrg4f6Ax/sDwiKNU3tCrkNVZp+gPZW+DKhtqbC2bC6XJI/2upU+rJrOnpDbPt/mq8/Z49+34gmq5DTPM8ISAEgECT94eOc7J1FfbkPr6m/VedYx4hpA6pV16Rlrj9R2W4mpp+psbX1hjoiBEaaIz+KnePX6lRBwupV+8R63GoEdptwIgJL0mUwBlylB2w61NZtVZZHjmuM7NB+113W0I8XG2GpCyr45MK7YB9aAHZlrnGhxcsAQr/qi/bomvdcgRVhJxZ1naiJPINRHzNuFmq5Ug1ILv3lBVePy1TjcusI13KwnCFVV4prcV49VUMDtU7yuNneuARuLolH1rHN/Il14QtdUFO78/v4RD/cPeHw8Zh18j7Bp+jKSzHMA6lqJ5hpbzVN1aqUfJaqX9lq12gELYlaSafNYliWDVIwM7x384DFq/LwMmv12tAC8UtVxVvgU2gvNd+2C/lTf9Oqw1W7twx4j0bMhAkBcAIARKOA03SHEgP3+Bn502O1iPmiRXOpvtc9yiq7uVLKykolpBCkzjXqh5zPSCvdGzOBqm8mThG/VX7aN6Ueqb72masHnqbne0h3L9AqRTCUngFJwKqLCU+m9BimRPFo2vHpg47qyBz1xN73WxLsqY2vF60bK+gqqQ5kgdhKaunXOrSrPWKDq5rx6/nLq9QcDvD5yYZ2cASt7FtXW6aWpdq4o/FilPWZ89tkD3n5+j3/xL/4FzucJyyybKK2KqJUo9K91WvhZJsuZA8gc+peZG/ZEXM2rTT01mZUKNA+VonpR4EMI+agTIsJuP+R3SpHa5+uy29QGml2HIGv7ot0+8eWSjD+wmvccEZJ0o/2ggL8sS5+h0HFM9ZvOCz7//AvEGPH69WvsPr7B4fGA3c0ONzc3YBZPwBgCmB0AD+cJSCchb9Kjah+T+azqRKgjiFzbT8oQ0Kr4gkNb2iJz4WJxT9EYBqhsgCYk98trTBp430GqSfUUb/VHhihSvNjvwsCYZSUvdafYsswgN8A/eZpofwEyYPbflUlI4DK/upOgIy5fvNCCeQcMq/cNGBvWilEAqhYmW4C2i8khG3L1ckThNhnCcKRyOIiX1d27R9zfP+B4PGI6ix3Kesm1ANU7OqKXrgWLr2oDek55vbIsMQX6koDmvwVeFrTUSaGNAN+rY1t2JSU36kj73f71mQNdeRa0nh4zrdNTTEd+3wgcWcXn5II9GkXtZFUNV1KZMJDLzDg+TpjOn+Lh/oDj8Yxf+dVvwfsRYQAY1hNS3AUiMxwPYN34m23IyBq8Ff8LHeuiluUscSZMiQbUn9THpVIYYMQKk7ha38UzMI87XTd/rbqzg4eFzjHAyVWfN5nvOv1CgVROXdrCmd4W8mtkkI1xUNfW7jOkk9AM0GbaIJimvvXrPYeQFgRk8opquc6/Q7JyrjZqRLdO1eKUHssqnq3XtCGqMizolRqmC99w45wWrbVZhIBpnnH37h3u7g54eDhgnidM2U5S6ndp/9NzbFJPLcJLqsVLz34Vhwmrimr/es+u7IQmb3v9kh3P5q1OB5dUpj0b29Mhp54al0zJVpO41VRsMhFNlkLkC+Nu3dyt958towVgsGzyneOCx8cz5nkBmPD69Qt4N8D7EW6oo32whD8RT9XqCJpST6I2AigBxKAEUhLFRd+Te5w31YqUtim9VH3au0fmJB3DQKKJNvNkMm1OY9fSLksOJJ6ow5NR4FP6xQSpC4mZjfKJ0qmc5ff1GQl3NQ43X1vd6OLEqq8L3+PByh0h5ve/Kv8vYMQoKj4GHJsjps2irt5LV6pJugZEuaI7+ENi4AjTecEXn3+Bt2/f4cc//hEODzOm85L2QPWJ23PUaNcQOKsufI4kdQlELhPt6/LVIzA0WfdpG1NPr7WE9pJUaeun4HTJi28rjx5AdfuwUWOs2KvVCdqSlmXBs5PJIkYJkdS2TdV9tk97SexywG63hyOP83nGH/3RH+P161f46OOP8d3v/QoclTPOOKkZyS3IDgwYhDkDACNViaDkso0LSEswPVawj9pu2W74xlHyGQ5jlxe4Mn+bCtufztjuPKGubClKDV9Pp95vkOpEeSYgMWKMWAVu5GqgamkBne/ltTrVz39ljZDRR9d5FTBaZiFAfhige4dsBUl2hhRwsbm4WDWBs9ot5vdhvnG0RKY++XhV9aoW1qJmICrL+QATpwWXCKeOCQORIx4fDvj8s3f44rO3eLybkws0sMyLOehuTVi30iUC3TMkW+DrBTnN7b6gWtvyVLsEElv2JmtHau9ZKcB+KljoxtUtJ4peak/TbcvrMQq2jIsAReUdIM0Ttvtm0Dz7fFC389bIJc0zdZ/b+rau6v1CABAhBMb5NGEJjNPxjLdv7nB39xYfffQRvvGNb2AchwQ80j45q8uJRGWyQibddRmZITTjEeHAK/suodjPDTBZ0riSLMu1DVZt/VJz15QO1fokeVXilAKFxpCxyCk4EV0pR73nIGUDiZLRgzIn4npNDP2LkRW7U+hrSFx/tx59vH4mckiEIILg6gj+mRBQmX1cc0iiC+emP2pWZqXLz6oFfcA6QvTqWxOFOr8S36/r5MESDPRwOOLh7oD7uwOmc1JLpWjmWw4SvdQjyu2zX5e9SfPqeYldKr+XR/vdSjWXQAookl97JtMWQLVgYuu+BbathNa2bSvPrbJtZJGn0vMlUQMBTVtaF3TLnOi1i8eNsMzLwAsiM87nGRwPmOYjmFk8AAcvdIORgMXBu0bKYIYaptq+KuuH4JxZz6TqUAvIKRHMGm/7q+S3QkT9t8eIdttfM7aF9tiKcPWryjMxv65HCzrpvQYpoC+uare3nP7lRKZrddGlO183RrUg1dyrh5sw7pJajyPSdskcFXydC+WjmmWRJBUGAMZ5k0stUljZhChwtJa4qiNCFMl7I3EtXWGHuDDefHaHt2/e4f7+PsVH60dquGQnWbXpGUkJ07Wqri2XZlt+e0TEU2CpG2adc/no9EsqxCxJeR0/znXrAdU16ZIHoc1vC5yuSV9W/fl1Je2bVo2qAKahk7aSgLXDOOwlYkaY8ebNOwzDiHHYYzfuZPzSacLEAwCXpAt1EvJpTxUQQpG4bCJoNHcA4OSKDpRjPCIqj8Deomu1SfnAS3v9+vFYSdXco8J18XXuIUkV13ndvvcgdSltSLv5LlWf5Z3rtpkxgK1QN0+8XwHF2vCZB5UoM0621M1SiOoJQwBoScxa5nnSfHRQtShHRj6aGtdIjpb7alQ6zJU6AVZ1QAxH1nImO/YfHo+4e/eIu3ePCIEzcdiSlq6Vonq/c1U7ksulZzS13nZb72wR7tbJoSch2br3yrKgl59nhxCF4hGt87WSrTLcZYhSOY66Nqm2D200963+bp0qAHFzryOXX07XjmUv1RoGam9CW98ejNiTaGwgW3tfo3NovUZ3i/t3J5yPP8S7t3d4+eolXr58iY8/+ggvXgwY/AimGSFECeIcbYBoXb52o7XdvlHolTxg27PxvbTEvGC/lz4i6uo4sCUGEDVLfCMVW5SCq7l3JfP/noOUdviFSb8xmC3xl2s6AZ6O6McbEomEs39O2hDLbZ3ynFLKwkkbt65vTRj4AreiNixCOduwJ9pTLs+8aqra6ymrDmC0aleO1k5GOJ0m3N894Hg8rQJ+toD0HBXfVroEDE+91/u7hnha8OoB1KU6bKmCKkLcpM06mXm/HtKiQrRRKbYkuafSlg3r2ufba9eClIxJ+i4XOvkqKGwfh6Jlrjz9zL3aYcVjmSOm8yMiLymuJLDf7bHb7bHbce7/bBtW6QYCUJXNDhr9Lpf4dOMvL8eOqs8A1VP559sEkG7qvlBwVi1qOYYzouvH8xcCpApn3t6mFYHsPdnyFRu5dUt/Ku/NF/ODHbTUO0Qg58Ac8pOU4vWJXtdw10YKquehw5aemjmFcCkIt25BBsUNqVE34jKh3vBbysy7zEl24quXlqipPI7HA96+fYuHh4fKptLbtNv7a9vU+76Vehxyj3C1jgrtvUsEtpWqes9aO4i+027ktec6tXXu2V1aQMu22q2+cLSSorbyf256Cgx6z18LhG3k9Erq2LAp9eyxtn7Xqnzbd3XMHHZYJsbh4YzP8DlUO++9ZSxq5q+w3LrWG4azxxSu6Jsyrtf29fXSaU0bzHu9E4SbIlqofY5U/F6DFFOspYs2VSE6mlud/CJzZ3JsFW4lj2vgbWMyZZtOeSwsi0CQY3g4OW6gKljZxDnjA0WX8pKYa/X8LmVVc6PZ+c/Q7XymNJJYhw5rALKcFDfqT/GUbfIOASFIBOmQwu/c39/jRz/6BJ999gWOxyOWZQEzw3svhyhmLtP+qVdYDQJPOSm0zgH2u73Wvm8jSFiQsu7vFnyUwLb39d5T88u+r+Xrbz0+YxxHEBH2+322P+lps61bfqViVKDS9vuaGBNR6vtY1bUHiF+Hrcvm0zIYbX9qHr066dwAINtKqEhKzB1nENbQQ/06bTmQdJ5MkpF8dSnA7PF4NLEKgbfuAcvE+M6vfmyWV8zIJM5ByaNzGNIjZq0zGUaxw0zqM90Nstx/PHVYS/e228zbP7N2pX43bRVD5Hr7ABGJ9/UV6b0GqZxMjKuSirx/lVTUITamAPOvXikTRR5/5gB38i/qYiEmHMVoKlkWzqsUsbF5jlR24a6knZun77XF25qRtpTKS5vEKRGVjH21jKpHajA4HXWwpOPfH3E6HbEsc7UnSuVFqXMjQWlZTwBTm3qODi1Baolwe68XoLVVC63VlLlUtL28NWdsMFZ7FpIFEGu/6x2sqHXPwFW1P+236rTtqdT2+1PSZSupPpVfC1L2uR5IVt+Bqpt1/dT1ebp9vblStSczaKVg/R5CxDIvcOQwDQMe6YAYIl5+4xajRpMnLutL1yDr8fRtf9rKtbcts/w8+pO1jtqe/Nl5uFMmmetPL8EG1a7kcd5rkPr6XcMTV1FxXKWklfemSc9VZ+g7bYZETsKsLLI3KIQU+4z0vkwGOzkAJLVb5r/W5bTXYqXnMzfKV0+A6xKE9WsVt8Zs+ory4rXSgZZz//CA83RC4BmRFzjvQUlqK3p72UelklUvYsY1ksqW9NTjmm1+qnJr1Ur2KPJN21QFTnKBGg+uXqw/Wx5QVH3WTdp7j2EYcDwecT6fcTqdVu0lIozjiNPpVPZ/6VxyDvubm8rdWiWynrrN9smWynLreft5zfdLzEf/mvGgJAeCS2rmdPAeB+hmWUB5KK72Rtl82z1TbdtEdbqW5Eo9PEIg2UpBRxyPR9zfS0T5jz/6CLcvbkEuYBgHOO/hh+KNGcLSldwzdlgVH2MlvdQ3e99zRxmmGAageuxqm3pl9ugZAGY48h2m5Tp6+V6D1M8uxTRBalnKpi8DUFnYMACU8xrE7Xy9OIz080x1S1OD5rtMUG+4Mmf2N2Upya4PZgAxu0oDyCesLiHAkUsETQ4s1I3CIQS8eSPHv3/yo08xTQs4EhwNQLXwrdrM1vnLt9su/hs9ZoGLAbxnG1KQsnuWlIhZCUrzUXUZGPkwxi+bbKBWlZTUTZ2IMM8z5nnGNE2b70/ThOPxCCI5Rl03CA/DgFevXiHGWKmn2ugV2g89NWavz/T5rd9bUtO10vCW9LZ1raT1OrY2LQCbm7htXXtqQKI01pzmUwyY5wQy/ka8WnnBp59+hnla8Pr1K3z7V19jTOd0+cHlPpjnuSq3ktpWjDJhU8WXRUlG14GKah1JXuMkkTnWWVpg2RK3vv70CwtSvene79LGk4/qt4sXH6EdmGyebSWKaiCfqlVdeC5ldRR1qVBWpzVuo1vqlY2imirZxdCXrFSyKeUI52R/R45J6pFoH/aE15iOeT+dznj37g5vPn+L03ESfXyUNms57V/VPq578pql0pOilCC1BLIluApOVkrr2ab0r7xPmRFpzCGmXpfr3bPTWODQ4zV6QVf12fYQQDXuD8OAYRiy5NSzZ7XlWZWj7U/tn7bOW+2xnz3b3TWpHY8tAGkHQCWsrXr11L2XJMctTRkl2sExgp3E4ZvOEw6HA5wjvJ5uMQ4M75LkRg4gVH1p7V3g9Vl41q7ctMhc4D5JojxDyyWV8jeHYV1ueaFV4dWS1XMZeU3vNUgxRXGeYI2ybVLqL1WNSZc2qiCqpZmqD7PB0ubrzM+tyWEHj+vvZtIUnqTlTmB01FQkrmYy5ZBCW6kihGt9PJGTGGPtpDKf6lWYy2+KlL5jzMuSdelC7JJqjhjLEjBNYjQ9Hs94fHjE3d0dPv3J5/j88zcACpHfVJnBDAOn3suDK3Yr2lBRXLJB2WRDAtl0KdCqgpG1A1nVUWRGDOLIUjh0u5D5AjHot8OqgGKMOJ1OckhhJ3Csfef29jZLT/qpJ9QqQE3T1FXxWccNba8FBAW91mbWpi2AslKs7X/bl1vzYms814krKeqSWridi239lBHJm+ZJch1GlYYIbkibrCltiCfKNOV4PCKEgBcvXoLiDvHGAcOC/W6UM7y8lz6ODNb9jIqzq34IIKexAh16qr+a727ojfX8JcCPIzim9YvqRWTbf0ZiM5fzY8mBCwx1pNL+KbTseem9BilEkr9OYq77ryu+QjiJq7017aCyTCCqBl1Ls9+/zLB0yu2mrYpfJVtscEMb5VvCarxyGEAMyb5FDssyi1t7HBABLFPA+RjwcH+P4/GIw+GAu7s7HI9nAEAbUWJLiqokvFw81YuPzDOwZ4CZFhoir8TGOZcjXFgpQZ+3f1qfdh+R5X7VDsQMHDmYBd/v5Wsl4GEY8rOq5gOwAoZWolOpsWdXeXh4yF6BWyq5LanHe4/dbgfvPaZp6gLUJemqLbOnNtTrtu5fRuq6NrV2qa06XUpE5g91eCtmxjRN2Yb4ve99Dx9/82N89PFLTOcoHr2Dgk0CVHPAbo8SlF7YVun11/aaPoVlWV17MlXqxstjQgD0gOdrh+/9BqmLnW+f4cyJrCY4Xe7WiwtBF+BGLVYid1v1zuU6+42blmAbNolMnvlVUsnMSgIE83JTr05r2mrkcjirnIRYO4QlgiMBacPuNC04n875XCgBqmM6/TVmpwNNreqs7oMnZjWr633p954zRNUUEg+5FqRaFVoPiFqAsvYNAaoB57Puc7vcDm0mJb6h9zSBTITs0jctQG0lS+xVejqfz5tqvnUd67Vj82r3ZV0Cqt74ts4jvfL02lNj2tbtUhtsnlvqvZ7UZVWvrdpx6x37nkrBDw8PGAaPFy9vMI4EwIFCWc/Xau17bSVS/VGRIDdftnlUNMU8dBG7qAhXq+ca+pTX5qX8SnrPQQro9lySrDf7oJZ/N7MplzsTO99RN1Jb2kbJmxVak6S8oDdfbCvMRcpuEHD9ZCsZtWWUayti0xBRZsZ5Okt8sagLFIhJQDoejri/v8fbt28zSJ3P58y9z/O8uffIJiEGWrdef6ylwkqzS9RV23nvMY5jOgo81T1GPD4+Ypqm7GSgz/bAyDmXCb3lusdxrO0LqW+q5mVilw6CUyfNDFqWUq1jAG6BuiW09pq2P4Qgh0kaCWgrbakAiShvzLYOHr33WgBrj93YCqTbpmulpy2QvQSCOn7ap1aF2TpU6PWtQynbvC2oWan8/v4eIQZ84+NXcO5F6tMAl57xA5Dne8N4rttatmZYtVytYDAALxVc55OBakN0K2+YuVrqQNVrOpFj3nOp/UK/jCfzXpcudcw1YPDTT19Wd5sTpSNKLnDV1mW3udP9KROY0/9qo2AJjBn0fBgPZjm4cJ4XTNOEzz77DD/60Y8wzzOWtIFXpZSs29/gwFcVMdIRd3W0FnB7XGX9uwLEfKwA4DzgPIN8kRTbgKOXVEAWONTzsRfnrqo5A5FLgFHqSbRUvC2ZZb9Zq4Ir+dUOAO3mXH2+dW23716y97TSU29j81oS3rY/9sanB7RfJdmyreS2pT7s9Ze9p3nqkSpAAVvVLtjnVbVt1avTNOHx8RFv3rzBr3//+/j4mx/j9sWI3X6f5hvJMR8wc54Z/CSB10gvz1NVSrpCfLP9YT0H21ieUhWoW7uetQUGllC8GC+l9xykbEdd+Tg1BCx/NapDbkkcCt2ryuHCiVTCS09Zs13vLO9YB5BrsLRjZ6ukpDynew2yzzRS4EVimnYpsUpM7UIo/Xg6nbL0JIs05nOh9H31VtoGzZKtZQa7ccY21AhWPbXFSavqPmsL9Rpzlq40QGr7viXI1i7TesBZjrf+TL3WSvibUoP22TUn4a65efu9Z8+yaUu9pu1pw1a1fbP1W/PojU1PnXgpn2tSC5pbAHkJsLbybFW+bTk9QG6lrtPxhMfHA3a7Ebub1wliojAuoNp8fKWaLNWyFaVWt+Vjm2HYTrrgEmFkRutxbJnL6uozxvH9BimDK08/aL5VgQ9LKh1n5VWCniRpI5aXcW9mTk9rtrpQBs6q9WowcZlYlrdZn1SlYLf9rcG3PshwncilclgaUI4G6KcSX48Rg0hQmXMz3Pfj42MOdZSfj4x8PGIq7/nSatwYd1rR9Z5E0HLSzrl6gy1B4tiRkxFPfbcsS/aOa4FJP5Vb3t4U2x8zGYM0vlnFF4v0pBWDMiJbdjtTUocgt4R0y2FCv29JGi1A9vLY+myZhZ5n5SVC+WWAqgViq661WxFs2W2bept7NR8L1m072vqqitnar06nEw6HA4bR4/U3b8EcAHaIMSCyS/Smx1yvkxbvqCNFWZrF9Xgy9JSCFTe+LpPVaaxhyEljeXbKq+p4jUempPcbpJ6TNvujI/XwxiCBGnH2GazNhot0dcWWu1UHCuiHgSrJ+S8j4psiMrjVNYwtYY4M54Srn+eA6Szi+/l8xuPjI/7ZP/tnWfqIya1V3LJjcq9VQtBfcZstbHXevWGgvuTUEiBV5/EpYBhkY+V+vwfBYxx2uHv3gBAK2KijR8sJt2ozK6E8RVQve4+ld0kOvwPEDhRicfm/lHSsrO3I3muf7fWVvdYDvK1yt8rRZDdJX0qt+rAFGesh2tuM26unXtO52ZOG2tTz9tPvMcYkDRNA5WDSXj9ataP+7XY7HA4HnM8nTNOM7333u3j56gX2t8Ue6rxy5Vq/NkYnkJnq0nsXetZKT+VnHYvGWtt739oslaFnXHte1FPp/QapbfrWTXZA8yS8Amj6KoDLQNGvgMnPMkXc1KF7Tx9RyUsluroOPdXOk4mBvMGRdRFsPMeyORG5T1B9Kkd4d3dXxZITjj29psCUgWKjWlZFUhWWlmoeQ1PBRn3UUyNZQqd1JOeNJCneC84VgmfBxx5IqNesNLWl9rlE0Htcpa3/es/QZfVa217L6bfvXAM0PZXYlvpsqxygEHVt06Vzq56bt/7u2bd6z27ZoK4B/q33ZeZIhJSnJAV7vpa+H0LE4fGAu7t7MEe44QXGcWfqp/2ucR1rzkzV52nGp4qhT6oaclbVt9IOqjaJK55Q7m+0MWtJ0ie1ezKvT+83SF2v7zOJ+9+ridlTQVEi5L28aH3pQtFVDVYLglPxPSV0RxLjvseS/f6UGwZz2smeimTeJmbceoIRoK6UzjkcDo+4u7vHmzdv8qbEaZqwLBKPzFHL6W7XbTVSDXG4RASsOs8ClVX7aCSMvIdlkIKmaU6SYuHirU2qdZduHQham5Q+s9nODaKn7dA/K61tSQdb+WxFpLCfW++2z/TAaUtSsfds3+kG4K1nLyXb91ug1GuHVW224N++v5WsBNY+r9HEYdp7CfTsJmHNU1V/b9++RYgzbl4OuL19AYCM5MyIHJI6r2h3KjCm50U2XbU583+mn1Lb1/bg/rtZqrKaH1Y19vV1e89B6kK62AmM/hkoRrpaxcNK3P8lHdTFIi2gmiOfqyy4cCbp3CgAgGv32VyfOF73HquYYzkj82oILHug0oLTc6HOp1MCohmPjwf8v3/wL3E6nTFPIbs3A8pBWzCNBjy/JIt1IenCUhuSbadVy+mm2NPphJubG9zc3FRgYD0RAVQAZO1TCmLzPK8A7KukXtn2PK5rUo9IXitl95igHkBdkhi1D+zmX1XT9aTOrby3+tLW75Ibu3Upt8/11MJ2O0EPEFuwslLY1jNtv2qb9Hlbp2ma8HBPePPZAzxucPtij5uXvgAtJWkqSTm5/s/l2TtppSxkACFg27epZfCb743bu7LO11b2/QaphBsA1u1NfbCer4Sqg7qLlWoVksk0F7fKeGug1td6clrOg5rf3evNWx0OulUprqrVwYcirdBKyIwxrlvI4gzx8HgQFcW7Ozw8PGKeFoQQV5yrqhXBYqT9KsQz12NDlaPEQjl2a9dobRmiZgk5gkOxofVD/PQIqKoNbRlb7fky4LLlKm7z3FJffdX0FHj0vhcC7eWMJefgyGeCaiXPS5LZJZDvtfmSQd7OiVYNvGV/23Ic2eojC1R2bliJvjc/eirpeZ7x9u077HZ7MCJ2ty9KvVCoEXMC3ZZOFGL1NHnauq3jcfFhSyzcJoHLl0n/+WUAKW3oxsmz5RnV1iIRR1cGeUunWjEETxFOs4mOXINvW0Qq+dBYdUA74NR770pCzuvn7CKp7xnddec9KVNDHxVTKhGBI+H+7gHv3r3DF5+/weHxtO3WTNgEqC9DzC8RJPuMPXOpBSlLUFUteXNzc9XmVPu9BalL9XlusvakXJ9m+LYI6Vadr6nT1vM96af9VAbAO4lHJ8CQjtBgksMvk50y89YXwK6t65a98VLdt0CJzJxGqY6pR9m/1gP+Hpi1/WFVi+19Iuo6fCzLgjdv3mC32wEU8eqjnWmHMn1SYeZEgdQhlMtZbFRx619Bst+kg2wQKFWqcu7S9a8xS2mTSe2lr+YG1kn/zX/z36xsAX/uz/25fP90OuG//C//S3z729/Gq1ev8Ff/6l/FJ5988uUKuwTGZB8hQXh2AA8gjEAcgNgPy/9lUhHqLMiwqO2oVe+xreJPJxGqiMbtGUbkJMQOZY+hRh25Cj4LgEXKePHyJbwfEJaAu7t7fPLjn+CzTz/H4XCoFuUWd/pVk0pHNpK3uvTacm3YHz2aoyddqW1KpRWNMfj4+LgKGaRqvWVZcvw1VWt+lfZd4uo1bUaGuFBkj9B/nWkrbx2TAlBFjXU+nbHMYvNjLuDbAyhbjk2XAGrr2nZSVXT9F4PsA1SNQaljeX4rQkovWemxHcd2K4B95t27d/j0J5/hD//lj3B4mBBmwuB3GPwO3o0gDUKr3t8RBbS+1sQbfyZRFPMERTnjzkXAsfwZD2fuvLqVfiqS1L/xb/wb+F//1/+1FGJ26v+Nv/E38D//z/8z/qf/6X/CRx99hN/6rd/Cf/Kf/Cf43/63/+3Z5RSyqr795kZKXP1sxF5jdFwnRf8Lar38Ljd3DedgH8lZNUZN89Albjffap0qqmrIvVatoI+vJRjzaaSoRJKz/EmO8kMhBNzf3+PNm7f45JNPcDgcunaSlsh8HcRyyyFCv9vFreWpc8Rut8M4jlnqaet5SUIA1iGbeuquL5PW7TGcvZ3W5OUQSiJEoe7pRl2nr6riu0b62kqthFLXq0gmpjSgWr0MbVpPgtLv14D61XXltUtUqRuZuqN6vrxe27C6ZaC39urfW1FBdA4fD2ecjhO8G3Bzs8/vZQmttQcxCm3J5GJDF3dVat5tu2xF61p7ZnmGTNWeSj8VkBqGAd/73vdW19+9e4f/4X/4H/D3//7fx7//7//7AIC/+3f/Lv71f/1fxz/+x/8Y/86/8+88qxyGA1cdl77nzlCRmJOfhCEqOZeNnqrGQnu2Big9/VNE6/KihvB3sBPRTKHKicKObajLtmCbF4jesO/bNnD9qaqgFUitVRycQChLDWC4BMTOAYHlhNzz+Yw3b7/Ap599jh//+MeY5zkvLhv7zKoWe4T8KQLYEtxWZdJG+QbKJlrLqc7znI3myjC10dc19cL72Pr0VaFfDXgzwc1yfzO+rG7FnO47EIIh7XZnSz/vL1PvLy8VmrPQjCrykvqw/m1mcbrX86Z7St33HPC6pB6MzCIIkJ7yq1SnJtpP9VerEmyBzQZbbsFXzmGbcDycMQwjXob0nEPa19jsZ1KASvSCza0vzcJYutW6lG+R0QtM97XR5X8qIPV//9//N37t134NNzc3+M3f/E387u/+Lr7//e/jn/yTf4J5nvGX//Jfzs/+uT/35/D9738f/+gf/aNNkDqfzzifz/n33d0dgCJs2v09ubcYImICKxDJYufFOdWov1I+mYhkjsAcQsYwIi01Y2jrVvK2OnmkyA1SR2t7aDikJ5Np7cq7r4OAuU9KM9JWW0QIF+kcEOKC4/GITz/9FP/yX/4LPNwfMM91oFCbvgqX+1R+PY5dI5mLK/wBQFENhhDw8PCAcRxLqzeA81owtZ5ZX7VNGZiYGhYzqZZi8vZkmUuOgEhy5tA1U6IF3K8rrdu/XiO9OddTRzFzZ76atdZInF/n3KrrqfUplyOLNsE5hxDLnFdAtvNyy8OwB5y9DcL6rOanjJd19PnWt74tazUKgyICuPWgpdKiSqNy3Xx5Mlnv569p0+5W+tpB6jd+4zfw9/7e38Of/bN/Fj/60Y/wO7/zO/j3/r1/D//n//l/4sc//jF2ux0+/vjj6p3vfve7+PGPf7yZ5+/+7u/id37nd9Y3KP+DAlA22CHMgrfeaUXKqC+twUCkVlceUialo7Koo0qI2qJwsjZApIIqsiQmhWkbzIRqK9pMcjLNKYydfabHZRcY0p/F5lITj2LgDJimMw7HR7x59wVO5xNCWPL+Hanu0wT/qyQFoBaorEOI/lbJyT4XY8zHrF+SLLbUXV9HW1oVVp1nISD2lOLqPgCi5KjDEDXsBrFeq9vq30/V76n0pNOK/ZKYMQWn0te2cDRuzq5gVCTA2lgv2KN611umppLwM5tZGFdKajFVBCoz3M4RIq7mXyvxtfW0GgZbFwUrvW/ruCwLvHPwziPMM46HAz7/7HO8fPkSw+DgBoL3SbZzSkM62iJahw3rJktDlMG/NC0a6cy229rgiMiEahKNwDXpawep//A//A/z9z//5/88fuM3fgO//uu/jn/4D/8hbm9vv1Sev/3bv40f/OAH+ffd3R3+9J/+09UzRI1dSq42ndvp6epSbwTNwhZqrgWiTONG8jGgUbgxqh1resTAxhRccdRcfZTqUe+ieba5l/eHRajfjFaFWcIW2Wf1xN0QZ5ynM47HA+7u3mGeJ+EuO/pz+/l1JUtkL3Gqmrz3VYBXoH84YEsQ2nza7z210ldpS86DCmGsxpTL/WpyGQZoNW8707gnSfXUmdemawCvd1+ZnnyNAa7mqz5o20YiJUSIduSKevZAygJGy1jVtIPKmMgLq/baNmzNx543X1u/3lxskzpVOBDCEnA8HPHZZ58lZ6A9xuTmr30jtBCGDinDrQxw6e9+TxZTgOZzaYwp90lpW1lXEZG5RNfwcqCj2l6vST91F/SPP/4Y/9q/9q/hn//zf47/4D/4DzBNE96+fVtJU5988knXhqVpv99jv99v3q9T4XvWnVCkqvr3dSmEiGWZUl1qY++6DNSnBlcLkPGssq2wiGe+q8FcV5UAOHvw2aMFzPHnIeA8TeIuHCccDkc8PhxweDgiBkYMjGWeTX5rL62vM7Gd7J3FbO1QqkJRTrSXl6aeNHjJ/vRV1EytdLfxVPma7QoNYcvX2/nwvD7/qmrASzavXCNzr7/vyXJ2emxre83lR5nF6845enIpbDly2HqtNACoVe1EhNvbW8Qo9ti2Da3al5mrs8fa+bo1B9o5V6k1mTFHUTmGGHA6HTE4h9cfvcY3dx8jxCC9RNJf6/IKA2R6JM8h1bJmJ2DFsVgzrl3mMEaEGDCdRUvhvcduv5O5FQUJnRsESLNkdv2c+9pd0Nv08PCA/+f/+X/wp/7Un8Jf/It/EeM44vd///fz/X/6T/8p/uAP/gC/+Zu/+ey824MNBZbUsEwgVaslJwcHl+65JGpS82fyIoZEpY7JMhPhCPWeGzDW0ltaRBpMNbbeTM9sI0ldQFzcOLNbewQjpPrZCa7BR1swNm3lMmFCDOl95PdENbbIuVDnBcfHCe/ePuD+3QHLxIiLkxN4c5u3vfe2COFTdoUegVEgtJtbtzaE6nvXPPecVKmKLvxdk4/Nbx1uClmIUMZTPsUmpQfjof0zLz9lx2u/X5vasavzkHURuZwftGqfbWcFynH1J0dWBHA6OC+X0jBFNvWkHd22cDHWItZjEmPMWw1sPMo2HJZ9vi2jtzF8BY5NXrUEiiqvwIz7hwc8PDzifJplPVsGsypT+lDJQMx/AseBNUTYnGmVlq/MniOS+YY11dRndrtRth0MNqq8AjIS7eJEW7UmT6evXZL6r/6r/wr/0X/0H+HXf/3X8cMf/hB/62/9LXjv8df+2l/DRx99hP/iv/gv8IMf/ADf+ta38I1vfAN//a//dfzmb/7msz37gC60lK/cXiscCScOgrDKoH05a6sBiOGUXVG/ZJVArc5ApeejJsd+O7aSli9V5wsPUxLfGUok6oyKyM8w1WMrURVCEiNjCSFJjwHn04z7uwc83B+wzECMspGXN4jQczj09tlL9gSrv7fEt9X1t2mLIFzz7JdNPY65tU1sPZefT9xvcno2clMZz1gtdpvHejyeU/6WimpVx640tU2MN9Pm3FZmsbCh7XzZknBt2/R+u/dts9R039oy9foliTpGBlGZo+3fVjk95qkaJyTJhhjEjMPxiGEccTqecbO/BZw9vbqRxstHfoY50UNmhBjk1M2h0RaosFpJ7qbu6dMRwQ1DBfKdHk31UKmu232r9LWD1B/90R/hr/21v4bPP/8cv/Irv4J/99/9d/GP//E/xq/8yq8AAP7b//a/hXMOf/Wv/lWcz2f8lb/yV/Df//f//dddjZz6C99lYn7hzfTZ59Ik+qgOpiGikbuibHWFyoVtzHkuoVSC1POQ0lRUJjBSlncegQNCjDieTqbGjN3egxDx7u2Czz97g4eHh5V6o13wusn2eDw+a7Njm2zcvXZDrTpH2Do8lXpSziW13s8ybUqVSdLQKSOx2rxMsTTOHj5LAZSet6pbufLVPLC+DhXnVp5fRaLVz6366fhqjMZL0lebrplTdty8GzLnHCDqSEcA3HUq4q16abxDBrBwlP2xQD7V9/PPP8ft7S12GGXfLBFADKaQAJyy44kj0TKFEI00xBi8B3xhhkwPakvRj3cK1AwxN18L2Oklkb4kAsk16WsHqf/xf/wfL96/ubnB7/3e7+H3fu/3vobSVHBdy1SaelziVaoY7WzT0bVKLXEW7C4CiuVITObbEpxKaPmdJBlds44N+HVyTjhWe/0QAKeecEE8+JTTiTFiOk94eHjEH/7hj3BKAGaJS089YV1me3p2aBNNP4imqkhGNmI2gCqQa27ThvR0SWraAqree18p5WlC6zGhej4+pfbM7zCh2Bi58BzMSb6gzDit2pfmqVXBPJWekmifo9qsCBaQIqDIPNtyv67f7WRpym+jjfSkkc1xXv18WiXck0LLOxGelHFARfRbetSuoZR7VTEbpDkuxRlot9shhIDT6YR3797hxYsbvHh1C/IpRiJTrgMTg2MQSsnCpDsnR9OLRkglVEqKoEaKVu2MrWKiNzK/up20+p1bVpkjLqf3O3bfVcxdoYSl4xntdNh+FzDUoCmc0Ead0LhUQCI+lbrPlsoll0pLyQb0DFBSW3Y/ERkvx/pjRaAyB5orwQhhyXksc8DD4wPevbvDF198sSqr1cW3QNBL+brZK5Zx3BCZlnD17CpVfheu9QDqq6r6LtYjd7hhnszjtr1tXi2gV+Wwbl4ngELJMnuFIo+jVe2QnaJEYFYOO180z6+ZujY9H8wtF65t7nPRZX5s55bH0/xuN8ZuMSH9uhtGgp6ew5v1SkRcVfNF8KgB0m6PsPXPdbH1Qh2o2TKCwzDko3Du7+X8qXH0CMOQ7JUOMZ02wIl5cRxB8PDegTwqilTkqjr+RqEaLXCVNtdXy/dqFmUARGrDdf37foPU1ann4PAUTG1LZ/mJvJAtWFE7NOmqAQjS4buQf7VBbsvm0E/OO3NkvLju9jhoRpJ6pjmrQwB5NoSAh/sHvH37Dg/3jyvO0RL71vj83CRZ15JFT4WjEpath/3eupzrva0gsV8mXZZ4DLFjAqBUgHFpw+NlSUrmiSMCZ2CpmaFKfWzVzxmEAJBGM2GAvJkPZSuFBbdrU09yqdeFbATvzY2aMGpdn2LGtpyVyqnJrSr6ypZoRqs5c/GtqsM4ZxM5AFFUWq7pGysZrctar9PeVop5njEMQ+7TaZpwOBxwOkk8SeEHSpzKYRiw2+2wSzEVX79+LSOvTEzylMxjsnVUfW9IunOGkPeXGmatHPYtzlfXpF8MkNqMv4ftTlUByxIPLg8UCYir9woAbcRXA9ZiLtIyrJiMmPPIZbVcJDWZazW5GG4vcrwAwHGVQ1YBZGABCC5vtFPAOk8TjscjzudzXlD6/jXeSpdTIZLCyffb0XL2rdqmJx31AHUr362qVZFEUrokzcmhcATwAK5U0PUKp86YtvkmJWz65jJDQ0TwTiNIF45U51wBeyu2MTSCfVbZwBA9BoodohCnLkuXCzX11d9U8qyYhKi9QGAX66mNejwlOVC7qR1I19C8XaSWXJ6ZB5eZJpu/AYa0NnIEm2Zadm3cK7VW/dM6+rjkqkmubOLVuto22Hy1j+yaV4Cya/FwOGCeZ+x2O1Dak+RIVHshBjlN2mzL8H4nDG3VTxX7czlxhyFfvUj52fX1XyaQ2koXbUX6jdcXm4WYJ81KFG/Aher5WUkvlE60JDKAxaulIohhwaq6K7+Zhc54JTJ1O8qisW3jMlEIWdKyC4JIdrWfwhHTNGGeZkzTgmUJ1ULZ+qvbvZUK/6x9wNUQbG867anx7EK9rvzLqajBbB3r+229KmKSQIWrqZTGOXc/5cyp+tR7iXUhBSfdO5TuMRDZ7NjXd8lwr1XdKRHftUqJMyu9eqn+ySp11RKGBFrlqi65j8oySmDiMoPU93IzIG0kQuk7RstutYDfjvtlaUgBtp1zl+fO1tzU7/pMmQOyDSU7ADHDJbOi2nJqWzey6rInnep8s7EqVYKcpilLWOOQTggYx9JO04YlBIwpr9or19ChTpcVktqez7tm2O0FbjxybX2eSr8YIKUE2G4AvOQdd0nyerqwXobgHDHcelQFZK4VgNoLKkKdnrxcxga3orYIhvHoS9xyZlVTnzAu9EkEOWAYHW5ub/H27Ts8PhxxOizg4EAYAEwZEKwL77OOqLCg2+x3AWrC1YKe6uXtMzaY7deZanPJ8za7ZpCjNPKJ2DpS6Qr50zkH79TLqV685ETFt54LMrbOeVAUoqeSkAPBDaXyUesQxeAuU2Q9T9Z2haompsYRJb6kbYkZQwO4rbehEqnN/mSAo419V5gqICaXkSIJdrO4ei4aBG0r8YxkVXEKFuKJF3PPMRtpKl1zsGrrsl7V6ch6tq6ZyVLvZVmwLAt2u11+fhxHfOP1a7z+xmsASHsdJyzznNWAEcAweAxDOuI9sQDKivT7jDpdZpjfjcTMOE/nfHyLtpfcdX39iwFS4LTJrx5wQAmfAS8ABUis1NIShC1D5hOJVCWS6tWTwiqmgztLfvVQWwiIuHvkkyZ1nKnsFvkmS1i0dCtGOfpBhLgUtgSuWiSXNjA+pTrTwjktBiI5+K7m5zmXpcm6n5eqP+159WXTlsrxWk885nSERrrtkgebHD+XAtsmoHD6XwIom6/S56IKriUmyUcIhqskaJeAMWVC6U0CQnBJeDDeXlm6UmbHSHCtE0Iqe4iG+6eYiSyxSoDS7sioVHQybkrQdQyp+s3ir136zZFhvnwC4xLOC8wNfSRwDE1HPZ3ygZ4lm6+UWkmI9LQG7TcExHz0e10skRO1XEelHELAOI4rRq2NZ3k6nTAMA/wgm5c5cj23ELEsZ4SwR4wjkOpBSBoWU65oJwstYg1plB8SkOJ8P69kOMN7jbtB1n3aVCxKgV8CkOLMAF4nqudkT43M423AxcwPWUtFNbBRE/M8V9cLl9qXEirOcgWU1XRprqsI1T5NaHullzKjatrHaRLK0e9lL9RT6r11u9Hcq2soXLWUpfSxl8dTYPRcCefS7+ZpZJVcU0T22CRUBEaJrq5fxQkl3BLhI91gRo58Qh4iabUgpQVrdBStV2K5cr/Vcy+mMrLtQ7LIoEMk0oo68tRdmN4hl5/ntC5UCqC8TnS2FYksqyoTeLLRKhAxolrNmQBSZsWyaAS41A9qY3OGIDJSntaOLA22NpXnJ/5yr/Vy4nYLgDIqRj0WAY2+UCRVymSiVe3p917IsZaRIyIsy4JpnnA6nUTCqvYkpfeDRm1pnWWKxKY/rS90mr7mtwWwej5Z6a94Xpr5+ssAUnp0AYArJtkVElHlROHKNYNpq/IBDdacuDhzO+3jihB3VI8nTgJejVmHSuqjK/fNrOgur15IepsIiLoQIOvn7uERd/f3eLF/UUlPW/uergOLBrzZELaNyl7vmfVVU6mDyJCFW9WQU7meiZ6R2aAp99XWksbBSCPqRQUUwI8smy49DYkwOTjS+cGI6WwxSnHY1KVY6xBjRESKnE2i9pvOs8xF70Qd6IqE5vd7kPbnFIW7BoB5Ttxzkq6dh/MecAIzzKK28j4deTLNyZbC8NEBcc2wABHeKxs9iBs0M7wjCRNGZcOozJ9YwN0lhyJBqGyzE085BnNAz3OZmS9sYv+TTAzAVdM/70VCzBt9yTACl9buPM9PMFjAy5cvMZ0nxBDx+vVrjOMoar2cFNg4O1rkejEqdbfhR4yZkGHt25padT1HVr4HLR0TAeOXYZ+UpguIrEbM9TsXfltQ2gIocAG1zEG09SiyDRiIIXE7jioOqWqH4VLailWqrlVxFyQbU/+odqVlgez6FgKwTAtOpzN+8ukbnM4zQgROpxPO53OOWdbthR5ANZLjU6mAwE8nteqRnGJRtVUg5XyuPsdyxhdl1RxAAxmiYhw4HCPrWtWuxAIkzMjvFPWeywDkyKVNrsLQEFE+w2gYBoRFwlSJfcNnpidCCMHNbkB0AXDpbC3IXBuGATwvOe7c4HcQAY4Qycv8hbhNq8tydDIuHh4cI5iBZQ55bAmUjglJXZU5cnEeUXtazFJ6BEePSBFAALkSJ87DIxoGL2/MZZddlsss07zTvGGJD+jgwFRiBVZJCS3FaloWe1C97sgu/g0i3CarbnPVIYRlHaujRGXzTBKInH1XIsJszlkUOmAdiuwz0zTletiAzFYt2FMp5uOM2LXkpKwHZjlkMY+BgpWRFpiTas/SDOXuCM9d6u85SPUJ8fqpgvxk5p/t+DpdIJlZ3jV2lMxqtLhR65zz5QuG41J6jY5b3nOVKo3Md32OtMrlmhpnvfdCkAEs84Lz6YyH+wfM84wYImYOlZPEJbVfVldkW8MahFvHVq1Xz0Pq60kFEPRXfYaNB8gSpQRAZH67Mm+IXFbHkCfkVhuQcjnGnCFYDMRg6kAkQTiZDHBRYl60HgQkCUpBSvpQx9UQsXQd3gsBdOmoEhRnBQm27EQCcsWt3XlkySgwgZxIYTDu4pEJSNKQGvxVCi7VUNsWgRLAFgQTW5LYnBLhcgFgTvHNZb6r+p5cdgOR0ycIyT4VU7uzWAoQJ/qo38kuitxneT1tMlVAEWNsGfZeP9X2RLXZUOmjRmrMoJvpdbIHE+f5YPOuqnpBBa7X9CRq66au96xa0NqaRTvMdf9WTe/sXazqZvq2qn+dS1I3ZMb9mvR+g5RGBH8qZbGjnYBlR4CRauv5SGR+K+dVsrQqBmYGNxOMLFBVIn9XZyGlq8WxY6Mp9iubR+t+nRZ9Ipf6WEwAFYPsqbi9fYH9Xgya5/MZ9/f3ONzfYzmdEeYZU5rAtmwFOVte0V8bcKpmYOfASemhdR4b6TqVogN1qIkCA5GrpCd2SpcKdyEERomsXBzGQVx+jQSsdVLnA708jJoPABczwYohESuWet7c3gIMhCVgCQGZo6/6SOw6zKI+IxrhnMOZAxJupPaZmUsAHGEYfPL80w3bjBgARwN8imrNzGA/JJUuw5GqCB0SBifOWSQhhygMf0ITB5/7m10ofZz6kZFpdQK4kFTGAd4Xr1cXJUSPrK6yAdRld/rUh0gepdYBAwzvk5dnlD6MlSME5zHmyj0/ZKBWtRNVajnCauE2BMJKL6vj7ZXZKfyrAJCzGdWpVaP3AKp1HLIek/p3Pp/luIzdrrpuI7jv93vM04SwiPpwGAl+MHnl3tF+SHMRxe1stdIqKYmFWbG39VeyoHD8sE9qI7VcAkHjS1vAKvfbZK+00o1ydU9IA3zpEdG7125Lxl01M5EWVsu1Is3INCkgmjz4onjvvbh9jXlecDo84Hg447PPPsPd/T0OhwOmacqurdM0dYGqlexaX70KOJu+utg1VwCWTQRxPADKwi6Sk/xLroBXzA4BVDhaBhwPeVkOfsz5RACDH+EHB/gSxDeEkEPOKOg5B/iRMY6iSglhSSDBIpkQw2PEgD3gPIgZw0BiA0ogZdUw4zimo144gZSC4V7azYTT6QxGAJGE2xmHPZhcmgIpiCgBtzcDdB/S4AbEkCJ7k0Qocb5IZ+LdRysCGCOLDSr1reeiQhocpaM5GDMXNc+QiR4jRoIExw2I5gj2SH31E0VVkaWYcpAzzxwDMQSozZchR5ew52TbKSF3WMKt6GTR4UPZsmJutFM0r9OmXpUw0d6j+joBRKHkdQ19sFXoMKpbyTooaLikw+GQz+NTN3V1rgBEQr2J2scMDhFylIZLDEa9PeI5qWiEvlr6xQGpC8iiHna1qFpekq60HVoAIJGgdcZZYmkLb1NvgLlfX6qBqJbgtidKVesGu9iCHTtwTOq7SJinGcfjCfd3j7i/f8DjwyPO5zPmeV6dm6PE6uKhhllse8ItvdcGo+67pMqQZ1wivgQrPWWQSiq2/NvIGo585g7teVgeo6iYHMH7UYCNCJ4kAC85AtNSOGa3gExfSLkAfIAbdkmSEhUVOQYHcaF2bsDO6wGeQlSViAPIAEckAURVyiESwkMgsBepB0wYRo8liqTqxwHwIxjlEMcsXI0lUJunEYEiXIjVpn8L8AERlPZxZVUmc9r2kOZoDCDv4IcBg/OQM9QiAifJkCF1U6bGC7fOMQDLksHDAXDDkMeRkZQkzoBUFFsXWPpsYZLQQ2nSywpR9+Zazc7J5mS1ChZ8riLBjA011nayTJMwcqjmnHkSl2iInfs2pNJWWUABKXv+nd53Tg45dU62QYQgkdUpUp4vjPUJD0qHJK/ak89KjTUBK22r1zXhWhLxXoNUkl0AwExv+1kmZfEgMlwTAdbpglYDgrTWepyW2hEkb87co0EJJvNKMVqW/SK5gPRmU38z8FtE3zpS2Eey2o9R2T1CWDDPcwrzf8DxeMTd3R3u7u7w8PCQAcqq9fR7u3HWOn+wtpuailxIzwGy2mYlf857UKPiI0qSCQuYueRtpw4jjoZsi4tcpDCHMe85wTCIdOEcnEMGbIaH96O8P8TcVtkaJwR6DgGRRnjHGEdVQzm4ZZBNl8OIFy9eiBMNxwyfpX5JOkkOD5axmqcZCy3VPLl9scPDSQ7Bczd7gLXtQGBRz/h0ZHduK41AZOx8TehcsiUxM47hCDd4+GEE5wCnSmxkHpynGX5PGPYDgJ1YkZhBCGDISdaPhxNCFHDd3Xj4cQRxhD+qfYbhUaTnGKVP4RycHwSAkuTjAZGUYkTkEyg4xOCSMnkpSxqAIzm2XA8M5SQtqCpOiW2aNbk/q58m6Xuyh8vSFzsv66ROKPbZ0AWjy2vb/l6WpZKY2jpqeRrabBhk3p1OJ8zzjJubG+z3e8QYMY4y5+d5BtwAZofBD+A0/5BAq2iYEnwx53WlTIGaFgpJM85kKlSSIQ2GGX8q/QKAVDOnKhsVJc+kRAiS4TUPMPMVwScqjX8puLoAc2Gr501NOfMqBaiq5y5kcf3lXCazHAUfEzfnvcc8zzgcRIJ69+5d9ggSAu7k0MMliLEajNpTx5TQkaqeK0X18tPUOlWo3YPUQ0Dtd9Zw7R0oGvCKQkCJgJsXL/MeosDGPmXVhIm4yIbMILHQiACK8H5IizLCe5/4CFXnEF74FFSUAEfqScmYzhH7vYSdAkY4lwgrpSO3k1Q1jiP8OBSjd9ZWMQI5YPCI55oIjLe3GLI0IX0wjqNIPqkfBYBS3dyQpeOAwoG7ZJ8KYcE43kpYnWHA4+M7yNYp6TfvpG/oxQ5EDoHImNMIzEJWvAO++XJXaL+nJB1FzFzWikugpwyhtsER5SPJQerVx+AY4G/3iPMMzAAvaQNvUlk5X6QxZlHnRwex4X3JuVnA7cqUTwVPPzfwb5NabKi9+155/dQ+pwGkbYQKBSlGAEeP4daX+ivt02VkGOrASzJBUY75WQqWD6e2a0ayxen49UF9K73XINWTpWphyCVOrDgREENOtlTCdtWktWqI/vNEqSzUzg1WpVA7E1Dnu+EuLlTtkp66/q6VMBJO4nxCWLCECfNyxvl8qo4DkPak7rmAu5c8ja5Ll9UcNu9K108u/6kbfTbYK5GDOV9IJSYiOCdBNgHAsbqaJ2+7rDYUFZ8jwqIqLe8BYnhXpA3vhWhHY5sh5zN4UWIQHEUQs0hn6T+RUl3Zy8QRxAHODXB+BPlBJC0vLRJQES85Pw4S2UKByRXAIk5ecTngrfSjc770lfNw0XqSpXOFvAdCgAPDuyEdt+6y6tN5D05gRY4wUCIfDCBI/xIAF9O4culHS/AiIni4SeOV9pglaX2JAc57eBKze/aBINUKRERXQJ2STSpGl8QwGOYvFcgOefNyrrDe25aeqnnY0pknkmg2kh3P0JmaONfc7mpdN3m2XoTdcrnE9pN5WN7RPpaj4pc6UoVnuEhJW1K8TEERcCRqX7NeQwwAEQbvUxtNz1jFk9IIE/WiaLeuoxvvOUihxiYyFwAQkodTZCyIEo2eAArCgBfPFWzSyoqTN9frx3WjYaxtQmwGgmqCnPXvXKpQTgzers9WDVZ3LeAkydzpdYqYwwlLnBDihGk+Y0m7z1FNIAW4tWjeurRuVq9ZS7ULuFyxm/qomu3pmpWSSMgayIOS2k7VH8SFkAmh8vDDADifxyWaIc9IDGDc7euFr19dAAYHGnTBuxxXLz8fSNRYJLawcSxx1CIzMAD7nfH+YsY0TWD2INonddwC8IRIBMIg7SOPYfDwg8cSFrhTBIWQvbY0xcjlbJ7svejgwwAgxQf0qVHKFTsGyCcvPhL7GZCiZ3uMvoDx4Paiuho8gg8ZWHfOZWK8KHEkh50H4rJkO8zgxYM0cjp9ljzcOJaJkhinOUw4nu7h9yM8HPbssB/3EIiImOcFkSNiTPEsiMFeJnaYGXERAixj6hI2CEA4yLHrsExklj01vhihv+fSvmPoy5Y0oEMRhSaAkPd25fdU2jDzKMYSP09rZhPZ9833Xj3GccTNzY3RPpRnOM8/Aa3b21vQInbPJaRzypJ0G0jslvt9vT7mpHZUMwIlBiUH0s5labtEE+Ocy2Afua+dadP7D1LKZQGwencgdRAzQoiY44xxGOCdB8cURZpUDJW0mp8NKln8WT8mRNExJbG4EN6YOD6tpw5Tdyb22nfVg+aVJD3qa9454YJJTvE8TyeEOON8PuE8HTHsGAEzsEQg7J7kcJ6UoPLlrTq3cL/dtuKpJ/YnBwfnBBDk2HQFGgK5IYeAIXh4N2AYhypCgYYhAgDnBijh9uOurk2qkic58oCdg/dDvumyjZFALNEiBMAA53YSv85JUFQ7aYgBx4z9qOo8nScjAJEu4AS2d7sdxt0obTgeMYwEJo8Y0kF20IPsCIjAEiJiGMHsU/sAXREzVB3ockVEEFpMBcWG5zyLyjRJJPsXr+QVR3kTsKwdl32/9mPilBkYF0Zwi4DGZJgYApyXRefdWLj19OH9DlBAdAxPsnkZHICYDvQLwBID4jyDwHDOYxzFNWZBxBxl/xUYUAcbUHxiTm+zn226ZIPK9wlJOtTcqexZsyU2eVTH4ZjnrtVTqJu5tUNZoNJn9DlV8YcQMO5krYAd9vsbjMOI/X6PQJwdT3I9GEAKn7bMJ9zc3spmajAcF4ediMRAhZD2Yaksqzv7fikkqbQEG8NSFurVswgsC3vQbqHMsVeT1+RDOa/0aQhzIZzlmZwfUVoj1vU2yqLnxLdRIR4rbomorpOZpTUH2CajtuB6AjBiqpMc2RxZNvPG5OlXosfXe6Fsm7dUjO2iVYeNvBy5v6BrCbVPJNYcoyucWx7DBBRUgEKkqyGF9qEqPJDd5SEbmaWMYCRC1fuTI3EcSE8RDcgwRi6pDQkOvgT61M2whgDr3phMoBgYxhExBCzzlG09ubWkfTogwmEJQIQH3ADnZYuCBkV13sHzAJBDjBPgdmCkk1kRMldLMeS5rx8OVM9v3beiel4CwIT9TiJXMzOWtDcJLACuY5bdAwgS6ocd3MBwHOEHGRemHDwqxTQvrzAzmBxGN4hglzQfcGl/E2nUBAIx42a/l/1Q6RhyR+LUsISAGJIEg6L2S9gBtOvL9gnQ0BI2t2pvtuptO5fzdy5EmdXDlJVS52e171dqPKsJQf18+z3XkMvRGwo+vfWpTlBLknZPpxOWMGCYhckb/IhxGIU2pA1zMYZckaw2TIcvjsNYAhKThvNKKkGWPvApaLCOvgD3L0VYJAeYDX8lEnrqSCNRxKgWaJKd766c91LoYhO0ipEJuCXUZVKZiZLxkhBJFlwMASERe8ecCAdK5GrSnQT1hLTzTwUp69FV27bKvM8T0iFpMISQxeSuy1w89Gyoo1qVUSaxvddT62k/rEHMfu9Hk2CUMDqWGHCzgO1nFYQVSnSS27hz4hyQjPrsxOV7jgG7YRQJwTnA24jRY26HGpUdkUjcXtRs481N8u6LgEY5B0Bq43EOg+r+nYMfR4BjFfFP8q1PVx13eyzzhHmZ4McCiqX7CHAO88JpT5EDuzEB4wJOLtzkRnjcgJzHPB+T+3vafIxZ5gADbpmzzQY5eweHAZzUhUvqVYJMKGbpj9vbF1Dnm2OQfXORI3w64Vf34lEaQAZAHnAeGEdKAC7tUQI3h5CJmINoGzwjl63rxKXNxaBkSwwe7AfsxxHzMmOeRW2FcQTHHeZ5wQJkVRInRobyouB8xEudlBs0G3p1f1NHntE12AModayB9iMgzgWpTzOjFRsXdSrMcy9tMXtt6p2U3a5jtU2FEPD4+JgjVMQQcXvzItMIN8om9hAj9EQvtbWptLZ3HpzWQFCaCuA8neBc8jhMhzASFxVhN4RVJ73nIGU4dkCICJA9okT0FmL18HgC0YjdHqAxMYpEIE5HADBW4V9lB3sEUehKPYW2U5LW0vZCZVbHAR4DzqdzcoENxiVVuCquXODRFZIiWWAy9htzPELBiHUG6vLOaYEOfgCYcD7NODyeAMj+IXaM0LRSiUqbWi5wW52ickh7zWa2ztcm3c+h93ySVvwwgFwJbeSNjWph9eT0cH4UjzTvAE9wlLzTlMgyY+/KPh1HXujZUg6s894D7DJIKWAWR44k1ZHKTDZ2edMeD4AinCfs93upV0relygOzjksIWJJ0qCDTwQvgsdYCBsnNc0Lj5BU2RLzTiQ/2Z/lk4MFo+i1KTNizgsx0DbZMT+dpwQUhNHvkbSJIhsQgx0XEAbgde2BsXCAhnobpFFw3mO/3+F8OgGRMZo1wRCjPrGT4LtIGgAKGAYvMRNHwuF4xLIAiMKVz/MZ88QY/Es4eEQ3pX1+QgyJgCjwBXCU+Y421l8DRjZCBYDKHpAAB66AgD3aBqYvrXRj1wpHAiX1ikoawnG2TGjf7rSVVKr3Xua3uq2rx6rmNyRmTNe4buJnZtze3uL2Vjw8XQpYvHAEk2gaOKnxwIzD4ZDXw82NqKxDFHWi1N/BTXqMvYcbpE3TPF3VnvcapIqHHlRZhezvw6LCcSQiZogqV+i7kioVCMpFst/JvJQmEVviy8X3xyrahLCIWBtDRCDgZhBVkEgBXPJNL7f+R/m+ilS24qqRsWsn67SNvGXCP6nKhDmF9J9mDH6XQLThFp+wTV18ru7g1XOFY1SpZjt/a5x15CowksgRVBEEqNTlNBp4krC8qI3yfigUouHISlhps3CKVJFZIYJ4nGmdWuJBWblWxpdU6qOUJ+CJxDZFBKQjvrW/nM7plJ9zKfgQUXbWc46Fs01jxskFk3yqUyzbLUSSTZtcc5/bOUJ57sInTYO+V42bLDI/aKibsnKK0Rz59AnSDbTkEnZrZJfSG97vQJ7hvUo6wsiJNMUgiikos76VwlU5cZd3foTz6f4i0SeE1o+Cv7QAtOS2UraFFPZBlnMLVKV9VersV7FOVc/3cmWZu9AxTNfoeqnpyRIabUftwUvVcwByUFq7JcV7L2GU0jaWxc1ZohKNVcxaK3ti8DxpVAsHFwk8CmMdVZq9zm/i/QYpR3pUtxFr9Y/EU8U7CbSZty0QgBRCvk6c5yUBfScfK5qjTGfZfrF+gdIBbqLemAEwxpsdvGslwLYmrUIv0xsI0eEV09c2paClcm6JkGkbImOeFpzPE9xuLx5TLPaOnE3DET5nEVoVogX8Akzlrz2SXMvUJJ57IvmoWkJdzpeUp3jzGWkrnWzqvHi4wTtx7UYBslwT9Qw0bc37pKDbhRU4tFtVPWdmgk48/UHa3wo+AlCDK2SenKsAUq5L/WT+IO8x0flLALAEBKhaR1WJ6pLPuQrMADuXj5Eh56GeW0CyDVVll3eQwIKpqIldeTSPnnz3GZjJPODzQuPcd5QYizFF5hCgkvdFjSmqOUZE4AmIqf/SvjYGww8jhlEO7ASAsDC8E5UsAqdxGEDuDEKJ1gGNWkGJIUx7AnP7K9V3HTW9bnlKsUwBjoWJrG20qIDCqqyV2ZXnih0tqwW/BDCt24JKOm7ztL/Vi09s1jGvt3EcsUyT2PxYrEnOOezHHdTuricTa1vnWUDKOQdaCIgSjzGGiGEAYriOnrzXIKXJSkg27Xa7pG+P8M5jmRcQCPsU3LOagAxQ5HIMQEWQ1cjegw99wn5Slko4AtNZXGedI0jMNXRR0PCmqFF0Md8tPD6d6kXncf/uDu/evcOP/vgTTKeA0d/k53JkhZWNqSyyXrpe3VfbEOVdlSJUXZScH3TDLulR2ypNeQR4Udn5AWMi8D4Bkh7JHpwXQz0Bu12y5VCK6J1rkBwdyAYzRT6KQPeIieRUt7ei1hCngMviINJR8UYdTE6Elyb55H5ORFiSSkpjqQl+OfEO9BGn6Yw5vXez32OexPY4DANC2gslRNkbhfGQJNB274/RD1BpuHNlz005ngYZDDNUJ4mpjpNbE9oc6Z2SJEkQMIoCP44IPnk+InLy5YjCWOr+K2IMzsPtR4zDHufzCcsYxaYVhTFl57EsAQsHENSRIO0Dcgy1NVOUdSmAzgjR7BekS5xgHq3SVmpBgDZXKjNX00XUyqXfvgo4WVWtRkQvm9O5Uk1qXVQ9aK/HGKtj7G/2+9xH1mNWpPoUheR8znkCBRw1ko2eSMwDbx7/06b3GqTUe641Dq4SIRtLQwwi9MeY1REAZJMu9yBIxS/llotoXm3wM1KYI4CZQGnf0UAeIeUvKuxEPJJU0ZuOmq+0SdUUeu/y4tGD+mRCIhNigsfDwwE/+clnmCY5z8e5YpfZ+rs2daVJsqpG2YBZ608hR9aTGo3TibXOG/lFeXYH72TfkzhIiNqSkNzDU7QHOPmzHL0SRlJqqoyAbp5LBDlLBxlIOgClar0i3prn2JRpiFWh4hYTGlwrhVYepHrKbq6mqg59ikUY1aRt6mWyA8D5QAxA1aNl/ur4ieoJVPqAwemAx1LHvEqUoYM4ORR1KTJv4sg2WKWD0oakZMqAVvKXkFHejWCKGQB0uGIQ+4gjAeBx2IEgRDjAYQGwRIanER5KuGcQmaNnkNqlmgmkMEqW7c3zt9S23utXTY6qbTqfylpNfZEFXQ1trf2friY7aLY5XpB8eslKcVa6Kf1fO3soQOmfpafaV6rGy+/F4kTRAlIrwVm73JwOzWyfu5Teb5CCaajpWGuX0eS8AyMFS40MTvuW6lMoS2fXSYCKWRykcgwrnW+MSqectfxB6rLzI6bEbZA6/ZAqPtQGkCtRtRBIRDwnjRnXBypRFSjBKh5m5MRJ5O7uHp988mnW9Ttyq2gTl4LIbk2sS9JXodWqPhGJSFVv+T9yRdXlhuyyr2NApGDkk71J9NpEBD8McsCeIwExWOlO+kNBQhZNck/XRYvCGFi2QYmq9i2lPGL1LBeQIjJlas3Lp83dBumkBMIZIEoFqo2ger84kXgQFoCNR2auQQFHCWxabEcKnPaoGTt3FBEpnx5rcs6iZaetDcDmkDkqVaEAve0bfUhKj+nJCO8LaMVkYx7IYYpTIu8Rjlw6fVa8Nc8k65uXGYPbwTkv3pkAljRfQlDPPQc9D0zqrvO+2WBuakuNejY30ICU9oV9L5enb6RJp4wB6zUuElkLVPbaVrLPtyo4tceqIweALEHpuWU2qLR+tyCV391Y7239VH2uThzqnHGttPheg5SE7IgmpH+5JRN7AQjwA+P1N17i8fEBIc4IMRT1hT5/AdWZCWFJZ9nYhd45/6f8yRhyTAfXJYQbWEEucY01/emXX/2yUpXV/+sxDoTRj3kfFKLD+TynWH0nnI5n7MYdeBC362VZqoMNgZoTu4bbuQRcNr/cmrQws0SQg8SmoLHOYUxu4yrpcro37G9yiB9HhLhDlpz0OpDC/CQukYbBSBeqmis1iipnEKHsf1JazsU1PP0uZEsJeusXmgg0LEGT3SG6+VXbVHhsCaBajE7r/KzTgaZxFA++EMSRQoPEpo7PahnvCiCynUJeno1p/VRtRWK+VDWW+yS9Y+q+GuP0XAVKeZoQuNdITqGcYgQQQBTh3Qg9N25ZFlEBxpgljRidOf5c5rQfPQYacetfgjADHBDPZww0wifbq9pcWhW3S8xLlJMWS91c36hQ1h9lkIK2efUwofYYbBhAPd2Y6sMJrddg12HniaTAouCkYKV5q/ffMAzY7XaZJmi/hBDysfWal81bAUjpiNb71SvZBK4bjO07wzD8cqj7gJaX4yyC1nflrJ+wSGiVoz/hxYsX8HbwuZkyWVCpOYZqahixnasaNA9k2YskaCuQJ0X05d1e3qvvuV52mScuPH2TQ/YWsYUtAdM8Yzqf8eaLNzidxOW8N8krKbSj7nvqdy+tyilMdAJWJfdF7aBAJeosDZsgjhPkNJZcaq8rtitRG6ljhMvSloKEfgJUvNBg/iiZmsi6mOsG4qqnUj8jD1om4HnqFVUl53dkKsVUcKVWVEmjklDQL5+Tqzo5zPOUpKnEHbPPMyOmColqKxHQdmnANKMZqsLtOvRsqDUslTpm5RhbjQHywHOG8F7SCSI2NHIjgAB1o0dSGTMzyHtx3WYGxyhM2SLYQh4YdiRaCGZE9phOLrmck0QtgRxZo5uMxfZHSXok6FlV0qJa5V7VmMwkQC3JmJlRJNDc4Zwl80LBkOlNz/lCf19jH7bPW/DQ61aiqt4h2UvKUTZHh2XG/eER+2FcAdWWPdo+tywL3DDAJ1f2KBO4oZXb6b0GKbV/VlodWLQp+y8cgGWOmM4Tju6E3X5f9izpK2Q4Wztw5pqRvbRAWUJ20bNdmJy5b3ACqbT4Bz8korVOZV+yzcs8wPWFSiXIDsvMmM4Lljlgmmacz2e8ffsW5/N5BTQ22QXQbujtLY5LIHWR49N+KayoEJ1hkHh7SF5BuglXPde82KyyuohKEE0roZbrxdWcjANGroIBi7TfFACb+Hx1zD2dK0WSEkmDTH56S2w8kiJi3tem7bUyWRlgqYvSM1WDKsCordEle9TEMwgSg887xhKLlBKpqP+izpcKSTOXBTtMwujZa33uvx5Zqj45tVA3EyswaTNr2K2RXoEFTAmkCEgOEUCah0GOPB+czJWwTMAcM0BJqEIZc2JGhMc0OUSUKPWyv4vSIZHiTegogEHwxFm6ZBQmooBWU3UqY5fnWmYKfPVsGQN93vRbtpWV3z2wsuq6XJUnQK3HYGr+lR2aBLgjIHujFsb94yP45hZjis5vpS+bp5abfQBCwLwsGNPp1m4YkpdqimJxRXqvQQrsJJRLjGtOFiiHSyRiv0wTptMZnhym/R6O0+mnyeMEWwS1X3guTLUZfhiwzCG5w8peDgk66rCcF8zLhMfHx6z7vbm5EVtBr9iUfeTkcah146JkyY8yMM9TRutlYUzTjGUJePfuDqfTKQeU1HQ6nbLKY1X0BhhtAdQ1KsG1moIh9rXkyecGjMOIYdzBe1kIWaIiEjsTuRQmJxG8LOkkp4BEcMi5zDQIgUlKNtPP2R5G/f7nyIgkBxZqRn613Tu1nWMifmTYIo3ZmAgyU4oXKQf/tRKNUaDIG6laqgErzwq4TfMM4jldTq7ZyQtQ6+XUrTtGLIaZo+y6nkpuxbTMO+iNxiZaP109aZ+XkIJFdersk8Y+iUgVQXXJ0cc7wA8OFEcAHiEughYu4ubGpzwiliUgsqicBroBTYQFAzhMOkggCtjtX2AYAmIM6RiaANAe7nwCcwA5RohTDtcjTn8674Ph/LcYLx03B9lRp7/LvImc8qnE2iabymOwVq9ZKah1hugly3C25g1rdzocDjifz1D7kezPA3bkEGLAcjzhyIzZF28/zVfz8d7nM6qGQcKSDd5j2O/yrOGa+7kqvdcgpTp9IQjJZhRijoUGyM5nOSr7jHmasEwzziDMN7cY1P3Ym7AtqrNNDg7d/VIAhCikvTohSNDL44SQjti+ub0FQaJCRyIEjpgSN0iJwxjmCeRTxISWK0rsrB5RXjHaRHkBWK5MN9Sp8ZuIMIwDhsVjXur8t1zNV31sOawrwCjX37Snt4AIXmLewUtgWDeAnEROYJaFrn8qSSHti8vebrqXJ+Uo6sAEXLq7SaWX7L8NgIVTpM36FYcT3ftC0JBISUpRAkMERJfOSbLHwKThImSxRNdnPp01jV1xHqmqgDzAmUER6QyJw1cwYQWF/InyTiKIDgp2jLydRyW2nHlqOSFHU5dbtmatmi4xKpZpQunqmOFONucinRgAZxSErjB7PoG9o7KfjIjkcCoaRH2Y7ITMAREBYdao90IoadwlhyAzX13E4AfEEBIxdghhAFGQKDHLgmme5MiPPDjayZy/E3e2oWiDlRmqbJhCX2QaFMms9Fydz8q2t4pLWktIl+zq7b4oewCjPt8yqefzuYpKoTYs9cyLPuYTo1tHCQUv/b67uYHuoVpMCLYcwyCueqCb3m+QEibMAJXsxEdS1TBLp4QYMJ3OCPOCsCw4x4h5mjAOwr3LmTdCaPI5XWz2EmQWlPJkU4IAlvAg07zkPQKOJJIwUpxRR4QQGXOMcuolAMcRp3nGiMQVNQbrrFpSh4bUFjcMKdJ2M3lVRGeASAiNc5SOenDwvp7M1xotu/1+peTUu1a4PwdHyTkCI4iGHPCVI1KoIPkT1Ynu00mySZKgVFbhBFpK8F12xnA1wdfv+RoqcaAMdSHfud3mM5q7BEohbqz9SW1APc6xSGeF6GQ/TAOiiQlTNQrrPjOrcjOO5GTjV6a3tT05qIX0b6bBVU6xtLhwQE0/1DYIrWPbR/Y9KSoRd9KjK7SftCxOTF0CG3LZ3qRo75ycwhtJCG7kBRxnRJ4AJC9I8nCjxG6cFyWMsp48MTgFVXbOwzmRsKJ3WPyMoBuJGbIeU1ipFLMGOurVAaBpHonQYxmi0m3OaU9RWsepn1oAWq2rLYmj4i7yu2QZIbb51XuyeoynAlIJZbRmnNVJwnufwU2dJuwerJQBxnTu2bIsQAqhxJEz87Z1Nl+b3muQEpdj8ezyqmpJnRxQiPrOe3gQjscjmCXq7/l8xjiO+Oijj4T7cskm4n1eOBp2hlKwUT32Q4+9loEYEU8Stn5mVakx4hww84SQCKeczsrgwLh9scd+v8P+Zi8cISXPmOSaCQAj9CRSZOPj+XTGy9evckwzm7zXg/uAgQiHQ8B5OuJ0PmCazwhB4pbFuCDGJU8q4DrQadNz37ETvo7FJ5543jk5FiPtgZLNuWPeLyW7ZgR+ssOLZFykoqQGXK3tnqSaf1P5SwSEVsKCJdjIZRdAEq5S4NRXHnzqkLN5ArQSk0oNiqy2q4lRqWuW0iBCBrMc1+4qO18OZVyYK2YMQ4INLlICR0aIVBMafXMFaLYupbjySJEYyD7QJOeUkUi/0xoWXkSJnSmRUstZvcOEKIMTA6MEM8qBca9evs7tCUFmEEeGox3O51lU8y5giSc4cpiGWSJYJBSP8QykEwTUnibsqd0rZmYCq+Re6swcMc2nvCmWoGfPcRW3kyMyoxpt/19aZg0YZt6nO3zi+WjVhK2kpbRRk57grVKTRp7Q+eFc7Vmpf8siDPvxcKhUkprGURwwlumXIHafOKqK9OS9h3cew26XuBQWzoiQ7AvI0c+VE1iWBVMIGAjgwJiOR9zcJhdnK9Rn/YWE16F83HVMYUKEMLgkTosNxXDFLGf9LCFgGEYMo/zJAWOiahzGEcG5PMO80g8AMYo76Pl8xu3LFylKgXoyahWVQxHiNgwD9vs9wMB0FlvYeTpjmifMi8YoKCL/c7327HM9rssG1CxJ+iOr48ilxZskWRQ1nYCN/LH2JyVHBhtWyqlklXnVivBVUET19Xz8mqGmoqooXK+CnkBR3RT9ovApvzRmXpK1Sd2Sbey4QlzUNlb1a/pTQSJLaWycPgwtlBwlYnhUdSAhG/uR7q/qTem4BCbpajvmK6Qu17OUr0yOyU+dhmDuYPUrucanrvIqHZu+SDOgqrPtJV6WDB77/Q0QFzAHcIhpvgQQvLhSR7GMyTiLNDAOgIPHQgv2w4B5mTBNM2ZGOsKGU+RyPVBS1dDJCabtl5Q3s3gMWiYQ4Kxed3a+GkaEnfYd51myFqwa9NHcVYqCfUlHxYGTY08PlNo1alV3NvhsVvmlCBbavnmeCwBTLWUrDbDqQyKSQxaNevCp9F6DVBppiSOV9NZej2Lg4kas5ydR8i4ZYkzGdY2HRgjMOM8zxhs5CVTtCjrUkUq+4jEUwVG4VybZwzMmroIggKV7VjiqG7A4VwhIDTjPE+TEUFFVKAc1eA8EO6FSLKxlkbwy5wZlkdHK/3IY3JjekwMOlyUgRAnRZLnjnvjfglTrOfQlBit/KkBpGH/Z2Fu89JQQa6ghPQ0Xeo/KBmiX3c+zfJH7zHL6rSzk8rVSlpbvWrsLa5nr/pD5IMBAWWwoe8DkihB15ziDHSVRyFqjMnmh8jvxI2Z0rdRVAIiR1EqJKSr9YZ7MY25ZfSN5GoDQvWJFfaOPuKJ+7nhnbc+Tuv84RWp3KH0P0xfa4FKjnEtWTena3I07cBCVa+CQ2+8S0dSAppSAVdaGg3OyxnZ70aDsxgNiDOCFAARhYqKuP5m3xVqp7cjf0v8pikTV/kLoxetU+1r7VtvNlbqOzJg03VABE+nv9iFTLwAJbIstaUu7oOvABotVCcnG6FOzgZ6/ZkGntUlb4NvtdpVE91R6r0Hq5vYFXr1+XXEYnI7XHocRuzF5orAYBP3gcXd3h8fHR3zrm9/Cq9ev8a3vfBtAOl9lSZtaYdwzE0/HiaMSx4xkgPQAuYCbG8LoZRCm4wnz+Swh69kjRuDh/gH7cY/Xrzy+851vY7gd4AaHgfcAM87ThD/60Q/x9rPP4Yjwa7/2awjnCd45vHz5EgBA3mN3s8/EAzCcKQGiKJEJGSETMoSy92MYR3i3x+D3WEYRx9szpZ7rrXfpWQs41gmC4OFoxOAHeD+kYzcG2bdChOjE0QQAPDsQl2CyShxCQhxHTkhJLssVo6yNCqDEwxJhu4g0bJTW11BTS9OVrKycQqwAggKAwuXoy5wBNY+g2p6SiijkNhqAc1lmTtc0hmRngRPVe/9QNvAuKQciQj68NgGrjqH3cooxx1h5MiqHn6E+B+PFSgqvqmM5IQCqfuR8X3rKq9RsEiN55yagcgpYlrCpuEEQWy17LOGc/GrS/Axyqi9IpDdmwO0cRkg7iVgYtxhxc7MHSKSFaZokKGpiQBkS6TumvsnRa1aAlKqmTgEEAOowAiwhSSKmvfkonaqvVG53hvnRvlNGtegOuv1PzT0WuqCq1HxQZwcslB4osAzDgGmasjS23++z+/lutysegSZ6haUpNlDttRKUpvcapHbjgMGLhnhZRCW2zLNIIn4A5kWcCULE4f4e8TzBR8arFy9xe3OD/W7Mm0WVQA3DkDeBhrBAXXVv9jdCyGISh4cBfhzx4sUt9uOIfTq47u7NG7x78xafffYZvFfX5yCRMXzEuE9HRjgCh3SOy/mMt1+8wfHhEd453L99i7gE7Hf7DFIqIagXo3JceoiYHfawRCCdx0PwGIc99jsWHXzaFa5nx7QHpD03JJIV47vXk9Tk/ZBByvuhAJMTV1UlBvC+hIlSL01XbE62rUQuB58FW/CxIII8vluJo7praztMnDvte5h6NMklJMv7rrRQLoSFkdzjk4S1zAtyaK4kXTGpvj43KYFiVQspE0mCypJOIf3a/7lTYCRJBUVV12W7VCGW5OVohdw/Jl82NhdKtmDHjLmJaJ37myzpTQpWNsHDiQAnG5AZxSWBgBRXXdqhUhdAcJRONo4RIaRNvGl8xt2YGLaIsCzwwwjyaS/VvEjMP8dyphok4PA8HQFA1IaIckoBM6IbEaOEDUPwIJbtIBEB4OTIEZpjPrRfXdoIzAA4wPmkyGMYqWq9bvIAZ9E5bStIIbRo9aCVqowXsAGvMhZlNPU9q7IHkIHHOVep5Xa7HU6nU2ZqFXDGUUJRqft5T32odVDJ63Q+g5zDNBezw6X0XoMUpU6npHoI8yzh5IdBdimkCbMsC06PjwjTDIqM/e0eg5dD7rKXWxrTHGhxGBAXl9y6I168EJd1PU1n2O2wu73Bd77zbdzu9tjvdnBE+Pz2BUY/4O3bt0UM9pD9NixcTDUvObm1xgikM4LOxxM4ct68qAClLyof5SxQJU12MpWl7VROPOVSAM5lPmZxvQdQ1iW1tUu1wGUn3yZICTogRzVPEc698+JyTkN2lBB7nwfZcFVUArkq3whKyrjcblt2syiblMm4yU8+GbrXSieCJbmZW+3ZabpSRMojEXStY1b1oJFk0ngWXChjvZYCOQFbgY4t1awS+NyGDC4J1JlRAlCWlsL2qXJD2oGJADInB4JMEENTdnpBu8xsbF71U7ojYaPMnDP1ATipBaUMnxxqkCQ/5VX86OVYF5azqLyXjbtgBmKxzbhB3h92ETHOEAeJEcwjiMRjN7gdljCLGj76NGi6FrVeNpamaXcVrSLZqhhJVbl22dbPrlRalkN9wZaoQGMYmt6zlfYw/dCQSbZOCjoqGe33e2EiEr30Xjbdj7sdOIakQh3SeAo412VJviGEfMzHstjTHbbTew1S9/cPmKcZ+xRvap5nhMMR08NjJrIqor548UICG0KmzWk647zMePfwAAAYhwGvX73C+XAEQfTZu90u8f7AFAGL++NuBz7f4ubb38EQGTzNuH31Ci9vb3H+xjfwq7/6qxkgHx4e8PZ0RAwL7h4f8OLFC5kARHjx8iVev3yJj15/A3/wz/85To8H7Pd7hBCwH0fsyOGMCHbAuBvhxgHkvbFXyDc5pLQchKcHlz08POSJ//j4iMPhkN1Mr3GQsM/0Nv72kqr4oJ/qOp0FJDlmg9wAP4xgL0crIJ20q4Ah3maUwtl5FVOadWeUa1RVQurf0td0TYBKwUdO69XnlHhaXXouO+ddTnWNqj4hl/ORgyYln8GP+cRSOSNpxv5mX/VZpKTucylkArQ+AeBkf3ExAWVqjYJg8beBhQJHLp91RuZ0Z+dkzjgGOKR448wFIpirY0r6SQlcsfkoUzbPEuk6i4RIjBnD9KnaPMQeDBQHCk06TjpMRVZkzMs53WMMg8uQULaNOIy3A5jlqPuwLLi9vQVHxul0TCELIwgR4yhbHpgI5G/g0xqbjw5+XuB4RuQZAQEhLHBuzuo+P6QydO1tJLuOLkn1es9GI//y9mDqfgVqVV/LjA7DkCUq+333Ip0dxQzvdzLXvcfeD2ZLQWEch8Ew2XA5+gQvy2rP1qX0XoNUXBbwOCJOMzgsoCVAT5bUXdQ3Nzf5GOTT6YSwLIhnOQjNp4gGABCY8fjwgHEYErER9QBBODDrmhlCyIvyiy++qPS38zznwK2qt2VmDN6nkCLIIUXGccTtMGI/jHg17rH/V/9VnE9nWeSOsvPD8eFOOBiiFP+KKg5eVPMKW3WMLg0WifSMckjTBffPro79ygVSHB+Kuk+jQ3i/hx92AHkwDSA3gt2AmLz4ojpBKHGCxKRD2lsmkz9xn7V2SxbbmuEXEmkPGaTSS1kKcy67A4uDi9mT5Vw5/6lxb3cKGINwsZ7kGTZGfSWkIDEwByaEpdRHk5wrq/a7AogcBQjG3QifXPUjR7HwJa5cw1Jk9ZKRP9TUz6gBOzsXJBVczPJcBefdMS5MS1EZCs3nLGxmdWOeC0i2ohRNYvDIkl6lKiwMh+6ZalW2Ohf0uZUzg6m5nNnl4EaXAvgmoEYEh4DgHQJ2IPLgwYGwgOBlw/XCIJoBcpiB7GAlR88TyEk0BmVkIpZUrlWQUlX3vG8Pa6nJaiQuOTZcUrvb58iqe7dxsTpvCihbXo7HI5ZlyYFn3QDZgwoJngCf4o9SmVs7pypHxjDuoKpv7wcsSwAmwpwkKL6S6X2vQQpJRRZDOnSrOTtFPU90h7SquQicwIhS1GMGh4A5qTBUj8asG9BiBhbVqwIyGd6+fZsjBqvXi4rHlYEdyGdWIUZwiICP6RhxwA0eH330MeYXMx4fH0GDSEvKfczzIvHkbKh9uyQUpAgSKZpjcqWVv1Y91+qKJYvnOU+0qVLxGY5K3OxFenLDTu6nc6FAIiFxMp5rgCsZB5dByrDRANYrbi1FldXJ9gGiyptMXdtFchAJQkJRybMt+OX9Xel5iX4gERAKaac0FAqC4racgdBUtvRy6Tstl7RfwMnBRE7qRYqsTyDxUFOQYim7DF3ZOKmgDMi2DQURvebI7M+xUlSl9gP04EFOasgqMZeoXSkjZQyQ+oF0XJ1Kbe3g6TDp5tyi4tPnqqc7m66tilLVnOKzySCOGMYRFBdw6lewAKZXjR4IwwDwPgLOITDgwiJhrkIAVDVMnLajpLIM88i5v039CnJmxtLl+lGuei+1a/apRFQK7D3fOv+0AGmP71F14EBmf6PGq8y69zSDXVrBpJE2qC7D1eVck95rkBqEic2nPjJzNt4Nw4BvfvObebPZ4XDA3d0dpnnGy9evgMjw5LBLIePV6OjT4sh7FYDKow4o4UWIxOdfkzWIqqulcinT4YjT3R1efGvA3g8YHAOnCW+Wt2Av+btYADHOZVG+u7vHPM/Y7Xb49re+KdxLWOCQQhtFFgKQKjzPJxxORzw8PIoi0Hg/atj9VnWnIL6VLnnxpW+wKj6NP+bcgMGPuL25QaQR0Q3wg89OE/nwQpKNsEJkhe0uR3WQMZ2U4y6UeBVniUIaMvdKljaIbAES21e6mDdys6psCLK3xOneJiEeS2TsRpeDZOY6DMIZZs/CwYSHSarZMzPO85Tr5DTEk0o0rmxSTtXCfr+TAKvZaC5Mjtf9dCzBWyWil7SN4cEM2SNoJCPtHQAp6ncaswxCNaceDF1zeehTP+terAakIseayOpAqGseaY243nPmSrlgJG2B8fgjIJ8IbFLWG1gMIGUQtPlm7nNiZnVuONmSEhzlE3oZnLc+7G5G0DzhfM8YEAAClrBA90wJyCSVKiIoe17KRgP1wGs6pOpL1v4kgJlUkfAzTz27smVasy0qEsbRY09e6OQSMQzirei9xxzOWQoGilS2zCeow5cyOLGZP1vpvQapTz75BDc3N7i5uUmHng2yQTe5Vt/e3uJ8FvXZ3d07HE8HzJOo4h7u7rEbR7x6/Tp58UmemXsDso5d0ziOciR9cjCwQRvV/qRJDKRC+B8eHnA8nxFCwNu3b3F88QLDOMgpwcOYQgABu0H2NR2OxwxuY/Jiiqr35iWtxghwAMCotX8pJBKJ7eF0PsqBbwxM01xxSD2Pvjb19lD19kNQ9Vt++aTiHFIbyQ3wfkwSgU/2J3neQhyThMXhHPYINbfNddm53flTJTipmUIvMxcaSyixIVhVVyXkEMckUY3F43M5T6DBgTzBedPpuneIZOGpVOVU1ccAc8xhm5y2W4XfogsTj9AMsEbOIgELPfdKl3iEeomp75v840aPqCpAmI2cSTrV6Cni0CPv5XMArQDA8oW1vgYBhECvwaPaB2Z25KasQCwRWkiBWjlxSu1LG141kggTV9KntlHCLMl13RA+z3OqvtYzzSrTJg4RwaVDL8cRWAp4unGUeJuprY48duNeDP3jCPIex8MJzOKZOS8Lslekqn+Zy2b+SvVXktxV/0Uq84ABUHJtT/M5z107j7GWkFoV4TUSlyadH+2x8krDWlf1EGMCVFHdeucxjEM+PQDeYSHjGMG6pmIK3UYXbXg2vdcgpcEQ1binjhKn0yk7S4iNaMJpOmJZJixxAc9AWBxiCOtjkQ3oqE9/qydWcdiqAJll97U+J/apCdM8S30SgTxPZ/Dg4VjUg0OaAAAQRyE852mSmHsxSSMkx3oMg08qQzH4FjsUCtPMQiSdF4InEy2kfVOhC0ZbBlr77KbnUX6g/kpI0ZS9SE4gykF8nStnPaXM858ubXVs6KoqOsWrNipjmrkha18hLAGvqWnSwBaVkqqLEtqTV1UbJZUr6S5UJOqSy3Paj2S4UxaSLuoOa7CGBNnU5zXvrOIqeas8pCEb1c4E0iNFLLOg7YBRyaUWp5BUnEM+lHswNFa/VKdPU2GIKPdz4piNmks/K1hpJSzzOzMqts9Qg1CHR6nnHKlnGkEZlfKiStcE3XfEANgxAAn4rJ7AMp6cOiMC5DEMOyTeQaLEOG+AMPUzl9iLsjZTIF0QVApc2Zj0jU7MLM5V31h31diWPrBAdUk92D7XU/Urow0Iw1k8+3x2vefE7HgfEaM4XXAK1A1akG2KWm7USDnr4LZb6b0GqY8//jh7TRFRllSmaYJzDr/yK78iqsAYMC0HRJrhPWG/28mhh94jBsZ+v8+E4+bmBupwACQ1TA7OKpPQum7f3t5mwFKnDWZOqj6HYRzw+vVLHI5HnKdy5DUQk1cW5wE/LMfcNpXMlnnByxtx/Li9vcUABwoMC1D6VeoU876GEBm3ty9wPJ66Hn3XSFKalAhsP2e4eRIX4d1uB5eOew8kYOuTKhTOpxAJwoESFaIroZDa3G1d+kBl7S5c6IcBLJFoRN2wIPohLSDKjAU5l11kvXdwgxzOtsQFw+AwOEomMk5amhRRMHGQPpUjpxNF7MYRjjwIA5awZABuGyWMhdMplp0W854wJK1ZliLlWZ+C8laUm1N7LBFKasfIQG8EWYlziiuXwSq1JVc1qZVFKkoegkyISTprjFISky4PWItUknwCYm+ZEtJmSZ2elgnSc46b00fs2lX3ageweE0GAG4gMEUgAMs8p1O4k8MPXHo+ImJBZMKwewHxGmQMkTHPExYTagxpu0XNIPSBBtYZp+kecg673Vhtol2/X5qowGNDkl2Spra8du17ShumacpMu+6NsiYC+6nvDrNqgwZoQAQA+fj4S85bNr3XIPXixQvsdrtKtVaOlY64vb3F8XhEjIzXLz7G7V5Cpjh4fPzRx9leZV0xdVC897i5ucmxttjo2xWc1JvPch7qrjmOI4ZRWNPdfp9PmiUCXn3jG9jf7BM3K0QlqptxymtMBDRGBiHImnVo6lH6wjuZOCDADYy3b0949+4tDg9HCZPPpX1btqctp4mnjLaFW4pw3qdgsarK80AYQY7g/Q7e7ZKzigMzZTfpCMoRGXplFEdz1IRe6w6Y0zjMvqrMWctXjwHq4GEUZDm0lnMOgyu7/GNkKIPuPCXumAuOEGGgUh+X1BgcIs6nCRQchkHORVLQsF5YNBhiYnVSZPGqSEm52WaIctvtDUI+Oj6ryljrHkDE8J6h2mNZF0Vi0821kiPnzo9aKRQ84qTSFCcFX20stzPJGUnJSn4UOcVLFEuFHvKoUiO1RN50gn4bHMnG/t2IuIizkEgbzUldqYv1zE1H4sTjwfB7h73bYQlBju1YkpMUz1h8gN973A63oMOCZT5jWWbEUUJ6gSOIxTqV1ZoqMlLptKr9FFEdEa0iKyl4BExTvXdRhzEGXY9oJkXMNLAwljo2On/N+HA5V6plYFvVX+n+BEJpQ292rBiGKi6qeiIroOs8srEAr0nvNUi9fPkS+/0+N1Y97FQyevnyZQ6ASMn4HDlimQM+/vjj7GQBlEFQ0FFpxIKeNSJqmA89KEz3aenkWJYFcjx64s5Cep8oeSLKwDryWQJhVbEwMPjkHDwAwoWsg8DKp14oEon3Am7n0yTlGhHbTsRLqVdWm1Zgkuis9eIhiCOEpwGkbbX2JlKniOQ8kYm1rltGDSqduubPwoWzEgYqKjJA9sNV9c+4kIAh1d3mzpmYFYlAJJtULzL5QaQiisXDMeWSQSqX7/QsM93bY5ljQ8Q7bc0N1WsWh8g+v1YX5Sj+BnyARBtZSWvttp7rt1EOcVHruPJ4daSJglQuS9tnsBlcbpbsCxhJrQuBzowLJWmWE7oxrQzz2qM6pySeX7J3OSQGZcAQhCmcEUCJUQk8QjaiixkAnIDQO7joRApuok8Ux4nizFMardOc83dlCaxk1DKUzO3a1F4xvWPMEqKGlB4kqp9vn7O0oac6VDu8ao2UKScntkedy/qO95TxOYaY28fJ4efa9F6D1He/+128fPkS5/MZu90ug4Yi+m63w5/6U38K6m2n6riHh4cqhpSq9krnyruqumtDBemRH4BwE59//jmWZcEXX3xRcQcq4Z3P58zP+cHj8HCA8x6vX73GbZIGx3GQycRADAuWwGlDsUhIepyz1kGSGhGAOU0cxx4DBgl+u0Q4PyDGpZxL1Unq0n4JvHrSVbEDrDlkIEkV8nY+qTMIdc+n6AapQJYk1D1cljcnVVBy1W2qxyicvZVO5J5yjpTVSM45vLx9gXwIm7ex5MSZQ7ax6ImXKSeySrc0jhmQ6/7Q58bRYfCj2BGJkrt9A+wKijnvzE5vpFJg5R9nBI3aAzxTpfKocrUsXqGUEEI91fLTqbsjl4MLhbgjE74sQRsks8BvvfiynS79bhkOWWeiiuMIsJMgSbZPV8kIIS7ZYRFiMt4PiMvSeysDmmXwsso8MGIY0nybMuOn6vcQF9zc3MA7IbYRC8AlNJMwwuqNqR3hTds3VGsEZBsWFyCWvjH1xGpI6wtURx/X+IJEAAcCUsSb1hnCSlRbKkINpxb11AnvxQPVJxAMwLI4DH5InqnSRyFGLIuhL0nE/6U4T8qiu3rxqb7U2ltCCDidTnj9+jXGccz2KHuMuw6Y5qebchXgbHrx4kX+7r3H4+NjZaeySe1cStHmeRZpaQm4v7vD/d0d9rsdvvu972EYhCOJut/LpUluzqwRiUknVtqkC1UPiJPE3adv8Plnn+Hu3bscCVrPktEgkPZk3t5RHfbzqdQCln3XOwdywhAsjhAI5ngmloPtOgAHqKSihSATyCw6MMvBdoSs9lUjvMsqO6RFKu7s5/mc8wwKZEAKE8gF+Ui5bd1SnOrCrNuTM6w457Ebx3xsvercyQHkCTC2OoNqOpwKwaWu2j35CVRX6t/1fUv8LTnk7BIN8ZYDgx3DiYMoHNIZbJ13TYyL/Jk5Kv2uJ+1mpwNpy8W4AqRWHwN4ttlpvKt5YIh3kYyAaZ4xJbd7PfDSOy8aCKjqqp7jWn2XDwUQKYYdMOwGUGDM5LAAGDlgccB5igjk4dwew+Bwvp9BYUEAoZZ5DLhS3ZslnJfZR2SkIG08qRTMxrq18ogztMFeTVKY9KuDSx6Dck+e6Xn0tclqqXrBaJclwDGD0+kTjsaUp8fhdEzrgDAtxSyCEDEvCw6Hw2a5Nr3XIKWdpiCioAMgE00r8YzjiP1+X0Xq9d7nQIrqAGGjNNhkdbFW6tLyy5kxtYt2lhKQFkd6jpJrORFjWSb4YScESs8zIgUgzlysTmcCyoTODLgsvGmacE5/Voy3faKEtKcXtjaorbSyLVS/k6KH1NNQDpUUCSphgHLlRHBU4o5l0t0Q81JuUnmkH7rEyf6bCB6Z9ylJLazcojP9SG20inTcApAIipIMqjUrmVojA20WKkTUSHtukPMrz5Z3M0ki86oWY9Qz2+NS3qh7q9gBONkE1H60hj5rwcsFQwUlYv00PL6ppKquildd+1Bph15WNS5B3fXrVnEsBLTP2xuGKiKdm0R5rAtBZWEUDFhQLOU5yK0IFicJx3CBsSSHCUSCG2TDdHAAwwsoD4TFyXlo3nmJmAJVlMZSthaUJT+dS4VJKt0jI0BGmtI5kEeElXEt9AEAWgAp/ZMcQZTnNfOoRx/s+U9Pq/pFIg8cEcMMR+IIxixReijR2GWR4L4xBWAQr+djN+82vdcgpa7nNpy8JiLKUR+ICN/61reyo4VKTjYtyyIHA6b9TECxcdk8tVzN17qwH4/HctigeV6AQI76CGnjMYCkonRwHjieHjHufFJVFvWS7oap2NisFTAAk4iezh3RHc+QkwrKc7bddvPxVupNUjuJ25Q5YgqACyCPdOS9nqMji0pUMkhto3zdgpLaU2w5zCj2JgC7cQRIJDZV8Unj0qKjHJgpqQ4d1MKVeVYSLljpZP7UjuWSr4JpiIzBFTAMy4JAZQsnCIB3Jl8DB1SDYacTCx/eEBTtma2kpAssUS5iAiWVknJECgYKoiapV0keSzRytRux+VfmX1biFbHYYExvbnBjxHIp1FXe9p0R0oAOO4AGyByJuXhbr3I4rbVamrJdCeoLKu0gX2KsO2IwRXBg7PYjiIHT+Ywh+SJxZAmWFF2yew2pzR7kRvghYhyTg0CMQAgyD3KbTbtXAGqHQMfdpTFiiBdiZh3SR+kPq+qr1oqZIgyugsi241J55Bna1lPhxxizOUXmJsBMOVaorQORxD/d7/cI56naHqSHuF6T3muQspKTdpxyBXpvv9/nkPIq9RBRFXb+5uYGp9MJP/rRjwDIWSmvX7/GN7/5zSx5nE6nLGUpt6Hg+PLlS7x8+RJEhGEcsNuPchSD8QQjkgU57m4k4kMQF/SYdLaH4wNevXoBoqGWkDIHngbe0idWzo0yZ8oAHu7vcTwesISAEIq0pzrnGCOOx2MGrFZNabmrnqSlOmlNdhILMLjEXQ4AuRTvDECyG4j2qyZavZQNz8woygpDwLVsEugxpFM89LS+KeQSwRmaymDR8aV3HHIYCdMPCyJCCrI9eFcBWUiqGN8QaLBCEKARM7RihBacWOXkTGSf6pVLKepflKDE7RtEYr/hKPXXyBIM1tgaAGTjsIKTHu2ht60Ly7o2/drXc6T8repnn0lEuibzJWUhsyW8oHJ2rkZfzxK+lebUGUZjL0qlYpYWGc4Du/0At+j+PsLOeUyzHCQ67F9iiR48A+RmgJN922hAiqTT7Zpeb2WGTvgBK/pGPDUHbD72U/YCcvqrK6Nr2mqJegyqmlZsQGGNlapJAyoAwP39fWXLUtsjGAjhlyAKujpJAEguj36l1lIwUamKmXE+n3F3d5c3375+/Rqn0wn39/eZ+L569SqrBgFkd3PtfKLimSUgkLgsaGQAzmI5EYHSGVIahSAsydjKImHNORpEzCoaKUg+VhMm/y7qRLAcMXA4nDCfNTJF4WpU6mzdyZ9rg9LiddkXtY2QE3JeVJduFM49irFeOedr16qo2wBQA9T6QGq3VfZpuzKAASkMEFXOJyUnQykVX0CWJmfizZm6lt8RpX6aG6mar2kroSpReZBcDrAmHvWY9MenuprEtDL0hQtXzleuFOLZK4H0l3l+1WcANKKGFl0+64tVTxiA+v+T92+h1m5ZeTD6tNb7+44xv2+tVcsVLcvaGDCHzZafxPxbwS3kQlNFrBIkUW8K6sIkohCoi+CFYEgCGkEIXgQlxLscQK+9yIVB4hb3hqJQQQLBiyiSXxOrSqtqre8w5xzjfXtv/0U79Pa+Y8zvYAz//1l9Mdc35xjvoR/buT1t9/FWAOEkoolc3KD92s8qNntFL6LYo9c4hQa2ADAhRkygosrgrikrQip0dmlYreomqZlAtbJSwWVCt+hCWs0CEqY5GRvaf6XxcZ75MBREv53Z+WWXQqWPI37f7e3Llb7esnUofNVBH/QZ3Wio0093Hbi7w6HXXNB1N0r0sT8cnHGtvdFM6ktf+hLu7u42aqlLA64xnU8nPH7rLYXptwl7/vw5/uiP/gh3d2oT/fCHP4x1XYNJiQjeeecdAMM8djwe45mqgY3NJ+gQ6Wq6EwGdF1QIyELQCzNQa2D0ERFWXnE6GRqwLfr5fA5GggIlfpLOVdhpbO87AWB1jq6rVtx98sFznO4b0KuFo+tmc6a7Z1K5vVqYOo+Dpk+KHy1wOGM6vAWuBWujCNogS5ClDgjT9SMj+Rc11O2tJvqn4/0NqTvI4YYzEBzEdkDyQilgqFX2uQxflPunC5P50MhtNcrMiOKnszNFoJIn5GqCq1OjoNvBVO2DoE2ypbXbWuub9blsJbEQNaMq5qwKPEQEaVfWdPfnNoBaf7pfOLKI0yzmseR7dSy+W13T19sHa9l2QtfN8+YJDlulzxsWBBV1/Dcn5oGBBz0PIzfMdxLvNIhtFC4BkOJrCqAySiKPYo6rtQH3t55cy+hMkFpB8wxuHSwrqC8AGiBsc9UMPkoUXd2FHJHIw3RpgIAQtMM3bbyuvSSvaBMyvplbYGOiTYLrtf3kVqdlWdCWJaJB0fXftSmdab1jXRasLWGIwgSgJFN4nxwODQytqt0u98619kYzqfPtLbA23Kdsb9eiyAhYseRSGCinHxz3N53PZ3zhC18AEYWN9I//+I/xB3/wBzgejxFwsbfLai2hjseP38IXvvA/8IUv/iHmAwO94SwrAK1Yyhb22c4NwmTRh3rol+UMMXw1lU7OKFwHgjoBlCqb6gbGBVmQLpiPE549fYrPf/7zePb0qeL17ebk7bffDo0wSzbZ7/ZyBjWu8/v1kBdAKirNYDpAC8hpKZQya90ZomIJlGyHVZnaxl0hu7TdK/tYgI1ljsQPuDGL/Lxd697nhy4YbwYx4XhzMABiNc86BJELL2xachThax3JUXKhST38vq2GELFoaT0kBS24vKJzp2VcmBmdLB/FrbFXltJrQAGIpNbNEmDwJM59ATQ688pc8QNbpj006qR1uebljNxN4wOqiBJbu2Rv/kdoJ1feI2LBSr4mYf7bXtp7i7lhoxndfwA1Eac24xAM5VRXl3mwtKLVDrrjoDgEWBsCESE0FeIQB3TWyEuZIMz4r9IGoxoC7f77/K/vYwedfv78Ob7ua78Wb7/1Fr74hS/i9v6EZkKm5PstAELPmwu+Q+vS5vlQxjgtOEVTU1QrfZX2RjOpYqa2CRKMqfW+2c6uPfV1SRYyK7rGjFqL4Uwh+VkEra24u7uNKr3+PMCJhQHf9BXresbxeFD4D5EwTbAoAe0ieH661xDZWmP/KIzMGM+yLDjXMw6HGV1czXeb+iAkOYdijJWwtoa7u3sQsfFkQdYwHz16FKr3NUa0d6K+SpNO6rMgl2SNrFm1YKICBZMd/pzRbzJ+7RrjJSEaLX2W+ifIMvFAsnfCtiFFweGTpH21kTEk14rEiOfWkeyChPZBpX12IuGKUUgWLxhT2Mm2OkZs2CuaVNCE2CMxo8EcfV7WdVVBencv5ev9VdadeBrpmLxfDmi76b8MdIjctkxDNsPdPCLdu2EB16bsYt9mzeHyOrL1y9GTetf24VtT2Y5hJ/NxLZpu4H4Y4aIh6WC4hxhgDaIh0ohWY7j+5ji/JlBQOtuj+5L6S2opMOK+UZAeOMcvanmczng8p3E9nfDoeMTXvPsu2rKiEOHu9jZMez5X48fPtZ0CGXQ3xpsH10W1/KR5vay90UxqPh7x6OYRbghRFuP+/t4QFtKpg1xU/iZOErtBEklcZFnR/pcg8qf0c7INtuL2/immueDDH/46LalMY/uTCKR13N3eov3xF3E6n130tWduD/fpfA8uhLfwOBEf8SFctLI5Z4S2dtzfn6Kyby7OOM8z3n777ajOuw928E3lCB0vwumLPjtTkoIALgqjPhmDYkPejm7CgTtz31mc2GS2YqTSazUlUwbZ6xsGc9PERQ7EgWyXf5lG40Qoxshet0u1KCf8HhiymfcuWvwOwGyRWERkjv/R76sv9uAVDBOj0lXL47E1UB6zNcXG+pASwo5RIsHnbW3qtI6YRlKhIheN9BkazDx/PvaH4DIJN4J2HpjTHjc4k1KGvYnYhDMCCu3CxcCrGz+9YdhGxnkWUYGBDGqJSExk8TpQuYfag+zLCeJt/YKZkqtZZcSAq0/nM3qt4HVVwTd117XtbsKB/6eWAh9Zj7pl2u/cpz7Ol+1jPwNKD9I5yUzrym/7lpkUTDCduaIUQrs/4+2bR/jI134d3r55hD+aJnzl/ffxJ3/yJ5v0Ayol5oaLdrd1Fe6k58hGFaQ8X9MDJ9SC81UAi9QArYUpwP35fGFjFUBrnRgq78aU5TVcAMAz7a+sq0sMU63oSQtRZzyDi+J2+fObSSXsPovCqMcj7k8dT75yh/ZYC/s16UArePTOI3AhPHnyBLe3t+it452330HheRzkvZh1pT19+hRPPniCZ0+fofAcTNVzwzzs/vb2Fl/5yleCCV1jRD7m/XcbDYsAzZBvWHsHU0Vlq7MlAmod3P0oiiZWAhhAOe7X03XoVrZgc8zMP1DSp5S+0/M9yGYpnrNSsTbZKDA7YQ4kXirishEppEspDGLFReRrqoKRtLqbwoyvyFziHW5WuwJ6nUKWtW5V5NsZn+1dIL0BqURM2Pq7QWh1hJ+DuQBtAa0LcD6Bq5X7Fs/jGWPIm8uJ+LaPFATNySVRiqC7vEHnwZmST+qVA+ZjKMU8UJ1gLBlOzClrDyqmw5/scD/6VzPGIjrZzCi2fwiEFs+7DGiJnjqd2FlkpqmGFeJgyfmn8xmFBIWBwsCpdQXUhQLDrm0FmqA1V3uNpVrhSHBgm2whswSQjXViaLKbXsdm2hH7NFcP4XQyM1rvaCJaULXqvnAwBCIFITi+9RjT+YwF0OAvEci6o6WBQdi05I1JWw2KlSqQgIyKKEoBWv8qMPfllifNo5gAl1Btg+wJcohLQ/X1NswDlCRh/V0/9vheXbgs4Yj4ATXcOp6xLg339yc8unms+SsuaYgAXcN/1UGpZsQxLmw6d0FerGvPnz+PHASmBpCAC+FwmDHNE7gQvvKVr+DJkycRMHKteXDItbbXviKqjsZ3G3u4uD3eiAaRmYu848mkQVulw0dLubogknRr66Ff+3cJloZkECSjw9etiWMMMS6SuGe8j2K7BA3wA5gemPHiNsTPPtmGDNB2QW0knTA0CdvLLnu7IEQJCQCdLG1GC/WpYKZEHl1QucDLxF8KJWNzhaS+MTWNMcSKB4OkNM5re2ZUnr2YFKREXX9e8DPVNq75DSVMtt4rJAY4xqG6vaDELrUz5vtRLnsswYRtJQJB15gIM1CU5a3LikJq1iusUECFq53tDupsprAeYxtmvjxrV0Ypnhqy27BpjpwwKCYpjbnJM7PXuPfz6GO2IKbWO2RZ8KU/+RIeP3qMr/u6r8XxeMTNoxvMh3mDqtNlG0U9BAvAkfRd+86RpMCwGPQr/brW/twwqX3LJqC8IUNSzFxs3BS/5owVXxRfGGdUIqwSbpJGycxdmt5AACzjeuk43Z8uuOG6LLr3mTV81SRFN1nk5taj2KcBRQN88OSDYD5NVvNDMY43x4jo+6M/+iM8ffp0gz3o4/M5y7BS1+BStj4ZzT1SBOsUXE4A4MyWjKHrXy0InV+nOVRDUckk3QMhhsaV1yrmIebUmHwnw/SjuG2fgUTE8aqBTm77JiLtZAg5ZiaLe+DybQIyTaZe33Ns9/YcBp3WNZvshu9DLNwZG62h26w2AyjuXRMi26lDVt0zb739FqaDlomZi47tUI9orqntCIP7OK7nxox8IZ9I9r2+4fhj/+fnj3nXv7YwsQgiF/60/FiovwO9W70lM5ftuOe4Z5j4iwCTmTSJRk4aA8MEi7EMeezeX2bSPFq73sume4HS1Ux/atKumCZgmmYNSGotysYwWP1WKSk3BDnfn5RZlc6aLpWM6wkIbTUJEZJoxn79su/6Gqao/x6IOb3j/nTCH/zBH6CvK772L7yHeT7g0aNHOB6PUQF9Dw6Qi78Si1mVvKqD89ZB+Pru35e1P7dMat8k/XsRiZQFsmiZmOgvLFuZYGyrgm3m5PYKJ/6H48GiwERNYdQhjS1IglB5wsQzIAdQqyBh0xa2koi/SLpgWReczvf4H//9f+B8PhsCt13ROp588BTPnz/H8+fP8eTJk400tM+veJ08KWdWbLKqzt0ClIJOGgVWa9E6RuyEN81aEhwYAi8h4NFkeqWnZbqpRolhT+s0jDLer8v1KYkBqPayRSbo3hc3ARdgla45MsmfdjVjH8PYkki1faf+KogoCGfye1y2tLdMk1sN76yLloyX1tDXFXfPb7GcBMtZa/JI16TP3jvO7TlqvQcR4b13H2Oep2EO09KoGFICElPJDGTPyFxAMMuA+Sz72nAhve/nXocz/IfG4NRcawE2lDQKGntSPzBLRdtCLvm6jF5rnPaGxSYnn56LEfTRUyfd0gLBqOIMXF0l/5KIMdUJJ75XIQRAnQ7QdIcFy5rn1seXnkgEyBTo8WTYguMWib6NPL1c6HRoLdrZvdD98FneC6jMjLNFIq5txf/xP/47/vB//Hf8f////z+8+6EP4fHjx3jv3a9BrxwBR1GodRcWnzFBw4f1wsl8eftzz6Tyggy1+/qc0ZXfXAbQ3/Qrj96TeGZ6Iqk2tZf6PEpwsdBvV4OnquCrp/MJES3jGzoI+UgWde1DBGAhyNqx3C2KcGEluQEETtba73F3d6f+rh2ae56jPcN61Qi/rFmBVHIXVkieRRoYDURsfGHEP2UpWzUO/X0XgG6WnLxi+vuFC2Rz036F84HlNJH2EwurvYty4OkZKmX7aqQ9ZAcx56e4tt27SrqhnIUweY2ADEmztYa1rWhWUVmkY+kd7dzRlobzqeF037CeFc29OL6gAMt5VdBPItzeVrTWLRBGfQ5KlDC00xifhUDv+xcaq+1YspLxIdhRngz9LHgDjXJJQlrd1mqGwRmT9522TGqznqJMZj9tnmbi/c7stYe2MZix2Fkdkj3SyLx5KPilF8jbunZIA4hMQGNLSQiNzPtrPhxH/c4PDIsMJenGAqV243TGJMm0smPH49+kVb2o7c+794mYtcS750QtCxbP4aRZNcmQcyi0YactGx+YIO1q4MEJfUn7c8+kHmr7UM5tCOqLZlNCesnSuW8gRzjen6gyTZimWQMmMA6mJwmfzgPHai8lqvXJJcBBCFgIOHcst+eBh8MAhNCaIp93LJEd/lBAxEOM61rbM7Hx+5CwNPxWsPQVVVgDKkg24yAI2DRFBkfi5jWd8Zp/RJnBPmTTfx5gUjtzXNwQhHb7or0JbNDhJLgQbSvgQqPABFqHCEaIWTxQfKQobHx89mxiwnpecXdSbUi6QlOdzmes9x1tUYn3fL9iOWs+XmE2jELCcl7RTdN49vwOy7Li5niDOiEFfxhMlRDAyaTnCoWM1AX9RgYvh6BJszxBEyloOyfBcPL5gkZFeukLf2X2RbkGOWZkCACEyz2ZV7/nHsgISHBBJz0t5sDfO3QTDyoRdOmpLpYAye+zLio8MBXAxuRmS58jYgaZ6XmMLwl0EEWmiOEOgxgxhXoe475ipvT+bJtshhiPv3Lu988gMlQa0cChR48fYyLdX8vphGmqmnOagkoyso9G7mnH1cUQI8D1zl/tzkW7dDq8pP36r/86vvd7vxcf/ehHQUT4pV/6pYsB/7N/9s/wDd/wDbi5ucHHP/5x/Nf/+l8313z5y1/Gpz/9abzzzjt499138UM/9EN49uzZ63blT90Eg6bnfofEAzVT+A+l/+BXUJr8jBZgpgw1kWg1XoHWvvrmb/5mfMf/5zvwv/+//3f8b3/9r+Gvfcu34P/5zf8v/D/+4jeiA7i9vX0pfD2LoIiVj2DC6XzCl770JWVeHNkKaKKRO47J5YUgvW00IDzMlC7mLjE0NUX1tMndfMOgUgJYVimczWAnLPdn9EVDtgvxRkPpBKz2sxBwJmDZrRVhq3Fdax6BpkC2OzAmcYK1NXumyVGCI2pOO92fcL5XIcKLW8aldq+DZra1Y1062ro1g/QuaK1jbR1LEyxNsKYQ3GVZcXd7jyfvP8Xt03ucn3XcP11x/7Th9ERweiK4f7ri7ukZd88WyH3D3IAbrTVsc0KYueLAFTMK1vsFz58+x/tffh+n04JlbTgvDUsHFiGsGE5sQEFzHbeu2Rps4q9EF4ga6b9526effC4ElhdTsGVQ+yZQLccYBaOjCjBDJemXafZCBGEFCm4MrKz7x8ewItFE2f50dDQ0dNJy8nEPAY0U31AElmsG1KlgmibM82wCKoNKxfQW4fAWYX6LMR/mQL7hUlJxSxsHAaAV4AZwIC6GJqmFBF8sLr+suZbzIkDofau1oh5m8GHWAB4acGLNIkuV/I1inV5VAtBUIC8WSxadSiaIegmVS3r64vbaTOr58+f4lm/5Fvyrf/Wvrn7/L/7Fv8DP/uzP4ud//ufxuc99Do8fP8Z3f/d34/7+Pq759Kc/jf/yX/4LfuVXfgX/4T/8B/z6r/86fuRHfuR1u/Jgyz6XfQLmRkXBVnLfaC8P/Z4lTBo/++du3kuER48e4d133w0w2rfeegtvv/02Hj9+jJubmw047uZ+uF/Gf8ZBJla0iLu7W72PLJM7UR5/3rUgiGt5N6/tm7KBE8YkEAiFCtic19KT6YQEU60gVoTwFoTAiIHN1zCH7IjL5l27voSmROOQu8DgALJpaHuhzs39ZOXf+9qsCF5Hbx3L/Qn9vECWFX3Vn7a2KO3iJlbPUfPPA5MxfjxfROz+huW04u52wf3tguWkoczn04rz/YLT/Vml96Y+JXItPioFb2fDZ6FYOD6ZVuf5KRTapgtYMv6GMRYR5WAyfigxEQ9AJ5HNI7Yr48KcHpLuz87zns+qqH+SIZoIn8aS18lJ+naX+jqT/W7XCiLoSfPZuqFHjJ8t/l8+0Ek786+sz671FVa0kcrKiKIcTbxjeCx97nNOGALw1Z/Zhw9xt6dz/3xZ8hzG9S/5eahtrElm+tMkfA7Bd+19I9hE8Is9d28d2Lw3CQavw6Re29z3yU9+Ep/85Cevfici+Jf/8l/in/yTf4K/83f+DgDg3//7f4+v//qvxy/90i/hU5/6FH7nd34Hv/zLv4zf+I3fwLd927cBAH7u534O3/M934Of+ZmfwUc/+tFX78yr09IHbrcERYe4Sdth8+goZ50OGHmY+6Uae7ERCMqIalGUdAiKQed74m0gkmPLWCO8M9uj/b0CrG3B/ekOcYKg0C7ZJOcb6WL8L2BIL8yRimERmLQkoB9q96uBzIQnsBpOHLynVK294wUQ1RxjzBRuFBs5OdohZ1q0qd7rFhzHOdOxpkMJDz7eHh5xq4j9kUdLHWbO2mqOy+19MIcWQo+FwSdi7N3qlsOicz/CrWMtO7Catnt/v+Lu2WJ7QQGLl3NDW405GdMI0skuAF1dPnjUVpbG1SxTN4Rwu6zJHyI2FBf0wgnYdR2Ewq+V867CirCTBIUcmZ3htTw3Qp3vnyxg5Z7ZGnnMqFbOlZ2U7bMj434RNHsQ23hzZOe4bxj3bAI9gxoe3HHBFEhQq4GxkgLRksEHdenxo48baTH+9+j8lbPtn5P1BbuzGhGP19v+vF6a5y+v3zOpoQ0pMvzaBcJdIxZp3LNnVDo/Oq0ku1Dz5AvlK/241v5MfVK///u/j89//vP4+Mc/Hp996EMfwrd/+7fjs5/9LD71qU/hs5/9LN59991gUADw8Y9/HMyMz33uc/i+7/u+i+eeTqdN7ZEnT54A2OKK/Wn5lYioukqkxQih87jf/BE2eoE3tZPCdhxOzBextBVLb7hL6MHvvPNOSN7LskDQTaICHDJFB7rNQVjXRYMhnt/j/Q++AmAF5GCSvErmOVTUN9K1shsvm5sY5atsKCFIxzBHSg/tZSKFiekgbM30u8O005SCxDqHy9cKoCCeZD4t/dFQ8eQv2dNMiThBY1RKpNyGnoWV+eo4EXhmxASJXBFl2r1L6qtWS+69BdPww7m0FefTgue3z3G+6zjdddt7bNVoSW1MgX6SzEExd5S0E09gJszTFGyfazHgZY4EZV+BodV1LS8+WBqcCDLX4OhkwhDZAjhKffQJOxPhrvVuUKtMFtYNRK0o0egyHzuZQ35zvxFr6RLv4Z1vUvZ/uKDhHzVHEDHCHKAOY2Nqlas+1OsYn4ahi2mmH3rnHZzOJ9zd3eLUGULjp0MjdysxPCSbNqCKF+JwjDJeKARmz4/Ty4fgZPe/gj3sRT6pfWPSPrv4oekLHbV3UCetNp0O1DzrKam1BiboBVPdioGv1A9vf6ZM6vOf/zwA9b/k9vVf//Xx3ec//3l8+MMf3naiVrz33ntxzb799E//NH7iJ37i6ne0OVSvz6x88fbku6fvQ5j3v2Eaz/YLkxyuKLEuaRuzmA/zYHqqSEOkYZor5nkypJwsTe00KZMnnzx9grs782EZsWlmctqXhH+VTboPFHj5tXZsnVgm9UTPvEc87c2tofMDeR6h3/lfwa6Iwgyo2pEWOCQicJ30KnLNxe/ylICh/47njnd5l12ryometLl4tK3/0s076qpvUKGHwGnNzGwpPXAKlbGoP4KLSuXSstEUUCKpxq3Yhy7B5r7F86KDisFYBtrFuq7oVpyOqpmpag2TnmzpcUwU2dg8VNrXMmuxeuOVIJbNJZLCvrendpj7Oko30AkISBr2AU7BjsW9ip6n5kLkdgg8vkk1slI3fa/T1vKwfdCWxjClNAhbD+aCyhWVKwptCwdqFrGvmTPCpLH50yUlkZP2TSspyxieuPAQ3PSiXRP8/N9rdOCqoEjDXB5aFrQrOSgFsL2ViirutbLNrNP4u8urCc1vRHTfj//4j+NHf/RH4+8nT57gG7/xG0N2HrgG46BubKN+YDYna0eUZes38eb8Z3N+ZRAnV18zDR43pn4YkS3hRLV3kwTTOcwzDseDMakkTVEemW1SUcTi+9NpfGMScc6F4otDsNuQGxX91Vj89jBvparM1OP7mBtlys5UginYZDn7z1IuG6VUoq9XqOSsB6KWCSCypOpNL501BL1x4p7OSeqb/ZvOvpp4BjFxYuuEWRPFSYtXdoeAwTCxSSJ4G5ONaS7oIO4o1aoUmwbmfdR9aUzKwY+JzHQ6THXKZxx8VO9praGw5jWJCNa1gbqgrQTqiufIRcPktTFEWtIkxv9FOlzbjDkJojXWztl2mAntyeJj93UhNweNtelN/a0seY5fTMQEVkbFzvU4B9vreOysGN/mEverkSXxuskrfZ/5lfuT3ITn5q7CFYUKSgTq6Fo59qM/0/eJdyT2nhMapvTOwaD2o99u5PSM9G8et5o57bzt9/+GXl76r5gYTqlG2L8+xH2vD1lbrn++l4gebn+mTOojH/kIAOALX/gCvuEbviE+/8IXvoC/8Tf+RlzzxS9+cXPfuq748pe/HPfv2+FwiJLsuflhdpOfr+Ulo8qSOxLxeFG7PoFiL5b8x7W2+3jUUyrxHZOFGFuU14fe/RBubm4u370r870sC54/e451OaN7XkJiMi7RdA8VlXT4sd2EL2JMD9mux1x0dJzVz4ECpgnMRZmIiPoXSHNqSpGIXdB1c0d5IipGAPQilYElCQB5aoOQrx0IJGa+umqXrNQ+F2dlI2GX7H9OgEfU9j6AWcclBEjLhSQXnJcFvQmWc8NsNbxqZfRuY2mjly5oteWEu+eKun+YZpTpkMZt9xVKwRJpHZpri6RkhEy/aE0RL+BgxKx1vFZND2hNMNcR/bW2ZpB3rIELJJZkCvM/dYUGso6FBiOynfmwNowmAMCr3QcldaR7hYEAYgWS0GhgpQAChUwxDM1PZth8uhpphSX7nMY89cyleCSQ61LqvK59WC2qDNrS0ngYiGKnp5OCGhcuaJ1wXgSnc8e5aQFEoQLpT0dXzJcFEaBVRN4ebxky+TVoo57Tn0HzStHioYrxeWJSFnTkn/t3XSTQ2L25YJyTeP+s+urtz5RJfdM3fRM+8pGP4D/9p/8UTOnJkyf43Oc+h3/4D/8hAOA7vuM78P777+O3fuu38K3f+q0AgF/91V9F7x3f/u3f/novFABdkupN+Z+LFha23SHXR+2BN7NIma4NtQxDI9i8ZHuNq8bOMEopxpQEZZ7tzOumYc+3IEZHw1430Oq2GhF2f7q3kFgaXaVtYp06vM3hLN0YupLl9SVS6ssY1JgEPdREyRFKLkGaNJmnRjxfxyCVRG33AooQVcCDAiwogd3P5Oc8sR1neuLYFPs+M4i9dtb+8DhGWo9D6QL+0CK2dzg6QUdXAs4YggIIEA7EZzKoBRGtxMyWLyZoSnx9D/WCwgXzpBpOLQTihlorRNSfoUxqOLJFNEqtratpUaMLrreElkob43QwhN4aSASFBaVwKC6ayAnzDckGKcRhhSh9pvNmO12GCdWmYxwhyjta54+powigxLhbjaEx9x4NOdQkFWLYERoCVSLVO0qZ3iI8sOLsXJRSgjGp9pL6Ff0l9ATcs9lXopaNbsIUcQFxRS0zDnOHCKG3hoU0zH9ZfUXSw8gmB+PDPKXDdJ7mMkviV8Sxa1rUuN/2ZNw5ftvff+3pYX5MTCozJK+24P72a4xq7354JT83/hRM6tmzZ/jd3/3d+Pv3f//38du//dt477338Bf/4l/EP/pH/wg/9VM/hb/6V/8qvumbvgn/9J/+U3z0ox/F3/27fxcA8M3f/M34xCc+gR/+4R/Gz//8z2NZFnzmM5/Bpz71qdeL7AOGxLv7zNsw1fgHlzwlN8m/pQ3ykHT+IDfMz5MklRsVGM5gJcTEyWxgCYIbgBbboN6P1jvO5yUQDfKhvNioyQTIppU81Ou9hPTQNeETic/UQpGRwj0/bHu4EYxSGZHnVdlhp8GgCDasZP4aY7IIMknfmRniKhuKEOvcF2dKEv9eOdqJIWpTSb5H3jRBiZkTba/kAri5EZBu+NwmRXd0i9gjsHBI4vOhYrLaZaCOouXJzNdmEq45rYPRjBeOfodUYEIEupn9lDBK7Mk+qvjK+HFm3qECfreKxATaTGOJRXUfle1vI4ZqIRRdZzK6bCHgGuauGpWGsTeQNHRZ4Unansict6IishQ7E04kCab6mbrkP3aubP486rUUTmfGw7l3mqAghF+KsSpj67aW6hcbjKqWCfOkTHKZ7nWuumCBC1guTOyZiWz+iZYFYaMPqr0/rKlcI/5Ohx70E+3uj9mz30Ng9DtdAN69K4NTv26Q1ovaazOp3/zN38R3fdd3xd/uK/rBH/xB/Nt/+2/xYz/2Y3j+/Dl+5Ed+BO+//z7+5t/8m/jlX/5lHI/HuOcXfuEX8JnPfAYf+9jHwMz4gR/4Afzsz/7sa3degDAfZYnnYvr3YuYLnhcP8c9eRNRx+Z0K4bTxBfUuENM4OlbDWyWs64qZZ6AXSGcUnlHLbOKnj4RQStUDsS6KlCcFbREsS0NvAkDLo2c8Ld9Ad4aOfj6fNfnQDj5jhIa+jnKuTAabTaqJxVBwSSxG2Cd4kINitJnmBAYpJELMntGyi43vuTLQpw1ZIy12Pq+CFrSnl9Vufij0SdDRjHjSBUiFm+4huyMS3n/PJQKqScRdgKWdLVrUXAvLWX1GsLB1UkIb6AtGvefDAfy2a8FNiXlfIUEygNYWdAM97f0SsFV7NiLuyKIrNYdLw9anqaKLpagT4bx0MEnAJjnbJlEfQ2EGTQeUomVpNOJOrLqvaqAbnw8EwDkII5sptFQ1Yz579gzn+1vc3n6Ax2+9hcKMZ7KiRNK3Mf7e0Lx6NBcU8z1OdbbiosDSVyx9BYFQpKB4uRdZAXQUrhgp+bZ4BGhwzstD4rJG2GI/ZGut7W4i1EIW3t8AMI7lkZbxYcZ9P4RmW0j9yR0uONnGyzhf1E3LyizNMDWMdjxkUctCajClpCl6rt6L7nUTXillw3gyA3ITca01XAtnK5f0Ku11zIKvzaS+8zu/86V+jJ/8yZ/ET/7kTz54zXvvvYdf/MVffN1XX7ROHvWFK8f1ome7f19wjZ5SmByTyGO+0gjpBqoeCO1qA3OCpH2oZEnEscCZMLtpw1HFBYCstlk7ayJjg1bE9dwbI0yOmu31rKwbSdswaRyMtblkdj3X4qHNdinsWW0s4ZSHMp6h2qFuaKGh82iejY9bF5DTPGw0NtmanExEBUjRxZ2a2PC1XwYG2Gl7IEOwINPoaKzyNYE27/URWWV/a2GqYBYioiU0RDTEOmuWwqahECCqiWm+kpmpHH0imdMuY06zEHZd88u9F3uelz53NPHCPucmIRNZfyTmxs/VKoL1dI9SKqZeMc2Hsd9tyrp0TGyJ5NJxPp1j3grE9gChVUJbtcbVjAY53aIzoxYOtPgm6muCaDpFLRoK3tdFIaN6h6yr1Yhr4N4MyodCd1XFnFAro60utCnUjxChTpyiGSWEnawpjD2Vv3Btc3ygVjs7nzQQHgQCLqxQQruVEpD5+Ma6Za3KKYdH2F1WsCWQ1QYzYrGhI/mn964mTNHCnJGonTZJZkR7dApnSI4qEflQZPlyTFZzjXDkCWvTcPQRiv4/396I6L5XapmoXmGiL+fw1xnRCL21z4bnf9w16NuGSPv3qhRRXOyRa9eklPyeEO5N6mCwRdJ4ITV2md5PDxCmFmNhIsGoAmPuFfIqXpQj5f0ygT0cqjDiQJk4+0HbzgjCzCdjvjjfE1M8BId9hKOa2vw32pF02f5fxrOH0jTQGyT8YXnsZNqmP2U7ikhadFBSwbDbm79iEykmCPNsBMwZsrWvka/hjtVsRyZiAoyPi2LPixMt+9372OFeKTOtGXN0qCISCWFKOzxW+XQ+o1YlcFxL7EfKlN0jx3rHenYmJehtECvmFloB9xX9ftHk3mnWgA5idIPUQu9AX8FVn6umOvXVdixqFhUNzims5vGBxKEno5DvCWOYhgjBbBlyPQXDUFr3nbAyjoKZKfM9dp0nmBN7ErVia5ZqOZj+zPAhE7KZxs/s4I8b6AAAQzMa9MROYUKO36M/RCkNlhB2920vKO/N/vuAK2ZnonYO/f6i5uq2tujra+daXmlvNpPaCsAPX/bakzNMGHuMuEzscy4VBUF7qKnJS2Nm06dZOrnats+8v7/H82fP8OTJExBq0sAcekeiBLyHLftmL1Y59yIU/RXV7tyjZsywYLuRp2kCqAxnNTRslaRb8qZ7chIjsx90sdpUMKKh3+ynpkVipALZghKTMs3IfTVOjPNYfeyrJeTWWtPXQ39Rs8dArR9RhCoAdIc7uqLxdCK087XJI7AURPVeIbSuSOXzNCkChexW3cavYet5FYxkdvM3QU1gICj0VNeYtN66QvaQOvTvl8GmCw9BqUxDiu7cQ+C4P92jrgWtrRA+64p2wTFgkwinaYb7P5fb5zrv0nF/65icHUyLDUcwQRG2NbHXCGkh8FwNdLlq8cZuzGMdggBAEJlAVMFS0E5noBtAIACqBVQKelvRPChHCId5RqnFcsgAFEAawMWYyNoRMfmh5RDgkZwhEwwGEqmrBPDEqDSpxrfMOJ/PWGWBVNUMBQSsFfAk9+JmeZ2fiwU3xBIiFy6QBJ8XV7UdDIWDUXmo+J+2ZR8g8zRoTzON3Pru733Ru/6XBk78363JlYE+NPgHTViyNZ0QRm4F006ygPsCMqSMQPUcTaLsokRnCw+TpRPXuIZ0dHd398CiUlyr9xLWVXC6X3CYy1jsgB2Sgchsph5iRnEEAttMXEqEjfrcvG44OjAcpJrnw/pjYdJsOGYCCZQDIgvgQL+yUUWZLRyNwCPGXNx03SrEUnhBRTU76ufMjC5WXrF3dNfMiIDWQ5CNYm9rQymM1g1nD9sw/pCCCyJZW4ABrptRSGhITXuDnH2ozMMpnqmjbRWcetOChi6t7wSJIccHe0qfuFTtwQurmtDspxvKgn8EUjNsN6kdQJQFb71BStPoO2Ys7Q4dBUIrjqcptFAmRlu1z+uiiDDSO1ZD9HdiqsnPDcv9c3uv4jWOwAMzybH60ToIKCt6LWjLquZKw0IU036YGKVUFJ7saHl+A6NOFaVWTG0C4QCiCVwr+rKAIThME1ZS4anRiCZUjd/XL3Olq8uo92RzMAvUDVYwrQes0iArYb55hHU5Y10WSJOY/5gjga3b0GTCFxmPH5GISXFO227slSz0DnfE9TO8N6/vBeZLuCPZ+LwhsDpX/jy9x4un9m5BQhjBYaE9fjUwqVeV//Nk7FXZ/FmYm4CRH0PJfCX5qqGhxDEngh9K1xb2hMrfGREy+gfWZUEyHiAe530IaVpRJdragDkxr1h8JT5qzVBATdjmSwYhtfubZD7MCK+GSrElnvENNCHUtCTnz4mRB5AmMA6l2rygeTM9ZlPCue072p3fg0mJJLItiDIoMEiboDHCVmSPIL1Fd6lWY+QaBN96G7b0WkIC9UG6AAwXAJxNJEe0Cwd7bdHvgUgIwWT9JighWS0IIOPg+lOyqdOFD0HaT2lBPFk23mf3+2fdiQUHxwIA9GUwKfTBpNq6QKRB4xhdyCDwPGvNq3XF2tboVzudMUyWxnRbw2qO9U5AZ4n1JwBrb0GEqYtaAWoF6Kxap2kCgaQiotpWqZHLZZIRpqWi1gltnlDpEQofUWRGL4JOHWWuoGIpC9I1ShbK4Bo563dOsD0P+S83acYqE1QbFKBMBVQZYEY9zCpAtY6WhAI/RmG29WND49nx1lQeaM+hwnNO2x99x/bfF7WH7nvxvaIHI/YshQ8rhGBKgmbq9atacN5oJvWnbYP4jPbwMgzHuEsO9kfaniPHiklNDW5vHldc74cHOUy1ohQoRA66EmwjiP67ml6e4bw8BxWV/olsLI4uQAAKB8E4n88RpRMjfsGeexVGla6GR04RCpiLSrrsuGruLXKGAtubZq6gDkIxBmtEyn4EpA5fUuanQRmWI2Qh9WtbsYiZFEXQoEC9XGbrk62XC8vEEOE4G4Q1DmBrK9b1jGVVk1TrrHlN2B5ULwaXTYIDRY5S/AyhYNxfuFgV3VxL2CX3NoIb0hqoMCGRS6ex4taPlqvijl3GRs9KgeVQiSHOe9h3xwoN82ZihaEH1DRnTGSaKmpXDbGtAjmd0Zmx8oLncovCjFoqHh0OSjvXhrvbJzgvZyyL1i/zOavWd4YRG2eU6IFSUKcJp9OtRqqayU6IISZEiHkep2kCQbWydV3NhCwWgGQPB8BlQuEJtRTMxyMO81GrDcjbKO2IhhNuHr8NLhPOEJxJS3xICu/3+b9+EmTHRACHVRKXmMx6Md8cFSqLgJUE5/vnYCIUJkwJ02/spSQg27si+fdqG5JgLp+xZzhuNfCWr8vNtaQLlwDUlH+VYdGYiwgSIw3uKKxWk0hI3gi1r9beaCb1MunA1ddr1zKzSbHehpQvMsKziXI4uUv+LqEHtTOpFqq1jKvhSYshn5GFBDsjMkKmibwAG5isu2eJACoavi4ieH7/AZZ+h+NjdRw/yFBM4hKjm54jdW1+8sZ86bzKGCfZfETdHJ9vu79iMBtFJtf5sBWwGRobXCVbhMRIqU786tYP2+gEM0WsXYmViOaPELA6qrzdXgzRtomA+jisK5LTyEx7mtei0p/PQzUmr0AGYtrG2TD6KPqraOanSMjmCH3WawLwF4RaDd/P0awlzYQYYYopd0KUTHY0Uk3JEBbWVREmSEQLT5rGQUQagUY+8wQiRYkQwBi+ak8AsPYVwsOcTV3nWDqwtB7M40/++PNY1wXruuLclAGLuHBlCa9dx2fZWhrSbguqYxYs4jl/gOdaiQhwNkJJHZ20ThdDx+emTH2HSwY21lVTEQoz2rrijHsQCU73Z0OYL7h5/Dbm4w0eP34E5kMIJALl210QWtVYn/HO2IcJCJrYmE4nsFXiLhb+35oC0haebHw9abIb/Wzj2yZiSAjU+fPrZ9QZ0l4Dymd7TxOvaUv75ziTCwbELkhT7FUCUCejI4t76+x606j2wSCv0t5oJnW1Je0lLEVuVnOamC7k3b3anPmkxUvqrF6ygwex/2d1XIzYuAofPK6bEzWhDsIqgbKbP1LJB5cSRTpO53usbUGtDCwIO3G2G2/+9S7TdmPvTQP7Zzzov/P7jQESNNS2mGPekqjSAbD/xRiSCcP/DmFwmLF4DFyJXxdIN0Iv6g+Z5xlYWxBHR7Pu1LQQna13IUetlg3jK1StJ9vQB4+E8/XuqCC04ZuCQNoZXbPWNB2AWNHO1wUw5qVSskXXdbHaPBbd2X3dh48r9peH2AMGSCtImzNEmDjwFoTRpQOtb/wkvvEzIr4+g+IJvTU16fUGGbfYv4TSfa2BtanprTFhXe8VeV96QmfQvjufVRtD2ov2fDLEDOXe0PXNBFuUkDuyBNDVn+OaRV6vLnFGAL1PTBh04UP3zT0gqm3cn844HG+AvmI+vAUuNUqqkAAsAquJACLE+DKDCuuANa4FYnlsYI1SpFIQyckiqFQCOSY2vsAnK2jUdoQ0pkWy0DLGvD/PDzGeF/mlrl1z/Tn2XgKIdz0tBCoqROU9O/xp5CPajfHh9sYzqT3f2Xxh5EcEqJQX9sUcfOQCGKSNOwpf1IvgBkoMWu/xOxGDS3JIGhLAKmdMNKH0Ff18i0kIR8wgAu56M5QCaDACadjy3fMTTveLIlO8pLmGtOlpkp7+dM0d3hbSzOo64EoaN1FITY/GmAiOz7chw/acZgd2zG2T1eYOGiIsGn7MK4CGSExlZuDYsDaE6Q9lHM7mWheAMpnW2TvW7hBJhOOjCRBDjk9sqhCFNiRSATmj7dM+OtAX9ZGcT6smuzKjoqDaUFtbsS6LBhaI4HhzA7CWcVjWxQiOahZu9tUEX90fMFKmysXWZzh2F5S57PKsvJO660sE9GTSsBrxXNcV5+UE0NnkCdK8LluVFePEHACc1xWtWa1bF0JMQBCbZ2AwXl/vRkCzsXASWLZe3hQgQojKzmXHyr05AxgMSsLXen86gVOCaVu1N9M0gZ48BXPBB1/6Ej707tfgcLzB4XBANYYFIqwJAtDNvPucpZ6Iz+F4RDufsK4LOgGNCVIYx+MB5+UMAmEurFGSAqAdNJpQGlDOxqhox6jG+9zFeI24F3MdAMOUt52nLWTaQ+0avcjP2wtMQ6gy7YsZrekAiFxXT89+ZVfCaG80k1JjkhNiyl9Ec3SE3cepXc++duds8B6E3AnT8zc9cYd5llALl8gp0OgqDcIorIgEE1c8e36HD26foxyPkFrR2BEqCJ5L1CIxrqKvBX1RP84msihpUJuQ8KJh6goPNKSi8j8R3UehliKeRaRh376Fw/TnofeuGdltDWLR+D0iKbXA33COc9fk2Gb1sdBVg2JRTWQ5dQgKIFAzkPkSem9AIDN0rDcHFR+ko8s5xvP06R3cZNqtfxrWboUTWcuCe6ebZ/oLIG2Yh2DoC67fdPNRkRBkXRUfD0BpKjCt1OOwC1QLFahPsUszDdCTvqF2fniY+XYX996VSfmcuaIhJqKZz6yLmpi3SlZDX88azddWFNakVyKConfYfKwteNEqvmeMkYQytNX0dGI0CCLHH/rW0QRY10jGcdpq2qoVbfebRcfB97JcHmwmw4jEhq+RmZvWtoDA4K6WiefPn+J8vsdtKZhv7lDKpNaB4wFlKqiloqkuDQnrB23PHDrO9zqeQz3gdD6BDfbq0XxEP64oXX3EbJVu0bFb05z7FtNjzCkFw1yMuQNUNp/tmU0IN72PFJUrJsGMZp4TeHOSr2JQusDDsTcIALrCVk1lhhSN5u3NhC0xJuyDu0hSvt7eaCaVm+9XIP1rnw9HpJgL4NI/47fs1VClXXTlymQSk3Gd0h4tLKfll/WgRX+gW5zNcX1eV5zXhjJNADM6NJRaQHE4PTu+9zZAZUFWJv7S1LcdmARlICBtNH7wnlfx9Y3Hu98gJyJedmHce9FB/b6LRm+1FdKsZETvVsq9Kdq5iAb5xV7XIpEC0lBzKLPQMus67713rIu+V+Px1lgHR+gweoogo0SAH06whZoDTdikaYRsEzSw2f0yJNUAtiZCqWx+HUn1p7z0BZRJeVmHmEPTprLlJE+mzZFK4z3AX7Ejal3cotA3AUPSO9p6wvCLqCAlBgQbwl9fY3oUAFbG+/3OXfRWRBb2cb2XXQnOFBM4pPMYXpj0jJnHGUqm+/h3cLkIbrLPs/0jzpOImQQFy0K4v7/FulYULlh7RykTSmXMOGDqM6abGxBp0AZRB6RERyMsWzraOiwXfsY1aEkwlYq1VrRS9Yy0EZC1OTFeyDCvcV7TmDsXLmXMQYxtCJ77tg8hf5Hv6mGTYfwVVhCnazZyMFeUkvyLMSSft+2wX9TeaCalDrytausTlgQoABhqJwFDzMImJ06A8LNcoaYX744HJlNgOBoxHqE03CQPY2LMBYRihVdF6yJBmVTHVqIkMJbljGfPnxtTUKnmvHg01nS1fyKC0/mEWqpF5kiYstqV8V3brK/eCB6CHhKfE0RxuKQBEMOwkBEBxExRcj6jnU7opjmqVmD9MHzDvgrADUQCLgXTBKALTqf7EZUlMpgwBHfnuxEhF+MkIExaAw09zDm1gqAReut5xfm8oJEnQ7u/CUMydJrc1cwnraN0wWGeMR1mzDdzMLDmBJeV47YVGhHKGUDH9pQTw9DREZFSYr4NahLzJK2HubSLS7Bm9usd7XwKQrUsC2B+n2medC8OI2K803E9VPhtF7SFzBQt1l9n+iId6Cc7In5OLDgmnzsjWBuCKFe0gfiFYy7yJRfr68vjhDeqIhOW84LWRCM6l5OWSDkcQPe3MR+46zg+OqJMX4s6fQgFilu4nnUNem+JzijSPWicnWnSCMPltILAKFRxOD4G7u+xCEH6YvRCKwGMUV5hLvqGLCNfXOYCo0MZvajt4Y/yv9e0J78nnmvMyt0gBArTK1EFc8U0aWf3taZel7a84UzqBd/BFQ6HzRzERFHH9VTFUprEyYFr9ko9uNKnB24UHtKhvbvWasEAMBVaJSNiWH6oSnwgQusrnt8+AzC0oD1zcuLj3zMxKrMyJlKYGIN7BcECCpi1hs7/ZCuloJbj0CbFDSNab0fEEmNdY+pq2judz2au0rFSoBhkadGkMyJQYdMcALQVy6IQPL11NPVaQ4BRZ0gEhTj45bquEeHlJjUmAVxLgqHGL5aX0zSwIER82xo9jo4yu259qh0B8MpFANFE1/bkjGme0LPKzwVS9Dnq5FfhJYjEngiFHU9GUnGogX7wFQFDAxpGsIma+gZzEwClVpD0oX2EyA6AnAArWoh/XhIWYg4IMOpsWkWL51kOd8BmiQmAah52U6AmoKnVQ4WGrYlPxQWCxNaIgB+m0WUYEr8pFyo0rCG0MBUjpqRM2ZfUzLvaG0tOPq1AU38001fw9oegvioIZH0EiMZuNhnpJ53H80rRPdE6AWUCTx3cgXa/oHHBSjxywEmDLnSN+0XFWtms7/WWg532DCprV7XWC+Yz1pNwOBwwTVOY+vzzrbVmf9/2faUwbo5H3N7eXzx/39dXaW80k7rWhiZq6ip8E6dF7lkt1+ZTT+O8DfNAujdrUJtQijTp1xdAH6zmHWWEbOUsPJfFJUA9SGYGMwKiKMOnMNn5wctSyZ5JKdNjq9tDIf34AEMSekDdz3+/qKlEb9hoNtJBPPVfh2wSAdZlRW8L+rpisdyhoejKsF44tQHA1BXnLUWB6fwI3BQSpigamHseXSe2BxxRgsgPSzf/Xwni3Zv7ZgQk5/GMqD9OEKcwEYWZTBuwubXPpYmikAOKURdMypgyEcw+DCoCklGFd9PMtLOJ3osxOsySF59rxqCM6bqGg63vkWzTjyJ4MuZFxto5RWd/Do21cg1Kb+/jGZtebvdDxwiRNy4Wqx9AvHno5BqvMTZWZBOyiSYTSNX/OhBXxlkx5k9XzNw0nqNj6QpN1QTrsuL+/oT5cGd+HEZfJhBpXmAPEEbtvW8u1xxBpNGetaC0ijpPuDvda5qEeEK/0RLdINHnmK9gMknAGZ1P/76YBrmWtI/e23/veZWX8/9wozi0ZEPmzfM2Ne7oeh7WQ+2NZlLDP+ObVD8fyxYhR6MshXTAkZOTBMAYqNVxRIJz7SczW2Ltk5dMeK4wy7bBS6nqJZGmQJogG4/mTLn9eV1XrOuK+7t7ZTpgi65Kz7eN3FoLLDrpHVSLlljwz5x6xigu/VOvq4631lCkY/I6RIIw+4EEQg3n5Yy+dnQLgpB1BVqLWdzMnn2Qu9FFgNYuoaMcIUBMIocuVwaGXVvfXN/7iiVDGYEQJTlcgCAtyLfIKSR1kTIIfUpXhtc3gqBZ4rUHd7gvpfeOxSQfHxYzg2qLYJpSqub/sGhkowPzusCQATjcfEwwH5LmOpFpfZ7gHCHT0OAVNRWOpFPXzvRPLQ1CANCT9tgH01l8xojA3GNPx+U5v2tHWGXzuc+fTq6junRRtPLtjnD/CA/oS9IgJGYFcc3NS7gDln+ILbQPoNWte+9aTsPGE28lFSSaAK0JlvOKpx88xTRNmKYZ1LXAIVfnQ2bqklH6RUkTacRpLeA+oYDwCMAHT5/i7v6EG3TUYpWsHSudOgoTWnPEFA/vl9ibLkzozFjZF4Mp2Qog12nSixjYtc8vtS439Zm/nwajdgFNBe+Ra3V/f5+Y7+u1N5pJRdsoNGPDjrpmFOCqEK2i6ZtyK5kM7UqQVNwXvDYOzO67a/IOEYENDYIKo04Tnj97hqdPn2itJxI0GczHN87/8Qf/Dc+ePcPalyGlXWmlFByPRzDzAJPMmpb3iwilViyW9Jg7+6fdSFnDy0yjdzWZ3N3dQZYGGD6cl/XYPuF1WmZM9jv577i6aFcDPqKt0Q0RQuvGhqSEVEzdiS1t4D2HP2HbWkjCCn66Z7DCmqhZSkErxgh4NadzRUFBJxqVp13bzOa5Lsr8W49kXB9fMCgyzcolL+lw45aO2x+81YLE52nMuMvKekfi/WGh2mjQIwge2JeY9+uvaQd+gUaGCmktLrbii8wmRFRN7eBioMX2zCKMYgR9ippIQxDrzase63tVu3Y/pu7hdV2tXmTH0k9Y7s7KEJlR+Ba1zpjmCdPxMWqdUGpFJ/MXQzDPM9Z1xSICPlSIrGhdsFIDz4T5UcVxY+7EEFbmA86nM9a2WroFRrLzxTRJaNixDoGQjgsB9OJ2Gj6obOLL310LsqDxigeb0wT3m7drQuYrtDeaSTktIti6uMq84Vr2T57cHIm0OTdJFXO1dPu0uCE0gPTcfAWFVOMEgMdxFdHYDSKclxNOp1OEqu9DwrsI3n//A9zd3Zq5wc10bh5BmEzcFu6BFQILD0/mP9jG64bIIOld2Xn6ok29/91NJdsibmIMqqE1y6tpbRD6VLsIeP1M9GGK8ne6RiAPMqiHme+Yd1Ne4cxODVAWYJESrEcX90R2XLMl1WbKSnq+e0tVZO4WxUlxMwPoDnoKCi1FgkkhADy9HP1mjPlXGvvRGdQA6vUuycVKbP7YXJ6uiznzz2V/CzZrTLQ9g3HKBqOxv9L+MKblWgubeZTGfo+gJSFjUupHIgdchk+ji6G+xtDo0G7mQY+mbXZNE4ANc5AYnU9mom4gVsDdQgAKA87UPekc1m3WM0vomKeKdphBy6rvFfWFsZW616ArHw+G60GJBy5bLGD8S2ku3Q2QzZ+5XfNP5c/jh20lXNixcz9cJ9rJYeofz2Qqo24Ztve9rP25YFIiiEqnV4UNokBy0A/GA5hGNv74ekQN0u6JcvGLL6ZstwoB0lI4shOortFYQAcLcD6dcH+62wRBBKEVwdoavvylL+N0PuO9r3kPTIJSDGbFaZzB5RCbQ52GXXs6HNK4MHD8evahPEzEX2UjlVLAhdDZtBHN6kXrDcuyYFlOaH1FjmEiTeuH0/6Q13vop2CMbMrrR5N8tpBEjKvt2tiyQxmwnKPE5FTb1tyszDyIaFi1gi44I0N84blDRACxoh9owUcNinBaqxUrezyzU0czeCyUAi8AJkCYrYO3iaR8E2cEJsDQlbIf1iEXlnJdosD+fWDOQzyLfbed2/j3Yqa1Z41gSOcIzUYjaRUeiixkO/oYgiIBXSsDgzQZeoiJqm05FmEpBRWM6gxOSLUxqIFRsS0VgNgbA2jSsCznEcYPRl9HHuE018hlaZYioY/XXDsmgjjkEYDldPbZMhYrenYheHSYUfEI90/u0Dosx25F96CbRZNgaSdwb8PN86psd/4wQSL6uyxL+n5//SWTughFZwJRg2ttYsgcg046RyyDKKfVZ5kg0sGp4vAodPLi9kYzKQMxsaJnly00nSvmOAAjJHUvDDNt133HkACNItpmYgN7KWcgjyNQGFyln2qNHKrA5qIOUAOko5QZp9MZf/Inf4I6FRDNAMnAR0PHKIu9G7T1s5RytUKmiOD+/j5QpV8WrnqtedCGiETEkEPbaGivlpNu5zu00/OBc8dOfCT6mmT6zbx3DAGj7xgKyCW2Ca4JKTbBSKp9Wcv4CzmiKmQYE7M1r4SjoKGnAfjRZHuvPtMHRYj1EYzDi93eigu2qjh1gXTZzVPqXTDNtrnXEfkikOJaowfmx/1yEGQCEu/NZsY8R+nK608OvQhh3iOHgh/aeGir5LNqUaE0PFgsDpnFkE5oIDBVRR4XoFqyN+KNBbhqjk1uAa6gCZiroAhCC/Ek6d4bel/gZrnD8Yhaqvp+SwFxR+tnVJrMHMibcydtxYEY0zTjfFwhbYW0gvbogNOzFcu6ovr0iiSGovuCIjlwN7uU54/VHFk0ilgsYjJrUQ+1fbSfJ/mXUozWaHRt1rxDvxdA2QhFXy52gEiUoCEmoL38bOb2ZjMpGlAgL5L46YHvg+EQmwM536C/7p3Bft8+LPPq+xM9YjO7jcguRPKpQMDFo5r0RwuxdTx99kRNCLzlpKFSO4+Vl2s93meHR/HN+7o+qGtmQRIos+0CWQWyALKsKo02Cy+35OYwXThDxfh9fJGinkz6zjlJMQ9+eEnsa4aWDnBxf4j8eznf12JoAOO7LaMim/CUQ2Sh2zm1Qdficg2uzq7vnS7G5XYMGJ6YiVTVJDEje5cIY/PKHfRR3CMuASNC5LVvPjYn7H43WxRhDCyeHxrUZsYQmu1VCS80ANmNRdKGAESG9tTI86Eo+u6RlrR7vL9ieJopjWj8tmXo2phd4IFWKPZ5IwK4g6UY5qPupcLVAGlVUGuaHGdwWLLRimHzrSY81fZqYQUYZoLAQ87N3xevSXMfAvB+XrOWhYuq2A9ZD8Y914IicPlZMuOPcakgQVZUMvqZWgRw2SbW9AOrEhwb++XtzWZSObkMu0OcDlhWfe3KuN4JJG02r0c7bc5PutuI4nixMpLEtOL59oJpruBSsCyLbmwAqwFzgppGNLkERECtBYKO99//Crolw0nvqUOuZtv7E4K70uaRWZ43ppv7MiTSPuw8//4i31S+X7oiHkjvwNLR7ztkWRSRuhW0BqAKDIpNCTHFw0akZVfzVvi5oIfPKwDrgdVyG0KKwuEgvnBcbwLIAgJ0HXOC6v63XZPdv34lAUDfEFeBjDLx8Nwlv3H4YNQGH/qIfhK8QzWmYgUigzjCcrvMj6GVddMaWdT+RU5M0+g9zTVyrY9gmFHagU5BiAViPpg8HwTHsN/SRf/bNErA/GN2YoiGIIHd4yAArbZXKWju5tyJZ9UxBIwuXgl5CKOCbV+3PjhyPgKQa+LjHTkiN/Hpzb7vVhRTmKBIZkUhkkiLK7piK9AcKWkCdLUeHB8D0hpaUzQRH3v1SgNQH1UtFXOtuGWfSw155+K7Z3uWg7dbMEgEW9g+ovB5vdi39CrNLTADAR2mWe7WVtRWTc5Crjz/cDiMskh8htszuTjay1cBk8otE7UsZCgGG7ARbXqeULr4TRe0hwAThWGRJI8r0scefNJPC7F+10SjW5ywnM9n3N/d4/7ujLfe2m6C83LG7d0dnj57BhJBtUTC2LTYHnB9nasMEpqSR9O4+n4+n8NUt8+ZeJ0cKX/e8XjUKfWQWRFFaDitIGaNfCrVkmjVzl5KGXIiqSkhUB/Y/D9pXj2fLPrU2Q6twFKzQigRuHaaTKE08ObcLKfspMNl7m5E4HW1SmVOLjwIhhnJ0CAA1ZAFZpYePkNAoZCIFS9wkDbdy1kqvmjuVwqqrM2hnACgdwpzlWQYVxralqBAWINcNpJtltxlMOqILDSeF9po9GurmedSm4Sxp7xasvfE5BZkb9GLcmmCgJIl7IKBUi7vSdh42/jCB4SvwtEf1wS8/xd3iQomdaqY5xnn+xNqVXSX/dOJCCiMw/GRJnFDgwmIPPKwm3kXagzI5mcmxcSL+faLEAJGdwhelk2kXoZAOp/Pm3ndC/kALMx+SjQCIRhvliJrirT33I/mVXqdFjU0SGc78V9FTMoZVBBXqLpNlTH4iUuBlIQcQbZXb3WpwYdMH0MIYtdM3LLVLgBEAmH2D0SkGBDlI5R+kmoQmMBEeP7sHs+f3aOvLi0b5FDkrCRTk3dgF87rYciDdw2fyrVE4Dyf17Sr/Zz7RgfMoW9MCkSB+r6JqiyCwu7PSc9JTMpBdcd70zPsk2R5sWdlaTqevPmAXHrHCBqgSA6F4urtFIfxzEzoEvHO8wFjd+krdSxTjMnnYVhHbA39Q/ssnmgMbDeUQEVxttxNG/MXOAMsXOJ53eo8xTy4po3Urz00j04UhrkTcEle0keDDV2ooRiqjQkJ9qweGo7tf2PyTbr5lCyE3q5xIWKsQZpj56C43MdIpc112Nf2xWBsJZmovHTJhmGHNuXzY6PoYs6yNO60lsXqrK2iiCdksGi1TGi1Acv9EHScHmw4gf1vs0dNcycKLYsSHfTWWtv4yFw49eRmJg6frDO3bNJ3axCw7WLMH23F5UxngiGaS0UCMEYuBP2H2hvNpIZWgzHgdMY1yc+Jumd3A2HkT1rJtWd7Y18kF2DCvJS42JV+eTHA83mB4YnqBiX1gymUDsHNFCQTWGYULnjylS/hyVeeA71qV6Wgd4asqwVzJck4mG3HpXAiiTiNg+l5IjlvYe9jI9pGPj4UBdS7gBPR4sKYDxTAqERAnYwhUCYiOockg/O7SYaJt+uab+qq547+Oh2VtCpmFonDnVi6jPfoLa7DXCOy44kC93RIMDxXMYLOcog08JCKMG9h0B+OZaeozgBYsiqZb4FM6+Itoy7MZu6zYoIJ1Fad7DaHrGG/eZsXLgGVFJFsBHOLDwEINPaB+GxKD82n+2hcEAqWiWBuBGzRMcTmh8hKtIwlHa7DPoSC0Mqc0doqed9snIRBJHvrISDp8xoiSENUm/I9A0FgOkYUX9WQ9XVdsZqqIpRFS6CIvTEJW+u6Yq4Ze9F3zohaZGbNe6ICpgpCwVQPkAYsy2nstZzqEB3fsIHx7LTXtPKAm+ZGAMS6rri/v7e5HrlPHuRRmLA0fU51M59fu5ndwZxd0wt6QJdWiBywoQgdts6ptMertDebSZmjcE9aNDeCQMVNHV61xw/TNe3pgXfYZvdKr51SaZCdygtS3wm7VtWbJg4CasbamG8GkYz8mNS+8IUv4Ctf+Yqq7oY7BhCW5tIJds9y6VdAXWsi1YBDAgiMUg9RAvr29vYVZvjF8wJoRv40qlkAMMQa1oRYo2Ojm5s+a78UbNc0u7HnNy1vf+aSIv4UCX2fRgA2iKTuRh6Xv/nKExPTChKcF7dHf4cUn4UE+7cL0BFJpxurE9zKTOiiPhcXsJi2UaAami7gIqiVUUrFVOsmwpFSAqubWTUKTcuft+4BDsVCt9cQVkonxToEo606T7ajgwEIRj5V1pFyFKRQU4IjNi+uzKVRb6MtlQCLiEa7Iu3jjQQhAClD1Bz8/ERELa3B5IY2r6kmFIT2oQhOxIi37fb+1vjBViBzoq192NKPUgqmqYKnmsxnwyJQEtOsh0foNKGjoNZHKMXSEGQaGmdZEfkl1NSC4lyVxjyLodY7k+1d0JtGO7pwRrtxZgtIDdrC4KLWj6Ml3UqXJCg50zK/KChKfbyo5cjCLCA7TiL1F9/v7Y1mUm7L28sYZFF/hK75C0q7/abwSW1lhF0L0dhfMwhK3EUuUWWSq0akUovWR/LwZcoHbWydtixYl3OYFES05szTp0/x/Plz8/kMidEXfYuttTVxuIUoNEAfSpeoTXUtoe+hPKmHk/9GwIOMSbAuSfji0p2DCblzP2YiRWNRXD0OZHq2CoouiUsQO/G5MHWFkGdGWZD/PzSbkNTzlbt5SNJfdEbSdZkLB1NOQQsh0HjGjJpIdP5GkinIwvnLCCcuhZRQl0SfgonAosyM+KNi7fZ9cww/758TOQDmByEicB9MKeaOYjumcQ6vzNgPWQIZV4xsLUFPPqoS7I5M06JgjL682zQDRH/yHOp/uncyun0IfmkFRcZPXALVBASwSrq6p9RXatGDZMnXSp3jxjDz2d9sWu+1agzemhN60tAJIQK4YJpuwLwAWIBuOVYkGGkFw8871nD3cFtT4rTXRk8311Lqx/Cr05jn7gFQdu0V68E3YgABAABJREFU5ua/I/880LbWmGF9cAv9V4UmhTRx8RHSYsClrLxt3Qfij7icqDDljNfAFz3j/QGJaSXCymQoywD6AlDJb4y4HJAAy3LGeTmBvDaSCO5P93jy5Alub29xc3OzkUSuJeGOsfsmUvMbEeAFBUXUwdzWsemvMZ9r7aHPOWFzATvS7nt/c3CyF40ReGUWrRfS5xXRgZBMhUZVIn5ORLP6e3Lww7L/u8veO23VeZns+p36v2mZR11QPRkM1Qs8yvgzxus5QC445UNuv9apGmOyuWUyq+HAJRQfEzGoEkiqEm2yGREB2mrgsrCQaAnCHEUBS9HikCb49N5ir7svx3mU+LhjcLIprujLYKwpGNoa2lJgRujvIhbgYQgPMU/jHexj8WV3CzAYLGqmCiZl+8w1rAiUF4J0Lcg5UjyGiVR6N6RzAvMhvheBBa74fiwXe8JR+alwrONGKLJ+NRrmW3RCsyjGeX4E5ntATpA+6z3cbDKCO4/5HzvQtoxr683SOzID8mUad11CH2mfOhC5mn21wK7MyIgQpk0Xqqz11JcXtQhcMsScLnJRev6h9mYzqRc2AmTe+ilMqk8K+eYONwa4CZGSA7FW36Rx1Vaazq8lwbmd0akDVaXHZtKutkHkNKiim7otaHKPL7//RZSqUTHrum6ijIDBqLba1BiLSuqav3B7e6t1beoUknUXRbJ43Ui2zTBJpUeqpMQjMYZNePdeSUmSXvTXGGZbW/jx9k3oiruNxli13gkF7NKYC73QCahzJie6l3NAeMl5s5Z74ygJGJzJiVZ3wqFmYFE6NQiJScJTrZjnqqZSw6MrpaAxoxdGmeYhy5PpEUSopcDh2kCE2g7gzuC+GtCvmlwKVkBWLMuqBfcU5hv1MGsNqrZiWdwnNebBSAucoFHMnzPKwcmuG1L36zWmysU1FlYBg4COht6gZeOZLLijG1oJgasybyYFlq1TQak1snXWdYXQqBPc2XxSIjidGG293stOBDl3BXo2cxh19SdFUr6Ye8HSDsBsiez6OZcptCXXVrmwlWex4fqeI8Ljt2/w9BmBiuDm0YTzedn6gGPfxoqMXjuoq8fEUwqMgQfEKFCtPzPTEWdE/rZm83ffVkxUzaSue0phpfZMUjAE/oeF2wjQKBT13rJ2/irtjWZSTiivfQ5cakSS1jRfBwy5JXxP/vyhEsDLTSgRHURG7zXpngBQUm1dosldSX0YEobVoFkabp89g0gDcUeXFcDQVrLjM8br2pJvHNrOwUhWHQnEL5vXh8LR8985wCJGZIRsmH5gIc+4nAQNf9Dxg1DYJUPv75DSZHdEdj2OWbi27fP4aXx45RnXn35xucsY9pkmJ/q95qeBRmpuX6GEXs2xI2k00AJStFXhgsIFPFX0qaJOM8SKNNY6WY0w1eSbcIJgsvyeXiK3SrqAu5ZGkX4CSVNCVnSji2kMPQsYEVxhQLmZsJBNgJ0n1YzGqvnlqpTthThSLSzN5SB4tp9U7YeggQtA1MEEFAOURSXQxEBloDDqPBnoLDDhCCLXmgAiHavWBuvoTaKuWV7PocGLAdbC4Jc831BhjZwVD4HBsAMtApesJhjImJdrYjKGqxqNXV/UZyiOpWkCiApWOSoYEbAlvgbkNIc3tMpN2DrVqiU6g7oIfhrE0PYyx0+2VAkyvQxpDPt2LWJ4MCtLi7Dz/lVh7tvYV3ef59898TAW4sp13kopSkiuvE8UUCEcoZsvANsswFg8XUh3HUef/FsZTmmPtlkFeP7smTIn8nLnB3jk1UXoeN799uzt+Me4Nfl1lC15Udsw8PCtjNZ7B4dzHsHI9XfXEvV3slm4LnE53iBA1dET+ma+3CmfZ3X8n0yyf6GHcWeiu+zD9jcjpJTuRSwz4BJudGh0jrgZUy6ADCbVARRzFhoJCtaqBSnVj1qMWVUrzNinGf1wQC0VggqigpvjEQUUPsfWLfITAuIFbPW5JiPeIoJ+OmOlM9pKIFkg1MDQ4BbN1eqq+ds+o1XDvhtZXSoRC4K0UIQLQWfgTfjv0fJRQbCjzdcerVcIyqAMOARF84m4EGolVVqLgCopqmshlLkqgy9FNZuob01gbgAaTqcTltMa1QGky4ZRuWKrJTcEVLGp/eVh95qBl9IrTKMiKqZ1qGZHCSrIAj6DDqm5raDLCq4FXCs6c0RiQgwGCX7uzAfJvjuHlEtkEG3kTMkFR5MH0C0ZfpQkeagkB2DIOMY499/ludov7MusMjlnK3/2Ku3PBZN6lesGV8dGAFANRjdYo8v7vEkX1MkS9SxXqUu3yCy+ukibWjC5pQ0pNH5vy4rT3R2efvBED93hoKHi1JSYrCuyxO81jcxbBtu+qbw4MB0OIErow3j5hnqV1s2W34GQGOHlykOzG727Fg039rrPEY/RRUKu3tdcOo8FHA5y/yl2v/6/pfd4pBqwX4rRMgtMJt3oj/97RYIUr6q8126Hf0CQzHywXBXXHKmDuKMWQa2EYj/TVLCWI1a8BUbB4XDE4XDAO++8DQihd8Vg7DbCwgQuWqtMozhVCzidTnj65AmEC27qhLoqftxyXrDO91jXM86nEwqR+S7FAjKMPC6KL9mlowhrThkY6nFyP4POiwdceADCQzOcydO1E6w+KwG3DoYmbfdl1YjZrhoOr1biHIhqsufTSb2dRJgPB2VKfcHaOqbDDKKK0/neOrNd33Vtqm0xA5PW+uIiljvVQzucDgcwG14lyLQDD9/WnxzpQ/t/mSFUwZXwoXc+hEKE90/3yoT2gAB7TfRypiLQw5uX6yHSBN7e+1W3wZ5hlFJwSIDU+2vyOzWVhpIS4ILpNRP6YI57S8yrtDebSSETuhdoU+lvL/pZLNs6JAvampT8biaNYFv6on4A2xAdI4giq66baRd3SGNjgvPFXXvDVCfM0xECxul0wv3pHm1dIV2lNK3SasQj0UdJFFeHlE1qLkFJJvtqBulNzScJIPZiXh9g/Plzj1pkUVRvnTHPEzG7vWkYQeCTRkSQeD/F59t6SK52hI5ESry6aYdDL1bGIuiWMCgBGyR2TwZVFVHTyaZfm+JICMl39GWr28WrQRBqCFTviHYUDdGGJg2rOUazkRwTwkuqu4jBljKgvr6CMk1aXr4UzIfZIv80ERSmSc2HA7rofDORFthjMyNBwNSBA2E5LmAuON3fq28MhDIxhBaArLZS09waghj48VYIJCKFrYpx2JynWaSx3S65j39JW0FlswM99FkUyUMIaLJA5M7ywwqoVYUjqhVcK9rdPXBe0FhBXDtBBb/WsfYVXbQkvHRBs3pmjmTuwMdhkhPbS83WVACgQYonfruGpsJtN/8UkdaH4+JmsrFXgiG7Zh4rLmbeLYp0saNBPufuJ9yfVErvcT9XlgzcHwk8rLVkoak8oD1dowfXFIS9SyArB/tnPaTRXWtvNJNy5/G1xdN/02fAZpW5FBQepdVVursk2GxmuLZujUmap3P5zqw36Z63ZFceGyqY1LqilAnzdACBcTqdcX9/UsBKcQBLPShEtGVMOSAEMvJVxA4FqVM7HxaE1NtN4qOrG2nf9sxVxAlqZlJsx9FZipspUpM0N4bk3sXK4kliBnGTVX71WlzOG9h9WYD6Afy2EUq9YSvuQ/ErMgOK078L+Ng7oWKy93Plc6zRdvlzkRUg07KMgZPPVMRdu28KUW6GuYBKRZlmoFRQYczzpNqNYfyppEWo5YC+IqRpqql+GHSdJyo4Hm9AxFjXZnEEyggZliwOQl9Ea/6IzjEnIqe+Iui+NFMo02rz3AeLj6miMb++DFeI3dCJMYixjG97UwGj9wYpFUwVhTqkFNO2CG1tURSxPlIGBQJWEaxd1Aohar7sosyqW+BQLZau6n4c71QXDcYBgJWwBUN1eC9WiB8LrihTtb1AY6+GpmF/Oy8xQc79WVQ88TrPkWvjev1Lz+rmfOk56lbX6ppLJDMaZ1KRwJ0YzrVI4v13+fv9Zw/d/1XBpMIui+tWnKv6gElZq6hvZmZGt9DNPBn7ia2HOX2H8d2O8TCGKtuhTm1I2249e+b5fNZyFq3heLzB+1/6Mp5+cAe0Ehz2WimN/UYNydAbl0h4PJ2WKCtQaIbgjC4rWttGEl1M0wvNqEZuhQGpKJhDu1QzD1n/uzGP/CztexcaGkDHCDfO7CU2fc5uN7BZe1pzWIvRK5N1e2JaQ+K/DBrZMyNXAV7MuLfzdClVdunoq6MfWMKmRWCBCF4SSWB+AKqAFBAqIAdQf4y5fAitVqxF4XNc6BGRDT+UYmHcCIwL7UOa91oOkMpYJ8GKk+bnnU6o0w2orOi0YmZC7R197uBFzYEnusf9PSVJPSOQlNCUiVrOM431GIKC6l7jzPS4Ziv9A5onpFppRBsKqdlOtIzF8XgA+op2Xoz56n7uFiHKzIoVZ/603hoWO/NEmmjvZmD0Bo3cK6hcLK0EKFSjDt0EMz9CgHZGnQseHytuF61yPU3TZtsWHsFOXqYizm3SzGutmA8HPLp5jHVZcQ9GP9+O4AeptiMbhNYklAsu0EiIUaptDFFNnWQrKI4gBt6Y3163XM9e04qxXqm86+971ZSXfXujmdQwA+0ctcnWQDRsCy68cuEw9yGihZOj3J4ZIjoyLJeY3c2hcAhushlMk8JJWC1c1sMKs/aiC9pBNLSKLrIhLtekEydUYqCUXTSE2OnI42O1EtqM82lVC40wpgNFeZOtKesFc7zToiBmShF17HOdIhIIIJU+qUPEsAZDVvawb6t3ZJKkCEOTdfQ6D+oIXSz5EgWmrIhLvm40GXlK3eyiEozJmdTQskLDcw0s519t9lDMwnY+JH/mYbhDe7jaRCPgIu0pPbtyRS0TSp3Vt3J4hHp4BCEOfxOXYiCwKn0LpR4SpRwZTuqhJy6rhkSFUWrFsp4V/HeeUERUG6YC6YR1XbCcFxB3Jb5iQRua9YvWlpgRwWpz1zHeuG0RgiTWz/T7+GKEJ/hngpEITq5RbycUHQlNhi2ij1doTpq+1xPbFYJVhYXCjCZFc6SWs11LKBALgtCUE3aCTmQRlxpyPtcJh1I1FKGvoE5gFLuXjCHTdn1Sv51ukJAhyqhpt04zyrrifHaaovo3weer+Mwg+6qIBCX1NwIUZKSakGlKmUldM9m5ULynO3utaRvZi821D7VrmtirtDeaSQUlA65u4o2q5ZebWYCL41xJut8vNIaCZAJIdgi119t3LhknDif2LytXUgJjhHLLpCzPhARdVqxr001F1w/8fpN4DkRrLSQYIgLNmnvCwmirOp4VM4zi55WmN10XkpaZfcSiqUopBm1hP+SMZwQfuP9Hj5exFel2na/V8FEBcnEoxnpwmuTNFtA5y8JEgOL483u62MlimtNEJMfTkcjNRh+2/5P2yRfMmbpvgvRkSpc4KCoRWSRfRSkTuMwodUaZjhqjJmYCooKR0OufxZIE83Kg0TGokQ4BUrOSM7VCFSwd6BadZigdbdUqwhomPWGeqprbRCBLHlOe3wdaaBeDQdGmezY3MsSI0W+KNzli9vD9CoAGkWYahb2D9Tz5f664EbS6O4hQq/oEW4MVGJRgLmSlJGrlWB9HsGEuVmZjUsgxgWlhrPNYtXKwMortVqEMq+FDJEQ+XKkFZargpaKnsi7VBOURC0o279uWi7BmAbR5TiVwoT1dLJUJzq9q1tu3MG3ufN3/s4FabzST8iNyOWXXJzE7pj26Si8fJ0fhqR4g4kSqRRFZkUIe2oVsjUV10vIU1E17gVruNXFXNbguZy2xvq549uwZvvjHX8TTp88BIqznc/RpUzJARhmO+/v7zQaotWKehlmSmHE8HHE8HnG8OeJgCb3XqvVuhzmkrYeahvyyYbDBGKsM+J7mzne1EA3U7MsN2wNdz+znaFb23GpF2bwTAZUdKJCuPssl38EUryQBv3Lb767L+RiodzAfg+0fJpSJIpITUA202zwJGtQvWEFcQTxBaMK5QSVpYRwOCj6KFVhL0cK5VdRFsh106ltCE7D/R6lNy7uptWo9s3VVzUuXEdIIXCfMR8LStKxDAfD22zdYlwXrukBW23+iCaMvJ0A5UvJKJWmbP/Wh+O8+99cbMWFZzuELAlTz0TIZ1RjcAukrukZR6Lkuypw1YZpQpWA6MkAqwNWpospsPlZFRPfoQaJi44VGAYugryumQsr8pAHlgBBtI3cOIVcRSPHqbA80ANPNDVAK7u6fo84z6nJG2E0D4v26ICDSwSgb1Jc9A5kmPfOcNKkXnWuHXcuMye9/UfOSHF4v788igtjbG82kALcSUWyI4Dt+jJMEkzH0lFERmripwgIO7Pqr0oLb0wBjTN2irZQ4aZKg4rJ56WXxSqkEM310MOl3rY0NETb0pjb1dV03mySr1iIStWGK54YY42VWJO1OGEgEbCaiMBPilTbSNZXepXb9XU1I4zmmBUGdyq21UdbA7fBicqFYoXMRUJbIyYmVMb1wAOo/q0Wf+Qq7T2iYVGyWPSQ6aV0vGucLvs369NCqN7bh1EGkuaFhUlFjksWRJdNvtfUDE86iATaVGWWadN2ggRLnZcFUOYA9ycxS3UzPPkxKfc3r1boZkbuWSGdmrRIgLeCT3IwkJJBqUFUdwGJBQn0QKxJ7ZuzJ7Ni/Pp+CnCfj8wSNZPX0hYv5fGBVxLyMUQi0onjYvAh602eEOT6D+CLNv4+HCVwruCcTHw+MQw9uoDJqmxERSp0sNWXGatqtGVlCcxy/QJVuG2QHGY4fgQ8z5Fb946WyArwCcLQMv18ixNf3wDag4po285B5b2+qy/TFmVWGYZumaniSZXOtt1zxe9+vfXsZs8ztjWdSwKW8O0xvTljGv5tQYT0hmyPlSYUPnpErzj826QwCdJv3Uhm8ujnRM6xVa1CiRWie/AON7PKN9SLJKG8Odhv5PG8WPAxdRqBC05Ee2koe78W85bm4wqz9IztfSVuxcNndLXuLfJj0koCwh9i57IdpZTLmsxj0E8VyuTHEGdflvvCop6yJ6V0v4WSZ0CQCi113xb4gwHx19jfRSNb07rESwmKIA40IIAZK0YRx1nscJNnNTWymO31OHw/c9SV9Gr9TrKmV8jDTbJch4DEBpZjPkBlsficEWDJU46Vs1uXN2m8Fl91EbQQuWxdXN65px/4v7RYUvhMMR4+UsVpAm4orvld5+DvjmaTCFMzUFykp8buhQpjVwBlVPIMIVMxUWyvW3mM8eSlyl90EuTG7mFBCtYJKQS2MBgd6DQ/uTgBwBjzowZ5xOD3JDOqaH2rfnMm01jblNtxFwnz5Hp+P/M5YsyuNiSya6OXtjWZSDCeSWdpOx3LQoYCbGRQ2/b4ho6PtP8umI5WuKDYwF8udsloziufl6joNwgCtJ9MJWup7qDvBQHNuwzUbsmLxvXjpXFLxsNJlWVAmgqChY91ISa/qwLw2P+Slz6Ehvw5xPAiCE3QNXQ57+oZg5MN3yTRFJILK9CMLPdj0exxajdjSXCKKdw08M5dj/d1KBK4kXe/64RJ4Hpc3tncrMxl7q8MiF6lZmW9lMJpeo2U4ap3RpgPW6QDUCkw1ov9qZZSp4K233lLzU1WzU+uWsUNl2x2iLdPcEawxHisIykN7kL6o5t8Q+1Wj+jpEVrR+HsEMRCioFugjADQISOL36EHilkNITKsGogoqMvakJMHF3uXnbfxtAQdFhbRaJzBNWM8do6aW00HZrNF4N4NRwp+jeULFznQBmtY08IAJJt70oVTVqnrvauZyENm9JpsFh6RduJEbTHh0c8Dy1iOwrOhPq1awlaF5ahmOtBcJKjDg4ZbX/FXMdf7j9OLJkyfBcA6Hg1p/Jl3baZrh1pmH3jnm+WrnsHXcPdzeaCZFolF3PQIdbEeQnwuGq/W+wQiGiOz3yvAr+fnpvaOdF8yHA7yImEfNCHrUZGKicHrWqkxmSB5+6MQc0NZnu2eYDCyZj3ggRUBQJpfGGxAlLWzQwLApeHMT2Y4YeV9FBKVOwHJWc475e9R+/7Dmtm8+vlqrzXFHIasAa+/x/DGtG6PSKAA02RVYtIAKx1MMspQ0NSOxSfvZMqYwc5GC+Iq+WOt39SEVknlmYm582uCEY/9p6uduXoacbITQzcix15QJCLHCHRnMjBIVDdvX0OYJRBXm1tZS5Kzz2lpDJUbvC2Rd0FrFumqAw6PHj8de36mLxIgKtqNgnYL3ikmv66Ixg7VU23+CwgUnJqwNWCGoVrZBSzfoexWFopnm6cxCgxp6rJFocnNop7ZmkpgMtow0zzWTR7ClvDOzPLiFw89QrRoVOU2TjxI3Nzc6n6J+qz0AahaN1MfEZv5gEBfD67O3OfZeAQorqfQAJBNFzUQpilRBFeOsjl2iQoqrmD16QtC8xcLAXAsO84R+nNHeegv39/dYV6D1yRDKG5gTAyCNolVBYivQBq1KdOBF5zrfm5na8XgMQdaRS87nBeuy4u23GbUm2C9DtchWoIv1Tb+LCNor+q3ebCbl/9L2X2dUucagmqYovoY5av3AuDnJHaVqo7fIHoteY5dupynyJ6IoIRfDCQN6t4J7uZ/+AgymCfgGMfh8kySZ/Z1J1ffxufkwJDQKWpXXfCs1mzmAPenWL04M74qp76HmfhAiMm3WTFmiaNIsHbBkYSGyg+8akcPLOAELT42ZkLJmPAJ5ozc0+HTqrL3bQq1pEBCtGkxDCQomj5ifiNTcLNho2Y+yZ1V5Lf1vsrkRDMBYhxkiYWhgMYOpgkoFyFEc1IRJYqYpsYhE6VpmpREi+4kQ79nOxfDDsD1TtSH9mkvBuugDxObJ5AsIFLG7+3gEAdOjmkwJ6ZmsE0wOkeSShjIX9TsmNYJoM3v7SQ5BgWgwqIhyS5qfKqLKpDz/z/y/QEedtCy7htPbfFwJeAmtusMCWxiDrTM80pFc+LU95HtFhAxb0IN0NBpRr81JbMMkudHUQ5DW5r4eN98vy4LWC9DdDZCFJTG5T5PaRS7Neb4v9qa+i3nIwtfu93mew8ekmIc6xyKCw2GByLbcvAuEGc0mojcv3vxiGpPbG82kAlUBWyIWgItBAIEw9xhz6Z79aM1pdqkVhVVymqcZoJEAWkrBobKW7QDCpNV6w3q6jGophTFdKTsBS2IFGLXMmKcD5kmLG/qmCMqRGTDnkVKSznaPT304n8/hu5LOGtjRCCwltDadSiMyHk39gDQkYnQIam6ptYYJS4kshfYJKB7asqxm1bBcjmUZBCsxCX1qD+nU3+fT4dKnR4Hl8ba1gyuixo8CeALSVRN1hs/G8nuOmhLszFDXTH/5nTpR7gcBBnPKhK7YXkMnSCuDgJFG2JXDDLo5os1VGZpo6oB2QQwJxRhN12fG+x5YI4pIssvGzJjnGegSVgFBtc1fAKkgASoVSD1pBKCIlpows/Y8zwHSqu9T4aqIlbJPJ85zqDZT+ILmQo32xZFTaBQWBJnJVHA4HNRUWqxCrBFh5oLAUrRglfH6LIhcI9qKLqPCl73PmFxbPECDIChonXFeBNWEMQ1ycRNz1kp8H4097pGu+h43lhZbvALiirUJ1tW0mPDzpKTq1HrvatFJDGNdV/TerWjqYFb7loOjMiPLzMZ9U1nTev/991Frxdtvv62m6Ct0rr2gHBCR0opXaW82k0LIs7FujOEXAjDCzOHSIG82KKeFdc2ImVEOJRaFuy0w66brg2pumjvlGQgzFqfFFpM2YIe39dVKfvf4cUa3LAuIgHmeRxRP1BrJBNoG3UduSJZi3Bbt0lCo7zZ5IxdlaDeEsXlz611LHayrVw51Kc0gXUhNX46dBiiizMpAt/wLLuoH0CqgGllG7PgYNiDZEmDmIRAoHJVKtZ4DouaWZn5ABjUlKCTqdVAp35memrr0UwWhzXvEu0C79d1oUKRMPohYMOW07wAQlfBzCIpCGxWFPDoeD0Cd1SxJztw4tGOCLTUcEkrRtUupmz6pQpPmql/bmUAtEwg81s4EpQ7TEMCYp0cgLDj3E5gKwB3CHScIpBaUcgTu78Em3LTWw7zZjNEX8/85HE/EX4c50Dt/SbwijcEG1yzZNcD1WTcDFQbVCXC0GCAYw3JeAGkaMYjLuQhTnLhFwgyCMgx1YaC2HDVZO2phFNLyJ0IFIuoXdP0LYuUwnGYkbWJIWXZ2NxhSup86RDVQKjhzxZkKTp0gJ10vNcta2P+VpIrMYNy3lxFlnJE91PYMyudNYDTyeNzQyNYasnXI27quQWfIGCrDgy0GrZGro7je3mgmFZMKIO3tUNEd+TsIyBWV2J2keWHc5g2YiQpG9IxyuPlkT8ji/U6oHJfLIUEssMDNMd1NX1c2V9YutlI8bzaG7hMjwlecltFENkwwzmkagth1eW4vHgMJIp4Js4/JU/zD8s4UUTxMDoHEZqZAMOwwYcYsDtMKUaZpbjp5uEWE5sakRyASO+ROMpJsHS+4NikxGFt3hsvaYx9Q+syICtzfWNCpaLJuLaBpQplnCFc0C+BRUuml4GPJ9E3kDnz1f4YA4fLK1oo09nH+jBksAuauodoCiy71/SQKndQJjTuYVcNiFi3lYedDSlWt2/wwjnIQQYZuRmcKU2GkYWwnc/TZ93/utI/EhB+ywep5KiFs+lOL+ZLW1VR9MQzCPDeEzTspr12yWvjrRcZpZlJgX+YSa+Z+TDFBdFPbbveshzasC8LhEyU1t64grAJgFUtzGQ/VcmU74fsBLWk/xw9dt7k2dd+11Gw2DI0c0FD5LDS3jt6aCY0GpJz2qgCRFfRVYe5zqSdym65cQVw2EkQkAGcNardwsvnbbScCUAcVyRduLmNmjaQ2wlonqLTXu2k60GRXLiDSxaylbKL1PFJomqYrETm0Pcm0l0VcPtlu4FfdDC9qvjkBQmdPdBw1ashMMg6A6m8knjFXlUC5ddCy4v50Qjt3iEHsuKNc8yxyfymYMHk9HFV9tjSAWbP980xYjpoypXE9MymUVO+hZREAsPoRVfgrG5OjPpAA1Ast3U14IaGjqMTNBbXMKEXDimlSyCMpDKkV3dDMmaoGWHBF5YNtTiV8rXcUTKg8gcGoFuoMqFIfDCpNRtkFCsQcoYMYqFMB01Fx4u7uLHFVxzmVqn4sYUBOaDiD+hlTuRuYh8eCxmcQnSG9abE+Zq1ia/PVrdIusYAMzLXHybvaPV/thIaSTak6YC41EBr2TXXjiqWZ7mlMmNBABMuhGi8OMyEAVFaUc6uiqwC1KpB4kqomXSv00Zgx3RZaIXhSzZ7IAoYkxkxOP9z8twc5BCxqkkxTdOxN0UKOk50fauYXGmyEiC1o62Hm87LACb/2Gk7otetaa/iar/kaTQpfW2B29t6VoYiW1qkpT3BPgdJueGl7o5mUy7PaXOJRwiEOJyMExa1TWzpEIvnVCXgQWTMzKeOzDSYGKnlNbyI3N8FOkvpDGC7lrUp+2xnu+JXezdQCLIvj7anmptEzZ3zoQx/Sx+9UeNdccnMTo0urak3weG3EcwTA+XS+CgD5yvNtyWBcDU2dFUKGeDjSdV0MZsfXxc1wAIAJR2b0uaCvs8HtrIB0MBPW04LWGgoXQzYQrOsCogISjSNTy5EGFWwPH212vgYniAV6ePAJA9wjL830wMFUCYD7xZBC6V1LsCXIiZ4AtI9gHIwZlVpRDmYi4YoyHTUayvKhRkXXgql6lJ+bV8hKPogRO4nQ9rkq4kaTFL3lmm1odEmyNcIoOaGtKrOeeAbuF6tY29BpBZWOOneUcsS6FuBccGxvYVkWjd4CYyoMmioEa6wR+T4T86WxaiIFGryimpabdLHRBEZoOI1S63aG1aRqRF0Y6Go6hUXbKZaxCgcet5/j50qphgjhL1ZfMZtWVJjR2QCdRU161cLaRVQ7KrWgG8Rs14MwIuJKDZeBnyzFWMwCpJv4Y2ShXfmWKoVxOMzoAa6rgVqldhCtQ8giM1Z3E9oKhS/KGUhm4h5xly1De4Z1IaQjyT6uPdka+ZXvvPsO6jTh+bNnOJ3OaKsi56gMacFkFAQoPVxwMiF8mnaVqx9obzST2ksIFCYX/YvT0VUFZKirFwsFs1QRwct7R7siYeS7u73AI70IBUQarKuP4SDi5IdKgNZ6BDUAQ3Kvtb48h8mZggyzmM+Byyl7Dar1Fnku11R8HbVcnx9ys54SOCfWwgRh0xIjkootis2Ij29uYmNoGr0oXAaT0tmCdAZRU+LRO3rTKq0q3avQIZTQnTdK71i1YR0kJRhOFxIz3bK3bKohQJQRK6MYV2VTcc67ITfx1YoyTfpzmGz4FVwmi+Sz4BKNAECU5thpv54w6VtoFLK0EGgBssaJh/aJtcKa4tDFS6EDFQxZeYyRuwllQCkThAkVhMPhRjUJWtDbAgUEJjQ5WFl2R0WwM5gs0kxA6Rp8MeJUR7/dZ+v6tK+DR8SF+RQqYGhkZAEC99E2Io1UB/H/kwtI3h/dBFzJmJRqpmMtCKVMhqM4oxFSAm814Qj6t2uQ5lPc2jSypkRj49EY9x6dnEmLJk61oBJhKqzlSBigzOBMfd6nGGU/9P7zcd8L6Mm1lmlrSs4l0oKStVbcWWRl6w2trbYUWXW41Jh6a8BVS9H19kYzqdGS9Bg1WihqQQEa5Zbhg3yxlXFZfRgakWk+fRr6q0QTouaLYc/WBSiuppNBqUAX7r51LOc2gjUwAjMAjX452GIDWlHzaA7KDG1/jWGIO6Ulxy8pASjm2NTItssmXQy3zQ+/m60enl/AxyAADISSgbVYmLwUFBQNihAtasCh+VlAgJi/ABpggTqjtw7iBaBVc3IwoVc1HZTWUaiDUCJSqKAooyXXdFoMXWh7KNxk5wdM/YsNA1RwaBpb/UP3hIigbHJTnLgNH5v7MqbpRjH46hH1cECdJ9TjbEEZo5Q8gVCooHAyHwmUUbhjh4bW7Npab02DaXjkZcW+oCH9bo69DCI/VS1wsmBBs8BWZkBuCngV4Ayw7VtFwphRawemFYUJx64wXndPn2o14A4cp3ewYMGKBev6PF5bSoIfA8C1QkRzqWJfWhlsF9JWk8IBjr3tp5Csr6VMKKw1pcLkavMJygKKhHAkRdA931klLGAqIDqg0Iw6TcGguKowAWasAJZagGJCpMUJBIgrqdbXizLe1psJqEMrHNqb5n5BXJU1huXmPwDoK2oBjrXg7cOM9XjEnSw4LdUiBxOiRD7xXedUA610T+RArGt++NdpFwEVROGK8PSb0/msycyabWwyXogKIbG8qnlv395oJuUI0S5xEZEL27GM0hXotDArcnEp8Z1rBi4VF19IpMNODHDx9A8AHuYKRFiobUrNSlcmRVzB92eTfO1pRJtkt4cS7vYAsC9CLt77nIhog4guojBLhRl9bRou31asy6JlRIoWXXdiK+l5450CEQvlNkle6+WUMAtoZJpLuGTC/b7fKvEu5xNcq1VYGjOlyAoq5qzvDWSW+akymDQSsvUei6OmoMGW1pazqiQkUAr8RL3eA++979vD4weS4eXXXQv2vJxwkJs03AjgaUKpB5TD25gPRzPBBHUEWfFDr8RapwlackPfE6kABAAdvZ0Np8/MgoXQ+orzSfNXXLBi97VEDa8rTQzZXHcTClcr/tcM7ISBUoHuQojNDwGgghVFowC54PDobVCZUaYTnj1/CqpkydzN+QKmkohU72aiEjRphvVYITLqLwmJAcGq1rW2BkgPkxbXgsIF3HVviYbiRXFD8sgxp/fmj5ymGYjoSQCuzdYJ4COE1TfYzBxd64RGdcwj69wLiqay6QKiuoYWWmDe38OXJqmemqd70Bg0FD1YaZBW+VUBIYISGgH9oEEh1CFyj5DGYADBLIEV6rTF/UvXTPt7c5+Lka/CwDIdWs4r2tpxf3+GrIZSAsb5fAdAfX4uJHqu1IWV4xXbG82kAN0AUXI78MzstIQPgYLzbxN8CcWgTDwKhdK/fk0ECMCiVazsnngkk23OwhVh7iKVMoo5WjuNLP2Xjimp7fkQbDaXS1U7BnUtokfnSUmPIkK4MZzSj45w37stw5IYg4Z7Z6V+/ITUlvQTcukRCHiq0Bj1RVBKMHLfuOjzOpF5A9xHlHvppsYkmAQEkpugxnVMilMnMvLrthrUkBpVINGr3G+YNWGV3lVIqdOMMh1Q5iPYIWPS6F3C9PmmjeAh8CRtJQQASBBWJRIUk7y3kZ9+99BaZLeCPg9eG1KXmU070eRqNTmyBT44m0+hz2WCR7VSB6aZACqYlgWlaaIxeIm3E2mYv3SPJDUzMRAxA+HUN+2aixjSvdUwFmPsMOsDqXkuos3IgxySHmwCp28IKjUCOzR/blYmVSaAJxU+rSIumCGs+Wo+a1zU8oK0J3zdODHHvMcjwhPOnHzvjWjAzX7z35jgho/pMANE5usrwZSICdvIWm1Z2IW9Z2/625xjyedC92Smd5Q+22tSAWJt71QLh+09GU/1KGBYP0bKC167vdFMSryUdimqqieCHoti2pOWdnZGoX4VNTWMqDHyWUz2YjbAT8AWyRAB9IBp3pB/XmkeZRlEULngUExq1Qdc3TjesoruUlCgXr8ic/N+5pbrTXWof0NNGxrKG1qF3bZhAe4HibwPjHk1H0Fu4ZtJHsHwK9j4Dwdj8jbLznhKKVg7VFsrDI1I6+jrCobOQzNC6cTYAxsiSs8NsE4czHQ0UgBeXKbEW67P4xphhFsTATBE8qLQPMfjEaXOwHQDR9wW4wxOeIeg0FE8aCP4lJr7uNTwic5zgdLIhokZKZss9me4JujS3PfgjhECo6hWE3FWxar7Wi9lVQmfGeXR22hr0xyY1lEPFfVwRKkF67piWc/AqVkUawdZQIVYUIUzj5qc990IGETAXcI6ASmYoOex2mi6Ebl5OpjSaN4rGmgmwVBZ65sJk+VSWYj4NKkQRBZ0UWYIFwPQNXOhpNkjRp3MXGsCjSOjR/xkRHru98aYZx+vmvuAUa7EBRMdLJFGQQLAo7ffhhTGKi6YQTVnJOEJanbTXL+8pz16dTCp/G8OyXeBU831GjEaIeOZMe+YVA7CCEuDSXzzQVEqWkp3CcvFn9Le90Yzqcoavp1LNQNjMus8Jco7UlWpQEM6E4KDEBTrTTQJkQFoOWkN/QUQKrSHr7qKXa18dKkFvWnhwme3t1oTSrqhdStWFSP5MgDc3NzEJlqWBcuyRNhrHo//vE44uYjgdDpt+uq/u0nhRfdeY4wqiAl6X9HXitKVkQoMuRkltFVVmIb5A67gRvChX2jIAF39aR5VRybpp1RINKxmPjXi4SU5VE3Sq0TUVCJ+QHswhI6iyZUdkGBYQyJWDYOCGBBGJJdD1zAzapksuqsA8yMVdmjCgoPtOTEzIzYF7zSxkYeU6eyGOYrtHW8e4Xi8wd39vQHfEM6rjrsURoGgMdBIg1ZyEraPJqXV6HpuVzHmf+aKDkYXBmZDIemClsBDp3LA6WSm6+NNPIVLwel8giwFb02wJM6GwgpE21vHen9K/TChpHecz2eIqBA4zxWTCYY99TE0FIGC4NaqgTfcTRBgZSwCNFHdr0FQeEadDnj7rbeGYAAB3AfIbFo7oQGWVuCsR03Q83wImuEClpr+zddqFokoMR/0xfvebN+LmR9x0UZOLwFFg25IOpgEy3rGeTlhchR00crCnrdJrH9rMNJs/vaCUgirKDitR/p5Wkut1Vwh2svzotG+jx492vQrd1US1FFfV5AoBmgxWsQA5vmA1lYsy2IguLbOsMjUPy13svZGMymtCVXChOKKEFsJBFfNsz0UhPSdfSZKTEstIM/T6RqFRkly8CJrmSl6jlOtmsPRLPKtMGlxOvu9iWKZBRgp9FBn89E8z+ZvuC4DO5N60aI/FN2zZzpZZc/Pvs6YaHNNEB0jaNlmNq691OhsqofgMN4ADcxww42tGENNR1FbhxTRoAngzn94xJthyG3MK8awOuBlMUgE7I5lz7ciCuahSZkIKB4mL7nNWn4lGNZkeWEVOBzUFIMKNK9blMw6m1+MaUsYKnU9aAhXRAPQS4Fdtb4QF1Y/Iqn5OIgoBhN3oXZj6t1sie3cK5IBVIy2ixX9wOqh2aJt/IcmQZdaUc2c1zuDeUFrC5iLIRI01Nmfi2CmvTf0tZtFQ8fkVrpVfPV0T4T5TijWRv2WyqBEHCfQni+CUg+o9YAyzcGkeu+jKjENIUREgnB7yDqbP7GHdj60af3Ryp7UcyQsRZ4Ykfo+idzE7D6psSA6JD80rlER3KGulRVYAynFBTHZLjKM1nl1XxOgonz8tZb2nVtuitXEi02U9mXeOr0rGK6b6Melw+fk1hZ3K7yIXn11JPNaCKgvmhJAaI0XQ+nenGLoZqtlRAcxPGlO4egBqAS4Drw+xb0TcB3h4r5h53nWTWI+sdYLSiWs7YAuK7osmkvUVFPgMvKz7u7u8OjRo2CCH/rQh5Kddzg/XxSZkzeB33MxT0RbAnjl+mt5Y37N/v0j2s0neL/ZUl9pKx07DNOWVpLWlmLEYfYzAwATCjqfganj5njA+fbe0A5S1F5Az+hdvZg1CYLlvIBKwTRPIFEkhd4FHUvMTyVgmiet7Eo+ZsM8NEmYTRACEcT9I6ViPRwNj43RooaZ7kOfoiC8NNbN363/OqFhtLXhvt+biWxCLVrxdK4z5tmw2CzoIk/mxe4wonNV8ICvnU02WE11pL5AAqF3Min8DIhgKhUoiIKcRBWHQ8VhPqLLGfenk0Z5YUGTM0RWlCOHmclrKkvvKKIh1+7XZGLTmK0QKAyA1/eu95sJXNmYyAj9diguEaCUGcyTmunJAhNsrgfTsX1iSbS6OgV1PqAU1kAR6knYcnPiyAEsZikAGYOKtZcNzCZxQZNtFYBkWLUBdKx9WHEOxyOONzdR3BSCAfW0Mcdta8oR0SYpN5/9eK/tRz/bbrUZ5jvCDiNXESTsQzbUky5qbDwbluNqkZuROoItk/I5zzldr9LeaCY1aNKWE5lffggo9lPcEeqSITGmfVXb1g0YlpTRGQHMEz0fZnPmmo26qHRXmBTnby3ArfoeVFIZRKmWGgmIWyc9cDgccDwesa4rTqcTgN3GeUg6ekHLqBrZPLksy+a6PYJxTPHuncwVU5lRcKNEpLLlSakU63U3Ooz2XazPQGfYOvn1/s7NgEmBCndQezSY9rFOR/X3CHA+L/H7eAdA3SIwAeD2mUZ3zjPWdRycuVTTjgmVGNWRP2afbzWvuX2/EGE1sE1A94fwhNaV/DposI+YAEzzDEJHW05udIo+eiuGPiGogCWNSmccjkfM84ypVIv4pHiwQzjmJxEQRL9HT8aXoRVtPteoyqWtmLx3BLCwxXIYd7G7TvdL7KPzuoTZ6345g5gxHw9oTRm0+qVsn4meh2Vd0dczaALqcTIU84q2ruhi6Ry60yAegi4ILQWs2H21FKBq4MOyLEAntE5Y1wVr6yBa8OjRlPVZzaAkPVNRSl065nIAoVhahk4QEVCrWzV80tP+coFt7N4kgACT30WEaZ7QT2fIaqgmNLQN99l0AXrnsCJUYRQZ4e4QUk5sWjiwotbB5D3FxnMspXewALNFo5J3LBkkXetyhuKDEQioWUI5EBh8zpQArRSdy3g4CG13TYowAmceaF8dmtQQKIbFj4ywkWgsni0M80ClVtOJBTsYbFKElJoWoEzNCvURmT9gEHnXnJgZCkjXg/kwF0x2+Dwy0KurhvVit0DukGTmkFSzxnNNw7k+J5cRfsEMgbRZx3f79+yft/kbbgIzvI/4XkwDATbmi8u7EfZ9m+sevhkzN0a2vpmz2D6yPJRSzG/VOoibmXgZyEEcVi1FGYXV/yoMlgqXJjVlwNaWzdRTK6g2o0mUQtIEABtz0HB7zXOaIN2TsTEWGGaTF8m0DUjSf/jruBhUVrGQdCPQxOhCaF2lcU0aVS0DIING8qgxe/XGZEtB08YqCRI9sjUoKAXGpMwUZIC9OgsDWSUX22SYBkuMOh0CYUDIAih6V+R9cb9KU3OrTCgyK5Yha9qGFAqQ1zFPTvVH/z0Qitlqhln0nu8tLgLXeramPduOhGTWJYVzMkGuoLjd9eLesV5Zi0n725iZz2uG/WkiUUZmMIp0zgTDbA63ALjfjLGx8cVa+9qMcxvBKq1vilNGL42WxXxY5HHrDhjtwv1g5jFGN/PbZstVe82qfjGG4Nx274uCxl7U3ngmBQN/1dLGGlNWWBlVIcCF7EJDmiBRS0A17aKWlKB5BQfLJVJnVJ7MVowBdepo6GiieHCFNUt/ni3JDeqzdfUuS1G5iKBH8t3d3W1Mb1l9d0doVpX3/X0wDN02sZhZwTWn/KyMlrzfVBviF/MfH5kU2oNA+BchEWNEUoGMYfHwHek1PYwvoBG9Bwy8ApDFylGzwwxwnUCkuUcAsJ7P0f/5eIyDOiUCM5VjEB4CBiJ+WeAkp6MroogTzVLU3yAHY7SGRpD2ynAWq+bABExJr/FwXDWrVFCZQDxpqDcKBBxRl8vasRLAZYaQJpkWE+rZVFb3bbhs0EWiLHp8p4toM2lCD5QgFZowHw6Y0QFRInfuZ6BYmLo0c/4L+DCHZtmSwDUd5tjPHRrdRbbXWu/q+C9NCTJp1Kv6CAFphq1HSLBIThMT04HVXZp0f65QBqCQT8p4NCCI4FVrKXy+BGcd1TAAiVQIEVOdyjxrmRdKfmw/J5GXljWRdLbMhEZQLaLBhAcRnJoKnSpFcKyDUm4jUKHFENZOEKoAaa0x8rWi7ftU2EpCq1t9WjPzN4Kp+isSGHm4SxYDg9VzQBtTe25+K4mgrSuaVVZw5iSGW6r2QSBXWEhcK/r7VaFJIanmzrG7jPIURABZrg2oB4Zc5WomO01GrfMcyBR1mvQ5nhDrrzHbChFZ6LElsZDlYECwPn+ueS2FcDwe8fT58wgODSxaQpgPfZGYGY8fP45S7733Db7gsiyvJXnkNpUaZRP62ixEGEo8PefD1H5v2bTpn3vfWl/R13uAOkqbUA9HHOYZCnPkpkI35Lk0a2ZHzcC0edeDo0EoQxBoYU7DphpQo5rOtm14Jszz4zjApSjO2jRNuLu7CwGgO84ZgGqEQ8/0TjI1os4ymSYlAM4g165lUsJEDIj5L8FgrjFuT+AkmDAkPraR5AzANPECogmgCiEFFXXEcYC0RhZ7TpqWh+jNWH6SAzZwu878iLBJCoSLFsD5dAKBME1V51gasDYl+KL+OjKRIDTQjmBUhI61NSyn2xCuys2sptcVONbHUXNKupn4egOoYCUzebFgNf+FI6+QhdmP87yjlkSodcLhcNSoVXSrrp0iAcnZN6OgoPAEYo26LWVA8XjOVTdQWKEcnWd7rrcoHd+Wc0TFSoKB8lUnDJ1niFX+002TGtdfnmYbb9ylfVpszxTS6lgQM82tBU00ERxRbFUCPw9lANVeYzg+V6/jRpip4ECM0gQLvGqzKgBiIfKtd3QJ9NDUhpndLUVfHbBItp6lDBgkyFBxR9IlAqKHyLQl5S+2SDCpHRuwTH+HWqcyIeAgavDADTP1aK0bNqgkCubkkk/OdOkJFJZoVLl0GKZc4sOlmPz3q7AtH5sIhs3YnxPXbM1+Mb1JewqGSjp3pbDV2CHLiKdIoQ1+bI4TlUR9/tkQCOxv+7/fwwZpBQBsyN3aATcbASJm4iMO7uZ5TQEcWgzJoQMlRehh7cakBCIpcdO+97BhJxjMngpAABSLTwyFwNlx9nto5VqdCQf22XwfxNT66mVi2EFlVaMrxUKbzQyISCI1wsI6v8NnYkKF/0UjfHpjWSKH7jLN1o1qxrx9pOBE+L1ch5AF8uh811pQq5YPsYSuwEZ07UW4W54PQWS1eli6Z9Rv0sG9jsi7YORGPM1U5IKCAAPXEm4xGTW5dJ+pmWxoUDqPzCOXMiCXbL/584E0X9bvQgokW0oSB/xcQGUmHzfJ5am0GEllLkmgdp1MBQvdNyKCZV1sKSjObrDE0QFN5WBlXJr6ofuoo8eeelF7EXPKDNv3h/SG3jg0ti7dtDwF4SaoIOOa9oVgLWNOAVx+/0B7s5kULJrHbcyJIPoi5c8094Y1OsUEl2kqZrcXja7pSvzUc7F7my8qpUVM2ruWjJi0XpDcATScvyINoFwwUUy6FP1cOqZJ861aa+jFw9MGg2Jo+YZXYVLhZ3JVXwSrtAgLzddcjC/9nTeU2/GnWjAfFC1aS0wkJ/fmR+zHTC9k2oP9jnFMU/TWLh3VulStD10Evbk0KqFRBIMi1dZq0RyYDjGJWhnCqS0uTxhh8qRjZ1QIAqH7psZnPSG7N/LqzPYsGkXumAUkjCrXiYA+V0OMeTLJvmhiOZGg1oLJ8nSG6VRnsRCBimNMwva1EW0uZv7zn7RfAzZJMNUZTmJZPAwfAFmyJ0P9ueFbZNvno5wDkVoLivmITtimelApKCyKWt7VjNdaw1wnjbTkbWTXuhpyQdqSWXDysywiOEfQj65b9XD0YExqhtVQaQNNLjwQJGzsyhws6tVmpCcxS31katafi6asbOoniQyPUZjr8kLbjAg2ulP4iWzviAAwgbR3RQnvkn1YW1NZrBc8LF2pUSkVdSqRG5nP7tZUOISa7FaI733PILtkO9auRW802EJzsZxJ6U8PQdhsfDsaY8KnpfBci0S+1t5oJuXRee5D8YkObchs1FxKitDT2a9l5DdpE0wVUANODxMKl4KpzGZTVsm5LYuiXTNrzs66QtqK+ugmJCzXIFwr04x3e5OMLetScZcVX/t1XwcR4L/9t/8GXlvk7gDAJIQDKXJ4s6hB31b7wAc3obTWAgfQt7malGiDvP4qEk3vXROWS4ViY0+h3bR11XDrUlWwMy1RS1CYk59LSK/ehrSWiPlGfEus2PyNjrEoveN0PuvcwgJPquPYCSauKCA0NCgqvVJuIguGgZgQYcIKb7XjAR7rDEB3xlC7y5BOdk0ZXwdf+Z5IowJV+xh5TmUqODy6Ufu+mWkGErohn7NRcatJttUKtO8kCqOTdCKdvjTFpSoEUhfN3Rv7aPQHfD082DVNIvXvuXZzvr0FRGDAVpvlrKyF/JgOmKoywgldzYNdEfBVKNAbXNMWUTOXaxG1aGhHc5MXnE1pxJ/7pbxMT+ES0EmbvQSElqH7Z+zLm+kQGlPTC7ACmNgCbnpLTEJpycCDfGA/iCk8u7lJkwqCWSYMi5G52lLrc/ni2ZlpKZPtfZR69whh93PHXYlBbZ6WLSsueJW8NwZtUdzHJCyTpwDYzZJKGF1p6scqXx2aFJs0LK6qk/tXbCFKRY+QTZckzYJAKo1OtVrggJLM0AJiDUcIuD40EXU/+Z5nEpJJB7qVS7d8LcWAtv8EtpEWeHTM+ayAr/Ns9YgWtbf7AWAQKldQ75YoSFd3wd6Ml/1N+5yFV90ke6lMAKxLAwqBqliemWHNwbQ3lxZpM/VJYh1mKYQuM66P37M0Zn+3ruHMxIRimo5HG9odqrGRMhcSDwSxHA2CMSpO76QhWASDgib8mnCyxeJz7cQlTfeyJe33CiHQ17jdQ0YSLXl0mml3QrbOOjtq/h2mYZd04fOY9i9oEBefeHJLEaWco7TftW8M11Fd6wIRKo9gHSqAm0KpD+JeSxnhxy2rFbYWNp6xF7SAqED9bEOlUPnd3jBgxmwtnGS79gC4ZmJAvKwRkcbtNmsZc0HZrKgz4SVDit8TW9Ouqwo31LoopFVMvu8IDdjyciji1DutDmDnIx27GAU5ernPnf7Ljpm4Z1I7Ya4rXA7QKNZq5LNt92FuV2nAFa0KNJjR2nuYMAGDrdq5EZxhX3u8gyK8anujmZQybjEmZaCuZYLWJwJQKrqXkYaHpg/iTsSYp4KzNHMW61OzD0EEWHsbuGOwRWfSbD57Du1MO92YlFdS7c0qmYr5vnozaBgBOuF8v6iEbSHutSvSgm5QCrNDHLgriy9mKwaGPy4CHixBOIezX9ug+yRA3+D+HLHorQWrEX1NdiVSP5wfOKluLkqE2TYyJe7lvoRBQLAhHoDWP8oHYF0bBBJ+OzYiFXcZ8Q+BoxfAc504SXBGzMJXYEER2eTYfb7tlLr8rgRUxWMmDCw6k7S3vgljNmTmRFJnvSJWZIrF4bMhUGg5btZzpjsQDLbrF8QVuiezlcg/Z9dYgAHXJBITr/3uwbQBoNYJa1M8Pi7+bgJWe5AIptkw25qgtWSGksFyuqw6LtKIWPUFramkzBDuhFKUnZ23BhpzGFogGePlAC5WDXUIHfFvWo/8mQsUNeUU9TRxzAQpQGetAefFuccSa5+mYrBoAjN+GTKi0xvoemStyjVRwPONVjOl6eIxeRrDWGsxQQi2N8BaKiSMbjsmdblHKPr/Av5l77LG6jPrUCYV/QDQugS+IuDCcHrGTnB2Gveq7dXZmbVf//Vfx/d+7/fiox/9KIgIv/RLv7T5/u/9vb+3IXBEhE984hOba7785S/j05/+NN555x28++67+KEf+iE8e/bsdbsCMyyBoOUHpmnWTO1Hj3C4uUGidrbZ1aEqq6BSxcwK5lkLoXhcL0lE5zkh76JZ1YuFXbbetvZUy3XJL2utACiaUwQ1RUjXf1033hZv20oYdSqokwY0O6Eml2ZE/WcbzUgifsyIkdrqAUS9mS1aMi7Wac+grs64ET6tbsro0sCFNjWEXFv0zdo60LoSGaebQhrhBf/JHc+/M6lTPv2USQsLUq2gyCUZNFmlvwriGaUcUSwXLgQAB9cNIqUMoaCgcsVKjJULGtsaYgKTIWdThSCZl8kOK3VsHSoYY+Nu33cIdO8QE2o94ObwNh4d38FUjugNWM4NsooKX+uKZTnjdDrZ2pGZtGrY9ZXoVDAmnzBE+HWeSrpOkJxYJnobocm+J8pEmA8Fx0cKpOtJxmOPEJoUNCnoUH9vgOESlOGYL9hLtDcwGiqEZ/DhLfD8Fmh6DC2hcYTQjACEJQ3zJ5qgabITiGYQTxrCb4ElXrojGCzRCDyxPdJ3RBuw8RaOQCdXxKgoTqBIw/39Pc6ns+3dtMakMyjoaOtqwSCC2oG6CspZIKcGPgt4zVskrY4MiCLpAqwN1DrYBWeXbN2qkIQPX0EVFEeKChFpxOR+0TfMA1Y8kcwA4NqPReilfCtAN5GwBuesoggZp2XB2epJ6R5NE+Q0ALnL9hvJBtXiRe21Nannz5/jW77lW/AP/sE/wPd///dfveYTn/gE/s2/+Tfxt8MNefv0pz+NP/qjP8Kv/MqvYFkW/P2///fxIz/yI/jFX/zF1+oL2Wmw2ANARrlv1ZgAN31MRMPW3IHC6uB2s4mrtR4dJtASBVw1sgf2vUcEcnWQl/HPZfONZREvXbH+7s53OJ+X2ECtdZxOJxyPalp89OgR6P6si25L26VjbetI0sNun8K1RcUJRNKUHgqUiHl8mTjlrxExraabyarDkaMlmUWla36RZqwTgAIRQ5oeoj5gTCske8Jw9m+HNswOPk67wR83rD/6yx4OLcmy8Y4Naj4hItRynhdhmJyGaRk29oQ1aMKts2nXekbnfST2bk9d8IsESjFcYu2Z3uq6TqyRdIQ8Jn8mxXj3wdSuiUXrEvOZtwTHAyj2T28N66q7TYlX1sYH6aGwQOhc+IzFqAmh3WlByY5SFKXDkIl0vtjKnIgWtGytX1mTMepIINZZH/33dydtIWsOA++E4hYZyMdAGgsIW814Q9ATQfZNEKYvKOpCFEqlKDEf2pOMkhfj3TavkUNldGRH7rdN31FLwTxN4YPfR9LlczA6QRFC7jzQ32d2qljEsMCIC6OKtIMGLL2Pci+CYf5Nffa11se9Gt15bSb1yU9+Ep/85CdfeM3hcMBHPvKRq9/9zu/8Dn75l38Zv/Ebv4Fv+7ZvAwD83M/9HL7ne74HP/MzP4OPfvSjr9wXAoM8Oc6kjW5Yd07A9DpgLlr0EASsIBTNk1P1WBSWRKCF7ciQobnOGiBxsARByMt1z8QQ9Kejywov21DrhNae42xOf3dy3t7eAlCJ6q233sLd8j7aAkNl15wNhTIa/o5LVV6lQriWhS1jepF9eq9hPTg8CISaGTIYmQH23pTBd/VTERvxIqvWKzRSX2QIEu5w9XUb5END1VKYSYyT0++JKiKkDjdZiWsKGqASh54oMVGgO5wQEaKQIFQLcOw+DDKc5kg0h8jv8EKLjr5hn2mgiw4+a68iPlqVhh2WqvduYLJkvjfGVGrkvfXEiJJrKLErGWZuHkgqgGHICYwZbAMkInjEz0VrWNe8h4YGr/WoxMbuLFzPIm0faqZh3RuCMTcCgSRGRJMF9PQOdEYz0z2ZKkhpqbe+UsQawZlVvGHL3BRGizAAjQkudITAR5QWlUE9MQdBOlsjLD2b4SAaIu40203J+uewaHRb6xxWr7FGqp2F5kfG3GGmtHyWTdLzSuTzNCldcVoYfVWdb58WIelv8fMIpT38AMETjLkqzJFEnOc4WGo3gdZuGGV/rgvN+/a/xCf1a7/2a/jwhz+Mr/mar8Hf+lt/Cz/1Uz+Fv/AX/gIA4LOf/SzefffdYFAA8PGPfxzMjM997nP4vu/7vovnnU6nwLIDgCdPngBQLL5pmiLiqxDhEBtJwFQMfcLCjM18MU2PMdWDRtIIcD6tWJvGgR1vHmOaFUHZnzOk/ay/PEDIe4esC+7vb9HOd6B2QgW0bEiYBHWDTdOE29vb2Ehu1nn77bfx9MkT3JLgBN3UtTf0vuJ4cwPqHe10ShvCGFKyb+/DhV/W9swMGIxwe79qUmtbABAO0xzMGLD6RCE1cyIWaca2gv4LmiHVx0l3OXB/cFyjcvIsalG1g3TuCiBci1UoMkm4m6qifG7ME1FJBB1AKnLp2o0PQ4/fZVMpuif/ltaKmg4H3Nw8UslZOsQiIz0isgCbqNNq+WiaMA1AlBA4v9Xe7YQy+yWCA7El6NWsAESEZ++/D4LiUY7ZBFgK+tqwngREPXIRldD2mAsXvztW9dHtTZ47OqT1mQCX9ggCqRY5Z/PkZjoGY5rYcnJUAPKEARcc8uNd65SMBLHfaCagNGnQ0jDFTHQqoPZ7Ndk5cgbXgsN82Lzn7HPKbNlyl68QdCtSrYgeItf3iUfj+f6lwqCpgEqDUNN9XDSfDQQzqan5qAQMmGvkJrR0Lc1xd3d3Ed3nzEIAow22k9LGdjmOEz0ZY9N9qvt3N+aeBHTIhhG5pSW7Sf4vS+b9xCc+ge///u/HN33TN+H3fu/38I//8T/GJz/5SXz2s59FKQWf//zn8eEPf3jbiVrx3nvv4fOf//zVZ/70T/80fuInfuLqdwRoQEL3UFMeeVORAImoH8WFUecCKmQTt0JLXwumorWpMmHvporXWpMZ4AWNCFTUnzTNFXOv6K1HiGbaSxsp53xW38OyLHjy5AmaMZg6afQhO7L7Rnq0TTBEuQsJxbPCh4Qdl16VZK7lVrg5wkP9Ee8YG3FoGhQ8hYJwGuNCiQAT37rKCLV3Ec2VD5X3Q7bS39AX8vBjYu0KgTDARbH7xCbfAT5TyNuImNzMVAoyUME7mcdcwxOA3NE9vDvkc9MN5slC5BVpwpzxAIS6FugTT0QVmxeEQqA1lMZI/bL9TiRAk2J9/dIXWfvI86eCmmozUjwSVrHmwKyBPySK6tCdeYhCgfWzOs3dF4HR3xC0PRJwK/RvosMYfKnvxNitTIkMQdN9vJT24JZDj3XLhNSZtZsqfQcFnFTvsc+K5dyx5h7YlWa6K9Y5kTC7ElKavgXUuO7gkW/bFUvrIACoWw5kB+Gs/i3pmKbZTIEdLiAOlP7LZwmgAreZD6+d8SyEBt7BZsISszLTfdACqxOW5/V8XtDaCNIIGiQSka9u1vR2LajjofZnzqQ+9alPxe9/7a/9Nfz1v/7X8Zf/8l/Gr/3ar+FjH/vYn+qZP/7jP44f/dEfjb+fPHmCb/zGb0zqbtcIIfbEvWISxCC27ujnonWjdG9oEIQHSzhwrEdPAXui/wriv5kU68yYlorWJ4M1cv8LmWlCQrNzyWdZFpxOJ3zwwQehBTmMfokxPdyHqyp0t4zwTCVwffNee148pg+U9GAxQYiMKBmjcgYYPN0w/TQjVJ+3l/rF/ydDps5jpQfmfvCMzGAEIalTUmDJ4G+MuIAs14xMgnDKKNv36j4yLtWTb8Dn0kx12czlTNxEUoXXsWRSEYn6PUJqPiUqI/LONBIiMgal7x7GRmx+z7PDxuS1ntOWKflV7gcTUTTwyCFMfguxeSlTsfHp15rHo/Pb+hlrc+JKsQbEuKgzNubzQrmCR5zpnNHmcx2UAFLQeoP6Xdn6o0R9sxc4Rgnn5PmTEDqcr8nADBFL7wDMRFp8c8ah0XGlzAUfy9XTpHwMLSo05y8FiuPna9LR+4reVqCf0dcVELFSMQKrBRMCiq/zRp6zn7W1UbF6Z8bfW0xG6gEuaItbSKRziuq8pDHLslwtu6Fd1Y2T02AAbHI4X9b+l4eg/6W/9JfwtV/7tfjd3/1dfOxjH8NHPvIRfPGLX9xcs64rvvzlLz/oxzocDhfBFwBAtaAeZsA4vTChC6FOM26ORzADy3JWwM1pRHR1GaqqiGCaJoOlAaiukffUl7NJ3K8dBKml7c2OXucZ1AtaV4nx+bPn+OCDDy7uWSxSBhgb5kVMaW9v3jctS9ID1qV1D0O3g0tbqeplTcPYEeH43pSB5U0oZr4Sq7OjhMCJ+wh3MBpj4cgglyq30XoX40ImEJdpjjY7cPLkJQdEBKthqLk52N/j5lydh6TuxvzYgXMabf0W6UDruDp7XYl64YK5TqgWkHMWTUQ/HG5w8/gxnj17ptGZBExzMXxAJUhcK8pUlSCxjlbEItc96MB9LADaeQEMIkrSOPLyzvME6YLT2XEidJx9NcgjYsCR4Am4X9aYYw7C1bE4MfQvW4/52izDlf2pljDyabI+cly6FUrU5MiJmOqvBRvke6jvdlRuycKGaXhDUUtPF+dnESTTAKANDTACsnycFnXqgmceBwDME6M1Df4gAta2oHfBVCeNHjDNm9AUvBaaE9XRcbq/V0YEwrJ2LOcVkBVTSUIVTBAgmFCAi8PiPu+NsGeuDy/tMU2CaZoflr/FA9thQtZWPPI5d6G6J5+lY0FeO8X/l/ukcvvDP/xDfOlLX8I3fMM3AAC+4zu+A++//z5+67d+C9/6rd8KAPjVX/1V9N7x7d/+7a/17GZ+hcDfYjbmwJBiGHMygfqOgKaEOYIEmCuYTZVtIF7D3EBUHu7E1Uao0yMcDgSCOjFbJ7QuUcfJNalsvuutQVoHAxEF5HkFjun3Mm0K6b7hq4JqEfZ7bI3dHnmR1DU+U39U6RylSPzgtCxpOdFjYEwfQdDBAflEILL8iu68KTE1cUc8IOkUOY8YFCdF88V7jAlDCb6Gifu3NHhQjAtwy0v6Wu+PeZDYMwokKyHJ+8F1aVOT4WCMRdHNQFWheaZZ0cGb4P7ubNBLLp47ITXNEwIhNb95eZhu/tO4SqAIEq1rvR9DWvEy3qFk2ljDNENI+G4E6prU3mXLbBREQJ34nTzCzzUon2/3r9g8xxzKfpuZ+MAx39kETPmazT5nDARymNCTbrBvFBA9CWL+DQHbNIHrujl5GIiYqBXasvXOtD4/SJej07a2ZmCrunMLq1GzG3CA825PynAIJhZGXwDqGhTmKPIqbI7+qDCBsGTEeVdIme3oZGwAETfHFzBVMFU4MvtVC4bTqNCLBpPy+RcYGn2/LHQIv27X/pcyqWfPnuF3f/d34+/f//3fx2//9m/jvffew3vvvYef+ImfwA/8wA/gIx/5CH7v934PP/ZjP4a/8lf+Cr77u78bAPDN3/zN+MQnPoEf/uEfxs///M9jWRZ85jOfwac+9anXiuwDhs2XXI8nA9gMYkNKEF1HluSL0NUFQcIPBWKg6QEkWEVcdWi9Vr80MfCIeSYQKpohTHfpG7U3+740dNvhRtKzgLjuWnTeNWbiz7cLlEE5M8Dlphl+pbFJH9KwNCBjRe/q7FZtTTdvEwzGAlG06HiGJ2gqGKY9DHn7e8eoD7NGjuwbrMLChv0o2iZItHhDmfueQe0bURy2GP8YsR1q0wpd+3QN8co58ygxCqmAAGEQCpgnlOmA8/mM1oDel1FeAn74Bc4KnZEIoKYsQ38IAcRGxSKaw5c1G4H5XvRZROoLa30d3xVn4ogxhWZhc1Z8TK1jhYTzfq9FrM2KfNaRsgFDzcjTtGFASQChfEH849zOTFu7Oc9rRiC1KBuxpL0FJAd2XKWPW/3rkgElSSbLLjviLmJ5jJbkyqTZdsIqpHp+nUk1RpckUDk0T25ELkcVA3Lh1hiclf+V3qNskFtK9IwM5r/NXVIhQUFpq1b8TdGoD0UBb/ZIWlPfsy3RoWv5aNmFkv99WXttJvWbv/mb+K7v+q74231FP/iDP4h//a//Nf7zf/7P+Hf/7t/h/fffx0c/+lH87b/9t/HP//k/35jrfuEXfgGf+cxn8LGPfQzMjB/4gR/Az/7sz75uV6Aocqbw9wZ0DY2W9QxpBZ0mOxApVLoL2rqkqpYEEseVEw1O4AKwQ8y+XGt5qE3zjFIrbm9vUWRE47g2tK/bREEsh5nMCUHWuLw9JI2EVCWC+XjE+XzW+kpIElC6f/+cl1UCHvd4kiqgpi+YqUUgsoLrFARaLVIKF9M6hdnBtR4RKJwOCTxUN4N9akJskrONFlsA1X4GFJl9/DUIDj0s/frccbpXpMGR2YxngMSi/kyUZAs46L0bgdHdNFleXhGgdC11QOTlKLZ+TyItB7+2jmL+HRGg1AnHww3uTyedJ0s4vV/PWNamIL+NwUaEmGv0J29dhVsypjMZ0esaQNCaQR51giqIAmlQNP9SNLFUFGng+bOnlr9HqPModHhqw7/Q17GfM5+IWSfC8IPAQyhT4yunbjAQuvhUiS57YAyPCMTN2l48M7rz0pZ7JFA6wgSzEgzm5P9KGwFFYbgBIKVAFSM93+dFo3YBQ5xYV6zSzSph/TMYMOFRZJBIi2F2aVjbGTcG9tt6D3QQPXfuU/WcK5/BEYDiDPMacwrLwOYzXX+fOPebvShYY/M8uYz0e1F7bSb1nd/5nS/sxH/8j//xpc947733Xjtx91obWggAkxwePX6Mw2GEl7tIHtcWQikHlGo5KBihkALSaq8oxkD+55iU93Gaplic7HMSETx+/BiAmkrubm9B57PCjqQNv6xr9H+e51eQREy2ieRETy6lMJxfqx/1suZMNSNTHA6zZuKH1mMHgFKukYgFLGh+D3UCkSKFOFI19C6T1sJLkUZUhsPa13v8EVeSEUCvKgtYrpm/g3YZIml5KV0zPht5UvknN4roNAJ6j+AbU10gpEmcq3RQa5BlNYFec2m0bhSHeU4m+w7A6bSgdWBZF9RFS4YXo47qA/BdagFCJip3UdSuwXQdtcTVFI3sWj1KrncLhe+atwREZFchnXthwbFOKvSsK0RI6xYx5/gFiPuIgUtNk3ZzLAMZxZd+KChJpEpqlpPYbgpJWCTIKieruUQT6JOniNJz8qmm0a1ggq7JWxdjb1wRCS0favuNIzmoRpz3s2otEEvkheezbccqljRfsaJSU+zPLEySr6egUoGbtMVAZpWJlt18+hK4dmsaXD4R8XcKttp8PsbqpefXdb1As8n37QXhUdH3f5Em9X+nRrtdx8w4Ho7q4EsZ6llU+j/J+5eQW7vsLhz9jTmfZ6317r2/S9VnqspzKkqIHDQoRCSQQhARSRrpiGl7AVuhEtCAiGLDCxqwYyvakthKx4YIUcQLqKgR+YfT0fyP54DnkL+m6kvVd9mX911rPc+cY5zGuMw5n7XW3u/+Kja2Nav2977vWs9lXsd9/AYRrBJvtgO8lRxsk79FsIRP+jYnKSRXI+59Rd2e6eSccTqdsD8csJaC3bIEirn/I6LYDADCR6V9bovtZqD2Nwev3QZLvG3bhr8DDUqpPdLMLi7Vml2EOxOBSIUboZq5h4Iw9Ca++GGRUGRn3AU56uayMcg+nNw+t3siOZGALZPC8CwfVIIb1jh0qig+gvBbdDTGwUoBGMSQdpSNGYgVnAQMcYFVBdU9Cx2naUbrWrEWtr3TCgRGuL6ZlCBj9WkNKdB5SPb+Row7RsVsYMBq2hYhoFIMn6vEc1MS7KYZdS0KMsorSCak7GsYE9d+yiBZxFVhPcBGc6WGGtPt6g13oLYCpEnCscTUkvsbXmRTu5sgMuyOcRv43jK7WoiqHdPSnwKPuhvggzAKMl3Mpz7YED+4O6vi2jrEgmCVSSUwEnH0w//TzK1oZj9RMyxXT9buzPYdrPDQz3YS2zfOkGKO+oHbGGwvs9GnW0Kva0393yJvR4PeaSaVyHw1RBapMoOrqvuRAIB+w7tkLSjLgpQS9vudLTZhztMX8kGdTiccj0d86Utfumoi88TMUgqOx+NFuKZX5k0p4aOPPkJKCd/61rfw2Wef4eOPPx6CJdxMOE0TXr58eX1zdBI/MwKS6ZYEc82vtdXWnEmmlILp1lpxOp7tQBKADBKF8hAkFI+ISDDHMJmZwvtckaVG/hKbNNwLCEReSkHUbhLh2I3g9NeGj0RSs91LGrLsqScaHfXbLp3As1OSJUp7xVECaEblFVwZycNceJTak6FEePpDhvklvYCjEIRJfVMkIFTM86T+CEzasw6JQIpCz4isWEpBFY1MLase/MMua1FDIXCpSFkjsvaHHcAFMEw27x9Z7S0wGXJ4P/gcU36qxepXzah5QkkJCwzihwuIKwQa/OGyoTOG7L4iOD03aRptXGkToedJpFclc2iZRlVUTIsLMtv2KmAkQAxmSoyhXHMivqENARjYJOXGml/XDIYdZkJY3RBu7VNVcFkpWhbGwtGzj+eSrKjVJym5qpaQzKJ4mmLzesu/FAKWefa3abvuI2sCggm7iazArPar2Sge15xJ2Ql51D3vNJMyPM2hedhlKYJpbptf/QD6uypKuvBFxPKjMmjysuFvM+2I7O4vfelLF99ds/EuyxJmP79mmiYcnuxR64rzecH7778XyOM9M3I0c39Wr0L7s3r1+pYavu3Tmz4f3unuoU4iSknD3AVmqiAzUVqRQ8AFsc6kATcheDAJtKwGeV6JhZc7cSO20iWw7/NABBqGgXKnht9HnRYhTUI0k5wpL22scAl86PnwfamtLLqGPai0mjOFRu170dQQjcYiRE2elKzaK7kgoZFc5/MZa6kWzNP2LaVk4fQmKQsga0ViatKtDU5NMQk1C8QR9IX02nioFodMxMh5wnouOnaiMEdqQnSzECTMSJhBsuJ0PikDzhmUlGASt9OjrzBtVWzeKVSe0HZZRh/oaA3Y6jndhLgYQcm0dBdCbF9JS69uqQXtqdFHg/ECGkoHizQsw07hdmVi+BnfXdcmpfuImaHGO7ZwfrHnC6aUUCWhJJ03NQVqVCcoY63SnujniE3AIS/10QKztIROS64fps5uFrABb7cuN9Nm0xRZqloIkJApgUnzv67lRz2m9ZbcN7V3m0kZ5dlubjd/+PZo22UkOgIj7uY7oPx2GpQ3T8Z9jArrWH0lTD7acs5A0sO+rIK7J3cgSjifzzgej/H8AHHd2Hl9/H17G7vvreaSWPvZnQ8z45ET++SJsqwI525yEy/WEOwFHrnWDpyvDWtUkxM4hDrUEQXXGCWICHoi5SHN9siWk+PURuK0akTYSFyIEH6GIJgdQQli49GYQOxDrRDcnufYiyStbAPDkQ0YNJlW6HuYGUUEqIzJks6t5ya0tD1KgJWESUhIWmssG3Fmi/ar+o/MWOlF9QAyE3WOfpdF892CzXRatM+6hjirdFhKhfcuuX5hgoUzfq61mcFdEvcBxYrLUGeJ/W3dsrQ+desfGobEVe1n95lfJz2jkvi4TzHqGQB7n4GoqNKi57ruXPm919jaETSDXttG6C9ydhxaVsgdXgqItnepNuQ5SsakxLTGxpuk06i0/4p40vD14vluVXFh3Wlp1z/1I98Wgm9FHF/O1ONo0zvNpFoMvx5ujYwRNZlMGXWLlmX26bCJEsCJQLVAiJD7U/EF+vKm5hEwbsPd5jwlUhTju8MBtagh5NmzZ/jud7/bak91rbcF94zri/T9TRE5fT/9DK1VcKxVIyVRkHAOwQEwvwwTRDIS15awCzX9JXjpczM7iZlwUzLCTo2+SMjHIZUrZSM086A9xzUPXFuXy4O+/X6/O2gSOK/KiMznUFaNwiN/uYeXd7RRq1I04qmhuQyuRYlhShAPF+YCZrF1nzFNsxExJR9s+UzOHDkl5JzAXZkRADr/UwaxRKL1cjpjQbUaZgI+nzDPGR9+8MFmuL5/uvWGFvcbLrM9cjqfUGqJs+eI7cEsrY8ggrDg/nTG3d2dJsyjsQhnb87+HLKYQx/GgIt33bphJNh8KOMYoAxRkkYtAhG8EyszaPUArF6SP6ZGvS1LmjD+FZUIqL1z1P78+6Yx9MJdM2HX4d7KjFIqjsdjaJcQDaypbIzHJa/M/uLQnAE0bUouIyQVLcMEvtRoDxvGpJ8tZ6SuiercUqcJSyBGeCSh4M00cIgK7ubpTe2dZlKNGqkWlOyA5pwxTzMEaxO8usMeMhYpNpmXRcfVg/Dm1vuaXCq91frwy4FBpQTBrOaunMBlxTRN+OCDD/D06VMwM06n062njv/jG/bxK9rXm9rFtQJImOdYIV/YtAg2qdklYktC1MOoCAjUU/TI3WhcZQh4EAI4NR+hH3pH80ATWOO5ZH+ZJBhPE2pOdRuHooe7JtYEGoeoWtc1UDogsMz+ag7vijyl8PV5EA513fAsMCVAGi6s72mF/YQU6ieniilXJe52r0u9RAqmrNKBaplsZVvm3Q5cBEWUvC98VK0OGqUqzFiXBbJWcGF8Xl8C1HAhWTpoGhHkBKR5Qske0Sjoossx3e3AE1CzYFf34OoFQ53gCSBuPGtMs5rZU6df8+Uio84TY4kgVOM6thlU61eLkAvT4AUV7jUsahUBnBFzC32HbDUAZaHN8Odalf0WRJ9iI7IT/bhBV86Vks3HOj9uYoOA6xpAtlr6XbXeRsR93zQNzvvV+i4hNepnDrXUtF/dTY05k8OUdSZQQYf8ARcMRXEIk6HmJNWXhQ3nETlMwV+EpowhXrfbO82kQmClhJSyISAowGxOCYzUFj2gSIygkUY9OZ7aYzShW21dVzw8POB8Pr+RSQFN2ul9F9ovdQIzCQCVVud5jpLyp9MpmAwFQfdhSfxjHtXwC3v/W2pbvcmvR1l1IjbAH4k0/7T1yxEaKPDHuhN81VyD7m9nIu0TX8W4S8W4zZ1jnheNLMtH1kwd3cfMWscokla7eWRpJj5KHeNL7hcZfVmN4ZhvwIvXkTL7akSCk2okKTWYpyZMmSZDZiatQC0rhBnTlFCFIJJQE4WgBAC1pOirLMoMa1WClaeEHe+gDBPGKGclvmBloLDgDkHUkUpzRkJGkoz9fo/zcgavJbafyJh0ikShfUjS8ROxxsebmbdX2oa5E4BIHfhtDXw1Rzn8VoBAWJxFWqkWNH7mu2PzgW3dJtA4fqF+42XsBWPnuV3fk4A+DNwj9cxU7NG3bEyvuskO7rpAlBHycfZnpu+n/jBdUDom0G17gp1haC24ft5aJCDaeXIYsdTGpKHzeRAG6IrZ75ag7D+/L0LQp0xQsz1hlzPmecZ+t8NkUD0ZCeflhLKumgdCyrymnZYlUPy0A8Zkgrdvr169wscff4wPP/wQH330ET788MOb13rouIcSjwyNhp/MHBhbbhpkWZEgyHnqSsOX0BQ9VLnfBB4R2Ew038Nge7HQJDqxpFBAy1hrlFcO804C9KRZQAQSBt+HdNUBNdB7JDZs6oR/mkBR8HKYtistgiauXOQSvJaPSMC1yC/ntoKQ6nsfBoDAhHSC2MVbhvzar7KbixQhQk0+nCp4Ysup62im+fqWZYn8qwJoFBgJzksz97IcoIYpHeugeRcgpwlPnj7FBAavDCmEGYKcVat6dneHUgtO55PW1zLTazpkLOuKcl6QJGES3Yv7/R4vnouer25++/1VixHGLEAGKul4k5MeIqCHHdswEZHrR3Mobrpp8X5mpNQi1/qde/PAe50p5xB2pgoQQLw5jWZ613IGEahi3Jd2bkgEqFWh2tgxHyXym0pZUeoaqBkF4hkBDShnGMmQoGav8TOuWJuUUhfmz90ctwoPA7Padh1ALRU47AFKCgxbWzqM05YeMNZpUUyHuSY88Ov7hkkRZaQ0YV08koxwd3dnOGPqBL6bd5BpRq2r0kdK2M07nVDq1P8vaOrzVmvFb/7mb2K3272WSfVRd1vtxvOgoiKvJ/R19YX6QmoaFtwYm675JRNyRuhM7W2ab97m+0qatwFAUMFYkTO0Dg46c52MZgd7mjp5KY+Rd2b5S2HCMVOEMwiyjH9xM4QSvp4xuHHIJU6WxiQptJNxjUMTt7t71WyaElKasZRiyo8gcVXkC0iH6UouUMJzS2Ks3Ru1ymy1nACXJi3fx8ch0vJOqjEs0vBwSglURXOaiCzZU+F3NOpKwZSJ5iBKSZT4rGsBr4JEgloesJ8nS99gTLPWippWrWulc5KjQCgAFNFzM80K8YVJ1+3ViweU1TWLNn0hcpjmUWuFQAyc1Car87mImT9hGuHlSklI9rp/m3QSeYM9s+qWYQieIqg2ytKjBFlBw/ZWYfVdJTtsCYLCLXQ6AoBI88h6jX44XfZHIjvPps2trOHmUS6SNE1lXdcoglhrRS0FqTbEE04McDXz5bWoOt9x2zMuEKk2h2MnW55j+9CfoL5Jw51MhAkWGDTPWLtSG9uyG9KNof/7MZrWtfZuMykjXE7YI/EVTjTFuDxQ3a1Bmucz+IS+AMr5tfb8+XO8fPkSx+MRh8PhpgmxmezaadKFrKEF9aKkL6bmJ5H5QZxIU3ep2cnx+A3wmNar6GrPdomLAakGVKlSojMp6RJS0d0T4l7ve+oucTOHHhSTPv0+QAULMxMpU2zfiT++O6hRCPKaKSg+l8E8QqTEJBGBqsUiumQQmf4pTC+uYW3lnFEI0VIMYG5EUQiEHPtAquVP2X5mbjlB0zyhQnOgYEwqsOjMPyelmunFrGkQDVGuoviRYFi9vIhArAAmSZCU1Dmfk0Xgpd5VaFaIDCZldprX5YESxrwuN46Oh8gwKR3FxfaUL5iZMhEm2e1CdYJXRCOg3xUDWaZuLXyNKms9tESk5Xlu3NtrctI/q6m2aL4qC1Zqw73RuDvHDJHayqOE9u6fG1OSCkg1c6fV9/LF63x+m4kCYH4kuDCn89Us2i1wbGvmiyno5tgjHEma75VsPXuBe8ukek3JrS39yb7a/RvtnWZSENG6K/Gn4PTwgLu7O+S0s0KH+m+achBQACDamUOeLgjmrXdF2xC8bKbG73znO/j2t7+NeZ7xwz/8w29kUi3UVDdl/wqxPQkknE4nrOsa+IciikSgZc6b2u3h7dt39Uz8McxrkKq6MfSRhESkIc9lxbzfgdD5prygHIuVR3C5TAMnREbnskfLVVegkhMxe1/O5qfTuUpIbau7RA6Xwu33TtNim7Q+IRpoTHAkea7ltvFT1ErS4nPqa6m2SIRscaEureuTOnlUGFxWcC0gYUV5EIEnmgoAlARJBTTlKPy4rmesrMCdT9ITEBK8krs716eZIFNDTlGsQf1ul7uABqlmUlqBtJo/jTBThqQddjkhmXNcRNS8ZftAQ/gThDIqmeVWEAnoOWUc7++beRmNFNVazUxpkEVwH1e9OEeqpVMID72rkMNv29E2aaY8tj2RTIhx2UWSrudyXvD02RNM8wRBRV3bg/rntZ/mj/O3GqMSYcU1JNPT+zPblh/ODCCCitrApVmjdCMlwXD5Ki+o9QThM2YwMq8QKcCkwL0QbowBcTRsn/c9aJ8jZqfNu0dg9mY3AEEnYxwdA2NhUCWsy4qaNEq3WA0pMTPeNbriYfH6Hk9fcSbpq/nm9m4zKehE7Cyzf7J/asqAhqJ3DkDKfnySogQoJVUJ73XzJQItGu2beD8csGfPnuGrX/0qvvWtb+GTTz4BEeGHfuiHrgZQ9FL1shbM84SUphiL23a9OJ4HZNRaMc9zlPron+eM6Zo6fQ1l4m38Urcc0n5YXCoFBEhF0/y7AAlhBcvMuTOrkucKXYOlEhDLxdyJMQoSM3UROyTw1eeMBHAkH+2zrfzt2pFGrHmwQERTMVuACLq8FXsWmcYVxFRC4hauqMtJ+2iwNeLaIPnaZaDO6qNK6tMTMFBV6iaeAWQMHnmCVnDljgGrUQZEhLUujtWBeZrBrPuJUglN6nwGdqviQTJU4MopY3/YtxyqaW6CQEqYZyt5zgKpFWVZhj1C0rTcnFvQTdOG/DrTooZ1ZlepnVoG00h5e566Pd1F/7F9lZOGn2fK2M973Yur1tDybaqlM2wtjcEZb4l117lRMy+LQKqaLnO6DPNu/WoaU8aqMFVeicAEp2rfs+fbse6xcj6DmBWtY5fA51X3TP94adqe0Og66Pvh0aW6RSloQt6kMby22WUsrJZ4USgrJi8jf0l34lZyl4R0/96uvfNMSkN/vVw8RW0o8tLPYVs3QkIEWGiugFHXVbPlreAabRcvnECdu3yztnd3d/jShx8ip4TldMKL589xPp6Au0P4kgBdvHVdLb+gxrP8cVtnbK0V5/P5aqnla39vmVH/2WOQJ966uRrPDCTLjvcDGgEQJnG2mzoTB+IA+We6nW8dng5tAm27O03zJ8Z8eh8hrS9uHqV2b5Og9R87cfI5Mwd6H20cPfS9ZQ9Tmtr2CXPVf9XyxEQ0jL020Frdu4pBV1lH6bC7SRgZDJQVKlB5onMKAq/lIEYhQDUiRgUMbJRQK2NdzyCyY0GkTB7QgoxE5v+ckXLCNEN/t/NEUN9JNSXIfSnTNBlTcg27MW9xYUO0H1F5ADKoRS3i0xenGYd6AtzrBv3ZgQsE3W7xWk4wouoC0+aW2824lUv/jTEIhMnAd+niFu2d04vOU+oamq1vbIDok76rrgVSFbxVaFUtuAM51lupn6qwB1yccfLTclvgjL7Hvu0EDl9TZ3BgJI9udM1oe/+VfjQNyoS8W6bdK+3dZlK16iYhisXYH/atPhQSaNqZBtVCXpEmVbtLwXo8Y97NACVUZMyHw4g8Iaqi6wt6cFLAt+R7z57hMO/wdH/A/f09Xn72GT75rd/Cl3/gd+Dpe++1R4ng1atXeHg4YjmvePr0qcLVdGYoZyjLsuB8PuPh4QGllAF+ZLD13mA8ffb5FgCyf9f30kRUkuaqEX0sydwjAqAC5Cm7zbHtJrZhLD3KAfV5F3aoRTaCgUn1DOTJiDKcMLSEX4LapsShecjiaIm6g7sZj5k3GoPyGl8WoRc05Rpb9L553xnLuoAt3wqGYlFL1XpSRROE9/s95h1ht7NwcIvKyHnCzl+63INZc1MAANOMiIpL2WCXPM8ogdIUZs5SK9Zy0kTR0wPWpSAlMQzIZ0BlPFif5t0O+8MBAsHhyRM8nWdlUMaAd7sJy1JRixLNaZpwOBzwksypTkm75XuzI2RssD7JrWTiR5e63wEV19s6+1yLAbOCWlJ1L6gA455W/7QLrObL7dQR1XQ7AaojuI1NYhPogvBHllqQpzn2Djb3EsyMxkn3oW9jZ3ybfEYRLVy5nFcsS0GpC5byslkJHMzWk8ihmjkGBn+pyVxrg18c0P1Ol+kzrgSgE76qaH28yhdwCRfvb4Iyx37QdXo8/XmnmVSe98Cmam4vxZIXMjQA07bxE2h+oqaW8yuUVXPdq1RMV8rUv74V1HVBPZ/we/8fP4z/8X/9T/zWx9/B//P/+D/w/pe/hA8++gg/+qM/GlqUiGIF7vd7K7sB1KrEVaVuNXU5k+qZS+lKdgC6gXa7HVJKuL+/f61tmGjMobmmeT2m+T19bhkzA1Wz4pUhQX1RpoEqXh0AyoYTJmhJk4SMpInwRFZayPxJ0JwapdMJHtGXjIiT2bxTCCmAh1gpETOhJPgdoytn2mnZ/lmC2v7dvyXIxmRZZEAwuWXmkSHqSm1KnowJIMLPp5SQJrFqqytqYazQtAQCABaUcg7mAHieVDEzliVIM2uNoqypFTo0QV2g14gAwiiLRovhvICtjLoQoaQT8jSj7nZ4kj/SwIlS8PDylUr0a0GaH0xjUoHKo08pEZAJlAmHWUmJ+/A0SRWAaTDCbFlhjhzS70M2/6qtSwgstp6xRtJp4Z7q26/G5V72pd6WrOr/HEQM11RAKLWT/Lm72ggtASjdg3Moje5vtvPAMCYlkVgMEFKaQKS4eGVZsK4rlmVFrYKUJkwkQNJ0GeYKLqsbA+Bh5r1FAZu/tm1rEnSBOBD79UUQUSSenmE1LVjbaj6p19WEGn3v7rc1RhUM683tnWZSPlaK0FYlmJJso+fcQfG4U10bUYZHqqnkLIqZJq6m+0EhJax24Am0AbXVcFXhivffe4aPvvwlcKn4/PPPcLYk36/9jh9AmiespURlzt4mLCFdIAIcHh4e8PDwcAGH1DMXf4bnUPWbb+jhRlO7ds1jWp8UHFKuJHBVk0YigQYgdmYM16yENVLNzKoR62PRSxorBgutNue3aUhaKdlDjf1A6TVJmlnIz6jbwAcFzH0bPn7yiVdmyNxppuJaN5vEl9AnPwJAuphjXyB0fsDmaxBWyVPNOC26y0PTWTKAGdMMCwzRfdWCfbrKSIZE35elgGSQ1Bg3mCFVzUalFJxPD6ilYC0rcmxtAtOMed6BcIfTw4y6nlCWHdLuAJKKDEa+e2KRp6qxuQku3OzU0htCTzbTZhTCdbOUMJg3BNC0/ZQMrNYwGym0cor3iK+zv9vymrbKduxTaRrRhWARa9bOhHBwqc5E1S4fV12GXF7q/gj4JSNS/XP8WVPynCMV8qRqiDmoqrYoamFwwUY2bydn+J2VwgOTbjX/LsAEgFbAMUYl6t2wAKZ4bywuDGmEL567tfL43x644drg27R3mkmxoQJMXXJdKUXNGETAvOuoxxXZl4CcVWsptWLhiieoVkHXY9b1YGK91wVK7op24jHBTQaHwwE/+INfx1d+xw/gV//Tr+LFd7+LT771bfDDCU+//CVMdwfF5eqkF1e5e63kfD7j008/xf39faClb80YPQRTf+B7s+CbkndvhaDeamGfhvsotDRHXYHMgnkSzHYYCxmel1dwEkYFYWVG8pwqGD+jNpvNyQqQ+bUaIneCer0y3M7vjm47U+bbaAwyAi50AGCpMYYwR4E20U4JblYRK28hkjoJ0DNcYOG+HLyYAM1LqhW8GtK9MabldEItRTUa1yZJ4ZfylMGyQ56KIo3TTkFAbW36WmKaSLvTwCB7b2IAtbiBVAlPqeB1xen+Hi/vv4tSFkC4nQQiVMzY7+8AeYbTw4OuWCJ8+as/CClnSDnjyW7SkvMgSMqY5lk1K5GWaD3ngEgSdHPPGjbtMoEzozFqVKMOU87Y5WzCyy1tlRqt9P8ITEi6FMbIGVnc7UPvBBY04bAxfXTP6IUaGFO0IAojvhRP8ue297M9s5kX9cwf8oT70xl8PgO8ItWKhIo0u3m4mmYzQiU1s9nIOP33y6AjGefEtOEEMj+pw3rZ9UkFSk2/IHBi8+m1ICJHxrgm+F5aaKhbAxcMNf3iMe2dZlLoJr2vc5TnGfPhzWY7Sgl5t4cUJTlzTtCKslfypmgHGATmtk37O+TdHrv3PgRKwXI84ge+8hWspeL+00/x//7v/x/k37zDtN/j2bNn+Na3voVXr17ho48+GhiUR/K9evVqMM31EXo9MT2fz5imCbvdLir2bkPQo/s2R29TtvnqczrSoXZ5xmQmHw9rlnaBkmKqZjSzkH9xCCEEgoM+WwMFyHxGLgoo85Fmi7nVgojoce0lQyUk9jQBhFgBRHF5sMIvJaJVMcQFk8t5EwZKXVvAjo9JuAFwlgI+LVjPK5Zywnl5ZeNVP1dKWmPrVFWjn6cdpkkj8CZoafgJAscyhwBcKopJzml2LEMOJZZLwfn0EqfjAz799FMkWM6NCwH2oywvgXpElhPS/Ax5UqDb8/0rUK0grqBPJq1knTKWPGF/2AP7PVbhgOCanzxBNmZ8fHiwZSCDKnOzT1ImJmJmbV2hlAgpzeHrGYQnJ4Kvcfj3+9GJZlgPbm2Xzed+PsirEkMiX2irPfV/+5bT3SHxfYIFYvk3zrisQ7VW3D/cq/AhK9K0gPIJlBZ4kFYfXej+qkdH5N2Ym76xOekSpRA2FBTaKgGggglIyED23d+jG97WoHxSCEnz6sz8r1rh1rf/+vZuM6ltM0KUBrPUjUurmmJKNYk/JcyTFm7bzp5td5MEGWkez4wiCCdNu0oZIsCXP/oI98cjzuuCTz75BMeXLyH3uimLmfx6SaSUglevXuF0OkVeVM+QnFFx/5mbnjYYgEPfgzhLyzUhGjb/mzSpgXB0xL/Njf5GPUExU5rboAPXLt6nB9ZDtuHfD0+E3euJwTS8UW384oJv06J84E6JTTSPg2RScRjQpJGeZuIR86X4gRM0HdpClUUAaTlobDA3LmV6AAyXFVwWrPWMWhcItzQC7VKy51QUEaS9WNSY4k9qDS0xZpktj4xRixHlpN+v9jyurKbi4wss5yPWsiDDkCmINFnXBs6lorDghCNySZjmHbCreHj1HFxXQCpS3kFYQZhrFaxk2m1K4/4hX0eMwopa5KBlH7UWloPS9qboSxOdrSFp2kETkKhpWt3a+fr5PnXTcOr7F0xnK/DYzrP+S6/92AYWSLu/17L8WnJtKUQfM5cZFl8HF8PMKHy20PaKlCoSVVCkxnR7vNPq9Hh1fd9Gyn6BpufIQ/e788uApO7MEiItAxjpSOtr62ffIZ/HgW5+XzApaiYrFgUw9XDZXgUemn9RC+pacDqdAahN/XDYD6Wst40roy4F6e41fcoZab/H137n70Sx8hQPDw949elnuH84YjmfASIcDgftjmlHx+MRn3/+eeCteW7UVouqFo7OFk7bbxh7oA089IbBDHBZynnYSa9tGqSQILnN7ta34GYSZRiqoQonwOocAYBwNfw+72Hrt/Q7N4ai2heQtNZUjMj8eKRmPy9K7wSH0KIK/Q6O+QEcx0zidc3+LmzoHTFf/kyT/sGGkO6YZM0PBUiYos/nM8p6BviEta4QXjFJe2NgxEHpTVlXTBBQyoBoWHhLrNWgkylNmt9jY0uipRbKugICLMuK7373u1iXlxAuGtpeK6pYtFlupmZmDU2v5YycBbvdDJQ9Ho4Lnp6fArxi2h0AqRCewImwsoCXgunuYJPnBExrbCFRExrsa+fFBDcbtbwfIkKU9I19pKgfLriIl20hArnf0nbPVsbyfU3J/ZbG0cRTIzzNoO8cQKIY5UIuwHQ4c8jxHjffuR9c9zxjPErO3PRZzNXK0riAWcFYlPmSlonP5KXi2/53dBnlHQ3jUu27CKbY5rl14qrQekOYdfM1i/p5nQElTlBQXLJ1uBQI+jm//NzPnDRiLC5APq6900yqlopSi9rH84RpNwPzFGUcbjYBpGpi5s7BZqeMNB9wc/IMUiVN+Y0iABHh7oMP8QNSMd3tcaoLnj884LuffornL54H/trdXeN2Ihr19+TJE3V0G5MCEEmk1F3r7/E2pYyilEIZFCne3eTSDgQTlBkzJSxlBUwz2wSnd3NwxbwVEqjmlqniyeqzWIGcsiJoi/fPxWvzQ4kgGzK2loO3BTHpOLFpw7mbY9vkBCcOhl1BSlhIqPMV+kFpfqi+JTOJAQSvXQRgKKvOYKQEBSf1vgLIvVDA2i/pGZkwatHKyrWsWJcz1mUB1xVSFgXfrdJNq6kYGyJb1oITjphnzVciz1MiRbxQHLam8TOTYSQLUDNqYexSxm7/REdJCVrXywgGuUlYTYseMLCuK85nLRmxe5pReMXx/Ar1+Xew2+2x2+3BU8U8qT+sCEe6hyRCygnzNCMbooFWINZkWbVCGH4fwczqr28iTejoPtV1jOhNn4YmBGklCrJABmU6MA3Et8O0FU1cWxEOQh9RmSF8INbeNSXraOtG188QLqmiiubLJZimQoot6jBHZak4Hs84Hc8agd+lZfog1Rx6Nei7/e8Kbei11fGJ19vbsZDbDCpM5vryNp7+90e0d5pJxaIkAnICZcMbS0607DoRoLTIJy3doRpJSkDKkyXxvgbDz8syvIkBWqOUMM0zDk/u8NWvfQ3Pn9+DGfj4tz7G/cPD4HOKPqJBG9WNWa+HRImTZt+XUjDnyRIwp5DwCCFwQQJNQadmylkJLPGoXYj/uL2LmnTZtDyRDhj3yjYX0xqyMZg4TGJQSgAoWe0giCaEmj1Hl9SZlENJeZE2MwUJb8+0JrKCkTH6OnzuEnqtMoyTg9CnUr40gdtNIcYjTE4O7bbUFWVdUMqKykVRBaQ6Xzcm22bFZ9oZqvuM1FxYXY8IHkzOnE0rUbOaEvNMCWtZwUs1aTg1/57B5ulQLA3AtA0dvCCZn8ihkbgUrUU1nZuWKQebGC13LsIgVp/GPE+QnOFh4675uAkp4OdAY9kM33s2yQoODSt93hiAm/gGxjWY/fwjarKP7ZlIaehAXX0OYBYAF4AaoRXTLvx8bgWnfgUxfOeMD6bh136d/TtkSzsBeC1aO8qCknxAZEH70m9KbLSVjiFuTfdbBjXQnPhsfGbv13OtsVuuN2pTl34q9MvVyxOPau80k3K7tyQtekgpayVVokhaEBUfIeclCGsUerMDnOcJKb9mKsIA/nj5QglAwm6/x9d/1++CIOPps/ew1oLT+YzT6RSBHr3PqVZL9OyYlFeH9QqYwQbMr8XMWvZjmrA7HCy+QBkHrTUIayUOKX7Ok4W8psj/ESNW9sfNcQEWzEEE4YZAn9Ddf20+jGFEBr4+KLD4PIVENSqtS6TETUDZMvfNVKhz4IfZCSHixLH5pEQ0P34b5ajErv2d0WGLaY/1C7MnCTr/mc0vEys0jGiFXa4VZV2wlkXLpxjahLCanLyLEnMk5l8AkDSqrhVOVCalkYMe+m5RUUQB/aW3KjOapozzqxPKclYzV2pnhG0sZHPSBA2fcGCmbMEOCVIZlQpWAGxMikWQaTEiw0jTpOOzfctVNaw0Wf5Z6sxxogxQayNJMC9nQbESwhYBSKFt6bI2c6vvb+0HddeQLRlF8Epo4ZSQEuubOu3VTdRwZtW6sdnAuDwTF9TX90orGAlRFHmOytIdN8UEripkHs8FBQnIExKpcIMLPyyHEkLeZd+tVxjEY+rk+d4YPPEmVIgobqQjd2wtORfPkoZHOl5jYm8XZfl9o0khE/Kkfpbj8YjT+QTKWYsEThNSylb6oKCcT6hFM/p3hwnTYQ/KE0DThUnot6tN04Q96cH9wR/8Oj784EvBWD7++GMcDgcIE2phHE8nnM5HlFJR6+igvNW2kpPnTMHRJipUuxSV3JIkhX8TLWme5xnICev5/MZ33eiAvmdq2pR2pNuDtrspRFvD90Mfgqo6j0qNrR6SYtOatM+aO6J+NddfOPDlQC0mqoWt6zln0+LSEFhhErtOnGqag7Sr83nY77CWolFY9hlBo9pO5YR1XSBgi+LTaLiJlFAWFsykeHfMOQikp2ERoH7UDpMuknfJHe2GVkBd8jNlLUnPlm9n2k8FQIcZOROoaqkJEVF0dHfqw+YhglwaoU3U5p4AxeUTwSQVtSyQWjCl96ARWyrxl7JGRGlKGl4+7RKcfOqcoQvh7PaZm4MNbE+koTS4dhSrIeIuTtPSRvMRWW4ZgVqONiPQ+c0L1T3XCwu6dQWxe1ToUdLtwQ5BoIFRA78luIaFwDRhkSE2WMzHWotgXRgsBJYEroT1fIb3BjYfEAmczBaUMjLWvt3yPW2vudYYUBoCwX6/C2i3/nr3RT+qPpQIpD4u3Pxae7eZlHF5z73IlBVbrFScjkdQAG+aI1scficBpJrXb1eZDm9auKyENuRqj0LQaKl6xzt78uQJuKpvrXLF+XwCrjAeHepttdqbbxwPJwURGl4cm/Ql4ahOREAlEC3Ds9/U/BlO0B31wXHanLb4QXLJuSHvWTC1IWErgeHQsNT/RkHEXPCl8Cd11WuNAPXBMs0049pnAkjAEXJOIYlS9FjC6tM3tvrdRO7j0vfnPGmPqqDWRQMGmJHF0PBIx+Eh9kSwkGsbpSWIhWRMrR+qAUrkYKmpVFHTBISVixFyXdfMhqaWNRlWg0xFq6UaggCkBPsJJgAoLpwRu0SjfunSc2Kv4EFqRhRGEvUtriyBiJ1AiqTRQQ2lZLsga4rIsiwWCGC98TlvymVoTN3E2FppAEXTmI2hx/5AmOO13Aqpz7LHhQyTVnufd0REEFWAQxOyuQnNa2RSUwc07JiPqsGZU0kkZlSxHymeS54OIVDTu5DtTB1Uz0IIRqqknafYo1dSSrZM6k2/h+wWR4QiGvR1NMh/79FurgZQxNkPETDm7U3tnWZSAjM7lYJ5VqTm3U4LGpZlhSwLdrsd8mT+F5t8FgIoYwup9NvRPOjhfD5DHdyaI+D5Id5yznj69Cm4MtZVkQDu7wGYc1f5zGWoeLPRhwdF/7LNFBF8ShV9kowZtHlzPC7Q6BsD3syovB/uG2HmCG+XMIE44U8WLaVvDjZm5gwnmF57ikKjInheVYvjQhx+p1/MWkPJ56GfN4c5kNRHXml02JDguRmu83hAE3Pdr9OfryntkGiCCGE9LxAoISM2qdk0N2YnMGbya6x4wxFtvkz/aHgO6lxnEFjUo7cWDRZKOalJsCr8krpiW2APGYqBCCJ1gUj9kU581gI4rpprJ70mSSBN1Bb1eYkIMgsSswLd1IqyrFjOZ8xJk3GpWoK5az0gRXWfDhoFyQWpQ+9wxBFn2lvS1RiUdyw1IaybQwOfiLUj/9sn2pkPXBDS/Vi7/eoaUISLi6iwOzAN15LMFmAdK9JwHwmN+cecuuIjaEzKoLOkSqSLkEF3SZfHpzKbKDxR7P/rifp9VKXf2/0AcJnw64zfmT1Bg8m2ScDhJ98wpW0/RkblqnQTUwmX/b7V3mkmlbNqA0s5KxHIAB/PhjGWsAIotaByxbqs2O13mC1Z7Zaa/L02J6caWaQHr5wYdamhGkfioGs6EKylmdxyTnAMLRHBuhSwKLy+o1sQAfupFVZUs1frR4sMVF+KBGYdDWNX9IJ9aH7XgjlutybpswhKJZxOD8i7GflwMMLOHReIaAMQKlJm1WbZET5sMzeVRQ+taUkiGpFXURUME8mYo6GGG+caCBeg+UQ21l6Aa9lCV0ZmEvuloq1rd/dkj/dKQSLg/PACUs13WC5t8s7MbakDSie0M5gfJW4RgKoyKKEWRJIqYGY/STkcPiwUhVqtSpESlExAzsiTFjaE5RolgeYIlhIMykbW5p3UlJhzhnBFWRlcC9J+VfiwteDlJ59iWVfwuuq0skDMIrDb7dVykGZlvZJQV2CmHVLOOHe5YrEkW+2m/536D9w0OC5Q+HBJGhY0S+yHXt8IhHQABNV8RCRQM9iLp6Kd6e5FzdzmvwnCP0eQ0Kpck4qyL32UIOx4GD2q64rqaPcAkARp8nH5GPy5eG2jG//i+9eYAikrbcrzJiF0mIIud/ON4ACh56LN2RVp5EZ7p5mU1/wJJVkAlUKzSZUtBycllQS9KNr11jbdIDa/RdsmJQLKMNayRi0oR8cIZpW0hlT1aC4jEAGDk2DlD4whmeaUs4b76vPaxusraBqf1E3ukhC5uau9a5tDFTPyWkZlpgefOSGDHhOFeHE0eWmSa0xKapuWegLkGhlcYuyeH9/poDRB2D5j0YmyzxsLaiuqki1Fnu+oQXkypxNpmGjepD+Iz7H6FXPO2O13uLt7oikDpYTmEh4Z8/mk8EVRmMPCrBkrK11v4q3NFCOwcjRzaCoR5h3MeWSOzlQb8h9FYnSegGUdi20GI8vUsJnREm8zFRMnBKWsECjMlVStKsulQqYZmnKQgSnreRMCOKmf2F6YTNPnwDLQwTYIoo2ahFF70D3X3XuV7nXaiIz7OYKAeqm/YyxtXbpFwPgMT4CV+FwaExPAn6Rl4Q3k1+7hqogkIhx1w9gCp6ZpGjeoDWMrRL6W2aDRI4JZTwzL9Np9vfaz/V5LtNSBOW0Z1G2fVPynzRoRQFeqOV9p7zSTKsxWS8ddpoBRRJVcc8Nnm/JkFUIpbO9jkys/+8DLbbu+Ofww9ypwrRVlLYGC7v4pZ1IpJazrapKYBwO0khXKbM22Tq30wJRnfVaeQLkMfQ3Tn0njwaD6vtpGnKYpMAL7z2+12IzikrvZ1t1CwurLmXLuptMOsCszvXAFhN9g+x41fYpmvhNCuxBDaXDtjIWRLDrNTW3xapLunIyBFQOFpp5lOIOiwf/m/ym1gnLCvNvhybNnkVhdUulKSmjCi5uKHArKgzQI6LSnJrQGrXNCaUKGaluEfZ7BST0jwuqOZ6ptbgefAyykXKmc2L5O5r+icx2le5uDHFWtKQIzUk7IiZGpKJPioqkWUwJzsTpZBcJ7CCvOIvIEuEZYlWERK5POKUMgqKUjdL2zaMMYFFAXwUhVi+z3la1bp6k6ce8ZCODQPs7sukhHZyud/6cdGmnr0o5Ad9Zl/N3uYkgkOpey2ngYy/lk5UMs3cD+resacx/j6MfUtdtCJI0/jB6oG+DNQRX9812o7iOQQ2utl4zmap9oaxIkXMvDvNbeaSblRDETkIlwYZnZtFrVfzE9edJw9dvToDaTvPkbuKLw3+4TWj2oaup/ooR5N+PABwXEtQAKQH1YzrwAlS6naRpsvvM8q5+tFGTKUa5+N+/as/KqUYwGRNqYWycxXg0PbRv4lo371lxqsUmdLxYBKoNIo+CYtLYUZdIIQ3ZEAQ8Hp/ClqB9uKyR4tF6yvKoU0nQyakxQP4Brn4o6YsganTTIMooUAgnTW3xO/k0zI/WlCXqxhlgL0wGaRP3e++/jaFBWPr8iTWDZqCrGJF3CN2251yY3zYmeRqSTFtTMk8ISGd4fJFmMpAZo7A93qIlV86KMTI52ANCk10zThC/fHeI9iVtKRCI2tw+Bk5aVj3pRlpDb0xhnOKvXF4OaaUkIlOYoSCpJFNkiqYDFlbF6ZemLkV/Og4aid4m8m1YrI5Nqrr7IEgxJBdhLyb5bHRIgiWnmjVFGZCDUgrPtl0A6BBiBZ+OqBlV0/EnBkTVtBBBUqzKtpl02H/bTp08vTINjH3Vf+Vl3RtHcBV5ghrCc1X9JEyFNk1lWrmtgW/+TW1k8Ivl4PEYk56OKqAqA3q82vg2PtVK900zK6EoT0ilhnndmRui0GSNG0zRhmmdouYHtBFH3r21qEfGYTFOTAeB2ufl1XXE6nTTfqZzBXDBPE9bKWNYTSllwPD3gdHrAs2dPrIbMEsjoW3OERwu69JImDbuf5qxlHbKor8Ku983k0s81p2b/fP/pzHFbnv51zSVQR5T2v+EMixmgpNKxKyfJGb6RGYuyVkaDwdGtCcgE9nl3wTiIh37o/2UPI2eL4jTpOnVPjPIfOnC9MxgUol/JpO2hQLibkwhoTm3qysFAJUayYGNp9zWGSPYO85O5tOzalvijRrOOP4vJw6jNdxcmqp6gpYZSYvBOU59p4SCvxAZma9V1xXPFGKvlv6WUMNFOtRjy+kk6q/v9PnwttFdGlFLGNO8093CaIJLCvKgI9AjiN00TKjHSZMzdoahes+N8vfs18R/J9k4iC9CJgIcx6Gi0JzRaKgJw/NS5iyAhEzyk+wm4ZtNpWP5fYzLtp66VR3yqH3dtzMj95K6h9f2VFigR+wG4ChYdZ50IkKT5n3EGzS9JDe9zuKd7R++SIKKovOB+62tm05t/hxrYFow6v+Gb2jvNpPyA6vqq5JYtKXeILtEVjoNxyxk4aEsubTkR6K8Js8Plc8pajEkVlHJG4QVEOyxrxXk5odQF5/MR5+UEkChCgUknYT/ufEuuQbmfSRmU/kuThfhSY1K11kgUnuf5QpvaMi3/2xOLt0zqddqVSGNU7ntxJgH/l0x6F6hky2TYbm4WhQV3wOqCOWExbQsWsZe6PW7VSCUYmpYdqCbB6n1piPRqq2cfbsckzTfmIoq/Dr7UHUH0aDOxD1pQRpuLGB86k00kGUu7b/Oum/PNLapR++tQTo246XU6986QtUhyCqLApiUwBGlWDefu7i4YNzOjrG7iy5iwgwOsrlytD2rq9PQJL4uTSJFW8jRHmL7eax2BmeLFqxZo8jCgjKEFcjSzufGm18rdtP0X89EJm93cNiGlrTebPCqEQOxywY/s+qjtJOMOCQ2qWwMRR9N3gFkxGC9lqJWb6Q/MEUAhtkYyqPpj631DF3MREp3n0RmKBSVzGfBgOdnmP/V0qKdF2/dtGWn/+a0Wr3rDevbtnWZSaymaaAkBYYdMwPHhQdVdI7pq+mGcy4Id9lrtErgxQzT+rKoJIM8Arx31Muy0fCWEvVbgvKA8nJATY0qEcjqFsaAy43B3wFouNRbfIPv9Hg9W7gDAsFl2u5Zc51KUMxg3M7oJcVmWyMvKV/rab7L+3Y7A/uYm6IH7CVq2QkTLLpRSFRCWE6YpKwE0xp86p1Rsagbc9KdPVl9GooYYT6nL4yHAUliDWzjzUwAmk+D1JYCYBYg8qtLNQU3LIcD8MA2KyaX/zWopr2NNOQAEKSfUlFGVDiCR+hfFnVH+JtM4lImwMVuEP3mYk76RCiT6lBbp2bRKhEZ7fjhi3s3IZo4tp1bmgaGgs2lOoJ2GlE8grDRp3zJhpjn6YIhzECHMeQ83ip64WmXeOV7PGykdQAR5CBTxJCGBHHBYlOGJqKCRUtZgAptjSNOGgNuEzZmLK5euX2iQd7/GNk1oLKXCs/QqDB0RAiBzjVDMIbleuuTeWCetdObmfuKi+xyCglZTigBULgqb5WY6N8MzQ4rjv3h44vVz6IKlaznbc9xGSdjtpsAo9RxJb1sT36139YJsHzjRf//Y5vTssfe800zK8fFg4dnJ7EUe1l2rFkDMiVDdIkO4gb+33cL6g4QBlE5Iej3/DwnHpQ4GxCLzLDdTjSWW3V+LRvMsy4L9fh+Rdq5a+zOJWgVeAMNP/73WGppQ/9m1sPKtJBYlpDcS1JtU+egfBOJAgfB3sCFFJLNGaXSRiDjIAPrzJKS+kOqOcQtHt9KHcKkcnSO52e7ISI1qUOxSqPT6sWvFBGFCDlObR0I68WkRcCAypAOxkiwuAl4/2L0076hrbioT9t+d8Fj0n0hDUxjYZTeXIVULvNyFR+yRI8QDoX0yNEmcSf1SjgTuOUsQUS2oLFgX4JwIafcMKU2WGD9FuDPJHGNDmqOH7HsFjV+2FCYZzqLtejVEiAQuIQjYmc+VhRUOCMMU+wph27YarzMidTvZvJEyWUACpFm2/yRmuGlu4sEvo/a1NRXGnSIgGdEXmnblhSZbjwlAElKttgpq50tuIzJrQqjj9j43Qdr8NzeBno8+lcCf07SrPpr29a2nEeOYxmu+SHsbxvZOMynkhDRNmISRk6JFG10BJVLoH1LYmORF+Xz93rBSYQ7wf8Hh7O987djY5HsFS4/y8ug6hqKvG4ikm+ccMdpNdO6o7JlUSim+6+3GEcXXRd70vq3+OVtm5Ztw+523x0g7vZ1ftQ6KNdADr4xKD5tpLuLBDhLir5us2GyDSky5nbXIsbJDOAqLcYWY2c6JXMN8u+wz+UEfSBy6yMMuUpMbYkR7te6LwaZv9zupaHh/yQg5D8nNriNs+e5WUh/XxxiVkz4xjcw0SNcmXEBRX5MzYsJEmivIUlFXxeeDCO5wB5oy8kzIpFF5+v45+KfqXNrhNk1tT+rZEzS4JYn9EIwUVkeNdW3maQJEUKQv4IFBY2nM+3rrWPmgOTHc50hAMPftPyeYo2AmHaeU7r6N/BBmPS2Y2PY30BhV+D2DpgCJNXSfqwoM7PSiPXrzwsaw+n0RNMFcHsDG5y5N0PW9/pgI3tf966+7df/Y2jo4Of3+YFLWHGATAGi/A5tWMD3ZQwBUIkz7J5ieHJDmN1fs9VZLgZdhyPOsjl2rPpp2e0x3l9PnG1bNZkv4m4ppTPcPDyAi7OYZy7kFTNzd3eHZs2dDPoKb7QBEqLozpXmew/SXc8a6rgHHdD6fB4eqM7B+Q29tzP31PeN7TPMx+L16ThigquG3rMCyrtNo5F2Xt9ORZTfx+aFusJyhaiDFXSYhx18NqSHF9aFq6Tr6W0Waryb3VygRSQLkROp3CXPKFWleOkc6C6gCmRsKYfdUG5/Vh7KeR50eMzjFGrxx1js9grgjajt43alqzDIjD9BMXFmV2pxwOq1IS0XOK+p5j7s9Iz95grtZgyJYGHXaq79GmkBjJD/eu9/turw+CpNp62vHbgwcNzkCOwv2hrc5pYTT8TQQ7Ec3e01Fi8ud0NYcSXOPqldEiL3hqSZKPSkpAkRKBBKrWtsxpL5lf62od0C7oezR0QE1YYKAlLCYGb6sBYkJFgoL8KQoHGAwtDqvJ2a3YAjjUTcmhqj96xtDw9+nbKDEW+3/htUkplXGsHP/7LECrHZ9FBVdfn1Me6eZVFm1lECeJmUGRKhSsTM/TIQQC8AFOJ9WcCXsD7ZtXytNJKQpQaplodcVZa04n1fNUUoJVOcGcGpNRENJUwao6OfrWgFhpCqgwlo+g4GH0wPOpxPKuioq+RX1GlA/lDMNDzl3JgVg0MaA0UfV+tVMBH7P9j3b/K63adp3J70VRVYtBwIjGuIMx/Oq1EnuZQnc76MRfapZkXAEsfVmCzYDYIo+o2ljpFJ6kqb1XDsLyqOULQTWtg870Gr8cNr3JBfMg0DY5T2eHt7DTDssICxnrb6bUKLnQEYpisww5WzPJUWOMI0hzJRAVBweW3yrYr5c7l+ioloWEUgylF1zzAcbWC0oWwqBBi7M84Q8EzhVnNYjdjE6ALQiUUImgAOEVqGRAPUZzvMcpuq8cz/opG5dy18LIC8RKKiKzmnU7eKEnHcQqeDKkEqRnjGIG9KPN1YUwaWoQ92gZmxLrsESwJIsos+sHtBghWyP2YokHsHaMtz0nWE58UhAE3LEyqUDFsVnHV3rgsqrVmLgatG7tTtzAob6swQMoTU+h1hAhQARvYxLLTsikQGknM0CILZ/mun0wlS9cSVcFFztaIwu4+vpxGNzsd7U3mkmxcyopSBNOYBmmaseFhFk6c1ChFLUv7SbOfKkrk+k8f1s2fG1QMxsVkrFlBMqM6gW7NI2IEE3gydBAmSSmysXAliy6+l4wrqs4FoxZTV5XAsrnaYpTHgeLtyjRLhJzzfNNE0XDGjLpK5F63zRpkSELOra80WMCVI7HskOMJFj6Tn8KgzmyAw2zi9M3NIepsbIxI00zri8J0o0BaTl1+3l11KyBVATmFgOlnI5HUeiUMKYK6jT1LGhByklzHkH2WkezGQmpVoEidY2N1Z4MBFU+GFuzJOgPjCiMDU2f4a9szMhNen/ylpQ7cxbEewM91WpucXNmDw8h1FRGKhSQEiK0JISCHMguJSw3REmm6+UM+Z5MkalRUfDt8pN041JI3urT7MxKSRCzhPWVeGuhAi1rFfXrv3hc+TcRXcbhV+TgpcH0koCpC8CCa2bVEWQRbEXL8xuphFp7bL2WrecBLPrGJUzMUfbV0GuKKivRf1VrmBuurXe75qeFbjsBt7O9dXlt7NjbJQoikzCWbFP1xUGdY0W9m6BtxFgr6f4fLH2TjOplBOm/Q5TnjBPM6Z5Mi1nVpu6qGkhZc3p8HZezgbQmZGn10xB2gMpg0xyn+cJKe+1jHwVHB9OmN/fDYubstb1ERHklJETR2QaAEU7X884no4RwZdzxv6wN8LZtBrPc+rNe/v9Pp7lKBG+eVJKWv7DnuG+qJ6BPbb1zO1xzQiCSccch8vt5LAqtqJMQNFYoZ50Gp7S+qDfe0KsBG1uWoSbAYly+90JMTxMvB+Dh+QbQ2WTPJMx/pyQJ48CEFC277sxepumycxehHnWdX96p3hnpUxAUkR+rhV1XTEddkhJ5SMWzzsxAkZm/yCYHcTeSbz53KlM7boyalWejiFYocUhCYwJJEmZN7TsgyyCFQWuoaZ8jD12xhH73Q6Hwx3uDs/gztzJErhTSsh7L4BIgCcFG+OptSoyvCWvElGHDEMgEmSC7Y3a2bEseEYK1lp1/sM09Lq9WMM8RoEm3rQkIsI0H0CUsJ6OA6EX5uA6ArW+hcrmZXi7ir06xxIaXpjD+v51TAu2z0QEmciNeKhSUHlB4WLaXQWbsVKMSQ3ve83ob7U+2i+qRVyLSt7cAzRNyrWoLy7QNg0tAjhg2ukj2jvNpGqpOJ/PEWYOINR5ApAF6hyvAl5XiwBU6ZKXFUwFNa2Y9vuG2uyts9OWUpWZkdb+CSn/yrZx/8T5vOB8XrAsChg5TTuwTBAWPNw/4OXLl2BmzLudMqD9Hsu6olpelCfw9ir4Ntm3/66Xsa85Od/EdG59/thQUZVKVVPwq9dlNfVRTV1pmpGmWc0/bNBPGQMD8qMYykCYuLIxnRad5LiNgPm9yPPsHfXvyvr0xj8CUrbwfCLkbAX/kgolQq7Zaf+8wq8+h2J9ksFtMQhMGfPhCTLPmNIBD8cHVDIk9UQQrqgdmLDyHmVEkRgLtqhSNOIZGoOPXQkrbfeEOc1FXDByE0/PaGGmI2WGEd0IQlmVKU+7A7gsWE+MHd7HlDXnLk8AGRpFnh0kOWF/dxeMaRWdMwhQasU0uWPf92yT9IHOiS8EkYwkEzSQpsZ93WwNDEJNvP6c9rnPTS+iqK8sNW3HJyJUI50rz+saokLJdQNBrV3QkVs//IndWoj1uweb9dIv2GhEWjhTXQWSmkYW5sPNVg7yQxRL6uTrdWY277cLwNvv+iAsjzr2PE1/9tszK+l61/r4WHPgu82kuBqSc3eQO96RgADd5FKRNYtQj6ybuhIh72ZcmE+6Z7JI+D+INLA4cfcik3Jc6ihrxbKsFsywIkAdjcielzNO5zNySsiGgpGnCWRj2UotHpUzJH52QRC+uciwudx2/FhG9abPH9v0YIXFG6VULUBnWbVCCZwVgSC5hiVtAwcUjQ6wza9+O6xNgMjKle+x1Z7g9C1+VQpnNXOSmpbITLRs5hFQY5FkJrfwqzjDEmhVaFsjpoQ875ElY0oT0lLAAmSwRZy2CstKxXoziv3rNal4X29GQlzvaArjQF2jbPd6OL+ScMPg6BiujqeiVuvDPINZcfVKPZtJbtJ/ERwjkduek5bD4a7f/Y8w9okzk/Hz+EuzuuFh1IkInBBM24XAGH+v9Tjz685lmBSlMYbWNRlePYpIm+86a6WW42j+KBbHEOzGiObfdYQUvZ6jD27a9ntiD3TjiWs3z/ek4i1jCqv0hgH0Fpr+72H2O580h3ujDDmTLQBChil6o3Xv2rWPtAC+00yqb72DT3Hxel9FkyCGZpL6zbliTWOc84wkpBD204TJpk16aYAF59MR9y/v8fz5iyFfyR3lAZe0rljWFU+fPsV+v9eaV10peRHBbtYs/1Iqcpo0f4UmEBpyRq+Wp5Q0DF8E3zkeh4TcawXJrs3ftfl8PKO6vG5dC6YJmCY1V3JS238ShpBXDHa/iAdAiMdVBEZfPJ/M/ECuL7lxbzx4oxlhS4j0FyIgp71pofpOZIJYpJ/Eq31/eIShP8/RHrQX06xaQKmCwzyDhVCkgqYJSdR3KgWoVVCYUUX9DSLFihuKEWL/5wzEzX5AX1bD2wUsDjQR1pOIoy4VrQA0snDCAcClyYe5Gm8mLKd7TPOM/X6H5y8/wW63w263x76+h3neYZpnLOXeqGLCWXokF9MukTBPGTlZPKb5wULbAaBxbyMZ8soAAFR4Y0bg2FWdNxccrRLguM4+d1IVcd6DFs5n7Rta9B+ACJbQm7qo2OFpaNGOVsx0KxTrO425dBofuANkFY6S7BJA2KT+c9GyK4G4Jf0z/fe+Nlrfmu+pb1vmFGPrKyUAw1l32lGKWgA84nic4q0QebupT7Z/vw/uceF97zSTcrw5byI67MIVqZaoh6IEKaOKBFT+vNsh54ScYNhr/ZNtArlAatF7pkmrkgKQUqNGUyaN96mV8cknn+D5/T2OlTHvW62nh4cHnM8LXrx6if/rt76N4/mMeZ7x5S9/eWA0vmmWZUGpNQAnKSmhP9ztBtiSLXT++XzGq1evWlXgTbvFdN6+RMetVoHUQs1THvHBhBUvrpJm92tu54yeKGu5dCX8nDTSL5lJw+3/kapITVsmO6SaMC29i6YJpi6ow8xixhApEzBniwRjMCGYlxcqJFjklw4mNHSBgtz656iKUsBccF7PKIuWlQcLlnVFKQtKXbGWApKKbLlIUeLcCFqEeweoxNsIDDZgAy71QAqfh4pFfbWUAJltXrXybwWjVLUUEDMKAevLCr47gABM+4P5loBa7oLcnJZ7e68KDXd3d5inGQLbx9D6X8nOo9stKBHmad80fyLcHWZMeQdCRkoZlT2No5W58OWtbnKzQIQ8TWb10OoIXux0MFNxCx3fCviuMSuzTmAGai2K7m4MIrSoWHf/4UY/y1uLMHJ051SFECGtP+YR6JU7XHaJLgzC5SCkOLyWky4yrfGGBuXWFhfke0tMf0Z7GLb+3oiitflvM9a0wW3bRgsOFz5SiwLedSbVRV0pOKUhFIigcEVBNgt90jiyHlkcwFbbutZETFNhViRvy5XS8NVWrZRLwel4UvOjRSaBVHM4rSuO5xNeHR9wfzppNNRuh8PhEFF2fXAFd5JjNkKfcgrtqXeEusQDoAOr3aji0v0cvpI4ENtBxzZ89GaSzkRnB7W3DEhMJlowOgG5+ZX01RKcxCpqQ816YccIJmXnMszygdW31QrtPnWTNKkuzC1EXUR3bwRywq6fRLHC7jMRDxnWz5KH9letDs2lQmoFuKCWBbUu4KJlLUjc0e8L0whR9MJyxhrBuD77V9cp8BRtDkM3rFr4kIAGddttE1EGVZMm+9ZaMFHGnAvWw2pCXUKptpYiWOsZ7rchSZjzZKwd8XZQBnlV4ISuV9RxDEJOCZIE87SDqZCom0qyLrB4YxZLuHYhX7TQp69Xdz3Z/m7bs803UduLLjtx4SEwIqwMw0mSdoN40IObztq/dp+ZCSG2r5q5L/4nMdKh/40x+Ib0a8ZNMPqPnMmMTKtvzsBeF8k3+PI279o216AeT0Out3eaSfVrokUDGfu7gwFkrjhLtTyBhEIZ7tdJdj0zQ2rCPMklIi8lYNpbxuIRXFbASkMDCDv0nDLqumI5nZFFcJhmYH/Aw8MDSq1Ya8Hx/h4vXr7Ey5cvw8TXh5H3avYQKZg0KtGBYoloiO5zpuRRgm5inKcZxaLKrLMh/ffC3y2CFwjfX2BzaX0vNp+FmoJciiQQJiSFjwFQBS4tdK1L+oUimzPIoH+84Da1A4AmlY+D6J5I7U9HNSdpYfhJBGstAHmydCNubtRzEZeMaHrhTB2bQVgxY2INOy+1AMsKKgXMZ6z1pVZfrgVUiw3b1gXKnJhrq480EEQfCXVj6wZ4YUp5cxOMfpT+DRqJN0FYSz0AQJEFq2ScSsbhjrE/AIu8QjByUET9ETLu7x+QT2dolGpCThlzmpDnHTDlCPNnLni4X+wJupqcU0coeUgivtU8MIUra5VugqLhp9SEiuF+Z1GqdWl4eXxi89P8MtILWr35DYCQCpXJMu/03hoMqaxG+ENKbAyJq667vng09WpL9hLGaKR8U7OzZzntRIjya+Gh2CTwO6B13y6tLNLzzdtalL3/MhTdnvHIfQq880zK1FWxyruJkFFaQAEr5CMzAVJChVbCcFBTYdqhnE9IdcLkIbU+saRIE/sPPgCdz2pHhqvtAAQ4vrq3zaxE+Hw+48WLF7i/v7ciZwXf+c538Jvf+k189vw5ppTDp+QAkd76v90evNvtcHd3dxF+7tE3WixRD9P5fMbpeAKgBxZC4GIo8RbCa6Rvw6CaxCXiId09QQQ8QqGXznp7dy/HiX3HtZqaQyBWfZZAWmOK1YGeagWTYUX0mpiYTGkmOWcQMgiMbe56adkREaL7AiQjUtFTarJnQzpzwx6MqPUTAKz9U6ljUoaZSMw4iCF9E+PuqeDly3vUcgavZ6CuIOEe38DoT5OuX2unF8Bzy/r1U8XBmTYDJOGn9Og4X3siVzW6yaFxzpr5SGeIkFCLovsTTVgASKmgqZUXFxDyTi0LEAbWFXVd9V576okIy36PeZ5wOFguHzQqdJ5nDWIRUQ3UmYGIBdooRqf7o5pWhCD4sVZ2X8oJEE2aTilpDUobI0f+HeCMIftamDmRqzLKeZ5R1rUFSLgmJQIkz3lSjbppwIAfvCD0oudLWIDKgV7DtTa/1ZU1b2Xa3TwnAF3uk17z8cANOKqHj5ObJaHf372Jb3u+L4I2ur+Tp5eIR2ne0KjaH/CT930R3debCRw8ksCdGdCos+hBiE0gLWeARUBOTK+9I5vjt1iGuEf1iW7odV3U18WM0+mE48MRD/cPOJ1OWM4axffi+XMcjydwrdjfPcHOo/moFRvsiUpI+AaF5AyqRz93MNneL6UhwFazZ7NZemH8tgb1Zunm1sZ1s8nWEatW96q+HQfLFNn4jySSfmkgNrDIqO5c3VincMbTOMAQ+rrP/Tox7ZLE3+HEW67MU2PsIezY56UWnXepKLwqEUoFU1pQ+YxazliXBS5FgxAmvv45V0jUpjnD6UXZoYsDjQhCPjBzurjHSQvZHwPrd6lbWMPJ14IKQmIgyWJ+R0W8rwBoYmjOmj8/vE+RTMtcAFGBzM3i6hvWar4iLYADUN8STCPyM1NqHWo8EfX7xrlRt1DCjTmDgi7on3ov+ZzZvG3N8L534xoF42yf9wKGbK7v1oRZQQfg+Vn9RpMWDRhrGNd02t/2fIv7jaQFA/mUDGdiq3V3wtaVAKtt3x/TooebrUZul5emfT+mvdtMys0urOCaUgkQy5Q3SJ7B0ejURTzHJSkxyZPRjz7qaNOyuVqlajkBXlHqivNywrpWnM4L/udv/k+8evUKx+MREOA73/0OPv3sMzw8POD9Dz7A1772NX02oeXE2MbqD8PxeIxcBiLC4XAInL5egzqdTkMOgyfqlU5FT5kgVU0acjGycZT6+tT9LleJ3nVbte0/L4ObXOPi8PcICCypBTuwrh2MWFUAKfVlPKQdsqGTW0IrcNPfVQ7cH167NiKoRYYS7s5Irh7ICmOwFoFFCjm0Vi3VIVRwlpfmFyxIWE1YOeF8fMBhvzdtEcYEPbrxda2Nd4Bb3ZiurjmnXVBI2yKfPocmsZFIp8Wi9TGa9ZEr6vGMXARcBMIvkfd7pGnC8f4VllNGzhP2T97zXiFZ8IMm1U+oRYNHTkeNwk2ZkGcA0LIic87IU4YgoXI2xpS0asBZrQ05E/jhGAxKx6DGYeq0ZWZukLYuzAahbCJCkIWBIZh2UbfmN2hAlQsZbAiG0pIvPEgoNCzxfun5WJc1ysgPyOwh2LX9178/dXt/4DnduRzSVEDDefRdtA34gPXrbTSobZ5T+93OokqCluKxRfdJ0b/HtHeaSQE6eWutzUckO0vy7K6xnyzNGVmZIWVVjXwnYZq7e/IEBAKvzeSgghajFtVeTudTezY3k9Gn3/0uPn/+HPcPD6i14pNPP8XxdMLv+T2/Bz/wla/g8OQOH3/8MU6WINcTznVdYzFLKRG56P4oNxF63sKrV68AtM1ysaGCJbUIQcT566ROb1FWW+fn2jz3P6+sBBwzLCKPzDSlPDPHgRVJJhgSuAo4CSSpdsUWAZhTRhY1waR4vhKjTElTArqeXpDVYQOMUiZMoy6AWyPtMte6PWJMUIuik+SUUJcxEbiyRuPlZYWAIVxQ6oKyLKi1QHjFuhbU2iK8QpvwNaLmbFf/Tb4NV7VlaMOgpfs53tu0dN0PEXpCdBEJGfO5YVSuFScolE9iQlpOKFX9qFMtig/ICceHYgJWBmSHeZq1vhuviuSfCEgVVRilALK2yNZMuu/zpP4rRf7PEEmovAQ6BHW+GzFt3Am2Rml6CrPjIiorIEc82U6tcaph3qVFtJHBO02UsQYArGuFW/HP/VKmiWVnlAp4VKCI77nWuFcTeS+1rsvWgi/c1XFNkNROVzjLFm6arC1wPC/2fHf/6xjUY5tCMpEp5KZfdYz2+wJxwptLx5Q830aJX1uREJXgYj2LhD1bkxArai3Y71TaZU+otftqVRTzh+ODMhHDNVNtZsXxdMKrly9xPB6xLgtSyupLujvgox/4Hfjwy1/CPM/49scfA2jMxc17Hnp+Pp9D+nUm5QEWAMKR64zMN9AQlRPcqCdcrvW7SLmdxO2cYmP6eURz7etCH3Wt0Z2/GX2EUx811Rhc0yFSEF4xydNREjpxUm4Zyyi0loFRoRGz3jsBUek4JGuTlusGH1HNVhXCgiwawcdcUNdi4KhsjMp8KsmAWU2Cl2F9sLHgtT+aUHFtWFe4yyOawH1vPpZQM+B+XuCSUemdDA0rJxCvliZhWjHsbJGAZEJKWUFbk4BoUixMNglfSoQB9G9hIjAXZJ6QwciZVLOmCUDVZFjTUtq26Nbe11Yk9k+gXPi279awjczH101p0qjS3k9luzb2L6RBKrXRSEQbszAo23oza5AF+Tw2pHPtM+PWLpa4xnprQuX2+rbH2xlG7KFNtKU/Wzof2/a9r2FOF5pbt2/ItKleCBx0/40m9rr2zjMpt1HvdjtlMAxI0YMCowsEAIaRRmTGC8M3cz+Pb6p1XTQpFp1pgFXTefnyJT759BMc9ofQcD7++GPcP9zj/v4en372GYgI+90OX/rwSzi8/x4Oz57ivffew36/RykFL1+9GhiUNxHBZ599hs8//xwiEn6ow6G9y4MjlmWByFgp002AqqHZAXgMFr5c+VUa9JD37XtpZFh1IMP1TQXiwK4uCMTFAEhUA0QCxIB1qZlzwBY4nTIaw7s2LmVkrrVdvcic0J3VBIwaB3Bn2mupFdUiSIVZoQfR4pdqXVHWBct5hWtGvBYQQYFn50MjBhGeHAPGgMsHIGIWb829ITN4j52re80rhexBJy8EycZALsi1X1xB9JdLQkLqvxVA8ehSim5rzA0hMyOhgDgBsgK1gPIOeb5DKQu4MAori5pyxt2uC/EU2DyvoLpiyoSZdgbHVABhNTVW9mn221RnEp86/TJZgvt22a/D83bDBJAnNfEXVmuEAlpr7V5nKjG9cdT0XQoca5GJU23rmyuQimqSWG2vi8KHGfDs9SaxL53hyvjKq0Tf1/CatWV73dswqa3pr//9Wg6WC0Ejo7ox1E17p5mUyyEE20Cs/iKCq5iA1E6qStnUCQzhlilbNpUI1ocTOKcLBIiXr17ixcsXePH8BV6lVxZ2XPDJp5+grIqO/uzpM3zpB34HPvzoy2pemAyqyJ7jwQ19bSipFWVZ8erlS5xPpyhNsCwLUkp49eoVDgeNOmRmPBzv9bsMKIgoQ1bGspxRLRrpAirqjY2a1jhQtrdr/TtdeLh2jYY+a2UlFrYoKgtQkaxO8pyRYNqXCJibr4TIctE2qA9XjujmV4KCBdpHCahSIUWGq6uUMPWyrZswA5WD/qFYuZBMOBxUABEQhJV4gwFiO17E8LLvSjvdRm+JnV3tpMqPWS8by+b3hNT5FjbpqpIaY7umIUkFV7VGpJQGpL+MLiKQzDTEQE4CFjUj1lCa9Q8NPiOACtZSwXJC4oeOGOobaiWcqpWTIE0tmKYJxBmyTjiWBWW3w35/h7wslhAuIKo2j35OOxOpa0pEZsbyfdOmLcLv3WRm65y47WH2iDdWAYl5xVLOIF5BIRBcmugEPbSZWF6cVR0oBVQ17SH2lqGhtwjPFsTA5GU7EFBI/p7Xtcf4j6Sbp8cyrWvvucWkrrbeunVDa9y2d5pJBT21TeYmLnFJgxF5TZE0iLZA19Tc2kGYBNFlCTPbWlb05rS7uzvQ0wxKGdN+h6fvvYf94aCJexsZXzW1NTQjZwwsjHVZIgDCzXtuCvQoPo3os0gyukyCjGl5CwYVfKnfM+T/aRFtb/Nc3/RDsAKMkMDAT91rIOqnkbD/k4GjNz3FzVEiAGWK9W0Ydw0ceDga1MbYPm/kl9CEhz50WNwxLoZe7YS/A30lVo2IhbEspLBHtVo6iwtG3oFOaBo65lqUfnZ1Vi/MgNcv6sjE+FVokd0/8TnpzoLawSxizr4luronIKpFqHtHr5VwbEkodkrJFYRDJCFJCdObP1iIUFC7sjoEoICQAZ7AnCDmAxPTvlT57Yvw2V4dBu2/dhPYy0ydyU+MEZVSvO5wrD/MvOjVhnusxN4U62ddMNITEdX6ojSOA8wacxrpj+9z16o5njxcR8DgzqH2rje1aykk3XR0n19nUo810V0PqnCmNvb7Te3dZlKAapFA2JoFzXQjERlDQJqCSUnnJOzVUbZnVGYs1U6yuC1bqUyFJt/t5h2eHA74nb/z/4an77+P/ZOn+O//3/+O0/mMsyXl+sbqQ8cfHh7w7NmzAYWYAZzXFSCKelHukxIRPDyoBHo+nzu4o+TdgwgwzzPcN/WFJjEaNalZzG9BBCfuw769oXT1zMn/RfRiApgdYNY1IMXy0+J89mAzx3p4uDsUpPJghoXlBW01qSCwNNaTUry8bNA5WZ8prSYQAGA16VUU7kpphgCR8aN4b8KMtTJO9/cmcVctIV6tnGJHSdT6rO5+lsYoW52nnkr0q9GyuGJ9/CIbnybfUvvWftdSGY630j1aLs14YudICLHfCdclYoELVxUQBQwW9Ga/6h0LrYmZQIWRLR8q+kAAE1uQhflcV8BBZhMn1FqAovlKBFhlg9Xe4WE1CREuxUaMY7z6j7qilYN/qqqGU5ZzIGLEzSyApXUkEKY8aWkNbJmLWXY8Dwq+rzQSshqjIrAxvtEf5c8TS9kItBAXkjb7QyIK1wXmbm363dKdQ6CV4bjUlLo9cmF6bs/att5a8lgGdtWM/Jr2TjMpJ8hiv5M7fqtufi3aBj0sKWEFTIJpdlp/AKPRZsX9ajBLIgKZgfe+9B4O791hStnKjidMecLucMA07zDtZ9TjAx4eHnBnpQucOHvgxTZy63g84ng8XpR89/Hd39/j4eEhIvz6TdHD6Pf3Tnlu0UKPzlKnRqWGJo34vFlQa3d1jKrHCgvG5YfLzC1JqoYZu6YjFVwFQgqcqzlWSTWpkCh9fEro+5EmI1SEhCLqd/HEw5SAKZMVVdeDfX8+GaMCMotiNtaq5h9xLL2qznQigCbUVQtWRr2kBKQ5Y2E1Y8JMlUooqYveujKRojh6Ggo5MrjbjQJB4+q3TqjJnh9amxJ0TZ7tAzoIqFBzWnIzWWrMRgQOleljElvDtZb4fco7XUeD56pVTbW73QyU1YCZVYtKKWFOOzOHaZAKpaSwUnZWFjnjjBPu7p5YINEMK+bRBb2YRu3T6Uyo66cYXwOAqYMtAgRIwG4/a7VhYc3vY0se5qJ5TTqpAYDMAlQrUAh40q3ufYdhY9aqCF6CPlPWIqrVNLZOB+sdTc0U2O/qzjTgfzaYDFzdVzEf+t1tIfbthNu3YTLX7v2+YVJskCmJLKS8FvVRZQXP1FpFHnvNgf8W9VSg0qbXkgoiTCYRuVxiqAeJMnaW3EtJkxhTTpaEiDDPua+rJ8yRbFvdOaovc2ija+U5AN1cHp7uZeP9mefzeag91RhVMwvc3Lebz3vNQ01PjZi+zuHq17w2yXYjzbndPoiESOS8hW7JVreLNOJLNS/c0N5G+TGULwBkIdN+M1kCd60urVp/uETkWK1WvtyJThOr7f+mAVQlXo7SodtsxJNo2rJLyuPcxE+nU2726y+8OrXNfE3b6/trQvAKewNauIfd3xdMDDOEPlOIAybKBTb366om6AqHmWNBjaCjC/92cyIcCb0tlngKiZnANKdJNRydw4oqC9aVAGFg1sPKhA4MOBmafDfOgVFJzFp8CdcudI1racm51O3RixxD177RgIAhCGw+AOCI3hOL8NRnlbraPeOCCXmUoMQ9sT+uLq7tt41gORiNr5gBH+ejvn6eex/W9ueYp+e0pGOq3T68HT16vb3bTMqgReqUlRCJmlumWU1mYN2ABEWMCAgPiEL4C2MpbGaE7qCG2g2bXM110P2rpRYytEieYtONVXC9Yu62CqYHNfQAsg0Ulm9uAmduRGRlSBBmQH9eHyzhDPY2h+o21NVNK5ufr/v9xhuoD/fGYPKrtQIpW4VaY1B9fS6bc8EEUAKLzjU5QY29L2gwRzZi1/r8vwKUsoIoATkj2/uDSTnzrBo5JswoJ00DIBAqUZSNECPEIhqdVq1sBJFBbjGA6qexmWn8PidA/fw5g9qufVuW7Tq6WS/Ffr61HETtev+A7F6FRwJcw3LBIa5mm18TEogcLYIt9kLvc5Oi5mTrWXBIL817Iru+zaES63qxTwQwM5ibVbWIpkZfrliWVYUCh6S0vuWcIYnAXtQwsutEMR9t7hOoYS6iEf+clagu5TwyfDXMIGcDRBbtn7OfKq1kh5uMm+bcSsIn83mKCM7LcRRGXZIMDXvrK+83dHc+BqNLJ7117ovY21+gXROY/fNb/qYmCPUan59XD0l/OwYFvONMqkLDQ8lqxiQQdvOdbvgKLEtRbScR5nkHThwTWdikYvNFZMPUI0NKKLUoAjlCCNRtL+rYrFxR64qcJtQ6Ax4I0WlP/UI4Izmfzzgv5yhRXuqCtSw4L5og7Em7sHd52LlHy3nU39bn0xohp8kSCTsnrMHCkBW843GXX2m3KJ88hkcNGlQ4a026kuR9N9QPT9cVhvAEkQQkQkoW3Zeg0FM2t2mXzOxmJePJD/smmtD6mfMU0j7qAj0wFAyzlhIRWKJUtpnxDL4KpJGA2zE6qfOSGBEYYHSnBYQ0RoWEIVLLu7p1oMd8b5r6n7a+qs2gu2+Vpk0IzckYzrXbm7anZlTVhBqhTKSCAo0wHag1gUgjHnNS/xJDoxV3O/VDSfXwdYFArQ3ChGVp6Cq6V/x1Snj7EHIuBassmKZJ3UXizI+UDrCviflsqYXVO/iCDp3heXZc9ZwIuihFAOTCnmlqzAXLqpW2IWbOjPnRRPMQFGvpUGWcCXVJ3fDgEwugcNYnWgk8GNSwRk78t4zAvrtQqW8xgi/GuB7TxoDeWMGBQb1te6eZlIadM7JICBehkZhYTaQ1gYBV8fISGWFsC+WmslIK5t2sm6ey1TFSoqNmHcQhEnu+5v8QwDkgmpgZ5/MZ885Kk5sGdV5OyFMyYUdCkXET0zQphMxut4sgCUcL2PYV/VjRbwAJZgRLetULEoRdbmtE0ImqMthucntJfvv5DeLWtwsGpR+2c+QM1iU+SLCYcNo7UXfmjxGyx8kpgdqhtk9Gvk0RtaZJ3m3szoxQm0Mb7AyFhn5WabWZRg1HYXdItOZUrz01kx7aHPdjj/m7nFMfSz8uPfAeEv76Q+86V0SChilmW6Kh/72XoF3raIJJfM1t/qHbHkS6t8mj8Py/QZQ9oMKSTbkRazGA5oRmOieiSNB3M5aADeXBrBjeZwGIOcYaBjpSyLRtU/OahyFaH1yOsoGGbmOb1lHRCW4SHM2WziDJtDa9x82F+o5gxBYd2jMn6fbOsIrivaLx84tBSfTHA72uXLS55brG9DbtavReT486Dat/xfeHuc/DRk3aJaJIdIUAZOo/iWpOOxjSeBprqXA4bV0qB2plLdKWACGF2ycihXexJkyocJOP5j7AtJ/j8QjQHinvICI4nh7w8PAK+4PilQGjaacWxv7JAfv9Hnd3d0PxsR4dfQSU5Tjoo5mI4nS1Eu3ovrdQ2o7INmbiBNy/CboafR58Kbi+2a6ZGRzANDRAmEksSZCVBhTcvV0YJFowL5MB4ojW50mp6w/c2d/uBho2n8DokmnDHhzDzMjug2KXYs12zk36rab5JdqYg60vza/mDKrNRR9RqsKNS8oevLElQm7Ka4JGFCr0vvk4N0RtMMHYc1P/fE4jMbtYqi76MCaywVYlMmBYJ+RGn5PJfoEkQYaKDoEGI9i5hAaZsBHn6mkiQkBVH69jvqV5Rt9FkgqRVTVUygA0AjZykklCyAGAggr2+UjdUKUxBzbm0odeRE0q8jIzbkEpGnDjC+w/VNpU5oWmcULsvRbNl5KaRlVQqvBq09JpWcOCPEIYuVg6whhP0bVe5PntbKM519/VdcLHYQz/bYIn3mkm5cTaw7mZOSKCIJqk64mz8zRhqQWJK6Z51pyWRkXiQLk9HQKcHD0AAEgjwlL2WwgshFoq9vuM3ZSUoNm/zz/5FCTvYU5PQQR88lvfxSeffIb3n3ygNu4qOL56wPPPn+PVq3us64rD4YCnT59inudAOQcwVB9el9K+c4akPWoT41D+W9Ncb4rqzX2+c2PPyKN3cm9uvJa8qxc1qVdx7JSxJKmYphnJgPYoTxgJN2xtEtgw7giI0iPJwtl1aAnc5Uv1zc0ujvsW6CK1hICCs9c0ApC6aET0JRQM906AC2QAk2IjdKXTlOJuByPt59kbNSm4H8QYWO8mvu0gu+sHYdvCsgWATPGsYcfIsPDdeLTfBASj1lfrp40osRXv0wrLqgGbEEgCwYplLUg1YbfXNAkChVndGTizCiK6bQVCFBGqmo6RlUETA7RCcR/mxtzFzcY0rE0ftcu2jh6JqcMUjf608U0W5t6CmZSeTAnIRJinjFQ4AqFiWgCs3O1bR5zgCrYaYw440ELPOwbnQmOLjd+c79e0gfMMHP3N9z6ybYMkXsdkmvDqAToNYDYRhZD+WH/ZO82k+qq2vgm2UWRuGiu1IolAUoIY8Q8NAtIAKM1R3hcj9FaqSlJDyXrygwqAGNOUsZt3OKUT1mXF6fiAJ0+egi0ZWIMpJtRa8eLFSxwfjljXFbvdDrvdLgBG3QflARM+Tj08Y8j5QOtMUwE5EUfb9OQmjW10kW2qIKrYULLuyhsb66ZU5MTOrBZqQlKZVf01Gxw0MuBOMEgmRJaTES7XUtD55MIPROIisPXV++B4bxIRfE5k2AtCdgyFPZ0B0Fh2+4asj0KX8yAyQtVsnd/dzN6apk6z7ea1J1KxMHTlmt6O4ozH0CU6utc/6zb5286ha9r6u5AE/WtjrBZqZwIcW36hC3XoTWqiQUiucbLAk4H7Pql5Vhk7syL6B6MUQ/gYfHOd+q/JUjbu7XxJYHuOZleDQBLBygUkjRkJAY7V6GbBZu6036TtFYUvtpynGLsHhfgZcKYkg4/Sz8rj1J7NQvo8Uve5xH/eum2j8bbM6TEakefb6b8UGhZdVJq93t5pJtXMXs0E5s0Jex9g4BpXSECkG9/YU1C1lK9PXikVpSx4+nQaFk7ETG+iTOpwOOD48IB1WfDAK54+ear9ZQEyIecJzILPPvscDw8PYBE8e/YsmNTpdIoAip7Yua/LzVw3N0enZRNJ5CQJqoFb9pqS2SuGjf5269CH2t/6vtmqm4ROZEC/YNOEGESGjdZhzSlIqT6LgyBszzBj62sBjAgZI1Ki1zL9fd8Q3FTV5tmZC+UxAEY6ojCMNiTi7r3+LGfC3bU2MR0nHTqNYRHjevgEuo5jH1MceGXxbu5KEV7uzM4J7EXngZC8XXCLz6UftzJq7vaPbp8C1WKAsKsRAZ2pmjtNo7L2VAQAKydTceQy4EZzqKAVfW33sFRo2HkBhcDTzKA+J67ZtVHqBKwWHk42bj8Ta1F3QSkrMppLoRqTYdPGtepCAz8W5tFHlQpg+p7veTGEFAkTn+2xK8e4eeG2679dM8CrOZJ0lgRqFaYHyCh/trS1vtauhZe/jYlO39udHXINtotIvcjJvN4ex8qs/cIv/AJ+7Md+DO+99x6+8pWv4E/8iT+B//bf/ttwzel0wje/+U189NFHePbsGX76p38aHxvyt7ff+I3fwE/91E/hyZMn+MpXvoK/+Bf/4kXZ4sc0JwQDAvggwY7EokcQX5YF59MJ5/t7EFckYXApKOuCdVmab8snKmlOVAspVwkoZ0Jl9UGdzmdwZcxEyACWdcGLhwe8Oh2xclV0VSKUWnFaFry8v8dq2tWTJ08iks81qw8++ABf//rX8cM//MP4+te/br6qatV4rzhYu9LrbY7UQazI1RVaUO56RvkXadtNu10Dt7ePDuJOshz+FkgVNdUbCxJU7bv9BATEjGRFBpUsqwnFgx4AmBArRifIrLCCtRSsq/5ziZalYkXBYv9cWleJXQZNndmxBquGrfs/LoDNr/9TQtqbdjagvxsGdaEjyfaSjVY1gMbaFQIkDhbVvvWiXn7vlTVbV6+flEBJOq2362/f/zKjLhnlrL5gBzkW4W7+KlgKmAvOa8FSq5rkYRqULRmLLheL+qiGvW0SuAIou/ZL9uwTmM5gnFFlgcja9oNUg7iyfW+YiqhAqoJUBSj6jwpAVVAXRbL3uVdQ2YJqyd0sjKUUrLVasdNGe2otlvjrIM9tfsPUZ0LTVqgeVpkshF4My9JxF4dFLvqPXGMTKPivoq67kuLC4LV/b2q3Qs237fXWlccztVvtrTSpf/tv/y2++c1v4sd+7MdQSsFf+St/BT/xEz+BX//1X8fTp6ot/IW/8BfwT//pP8U/+kf/CB988AF+9md/Fn/yT/5J/If/8B8AqOP/p37qp/C1r30N//E//kd861vfwp/+038a8zzjb//tv/3WAyC0vAxvjVsbJ3eGxdzMH+LStYYfJ0oAW4g2Qe0U1Mx/zErciQhrKciRA6XSY2VCSjOmDNRZsNvv8fz+JV49vMJudzCiCLx69QqlFA2sAHA4aLDEbreLjev4ftM0hRlQRPD+++/j1cuHYKCjX4Q6MXnDLDrptDXZ/BzbViu4OveP2OjmwbD3NFu0a3QhL1pJhNjXtlR6q3Sm+k4qZmgOjmsXNnyv2OqiemMyguYwb6YY7aUnbztxtWuuahzokj23X3dj7ISlcR6vzz1d+W1QeoFBgwGUEUUQBmxNCE2Dojb7rkEN6xm/auh4FEgkBWf1XKAmBTeznNYnSqqtSzZ/o4Z2K8iHaVlEURbFIb8SZYgz4Y4Aa0yBmFqlgSpqqcCgcRBpwRYF9WU4oRbzi6lmROi1a8+pc7+YLUqsPQD0RQx9p5GmVKpIY4w6BGRfIHuOTyp3DMkTvsX612qItbVo55OMREmzFMTAe8aNTfPz5c/rtKHNDY/1BXm/vkhuU7yyr5iMNkfU9fVN7a2Y1D//5/98+Psf/sN/iK985Sv4tV/7NfyRP/JH8Pz5c/yDf/AP8Mu//Mv4Y3/sjwEAfumXfgm/7/f9Pvyn//Sf8OM//uP4F//iX+DXf/3X8a/+1b/CV7/6Vfzoj/4o/ubf/Jv4S3/pL+Gv/bW/ht1u9+j+eCRYLcWkLYrII5cgLswpYomjHp1UGbwWK0+d/PTBj7XmgCaUssJNQ6UUUM6YAgeLIJIw5T3qBOTK2O33KKXi5Yt7EDIqM7hWvHjxAq9evcL5dAIR4e7uDnd3d5jnedDyDocDdvs9Jqsntdvt8P777+Pli1c4Hk9YljOWZUWYhuKQXhLFLYPqbfDdh1fbm5hVH9XzOpNfr/qPxNoOq025+zvcj9XC0iVqSYklUAsE2YWKbgiRGuZatDQ/VCTwdv4CojEBOpz5HZpAM83YyzzM+uLgtjnwqDFx7gBfi0t/ojIXczCHuc5W1Od2i+EGvYUjZN40foLVr2qfVemEtYFJqZaiAUaKZagmMAITgtBGhKklAYv1mRIhsTEctohLEkOCAJJHNcIghEQw5RmUp8agOsHDn+vh3DmruZdZNLfQtDBi1nw7JDPrC2B7Axbqridj6hiVv0faywDT0n2tO4BpmysN8jDkmBBuL601FKgRCPO/3lPs+Z1FAZf3DxvC1/e68nu9uTk27g3b34XJdfz+NY98SxPf2Hxz+7yHg7xjYG9u35NP6vnz5wCAL3/5ywCAX/u1X8O6rvjjf/yPxzW/9/f+Xvyu3/W78Ku/+qv48R//cfzqr/4q/sAf+AP46le/Gtf85E/+JH7mZ34G//W//lf8wT/4By/ecz6fcT6f4+8XL14M35OMUhZYcyOEkkVUNQBWEUGuGkZLBCDPEFaL+pq0gB1VBp0rGHukWkMGURhAAmrFWirKSQMw5t0Oh/0BD/cP3YYlvPf0GZbTGf+v//P/xHlZtCZRrZoQKIL33nsPT588weHuDvv9fthAnChgX05nzYQ/HA54eLjH8WiVgbmp04IyELDe/nuLWd1q1655rPS1DTh5jBTmqBMpkZrSAPPHadhyoknNUAKDs+JY8FrZCGxz7AfxkWaei3GENGdM3aG1RKGQmNmqxG/H60QQplm4cHB9nlr5cJMcHe3EVSF6HJHwuStr0SrGFkJPmJBI/ZaU0Mw7tu55KNmtqAzorvGfRITdboenT58GFqSa3ne4MxP3up6DaJeiFas1UjIjIUOItZqyYUt6pVxy2cnaRBNQkjKy2g6t0LhfRFQfEjafJREyKWyRpKTVgUuBpAzJliYiDKAgwm+NMKsGntVvFTiPSjDFzLli8FYsilnoy1Ij2Eb0fFlplRpMxRiWIchkZ+puymYVkNhL0pgWf8s10c7/QMwetUduRta+ZfviDOntnu1A2o9pX5hJMTP+/J//8/jDf/gP4/f//t8PAPj2t7+N3W6HDz/8cLj2q1/9Kr797W/HNT2D8u/9u2vtF37hF/DX//pfv/pdOCDFLUOiMC6gKCmtUmjLN3Gdg0S3r6ARFTbIGBJGKRWJFNFaQ2rVfCCiB6aWqmU3qtYK8pIaAmC33+NLX/oS5t2MFy9e4PPnn+Ph4QixZ+WsARa7wx2mWZdh3s1aoiIR5t0MyoRSi+ZfDWYkTwpBaBSAb4KRePbVezVgQ303A+r3Zj4f3TpzSq+8az7UbYntQqOyir3KPHI47smZESmEUTPTTY0dk0m33LNoa5av6awpnNfBtJVAioUKu0kmCgZ6X6+YVnwJXMNq8zdqcO3zGh4iwnY+0mC/b05/6yQINGfQ3F1jkY+e90dmBEhJtRs3RWtOUgZ5BVAgEEucsE3ThHlSTMhpmsx3uyLnjHmesZt3VqamAJwMeaOisgeAq/mOUrYz5NiViOClJiCZ5uzzR03C3gZMtImXMC36mMLCyYag7uvbIbAr5/bwckYfbOLaca/RkD/FtSXXrN1kNZj0hmWP7+MHd8j6oeGMO/Q6g/I9YfSryTQXRqH+Wv95jcn0wuk1H/L27+9Ne2r96J+TDNXec/002OV/sSb1zW9+E//lv/wX/Pt//++/6CMe3f7yX/7L+Pmf//n4+8WLF/jBH/zBtikssqtFMDU/TM5ZExk7TBRPKkwERaMQMR+0bUjLUq+lgBNAYEyUIdImttaK87IgTxNKrSDzJXnU0jTPeG+e8eTJEzzcP5gZ5nOklHB3d4fJShYwNVlpmmekaUKeJqRZ31VK0agdZ0RGhACYZAcADtx5iavVS2vTlNQJLMpI9HPEM3zu/N5HtcHm76o8X6jy1w5HEKVkc+7PoGRIAZ63774j9ZMIpXivfurEQ8Y9EFzTTTQexOBzaRQFxqR8DpjjycaJXjN+dEyszTVvmJQO3wltuykOMzoG5AfZGRQSpkn3cvbqueZQV5ObRnclIiA5PJM63fVeZVKOt5dzZ95zoSllpGxMhhnHE4VPFCKGR3mGVNVwhAhVVsD6mUxK1HUoMTfiFh63sAkF423rs5nSjelYo0D7OfJcNecIYmtv6xzzRnZGfJf08Ktkz9jkC8KFIdOcw6ciIShFPwkBidTG19ZfWA17A2+8YnK9akZP1r8+NL03lXU/r5XLeFPUXt+uMotubd6aJmyu1T1uwj+84Gu6GTiybV+ISf3sz/4sfuVXfgX/7t/9O3z961+Pz7/2ta9hWRZ8/vnngzb18ccf42tf+1pc85//838enufRf37Ntu33e+z3+4vP+7y31KwHupV9sUKS2TAw27CrEyyXcIs5X5NJnFbWoBYFuwy8L6jdvbr0xn01TjU1EVTC/b9//XfiO599guUzxg98+aNAyOhNmNo37cc8z8i7HBJ2Pa2o5qv64IMPME0T7u/vA1T2ddLT+J2EBC0mBTckBJvIG/vwtqnwGqWh4e9t93rpsfUThjIApOS+I8XxUwas1I6RwEUZSs7JDXBRo2iIPuv6FRo3Ksg0sro6Osnoq6LUSdMhyRpxHMbRz43PBHfRaZvAlkgu6hmVRm5Rr92IRneR3UME5JRxd1CzcAMZRuxloAkkMcc8gZCQc0LOc+yFnM0kOGltJNegDvsngKgJlfJLnS8CsiRMOUHmBAWUT8hZE7HVVwWcznY9klUk1rnLtOtC+gkpz6bVXa7RtT2i+wRIyf1ExmiofSlwQtg+d4YRWrtr0X5bHxQTmtKo3fTN/WIArNQ7R1a52WJQlBook3ICLICEmZZCU7/uj9q28Rx1kwMQo+UZvb32sxUCRj9lsx+L+XEf+2zX0v25/i/nqVkMJDVN+BHtrZiUiODnfu7n8I//8T/Gv/k3/wY/9EM/NHz/h/7QH8I8z/jX//pf46d/+qcBAP/tv/03/MZv/Aa+8Y1vAAC+8Y1v4G/9rb+F3/qt38JXvvIVAMC//Jf/Eu+//z5+5Ed+5G26owvsOHZAl+k+LoAGVHQxmU644j/2VfyuUkwtrNwvkyOKGSEDkAjTpBGAtbISTtGQVA81TUQN3sWO5kTJkJTtIJI571PSyFLD7qOszmj3kSzris8+/xwPx2MgUfQbc8uQbm78kGqUgSZsbOHB1K/dutnMBDSzHgXQrzaf705zuaJNcWQbk+G0+fOb1CrSGIG7uZuZhsN0ECUzYN0QfX8Cq6/BE3nhDFoZUYvUc6mbm/RjYxuMiR2VVN7YTFkCiS02rIBKTvotIbQnNceZuYwyIudJ0lAig2wHEU12nT0zOSFzTEljekYMPJEyOwYekSbYkjJ8oSl+r1YEkoXANt8EahiWRJh2yQo+UhBt1dJn10NBlRoToIxKTXhrGmKbl0Zr+9QIsmGpHZNJsTTZNkScZ9uzCcrMI78oNKyG3TcwBGPwvtP0USFpIEx9xtySsUfyXeICgr/Gnkkd3bnUmkaGdJs5db4qnysBgC79xf6R76U3aFB9i2tBLSvBhTKf04265tPZ3nf5vH5MzqziO0LQAhYGMUW8wJvaWzGpb37zm/jlX/5l/JN/8k/w3nvvhQ/pgw8+wN3dHT744AP8uT/35/DzP//z+PKXv4z3338fP/dzP4dvfOMb+PEf/3EAwE/8xE/gR37kR/Cn/tSfwt/5O38H3/72t/FX/+pfxTe/+c2r2tJj21Yl7TdJcoO9a0s03gM0ubb9TQHh4wxEoP4ul9ymSTfNmFBcUa0AHOUMgZtDlN8lAIUZVdwzprkNKU2AqCax22UINOG3rhVrKTieTnj+4gVOp5M6je2gSozDD0Q/Ky5dd1K/jd8JD6fUJTu+WbS5PARtW7smplM9EqLuUhMi9GNmj9hTwtiDa0evTAonMz0wJJzekUwpztT8YEgQnWQJlETK8PWsjIyqESTrWMesG/PrAHs3Q4LTN+rnaDjJtm/iIvh+dI2dkK2Krn6v+7a90QMAVNwx7TPlIJ8EQHgCkM3uT7EOQXZUZQVgAXSkhKoyBQI4m8bawrn9AYRp1khWIEWgCYtgYgtMsrelHqQ3mLx0kraduA4ZQeztPlo1a7tfkqJ6cwgIxoRZBMgd+qEfZtZAi1iiK35Yudj7jYnp/uFgUrCZTjbOsD54j0Pzbgm6o8Y0XG7bgi6ua9f3AmHD4Gxytp+LUXPZjq1/z7YNUaQDE5XhqvbMBvLsDOvauy/90GPSssDAuR/R3opJ/f2///cBAH/0j/7R4fNf+qVfwp/9s38WAPB3/+7fRUoJP/3TP43z+Yyf/MmfxN/7e38vrs0541d+5VfwMz/zM/jGN76Bp0+f4s/8mT+Dv/E3/sbbdAXA9YkfVUwPm+0IyObaXnbo92f8FAEqI19YKMbN4PlNVVyjYkgVZKgpxU1ZXFcQKXFNBCzmrJ8dDJcEy3pCxh7rWvHyxQv8j//xP4bqvbHh69zUQVrRzEku+XQDujgcCS4Gqv1bLi1lV+b4tc5Ocg2kh6q5uKin5u0zQKOk0rbPDdpHvK9d42BUgKJV+BP9My/1bqHacR+gSZ4edSVm/78t3W7FmHath247TI+/n2P+GAiN3s00ZJo2JWUxSZomNQZTOMXLis7A2SK3jXh3G5tlBpBtinuWmiK518wOMQ9+0at7Lwnj79R+FlRUYtTEmJHQmRwMnYWwmykCh3JOkbBaClsQh6CaadEPm2K4deeIGBGI6e/w0F3H7TN0CV/lVuaFNOoOMO3AdoCD126sAOGrHRcZrbwNQC2XAWIo+ap5609PWhZRobNVwRawRdsq2rruFM1nc2ajydLXztP1M2YRqY1Dfc+tR9YR1kIAdV2RcsabIIuo7SaXIS5MiFumNU1aFeLu7g7n8xmvXr16VD/f2tz3pnY4HPCLv/iL+MVf/MWb1/zu3/278c/+2T97m1dfbZSAbKHHV22yKUjVIOFeXWLxKB4jP8LmO4IRSfvuirSg5EjfR5xMOJRm0iGVCAVVI2RNkxERTFY7ab/fIecZzBWvXt3jfLzH6bhEXpUDXoKzHlLxxELvb9tULB0wrmsUgO5EIxBRO8kFWWOij1jiYey3PhtwANuHzWZgzaWy3k9VSjXtwp5gtnwizRVK5InVCTWRyftaiBKuRA3SX18KoeWqxPddF5u064xhM86RrkIl2aYNRFFGIgCTCULWJ2NSnsPkUU5IaiolD4SAa1muSVEQAu+x2HtYVMOIdSPTTINBOZPU4CDX5KhjcP2ai+gzFeXA/Ctks+WAtSZoJBfyTCPzRF1NvlUkhkxKDAlA5k5zdVA/sj6TwxolI+b6TEoudKbBI0hEw8KF6b/TFPu2ZVAaKLNlXgJCUebBLRCDWFQrY8eA5NgoYsZnUOfjcgZo56shQkh3DRBl7SV46UVfhxYaZwuUel271GZe83dSYyJPXter66deHHRiMC+CIp2HiADz5buC0Ecye2VxByh4bE7sO43dl0zq872pNDCobmdeGdVSbxf+lU6VEvRF7GB+i8asnN6qHdc+cypPXQixawzUAjKQzHTFmoyKRMhTxjzNWBbg4f4BL54fcTqqtHE8HgNvENUhUqgdZqCTLhGmFet4fNp+tsAA51IjUR8P9C01/tamvyahXp3nK+9UP4eZ1RwNXQBFHrB5Fq8bJJqkGgTLibtKev2zw9QTY5ZWTqjX7vq+mSYXzJJ6VZP8/yDq9xHZ+gMtnF4H4dqRR2N5tJO+ltr9cMbkicoakecE1X0VYn1Uepfa7XBIo7RZB/eYNfbV/93myvojHqggoZn1+8yDVfyjlFJLfPXrM0Xx0MQeNBAvg8MnaZ6b1aOyM9WS8lveV8+tqV8u63unJF40t5x45KWIWCVfE/ZEIuXEJ1a7J41BcduT0u0l2Dlhx++zvabjaNeMpvlmit8WwXxbZel15/Ladb3FKbRKEuQr+JfAhjF1+7J/lq4/ovKEotdPmOc5UHX2+32YfD0A6E3tnWZSU84W4dW4FION7jKSjAf9sREqCgDJKHWF5wLRLls1Uicmuu+WZQ3b/Dyr87iKAClpQnFKkGlCSQkrATxnN1AoMbNSE6UwUAX3Lxf8xv/v23j+/HkgoCsemm3i2hzgzX6tzwsinJxRu5Q7jo+IIq/mlonvMVrzb0djdt+KJXFyMUIoABUl1mlWMxdc07BxJSBbThMZY3QE+0zNd6GwdSb1hvZIqAV6jWPgGYNsFZGAtt4EpLGYX2+6sk+wLUsQT4nHXDejCAkkqe8smU9p3uXgqYfDXbvWiR8uo64ceDgI5aDRWli2/S42idtTIcwQauXP+7b1InSg86H1gIBptn09CAhTzFAGol4aRJCmCZRS1HFi6aLgxDxuBDOPtlE00as7CzeKKUUuXDALDxpqsEheO8ofLqxpIJerdjk3/v4qTVtvAqO9pxuX2PtGBvX4c7cNmHgMfcs5h5bj0GtE1OEujtGHLlgNJmqzUMVZMi16t9/HGIEWld1DvznE29u0d5pJCSUgTUiRCwEAgpxShPRu29ZROX5pPxngyoFllkgr+vZmKjHin+cJVTRTf1kXI7qe/2MHKinixWF/h3nadaW3BbP1KeUMKW0jHA6HgEjyhNxaGaBiZgJArHidiBgB9cNVR2VF0G06CglSN994aH1ubjGq1x2Ex0Uu9Q9zDpkUUXpgvE75YA5W0/VEc1/ITFbULjKNSv0h1eCS+qjAZobTw+VlUTwwgqzURyAgOK+034nm+Lj/LyXTNaSPIm0ayjATPVGxBwzMLNkYoQE27sMqbDWuQEhdCLcTu57I+vOH1wZTovhEp6Egklw9V0zEAhS6TXRRfK9Dqmty28jAAaQon+J6XGMgOU9I0gUZwKGr7FrzlWoxSSfEYy/8na23Jqh5BLg/e6PBwNk8CxxLT1j9TaEkFCO44cv0+xuTiffZ3Dvh3toS2uddDqH0ZsDvTSh8bHSf93Ge51EDumIV0f3WcuvC/GxnyAWhebfDbp6xPxzQp130pYc8XcaBxL3s0GPaO86kNKkwhSlI/7k0ffWeR2wGEUGpVZNqk9nUaZRJ/XR4jRsWxmpAty08VvsT9aFyRkqThkPbJck2AiVCqWPQhxOclDzxzQ5XMB47j8KAlH4AjZ9e48Wdmg9gIzld3+Rv8kFdRCPh+lzT1Y75AR6FCD8MOn9eMJCMkejZ9nAAEZi5FXA/i65F3zcJIipwE9PYn463dDRaiWMatKB2USLzD1JLrFTP0A2CESarXhLuvvP7hQFKyESozJHOwP1eB8w5r+9lQ1zQbtD4zijbwRgH6SbJHlprjPQafSyjIDHuDfepwfS0zhbv80ba85xa2JJDC3nwAcVac8gWMfu+Ny62kPdHxu8AE2JMo+nmTzzPSSQcQzFE0/IgI8txTdbfGVofd8wmukDBzqWfx5g/+/dmJeg2I9mcw1vXR99Frp7X/h4RBGOKBNzOSmBDA6AC+DTPmPIEz+sDWrFWETEE+xJmQGbG6XR686DxjjOpkRHpwXfO3wuNSvDY5LitRHjZKlc8nI7IU1aGR0Uz3rvy3b2ZxyWjWqthqRHqavdACdjxeMb5tKIhDhjBFH2eVOqeJTidToOZb9gYdkiYOnOCbDf65Ri3jHs0eVy2x5pHr7W3Uekd5kkEoTmIV8GF1g5y6IJs2eqNQFhYthN7ab4fwEyv8MN5qTV2vYgp06w4aYwkEmtbxJ3TF6e/uecH7mfy5wbDtYupET2/jIEIbCBjwEyAW+3VZ0BBEDkBQqKIKcwxZh8aV89VSXF/G2ZP2DxKSxAF+jZrU+sahFkDghDEuNPLLggmxfPbGH1+XBtU8xhA7LlOdhUxRAqqFBCAKQ6zfqedbAgSvZYovnRA01ZEkJwhsYQ/N/kN/ROk80FtmEorvaKfF+6qO/dNcjunWKPMS5/jOKzLI9s1nxIwwk89pnnlCH+/m16JyDAiNfdtmvZINGruPv6UEubdjMP+YLEBNSI2iSi0J2dSLnzf3d1F2aTHtHeaSbl91QfbjD+XIdAiJnWafOefbZvbV58+eYI8aZa00/BeOlVJT4lrSkDO0ERHImSilnsU6nR7fiIrkx7HQr9c1zNqLbi7O+D+xcsAuPS+TiCUgYQ0Ca0T3Zq58caG7uekt0FvHan9HN3a/BfmuRufXXvGrWdfMzeSCQWVnZBZVJEgzHNsa5zAxrOpQUhdpQUebp1QmWNlgdyIb5pM+Gl2eVDvbzLiZtpBE4SchIrh0zlRH4Wklsoc/MtkHsWWZFQUEeQpgz2OUdhgd5L6Pq2+1ZTH4yw2Vx4mHmvb75VemqO2bl5qQjUMCyYQQDFWfBpcI3SeYYIiTd0I2z7MQ0RDtzddLbbZ9Ag6gqWRQBmMayI9JNY1ebM37YWQ4JNt85t6818IjaY1+biHZ7aAjzg3wqhVgpmh09o9AJBFUIoqZfXKGXls2wZGjAE4by9MRsqM+b294jAhRUBDTqnROWuKlUAg5PBBruuCnJPCuqU0nF11U1ScTqeI/hx87I9o7zST0j3VJKCeMKvUOkqsvQnpgonFf2yhpjm0Jum/jOt9Y5t0nJRZtSPb3qzuLEUQILQoKK4trwJAlDPfHw767v6wSIvDUqLYm2u2B+rSod5vav+7Zwa3TAdvcwAe45N6jDni1uf2G8YCHhJh33Ewuv/2U7N9t2s36vPbRsLZdYHUgHiv/tqxothOBDVFOtF3FiTjEm27FjyiH6OEJF5JzP+m/6tcwZIUaRweEUkXc8eehCww7VSfPcxDNzepC91ugh+6fCEl1jaBg2nIGbmFr/aqDFyDpMjzGeeiZ3aARd5ZnSqzRVj9N7T34fL3Xnv1z8OUZxdHIMawJE3ICwYX1/bHcDSVKTOSK997iQ77znP0NM1r09fHte3aDgLolYddO//+HG+uAUZenH2Vu6RQ6eZPyaGbvptvisV9hq2vHo3sGpPmz1m0K/PNmIFr7Z1mUqfzyUoQdITBCW7aku437AvppTg79MWcuB4xRaKZoXA/TsU0N61MkY+9Aq7C9SQTEuc5Y7efQUmwOyjm2el4tE0Ne6ZCKT17+lQTIqU5kmMAYuHrb5BErmlSjuixdbK+DjL/Te/ZMrvX3fMYpudj/iKlB8J8CYBgVfJENSW94LLvMPNrztPVfgtg2lhjYkIeNdp0L/1JqOwcB4Zi3YtJPgfdw+GXtDIgidTPpaYlTTkodbVAnoxSCwqr8Q27naKcX5lXNsBkFkG281BrCYanrUY49n7emYZIeHh4hZQSdrsdktWB2kx2my8CIBlCihUo1FHiRvs26UvdvZuWJor3cTUCaKC2LNICWwQRGbcNVIi3NP4KVG5Ynt3lnsDru6dnTq2rjTH5fPVmciXmrW4Z92fB8+g6QUeHLjf7/aY2COPDJL7F8zyVhRuD6Z+kZ1onkCgFWDAngJKer5RyaEhA09CORtvcSrOfZ+ymCbtp0lp8Dl78iPZOM6m1Vqy1YErzuExNtI0NSTBinRKmaUYJlbMXy9qvsY9FtL7UlCEiKKslmpo2BFGMv3UpUGmWzKyQQJQhDLx49RIQTXROuSMohDAFEoD9XsE48TBqPmHm6Mezkaoew0x2u51tKJNc7xVrMOWOwfBt88Etc+E1RnWrvc7E14+lZ3gXY2VNfK1udlNh20xum+c75cflfGkyKgBSlG0EmfI+wTeOfdCYe0DDoDFB6S6NBNV4qvWrH7O9JParJSiDCELV/HP63VYSVkR+QlkKcgaIREtoGDMSZlSS5ufqmH7u5wLdHqMSwR4zAOGKej4B2aLqyGCrbE7anuxGRRe/2MIIPA9KP7vuBw2uRoZl6eU2EuBUcuDxObXyHDGxTStK0OjA6kEQbSlMA+riAmMtpGmM7aE2S9ejYSEmuMYO5LgDpNiRiZovjICGdP4o05cJXBcpDP7Ga/qU3XmLVmxu2DIo/5lSxn6/xzRnTDljt9/F9320nvvbeqBtD0P/8MMPNZnXqkYAiOvf1N5pJtVUarkgcsxipRh88mkwB/T/YosQTBOz/Sz+nYlVDET4hTERrmx5UnF62yFTqonzsgIgTNOsEon0jk5Ev/KUtPooCXLKmFJG9TFqdwYhsCdclz6c7ZZt2d85Z+QpYVlmrGvRkiRe9K0Jsf/L25bBbb+71dxU04Bhbe5N0wRU24kJQ8ursScEYfXQbJ3XS4m0h2EKMiAGxzSoBh4eLeMTSP1mI2TVKBhRaPAwWtQ0d+mIBURCS2F3zHMFFw5zsvu/ailRE9PTJ9Q0hAGNwoNCiAiFz1FAUvtkeyLKK3SM2aZXYnxwqm9z3jMwD7bQOb4oGLnhL9fWyTWZNm3SmVWpO6sIbEbXbgCDMuqam+f0ucM3F0wo3ndBN9pnjbmZiS/Ut5aDNZCgfpQXprhbDIeG68cmo7Cwefb1z7ZaWPd5SFz+TvVRTfOEw+EAIgpNcl3XwC/trTPTNGG/3+Pu7g7Pnj1re8iu/74w97mmEfZzakXbmK3chh/OJND8IsayWPiBHyZBJBEqFhcirabPiRDAQt4JSBkkhOV4RlmrIQ6lgJ/JARFjqNuUkTCBJKOu1U5SgkhVqbcqZNI0A0gFz+72SGvB/X2B5hFpRdDV6aRIF5p+2drGV0mfoNE2XsjusL9DWavOCWu9II5om61k2d75uuCK8b1v1qhuMShApbHe5Dc4ZMnKbRAQZjghJHa/mga7NLt5qFnoD6WYuUNMkvGih8FcUog3cPbjgtG6rKaZtmhNt9c7wVKmoCzwcjrEQuhN7hYBMwGO8W71taTT5PqAIJFs1wNctKDikydPAHDkoEQiZgLI/AxEpCC75AxKtZWcMu7lAbMRIeG1FQItgOViQBIhG1p7PxaVvGwech9p1q0vAX2Mn7chAtG0JYGCOfv3ivUqw+fBIlIKmC8RDf7397CwMiga92RvnfDfhTk0Jf8cznC8XhkBIOlApZ0+eP5T08I4vrPaYhsNpt/78Xvs0esEPNEtBuZzSVf/7hmgM8Hg04DSAY/ENN+TX8BcW8FWq0XWP/vh4SGiBXe7ndKXwwHPnj0LlIlpmrRwpmlPIvL9Ed3nYZN9mXQPdSQiTHOyeCg/kBsJzpquhUb3OB7eYBpgiSi9TIrwzGtBKQBEo5MykjoLH6mFDBsEjfCnlHA43OHJ4QA+rXh4dY91XSBQSWZnWfkCYDFCvrWPxzvsYCNDs/kFKGsFYcXd3R2ePHmCeZ67kuF+33Vpsv+u//u3o11jWlsm2EygrUCi/9QZye1ZPerGtWM9MCz9mYxluPHoFjFISQ/jGIjSIv/I35ssd8ZDxQF4nQeVh2x8gRje5oKFTQix0gYEEDWoLuaCFvWsZPS8NN8RObUR9SVIV204zxkggqACSTOTlOEp4Tg+PAwbNAr4CWPCjCIFVAnTvItQEsOxN/dLCmIqrTNwgbCb8otGtp/JsQPhWUYEpgT/xAwbjbn01hRhkJeB6PbyNmhgZFpjc8bUtCXf+4otOZjMNnlZZHOrgjFb/pVcecvbnx8FVFbN8VrEH4ALJhJ9ClXdl5ewxaaU7j9urnaR4P7+HsfjQzAln0O30MzzjLu7u9Cecs6oteJ4PEZCrwdSiMj3STLvFSJ2jYAKAHL10nwFzdGHIBou9YxJ4WKhvB6cYY5RPzwb4tLTxG203Kb3w18jk9rj/Q8/xJR3kJRwengIJnQyjYqZDXLmNkNRqa4j7qI+KJSmVbla7v18bLXMizd9AWZ1y0x5izle+50sgpMIQUyA0I8sAs70GbV1AaY5uX1tu0KButgTVPH32HeJgva62VbQrTdB90sijAQM0R8MxlsMv6uc1CRxEfN8kWqATbcHvMx4rap1gxDRoWJeJjFztMNPtc53Z8SijQqz6WydfcrmKsw0Pn8m3EU1HJ9bdO/otKmY0mHSG2N1a2G/kojRUqC/e3Xt/qpY0k7I7GnEdg+N+6yf0WEVumfcyLMLYWh7b/N3EV1e8dg27PcrovYtZtV/3mtqnWyzedLwhdGOJkAJM6oA5/O5QVpBk3l7RaGUEgVdvT8uBPtYPCjnMe2dZlLebjntXeIiEIoJVj3oIUA4n86hcbRcAYRf16N13LQWMUBm9hlyU8ZzEyaqC0QHDVeKewCVPokEOWe89+w9fPj+V8BVcDwecfzupzgdj7h/uMdnDw84LWecjifMkCGKaCsd6ueKVDBNk0rEZUWtBff3GU+ePEHOeTCluZmtN+u9zTq8bbsWMHFhAul+7//pvV7d16RXELKjglMzs5Hb16k3NzXkBbeq+X5pbA5xfReA3gdpxbODUaq6Y2HqORC0Ye/I5PWaeoLXU1yBlp7w5zkz6gUvk9L9ehKrlTVrUElXq8e1DSLCnGdUMttCbiZN3btW2lMck6Ilr7uEzVwjmrCUipw1R5AGUlLtHhonKuZq26T7721jVhBZB7+VjugJohbZAJJme4pD42rvdDP+KJXKZt2bBiWmUfZ9vjj0cHbXY/dh2Mfbc/I638zrztRAUzatZ1avfcYgDnQarzFeF3JBUHADe+48zxEs4XSj1opXr16FKRRQ39ThcICIYLfbBY6fW3Ee0/63YFLASNAiIEHad96qQQhxdcmw22QmZQsrTp4/M2dlLII6JOlVA590FVYlz1Gq6VsvZasqLdDMdNhDqUmA3JjOk4++hDv+AO+VivTd7+Lz55/jeDppxJb531yS8c3h2pMjE9daOhBXjs00z3Mwpn7TfC9mvO+FWfnvvWTWH7Z+rD0T1Uv0usqMnABwgiTBdRoguNShrl3lbWMAFIlkxpSblqB5plsiZLBaXqdo8CskaNFLZahNk21yfQOydzVD0RicKfeERvfzNicOIIvQYylInDRtgwnVI+6IQN2cCGB+MTHEgfCuqX+FPZ/G/UBqhiIikOQIZWcf42Y2b843tYAEln4ufAo6JiIAsZYGISErOigYzFheDoONuTW21B375sMitMCHZqXoQGBdObZrCKTlKSgZJJVcMLq3Ffb6NpjpAN1gVzW311tumj83hrFJkehEsEFNA7ANdLHmJTju7u4ipLwXcP13d0m4guDXPnZO3m0mZcRdhebL5FT1B1zeoxY+D2ftCF332GGjUQfn79d0EtHWzNd/PhJS30TJ6I1vc8X3amUbmort9l4RAU2MJ0+f4Hg6WiQeDePdBjX0xN6lIv98XVecz+eQgLx/rwvGuJj+K5vse2FQ1/xRfdsGbPhnr3myHcxGYPyXBn7QyIm4ecvVBr9J/ObuXV1oeCDKEwbi1O8o1/ZAo0a0uawjJt04Q3tqED/++6iNuZZBFsMgcNOfRx1KjJjQIMQ8MvHqFHag4vqURDB8zDYnJGgo6CDn1t3YGoPo/3vRggD7utlZkOYdCo17uExCgJSYB7/Xh9LP0xgu3gOjBoPyv7o9F9uNgDariO8cLSQcx5uxvu7MUP/w4bk+p93jusds6d62XZr97PfBl2Y5eXRLdLtkVD0jcovRNE1IHdJ6PL1WVGbUZUEtjLLWCEV/U3unmZRmoXeMotOkRKRV+exMOmFmD6WnJwQ1NiabRJeIwsSH7vKhHy4tdBf0m7En+to3xZsLYkgAUYaXRHcIlpQS9vtdaHUCwbNnz3B/f68F5Yx5bfvh0Y3MHHbfLVN1gMfX+aL+V5j7mqTe+tL/6zf8lglv3zG+zyPrBmVWxwa2+EaPjrMD15n+PAg9kGu3zKnXkAFQakX7UuIwmyXrEwvAkYPUF5RkMAGcyKxM45y1cSrhjDVjsfIZ1b63ObARtseYeY4Z3CGnY6q28RlMHkaUka3mVZg4zUSqR8PGbT89OCTlhGmyUvfkq6p5gWSIE46P0p08eGALhMFScVXACLlSQKSBSDyc5YE6I3GbH+aeuSRwrehZEahF3PncVunAi+FsGIMWp9WbTaTtrTWmUjmeYvjqAHjRSGWu9aop/mqjTpi9uLzXSG+wkg2jumU6j+eQM0jLR8Plo0WuC4Mu7ALNtfH0vfcwSZcaZEz+dDyhckWtDKoaHfp9Ed3XNhzgB0LQbRj4gmOjJo0tiqz5BQTQZHhWYCsj0WzzDnfSHkBATup7kEtifSH9O0EIn8iVTrlpKBHmKUOYUIpgNx/wwQfv4Qe+8hE++e6nrRiivafXihxJ3RGIXbrxudlGR/ozrv3+mOZq/e17t3aEkUH5faMGeHlAAlXeIhYhCISRkPhDG1KE8NCSgmAJGhJFr9UIIA0WpvdgUfxHfyQvxYIclW7dxaPI6PZ4r2wLaCIyiUWfCUDZUCBUM2JjLmzRaa7Btz7bc67Nq8jwRY+KsdX0GdAkWEvTyIpXrvcl6JhsoImckQHTPGsBwmT714X8icBJoyo3OudFE9H6aVPO49k0FYE77VDDvNu9LAwxXEHqsC0hjSUOVpCrHQAYtdNM++d3gq+VgNeyJYbhmNQ8SwRN6Pe9IoLj+RhjFzyOAL+50cVf4de+/Nq6stXM3vYt15957Xkt3UHpzcvnz+O7UqsJdIQaEZGEJ3fv6/l9ZP/eaSYF9AuC7X7bXBg6zsDAghQSQDQyOJATTXQaG7qKuJt8IYwMyRlGT4hbDSNdsD6VNMYBg2AySSfs8KQ5C5SAu7uD+ZM4Mry3WtWWAfjn/p1rW9u5vDV/l63v92NRJy6lhWuXbyW/QaMSn2sjTGZKcyGlf2ZcT5av1n02dqnfPDr3rgu0s0TBrLS4YI8/RtEX/yT1oKmuJVqiLIuop981gY7BvbZdEbba/BnxCt8XTCG0CD9j3E2jbIgY4hf7/hOFwkmUkCykXE3PnXkn3irIiQa0i0jc7AlR18+tYBRCRRp9QVzZNBVbN+bGnDqG4vNss217cBS4ZFOmI77vNYuudlQvujbkX/VZIyVQVz/MTajjP3sSyxvOxKZdEXSBNpWCLcRUd+sNq0P//VVzeacE+t+PaVvG5bSopy0uULo/6unTA1LKqN8PmtR2CVQCs+9eI01wrWa+0OPayh6PUrq/Q0QMDJbQRyu1UtJN5e+JvqMM++dusx1g8Xv0bTKzDUEL6ZmmWLnlZZzOJ7CsONztcHd3B2YZ3nN1nuzeW+r1NSYHtDpW+vG1XTsSgje3Xpx32pAAbPLLerrWaVhx+KC5acGk0EKyw3wp0giIaRgjE3WpJnSv1kUYxBUphFAkm0b33Ba/QWFAI8o9EWEzGcW4gVaSXKShNgRBRIu6awGIyjavMadh7jogNv/awGWbwuL7uINtSp5LqO+opYImQqYJJAkTTYaT2QlSzgwE2Kcm/BSpmIiQIyydhj2USEDTDETEnJbHAUmsg4io76JUcGVUVhSWDEVER9RucuHCmAghvvPAgLa3BRCT7o0x+o6KazoG0eqBGfO3BOtSBAoZmkC5WycP2LCyPh4x3EyHj2gCXLewmDAGE217Iay7dzim0jNPxLxuz/mbAi5e13qf1DbAqX+n5049efIE773/DDmn7w9znx+w7TReM1kN5iO7mUSAZPAeLKikTASkZgWu3MxJm5f4u7lb9G3gwTzP2O0U52q/3z8i5FIPmvcLQpBqB8ekscolDvSTpwfVtKji1YuHwZ/zaBv4G1qYS70a8IW62tvJv+j7NicLbaP3mp/3B8PVHpXVCRChbTQNJuYwdc+4BD+42iutUYXQQKhjXN5cswKNCNJqDBRjxVVd9C6UQ4DEWhKGC8BFNZWSAiFfslcOhj1JUVOCMN9c485aYD6nYKeUIiDHy9plACnNcZWkvV6TNLLPajSoRmNRo3N2Rt0iw1R5uqz2SgR0UfFGgh23ktU8yhW1mOBnZnZhZZ47tGjXGvOx0Yi6mVcLBLfz2QsBADywwtfJr1H8vb6XTejwdT/sJ8yGpOAm9hIgAv4C34/f6xlskoiI791RWEYIewRICq0zzGnUrz4QkYxdrp/Oyev7umVm1xiY5z/tdjs8ffo0mNMHH76PnKw+XxZlUuv3AZPy5tqO/w5cIWaDWYAQNl1pn9qdgFip9s07ho1ONL73yk9f1GVZBuj6bd7UuDkEve8BZPkJVTWzZPhZu92M/aGicsFaJtz3mydIEjXp8A2H5dYG7c1rwazsWX0c0OOPYkcCehNhfHX7SRFcIQ5ztAHbjDPp8rEKEV7HSK1DkaaK8J7Ye9tWaNqRKQ1GIEZCjO4qL7w47rtO6/ZoQBGAG+q6CCMlVmCCqqHOlAWZoJo0kwVgGMsz/xpZ4b+tpAw0gar/GX12puV9FQ10ILEax8bANIw9I+Wsb/Bwe/i9Sb8PbTIZ88qYdjmWsSf4KUzdbY5Hn7KhiZiWE98Endb5InT+ugtRtSfqTdMSV5tCQWnCpVc67t+53ac+fypw7rQS7ZQDQd2rgTtUledeXa0SvGnDnnmkmW3zhOhxCBkyKOHDtVuhQq+/7nPq21WBP5imP1d/5qy5qPO8i3/JEttT5ihX9Jj2zjOpRlgak7rmF+kXgY1HCSiisXTz+oYGeK1IU4PZaZYYatd2z/afW3VaRAIWJKUUTkZglEzGe2wkou+bdxm1EtayYJq1YKJYLRhBwVpmpESotT+sjQC3R2/m5C1YS9vASq1bUmPscnQUoB2WG+8gI/yp8230c3CrVAczGyxM73gdw2Z7P6WuR2M6wkaxoRF9PREVNHOx5/lg86xm3kMnoRqhR4eOThtp1+aiigUXMAURFWGUCjAxuFRkArIQuOpeRfhgdOOmIM8at9gjdqdeU8TIoNwnloyhEJHBD+k/EsJkKCSAaUw563kZmJpL6h74QBBJSGnGPM/YHw5YDJWgW3EkSu2MuHAgDvnXiJ0KE/5923ciYgyqoRe03LOWOO/P8HvCH+UvjH2h/1j4Yp8G2fft4uHZRDjc3SFlBfjVuU1GmBNqdW2eVZCwWmD9vhzec4UxULyzaXI3WwgDDVnFn6vjHpmRXuw2205Lv0Iz++f7O7yFpizaTzLfZVszRekX1ioRtdTAXJwzUCt/f5j73Ky1jRJ5o1TQG/MpRXKiQHA6KZxHAiCuTTVxo20G+6j3OV0rz05EuLu7w/l8fq063UskIUnaITqfFwAS4eQLJayrAjrO84xnz57h0/kBhGImwS2ahRPf3t7yeDu5SzzTNAMiWJb1CuvptZKYAfSMazxwHjIuVm7DgkXoMSaSbv2GZ7o/ZjzcMe/EqFxDW9CeXXFqG+wOUaABxjP1twSHBiQCKqHLEcJQFqPvMiWNmSMRTAb4ylCf4kwaHZVoRRJGFsaCBZkEua9hxg5eqqxKNRPLmRIx6R1NWjbGFEwUhCnP8X2edkiUkVPCfr9XNIlEWs23Y8jJkElyVqQWkCGx+zxXgROmsmrOTc6TGTh1/LUuusYe7ezBQUDUYIOo2Y9EujNYASogquBaI7rPmyZCtyjA3nogVCFJMQ25vU61VKmmRfWpvd4oVDiGKKIFJkx5Qo1triH7FYQiMCSOcvEsIk8V8Xf1372eCdFG0LkU3kzIcOH5TY0EoLrhWm+8KfoSvlctKtV8ma6RI4FLwul+0XMtgt1u3mCspo7evbm900zKKPkFrSIX09sHdrm0yzYazDYqDIRIBm6Pt6Tb8M+MtVMGSdslmc4v0mtO12y7rkC9LkLOk3CXZcH5tGJZKpZzxWT1rrhWY0Uq1bNzU49M6gfZv2KzX7aHp9nbDf/NGV/XTwoijlBCrsiJm+e2rmzXRrt6ObduehPThZrokKI7vgV0Lt3wYdTRJXBKkVsz+pjc56M4Cs6cUuhe6hAPlHQiq0x6uaba4ZGANClZQW1zNgR0w9dT9AQLbyZG6JqRi6d3ewqG90NM03ItTx34qjmpUHD5b5omZModOolFIHrJDkoKcmS/S7eHk/vfBFo92Oqsqeaax3W0NdqW1rFNBOYagSS+PhFYAgZJK844WisEWlvLo/d8WxujMubnmqcHOYnU0Ee7BIFxkazcbEqEKWVDiN9jmmYdCzPUnU1m5suAaJVnuNZpsGRdpiS2bTxrum6qxPlck2l0V4Tv0EBvPVvvGwQ78p9XBLkbLWiX7d04Z1HjiiINA1Dkk+VcMOWCZVewnNUKlHNG5N/hijB3pb3bTIrVVsCQm/ZNZ1gtr9f8ERvmtG3S/SLdAvdyEDMCdb0vuRy3ujmhC8l9XSTNeO/IZ/2zh4cjTqcTHh6OON4vOJ8XLMsSib1rOJV9rGQ/VYL1wmxtQ1/lJEPb2szVZEQWueTXNKYwak960+UhkMu5D4V1/HyMynNm6JqD4txbvvywts3k4ZF+LjGrGYJdwpXmlCY/wFCiX6kvkNH6w1Xfi0RIHh7dd1vQogtvCQDGCKc8GxJ5DXNJMKp27nXOAoLJIxx7orOZYtaeez+U4FqwgzGheZrNt0TNBGi1qRpyQPfIWO/UxqczCsoZlLIxywQPHHCzk5jVwiPe3DMowqElCre0DWVcqn2w1FHAAsJUVStDI++qMab+Gq/HJoBoblR1hPTYqv2ptrORlLBr6HTGbp4Nc+4OnHaoVcCiSPTMKgwmmgEUsCQTmFi1BnC3jtcRXYhSnBrhbFo6xR4hUlPuZbsmAOk4NN+NlEnR9vy9XWt+LLI59qnySbxkOsu5IKeCeV4x7084YB900v24j2nvNpPqm/S/jhLHF1meS5La/sUniSEo0NioxqSGd3da1FuXRLeXLUtBKSuWZcHnn73C8eGE+/t7vHr1Ksps9IR5fzdjXVesa0WSHIdDzMTHhmPW6OrjZmiLZBzS5tXbR3+bm/YePfQrjMrH5zWWXD/yf2OS73j/tj6Vjai/Qg9Nrw0BQSRMlLX6RAkik5kDCZSNpjGArHWtQCPxkM3P/hslQIScJtwdnmACMIlA7u+b5mA6nd4hEFohpBGkXJVqaHRh94bgTR7YoflO87RHzhNynkCiIeagBGHTlEC4e3Kn+13EQIiVie2mO3h5dKIuWlWa47yWJnlXysp8qmozHP6hGs8XNoDXgEFyX5MKIRALBu/9SJEMHRtknFVjjCwFbmIbk/+vNekXPbTLnDN28xPMaQeSGfO0U5QRInApUUPp+OpeU4TNVBnQSLEV6II2jc2DcJyQQwVMP2e4ZG4jrt4oJYnAko/9vL5m6Ld6RC7UGBOyR7WAGRUWhK7QNgJKXXE8PoDSCiKtTq5G74T0SJfD/z5M6kYbNqVJ1v3U3F43c/SiOWfFnjFck/IgEfgh6gnisixWtj0NxBbtqe1XlwQ7awhRQmXB/f0DHu4fsCxa2XJn5Zh9bN5KKTgejxCRAHpS2dvVf2rKjr/nkYxq7L+E2Q2Cq4fvFrP5XiU7f9atIJntu19nQtWL2QJqTJsyCbGPZZT4RK9HENxkTKQP9e6Svi9MNO1X/6pp4wnEGmad9ntIrQEi3EdVasCEmTtFhZSJWnn2/loyxuMMXH0nXR6g7fUojJgI0zxDRBRfjQBKGXmaMO0mradUazdSfVMMrpMcXENjKMhxR7fDXOBMSZmI+92UQQlXWOhjF4XX5+BcmupkQCDnYT9c3wPKBMhDxu14JJp0XiRjnjQoZMrTUAI+GeK+wJgsxRND0ZDuuN06J42ZdaoztWtdg7lGsOjWWfLowm6Pjfe9mWuphkf+OO3Ath/GQBnVytz0pkHVms/LgvzwEFUX5nl+1PuB/42Z1AVz0g/1O//4DfenSReo4xmbTU4tj6T7bvSfCM7nc9j/L4M6WkSZnlkarA+hGbAGdZzPZ0X5zhkHQx1OKQ2M93Q6GYEpEFkbkzXVQ1EPVKPyPlxVHX2Suk25JfYpkUUZ4kKabWNoX8X9Nw7cY1vPoP7/7b1LrGTXVTf+W3ufU3Xv7dvd13a7u20nNg7/kCgk8QeBWBZiFMsPRSg8BiHyICBERHAGQGDAgJhZeEgMQFGYYZgEyCAgIohk7NhRwDFgjCAJfyvmM9gJbju20+9bVefsvb7BWmvvfU6durc6cdx902e1qm/Vee7nej9K+5wJ0+cAAEDYSURBVN9eG7HMJtK9lhRZ5vxr4swh+deEi9R5cjYkVsVVPQ41y6w58TAY0Qgeo0OUhyLLnMuSdggBoQX8ZAOsBS9FrVe0I2Z1iREpcwYBMoEkIkljJD/gvE9qveRdSJAs6VZmHqTIWNRwARC1pvdwlYPjMliTlpePNyKZJRIAYKunESWlEQy5IyablDhQsKjIHIMRwCyEaqmeUyF5ddYGgqQ90nXGyvilgqY9MKbEk5NcfhDiI0RKmFBxp66lkB9LH0Sp55Gsog6Fai1rKpLGYl1Nglu1NTLjtO/eUQeaDqEbIArLxxI5Tar7zIwY99EH1vWOJE0boWKNV1ssQqK9VhBxXc3K9yaR6tKR1ZcVyHJoAoNykRbet1xTZhksq7ipl4xIWUqQ0jZVNnCVeyo5B25bTOoax667DjtHr8GklkC5GIFWHSleOHUKIUjSzo2tGs5vYTL1OH/uIto2t4mieUUql2VqgVWSRjJyluOT8wMachQVTJba+pxAqZWhApF/p9B3TAFystwh+1/fXpGdbIp5YA1TcBEOMQVHSqVVdN4jyCegiVl6dt5yBWYDc0mkqD8oS2DjzSBfg8ij5QXAgrDFWcIn1+aN6WbZQwAM3yfWhfdh4CDO686BqAF8BDtGCyk345zD7mKenhZZ8rAxS/hEZqKyzS0MLR0gqaJjSpibJZsQhDDJQm7VHmUVdSO4CeC2lXsGVF2mBrXv/ePCQGQmcCmuLvWiSqo1X6zJykucz8Z0QzxbCWhDC/BmQt2+atXDN8AS7NK+FMSABq4Voq0NQlajSksTw0OhewtZQHl+tnRpNYHqj0K3XfaM5TETRsyh7xBUMgyGG2JoseAg6mzNMmEFEa86IrW3Kqf8yl38ycqbdCSvLtLOUlSBdFaAqW1Kjq8ffV+ey2ozzT9H0PuVX1e/We89tg5tIQaRonzlgBhRkQN8jSNHt5PXX7sQ5ClBmw6kul8jJoOehSaRdAhw9i6yZVvq1PvqNpe8zDIBIpgUgM6GzIhj9YD2x2kvicmu6Z8zp5VV64OVQFnvOptb+8GaKiEGcTSwkj4sWBCBAO8K95rIqtaKwmSAJXOIctfRqvMlWs6J22egIAKF1AlZHyjXYKEWsqEt2IQ8LnqpqXqLnO+oVHqyfWFqwRAaqGiApCaPAYt5k5wvkiTG5fvV/1HroYXYZnVe1PL2+j2yEClqAygGCdK2ehvg5N0X9X4uJVIud7JJKmUbnGaMoWIwBtZMUstTIvqm7tvc2ErSE0CabYnyyraBTSnuPZg9GB6gJvUjPX/57TqN1DnUYV+LUILOfFq7dc3k5vSYkzWJFBX/l3+lB4WXoci4WRsgTrLShghN/i7z5TwjRgkSF3Wv1N2zKg7r2ugPNJFaae8A8gZmG+p0VfGr6+mXGNwiq0JnYac1t3rCk8G4yE7ezz5ube0vIFvGlnE9spVbAFwl6UZMShDEH+AqoPYeR48exoWLHvFcwGzGg7W0sksrOu8t21TelAgUUVLZKUnV/ympM9L46PCs1P0D2NsqmDnjPee3fMIKolnet0pSNCYhkyr5ljl0c4WWdeFVvZr7aEQTmg3CpDEuNjer5Erq8NZvX0yVngWJC5cqiLd014YEIzP3lmXGbMthqeX6spyCjOxLZtKS7YEs5ZCLanivtB0iSXhfo6qQNQvykqI/hKDZ3M3Vm5mBoDFdmsk8mr0ptABHkNmjlABZZpZSSk8OFQOQ51fWbSZQq/YrFeVG5HfpgTvd2Eg236jzD7L1D/0u9wst8QBVQqT0vCUNSCtrBbEoVWqU5nNZ0jJGI6uVi6Gnb59IdYeUrHMpHaEvpKe8amxOUnN1nWfG1LmIGKUwTAgBTdPgwoULAHB1VOYdUheFGJLnSclVJsrP4uWzFHiLcp44L4519cgK/SSzADol2odtIgJUssQWfOkI0+mWSFjOoYXGkyDCU9aHb29vY2NjA4e3t/F8879g3kXbcmpTCAEVOfFG62WgLu0LfVf+/FvUg4KXbfPrIu1rYlYMGWfFadnpweuHCE3fEWJIQrVzJfSTBi/DMqHkmB31Uyqf2KCNBIpA5S2gmwDOknNfkokhJDzJHMEBheYvEzmLHxJix6AoqrJQ5GEkMByZS/Uezd8LtDFRJbegdjUbshiEiLhK3eCjODR47+GqCmCHGBiNJmrNfXXJdhfCPMUPZv6bETiA2lacQRYNoKXHkZw4JIdGjBbDZGq+gnHp7dRyP6UuqsRTFlrsD1TpnWa/q6pCXU3gvVSORXQILYnWgjWrhKswoxyvFmqHGCQPht+Yoo4BU24R5+fyu7lrO5WWEIbQrxDC5XAWS4Bdat5FS2LOO6sJ1GpYsR+iEiSCBq1T0pJoh9AZV2fqSZelS8W/MbIImpGTpmc6naZk2+vAwSZS3C2tIbp6lwYIQEKChQal4E4y2kSHQKzm2vvv79tD0muSikTqrUwmk8FYqv491i6DTmXdfKFw7SSZMizjt/c1phOPnZ1rECPj4oWLIBb7BHmfvMWNezSkKkGaqsNf6veA+3jWuBRjYXTL7AF7E5lSrZEIF692Ex56XpmA1nWcAfL4l2tj2VZFnb3GBVLpHsuccGhDYl9FdSbmc5MOuJGYNaj6thyyRAhSG1Vms1QIHDUVf5Ag0diIOkylCFLJrZ9Y1QQwG9ch4VS8wyhVY02cL0uuwAhO1DOGoBkmhKEJ6lAimjqxG3HLaFpVPXsvhez03cQBFFRKQq6xFkMrzjytECuxV6mqlMVuFdUJhDv9Y5i3nkmvQ2uCiJYYxOXBoEINLufM+chsUKb5sGlxkcQ26NRVHznijjW0I4IkoMF7uMkETeNhzK4Log2R/VFmNS5tP7kPQLHiSPa2EKksQVPKmLJ/vFFXVeiK9d2VfuVLcbxYU1zamwENv8mOQnImKwLN45DteFqDwrjEtsWivQoq8xpCNJuSuIF2Y10ALK3T4WJbvHzhnq/ubpS9iFRZvfJSwOwH0P9Lc5EZsJNAowZW7z22trZw7tw5QdIsrsnsCRwsNgVSgdV6TXn8hqTTvQh2R3pIaq5lgrJ3PxXJ6mZY16BqhC+nW1ltL8vfu2jBtlK+r3yBfLLKBEmqcV4zNCihESIFhLZBTeaa3F+Hov8xIqXmIGRnAs2YwGbDaRQb5kwpyb5HKEWy1COGcQu9U2bPSreJJBh1jTggBbAyM2LbCsJlrUbFgnTFFVwcb+bzBeq6BnMNDyt/A81NaBK/SEUxCpEKoQGHCFJHntQ8FHYroLe/GKXDRfm3v/8su0R3VKjzm4qyJETiySjVhqsiVATKtAkh8Oq2z0rozZtNZl/GNQCIzoGqGtFVSNarnJyy05bUukJfl9FIZmSEWUgCp45xMbXrbRe52rKBLJ3J+Q7L9yPh1h5aNR1fr7323WzqRPb0fClBJPbSJLIXHGgiZR1v2TjqdYnA2rO6+gkdNVmvXYVqya4xbm0vw/9qyKoLp1KPGKXzxm2jeGbFyDh37lxyQ68nkyTmNHNVwyh3Zpxp6SxRQbMCYHmUpO3l9xJZlLaC73x814W+ym9/GBI1+oS0T2StgJ89ImfJF8F9kTd/cAjcIDoH56r8PCPEpcQCRiyaQgAQI2JoEYN6uzFh2CutHO8CCZfXdr4XLyqQUQgN2BuxNU2NIhWW5KiVVhLmdi5jASlEXdcMogYxtmDKmc8Rcosia27LENDuzpLNSXL7WdyievB14p/yGhuyT/b3npV4xwCJApbXiXib1djc2EBV+6TO8jQBQAgcdNociDwIVX6ecXpgRDhNfOMQImlORY+N6SZC2yC0rYaQyJ6iwrmou8z66jTAtD5WARokKn+RKtdD8NpbZNWeSo/FWFj8WXdblMyOnnCh22jSZ3eWluRQTFlO0F3guU7fEIYZhgNNpPow5EG3LOksD04ylqZkmBmRdaTfPTaNwSpPsrZtU1DvpYBx+kl4tnhDBxiikr0ZVNoS3a+5/pLme2P1QmNTh6q3lGwcFedNwirWZ1lWpOtppsvMxoKLVq4Ym5Vu7sW7VsE69/aDpYdVfyVi73O2K58u8rqpMMi4Z82XCICjgyPAuQViFHVs5NDhPpf7oFKnSoPeezRNo44aWsSS5f12tUk6lqOPiBBL9Tb3kEPKAJ+7nmVowNS+bdvCq13JEaFtGinv7iuQd+AQEDjCakABBIqZGi0hTacBxDEgNuKmjRi12CCDuU2SezTnFJT7q9hv5Yj19t7SeOqUahMT81o6EHjvUVcTVL5GVVWaek6OE1cAi3TFJPPiSgLcWS6qrmMWT9viKMEhwKGVBGKaySO3OduWMl7qPTq11y62tZTj42QeM2rjTgWiki9JK8QYEGcvYTimJFGTSvuA7cce8UxtsLahCzr+fUa8ZF9tn4c1RcCDT6Q4D34+VA5y91h/UDsMcr6688vUM3tJTyVCHOLsLU7J9N3Fnb0re/cWyJ/6l1Buv3mGMSIWizmaVoN47V1GrLSmC0fqvVkWZ0l8rF/lp3TWRodAdcdhSA26rhpvFfSN5EPeiUPXlMfTns93FfcvvTFt2OykraoRiHuzJPMmMSUBIG51kzoJWi3UHWyGiYRIjEhFECqwcxo7JPac2OFms52mZBacubgXfUiZEzr3o/Os9E3bJPnvAA8HdozYajAyIjypiisC7IpK0ipt6G7L7WTAsSaYDQGxbcRZohjHTrLYWATqJkJVEqTltVjO8dL8MRQZZwJV2p+qqlLXclHzSWZ2CVZGNCeJvJeTmr63PiyZA7HMoWfAVLxOs5NLstmY980laFH6nriUJBECkUcI5lgilaQJmRjlLJXa0DxrSc2sT5bvUXOnmKQHVkK2muFM7dPx7l/b2Y96T8nYrosPDjyR6qt5zXi6kivfhyasgr24fCNepc2p/90MzOV1tDSxqxrjiusySzeZ1MoZigTRNC1msxnOnD2DxWJeMmnSh+JnuXhY5HMpsNfTE1vfEmIAw9FQtPilLbwhyLEYQ5CR1dAm/3Y2/tAbht5ZyCKdcxEa24MIsCRpDezhrdyIqXjMoKCEpUssFKmEBjE0kNpRLSKybaZE1Pn1Mlbi8EBIRoqCqelw50azOMs8zgkhtXyw5pHnvZwLISKEBSaoE5JEzN6F7LqMjq2TGCMmmg1lPt/VgReMHs0lHdlJJjlFoOtqzkWdp70YxFVQhn6YBsN7j0OHDhXcCgNeHESodp1ZdrHqiiPSkZSXL0KczZmBEFkcLCJAgQCuxRYVAfiQ1OuxSNO0Z9udT6EOQig9prVU9zZ7aNO0CO0C7WJXMrNzRAiN5g5klLn+EtagMnt/0Vcq3S9MFrQkuZqP8JLU6sPgnOSHXB1IsAwHnkgBuv6ZgRDQMKfNtpzdYQCMC+h5YpUDmPjSnpqB9V0lASrVCiGEFF291GBnW7WMhRhARvkma2xxSeaCJTX+Ahcv7GKxWKjrp0upkAALEpabk9G652hgnxjjyhpYMdUvslYPq2D2U9ENueMzl5unPx7deexzmkPPL+djZcxIKa3022uxMYRUQrycLnHW0TLlkeCIkYrKsSI/NmlpuY1J6tfOS/LWBpHaTtskvkhsC4m1YWmfJMaN8N5pm7SfbLFspRcrsjoHgFnkHQjEkgw2gDUeK40KKl/BVz7XfQJAVh6mUMkZMbHinjnjOeAdabFOcWs3IsExIGiNKDNLyXOLmMDi7yAkCRJaJdfyFLqk4ktFHCE114R41ZoCqVAJ2vNKsZv7eEREyMViIYydrlvvxYbVBCcqz8JvnIjFG3cfQuUsoasRKMoeiQDBk8dkMsXh7QnaZo6LF6WYKmssnidRNQOkjNMAb277gWxJZPzSdb4pxqRY+mkM5GFYfsP+jOO6TMeBJlJs4jaQOMLQivcRGGuWK6H04f5hewlnEtFvwCoduRGwVG+KjOGlQuU0tFD34VYSNtA268YWu4IQRSNAxomlJ69A6lL2enUG8T0aUVy7eiwuTfLhFd+XYa9N0CdIHbVNcdzmBtA5LvuekHT2srRrS/WxCBiqCmVSrkbmx0WoSq1LorJHpb0je7cxWs3eXTRF1bnWmrKNDBaHiZQf1RCLEZpSIQgkGxcDIK9oSXOusUrURf9dkz3hUk48AC6INNHpBwMcNSYKgGPSmDNx1jECFTlnpWcrxRHNK8yIVJbqmYsG5VEsRqK7tsuPESvnPZz3ynDq3CkhoeRqWW6Y8ovl6ei2g4t4MGhBRwLl53pzWpAXOodEqNK8ls8kSimsDE8YLUlLAATvKxw5chRtO4fzARfOn09ppkKMCAGSX9C5LEWjty+o2EOU/is4IMCy4NhMDaOnDme5JxgTsx8DW8KBJlIlCLK2rUZ50V3KMwaPCR8KdBkGgiCfSVV13tNHhoa4nQN8TRgue5WE8TVaqRtJOXe2HUfCmZRlO/prwKnNoyRiJvGVi6a8JuudBzgl5Fq/l7LohmBpsxbv7j93P+l4SGJaJUmV7R5uv+xwZohDBFl2fAuCJZDziYuJYHEQUMQWYFJiL+UUddsRuUFEMzguuV0ao4QiPkvuBgC0IcJpSQ5r+VBvYswE0EUWVa9TIkcq2bO5KZNkPY/iNkyV2dlYYsbSnlBpATmpRjLit0JwQruQYGFrtTpLRGj6pJg5S0ZEJz9dwtJlbxySO3VRMsZsN50gekhORedFanKTLGG5SuPaUg8EgccC9yaHIiVI4vkYsbExRYyS7qdhcatuYwv2DkAlkpXWyoocc3B8RJIU0wFXEA9npVcA8pz615q3IDOuv/56OM+4ODuMs2fPSnYaMF5++WXM53M080UHNzGQPHf3Ro1mKxUinoOih/KZ7PGUnv2tNBvsFzNawoEmUrJXuoZ+oJhoO8DLQ7uE5PaatC5uSYcyF9KVSmyDzGYzzOfzvFmG3s3lU3uvXWp3l5MrDeiTjQncjNC2jbiKshHXWjhbzZs1pLIsoW3bJTHcCO+S22/RF1uQ6xCqdexHq6S+pXne4/7SWD5MpCgxgYxsK0z9LB9vXKzLUpMZgqOWpBeVYNS4IuizjZMt4rRs8xPDgmj69pehv2VjkhYhieWyFgIkvdCSVJAHsLNeIyS7haQlslgnglOVJSFnw4iR4C2zO4Bc/l3HIoobeEq/hyxlglkcFLIoqlJYznwu/S/XKHfewyUnTwDlQYalkvLewTsvn56TkiOSrCveCxGxNcE+yWSs9ZdNrZtbbG0yr1ibm2HVHTmVIkGg6KW6sT5HikoCSNWBbaRynJmtDZtf4XMY9aSG94SmXaCuK9RTDzghXG3TYLGY5XXMjMr7ZMMKIaib/H57NKv8LUFY/nuJjL9qlPr7zxjqdeDgE6kCOoQDWOb+VyC1PT3RGIWYLwd4j2flV1EiDLJZRPdvUdfdXqx6Vv/aTjM6l3kvMqTo2m1Di0tqWbJ7cEPpAuqf7xAKiE69q6Ia7vd+hGoV0enf3ydC5fG9pKkhW1Q+ZkhWkXjmZADkEIKlPojonNREdouQB0GWekmaMyEkmTBxeg0VNoCQHtbRNA60ofOThbOFpu/Ma7QnEfDyGFJvPdv/ck5REuf+cFSFpwMoGHIHiijVRIw4MmLIhCkNlkpfVibG1IK2No1bT3WgOl1XYl40m5JaQ73QFK8LkTL1Xs5I7lQCFgLhpEify2siZQ1O+7GUQDKatvVvBJJtLorJsdybwtDoGoTlYszPX21HXT4mpTciqko8D9u2UXWoR1XV2NrymM9nwqTqHgocsTmdoKoqeOckdpIIqUbPAPOe+lwwnZSI9ep9252X4efZ+bVsjAUcaCK1L3DxZw+aYgihL4Kua9iz+jx2TwgBVVWh0TIa0+lUqnmiMqwGrFweKzrROZQlBFtMoWG0TUTbRDhUECuDFrETXdVab/Ped9SBSUQnSFE8Npfh4QXeV9GtUrmVx9ZV55U2hlXv7RMluz59p8xdl8HQUi1U0Y63HIwlRUJvDZnh3/qrHipxWdUr7SraTMUSGJperNjAPcRtdqyujUqLcPbdh4tNYGrKTm+Y4aL2iQBW3p8jJN+gIvnQmoMNipxtSOp2k4iatlHVnoeUpwdALBIaMkMUmbWchsWeGdEs/L9WMU46p96J6pPIw2tJCO89qsrpfLsU72RqUlYCTmT14LIlb0luZQBFMt1y5Bx0rbQtwLK+XOWBGCQOiDiNqTxY0xJpUHSWRiV0IStn+msXgA/wtdh05rtzfOv0yzh8+DA2NjawvX0IAHD23BlEZjQhYNE0uP74cVReHGJefvllUAhJkmINsgYvK/Ls/SlvZG+frtq33w040ETKlvFa7ozG1VL21e8P9FCajv0moutOnt14V3L66XHrTGh/S+TjJZeTfkdGaFt4Vxf7Orel9O4rJasSmfbHZcgudKnSUiIg6NbaMS40qVZ6Ouyh5+1HwIbUehYr48iBUKU2eK0PZcZc45AJMq6uSN6Zn9dnc/NXGccwIClmTzXSf1wwKkMSIxfnumO93PfOnOn7Uj5AKOJjh/J9nJ6Wg7eFFzHkiGKtVnqd7p+SICviDtykdSSeelGFo1ayTtizgqnSKN1brtXcqf44l2PgNE2RzbcRIQdfeVROCaNWzZYaT1mKEsnGw7wloYQTug7TfKQ2cG4DZzeaPjhIUUjvHNo26JojdbsXlWKIZWJeMwEU64wh6j7rdrGeQwyYzWewNTCbzbC1dQhbm4exmM9x/txFfOvV02ibgMOHj+D48eM4ceIEEALa3Tku+DOI9QRUeWxMN3Dhwnns7u4KMxEtoFpWRbn+ShwwhCfKfXcpdul1idqBJlLACuJkA7l0gvLRYpPs+44BUdV+73X9ftddOmSkYziuq9O2xeORUswkLRB1JKO9VG65zb12cxb6S8JS3rMXoVlJl3Uu9hunIQLUP9clYvbdCrtbMlh5Z+ZkDZGzHifxlqPYUbF1Vck2JHuvoNJ2YW0quXV7becOJVJDm35ZFdZfl1xcY8mBAQ01XhbkCzzMeg2DcqlwGMrSczp2JgUawxehJTZYpLuEzFCoMct6TZYZIw3poFze6XmeS4ccpNuVOiyFkDMPuQH1HVSKSv2g5WvKNSSjx4mYFxctSXmkKkVyHi6y0r8GpJVyNbJNmcu+TLbHWiJRWXLKAuEwm82xmDcAAxcv7OLihRnmswaVn2D70CFcc+0ONjc30e7OEHiG2nug8vDTCY4eOYq68phUFc6fvwBqGljSo/QxdW0hLed+LhOoUvJaB64KIpVsAZzJj/0d1pbktCE5tdQwsh5EEAPny0zcQ/f2JZF1Df9D9+YTSAjBep3dVrvqJgCih+5tqFVtMHUfM1B5iWqPYN0cSITKOSGEgbtVevfuE/fwDqe+rLNgh6SpobHvECaixDEn1Y6Nq9wBEMP7Cil/nEodkQl+IBmn3cYF1iqH1tSjfaSTJCQuNnrRkiE7VIeZSFdnV22ToDMuYYBLjUCmQvK+TGDsWWTrIxEyhxDzvHrzlCUJVu5m3bY2x6X22/pcWr8MiD3LiMQyrVpeDyVxsu+5hIQxIA4+2Z7ycU1rxKq+lOWxBEI3pJ9EJPtGCTYHLocNKPEDkajCdQRFhTZBhNe8hBorSU4CvSlo7FgshsDWWQQG8vI55zCppzBGIUbC2dPnUbkpDm+fxze+8QJ2d3fBocI1Ozu49thRXHf9DtqmwWx3hjNnTkuWjekEk81NnLzhJNAGLHZ38fT//zR2Q+xkJWcWFXDrUrK41N++WcTmqqwy8FrCwSZSFOVTJNI0VfzgMHFaqzAD8gBzLN/345B74q5Bn7CUBGs/JLQK8iUE8vqcYC66ssQZLchHVLVUVY1mX+C9RfBktNYaUTGYW7vlj2NY8tiSW9fmrHY0eI2h5JbLYyuvH1KLrTxvHL5y0xxgQbPZ+24dicnmGUqI9rh+gIlZxRQNS70yb4wcnzXUQuacKSCphwspKT3T1jMATjFMck2IUv7bKUYtE8HmrBHLmUpszRgxT3ulHO40esIsZLWlXuOMaOQaUAR1itD5co5QuQreaaojVfO5wk0/awZyPJj9NrORs0aRqIZbNsaivF8lKp2HwBFwwsyEVLDRxkaChhEr2a+WncQgCZaUuWaScSgvIN3HoQFyiiM5d/7cOTz7X/8Xs9kuNjcnOHHsGG644QZsHNqEn1b48pe/jNnuLtog4QI1GB6M6cYGps6DtrdBRPiPr34VL51+FVtbh1BB7HyVrzQhcM61t98uL9eqH5D40R2BteBgE6mCt+yI6vvfmC8suKGVl++BoGxT9a9ZJfZmxCMNWC19lNcVbSQWAoLS20lVCZrSX03qXTX+0OMTXu2pGlKZ8tJmhf4wD47Faw2rpKd13pdtYfZPpMr8rI5Yl8aCkq0AsHyGK9WjSVBZlpQvhW53nSyG39VZZ50MAeYtOABUPCOt9xWqJS3HIWvahG8jNk4DfNVRg6xNMWfRzg9Cl0CZyk8RPlu7ivVtj+zNaZ5DLVkBk4pzlghR8/mcYQKlRoGSRJ1WQeFGn88hSZpZRZgGAZx0NLmnnS1Ey1KvqTodmx3SmEGdr440R4n2JClQCbJpAkzytzYza7Xb+Qyb0ykOH9rEkSPb2N7eRCRgd3cXu7u7krTYeqbhMeQcXFWhJsLOddfiuuPXYx4DvJPCqE7f31y4gLYJK/Fjh0Evf7NlaOlcnL46IgQMP7MPB5pIIdJKsrwOfjBpKm3eQiW23/22AVcZDC3eqCz3nr4njjtIHjzwwHSVG183sSME1rIGyIXoAIYrCrWR002wshOG5AHApcqZpcTEMcex5CBLwyyGvPLG/XZgPbvY/vfu9w4H9eijjMCcz5yrvTsqJ0xcAQiCVGg539pKNewlwioJtO8osSTBlt91HrhI29MnSoJETVoCaGDTCBJ1aT+Yc4QhzJwRO6oDSqLOidMu13VpF2NwIR1YmiAuBIIseoo6LxNHoGBO4CFlMzw8hDB5L16ZFgskF5ZrI0tPpJ5+RlXSGWNaKLnNKMIVYm5LnaFu9sXWKnfpkmrfxoQ1wzgDzF7wFgeJQy6a6koGSokVoQJFUlEvUVYAIuHWVYWtjSnecNNN2NiaYLIh43fmzBm88M2XlmKU6rrGxsaGxEzVNVxdYWtjB+/4P/8H/9/uLk6dOpWC+ReLBc4//zyaucZeUS5bX0K5Xj1sKjOpL+u9AUANScAbhzMbLMHBJlKF2GvAKJjMAvYUMdOeLoy8+4yfuZoDSMXSSqiqCmZInM/nmEwm6RhIHD6JHFgzGMsry/7kFld1BWZG07ai3kzIIF8T2hYco3AvS/WHBpBpKYVBJTT1ZhNvLNMvO8kzWAxgH2m+Fki7T+i/c6mM0F8fxulZMs2ULUM3iyTUZCB6kCNN38MiWZFkhdirzd8uDElQ+eRAt/o/Er3qSeUcM3FgnV9GB4kbs6E42v4DodK90Ns57BBTfJR5gxkDxul4Hv4isje6tM46HTGETJQQtVQmhlSVhthcK1clCdhXlV4ryVjFJmvqwCwZZSXEMp6wv2SqP2M4i4vED5C00OE+YK9z1DMr6dhQBNwczltYx7LUmG3Lee0Krc9MsQTCLuD9FqqJx9a1mzh07BAAxvnZLr7+4inMFwvMtVR70zQIIWBnZwdbW1vY2tpCjBGz2QxN0+DQoUNi85pMcPjw4ZSz88SJE9jZ2cHu7i4uXLiAb33rW5oXtMhgz4wzZ84kYrillY299xIaYAxG0d/Uv7De3jnwRKqTo5oLy8CaOK6PIPr0bS8bUlY15MkYQjhWwVUWsQVMouAuTW1jvep+KRdE3glAtkMwQmAVBr0xsINAlHvI6UJDHColQexRstPZmqfju3o8SiTZt7Ws6xixJ8Le597ub6AkUtknMROv8kwpF4rB3KlSLMdPEcyxAhga4G+HYC07GuSWFb2xK3rH9uZql6Txle20tRNVegTM8cTiiTLE9Js7BCzkN1B5vJCiCgKVaGUhPZQfp/kPnebVkxIbdZpn0xyYFEWUpb60k5WIJdprZyzpq+5bcyyicl1wdx8SSBP2FiPaS3NVggg+FvkVNdtGBJM5pPDSTUaYyvpXy0AyBySfyC3mzRy7mm1+MZ9jdzZL2Rym0yk2plMQEXauuQbTyQR1XecVxd0qB2Xm+Ol0Cucctra2cPjwYWxtbWGxWCQpy+6r6xqLxQJN04CCxdrJx3q5pBZc0bshONBEymSKzsLBEProwp4DRIaPi6eU1EOPW+qjuq47ExBC6AQB22RXtYfzSmiMawXSBha1QG/69L2NpTNiIMW7sF6vapHQshbeq/YcgCGpx7Kai5cb0DQBjJDRd0HAevqN9IOIOl4/RrhN4ixVDnb9EFIvPYSGCNw6dihSIpPIkHKozoI+1S5geR4L7CrKKWeZEERtmhkErYO0wgPrtQIjVf0j8jUvxp4j+/JzFDEOPaaEZNAuYrkctUZFAJRrakCiMImpXByloGRzZpZ0ZoCLOCe4rqoLur+cSEuWPWIymXSIkRBXFi1GySxFaTeb1GznlFBRZepf0qzohKjSmjEy0o5s9/XI/Jp0Ybh0CCmF04QWcC5C88oPjZziAurc7zvB6t05tnZ7xyAfMG928corjUhCVY2KCC4yvF53+NAhHD58GNvb29jc2JB2sxRGtazwli6trOJgfZNA4W1sbm6mfrdti7Nnz6JpRLMwn89x8eJFvPzyy/jmqRfleUDKNE+UbVyGRqQQxFUhSQkUOAbrMrODqK5gboweUf88ZCE1TYNF02BnZ6fzCMtJdeHCBeEsiLCxsSHun86Jl41Wxc3e4l0ZquSxhGk3brZHgtMCJ1FFUYCvAPgFTBUTFrphL4HL7xOU5QvWv98kTSATxr2kpTI4un/9nlAugsIN36W2dCW8vcHBKUrJ8gEDUQmcns2pcfJmvNSaR6s7s1+fzUYEIRCDjhOXLtkBioARQWbnCmafWjF2LIR9bUjIqmDKWG2FzicHCXOOcN6nLBJyjVwbwWjBaMm8EpWBdw4uCpMSyYNRlLrQ53ESsYDM0JROFkBVTYTSMLBoBSFbzj1STJs8JgGVtNVpyQVEHxEDo429sut6dXofkNV8S5fYXhFm0dTxTSsqvLqqcGhjAzURNshhKtQB1XSKjcPbuOnGm+DqSoqdQmxq0eZAGx5CSEx3f45NIquqqmNn39raApAlr6ZpcOzYMTRNg9nuLgBCM5ul0BVvuJQAjaG/eirzLnEn++EgLv+YmiNTt7RM+gSq/4zIiG3AubNnk+phpmK2SQ67u1LbaWNjQxwTND20vZtYdN1pXXQY5kywum0uulpIRVlNkhOZlUigr0pb5ahgz1rlWl6u4XWIx5D0tBehGGrj3u8pNzZ1jvTVDJ07TN+U9E5A4lTT3WWeO+PgTd0ncWLdJKfrJ9kdgksdT1tJlP6z4/Z/d/5WjXu+Jo9bvovTIlxSUFFPSluD/ieVlitVdEU5DeeT9JSJltM6TaL6Y5WMTflmBCqp7ViIkUmCcl+OsdKFAZArRrBLoMw2lTXyRlBEuhZp0+o25dGTJaXnUTrwKdNm6ngAQLf+XCZSlCSyYvmpipDTOjMJBW1E7Tw26hpbvkLlHOqtTWwePYJD24fAzqFlRgitSIyaELnUIpgGqGmatB4svVtVValeXpruHp4AgO3tbdx44404f14yWZwvHMcqk6CIsDmZiLQ1W4dh/B4gUpcMhXZHNGicULlbR+IobQgx4sVTp+CrClVd45vf/OZSiQtbTN7rBjOvJcrv7lCfYlNkdFHoGUxx0EE+AJFPBktRb8mG9J401ok7hKfMam5tLM8B3TRRQ2rCvYdp2T7V/77qvqH3DXrCldJT+rvPwrdMA3Y/GS+rhAbZBRsMULSxMa4TSpwqiLQqCX0NXToaDj94rZxCBr0MUTA6ctHKe1cTKn0mLBGqrdFMsshsomm4CwmhOJwEvN67ss1JAmxNunHO56SwrgIg+0UcjYq8i0qg2JFmR8hzmckCchHDRHQcvK9lrsmIm0f2+iv2q/7P0KDcpN2wtWWYQtuOkAi0kTrRC4oKMQCwgomSXNcn+RtamoUoS1TSHyOeMSF2OaxMniNUVGEymcAz4GLEZlVje2sLhzc24YhQbx/C9OgRTCYTKQ4MlsKITYvYiBqyhMV8jkWzkDRJWj3cGGyzTdV13SFgtidnsxnMrPGWt7wFZ8+exQsvvIC2aRAtPZT2wxHh2LXXYba7Cz59enAt9uHqI1IDkF12hzdw/yiDMZ/t4vSZ0zh9+rQYJzc2dBN3E9OWNZy8JR9N0hOhk4lbyw0Mt0A97Cy9hrxJ25+5FO89Njc3MV8s0DYhqUdW6dCBjDz6bbdznTHqnduLaJVIqnQuMWDuJvXtI/eOba+f5UAOdgi81drpuyKvVF8W6pQucDrujHgDsBILBNO3q3OBuneb7a7kLl8b9d/rAayZK0qbD5R6NcoJAcxD9pJlAlXCkrRAmgeiqiTfnqsTAvO+klgep4QFUG03IapkBHZJ0ineUny3WDeSZ8OjXbTwVSWZIcilazLhMWIkzxJibXakjB9MerBwhVTPii0uUSkWB5W+vGZjXyaEQfP5GWEr3536QTEVL2VETKfTZAtnZhw9cgRvOHkD3vqWt2BrcwuTiYwlEyE6QtO2mO3uYjabqf3cwfsKs94cNW2j00ydvdq2LZpGkgMYE1zXdVL7mR3LJDFmxtbWFm655RacOHY9mqZB0zbYmm6kud+absA5h9l8hr/8m79eXjA9GImUgqgMhhFxWv5JJWgfBoeAZrEAMSOqqi8UyNeIgyWZlGfqA9KCNs5+WRIwLp/LF2u7xEDM6VYjUiEAHGQzl/3YD0qJqv/d+nIpzyntS0uZy9muy0lKLS4ljbP97qmaOrbCQohKWQiMqyUo91q0mQqkYwO6Muq5r+pUjpayMV5JmahtC0KV1TIDlY47P2no4HqwwrZ3qVJvuicxQFmqTE00b7Tk6LP8/nKOBglTQn4Ex14yhpNmJ3e5lIbZntik3PSM1BiRwoqZlfZoBg7Le0QEy4Ur2dA9kLKe+6SWy8SqVFgQmCxfYUmoHMCltkSTsXJuiDg5mTrMp7WyNE+99yaJyq4jhiREln1ABDiq4FUFGmPE5sYmjp84gUNHDmNjOhUiT5IhMDCjDUFsdM5c/AE4j8lk0nHy6jNy5Zx5rYUnmeWrjhbGpK4+U+acQz0Vmx5DvDHN7lVNajjnUfN6DNyBJlJ9FdClqFEGgygLG0T6zqwuybZ/5XjlHCbewRMjzOe4OJPYgiZGtBxFstL7RDQuU6IYciy+F60ofzuX9d62Sbs2BEUmkeHIo/I1FrtaPjqlhNkbYZXjWBb+M0Jb5icccsnve+HZ4s52Beq8C4yMgCz7AcOULF0SQlKkjYr4sEGhiEhVRUjXOCdZCpBsCIRcYsPeUIx7fxqWv0ofnTmoKxNiSrFC7Sf/orpY8MpCcxnJxk6/9iMwiVHqS6d73rXiWTZvfsUAlC81hw2dR+uA2XbTksYygbJYQoIDYi2Jiwq7k6mpTTUnFr9lhsf+OpAWFjR5xwiGSUqW4V3cpOE0GJgIqYa3qteSmhOZLWKrMUVIhCmtTSI47iWDYvGQlGKB0k/vsMyg6fMdOaXzLMvSUyIo9jwH4x2E4XFKtFiJ4XRjiuMnTmBjcxOVr5LUJXxF3tN1XVvD4T2hqivMZjNRAarzxxD+rL1H7SsQJBDYe4+oqr4QQnIOMzODMeXmoh5iROCI2XyOqq4wcVKtOnJEs6aW4UATqagBrIkL+Q6JFJBklvQ9y07ocMkRDA8nnk+ck6nUlRc3cH1H27aYz+fY2p6k+1klpiwplM8vjafl8biMNzpspHCERB6zmXjXpIW5AkqOu0+oiKjDZS2Vky/GsG3bxAFXRZxFWbupuClVsiXy6keXOgGghW19U7OtDzJ+sk9JRSlT/CyvDRr0SDMkaE+U3HcMrchrWQgoj4crVLRixbBxzePlHBLCHO5UIbkgE/+9oJRgXi9Iq5OoYCek9Y6M8epmx7bK1BauwZHAbVWkMSqyEsBmMasd8xgqxWCbT/sbhWkwyd95mIu7vbuqKgT0VYTLUMrZpTRpEjEpESQwQA4BrRInloKDKoU6ENoQEVqt2jwoNRSSK5AIVKXEGixVn/MIexAqUZtwQAiMGNWk0Ab1hJQxa9tWAnrn824JIiVG08kEdVWjbVu8+uqrYEYapxhCYh/N/R/2G8Jg1pqYYLFY5EcTdVSBnZ4qU0Mkbv91Xacg+v3gQBMpR5JCMqH2kgVfAas2PpUIsSf25jOizmEwIjGiAwKxJd8vkmHK/TZZVVWJ2iGVKiCUuVW4t9kpLdwOFULeMFkNlwJ89W7bmOtoevrifTlGJQdsH9M5d7Ids0T8i23BJ6TTH8M0fERqhtMlz5koC/In5RQv3ZZTIsZsNC9GZ1AE65CGXlshc6pD7Ao+oetWn+N2HCqNN8ppq/JrOamkOg2ASpEDc7CSUFEOlBwULffq8xrnckPy+0ysMNSdchwywYJTTS2X50DUeZ48ENV5gR3Qk54AV8xdLneRwaLadI9Jo9I589wTQlLBVN3mdm7MZpbESvYzq+eQekiJaRR7k5FDgPNd8k6KQAQWYSGJeMlrfaYgav4B7YMjQouI2Mp5pxV3nSNUXpxHDH+YRsK0BZITTwhFVdVgiHkhJ90tMEfRr4661eW9Xde1VPVtWj1nanOdaY7q+JA/Ja4piWDTNAitOZxwen5omkQgS7yyDhxoImVckkHigvo4oHewXDQZvXTBpDMhKDmY04zkkRjBMQJBc1DlzWtgDhOTyUSQU1TVgs646cs7SMyYxSH7WNochp54qT/GzUjRNblrP468b4Mqa07Z+Fbeo9X+pOdpc52v4bQiqtFM7j1fWuJ0YTvJXwaXLpTeqH2AummY5BndZ1lf+39Tmy0PnZyAZecwmxVgyMYQX+qSqldUl276dkbyzmQATD6tiVIi9ZpwSWJRrJ6TSsL5DdaDxLh0DPRFn/YEWpYMSsJ2KZqFpUcjr7Dk2VaWPgc0/ZbZiqISJErI1kpqCBNTAWECW/CukLhF9BVJyBeu4lJaRVoT2RUqxQIRkzF2uTwH2AoaAsb/B/OwS2PSY/wMqapk77RERU+XIrNLRc7MZI9hLNo5fOUxcdDq1S04tihkQ3mGExzgQosmtAgxYlpP4UgCeauq0mDymGKYiEiCmzWOzJGoMCfVBgBJnVY6IZU2QPPYLRMPJHsZA5PpBHwOWCxaZc5UeiKC1DaRdGuOdJo448fSNZ2ZMZ8tNATHgTmk9Ehzk/S08GRB7faFA02kHHUJg4moBuUGLm0CVF5PpmbI1VOHQCbbgdUtNQaHEBwk8b0l2NT3KpI3vWxWc6CMM10JS2quMpg3SWCMMutB2y6waOdYNDO1YUVkF9s+UiyRpkh8Gxsb8N4nHbURK8sqDQAVOfiKEJ0Tjx9tZVUXaj0bZxJOVBLUyiFZm91aNEvMQTSu10FcdDNy78dN9TmyEgkR6hTPwprJ09BQZ2sk78qMjjPk1dVH/ibBCH7SNUSMyC2qSmKoYmw1Q4XapCyl0gqeoZtmaBlKqXANQfm1AcoEW15sNbmQ1DUyGpVk+46i9k1lJqlKufEYAJypgikjQgDsSNaGqwXxEw3m9Igqv3VTIPUJcoEJyIGVWPYmF5ndCwlpuoJh8emq/FeIhdc9HgGqECKjbQCaM1xgyUnHEDNWIBDXhZZGGD0ESZUkHnMVNqdTtflUIPbg0IAjoa6mqCtVjZnHqXOYTDyOHD2KrUOHhJgWDLW1dTBsA+U+ESbq8OFtnD1zTufWxtVhUm9qCikGeSCEBqy2uRgJHMWyhwiEENG2Ad5NQBwRQkSguTIs4lFY1ZW4s2uGkHXX8IEmUp1QIuOoej1PKoQIQZiAqhcUyamKQFxK5Z4QAhZNI0G4aiBM7ywQOJc2imIx2DUm6ZmI3UUz+/StXFyJMOV7BXFkouOcR11NESeKC/vX2zg4W8CZozK3VuO2vNdNGBlN28hiTJKM/K1Mfz6ZSBoXRRKhDQgxpih7eZcYUD1zzuQMDYw14pGx4DAiTx6QKtUoJyguxkWOsGJ87F9Eb/yLZ4s6l0DQsu9cjlrmgLPTRnHWri2OOaiqlUhiUSJJ9gaKIKfnsiCapHRRg2a7jK2pvHaKdnV1iOg2gXpt5IJxwFqQ95BJSYa4shpV9psZ6YUo5PIZ3khJ+gugKJNiUo8+r1MK3jKR9xpMWerNiWSLK6i8vuDSTXIu04+lR5oUK/8b4s7jmQPv0zpgBqkXJGvffeUx4Ro82ZJrDZVEB8QocYpRzAS+snfK+q98Decd6mqCupoIwxUdAhiOIuAl1RkRQD4qHYpo2oiNjSmm00lm3so5VKm0clZlgdI+7UvYPuU+lPXv1QtPyvXIfLiqQlXLXo8cERtldJNEVHgFO3ErclTpVBDgkTwDaaANe8GBJFI2KbuzWRYqCoaYimssRgFQLzsApOn9jUiFGKRAmaYAaZoGF3Z3ceTIETBzylEFZI56Npvh4sWLSVoqRd6+Smo+n6sTgxEKLmqcFeqLQm3U7XB2VS3HIL9LPuJ+67BYNGoL60oBQ2VDTBopibFT9Qt5oFksEJpWkCnyOwmyuDem0+68sHg42bOMmDdNI+6zXj0F0a2emjWImlomMhit5p+DlhIxSUpUS94TEBwYkrG8g5ss9yBlIjW0MTJyVPWoEZ4O09Ed90Rk+tNkc2L9SNVqY8fGlmwadi4CkiHbzts9pWqw934aDgegQv0YbX3YGiEM3pPVsbZ38q+MuI1QWx5G60kpvQpe5kBg9YCMCIrHCHARzB5k2VCirr/aaUZsUaObmp0TQ6NqYIj2xLtMHE0xEbUBWbeSHVso2v4qOZDl9SAOAqUKU12nRATUYRTkzFGkBRs/cgxfVWibFqFtQRB80oYWbQjJI65mn8eUAqq6UkaZdM0DHCJCZMQo4962AeQATxFNI5ntWcc1xpCylPdjDmPbYjGfp2Nt2+TkuujG8M3n85T9vK4rJawzgCoEDqg3NuB9jeClX7N5g7YVqbBZtOogtlDCJmMH1/VYLW1sFixcrr9VQHwpwRRXCHz961/HG9/4xsvdjBFGGGGEEb5DeP755/GGN7xh5fkDSaRijHj66afxtre9Dc8//zyOHDlyuZt0xcLZs2fxxje+cRynfWAcp/VgHKf1YByn/YGZce7cOdx44417evodSHWfcw433XQTAODIkSPjIlgDxnFaD8ZxWg/GcVoPxnHaG44ePbrvNes5qo8wwggjjDDCZYCRSI0wwggjjHDFwoElUtPpFA888ACmhXfZCMswjtN6MI7TejCO03owjtNrBwfScWKEEUYYYYSrAw6sJDXCCCOMMML3PoxEaoQRRhhhhCsWRiI1wggjjDDCFQsjkRphhBFGGOGKhQNJpD7xiU/g+77v+7CxsYHbb78d//RP/3S5m3RZ4bd/+7c7CTqJCG9961vT+dlshvvvvx/XXXcdtre38TM/8zN48cUXL2OLXx/4whe+gJ/4iZ/AjTfeCCLCX/3VX3XOMzM+9rGP4YYbbsDm5ibuvPNOfO1rX+tc8+qrr+K+++7DkSNHsLOzg1/4hV/A+fPnX8defPdhv3H6uZ/7uaX1dc8993Su+V4fp49//OP40R/9URw+fBjHjx/HT/7kT+Lpp5/uXLPOPnvuuefw3ve+F1tbWzh+/Dh+4zd+A63mDB1hGA4ckfqLv/gL/Nqv/RoeeOAB/Ou//ituu+023H333XjppZcud9MuK/zgD/4gXnjhhfT54he/mM796q/+Kv7mb/4Gn/70p/HYY4/hf//3f/HTP/3Tl7G1rw9cuHABt912Gz7xiU8Mnv+93/s9/OEf/iH++I//GE888QQOHTqEu+++G7PZLF1z33334Stf+QoeeughfPazn8UXvvAFfOhDH3q9uvC6wH7jBAD33HNPZ3196lOf6pz/Xh+nxx57DPfffz++9KUv4aGHHkLTNLjrrrtw4cKFdM1++yyEgPe+971YLBb4x3/8R/zpn/4pHnzwQXzsYx+7HF06OMAHDN797nfz/fffn36HEPjGG2/kj3/845exVZcXHnjgAb7tttsGz50+fZrruuZPf/rT6dh//ud/MgB+/PHHX6cWXn4AwJ/5zGfS7xgjnzx5kn//938/HTt9+jRPp1P+1Kc+xczMX/3qVxkA//M//3O65u/+7u+YiPgb3/jG69b21xP648TM/MEPfpDf9773rbznahynl156iQHwY489xszr7bO//du/Zeccnzp1Kl3zyU9+ko8cOcLz+fz17cABggMlSS0WCzz55JO488470zHnHO688048/vjjl7Fllx++9rWv4cYbb8Sb3vQm3HfffXjuuecAAE8++SSapumM2Vvf+lbcfPPNV/WYPfvsszh16lRnXI4ePYrbb789jcvjjz+OnZ0d/MiP/Ei65s4774RzDk888cTr3ubLCY8++iiOHz+Ot7zlLfjwhz+MV155JZ27GsfpzJkzAIBrr70WwHr77PHHH8c73vEOnDhxIl1z99134+zZs/jKV77yOrb+YMGBIlIvv/wyQgidSQaAEydO4NSpU5epVZcfbr/9djz44IP43Oc+h09+8pN49tln8eM//uM4d+4cTp06hclkgp2dnc49V/uYWd/3WkunTp3C8ePHO+erqsK11157VY3dPffcgz/7sz/Dww8/jN/93d/FY489hnvvvTfVDLvaxinGiF/5lV/Bj/3Yj+Htb387AKy1z06dOjW43uzcCMNwILOgj9CFe++9N31/5zvfidtvvx233HIL/vIv/xKbm5uXsWUjfC/Az/7sz6bv73jHO/DOd74T3//9349HH30U73nPey5jyy4P3H///fjyl7/csfuO8N2DAyVJHTt2DN77JY+ZF198ESdPnrxMrbryYGdnBz/wAz+AZ555BidPnsRiscDp06c711ztY2Z932stnTx5cskhp21bvPrqq1f12L3pTW/CsWPH8MwzzwC4usbpIx/5CD772c/i85//fKdQ3zr77OTJk4Przc6NMAwHikhNJhO8613vwsMPP5yOxRjx8MMP44477riMLbuy4Pz58/iv//ov3HDDDXjXu96Fuq47Y/b000/jueeeu6rH7NZbb8XJkyc743L27Fk88cQTaVzuuOMOnD59Gk8++WS65pFHHkGMEbfffvvr3uYrBb7+9a/jlVdewQ033ADg6hgnZsZHPvIRfOYzn8EjjzyCW2+9tXN+nX12xx134D/+4z86BP2hhx7CkSNH8La3ve316chBhMvtuXGp8Od//uc8nU75wQcf5K9+9av8oQ99iHd2djoeM1cbfPSjH+VHH32Un332Wf6Hf/gHvvPOO/nYsWP80ksvMTPzL/3SL/HNN9/MjzzyCP/Lv/wL33HHHXzHHXdc5lZ/9+HcuXP81FNP8VNPPcUA+A/+4A/4qaee4v/5n/9hZubf+Z3f4Z2dHf7rv/5r/vd//3d+3/vex7feeivv7u6mZ9xzzz38Qz/0Q/zEE0/wF7/4RX7zm9/MH/jABy5Xl74rsNc4nTt3jn/913+dH3/8cX722Wf57//+7/mHf/iH+c1vfjPPZrP0jO/1cfrwhz/MR48e5UcffZRfeOGF9Ll48WK6Zr991rYtv/3tb+e77rqL/+3f/o0/97nP8fXXX8+/+Zu/eTm6dGDgwBEpZuY/+qM/4ptvvpknkwm/+93v5i996UuXu0mXFd7//vfzDTfcwJPJhG+66SZ+//vfz88880w6v7u7y7/8y7/M11xzDW9tbfFP/dRP8QsvvHAZW/z6wOc//3kGsPT54Ac/yMzihv5bv/VbfOLECZ5Op/ye97yHn3766c4zXnnlFf7ABz7A29vbfOTIEf75n/95Pnfu3GXozXcP9hqnixcv8l133cXXX38913XNt9xyC//iL/7iElP4vT5OQ+MDgP/kT/4kXbPOPvvv//5vvvfee3lzc5OPHTvGH/3oR7lpmte5NwcLxlIdI4wwwggjXLFwoGxSI4wwwggjXF0wEqkRRhhhhBGuWBiJ1AgjjDDCCFcsjERqhBFGGGGEKxZGIjXCCCOMMMIVCyORGmGEEUYY4YqFkUiNMMIII4xwxcJIpEYYYYQRRrhiYSRSI4wwwggjXLEwEqkRRhhhhBGuWBiJ1AgjjDDCCFcsjERqhBFGGGGEKxb+H7kyiRHyHFrUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(frame)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "583b46c0",
   "metadata": {},
   "source": [
    "## 2.3 Data Augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0160954e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_aug(img):\n",
    "    img = tf.cast(img, tf.float32)\n",
    "    data = []\n",
    "    for i in range(9):\n",
    "        img = tf.image.stateless_random_brightness(img, max_delta=0.02, seed=(1,2))\n",
    "        img = tf.image.stateless_random_contrast(img, lower=0.6, upper=1, seed=(1,3))\n",
    "        #img = tf.image.stateless_random_crop(img, size=(20,20,3), seed=(1,2))\n",
    "        img = tf.image.stateless_random_flip_left_right(img, seed=(1,3))\n",
    "        img = tf.image.stateless_random_flip_up_down(img, seed=(1,2))\n",
    "        data.append(img)\n",
    "    return data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "47025b5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import uuid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5a844818",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_path = os.path.join(ANC_PATH, 'b0a1ad92-fb7d-11ed-b171-90e8685c3a0d.jpg')\n",
    "img = cv2.imread(img_path)\n",
    "if img is not None:\n",
    "    augmented_images = data_aug(img)\n",
    "    for image in augmented_images:\n",
    "        cv2.imwrite(os.path.join(ANC_PATH, '{}.jpg'.format(uuid.uuid1())), image.numpy())\n",
    "else:\n",
    "    print('Failed to read image')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b9a7c0fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "for file_name in os.listdir(os.path.join(POS_PATH)):\n",
    "    img_path = os.path.join(POS_PATH, file_name)\n",
    "    img = cv2.imread(img_path)\n",
    "    augmented_images = data_aug(img) \n",
    "    \n",
    "    for image in augmented_images:\n",
    "        cv2.imwrite(os.path.join(POS_PATH, '{}.jpg'.format(uuid.uuid1())), image.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86b0c67c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2665d720",
   "metadata": {},
   "source": [
    "# 3. Images Loading and Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d5b83e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "df788f5b",
   "metadata": {},
   "source": [
    "## 3.1 Get Image Directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "87275fcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "anchor = tf.data.Dataset.list_files(ANC_PATH+'/*.jpg').take(300)\n",
    "positive = tf.data.Dataset.list_files(POS_PATH+'/*.jpg').take(300)\n",
    "negative = tf.data.Dataset.list_files(NEG_PATH+'/*.jpg').take(300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ce8b6c54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data/anchor\\\\*.jpg'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ANC_PATH+'\\*.jpg' # testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0e78fc8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-26 09:59:31.228096: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [311]\n",
      "\t [[{{node Placeholder/_0}}]]\n",
      "2023-05-26 09:59:31.228300: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [311]\n",
      "\t [[{{node Placeholder/_0}}]]\n"
     ]
    }
   ],
   "source": [
    "dir_test = anchor.as_numpy_iterator()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "35df7479",
   "metadata": {},
   "outputs": [],
   "source": [
    "abc = dir_test.next() # grabing a different image everytime we run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "66e81e22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'data/anchor/b52815cc-fb7d-11ed-b171-90e8685c3a0d.jpg'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dcfdbdb",
   "metadata": {},
   "source": [
    "## 3.2 Preprocessing- Scale and Resize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a92a780a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess (file_path):\n",
    "    byte_img = tf.io.read_file(file_path)  # reading the image\n",
    "    img = tf.io.decode_jpeg(byte_img)      # loading the image\n",
    "    img = tf.image.resize(img, (105,105))  # resizing the image to 105x105x3 same as in the research paper\n",
    "    img = img / 255.0                      # scaling image to be b/w 0 and 1\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ec7a1278",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = preprocess(abc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a3c977a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff7e4115030>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5e4257d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11376774"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.numpy().min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "83b42ec8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.numpy().max() # make sure it's not passing '1'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8203f2a",
   "metadata": {},
   "source": [
    "## 3.3 Create Labelled Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "82276a82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (anchor, positive) => 1,1,1,1,1\n",
    "# (anchor, negative)=> 0,0,0,0,0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ece0c4c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(300,), dtype=float32, numpy=\n",
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.], dtype=float32)>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.ones(len(anchor)) # effectively creating labelled positives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "df479cc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(300,), dtype=float32, numpy=\n",
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.zeros(len(anchor)) # effectively creating labelled negatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ae8579fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# </ checking with an example\n",
    "class_labels = tf.data.Dataset.from_tensor_slices(tf.zeros(len(anchor)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "68247551",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-26 09:59:35.028360: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype float and shape [300]\n",
      "\t [[{{node Placeholder/_0}}]]\n"
     ]
    }
   ],
   "source": [
    "iterator_labs = class_labels.as_numpy_iterator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a4a9ded2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iterator_labs.next() # effectively looping through each lable in the class/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7c7a425b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating 2 different datasets for positives and negatives\n",
    "positives = tf.data.Dataset.zip((anchor, positive, tf.data.Dataset.from_tensor_slices(tf.ones(len(anchor)))))\n",
    "negatives = tf.data.Dataset.zip((anchor, negative, tf.data.Dataset.from_tensor_slices(tf.zeros(len(anchor)))))\n",
    "data = positives.concatenate(negatives)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a0287bbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-26 09:59:35.580282: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_30' with dtype string and shape [13233]\n",
      "\t [[{{node Placeholder/_30}}]]\n",
      "2023-05-26 09:59:35.580870: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_39' with dtype float and shape [300]\n",
      "\t [[{{node Placeholder/_39}}]]\n"
     ]
    }
   ],
   "source": [
    "sample = data.as_numpy_iterator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "689d6b07",
   "metadata": {},
   "outputs": [],
   "source": [
    "example = sample.next() # as of now it is not shuffled, we have to compleate all positive iterators to reach negatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c116de1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(b'data/anchor/bb819948-fb7d-11ed-b171-90e8685c3a0d.jpg',\n",
       " b'data/positive/de4a0406-fb7d-11ed-b171-90e8685c3a0d.jpg',\n",
       " 1.0)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca63fcce",
   "metadata": {},
   "source": [
    "## 3.4 Train and Test Partitions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2678cb0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_twin(input_img, validation_img, label):\n",
    "    return(preprocess(input_img), preprocess(validation_img), label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1cd10523",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = preprocess_twin(*example) # '*' is to unpack the values of 'example'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b605defa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(105, 105, 3), dtype=float32, numpy=\n",
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       "         [0.5082833 , 0.5433173 , 0.53163934],\n",
       "         [0.51101774, 0.55172735, 0.5372549 ]],\n",
       " \n",
       "        [[0.58646125, 0.5951447 , 0.5903828 ],\n",
       "         [0.5803922 , 0.59689873, 0.59187675],\n",
       "         [0.5768774 , 0.6004068 , 0.5887089 ],\n",
       "         ...,\n",
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       " \n",
       "        [[0.57701975, 0.5966276 , 0.5809413 ],\n",
       "         [0.5787448 , 0.6002801 , 0.58459383],\n",
       "         [0.58431375, 0.60784316, 0.5921569 ],\n",
       "         ...,\n",
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       " \n",
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       "         [0.6295985 , 0.6295985 , 0.6295985 ],\n",
       "         ...,\n",
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       "         [0.30508915, 0.30116758, 0.3209021 ],\n",
       "         [0.25795427, 0.26187584, 0.2775621 ]],\n",
       " \n",
       "        [[0.6273643 , 0.6273643 , 0.6273643 ],\n",
       "         [0.6313726 , 0.6313726 , 0.6313726 ],\n",
       "         [0.6313726 , 0.6313726 , 0.6313726 ],\n",
       "         ...,\n",
       "         [0.5721291 , 0.571849  , 0.58053243],\n",
       "         [0.67056835, 0.67028826, 0.6789717 ],\n",
       "         [0.45835704, 0.4622786 , 0.47068194]],\n",
       " \n",
       "        [[0.6223156 , 0.6223156 , 0.6223156 ],\n",
       "         [0.630072  , 0.630072  , 0.630072  ],\n",
       "         [0.62717086, 0.62717086, 0.62717086],\n",
       "         ...,\n",
       "         [0.588142  , 0.588142  , 0.5832645 ],\n",
       "         [0.64325064, 0.64325064, 0.64043623],\n",
       "         [0.6576097 , 0.66545284, 0.66153127]]], dtype=float32)>,\n",
       " <tf.Tensor: shape=(105, 105, 3), dtype=float32, numpy=\n",
       " array([[[0.49925303, 0.49925303, 0.4914099 ],\n",
       "         [0.49803922, 0.49803922, 0.49019608],\n",
       "         [0.50074697, 0.50074697, 0.49290383],\n",
       "         ...,\n",
       "         [0.4678805 , 0.4678805 , 0.46003735],\n",
       "         [0.4740363 , 0.47039485, 0.4625517 ],\n",
       "         [0.47058824, 0.46666667, 0.45882353]],\n",
       " \n",
       "        [[0.5058824 , 0.5058824 , 0.49803922],\n",
       "         [0.5016807 , 0.5016807 , 0.49383754],\n",
       "         [0.49803922, 0.49803922, 0.49019608],\n",
       "         ...,\n",
       "         [0.47030813, 0.47086835, 0.4627451 ],\n",
       "         [0.4742097 , 0.47424972, 0.46638656],\n",
       "         [0.46666667, 0.46666667, 0.45882353]],\n",
       " \n",
       "        [[0.50262547, 0.50262547, 0.49478236],\n",
       "         [0.5000334 , 0.5000334 , 0.4921902 ],\n",
       "         [0.5019608 , 0.5019608 , 0.49411765],\n",
       "         ...,\n",
       "         [0.46328756, 0.4735383 , 0.46097106],\n",
       "         [0.4685341 , 0.47238895, 0.46261838],\n",
       "         [0.46666667, 0.47021475, 0.46059754]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[0.52726424, 0.5211951 , 0.5197946 ],\n",
       "         [0.5294118 , 0.5232159 , 0.5221956 ],\n",
       "         [0.5294118 , 0.52156866, 0.5254902 ],\n",
       "         ...,\n",
       "         [0.50138515, 0.4974636 , 0.48962048],\n",
       "         [0.49854606, 0.4946245 , 0.48678136],\n",
       "         [0.46656215, 0.46264058, 0.45479745]],\n",
       " \n",
       "        [[0.5266173 , 0.51877415, 0.5226957 ],\n",
       "         [0.5254902 , 0.5176471 , 0.5220888 ],\n",
       "         [0.5254902 , 0.5176471 , 0.52885157],\n",
       "         ...,\n",
       "         [0.52716434, 0.5232428 , 0.51539963],\n",
       "         [0.53821546, 0.5342939 , 0.52645075],\n",
       "         [0.46087766, 0.4569561 , 0.44911295]],\n",
       " \n",
       "        [[0.5306256 , 0.52278244, 0.53454715],\n",
       "         [0.5257703 , 0.51792717, 0.5296919 ],\n",
       "         [0.53333336, 0.5254902 , 0.5372549 ],\n",
       "         ...,\n",
       "         [0.5096616 , 0.5018185 , 0.50574005],\n",
       "         [0.5064226 , 0.4985794 , 0.502501  ],\n",
       "         [0.46629316, 0.45845002, 0.4623716 ]]], dtype=float32)>,\n",
       " 1.0)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0f746b4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(105, 105, 3), dtype=float32, numpy=\n",
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       "        ...,\n",
       "        [0.5222222 , 0.5538749 , 0.543324  ],\n",
       "        [0.5082833 , 0.5433173 , 0.53163934],\n",
       "        [0.51101774, 0.55172735, 0.5372549 ]],\n",
       "\n",
       "       [[0.58646125, 0.5951447 , 0.5903828 ],\n",
       "        [0.5803922 , 0.59689873, 0.59187675],\n",
       "        [0.5768774 , 0.6004068 , 0.5887089 ],\n",
       "        ...,\n",
       "        [0.51994795, 0.56607306, 0.5410497 ],\n",
       "        [0.51064426, 0.5610644 , 0.5338935 ],\n",
       "        [0.5140056 , 0.56442577, 0.5372549 ]],\n",
       "\n",
       "       [[0.57701975, 0.5966276 , 0.5809413 ],\n",
       "        [0.5787448 , 0.6002801 , 0.58459383],\n",
       "        [0.58431375, 0.60784316, 0.5921569 ],\n",
       "        ...,\n",
       "        [0.53058785, 0.5560602 , 0.5394024 ],\n",
       "        [0.5292584 , 0.55160064, 0.535761  ],\n",
       "        [0.5311858 , 0.5529412 , 0.5372549 ]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0.62530345, 0.62530345, 0.62530345],\n",
       "        [0.6297253 , 0.6297253 , 0.6297253 ],\n",
       "        [0.6295985 , 0.6295985 , 0.6295985 ],\n",
       "        ...,\n",
       "        [0.35994497, 0.3560234 , 0.37740526],\n",
       "        [0.30508915, 0.30116758, 0.3209021 ],\n",
       "        [0.25795427, 0.26187584, 0.2775621 ]],\n",
       "\n",
       "       [[0.6273643 , 0.6273643 , 0.6273643 ],\n",
       "        [0.6313726 , 0.6313726 , 0.6313726 ],\n",
       "        [0.6313726 , 0.6313726 , 0.6313726 ],\n",
       "        ...,\n",
       "        [0.5721291 , 0.571849  , 0.58053243],\n",
       "        [0.67056835, 0.67028826, 0.6789717 ],\n",
       "        [0.45835704, 0.4622786 , 0.47068194]],\n",
       "\n",
       "       [[0.6223156 , 0.6223156 , 0.6223156 ],\n",
       "        [0.630072  , 0.630072  , 0.630072  ],\n",
       "        [0.62717086, 0.62717086, 0.62717086],\n",
       "        ...,\n",
       "        [0.588142  , 0.588142  , 0.5832645 ],\n",
       "        [0.64325064, 0.64325064, 0.64043623],\n",
       "        [0.6576097 , 0.66545284, 0.66153127]]], dtype=float32)>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "145c66f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff7e4163cd0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(result[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e5fc6f63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0b6eb9ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# building dataloader pipeline\n",
    "data = data.map(preprocess_twin)\n",
    "data = data.cache()\n",
    "data = data.shuffle(buffer_size=1024) # shuffling the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f4e53e1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_ShuffleDataset element_spec=(TensorSpec(shape=(105, 105, None), dtype=tf.float32, name=None), TensorSpec(shape=(105, 105, None), dtype=tf.float32, name=None), TensorSpec(shape=(), dtype=tf.float32, name=None))>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "21f45dcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-26 09:59:38.774202: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_30' with dtype string and shape [13233]\n",
      "\t [[{{node Placeholder/_30}}]]\n",
      "2023-05-26 09:59:38.774768: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_30' with dtype string and shape [13233]\n",
      "\t [[{{node Placeholder/_30}}]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.data.ops.dataset_ops._NumpyIterator at 0x7ff7e41cbfa0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.as_numpy_iterator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2393ab5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = data.as_numpy_iterator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fac473d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[[0.58636564, 0.58636564, 0.58636564],\n",
       "         [0.5771242 , 0.58552754, 0.581606  ],\n",
       "         [0.5743119 , 0.5899982 , 0.5860766 ],\n",
       "         ...,\n",
       "         [0.5329598 , 0.54509807, 0.5411765 ],\n",
       "         [0.52455646, 0.54024273, 0.53632116],\n",
       "         [0.5269841 , 0.54267037, 0.5387488 ]],\n",
       " \n",
       "        [[0.58067894, 0.5890823 , 0.58516073],\n",
       "         [0.57511   , 0.5840336 , 0.58011204],\n",
       "         [0.5686275 , 0.58431375, 0.5803922 ],\n",
       "         ...,\n",
       "         [0.537535  , 0.54537815, 0.54089636],\n",
       "         [0.5266106 , 0.5419968 , 0.5372549 ],\n",
       "         [0.5254902 , 0.54901963, 0.5406163 ]],\n",
       " \n",
       "        [[0.5674025 , 0.5866369 , 0.5809413 ],\n",
       "         [0.564986  , 0.58422035, 0.5785247 ],\n",
       "         [0.57335156, 0.5925859 , 0.5868903 ],\n",
       "         ...,\n",
       "         [0.5340803 , 0.5509626 , 0.53645235],\n",
       "         [0.52791786, 0.5492997 , 0.53361344],\n",
       "         [0.52267796, 0.54620737, 0.5305211 ]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[0.63529414, 0.63529414, 0.63529414],\n",
       "         [0.63529414, 0.63529414, 0.63529414],\n",
       "         [0.63432264, 0.63432264, 0.63787067],\n",
       "         ...,\n",
       "         [0.27623934, 0.27623934, 0.2797874 ],\n",
       "         [0.31163162, 0.3131522 , 0.3246701 ],\n",
       "         [0.30837035, 0.31229192, 0.3236831 ]],\n",
       " \n",
       "        [[0.6313726 , 0.6313726 , 0.6313726 ],\n",
       "         [0.6313726 , 0.6313726 , 0.63189274],\n",
       "         [0.63301986, 0.63301986, 0.6405562 ],\n",
       "         ...,\n",
       "         [0.51469237, 0.51633966, 0.50386137],\n",
       "         [0.4793118 , 0.4793118 , 0.4793118 ],\n",
       "         [0.5703615 , 0.57064164, 0.5784848 ]],\n",
       " \n",
       "        [[0.63987154, 0.63987154, 0.63987154],\n",
       "         [0.6315659 , 0.6315659 , 0.63212615],\n",
       "         [0.63622785, 0.63622785, 0.644071  ],\n",
       "         ...,\n",
       "         [0.53782624, 0.53427815, 0.5360522 ],\n",
       "         [0.53380024, 0.53380024, 0.53380024],\n",
       "         [0.5953226 , 0.5953226 , 0.5953226 ]]], dtype=float32),\n",
       " array([[[0.94631183, 0.96591973, 0.9384687 ],\n",
       "         [0.937535  , 0.95714283, 0.92969185],\n",
       "         [0.9411765 , 0.9607843 , 0.93333334],\n",
       "         ...,\n",
       "         [0.9372549 , 0.9529412 , 0.8862745 ],\n",
       "         [0.9408964 , 0.9529412 , 0.8862745 ],\n",
       "         [0.9490196 , 0.9529412 , 0.8901961 ]],\n",
       " \n",
       "        [[0.94631183, 0.96591973, 0.9384687 ],\n",
       "         [0.937535  , 0.95714283, 0.92969185],\n",
       "         [0.9411765 , 0.9607843 , 0.93333334],\n",
       "         ...,\n",
       "         [0.9372549 , 0.9529412 , 0.8862745 ],\n",
       "         [0.9408964 , 0.9529412 , 0.8862745 ],\n",
       "         [0.9490196 , 0.9529412 , 0.8901961 ]],\n",
       " \n",
       "        [[0.94631183, 0.96591973, 0.9384687 ],\n",
       "         [0.937535  , 0.95714283, 0.92969185],\n",
       "         [0.9411765 , 0.9607843 , 0.93333334],\n",
       "         ...,\n",
       "         [0.9372549 , 0.9529412 , 0.8862745 ],\n",
       "         [0.9408964 , 0.9529412 , 0.8862745 ],\n",
       "         [0.9490196 , 0.9529412 , 0.8901961 ]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[0.915324  , 0.76575524, 0.48842204],\n",
       "         [0.9218487 , 0.7728291 , 0.49010268],\n",
       "         [0.9282357 , 0.7792161 , 0.49294162],\n",
       "         ...,\n",
       "         [0.96686906, 0.93924916, 0.8418079 ],\n",
       "         [0.9729358 , 0.96270514, 0.88704145],\n",
       "         [0.9626851 , 0.95863676, 0.89282155]],\n",
       " \n",
       "        [[0.9094304 , 0.7604108 , 0.4743097 ],\n",
       "         [0.9257503 , 0.7767307 , 0.48317322],\n",
       "         [0.9292584 , 0.7802388 , 0.48612112],\n",
       "         ...,\n",
       "         [0.9351943 , 0.8856212 , 0.76704043],\n",
       "         [0.9708683 , 0.94705874, 0.8453581 ],\n",
       "         [0.9543484 , 0.94639856, 0.8645191 ]],\n",
       " \n",
       "        [[0.8919701 , 0.7429505 , 0.44883284],\n",
       "         [0.91792715, 0.7691876 , 0.4742297 ],\n",
       "         [0.9290382 , 0.7839402 , 0.47695968],\n",
       "         ...,\n",
       "         [0.78310037, 0.7231676 , 0.5871042 ],\n",
       "         [0.97396964, 0.9381153 , 0.8235495 ],\n",
       "         [0.965544  , 0.9481815 , 0.8509804 ]]], dtype=float32),\n",
       " 0.0)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples.next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ed318a51",
   "metadata": {},
   "outputs": [],
   "source": [
    "samp = samples.next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d241b582",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff7e4145f90>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(samp[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "03ed9b74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff7c4781f30>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(samp[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0e55cbb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samp[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1d27baf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "420"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(len(data)*.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "815293b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_ShuffleDataset element_spec=(TensorSpec(shape=(105, 105, None), dtype=tf.float32, name=None), TensorSpec(shape=(105, 105, None), dtype=tf.float32, name=None), TensorSpec(shape=(), dtype=tf.float32, name=None))>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e7ef56b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "00afe7a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating training partition\n",
    "train_data = data.take(round(len(data)*.7))\n",
    "train_data = train_data.batch(16)\n",
    "train_data = train_data.prefetch(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8d5403d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_PrefetchDataset element_spec=(TensorSpec(shape=(None, 105, 105, None), dtype=tf.float32, name=None), TensorSpec(shape=(None, 105, 105, None), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.float32, name=None))>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "da1b2b4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-26 09:59:41.101065: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_30' with dtype string and shape [13233]\n",
      "\t [[{{node Placeholder/_30}}]]\n",
      "2023-05-26 09:59:41.101668: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_9' with dtype string and shape [3020]\n",
      "\t [[{{node Placeholder/_9}}]]\n"
     ]
    }
   ],
   "source": [
    "train_samples = train_data.as_numpy_iterator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "86b21873",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_sample = train_samples.next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f68593b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_sample[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "382c6872",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating testing partition\n",
    "test_data = data.skip(round(len(data)*.7))\n",
    "test_data = test_data.take(round(len(data)*.3))\n",
    "test_data = test_data.batch(16)\n",
    "test_data = test_data.prefetch(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62d8e0bc",
   "metadata": {},
   "source": [
    "# 4. Model Engineering "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d76df6d",
   "metadata": {},
   "source": [
    "# 4.1 Build Embedding Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "23699f3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_embedding():\n",
    "    inp = Input(shape=(105, 105, 3), name='input_image')\n",
    "    \n",
    "    # 1st block\n",
    "    c1 = Conv2D(64,(10,10), activation = 'relu')(inp)\n",
    "    m1 = MaxPooling2D(64, (2,2), padding='same')(c1)\n",
    "    \n",
    "    # 2nd block\n",
    "    c2 = Conv2D(128, (7,7), activation = 'relu')(m1)\n",
    "    m2 = MaxPooling2D(64, (2,2), padding= 'same')(c2)\n",
    "    \n",
    "    # 3rd block\n",
    "    c3 = Conv2D(128, (4,4), activation = 'relu')(m2)\n",
    "    m3 = MaxPooling2D(64, (2,2), padding= 'same')(c3)\n",
    "    \n",
    "    # 4th / final embedding block\n",
    "    c4 = Conv2D(256, (4,4), activation= 'relu')(m3)\n",
    "    f1 = Flatten()(c4)                              # flatenning the output\n",
    "    d1 = Dense(4096, activation= 'sigmoid')(f1)     # making the flatten layer to a dense layer\n",
    "    \n",
    "    \n",
    "    return Model(inputs=[inp], outputs=[d1], name='embedding')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2ddc1909",
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding = make_embedding()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "dbb681d3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"embedding\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_image (InputLayer)    [(None, 105, 105, 3)]     0         \n",
      "                                                                 \n",
      " conv2d (Conv2D)             (None, 96, 96, 64)        19264     \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 48, 48, 64)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 42, 42, 128)       401536    \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 21, 21, 128)      0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 18, 18, 128)       262272    \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 9, 9, 128)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 6, 6, 256)         524544    \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 9216)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 4096)              37752832  \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 38,960,448\n",
      "Trainable params: 38,960,448\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "embedding.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9f9d063",
   "metadata": {},
   "source": [
    "## 4.2 Build Distance Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6b1c17b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "class L1Dist(Layer):               # siamese L1 distance class \n",
    "     \n",
    "    def __init__(self, **kwargs):  # inheritance \n",
    "        super().__init__()\n",
    "        \n",
    "    # similarity calculation     \n",
    "    def call(self, input_embedding, validation_embedding):\n",
    "        return tf.math.abs(input_embedding - validation_embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a953cde4",
   "metadata": {},
   "outputs": [],
   "source": [
    "l1 = L1Dist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b1b9b3bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.L1Dist at 0x7ff7c4618940>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c801ea22",
   "metadata": {},
   "source": [
    "## 4.3 Make Siamese Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "253b03d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_image = Input(name= 'input_img', shape= (105,105,3)) \n",
    "validation_image = Input(name= 'validation_img', shape = (105,105,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e9a3767a",
   "metadata": {},
   "outputs": [],
   "source": [
    "inp_embedding = embedding(input_image)\n",
    "val_embedding = embedding(validation_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "262153e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KerasTensor: shape=(None, 4096) dtype=float32 (created by layer 'embedding')>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp_embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "904c56b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KerasTensor: shape=(None, 4096) dtype=float32 (created by layer 'embedding')>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "4d51e157",
   "metadata": {},
   "outputs": [],
   "source": [
    "siamese_layer = L1Dist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "bb4f2411",
   "metadata": {},
   "outputs": [],
   "source": [
    "distances = siamese_layer(inp_embedding, val_embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e408cf44",
   "metadata": {},
   "outputs": [],
   "source": [
    "classsifier = Dense(1, activation='sigmoid')(distances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "34330b09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KerasTensor: shape=(None, 1) dtype=float32 (created by layer 'dense_1')>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classsifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "cf5f0015",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_siamese_model():\n",
    "    \n",
    "    input_image = Input(name= 'input_img', shape= (105,105,3)) # anchor image in the network\n",
    "    validation_image = Input(name= 'validation_img', shape = (105,105,3)) # validation image in the network\n",
    "    \n",
    "    \n",
    "    # combine siamese distance components\n",
    "    siamese_layer = L1Dist()\n",
    "    siamese_layer._name = 'distance'\n",
    "    distances = siamese_layer(embedding(input_image), embedding(validation_image))\n",
    "    \n",
    "    # clasification layer\n",
    "    classsifier = Dense(1, activation='sigmoid')(distances)\n",
    "    \n",
    "    return Model(inputs=[input_image, validation_image], outputs= classsifier, name='SiameseNetwork')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0dd4fbe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "siamese_model = make_siamese_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "0d03ce8b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"SiameseNetwork\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_img (InputLayer)         [(None, 105, 105, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " validation_img (InputLayer)    [(None, 105, 105, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " embedding (Functional)         (None, 4096)         38960448    ['input_img[0][0]',              \n",
      "                                                                  'validation_img[0][0]']         \n",
      "                                                                                                  \n",
      " distance (L1Dist)              (None, 4096)         0           ['embedding[2][0]',              \n",
      "                                                                  'embedding[3][0]']              \n",
      "                                                                                                  \n",
      " dense_2 (Dense)                (None, 1)            4097        ['distance[0][0]']               \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 38,964,545\n",
      "Trainable params: 38,964,545\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "siamese_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1da99d78",
   "metadata": {},
   "source": [
    "# 5. Training Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4f53887",
   "metadata": {},
   "source": [
    "## 5.1 Setup Loss and Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "08eb3375",
   "metadata": {},
   "outputs": [],
   "source": [
    "binary_cross_loss = tf.losses.BinaryCrossentropy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "72ac9490",
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = tf.keras.optimizers.Adam(1e-4) # 0.0001"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f909f699",
   "metadata": {},
   "source": [
    "## 5.2 Establish Checkpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "48fa088d",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_dir = './training_checkpoints'\n",
    "checkpoint_prefix = os.path.join(checkpoint_dir, 'ckpt')\n",
    "checkpoint = tf.train.Checkpoint(opt=opt, siamese_model= siamese_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b892f0b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e63cc082",
   "metadata": {},
   "source": [
    "## 5.3 Build Train Step Function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "1f42f78c",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_batch = train_data.as_numpy_iterator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "75d301cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_1 = test_batch.next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f0f659e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(batch_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "673063e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_1 = test_batch.next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "eed49603",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = batch_1[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "97ebbaf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 16, 105, 105, 3)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(X).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "de7314c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = batch_1[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "caaa7f9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 1., 0.],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "a43b610e",
   "metadata": {},
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def train_step(batch):\n",
    "    \n",
    "    with tf.GradientTape() as tape:\n",
    "        \n",
    "        X = batch[:2] # get anchor and positive/negative image\n",
    "        \n",
    "        Y = batch[2] # get label\n",
    "        \n",
    "        \n",
    "        # forwoard pass\n",
    "        yhat = siamese_model(X, training= True)\n",
    "        \n",
    "        # calculate losss\n",
    "        loss = binary_cross_loss(Y, yhat)\n",
    "    print(loss)\n",
    "        \n",
    "    # calculate gradients\n",
    "    grad = tape.gradient(loss, siamese_model.trainable_variables)\n",
    "    \n",
    "    # calculate updated weights and apply to siamese model\n",
    "    opt.apply_gradients(zip(grad,siamese_model.trainable_variables))\n",
    "    \n",
    "    return loss\n",
    "\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9768268",
   "metadata": {},
   "source": [
    "## 5.4 Build Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "32c28efe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import metric calculations\n",
    "from tensorflow.keras.metrics import Precision, Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "b248346d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(data, EPOCHS):\n",
    "    # Loop through epochs\n",
    "    for epoch in range(1, EPOCHS+1):\n",
    "        print('\\n Epoch {}/{}'.format(epoch, EPOCHS))\n",
    "        progbar = tf.keras.utils.Progbar(len(data))\n",
    "        \n",
    "        # Creating a metric object \n",
    "        r = Recall()\n",
    "        p = Precision()\n",
    "        \n",
    "        # Loop through each batch\n",
    "        for idx, batch in enumerate(data):\n",
    "            # Run train step here\n",
    "            loss = train_step(batch)\n",
    "            yhat = siamese_model.predict(batch[:2])\n",
    "            r.update_state(batch[2], yhat)\n",
    "            p.update_state(batch[2], yhat) \n",
    "            progbar.update(idx+1)\n",
    "        print(loss.numpy(), r.result().numpy(), p.result().numpy())\n",
    "        \n",
    "        # Save checkpoints\n",
    "        if epoch % 10 == 0: \n",
    "            checkpoint.save(file_prefix=checkpoint_prefix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcc4ddbb",
   "metadata": {},
   "source": [
    "## 5.5 Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "3a5e2fa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "097ff6f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Epoch 1/50\n",
      "Tensor(\"binary_crossentropy/weighted_loss/value:0\", shape=(), dtype=float32)\n",
      "Tensor(\"binary_crossentropy/weighted_loss/value:0\", shape=(), dtype=float32)\n",
      "1/1 [==============================] - 0s 382ms/step\n",
      "1/1 [==============================] - 0s 325ms/step\n",
      "1/1 [==============================] - 0s 309ms/step\n",
      "1/1 [==============================] - 0s 315ms/step\n",
      "1/1 [==============================] - 0s 309ms/step\n",
      "1/1 [==============================] - 0s 334ms/step\n",
      "1/1 [==============================] - 0s 331ms/step\n",
      "1/1 [==============================] - 0s 331ms/step\n",
      "1/1 [==============================] - 0s 322ms/step\n",
      "1/1 [==============================] - 0s 304ms/step\n",
      "1/1 [==============================] - 0s 300ms/step\n",
      "1/1 [==============================] - 0s 302ms/step\n",
      "1/1 [==============================] - 0s 306ms/step\n",
      "1/1 [==============================] - 0s 304ms/step\n",
      "1/1 [==============================] - 0s 309ms/step\n",
      "1/1 [==============================] - 0s 303ms/step\n",
      "1/1 [==============================] - 0s 323ms/step\n",
      "1/1 [==============================] - 0s 328ms/step\n",
      "1/1 [==============================] - 0s 308ms/step\n",
      "1/1 [==============================] - 0s 310ms/step\n",
      "1/1 [==============================] - 0s 305ms/step\n",
      "1/1 [==============================] - 0s 308ms/step\n",
      "1/1 [==============================] - 0s 311ms/step\n",
      "1/1 [==============================] - 0s 303ms/step\n",
      "1/1 [==============================] - 0s 308ms/step\n",
      "1/1 [==============================] - 0s 306ms/step\n",
      "26/27 [===========================>..] - ETA: 1sTensor(\"binary_crossentropy/weighted_loss/value:0\", shape=(), dtype=float32)\n",
      "1/1 [==============================] - 0s 117ms/step\n",
      "27/27 [==============================] - 40s 1s/step\n",
      "0.056189932 0.92537314 0.9893617\n",
      "\n",
      " Epoch 2/50\n",
      "1/1 [==============================] - 0s 320ms/step\n",
      "1/1 [==============================] - 0s 316ms/step\n",
      "1/1 [==============================] - 0s 306ms/step\n",
      "1/1 [==============================] - 0s 303ms/step\n",
      "1/1 [==============================] - 0s 305ms/step\n",
      "1/1 [==============================] - 0s 302ms/step\n",
      "1/1 [==============================] - 0s 327ms/step\n",
      "1/1 [==============================] - 0s 315ms/step\n",
      "1/1 [==============================] - 0s 303ms/step\n",
      "1/1 [==============================] - 0s 311ms/step\n",
      "1/1 [==============================] - 0s 312ms/step\n",
      "1/1 [==============================] - 0s 319ms/step\n",
      "1/1 [==============================] - 0s 324ms/step\n",
      "1/1 [==============================] - 0s 307ms/step\n",
      "1/1 [==============================] - 0s 313ms/step\n",
      "1/1 [==============================] - 0s 306ms/step\n",
      "1/1 [==============================] - 0s 304ms/step\n",
      "1/1 [==============================] - 0s 304ms/step\n",
      "1/1 [==============================] - 0s 308ms/step\n",
      "1/1 [==============================] - 0s 319ms/step\n",
      "1/1 [==============================] - 0s 312ms/step\n",
      "1/1 [==============================] - 0s 304ms/step\n",
      "1/1 [==============================] - 0s 317ms/step\n",
      "1/1 [==============================] - 0s 331ms/step\n",
      "1/1 [==============================] - 0s 302ms/step\n",
      "1/1 [==============================] - 0s 298ms/step\n",
      "1/1 [==============================] - 0s 109ms/step\n",
      "27/27 [==============================] - 39s 1s/step\n",
      "0.008811635 0.98623854 1.0\n",
      "\n",
      " Epoch 3/50\n",
      "1/1 [==============================] - 0s 303ms/step\n",
      "1/1 [==============================] - 0s 309ms/step\n",
      "1/1 [==============================] - 0s 309ms/step\n",
      "1/1 [==============================] - 0s 311ms/step\n",
      "1/1 [==============================] - 0s 315ms/step\n",
      "1/1 [==============================] - 0s 306ms/step\n",
      "1/1 [==============================] - 0s 309ms/step\n",
      "1/1 [==============================] - 0s 338ms/step\n",
      "1/1 [==============================] - 0s 320ms/step\n",
      "1/1 [==============================] - 0s 302ms/step\n",
      "1/1 [==============================] - 0s 303ms/step\n",
      "1/1 [==============================] - 0s 303ms/step\n",
      "1/1 [==============================] - 0s 304ms/step\n",
      "1/1 [==============================] - 0s 300ms/step\n",
      "1/1 [==============================] - 0s 312ms/step\n",
      "1/1 [==============================] - 0s 307ms/step\n",
      "1/1 [==============================] - 0s 311ms/step\n",
      "1/1 [==============================] - 0s 304ms/step\n",
      "1/1 [==============================] - 0s 319ms/step\n",
      "1/1 [==============================] - 0s 325ms/step\n",
      "1/1 [==============================] - 0s 309ms/step\n",
      "1/1 [==============================] - 0s 305ms/step\n",
      "1/1 [==============================] - 0s 308ms/step\n",
      "1/1 [==============================] - 0s 309ms/step\n",
      "1/1 [==============================] - 0s 306ms/step\n",
      "1/1 [==============================] - 0s 308ms/step\n",
      "1/1 [==============================] - 0s 114ms/step\n",
      "27/27 [==============================] - 38s 1s/step\n",
      "0.00011113278 1.0 1.0\n",
      "\n",
      " Epoch 4/50\n",
      "1/1 [==============================] - 0s 314ms/step\n",
      "1/1 [==============================] - 0s 303ms/step\n",
      "1/1 [==============================] - 0s 301ms/step\n",
      "1/1 [==============================] - 0s 327ms/step\n",
      "1/1 [==============================] - 0s 308ms/step\n",
      "1/1 [==============================] - 0s 301ms/step\n",
      "1/1 [==============================] - 0s 314ms/step\n",
      "1/1 [==============================] - 0s 307ms/step\n",
      "1/1 [==============================] - 0s 320ms/step\n",
      "1/1 [==============================] - 0s 312ms/step\n",
      "1/1 [==============================] - 0s 305ms/step\n",
      "1/1 [==============================] - 0s 303ms/step\n",
      "1/1 [==============================] - 0s 304ms/step\n",
      "1/1 [==============================] - 0s 309ms/step\n",
      "1/1 [==============================] - 0s 324ms/step\n",
      "1/1 [==============================] - 0s 316ms/step\n",
      "1/1 [==============================] - 0s 305ms/step\n",
      "1/1 [==============================] - 0s 303ms/step\n",
      "1/1 [==============================] - 0s 300ms/step\n",
      "1/1 [==============================] - 0s 301ms/step\n",
      "1/1 [==============================] - 0s 308ms/step\n",
      "1/1 [==============================] - 0s 302ms/step\n",
      "1/1 [==============================] - 0s 305ms/step\n",
      "1/1 [==============================] - 0s 307ms/step\n",
      "1/1 [==============================] - 0s 302ms/step\n",
      "1/1 [==============================] - 0s 315ms/step\n",
      "1/1 [==============================] - 0s 116ms/step\n",
      "27/27 [==============================] - 38s 1s/step\n",
      "6.374998e-05 1.0 1.0\n",
      "\n",
      " Epoch 5/50\n",
      "1/1 [==============================] - 0s 304ms/step\n",
      "1/1 [==============================] - 0s 300ms/step\n",
      "1/1 [==============================] - 0s 303ms/step\n",
      "1/1 [==============================] - 0s 312ms/step\n",
      "1/1 [==============================] - 0s 305ms/step\n",
      "1/1 [==============================] - 0s 312ms/step\n",
      "1/1 [==============================] - 0s 305ms/step\n",
      "1/1 [==============================] - 0s 305ms/step\n",
      "1/1 [==============================] - 0s 307ms/step\n",
      "1/1 [==============================] - 0s 306ms/step\n",
      "1/1 [==============================] - 0s 344ms/step\n",
      "1/1 [==============================] - 0s 320ms/step\n",
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      "0.10924246 1.0 1.0\n",
      "\n",
      " Epoch 6/50\n",
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      "\n",
      " Epoch 7/50\n",
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      "\n",
      " Epoch 8/50\n",
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      "\n",
      " Epoch 9/50\n",
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      "0.13678963 0.98067635 0.9902439\n",
      "\n",
      " Epoch 10/50\n",
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      "\n",
      " Epoch 11/50\n",
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      "\n",
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      "\n",
      " Epoch 13/50\n",
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      "\n",
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      "\n",
      " Epoch 15/50\n",
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      "\n",
      " Epoch 16/50\n",
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    },
    {
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     "text": [
      "8.590427e-05 1.0 1.0\n",
      "\n",
      " Epoch 17/50\n",
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      "\n",
      " Epoch 18/50\n",
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      "0.00017777579 1.0 1.0\n",
      "\n",
      " Epoch 19/50\n",
      "1/1 [==============================] - 0s 306ms/step\n",
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      "4.3958685e-06 1.0 1.0\n",
      "\n",
      " Epoch 20/50\n",
      "1/1 [==============================] - 0s 321ms/step\n",
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      "27/27 [==============================] - 39s 1s/step\n",
      "2.5481072e-06 1.0 1.0\n",
      "\n",
      " Epoch 21/50\n",
      "1/1 [==============================] - 0s 310ms/step\n",
      "1/1 [==============================] - 0s 309ms/step\n",
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      "27/27 [==============================] - 38s 1s/step\n",
      "0.00017181538 1.0 1.0\n",
      "\n",
      " Epoch 22/50\n",
      "1/1 [==============================] - 0s 304ms/step\n",
      "1/1 [==============================] - 0s 304ms/step\n",
      "1/1 [==============================] - 0s 324ms/step\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 300ms/step\n",
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      "3.9356666e-05 1.0 1.0\n",
      "\n",
      " Epoch 23/50\n",
      "1/1 [==============================] - 0s 306ms/step\n",
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      "3.2931666e-06 1.0 1.0\n",
      "\n",
      " Epoch 24/50\n",
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      "2.875932e-06 1.0 1.0\n",
      "\n",
      " Epoch 25/50\n",
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      "5.2154087e-07 1.0 1.0\n",
      "\n",
      " Epoch 26/50\n",
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      "1.926793e-05 1.0 1.0\n",
      "\n",
      " Epoch 27/50\n",
      "1/1 [==============================] - 0s 297ms/step\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 314ms/step\n",
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      "0.00011947597 1.0 1.0\n",
      "\n",
      " Epoch 28/50\n",
      "1/1 [==============================] - 0s 314ms/step\n",
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      "1.1920946e-06 1.0 1.0\n",
      "\n",
      " Epoch 29/50\n",
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      "1.3411046e-07 1.0 1.0\n",
      "\n",
      " Epoch 30/50\n",
      "1/1 [==============================] - 0s 306ms/step\n",
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      "7.450585e-07 1.0 1.0\n",
      "\n",
      " Epoch 31/50\n",
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      "1.2666003e-06 1.0 1.0\n",
      "\n",
      " Epoch 32/50\n",
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      "6.258491e-07 1.0 1.0\n",
      "\n",
      " Epoch 33/50\n",
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     ]
    },
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     "text": [
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      "4.8237413e-05 1.0 1.0\n",
      "\n",
      " Epoch 34/50\n",
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      "\n",
      " Epoch 35/50\n",
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      "1.4454533e-05 1.0 1.0\n",
      "\n",
      " Epoch 36/50\n",
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      "2.9802322e-08 1.0 1.0\n",
      "\n",
      " Epoch 37/50\n",
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      "1.4156499e-05 1.0 1.0\n",
      "\n",
      " Epoch 38/50\n",
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     ]
    },
    {
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     "text": [
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      "\n",
      " Epoch 39/50\n",
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      "\n",
      " Epoch 40/50\n",
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      "0.0 1.0 1.0\n",
      "\n",
      " Epoch 41/50\n",
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      "\n",
      " Epoch 42/50\n",
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      "1.639128e-07 1.0 1.0\n",
      "\n",
      " Epoch 43/50\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 306ms/step\n",
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      "2.9802322e-08 1.0 1.0\n",
      "\n",
      " Epoch 44/50\n",
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      "4.4852873e-06 1.0 1.0\n",
      "\n",
      " Epoch 45/50\n",
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      "1/1 [==============================] - 0s 303ms/step\n",
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      "1/1 [==============================] - 0s 339ms/step\n",
      "1/1 [==============================] - 0s 121ms/step\n",
      "27/27 [==============================] - 38s 1s/step\n",
      "2.0861633e-07 1.0 1.0\n",
      "\n",
      " Epoch 46/50\n",
      "1/1 [==============================] - 0s 308ms/step\n",
      "1/1 [==============================] - 0s 304ms/step\n",
      "1/1 [==============================] - 0s 305ms/step\n",
      "1/1 [==============================] - 0s 297ms/step\n",
      "1/1 [==============================] - 0s 307ms/step\n",
      "1/1 [==============================] - 0s 313ms/step\n",
      "1/1 [==============================] - 0s 295ms/step\n",
      "1/1 [==============================] - 0s 302ms/step\n",
      "1/1 [==============================] - 0s 309ms/step\n",
      "1/1 [==============================] - 0s 330ms/step\n",
      "1/1 [==============================] - 0s 326ms/step\n",
      "1/1 [==============================] - 0s 310ms/step\n",
      "1/1 [==============================] - 0s 306ms/step\n",
      "1/1 [==============================] - 0s 305ms/step\n",
      "1/1 [==============================] - 0s 304ms/step\n",
      "1/1 [==============================] - 0s 294ms/step\n",
      "1/1 [==============================] - 0s 309ms/step\n",
      "1/1 [==============================] - 0s 299ms/step\n",
      "1/1 [==============================] - 0s 298ms/step\n",
      "1/1 [==============================] - 0s 305ms/step\n",
      "1/1 [==============================] - 0s 307ms/step\n",
      "1/1 [==============================] - 0s 317ms/step\n",
      "1/1 [==============================] - 0s 318ms/step\n",
      "1/1 [==============================] - 0s 306ms/step\n",
      "1/1 [==============================] - 0s 305ms/step\n",
      "1/1 [==============================] - 0s 305ms/step\n",
      "1/1 [==============================] - 0s 112ms/step\n",
      "27/27 [==============================] - 38s 1s/step\n",
      "4.4703487e-08 1.0 1.0\n",
      "\n",
      " Epoch 47/50\n",
      "1/1 [==============================] - 0s 306ms/step\n",
      "1/1 [==============================] - 0s 304ms/step\n",
      "1/1 [==============================] - 0s 304ms/step\n",
      "1/1 [==============================] - 0s 301ms/step\n",
      "1/1 [==============================] - 0s 310ms/step\n",
      "1/1 [==============================] - 0s 320ms/step\n",
      "1/1 [==============================] - 0s 326ms/step\n",
      "1/1 [==============================] - 0s 303ms/step\n",
      "1/1 [==============================] - 0s 304ms/step\n",
      "1/1 [==============================] - 0s 299ms/step\n",
      "1/1 [==============================] - 0s 305ms/step\n",
      "1/1 [==============================] - 0s 306ms/step\n",
      "1/1 [==============================] - 0s 312ms/step\n",
      "1/1 [==============================] - 0s 302ms/step\n",
      "1/1 [==============================] - 0s 299ms/step\n",
      "1/1 [==============================] - 0s 306ms/step\n",
      "1/1 [==============================] - 0s 341ms/step\n",
      "1/1 [==============================] - 0s 337ms/step\n",
      "1/1 [==============================] - 0s 343ms/step\n",
      "1/1 [==============================] - 0s 305ms/step\n",
      "1/1 [==============================] - 0s 308ms/step\n",
      "1/1 [==============================] - 0s 305ms/step\n",
      "1/1 [==============================] - 0s 309ms/step\n",
      "1/1 [==============================] - 0s 317ms/step\n",
      "1/1 [==============================] - 0s 307ms/step\n",
      "1/1 [==============================] - 0s 305ms/step\n",
      "1/1 [==============================] - 0s 110ms/step\n",
      "27/27 [==============================] - 39s 1s/step\n",
      "1.1920932e-07 1.0 1.0\n",
      "\n",
      " Epoch 48/50\n",
      "1/1 [==============================] - 0s 308ms/step\n",
      "1/1 [==============================] - 0s 324ms/step\n",
      "1/1 [==============================] - 0s 301ms/step\n",
      "1/1 [==============================] - 0s 300ms/step\n",
      "1/1 [==============================] - 0s 306ms/step\n",
      "1/1 [==============================] - 0s 300ms/step\n",
      "1/1 [==============================] - 0s 303ms/step\n",
      "1/1 [==============================] - 0s 307ms/step\n",
      "1/1 [==============================] - 0s 304ms/step\n",
      "1/1 [==============================] - 0s 303ms/step\n",
      "1/1 [==============================] - 0s 301ms/step\n",
      "1/1 [==============================] - 0s 290ms/step\n",
      "1/1 [==============================] - 0s 319ms/step\n",
      "1/1 [==============================] - 0s 324ms/step\n",
      "1/1 [==============================] - 0s 303ms/step\n",
      "1/1 [==============================] - 0s 304ms/step\n",
      "1/1 [==============================] - 0s 299ms/step\n",
      "1/1 [==============================] - 0s 296ms/step\n",
      "1/1 [==============================] - 0s 310ms/step\n",
      "1/1 [==============================] - 0s 304ms/step\n",
      "1/1 [==============================] - 0s 308ms/step\n",
      "1/1 [==============================] - 0s 299ms/step\n",
      "1/1 [==============================] - 0s 309ms/step\n",
      "1/1 [==============================] - 0s 316ms/step\n",
      "1/1 [==============================] - 0s 325ms/step\n",
      "1/1 [==============================] - 0s 311ms/step\n",
      "1/1 [==============================] - 0s 112ms/step\n",
      "27/27 [==============================] - 38s 1s/step\n",
      "2.9802322e-08 1.0 1.0\n",
      "\n",
      " Epoch 49/50\n",
      "1/1 [==============================] - 0s 309ms/step\n",
      "1/1 [==============================] - 0s 298ms/step\n",
      "1/1 [==============================] - 0s 331ms/step\n",
      "1/1 [==============================] - 0s 304ms/step\n",
      "1/1 [==============================] - 0s 304ms/step\n",
      "1/1 [==============================] - 0s 301ms/step\n",
      "1/1 [==============================] - 0s 304ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 315ms/step\n",
      "1/1 [==============================] - 0s 321ms/step\n",
      "1/1 [==============================] - 0s 303ms/step\n",
      "1/1 [==============================] - 0s 301ms/step\n",
      "1/1 [==============================] - 0s 306ms/step\n",
      "1/1 [==============================] - 0s 309ms/step\n",
      "1/1 [==============================] - 0s 308ms/step\n",
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      "1/1 [==============================] - 0s 335ms/step\n",
      "1/1 [==============================] - 0s 308ms/step\n",
      "1/1 [==============================] - 0s 302ms/step\n",
      "1/1 [==============================] - 0s 308ms/step\n",
      "1/1 [==============================] - 0s 306ms/step\n",
      "1/1 [==============================] - 0s 301ms/step\n",
      "1/1 [==============================] - 0s 306ms/step\n",
      "1/1 [==============================] - 0s 110ms/step\n",
      "27/27 [==============================] - 38s 1s/step\n",
      "4.1723547e-06 1.0 1.0\n",
      "\n",
      " Epoch 50/50\n",
      "1/1 [==============================] - 0s 307ms/step\n",
      "1/1 [==============================] - 0s 301ms/step\n",
      "1/1 [==============================] - 0s 308ms/step\n",
      "1/1 [==============================] - 0s 323ms/step\n",
      "1/1 [==============================] - 0s 327ms/step\n",
      "1/1 [==============================] - 0s 315ms/step\n",
      "1/1 [==============================] - 0s 307ms/step\n",
      "1/1 [==============================] - 0s 327ms/step\n",
      "1/1 [==============================] - 0s 320ms/step\n",
      "1/1 [==============================] - 0s 321ms/step\n",
      "1/1 [==============================] - 0s 304ms/step\n",
      "1/1 [==============================] - 0s 325ms/step\n",
      "1/1 [==============================] - 0s 307ms/step\n",
      "1/1 [==============================] - 0s 307ms/step\n",
      "1/1 [==============================] - 0s 337ms/step\n",
      "1/1 [==============================] - 0s 314ms/step\n",
      "1/1 [==============================] - 0s 314ms/step\n",
      "1/1 [==============================] - 0s 324ms/step\n",
      "1/1 [==============================] - 0s 301ms/step\n",
      "1/1 [==============================] - 0s 303ms/step\n",
      "1/1 [==============================] - 0s 316ms/step\n",
      "1/1 [==============================] - 0s 312ms/step\n",
      "1/1 [==============================] - 0s 307ms/step\n",
      "1/1 [==============================] - 0s 301ms/step\n",
      "1/1 [==============================] - 0s 305ms/step\n",
      "1/1 [==============================] - 0s 325ms/step\n",
      "1/1 [==============================] - 0s 113ms/step\n",
      "27/27 [==============================] - 39s 1s/step\n",
      "1.6391282e-07 1.0 1.0\n"
     ]
    }
   ],
   "source": [
    "train(train_data, EPOCHS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f5e4a58",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "08a2f03a",
   "metadata": {},
   "source": [
    "# 6. Evaluate Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdca1dfd",
   "metadata": {},
   "source": [
    "## 6.1 Import Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "1773c912",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import metric calculations\n",
    "from tensorflow.keras.metrics import Precision, Recall"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17257fae",
   "metadata": {},
   "source": [
    "## 6.2 Make Predictions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "fad07ec4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-26 10:31:53.442696: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_18' with dtype float and shape [300]\n",
      "\t [[{{node Placeholder/_18}}]]\n",
      "2023-05-26 10:31:53.443140: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_39' with dtype float and shape [300]\n",
      "\t [[{{node Placeholder/_39}}]]\n"
     ]
    }
   ],
   "source": [
    "# get a batch of test data\n",
    "test_input, test_val, y_true = test_data.as_numpy_iterator().next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "f4ba027c",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_var = test_data.as_numpy_iterator().next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "6b8800f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 1., 1., 0., 0., 0., 0., 1., 0., 0., 1., 1., 1., 0.],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_var[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "dd2b3af0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 0., 0., 1., 0., 0., 0., 0., 0., 1., 1., 0., 0., 1., 0.],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "430555d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 298ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1.0000000e+00],\n",
       "       [1.0000000e+00],\n",
       "       [2.8399708e-07],\n",
       "       [8.6050328e-08],\n",
       "       [1.0000000e+00],\n",
       "       [8.6433653e-07],\n",
       "       [4.1240259e-08],\n",
       "       [1.4265764e-05],\n",
       "       [8.9842437e-08],\n",
       "       [6.3369621e-06],\n",
       "       [9.9999809e-01],\n",
       "       [1.0000000e+00],\n",
       "       [9.5334990e-08],\n",
       "       [1.0323652e-06],\n",
       "       [1.0000000e+00],\n",
       "       [9.2817302e-08]], dtype=float32)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predictions\n",
    "y_hat = siamese_model.predict([test_input, test_val])\n",
    "y_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "c2f8cc55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# post processing the results\n",
    "res = []\n",
    "for prediction in y_hat:\n",
    "    if prediction > 0.9:\n",
    "        res.append(1)\n",
    "    else:\n",
    "        res.append(0)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "997d57c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 0., 0., 1., 0., 0., 0., 0., 0., 1., 1., 0., 0., 1., 0.],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_true"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52d17814",
   "metadata": {},
   "source": [
    "## 6.3 Calculate Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "1d2756dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a metric object with Recall\n",
    "m = Recall()\n",
    "\n",
    "# calculating the recall value\n",
    "m.update_state(y_true, y_hat)\n",
    "\n",
    "# recall result\n",
    "m.result().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "1b8063b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a metric object with Precision\n",
    "m = Precision()\n",
    "\n",
    "# calculating the recall value\n",
    "m.update_state(y_true, y_hat)\n",
    "\n",
    "# recall result\n",
    "m.result().numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75a2df29",
   "metadata": {},
   "source": [
    "## 6.4 Visualizing results  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "e7d5d572",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))  # plot size\n",
    "\n",
    "# first sub plot\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(test_input[9])   # res indexing image references\n",
    "\n",
    "# second sub plot\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(test_val[9])     # y_true indexing image references \n",
    "\n",
    "# rendering \n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c52b3ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "590a7b51",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d6529b8e",
   "metadata": {},
   "source": [
    "# 7. Save Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "d7300bd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    }
   ],
   "source": [
    "# save weight\n",
    "siamese_model.save('SiameseModel.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "479ee888",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    }
   ],
   "source": [
    "# reload model\n",
    "model = tf.keras.models.load_model('SiameseModel.h5',\n",
    "                                   custom_objects={'L1Dist':L1Dist, 'BinaryCrossentropy':tf.losses.BinaryCrossentropy})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "381125c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 358ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1.0000000e+00],\n",
       "       [1.0000000e+00],\n",
       "       [2.8399708e-07],\n",
       "       [8.6050328e-08],\n",
       "       [1.0000000e+00],\n",
       "       [8.6433653e-07],\n",
       "       [4.1240259e-08],\n",
       "       [1.4265764e-05],\n",
       "       [8.9842437e-08],\n",
       "       [6.3369621e-06],\n",
       "       [9.9999809e-01],\n",
       "       [1.0000000e+00],\n",
       "       [9.5334990e-08],\n",
       "       [1.0323652e-06],\n",
       "       [1.0000000e+00],\n",
       "       [9.2817302e-08]], dtype=float32)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make predictions with reloaded model\n",
    "model.predict([test_input, test_val])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "617e6b52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"SiameseNetwork\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_img (InputLayer)         [(None, 105, 105, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " validation_img (InputLayer)    [(None, 105, 105, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " embedding (Functional)         (None, 4096)         38960448    ['input_img[0][0]',              \n",
      "                                                                  'validation_img[0][0]']         \n",
      "                                                                                                  \n",
      " l1_dist_3 (L1Dist)             (None, 4096)         0           ['embedding[0][0]',              \n",
      "                                                                  'embedding[1][0]']              \n",
      "                                                                                                  \n",
      " dense_2 (Dense)                (None, 1)            4097        ['l1_dist_3[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 38,964,545\n",
      "Trainable params: 38,964,545\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary() # model summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "137a9de1",
   "metadata": {},
   "source": [
    "# 8. Real Time Test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09b7e8f1",
   "metadata": {},
   "source": [
    "## 8.1 Verification Function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a45f5b91",
   "metadata": {},
   "source": [
    "- I have created 1 main folder 'application_data' and 2 sub folders 'input_image' and 'verification_images inside 'application_data'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dd7a995",
   "metadata": {},
   "source": [
    "- I have also copied 50 positive samples from 'POS_PATH' to 'verification_images' "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4f7d181",
   "metadata": {},
   "source": [
    "- We will save our live input image to 'input_image' folder and compare it with our positive samples in 'verification_images' "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4c0beea",
   "metadata": {},
   "source": [
    "- For better accuracy we will only verify the input image when it is matched more then 60% of the images in 'verification_images'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "3ce23f85",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(os.path.join('application_data', 'verification_images')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "dac856b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def verify(model, detection_threshold, verification_threshold):\n",
    "    # Build results array\n",
    "    results = []\n",
    "    for image in os.listdir(os.path.join('application_data', 'verification_images')):\n",
    "        input_img = preprocess(os.path.join('application_data', 'input_image', 'input_image.jpg'))\n",
    "        validation_img = preprocess(os.path.join('application_data', 'verification_images', image))\n",
    "        \n",
    "        # Make Predictions \n",
    "        result = model.predict(list(np.expand_dims([input_img, validation_img], axis=1)))\n",
    "        results.append(result)\n",
    "    \n",
    "    # Detection Threshold: Metric above which a prediciton is considered positive \n",
    "    detection = np.sum(np.array(results) > detection_threshold)\n",
    "    \n",
    "    # Verification Threshold: Proportion of positive predictions / total positive samples \n",
    "    verification = detection / len(os.listdir(os.path.join('application_data', 'verification_images'))) \n",
    "    verified = verification > verification_threshold\n",
    "    \n",
    "    return results, verified"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba727ade",
   "metadata": {},
   "source": [
    "## 8.2 OpenCV Real Time Verification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "f36a455e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n",
      "QObject::moveToThread: Current thread (0x6a7a000) is not the object's thread (0x729d270).\n",
      "Cannot move to target thread (0x6a7a000)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 59ms/step\n",
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      "1/1 [==============================] - 0s 56ms/step\n",
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      "1/1 [==============================] - 0s 64ms/step\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "1/1 [==============================] - 0s 52ms/step\n",
      "False\n",
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      "1/1 [==============================] - 0s 49ms/step\n",
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      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 47ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "True\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
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      "1/1 [==============================] - 0s 50ms/step\n",
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      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
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      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 45ms/step\n",
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      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 45ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 54ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "1/1 [==============================] - 0s 54ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "1/1 [==============================] - 0s 56ms/step\n",
      "1/1 [==============================] - 0s 53ms/step\n",
      "1/1 [==============================] - 0s 53ms/step\n",
      "1/1 [==============================] - 0s 53ms/step\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "1/1 [==============================] - 0s 47ms/step\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "True\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 49ms/step\n",
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      "1/1 [==============================] - 0s 54ms/step\n",
      "1/1 [==============================] - 0s 52ms/step\n",
      "1/1 [==============================] - 0s 55ms/step\n",
      "1/1 [==============================] - 0s 56ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 58ms/step\n",
      "1/1 [==============================] - 0s 55ms/step\n",
      "1/1 [==============================] - 0s 52ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 57ms/step\n",
      "1/1 [==============================] - 0s 55ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 55ms/step\n",
      "False\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 52ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 56ms/step\n",
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      "1/1 [==============================] - 0s 57ms/step\n",
      "1/1 [==============================] - 0s 56ms/step\n",
      "1/1 [==============================] - 0s 52ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 56ms/step\n",
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      "1/1 [==============================] - 0s 51ms/step\n",
      "1/1 [==============================] - 0s 55ms/step\n",
      "1/1 [==============================] - 0s 53ms/step\n",
      "1/1 [==============================] - 0s 54ms/step\n",
      "1/1 [==============================] - 0s 45ms/step\n",
      "1/1 [==============================] - 0s 56ms/step\n",
      "1/1 [==============================] - 0s 57ms/step\n",
      "1/1 [==============================] - 0s 53ms/step\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 53ms/step\n",
      "1/1 [==============================] - 0s 55ms/step\n",
      "1/1 [==============================] - 0s 55ms/step\n",
      "1/1 [==============================] - 0s 53ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 58ms/step\n",
      "1/1 [==============================] - 0s 56ms/step\n",
      "1/1 [==============================] - 0s 54ms/step\n",
      "1/1 [==============================] - 0s 45ms/step\n",
      "1/1 [==============================] - 0s 47ms/step\n",
      "1/1 [==============================] - 0s 55ms/step\n",
      "1/1 [==============================] - 0s 58ms/step\n",
      "1/1 [==============================] - 0s 53ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "1/1 [==============================] - 0s 59ms/step\n",
      "1/1 [==============================] - 0s 55ms/step\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 46ms/step\n",
      "1/1 [==============================] - 0s 54ms/step\n",
      "1/1 [==============================] - 0s 56ms/step\n",
      "False\n",
      "1/1 [==============================] - 0s 46ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 46ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 52ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 45ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 45ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 52ms/step\n",
      "1/1 [==============================] - 0s 53ms/step\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "1/1 [==============================] - 0s 47ms/step\n",
      "1/1 [==============================] - 0s 52ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 56ms/step\n",
      "1/1 [==============================] - 0s 59ms/step\n",
      "1/1 [==============================] - 0s 47ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 68ms/step\n",
      "1/1 [==============================] - 0s 58ms/step\n",
      "1/1 [==============================] - 0s 55ms/step\n",
      "1/1 [==============================] - 0s 63ms/step\n",
      "1/1 [==============================] - 0s 57ms/step\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "1/1 [==============================] - 0s 59ms/step\n",
      "1/1 [==============================] - 0s 58ms/step\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "1/1 [==============================] - 0s 63ms/step\n",
      "1/1 [==============================] - 0s 59ms/step\n",
      "False\n",
      "1/1 [==============================] - 0s 46ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 51ms/step\n",
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      "1/1 [==============================] - 0s 53ms/step\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 52ms/step\n",
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      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 52ms/step\n",
      "1/1 [==============================] - 0s 54ms/step\n",
      "1/1 [==============================] - 0s 54ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 52ms/step\n",
      "1/1 [==============================] - 0s 55ms/step\n",
      "1/1 [==============================] - 0s 52ms/step\n",
      "1/1 [==============================] - 0s 55ms/step\n",
      "1/1 [==============================] - 0s 57ms/step\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "while cap.isOpened():\n",
    "    ret, frame = cap.read()\n",
    "    frame = frame[120:120+250,200:200+250, :]\n",
    "    \n",
    "    cv2.imshow('Verification', frame)\n",
    "    \n",
    "    # Verification trigger\n",
    "    if cv2.waitKey(10) & 0xFF == ord('v'):\n",
    " \n",
    "\n",
    "        cv2.imwrite(os.path.join('application_data', 'input_image', 'input_image.jpg'), frame)\n",
    "        # Run verification\n",
    "        results, verified = verify(siamese_model, 0.7, 0.7)\n",
    "        print(verified)\n",
    "    \n",
    "    if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "        break\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bc64943",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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