AMZ_Products_review.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AMZ_Products_review.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU",
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "3d128511d9c74582aec60e99e35f8ab6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_6ba6b7bea75e4b4984be11ad772c0db1",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_1e4e35363d864130a333a63c5924ffb6",
              "IPY_MODEL_4417362d27144f97a9930abc59a680bd"
            ]
          }
        },
        "6ba6b7bea75e4b4984be11ad772c0db1": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "1e4e35363d864130a333a63c5924ffb6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_b7a12a32d1e54d6c8f07b3cf5d93694b",
            "_dom_classes": [],
            "description": "Downloading: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 213450,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 213450,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_c6fbcc9a8f024e1a8daab1a6e657fea9"
          }
        },
        "4417362d27144f97a9930abc59a680bd": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_05afeb24ba6241db9b53ea8f5ba34e10",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 213k/213k [00:00&lt;00:00, 965kB/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_0f6b9b1da4e6457e956e84f63cf6bd3b"
          }
        },
        "b7a12a32d1e54d6c8f07b3cf5d93694b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "c6fbcc9a8f024e1a8daab1a6e657fea9": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "05afeb24ba6241db9b53ea8f5ba34e10": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "0f6b9b1da4e6457e956e84f63cf6bd3b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "2f420720e3304d3b940d95de78e4e149": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_702967e44f754a9798056d71c96f5e96",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_0d76412c584044fbb0cf87e4f4a05ab1",
              "IPY_MODEL_c11bb0167f7b462e8017e95aec0a075b"
            ]
          }
        },
        "702967e44f754a9798056d71c96f5e96": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "0d76412c584044fbb0cf87e4f4a05ab1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_011248919a0846389b3c71c2e9f5beb2",
            "_dom_classes": [],
            "description": "Downloading: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 433,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 433,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_1abdde1835aa4e4fb10c6cd85392b0e9"
          }
        },
        "c11bb0167f7b462e8017e95aec0a075b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_10e979953bd9430b9dd50347761026e8",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 433/433 [00:00&lt;00:00, 3.03kB/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_73eda9339fa34917a0cbc92dc16ee401"
          }
        },
        "011248919a0846389b3c71c2e9f5beb2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "1abdde1835aa4e4fb10c6cd85392b0e9": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "10e979953bd9430b9dd50347761026e8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "73eda9339fa34917a0cbc92dc16ee401": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "cf4b843b6da145e283226f360dc8b125": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_765c95b70ac64adaa8c6eb3f1593a240",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_26af46edaefc491ca8b977ccba448e16",
              "IPY_MODEL_e34b2f4de8c943759ebd9305711d29fa"
            ]
          }
        },
        "765c95b70ac64adaa8c6eb3f1593a240": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "26af46edaefc491ca8b977ccba448e16": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_03de5d4bac2f4a40a79ebe0dc5a5360c",
            "_dom_classes": [],
            "description": "Downloading: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 435779157,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 435779157,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_40c7c39b362440a2af300a63e3a19b39"
          }
        },
        "e34b2f4de8c943759ebd9305711d29fa": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_8d18eb6c51c7472e9560c8461065c306",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 436M/436M [00:16&lt;00:00, 26.0MB/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_e0edbd29bc7643d9bf2a3eb1a7929e57"
          }
        },
        "03de5d4bac2f4a40a79ebe0dc5a5360c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "40c7c39b362440a2af300a63e3a19b39": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "8d18eb6c51c7472e9560c8461065c306": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "e0edbd29bc7643d9bf2a3eb1a7929e57": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/BenCashman/AmazonProduct-Sentiment-Analysis/blob/master/AMZ_Products_review.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WdXeXy6870dY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "aad8571c-9d1f-4bee-947f-fe3a1bbecbd1"
      },
      "source": [
        "!pip install -qq transformers"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 675kB 4.6MB/s \n",
            "\u001b[K     |████████████████████████████████| 3.8MB 25.1MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.1MB 45.5MB/s \n",
            "\u001b[K     |████████████████████████████████| 890kB 45.7MB/s \n",
            "\u001b[?25h  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "idpfqvAT8RWO",
        "colab_type": "code",
        "outputId": "5560b28b-57ba-438c-89ce-7e481a433784",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "import transformers\n",
        "from transformers import BertModel, BertTokenizer, AdamW, get_linear_schedule_with_warmup\n",
        "import torch\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "import matplotlib.pyplot as plt\n",
        "from torch.nn import functional as F\n",
        "from matplotlib import rc\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from collections import defaultdict\n",
        "from textwrap import wrap\n",
        "from torch import nn, optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format='retina'\n",
        "sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n",
        "HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#ADFF02\", \"#8F00FF\"]\n",
        "sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))\n",
        "rcParams['figure.figsize'] = 12, 8\n",
        "RANDOM_SEED = 42\n",
        "np.random.seed(RANDOM_SEED)\n",
        "torch.manual_seed(RANDOM_SEED)\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "\n",
        "#Lets ingest our data into our server for data exploration training and model delivery\n",
        "df = pd.read_csv('https://amazonproductdata.s3-us-west-1.amazonaws.com/train.csv')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IANrwgEcyxr8",
        "colab_type": "code",
        "outputId": "f628eeb4-be35-4287-85c2-23c03d2247ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "'''\n",
        "import transformers\n",
        "\n",
        "import torch\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas_profiling as pp\n",
        "from sklearn import svm\n",
        "from sklearn.datasets import make_moons, make_blobs\n",
        "from sklearn.covariance import EllipticEnvelope\n",
        "from sklearn.ensemble import IsolationForest\n",
        "from sklearn.neighbors import LocalOutlierFactor\n",
        "from sklearn.cluster import DBSCAN\n",
        "from transformers import BertModel, BertTokenizer, AdamW, get_linear_schedule_with_warmup\n",
        "\n",
        "\n",
        "#import sklearn important functions\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix #needed for classification problems\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import roc_curve #receiver operating charecteristic curve for binary classifier model diagnosis\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn import svm\n",
        "from sklearn import metrics\n",
        "from sklearn.datasets.samples_generator import make_blobs\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from torch import nn, optim\n",
        "\n",
        "#import python based open-source system for mathematical , science and engineering functions\n",
        "import scipy \n",
        "\n",
        "import time\n",
        "import numpy as np\n",
        "\n",
        "#Lets ingest our data into our server for data exploration training and model delivery\n",
        "df = pd.read_csv('https://amazonproductdata.s3-us-west-1.amazonaws.com/train.csv')\n",
        "\n",
        "'''\n",
        "\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\nimport transformers\\n\\nimport torch\\nimport pandas as pd\\nimport seaborn as sns\\nimport matplotlib.pyplot as plt\\nimport pandas_profiling as pp\\nfrom sklearn import svm\\nfrom sklearn.datasets import make_moons, make_blobs\\nfrom sklearn.covariance import EllipticEnvelope\\nfrom sklearn.ensemble import IsolationForest\\nfrom sklearn.neighbors import LocalOutlierFactor\\nfrom sklearn.cluster import DBSCAN\\nfrom transformers import BertModel, BertTokenizer, AdamW, get_linear_schedule_with_warmup\\n\\n\\n#import sklearn important functions\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.metrics import mean_squared_error, r2_score\\nfrom sklearn.linear_model import LogisticRegression\\nfrom sklearn.metrics import confusion_matrix #needed for classification problems\\nfrom sklearn.metrics import classification_report\\nfrom sklearn.metrics import roc_curve #receiver operating charecteristic curve for binary classifier model diagnosis\\nfrom sklearn.metrics import roc_auc_score\\nfrom sklearn.metrics import accuracy_score\\nfrom sklearn.neighbors import KNeighborsClassifier\\nfrom sklearn.svm import SVC\\nfrom sklearn import svm\\nfrom sklearn import metrics\\nfrom sklearn.datasets.samples_generator import make_blobs\\nfrom sklearn.preprocessing import StandardScaler\\nfrom torch import nn, optim\\n\\n#import python based open-source system for mathematical , science and engineering functions\\nimport scipy \\n\\nimport time\\nimport numpy as np\\n\\n#Lets ingest our data into our server for data exploration training and model delivery\\ndf = pd.read_csv('https://amazonproductdata.s3-us-west-1.amazonaws.com/train.csv')\\n\\n\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qkFr4OcbMzcl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#First look at the data\n",
        "df.head(15)\n",
        "df = df.sample(2000)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mYXJTY1jM7-A",
        "colab_type": "code",
        "outputId": "5e6746a9-fd31-41c2-962e-91c1727e4580",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#See our column names\n",
        "list(df.columns)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['polarity', 'review_subject', 'review_body']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kbC7xhfWK8Xa",
        "colab_type": "code",
        "outputId": "4076ca15-69f6-45a0-e3b6-c51ac5dda1dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "#Another quick overview of our data\n",
        "df.describe"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.describe of        polarity  ...                                        review_body\n",
              "75721         2  ...  I have read all of Patricia Cornwell's books. ...\n",
              "80184         2  ...  Lively and danceable, fun African music: Souko...\n",
              "19864         2  ...  Recently purchased a jar of this from a shoe s...\n",
              "76699         2  ...  This is a great dvd for the Def Leppard fan. I...\n",
              "92991         2  ...  I thought Carrie was both an awesome movie and...\n",
              "...         ...  ...                                                ...\n",
              "58380         2  ...  I Loved this movie when I was a kid, what a wo...\n",
              "44243         2  ...  The vest is fine and it is constructed like th...\n",
              "8745          2  ...  Definitely my favorite band. We saw 4 hits com...\n",
              "96758         2  ...  I know a book is good when I come to the end o...\n",
              "45067         2  ...  \"Solitude\" is a stunningly good album - warm, ...\n",
              "\n",
              "[2000 rows x 3 columns]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Py9rgzLlLyRC",
        "colab_type": "code",
        "outputId": "1d163387-c975-4290-ab61-6edae82acaf3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "#Null analysis - seems our data has 3 nulls in the subject section!\n",
        "df.isna().sum()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "polarity          0\n",
              "review_subject    1\n",
              "review_body       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k6RUKFPQW4Sg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Let's drop the 3 null rows\n",
        "df.dropna(inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rs0jLw7VMRqt",
        "colab_type": "code",
        "outputId": "ffe8a560-9e1a-439b-94a1-453207e8a890",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Now let's see how many rows and cloumns we have (#rows, #columns)\n",
        "df.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1999, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s9M5LQgfOtq2",
        "colab_type": "code",
        "outputId": "2aac9e19-d506-42a1-87d3-a5fe8e490734",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "#let's take one more look at our data\n",
        "df.info()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1999 entries, 75721 to 45067\n",
            "Data columns (total 3 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   polarity        1999 non-null   int64 \n",
            " 1   review_subject  1999 non-null   object\n",
            " 2   review_body     1999 non-null   object\n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 62.5+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ek8kZcOTWHv7",
        "colab_type": "code",
        "outputId": "88fa620a-647c-42e4-fb80-96bb65e88069",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        }
      },
      "source": [
        "\n",
        "#let's take a look at our polarity to see if there is any imbalance\n",
        "#Based on our data is looks like we have a few more positive sentiment scores but nothing to worry about\n",
        "sns.countplot(df.polarity)\n",
        "plt.xlabel('polarity score');"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 745,
              "height": 489
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VYzaULD06UCK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def to_sentiment(rating):\n",
        "  rating = int(rating)\n",
        "  if rating == 1:\n",
        "    return 0\n",
        "  else: \n",
        "    return 1\n",
        "\n",
        "df['sentiment'] = df.polarity.apply(to_sentiment)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B7AsJoFDWl2L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class_names = ['negative', 'positive']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kdU-9sdgWnmH",
        "colab_type": "code",
        "outputId": "94c9b5d8-8a99-4620-8eb3-0f8754cd18ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        }
      },
      "source": [
        "ax = sns.countplot(df.sentiment)\n",
        "plt.xlabel('review sentiment')\n",
        "ax.set_xticklabels(class_names);"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABdIAAAPTCAYAAAC0evs4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAWJQAAFiUBSVIk8AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdb5DdVWH/8c8N2WyAZElWQ5wVIonBGCI0U2IrapNo0s7oNOOQKTQiTsWEkFQrQp1WrVT7xMgDf6QdKHU2NQFrmkLHrYFapxiQoCRT3U6iJTYlEDRxa2ZrWJcV9h+7vwdMrtnN3pN/uwTD6zXDzPfe8z3nng15sPPmy7mVwcHBwQAAAAAAACMad6Y3AAAAAAAAr2RCOgAAAAAAFAjpAAAAAABQIKQDAAAAAECBkA4AAAAAAAVCOgAAAAAAFAjpAAAAAABQIKQDAAAAAECBkA4AAAAAAAVCOgAAAAAAFAjpAAAAAABQIKQDAAAAAEDB+DO9AV659uzZk56enpxzzjmpr68/09sBAAAAADhlPT09efHFF1NfX5/LLrvspOYK6dTU09OTgYGBDAwMpK+v70xvBwAAAADgtPX09Jz0HCGdms4555wMDAxk3LhxOe+88870dgAAAAAATtnzzz+fgYGBnHPOOSc9V0inpvr6+vT19eW8887LnDlzzvR2AAAAAABO2d69e9PV1XVKx1j7slEAAAAAACgQ0gEAAAAAoEBIBwAAAACAAiEdAAAAAAAKhHQAAAAAACgQ0gEAAAAAoEBIBwAAAACAAiEdAAAAAAAKhHQAAAAAACgQ0gEAAAAAoEBIBwAAAACAAiEdAAAAAAAKhHQAAAAAACgQ0gEAAAAAoEBIBwAAAACAAiEdAAAAAAAKhHQAAAAAACgQ0gEAAAAAoEBIBwAAAACAAiEdAAAAAAAKhHQAAAAAACgQ0gEAAAAAoEBIBwAAAACAAiEdAAAAAAAKhHQAAAAAACgQ0gEAAAAAoEBIBwAAAACAAiEdAAAAAAAKhHQAAAAAACgQ0gEAAAAAoEBIBwAAAACAAiEdAAAAAAAKhHQAAAAAACgQ0gEAAAAAoEBIBwAAAACAgvFnegNjYXBwME8//XR+8IMfVP/Zu3dv+vr6kiTbtm3LRRdddNx1+vv7s2XLljzwwAPZv39/ent709TUlKVLl+ZDH/pQGhsbj7vG4cOHs2nTpnzrW99KW1tbJkyYkJkzZ2bZsmVZsWJFxo8//r+CvXv35p577smOHTvyf//3f7ngggsyb968rFixIu9617uO/wcCAAAAAMApqwwODg6e6U2MtoMHD2bJkiU1x08kpD/33HNZuXJldu/ePeL4tGnT0tzcnLlz59ZcY8+ePVm9enXa29tHHJ8/f342bNiQyZMn11yjpaUlt912W/U/Agz3/ve/P5/73Odq/yCnYe/evenq6sqkSZMyZ86cMfkMAAAAAICXw+n0zrP+aJfXve51+d3f/d0sWLDgpObdeuut2b17dyqVStasWZOHHnoojz32WNatW5fJkyenvb09N910Uzo6Okac39HRkTVr1qS9vT0NDQ1Zt25dHnvssTz00ENZs2ZNKpVKdu3alVtvvbXmHlpbW/OZz3wmfX19edOb3pS///u/z44dO/K1r30tS5cuTZL84z/+Y5qbm0/qZwMAAAAA4MSdlSF9ypQpueuuu/Kd73wnjz76aO6888687W1vO+H5jz76aLZv354kufnmm3PLLbdkxowZufDCC7N8+fL83d/9XSqVSg4dOpQNGzaMuEZzc3MOHTqUSqWSu+++O8uXL8+FF16YGTNm5JZbbsnNN9+cJNm+fXv1s4b7whe+kP7+/rz2ta/Nvffem3e+851pbGzMvHnzcuedd+Yd73hHkuRv//Zvc/jw4ZP5IwIAAAAA4ASdlWekT5o0qfrE9qnYvHlzkmTq1KlZuXLlMeMLFizI4sWL88gjj+T+++/Pxz/+8SFnnff39+e+++5LkixevHjEp+FXrlyZTZs2paOjI5s3b87ChQuHjP/whz/MD37wgyTJqlWrMnXq1CHjlUolf/qnf5rvfve7ef755/P1r389N9xwwyn/zJyYWS37z/QWAICzxNNXzzzTWwAAAE7QWflE+uno7u7Ojh07kiRLlizJhAkTRrzvPe95T5KXjnBpbW0dMvb9738/nZ2dQ+4bbsKECdXY//jjj6e7u3vI+COPPHLMZw03b968zJgxI0ny8MMPF38uAAAAAABOjZA+zJNPPpmenp4kL30ZaC1Hjz3xxBNDxo5+fSJr9PT0ZN++fSOuMX369Lzuda+rucZv/MZvjLgHAAAAAABGh5A+zP79vzq646KLLqp5X1NTU8aNG3fMnKNfjxs3Lk1NTTXXOHr9WmtcfPHFxf0eWeOXv/xlDh06VLwXAAAAAICTJ6QP8+yzz1avX/Oa19S8r66uLg0NDUleOt5lpDUaGhpSV1dXc43Gxsbqda01SnsYPj58DQAAAAAATt9Z+WWjp+OFF16oXtfX1xfvPTL+/PPPj7jG8eZPnDixel1rjVpntJ/IGqOlq6vrmHPgX22uvPLKM70FAOAs9Wr/PQsAAH4deCIdAAAAAAAKPJE+zLnnnlu9PvKlo7UcGT/vvPNGXON487u7u6vXI63R19eX3t7eU15jtEyaNClz5swZk7UBAF7t/J9vAADw8ti7d2+6urpOaa4n0oeZOnVq9frnP/95zfv6+vrS2dmZJJkyZcqIa3R2dqa/v7/mGocPH65e11qjtIfh48PXAAAAAADg9Anpw8ycObN6ffDgwZr3tbW1ZWBg4Jg5R78eGBjIT3/605prHL1+rTUOHDhQ3O+RNc4///xMnz69eC8AAAAAACdPSB/m0ksvrX5J6O7du2vet2vXrur1vHnzhowd/fpE1qivr8/s2bNHXOPQoUM5dOhQzTWOrD98DwAAAAAAjA4hfZiJEyfmqquuSpJs27at5hnl3/zmN5O8dJzK8HMtFyxYkIaGhiH3Ddfb25uHH344SfL2t789EydOHDL+rne9q3r9b//2byOusWfPnvzkJz9Jkrz73e8u/lwAAAAAAJwaIX0E1113XZKXzjDfuHHjMeOtra359re/nSS55pprMn780O9sHT9+fK699tokySOPPJLW1tZj1ti4cWP1jPQjn3e0yy+/PFdccUWSZMOGDeno6BgyPjg4mC9+8YtJXvqS0fe9730n8yMCAAAAAHCCztqQvm/fvuzatav6z89+9rPq2I9+9KMhY0d/6WeSLFq0KAsXLkySrF+/PuvXr8+BAwfS3t6elpaWrF27NgMDA5k+fXpWrVo14uffeOONmT59egYGBrJ27dq0tLSkvb09Bw4cyB133JH169cnSRYuXFj9rOE++clPZvz48Wlvb88HP/jBfPe7383hw4fzox/9KB/72Mfyne98J0nyx3/8x2lsbDztPzMAAAAAAI5VGRwcHDzTmxgLH/zgB/Mf//EfJ3TvunXrsnz58iHvdXZ2ZtWqVTXPOJ82bVqam5szd+7cmuvu2bMnq1evTnt7+4jj8+fPz4YNGzJ58uSaa7S0tOS2225LX1/fiOMrVqzIX/3VX9Wcfzr27t2brq6uTJo0KXPmzBmTz/h1M6tl/5neAgBwlnj66pnHv4lXrWd2+PsBAIyOS67Ss444nd45/vi3vDo1NDRk8+bN2bJlS7Zu3Zr9+/enr68vTU1NWbJkSW644YbjPgV+2WWXZevWrdm4cWO2bduWtra21NXVZdasWVm2bFlWrFhxzLEww1199dW57LLLsmnTpuzcuTPt7e254IILMm/evLz//e8fcpY6AAAAAACj76x9Ip3T54n0Y3kiHQAYLZ5Ip8QT6QDAaPFE+q+cTu88a89IBwAAAACA0SCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABSMP9MbeKX78Y9/nK9+9avZuXNnDh48mJ6enkyePDmXXnpp3v3ud+faa6/N+eefX3N+f39/tmzZkgceeCD79+9Pb29vmpqasnTp0nzoQx9KY2Pjcfdw+PDhbNq0Kd/61rfS1taWCRMmZObMmVm2bFlWrFiR8eP9awQAAAAAGCuVwcHBwTO9iVeqlpaWfPazn01PT0/Ne5qamtLc3JzZs2cfM/bcc89l5cqV2b1794hzp02blubm5sydO7fm+nv27Mnq1avT3t4+4vj8+fOzYcOGTJ48+Tg/zcnbu3dvurq6MmnSpMyZM2fU1/91NKtl/5neAgBwlnj66plnegu8gj2zw98PAGB0XHKVnnXE6fROR7vU8IMf/CCf/vSn09PTk8bGxvzlX/5lvvGNb2THjh25//77s3z58iRJW1tb1q5dm97e3mPWuPXWW7N79+5UKpWsWbMmDz30UB577LGsW7cukydPTnt7e2666aZ0dHSMuIeOjo6sWbMm7e3taWhoyLp16/LYY4/loYceypo1a1KpVLJr167ceuutY/pnAQAAAADwauZMkBruvffeDAwMZNy4cfnSl76UK664ojrW2NiYK664IhMmTMiWLVvyk5/8JNu3b8/SpUur9zz66KPZvn17kuTmm2/O2rVrq2PLly/PjBkzcv311+fQoUPZsGFDPvGJTxyzh+bm5hw6dCiVSiV33313FixYUB275ZZbMnHixKxfvz7bt2/P9u3bs3DhwrH4owAAAAAAeFXzRHoN//3f/50kecMb3jAkoh/tfe97X/X66aefHjK2efPmJMnUqVOzcuXKY+YuWLAgixcvTpLcf//96e/vHzLe39+f++67L0myePHiIRH9iJUrV2bKlClDPg8AAAAAgNElpNcwYcKEJEmlUql5zznnnFO9fs1rXlO97u7uzo4dO5IkS5Ysqa413Hve854kLx3h0traOmTs+9//fjo7O4fcN9IejzwF//jjj6e7u7v4MwEAAAAAcPKE9BrmzZuXJHnmmWeqT6cP941vfCPJS0H7bW97W/X9J598svoFpfPnz6/5GUePPfHEE0PGjn59Imv09PRk3759Ne8DAAAAAODUCOk1rF69OhMnTszAwEBuuumm/Mu//EsOHTqU7u7uPPXUU/n85z+fe+65J5VKJX/2Z3+W17/+9dW5+/f/6ptwL7roopqf0dTUlHHjxh0z5+jX48aNS1NTU801jl5/+BoAAAAAAJw+XzZaw8UXX5x77rknt9xyS9ra2vLnf/7nx9zzzne+MzfccEPe+c53Dnn/2WefrV4ffeTLcHV1dWloaEhHR0c6OjpGXKOhoSF1dXU112hsbKxeD19jtHR1dR1z9MyrzZVXXnmmtwAAnKVe7b9nMZTfOwGAseL3ztPjifSC+fPn56677sqb3vSmEcd/9rOf5cCBA8e8/8ILL1Sv6+vri59xZPz5558fcY3jzZ84cWL1evgaAAAAAACcPk+k1zAwMJDbb789mzZtypQpU3Lbbbdl0aJFaWhoyP/+7//m61//eu6555587nOfy3/+53/m9ttvrx7TcraZNGlS5syZc6a3AQBwVvIEMgAALwe/dyZ79+5NV1fXKc0V0mu46667smnTptTX1+crX/nKkKfSL7jggrz5zW/OrFmz8pnPfCZbt27NlVdemRUrViRJzj333Oq9R750tJYj4+edd96Q94+scbz53d3d1evhawAAAAAAcPrOzkeoT1Nvb282bdqUJPn93//9mke7/MEf/EEuvvjiJMl9991XfX/q1KnV65///Oc1P6evry+dnZ1JkilTpgwZO7JGZ2dn+vv7a65x+PDh6vXwNQAAAAAAOH1C+gj27dtXfcT/LW95S837KpVKdfypp56qvj9z5szq9cGDB2vOb2try8DAwDFzjn49MDCQn/70pzXXOHr94WsAAAAAAHD6hPQRHH2cyuDgYPHeIyG8UqlU37v00kurXxK6e/fumnN37dpVvZ43b96QsaNfn8ga9fX1mT17dnGvAAAAAACcPCF9BNOmTateP/HEEzXvGxwcrI43NTVV3584cWKuuuqqJMm2bdvS29s74vxvfvObSV46kmX4Yf8LFixIQ0PDkPuG6+3tzcMPP5wkefvb356JEycWfy4AAAAAAE6ekD6Ciy66KDNmzEiS/Ou//mv27ds34n3//M//XD1a5Xd+53eGjF133XVJXjrDfOPGjcfMbW1tzbe//e0kyTXXXJPx44d+7+v48eNz7bXXJkkeeeSRtLa2HrPGxo0bq2ekH/k8AAAAAABGl5Bew0c+8pEkSXd3d66//vp89atfzYEDB9LZ2Zm9e/fm9ttvz2c/+9kkyeTJk/PhD394yPxFixZl4cKFSZL169dn/fr1OXDgQNrb29PS0pK1a9dmYGAg06dPz6pVq0bcw4033pjp06dnYGAga9euTUtLS9rb23PgwIHccccdWb9+fZJk4cKF1c8CAAAAAGB0VQaPdwj4q9hdd92VO++8s3oO+kgaGxvzN3/zN3nrW996zFhnZ2dWrVpV84zzadOmpbm5OXPnzq25/p49e7J69eq0t7ePOD5//vxs2LAhkydPPs5Pc/L27t2brq6uTJo0KXPmzBn19X8dzWrZf6a3AACcJZ6+2hfFU9szO/z9AABGxyVX6VlHnE7vHH/8W169PvKRj2TJkiXZsmVLWltbc/DgwfT09GTSpEmZNWtWFi1alD/8wz9MY2PjiPMbGhqyefPmbNmyJVu3bs3+/fvT19eXpqamLFmyJDfccEPNuUdcdtll2bp1azZu3Jht27alra0tdXV1mTVrVpYtW5YVK1YccywMAAAAAACjxxPp1OSJ9GN5Ih0AGC2eSKfEE+kAwGjxRPqvnE7vdEY6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQMH4M72BXxc7d+5MS0tLWltb097engkTJmTatGm5/PLLs2jRorz3ve8dcV5/f3+2bNmSBx54IPv3709vb2+ampqydOnSfOhDH0pjY+NxP/vw4cPZtGlTvvWtb6WtrS0TJkzIzJkzs2zZsqxYsSLjx/vXCAAAAAAwVhTY4+ju7s5f/MVf5MEHHzzm/c7Ozjz11FP53ve+N2JIf+6557Jy5crs3r17yPtPPfVUnnrqqXzta19Lc3Nz5s6dW/Pz9+zZk9WrV6e9vb363gsvvJBdu3Zl165deeCBB7Jhw4ZMnjz5NH9SAAAAAABG4miXgv7+/nzkIx/Jgw8+mLq6uvzRH/1R7rvvvuzYsSPf/e538w//8A/58Ic/nAsvvHDE+bfeemt2796dSqWSNWvW5KGHHspjjz2WdevWZfLkyWlvb89NN92Ujo6OEed3dHRkzZo1aW9vT0NDQ9atW5fHHnssDz30UNasWZNKpZJdu3bl1ltvHcs/BgAAAACAVzVPpBd8+ctfzne+853U19enubk5v/3bvz1k/LWvfW3e+ta3jjj30Ucfzfbt25MkN998c9auXVsdW758eWbMmJHrr78+hw4dyoYNG/KJT3zimDWam5tz6NChVCqV3H333VmwYEF17JZbbsnEiROzfv36bN++Pdu3b8/ChQtH48cGAAAAAOAonkiv4Re/+EXuuuuuJMmaNWuOiejHs3nz5iTJ1KlTs3LlymPGFyxYkMWLFydJ7r///vT39w8Z7+/vz3333ZckWbx48ZCIfsTKlSszZcqUIZ8HAAAAAMDoEtJr2Lp1a7q7u1NXV5cPfOADJzW3u7s7O3bsSJIsWbIkEyZMGPG+97znPUleOsKltbV1yNj3v//9dHZ2DrlvuAkTJmTp0qVJkscffzzd3d0ntU8AAAAAAI5PSK/h0UcfTZK85S1vyQUXXFB9/8UXX8zAwEBx7pNPPpmenp4kyfz582ved/TYE088MWTs6NcnskZPT0/27dtX3BcAAAAAACfPGek1/Nd//VeSZPbs2ent7c2mTZvS0tKSH//4xxkcHMzrX//6LF68OKtWrcrrXve6IXP3799fvb7oootqfkZTU1PGjRuXgYGBIXOOXmPcuHFpamqqucbR6+/fvz9vectbTvyHBAAAAADguDyRPoLu7u48++yzSZK6urpcf/31+eIXv2vEdrMAACAASURBVJinn366+kT6gQMH8pWvfCXLli3Lzp07h8w/MjdJXvOa19T8nLq6ujQ0NCR56XiXkdZoaGhIXV1dzTUaGxur18PXAAAAAADg9HkifQTPPfdc9fr+++9PX19flixZkj/5kz/JG9/4xnR0dOTBBx/MHXfckc7OznzsYx/L1q1bq0+mv/DCC9X59fX1xc86Mv78888Pef/IGsebP3HixOr18DVGS1dX1zFnuL/aXHnllWd6CwDAWerV/nsWQ/m9EwAYK37vPD2eSB/B0Weg9/X1ZdGiRbnrrrsyd+7cTJgwIRdeeGE+/OEP5/bbb0+S/OIXv8iGDRvO1HYBAAAAABhDnkgfwfnnnz/k9Uc/+tFUKpVj7nvve9+bu+++O//zP/+Tbdu25TOf+UyS5Nxzz63ec+RLR2s5Mn7eeecNef/IGseb393dXb0evsZomTRpUubMmTMmawMAvNp5AhkAgJeD3zuTvXv3pqur65TmeiJ9BOeff34mTJiQ5KWjU0pf4LlgwYIkSVtbW375y18mSaZOnVod//nPf15zbl9fXzo7O5MkU6ZMGTJ2ZI3Ozs709/fXXOPw4cPV6+FrAAAAAABw+oT0EVQqlVxyySVJksmTJ2fcuNp/TEe+LDRJ9b9mzJw5s/rewYMHa85ta2urHiNz9JyjXw8MDOSnP/1pzTWOXn/4GgAAAAAAnD4hvYbLL788yUtPhB99ZvpwHR0d1evJkycnSS699NLql4Tu3r275txdu3ZVr+fNmzdk7OjXJ7JGfX19Zs+eXfM+AAAAAABOjZBew5IlS5K8dEZ5KWR/73vfS5Jccskl1TPKJ06cmKuuuipJsm3btvT29o4495vf/GaSl45kGX5G0YIFC6pPux+5b7je3t48/PDDSZK3v/3tmThx4gn9bAAAAAAAnDghvYaFCxdmxowZSZK//uu/zosvvnjMPS0tLXnqqaeSvPTFo0e77rrrkrx0hvnGjRuPmdva2ppvf/vbSZJrrrkm48cP/d7X8ePH59prr02SPPLII2ltbT1mjY0bN1bPSD/yeQAAAAAAjC4hvYa6urp8+tOfTqVSyY4dO3LjjTemtbU1HR0d+fGPf5w777wzt912W5Lk9a9/fW644YYh8xctWpSFCxcmSdavX5/169fnwIEDaW9vT0tLS9auXZuBgYFMnz49q1atGnEPN954Y6ZPn56BgYGsXbs2LS0taW9vz4EDB3LHHXdk/fr1SV6K/kc+CwAAAACA0VUZHBwcPNObeCXbvHlzPv/5z6evr2/E8Ysvvjhf+tKX8sY3vvGYsc7Ozqxatarm0TDTpk1Lc3Nz5s6dW/Pz9+zZk9WrV6e9vX3E8fnz52fDhg3V89lH0969e9PV1ZVJkyZlzpw5o77+r6NZLfvP9BYAgLPE01f7onhqe2aHvx8AwOi45Co964jT6Z3jj3/Lq9t1112X3/zN38y9996bnTt3pr29PfX19Zk1a1Z+7/d+L9ddd131bPThGhoasnnz5mzZsiVbt27N/v3709fXl6ampixZsiQ33HBDGhsbi59/2WWXZevWrdm4cWO2bduWtra21NXVZdasWVm2bFlWrFhxzLEwAAAAAACMHk+kU5Mn0o/liXQAYLR4Ip0ST6QDAKPFE+m/cjq90xnpAAAAAABQIKQDAAAAAECBkA4AAAAAAAVCOgAAAAAAFAjpAAAAAABQIKQDAAAAAECBkA4AAAAAAAVCOgAAAAAAFAjpAAAAAABQIKQDAAAAAEDB+LFY9FOf+lQqlUo+/vGP58ILLzyhOe3t7fl//+//pVKp5POf//xYbAsAAAAAAE7amDyR3tLSkpaWlnR2dp7wnOeee646DwAAAAAAXikc7QIAAAAAAAWvmJDe39+fJBk/fkxOmwEAAAAAgFPyignp+/btS5JccMEFZ3gnAAAAAADwK6Py+Pf3vve9Ed//4Q9/mGeffbY4t7e3N88880w2bNiQSqWSN7/5zaOxJQAAAAAAGBWjEtI/+MEPplKpDHlvcHAwn/70p094jcHBwVQqlSxfvnw0tgQAAAAAAKNi1A4kHxwcPKH3ajn33HOzcuXKvPe97x2tLQEAAAAAwGkblZC+bt26Ia8/9alPpVKp5Oabb8706dNrzqtUKqmvr8+FF16Yyy67LOeee+5obAcAAAAAAEbNqIT0q6++esjrT33qU0mSpUuXZvbs2aPxEQAAAAAAcEaM2tEuR7v33nuTJBdddNFYLA8AAAAAAC+bMQnpv/VbvzUWywIAAAAAwMtu3JneAAAAAAAAvJKNyRPpR+vo6MiuXbty4MCBdHV15cUXXzzunI9+9KNjvS0AAAAAADghYxbSf/GLX+QLX/hCHnzwwfT395/UXCEdAAAAAIBXijEJ6b/85S9z/fXXZ9++fRkcHDypuZVKZSy2BAAAAAAAp2RMQvqXv/zlPPnkk0mS2bNn5wMf+EAuv/zyXHDBBRk3zrHsAAAAAAD8+hiTkP7v//7vqVQqueKKK3Lvvfemvr5+LD4GAAAAAADG3Jg8Hn7w4MEkyapVq0R0AAAAAAB+rY1JSK+rq0uSXHzxxWOxPAAAAAAAvGzGJKS/4Q1vSJIcPnx4LJYHAAAAAICXzZiE9GXLlmVwcDAPP/zwWCwPAAAAAAAvmzEJ6dddd13mzZuXf/qnf8rOnTvH4iMAAAAAAOBlMSYhffz48Wlubs7ll1+eVatW5fbbb8+ePXvS3d09Fh8HAAAAAABjZvxYLDp37tzq9eDgYDZt2pRNmzad0NxKpZI9e/aMxbYAAAAAAOCkjUlIHxwcLL4GAAAAAIBfF2MS0q+++uqxWBYAAAAAAF52YxLS161bNxbLAgAAAADAy25MvmwUAAAAAADOFkI6AAAAAAAUCOkAAAAAAFAwJmekt7W1ndb8pqamUdoJAAAAAACcnjEJ6e9+97tTqVROaW6lUsmePXtGeUcAAAAAAHBqxiSkJ8ng4OBYLQ0AAAAAAC+bMQnpH/3oR497z/PPP5+nn346jz/+ePr6+jJ//vy84x3vGIvtAAAAAADAKTtjIf2I9vb2fPKTn8zOnTuzfPnyXHPNNWOxJQAAAAD4/+zde4zV9Z3/8dfB4aLCOIwCFoUWt5QKiZcttSoWUdltvNBU0lpq1KqggrstlaZRY1wbd7f628SV3brFBrq43pa1FnbVdN21gEqV9YKCDSrVFhXE4ii3cgc5vz8MUweYDzhyHNTHIyH5nvl+vp/znkKak6fffA9Am3Ro7wF69OiRSZMm5Ygjjsj111+fF154ob1HAgAAAACAZu0e0pOkU6dOueCCC7Jly5bcdttt7T0OAAAAAAA02ydCepJ8/vOfT5I88cQT7TwJAAAAAAD8yT4T0rdt25Ykefvtt9t5EgAAAAAA+JN9JqQ/+uijSZJu3bq18yQAAAAAAPAn+0RI/6//+q9Mnjw5lUolxxxzTHuPAwAAAAAAzepqsenVV1+92zXVajWrV6/OwoUL09TUlGq1mg4dOuTiiy+uxUgAAAAAANAmNQnpM2bMSKVS2aO11Wr13UHq6nLNNddk8ODBtRgJAAAAAADapCYhPflTIG9Nhw4dcuCBB6ZPnz457rjj8s1vfjP9+vWr1TgAAAAAANAmNQnpL774Yi22BQAAAACAD90+8WWjAAAAAACwrxLSAQAAAACgQEgHAAAAAICCmn3Z6HbVajWzZs3KY489lkWLFmXVqlVJkoaGhnz+85/PkCFDcsopp6RSqdR6FAAAAAAAeN9qGtKfeeaZXH311Xnttdeaf1atVpMklUolzzzzTO6+++707ds3N954Y4499thajgMAAAAAAO9bzR7t8sgjj+SCCy7Ia6+9lmq1mmq1ms6dO6d3797p3bt3unTp0vzzV199Neeff37mzJlTq3EAAAAAAKBNanJH+sqVK/P9738/W7duTYcOHfL1r3893/rWt3LkkUc2P8KlWq3mhRdeyLRp03Lvvfdm69atmTBhQh566KE0NDTUYiwAAAAAAHjfanJH+p133pm1a9emrq4ut9xyS/72b/82AwcObPEc9EqlkoEDB+b666/PT37yk+y3335Zu3Zt7rzzzlqMBAAAAAAAbVKTkP7II4+kUqnknHPOyamnnrrb9cOGDcs3v/nNVKvVPPLII7UYCQAAAAAA2qQmIX3JkiVJkr/4i7/Y42u2r33vF5MCAAAAAEB7q0lIX79+fZLkoIMO2uNr6uvrW1wLAAAAAAD7gpqE9O1fFrp48eI9vuaVV15JknTv3r0WIwEAAAAAQJvUJKQPGjQo1Wo1d9111x5fc+eddzZ/ASkAAAAAAOwrahLSzzjjjCTJs88+mx/84AfFx7Vs2LAhV111VZ599tkkyZlnnlmLkQAAAAAAoE3qarHpiBEjcscdd+Q3v/lNHnjggcydOzdnnnlmjjnmmPTo0SNJ0tTUlAULFuSBBx7I22+/nSQ56qijMmLEiFqMBAAAAAAAbVKTkF6pVHLrrbfmwgsvzEsvvZS33nort99+e26//fad1lar1SRJ//79M2nSpFqMAwAAAAAAbVaTR7skycEHH5x77703Y8eOTUNDQ6rV6i7/dO/ePZdffnl+8YtfpLGxsVbjAAAAAABAm9TkjvTtOnfunO9973v567/+6yxcuDC//e1vs3LlyiRJ9+7dM2DAgAwcODB1dTUdAwAAAAAA2uxDKdh1dXU5+uijc/TRR38YbwcAAAAAAHtNzUL62rVrkyT7779/9ttvv+Lad955Jxs2bEiSdO3atVYjAQAAAADA+1aTZ6Q/+eST+eIXv5ghQ4Y0P8qlZOXKlTnxxBNz3HHHZf78+bUYCQAAAAAA2qQmIf1//ud/Uq1WM2zYsBxyyCG7XX/IIYfklFNOybZt2/Lf//3ftRgJAAAAAADapCYh/dlnn02lUslJJ520x9cMHTo0SfL000/XYiQAAAAAAGiTmoT01157LUnyZ3/2Z3t8zRFHHJEkWbp0aS1GAgAAAACANqlJSN+4cWOS5IADDtjja/bff/8kybp162oxEgAAAAAAtElNQnq3bt2SJE1NTXt8zVtvvZUkOfDAA2sxEgAAAAAAtElNQnrfvn2TJHPnzt3jax577LEkyWGHHVaLkQAAAAAAoE1qEtKPP/74VKvV/Md//EfeeOON3a5//fXXc88996RSqeSEE06oxUgAAAAAANAmNQnpo0aNSl1dXdavX5+LLrooL774YqtrX3zxxVx88cVZt25d9ttvv4waNaoWIwEAAAAAQJvU1WLTT33qU/nOd76Tm2++Oa+++mpGjhyZE044IV/60pfSs2fPJMmbb76ZJ554InPnzk21Wk2lUslf/dVfpU+fPrUYCQAAAAAA2qQmIT1JLrvssqxatSpTp05NtVrN448/nscff3ynddVqNUkyevTojBs3rlbjAAAAAABAm9Tk0S7bXXnllfnZz36WwYMHp1KppFqttvhTqVRy3HHHZerUqfnBD35Qy1EAAAAAAKBNanZH+nZDhgzJkCFDsmbNmjz//PNZsWJFkqSxsTEDBw5MfX19rUcAAAAAAIA2q3lI366+vj7HH3/8h/V2AAAAAACwV9T00S4AAAAAAPBRJ6QDAAAAAECBkA4AAAAAAAVCOgAAAAAAFAjpAAAAAABQIKQDAAAAAECBkA4AAAAAAAVCOgAAAAAAFAjpAAAAAABQIKQDAAAAAECBkA4AAAAAAAVCOgAAAAAAFAjpAAAAAABQIKQDAAAAAECBkA4AAAAAAAVCOgAAAAAAFAjpAAAAAABQIKQDAAAAAECBkA4AAAAAAAVCOgAAAAAAFAjpAAAAAABQIKQDAAAAAECBkA4AAAAAAAVCOgAAAAAAFAjpAAAAAABQIKQDAAAAAECBkA4AAAAAAAVCOgAAAAAAFAjpAAAAAABQIKQDAAAAAECBkA4AAAAAAAVCOgAAAAAAFAjpAAAAAABQIKQDAAAAAECBkA4AAAAAAAVCOgAAAAAAFAjpAAAAAABQIKQDAAAAAECBkA4AAAAAAAVCOgAAAAAAFAjpAAAAAABQIKQDAAAAAECBkA4AAAAAAAVCOgAAAAAAFAjpAAAAAABQIKQDAAAAAECBkA4AAAAAAAVCOgAAAAAAFAjpAAAAAABQIKQDAAAAAECBkA4AAAAAAAVCOgAAAAAAFAjpAAAAAABQUNfeA3yUrFixIqeffnpWrVqVJDn77LNz4403trp+69atmTZtWu6///4sXrw4mzdvTu/evTN8+PBceOGFaWxs3KP3vO222/KrX/0qy5YtS6dOndKvX7+MGDEio0aNSl2dv0IAAAAAgFpSYd+HH/3oR80RfXf++Mc/ZvTo0VmwYEGLn//ud7/L7373u0yfPj2TJ0/OkUce2eoezz//fC699NI0NTU1/2zDhg2ZP39+5s+fn/vvvz9TpkxJt27d2vYLAQAAAACwWx7tsod+/etf5/7770+fPn32aP2ECROyYMGCVCqVjB07Ng899FDmzJmTG264Id26dUtTU1Muu+yyVsP8qlWrMnbs2DQ1NaW+vj433HBD5syZk4ceeihjx45NpVLJ/PnzM2HChL35awIAAAAAsAMhfQ9s2LAhP/zhD5Mk11577W7XP/LII3n00UeTJOPHj88VV1yRvn37pmfPnhk5cmRuvfXWVCqVLF++PFOmTNnlHpMnT87y5ctTqVQyadKkjBw5Mj179kzfvn1zxRVXZPz48UmSRx99tPm9AAAAAADY+4T0PfDjH/84S5YsyVe+8pWcfPLJu11/9913J0m6d++e0aNH73R+8ODBGTZsWJLk5z//ebZu3dri/NatW3PPPfckSYYNG5bBgwfvtMfo0aPT0NDQ4v0AAAAAANj7hPTdeOGFF/Jv//ZvOfDAA3PNNdfsdv3GjRszd+7cJMlpp52WTp067XLd6aefnuTdR7jMmzevxbmnn346a9asabFuR506dcrw4cOTJI8//ng2bty4Z78QAAAAAADvi5BesG3btlx77bXZunVrxo8fn169eu32mpdeeimbNm1KkhxzzDGtrnvvuYULF7Y4997Xe7LHpk2b8vLLL+92NgAAAAAA3j8hveD222/Pb37zmwwaNCjnnXfeHl2zePHi5uPDDz+81XW9e/dOhw4ddrrmva87dOiQ3r17t7rHe/ffcQ8AAAAAAPYOIb0Vy5Ytyz/90z+lQ4cO+eEPf5j99ttvj65buXJl8/HBBx/c6rqOHTumvr4+ybuPd9nVHvX19enYsWOrezQ2NjYf77gHAAAAAAB7R117D7Cvuv7667N+/fqce+65Oeqoo/b4ug0bNjQfd+7cubh2+/n169fvco/dXd+lS5fm4x332JvWrl2703PcP2m+8IUvtPcIAMDH1Cf9cxYt+dwJANSKz50fjDvSd+GXv/xlZs+enR49emTChAntPQ4AAAAAAO3IHek7WLNmTX70ox8lSa666qp069btfV2///77Nx9v/9LR1mw/f8ABB+xyj91dv3HjxubjHffYm7p27ZoBAwbUbH8AgE8ydyADAPBh8LkzWbRoUdauXduma92RvoNbbrklTU1NGTJkSM4666z3fX337t2bj99+++1W123ZsiVr1qxJkjQ0NOxyjzVr1mTr1q2t7rFixYrm4x33AAAAAABg73BH+g6WLl2aJHnsscd2exf2jBkzMmPGjCTJv/zLv2T48OHp16/fTnvtyrJly7Jt27YkaXHNe19v27Ytr7/+ej796U8XZ93VHgAAAAAA7B3uSN/L+vfv3/wloQsWLGh13fz585uPBw0a1OLce1/vyR6dO3fOZz/72TbNCwAAAABAmTvSd3D11VfnO9/5TnHN1772tSTJKaeckvHjxydJDj/88CRJly5dcsIJJ+Thhx/OzJkz8zd/8zfp1KnTTns8+OCDSd59JMuOzycaPHhw6uvrs2bNmjz44IP56le/utP1mzdvzqxZs5IkJ554Yrp06fI+f1MAAAAAAPaEkL6DPn367PHahoaGHHnkkTv9/Nxzz83DDz+cFStWZOrUqbnssstanJ83b14efvjhJMk3vvGN1NW1/Guoq6vLOeeckylTpmT27NmZN2/eTrF96tSpzc9IP/fcc/d4ZgAAAAAA3h+PdqmBk08+OUOHDk2STJw4MRMnTsySJUvS1NSUGTNmZNy4cdm2bVt69eqVMWPG7HKPSy65JL169cq2bdsybty4zJgxI01NTVmyZEluvvnmTJw4MUkydOjQ5vcCAAAAAGDvc0d6jdx0000ZM2ZMFixYkEmTJmXSpEktzvfo0SM//elP09DQsMvrGxoacuutt+bSSy9NU1NTrrrqqp3WHHPMMfnHf/zHmswPAAAAAMC7hPQaqa+vz913351p06blvvvuy+LFi7Nly5b07t07p512Wi666KI0NjYW9xg4cGDuu+++TJ06NTNnzsyyZcvSsWPHHHHEERkxYkRGjRq102NhAAAAAADYuyrVarXa3kOwb1q0aFHWrl2brl27ZsCAAe09zj7hiBmL23sEAOBj4vdn92vvEdiHvTLXvw8AYO/4zAl61nYfpHd6RjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFQjoAAAAAABQI6QAAAAAAUCCkAwAAAABAgZAOAAAAAAAFde09wL5q06ZNmTNnTn7961/nueeey5IlS7J+/fp07do1/fv3z6mnnppzzjknXbt2Le6zdevWTJs2Lffff38WL16czZs3p3fv3hk+fHguvPDCNDY27naWFStW5LbbbsuvfvWrLFu2LJ06dUq/fv0yYsSIjBo1KnV1/hoBAAAAAGqlUq1Wq+09xL7oz//8z7Nu3brimkMPPTQ//vGPc9RRR+3y/B//+MeMHj06CxYs2OX5Hj16ZPLkyTnyyCNbfY/nn38+l156aZqamnZ5/phjjsmUKVPSrVu34qxtsWjRoqxduzZdu3bNgAED9vr+H0VHzFjc3iMAAB8Tvz+7X3uPwD7slbn+fQAAe8dnTtCztvsgvdOjXVqxbt26dOzYMaeffnpuuumm/O///m+efPLJPPDAA7n00ktTV1eXP/zhDxkzZkyWL1++yz0mTJiQBQsWpFKpZOzYsXnooYcyZ86c3HDDDenWrVuamppy2WWXZdWqVbu8ftWqVRk7dmyamppSX1+fG264IXPmzMlDDz2UsWPHplKpZP78+ZkwYUIt/6cAAAAAAPhEE9Jbce6552b27NmZOHFizjrrrHz605/OQQcdlP79++f73/9+brzxxiTJ6tWrM2nSpJ2uf+SRR/Loo48mScaPH58rrrgiffv2Tc+ePTNy5MjceuutqVQqWb58eaZMmbLLGSZPnpzly5enUqlk0qRJGTlyZHr27Jm+ffvmiiuuyPjx45Mkjz76aPN7AQAAAACwdwnprbjuuuvSo0ePVs+PGDEin/vc55JklxH77rvvTpJ07949o0eP3un84MGD8nhYvgAAIABJREFUM2zYsCTJz3/+82zdurXF+a1bt+aee+5JkgwbNiyDBw/eaY/Ro0enoaGhxfsBAAAAALB3CekfQP/+/ZMkb775Zoufb9y4MXPnzk2SnHbaaenUqdMurz/99NOTvPsIl3nz5rU49/TTT2fNmjUt1u2oU6dOGT58eJLk8ccfz8aNG9v4mwAAAAAA0Boh/QN46623kmSnL/p86aWXsmnTpiTvfhloa957buHChS3Ovff1nuyxadOmvPzyy3s4OQAAAAAAe0pIb6O33norzzzzTJLk2GOPbXFu8eI/fRPu4Ycf3uoevXv3TocOHXa65r2vO3TokN69e7e6x3v333EPAAAAAAA+uLr2HuCj6qabbsqWLVuSJN/61rdanFu5cmXz8cEHH9zqHh07dkx9fX1WrVqVVatW7XKP+vr6dOzYsdU9Ghsbm4933GNvWbt27U6Pnvmk+cIXvtDeIwAAH1Of9M9ZtORzJwBQKz53fjDuSG+D++67L9OnT0+SnHrqqfnyl7/c4vyGDRuajzt37lzca/v59evX73KP3V3fpUuX5uMd9wAAAAAA4INzR/r79Nxzz+Xaa69NknzqU5/K3//937fzRLXXtWvXDBgwoL3HAAD4WHIHMgAAHwafO5NFixZl7dq1bbrWHenvw+9///tceuml2bhxYxoaGjJlypQWj1bZbv/9928+3v6lo63Zfv6AAw7Y5R67u37jxo3NxzvuAQAAAADAByek76Fly5bl4osvzsqVK3PggQdm8uTJ+exnP7vLtd27d28+fvvtt1vdc8uWLVmzZk2SpKGhYZd7rFmzJlu3bm11jxUrVjQf77gHAAAAAAAfnJC+B956661cdNFFeeONN9KlS5fceuutOeqoo1pd369fv+bjpUuXtrpu2bJl2bZt207XvPf1tm3b8vrrr7e6x3v333EPAAAAAAA+OCF9N1avXp2LLroor7zySjp27Jh//ud/znHHHVe8pn///s1fErpgwYJW182fP7/5eNCgQS3Ovff1nuzRuXPnVu+QBwAAAACg7YT0gnXr1mXMmDH57W9/mw4dOuQf/uEfcvLJJ+/2ui5duuSEE05IksycOTObN2/e5boHH3wwybuPZNnxYf+DBw9OfX19i3U72rx5c2bNmpUkOfHEE9OlS5c9+8UAAAAAANhjQnorNm/enHHjxuW5555Lklx//fU544wz9vj6c889N8m7zzCfOnXqTufnzZuXhx9+OEnyjW98I3V1dS3O19XV5ZxzzkmSzJ49O/Pmzdtpj6lTpzY/I337+wEAAAAAsHcJ6bvwzjvv5Hvf+16eeOKJJMl3v/vdnHHGGVm3bl2rf6rVaos9Tj755AwdOjRJMnHixEycODFLlixJU1NTZsyYkXHjxmXbtm3p1atXxowZs8s5LrnkkvTq1Svbtm3LuHHjMmPGjDQ1NWXJkiW5+eabM3HixCTJ0KFDm98LAAAAAIC9q1LdsQCTpUuX5rTTTntf18ycOTOHH354i5+tWbMmY8aMafUZ5z169MjkyZNz5JFHtrrv888/n0svvTRNTU27PH/MMcdkypQp6dat2/uad08sWrQoa9euTdeuXTNgwIC9vv9H0REzFrf3CADAx8Tvz/ZF8bTulbn+fQAAe8dnTtCztvsgvbNu90toq/r6+tx9992ZNm1a7rvvvixevDhbtmxJ7969c9ppp+Wiiy5KY2NjcY+BAwfmvvvuy9SpUzNz5swsW7YsHTt2zBFHHJERI0Zk1KhROz0WBgAAAACAvccd6bTKHek7c0c6ALC3uCOdEnekAwB7izvS/+SD9E7PSAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoENIBAAAAAKBASAcAAAAAgAIhHQAAAAAACoR0AAAAAAAoqGvvAdgzs2fPzrRp07Jw4cKsXr06hxxySE444YR8+9vfzoABA9p7PAAAAACAjy13pH8EXHfddRk7dmwefvjhNDU1ZfPmzVm2bFl+8Ytf5Otf/3r+8z//s71HBAAAAAD42BLS93GTJ0/OtGnTkiTDhw/P9OnTM3fu3PzsZz/L5z73uWzevDnXXHNN5s2b186TAgAAAAB8PAnp+7AVK1bkJz/5SZLkpJNOyi233JJBgwalsbExJ510Um6//fYccsgh2bp1a/7f//t/7TwtAAAAAMDHk5C+D5sxY0bWr1+fJJkwYUIqlUqL8927d8+YMWOSJAsWLMjChQs/9BkBAAAAAD7uhPR92OzZs5Mkffv2zaBBg3a55vTTT28+njVr1ocyFwAAAADAJ4mQvg/bfof50Ucf3eqaQw89NL169WqxHgAAAACAvUdI30ctX768+bEuffr0Ka49/PDDkySLFy+u+VwAAAAAAJ80Qvo+auXKlc3HBx98cHHt9vOrVq2q6UwAAAAAAJ9Ede09ALu2/W70JOncuXNx7fbz69at26szbNq0KUmydu3azJs3b6/u/VHTtWvXJMl/D2znQQCAj41FixYlefezFmy3/XNnGh9s30EAgI8Nnzt3tr17vh9COq1655132nuEfYb/owEA4MPgcycAQO21pXsK6fuoAw44oPl4d/+FZPv5Aw88cK/O0Llz52zatCn77bffbu+KBwAAAADYl23atCnvvPNOm1qnkL6P6t69e/Px22+/XVy7/XxDQ8NenWHgQM8xAQAAAADwZaP7qJ49ezbflb5kyZLi2qVLlyZJ+vXrV/O5AAAAAAA+aYT0fVSlUsmgQYOSJM8991yr6/7whz9k+fLlSdK8HgAAAACAvUdI34edcsopSZJXX301L7zwwi7XPPjgg83Hp5566ocyFwAAAADAJ4mQvg87++yzmx/vctNNN6VarbY4v2rVqkyZMiVJcvTRR7sjHQAAAACgBoT0fVhjY2Muv/zyJMmcOXPy3e9+Ny+88EJWrFiRxx57LOeff36amppSV1eXK6+8sp2nBQAAAAD4eKpUd7zNmX3Oddddl2nTpu3yXMeOHfN3f/d3+drXvvYhTwUAAAAA8MkgpH9EzJ49O//+7/+ehQsXZvXq1enRo0eOP/74XHjhhRkwYEB7jwcAAAAA8LElpAMAAAAAQIFnpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAMAAAAAQIGQDgAAAAAABUI6AAAAAAAUCOkAAAAAAFAgpAPAPu6JJ57IgAEDMmDAgCxdurS9xwEAoJ2cf/75GTBgQK666qoPtM/2z5bTp0/fS5MBfPwJ6QDQTq666qoMGDAg559/fnuPAgDAR9z06dObAzkAe5+QDgAAAAAABXXtPQAAUPalL30pixYtau8xAABoZ3fcccde2cdnS4D3zx3pAAAAAABQUKlWq9X2HgKAT56rrroqM2bMyHHHHZc77rgjL774YqZMmZInn3wyK1asSPfu3TNkyJBcfvnl6du3b6v7rF69OnfddVdmz56d1157LevWrUtjY2MGDx6c888/P8cee2xxjhdffDE//elP89RTT2X16tXp0aNHhg4dmksuuSSHHXZY8zMmb7jhhowcObLFtZs2bcrcuXMza9asPPvss1m6dGm2bNmSgw46KAMHDsxXv/rVnHnmmenQoeV/t54+fXquvvrq4lxnn312brzxxiTvftnoBRdckCSZOXNmDj/88CTJXXfdleuvvz4dOnTIww8/nF69erW631NPPZXzzjsvSfKv//qvGTJkyE5r5s6dm3vvvTfPPPNM3nrrrXTq1Cmf+cxn8pWvfCXnnXdeDjjggOLMAAAfFTt+Fn3qqacyderULFiwIGvWrMmhhx6a4cOH57LLLktDQ0Or+yxatCi33357nnjiibz55pupq6tLnz59MmzYsHz7299OY2Njq9c+88wzufvuu/Pss8+mqakplUoljY2N6dmzZ774xS/mL//yL3PUUUe1uOb888/Pk08+2eKz4tKlS3PaaacVf9/DDjsss2bNan69q8+4L7/8cs4888wkyU033ZSzzjqr1f02bNiQE088MevXr8/YsWNzxRVX7LRm8eLFufPOOzN37ty88cYb2bZtWw499NB8+ctfzsUXX5zevXsXZwbY13i0CwDt7pe//GWuvPLKbN68uflnb775ZmbMmJFZs2bljjvu2OWXJv3f//1fxo///+3deXhN597/8XcSCZIg5jEVVJQoooYEHYytKabHUVT7FPUoVfXQKqfXo6faiqnlVE1FkUMMFZrEEDXGFGPMYx2JCJXIJERl/P2R317NtrO3CKqcz+u6XNlZ932vda+VfbVrf/d3fe+RJCcnm22/fv0669evZ/369QwbNoyRI0fme9zg4GDGjRtHZmamsS02NpbAwEA2btzIwoULbc57+vTpLFmyxGL7jRs3CA8PJzw8nJCQEGbNmoWTk5PNfRVGp06dmDRpEhkZGYSEhDB48GCrfUNCQgAoX748vr6+Zm13795l/PjxhIaGmm1PT0/n5MmTnDx5klWrVrFgwQI8PDwe+XmIiIiIPEkrVqzgH//4B9nZ2ca2y5cvs2jRIkJDQ1myZAk1a9a0GLdw4UKmTZtmNu7u3bucPXuWs2fPEhgYyPfff0/Tpk3zHTtlyhSL7VevXuXq1ascPXqUCxcuMG/evEd0lvf3/PPP4+XlxalTpwgODrYZSN+6dStpaWkA+Pn5WbQvWrSI6dOnm91nA0RFRREVFcVPP/3Et99+S+vWrR/tSYiIPEYKpIuIyBMVHR3N2LFjadiwIe+//z5169YlPT2dsLAwpk2bRkpKChMmTGDFihVm406dOsV7771Heno69erV47333qNRo0a4uLgQExPDsmXLCAoKYvbs2VSpUoXevXubjT979qwRRK9YsSKjR482Asz79u1j2rRpfPTRRzbnXqJECf72t7/RokUL3N3dKV++PPb29ly7do2NGzeyfPlydu7cyYwZM/jkk0+McX5+frz++utMmDCBkJAQXnrpJX744QezfTs6Ot732pUuXZqXX36Zbdu2ERwcbDWQnp6ezqZNmwDo0qWLRYb8xx9/TFhYGI6OjgwYMIDOnTtTrVo1fv/9dyIiIpgxYwYxMTEMHTqUoKAgZaaLiIjIMyM6Opovv/wSLy8vRo0aRd26dUlNTSU0NJQ5c+YQFxfH+++/T3BwMEWLFjXGhYSEGIFwT09PRo0aRcOGDbl79y7bt29n5syZpKSkMGTIEIKDg3F3dzfGXrp0ienTpwPg6+vLoEGDqFWrFq6urty8eZOLFy+ya9cuUlNTC3QOVatW5ciRI4SEhDBhwgQgN9s9r3vv/6zx8/Pj1KlT7Nmzh8TERKsZ9cHBwQB4eXlRq1Yts7Zly5YxefJkADp06EC/fv2oXbs29vb2nD59mlmzZhEZGcnIkSP56aef8PT0LNDcRESeNAXSRUTkibp+/Tovv/wyc+fOpUiRP/639M4775CdnY2/vz+RkZFcvHjR7CZ93LhxpKen06hRIwICAswyvkuVKsWkSZMoX7488+bN45tvvqFr164UK1bM6DN16lQyMzNxdXVl2bJlZh9uunXrRqNGjejevbvNuY8YMSLf7eXLl6dBgwb4+vry3nvvERgYyLBhw3B1dQWgSJEixj8ABwcHXFxcHuCq/aFbt25s27aNc+fOcf78+Xw/iISHh5OSkmL0z2vz5s2EhYVhZ2fHzJkzLR4L7t69Oz4+PvTo0YNLly4RGBjIoEGDCjVXERERkb+a69ev88ILLxAQEEDx4sUBKFOmDMOHD8fd3Z2PP/6YqKgoli1bxsCBA4HcJIVJkyYBULNmTQIDA437PID+/fvj7e1Nnz59SEtLY/LkycyaNcto3717N1lZWZQtW5b58+eb3ceWLFmSatWq8eqrrxb4HOzs7HBxcTHbT2HvLTt37syUKVPIzMxk/fr1DBgwwKJPYmIie/bsASyz0ePi4oySM++++y6ffvqpWXurVq1o3rw57777LgcPHmT69Ol/ata9iMjD0GKjIiLyxP397383C6Kb9OjRw3h94sQJ43VERATnzp0D4Ouvv7ZaNmXYsGE4OzuTmJjI7t27je1xcXHGzf+AAQPMgugm1atXz/eDw4N45ZVXKFOmDGlpaURGRj7Uvqxp06YNJUqUAP7IDLqXaXvt2rWpW7euWdvSpUsB6Nixo9XampUqVaJ///7AHyViRERERJ4Vo0ePNoLoefn5+Rk1yoOCgozt27ZtIyEhAYAxY8aYBdFN6tWrR58+fYz+iYmJRltWVhaQG7B/HOX/HkbeMoDW7vs2bNhAZmYmDg4OFuVfVqxYQXp6OpUqVWLMmDH5jnd0dDRKL+7cuZObN28+wjMQEXl8FEgXEZEnyt3dnRo1auTb5ubmZjxOeuPGDWP7vn37AKhSpQqVKlXi9u3b+f7Lysoy9n3y5Elj/LFjxzCttd2mTRurc7vfok2Qm5EzZ84c+vXrh4+PD15eXtSpU8f4Z/rQFBUVdd99FYaTkxNvvPEGAKGhody7hnhqairbt28HLDOG7ty5w9GjRwFo3ry51et4+/ZtI9P93LlzZrXsRURERJ5mzs7O+S7CbtK+fXsgdyFOU8D38OHDABQvXtxm5rjpHi0rK8us1IopseHChQtMmzaNpKSkhzuJR8z0BOOxY8eIjo62aDcF2H19fSlXrpxZ2969ewFo2rQpd+/etXpvaXrSNCcnh1OnTj3O0xEReWRU2kVERJ6oChUq2Gw3ZQf9/vvvxrZLly4BuYsxNW7cuEDHyZsFFBsba7zOb+GogrQBHDp0iOHDh1ssdpqfgta4LAw/Pz9Wr17NtWvXOHDgAM2bNzfaNm3aRHp6OnZ2dnTt2tVsXExMDBkZGQBMmDDBqKlpS3Z2NikpKZQvX/7RnoSIiIjIE1C9enUcHBystpvuB3Nycrh69SolS5bk6tWrAHh4eOT7VKVJ7dq1jdemMZCbwNCuXTu2bNnCDz/8wKJFi6hfvz4vvfQSTZo0wdfX94muSdOuXTucnZ1JS0sjODjYrJzh5cuXjUSM/BYZNd2nh4SEFPhJxrz36SIif2XKSBcRkSfK1geXvPJmWhcmKJ03izotLc14nd9jvCa2PsCkpqbywQcfkJycTNmyZRkzZgyrVq1i165dHD58mCNHjnDkyBEqV64M/PEI7+PQtGlTqlatCliWdzF9gGnatKkxl7znUBh3794t1DgRERGRv5r7Bazztt++fdvs5/3G5q1TbhpjYlqM3t3dnaysLI4dO8aiRYsYNmwYLVq0YOLEidy6deuBzuVRcXZ2NjLx7w2Gm+418/bJqzBz1r2liDwtlJEuIiJPHdOHlgYNGrB69epCj4fc8ib51bUE84D7vTZt2kRSUhL29vYsXbqU559/Pt9+f8YHIDs7O7p06cK8efMICwtjwoQJODk58dtvv3Hw4EEg/4yhvB/u5s+f/0CLWomIiIg8C2zd793bbrp3Mv0szFgTR0dHBg0axKBBg4iOjiYyMpJDhw6xY8cO4uPj+de//sXRo0dZuXKlzaz3x8XPz4+ff/6Z6Ohojh49SqNGjYA/AuumrPV7OTs7c/PmTQYPHszHH3/8p85ZRORxU0a6iIg8dUyLg8bExFjUBC+IKlWqGK9Nj5/mx1ababHTOnXqWA2iX7t27bGWdMnLVMsyb0300NBQsrOzKVq0qFGjM6+qVatib597KxATE/OnzFNERETkryQ6Otrmk4P//ve/gdzEBdM9pOlJwKioKDIzM62OvXDhgvHaNCY/1atXp3v37nz55Zfs2LHDWPD+5MmT7Nixo8Dn8ij5+voapfxMwfPjx48b6/7kl6QB5vfpIiLPGgXSRUTkqWNaECopKYmIiIgHHt+oUSPs7OwA2LZtm9V+W7dutdpmKhVj64PX/epCmrKLHkXZl1q1auHl5QX88cit6edrr71GiRIlLMaUKFGCBg0aALBhw4aHnoOIiIjI0yYtLY09e/ZYbd+yZQsAzz//PCVLlgTgpZdeAnKfbNy1a5fVsWFhYUBuKUNvb+8CzadIkSJmNckvXrxYoHGmsSYPe3/p4OBAly5dgNz7xMzMTOPesnz58rRo0SLfcab79N27dxuLs4qIPCsUSBcRkadOq1at8PT0BODzzz/nxo0bNvtfuXLFrEZ6hQoVjJv/gIAArly5YjEmJiaGgIAAq/usVq0akJu1Hh0dbdF+8eJF5s6da3Nebm5uAMTFxdnsV1CmzKCdO3dy8OBBI2velK2en3fffReAw4cP8+OPP9rcf1ZWVr7nKiIiIvI0mz59Onfu3LHYHhISwrFjxwDo2bOnsb1169aULVsWgGnTpuVbyu/s2bMEBgYC0LZtW8qUKWO0RUVFkZ2dbXU+ly9fNl6b7hcLIm/fR3F/abqHTExMZOfOnWzcuBGAzp07W13nqH///jg5OXH79m0+++wzY2F7a0wZ/yIiTwMF0kVE5KljZ2eHv78/xYoVIyoqim7durFw4ULOnz9PSkoKCQkJnDlzhtWrVzN06FA6dOhg8QFnzJgxODg4kJqayltvvUVISAjx8fHEx8cTHBzMW2+9ZfaB514dOnTA3t6ejIwMhgwZwtatW4mPj+fq1assX76c/v37U7x4cZsffkwZ5DExMSxbtoyEhAQyMzPJzMy0+eHKmi5duuDg4EBGRgZjx44Fcj9QvfLKK1bHvPHGG3Tu3BkAf39/hg8fzs6dO7l+/To3b94kNjaW8PBwpk6dSrt27ViyZMkDz0tERETkr6pChQpcvHiRAQMGsHfvXpKSkrh8+TLff/8948aNA8DDw4P+/fsbY5ycnIy2X3/9lX79+rF9+3YSExO5du0agYGBvPPOO6Snp+Ps7GxRK3zu3Lm0a9eO6dOns2fPHq5du8bNmze5fPkya9asMTLSnZ2dad26dYHPpV69ekbZvn/+85/ExsaSnp5OZmZmoTLU69atS+3atQH46quvjOQVa2VdACpVqsT48eOB3Iz83r17s27dOmJiYkhNTeX69escOnSIBQsW0KtXLz788MMHnpeIyJOixUZFROSp5OXlxY8//shHH33E9evXmTJlClOmTMm3r4ODg0XWTL169fj6668ZP348165dY8yYMWbtpUqV4rvvvqN3797GPvLy8PDgo48+4ptvviEqKophw4aZtZcoUYLvvvuOsWPHkpycnO+8Wrdujbu7OzExMXzxxRd88cUXRluPHj3w9/cv2MX4/8qVK0eLFi3YtWsXsbGxAHTs2BFHR0eb4/z9/XF1dWXlypVs2bLFeIQ5P/fbl4iIiMjTxMPDg/fff5+JEycaT+rlVaFCBebMmUPRokXNtnft2pW4uDimTZvGuXPnGDp0qMXYUqVK8f333/Pcc89ZtMXGxjJ//nzmz5+f77yKFSvG1KlTqVChQoHPpVy5cnTq1InQ0FCCgoIICgoy2qpWrWqzpKE1fn5+TJ8+3bi3zFtO0Jq+fftib2/Pl19+yZkzZ4wEj/zUq1fvgeckIvJjuwuVAAAX0UlEQVSkKJAuIiJPrcaNGxMWFsaaNWvYtm0b586dIyUlBQcHB8qVK0ft2rXx9fXljTfeoFSpUhbju3fvjqenJ/PmzePgwYPcvHmT8uXL06pVK4YMGULp0qWNvi4uLhbj/+d//odatWqxZMkSTp06RWZmJhUrVqRly5YMGjTIWGzJmmLFirFs2TJmz57Nvn37+O2337h79+5DXZNu3bqZ1eq0lTFk4uTkxBdffEGfPn1YuXIlhw4dMubi6uqKu7s7jRo14rXXXrNaD1NERETkadWvXz9q1qzJ4sWLOX78OKmpqVSqVIm2bdsydOhQq08YDho0iJYtW7J06VL2799PfHw8Dg4OuLu707p1a9555518n3AcM2YMvr6+REREcObMGeLj40lOTqZo0aJUr14dX19f3nrrLWNx0wcxadIknn/+ecLCwoiOjubOnTvk5OQ88H5M/Pz8+Pbbb42nJQtybwnQp08fXnvtNZYvX87evXu5fPkyqampFCtWjMqVK1OvXj1efvll2rVrV+i5iYj82exyHua/qCIiIs+w06dP06NHDwDWrFlD/fr1n/CMRERERORR+PTTT1m7di3NmjWzuS6OiIiIiWqki4iIWGF6/NXJyclY3FRERERERERE/vMokC4iIv+xrNUuB4iKiuLHH38EoE2bNjg5Of1Z0xIRERERERGRvxjVSBcRkf9Yn3zyCS4uLnTu3BkvLy9cXFyIj49n165dzJ07l1u3buHo6GixkKiIiIiIiIiI/GdRIF1ERP5jZWVlsWHDBjZs2JBvu5OTE5MnT6ZOnTp/8sxERERERERE5K9EgXQREfmPNWLECDw9PTl48CDXr18nKSkJJycnqlSpgq+vL2+//Tbu7u5PepoiIiIiIiIi8oTZ5eTk5DzpSYiIiIiIiIiIiIiI/FVpsVERERERERERERERERsUSBcRERERERERERERsUGBdBERERERERERERERGxRIFxERERERERERERGxQYF0EREREREREREREREbFEgXEREREREREREREbFBgXQRERERERERERERERsUSBcRERERecT2799PnTp1qFOnDkFBQU96OvIYBAUFGX/j/fv3P+npiIiIiMhjpkC6iIiIiIiIiIiIiIgNCqSLiIiIiIgAbdq0oU6dOgwYMOBJT+Wpp4x9ERERedYUedITEBERERF51jRv3pxz58496WnIY9SzZ0969uz5pKchIiIiIn8SZaSLiIiIiIiIiIiIiNigQLqIiIiIiIiIiIiIiA12OTk5OU96EiIiIiIi+QkKCmLcuHEALF26lGbNmhEaGsq6des4d+4ciYmJ1K5dm59//tls3O3bt1m1ahU7duzg4sWLJCcn4+LiQo0aNXjttdfo168fJUuWNBuTnp5Oq1atSElJwdvbmxUrVtx3fv369ePw4cOUKFGCPXv2ULRoUQD279/P22+/DcCkSZNslgBJTEwkMDCQXbt2ER0dTWpqKiVKlKB27dq0b9+e3r17U6xYMYtxvXr14uTJk3h5eREUFGTRnpaWRrNmzcjIyABg/vz5vPrqqxb9pk6dyoIFC7C3tyciIoJSpUrd97zvFR4eztq1azlx4gTx8fFkZWXh5uZG6dKlqVevHi1btqRdu3Y4OzvnOz47O5tNmzaxadMmTpw4QUJCAkWKFKFKlSr4+PgwYMAAqlevnu/YK1eu0LZtWwA++OADRowYwZkzZ1i8eDEHDhwgPj6eEiVK0LBhQwYOHEizZs0s9jFgwAAOHDhw3/PcunUr1apVAyzfm82bNzfrm197cHAwP/30ExcuXODOnTtUq1YNPz8/BgwYQPHixY2x+/btIyAggFOnTpGYmEj58uVp27Ytw4YNo3Tp0vedZ2xsLIGBgezdu5fY2Fhu376Nm5sbdevWpVOnTnTt2pUiRfKv8vnpp5+ydu1aAM6dO0dGRgaBgYEEBwcTHR1NRkYG1apVo0OHDgwcOBBXV1ez8Xnf+7b06NEDf3//+/YTERER+atQjXQREREReSqkp6czdOhQduzYYbPfvn37GD16NAkJCWbbk5OTiYyMJDIykiVLlvDPf/6Tpk2bGu1OTk507NiRFStWEBkZSXR0tNXgLUBMTAxHjhwBoGPHjkYQ/UGEhIQwYcIEbt++bbY9MTGR/fv3s3//fpYuXcrs2bOpXbu2WR8fHx9OnjzJmTNnSElJsQiAHzp0yAiiA0REROQbSI+IiACgbt26DxxEz87OZuzYsQQHB1u0xcfHEx8fz/nz51m3bh3Lli2jSZMmFv1iY2MZMWIEp06dMtt+9+5dLly4wIULFwgMDGTcuHG89dZb953TypUrmThxotm5JyYmsn37dnbs2MGECRPo27fvA53nw8rKyuLDDz8kLCzMbPuFCxeYPn064eHh/PDDDxQrVowpU6awaNEis36xsbEsXbqUHTt2EBgYSLly5awea+HChXz77bdm5w9//D3Cw8MJCAhgzpw5VKxY0ea8ExMTee+99zh58qTFvC9cuMDmzZsJCAgoUHBfRERE5GmnQLqIiIiIPBWmTZvG2bNnadWqFb169eK5554jNTWVf//730afPXv2MGTIEDIzM3Fzc6Nv377Ur1+fSpUqcevWLfbt28e//vUvEhMTGTJkCKtWrTILUHfv3t3IRF+3bh0jR460Op+ff/4Z08Od3bp1e+DzWbNmDePHjwegYsWK9O/fH09PTypUqEBSUhI7d+4kMDCQy5cv8+6777J27VrKly9vjPfx8WHBggVkZ2dz4MAB2rdvb7Z/U4DcZP/+/RZzSE1N5cyZMwAWGdUFsWLFCiOIXqtWLd58801q166Nm5sbaWlpREdHc/jwYbZt25bv+OvXr9OnTx/i4+NxdHTEz8+Pli1bUrVqVXJycjh58iRLly7l8uXLTJw4ERcXF3r06GF1Pnv27OHYsWPUqlWLd955hzp16pCZmUl4eDgLFiwgIyODr776Ch8fH2rUqGGM+/rrr7lz5w6DBg0iLi6O+vXrM2nSJIv93y/wbM3MmTM5evQob7zxBt26daNixYpcvXqVefPmceLECQ4ePMiCBQtwdXVl0aJF+Pr60qdPH5577jkSEhJYsmQJu3fv5vLly/j7+zNt2rR8j/Pdd98xa9YsAGrUqEHfvn2pUaMGZcuWJS4ujs2bN7Nu3TpOnTrF4MGDWblypdWnBACGDx/OuXPn6NevH23btqVMmTLExMSwYMECjh8/zoULF5g8ebJZZvmLL75ISEgIW7duZcaMGcb1ffHFF832XZgnH0RERESeJAXSRUREROSpcPbsWQYPHszHH39stt3X1xeAW7duMWbMGDIzM/H19WXWrFkWZSd8fHzo0aMHffv2JTExka+++orFixcb7d7e3nh4eBAVFUVwcDAffvghdnZ2+c7HFEB2d3fPN9PalpiYGP7xj38AuUH4L7/8EicnJ7M+rVq1olOnTvz3f/838fHxzJgxg6+++spob9KkCY6OjmRkZBAREWE1kN6uXTu2bNmSb+b6gQMHyMrKMq7Ng1q/fj0AVapUYdWqVRbX+6WXXqJnz56kp6dbZEgDjB8/nvj4eCpXrsyiRYuoWbOmWbu3tze9evVi0KBBHDlyhEmTJtG+fXuL45hERkbSqlUr5syZY3Y9GzdujIeHB2PHjiUjI4MVK1YYZVcg928I4OjoCICzszOenp4PfD2sOXr0KKNHj2bIkCHGNi8vL1q0aEGXLl24evUqixcvJj09nb59+/L555+bjW/RogVvvvkmJ06cYNOmTYwfP54yZcqY9Tl8+DDff/89AEOGDGHUqFHY2/+xJJaXlxetW7emTZs2jBgxgvPnz7N48WKGDRtmdd7Hjx/nhx9+oEWLFsa2evXq8eqrr9KrVy9+/fVXQkND+eSTT4z5mK5d3iz2atWqPdLrKSIiIvIkaLFREREREXkqVK9enVGjRlltDwwMJDExkeLFi/PNN99YDbZ6eHgwfPhwILcMTExMjFm7Kbv8ypUrHDp0KN99mEq/QG4W+4NauHAhd+/epXLlykycONEiiG7i7e1Nv379gNzA/e+//260FS9enAYNGgCW2ec3b940Ms0HDhxIyZIlyc7OtshKN41zdHR84C8DAG7cuAHkBmmtXW/ILZvj4uJitu348ePs3r0bgM8//9wiiG7i7OxsfOmQkpJiUR4lr6JFizJ58uR8r6efn5+R0X/w4EEbZ/Xo1a9f3yyIbuLi4mK8f27duoWbm5vxlEJeRYoU4c033wQgIyODyMhIiz5z584lJyeHBg0a8L//+79mQfS82rdvT4cOHQBYvXq1zXn379/fLIhuUqxYMfr372/M5+jRozb3IyIiIvIsUCBdRERERJ4KnTp1srpAIsAvv/wC5Gao35ute6+8C06a6pybdOvWzchCX7duXb7jTdvt7OwKVdZly5YtQG62+P1qq5vmmp6eblGr2pRF/uuvvxpBbcjNNM/OzsbFxYWGDRsateDvDbibfq9fv75FoLsgTKVODh48SFRU1AON3bx5MwAlSpTIt3Z7Xp6enri5uQGWf6+8WrRoYbV+uL29PV5eXgAWX548bl26dLHaVrduXeP166+/bvVLlbz9rly5YtZ2+/Zt9u7dC0Dnzp2tPkVhYnpPXb16ld9++81qPz8/P6tteUu1/NnXU0RERORJUGkXEREREXkqvPDCC1bbsrKyjMUqt23bRp06dQq83/j4eLPfq1atStOmTTlw4ABhYWH83//9n1mwOz09nY0bNwK5JUNMZUEK6urVq8YxAwICCAgIKPRcfXx8jHIeERERRsDWFCBv2rQpRYoUoXnz5mzdutUskJ6YmMiFCxeM/RRG79692b9/P8nJyXTt2pXWrVvz8ssv07BhQ2rVqoWDg4PVscePHwdy67Tb+tve695rkFfeuuf5MZW1uXXrVoGP9yhYy7aH3C8STGzNv2TJksbre+d/+vRpMjMzAZg0aVK+9d2tiYuLo1KlSvm22Zq36YuN/OYjIiIi8ixSRrqIiIiIPBVsLU6YkpJiBBIfVN5yKSamchupqalG9rjJjh07SElJMev3IBISEgoxy1z3zrVRo0YUK1YMMM82N702BchNPy9evEhcXByQu/ioabHUwgbSu3btyscff0yxYsVIT08nLCyMzz77jK5du9K8eXNGjBjBtm3bjOPklZiYWKhj3rlzx2qbrYUzAaPcSXZ2dqGOXVimv1F+8pZgKV68uNV+ebPM753/o3xP5WXretqaj4iIiMizSBnpIiIiIvJUsFbzGTAWzITccikjR44s8H7Lli1rse31119n4sSJ3Llzh59//pnOnTsbbaayLkWLFqVjx44FPk5+c+3Xrx99+/Yt8Nh7M4ednJxo3Lgxe/fuNYLnCQkJFpnmnp6elC1bloSEBCIiIvDz8zP6Fy1alMaNGz/weZgMHjyYHj16sGHDBvbu3UtkZCRJSUmkpqayefNmNm/eTLNmzZg9e7ZZ9rXpi4+KFSuyYMGCAh/PVrD5P1Xe99SoUaNo06ZNgcdWq1btcUxJRERE5JmjQLqIiIiIPPXc3Nyws7MjJyeHjIwMPD09H2p/rq6utG3bltDQUPbs2cONGzcoV64cSUlJhIeHA9C2bVuzwHBB3Vu//WHn6uPjw969e4mJiSE2NtZY+NHNzc0omWJnZ0ezZs3YuHGjEUg3LTzq7e1ttS53QZUtW5YBAwYwYMAAIDfzfefOnSxfvpyYmBgOHDjAF198wdSpU40xZcqU4dKlS6SmplK7du371vUW6/K+p4oUKfLQ7ykRERERsaTSLiIiIiLy1HN0dDTqoh87doyMjIyH3qepbEtmZiahoaEAbNiwwdh3Ycq6QG4GsKm+9KFDhx56nnnLskRERBiZ5s2bNzcLTpv6RUREcP36dS5dumQx/lGpVasWAwcOZM2aNcaCpGFhYWbld0wLf6alpRn17aVw6tatazyx8SjeU4+CvhgRERGRZ40C6SIiIiLyTGjfvj0AycnJ/PTTTw+9vxYtWlChQgXgj3IuP//8MwDlypWjVatWhdqvvb29UXrj/PnzRoZ7YdWvXx9XV1fAPJB+b4Dc9HtsbCyrV6+22P44lCpVigYNGgBw9+5d0tLSjLYOHToYrxcuXPjY5vAgTLXM09PTn/BMHoybmxtNmzYFIDw83Cjt8yTdu0CviIiIyNNOgXQREREReSa8/fbbRqa3v78/u3btstk/MTGRgIAAq+0ODg507doVgDNnzhAWFsaxY8eA3EU2HRwcCj3XoUOHGuVUPv30U06ePGmz/7Vr18yC3/fO0xRE3b59O5cvXwYsA+QeHh5UrlwZgMWLFwPg4uLCiy++WOjzWLt2rc0gaUpKinHN3NzcKFmypNHWtGlTY44bNmxg9uzZNo+Vnp7O6tWruXHjRqHnez+mL06io6PzXSD1r2zEiBHY2dmRlZXFBx98QExMjM3+Fy9eZP369Y9tPqZrCRAVFfXYjiMiIiLyZ1GNdBERERF5JpQsWZKZM2cyePBgfv/9d9577z3atWtH+/bt8fDwwNHRkZSUFM6fP09ERAS7du2iTJkyRl3v/HTv3t3Ilv7ss8/Mtj+M6tWr8+WXXzJ27FgSEhJ488036dy5M6+99hpVq1bF3t6epKQkzp07x+7duzlw4AANGzakd+/e+e7Px8eH7du3k5qaCuQu4FmzZk2Lfs2bN2fdunVGvyZNmlCkSOE/Enz66af4+/vTpk0bGjduTI0aNXBxcSElJYWzZ88SGBhIXFwcAP3797cYP3XqVP72t79x7do1Zs6cyZYtW+jZsycvvPACLi4u3L59m0uXLhEZGcnWrVtJTk5m8+bNlCtXrtBztqVJkybs27ePpKQkJkyYQPfu3SlVqpTR/txzz+Ho6PhYjv2wmjZtysiRI5kxYwZRUVF07dqVHj160LJlSypVqkR2djYJCQmcOXOGnTt3cvToUbp27Wq2kO6jVK9ePZydnUlLS2PBggWULVuWWrVqGe+3EiVKmAXbRURERP7qFEgXERERkWeGj48PAQEBjB49mtjYWH755Rd++eUXq/3vt1iop6cn9erV4/Tp09y8eROAOnXqGIt4Poxu3brh6urK3//+d5KSkli3bp1RQuZB53pv9nnz5s2t9st7jEdR1iU5OZmgoCCCgoKs9vmv//ovhg0bZrG9QoUKrFy5kjFjxnDgwAFOnTpls166k5PTQy+Masubb75JYGAgN27cYOXKlaxcudKsfevWrVSrVu2xHf9hvf/++5QpUwZ/f3/S0tJYvnw5y5cvt9q/MIvlFpSzszODBg3iu+++47fffmPUqFFm7T169MDf3/+xHV9ERETkUVMgXURERESeKd7e3oSFhREaGsq2bds4deoUiYmJZGZm4urqiru7Oy+++CKtWrXi5Zdfvu/+unfvzunTp81+f1Tatm2Lr68vQUFBhIeHc/bsWZKSksjJyaFUqVJUr16dhg0b8sorr1gNjkNucL906dIkJSUB1gPk1uqmF9b69evZtWsXR44cISoqisTERJKTk3FycqJy5cp4e3vTs2dPXnrpJav7qFixIgEBAezdu5fQ0FAiIyOJi4vjzp07ODs7U7lyZerUqUOLFi1o166dWXmYR61cuXKsWbOGBQsWsG/fPq5evcqdO3eeqjIvffr0oUOHDqxevZo9e/Zw8eJFkpOTsbe3x83NDQ8PD7y9vWnTpg0NGzZ8rHP54IMP8PDwYO3atZw9e5aUlJRHshCwiIiIyJNgl/M03RWKiIiIiIiIiIiIiPzJtNioiIiIiIiIiIiIiIgNCqSLiIiIiIiIiIiIiNigQLqIiIiIiIiIiIiIiA0KpIuIiIiIiIiIiIiI2KBAuoiIiIiIiIiIiIiIDQqki4iIiIiIiIiIiIjYoEC6iIiIiIiIiIiIiIgNCqSLiIiIiIiIiIiIiNigQLqIiIiIiIiIiIiIiA0KpIuIiIiIiIiIiIiI2KBAuoiIiIiIiIiIiIiIDQqki4iIiIiIiIiIiIjYoEC6iIiIiIiIiIiIiIgNCqSLiIiIiIiIiIiIiNigQLqIiIiIiIiIiIiIiA0KpIuIiIiIiIiIiIiI2KBAuoiIiIiIiIiIiIiIDQqki4iIiIiIiIiIiIjYoEC6iIiIiIiIiIiIiIgN/w8aWjwIi2z8lAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 745,
              "height": 489
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "keskrP6NjDX3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "PRE_TRAINED_MODEL_NAME = 'bert-base-cased'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MvNd2e7e6-VI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "3d128511d9c74582aec60e99e35f8ab6",
            "6ba6b7bea75e4b4984be11ad772c0db1",
            "1e4e35363d864130a333a63c5924ffb6",
            "4417362d27144f97a9930abc59a680bd",
            "b7a12a32d1e54d6c8f07b3cf5d93694b",
            "c6fbcc9a8f024e1a8daab1a6e657fea9",
            "05afeb24ba6241db9b53ea8f5ba34e10",
            "0f6b9b1da4e6457e956e84f63cf6bd3b"
          ]
        },
        "outputId": "8fb6c30d-7fa8-4dd0-910f-1b45032aa3b2"
      },
      "source": [
        "tokenizer = BertTokenizer.from_pretrained(PRE_TRAINED_MODEL_NAME)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3d128511d9c74582aec60e99e35f8ab6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=213450.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jRWDCj1N_E4e",
        "colab_type": "text"
      },
      "source": [
        "Sample sentence string to demonstrate token conversion"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_7YA_81N--Nm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sample_txt = 'When was I last outside? I am stuck at home for 2 weeks.'\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "px-ElR6F--ZG",
        "colab_type": "text"
      },
      "source": [
        "Here we will demonstrate what the tokenized versions of the words are"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "osdXeUHp7D6P",
        "colab_type": "code",
        "outputId": "66dcc20a-8800-46cc-b8d8-eabbc168cf95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "tokens = tokenizer.tokenize(sample_txt)\n",
        "token_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "print(f' Sentence: {sample_txt}')\n",
        "print(f'   Tokens: {tokens}')\n",
        "print(f'Token IDs: {token_ids}')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " Sentence: When was I last outside? I am stuck at home for 2 weeks.\n",
            "   Tokens: ['When', 'was', 'I', 'last', 'outside', '?', 'I', 'am', 'stuck', 'at', 'home', 'for', '2', 'weeks', '.']\n",
            "Token IDs: [1332, 1108, 146, 1314, 1796, 136, 146, 1821, 5342, 1120, 1313, 1111, 123, 2277, 119]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2IzFieTe_P4Q",
        "colab_type": "text"
      },
      "source": [
        "SEP tokens are utilized to mark the ending of a sentence"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-1uoYsdW-7qH",
        "colab_type": "code",
        "outputId": "30d32cc5-b783-4016-ea62-e9382b764313",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tokenizer.sep_token, tokenizer.sep_token_id"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('[SEP]', 102)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "03sinE9jAWWS",
        "colab_type": "text"
      },
      "source": [
        "Here is the token for the start of a sentence"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CYfYrptE_UFD",
        "colab_type": "code",
        "outputId": "5bba873f-8a74-416f-b47d-b5e25ebc4407",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tokenizer.cls_token, tokenizer.cls_token_id\n"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('[CLS]', 101)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zywC1ti8AdUd",
        "colab_type": "text"
      },
      "source": [
        "This will be the token for padding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eQOZHLnUAbSK",
        "colab_type": "code",
        "outputId": "db7a5c13-cf6c-4e05-8ff5-190804b5b31d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tokenizer.pad_token, tokenizer.pad_token_id"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('[PAD]', 0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EDeOzGB_Ayiq",
        "colab_type": "text"
      },
      "source": [
        "Finally we have an Uknown token\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bB1ksJwSAgYV",
        "colab_type": "code",
        "outputId": "c3e101bd-7356-4460-d21c-95b0155f6cbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tokenizer.unk_token, tokenizer.unk_token_id\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('[UNK]', 100)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u8pVyBnpBT_-",
        "colab_type": "text"
      },
      "source": [
        "These tokens will be added and implemented to our data set using the encode_plus() method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "je82wKqQA5wv",
        "colab_type": "code",
        "outputId": "de0f9ced-6a25-474c-f651-21831880485a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "encoding = tokenizer.encode_plus(\n",
        "  sample_txt,\n",
        "  max_length=32,\n",
        "  add_special_tokens=True, # Add '[CLS]' and '[SEP]'\n",
        "  return_token_type_ids=False,\n",
        "  pad_to_max_length=True,\n",
        "  return_attention_mask=True,\n",
        "  return_tensors='pt',  # Return PyTorch tensors\n",
        ")\n",
        "encoding.keys()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['input_ids', 'attention_mask'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VAfQO8M3B91m",
        "colab_type": "text"
      },
      "source": [
        "By running the method our token ids are now stored in a Tensor and padded to a length of 32"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g6UvoiC-BThz",
        "colab_type": "code",
        "outputId": "81f46005-6945-42b9-d896-9c5b02976ad5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "print(len(encoding['input_ids'][0]))\n",
        "encoding['input_ids'][0]"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "32\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([ 101, 1332, 1108,  146, 1314, 1796,  136,  146, 1821, 5342, 1120, 1313,\n",
              "        1111,  123, 2277,  119,  102,    0,    0,    0,    0,    0,    0,    0,\n",
              "           0,    0,    0,    0,    0,    0,    0,    0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NMgxS2kQCITo",
        "colab_type": "text"
      },
      "source": [
        "Now we can take a look at the attention mask"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YvDnzBKaCFWS",
        "colab_type": "code",
        "outputId": "f77f07cd-d46c-42a4-a846-103f2e2154e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "print(len(encoding['attention_mask'][0]))\n",
        "encoding['attention_mask']"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "32\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQNc-1jYCSoA",
        "colab_type": "text"
      },
      "source": [
        "Let's take a look at the special tokens in action - inverse"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CPutYbC5CLjV",
        "colab_type": "code",
        "outputId": "64fb5af5-809c-47b4-b904-15ad4f136c39",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        }
      },
      "source": [
        "tokenizer.convert_ids_to_tokens(encoding['input_ids'][0])\n",
        "\n"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['[CLS]',\n",
              " 'When',\n",
              " 'was',\n",
              " 'I',\n",
              " 'last',\n",
              " 'outside',\n",
              " '?',\n",
              " 'I',\n",
              " 'am',\n",
              " 'stuck',\n",
              " 'at',\n",
              " 'home',\n",
              " 'for',\n",
              " '2',\n",
              " 'weeks',\n",
              " '.',\n",
              " '[SEP]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_O1nX3LsDGoH",
        "colab_type": "text"
      },
      "source": [
        "Up next we will choose our sequence length"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1f7eY7ruCX8o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "token_lens = []\n",
        "for txt in df.review_body:\n",
        "  tokens = tokenizer.encode(txt, max_length=512)\n",
        "  token_lens.append(len(tokens))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KAHGonKfDycu",
        "colab_type": "text"
      },
      "source": [
        "Now let's take a look and see our token size distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XoxLe7QYDN77",
        "colab_type": "code",
        "outputId": "59aa22bf-ce76-45b1-dff3-66b82618edf2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        }
      },
      "source": [
        "sns.distplot(token_lens)\n",
        "plt.xlim([0, 256]);\n",
        "plt.xlabel('Token count');"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 731,
              "height": 489
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "igOrRyfTD-n3",
        "colab_type": "text"
      },
      "source": [
        "Seeing as most our tokens go up to 250 - we'll be safe and make the max number 260"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yCuLtjP-D5Dh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "MAX_LEN = 300"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gput1A0nEPx0",
        "colab_type": "text"
      },
      "source": [
        "Now that we've explored our data set we can generate our PyTorch dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SAs1B3VYENU4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class GPReviewDataset(Dataset):\n",
        "  def __init__(self, reviews, targets, tokenizer, max_len):\n",
        "    self.reviews = reviews\n",
        "    self.targets = targets\n",
        "    self.tokenizer = tokenizer\n",
        "    self.max_len = max_len\n",
        "  def __len__(self):\n",
        "    return len(self.reviews)\n",
        "  def __getitem__(self, item):\n",
        "    review = str(self.reviews[item])\n",
        "    target = self.targets[item]\n",
        "    encoding = self.tokenizer.encode_plus(\n",
        "      review,\n",
        "      add_special_tokens=True,\n",
        "      max_length=self.max_len,\n",
        "      return_token_type_ids=False,\n",
        "      pad_to_max_length=True,\n",
        "      return_attention_mask=True,\n",
        "      return_tensors='pt',\n",
        "    )\n",
        "    return {\n",
        "      'review_text': review,\n",
        "      'input_ids': encoding['input_ids'].flatten(),\n",
        "      'attention_mask': encoding['attention_mask'].flatten(),\n",
        "      'targets': torch.tensor(target, dtype=torch.long)\n",
        "    }"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cM1bywqGE1Fg",
        "colab_type": "text"
      },
      "source": [
        "Up next we will split our data and let the Tokenizer do it's magic."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fik6aQSlEZC0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_train, df_test = train_test_split(\n",
        "  df,\n",
        "  test_size=0.1,\n",
        "  random_state=RANDOM_SEED\n",
        ")\n",
        "df_val, df_test = train_test_split(\n",
        "  df_test,\n",
        "  test_size=0.5,\n",
        "  random_state=RANDOM_SEED\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M5tuBJxAFEk3",
        "colab_type": "text"
      },
      "source": [
        "Take a peak at the data shapes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wn6hlIDlFDCB",
        "colab_type": "code",
        "outputId": "c4906139-cf6a-4dc7-f8b6-7775a81cd6a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df_train.shape, df_val.shape, df_test.shape\n"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1799, 4), (100, 4), (100, 4))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U8_hQuVQFM_1",
        "colab_type": "text"
      },
      "source": [
        "Up next let's create our data loaders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QoUf0JSMFImc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_data_loader(df, tokenizer, max_len, batch_size):\n",
        "  ds = GPReviewDataset(\n",
        "    reviews=df.review_body.to_numpy(),\n",
        "    targets=df.sentiment.to_numpy(),\n",
        "    tokenizer=tokenizer,\n",
        "    max_len=max_len\n",
        "  )\n",
        "  return DataLoader(\n",
        "    ds,\n",
        "    batch_size=batch_size,\n",
        "    num_workers=4\n",
        "  )\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bJRz-NXeFVYs",
        "colab_type": "text"
      },
      "source": [
        "Now we will assign the batch size - let's make it small to start"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N7CNolbfFR0X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "BATCH_SIZE = 2\n",
        "\n",
        "train_data_loader = create_data_loader(df_train, tokenizer, MAX_LEN, BATCH_SIZE)\n",
        "val_data_loader = create_data_loader(df_val, tokenizer, MAX_LEN, BATCH_SIZE)\n",
        "test_data_loader = create_data_loader(df_test, tokenizer, MAX_LEN, BATCH_SIZE)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-WnK2_LWGFAu",
        "colab_type": "text"
      },
      "source": [
        "Up next we can see an example batch right out of our data loader."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e8bV9pMfFcYH",
        "colab_type": "code",
        "outputId": "ab813dd8-b0bc-4228-d307-caad13539cbd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data = next(iter(train_data_loader))\n",
        "data.keys()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['review_text', 'input_ids', 'attention_mask', 'targets'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hoz9U7MjGCTe",
        "colab_type": "code",
        "outputId": "623ab18f-eede-4e60-dacb-c358415d78a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "print(data['input_ids'].shape)\n",
        "print(data['attention_mask'].shape)\n",
        "print(data['targets'].shape)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([2, 300])\n",
            "torch.Size([2, 300])\n",
            "torch.Size([2])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ToCyqREzMmF8",
        "colab_type": "text"
      },
      "source": [
        "Up next we'll load the basic BERT pretrained model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7BgWSQwWMqdY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 114,
          "referenced_widgets": [
            "2f420720e3304d3b940d95de78e4e149",
            "702967e44f754a9798056d71c96f5e96",
            "0d76412c584044fbb0cf87e4f4a05ab1",
            "c11bb0167f7b462e8017e95aec0a075b",
            "011248919a0846389b3c71c2e9f5beb2",
            "1abdde1835aa4e4fb10c6cd85392b0e9",
            "10e979953bd9430b9dd50347761026e8",
            "73eda9339fa34917a0cbc92dc16ee401",
            "cf4b843b6da145e283226f360dc8b125",
            "765c95b70ac64adaa8c6eb3f1593a240",
            "26af46edaefc491ca8b977ccba448e16",
            "e34b2f4de8c943759ebd9305711d29fa",
            "03de5d4bac2f4a40a79ebe0dc5a5360c",
            "40c7c39b362440a2af300a63e3a19b39",
            "8d18eb6c51c7472e9560c8461065c306",
            "e0edbd29bc7643d9bf2a3eb1a7929e57"
          ]
        },
        "outputId": "3b21bb07-8fef-4973-c944-7b5455d9e44e"
      },
      "source": [
        "bert_model = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2f420720e3304d3b940d95de78e4e149",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=433.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cf4b843b6da145e283226f360dc8b125",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=435779157.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZjwyLVePNNs7",
        "colab_type": "text"
      },
      "source": [
        "Now we can try it out on our sample text from earlier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s89GLmSrNYWd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "last_hidden_state, pooled_output = bert_model(\n",
        "  input_ids=encoding['input_ids'],\n",
        "  attention_mask=encoding['attention_mask']\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7_gd8LbANeyX",
        "colab_type": "text"
      },
      "source": [
        "Let's also apply the Last_hidden_state which is a sequence of in the last layer of our model. Obtaining the pooled_output will be dont by applying the BertPooler on last_hidden_state"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DqAokDE7N6Pj",
        "colab_type": "code",
        "outputId": "02a1e2b0-ddbd-4ff2-8ddf-d9007dff95e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "last_hidden_state.shape\n"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 32, 768])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dNllOGUGN40w",
        "colab_type": "text"
      },
      "source": [
        "Next let's check our configuration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lUAjKN2QOpOr",
        "colab_type": "code",
        "outputId": "cb540cce-2559-4c44-d3c9-7d99047b80f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "bert_model.config.hidden_size\n",
        "\n"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "768"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PiF8mEP3PGhl",
        "colab_type": "text"
      },
      "source": [
        "Let's take a look at the shape for the pooled *output*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NtVJPTmDPhul",
        "colab_type": "code",
        "outputId": "a970278c-6865-42d3-af99-b9affd066d48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "pooled_output.shape"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 768])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mXSwJ2nRPxfh",
        "colab_type": "text"
      },
      "source": [
        "Here is a helper function to generate a classifier that uses the BERT model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SeM5Q_7dPseJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class SentimentClassifier(nn.Module):\n",
        "  def __init__(self, n_classes):\n",
        "    super(SentimentClassifier, self).__init__()\n",
        "    self.bert = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME)\n",
        "    self.drop = nn.Dropout(p=0.3)\n",
        "    self.out = nn.Linear(self.bert.config.hidden_size, n_classes)\n",
        "  def forward(self, input_ids, attention_mask):\n",
        "    _, pooled_output = self.bert(\n",
        "      input_ids=input_ids,\n",
        "      attention_mask=attention_mask\n",
        "    )\n",
        "    output = self.drop(pooled_output)\n",
        "    return self.out(output)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F41BbvN3QA6e",
        "colab_type": "text"
      },
      "source": [
        "Time to move the PyTorch model on over to the GPU"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OE9-uEvHPtxy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = SentimentClassifier(len(class_names))\n",
        "model = model.to(device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z9j14IylQKXw",
        "colab_type": "text"
      },
      "source": [
        "We will also move the example batch of training data to the GPU"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AYZF5UROQF8U",
        "colab_type": "code",
        "outputId": "a3da5072-7dec-49f0-9da6-28752c2b159b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "input_ids = data['input_ids'].to(device)\n",
        "attention_mask = data['attention_mask'].to(device)\n",
        "print(input_ids.shape) # batch size x seq length\n",
        "print(attention_mask.shape) # batch size x seq length"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([2, 300])\n",
            "torch.Size([2, 300])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0lTqu3t6QYAF",
        "colab_type": "text"
      },
      "source": [
        "To generate predictded probabilities we will now apply a softmax function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u1lWGK6AQRvR",
        "colab_type": "code",
        "outputId": "dada3065-da80-4cd1-c85a-1b32ac8594b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "F.softmax(model(input_ids, attention_mask), dim=1)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.7072, 0.2928],\n",
              "        [0.7760, 0.2240]], device='cuda:0', grad_fn=<SoftmaxBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L9f9AckpRkQZ",
        "colab_type": "text"
      },
      "source": [
        "Time to set up for training! Here we will use the AdamW optimizer which is from the Hugging Face library. Adam is usefull for correcting weight decay. Additionally as we are doing classificaiton we will utilize cross entropy loss for our loss function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M3WWxT_WQgdF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "EPOCHS = 5\n",
        "optimizer = AdamW(model.parameters(), lr=2e-5, correct_bias=False)\n",
        "total_steps = len(train_data_loader) * EPOCHS\n",
        "scheduler = get_linear_schedule_with_warmup(\n",
        "  optimizer,\n",
        "  num_warmup_steps=0,\n",
        "  num_training_steps=total_steps\n",
        ")\n",
        "loss_fn = nn.CrossEntropyLoss().to(device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dE87eGdFT3bA",
        "colab_type": "text"
      },
      "source": [
        "Next - we add a helper function to train our model for an epoch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w-nR3OBcRjOg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_epoch(\n",
        "  model,\n",
        "  data_loader,\n",
        "  loss_fn,\n",
        "  optimizer,\n",
        "  device,\n",
        "  scheduler,\n",
        "  n_examples\n",
        "):\n",
        "  model = model.train()\n",
        "  losses = []\n",
        "  correct_predictions = 0\n",
        "  for d in data_loader:\n",
        "    input_ids = d[\"input_ids\"].to(device)\n",
        "    attention_mask = d[\"attention_mask\"].to(device)\n",
        "    targets = d[\"targets\"].to(device)\n",
        "    outputs = model(\n",
        "      input_ids=input_ids,\n",
        "      attention_mask=attention_mask\n",
        "    )\n",
        "    _, preds = torch.max(outputs, dim=1)\n",
        "    loss = loss_fn(outputs, targets)\n",
        "    correct_predictions += torch.sum(preds == targets)\n",
        "    losses.append(loss.item())\n",
        "    loss.backward()\n",
        "    nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
        "    optimizer.step()\n",
        "    scheduler.step()\n",
        "    optimizer.zero_grad()\n",
        "  return correct_predictions.double() / n_examples, np.mean(losses)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vjn9mRiwy8if",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def eval_model(model, data_loader, loss_fn, device, n_examples):\n",
        "  model = model.eval()\n",
        "  losses = []\n",
        "  correct_predictions = 0\n",
        "  with torch.no_grad():\n",
        "    for d in data_loader:\n",
        "      input_ids = d[\"input_ids\"].to(device)\n",
        "      attention_mask = d[\"attention_mask\"].to(device)\n",
        "      targets = d[\"targets\"].to(device)\n",
        "      outputs = model(\n",
        "        input_ids=input_ids,\n",
        "        attention_mask=attention_mask\n",
        "      )\n",
        "      _, preds = torch.max(outputs, dim=1)\n",
        "      loss = loss_fn(outputs, targets)\n",
        "      correct_predictions += torch.sum(preds == targets)\n",
        "      losses.append(loss.item())\n",
        "  return correct_predictions.double() / n_examples, np.mean(losses)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wfnv39t3UD01",
        "colab_type": "text"
      },
      "source": [
        "Now using the two helper functions we've generated we can make a training loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BL31k5yJT76i",
        "colab_type": "code",
        "outputId": "df3b197c-2cc3-477d-e461-a2b1d4e269be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        }
      },
      "source": [
        "%%time\n",
        "history = defaultdict(list)\n",
        "best_accuracy = 0\n",
        "for epoch in range(EPOCHS):\n",
        "  print(f'Epoch {epoch + 1}/{EPOCHS}')\n",
        "  print('-' * 10)\n",
        "  train_acc, train_loss = train_epoch(\n",
        "    model,\n",
        "    train_data_loader,\n",
        "    loss_fn,\n",
        "    optimizer,\n",
        "    device,\n",
        "    scheduler,\n",
        "    len(df_train)\n",
        "  )\n",
        "  print(f'Train loss {train_loss} accuracy {train_acc}')\n",
        "  val_acc, val_loss = eval_model(\n",
        "    model,\n",
        "    val_data_loader,\n",
        "    loss_fn,\n",
        "    device,\n",
        "    len(df_val)\n",
        "  )\n",
        "  print(f'Val   loss {val_loss} accuracy {val_acc}')\n",
        "  print()\n",
        "  history['train_acc'].append(train_acc)\n",
        "  history['train_loss'].append(train_loss)\n",
        "  history['val_acc'].append(val_acc)\n",
        "  history['val_loss'].append(val_loss)\n",
        "  if val_acc > best_accuracy:\n",
        "    torch.save(model.state_dict(), 'best_model_state.bin')\n",
        "    best_accuracy = val_acc"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "----------\n",
            "Train loss 0.7579603529969852 accuracy 0.7359644246803779\n",
            "Val   loss 0.37663356244564056 accuracy 0.91\n",
            "\n",
            "Epoch 2/5\n",
            "----------\n",
            "Train loss 0.4285366311338213 accuracy 0.9182879377431906\n",
            "Val   loss 0.5771235775947571 accuracy 0.9\n",
            "\n",
            "Epoch 3/5\n",
            "----------\n",
            "Train loss 0.17882203994525803 accuracy 0.9699833240689272\n",
            "Val   loss 0.6389431643486023 accuracy 0.9\n",
            "\n",
            "Epoch 4/5\n",
            "----------\n",
            "Train loss 0.08118041806750827 accuracy 0.9877709838799332\n",
            "Val   loss 0.5766880178451538 accuracy 0.92\n",
            "\n",
            "Epoch 5/5\n",
            "----------\n",
            "Train loss 0.049820541342099504 accuracy 0.9927737632017787\n",
            "Val   loss 0.600138441324234 accuracy 0.92\n",
            "\n",
            "CPU times: user 7min 28s, sys: 1min, total: 8min 29s\n",
            "Wall time: 8min 31s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L496zA6l0llK",
        "colab_type": "text"
      },
      "source": [
        "Here we can plot our training accuracy vs our validation accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gNqiY3i4ut2P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "outputId": "b245bb6c-4b83-4c0a-f6c2-bb861c5d8f0d"
      },
      "source": [
        "plt.plot(history['train_acc'], label='train accuracy')\n",
        "plt.plot(history['val_acc'], label='validation accuracy')\n",
        "plt.title('Training history')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend()\n",
        "plt.ylim([0, 1]);\n"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABbkAAAP1CAYAAABMiOKUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAWJQAAFiUBSVIk8AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdeXRV9b3//+fJnDDPIAEJQ1BQqVK1cllU0bZXbdXiUKm1Wqut18qt1vZWWxAVh7a3vb0Wpfdbh1pRixMOVPHnUKyFOjVOFZBBwiwQIBBC5pz9+yMknANJSE4SknPyfKzFOjv7s89nf07csvSVd96fUBAEAZIkSZIkSZIkxaGk9l6AJEmSJEmSJEmxMuSWJEmSJEmSJMUtQ25JkiRJkiRJUtwy5JYkSZIkSZIkxS1DbkmSJEmSJElS3DLkliRJkiRJkiTFLUNuSZIkSZIkSVLcMuSWJEmSJEmSJMUtQ25JkiRJkiRJUtwy5JYkSZIkSZIkxS1DbkmSJEmSJElS3DLkliRJkiRJkiTFLUNuSZIkSZIkSVLcMuSWJEmSGjB79mxGjx7N6NGj2bhxY5vdZ+PGjXX3mT17dpvd53CbP39+3ed6++23WzRX7Tw33nhjK61OkiRJiSKlvRcgSZKkzmHjxo2cfvrpLZ7n61//Or/4xS9aYUWSJEmSEoGV3JIkSZI6NavEJUmS4puV3JIkSTosBgwYwIIFCxocv+mmm/j4448BeOCBB+jfv3+91/Xo0aNN1lefadOmMW3atDa/T3Z2NitWrGjz+8Qzvz+SJElqiCG3JEmSDovU1FRyc3MbHM/Kyqo7HjZsGNnZ2YdjWZIkSZLinO1KJEmSJEmSJElxy0puSZIkdXiRm1Zee+21TJs2jby8PObNm0deXh4FBQVUVFTw7rvv0r17dwB27tzJK6+8wltvvcUnn3zCli1bqKiooFu3bowcOZJJkyYxdepUunXr1uB9Z8+ezT333APAa6+9dlB1+YHjgwcP5tlnn+Xpp59m1apVlJSUMGjQIE499VS+//3v06dPnyZ/vkONL1++nIceeoh33nmHgoICunXrxrhx47jiiis46aSTDvk9/eSTT/jjH//IW2+9xY4dO+jZsydjx47l4osv5rTTTmP+/PncdNNNADz88MOcfPLJh5yzKV599VXmzZvH8uXL2b17N/3792fChAl8//vfZ8iQIQ2+b/To0UDDG48GQcDChQtZsGABy5cvZ8eOHYRCIXr16kWvXr047rjjmDhxIpMnTyYlpeZ/gyZPnsymTZvq5njmmWd45plnDpq7vlYpFRUVPPXUU7z88susXLmSoqIiunbtyvDhw5k8eTJTp06lS5cuTf48K1eu5JFHHuGtt95i27ZtlJaW8uyzz/LrX/+axYsXk5GRweLFixt9XgHuuusuHnroobrPM2bMmEavlyRJSgSG3JIkSYo79957L7NnzyYIggav+fKXv8yePXsOOl9YWMi7777Lu+++y9y5c/n973/PMccc0+I1lZeXc9VVV/H3v/896vy6dev405/+xEsvvcQjjzzC0KFDW3yvxx9/nFmzZlFZWVl3bufOnSxatIjXX3+dmTNnMnXq1Abf/+ijj3LnnXdSVVVVd66goIDXX3+d119/nW9+85sce+yxLV5npHA4zE033cT8+fOjzm/atIknn3ySl156iQcffJDjjjuu2XOXlpZyzTXX8I9//OOgsS1btrBlyxaWL1/O448/zt/+9jcGDhwY8+cAyM/P5+qrr2bt2rVR5wsLC8nLyyMvL4+HHnqIOXPmNOnzPPXUU9xyyy1R/zxrXXzxxSxevJiysjKef/55Lrnkkgbnqaio4NlnnwXgmGOOMeCWJEmdhiG3JEmS4sqrr77KJ598wvDhw7nssss4+uijqa6u5oMPPiA1NbXuuurqak444QQmTZrEUUcdRZ8+faiurmbz5s28/PLLvPzyy2zbto3/+I//4Pnnn6dXr14tWtf06dN5//33+drXvsZZZ53FwIED2bZtG3PnzmXx4sVs3bqVn//858ydO7dF91myZAkffvghI0aM4LLLLmP06NFUVVXxxhtvcP/991NZWckdd9zBF77wBXJycg56/6uvvsptt90GQFpaGpdeeilf/OIXycrK4tNPP+VPf/oTjz32GJ/73OdatM4D/e53v+O9997j1FNPZcqUKWRnZ7Nr1y7mz5/PX/7yF/bs2cOPf/xjXnzxxbpK66a655576gLucePGccEFF3DkkUfSvXt3iouLyc/P5+2332bRokVR73vggQeorKzka1/7GgCnn3461113XaP32rlzJ5deeikFBQUATJo0iQsvvJDBgwdTUFDAggUL+Mtf/kJBQQGXX3458+fPZ9iwYQ3O9/HHH7NgwQL69u3L5Zdfzrhx40hOTmbp0qX06NGDUaNG0b9/f7Zt28ZTTz3VaMj96quvsmvXLgAuuuiiQ37fJEmSEoUhtyRJkuLKJ598wkknncR9991HRkZG3fkTTjgh6rpnnnmm3nDx+OOP5+yzz2bJkiVceeWVbNu2jUcffZRrr722Ret67733uOuuu5gyZUrduTFjxjBp0iSuuOIK3nzzTd555x0++eQTjjrqqJjv8/777zNx4kR+//vfk5aWVnf+hBNOYNiwYfz0pz+lsrKSefPm1bUbqVVRUcGtt94K1GwE+sc//pHPf/7zdePHHnssZ599NldffTWLFy+OeY31ee+99+ptxfJv//ZvpKWlMX/+fNatW8ff/va3utYsTfXCCy8ANet/9NFHo37YAXDiiSdy0UUXUVxcHPU9O/CHAN27d290c1SoaQdSG3BfddVV/PjHP44aP/XUUzn++OOZNWsWe/fuZcaMGY3+YGPVqlWMHDmSRx55JOoHLePGjas7vuCCC5gzZw7Lli3j448/bvA3D5544gmgZhPXr371q41+DkmSpETixpOSJEmKK0lJSdx5551RAXd9GquehZpwtTZMffnll1u8rjPOOCMq4K6VlJTEd77znbqv33333RbdJz09nV/+8pdRYW2tc845h379+jV4n9dee41t27YBcOmll0YF3LVSU1O58847DwqKW2rMmDEN/iDhyiuvrDuO5fuzfft2AMaPH9/ourt27Vrv960591m4cCEAubm5XH/99fVe961vfYsJEyYA8M4777B8+fJG5505c2ajv0lw0UUXkZycDMCTTz5Z7zUbNmzgrbfeAuDss89utB+4JElSojHkliRJUlw5/vjjG92gsD5BELB9+3by8/NZuXJl3Z/aYHH16tX19kNujnPOOafBscj+1hs2bGjRfSZMmEDfvn3rHUtKSmLs2LEN3ufNN9+sOz7//PMbvMeAAQOYOHFii9Z5oK997WuEQqF6x0aMGEFWVhYQ2/dnwIABAPz1r39lx44dsS/yEN5+++265+T888+vC57rE9kTvbGq+IEDBx5yo9BBgwYxadIkAP7yl79QWlp60DVPPfVUXY96W5VIkqTOxnYlkiRJiivNafXx0ksv8eSTT/Lee+9RUlLS4HXV1dUUFRXRp0+fmNc1fPjwBsd69uxZd1xcXBzzPeDgFhsH6tGjR4P3WblyJVDTzmLEiBGNznPMMccc1MO6JRr7/kDNuktKSmL6/lx44YX89re/Zf369Zxxxhl86UtfYsKECYwbN45hw4Y1GK43V+33D2p+2NKYyPEVK1Y0eF1Tn+eLL76YRYsWUVxczMKFC6N+a6C6urpuQ8+jjjoqps07JUmS4pkhtyRJkuJK9+7dD3lNRUUFP/rRj3jllVeaPG9ZWVlLlkVmZmaDY0lJ+3+BMhwOt+g+tRXPh7pXffep3ZSwd+/ehwx+G6oWj1Vj3x9ofN2H8r3vfY9du3Yxd+5cSkpKeO6553juueeAms86ceJELrzwwkNWTB9KYWFh3fGhvj99+vQhFAoRBEHd970+TXmeoWaDyyOOOILNmzfzxBNPRIXcr7/+el0bGqu4JUlSZ2S7EkmSJMWVxlpE1PrDH/5QF3CPHj2aO++8kxdffJG8vDyWLVvGihUrWLFiBddcc03de2pbPSj+JCUlceONN/Lyyy9zww03MHHiRLp27QrAzp07ef7557n00ku5/vrrW9yWprU15XmGms944YUXAjWbj3766ad1Y7V9ujMzMxttmyNJkpSoDLklSZKUcObNmwfA0KFDeeKJJzj//PMZMWIEXbt2jQoVi4qK2muJ7aK2bcrOnTsPGeq3ZW/rtjJ48GC+973v8cADD/Duu+/yzDPPMG3atLqq6xdffJF777035vkjN4es3eyyITt27Kj7Hke2q2mJCy64gJSUml/GfeKJJwDYunUrb7zxBgBnnnkm3bp1a5V7SZIkxRNDbkmSJCWUwsJCCgoKAJg8eTIZGRkNXvvxxx8frmV1CLm5uQCUlJSwZs2aRq/917/+dTiW1GaSkpIYM2YM1157LY8//nhdu5QXX3wx5jlHjx5dd/zBBx80eu37779fd9ycPvKN6d+/P5MnTwbgueeeo6Kigvnz51NdXQ1QV+ktSZLU2RhyS5IkKaHUBn4ApaWlDV63dOnSQwaVieaUU06pO67dqLA+27ZtY8mSJYdjSYdFdnZ23YadO3fuPGi89gchFRUVjc5z8sknk5qaCsBTTz3VaP/wxx9/vO544sSJzV5zQy6++GKg5oc5r7zyCk899RQAo0aN4oQTTmi1+0iSJMUTQ25JkiQllN69e9dt5rdo0aJ6N/3bvn07P/nJTw730trd6aefTv/+/QF4+OGHo6qNa1VVVTF9+vRDBr4dxa5du3j11VcbDZw3bdpU18N6yJAhB43Xfk/Wrl3b6L369OnDWWedBcDKlSu5++67673uscceY/HixQCcdNJJrVbJDTBhwgSGDh0KwF133cXGjRsBq7glSVLnltLeC5AkSZJaU1JSEueeey5z585l27ZtfOMb3+DKK68kNzeXqqoq8vLyeOihhygsLOT444+vN+hNVGlpacycOZMf/OAHVFRUcNlll/Htb3+bSZMmkZWVxerVq3n44YdZunQpn/vc5+oq3UOhUDuvvGHFxcX84Ac/oH///pxxxhmMGzeOIUOGkJWVRWFhIR999BGPPvoo5eXlAHzrW986aI7Pf/7zrF+/nqVLl3L33Xdz2mmn0aVLl7rxESNG1B3feOON/OMf/6CgoID/+7//45NPPuGCCy7giCOOYPv27SxYsIAFCxYA0KVLF2bNmtWqnzcUCnHRRRfx61//uq4tT3p6Ouedd16r3keSJCmeGHJLkiQp4Vx33XV88MEH/Otf/2Lt2rVMnz49ajw1NZXp06ezc+fOThVyA5xxxhncfPPN3HHHHZSXl3Pfffdx3333RV3zzW9+k7Fjx9aF3Onp6e2x1GbZtm0bjz32GI899li940lJSVx99dWcf/75B41997vfZeHChZSWljJnzhzmzJkTNb5ixYq64969ezN37lyuvvpq1q5dy+uvv87rr79+0Jz9+vVjzpw5DBs2rEWfqz7nn38+d999N5WVlQB8+ctfpkePHq1+H0mSpHhhyC1JkqSE07VrVx577DEefvhhXnjhBfLz8wmCgH79+nHyySdzySWXMGbMGGbPnt3eS20Xl1xyCePHj+fBBx/k7bffZseOHfTs2ZMxY8Zw8cUXM3nyZP74xz/WXd+1a9d2XG3jBg8ezNNPP83ixYv54IMP2LhxI9u3b6eoqIjMzEyys7M58cQTufDCC6M2jow0cuRInn76aR588EH++c9/snXr1kb7uefk5LBgwQKefPJJXnnlFVasWMGePXvo0qULw4cP5/TTT2fq1KlR1eCtqXfv3px66qm88sorAHzjG99ok/tIkiTFi1AQBEF7L0KSJElSx3LTTTcxf/58UlNTee+990hLS2vvJSnCV77yFdauXUtOTg4vvfRSey9HkiSpXbnxpCRJkqQoe/fu5bXXXgNg7NixBtwdzNtvv123SaZV3JIkSQnariQIAtasWcNHH31U92fFihV1Petee+01srOzW+VeK1as4E9/+hNvvvkm27dvp0ePHowdO5aLL76Y0047rVXuIUmSJLWm/Px8cnJy6h2rrKzkZz/7Gbt37waot4e12tcf/vAHADIzM5kyZUo7r0aSJKn9JWTIvWnTJs4666w2v88zzzzDjBkz6sJzgIKCgrrNZ6ZOncott9zS5uuQJEmSmuMb3/gGRx99NJMnT+aoo46ie/fu7N27l6VLl/LEE0+wevVqAI455hhD1A5g165d7N69m6KiIp555hkWL14M1GwQ6oaTkiRJCRpyRxo4cCDHHnsshYWF/POf/2y1efPy8pg+fTpVVVXk5uby05/+lDFjxvDZZ58xZ84cXn31Vf785z8zePBgrrrqqla7ryRJktRS4XCYt956i7feeqvBa4499lh+//vfk5KS8P/L0OHNnTuXe+65J+rcsGHD+MEPftBOK5IkSepYEvK/WHv27Mm9997LuHHj6NevHwCzZ89u1ZD7F7/4BVVVVfTt25eHH36YXr16ATU7nd9zzz1897vfZcmSJcyZM4fzzz+f3r17t9q9JUmSpJb43e9+xxtvvEFeXh4FBQUUFhYSBAG9e/fmmGOO4d///d8566yzSEpyC5+OJDk5mYEDBzJp0iSmTZtGly5d2ntJkiRJHUJChtxdu3bljDPOaLP5//Wvf/HRRx8BcOWVV9YF3LVCoRA33HADS5YsoaSkhOeee47vfOc7bbYeSZIkqTkmTJjAhAkT2nsZaqJp06Yxbdq09l6GJElSh2VpRgwWLVpUd3zmmWfWe83YsWMZOnQoAH/9618Py7okSZIkSZIkqbMx5I7B0qVLARgwYAADBw5s8Lpx48ZFXS9JkiRJkiRJal2G3DHIz88HYMiQIY1el52dDcDevXvZunVrm69LkiRJkiRJkjobQ+4YFBYWAtCnT59Gr4sc37VrV5uuSZIkSZIkSZI6o4TceLKtlZaWApCWltbodRkZGXXHJSUlrbqGZcuWUV5eTnJyMunp6a06tyRJkiRJkiQdTuXl5VRXV5Oens6YMWOa9V5D7jhVXl5OOBwmHA5TWVnZ3suRJEmSJEmSpBYrLy9v9nsMuWOQmZlJZWUlFRUVjV5XVlZWd5yVldWqa0hOTiYcDpOUlNTqc3dUxcXFAHTt2rWdV6JE5POltuYzprbk86W25POltuTzpbbk86W25jOmttQZn6+SkhLC4TDJycnNfq8hdwx69epFUVERO3bsaPS6yPGePXu26hrS09OprKwkKyuL0aNHt+rcHVVeXh5Ap/m8Orx8vtTWfMbUlny+1JZ8vtSWfL7Ulny+1NZ8xtSWOuPztWLFCoqLi2NqzezGkzHIyckBYMOGDY1et3HjRgC6dOnCgAED2nxdkiRJkiRJktTZWMkdg7Fjx/L666+zdetWtm7d2mCA/eGHH9ZdL0mSJEmSJCm+BUFAdQBV+/5UE3EcNPO4kfeurezFycl72vvjxg1D7hicdtpp3HvvvQAsXLiQyy+//KBrli1bxvr16wGYPHny4VyeJEmSJEmS1CItCXNbFAIDVeHY7tkagfOhrg0ftn8COfQIVbGpOiArOXTY7hqvDLljcOyxx3Lcccfx0Ucfcf/993PeeedF9dwOgoDf/OY3QM2Gk+eee257LVWSJEmSJEmNiAxzmxOSLqvOIhxA2a6g1cLcprwv3Nz7xBg4H74wVw0pDpIproas5u/D2OkkbMi9evXqul1IAbZs2VJ3vHz5crZv31739dChQ+ndu3fd1/Pnz+emm24C4K677mLKlCkHzX/jjTfy7W9/m4KCAi699FJuvPFGjj76aLZu3cqcOXNYvHgxANdcc03U3JIkSZIkSe3hUGFua1fWRl0fbv57Yw1zY7lPbI6qeXm/pf9kFG+SQ5ASgmRqXlNC+881dNyca1NCULRzO6en7KJ/2qj2/rhxIWFD7ltvvZV33nmn3rFrr7026uuGguzGjB8/nttvv50ZM2awcuVKrrjiioOuufjii7nqqquaNa8kSZIkSWq5IIBKQuytDtq+TUIzrq3vnocMmVuhKtfKXMUiOYZwttnX1xMEN/V9Md8TSEmK7b1JQCjU9u1D8vLWt/k9EknChtyHw9e//nXGjBnDQw89xFtvvUVBQQE9evRg7NixTJ06ldNOO629lyhJkiRJUtyoDAfsqabmTxWHPC5u9LrjqSYEb7T3p1I8SKL5IWlFaQnJBHTv2qXp1bwxhsCNhbmHvDYpthA4OXR4wlypNSRsyD137tyY3ztlypQmV3aPHj2au+66K+Z7SZIkSZIUr8JBUBM0NzGU3lMNxY2MlbdqqbHhXH2aE+a2JIyNJTSOtbK2OWFufe+LNczNy/sEqPltf0ntK2FDbkmSJEmSFC0IAkrDrRdK761u70/UuGQC0pJCbdJeIbLatS3C2NZaY2uEuZLU0RlyS5IkSZLUgTWnhUfRIULp4uqaHs0dVRLQLQW6JUf8ifi6a0r95w/6OgVWffg+qaHAKltJ6gQMuSVJkiRJakUdu4VH6+uS3MRQup4g+sDjzKTWqzReG+rAab4kqVUZckuSJEmSOrXO1sIjLbQ/VO7ewlC6SzIk2/5CktTODLklSZIkSXGnoraFRyNhc+TXtvDYf5yWZCgtSUoshtySJEmSpDZXXwuPvKqulATJLN9ycGBdVEX9LT/2fV3RgUNp6LgtPCRJSkSG3JIkSZKkgxyeFh65NS/LD+cnq196UuuF0rbwkCTp8DLkliRJkqQE0ZotPPZUQUfe7zA51Hjw3LUZoXS3ZEi1hYckSXHLkFuSJEmS2kl1bQuPJobSexKshQele8gKhRnSu8chq6UP3CAxwxYekiRpH0NuSZIkSWqiWFp41H1dz/mSjlwqTeu28OiaDEkHhNJ5easAGD92fHt8PEmSlCAMuSVJkiQlNFt42MJDkiQlNkNuSZIkSR1KZ2vh0Vjw3NxQ2hYekiSpMzLkliRJktQiQRBQEm69UDpRWnh0b0Io3aWeFh6SJElqHkNuSZIkSVGKqgJWlcLKEvhb+UB2hlPJXFbT8qOonlC6uNoWHpIkSWo/htySJElSJ1RWHfBpWU2QvaoEVpbuf91aEXnlETUvWw/v+mzhIUmSpKYy5JYkSZISVHUQsG5fkF0bYtcG2evKoDVbVWcktV4obQsPSZIkNYchtyRJkhTHgiBgS8X+IHtlCazaF2h/WhrbpotpIRiZCblZ0L1oC/2TKhk7bEiDoXRXW3hIkiSpHRlyS5IkSXGgsHJ/n+zaILv2tbi6+fMlAcMyYFQWjNoXaOdmQW4mDMmA5H2V1Hl5mwEYP2hoK34aSZIkqfUYckuSJEkdREl1wOrSA3pk7zveXhnbnIPSasLrUZk1gXbuvkB7eCakW30tSZKkBGDILUmSJB1GleGAtQf2yd5Xlb2hPLY5e6TA6H3hdWRl9qhM6JZikC1JkqTEZsgtSZIktbJwELC5fH+P7JUlsHrf8ZoyqIqhT3ZGUnR4HfnaNxVCbtQoSZKkTsqQW5IkSYrRjsqgLsSObC+yuhRKws2fLzkEORk1LUVGRfTIzs2CwemQZJAtSZIkHcSQW5IkSWpEcVXNho91Gz2W7K/QLqyKbc7s9JrwemREiJ2bVRNwp9onW5IkSWoWQ25JkiR1ehXhgDWl+8PrVRFV2ZsrYpuzT+r+AHtURGX2yEzokmyQLUmSJLUWQ25JkiR1CuEgYEM5Ue1FVu97zS+FGLqLkJW0vwp71AGBdp9Ug2xJkiTpcDDkliRJUsIIgoCCyv0hdmR7kdWlUB5Dkp0aguGZ9ffJHpTmho+SJElSezPkliRJUtwpqgrq2opEvq4sgaLq5s8XAoZm1N8n+8h0SLFPtiRJktRhGXJLkiSpQyqrDvi0LLoau/Z1a4x9svun7mspEtFeJDcTRmRCpn2yJUmSpLhkyC1JkqR2Ux0ErCuLbi9S2yd7XRkEMczZLXl/eD0qol/2qEzoaZ9sSZIkKeEYckuSJKlNBUHAlooD+mTvq8r+tBQqYkiy00IwMnN/VXZuxKaPA+yTLUmSJHUqhtySJElqFYWV9ffJXlUKxTH0yU4ChmUc0FpkX6A9JAOSDbIlSZIkYcgtSZKkZiipDlh9QDV2bYX29srY5hyUFtFSJKIqe3gmpLvhoyRJkqRDMOSWJElSlMpwwNoD+2Tve91QHtucPVMiWopE9MselQndUgyyJUmSJMXOkFuSJKkTCgcBm8v3h9iRldlryqAqhj7ZmUkRfbIj2ouMyoS+qfbJliRJktQ2DLklSZISVBAE7Kjc3xt7ZUR7kVWlUBpu/pzJIRieEdFaJKK9yOB0SDLIliRJknSYGXJLkiTFuZIgiQ3hdFZvDfb3yd4XbBdWxTZndnp0S5HaquycDEi1T7YkSZKkDsSQW5IkKQ5UhAPWlO4PryM3fdxc8bmai5Y1b84+qRF9siMqs0dmQpdkg2xJkiRJ8cGQW5IkqYMIBwEbyvf3yK5tL7KqFPJLIYbuImQlRffGzo3Y9LF3qkG2JEmSpPhnyC1JknQYBUHAtsroliK1x6tLoTyGJDuFMNlJFRzbK+OgPtmD0tzwUZIkSVJiM+SWJElqA7urgrrwurYau7ZCu6i6+fOFgKEZ9ffJ3r70A1JCMP648a3+OSRJkiSpozPkliRJilFZdcCnZfvD68hNH7dWxDZn/9R9rUUi2orkZsKITMhsoE/2Lgu1JUmSJHVihtySJEmNqA4C1pXt75Fd215kVSmsK4Mghjm7JUf3xo487pFiYi1JkiRJzWHILUmSOr0gCPis4oA+2fte15RCRQxJdnoSjMysaSsyKqJHdm5WTbW2fbIlSZIkqXUYckuSpE6jsDKICrAjX4tj6JOdBAzLqAmuR0aE2LmZMCQDkg2yJUmSJKnNGXJLkqSEUlIdsDpik8e641LYXhnbnIPS9vXJzox+HZ4J6UkG2ZIkSZLUngy5JUlS3KkMB6w9oE92bZi9oTy2OXum7G8pErnp46hM6GafbEmSJEnqsAy5JUlShxQOAjaV728pEtleJL8MqmLok52ZtL+tyKgD2ov0sU+2JEmSJMUlQ25JktRugiBgR2VNNXbtpo+rIgLt0pKE8XcAACAASURBVHDz50wOwfAD+2Tvex2cDkkG2ZIkSZKUUAy5JUlSmyuuCvZXZJfC6og2I4VVsc2Znb6/pUhkZXZOBqTaJ1uSJEmSOg1DbkmS1CoqwgFrSveH15F9sjdXxDZnn9SIPtkR/bJHZkKXZINsSZIkSZIhtyRJaoZwELChfH+IHdleZG0ZxNBdhC7JEQF2RHuRUVnQO9UgW5IkSZLUOENuSZIUJQgCtlVGb/RY2y97dSmUx5Bkp4ZgRGb9fbIHpbnhoyRJkiQpdobckiR1UrurgqjNHldFtBkpqm7+fCFgaMb+Kuy6quwsODIdUuyTLUmSJElqA4bckiQlsLLqgE/LotuL1G76uDXGPtn9U/f3xo7slz0iEzLtky1JkiRJOswMuSVJinPVQcC6sv0hdmR7kfVlEMQwZ7fk6N7Ykcc9UgyyJUmSJEkdhyG3JElxIAgCPqs4oE/2vtdPS6EyhiQ7PWlff+xMGBlRlZ2bVVOtbZ9sSZIkSVI8MOSWJKkDKawM6iqyo/pkl8LeGPpkJwHDMva3FxkVsenjkAxINsiWJEmJKAgIUUZSqAyqt7f3apSgkkO7ag58xtQGkkNFVAfd23sZccOQW5Kkw6ykOmB1xCaPq0r3txfZXhnbnIPS9vfGrq3GHpUJwzMh3Q0fJUlSPAmqIbwHwrshKKp5De+GcH3HDY+f0Gvff1ita9+Po8T1uZ77DnzG1AY+1xN2V06AYDFYnHRIhtySJLWxvD0B/12azZpwBlv/EbChPLZ5eqZEbPQY0V5kZCZ0s0+2JElqb0EAQVl02FxvSH2I4Doobu9PIkkdQo/Uf0DVekg9sr2X0uEZckuS1EaW7Q24OR/mFwD0rzl5iJYjmUk1FdijDqjKzs2EPvbJliRJbeWQ1dNNrKgmxl9LawPhIJVwkElKSmp7L0UJqqqqCoCUFOM1tb7KKthZ8RUGGHA3if8WSpLUytaUBtyaD49shfr2g0wOwfCM/VXYuRFV2YPTIckgW5IkNVWj1dOHCqY7cPV0qBsk9YCk7vtem3IccS7UnfffXwrA+PHj2/nDKFF9mJcH+IypbXy07/ka0M7riBeG3JIktZJN5QG3r4UHPoOqA9LtySmFnJ26k7OPG0FOBqTaJ1uSJNVbPd2MYLoDVk9DWsPB86GC6drjUDcIJbX3B5EkxRFDbkmSWmh7RcAv1sOcTVAWjh47szfMGg7BynwAcrMMtyVJinstqp6OfE+8Vk83MB7qDkkZ7f0pJEmdkCG3JEkx2l0V8Jv18L8bofiAXtuTesDtw2Fiz5pQO68d1idJkuoRU/V0Pec6VPV0akTobPW0JKnzMeSWJKmZ9lYH3LMRfrUeCquix8Z3gzuGw5d6uUmkJEmt6sDq6Xo3R2zCRolxVT3dhH7UVk9LkmTIrfiREtpO99R3oHgFkA6hdAhl7Htt7DgdQsntvXxJCaA8HHDfZrhjHWytiB4b2wVuy4Hz+hpuS5J0kNrq6QOC6V6p75McKoZdryVo9fQhQupQV/9fRZKkVmDIrfgQ3svY7heRklQE22KZIKWZoXhTrtt3TPq+yommzGXgLsWjqnDAw1vhtnxYXx49NjwDbsmBqQMg2XBbkpRoggCC8nr6TrdO9fTwrvsOdh6+jwS0rHo6VHvO6mlJkjoKQ27Fh2AvyaG9LZigCoIqCFoyR2tpTuDezCC+0cC9vmN77kmNCQcBT26DmfmwsjR6bHA6zBgG3xkIqUmG25KkDqiB6ukmB9NxVT3djB7UVk9LkpRwDLkVH5L7s7r4f+mV9gp9e2fVVJMEZfteI44ph3BZzWvU+Y6kowXuLahib+zYwF1xLAgCXtgBM/LhwwMKz/qmwk1HwtVHQGay4bYkqQ00qXr6UMF1PPSe7s7O3WGqg6706z/c6mlJkhQzQ27FjaKqUyiqOoW+R49v3huDAKisPxSv9+uWHMdj4F7cQf4H6MDAvZXayTSx0j05VEQ4SIMgbODeyS0qDJi+Bt4sij7fIwVuGAI/zIZuKYbbkqQGNFo93YRgusNXTzdhI8RmVE/nb84DoF+fZv43viRJUgRDbiW+UAhIg1Bae6+kCYF7W4XvEcG7gXu9Ptdz30E+ND1wb0EQ3+RKdwP3w+Xt3QHT8+G1wujzWUkwLRt+MhR6pxpuS1LCapXq6SII9rT3J4lWT/V004Npq6clSVJ8MOSWDqe4DdzbIIg/sNK9Q+lIFe6pbdNOJqbWMokZuH9UHHBzPjy/Pfp8Wgi+dwT87EgYmG64LUkdWouqpyPOxW31dAPj9p6WJEmdhCG31FklXODegvCdcqqr9hKikqRQRXt/Nw5QCUFlBwzc26CdTKOV7q0fuK8qCbglH+ZtgyDifHIILhsINw+DoRmG25LUphqsnm7mRokdsno61mDa6mlJkqTmMuSW1P46QOD+QV5NP8jxJ5wAVByGFjLxWOEeb4F7/cdF1Wn8vSiDd/ekMyRIZ1q3DMqDdMqDdI7vnsH5/dM5IiMDggwoO1Sle+JWuEvSIR1UPd2MftNWT0uSJKkVGXJLUqRQiLows70FAQ0H7u2weWqHEnvg3h04OxnO7tnABbuaO2PTAvcRXUoJSIItDd1Yit3wLvseXJ8vtYERXQoJhSpgE4lfPR1K3/ffApIkSYonhtyS1FHFZeDexkF8HAfuPWt/UaGk7VekzqeXz5faUN3fX2321++hqqcPEVyHukNSN6unJUmSOjFDbknSocVh4F5aVcpftpezcEc54XAZ6aFy0kPlZITKGJlRzpd6lZOTXpFggbskHWYNVk8fqqLa6mlJkiS1HkNuSVJ8OUTgXlYd8H+b4a51UHBAm9fjusCs4fDVPhBqjUAlKnBvvHJ99eplhAgYMWJEy+8rHeDTTz8F8PlSm/j0008JB6mMGv15q6clSZLUIRlyS5ISQmU44I9b4Pa1sPGAAutRmXBbDlzYH5Jas1owKnDv3uiluyv71xx0Gd9695f22VVZs3muz5faQt3zleHzJUmSpI7JkFuSFNeqg4B5W+GWtfBpafTY0HS4OQe+PQBSkvxVeEmSJEmSEpEhtyQpLgVBwHPb4eZ8+Hhv9NiANPjZkfC9IyDdcFuSJEmSpIRmyC1JiitBEPBKIcxYA+/uiR7rlQI/GQrTsqFLsuG2JEmSJEmdgSG3JCluLNkVMD0f/rYr+nzXZPhhNtwwBHqmGm5LkiRJktSZGHJLkjq89/cEzFgDL+6MPp+eBNcMhhuHQr80w21JkiRJkjojQ25JUoe1fG/AzHx4qiD6fEoIrhgE04+E7AzDbUmSJEmSOjNDbklSh5NfGnDbWpi7BcIR50PAJQNgZg6MyDTcliRJkiRJhtySpA5kc3nAHevg/s1QGUSPTekHt+bA2C6G25IkSZIkaT9DbklSu9tRGfDLdXDPJigLR499pTfMyoHPdzfcliRJkiRJBzPkliS1m6KqgN9ugP/ZAHuqo8cm9oDbh8OknobbkiRJkiSpYYbckqTDrqQ64N5N8Kv1sKMyeuyErjXh9ld6QyhkwC1JkiRJkhpnyC1JOmwqwgH3fwZ3rIXPKqLHjs6C23Jqem8bbkuSJEmSpKYy5JYktbnqIOCRLXDrWlhbFj2WkwEzc+CSAZBsuC1JkiRJkprJkFuS1GbCQcDTBTAzHz4piR4blAbTh8F3B0FakuG2JEmSJEmKjSG3JKnVBUHAwp0wYw28Xxw91icVfjoUfjAYMpMNtyVJkiRJUssYckuSWtXfCgOm58OS3dHnuyXDDUPguiHQPcVwW5IkSZIktQ5DbklSq3i3KGD6GnilMPp8ZhJcmw3/NRT6pBpuS5IkSZKk1mXILUlqkY+LA27Oh2e3R59PDcFVR8DPj4RB6YbbkiRJkiSpbRhyS5Jisrok4Na18NhWCCLOJwHfHgg3D4NhmYbbkiRJkiSpbRlyS5KaZWNZwKx18OBnUB1Ej13UH24ZBkd1MdyWJEmSJEmHhyG3JKlJtlUE3LUO/m8zlIejx87uA7flwPHdDLclSZIkSdLhZcgtSWrUrsqAX2+AuzfC3urosVN7wu3DYUIPw21JkiRJktQ+DLklSfXaWx3wu43w3+thV1X02Ind4I7hcHovCIUMuCVJkiRJUvsx5JYkRSkPB/y/zXDnWthWGT12TBeYlQPn9DXcliRJkiRJHYMhtyQJgKpwwJ+2wG1rYUN59NjITLg1B77RH5IMtyVJkiRJUgdiyC1JnVw4CHh8G9ySD6tKo8ey0+HmYXDZQEhNMtyWJEmSJEkdjyG3JHVSQRCwYAfcvAY+2hs91j8VbjoSvn8EZCQbbkuSJEmSpI7LkFuSOqHXdgZMz4e3i6LP90yBHw+B/8yGrimG25IkSZIkqeMz5JakTuTN3QHT18CiXdHnuyTXBNs/HgK9Ug23JUmSJElS/DDklqRO4MPigBlr4C87os+nheDqwTWtSQakGW5LkiRJkqT4Y8gtSQlsRUnALfnw+Lbo88kh+M5AmDEMhmQYbkuSJEmSpPhlyC1JCWhdWcCt+fDwFghHnA8BUwfAzGEwKstwW5IkSZIkxT9DbklKIFvKA+5YB3/YDJVB9Ni5feG2HDi2q+G2JEmSJElKHIbckpQAdlYG/Go9zN4IpeHosS/1glnD4aTuhtuSJEmSJCnxGHJLUhzbUxXwvxvhN+uhqDp6bEJ3uH04nNrLcFuSJEmSJCUuQ25JikOl1QG/3wS/WA/bK6PHPtcVZuXAWX0gFDLgliRJkiRJic2QW5LiSEU44MHP4Pa1sLkiemx0Vk3P7fP7QZLhtiRJkiRJ6iQMuSUpDlQHAY9thVvyIb8seuzIDJg5DL41AFKSDLclSZIkSVLnYsgtSR1YEAQ8sx1uXgPLSqLHBqbBz4+EK4+AdMNtSZIkSZLUSRlyS1IHFAQB/99OmJEPeXuix3qnwH8NhWuzISvZcFuSJEmSJHVuhtyS1MH8fVfA9DXw993R57smw4+GwPVDoEeK4bYkSZIkSRIYcktSh5G3J2DGGnhpZ/T5jCT4wWD46VDom2a4LUmSJEmSFMmQW5La2bK9ATfnw/yC6PMpIbhyEEwfBkekG25LkiRJkiTVx5BbktrJmtKAW/Phka0QRJxPAr41EG4eBsMzDbclSZIkSZIaY8gtSYfZpvKA29fCA59BVRA9dkE/uDUHju5iuC1JkiRJktQUhtySdJhsrwj4xXqYswnKwtFjZ/aGWcPhhG6G25IkSZIkSc1hyC1JbWx3VcBv1sP/boTi6uixST3g9uEwsafhtiRJkiRJUiwMuSWpjZRUB8zeCL9aD4VV0WPju8Edw+FLvSAUMuCWJEmSJEmKlSG3JLWy8nDAfZvhznWwpSJ6bExWTVuS8/oabkuSJEmSJLUGQ25JaiVV4YC5W+HWfFhfHj02PANuyYGpAyDZcFuSJEmSJKnVGHJLUguFg4CnCmBmPqwoiR4bnA4zhsF3BkJqkuG2JEmSJElSazPklqQYBUHACztgRj58WBw91jcVbjoSrj4CMpMNtyVJkiRJktqKIbckxWBRYcD0NfBmUfT5HilwwxD4YTZ0SzHcliRJkiRJamuG3JLUDG/vDpieD68VRp/PSoJp2fCTodA71XBbkiRJkiTpcDHklqQm+Kg44OZ8eH579Pm0EHzvCPjZkTAw3XBbkiRJkiTpcDPklqRGrCoJuCUf5m2DIOJ8cgi+PRBuHgZHZhhuS5IkSZIktRdDbkmqx/qygFlr4aEtUB1Ej13cH27Jgdwsw21JkiRJkqT2ZsgtSRG2VgTcuQ7+3yaoOCDc/lofuG04jOtquC1JkiRJktRRGHJLElAUJDO3fABPvAkl4eixyT3h9uHwhR6G25IkSZIkSR2NIbekTq24KuDujfDLPWMpPuCvxC90rwm3J/cy3JYkSZIkSeqoDLkldUpl1QH/txnuWgcFlRD51+FxXWDWcPhqHwiFDLglSZIkSZI6MkNuSZ1KZTjgoS0way1sLI8eG5pUxi+PyuDC/pBkuC1JkiRJkhQXDLkldQrVQcC8rXDLWvi0NHpsSDpcHlrHWak7OHnA+HZZnyRJkiRJkmJjyC0poQVBwHPb4eZ8+Hhv9NiANPjZkfC9I+Dj93e0zwIlSZIkSZLUIobckhJSEAS8WgjT18C7e6LHeqXAT4bCtGzokmxbEkmSJEmSpHhmyC0p4SzZFTA9H/62K/p8l2S4LhtuGAI9Uw23JUmSJEmSEoEht6SE8f6egBlr4MWd0efTk+CawXDjUOiXZrgtSZIkSZKUSAy5JcW9T/YGzMyHJwuiz6eE4IpBMP1IyM4w3JYkSZIkSUpEhtyS4lZ+acBta2HuFghHnA8BlwyAmTkwItNwW5IkSZIkKZEZckuKO5+VB9y+Du7fDJVB9NjX+8KtOXBMV8NtSZIkSZKkzsCQW1Lc2FEZ8Mt1cO8mKA1Hj32lN8zKgc93N9yWJEmSJEnqTAy5JXV4RVUBv90A/7MB9lRHj03sAbcPh0k9DbclSZIkSZI6I0NuSR1WSXXAvZvgV+thR2X02AldYdZw+PfeEAoZcEuSJEmSJHVWhtySOpyKcMD9n8Eda+Gziuixo7PgthyY0s9wW5IkSZIkSZ0g5F60aBHz5s1j6dKl7N69m759+3LKKadw2WWXMXr06BbNvWfPHv785z+zaNEi1qxZQ3FxMRkZGQwdOpRTTjmFSy65hMGDB7fSJ5ESX3UQ8MgWuHUtrC2LHsvJgJk5cMkASDbcliRJkiRJ0j4JHXLPnDmTefPmRZ3bvHkzTz/9NAsWLGDWrFmcd955Mc29bNkyvv/977Nt27ao88XFxSxbtoxly5bx2GOPceedd3LWWWfF/BmkziAcBMwvgJvz4ZOS6LFBaTB9GHx3EKQlGW5LkiRJkiQpWlJ7L6Ct3HfffXUB9xlnnMH8+fN58803eeCBB8jNzaWiooKf//zn5OXlNXvu4uLiuoA7NTWVK664gmeffZY333yTBQsW8MMf/pCsrCxKS0v5r//6L1avXt3aH09KCEEQ8OKOgBP/CRctjQ64+6TCr0bA6i/AfwwOGXBLkiRJkiSpXglZyb1z507mzJkDwMSJE7nnnnvqevdOnDiRsWPH8tWvfpXt27fzy1/+kieeeKJZ8y9cuLCugvv666/nu9/9bt1Y7969yc3NZdiwYVx//fVUVlbyxBNP8LOf/ayVPp2UGP5WGDA9H5bsjj7fLRluGALXDYHuKQbbkiRJkiRJalxCVnI/88wzlJTUlIT+6Ec/Omhzul69enHllVcC8OGHH7J06dJmzb98+fK643POOafea77yla+QkZEBwJo1a5o1v5TI3i0K+PcPAk77IDrgzkyCnwyFNafAzTkhA25JkiRJkiQ1SUKG3IsWLQJg6NChjB07tt5rzjzzzLrjv/71r82aPz09ve74wAA98nztWJ8+fZo1v5SIPi4OmPKvgJPz4OXC/edTQ3DN4Jq2JL8cEaJPquG2JEmSJEmSmi4hQ+7ayuxx48Y1eM3AgQMZMGBA1PVNNWbMmLrjl156qd5rFi1aRGlpKQBf/OIXmzW/lEhWlwRcuixg3Lvw7Pb955OAywfCipPhntwQg9INtyVJkiRJktR8CRdyb926ta5VyZAhQxq9Njs7G4D8/Pxm3ePMM89k5MiRAPzqV79izpw5rF+/nvLycjZt2sTDDz/MjTfeCNS0LTnrrLOa+zGkuLexLOD7KwLGvAOPboUgYuyi/vDxSfDg0SGGZRpuS5IkSZIkKXYJt/FkYeH+PgiHahNSO75r165m3SMlJYWHHnqI6667jn/+85/cfffd3H333VHX5Obmcv311zN16tRmzS3Fu20VAb9YB7/fDOXh6LGz+8BtOXB8N4NtSZIkSZIktY6EC7lrq7ghund2fWrH9+7d2+z79OvXj9/+9rfccccd9bYs2bFjB5s2baKkpIQuXbo0e/6mKi4uJi8vr83m74g62+eNF3uCZB4p78+fK/pTSnLU2PjkPfxH+mbGVe4lvBI68j9Bny+1NZ8xtSWfL7Ulny+1JZ8vtSWfL7U1nzG1JZ+vpkm4kPtweeGFF7jxxhuprq7miiuu4Nxzz2XQoEEUFRXxxhtv8Lvf/Y7777+fJUuW8MADD7j5pBJWaZDEvIp+zC0fwJ4D/koZk7SXazI2c1LyHhrYo1WSJEmSJElqkYQLubOysuqOy8vLG722dry5ldZvvvkmN9xwA0EQcPvtt3PhhRfWjfXo0YNLLrmEE088kQsuuIDly5dzxx138D//8z/NukdTde3aldGjR7fJ3B1N7U+uxo8f384rEUB5OOD/bYa71sHWiuixY7rArBw4p28XQqHc9llgM/l8qa35jKkt+XypLfl8qS35fKkt+XyprfmMqS11xudrxYoVFBcXx/TehNt4slevXnXHO3bsaPTa2vGePXs26x73338/QRAwdOhQLrjggnqvyc3N5eyzzwbgpZdeYs+ePc26h9RRVYUDHtgckPsWXLcqOuAemQmPjIH3T4Rz+4UIWb4tSZIkSZKkNpZwIXf//v3rqrk3bNjQ6LUbN24EICcnp1n3+OCDDwAYO3ZsoyHescceC0B1dTX5+fnNuofU0YSDgHlbA8a+A1etgA0RvyiRnQ5/GA1LT4JvDgiRbLgtSZIkSZKkwyThQu5QKMTYsWMB+Oijjxq8bsuWLWzduhWg7vqmqm1zEgRBo9cdalyKB0EQ8Pz2gBPehW8ug1Wl+8f6pcJvR8LKk+HKI0KkJhluS5IkSZIk6fBKuJAb4LTTTgNg3bp1LF++vN5rXnrppbrjyZMnN2v+/v37A7Bs2bJGg+yPP/647viII45o1j2kjuC1nQET3oPz/gUf7d1/vmcK3J4Dn34BfjgkREay4bYkSZIkSZLaR0KG3F//+tfrWpb85je/OSiI3rVrF/fffz8A48aNa3Yl9ymnnALA+vXrmT9/fr3XrFy5khdeeAGAMWPG0Ldv32bdQ2pPb+4OOP39gC99CG8X7T/fJRluOrIm3P7ZsBBdUwy3JUmSJEmS1L4SMuTu3bs311xzDQB///vf+c///E+WL1/Ozp07WbJkCZdeeikFBQWkpKTw05/+9KD3z58/n9GjRzN69Oh6Q+wrr7yS9PR0AGbMmMF///d/s2LFCoqKitiwYQOPPvool156aV1bk2nTprXhp5Vaz4fFAed8FPBv78GiXfvPp4XgP7Nh9RfgjuEheqUabkuSJEmSJKljSGnvBbSVq666io0bNzJv3jxefvllXn755ajx1NRUbr/9dsaPH9/suXNycpg9ezY33HADe/bs4f7776+rDI9UG6I3tx2KdLitKAm4JR8e3xZ9PjkE3xkIM4bBkAyDbUmSJEmSJHU8CRtyA9x6662ceuqp/PnPf2bp0qXs3r2bfv368YUvfIHLL7+c0aNHxzz3F7/4RRYuXMi8efNYvHgx+fn5FBcXk56eTnZ2NieffDJTp05lxIgRrfiJpNa1rizgtrXwp88gHHE+BEwdADOHwagsw21JkiRJkiR1XAkdckPNJpS1G1E21ZQpU5gyZcohr+vXrx/Tpk2zHYnizpbygDvWwX2boeKAvVPP7Qu35cCxXQ23JUmSJEmS1PElfMgtab+dlQG/Wg+zN0JpOHrsS734/9m792gt6zr//68bNmdUTgKmoNBh52GwQlGUSVSyTEcBM5mxyFM1efr6tRxrxAwPP6X5WVNpM2uwdJTfSCZbTU1TEovQMjUzEbUUERAROSUnOV2/P1zs3KPCBu4NXjePx1qudXN/Ptd1vfda119Pr3Xduax/MmhncRsAAACA8hC5YQfw+toi/z4nufql5K/rmq4dsnNyef9kaFdxGwAAAIDyEbmhhq1cV+Q/5iZXvZS8tqbp2kc6J5f1Sz7dPalUBG4AAAAAyknkhhq0Zn2RH89LLnsxeXl107X6jm++c/uEXZNW4jYAAAAAJSdyQw1ZVxT5n/nJ2JnJC6uaru3ZPrlkr+RzvZK6VuI2AAAAALVB5IYaUBRFbnst+eYLydMrmq71bptctGdyxvuSduI2AAAAADVG5IYSK4oi9y1KxsxMHnu96Vq3uuRf+iZn75F0bC1uAwAAAFCbRG4oqalLiox5IZm6tOn3nVsn/7dPcn6fZJc6cRsAAACA2iZyQ8k89nqRi19I7l3U9Pv2rZKzdk8u7Jv0aCtuAwAAALBjELmhJJ5eXuSSmcmkBU2/r6skZ+yWjNkreV87cRsAAACAHYvIDe9xL6wsMnZm8v/NT9a/5ftWST7XO/nmXkn/DuI2AAAAADsmkRveo+a+UeTyF5MfzUvWFk3XTtg1Gdsv2aeTuA0AAADAjk3khveY11YXueql5Idzk1Xrm64d3S25rH/ysZ3EbQAAAABIRG54z1i6tsh3ZiffnZ0sW9d07e93Sa7onwzpIm4DAAAAwFuJ3LCdrVhX5Adzkm+/lCxe23Rt4E7J5f2So7ollYrADQAAAAD/m8gN28kb64uMfzn5f2Ylr6xuurZPxzdfSzK8h7gNAAAAABsjcsM2tnZ9kZvmJ5e+mMxa1XStf/vkW/2Sf+yVtBa3AQAAAGCTRG7YRtYXRW5dkFwyM3l2RdO13dslF++VnNo7adNK3AYAAACA5hK5oYUVRZGfL0zGzEz+uKzpWo82yTf2TP75fUmH1uI2AAAAAGwukRta0JTFRca8kDz816bf71KXfLVP8n/2SHaqE7cBAAAAYEuJ3NACHvnrm3F78uKm33dslZyzR3JB36RbG3EbAAAAALaWyA1V9KdlRb45M7njtabft60kX3pf8q97Jr3bidsAAAAAUC0iN1TBn1cU+dbMZOKrSfGW71tXktG9k2/ulezZXtwGAAAAgGoTuWErzF5V5NIXkxteSdYVTddG9Uy+1S/5UEdxGwAAAABaisgNW2D+6iJXzkr+c26y+n/F7X/onlzaP9m/s7gNAAAAAC1N5IbNsHhNkX97Kfn+nGTFUO69lwAAIABJREFU+qZrR3RJLu+fHLyLuA0AAAAA24rIDc2wbG2R781J/t/ZydK1TdcO3vnNuH1EV3EbAAAAALY1kRs2YtW6Iv/5cnLlrGTBmqZrAzoll/VPju2eVCoCNwAAAABsDyI3vIM164vc8Epy2YvJnDearn2wQzK2X/LZnkkrcRsAAAAAtiuRG95ifVFk4qvJJTOT51c2XevTLrmkXzK6V1LXStwGAAAAgPcCkRuSFEWRO15LvjkzeWp507WebZKL9kq+9L6knbgNAAAAAO8pIjc7tKIoMnlxMuaF5PevN13rWpdc0Dc5Z4+kU2txGwAAAADei0RudljTlhQZMzP51ZKm33dqnZy3R/LVPkmXNuI2AAAAALyXidzscP7wepGLX0h+vqjp9+1aJV95X/L1PZOebcVtAAAAACgDkZsdxjPLi1wyM/npgqbf11WS03ZLxuyZ7NFe3AYAAACAMhG5qXkvrixy6YvJja8k69/yfSXJyb2SS/ol7+8gbgMAAABAGYnc1Kx5bxS5fFZy3cvJmqLp2ogeydh+yX6dxW0AAAAAKDORm5qzcE2RcbOSa+cmK9c3Xftkt+SyfskBO4vbAAAAAFALRG5qxl/XFvnu7OQ7s5PX1zVdG7JLcnn/5ONdxG0AAAAAqCUiN6W3cl2Ra+cm415KFq5puvaxzsll/ZNPdUsqFYEbAAAAAGqNyE1prV5f5Lp5yRUvJvNWN13bu2Nyab9k5K7iNgAAAADUMpGb0llXFJnwSjL2xeTFVU3X9mqffKtfcnKvpLW4DQAAAAA1T+SmNNYXyZS1XTL6kWTGiqZru7VNxuyVnL5b0raVuA0AAAAAOwqRm1JYVxS5YGX//Hptlybfd2+TXNg3OWv3pENrcRsAAAAAdjQiN6Uw9400Cdw7tU6+2ic5r0+yc524DQAAAAA7KpGbUnhf2+TTbRbmybWd8o992udf+ibd24jbAAAAALCjE7kphbpWlYztMCtJMvD9A7fzNAAAAADAe0Wr7T0AAAAAAABsKZEbAAAAAIDSErkBAAAAACgtkRsAAAAAgNISuQEAAAAAKC2RGwAAAACA0hK5AQAAAAAoLZEbAAAAAIDSErkBAAAAACgtkRsAAAAAgNISuQEAAAAAKC2RGwAAAACA0hK5AQAAAAAoLZEbAAAAAIDSErkBAAAAACgtkRsAAAAAgNISuQEAAAAAKC2RGwAAAACA0hK5AQAAAAAoLZEbAAAAAIDSErkBAAAAACgtkRsAAAAAgNISuQEAAAAAKC2RGwAAAACA0hK5AQAAAAAoLZEbAAAAAIDSErkBAAAAACgtkRsAAAAAgNISuQEAAAAAKC2RGwAAAACA0hK5AQAAAAAoLZEbAAAAAIDSErkBAAAAACgtkRsAAAAAgNISuQEAAAAAKC2RGwAAAACA0hK5AQAAAAAoLZEbAAAAAIDSErkBAAAAACgtkRsAAAAAgNISuQEAAAAAKC2RGwAAAACA0hK5AQAAAAAoLZEbAAAAAIDSErkBAAAAACgtkRsAAAAAgNISuQEAAAAAKC2RGwAAAACA0hK5AQAAAAAoLZEbAAAAAIDSErkBAAAAACgtkRsAAAAAgNISuQEAAAAAKC2RGwAAAACA0hK5AQAAAAAoLZEbAAAAAIDSErkBAAAAACgtkRsAAAAAgNISuQEAAAAAKC2RGwAAAACA0hK5AQAAAAAoLZEbAAAAAIDSErkBAAAAACgtkRsAAAAAgNISuQEAAAAAKC2RGwAAAACA0hK5AQAAAAAoLZEbAAAAAIDSErkBAAAAACgtkRsAAAAAgNISuQEAAAAAKC2RGwAAAACA0hK5AQAAAAAoLZEbAAAAAIDSErkBAAAAACgtkRsAAAAAgNISuQEAAAAAKC2RGwAAAACA0hK5AQAAAAAoLZEbAAAAAIDSErkBAAAAACgtkRsAAAAAgNISuQEAAAAAKC2RGwAAAACA0hK5AQAAAAAoLZEbAAAAAIDSErkBAAAAACgtkRsAAAAAgNISuQEAAAAAKC2RGwAAAACA0hK5AQAAAAAoLZEbAAAAAIDSErkBAAAAACgtkRsAAAAAgNISuQEAAAAAKC2RGwAAAACA0hK5AQAAAAAoLZEbAAAAAIDSErkBAAAAACgtkRsAAAAAgNISuQEAAAAAKC2RGwAAAACA0hK5AQAAAAAoLZEbAAAAAIDSErkBAAAAACgtkRsAAAAAgNKq294DtLQpU6Zk4sSJmT59epYuXZoePXpk8ODB+cIXvpD6+vqqXGPmzJm55ZZbMnXq1MybNy/r1q1Ljx498oEPfCAHH3xwRo0alfbt21flWgAAAAAA/E1NR+5LLrkkEydObPLdyy+/nEmTJuXOO+/MZZddluHDh2/VNcaPH5/vf//7Wb16dZPvZ8+endmzZ2fKlCkZNmxY9thjj626DgAAAAAAb1ezkXv8+PGNgXvYsGE588wzs9tuu+Xpp5/OuHHj8txzz+Wiiy5Knz59MnDgwC26xrXXXpvvf//7SZIjjzwyo0aNSn19fdq2bZt58+bloYceyh133FG1vwkAAAAAgKZqMnIvWrQoP/zhD5MkQ4YMyTXXXJNKpdL473333TfHHntsXnvttYwbNy633HLLZl/j8ccfzw9+8IMkyde+9rV88YtfbLLetWvX7LPPPjnjjDO28q8BAAAAAODd1OQPT952221ZsWJFkuT8889vDNwbdO3atTE+//GPf8z06dM3+xrjxo1LURQZPHjw2wI3AAAAAADbRk1G7ilTpiRJ+vbtm3333fcd9xx99NGNnx944IHNOv+zzz6bJ554IklyyimnbNmQAAAAAABstZqM3BuezN5///3fdU/v3r3Tq1evJvub61e/+lWSpHXr1hk8eHCTtbVr127WuQAAAAAA2HI1907u+fPnN76qpE+fPhvdu8cee2T+/PmZOXPmZl3jqaeeajy+Xbt2ueeee3LjjTdm+vTpeeONN9KtW7ccdNBBOe200zJgwIAt+0MAAAAAANikmnuSe/HixY2fu3fvvtG9G9aXLFmyWdeYN29ekmSXXXbJpZdemvPOOy+PP/543njjjSRv/vDlPffck5NOOinXX3/9Zp0bAAAAAIDmq7knuTc8xZ0k7dq12+jeDevLly/frGu8/vrrSZIZM2bkySefzAc/+MFceOGFOeCAA7J27dpMmzYtV111VebNm5errroq/fr1y9ChQzfvD2mmZcuW5bHHHmuRc79X7Wh/L9uW+4uW5h6jJbm/aEnuL1qS+4uW5P6ipbnHaEnur+apuSe5t4WiKJIka9asSa9evTJhwoT8/d//fTp06JCddtopn/rUp3LjjTemY8eOSZKrr756e44LAAAAAFCzau5J7g1hOUnj60PezYb1Tp06bfE1Ro8enS5durxtT9++fTNy5MhMmDAhzz33XGbPnr3Jd4Rvic6dO6e+vr7q530v2vB/rgYOHLidJ6EWub9oae4xWpL7i5bk/qIlub9oSe4vWpp7jJa0I95fzz77bJYtW7ZFx9bck9xdu3Zt/Lxw4cKN7t2w/k6RurnXOOCAA95131vX/vKXv2zWNQAAAAAA2LSai9w9e/ZsfNJ69uzZG907Z86cJEm/fv026xr9+/dv/Lzzzju/675ddtml8fOW/l8IAAAAAADeXc1F7kqlkn333TdJ8uSTT77rvldeeSXz589Pksb9zbXffvs1fl6yZMm77nvr2k477bRZ1wAAAAAAYNNqLnInyeGHH54kmTVrVmbMmPGOe+69997Gz0ccccRmnX/o0KGpq3vzdea///3v33Xf7373u8bPe++992ZdAwAAAACATavJyD1ixIjGV5ZcffXVKYqiyfqSJUty3XXXJUn233//zX6Su0uXLjn22GOTJDfeeOM7vvv7+eefz+23357kzXdz9+rVa7P/DgAAAAAANq4mI3e3bt1y5plnJkmmTp2ac889NzNmzMiiRYsybdq0fP7zn8+CBQtSV1eXCy+88G3HNzQ0pL6+PvX19WloaHjHa5x33nnp0qVLXnvttfzjP/5jfvGLX2ThwoV59dVXc9ttt2X06NFZtWpV2rRp847XAAAAAABg69Vt7wFayhe/+MXMmTMnEydOzH333Zf77ruvyXqbNm1y+eWXZ+DAgVt0/t122y3/+Z//mTPPPDOzZs3Kueee+7Y9HTt2zLe//e0MGDBgi64BAAAAAMDG1WzkTpKxY8dm6NChufnmmzN9+vQsXbo0u+66aw4++OCccsopqa+v36rzf/SjH83dd9+dG264IQ888EDmzp2b9evXZ/fdd8+QIUNyyimn5H3ve1+V/hoAAAAAAP63mo7cyZs/Qrnhhyiba+TIkRk5cmSz9nbr1i3nn39+zj///C0ZDwAAAACArVCT7+QGAAAAAGDHIHIDAAAAAFBaIjcAAAAAAKUlcgMAAAAAUFoiNwAAAAAApSVyAwAAAABQWiI3AAAAAAClJXIDAAAAAFBaIjcAAAAAAKUlcgMAAAAAUFoiNwAAAAAApSVyAwAAAABQWiI3AAAAAAClVfXI/d///d9ZunRptU8LAAAAAABvU/XIfeWVV+bjH/94Lrjggvz+97+v9ukBAAAAAKBRi7yu5I033shdd92V0aNH51Of+lSuv/76LFq0qCUuBQAAAADADqzqkfvaa6/N0KFD06pVqxRFkRdffDHf/va3c9hhh+X888/Pww8/XO1LAgAAAACwg6qr9gmPPPLIHHnkkZk/f34mTZqUhoaGzJkzJ2vWrMk999yTe+65J3369MlnPvOZnHDCCenevXu1RwAAAAAAYAfRIq8rSZJevXrlzDPPzOTJk/PjH/84Rx99dOrq6lIURV566aV897vfzWGHHZZzzz03U6dObakxAAAAAACoYVV/kvudHHLIITnkkEOyePHi3H777bn11lvz/PPPZ+3atbn//vtz//33Z7fddsuJJ56YkSNHplevXttiLAAAAAAASq7FnuR+J127ds2pp56au+++O//zP/+TESNGpH379imKIi+//HK+//3v58gjj8xZZ52Vhx56aFuOBgAAAABACW3TyP1WHTp0SLt27dK6detUKpVUKpUURZG1a9fmgQceyOmnn55Ro0bl2Wef3V4jAgAAAADwHrdNXleywbJly3LXXXfllltuyYwZM5IkRVEkSfbee++MHDkyzz33XO6+++6sWLEiTzzxREaNGpWJEyemvr5+W44KAAAAAEAJbJPI/fjjj+enP/1p7r333qxataoxbHfo0CFHH310Ro0alQEDBjTu//rXv54JEybkhz/8YVatWpUf/OAHueaaa7bFqAAAAAAAlEiLRe4lS5Y0+ZHJ5G9PbX/wgx/MSSedlOHDh6dz585vO7ZTp0758pe/nI4dO+aKK67IE0880VJjAgAAAABQYlWP3A899FB++tOf5pe//GXWrFnTGLbbtm2bT37ykxk1alQGDhzYrHMdfPDBSZKFCxdWe0wAAAAAAGpA1SP3aaed1vgjkkmy11575aSTTsqIESPSpUuXzTpXu3btqj0eAAAAAAA1pEVeV9K6det84hOfyEknndT4NPaW6NWrV2688cYqTgYAAAAAQC2peuQ+//zz85nPfCbdunXb6nO1a9cugwYNqsJUAAAAAADUoqpH7i996UvVPiUAAAAAALyjVtt7AAAAAAAA2FJVj9zz58/P2WefnbPPPjuvvPLKJve/8sorOfvss3POOedk4cKF1R4HAAAAAIAaVvXIfccdd2Ty5Ml5+eWX07t3703u7927d+bNm5fJkyfnzjvvrPY4AAAAAADUsKpH7t/+9repVCr5xCc+0exjPvnJT6YoivzmN7+p9jgAAAAAANSwqkfu5557LkkyYMCAZh+z3377JUn+/Oc/V3scAAAAAABqWNUj95IlS5Ik3bt3b/Yx3bp1S5IsWrSo2uMAAAAAAFDDqh6527VrlyRZsWJFs4/ZsLeurq7a4wAAAAAAUMOqHrl79OiRJJkxY0azj9mwd8MT3QAAAAAA0BxVj9wf+9jHUhRFfvKTn6Qoik3uL4oiEydOTKVSyUc+8pFqjwMAAAAAQA2reuQ+5phjkrz5I5Jjx47daOguiiJjx45t/MHJf/iHf6j2OAAAAAAA1LCqR+4hQ4Zk0KBBjU9zn3jiibn77ruzYMGCxj0LFizIXXfdlc9+9rP5yU9+kkqlkgMOOCBDhw6t9jgAAAAAANSwFvmlx3//93/PqFGj8tJLL2X69On52te+liSpVCpJ0uTp7qIosueee+Z73/teS4wCAAAAAEANq/qT3MmbPyA5adKkHHPMMalUKimKIkVRZP369Vm/fn3jv1u1apXjjjsut956qx+dBAAAAABgs7XIk9xJstNOO+Xqq6/OeeedlylTpmT69OlZtGhRkjcj+H777ZehQ4emT58+LTUCAAAAAAA1rsUi9wZ9+vTJ6NGjW/oyAAAAAADsgFrkdSUAAAAAALAtiNwAAAAAAJSWyA0AAAAAQGm12Du5165dm7vuuiv3339/ZsyYkcWLF2fVqlUbPaZSqeTpp59uqZEAAAAAAKgxLRK558yZk7POOivPPfdckqQoipa4DAAAAAAAO7iqR+7Vq1fny1/+cp5//vkkyT777JOePXvmwQcfTKVSyXHHHZelS5dm+vTpWbBgQSqVSvbZZ5986EMfqvYoAAAAAADUuKpH7kmTJuX5559PpVLJFVdckZEjR+bPf/5zHnzwwSTJuHHjGvfef//9GTt2bF544YV85StfybBhw6o9DgAAAAAANazqPzw5efLkJMkhhxySkSNHbnTvJz7xidx0001p1apVLrzwwsyePbva4wAAAAAAUMOqHrmfffbZVCqVHH/88c3a369fv3zuc5/L8uXLM2HChGqPAwAAAABADat65F6yZEmSZPfdd2/8rq7ub29FWbly5duOOfTQQ5MkU6dOrfY4AAAAAADUsKpH7jZt2iRJOnbs2Phdp06dGj8vWLDgbcd06NAhSTJ//vxqjwMAAAAAQA2reuTeddddkySLFi1q8l379u2TJE899dTbjpk1a1aSZN26ddUeBwAAAACAGlb1yP3BD34wSfLcc881flepVPJ3f/d3KYoiN998c5P9a9asyQ033JAk6dOnT7XHAQAAAACghlU9ch900EEpiiLTpk1r8v1xxx2XJHn00Udz8sknZ8KECRk/fnw++9nP5qmnnkqlUsmwYcOqPQ4AAAAAADWs6pH7qKOOSpL89re/zbx58xq/P+GEE/KRj3wkRVHk8ccfzxVXXJHvfOc7eeaZZ5K8+RT36aefXu1xAAAAAACoYVWP3L17987vfve7TJ06NT169PjbhVq1ynXXXZcRI0akrq4uRVGkKIpUKpUcccQRmTBhQjp37lztcQAAAAAAqGF1LXHSXXbZ5R2/79y5c6688sqMGTMmL774YtatW5e+ffumS5cuLTEGAAAAAAA1rkUi96Z06tQp++677/a4NAAAAAAANaTqrys58MADM2jQoPz4xz+u9qkBAAAAAKCJqj/JvXLlyqxbty4DBgyo9qkBAAAAAKCJqj/JveuuuyZJ2rdvX+1TAwAAAABAE1WP3Bvetf38889X+9QAAAAAANBE1SP3iSeemKIocvPNN1f71AAAAAAA0ETVI/dhhx2WE088MU888UQuuOCCLF++vNqXAAAAAACAJC3ww5O33357Pvaxj+VPf/pT7rrrrjz44IM54ogj8uEPfzg777xzWrduvdHjhw8fXu2RAAAAAACoUVWP3F//+tdTqVQa//3666/nZz/7WX72s59t8thKpSJyAwAAAADQbFWP3ElSFMVG/w0AAAAAANVQ9ch94403VvuUAAAAAADwjqoeuQcNGlTtUwIAAAAAwDtqtb0HAAAAAACALSVyAwAAAABQWiI3AAAAAAClVfV3cl9zzTVbdfzZZ59dpUkAAAAAAKh1LRK5K5XKFh8vcgMAAAAA0FxVj9xJUhRFs/dWKpXG/VsTxwEAAAAA2PFUPXL/8pe/3OSelStX5vnnn88dd9yRBx54IAMHDsxll12Wdu3aVXscAAAAAABqWNUj9+67796sfR/4wAfyyU9+Mg0NDbnoooty1VVX5b/+67+qPQ4AAAAAADWs1fYeYOTIkTn22GMzderUNDQ0bO9xAAAAAAAoke0euZPkmGOOSVEUmTRp0vYeBQAAAACAEnlPRO5evXolSf7yl79s50kAAAAAACiT90TkfvXVV5Mkq1at2s6TAAAAAABQJu+JyD1hwoQkSe/evbfzJAAAAAAAlEnd9rrw0qVL86c//SnXX399pk2blkqlksMPP3x7jQMAAAAAQAlVPXLvvffeW3Rcz54986UvfanK0wAAAAAAUMuq/rqSoig2+78DDzwwEyZMSLdu3ao9DgAAAAAANazqT3KPGDFik3tatWqVTp06pU+fPhk0aFDq6+urPQYAAAAAADuAqkfuK6+8stqnBAAAAACAd1T115UAAAAAAMC2InIDAAAAAFBaVX9dSZIsW7YsSdKhQ4e0bt16o3vXrVuXlStXJkk6d+7cEuMAAAAAAFCjqv4k9yOPPJIDDzwwhx56aBYvXrzJ/YsXL84hhxySQYMG5Yknnqj2OAAAAAAA1LCqR+5f/OIXKYoiQ4cOTY8ePTa5v0ePHjn88MOzfv363HPPPdUeBwAAAACAGlb1yP2HP/whlUolQ4YMafYxH//4x5Mkjz76aLXHAQAAAACghlU9cr/00ktJkve///3NPqZ///5Jkjlz5lR7HAAAAAAAaljVI/eqVauSJB07dmz2MR06dEiSLF++vNrjAAAAAABQw6oeuXfaaackyYIFC5p9zGuvvZYk6dSpU7XHAQAAAACghlU9cvft2zdJ8vDDDzf7mGnTpiVJdt9992qPAwAAAABADat65D744INTFEV+8pOfZN68eZvcP3fu3Nxyyy2pVCoZPHhwtccBAAAAAKCGVT1yjxo1KnV1dVmxYkVOPfXUPPPMM++695lnnslpp52W5cuXp3Xr1hk1alS1xwEAAAAAoIbVVfuEu+22W84555x897vfzaxZszJy5MgMHjw4Bx10UHr27JkkefXVV/O73/0uDz/8cIqiSKVSyVlnnZU+ffpUexwAAAAAAGpY1SN3knz5y1/OkiVLcv3116coijz00EN56KGH3ravKIokyemnn56vfOUrLTEKAAAAAAA1rOqvK9ngwgsvzI9+9KMccMABqVQqKYqiyX+VSiWDBg3K9ddfnwsuuKClxgAAAAAAoIa1yJPcGxx66KE59NBD89e//jVPP/10Fi1alCTp1q1b9tlnn+y8884teXkAAAAAAGpci0buDXbeeeccfPDB2+JSAAAAAADsQFrsdSUAAAAAANDSqv4kd1EUefbZZ5Mkffv2TceOHTe6f/ny5Zk9e3aS5MMf/nC1xwEAAAAAoIZV/UnuX/3qVxk+fHhOPvnkrF+/fpP7i6LIySefnBEjRuShhx6q9jgAAAAAANSwqkfuyZMnJ0mGDRuWzp07b3J/586dc9RRR6Uoitx7773VHgcAAAAAgBpW9cj9xz/+MZVKJYMHD272MYccckjjsQAAAAAA0FxVj9xz585NkvTv37/Zx+y5555NjgUAAAAAgOaoeuRevXp1kqRNmzbNPqau7s3fv1y1alW1xwEAAAAAoIZVPXJ36dIlSTJv3rxmHzN//vwkadY7vAEAAAAAYIOqR+5+/folSX796183+5gHH3wwSbLXXntVexwAAAAAAGpY1SP3oYcemqIo0tDQkGeeeWaT+5955pk0NDSkUqlkyJAh1R4HAAAAAIAaVvXIfdJJJ6VDhw5Zs2ZNTj/99DzwwAPvuveBBx7I6aefnjVr1qR9+/b5p3/6p2qPAwAAAABADaur9gm7du2aiy++OP/6r/+aRYsW5ayzzsqee+6ZQYMGpWfPnkmSV199NY888khmzZqVoihSqVQyZsyYdOvWrdrjAAAAAABQw6oeuZNk5MiRWbFiRa666qqsXbs2s2bNyqxZs962ryiK1NXV5Rvf+EZOOOGElhgFAAAAAIAaVvXXlWzwuc99LnfccUeOP/747LzzzimKosl/u+yyS0aMGJGf/exnOfnkk1tqDAAAAAAAaliLPMm9wfvf//6MGzcuSTJ79uwsXrw4yZuvNOnTp8/b9j/66KM54IADWnIkAAAAAABqSItG7rfq06fPO4bt+fPn5/bbb09DQ0Nmz56dp59+eluNBAAAAABAyW2zyP1Wa9asyeTJk9PQ0JCHHnoo69evb/wBSgAAAAAAaK5tGrlnzJiRSZMm5c4778xf//rXJG/++GSStG3bNocddti2HAcAAAAAgJJr8ci9ZMmS3HnnnWloaMgzzzyT5G9hu02bNhkyZEiOPvroHHnkkenUqVNLjwMAAAAAQA1pkchdFEV+/etfp6GhIVOmTMmaNWsav0+SSqWSU089NWeeeWY6d+7cEiMAAAAAALADqGrknjVrViZNmpQ77rgjr776apK/he099tgjw4cPzzXXXJMk2W+//QRuAAAAAAC2ylZH7hUrVuSee+7JpEmT8oc//CHJ38J2p06d8qlPfSojRozIAQcckCSNkRsAAAAAALbWVkXub3zjG/nFL36RlStXNobtVq1aZfDgwRk+fHiOOuqotG/fviqDAgAAAADA/7ZVkfu2225r/LzXXntlxIgRGT58eHr16rXVgwEAAAAAwKZs9etKKpVKOnXqlGOPPTbHHHOMwA0AAAAAwDbTamsO3mWXXVIURZYtW5Zrr702Rx11VD7/+c/n1ltvzfLly6s1IwAAAAAAvKOtitxTp07Nd77znRx66KGpVCpZv359Hn300Vx88cUZMmRILrjggkybNq3xfd0AAAAAAFBNW/W6krZt2+bTn/50Pv3pT2f+/PmZNGlSbr/99rz00ktZuXJl7rrrrtx1113p2bNnjj/xdFO1AAAgAElEQVT++Bx//PHVmhsAAAAAALbuSe636tWrV84888zcd999uemmm3L88cenffv2KYoi8+fPz/jx43Psscc27l+3bl21Lg0AAAAAwA6qapH7rQ488MCMGzcuv/nNb3LppZfmIx/5SIqiSFEUqVQqSZJvfOMbOe200/LTn/40S5cubYkxAAAAAACocS0SuTfo1KlTPvvZz2bixIn5+c9/ntNOOy3du3dPURRZu3ZtHn744Xzzm9/MoYcemjPOOCMNDQ0tOQ4AAAAAADWmRSP3W/Xv3z//8i//kl//+tf5j//4jwwbNiytW7duDN6/+c1vMmbMmG01DgAAAAAANWCrfnhyS7Rq1SqHH354Dj/88CxatCh33HFHGhoa8uc//zlFUWzrcQAAAAAAKLFtHrnfqlu3bjn11FNz6qmn5sknn/S6EgAAAAAANst2jdxvNWDAgAwYMGB7jwEAAAAAQIlss3dyAwAAAABAtYncAAAAAACUlsgNAAAAAEBpidwAAAAAAJSWyA0AAAAAQGmJ3AAAAAAAlJbIDQAAAABAaYncAAAAAACUlsgNAAAAAEBp1W3vAVralClTMnHixEyfPj1Lly5Njx49Mnjw4HzhC19IfX19Va9VFEVGjx6dRx55JEmy++6754EHHqjqNQAAAAAA+JuafpL7kksuyT//8z/nwQcfzIIFC7J69eq8/PLLmTRpUj7zmc/k9ttvr+r1br311sbADQAAAABAy6vZyD1+/PhMnDgxSTJs2LA0NDTk4Ycfzo9+9KN86EMfyurVq3PRRRflscceq8r1Xnvttfzbv/1b6urq0rt376qcEwAAAACAjavJyL1o0aL88Ic/TJIMGTIk11xzTfbdd99069YtQ4YMyY033pgePXpk7dq1GTduXFWuecUVV2Tp0qU55ZRT0rdv36qcEwAAAACAjavJyH3bbbdlxYoVSZLzzz8/lUqlyXrXrl1zxhlnJEn++Mc/Zvr06Vt1vV/96lf5+c9/nt133z1nn332Vp0LAAAAAIDmq8nIPWXKlCRJ3759s++++77jnqOPPrrx89b8OOSKFSsyduzYJMmYMWPSoUOHLT4XAAAAAACbpyYj94Yns/fff/933dO7d+/06tWryf4t8b3vfS9z587NsGHDcsQRR2zxeQAAAAAA2Hw1F7nnz5/f+KqSPn36bHTvHnvskSSZOXPmFl3rqaeeyk033ZSOHTtmzJgxW3QOAAAAAAC2XM1F7sWLFzd+7t69+0b3blhfsmTJZl9n3bp1ufjii7Nu3bqcc8452W233Tb7HAAAAAAAbJ267T1AtW14ijtJ2rVrt9G9G9aXL1++2de54YYb8vTTT6e+vj6jR4/e7OOrZdmyZXnssce22/W3hx3t72Xbcn/R0txjtCT3Fy3J/UVLcn/RktxftDT3GC3J/dU8Nfck97YwZ86c/OAHP0ilUsnYsWNTV1dz/68AAAAAAKAUaq7OduzYsfHzG2+8sdG9G9Y7deq0Wdf41re+lZUrV+akk07KRz/60c0fsoo6d+6c+vr67TrDtrLh/1wNHDhwO09CLXJ/0dLcY7Qk9xctyf1FS3J/0ZLcX7Q09xgtaUe8v5599tksW7Zsi46tuSe5u3bt2vh54cKFG927Yb1Lly7NPv/kyZMzderUdO/ePV/96le3bEgAAAAAAKqi5p7k7tmzZzp27JgVK1Zk9uzZG907Z86cJEm/fv2aff4NxyxcuDCDBg3a6N65c+c2PmU9evToXHTRRc2+DgAAAAAAm1ZzT3JXKpXsu+++SZInn3zyXfe98sormT9/fpI07gcAAAAAoFxq7knuJDn88MPz+9//PrNmzcqMGTOy9957v23Pvffe2/j5iCOOaPa5jzvuuBx00EEb3XPRRRdl+vTp2XXXXTN+/PgkSbdu3Zp9DQAAAAAAmqcmI/eIESNyzTXXZMWKFbn66qszfvz4VCqVxvUlS5bkuuuuS5Lsv//+m/Ukd7du3TYZrDf8kGXbtm3fMbADAAAAAFAdNfe6kuTNEH3mmWcmSaZOnZpzzz03M2bMyKJFizJt2rR8/vOfz4IFC1JXV5cLL7zwbcc3NDSkvr4+9fX1aWho2NbjAwAAAADQTDX5JHeSfPGLX8ycOXMyceLE3HfffbnvvvuarLdp0yaXX355Bg4cuJ0mBAAAAABga9Vs5E6SsWPHZujQobn55pszffr0LF26NLvuumsOPvjgnHLKKamvr9/eIwIAAAAAsBVqOnInb/4I5eGHH75Zx4wcOTIjR47c4mvedNNNW3wsAAAAAADNV5Pv5AYAAAAAYMcgcgMAAAAAUFoiNwAAAAAApSVyAwAAAABQWiI3AAAAAAClJXIDAAAAAFBaIjcAAAAAAKUlcgMAAAAAUFoiNwAAAAAApSVyAwAAAABQWiI3AAAAAAClJXIDAAAAAFBaIjcAAAAAAKUlcgMAAAAAUFoiNwAAAAAApSVyAwAAAABQWiI3AAAAAAClJXIDAAAAAFBaIjcAAAAAAKUlcgMAAAAAUFoiNwAAAAAApSVyAwAAAABQWiI3AAAAAAClJXIDAAAAAFBaIjcAAAAAAKUlcgMAAAAAUFoiNwAAAAAApSVyAwAAAABQWiI3AAAAAAClJXIDAAAAAFBaIjcAAAAAAKUlcgMAAAAAUFoiNwAAAAAApSVyAwAAAABQWiI3AAAAAAClJXIDAAAAAFBaIjcAAAAAAKUlcgMAAAAAUFoiNwAAAAAApSVyAwAAAABQWiI3AAAAAAClJXIDAAAAAFBaIjcAAAAAAKUlcgMAAAAAUFoiNwAAAAAApSVyAwAAAABQWiI3AAAAAAClJXIDAAAAAFBaIjcAAAAAAKUlcgMAAAAAUFoiNwAAAAAApSVyAwAAAABQWiI3AAAAAAClJXIDAAAAAFBaIjcAAAAAAKUlcgMAAAAAUFoiNwAAAAAApSVyAwAAAABQWiI3AAAAAAClJXIDAAAAAFBaIjcAAAAAAKUlcgMAAAAAUFoiN/z/7N15lFbVnS7gt5gFFIpBVIaOilaQqNfGVnGOUZwHEDXRlqAtseMQO2qiXjWO1ykxK1Gv6xo1cRYnECWaqKAdUSMKXgdEpA0ghYooVWAVIhRy/6CrrkhRUEWV5QfPs5bLj3P22ed3Tm1w+X6bvQEAAACAgiXkBgAAAACgYAm5AQAAAAAoWEJuAAAAAAAKlpAbAAAAAICCJeQGAAAAAKBgCbkBAAAAAChYQm4AAAAAAAqWkBsAAAAAgIIl5AYAAAAAoGAJuQEAAAAAKFhCbgAAAAAACpaQGwAAAACAgiXkBgAAAACgYAm5AQAAAAAoWEJuAAAAAAAKlpAbAAAAAICCJeQGAAAAAKBgCbkBAAAAAChYQm4AAAAAAAqWkBsAAAAAgIIl5AYAAAAAoGAJuQEAAAAAKFhCbgAAAAAACpaQGwAAAACAgiXkBgAAAACgYAm5AQAAAAAoWEJuAAAAAAAKlpAbAAAAAICCJeQGAAAAAKBgCbkBAAAAAChYQm4AAAAAAAqWkBsAAAAAgIIl5AYAAAAAoGAJuQEAAAAAKFhCbgAAAAAACpaQGwAAAACAgiXkBgAAAACgYAm5AQAAAAAoWEJuAAAAAAAKlpAbAAAAAICCJeQGAAAAAKBgCbkBAAAAAChYQm4AAAAAAAqWkBsAAAAAgIIl5AYAAAAAoGAJuQEAAAAAKFhCbgAAAAAACpaQGwAAAACAgiXkBgAAAACgYAm5AQAAAAAoWEJuAAAAAAAKlpAbAAAAAICCJeQGAAAAAKBgCbkBAAAAAChYQm4AAAAAAAqWkBsAAAAAgIIl5AYAAAAAoGAJuQEAAAAAKFhCbgAAAAAACpaQGwAAAACAgiXkBgAAAACgYAm5AQAAAAAoWEJuAAAAAAAKlpAbAAAAAICCJeQGAAAAAKBgCbkBAAAAAChYQm4AAAAAAAqWkBsAAAAAgIIl5AYAAAAAoGAJuQEAAAAAKFhCbgAAAAAACpaQGwAAAACAgiXkBgAAAACgYAm5AQAAAAAoWEJuAAAAAAAKlpAbAAAAAICCJeQGAAAAAKBgCbkBAAAAAChYQm4AAAAAAAqWkBsAAAAAgIIl5AYAAAAAoGAJuQEAAAAAKFhCbgAAAAAACpaQGwAAAACAgiXkBgAAAACgYAm5AQAAAAAoWEJuAAAAAAAKlpAbAAAAAICCJeQGAAAAAKBgCbkBAAAAAChYrZq7gKb27LPPZuTIkZkyZUoWLFiQbt26ZeDAgfnxj3+ckpKSBvW5fPnyTJo0Kc8//3wmTZqUf/zjH1m4cGHatm2bXr16Zffdd8/xxx+f3r17N/LTAAAAAADwVet1yH3JJZdk5MiRKx374IMP8sgjj+Txxx/PFVdckaOOOqre/f70pz/Ns88+u8rxpUuX5p133sk777yT++67LxdffHGGDh3a4PoBAAAAAKjbehty33rrrTUB9/7775/TTjstm2++ed5+++1ce+21effdd3PhhRemd+/eGTBgQL36rqysTJLsvPPOOeSQQ7Lzzjtn0003TWVlZSZMmJDf/e53KSsry0UXXZRu3bpl3333bezHAwAAAAAg62nIPX/+/Nx8881Jkj333DM33XRTioqKan7dv3//HHbYYfnkk09y7bXX5sEHH6xX/wMHDswFF1yQ7bbbbqXjxcXF+eEPf5hdd901Q4YMyaJFi3LdddcJuQEAAAAAmsh6ufHk6NGjs2jRoiTJ2WefXRNwVysuLs4pp5ySJHn99dczZcqUevV/2mmnrRJwf9WWW26Zo48+Okny3nvvZc6cOfXqHwAAAACAtbNehtzV62X36dMn/fv3r7XNwQcfXPN5/PjxjV5D3759az5//PHHjd4/AAAAAADrachdPTN7xx13XG2bzTbbLD169FipfWP65JNPaj5vvPHGjd4/AAAAAADrYcg9d+7cmqVKevfuXWfbXr16JUlmzJjR6HU8/fTTSZLOnTtnyy23bPT+AQAAAABYD0PusrKyms9du3ats231+fLy8kat4dFHH80777yTJDn22GPTsmXLRu0fAAAAAIAVWjV3AY2tehZ3krRt27bOttXnKysrG+3+7733Xi6//PIkyeabb54RI0Y0Wt+1qaioyKRJk5r0Ht82G9rz8s0yvmhqxhhNyfiiKRlfNCXji6ZkfNHUjDGakvG1dta7mdzNqaysLKeddloqKyvTunXr/OY3v8kmm2zS3GUBAAAAAKy31ruZ3O3bt6/5/MUXX9TZtvp8hw4d1vm+ixYtyr//+79n5syZadGiRa655prsvPPO69zvmnTs2DElJSVNfp9vg+pvrgYMGNDMlbA+Mr5oasYYTcn4oikZXzQl44umZHzR1IwxmtKGOL6mTZuWioqKBl273s3kLi4urvn86aef1tm2+nznzp3X6Z5LlizJGWeckf/7f/9vkuRXv/pVDjvssHXqEwAAAACANVvvQu5NN920Zjb37Nmz62xbWlqaJNlyyy0bfL9ly5bl7LPPzgsvvJAkOffcc/OjH/2owf0BAAAAALD21ruQu6ioKP3790+SvPHGG6tt99FHH2Xu3LlJUtO+vpYvX54LLrggTz/9dJLk3//935t8o0kAAAAAAP6/9S7kTpLvf//7SZJZs2Zl6tSptbb5y1/+UvN5v/32a9B9Lr/88owZMyZJ8q//+q/5+c9/3qB+AAAAAABomPUy5B48eHDNkiXXX399li9fvtL58vLy3HbbbUmSHXfcsUEzuX/729/mvvvuS5IcddRRueiii9axagAAAAAA6mu9DLm7dOmS0047LUny/PPP52c/+1mmTp2a+fPn54UXXsiJJ56YefPmpVWrVjnvvPNWuX7UqFEpKSlJSUlJRo0atcr522+/PbfcckuSZO+9985FF12URYsWpbKystZ/qqqqmvaBAQAAAAA2UK2au4CmMmLEiJSWlmbkyJF56qmn8tRTT610vnXr1rnyyiszYMCAevd977331nz+29/+lp133rnO9ldffXWGDBlS7/sAAAAAAFC39TbkTpLLLrss++67b+6///5MmTIlCxYsSPfu3bPbbrtl+PDhKSkpae4SAQAAAABYB+t1yJ2s2ISyeiPKtTVkyJA6Z16PHz9+XcsCAAAAAKARrJdrcgMAAAAAsGEQcgMAAAAAULCE3AAAAAAAFCwhNwAAAAAABUvIDQAAAABAwRJyAwAAAABQsITcAAAAAAAULCE3AAAAAAAFS8gNAAAAAEDBEnIDAAAAAFCwhNwAAAAAABQsITcAAAAAAAVLyA0AAAAAQMEScgMAAAAAULCE3AAAAAAAFCwhNwAAAAAABUvIDQAAAABAwRJyAwAAAABQsITcAAAAAAAULCE3AAAAAAAFS8gNAAAAAEDBEnIDAAAAAFCwhNwAAAAAABQsITcAAAAAAAVLyA0AAAAAQMEScgMAAAAAULCE3AAAAAAAFCwhNwAAAAAABUvIDQAAAABAwRJyAwAAAABQsITcAAAAAAAULCE3AAAAAAAFS8gNAAAAAEDBEnIDAAAAAFCwhNwAAAAAABQsITcAAAAAAAVLyA0AAAAAQMEScgMAAAAAULCE3AAAAAAAFCwhNwAAAAAABUvIDQAAAABAwRJyAwAAAABQsITcAAAAAAAULCE3AAAAAAAFS8gNAAAAAEDBEnIDAAAAAFCwhNwAAAAAABQsITcAAAAAAAVLyA0AAAAAQMEScgMAAAAAULCE3AAAAAAAFCwhNwAAAAAABUvIDQAAAABAwRJyAwAAAABQsITcAAAAAAAULCE3AAAAAAAFS8gNAAAAAEDBEnIDAAAAAFCwhNwAAAAAABQsITcAAAAAAAVLyA0AAAAAQMEScgMAAAAAULBaNXcBNI/Fixdn4cKF+eyzz7J06dIsX768uUtaa1OnTm3uEliPGV80lqKiorRs2TLt27dPhw4dmrscAAAAWG8JuTdAFRUVKS0tLahgO0natWvX3CWwHjO+aGzLly9PVVVVFi5cmIULFzZ3OQAAALDeEnJvYBYvXlwTcG+yySYpLi5Ou3bt0qLFt3/lmsrKyiQxI5ImYXzR2L788stUVVWloqIiZWVlWbJkSaqqqlJVVZVWrfznFwAAABrLtz/ZpFEtXLiwJuDeYost0r59+4IIuAEKTYsWLdKmTZt06dIl3/nOd9KhQ4e0bNkyCxYsaO7SAAAAYL0i3dzAfPbZZ0mS4uLiFBUVNXM1ABuGli1bplOnTkJuAAAAaAJC7g3M0qVLk1h/GOCb1q5duxQVFWXJkiXNXQoAAACsV4TcG5jqzSYtUQLwzSoqKkpRUVHBbfoLAAAA33aSTgD4BlgiCgAAAJqGkBsAAAAAgIIl5AYAAAAAoGAJuQEAAAAAKFhCbgAAAAAACpaQG9ZjpaWlKSkpSUlJSV5++eXmLgcAAAAAGp2QGxrJjTfemJKSkuy3337NXQoAAAAAbDCE3AAAAAAAFKxWzV0A0HR69eqVadOmNXcZAAAAANBkzOQGAAAAAKBgmckN6+jll1/OsGHDan49Z86clJSUrNRml112yd13371K+3HjxmXjjTfOH//4x4wbNy5z5szJokWL8uijj6Zfv35JklmzZmX8+PF5/vnn8+6776a8vDxt2rTJFltskYEDB+bHP/5xevXqVWttpaWl+cEPfpAkueuuu7LrrruudH6//fbLnDlzcsYZZ+TMM8/MuHHjcu+99+btt99OZWVltthiixx00EEZMWJEOnbs2KD3U1VVlVdffTXjx4/PK6+8kvfffz+LFy/OxhtvnG233TYHHXRQhg4dmjZt2tTZz7JlyzJ27Nj85S9/yVtvvZWysrJ07Ngxm222WbbffvscdthhqzxftU8++ST33HNPJkyYkNmzZ2fRokXp3r17evbsmYEDB+boo49e6fluvPHG3HTTTenZs2fGjx+/2ppOPPHETJw4MYMHD84111yz0rnzzz8/o0ePrvnZv/baa7n77rszadKkfPLJJ+nbt2/GjBnTbO/o8ccfz7nnnpskeeKJJ7L11luvtt8333wzQ4cOTZLccsst2XfffeusAwAAAOCbJOSGZjR79uxccMEF+fDDD2s9/9lnn2XQoEGrHF+6dGmmT5+e6dOn5+GHH87vf//77L333utUy1VXXZU777xzpWMzZ87M//k//yfPPfdc7rvvvnTo0KHe/d5777256qqrVjleVlaWl19+OS+//HJGjx6dW2+9NZ07d661jzlz5uT000/P1KlTV+mjrKwsU6dOzZNPPplXX311lWvHjh2biy66KJ9//vkqfc6ZMycTJ05MWVlZ/uM//qPez7a27rvvvlx55ZVZtmxZreeb4x0NGjQom2yySRYuXJjRo0fXBN61GTVqVJKke/fu2WuvvdbqmQEAAAC+KUJuWEc777xzJk+enFtuuSW33HJLtthii4wdO3alNi1btqz12vPOOy9ffPFFLr744uyzzz7p0KFDpk2blu7du9e02WGHHXLggQdm++23T/fu3VNcXJzy8vJMnTo1f/rTn/LGG2/k7LPPzhNPPJFNN920Qc8wZsyYzJ49O8cee2yOPfbY9O7dO59++mnuuuuujBw5Mu+8805uueWWnH322fXuu127djnssMOy1157Zauttkr37t3Ttm3bzJ07N+PGjcvdd9+dN954I5dcckl+//vfr3L9ggULMmzYsJSWlqZly5Y57rjjcuSRR6ZPnz758ssvM2PGjEyYMCHPPPPMKtc+/fTTOeecc5IkPXr0yKmnnpo99tgjxcXFWbhwYd566608/fTTadWq6f4o/Mc//pH/9b/+V3bcccecfvrp6devX7744otMnz69Wd9R27Ztc/jhh+fee+/NmDFj8vOf/7zWcbpkyZL8+c9/TpIcddRRqx3LAAAAAM1FyA3rqGXLlunQoUNat26dJCkqKlrrGc/z58/PQw89VLM0SZIMHDiw5vPGG2+chx56aJXriouLs+WWW2bQoEE58cQTM3ny5Nx///0566yzGvQMs2fPzn/8x3/kpz/9ac2xzp0757LLLsvcuXPz7LPPZtSoUQ0KuY877rgcd9xxqxzv0qVL+vXrl0GDBuWoo47KX//617z//vvp06fPSu2uv/76lJaWpqioKL/73e9WmdnerVu3/Mu//EvOPPPMlY4vWrQoF110UZLkO9/5Tu6777507dq15nynTp3Su3fvHHzwwamqqsoXX3xR72dbG5988kkGDBiQO+64Y6XlRrbYYouaz831joYOHZp77703H3/8cSZMmJB99tlnlRqeeeaZLFiwIEkyZMiQ+r8AAAAAgCYm5KZW17+/PJfNTCpqX12hmbT/738vb3APHVsml3wnOadPUaNUtK6OPvrolQLu+mrVqlUOO+ywTJ48OS+++GKDQ+7NN988P/nJT1Zb47PPPpt58+blww8/zOabb97gemuz7bbbZrvttsubb76ZF198caUAt6KiIqNHj06SHHnkkbUu3VLt67OxH3vssZSXlydJLrvsspUC7tqubaqQO1kxY39N62nXpane0XbbbZf+/ftnypQpGT16dK0hd/VSJTvttFO22mqrBj8DAAAAQFMRclOr387+tgXcjaNi2YpnO6fPmtt+E9Z2A7/nn38+jz76aN566618/PHHWbRo0SptZs6c2eA6dt9999UuQ7HlllvWfJ43b16DQu7Kyso89NBDee655zJ9+vQsWLAgS5cuXaXd159h0qRJWbJkSZJk8ODB9brnSy+9lGTFjOnddtut3jU3ls6dO2fHHXdcY7vmeEfJii8xpkyZknHjxmXBggXp1KlTzbm5c+fmhRdeqGkHAAAA8G0k5KZWZ/fOt3Am97rr2HLFs31b9O5ddzFVVVU577zzVlnjuzafffZZg+uoay3vdu3a1XxevHhxvft+7733csopp+SDDz5YY9uvP8P7779f87m+M95nz56dJPnud79br+sa25p+xknzvaMkOfzww3Pttdfmiy++yNixY3PCCSfUnHv00Ufz5Zdfpn379jn44IPr3TcAAADAN0HITa3O6VP0rZntXK2ysjJJ1nq960Lw1QC5NrfeemtNwL3//vtn8ODB6du3bzp16lSz/MVjjz2WSy+9NMuWNfwbibXdTHD58votFVNVVZUzzzwzH3zwQdq3b5/hw4dnjz32SK9evdKhQ4e0aNEiSXLKKadk8uTJqzxDRUVFzef6/tyrr23u8bLRRhvVeb4531GSbLLJJhk0aFAef/zxjB49eqWQu3oZlAMPPDAdO3asd98AAAAA3wQhN3yLjRw5Mkly6KGH5re//W2tbZpyLel19corr+S9995Lktxwww3Za6+9am1X2/IrycqhbWVl5UpLaaxJ9bXVX47UR1HR2q3ZXlVVVe++v64531G1Y445Jo8//njefPPNTJ8+Pdtss00mT56cGTNmJLHhJAAAAPDt1qK5CwBqV15eno8++ihJcsghh6y23bvvvvtNlVRv77zzTpKkU6dOqw1vlyxZUhOmft0//dM/1XyeOnVqve5dvTnjtGnT6nVdkppZ8mtanuXjjz+ud99f15zvqNouu+xS876qN5qs/nefPn3yL//yLw3qFwAAAOCbIOSGRtKq1Yq/GLEuy4Z8VfVmgkny5Zdf1tpm0aJFGTduXKPcrylUP0Nd7+Tpp59e7Wz0AQMGpG3btklWrA9dH3vssUeSZM6cOXn55ZfrdW31GuXz58/PggULam3zj3/8I6WlpfXqtzbN+Y6qFRUV1Wws+fjjj6eioiJPPvlkkhWbWa7tzHYAAACA5iDkhkbSuXPnJCuC0cZYxqJLly5p3759kuTZZ5+ttc3VV1+d8vLydb5XU+nVq1eSFetGT5w4cZXz8+bNy69//evVXt+xY8cMHjw4yYoA95lnnllt26+/88MOO6zmZ3LJJZdk/vz5a33tDjvskGTFGuS1BcdVVVW56qqrVttffTTnO/qqwczz+1QAACAASURBVIMHp2XLlpk3b14uvvjiVFRUpEWLFjV9AwAAAHxbCbmhkfTv3z/Jipm5N9xwQ+bOnZulS5emqqqqQbO7W7VqlQMOOCDJiqUjrr766kyfPj1lZWWZPHlyzjjjjDz44IPZeuutG/U5GtNee+1Vs2b02WefncceeywfffRR5s6dm8ceeyzHHXdcysvL07Nnz9X2cfbZZ6dXr15Zvnx5zjrrrFx55ZV5/fXXU1ZWlk8//TSTJ0/OjTfemKOOOmql69q3b58rr7wySTJjxowMGTIk9913X2bNmpWFCxemtLQ0zzzzTM4999xV1jvfaqutstNOOyVJfvOb3+See+7J3LlzM3/+/LzwwgsZPnx4Xn755fTo0aOg39FX9ejRI3vvvXeS5IknnkiS7L777tl8883X+RkBAAAAmpKNJ6GR7LDDDtlpp53y2muv5ZZbbsktt9xSc26XXXbJ3XffXe8+f/GLX+TVV1/NnDlzcscdd+SOO+5Y6fyBBx6YvffeOxdeeOG6lt8kNtlkk1x66aU577zzMm/evPziF79Y6XybNm1y7bXX5v7778+cOXNq7aNTp065884789Of/jTvvvtu7r777lrf5cYbb7zKsQMOOCDXXXddLr744nz44Ye57LLLar3HsGHDVjl2xRVX5F//9V9TXl6eK664IldccUWtdc+dO7fOd7Amzf2Ovmro0KEr/a0BG04CAAAAhcBMbmhEt956a/7t3/4tffv2Tbt27da5v+7du+fhhx/OiSeemJ49e6Z169YpLi7OLrvskquvvjo33HBDWrT4dv82PuKII3LXXXdl7733ziabbJLWrVtniy22yFFHHZWHHnqozk01q/Xq1SujR4/OlVdemT333DNdu3ZN69at07Vr1/Tv3z/Dhw/PH//4x1qvPfLII/P000/nlFNOyXe/+9107Ngxbdu2Tc+ePTNw4MBcdNFFOfXUU1e5bptttsnDDz+cIUOGpEePHmndunU23XTTHHbYYWtd99pq7ndUbd999023bt2SrAjOq/8mAQAAAMC3WdHy5cuXN3cR1N+0adNSUVGRjh07pqSkZK2vmzp1apKkX79+TVVak6msrEySmqUdoDEZXys2ON1vv/3y4Ycf5vjjj88ll1zS3CWtVyorKzNz5sy0aNGiIP8M5ttt0qRJSVZsRguNzfiiKRlfNCXji6ZmjNGUNsTx1dC8MzGTG4D/9tJLL+XDDz9Mkhx99NHNXA0AAADA2hFyA5Akueuuu5Ks2ET1e9/7XjNXAwAAALB2bDwJsIFavnx5li1blsrKyjz44IN57rnnkiQjRoxo3sIAAAAA6kHIDbCBmjhxYoYNG7bSsd133z0HH3xwM1UEAAAAUH9CboANXIsWLbL55pvngAMOyJlnntnc5QAAAADUi5AbYAO16667Ztq0ac1dBgAAAMA6sfEkAAAAAAAFS8gNAAAAAEDBEnIDAAAAAFCwhNwAAAAAABQsITcAAAAAAAVLyA0AAAAAQMEScgMAAAAAULCE3AAAAAAAFCwhNwAAAAAABUvIDQAAAABAwRJyAwAAAABQsITcAAAAAAAULCE3rCdOPPHElJSU5Pzzz1/l3KhRo1JSUpKSkpIG93/jjTempKQk++2337qUuc7qek4AAAAANjxCbqDZlZaW1oTwL7/8cnOXAwAAAEABEXIDAAAAAFCwWjV3AUDTGzJkSIYMGdLcZTSKu+++u7lLAAAAAOBbxExuAAAAAAAKlpncsI4WLFiQPffcM0uWLMnZZ5+dU089tc72+++/f2bPnp3DDjss119/fc3xioqKTJgwIePHj88bb7yRjz76KFVVVenSpUt22GGHHHPMMdlnn30aVOOoUaNywQUXJEmmTZtWa5uqqqrce++9efTRRzNjxoy0adMmW2+9dY499tgMHjx4jfeYNWtWxo8fn+effz7vvvtuysvL06ZNm2yxxRYZOHBgfvzjH6dXr16rXLfffvtlzpw5Nb8eNmzYKm3GjRtXc+2JJ56YiRMnZvDgwbnmmmtqraWioiL33ntvnnnmmcycOTOLFy9O165dM2DAgBx//PEZMGBArde9+uqr+clPflJzzy5duuSPf/xj/vKXv6S0tDQtW7bMdtttlxNOOCEHHXTQGt/J6pSVleU///M/M378+EyZMiXz5s1LknTr1i077bRTTjjhhPzzP//zGvv55JNPcs8992TChAmZPXt2Fi1alO7du6dnz54ZOHBgjj766PTo0aPWa1955ZU88sgjmTRpUubNm5cWLVpks802S9++fXPAAQfkoIMOSuvWrWvaV/+czjjjjJx55pm19llaWpof/OAHSZK77roru+6660rnqzc+vfrqq3PkkUdm5MiRefzxxzNjxoyUl5fnggsuyPDhw5vtHZ188sl54YUXsuOOO+bBBx+ss9/LL7889957b7p3757nnnsurVr5zykAAAA0F/9XDuuoU6dO2XffffPUU0/l8ccfrzPkfu211zJ79uwkyRFHHLHSufPOOy/PPPPMKtfMnTs3Tz/9dJ5++ukcc8wxufLKKxv3AZIsWrQoI0aMyKuvvlpz7PPPP8/kyZMzefLkvPTSS+ndu/dqr//ss88yaNCgVY4vXbo006dPz/Tp0/Pwww/n97//ffbee+9Gr/+rpk2blhEjRmTu3LkrHf/www8zduzYjB07NieffHJ++ctfpqioaLX9fPLJJ/nJT36S9957b6XjEydOzMSJE3PWWWfltNNOa1CNJ510UqZOnbrK8Tlz5mTOnDkZO3Zsfvazn+X0009fbR9jx47NRRddlM8//7zWPiZOnJiysrJceOGFK51fvHhxLrzwwowdO3aVPt9777289957+etf/5q+ffumX79+DXq+NVmyZEmGDx+eiRMnrrZNc7yjoUOH5oUXXsjrr7+e9957L1tvvfVq669+f0ceeaSAGwAAAJqZ/zOHRnDEEUfkqaeeyvTp0/P2229nu+22q7XdY489liTp2rVr9thjj5XOde3aNcOGDcuuu+6anj17pnv37qmqqkppaWnGjBmTRx55JA899FD69euXE044oVHr/9WvflUTcB9xxBEZPnx4tthii8yZMyd/+tOfMmbMmFpnYX/VDjvskAMPPDDbb799unfvnuLi4pSXl2fq1Kn505/+lDfeeCNnn312nnjiiWy66aY11/35z3/OnDlzcuihhyZJ/vCHP2TnnXdeqe/27duv1XOUlZXl3/7t3zJv3ry0a9cup59+eg466KB07Ngx06ZNyw033JDJkyfnj3/8Y7p06ZIRI0astq9zzz03FRUV+dWvfpW99947HTt2zDvvvJOrrroq7777bm666aYceOCBqw1C69KzZ8/stdde2XnnnbPZZpule/fu+fzzzzNr1qw8+OCDefLJJ3PDDTfke9/7Xq2z959++umcc845SZIePXrk1FNPzR577JHi4uIsXLgwb731Vp5++ulaw9dzzjmn5suUPffcM8OGDUu/fv3SunXrfPTRR3n55ZczZsyYej9Tfdx8882ZN29eTj755Bx11FHp0aNHPvzww5XaNMc72n///dO5c+eUl5dn9OjROffcc2ut/5lnnsmCBQuSZL1Z6x4AAAAKmZAbGsE+++xTE4499thjtYbcVVVVefLJJ5Mkhx566CoB5OWXX15r35tttll23nnnbLfddrn00ktz22235fjjj69zFnJ9vPnmm3n88ceTJMcee2yuuOKKmnPFxcW5/vrr06ZNm4waNWq1fWy88cZ56KGHVjleXFycLbfcMoMGDcqJJ56YyZMn5/77789ZZ51V02ajjTZKu3btan7drl27dOjQoUHPUh2eFhUV5aabbspee+1Vc27gwIEZMGBAhg8fnkmTJuWGG27IkCFD0rVr11r7+vTTT/Pwww+vFGIPHDgwt912WwYNGpTFixfXGYTW5X//7/9d6/GePXtm9913T69evXLrrbfmD3/4wyoB7qJFi3LRRRclSb7zne/kvvvuW+kZOnXqlN69e+fggw9OVVXVStf++c9/rgm4hw0btsos7+Li4vTr1y/Dhw9f5drGNHfu3FxyySU5/vjja4517tx5pTbN8Y7atGmTww8/PHfffXfGjBmTn//852nZsuUqNVT/Xthpp50a9CUHAAAA0LhsPAmNoE2bNjnwwAOTrAgSv/zyy1XaPP/88ykrK0uy6lIla+Ooo45KknzwwQeZMWPGOlS7stGjRydJ2rZtu9rA9pe//GXatGnT4Hu0atUqhx12WJLkxRdfbHA/dVm2bFnNs+y///4rBdzV2rRpUxN+LlmypGZmfW1OPPHEWgPMHj16ZPfdd0+y4guCplD9s37ttddWWWrjscceS3l5eZLksssuW21In2SVL1LuuuuuJEmfPn1y3nnn1VlDUy7BsfXWW68UcDdEU72jY445Jkny8ccfZ8KECau0nzt3bs0YNosbAAAAvh3M5KZ25dcnZZcmyyuau5IaDZvb+zVFHZPiS5PO5zRGbys58sgj88ADD+Tjjz/O3//+95ogtFr1bOmtttoq22+/fa19zJkzJyNHjszf//73zJo1KxUVFVm2bNkq7WbOnJmtttqqUeqeNGlSkmSXXXZJp06dam1TXFycXXbZpdbQ76uef/75PProo3nrrbfy8ccfZ9GiRau0mTlz5jrXXJt33303n332WZLUuSnkdtttlz59+uT999/Pq6++mpNOOqnWdnWtHb7lllsmWbFud0NNnz49DzzwQF599dWUlpamsrJylS9Hli1blvfff79mw8Ykeemll5IkW2yxRXbbbbe1vl9FRUVNKH/44Yc36zrSa7uB6jf9jpIVm2Nuv/32efPNNzN69OhVah0zZkyWLVuWjTbaKIcccki9+gYAAACahpCb2i24/lsVcDea5RUrnq0JQu4BAwakV69eKS0tzWOPPbZSyF1RUZHx48cnWREw1ubJJ5/MBRdcsMqs1NpUh7mNYc6cOUmyxtB8q622Wm3IXVVVlfPOO6/WzQy/rjFr/6rq50iSvn371tm2b9++ef/99/PBBx+sts1X1w3/uo022ihJ1upnVZs777wz11133VotCfL191W9cel3v/vdet1zzpw5NV+YNNWGkmtrTeu7J83zjqoNHTo0b775ZsaNG5cFCxas9OVP9VIlgwYNSseOHRvUPwAAANC4hNzUrtM537qZ3I2iqOOKZ2siRxxxRG6++eY89dRTufTSS2vWmn7mmWfy+eefp6ioqNalSmbPnp1f/vKXWbJkSXr37p2TTjop/+N//I/06NEj7dq1S1FRUZYvX54BAwYkSa2zuxuqerb1mjZ3rOv8rbfeWhNw77///hk8eHD69u2bTp061Sxz8thjj+XSSy9t1Nq/qrKycq1qTVKz5vdXr/m6Fi2aZjWnSZMm5aqrrkqyIoQdNmxYvve976V79+5p06ZNioqK8sEHH9Qs7/L191VRseL3ZH3XLa++riHXNrbqLwlWp7neUbXDDjss11xzTT7//POMHTu2ZqPX1157rWapoKOPPrpBfQMAAACNT8hN7Tqf0ySznddFdSDZ3AFdXapD7srKyowbNy6HHnpoktSs/fzP//zPtc5ifeSRR7JkyZJsvPHGeeCBB2pdQ3jhwoVNUnP79u3z2Wef1bq0yFfVdX7kyJFJVmyo+dvf/rbWNl988UXDi1wLXx0Xa/sszTGWqt9V796988ADD6y06Wa1umYvr01AX9d1Dbl2bTXWFxjN9Y6qdezYMQcddFBGjx6dUaNG1YTc1bO4e/XqlV122aVBfQMAAACNz8aT0Ii23HLL7LDDDkn+/xrc8+bNy9///vckq99w8p133kmS7LrrrqvdJO/dd99t7HKTJD179kyS/OMf/6iz3erOl5eX56OPPkqSOtcobqr6q331y4P/+q//qrNt9fnqZ/8mVf+s99tvv1rD2ySZNm3aaq/v06fPGtvUplevXmnZsmWSZOrUqfW6NlmxMWmSLF68eLVtPv7443r3W5vmekdfNXTo0CTJW2+9lenTp2fx4sV54oknkiSDBw9OUVFRg/sGAAAAGpeQGxpZdZA9YcKEzJ8/P3/+85+zbNmytG7dOgcffHCt1yxdujRJ3TNhq2eDN7bqJVAmTpy42tniZWVlmThxYq3nlixZUvP565sCVlu0aFHGjRu32hpat269xj7WZJtttsnGG2+cJHnqqadW2+6dd97JrFmzkvz/Z/8mVb+vup6z+guS2uyxxx5JVqyx/fLLL6/1fTt27FjzBczYsWPXaq3rr+revXuS1CzXUZvnn3++Xn2uTnO9o6/aeeed853vfCfJihncf/3rX1NRUZEWLVpkyJAhDeoTAAAAaBpCbmhkhx56aFq1apWlS5fmySefrAmn991335U2sPuq6hnFr732WsrLy1c5/8orr+Thhx9uknoHDx6cZMVyIr/+9a9rbXPdddetFGZ/VZcuXWrWwH722WdrbXP11VfX+lzVNtlkk5qZsXPnzl3r2r+qZcuWNc/y1FNP5cUXX1ylzdKlS3PllVcmWTEz+cgjj2zQvdZF9YzzCRMm1PpOH3vssdVu8JmsWC+6c+fOSZJLLrkk8+fPX23brwfZw4YNS5LMmjUrv/nNb+qs8+tfuOy4445JkhdffLHWGdvvvfde7r777jr7XFvN+Y6+qnrd7ccff7zm999uu+2WLbbYYs0PAQAAAHxjhNzQyLp06ZI999wzSXL77bdnypQpSVa/VEmSmhne5eXlOeWUU/LSSy/l008/zaxZs/KHP/whP/nJT2pmlTa27bffPocffniS5MEHH8wvf/nLvP322ykvL8+UKVNyzjnnZNSoUbWuJZ4krVq1ygEHHJBkxYzXq6++OtOnT09ZWVkmT56cM844Iw8++GC23nrr1daw0UYb1Zy/55578s477+Tzzz9PVVVVvWYcn3baaenevXuWL1+e008/Pbfeemtmz56dsrKyvPTSSxk+fHheeeWVJMmZZ56ZLl26rHXfjaX6Zz1jxoz89Kc/zWuvvZb58+fnv/7rv/LrX/86F1xwQZ3vqn379jVB/YwZMzJkyJDcd999mTVrVhYuXJjS0tI888wzOffcc1dZH/2QQw7J/vvvnyT505/+lBEjRuRvf/tb5s2bl/Ly8rzzzju55557MmTIkFWWlznqqKPSsmXLfP755zVjtLy8PKWlpbn33ntzwgknpFu3bgX/jr5q8ODBadWqVebNm1fzNxlsOAkAAADfPjaehCZw5JFH5rnnnsucOXOSrJipvO+++662/cCBA3PcccflgQceyJtvvpnhw4evdH7TTTfNjTfeWOea1+vi8ssvz4cffphXX301Y8aMyZgxY1Y6f/jhh+ef/umfctNNN9V6/S9+8Yu8+uqrmTNnTu64447ccccdK50/8MADs/fee+fCCy9cbQ3Dhg3Lr371q7z55purzLAeN27cakP2ryouLs7tt9+eESNGZO7cufnNb35T64zlk08+Oaeccsoa+2sKgwcPzlNPPZX//M//zIQJE1aZkbzVVlvlqquuynHHHbfaPg444IBcd911ufjii/Phhx/msssuq7Vd9cztr7r++utz/vnn58knn8zf/va3/O1vf1ururfeeuucddZZ+e1vf5tp06atMka33nrrNda9tpr7HVXr3r179tlnn5qldjbZZJOaL3QAAACAbw8hNzSB/fbbLx07dkxFRUWS5KCDDkqbNm3qvObyyy/P9ttvnwceeCDTp09PixYtstlmm+X73/9+TjnllCadddy+ffvceeedueeeezJmzJjMmDEjrVq1St++fTN06NAMHTo0N95442qv7969ex5++OHcfPPNGT9+fD7++ON07Ngx22yzTQYPHpwhQ4Zk1KhRddZw3HHHpUOHDnnggQcybdq0fPbZZw1an7ukpCRPPPFE7rnnnjzzzDOZOXNmFi9enG7dumXAgAE5/vjjm2Ut7motW7bMzTffnDvvvDOPPvpoZs6cmdatW6d3794ZNGhQTjrppDqX16h25JFHZrfddstdd92VCRMmpLS0NEuXLk23bt3Sp0+f/OAHP6h1Dfh27drld7/7XY455pg88sgjee211/LJJ5+kXbt22XTTTbPddtvlkEMOyTbbbLPKtaeeemq22mqr3HXXXXn77bdTVVWVnj175pBDDsnJJ5+8VnUXwjv6qqFDh9aE3IceemjNBpwAAADAt0fR8uXLlzd3EdTftGnTUlFRkY4dO6akpGStr5s6dWqSpF+/fk1VWpOprKxMknTo0KGZK2F9ZHxRmxdffDEnnXRSkuShhx6q2byzISorKzNz5sy0aNGiIP8M5ttt0qRJSZpnQ13Wf8YXTcn4oikZXzQ1Y4ymtCGOr4bmnYk1uQFgtR555JEkybbbbrtOATcAAADQdITcAFCLuXPn5q9//WuSNMpa4wAAAEDTEHIDwH/78ssvU1VVlRkzZuQXv/hFli5dmi5dumTIkCHNXRoAAACwGjaeBID/9j//5//M6NGjVzp2/vnnp3379s1UEQAAALAmQm4A+Jp27dpl6623zimnnJJDDjmkucsBAAAA6iDkBoD/ds011+Saa65p7jIAAACAerAmNwAAAAAABUvIDQAAAABAwRJyAwAAAABQsITcAPANWL58eXOXAAAAAOslIfcGpqioKEny5ZdfNnMlABuW5cuXZ/ny5TV/DgMAAACNQ8i9gWndunWSZPHixc1cCcCGZfHixVm+fHnatGnT3KUAAADAekXIvYHZeOONkyRlZWX+6jzAN2TZsmVZsGBBli1blk6dOjV3OQAAALBeadXcBfDN2mSTTTJ//vwsXLgwSVJcXJx27dqlqKjIX6EHaCTVS5MsXbo0lZWVKSsrS2VlpZAbAAAAmoCQewPTrl279OrVK6WlpVm4cGFN2F0IqtcRb9HCX0Cg8RlfNLWqqqokSatW/tMLAAAAjUmaswHq2LFjttxyy3Tt2jVt2rQpmBncixcvtpY4Tcb4orEVFRWlVatW2WSTTbL55ps3dzkAAACw3jKdbAPVtm3bbLrpptl0002bu5S1NmnSpCRJv379mrkS1kfGFwAAAEBhMpMbAAAAAICCJeQGAAAAAKBgrffLlTz77LMZOXJkpkyZkgULFqRbt24ZOHBgfvzjH6ekpGSd+582bVruvPPOvPTSS/nkk0/SqVOn9O/fPz/84Q/z/e9/vxGeAAAAAACA1VmvQ+5LLrkkI0eOXOnYBx98kEceeSSPP/54rrjiihx11FEN7n/06NG5+OKLs3Tp0ppj8+bNy3PPPZfnnnsuP/rRj3LppZc2uH8AAAAAAOq23i5Xcuutt9YE3Pvvv39GjRqVl156Kbfffnu23XbbLFmyJBdeeGHNZnP1NWnSpFx00UVZunRptt1229x+++156aWXMmrUqOy///5Jkvvvvz+33nproz0TAAAAAAArWy9D7vnz5+fmm29Okuy555656aab0r9//3Tp0iV77rln7rrrrnTr1i1VVVW59tprG3SPa665JlVVVenWrVvuuuuu7LnnnunSpUv69++fm266KXvssUeS5Oabb878+fMb7dkAAAAAAPj/1suQe/To0Vm0aFGS5Oyzz05RUdFK54uLi3PKKackSV5//fVMmTKlXv2/+eabeeONN5Ikp5xySoqLi1c6X1RUlHPOOSdJsmjRoowZM6ZBzwEAAAAAQN3Wy5D72WefTZL06dMn/fv3r7XNwQcfXPN5/PjxDer/6/18Vf/+/dOnT58G9Q8AAAAAwNpZL0Pu6pnZO+6442rbbLbZZunRo8dK7evbf48ePbLZZputtl31/evbPwAAAAAAa2e9C7nnzp1bs1RJ796962zbq1evJMmMGTPqdY/q9mvbf2VlZebOnVuvewAAAAAAsGbrXchdVlZW87lr1651tq0+X15e3qB7rG3/DbkHAAAAAABr1qq5C2hs1bO4k6Rt27Z1tq0+X1lZWa97fP7550mSNm3a1NmuXbt2tdbVGL744oskSUVFRSZNmtSofX/bbWjPyzfL+KKpGWM0JeOLpmR80ZSML5qS8UVTM8ZoShvi+KrOPetjvZvJvaFYtmxZc5cAAAAAANCoGpJ7rnczudu3b1/zeU2pf/X5Dh061OseG220UZYuXZolS5bU2W7x4sW11tUY2rZtmy+++CItW7Zc44x1AAAAAIBvsy+++CLLli1rUNa53oXcxcXFNZ8//fTTOttWn+/cuXO977Fw4cK17r8h91iT7bbbrlH7AwAAAAAoROvdciWbbrppzazp2bNn19m2tLQ0SbLlllvW6x7V7de2/w4dOqRHjx71ugcAAAAAAGu23oXcRUVF6d+/f5LkjTfeWG27jz76KHPnzk2SmvZrq7r93Llza/qozeuvv96g/gEAAAAAWDvrXcidJN///veTJLNmzcrUqVNrbfOXv/yl5vN+++3XoP6T5Mknn6y1zdtvv53333+/Qf0DAAAAALB21suQe/DgwTVLllx//fVZvnz5SufLy8tz2223JUl23HHHes+03n777bPDDjskSW677baUl5evdH758uW5/vrrk6zYcPLII49s0HMAAAAAAFC39TLk7tKlS0477bQkyfPPP5+f/exnmTp1aubPn58XXnghJ554YubNm5dWrVrlvPPOW+X6UaNGpaSkJCUlJRk1alSt9zj//PPTqlWrzJs3LyeeeGJeeOGFzJ8/P1OnTs3PfvazTJgwIUly2mmnpUuXLk33sAAAAAAAG7BWzV1AUxkxYkRKS0szcuTIPPXUU3nqqadWOt+6detceeWVGTBgQIP6HzBgQK688spcfPHFeffdd3PyySev0uaHP/xhRowY0aD+AQAAAABYs/U25E6Syy67LPvuu2/uv//+TJkyJQsWLEj37t2z2267Zfjw4SkpKVmn/gcPHpztttsud9xxR/7+979n3rx56dSpU/r3758f/ehHw/Z07AAAGH5JREFUK63dDQAAAABA4yta/vUFqwEAAAAAoECsl2tyAwAAAACwYRByAwAAAABQsITcAAAAAAAULCE3AAAAAAAFS8gNAAAAAEDBEnID/6+9ew+K6j7/OP6BFbyAghoVAxqNzRJAROolrfqLCjSpVasyYiVmJtHBGDNeWrVaxzpeMLVNY2ckjnFGa5MqVSctRE1Nk0BMiLcao/HKwCTxAkRRuYmsUS77+8NhCwILu7ALy75fM8wsfM95zjnOk4/Jk8M5AAAAAAAAgMtiyA0AAAAAAAAAcFkMuQEAAAAAAAAALqtDa58A3M/hw4e1d+9eXbx4USUlJXrsscf005/+VC+99JKCg4ObXT8rK0vvvvuujh8/rtu3b8vPz09hYWGaOXOmxo8f3wJXgLbMUf0VFRWlvLy8RrfbvHmzfv7zn9t9HLRNZrNZ3333nc6dO2f5ysrKUnl5uSQpPT1dQUFBLXIsMsz9OKO/yDD3df/+fX3xxRc6cuSIzp07p5ycHJlMJvn6+uqpp55SVFSUZsyYIV9f32Yfi/xyP87oL/LLPV2/fl2ffvqpLly4oKysLBUUFKiwsFAGg0F9+vRRZGSkpk+fruHDh7fI8cgv9+OsHiPDUFNhYaEmTJig4uJiSdK0adP0xz/+sVk1ya//8TCbzebWPgm4jzVr1mjv3r31rnl7eysxMVFTp061u35qaqpWr15tGQw8Kj4+XmvXrrW7Pto2R/YX/3Li3nJzcxUdHd3geksNuckw9+SM/iLD3NePf/xjlZWVWd0mICBAb731loYMGWL3ccgv9+SM/iK/3NPu3buVmJjY6HZxcXFat26dDAaD3cciv9yTs3qMDENNy5Yt08GDBy3fN3fITX7Vxp3ccJrt27dbBpAxMTF67bXX1LdvX126dEl/+tOflJ2drVWrVqlfv34aNmyYzfW/+uor/f73v1dFRYWMRqNWrFih0NBQXb9+XVu3blVaWpr27NmjwMBAzZ07t6UvD63M0f1Vbd68eZo3b16D6506dbK7NlxDQECAwsPDVVRUpFOnTrVYXTIMkuP6qxoZ5n7Kysrk5eWlmJgYxcTEKDw8XP7+/rp586YOHDignTt36saNG0pISNDBgwfVp08fm49BfrkvZ/RXNfLLvXTs2FFjx47VM888o9DQUPXu3Vs9evRQUVGRLl26pB07digzM1Pvvfee/P39tWzZMruOQ365L2f1WDUyDEeOHNHBgwfVr18/5eTkNLse+VUPM+AEBQUF5qFDh5qNRqN5zpw55qqqqlrrhYWF5lGjRpmNRqM5Li7OrmNMnz7dbDQazaNGjTIXFhbWWquqqjLPnj3bbDQazUOHDjUXFBTYfS1oe5zRX+PHjzcbjUZzUlJSS5wyXExpaan5k08+Md+8edPys6SkJLPRaDQbjUZzTk5Os49BhrkvZ/QXGea+1q5dW6u3HnXgwAFLr61Zs8auY5Bf7ssZ/UV+oT737983T5061Ww0Gs0RERFmk8lkVx3yCw1pqR4jw2A2m80mk8kcHR1tNhqN5s8++8zyd+OKFSvsrkl+1cWLJ+EUqampMplMkqQlS5bIw8Oj1nr37t2VkJAgSTp79qwuXrxoU/3z58/r3LlzkqSEhAR179691rqHh4eWLl0qSTKZTNq/f79d14G2ydH9Bfj6+iomJka9evVySH0yzL05ur/g3tasWWO1tyZPniyj0ShJysjIsLk++eXeHN1fQEO8vb31y1/+UpJ07949ffvttzbXIL9gTUv0GFDtrbfeUk5Ojp5//nmNHTu22fXIr/ox5IZTHD58WJLUv39/hYWF1bvNhAkTLJ8//fRTu+o/WqemsLAw9e/f3676aNsc3V+Ao5FhAFrTU089JUm6efOmzfuSX2hMc/oLsKZDh/89fdXb29vm/ckvNKa5PQZIUmZmpt599135+Pho1apVLVKT/KofQ244RfWdsxEREQ1uExAQYHlOn6132lZv36dPHwUEBDS4XfXxuZO3fXF0f9WnvLxcZt7bixZChsHZyDDUdPv2bUlS165dbd6X/EJjmtNf9SG/IElVVVX66KOPJEndunXTgAEDbK5BfsGaluix+pBh7qWqqkqrV69WRUWFFi9e3Kx3U9REftWPF0/C4fLz8y2PkujXr5/VbYOCgpSfn6/Lly/bdIzq7ZtSX3r4kpz8/PwWCxi0Hmf0V02pqanas2ePCgoKZDAYFBAQoJEjR2rWrFkKDw+3uy7cGxkGZyHD8Kjbt2/r9OnTkqTIyEib9ye/YE1z+6sm8gtms1kFBQXKysrSjh079OWXX0qSFi1aZNddtuQXHtXSPVYTGeae/v73v+v8+fMKCwvTiy++2GJ1ya/6MeSGwxUVFVk+9+zZ0+q21evFxcV2HaOp9auP0d7/AXcHzuivmvLy8iyfKysrlZeXp9TUVKWmpmrOnDlavnx5nWeCA40hw+AsZBgetWnTJpWXl0uS4uPjbd6f/II1ze2vmsgv97Vo0SLLHbU19ezZU4sWLdLMmTPtqkt+oZqjeqwmMsz9fP/999q8ebM8PT21du1aGQyGFqtNftWPITccrvouW0nq2LGj1W2r18vKymw6xr179yQ1/pysTp061XtecF3O6C9JMhqNevHFFzV8+HD17dtXfn5+unXrljIyMvT2228rPz9fO3fuVMeOHfXrX//a5vpwb2QYHI0MQ30OHDiglJQUSVJUVJT+7//+z+Ya5Bca0hL9JZFfqJ+3t7fi4+M1fvx4u2uQX7CmJXpMIsPc2fr162UymfTCCy9oyJAhLVqb/Kofz+QGgCbYtm2b5syZoyFDhqhXr17y9vZWYGCg4uPjlZqaqieeeEKStH37dl27dq2VzxYAaiPD8Khz585p9erVkqS+ffvq9ddfb+UzQnvSkv1FfuHPf/6zTp8+ra+++krp6el644031L9/f23ZskVTpkyxPBIHsJcje4wMc0+HDh3S4cOH1atXLy1ZsqS1T8dtMOSGw3Xp0sXy+f79+1a3rV738fGx6RidO3eWJD148MDqdj/88EO95wXX5Yz+akzPnj0tb0muqKjQhx9+2KL10f6RYWhNZJj7+e677/TKK6/ohx9+kL+/v3bs2KEePXrYVYv8wqNasr8aQ365h44dO8rHx0e+vr4KCgrSlClT9K9//UsREREqKirSa6+9pjt37thcl/xCNUf1WGPIsPbpzp07+sMf/iBJ+t3vftdiL16uifyqH0NuOFz37t0tnwsKCqxuW73u7+9v1zGaWt+eY6BtckZ/NcXo0aMtj0O5dOlSi9dH+0aGobWRYe7j+++/15w5c1RUVCQfHx9t375dP/rRj+yuR36hppbur6Ygv9xTp06dtHTpUkkPn0176NAhm2uQX7CmJXqsKciw9mfLli26deuWRo8erUmTJjnkGORX/Rhyw+F69+5t+T9GOTk5VrfNzc2VJA0cONCmY1Rv39T6Pj4+7f6B++7CGf3VFB06dJCfn58kqbS0tMXro30jw9DayDD3cPv2bc2ePVvXr19Xp06dtG3btmY/I5L8QjVH9FdTkF/uKyIiwvI5KyvL5v3JLzSmuT3WFGRY+1OdGUePHlVwcHC9X9VSU1MtP0tLS2vyMciv+jHkhsN5eHgoLCxM0sPn8zXkxo0bys/PlyTL9k1VvX1+fr6lRn3Onj1rV320Xc7or6YoLy9XcXGxJDnk15HQvpFhaG1kWPtXUlKi2bNn68qVK/Ly8lJSUpJGjhzZ7LrkFyTH9VdTkF/uq6KiwvLZw8PD5v3JLzSmuT3WFGQY7EF+1Y8hN5yi+o3EV69eVWZmZr3b/Oc//7F8joqKsqu+pAafY3Xp0iXLixxsrY+2zdH91RQZGRmW52G5y18gaDlkGFobGda+lZWVKSEhQdnZ2fL09NQbb7yhsWPHtkht8guO7K+mIL/c16lTpyyf+/fvb/P+5Bca09weawoyrP1ZuXKl3n//fatf1caPH2/52TPPPNPkY5Bf9WPIDaeYNm2a5ZESmzZtktlsrrVeXFysHTt2SHr4K0G2hnt4eLjl1yF37Nhh+T+h1cxmszZt2iTp4cP2p0yZYtd1oG1ydH/duHHD6np+fr7lxRJeXl6aMGGCTfUBMgyORIa5twcPHmj+/PmW33Zav369fvGLX7RYffLLvTm6v8gv9/Xtt99aXS8pKdGbb74pSTIYDHYNcMgv9+aMHiPD3FO/fv0UEhJi9auav7+/5We23MlPftXPsHbt2rWtfRJo/zp37iyDwaBjx47p2rVrys7O1sCBA2UwGHT69GktXbpUOTk56tChgzZt2qTHH3+81v4pKSmaOnWqtmzZosDAwFqhUG3QoEHav3+/7t69q4yMDD3xxBPy9fXVlStXtH79eh0+fFiStHjxYo0ZM8Yp1w3ncHR/rVixQrt27ZLJZJKHh4cMBoMqKiqUk5Oj/fv3a/ny5ZZfEZo/f76ee+45p107nOebb77RtWvXdOPGDd24cUMnT560vBxm5MiRKi0ttax5e3tb3ngtkWFonCP7iwxzX5WVlVq8eLG++OILSdKiRYsUFxen8vLyBr+8vLxq/Uo2+YWGOKO/yC/3NWbMGF26dEnl5eUyGAzy8PDQ/fv3de3aNf373//WihUrdPXqVUlSQkJCvcNB8gvWOKPHyDA0ZMuWLZKkkJAQxcTE1Fknv+zTobVPAO5j7ty5ys3N1d69e/Xxxx/r448/rrXu5eWlDRs2aNiwYXbVHzZsmDZs2KDVq1crOztbc+bMqbPNzJkzNXfuXLvqo21zZH+ZzWadOXNGZ86caXAbg8GgefPmadGiRTbXh2tYt26dTp48We/aggULan2/ceNGxcbG2lSfDHNvjuwvMsx9Xb9+Xenp6Zbvk5KSlJSUZHWf9PR0BQUF2XQc8ss9OaO/yC/3VVlZqbS0NKsvYjMYDEpISNBvfvMbu49DfrkvZ/QYGQZHIr/qYsgNp1q3bp3GjRunPXv26OLFiyopKVGvXr30k5/8RC+//HKtt8zaY9q0aQoNDdU777yjEydO6NatW/Lz81NYWJji4+NrPbcI7Y+j+mvevHkKCQnR119/rWvXrqm4uFgmk0k+Pj7q37+/RowYobi4OD355JMtfEVwN2QYHIEMgzOQX3AE8st9JScn68SJEzp16pTy8vJUUFCgBw8eyNfXVwMGDNCIESMUGxurgQMHNvtY5Jd7ckaPkWFwNPKrNg/zow+vBQAAAAAAAADARfDiSQAAAAAAAACAy2LIDQAAAAAAAABwWQy5AQAAAAAAAAAuiyE3AAAAAAAAAMBlMeQGAAAAAAAAALgshtwAAAAAAAAAAJfFkBsAAAAAAAAA4LIYcgMAAAAAAAAAXBZDbgAAAAAAAACAy2LIDQAAAAAAAABwWQy5AQAAAAAAAAAuiyE3AAAAAAAAAMBlMeQGAAAAAAAAALgshtwAAAAA2pSUlBQFBwcrODhY//3vf1v7dAAAANDGdWjtEwAAAADcWW5urqKjo23eLz09XUFBQQ44IwAAAMC1cCc3AAAAAAAAAMBlcSc3AAAA0EYMHjxYGzdubNK2ffr0cfDZAAAAAK6BITcAAADQRnTp0kVGo7G1TwMAAABwKTyuBAAAAAAAAADgsriTGwAAAHBxNV9euWDBAi1cuFAnTpxQcnKyzp49q6KiIvn7+2vEiBF66aWXFBER0WjNgoIC7d69W59//rlyc3NlMpnk7++vwYMHa9KkSZo4caI8PDwarVNYWKh9+/bp6NGjunz5skpKSuTl5aXAwEBFREQoJiZGzz77rAwGg9U6aWlp2rt3rzIzM1VSUqLevXtr1KhRmjdvnvr169e0PygAAAC0Sx5ms9nc2icBAAAAuKuaA+qRI0dq165dzaqxYMECGQwGJSUlqb5/1ff09NSSJUs0d+7cBuulp6frt7/9rcrKyhrcJjIyUlu3blWPHj0a3CYlJUWJiYkymUxWz//9999XSEhIrf1WrlwpSXrnnXd04MABpaSk1Ltv165dtXPnTg0ZMsTqMQAAANB+cSc3AAAA0I58/vnnOn/+vIKCgpSQkKCwsDA9ePBAx44d09/+9jeZTCa9+eab6t27t6ZMmVJn/5MnT2rhwoWqrKyUwWDQjBkz9Nxzz6lbt266fPmydu3apbNnz+rMmTOaPXu23nvvPXl7e9eps3v3biUmJkqSvLy8FBsbq2effVZ9+/ZVeXm5Ll++rGPHjiktLc3q9SQlJen06dMaN26cYmNjFRQUpOLiYqWkpOiDDz5QaWmpli1bpkOHDqlDB/7zBgAAwB1xJzcAAADQimrehT148GBt3Lix0X18fX31+OOP11tDkoKDg5WcnKyuXbvW2i8zM1MvvPCC5dEj6enp8vX1taxXVlbqZz/7mfLy8uTp6am3335b48aNq1WjqqpKS5Ys0Ycffijpf49Hqembb77R1KlTVV5erh49euivf/2rQkND672WO3fuyNPTs9Z51LyTu6FjSNLKlSstd3hv3bq11p8BAAAA3Ae3OgAAAABtxIULFzR58uRGt4uOjtbWrVsbXN+wYUOdAbckhYSE6NVXX9Vf/vIXFRcX6+DBg4qPj7esp6enKy8vT5I0Y8aMOgNu6eHjThITE3XixAkVFRUpOTlZr776qry8vCzbbN++XeXl5ZKkxMTEBgfcktStWzer1xoaGqoFCxbUu5aQkGAZcn/55ZcMuQEAANyUZ2ufAAAAAICWYzQarT6fevr06ZYXRh49erTW2pEjRyyfZ86c2WCNrl27atKkSZKkoqIiZWZmWtbMZrM+++wzSdKAAQMUExNj8zXUNHny5AZfcDlo0CB16dJFkpSTk9Os4wAAAMB1cSc3AAAA0EbY++LJmsLDw62u9+zZU4GBgcrNzVVWVlattezsbElSly5dFBwcbLVOZGSk5VyzsrIsg/Xc3FwVFxdLeng9zfXkk09aXffz85PJZNLdu3ebfSwAAAC4Ju7kBgAAANqRxx57rMnbVA+jq1V/3717d3l6Wv9PhZrHKSoqsnwuLCy0fO7du3fjJ9yIzp07W12vPs+qqqpmHwsAAACuiSE3AAAAAAAAAMBlMeQGAAAA2pHbt283eRt/f/9aP6/+vqioqNE7o2sep3v37pbPPXr0sHy+efNm4ycMAAAANBNDbgAAAKAdOX/+vNX1goIC5eXlSVKd525Xf28ymSzP527ImTNn6uwnSUFBQZZh+cmTJ5t+4gAAAICdGHIDAAAA7Uh2drbOnTvX4Po///lPmc1mSdLo0aNrrY0ZM8byed++fQ3WuHv3rj744ANJD+/cDg0Ntax5eHgoKipKknTlyhWlpaXZfhEAAACADRhyAwAAAO3M6tWrVVpaWufnmZmZ2rZtmyTJz89PkydPrrUeFRWloKAgSQ+H3BkZGXVqVFVVac2aNZaXTc6aNUsdOnSotU1CQoK8vLws55KZmdnguZaWluru3bs2XB0AAABQW4fGNwEAAADgDE15TEi1gIAAdevWrc7Pw8PDdf78eU2bNk0JCQkKDQ3VgwcPdPz4ce3cuVMmk0mStGrVKvn6+tba12AwaOPGjXr55ZdVWVmp+fPn61e/+pViYmLUrVs3Xb16Vbt27bI8quTpp5/WK6+8UuccBg0apJUrV2r9+vUqLCxUXFycYmNjNW7cOPXp00cVFRW6evWqjh8/ro8++kjJyckKCQmx9Y8LAAAAkMSQGwAAAGgzLly4UOfu6oZs3LhRsbGxdX4+duxYRUdHa/PmzVqzZk2ddU9PTy1ZskRTpkypt+7IkSOVlJSk5cuXq6ysTMnJyUpOTq6zXWRkpLZu3Spvb+9668yaNUve3t56/fXXde/ePe3bt8/qI1AAAAAAezHkBgAAANqZ+fPnKzIyUsnJyfr6669VVFQkf39/DR8+XLNnz1ZERITV/WNiYvTJJ59o165dysjIUE5Oju7duyd/f38NHjxYEydO1MSJE+Xpaf3ph3FxcRo/frz+8Y9/6MiRI7p69apKS0vVqVMnBQYGaujQoXr++ef19NNPt+TlAwAAwM14mKvfOgMAAADAJeXm5io6OlqStGDBAi1cuLCVzwgAAABwHl48CQAAAAAAAABwWQy5AQAAAAAAAAAuiyE3AAAAAAAAAMBlMeQGAAAAAAAAALgshtwAAAAAAAAAAJflYTabza19EgAAAAAAAAAA2IM7uQEAAAAAAAAALoshNwAAAAAAAADAZTHkBgAAAAAAAAC4LIbcAAAAAAAAAACXxZAbAAAAAAAAAOCyGHIDAAAAAAAAAFwWQ24AAAAAAAAAgMtiyA0AAAAAAAAAcFkMuQEAAAAAAAAALoshNwAAAAAAAADAZTHkBgAAAAAAAAC4LIbcAAAAAAAAAACXxZAbAAAAAAAAAOCy/h91hXq2kec9+wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 732,
              "height": 506
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sDCbSli70uSg",
        "colab_type": "text"
      },
      "source": [
        "Let's now go deeper into our model evaluation, looking at test data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XWx9tF2uzywy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "13b7e17d-7fc4-4dbc-9da1-ebfaa48d441d"
      },
      "source": [
        "test_acc, _ = eval_model(\n",
        "  model,\n",
        "  test_data_loader,\n",
        "  loss_fn,\n",
        "  device,\n",
        "  len(df_test)\n",
        ")\n",
        "test_acc.item()"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.91"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WJESzjEF05KR",
        "colab_type": "text"
      },
      "source": [
        "Seemingly we can be pretty happy with 91% accuracy, lets now create a helper function to obtain predictions.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jnFHx5KN0HJM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_predictions(model, data_loader):\n",
        "  model = model.eval()\n",
        "  review_texts = []\n",
        "  predictions = []\n",
        "  prediction_probs = []\n",
        "  real_values = []\n",
        "  with torch.no_grad():\n",
        "    for d in data_loader:\n",
        "      texts = d[\"review_text\"]\n",
        "      input_ids = d[\"input_ids\"].to(device)\n",
        "      attention_mask = d[\"attention_mask\"].to(device)\n",
        "      targets = d[\"targets\"].to(device)\n",
        "      outputs = model(\n",
        "        input_ids=input_ids,\n",
        "        attention_mask=attention_mask\n",
        "      )\n",
        "      _, preds = torch.max(outputs, dim=1)\n",
        "      review_texts.extend(texts)\n",
        "      predictions.extend(preds)\n",
        "      prediction_probs.extend(outputs)\n",
        "      real_values.extend(targets)\n",
        "  predictions = torch.stack(predictions).cpu()\n",
        "  prediction_probs = torch.stack(prediction_probs).cpu()\n",
        "  real_values = torch.stack(real_values).cpu()\n",
        "  return review_texts, predictions, prediction_probs, real_values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r0tGcDmd2tQY",
        "colab_type": "text"
      },
      "source": [
        "Next we will add the ability to store the text of the reviews along with their probabilities:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OafGHNYq1FCn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_review_texts, y_pred, y_pred_probs, y_test = get_predictions(\n",
        "  model,\n",
        "  test_data_loader\n",
        ")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WEaLy7wK2zwl",
        "colab_type": "text"
      },
      "source": [
        "Here is our Classification report"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pKIfSPib2q9_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "outputId": "0e7aac70-66b4-4859-fa58-b3fac7f115b7"
      },
      "source": [
        "print(classification_report(y_test, y_pred, target_names=class_names))"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.90      0.92      0.91        49\n",
            "    positive       0.92      0.90      0.91        51\n",
            "\n",
            "    accuracy                           0.91       100\n",
            "   macro avg       0.91      0.91      0.91       100\n",
            "weighted avg       0.91      0.91      0.91       100\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z4-X_02J3wiD",
        "colab_type": "text"
      },
      "source": [
        "Next, we will plot a confusion matrix:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jp7ggtEx3wNk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "outputId": "e3579a33-8b36-4d4b-9f4d-65463dc86030"
      },
      "source": [
        "def show_confusion_matrix(confusion_matrix):\n",
        "  hmap = sns.heatmap(confusion_matrix, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "  hmap.yaxis.set_ticklabels(hmap.yaxis.get_ticklabels(), rotation=0, ha='right')\n",
        "  hmap.xaxis.set_ticklabels(hmap.xaxis.get_ticklabels(), rotation=30, ha='right')\n",
        "  plt.ylabel('True sentiment')\n",
        "  plt.xlabel('Predicted sentiment');\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "df_cm = pd.DataFrame(cm, index=class_names, columns=class_names)\n",
        "show_confusion_matrix(df_cm)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 716,
              "height": 516
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EfOk3UUK38Wy",
        "colab_type": "text"
      },
      "source": [
        "Here we will take a look again at model performing inference on a single piece of data from our test data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PHybZG0y28_8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#loading in data from the 3rd index\n",
        "idx = 3\n",
        "review_text = y_review_texts[idx]\n",
        "true_sentiment = y_test[idx]\n",
        "pred_df = pd.DataFrame({\n",
        "  'class_names': class_names,\n",
        "  'values': y_pred_probs[idx]\n",
        "})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D3ngItfK4Mch",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        },
        "outputId": "e6a49b22-e409-41fa-ce6e-4c1fe8daf89d"
      },
      "source": [
        "print(\"\\n\".join(wrap(review_text)))\n",
        "print()\n",
        "print(f'True sentiment: {class_names[true_sentiment]}')"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "I got these as a gift and really do like them. I just stick them to\n",
            "the fridge (magnets) and always know where they are. They are easy to\n",
            "use altho I do find they release less salt or pepper than you first\n",
            "think....so you have to squeeze more times than you normally would. It\n",
            "takes some getting used to.This is very handy to have, easy to use and\n",
            "its nice to have a small set to pass around at the table. I am also\n",
            "glad you can graind the salt...some sets are for grinding pepper but\n",
            "not the salt.\n",
            "\n",
            "True sentiment: positive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ohJ7WepG4K6r",
        "colab_type": "text"
      },
      "source": [
        "Now another look at the confidence for the sentiment of this prediction:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DL8_9-SO4gb0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "outputId": "1bdc9e5c-71f7-45a4-b5a5-9727ec828610"
      },
      "source": [
        "sns.barplot(x='values', y='class_names', data=pred_df, orient='h')\n",
        "plt.ylabel('sentiment')\n",
        "plt.xlabel('probability')\n",
        "plt.xlim([0, 1]);"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 779,
              "height": 489
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PIRNOp8cWB82",
        "colab_type": "text"
      },
      "source": [
        "Let's take a shot at predicting sentiment on raw text."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "753667Ul4hJ_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "review_text = \"My god, I will never look at thousands of reviews ever again. It's really just a waste of time.\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "intUAPSZWZz5",
        "colab_type": "text"
      },
      "source": [
        "First we'll need to tokenize this text."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SIQDvYdBFee2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "encoded_review = tokenizer.encode_plus(\n",
        "  review_text,\n",
        "  max_length=MAX_LEN,\n",
        "  add_special_tokens=True,\n",
        "  return_token_type_ids=False,\n",
        "  pad_to_max_length=True,\n",
        "  return_attention_mask=True,\n",
        "  return_tensors='pt',\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jnVXqszgWgJW",
        "colab_type": "text"
      },
      "source": [
        "Up next, we will feed the tokenized  string into our model. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qHuFeaGhWeyV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "4d59a149-a4d4-4a32-b1e7-ccb7cf04541a"
      },
      "source": [
        "input_ids = encoded_review['input_ids'].to(device)\n",
        "attention_mask = encoded_review['attention_mask'].to(device)\n",
        "\n",
        "output = model(input_ids, attention_mask)\n",
        "_, prediction = torch.max(output, dim=1)\n",
        "\n",
        "print(f'Review text: {review_text}')\n",
        "print(f'Sentiment  : {class_names[prediction]}')"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Review text: My god, I will never look at thousands of reviews ever again. It's really just a waste of time.\n",
            "Sentiment  : negative\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JASCCmpyWlzk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}