Initial_Notebook.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Handwritten_OCR.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "accelerator": "GPU",
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "863bfd50e00a43909f7e36186bcb0e45": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "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_3d1abccd6bf04921a2f2d5a06e485efe",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_3cc7c624c1ca4d958b926e040bc91761",
              "IPY_MODEL_c2a3f91652e443c59e180ae7028a5490"
            ]
          }
        },
        "3d1abccd6bf04921a2f2d5a06e485efe": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "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
          }
        },
        "3cc7c624c1ca4d958b926e040bc91761": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_0053f7f84f754596963b7544b8080d06",
            "_dom_classes": [],
            "description": "100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 115338,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 115338,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_b2d91c5aeb7a4220a7778b228cb6b833"
          }
        },
        "c2a3f91652e443c59e180ae7028a5490": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_062bdcf2881b4f54a8750c8c30190554",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 115338/115338 [00:00&lt;00:00, 795007.13it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_6433d6803ba84d37bfd4abe39c9c1a9a"
          }
        },
        "0053f7f84f754596963b7544b8080d06": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "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"
          }
        },
        "b2d91c5aeb7a4220a7778b228cb6b833": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "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
          }
        },
        "062bdcf2881b4f54a8750c8c30190554": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "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"
          }
        },
        "6433d6803ba84d37bfd4abe39c9c1a9a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "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
          }
        },
        "dbf74a73d9824a288c69d6d1e0f3ab96": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "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_e5ca3abd1b634507aa115c823e8cc660",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_d7b83d9b73dd42c9ac876217de6181ba",
              "IPY_MODEL_1783481b39a14cda842025ea2aba6be7"
            ]
          }
        },
        "e5ca3abd1b634507aa115c823e8cc660": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "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
          }
        },
        "d7b83d9b73dd42c9ac876217de6181ba": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_361567a72c9e4750adc15c850186b313",
            "_dom_classes": [],
            "description": "100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 86810,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 86810,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_e1ece62e03cc4c5a901e51a42bc60d2b"
          }
        },
        "1783481b39a14cda842025ea2aba6be7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_87a354d4275b47cda0f09a224bce94e2",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 86810/86810 [01:36&lt;00:00, 895.40it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_c7692d2fe18d431f8038a8ab49349b0d"
          }
        },
        "361567a72c9e4750adc15c850186b313": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "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"
          }
        },
        "e1ece62e03cc4c5a901e51a42bc60d2b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "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
          }
        },
        "87a354d4275b47cda0f09a224bce94e2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "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"
          }
        },
        "c7692d2fe18d431f8038a8ab49349b0d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "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
          }
        },
        "d359280a815b445aa70af200057fb4a0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "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_d5a78f3ea43a42a9814f1270ae7bdac5",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_00bf20c6598b42abaa6ec7a632f8bf0e",
              "IPY_MODEL_0a0c4c34c04540d4bc9d43a39e31ae54"
            ]
          }
        },
        "d5a78f3ea43a42a9814f1270ae7bdac5": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "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
          }
        },
        "00bf20c6598b42abaa6ec7a632f8bf0e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_501285dbd7654b54b8da9633ee3c185f",
            "_dom_classes": [],
            "description": "100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 4823,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 4823,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_e4083ace92034ceb8967aa20c78fd545"
          }
        },
        "0a0c4c34c04540d4bc9d43a39e31ae54": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_1638930591bf4a1bb280dd4039aa755b",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 4823/4823 [00:34&lt;00:00, 141.57it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_0c7932e4feb04f3c95f5329ea146edcb"
          }
        },
        "501285dbd7654b54b8da9633ee3c185f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "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"
          }
        },
        "e4083ace92034ceb8967aa20c78fd545": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "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
          }
        },
        "1638930591bf4a1bb280dd4039aa755b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "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"
          }
        },
        "0c7932e4feb04f3c95f5329ea146edcb": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "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
          }
        },
        "38d81bcf6f484015bb8144b87ba244c8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "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_dbbf026f7d4a4cfa8534df717ccaac16",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_66a1177ecac84aee8621799f315cb0ae",
              "IPY_MODEL_5fef5c3fa26c4e748a2f67e57d2b2d76"
            ]
          }
        },
        "dbbf026f7d4a4cfa8534df717ccaac16": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "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
          }
        },
        "66a1177ecac84aee8621799f315cb0ae": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_a96ebacfc6aa49f88a2a6af3c07e5610",
            "_dom_classes": [],
            "description": "100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 4823,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 4823,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_d8c9ff19908a488098f52232afefbf83"
          }
        },
        "5fef5c3fa26c4e748a2f67e57d2b2d76": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_e7a3d9a582534898a53130abd42ca2ac",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 4823/4823 [00:31&lt;00:00, 152.10it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_a8e7018577444f0fa90f0de6a17f616f"
          }
        },
        "a96ebacfc6aa49f88a2a6af3c07e5610": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "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"
          }
        },
        "d8c9ff19908a488098f52232afefbf83": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "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
          }
        },
        "e7a3d9a582534898a53130abd42ca2ac": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "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"
          }
        },
        "a8e7018577444f0fa90f0de6a17f616f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "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/sayakpaul/Handwriting-Recognizer-in-Keras/blob/main/Initial_Notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D6B83kI1VDpQ"
      },
      "source": [
        "## Reference\n",
        "- Keras example on [Captcha OCR](https://keras.io/examples/vision/captcha_ocr/)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BOJ3n-LZbxls"
      },
      "source": [
        "## Dataset collection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w1MdZdnrJq5l"
      },
      "source": [
        "!wget -q https://github.com/sayakpaul/Handwriting-Recognizer-in-Keras/releases/download/v1.0.0/IAM_Words.zip\n",
        "!unzip -qq IAM_Words.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jo2n2mBxL3N6"
      },
      "source": [
        "!mkdir data\n",
        "!mkdir data/words\n",
        "!tar -xf IAM_Words/words.tgz -C data/words\n",
        "!mv IAM_Words/words.txt data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OwwnL7cwL8q0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42be5c86-2b9e-4026-80c6-86a37a638ded"
      },
      "source": [
        "!head -20 data/words.txt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "#--- words.txt ---------------------------------------------------------------#\n",
            "#\n",
            "# iam database word information\n",
            "#\n",
            "# format: a01-000u-00-00 ok 154 1 408 768 27 51 AT A\n",
            "#\n",
            "#     a01-000u-00-00  -> word id for line 00 in form a01-000u\n",
            "#     ok              -> result of word segmentation\n",
            "#                            ok: word was correctly\n",
            "#                            er: segmentation of word can be bad\n",
            "#\n",
            "#     154             -> graylevel to binarize the line containing this word\n",
            "#     1               -> number of components for this word\n",
            "#     408 768 27 51   -> bounding box around this word in x,y,w,h format\n",
            "#     AT              -> the grammatical tag for this word, see the\n",
            "#                        file tagset.txt for an explanation\n",
            "#     A               -> the transcription for this word\n",
            "#\n",
            "a01-000u-00-00 ok 154 408 768 27 51 AT A\n",
            "a01-000u-00-01 ok 154 507 766 213 48 NN MOVE\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T5Br7NfLbz_E"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JdA5uc6ATAWx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8be0648-3d44-43af-835c-589dcc1a335e"
      },
      "source": [
        "!pip install -q -U imgaug"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[?25l\r\u001b[K     |▍                               | 10 kB 30.6 MB/s eta 0:00:01\r\u001b[K     |▊                               | 20 kB 37.9 MB/s eta 0:00:01\r\u001b[K     |█                               | 30 kB 23.9 MB/s eta 0:00:01\r\u001b[K     |█▍                              | 40 kB 18.9 MB/s eta 0:00:01\r\u001b[K     |█▊                              | 51 kB 8.1 MB/s eta 0:00:01\r\u001b[K     |██                              | 61 kB 8.7 MB/s eta 0:00:01\r\u001b[K     |██▍                             | 71 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |██▊                             | 81 kB 8.3 MB/s eta 0:00:01\r\u001b[K     |███                             | 92 kB 6.8 MB/s eta 0:00:01\r\u001b[K     |███▌                            | 102 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |███▉                            | 112 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |████▏                           | 122 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |████▌                           | 133 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |████▉                           | 143 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |█████▏                          | 153 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |█████▌                          | 163 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |█████▉                          | 174 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |██████▏                         | 184 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |██████▋                         | 194 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |███████                         | 204 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |███████▎                        | 215 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |███████▋                        | 225 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |████████                        | 235 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |████████▎                       | 245 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |████████▋                       | 256 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |█████████                       | 266 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |█████████▎                      | 276 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |█████████▊                      | 286 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |██████████                      | 296 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |██████████▍                     | 307 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |██████████▊                     | 317 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |███████████                     | 327 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |███████████▍                    | 337 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |███████████▊                    | 348 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |████████████                    | 358 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |████████████▍                   | 368 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |████████████▉                   | 378 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |█████████████▏                  | 389 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |█████████████▌                  | 399 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |█████████████▉                  | 409 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |██████████████▏                 | 419 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |██████████████▌                 | 430 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |██████████████▉                 | 440 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |███████████████▏                | 450 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |███████████████▌                | 460 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |████████████████                | 471 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |████████████████▎               | 481 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |████████████████▋               | 491 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████               | 501 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████▎              | 512 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████▋              | 522 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████              | 532 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████▎             | 542 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████▋             | 552 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████             | 563 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████▍            | 573 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████▊            | 583 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████            | 593 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████▍           | 604 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████▊           | 614 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████           | 624 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▍          | 634 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▊          | 645 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▏         | 655 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▌         | 665 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▉         | 675 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▏        | 686 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▌        | 696 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▉        | 706 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▏       | 716 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▌       | 727 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▉       | 737 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▎      | 747 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▋      | 757 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████      | 768 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▎     | 778 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▋     | 788 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████     | 798 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▎    | 808 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▋    | 819 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████    | 829 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▍   | 839 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▊   | 849 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████   | 860 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▍  | 870 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▊  | 880 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████  | 890 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▍ | 901 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▊ | 911 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████ | 921 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▌| 931 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▉| 942 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 948 kB 7.4 MB/s \n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.4.0 which is incompatible.\u001b[0m\n",
            "\u001b[?25h"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AzAn4MGOMGyP"
      },
      "source": [
        "from imutils import paths\n",
        "from tqdm.notebook import tqdm\n",
        "from itertools import groupby\n",
        "\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import imgaug\n",
        "import cv2\n",
        "import os\n",
        "\n",
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eFUUdA4Ib1q4"
      },
      "source": [
        "## Dataset preparation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5nw0wiDWssNI"
      },
      "source": [
        "BASE_IMAGE_PATH = os.path.join(\"data\", \"words\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M0OnjVA6MQTu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa95e066-14e3-4abb-d1c9-f70f43d1ec6a"
      },
      "source": [
        "# Image path: part1-part2-part3 --> part1/part1-part2/part1-part2-part3.png\n",
        "# The above format DOES NOT include the base path which is \"data/words\" in\n",
        "# this case.\n",
        "all_images = list(paths.list_images(BASE_IMAGE_PATH))\n",
        "all_images[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['data/words/j01/j01-045/j01-045-07-04.png',\n",
              " 'data/words/j01/j01-045/j01-045-01-01.png',\n",
              " 'data/words/j01/j01-045/j01-045-00-06.png',\n",
              " 'data/words/j01/j01-045/j01-045-03-00.png',\n",
              " 'data/words/j01/j01-045/j01-045-07-00.png']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bWcMSzy9NCdp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83,
          "referenced_widgets": [
            "863bfd50e00a43909f7e36186bcb0e45",
            "3d1abccd6bf04921a2f2d5a06e485efe",
            "3cc7c624c1ca4d958b926e040bc91761",
            "c2a3f91652e443c59e180ae7028a5490",
            "0053f7f84f754596963b7544b8080d06",
            "b2d91c5aeb7a4220a7778b228cb6b833",
            "062bdcf2881b4f54a8750c8c30190554",
            "6433d6803ba84d37bfd4abe39c9c1a9a"
          ]
        },
        "outputId": "0ba72633-3d43-4e12-930b-d95c8c208209"
      },
      "source": [
        "words_list = []\n",
        "\n",
        "words = open('data/words.txt', 'r').readlines()\n",
        "for line in tqdm(words):\n",
        "    if line[0]=='#':\n",
        "        continue\n",
        "    if line.split(\" \")[1]!=\"err\": # We don't need to deal with errored entries.\n",
        "        words_list.append(line)\n",
        "\n",
        "len(words_list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "863bfd50e00a43909f7e36186bcb0e45",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=115338.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "96456"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2MkkUDt6SCr5"
      },
      "source": [
        "np.random.shuffle(words_list)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q_1JJr-joiMb"
      },
      "source": [
        "### Prepare the splits (90:5:5)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OlWJ8FP2Rpht",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4611f0eb-c891-45d9-c691-fbe5715edf4a"
      },
      "source": [
        "split_idx = int(0.9 * len(words_list))\n",
        "train_samples = words_list[:split_idx]\n",
        "test_samples = words_list[split_idx:]\n",
        "\n",
        "val_split_idx = int(0.5 * len(test_samples))\n",
        "validation_samples = test_samples[:val_split_idx]\n",
        "test_samples = test_samples[val_split_idx:]\n",
        "\n",
        "assert len(words_list) == len(train_samples) + len(validation_samples) + len(test_samples)\n",
        "\n",
        "print(f\"Total training samples: {len(train_samples)}\")\n",
        "print(f\"Total validation samples: {len(validation_samples)}\")\n",
        "print(f\"Total test samples: {len(test_samples)}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total training samples: 86810\n",
            "Total validation samples: 4823\n",
            "Total test samples: 4823\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c9oBlfUkNGVz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "990d7f0b-98ec-49ee-dcd2-f782f1c7c941"
      },
      "source": [
        "# Since the labels start appearing after eighth index we use that\n",
        "# to retrieve the grounth-truth labels. Remember indexing starts from\n",
        "# zero in Python.\n",
        "start_idx = 8\n",
        "train_words = [line.split(' ')[start_idx:][0].strip() for line in train_samples]\n",
        "max_label_len = max([len(str(text)) for text in train_words])\n",
        "print(f\"Maximum label length: {max_label_len}\")\n",
        "\n",
        "padding_method = \"post\"\n",
        "padding_token = 99\n",
        "tokenizer = tf.keras.preprocessing.text.Tokenizer(filters=\"\\n\", char_level=True)\n",
        "tokenizer.fit_on_texts(train_words)\n",
        "tokenized_words = tokenizer.texts_to_sequences(train_words)\n",
        "padded_train_words = tf.keras.preprocessing.sequence.pad_sequences(tokenized_words,\n",
        "                                                        maxlen=max_label_len,\n",
        "                                                        padding=padding_method,\n",
        "                                                        value=padding_token)\n",
        "\n",
        "# Maximum sequence length is 4, hence a word is padded to that length.\n",
        "print(f\"Integer representation of a word: {padded_train_words[0]}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Maximum label length: 21\n",
            "Integer representation of a word: [ 7 13  8  1 99 99 99 99 99 99 99 99 99 99 99 99 99 99 99 99 99]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6aBAK36wos4G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "655dfad9-e7d4-4695-87f8-94bdc83d737c"
      },
      "source": [
        "# Unique characters.\n",
        "len(tokenizer.word_index)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "52"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nU9pG8p8nRd_"
      },
      "source": [
        "# View some word index mappings.\n",
        "def process_word(word):\n",
        "    processed_word = []\n",
        "    for i in word:\n",
        "        if i != 99:\n",
        "            processed_word.append(tokenizer.index_word[i])\n",
        "    return \"\".join(processed_word)\n",
        "\n",
        "def view_sample_mappings(sample_padded_words):\n",
        "    for t in sample_padded_words:\n",
        "        word = process_word(t)\n",
        "        print (f\"{t.tolist()}----> {word}\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V5Z3bGvk7n9e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa9f6af3-f929-466a-e548-ca7ea28d86e2"
      },
      "source": [
        "view_sample_mappings(padded_train_words[:15])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[7, 13, 8, 1, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99]----> sure\n",
            "[9, 1, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99]----> he\n",
            "[11, 13, 8, 5, 6, 17, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99]----> during\n",
            "[4, 15, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99]----> of\n",
            "[20, 4, 4, 2, 19, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99]----> booty\n",
            "[17, 3, 7, 2, 8, 4, 6, 4, 14, 19, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99]----> gastronomy\n",
            "[20, 4, 19, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99]----> boy\n",
            "[2, 9, 1, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99]----> the\n",
            "[3, 6, 11, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99]----> and\n",
            "[5, 6, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99]----> in\n",
            "[9, 5, 7, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99]----> his\n",
            "[15, 10, 19, 5, 6, 17, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99]----> flying\n",
            "[18, 8, 1, 7, 1, 6, 2, 1, 11, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99]----> presented\n",
            "[6, 4, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99]----> no\n",
            "[3, 7, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99]----> as\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FhOEwqXcpMPH"
      },
      "source": [
        "valid_words = [line.split(' ')[start_idx:][0].strip() for line in validation_samples]\n",
        "tokenized_valid_words = tokenizer.texts_to_sequences(valid_words)\n",
        "padded_valid_words = tf.keras.preprocessing.sequence.pad_sequences(tokenized_valid_words,\n",
        "                                                        maxlen=max_label_len,        \n",
        "                                                        padding=padding_method,\n",
        "                                                        value=padding_token)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1d8444YpgQa"
      },
      "source": [
        "test_words = [line.split(' ')[start_idx:][0].strip() for line in test_samples]\n",
        "tokenized_test_words = tokenizer.texts_to_sequences(test_words)\n",
        "padded_test_words = tf.keras.preprocessing.sequence.pad_sequences(tokenized_test_words,\n",
        "                                                        maxlen=max_label_len,        \n",
        "                                                        padding=padding_method,\n",
        "                                                        value=padding_token)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RROe32g7GD6k"
      },
      "source": [
        "# Credit: https://github.com/githubharald/SimpleHTR/blob/master/src/SamplePreprocessor.py\n",
        "def distortion_free_resize(image, img_size):\n",
        "    # Target size and current image size. \n",
        "    (wt, ht) = img_size\n",
        "    (h, w) = image.shape\n",
        "    \n",
        "    # Compute the individual resolution scales and take\n",
        "    # the maximum between them.\n",
        "    fx = w / wt\n",
        "    fy = h / ht\n",
        "    f = max(fx, fy)\n",
        "    \n",
        "    # Compute the new image size such that the aspect ratio is respected.\n",
        "    new_size = (max(min(wt, int(w / f)), 1), max(min(ht, int(h / f)), 1))\n",
        "\n",
        "    # First, resize the image to this newly computed size. Then\n",
        "    # copy its pixels over appropriately to another blank image\n",
        "    # having the target size. \n",
        "    image = cv2.resize(image, new_size)\n",
        "    target = np.ones([ht, wt]) * 255\n",
        "    target[0:new_size[1], 0:new_size[0]] = image\n",
        "    \n",
        "    # Tranpose to (w, h) format.\n",
        "    image = cv2.transpose(target)\n",
        "    return image"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f5XfPrBuQdGd"
      },
      "source": [
        "IMG_WIDTH = 128\n",
        "IMG_HEIGHT = 32\n",
        "\n",
        "def process_image(img_path, img_size=(IMG_WIDTH, IMG_HEIGHT)):    \n",
        "    # Read image in grayscale mode.\n",
        "    image = cv2.imread(img_path, 0)\n",
        "    # Scale pixel values to [0, 1].\n",
        "    image = image.astype(\"float32\")/255\n",
        "    # Resize image.\n",
        "    image = distortion_free_resize(image, img_size)\n",
        "    # Add channel otherwise Conv2D won't be compatible.\n",
        "    image = np.expand_dims(image, axis=-1) \n",
        "    return image"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HBVTcwqBZMnX"
      },
      "source": [
        "def prepare_images(samples):\n",
        "    images = np.zeros(shape=(len(samples), IMG_WIDTH, IMG_HEIGHT, 1))\n",
        "    for (i, file_line) in enumerate(tqdm(samples)):\n",
        "        line_split = file_line.strip()\n",
        "        line_split = line_split.split(\" \")\n",
        "        \n",
        "        # Each line split will have this format for the corresponding image:\n",
        "        # part1/part1-part2/part1-part2-part3.png\n",
        "        image_name = line_split[0] \n",
        "        partI = image_name.split(\"-\")[0]\n",
        "        partII = image_name.split(\"-\")[1]\n",
        "        img_path =  os.path.join(BASE_IMAGE_PATH, partI, \n",
        "            partI + \"-\" + partII,\n",
        "            image_name + \".png\"\n",
        "        )\n",
        "        if os.path.getsize(img_path):\n",
        "            preprocessed_image = process_image(img_path)\n",
        "            images[i] = preprocessed_image\n",
        "    return images"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E806c2cVa2mI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164,
          "referenced_widgets": [
            "dbf74a73d9824a288c69d6d1e0f3ab96",
            "e5ca3abd1b634507aa115c823e8cc660",
            "d7b83d9b73dd42c9ac876217de6181ba",
            "1783481b39a14cda842025ea2aba6be7",
            "361567a72c9e4750adc15c850186b313",
            "e1ece62e03cc4c5a901e51a42bc60d2b",
            "87a354d4275b47cda0f09a224bce94e2",
            "c7692d2fe18d431f8038a8ab49349b0d",
            "d359280a815b445aa70af200057fb4a0",
            "d5a78f3ea43a42a9814f1270ae7bdac5",
            "00bf20c6598b42abaa6ec7a632f8bf0e",
            "0a0c4c34c04540d4bc9d43a39e31ae54",
            "501285dbd7654b54b8da9633ee3c185f",
            "e4083ace92034ceb8967aa20c78fd545",
            "1638930591bf4a1bb280dd4039aa755b",
            "0c7932e4feb04f3c95f5329ea146edcb",
            "38d81bcf6f484015bb8144b87ba244c8",
            "dbbf026f7d4a4cfa8534df717ccaac16",
            "66a1177ecac84aee8621799f315cb0ae",
            "5fef5c3fa26c4e748a2f67e57d2b2d76",
            "a96ebacfc6aa49f88a2a6af3c07e5610",
            "d8c9ff19908a488098f52232afefbf83",
            "e7a3d9a582534898a53130abd42ca2ac",
            "a8e7018577444f0fa90f0de6a17f616f"
          ]
        },
        "outputId": "6fea8a9c-feee-4b3f-b47d-bf88f1dfc2d0"
      },
      "source": [
        "train_images = prepare_images(train_samples)\n",
        "validation_images = prepare_images(validation_samples)\n",
        "test_images = prepare_images(test_samples)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dbf74a73d9824a288c69d6d1e0f3ab96",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=86810.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d359280a815b445aa70af200057fb4a0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=4823.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "38d81bcf6f484015bb8144b87ba244c8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=4823.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9cv3QeQ-SGJS"
      },
      "source": [
        "# Construct TensorFlow datasets.\n",
        "\n",
        "BATCH_SIZE = 64\n",
        "AUTOTUNE = tf.data.AUTOTUNE\n",
        "\n",
        "augmenter = imgaug.augmenters.Sequential([\n",
        "    imgaug.augmenters.GammaContrast(gamma=(0.25, 3.0)),\n",
        "    imgaug.augmenters.Sometimes(\n",
        "        0.3,\n",
        "        imgaug.augmenters.GaussianBlur(sigma=(0, 0.5))\n",
        "    )\n",
        "])\n",
        "\n",
        "\n",
        "def augment(images):\n",
        "    return augmenter(images=images.numpy())\n",
        "\n",
        "\n",
        "def make_dicts(images, labels):\n",
        "    return {\"images\": images, \"labels\": labels}\n",
        "\n",
        "\n",
        "def make_datasets(images, labels, training=True):\n",
        "    dataset = tf.data.Dataset.from_tensor_slices((images, labels))\n",
        "    if training:\n",
        "        dataset = dataset.shuffle(BATCH_SIZE * 25)\n",
        "    dataset = dataset.batch(BATCH_SIZE)\n",
        "\n",
        "    if training:\n",
        "        dataset = dataset.map(\n",
        "            lambda x, y: (tf.py_function(augment, [x], [tf.float32])[0], y),\n",
        "            num_parallel_calls=AUTOTUNE\n",
        "        )\n",
        "    dataset = dataset.map(make_dicts).prefetch(AUTOTUNE)\n",
        "    return dataset\n",
        "\n",
        "train_dataset = make_datasets(train_images, padded_train_words)\n",
        "validation_dataset = make_datasets(validation_images, padded_valid_words, False)\n",
        "test_dataset = make_datasets(test_images, padded_test_words, False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "doV5CtiBVgMQ"
      },
      "source": [
        "def plot_samples(images, labels):\n",
        "    _, ax = plt.subplots(4, 4, figsize=(12, 12))\n",
        "    for i in range(16):\n",
        "        img = (images[i] * 255).numpy().astype(\"uint8\")\n",
        "        label = process_word(labels[i].numpy())\n",
        "        ax[i // 4, i % 4].imshow(img.squeeze(-1).T, cmap=\"gray\")\n",
        "        ax[i // 4, i % 4].set_title(label)\n",
        "        ax[i // 4, i % 4].axis(\"off\")\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SvslZTljVtQ6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "outputId": "c28c0e90-e091-4396-d4b1-48e697d9f978"
      },
      "source": [
        "batch = next(iter(train_dataset))\n",
        "plot_samples(batch[\"images\"], batch[\"labels\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x864 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7KQ8ueDJb46h"
      },
      "source": [
        "## Model building"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QZRjUkjyWUXl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38ae65d6-9ddf-4493-fe28-dc163090ea02"
      },
      "source": [
        "class CTCLayer( tf.keras.layers.Layer):\n",
        "    def __init__(self, name=None):\n",
        "        super().__init__(name=name)\n",
        "        self.loss_fn = tf.keras.backend.ctc_batch_cost\n",
        "\n",
        "    def call(self, y_true, y_pred):\n",
        "        batch_len = tf.cast(tf.shape(y_true)[0], dtype=\"int64\")\n",
        "        input_length = tf.cast(tf.shape(y_pred)[1], dtype=\"int64\")\n",
        "        label_length = tf.cast(tf.shape(y_true)[1], dtype=\"int64\")\n",
        "\n",
        "        input_length = input_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
        "        label_length = label_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
        "        loss = self.loss_fn(y_true, y_pred, input_length, label_length)\n",
        "        self.add_loss(loss)\n",
        "\n",
        "        # At test time, just return the computed predictions\n",
        "        return y_pred\n",
        "\n",
        "\n",
        "def build_model():\n",
        "    # Inputs to the model\n",
        "    input_img =  tf.keras.layers.Input(\n",
        "        shape=(IMG_WIDTH, IMG_HEIGHT, 1), name=\"images\")\n",
        "    labels =  tf.keras.layers.Input(name=\"labels\", shape=(None,))\n",
        "\n",
        "    # First conv block\n",
        "    x = tf.keras.layers.Conv2D(\n",
        "        32,\n",
        "        (3, 3),\n",
        "        activation=\"relu\",\n",
        "        kernel_initializer=\"he_normal\",\n",
        "        padding=\"same\",\n",
        "        name=\"Conv1\",\n",
        "    )(input_img)\n",
        "    x =  tf.keras.layers.MaxPooling2D((2, 2), name=\"pool1\")(x)\n",
        "\n",
        "    # Second conv block\n",
        "    x =  tf.keras.layers.Conv2D(\n",
        "        64,\n",
        "        (3, 3),\n",
        "        activation=\"relu\",\n",
        "        kernel_initializer=\"he_normal\",\n",
        "        padding=\"same\",\n",
        "        name=\"Conv2\",\n",
        "    )(x)\n",
        "    x =  tf.keras.layers.MaxPooling2D((2, 2), name=\"pool2\")(x)\n",
        "\n",
        "    # We have used two max pool with pool size and strides 2.\n",
        "    # Hence, downsampled feature maps are 4x smaller. The number of\n",
        "    # filters in the last layer is 64. Reshape accordingly before\n",
        "    # passing the output to the RNN part of the model\n",
        "    new_shape = ((IMG_WIDTH // 4), (IMG_HEIGHT // 4) * 64)\n",
        "    x =  tf.keras.layers.Reshape(target_shape=new_shape, name=\"reshape\")(x)\n",
        "    x =  tf.keras.layers.Dense(64, activation=\"relu\", name=\"dense1\")(x)\n",
        "    x =  tf.keras.layers.Dropout(0.2)(x)\n",
        "\n",
        "    # RNNs\n",
        "    x =  tf.keras.layers.Bidirectional( tf.keras.layers.LSTM(128, return_sequences=True, dropout=0.25))(x)\n",
        "    x =  tf.keras.layers.Bidirectional( tf.keras.layers.LSTM(64, return_sequences=True, dropout=0.25))(x)\n",
        "\n",
        "    # Output layer (the tokenizer is char-level)\n",
        "    # +2 recommendation came from here - https://github.com/MaybeShewill-CV/CRNN_Tensorflow/issues/69#issuecomment-383992527\n",
        "    x =  tf.keras.layers.Dense(len(tokenizer.word_index) + 2, activation=\"softmax\", name=\"dense2\")(x)\n",
        "    \n",
        "    # Add CTC layer for calculating CTC loss at each step\n",
        "    output = CTCLayer(name=\"ctc_loss\")(labels, x)\n",
        "\n",
        "    # Define the model\n",
        "    model =  tf.keras.models.Model(\n",
        "        inputs=[input_img, labels], outputs=output, name=\"handwriting_recognizer\"\n",
        "    )\n",
        "    # Optimizer\n",
        "    opt = tf.keras.optimizers.Adam()\n",
        "    # Compile the model and return\n",
        "    model.compile(optimizer=opt)\n",
        "    return model\n",
        "\n",
        "\n",
        "# Get the model\n",
        "model = build_model()\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"handwriting_recognizer\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "images (InputLayer)             [(None, 128, 32, 1)] 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "Conv1 (Conv2D)                  (None, 128, 32, 32)  320         images[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "pool1 (MaxPooling2D)            (None, 64, 16, 32)   0           Conv1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "Conv2 (Conv2D)                  (None, 64, 16, 64)   18496       pool1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "pool2 (MaxPooling2D)            (None, 32, 8, 64)    0           Conv2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "reshape (Reshape)               (None, 32, 512)      0           pool2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "dense1 (Dense)                  (None, 32, 64)       32832       reshape[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dropout (Dropout)               (None, 32, 64)       0           dense1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional (Bidirectional)   (None, 32, 256)      197632      dropout[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional_1 (Bidirectional) (None, 32, 128)      164352      bidirectional[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "labels (InputLayer)             [(None, None)]       0                                            \n",
            "__________________________________________________________________________________________________\n",
            "dense2 (Dense)                  (None, 32, 54)       6966        bidirectional_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "ctc_loss (CTCLayer)             (None, 32, 54)       0           labels[0][0]                     \n",
            "                                                                 dense2[0][0]                     \n",
            "==================================================================================================\n",
            "Total params: 420,598\n",
            "Trainable params: 420,598\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GUCgn5jJb7hP"
      },
      "source": [
        "## Model training and inference"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zUwk56nCWxxQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4ac1ead-2c12-4dda-b908-3a4591a6af15"
      },
      "source": [
        "epochs = 100\n",
        "early_stopping_patience = 10\n",
        "early_stopping = tf.keras.callbacks.EarlyStopping(\n",
        "    monitor=\"val_loss\", patience=early_stopping_patience, restore_best_weights=True\n",
        ")\n",
        "\n",
        "# Train the model\n",
        "model = build_model()\n",
        "history = model.fit(\n",
        "    train_dataset,\n",
        "    validation_data=validation_dataset,\n",
        "    epochs=epochs,\n",
        "    callbacks=[early_stopping],\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "1357/1357 [==============================] - 66s 42ms/step - loss: 13.7039 - val_loss: 12.8109\n",
            "Epoch 2/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 12.3833 - val_loss: 12.3740\n",
            "Epoch 3/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 12.0636 - val_loss: 12.0246\n",
            "Epoch 4/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 11.3540 - val_loss: 10.9796\n",
            "Epoch 5/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 10.4784 - val_loss: 9.9197\n",
            "Epoch 6/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 9.8998 - val_loss: 9.4077\n",
            "Epoch 7/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 9.4814 - val_loss: 8.9178\n",
            "Epoch 8/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 9.0652 - val_loss: 8.5845\n",
            "Epoch 9/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 8.6633 - val_loss: 8.0102\n",
            "Epoch 10/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 8.4094 - val_loss: 7.8673\n",
            "Epoch 11/100\n",
            "1357/1357 [==============================] - 56s 42ms/step - loss: 8.1767 - val_loss: 7.5236\n",
            "Epoch 12/100\n",
            "1357/1357 [==============================] - 58s 42ms/step - loss: 7.8461 - val_loss: 7.1450\n",
            "Epoch 13/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 7.5472 - val_loss: 6.7844\n",
            "Epoch 14/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 7.3233 - val_loss: 6.5515\n",
            "Epoch 15/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 7.0660 - val_loss: 6.3180\n",
            "Epoch 16/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 6.8688 - val_loss: 6.2760\n",
            "Epoch 17/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 6.7193 - val_loss: 5.9512\n",
            "Epoch 18/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 6.5804 - val_loss: 5.8578\n",
            "Epoch 19/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 6.4279 - val_loss: 5.7647\n",
            "Epoch 20/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 6.3582 - val_loss: 5.6309\n",
            "Epoch 21/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 6.2170 - val_loss: 5.5342\n",
            "Epoch 22/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 6.0912 - val_loss: 5.4642\n",
            "Epoch 23/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 6.0353 - val_loss: 5.3556\n",
            "Epoch 24/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 5.9217 - val_loss: 5.3582\n",
            "Epoch 25/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 5.8324 - val_loss: 5.2444\n",
            "Epoch 26/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 5.8023 - val_loss: 5.2309\n",
            "Epoch 27/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 5.7345 - val_loss: 5.1590\n",
            "Epoch 28/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 5.6841 - val_loss: 5.0648\n",
            "Epoch 29/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 5.6088 - val_loss: 5.1474\n",
            "Epoch 30/100\n",
            "1357/1357 [==============================] - 55s 41ms/step - loss: 5.6234 - val_loss: 5.0841\n",
            "Epoch 31/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 5.5251 - val_loss: 5.0094\n",
            "Epoch 32/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 5.5101 - val_loss: 4.9668\n",
            "Epoch 33/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 5.4147 - val_loss: 4.9486\n",
            "Epoch 34/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 5.3793 - val_loss: 4.8842\n",
            "Epoch 35/100\n",
            "1357/1357 [==============================] - 55s 41ms/step - loss: 5.3687 - val_loss: 4.8651\n",
            "Epoch 36/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 5.2890 - val_loss: 5.0575\n",
            "Epoch 37/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 5.2567 - val_loss: 4.7835\n",
            "Epoch 38/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 5.2343 - val_loss: 4.8270\n",
            "Epoch 39/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 5.2059 - val_loss: 4.7774\n",
            "Epoch 40/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 5.1838 - val_loss: 4.7607\n",
            "Epoch 41/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 5.1533 - val_loss: 4.7303\n",
            "Epoch 42/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 5.1413 - val_loss: 4.8708\n",
            "Epoch 43/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 5.0948 - val_loss: 4.7127\n",
            "Epoch 44/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 5.1394 - val_loss: 4.6977\n",
            "Epoch 45/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 5.0313 - val_loss: 4.6310\n",
            "Epoch 46/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.9796 - val_loss: 4.6168\n",
            "Epoch 47/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.9697 - val_loss: 4.5964\n",
            "Epoch 48/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.9318 - val_loss: 4.6240\n",
            "Epoch 49/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 4.9080 - val_loss: 4.5938\n",
            "Epoch 50/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.8800 - val_loss: 4.5267\n",
            "Epoch 51/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.8973 - val_loss: 4.5433\n",
            "Epoch 52/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.8488 - val_loss: 4.5200\n",
            "Epoch 53/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.8546 - val_loss: 4.6398\n",
            "Epoch 54/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.8663 - val_loss: 4.5372\n",
            "Epoch 55/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.7947 - val_loss: 4.5131\n",
            "Epoch 56/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.7804 - val_loss: 4.5624\n",
            "Epoch 57/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.7609 - val_loss: 4.4406\n",
            "Epoch 58/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.7453 - val_loss: 4.4943\n",
            "Epoch 59/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.7298 - val_loss: 4.4358\n",
            "Epoch 60/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 4.7277 - val_loss: 4.4348\n",
            "Epoch 61/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.7062 - val_loss: 4.5103\n",
            "Epoch 62/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 4.7812 - val_loss: 4.6003\n",
            "Epoch 63/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.7257 - val_loss: 4.4368\n",
            "Epoch 64/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.7109 - val_loss: 4.4251\n",
            "Epoch 65/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.6816 - val_loss: 4.3785\n",
            "Epoch 66/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.6478 - val_loss: 4.4075\n",
            "Epoch 67/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 4.6296 - val_loss: 4.4149\n",
            "Epoch 68/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.6126 - val_loss: 4.3693\n",
            "Epoch 69/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.6054 - val_loss: 4.3311\n",
            "Epoch 70/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.5899 - val_loss: 4.3718\n",
            "Epoch 71/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.5992 - val_loss: 4.3356\n",
            "Epoch 72/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.5682 - val_loss: 4.3345\n",
            "Epoch 73/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.5591 - val_loss: 4.3392\n",
            "Epoch 74/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.6918 - val_loss: 4.4846\n",
            "Epoch 75/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.5943 - val_loss: 4.3333\n",
            "Epoch 76/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.5429 - val_loss: 4.3719\n",
            "Epoch 77/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 4.5101 - val_loss: 4.3205\n",
            "Epoch 78/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 4.5086 - val_loss: 4.2883\n",
            "Epoch 79/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.5113 - val_loss: 4.3032\n",
            "Epoch 80/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.5394 - val_loss: 4.3564\n",
            "Epoch 81/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.5076 - val_loss: 4.3425\n",
            "Epoch 82/100\n",
            "1357/1357 [==============================] - 58s 43ms/step - loss: 4.4651 - val_loss: 4.3243\n",
            "Epoch 83/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 4.4624 - val_loss: 4.3878\n",
            "Epoch 84/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 4.5095 - val_loss: 4.2975\n",
            "Epoch 85/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.4493 - val_loss: 4.2531\n",
            "Epoch 86/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 4.4423 - val_loss: 4.2563\n",
            "Epoch 87/100\n",
            "1357/1357 [==============================] - 58s 42ms/step - loss: 4.4308 - val_loss: 4.2949\n",
            "Epoch 88/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.4231 - val_loss: 4.2685\n",
            "Epoch 89/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 4.4293 - val_loss: 4.3003\n",
            "Epoch 90/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 4.4332 - val_loss: 4.3408\n",
            "Epoch 91/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.4365 - val_loss: 4.2450\n",
            "Epoch 92/100\n",
            "1357/1357 [==============================] - 58s 42ms/step - loss: 4.4231 - val_loss: 4.2451\n",
            "Epoch 93/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 4.3929 - val_loss: 4.2030\n",
            "Epoch 94/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.3967 - val_loss: 4.2767\n",
            "Epoch 95/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 4.3831 - val_loss: 4.2333\n",
            "Epoch 96/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 4.3584 - val_loss: 4.2771\n",
            "Epoch 97/100\n",
            "1357/1357 [==============================] - 58s 43ms/step - loss: 4.3949 - val_loss: 4.2621\n",
            "Epoch 98/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.3742 - val_loss: 4.2439\n",
            "Epoch 99/100\n",
            "1357/1357 [==============================] - 57s 42ms/step - loss: 4.3431 - val_loss: 4.1714\n",
            "Epoch 100/100\n",
            "1357/1357 [==============================] - 56s 41ms/step - loss: 4.3499 - val_loss: 4.2090\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aakOMS1c4c1I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "ed5fe7dd-ccc7-41bd-bd4f-f75d0a79296c"
      },
      "source": [
        "plt.plot(history.history[\"loss\"], label=\"train_loss\")\n",
        "plt.plot(history.history[\"val_loss\"], label=\"val_loss\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"CTC Loss\")\n",
        "plt.title(\"Model's Loss over Epochs\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-yyn2luJQ1OU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "179b2264-fbe1-431b-8030-720f726a022e"
      },
      "source": [
        "# Get the prediction model by extracting layers till the output layer.\n",
        "prediction_model = tf.keras.models.Model(\n",
        "    model.get_layer(name=\"images\").input, model.get_layer(name=\"dense2\").output\n",
        ")\n",
        "prediction_model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "images (InputLayer)          [(None, 128, 32, 1)]      0         \n",
            "_________________________________________________________________\n",
            "Conv1 (Conv2D)               (None, 128, 32, 32)       320       \n",
            "_________________________________________________________________\n",
            "pool1 (MaxPooling2D)         (None, 64, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "Conv2 (Conv2D)               (None, 64, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "pool2 (MaxPooling2D)         (None, 32, 8, 64)         0         \n",
            "_________________________________________________________________\n",
            "reshape (Reshape)            (None, 32, 512)           0         \n",
            "_________________________________________________________________\n",
            "dense1 (Dense)               (None, 32, 64)            32832     \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 32, 64)            0         \n",
            "_________________________________________________________________\n",
            "bidirectional_2 (Bidirection (None, 32, 256)           197632    \n",
            "_________________________________________________________________\n",
            "bidirectional_3 (Bidirection (None, 32, 128)           164352    \n",
            "_________________________________________________________________\n",
            "dense2 (Dense)               (None, 32, 54)            6966      \n",
            "=================================================================\n",
            "Total params: 420,598\n",
            "Trainable params: 420,598\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0n-ZPN5sRLui"
      },
      "source": [
        "# Reference: https://github.com/rajesh-bhat/spark-ai-summit-2020-text-extraction/blob/master/CRNN_CTC_wandb.ipynb\n",
        "def ctc_decoder(predictions):\n",
        "    '''\n",
        "    input: given batch of predictions from text rec model\n",
        "    output: return lists of raw extracted text\n",
        "\n",
        "    '''\n",
        "    text_list = []\n",
        "    \n",
        "    pred_indcies = np.argmax(predictions, axis=2)\n",
        "    \n",
        "    for i in range(pred_indcies.shape[0]):\n",
        "        ans = \"\"\n",
        "        \n",
        "        ## merge repeats\n",
        "        merged_list = [k for k,_ in groupby(pred_indcies[i])]\n",
        "        \n",
        "        ## remove blanks\n",
        "        for p in merged_list:\n",
        "            if p != len(tokenizer.index_word) + 1:\n",
        "                ans += tokenizer.index_word[int(p)]\n",
        "        \n",
        "        text_list.append(ans)\n",
        "        \n",
        "    return text_list"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JI5nbx2xRm7R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "outputId": "c2332c59-b7fd-4a9d-ec96-8e06912dbd91"
      },
      "source": [
        "#  Let's check results on some test samples.\n",
        "\n",
        "for batch in test_dataset.take(1):\n",
        "\n",
        "    batch_images = batch[\"images\"]\n",
        "    batch_labels = batch[\"labels\"]\n",
        "\n",
        "    preds = prediction_model.predict(batch_images)\n",
        "    pred_texts = ctc_decoder(preds)\n",
        "\n",
        "    fig , ax = plt.subplots(4, 4, figsize=(12, 12))\n",
        "    for i in range(16):\n",
        "\n",
        "        img = (batch_images[i, :, :, 0] * 255).numpy().astype(np.uint8)\n",
        "        title = f\"Prediction: {pred_texts[i]}\"\n",
        "        ax[i // 4, i % 4].imshow(img.T, cmap=\"gray\")\n",
        "        ax[i // 4, i % 4].set_title(title)\n",
        "        ax[i // 4, i % 4].axis(\"off\")\n",
        "        \n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqwAAAJBCAYAAACDGOdsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9eXBc13Xu++1GAz0BaMzzRBAkCIKzSEqUKFETqVkeJNuRk8h+iRI7juOKU+89+926eeVKfIdKJc+pejexnWs5SjykriVb9tNk0RLFSRzFSRwAAiBBDCQBYmqgGz0P7w9gbe4+bICUTItN6vtVoQB0nz5n9znr7PPttddaW6VSKRBCCCGEEJKt2G50AwghhBBCCJkPClZCCCGEEJLVULASQgghhJCshoKVEEIIIYRkNRSshBBCCCEkq6FgJYQQQgghWc0tIViVUi8opb49+/fdSqnTH3I/31NK/fX1bd2HakdKKdVyg47dNHt8+404PrnMzWbXSqlvKaV+/Ls+Drn1uNlsnXw8uBXsUim1XSn13I049vXmIxOsSqlzSqmQUiqglBqeNYT8632cVCq1K5VKtV5De76olNpt+eyXU6nU317vNv02mDcMyT5o1+TjAm2dZCO0y7Rj39JOg4/aw/pEKpXKB7AGwFoA/9m6AT171xeez48E2vV1hucra6Gtk2yEdvkx4IaEBKRSqfMA3gCwDNBT4H+ulOoG0D372uNKqaNKKZ9Sao9SaoV8Xim1Wil1WCnlV0r9LwBO4717lVKDxv/1SqlfKKVGlFJjSqn/oZRqA/A9ABtmR2W+2W3TvJlKqT9RSvUopcaVUv+fUqrGeC+llPqyUqp7to3/pJRS1/L9lVLrlVJ7Zz93cbZNeRm2+1MAvw/g/5xt5yuzr9copX4++516lVJfMz7zLaXUS0qpHyulpgB8USnlVUo9P3us80qpbyulcma3z1FK/b1SalQpdRbAY9fyHciVfNztepY8pdS/z36Hk0qptca+r5vdkhsLbZ1kIx9nu1RKPQzgPwH43OyxjxlvNyql3p39XluVUmXG5+6YPQ8+pdQxpdS913q+P3JSqdRH8gPgHIAHZ/+uB3ASwN/O/p8C8BsAJQBcAFYDuATgdgA5AL4w+3kHgDwAfQC+DiAXwNMAYgC+PbuvewEMzv6dA+AYgO8A8GDG+DbOvvdFALstbXzB2M/9AEYxM2JzAPh/Aew0tk0BeBVAEYAGACMAHp59rwGAD0DDHOfiNgB3ALADaALQAeAvLftusbZp9n8bgEMA/u/Zc9EM4CyAh2bf/9bs+fjk7LYuAC8D+P7sOagAcADAl2a3/zKAztlrUgLgndnj2z8q27iZf2jXacf5FoAwgEdn2/jfAOz7Xdgtf2jrN9LW+ZM9P7TLtON8C8CPLa9tB3AGwOLZc7AdwH+ffa8WwBhm+mwbgM2z/5ff6Oua8ft9xEYVmD3ZfQD+GYDLuED3G9t+VwzOeO00gE0A7gFwAYAy3tszh1FtmL3YV4ivazCq5wH8nfFe/qzxNhlt3mi8/zMA3/yQ5+YvAbxsMdi5BOvtAPotn/+/APyrYbCm8VcCiMi5nn3tGQDvzP69DcCXjfe2gIKVdv0h7HrW9t4y/l8KIPS7sFv+0NZvpK3zJ3t+aJdpx/kWMgvW/2z8/xUAv579+xsAfmTZ/k0AX7jR1zXTz0cd0/HJVCr11hzvDRh/NwL4glLqL4zX8gDUYOZink/NntlZ+ubYZz2AvlQqFf8Qba0BcFj+SaVSAaXUGGZGJOdmXx4ytg9ixvCuilJqMYD/BzOxNm7MeFoPXWO7GgHUyFTDLDkAdhn/W89lLoCLxqyCzdimxrL9XOeSzA3t+jLWzzrVTOzY9bZbcmOgrZNshHY5P3PtrxHAZ5RSTxjv52JmpjXryKYgZNNIBgD8l1Qq9V+sGymlNgGoVUopw7AaMOPytjIAoEEpZc9gWKkM25tcwMzFlON6AJQCOH+Vz10L3wVwBMAzqVTKr5T6S8xMP2TC2s4BAL2pVGrRPPu3nssIgLI5bq6LmLn5hIZ5W04+KB8nu56P6223JPugrZNs5ONkl1c7tpUBzHhY/+Q6HPt3TrbWYf2fAL6slLpdzeBRSj2mlCoAsBdAHMDXlFK5SqlPA1g/x34OYEaQ/ffZfTiVUnfNvjcMoE5lSHaa5T8A/G9KqVVKKQeA/wpgfyqVOncdvl8BgCkAAaXUEgB/Ns+2w5iJ9xMOAPArpb6hlHKpmaSpZUqpdZk+nEqlLgLYCuAflFKFSimbUmrh7M0JzEw3fE0pVaeUKgbwzd/2y5E5udXtej6ut92S7OaWtnU1k4DzQcUBufHc0nY5e+wmpdS1arsfA3hCKfXQbJ/snLXtuuvQlutOVgrWVCr1HoA/AfA/AEwA6MFMXAhSqVQUwKdn/x8H8DkAv5hjPwkATwBoAdAPYHB2e2AmdvMkgCGl1GiGz74F4K8B/BwzhrkQwO9dS/uVUg1qJktvLm/l/w7g8wD8mLmB/tc8u3sewFI1k8H3y9nv9DiAVQB6MRO8/QMA3nn28Sxmpj1OYeZ8vgSgeva9/4mZmJVjmJmmyHguyW/Px8Cu5+R3YLcki/kY2Ho9ZuIbyU3Ex8AuX5z9PaaUOjzHNmZbBgB8AjPVBUYw43H9P5Cl2lClh2sQQgghZD6UUj8A8GIqlXrzRreFkI8LFKyEEEIIISSryUq3LyGEEEIIIQIFKyGEEEIIyWooWAkhhBBCSFZDwUoIIYQQQrKaeRcOmJqaSqVSKeTm5sJmm9G2qVQKiUQCAJCTk6NfU0rpHwBQSiEajep9ydJa5jbyuuwHAJLJpCwPhlQqhW3btuG1117D2NgYysrKcPvtt+Ohhx5CUVER8vO5KMnvgp/+9Kf4/d///eu2v1Qqpa6+1fUlFoullFJIJpMAkGZ3YodiZ/F4HJ2dnfj1r3+NHTt2YOPGjXjwwQexatUq5OTkpNmr+Xcymcxoz/F4HP39/QiHw1iyZAny8uYqx3fjKS8vx+joFZVXyCw3wnZfeOGFVGlpKaqqqlBZWQmv1wuHw4Hc3Nw0u1VK4Ytf/CJ+9KMffdRNJDcBH7XtpmYAgLS+1kSe74lEAtFoFD09Pfi3f/s3bN26FalUCnfccQe+8Y1voKmpSdt7Mpm8oh+WPnyOdqC3txc/+clP8Morr8DhcKC9vR1///d/D5fLBQCw2WxX7E+w3mPmsazHna8d18I//dM/4S/+4i+uvuHHiPnsdl4Pq4hUYMbQ4vG4FquzO0Y8HtcPbtkuFoshHo8jNzdXbysPdhGuIlLNB34ikUjbfzAYxNtvv43BwUE4HA7E43GcP38eFy9e/EAngHz8MAdWmToUscNkMolQKITe3l50dHSgqKgIDz744BWDIdnWXNfYup9EIoFgMIj33nsPL7zwAiYnJ7VgJuRaiUQiCAaDmJycxMTEBHw+HwKBACKRyI1uGiHXhFWoWh1ayWQS09PT6Ovrw+HDhxGLxQAAW7ZsQWlpqdYeSqkrxOrVkD49EAjon2AwiFgslqYv5mufVcCS7OCqS7NmGtmYJBIJ2Gw2LVrlfRkVmSMVqziVzyUSiSsEQSKRwPT0NAYHBxGLxdDe3g6Hw4FAIIDTp0+jtbU1Y3uTySSi0age+UQiERw8eBA+nw/Nzc1YsGCB9hgnk0nk5ubqYyulYLPZ9M0Sj8+suGaz2a743vL5eDyOnJyctG1McS7/J5PJtFGdeWOYwsr0RJsDBuu+RQiZwsncv7yfk5Ojz6vVG2gONMz2SudxM2MdIcvfVhKJBHp7e3Hq1CmMjo6ioaEBDQ0NV4zAzf1muh+SySQikQgGBwexbds2dHR04IEHHrjCbgi5GrFYDIFAQNtuNBqF1+vV/ZW1LyUkG7A+T662rc/nQ19fH0ZHR7WDa8GCBXA6nRmflQDm7MvNfjaVSmF6ehqBQADxeBx2u11rjLn6Y2u/nmlGLtNxr/UelOc/+e24qmAVwzEvtJx8U2Cabnt5T0STVawBuGJ/gmwTj8cxMjKCeDyOhoYGrFq1CrFYDEeOHEFnZyceeeSRjO1NJBIIh8O6PRcuXMBrr72GixcvYu3atdi0aRMaGhpgt9u18BPBaLbRZrMhkUhkbLv5fibBmml7U4Saol1+W6epTQ+0nCPrCFUplSb8zX2bbRFRbLfb09ojn5HvIMcToX6zY+0gzMGAEIvF0NHRgc7OTiQSCTQ1NcHlcsHhcKR9/modZCqVgs/nw+HDh3H48GFEo1EUFhaykyIfGBmsx+NxRKNRBINBBINBJBIJOBwOOJ3OtHuZkGzBqhVMrGJzZGQEZ8+eRTgcRiqVgsPhgNvtvmIfc03XZ9pGfk9OTiIYDKbpEPn8tYjM6zkYnE8okw/GvL1ePB6HzWZDbm5umqgyL7rVA2s+oDOJXXMfIpisF1TEVFdXF3JycrBx40Y0NDRgaGgI0WgUg4ODCAQCKC0tzdhuEWhKKQwODqKnpwejo6Ow2WwoLS1FfX09JDbXKrbFayoxY9FoFIlEIi1+TG4C06uZ6Ua12+1a/Fk9nxJOYY3flW1FONrt9jShZZ2SlvMs4lq2k8+JoLUOFsybOBaLIRQKwWazwel03hKem0yDBglXEQ97KpVCJBLBwMAAzp8/j9LSUtx+++3Izc3NKAjmGnDJ9bh06RJ27twJn8+HtrY2LFy4MKvjV0l2Eg6HoZRCLBZDOByG3++H3+9HLBaDw+FAcXEx3G53WsgVITcaq7Mk0/S69JXxeBzDw8Po6enR2xYVFcHlcmXMk5lv4G/qCTnW+Pg4pqamAMz0+3l5eVfMoGZqe6b2X+vzMFM8q+k4Ir898wpWuQDWWEC73Y5YLKYfxma8CZDu/RNRJwLKjHk1hZb52VQqhVgshvfffx+pVApFRUVwOBxwuVxwu92YmprC2bNn0djYmLHdEveSTCYxNDSEeDyOeDyOYDCopwlycnK0IJSbQgS0TEmYHuRYLJYmwEX0iadWXpNtbDYbYrFYmsfYeiPYbDbk5eXpG9T09Fo9p7m5uVeETOTl5aWdezPex2y/CHIAaedb2piTk4NTp04hFouhqanpAxnQzYKcN1OIJhIJTE1NYXJyEkopVFVVYdGiRdp2zKRCwezEIpGIDnvx+/3o7+9HT08P8vPz8Vd/9VfweDy3hPgnHy2RSEQ/WIPBoLavWCwGp9OpZ3Tm82YR8lEj9midWhfk2ZNIJBAKhTAyMoKLFy/qz+Tn5yM/Px92u13bfyaP6HyzXeLouXTpEnw+HwDA4XCgqalJ9/2Z4mvn+/+DkEgk0pxQ12Of5DLzClYZ7QgyBZ5KpeByuXSsowgzm82W9pA3vXgiEnNzc3W8is1mQzQa1a+Zx5LM7YqKClRXV8PtdqO0tBSVlZVIJpMYHx/P2GZT/E1PT+Oll17CxMRE2tRAbm5ummdYhLUZ2xqPx7WnErjS4Mw4T2m7GQsrN6GIH+CyN1puXBHNcl6kLbIP0wNtvRHkXIlwNQWptFfCAMTLK++ZxwKAM2fO4J//+Z+hlMLDDz98S2SNZ+o0zdcikQgikQh+9rOf4cSJE/B4PGhsbITH47nCjjN1NkopOJ1ORKNRRKNR7N69Gy+++CJisRjWrVuH6urqjB0XIVcjHA7D4XDogXIkEtFJV7m5uXC5XHrGZ64kEkI+ajKJtUwDqkQigTNnzqCzs1OLSrfbjbVr1+oBmTkoMz9n9VRmChGIx+OYmppCKBTSz/GCggIAl3Nr5Jksz9e5vMPm98gkyK3vW/NOKFSvL/MK1mg0qr2jcoElgFmmrcy4TEkQAGbEmRiYKa7kPZmOtRqbxFT6/X7k5eVhbGxMCyyZnjeTuTJhToVHo1EdG2O32xGNRrV3WDyMEvIgwk4EtBmuIB5WEZ3SBhkxyqjQ9GjKjWH11okQls9K+IK8L99N2i77N8Mp5PjWh5b5Wflf2iHXSI4n8XH/+I//iNHRUYRCIWzfvh1+v/8aTOfmQc6FOfjIy8vTnvpwOIz169fjgQce0LZgnTUQ5PyaMcWdnZ3Yv38/ent7UVlZiSeeeOIDZ7YSYiKD4Egkoh+80WgUTqdTl7lyOp30sJKsYb5pb9OBlUwm0d3djb6+PuTk5MDj8eBTn/oUnnvuOT11L9uZInAuB4A8Y83+1nRQ2Ww2FBYWpj1jzdBGa27DfELzaq+zz//dMq9gNQUYkB4bKqLJWvpKPiOCTwzHmn0vnzdjPMXTKSIuJycHDocjzZDk+HV1dXO2W/Yt3oloNAqXy6VFhDl1L8IauHxDSFkuEaHm1L95A4l32JyqF+R7iWC0hgzI9LSIZnNkZ4pi4PIgwYxxNQUrcHl0K55ZabOIbzN2U4RyLBbTo9xQKAQAupTOzY55LaxecrGjnp4eRCIR5OXloaysDBUVFVcMhjIJAvm8VKTYs2cPOjo6UFFRgY0bN6KpqYnTteRDk5ubi1gshmg0inA4jHA4rEOpJiYmMDQ0BK/Xi8LCQnpYSdZgPsPMXAt57gmxWAz9/f24cOECAKCgoAD3338/vF5vWjiAua+5HAemo8t0xPj9foTDYf2eCOFM5TTNfWZ6Xba/lu8/VzgEuT5cUySwOYIRg7LGoJpT0WaMpvm/NQBZvAhmsLSIgNHRUUxPT6Ourk4nR50/fx69vb1wu90oLy+fs71yPMmsjcfjqKqqwrJly7Bw4UIA0DXZTIFshjCImDbLYVjjbOUGML1p5muyrdyA1ix+06trHn8uoWOKXdlW2id/m7Gw5j5FtMkxpWboa6+9hnA4jIKCAu2BNhd8uFnJFHdqxvQmEgm89957mJqaQn19PZqampCfn5+2QIb5eWtHJPfEsWPH0NPTg1gshubmZtx///1pgxhCPigejwcAtFiVvkri8IeHhzE8PIzx8fFb4l4ltwbWPjLTDKwsGDAxMYFAIIDc3FyUlJSgvr4eQLqX1qobTI1g9q2iTcz8DVP4irNJnFPW2thmW6/GfH36XKECVmcW+fB8qNQ103CuJrRMYzNjYK1T4OZPMpnURdcLCgq013Z4eBj9/f2w2+26U5+vfeIpzcnJweLFi9Ha2orS0tKMxzSPDaS7+OUmsnpSJdZVtrPepEDmbHVT7Iu4NCsPmILI9AibP2Z7reXFrO+Z0yXm9QgGg+jq6tJ1bl0uV8Yb+mYl03kHZs51IBBAT08P/H4/mpubsXDhQt2xCea5spZHAWbiYN9991309/ejpKQEK1euxMKFC/X1ZSdFPgxFRUV6ZkQ8q9J/hkIh+Hw+DA8P48KFCwgGgze6uYSkYe33rE4ayR+Ix+PIy8tDZWUlCgoKrohbvVahZ3phpa+WZ7/M9k5NTaVVOvqwfNDP0st6fbkmwWp6C01vqim2gMwXxxRU8lnphGV6GriyRqbEjIrwi8Vi8Pl8mJ6eRnFx8Zx1CE1xEY1GkUzOFNtubm5GTU2NnhqQEZi024xnke8lFQTM103RIsewVlEwv7MZu2otDSavm+2W85BpIGAe27yZZVszZscUzeaxJQ43lUrpmziZTGLJkiXweDy3jMjKJFbN8ImBgQGduNfY2Ii6urp5k6TMayr/yyotY2NjaGxsxJo1a3RCn1X8EnKtlJSU6KQrc9ZEys9NT09jfHwcg4ODmJ6evtHNJURjPs+szzTpDyORiH5m5uXloaqqCrm5uWmLYli9qObnrfXSzSQqEagSRiP5Nj6fLy2ccb62X29uxDFvVa6pDqt0mCJ4TLGk1OVC85Ktbnpec3NzEQgE9HvS8UYiEV3zUwzJjMuUY3u9XgBAIBDA5OQkiouLsXnz5jkNIJlM6kLEFy9ehN1u1z8VFRXIz8/X5bhENMsNIkgb5LvLjWcd+ZnT7PK/jBLD4bCeGs6UiWjGnMrn5fubFQgkQUziXOUmlHhUOd+SYGbG61hvBvOmjkajGB4eht/vR25uLs6dO5cWt3srYL2m8loikcDevXsxMTGB+vp6VFZW6lhpc5BhftZ6ThKJBH75y19ibGwMpaWlWLRoEerq6tLioa+lSoDYllxX05bcbvdvewrITUhhYaG2R9MmxD5l6VZZXICQbMF87ljD6ICZfjEajWq7dTqdKCsrAwCdZ2Hu61qOY81FcTgcKCsrQ35+Pqanp5GTk5M2e2jtzz+saDSP/2GhU+ODMa9gFSEnwkiSd6wZ72JkMoUlHszp6WnY7fa0pdYkpiQajV4RUG2OxERklpeXw26349ChQxgaGkJ5eTmqqqrmFAM2mw0Oh0N7PlOpFBYsWICGhgYUFxenGYiEC5hiUfYh4s/M+M/kJbWuHmWKd1P0yn5FMJkeXTl+phGkiEu5Kc1zbQrnVCqFvLw8PR1i3oTSSZhtDYVCOHToECKRCFavXo2+vj74fD5UV1fPbzE3CabX37xeqVQKExMTePvttxEMBvH4449j0aJF+jPmYhLmtL4ZLgIAly5dwp49ezA9PY3HH38ca9eu1YMHh8Oh92cNIzAFbTI5s572uXPncOLECZw4cQLnzp3D1NQU1q9fj29+85tpsVmmTUhpOCvWKS9r9qz8bV20gmQPbrdbl64C0kvVAbgirIqQbMCavGw6emKxmH72DQ4Owufz6T7I4/Fox4xUH8pU2iqTsDRFo2wnM1zy+dzcXJ2fMNdS8+a+r/W++qD3H+/Z3555BauINPH+mOLULF8lGenA5QekZKibVQMy7d/03Jo1UKWMi9/vx/j4OA4ePIju7m6sWrUKJSUlc7bZDD34zW9+g1gspr2q1oQas3C+GYKg1OUam1J+yhTupniRh4dZqF/Og5SkAi5XQZBzaI25sY4YAei2Wb27IoSl/q0Zq2oGmUt5JrM0mQw6IpGIFlY1NTXwer0YHR29ovbuzYoZxiHXVBLNenp6cOnSJdjtdh0/JdfETIST8wOkr9ASCATwox/9CKFQCC0tLVizZg2ampr0IhDz1SI029Lf34+f/exnOH36NPx+P1wuFyorKwHMLFsoNmTGNosdWAdKmTA96qb9iJ2Q7MTr9aKkpATFxcUIBAJ6ACoPcgB6FSxWCSDZgnVxGuDyjJbNZkMkEoFSCkNDQwgGg3o2sLCwUDuIzDBBWXEwkUjoEBmz4ov5HDb7NxGp8my02+1YsmRJmlg1P2fmq/yusM5Kkw/HVUMCrMgFzsvL0/VBrbEhYkimiLXuQ4SsPMBFSEmA9LZt2xCNRlFVVYVt27ahu7sbhYWFaG5u1sWFMyFGHwqF0NXVhWg0ivz8fH0MMVoR3OY0sFmkH5gR5eZIT76btFum0M1MfVNQirdTQgPMY1hvGFO0WsMLTHErWAWv/G92ECJSzbhYaVsoFEJHRweSySQaGhrw4osvYmpqCnfffXdaSMLNjAxCzNjpQCCA3/zmNwiHw3jwwQf14CcajSKVurx4hHUAYdrG9PQ09u/fj3g8jscffxzNzc1pdmStFWy9dvF4HENDQ9i3bx9GRkbQ0tKCqqoq+P1+HD16FA6HA5/+9KfTYrPmi4OyYgrSTJ+Rc8KOMztxu93Iz8/XoQHT09O6TzIH9+ZDkJAbjZmrYoY6SZWLYDCIqakpHDhwAENDQ0gmk4hEInr59JKSEpw9exZHjx5Fc3MzWlpa9DLEbrdbF/83sc6CSV9dX1+P8vJyDA4OArjcf1oH7FZHgBXzeT/fd86UXG2S6Ri8fz848wpW4HLxetPbao5SzKL3wGWvKXB5EYBMHh4TuZjygI5GozrBSimFEydO4NKlS9i4cSOWL1+elvhlxTQwt9uNQCCAkpKStILEALQXUkSntMEUFzK1IB4OU7hKjK35Y/WmSqypNXNfzo3pHTXPhQhda6iE6QE2PyftMsWwbCPn3izPJddxaGgIiUQCly5dwuTkJEpKSlBbW6tXH7mZMcM1xF6k0+zq6kJOTg5Wrlyps7HFAy+Dmrlq505PT+ONN95AIBBAdXU1mpubdUdqenNNTztwuVxLJBLB0NAQfv7zn2NwcBBr1qxBc3MzBgcHceLECQSDQdx1111oa2tLs8lM01jyt1xvs1M1Bak5oMwUpkCyC4fDAY/HA6fTmTYzZM6YWMvkEXKjkTC8SCSCyclJjI2NYWhoCBcuXMCFCxcwOjqKYDCIkydPYnJyEvF4HBcuXMDLL7+M3bt3Iy8vD5OTk/D5fCgsLITX69WDt5KSEnz961/XOS2C6bgxvbPi/JJn/Pnz57FmzZo5B/NzTddn6nszzYbKe9bX5uODOCHIDFddOEBOqhiD+cAzBZBZScAUtNYRhPUCmytKyfuxWAzDw8MIh8PYs2cP+vr64HA4UFtbi8rKynlHJeIFi0ajCIVCSCQSqKmpgdPpTGufPLzNdsvxzdhVqyGbIzKryDXPVaY2mkle5nGs59I6qpO2Wrczj2s9B+brcwli8dx0dHQgFAph2bJlqK6uviVK5VinepRSmJ6exunTpzE2Noa6ujosWrQIbrf7ivMogyYznjWVSun6wHv37gUA3HfffWkJW9Z4K9NjLpmrAwMD2LFjB06fPo1169Zh6dKlCIVCGBgYwNTUFFpbW3HXXXehqKjoilkL+S7m77kwBbM8SGKxGPx+Py5evAifz6c7cJJdmEstW23ALHVFSDbR39+P48ePY3R0FKOjoxgfH8f4+DhGRkYwMTGBcDisV1cUR1Y4HEZ/fz+Ghoa0c2jt2rUoKChAIBDA1NSUXpXQ+ly1PguBy7NZk5OTCIVC+hkqM2iZZpbMvvKDhkpZnUpmArnA8Kvrx1U9rOKlshqKGfNpTjXLRTe9fMCVhYTFeEzPn+w7kUhgfHwcqVQKb7/9tk5CaWlpgcvlusKbZCKfD4VCGB8fRzKZRFlZmY6BAS4LuEQioeMO5bNWMZvJa5bJY2x+VsSmGRtrjWGxhgVIu02RbxXBpgAypz+sAwTrNIVV7MixZNvu7m5EIhE0NDSgvLwcvb29VzOLmwLxLAMz52Z8fBz79+9HNBpFXV0dQqEQJiYmEAqF9LZmIp4E6judTuTm5mJqagpnzpzB4OAgKioqcM8996CoqChjIL/ZgcbjcUxOTmJgYACHDh3CiRMnsHDhQmzevBmxWAzvvfce+vr6UFpaik2bNmHp0qU6Bkv2J/vKNEDJJGzl71gshsHBQXd4JdAAACAASURBVJw/fx4+nw9+vx/9/f04f/48gMxhP+TGYoaxWAe+IlpvpXrJ5Nbgrbfewu7duzE8PAyfz6fLSiaTSe0ptdvtGB8f1yUVPR4Pqqur4fV64fF4EAgE8Pjjj6OsrExvV1ZWBpvNljG/wny+SSgWMLNokJmsnClEK5P4/aDiUu7HYDCo480lfMFMGqMn9fowr2A14yplSjUej8Pv9yMQCMBms6GgoEB7mKxTsBLnagpM88JJDKz8LcZtt9uRn5+P4eFhBINB1NbW4uGHH8bq1avTaqNmQh7S4l0167tZxadkz1vFgUwlSHtMoSBhAuLlEFFqxhtm+q6mSJW/zWPLtuFwGMFgELFYTJf8MG9EUxTL95HzJ3+byWtmPVBzkCCfAYChoSG43W5UV1ejsLDwajZzU2AdAE1OTqKnpwcHDx7U1+5Xv/oVamtrUVxcjJycHH3u3W43cnJyUFlZidzcXDQ2NqK4uBgXLlzAvn37AAAbNmxARUWFrsxgJlNZB0KRSARnz57Fu+++i9OnT+POO+/EJz/5SeTm5mLfvn3o7OyE3W7H+vXrsX79+rQBn7UkmvndzP+t94PYYjQaxf79+7F9+3b09/dDqZmEwunpaWzbtu2WiVe+lZB716xZDaQvySzvMemKZAs//vGP0xIB3W43PB4P3G43Fi9ejLKyMhQVFWH79u3o7OxEOBxGY2Mjnn76aSxduhRFRUW4cOECmpqa4PV6dU1rpRQcDsecz1XgckUWCeuSvyXZS1a9lKRx0yMaDod16c3S0tIrQm0yOZrk73A4jImJCXR3d2NgYACRSAQ1NTVobW1FeXk5XC4XQ3euI1f1sMpFlyn2/v5+vPjii/D5fEilUli9ejXuvfde1NTUaFElvyXzz+l0XlHHVYxQxJ7pnTW9uk6nE3/+53+OVatWaS+plMjIhBjG9PS0zjKUbG8xNjFaEQR+v/+K6XprlQMzfEBGbtJOU3TKTQCkhx+YgteMlRRhnEgkEAgEcPr0aRw4cACnT59GXV0dvva1r+naqKYn1zzXMpKV7yQ3o0z9Z2qj1MKrqKjA0NAQ7r77brS0tOj1nG925BpItYmTJ0/ijTfegM/nQyQSwaFDh5CXl4cDBw6grq4O1dXVqK6uRnt7O/x+P4qLi1FRUaGT5i5evIj9+/dj3759KCsrw9NPPw2v15sWUiLXxiw3FYlEcOzYMWzduhXd3d1YsmQJPvGJT8But6O/vx8vv/wyHA4HNm/ejLvuukvXCLaG2lgxRat05GID8XgckUgEqdRMLWIAaG5uhsfjwaVLlxAMBlFYWIiNGzfi+eefRygUus5nn/w2yPLIshpQpsQ7q4eIkBuNZPPn5eWhvLwcK1euxPr161FTU4OKigo987lz5079bK+oqMAdd9yhnQN1dXVacwBIy/iX/tV0JMnz2VxGfXR0FGfPnsXo6CiSyZlVDbdv345PfvKTesAnz7jp6WmcPHkSL7/8MhYuXIhnn31Wr6KZyTMqA0WlFILBILq7u7Ft2za88sorCAQC2pv80EMP4dFHH0V7ezvcbvcVnttMTgZyda6qTCQG7vjx49ixYwcOHz6McDgMl8uF1atXY8eOHQiFQlixYgUWLlyIioqKtFGPxKXI/5LgAlxOBhFhJXF2Pp8PPp8PDocDGzduRGtrq56ezZRgYkUywWOxGOrq6uB0OnV5IDMGN5FIoLe3F7t27UJfXx/Gx8dRWlqKtWvXoqWlBS0tLXqUZ2IugGB+B3NKQerKyfbSZvHAyTmKRCIAZh5SP/nJT7Bz504EAgHY7XZcuHABv/zlL/H444/rm8dcbEAeZpLYJW0Ih8MYGhrCjh07cPLkSTz33HOor6/X1RXMBLrBwUGsWLECzz33HCoqKvTrNzsyIEgkEvD7/Th37hw6OzsRCARQVVWF4uJiLF++HLfddhsWL16svazSQcq1tNlsmJycxN69e/HOO+/AZrPhjjvuQElJib6GZgdrjsDj8TiOHTuG//iP/8ClS5ewdu1aPP3001BqprTL9773PYRCITz99NNYvny5HmCZ01Om59wULnIswezE5Wf//v147bXXtP3K6i9erxef//zncfvtt98Sg5NbDZle9Pv9euABXJ79kXvY4XDw+pGswW634+mnn8Y999yjY/tlRhSY6ZNDoRACgQDC4bCuxy4ziPIjziHTgWQN57N6O6VPzMnJ0cnaeXl5+hk5Pj6OQCCgnVAibiXMa/fu3di/f79+JkiyoymUzVngSCSCt99+G6+88gqOHj2qc3HsdjtCoRDefvttFBUVobi4GI2NjWkagiECH555ezvxwr3xxhs4duwY+vv7EYlEsGjRIjz77LPwer2455578O///u84ePAgNm3ahE984hPIz89PW9JUkAslAdfAjGAzV8ianJxEb28vwuEwACA/Pz+tphoALSjma3dvby8SiQTa2tp0Jri5ZGY0GsWOHTvwk5/8BMDMw1ySX/r7+7F06VL82Z/9mRbcpidNBIoIcREVMro7evQodu7ciT/5kz9BbW2tbpMIEjF+mTZOpVJ455130NHRgdzcXCxevBgejwcnT57E66+/jk2bNumbX4SvNX5YKZU29bxr1y4AgN/vx8DAgB65RqNRhMPhtJie5uZmXQLrVpm+MFcZO3bsGA4dOgS73Y6HHnoIv/d7v4fCwkLk5eXB6/WmrSokscfmgMLn8+HixYsYHR1FZWUlnnrqKeTk5OgBiSD2Lnbh9/vxi1/8AgMDA1i3bh02b94MAPjud7+Ljo4OTE1N4Stf+Qra2tpQUFCg7cEc1JlehVgshmg0iry8PH0PWEWrJBy+9957+M53voOxsTHY7Xa9BOKGDRtw//33Y9GiRXpBD5JdSKyxGQNoDmIcDge8Xi/Ky8t1MikhN5qVK1fi/vvvx4IFC+B0Oq+Y4bPb7XC5XGnPMumvAOjBujltD6SHJmbyVMr+5e+ioiKUlpaiuLgYADA5OalXdfR6vbpNoVBIe1cnJyeRl5eHEydOoKWlJa3Wu5kzIpVmBgYGsG/fPhw/flxri/b2dtx111148cUXEQgEsGPHDhQUFOCZZ55J86ias6vkgzGvYE2lUti9ezd27tyJCxcuwOFwYMWKFXjqqadQU1Ojhd/U1BQmJiZw5swZnDx5EuvWrbtidSggPf5SBJ7EAIq3b9euXfjFL36hL7C4061JRnNdbIlJmZycRE5Ojs62FpLJJPx+P3bv3o1f/epXGB0d1SED+fn5AGY8HKOjowiFQnqKI1PyktkuiZs9ePAgfv7zn2N4eBjPP/88vvnNb+obUQYA4imWNZXHxsawbds29Pf36weQxP/K/2aSm5npaI5eu7q6sGPHDhw6dAjRaBRtbW04c+YMjhw5gpUrV6atOGZON7a1taWNhm+FG0nsTgYhly5dQnFxMe666y40NjbC7Xbrkb1cRzPmyVxl6MCBAzh27BhcLhdWrFiB4uLitBAN85zJfmKxGLZv346zZ89i8eLFWLVqFeLxOLZu3YqDBw8iLy8Pf/iHf4hly5bpuGHT02CNTz5//jwOHDiArq4ufPKTn0R1dbUOPxCR7PP50NfXhyNHjmD37t0YGBhATk4O6uvrcfvtt2P58uVoamrSVTNuhet8KzI1NYVQKJQ2sJffNpsNHo8HxcXFFKwkq/jqV7+aNpMnfaEZ1mKz2bQTyhSzwOWFiszqPGbfnCmW1AzFkm2KiorwzDPPYPXq1dizZw9ef/11hMNh9PX1ad0SiUTQ3d2N/fv349y5c3pwODg4iGAwqBPEzOdrTk4OJiYm8IMf/ABnzpxBb28vQqEQ8vLy0NbWhs985jO6mkEyObNEvMygSl9rzpKSD868gnVoaAhvv/02+vv7kZubi6VLl2Lz5s1oamrSD+WxsTE9fXXu3Dl0dHTgtttuSwsHMAWWOWVpFsM+d+4c3n//fRw7dgyDg4PaCE0vkJnYMpeHVYShJF1VVFQAmBF8Iip7enrwzjvvYHh4WHufzESn4uJirF27Fk6nMy1pSdovN4u0UQTDwYMH8c4776C3txdKKZw5cwYHDhzAwMAA/H6/vlldLhcikYjOTr906RK6urrg8/l0kLbf70deXh4qKip0wLk50jMfZHIdDhw4gPfffx/T09Oora3FsmXLcP78edTW1moPdjKZxNDQEPbv36+nOCRG1vyONzsyCOrp6UF/fz9isRjq6+uxZMkS7dG3xlybHaJ41YeHh9HT0wOfz4e6ujps2LAhbVUs87xZA/l37dqFQCCA9vZ2VFVVYXh4GF1dXUgmk9iyZQvWr1+vQ13MkALZhxnv3NXVhV27duHMmTOIRCIoLy9Pi8eWckc+nw/vvfceLl68CJvNhqeeegpLly5FS0sLqqur4fF4tPf2VrjOtyISCiAzBOZqd3l5eSgqKkJZWRkqKiooWEnW0NzcnJYfYvWKysqRVVVV8Hg8mJyc1B7LTCUe50o0tTrBTMeRxMY2NjYilUphYGAADocD4XAYZ86cwbp162CzzSycc+rUKT2dL7NYY2NjWmCbxwFmNMSxY8dw4MABXLhwAdPT01rfTExMYPfu3RgdHcX09DQAoLS0FGVlZVf0s+x3PzzzClbx6Ii37o477kBbW1vadLKZIDA9Pa1rrJlZ8xKTYhVaIi4HBwfx7rvv4tixY1rsyY94n6yjq7mIRCIYHx/X8Xrnzp3TU6x5eXmYnp7GqVOn0NnZqT28MqorLy9HfX096uvrsW7durRkLdOra42DjcfjOHv2LPbu3YuOjg7E43G4XC6Ew2Fs3boV586d03GpHo9HC9ZAIICcnBz4/X593oCZQPD8/HysX78eixcvTvMyW88fMHMj9fb2orOzE6OjoygoKEBJSQmGhoYAACtWrNDfxfTWmd5hM1HrVknmiMfjeP/999HX1wen04mFCxeivLw8rYMzxbo5CJHRf0dHBy5evAiv14vly5ejtbVVd26mt9v0sMbjcVy8eBFnz57VgnRgYADd3d0Ih8PYsGEDNm3ahNLS0rT2SgcpAzKxLbELm82GqakpHD9+HFVVVYhEIjp0RmwLAC5evAi73Y7bbrsNTz75JGpra/Xa9JlqC5PsYmJiQg+grYmWTqcTRUVFKCkpQVFRUVqCHyE3EqtzB0ivbAHMOAdkoDU+Pq5rWwOXdYJVnFpnNq3PJ2vogMSSer1eVFRUoLi4GIODg+ju7tYJ04ODg+js7NTl/USvnD9/HtPT02kLAwnhcFiv0hUIBHRtV+nvh4aGtNZwuVxobW1FS0tLWl6C1TlCPhjzCta3334b4XAYxcXFuOOOO7Bq1aq0NekDgQCCwaA2FFlOUOIqzelvM9MduFzG6sKFC3jzzTexY8cO7RUqLi7GxMQEksmkToYxDR6Yuxiv3+/H2bNntWB94403tAEXFBRo40omk9qwXC4X6uvrcdttt2HlypWoqKiAx+NJi5sV47V6OaPRKMbGxrB9+3acPHlSe1LlPJ04cQLA5ZttamoK4+PjAKCNOxgMamMuKirCwoULtTe7vLwcDodDixZTIEkGezweR0dHB8bGxpCTkwO3241IJIK33noL7e3taGho0O2ZmJhAb28v+vv708SZGSd3q9R3DIfD6OjowPDwMJYsWYJly5allUoBMg+A5LyEw2G8//77mJqawuLFi3VBa+mEzSQ6M0QgmUzi6NGjCAQCaG5uxqlTpxAMBhEOh9Hc3IxnnnlGx0hlKutmDgLN2n6tra0YHBxEUVERVq5ciVAohLGxMSSTSeTn58PtdmNychIejwcOhwN/8Ad/gKamprRV3sxwFpZEyk4uXbqk6wObFVFyc3O1YC0sLITb7WbSFckarJ5OIPNCPFLuKZlMIhgMYnh4WH/eDCUwZzPFzs1QOInZl+3NMplScrOurg719fUYGBhAV1eXjg3fv3+/dsbZbDaUlJRgdHQUvb296O3tRUlJCbxeb1pY1tjYGI4fP45wOHzFMzIUCukKAeL4WrduHRYvXnxF5RcmXX145u3tLl68iHA4jCVLlqCurk6feIm/3Lt3L1577TXE43E4nU5Eo1FMTk6mTQdIQLVcLJnClmnTF198EVu3bkUwGERpaSkWLFiAiooKvPrqqzoA25y+FRE8Fz6fDx0dHQCg4zLdbreuHxsOh7VnNT8/H2vXrsWaNWuwZMkSnYhjLodoZiuaBifTClNTU3jjjTewc+dOXLp0Ka2cksvlwrp16wBcXmlDPiM0NjZiz549Om5y06ZNeOqpp3QWutyIkt0o51IEtySq9ff3w+/36xJiXV1dWL58Ob7yla9oL3I4HMbOnTvxxhtvaA+ieP9SqVRaUtfNjiz7Nzk5CYfDgcbGRixatOiKOCkgfblgGZjk5eVhcHAQIyMj8Hg8WLRoEVpaWtLKrMhUkikaJNFg37592vMty96uXLkSjz76KKqqqnR2rJncFQqFEAwG4ff7cfLkST2wCIfDcLvdKCoqwrJlyxCLxXD//fdrD5vY5cjICPbt24eCggIUFxejoaEhLctV2jdXqSySHVy6dEn3VebUp/RZ8jB1uVy8liRrMB065vLsANL63Pb2dtTU1KCvrw/RaBTDw8Npi7ZI/XCrc8rsu6wOLNEKZq11h8OBiooKLFmyBPv378fFixcxMjKCSCSCPXv24MyZM0gmZxYWWrduHV555RWEQiHs2rULFRUVuhJHIpGAz+fDiRMn9IpcbrcbwWBQ54I4nU4UFhZiwYIF2Lx5M+644w4dz2uK91spsflGMK9gFQ/gnXfeibq6Or1O8PT0NA4cOIDvf//7Oj4EAIaHh3HkyBGsWbMGy5cv19PQMsKSUlBjY2N477330NnZiR07dsBms2Ht2rV48MEHUV9fj61btyIajeqyENayPgDm9A75fD50dXUBALxeL7797W8jFAqhoKAAoVAIhw4dwuuvv45UKoXFixfjS1/6kvZAifgwy1CJy98UjbKyRX9/P/7hH/4B58+fTxPChYWFWL16NZ555hk0NDToz4rBioBIJpMIhUKor6/Hr371K0xNTWFkZASDg4MoLS3VxzbXtTcDzWOxGMLhME6dOoXu7m74/X4UFBRg4cKFaG1txYMPPqgFfzgcxiuvvIKtW7diYGAAwOWVoI4dO6Y9c9Jh3OyEw2G8/PLLGBoaQkNDAxYuXKgTjXJychCJRDImrolgjcfjeOmll3Du3DmsXbsWCxcuhMfjucIWzUoBck1zcnJQWFiIeDyOe++9F21tbaivr0dlZSW8Xq8e2CUSCbz//vsYGxvD+Pi4rgecTCZx5swZnXjjcrkwMTGBrq4u3fk1Nzfj6aef1glaYrtSxqu8vPyK4H6zo7SuMEeyB3MZS0Fmg4qKilBeXo7i4mImzpGsQ/oUeXZan3cygyozg4FAAGfOnEE0GoXL5dKJhmbxfzMUQPot0ylmOh6slJaWYvXq1fjXf/1XhMNhHD16FLt27UJvby/i8TiqqqqwadMmbNmyBQcOHMD58+exbds2RKNRLF++HEVFRfD5fOjp6cGhQ4fg8/lQVFSETZs2obOzE4cOHdIC+4knnsCnP/1plJWV6dUR5fuY5bTEE3wrzWZ+VMwrWJcuXYrjx4/j+9//PlpaWuDxeDA6OooLFy4gmUzC4XDgT//0T+H1evHrX/8aR44cQV9fH/7u7/4OGzZswGOPPabLsIyNjenkoh07dgCYWQmjpKQEf/zHf4y2tjb9MF+/fj1efPFFAEB1dXWaB0sExVwdtXjAPB4P2tvbUVdXp8MRxMt65MgRDAwM4OzZs7h48SJqa2vTahqaQdjA5Wlbs1zRvn378NOf/lRP70vNt7y8PDQ1NeGJJ55AXV0dcnJy4HK50hLFzJu6oKAADzzwAPbt24dgMIgTJ05oT9uyZcvg8Xh08XvJYJeR3f79+3Hs2DGcOXNGh1DcdtttePrpp7W4SqVS8Pv9ePPNN/Hqq6+iv78fHo8HDQ0NyM/Px549ezA6OqrPmyyYcLMTCARw6tQpxONxbNiwAXfeeSc8Hk/aNbWKczkHMhV/+vRplJWVYePGjViyZElah2hO35siVmznsccew7vvvovOzk4UFBTAZrNpj28kEkFXV5e+lmJfHo8HtbW1KC0txac//Wnk5ubqVbeSySR6enrw61//Gu+//z7eeecdPPXUU7Db7Vqo5ufno7a2FvF4HENDQ+jo6MCKFSt09Qv5juY5oODJPmKxWFrcstvtRmlpKWpqanSta0nOJCRbkD7RrGZjik5J1C4oKEBpaSny8/MRCAT0TNKSJUt0OULBdByZxzE9q+JokOezNYRQloUNBAL44Q9/qKf0CwoKsHr1anz+85+H0+nEs88+i+985zsIBoPYunUrtm/frnUDAL0C52c+8xmsX78eTqcTfX19GB4eRiAQ0OWsHn74YSxdujStVGQmj7P5P7k25hWsX/jCF/A3f/M3mJqawokTJ7RXr6SkBF/84hdRVFSE5uZm5OXlwePxwOPx4K233oLP58O2bdvQ1dWFYDCo61zG43EdW5pMJtHY2Ig/+qM/QmNjI1wuF4AZA62srITb7UYgEMD4+DgqKirSVr+ab9rajOE0E7akBNCiRYvw6KOP4rvf/S4CgQB+8IMf4Otf/zqKior05804P7npAoEAjh49iu7uboyMjODs2bNQSqGxsRHT09Pw+XwIBoP6O0hguSTNmEYroy0JAygpKcHXvvY1/PSnP8XBgwdx8uRJnD9/Hk6nE8uWLUN9fb32nA0NDWFoaEiLVgBp9Rol2Wf58uXaI/7CCy/gnXfewejoKIqLi7FhwwY88sgjOHXqFPbt26eTd6R01q2AZHGKZ1Omi8yYKvFKxmIxPeUKzHSIr776KiYnJ3HfffehsbFR27C8b43HNkf9Sik0NDTg2WefxZtvvok9e/Zgz549ulKF1+tFUVGRXhBj8eLF8Hq9elpJ4sGtlTUKCgpQX1+P8+fPIz8/X89gyDZOp1NPFU9NTWHnzp1YsGCBTtoz6x2a7SfZRTQa1Q9fCQOoqKjQ8XgFBQVpy0kTkg1I/2oNsRJxJs+rnJwcNDQ0oLq6GqdPn8bExAS2bt2KpqYm/T6ANA+tOQtmzjaaK1z5/X4UFhbqqjpSRaW6uhr33nsvXn/9dV1a0ul04o477sDDDz+sPb4bNmzQs75TU1P6/rLb7SgtLUVTUxOefPJJvYzs/fffj0gkgldeeQWTk5OYnp7GG2+8gaNHj+JTn/oU1q1bp0P7PB6PbhuTrz488wrWuro6bN68GUeOHIHX60VZWRk8Hg/q6uqwZs0aHUPlcDjQ3NyMxx9/HHV1ddi7dy+6urrQ19en3eXivSsoKEB7ezuWLl2K9vZ2LFq06IrOt6ioCPfdd58uPbVw4cK0YsJm5rQVWept9+7dWLp0qS7wLoLX6/WipaUFq1evxvHjx9Hd3Y0f/vCHaG9vR2lpqV4VSwx+enoap0+fxsjICIaGhuD3+3UMy6ZNmxCLxfDmm2/qkh2tra3YsmULCgsL05ZeNUeechOb8ZS1tbV44IEH4HA4cOzYMQwPDyMvLw9TU1PaQybLsEoWIwC0t7dj5cqVeP311zE0NISTJ08iGAziwIEDAICRkRGcOnVKxyLffvvt2LBhA2pra3XpJAl+lxv5VhCt7777LuLxuI6XMr3GkqxmZpXK68BMx3rgwAEUFxejra0NhYWF+jqZSW92u117VM3wApvNhsLCQr3ii8/n0wk0NpsNlZWVyM/P11NH0pHl5OTodppx0wD0gEvKrsmMgHTcMpPR0NCAL33pS3j++edx+PBhlJSUYMuWLaiqqkpbuIJiJ3uR+GZZVrqoqAiVlZV6iUuZPeE1JNmENavenMqXxCkRkbfddhv6+/sxMDCAYDCIbdu2oaKiAq2traivr4fNZoPL5dKzYub0uvxEIhH4/X6MjIxg7969OH/+PL785S/D7XbrNkgi84MPPojt27cjFovB4/Hg7rvvxpYtW9Da2qr7xaqqKnz2s5/FsmXLMDg4iFAoBJfLhYqKClRVVaGoqAiNjY26jmxzczM2b94Mu92OV199FSMjI9pj/NJLL2HXrl0oKSlBSUkJmpqasHnzZl3fNTc39wpvMrk68wrWkpISPPzww1i0aBHy8/NRXFys46gkW1oe5JK9Wl9fjwULFmBgYEB7dCRuUIReVVUVFixYgMrKyrSRhhh4Xl4ePvvZz2Lx4sVYvHixfqCbMaDWVYaE4uJirFq1CqOjozpDT4SAJCpVVVVh8+bNqK2txeuvv46DBw9iZGREey6ktqF4gvv6+hAMBuHxeHQNxPb2drS3tyMQCODAgQOIRqP62KtWrdJZkPJgMWvMyd9mRmRubi7a2trgcrnQ0NCAc+fOYWxsTC8ZW1RUhAULFujQA8kyb21tRWtrKxKJBHbt2oX+/n6cOHEC586dAwCd4LVixQqsW7cOa9as0ck4dXV1uO+++7B792709/ejrq4Obrf7lriJOjo6kEgkUF9frytXiAgwQ0zMgYPYVigUwsjICJYvX47q6mptD+Z5MT39ZrKBWS+zqqoKXq8X09PTad4FWdVKEqasVQvEXqzCUo5XVFR0RRaubFdcXIw777wTZ8+exW9+8xvs2rULNptN328yeyGhBhzhZx8St+7xeFBZWYm6ujrU1NSgtLRUVwYw4+wJyQbMuFJrqT+zvxIP6+rVq9Hd3Y2jR4+iv78fr7/+Ok6ePKlrj5eXl6OmpgYulwtutxtnz57VK2hNTk5iamoKfr8fly5d0nkYd999N6qrq7WTKplMwul0orW1FWvWrEFPTw9Wr16NzZs3Y/ny5SgpKdH9p9PpxOLFi1FTU4OxsTGEw2Fd97iwsFAfW5wSXq9Xa4y8vDy8++676OvrQygU0mEO4pCoqKjApUuX9MqKlZWVKC4u1vkk5NqYV7AWFhZi+fLlWL58+TXvsKSk3mjhhAAAIABJREFUBAsXLvytGlVQUICKigrce++9H/izUjR448aNqK2t1Zn0cgPl5OTA6/Vi/fr1aG9vx9DQEAYGBjA2NqZFtjwQwuEwSkpK4HQ60dzcjObmZlRWVqKsrAwrV66E3W5HJBLB+vXrMTAwgNraWqxcuRKlpaX6ZjEfKtZ6cqbQsdvtKCoqwooVK7BkyRKMjo6iq6sLJ0+exOTkJKqqqrB8+XK4XC6kUim9YkdhYSFsNhseffRRAMDhw4f1SM/hcKC2thZlZWV48MEH09qWSqVQUlKCp556Crt378axY8f0VMetUO5oYmJCx/76fD4kk0m4XC7dMQGXQz7MqfxEIoFAIIB4PI41a9bA6/VeEdMrwf9mPLU1gF5sze12p3lN5cdc6lcEtLUGrjXBK1PSlGxj2lR+fj6efPJJjI+Po7e3F/v27UN3dzeam5vR2tqKwsJCPVi8FQYntxq5ubnIz89HZWUlmpubsWDBAu3hoYecZCumQDX/Nt+Tfs/tdqOtrQ333Xcfpqen0dfXh1OnTumEaRF1tbW1KCkpgcfjwZEjR3Qyqyy1KuFsdrsdtbW1OnFV+kVxBpWUlOCxxx7D0aNHsWXLFixevBgFBQVafJoOpYqKCpSVlQFIj8t1Op16WXPpx4uKitDW1qZnP3bv3p0mpmVG9MSJE+jo6EB+fj6cTieWLFmClpYW9Pf3f0RX59bglivil5OTo71M8oAXb4Q5hZqbmwuv14uvfvWrOHnyJDo7O3HmzBmMjIxAKYXS0lKMjo6ira0NdXV1WLt2LWpra9OWnUskEsjLy8OTTz6pp9PFcwVczhqXUZlZqkPibyTxR8SHiJmGhgbU1NTgnnvu0duaJb7EYygluJRS2LJlC1asWIGzZ8+iq6sLVVVVaGxsRHt7O9xut165C7icES/LdJ48eTKt3NbNTmFhIYaGhnDo0CGEQiG0tbXp0BJzECE2Ya66FolE4HK5dN1VEZzWun+SZCevA5fXtJZraiY2yXUyp4LMNbCBywl/DocjLVbWXOoYuOzNkNho04tht9tRX1+Pb3zjG9i5cycOHz4Mn8+H0dFRnVk+OTmp48lJdlFZWYmSkhJd3aKurk7HJpuhUcwyJtmEWQrSilkeUvrDuro6PPLII6itrcVLL72Ezs5OLfBkeXURsNZ6wzabTc+WScjMZz7zGTzyyCM6UVme/Q6HAw6HAw899BA2b94M4HIeiTn7G4vF9IysOXNhtltKdcoMr4QuOBwOfPazn8X69esRi8XQ09ODEydO6DrZvb29GBsb0+cilUohPz8/LSGWXJ1bTrDKiEpEhRikJNaY8aNSYuPOO+/E7bffroVhLBbT4tKsHiD7EmEZiUR0AovcQLIPqeUpbRJRZCb+mDGQIkxkdCptlBAA8QrK+5KkFgqFkJubC4fDgerqapSVlWHRokXYvHnzFWXF5EeWow2HwygsLERLS4uOT75VvDfPPvssnn/+eVRXV+O2227DokWL0sR9pu8o56axsRGf+tSntHfVzDiVwYKcQxGrYgdmoL75+7fBTK7KxHxVHT73uc/hc5/73Jzvf/WrX4Xf7//QbSPXn7q6Oj3YbGxsRFlZWVocviSaEJLNmPGmpgdTkPJ/69evx+rVq3HkyBEcOXIEp0+fxvDwMKanp3WSlIjgsrIyVFdXo6CgADU1NaiurobL5UJbWxsWLFig82rM56k8n80qMWYYlrTpav0sAB3qZ66QKA4KCeuz2WxYtmwZHnvsMf3M9fv9OH78uA4Vq66uRmFhIf7lX/7l+p3wjwGKMWyEEEIIISSbYcQ+IYQQQgjJaihYCSGEEEJIVkPBSgghhBBCshoKVkIIIYQQktVQsBJCCCGEkKyGgpUQQgghhGQ1FKyEEEIIISSroWAlhBBCCCFZDQUrIYQQQgjJaihYCSGEEEJIVkPBSgghhBBCshoKVkIIIYQQktVQsBJCCCGEkKyGgpUQQgghhGQ1FKyEEEIIISSroWAlhBBCCCFZDQUrIYQQQgjJaihYCSGEEEJIVkPBSgghhBBCshoKVkIIIYQQktVQsBJCCCGEkKyGgpUQQgghhGQ1FKyEEEIIISSroWAlhBBCCCFZDQUrIYQQQgjJaihYCSGEEEJIVkPBSgghhBBCshoKVkIIIYQQktVQsBJCCCGEkKyGgpUQQgghhGQ1FKyEEEIIISSroWAlhBBCCCFZDQUrIYQQQgjJaihYCSGEEEJIVkPBSgghhBBCshoKVkIIIYQQktVQsBJCCCGEkKyGgpUQQgghhGQ1FKyEEEIIISSroWAlhBBCCCFZDQUrIYQQQgjJaihYCSGEEEJIVkPBSgghhBBCshoKVkIIIYQQktVQsBJCCCGEkKyGgpUQQgghhGQ1FKyEEEIIISSroWAlhBBCCCFZDQUrIYQQQgjJaihYCSGEEEJIVkPBSgghhBBCshoKVkIIIYQQktVQsBJCCCGEkKyGgpUQQgghhGQ1FKyEEEIIISSroWAlhBBCCCFZDQUrIYQQQgjJaihYCSGEEEJIVkPBSgghhBBCshoKVkIIIYQQktVQsBJCCCGEkKyGgpUQQgghhGQ1FKyEEEIIISSroWAlhBBCCCFZDQUrIYQQQgjJaihYCSGEEEJIVkPBSgghhBBCshoKVkIIIYQQktVQsBJCCCGEkKyGgpUQQgghhGQ1FKyEEEIIISSroWAlhBBCCCFZDQUrIYQQQgjJaihYCSGEEEJIVkPBSgghhBBCshoKVkIIIYQQktVQsBJCCCGEkKyGgpUQQgghhGQ1FKyEEEIIISSroWAlhBBCCCFZDQUrIYQQQgjJaihYCSGEEEJIVkPBSgghhBBCshoKVkIIIYQQktVQsBJCCCGEkKyGgpUQQgghhGQ1FKyEEEIIISSroWAlhBBCCCFZDQUrIYQQQgjJaihYCSGEEEJIVkPBSgghhBBCshoKVkIIIYQQktVQsBJCCCGEkKyGgpUQQgghhGQ1FKyEEEIIISSroWAlhBBCCCFZDQUrIYQQQgjJaihYCSGEEEJIVkPBSgghhBBCshoKVkIIIYQQktVQsBJCCCGEkKyGgpUQQgghhGQ1FKyEEEIIISSroWAlhBBCCCFZDQUrIYQQQgjJaihYCSGEEEJIVkPBSgghhBBCshoKVkIIIYQQktVQsBJCCCGEkKyGgpUQQgghhGQ1FKyEEEIIISSroWAlhBBCCCFZDQUrIYQQQgjJaihYCSGEEEJIVkPBSgghhBBCshoKVkIIIYQQktVQsBJCCCGEkKyGgnUWpdQLSqlvz/59t1Lq9Ifcz/eUUn99fVuX8Tj/SSn1g9/1cUj2cbPZKvn4cCvZplIqpZRquZFtIDeWW8GelVLblVLP3YhjX29uKsGqlDqnlAoppQJKqeFZY8q/3sdJpVK7UqlU6zW054tKqd2Wz345lUr97fVuk5VUKvVfU6nUc7PtaJrtXO2/6+OSa4O2SrIV2ia5laA9px37W0qpH/+uj3OjuKkE6yxPpFKpfPz/7N15lJzHed/7b/U+PT37PgAGO4iFAFeJZEhRPFosWjJpmbZM24pC6yZOlKObHEmRneXaNzqOYim5jsVzTStyJNvylW1ajszYpCXLkiJzkUVxXyASBLHNYAaz70vv3e/9o7te1DR6BgMQwDSA3+ecOTO9vf32O9X1Pm/VU1VwI3Az8OuVT1DgJjVCZVVqlcqmXElUnq8Cl2PACoDneaeAvwWuBb/75uPGmCPAkfJ9P2WMedkYM2uM+aEx5oB9vTHmBmPMi8aYBWPM14GY89hdxpgh5/YmY8wjxpgJY8yUMeYhY8we4EvAbeUru9nyc/0uhPLtXzHGHDXGTBtjHjXG9DqPecaYjxljjpT38feMMWYtn7/iSurJ8u/Z8r7cdi7HUi4uldUz3qdyn/uNMf/eGPO6MWbGGPNHxphY9a3JhaSyaYKmlF51rPwZXjDGbKryvDZjzGPGmHljzHPGmM8apxXNGLPbGPPd8v4dNsb8vPPY+8tle8EYc8oY8+m17Jucu6u5PBtj7gb+A3B/+b1fcR7ebIz5h/Ln+o4xpt153a3l4zBrjHnFGHPXWo/3pXbZBqzlSuX9wEvO3R8EbgH2GmNuAP4Q+BdAG/D7wKPGmKgxJgL8FfA1oBX4n8DPrvA+QeBvgAFgC7AB+HPP8w4BHwOe9jwv4Xlec5XXvgv4HPDzQE95G39e8bSfAt4GHCg/733l1/aVC1DfGg7HneXfzeV9eXoNr5FLRGV1TT5c3t52YBdVWkjkwlPZ5FPAL5aPQSPwfwDJKs/7PWAJ6AYeKP/Y/asHvgv8GdAJ/ALwRWPM3vJT/gD4F57nNVAKpL6/wr7IW3Q1l2fP874N/Bbw9fJ7X+c8/EvARymVzwjw6fL2NgDfBD5b/syfBv7SGNNR7XOvt8sxYP2r8lXLD4AnKP2DrM95njfteV4K+OfA73ue94zneQXP8/4YyAC3ln/CwIOe5+U8z/sG8NwK7/d2oBf4Vc/zljzPS3ue94MVnlvpw8Afep73oud5GeDfU7ry2uI85/Oe5816nncS+HvgegDP8056ntdcvl8uTyqra/eQ53mDnudNA/+ZUhAhF4/KZsk/A37d87zDXskrnudNuU8oByc/C/xHz/OSnue9Dvyx85SfAvo9z/sjz/Pynue9BPwl8KHy4zlKwVKj53kznue9uMbPLWun8ry6P/I8783yMfgLuz3gHwPf8jzvW57nFT3P+y7wPKWgv+ZcjjkdH/Q873srPDbo/L0ZeMAY86+c+yKUCpkHnPI8z3MeG1hhm5uAAc/z8uexr72AXzl5nrdojJmidDXWX7571Hl+ErjgyeKyblRW1849HgPl/ZGLR2Xz9H4dO8tzOiidK93jUnmMbrHdv2UhSi11UAp2fx34vDHmVeDfqRfsglN5Xt1K29sMfMgYc4/zeJhSkFxzLscW1tW4BW0Q+M/lqxH7E/c872FgBNhQkReyUpfRINBnqidse1Xucw1TKhCA33XUBpw62wc5R2fbD6k9V1NZXQLizu3uKs9x8wb7yvsj6+NqKpuDlNJQVjMB5IGNzn1ueR0Enqg4RgnP8/4lgOd5z3me99OUumP/ilILl1w6V1N5PtdYYBD4WsXxqPc87/MXYF8uuCstYHV9GfiYMeYWU1JvjPmAMaYBeJpSBfSvjTFhY8x9lJr4q3mWUkH+fHkbMWPM7eXHxoCN5dyXah4GPmqMud4YE6XUTfGM53n9F+gzWhNAEdh2gbcrl8aVXlZfBt5vjGk1xnQDn6jynI8bYzYaY1qB/wv4+gV4X3nrrvSy+RXgPxljdpY/3wFjTJv7BM/zCsAjwGeMMXFjzG7gnzhP+RtglzHmI+XjEDbGvM0Ys8cYEzHGfNgY0+R5Xg6Yp1RXy/q40svzGLDFGLPW2O5PgHuMMe8zpQGIMVMaXLbxrK9cB1dswOp53vPArwAPATPAUeCXy49lgfvKt6eB+ylVSNW2UwDuAXYAJ4Gh8vOhlDz/GjBqjJms8trvAb9BKZ9phNKV/C+sZf9NKbl60axhIIvneUlKeX//YEoJ2beu5T2kNlwFZfVrwCuUuru+Q/Vg9M/Kjx2n1EX72SrPkUvsKiibv0OpxfM7lILJPwDqqjzv/wSaKHWtfo1S0JEp798C8BPlfRouP+e/ANHyaz8C9Btj5ikNyPnwWvZdLryroDz/z/LvKWPMWXOlPc8bBH6a0uwCE5RaXH+VGo0NzfJ0DRGRS8sY0w/8s1Vy0ERqijHmvwDdnuc9cNYni8gFUZNRtIiISK0wpXlWD5S7kd8O/FPgf633folcTS7HWQJEREQupQZKaQC9lPIE/xvw1+u6RyJXGaUEiIiIiEhNU0qAiIiIiNQ0BawiIiIiUtNWzWGdnZ31/uzP/oxHHnmEqakpbrrpJn7xF3+Rd7/73W/pTfv7+9m6detb2oZcPjzPM2d/1oWVzWa9TCbD9PQ0Bw8eZHh4mLa2Nu68804aGxsBCIfD7j5SLBaxKTKe5xEOh8lkMnieRzAYxBhDoVAgl8v5rw0EAv7risUixWIRO+90Pp+nv7+foaEhxsfHmZ2dJZPJEI1G/e0nk0l/O/F4nI0bN3LnnXcu27dAIEAwGOTrX/86H/3oRy/J8ZOS9Si7uVzOM2b527qpW8YYisUigUDAfyyZTHLo0CG+9KUv8dWvfvVS7u4yCwsL/vdL1telLruZTMZbXFzkxRdfZHBwkHw+T2trKzt27CASiRCJRMjn88TjcUKhEIFAwK8r5+bmiMfjfr3n1qlQqkvT6TTFYpFMJgPgl/9CocD8/DyLi4ukUimKxSKxWIxUKkUgEGDPnj3s37//Uh6Ks7rlllt49tln13s3atJq5XbVgDWXyzE5OcnS0hL5fB5jDKGQxmlJ7SsUCniex5tvvsng4CBLS0vE43Hy+bx/8s/n836FaIzxf2zQmclkKBQKhMNh//5CoUAmkyEcDhMIBPA8b9lrbSU8NzfHSy+9xPj4OOl02g98A4EAuVyOQCBAMpn0XweQSqUYHBzkiSeeYPfu3WzatGnZ90355lcXz/OWlS/P8ygUCv7JvlAo+Cft+fl5XnvtNb88i1xqi4uLHDx4kImJCSKRCJs2bWLLli20trb6F9324t8VDAaJRqMYYwgGgwSDQQqFAnBmQ4Ktc926sFAokEqlOHjwIHV1dWzfvp1YLEY2m6VQKNDc3HzpDoJcVKtGn8YYksmkf2LP5/N+QRKpZbYldHJykoWFBcLhMI2NjUSjpbm8bSVoT/iwPCAMBALk8/llFWQymWR4eJj5+Xm2bNlCS0uLH1DY98zn8ywsLHDkyBFOnDhBOp32K+L6+nqi0SgNDQ2EQiFSqRT5fJ6lpSXS6TS5XI5kMsmxY8fIZrMEAgG6u7v9oFXByNXBDVJd9oLIvQ2lcpFOp5mamlIZkXUzNDTEyMgIjY2NdHR00NHRQXNzM3V1df6Fv9sD5fZI2TrOXojZVlTbMGCfY3sWbN1sL+SMMbS2trK0tERzczP19fX+9iORlRacksvNqgGrPUm7Vzlq5ZHLxfz8PKlUCoCmpia6u7upq1u+iI2tPG25thdkbmCQz+eBUnfnsWPHmJ2dpa6u7oyuz3w+z+LiIkePHuXo0aN+C2oikaC5uZmGhgYSiQQtLS1EIhEymQypVIqZmRnm5+eZn59nenqadDrN8ePHCYfDRCIRWlpa/FZeufK5LauVbApKIBBYVkaLxSLZbJZEInEpd1XENzo6CsDGjRvZsGGDX9faINTtiXLjCNtwYC+2kskki4uLBINBEokEkUiEYDDoB6r2ecVi0U/VCgaDNDc3+4Gte5HvplfJ5W3VgDWTyTA7O+t3PYXD4TNO+CK1amRkhGw2SzQapa2tja6uLr9idANVe9vtdrXBgL1Kd1ux5ufnmZiYYNOmTctabFOpFOPj47z55puk02lCoRBNTU1s3ryZjRs3+hWqDTZCoRCFQoFsNksymWRkZIQjR44wMTHB4uIihw4dorm5mVAoREtLy7IARa5cbsNAZe6qLa9uWbDdreFwmN27d1/y/RWBUkpTb28vPT09JBIJv3fKDTBtq2gul/PvtwGrHSMwPT3N3NwcsViMYDBIJBJZVu7t6+z2ASKRiJ9qZbfl1un2ArDyQtC+vjKIdh93zxUXqtFAPSHn56w5rHNzc8vyVyvzT9ZqtVYDkQutUCgwMjLiJ95HIhFisZjfbW9P8rabyVYgoVDIr/zslbtlr/Lt4KnXX3+dDRs2+DlS4+PjPP/88ywsLOB5Hu3t7bzjHe+gvb3d347NhbVBciQSIRQKEYvFaGpqorOzkx/84Afk83mSySTPPPMMwWCQXbt2KR3nKmLryWqpAZV1cLFYJBqNsm/fPnp6ei7ZPoq4kskkN954Iw0NDQSDwWVpV5VBonufbX21MpkM/f39AGzfvp3GxsZlDQ2hUOiMFtpcLseJEycYHx+nubmZaDTqB7q2UaCysaKS21DheZ7fs+YO7nK/e26jhv1tW3NXakF230PO3aoBqztAxb1SqbRSi4C9KpqZmeHUqVNkMhlaWloUtMpFl06nGR4eJpfL0djYSF1dnR+cRqNRisUioVCIXC4HnL56DgQCpNNpfzt2EACcvoCbn5/nySefpKmpiZ/4iZ8gEokwNzfH0aNHGR8fB6ChoYFbbrmFtrY2v4J1cxNtl5V9Txs8d3R08N73vpdvfvObfm7rj370I79bTa58wWBw2UwUbmtMtdkDFhYWGBgYYGxsjDvuuOOS7quI1dHR4det7kApd1YL9z44nW5lA9xisUhXVxeHDh3i1KlThMNhrrnmGr/uzOVy/sCtysCvWCyyuLjojwew6QLu+9lBi3af7Lbsftjbtq6t/BzBYJBsNls1v9zdrh1gZgNkN1g930Y/WcOgKzh9Mg+Hw1XzQWxgW62bdWFhgYcffpjnn3+e2dlZduzYwV133XXhP4mIY2xsjLm5Of/qNhKJ+An8tuvIllu38rMVWuWIfsBPi4lEIn43/uDgINFolJGREY4fP47neTQ0NHD33XfT2tq6rEvMXum7OVbV0g/q6+u5++67+eu//mtmZ2dJp9OMjY0xOTm5DkdSLjVb19q/bRlyW+nhdOPA6Ogozz33HK+88gr33nvv+uy0XPXa2tr8FKnK3lS3y912/bspAnaQVSgUIhqNksvlSKVSZLNZlpaWSCQSfnqADVzhdG9DNpsllUqxtLTEwsIC2WzWf8y2bubzeb9ut/GJDaJtwGwbBmwvnNtQYJ9bOYjLncHA/XyVgyTt85QOcP5WDVhtN6ptHQqHwzQ0NJzxPNvsbk++NhgIhUIsLi5y4sQJBgYGSKfTtLW1+VczIhfL+Pg4+XyeaDRKa2vrshxQd3QpnO4hsJVsPp/3g1s4HUjaiiubzfr5VYcOHWJqaop8Pk8ulyMajdLX1+d3Y9mr/MpRrnZb9uob8FtZ0+k08Xica6+9lqeeeopiscjk5KQ/qEGubJW9WJUnvsrn2IufmZkZDTCRdWMvxCt7lOzfNoB0W2BtPWjz+aFUD8bjcaLR6LJeXnd8gZtS4DZKQGmwbS6Xo76+HjgdaLqpWJU9xzbAtN8lN+0gHA77MYvbWlxtwK57jrHv576/WlffmrO2sMZiMf+fYPPtqj2vsqnf5pUMDQ0xOjrK4uIi0WiUlpYWWlpaLvTnEFlmdnbWrzwbGxv9QQDu3Kk2cHRTWtwcJps2kM/nyefz/tW+DVjtNG+jo6P+IKqOjg527drlf1fcis2dDNv9nrgVsu128jyPDRs2+JNtp1IpFhcX1+FIyqW22rRW7uNQ6t2KRCI0NTVV7SYVuZTcwBCW51tXdq1Xpji5AZ/NQ00mk6RSKb+hzJ0lA07XqXY8wOLiIslk0q/X7XPcfbPbcYNVN7i2+1ipcvCY3aZ78Vit9bQyb1UzLZ2/VWs3tyk8Go0SjUarBqxuAOD+A3O5HP39/czMzJDL5WhqamLTpk1aCUUuulQq5VdQsViMWCy27HEbNLp5TpUrXdmRrHY72WyWhYUF/3thp1hJJpP+FFatra10dHT4wUO1Cm2lnES3JSIQCNDU1ER9fT2hUIhsNks2m71ox0tqS7VW1srxAXb1n3w+TywW82ehEFkPtrve9izZeqyyRdQ+xzYo2Cn73NdEo1F/PIG9ULevcbfrzgYQj8eXNSq4QWi1aeDsvrgXh5XnAnfwlfs6d/YY27vstrRW3ldtjI+cu1VbWO0EvoVCgbq6OqLR6IpdTm5Ss20hyufzDA0NsbS0hOd5dHR0sHXrVk3kKxedLbs2d9WdDcBdJchOZm0rEndkqF18AJYHCACxWIx0Ou1XSja/qqOjw6+4q02jYrdlWxgqu4hspWq319bW5i8woEFXVwc3TaSS22qUz+eZnZ1lcnKSQqHAli1bFLDKumlubiYSiZwx5RqcbiCoFrjZC/vK5aihNHh2cnKSHTt2+IOaKrvjbUBaV1eHMYalpaVlKwvarnj3O1WZhmVTD2x97da1boOcnTHJnj/cNAPbI+eqTAPQ9/OtWfNKV/YKqFqXU2Weih1cks/nOXnyJMlkkkAgQFtbG52dnUo6lovOlsV4PO5fILnTp9jKzwakgN/aass7sOwK3bYIRCIRurq6SKVSLCwskEwmaW1tpauri23bti1rAbDvY78TtlKzFaCdPsversx72rp1K5OTk6TTaV3oXSXsCbJYLLK0tOTPbGGXr3QHj4yNjXH8+HEymQwHDhw4p6WzPc/zL7rsYEKdUOV8dXd3r/jYuaaqJBIJQqEQU1NTHD58mFtvvXXVsm0D3nA47DeyuXmjbuOAOwOAu2+e55HNZv2FCirTt+D0xaTbamtf7w7UcoNet6fanUdWzt1ZW1jtSX7Dhg1s2rSp6sIB9nlw+oolFAoxOTnJ5OSk32XV3t5OT0+Pv/qQyMVipwXKZDJ+BWYvumxuaT6f9yszNy+p8mrc5jnZq/BIJEJnZye5XI6XX36ZxcVFNm/ezKZNm/xWAvudcAdvuYFsLpcjm80uW8HKvVq3QbPNlQ2FQlq04yphL5Lm5+d58MEHGR4e5r777uPd73633yVqy2wqlWJ0dBTP87jzzjvXNKgjk8lgjGF+fp4vfvGLjIyMcPvtt/OBD3yAaDRKPB6/BJ9SpDo3JzWRSFQd6F1Nd3c3uVyOTCazbEUsNzZxB3m5gxnd+r/aXKm2FbYy/9bN2bVjHdzlZF02faAyvUfWbtWAdXFx0T+pd3Z20t7eXvV57lxqbjdoKBTyl3a1j59LC4DI+VpYWCAQCFBfX08sFls2uhNOT/JcrZu9UCj4Oa82WE2n036Lqu36z2QyfuA7NjbG0NAQ+/bt86++bVDh5kO506jE43E/OHWT/e13JxAIsLCw4AescnXI5XIkk0k+9alPMTc3RzQa5YknnuDd7373sry5P/zDPwRgbm6OXC6WvNYBAAAgAElEQVRHc3Ozf6J+7bXX+OpXv8odd9xBS0sLMzMz7Nq1i+uvv55iscjAwACPPPII/+gf/SMaGhpYXFxkYmKCvr4+v9y63Jlgkskkf//3f8/09DTHjh0jHA6zf/9+3vGOd+ii6ip39OhRuru7qa+vf0ut9XNzcywtLRGLxejt7V3ThZgdR2CnIqyc5N+t691g1m1EsAPFGxoaaGpq8ged2++V+5mKxSKZTIaJiQl+/OMf8+yzzwJw3333+Ssb2p6QldIh5NysehZ056qcnp5menq66gk+HA77OX62Usvn80xOTjI3N0ehUKCjo8OfWHh+fv6ifSAROJ3D6g4CqEz8t4GjWwnZq+dcLueX42KxyNDQEIcOHaJYLJLL5YjFYuzYsYMf/vCHBINBYrGYP3+gO3AKTo9sdd/TTd6vTAkA/NZhO9uB2xIrV7ZsNsvv/M7v8LM/+7Ns3bqVw4cPc/z4cZaWlhgeHubBBx8kGAxy4403MjU1xeDgIC0tLX5ZTqfT/NZv/RYf//jH2bt3L8VikTfffJPvf//7XH/99eTzeX7/93+fp556isOHD7Njxw52797N2972Nv/9be+AvdAbGRnhH/7hH3jqqaeIxWLceuuttLa2snHjRvL5PEeOHOE3f/M3uf/++9f56Ml6mpycpKGhYdXxLmthu+VzuRxLS0v+LEOricfjfn1drdu92ngCWD7N1htvvMHJkydpb29n586dxOPxZQ0OxWKRubk5FhYWmJycZHBwkOHhYQqFAm9729vIZrO89tprFItFFhYWqK+vp7Gx8YxBv3J+Vg1YbcXlNndXu9KpHBFnB7jY/NdgMMi2bdvYtGnTGQGCyMUQj8dZWlryu35sq2Xl1a2bg+TOtepOc2WvooeHh/3WzkQi4S/3ZytWO5rVdhu5KldPgVJQWjkBtmW7tiYnJ/2ZDjTH5tXh0UcfJRKJcOjQIY4dO0Z7ezt33nknuVyORx55hOHhYT7wgQ/Q0tLC6OgoS0tLNDY2+vXu1NSUPyuLbcVvbm72y3Q6nebUqVPs2bOHu+66i71799LR0bFsMQuA6elpXn/9dV5//XXS6TQ7duzgwx/+sJ8SEwwG/TkqN2/eTE9PD3/yJ3+ybsdN1t/u3bv9VQXPl00HgFIv78zMzFlzYI05vbDR0tISqVRqWd6qDUqrxS82XzWbzXLixAm/TrbjDowxZLNZ5ufnOXTokD8+B/BX1Oru7vZbZG3rcDKZpLGxkXA47PfyVc4aI+fmrDmsdpBIZ2cnHR0dK16huCd4e9vmkoTDYTZs2EBHR8fF+RQiFeLxOOFweFnLqjvhs3u1bbs77eNuGQaYmJhgbGzMT29xR4wCfqvW0tKSvz3XSvP7VbaqVs4NOzY2RiaT8QNkOxG2XNmefPJJ7rnnHr/cdHd3s3XrVhYXF3n22Wf9wVfd3d3+muu2fNsV2Gxua29vL4lEYlnqydTUFNdeey379+9n3759fs+XLcdDQ0McO3bMvxjr7e0lHA6zc+dONm7c6L+fDUqKxSINDQ1ks1keeuih9TpsUgOam5svyHbsFJq2Xl1LI5fN+be9XJVzbK80a4ttcBsbG2NmZoZYLOYvXFAsFkmn0wwMDDAxMcHx48eJRqM0NDTQ2dnp5822trYSDoeJRqO0tbUxNTXlNzasNhhNzs2qAWs6ncbzPEKhEL29vXR1dVUtOLb7FU7nddikfrvaUHNzM8FgkPn5+WVrtYtcDJFIhEgksixvyA0MYfmUK5VTntjXZTIZ+vv7GRoa8luebNeUO62VXRqw2koq7jbdINXdJ/tc+1MsFhkcHPR7KBobG9c8+EAub8ePH2fPnj10dnYuGyBoux/37t3rz0DR3d3Nzp07aW9v98teOBxmYWGBZ555hubmZhoaGjh69CjRaBTP8zhy5Ag33HADt9xyi7/M8PT0NLOzs9TX1/tpBm1tbVx33XX09vYSiUTOyPu2+dj2e2QHEoq8VTalwF6EraVV0p3aym2EsI/B6WmmKhcRKBQKDA8P+1MhNjQ0+K21o6OjvPHGG0xNTeF5Hj09PXR2dtLT0+PPfezOsDE/P8/IyIifxuC2Nq+UliBrs2rAOj097Td5NzQ0rNjCMzExwdTUlD+5+eLiIk1NTRw/fpxcLkcikSCVSnH8+PFlk/SKXCzRaJRIJEImk/HXpHaXDLR5127PAJxeB9p2HU1MTHD06FGGh4f93oZEIkEsFiObzfoXX3Ywil0SsHIuP1spugOoCoXCGXMA2v1ZWFhgYGCATCZDPB6nvr5eLaxXCc/zmJub8wd8eJ7H0tKSP30VwL59+xgZGWHHjh3ccMMNfvd8IBDwVxP8zne+43dFjo2N8f73v98PWDs7O/3ZBebn5+nv7+fQoUPcddddbNu2jQMHDiyb4QLwg2dgWeqM3d+BgQF6enp4+eWXL/1BkyuK7d6H5SP4VxONRkkkEn53fDabpa6uzq/L3UGvNmB1l8+em5sjk8nQ0dFBOBz2exsOHz7MwsICxhg2btzI29/+dqLRqL88t92OTSmbmJigv7+fYrFIY2MjTU1NywZ5aQDt+TvrPKx2jd5EIrFibsq3v/1tnnnmGSYnJ8nlcn4agB04MjExwZ/+6Z9SLBZpaWnh2muvvWgfSATw0wGy2SxTU1O0tbXR1tbmr6ZiZbNZP23AtnrainJxcZEf/ehHnDp1alnlNjs7C7DsSt1uY35+nqamJn8KFLdytBWjbamyy2ra/Fa7/GsymeSHP/whMzMzfuBr58mUK98999zDF77wBXbs2EEymfQXXpmfn6enp4fh4WG+8IUv8OlPf5prrrnGP3Ha8h6Lxbj//vv5yle+wuOPP87P/MzP8MADD1BfX08wGOTnfu7n+NjHPsZDDz1Ec3Mzd9xxB3fffTf33nuvP8jEtpS6AxXd1Br7Xtabb77Jk08+yec//3n+9m//dl2Om1w5bE/BudR59mI/lUoxNjZGb28vDQ0NflDpztTipn7Z8Tm2x2z//v3EYjEGBgZ48803mZ2dpa+vj507d9LZ2bns+2a3adMKBgcHefbZZwkEAuzZs4dt27YBp3sj3P2Uc7dqwDo3N0c+n/e7ldyrFVcmk2FqaoqhoSHS6bTfmhQKhfypIjKZDO3t7ezbt4877riD3/3d371oH0qku7ubkZERPM/j1KlTfhdqa2urP5WJ29IKpwc+2S6oN954wx/UUrmKST6fp76+nng87k9vZZduBfyK1lZqcHp1IhugVk77lk6nGRkZ4fDhwxw7dgworah14MABNm7cyMGDBy/NwZN19alPfeqcnm/rWHuSz+fz3HvvvSwsLBAKhbjxxhuXpZP09fXxrW99a8XtpdNpf3U4WL5eujuzRiQSIZ1O8+ijjzI4OMiv/uqvXrAcRpFzHaAUCATYvHkzw8PDjIyM0NfXR09Pz7JUrMqeAc8rLcE9ODjIyy+/TGtrK01NTRw9epQXXnjBn+pt37599PT0ACwLgC07k8x3v/tdmpqa2LlzJ1u3bqWxsfGMRgs5f6sGrCMjIwC0tbX5ifvVpou47777uOaaa3jzzTcZGhry81RfeeUVcrkc27dv59Zbb+Wmm25i586dymGVi66vr48TJ074ZXFiYoKWlhY/N8ntyoHTifc2D/Do0aP+ogDhcNhPA5icnPSD4KWlJb9HIRgMkkwmOXbsGLt37/ZP7LYFF5avX+3Op7m4uMjg4CDHjh1jbGzMv3oPh8McOHCATZs2+Su/iKzEzkFplyO+7777eOihh8jn8+zZs2fNs0ycyxQ84XCYn//5n6dYLBKJRPyZMkTOl5syGAqF/NSYswkGg2zcuJFIJOK3mNoFZCpXEAT8KQonJiZ4+umnmZ2dZffu3UxMTPDmm28yPT1Nd3c3t956K52dnRQKBT+X2x2wa2dEOnr0KD09PVx33XV0d3f7cxK7CyEAy1ZXlHOz6hnQthZlMhnS6bQ/KrpSS0sL+/fvZ9u2baTTaZLJJKOjoxw9epRischtt93GO9/5TrZs2UJ9fb0fCItcLLFYjK1bt3Lw4EGy2SxjY2PMzs7S39/PLbfcQkdHx7J5hm2FZufRe+2115ibmyMUCtHc3ExfXx+JRIIf/ehHfquprbTcwVgNDQ1kMhmCwSCpVMpv8bLzFNsu/8nJST9dJpPJ+F2/dvnWaDTKzTffzObNm0kkEstaakVc7iTpbhdqIpHwl3fNZDIXZS5I90Rsb4u8FZ7nkUqlSKVSeJ7n52GfjZ3vvbGxkdnZWSYmJpienvandnPn4LatrRMTExw8eJCJiQlaW1sJBAK88cYbzM7O0tHRwTXXXENPT4//GncaRMumTe7bt49wOExzc7M/CMuts22amL4j52/VgNWdLzISiax4lRMKhWhoaCCRSOB5nr/uue1WamxspKWlxT/x6h8mF5ud2WJsbIx8Ps/U1BQTExPMzs4SDAbp6emhtbWVSCTid6na3KfBwUFGR0eB0lX79u3b2blzJ8VikZMnT3Lq1Cnm5ubo6enxr+ZtEPr666+zsLDAhg0bSKfTfiBhW1VtYGoHBdiLQvu9CAaDtLW1cc0117Bz506/dUEVnazG5tEBfot/IBCgr6+PiYkJXnrpJe6888513kuRs7NTYto0F2DNLazxeJyNGzeSTCaZmpryp3ZzGxfs3Nq2Ue3UqVM0NDSQSqWYmJjwF47Ztm0b27dvJx6P+w0O7iIvdl9tKk5HR8eyBWNsS6z7XPe3nLtVA1Z7YDs7O2lqalo1aHVX9THGUFdX5zfFLy0t+SOvlcMhl4IxhkQiwZYtW4DlU+4cP36cqakpWltbicVifsvq4uIi4+PjfldSLBZjw4YN/lX20tISGzZs4OTJkwwMDLB7927q6+uJRCIUCgXm5uY4cuQIo6OjnDhxwl/9yk09cHsqbIARj8dpbGykrq6OaDRKV1cXu3fvJpFIVF1ZTqRStXl+A4EA+/bt46mnnuLFF1/k9ttvX6/dEzknttfLDkZdq2AwSF9fH6Ojo8zPzzM+Ps7c3JyfW23HEdhgdWhoiHA4zLZt25ienmZmZoZQKERdXR2tra20trb66WN2CkM704sbvNqgtXJOevc5bt6sgtbzc9aVrgKBAL29vTQ1NS0bMeqyzeT2n2NbZG136OjoKMlkctnIPJGLyY4G7evr8yd0bmlpIZVKsbCwwOzsLJOTk4RCIX+FILuym+d5xONx2tvbufHGG+ns7PQDz66uLhKJBIODg366QCKR8POybTfWyMgI27dvp6GhYdmE1XbCdZtPmEgkaG9vp62tjZaWFpqamvx8VfcKXd8ZWY2tl22jQKFQwBjDnj17OHjwIG+88cY5nfhF1oubY2rnt15L2bV1t50n1fZ6nTx50j8H5PN5ZmZmOHLkCENDQwQCAbZt28b+/fs5cuQIAwMDQKlejsfjflzjzgpg960yHnKD0GqpA3bGF/WWnb9VA1a7woT9R9npHyoHfwQCAX/daZv4H4vFaGxsBGB0dJTp6WnS6bQ/tYrIxWQXvCgUCnR1ddHc3Mz09DSHDh0iEAiQTqeZnZ1dNkOArdDC4TCbN2/m+uuvp7Gx0e/mCYVCbNu2jRtvvJFXXnmFYDBIZ2cn/f39/mCTQCDgz6php9GyV+OhUIhIJEJdXR1NTU20tbXR3t7ut9JWTq9lK0TbjaXvjVRjl5C0uc/2YgdKrfdNTU0Eg0Hm5ub8OlmklqVSKTKZDIVCwR/cuhbFYpFEIsE111xDKpXi1KlTvPbaa6RSKbZt20YymeTw4cN+GoBNvQLYsmULr776KolEgo6ODn/1Klsn29jGpm/ZNByb7mWMIRqNVu0VW2m1Qzk3qwasMzMz5HI5pqam/EITDAbPSN63k5/bgSu2Od9eoUxMTPD888/T2trKddddpzXR5aJzl2K1cwLX19fT0dHB0tISU1NTDA8Pk06nyWaz/kwA7e3t9PT0EIvF/InS3RWoWlpauPXWW/0psW644QYOHz7M5OQkiUSC7u5u7r//fj8VpqGhgXg8TigU8gPSeDxOPp/3J7m2j9nWXjuzQOXCA6ropJqlpSV/jtZ8Pu8P+rNdlNdeey0LCwt84xvf4JOf/OR6767Iqowx/tLaNm90LT1Mbq7oli1b/Dr/xIkTvPjii/z4xz9mYWGBYrHIli1buPbaa9m8eTOxWIx0Os3U1JQ/U8ypU6fo7Oz0W1ptkOrOMGODULcnzKad2RXhbL2dSqXOKR9XqjtrC2tdXR3XXXcdXV1dK07k685zBvhzVtru0HQ6zeDgIOPj4/70QSIXk72Isl1JNnCNRCJ+2XTn6LPd9O5SqjatxeYvWc3Nzdx1112EQiHq6+v9PNjFxUXS6TTRaJS6ujo/f9bmL7lLZ9rK2F6R2y4ku0iAbVFwW1yV9yTVDA0NcezYMX8QoS3HdsngDRs2sGnTJr72ta8pYJXLQnd3N9u3b2dychJgzb1Lti7P5/N0dHRw2223sWPHDl544QWGhoZoaWlhx44d7N69m8bGRr/ODQQCtLe309vbSy6XIxQK+SvB9fT0EAgE6Ozs9OttG4jaets22Nke6copE91FNxT/nL9VA1abO2IPuG0JqmSbyt15zkKhEF1dXf7VxeDgIBMTE8rFk0uiMsfIThDttjzZWSxs1467Lrp9nV1EoHK0pw006+vr/dkI7CpDyWSSzs5OP+h198ENOt28Q5tyYwNUd1UsOJ2fJVLJ9gy4ywq7k//H43Hq6uo0R6pcFuwCL+985zt5+9vfjjFmTQtS2GDRXvTbHqu+vj5aW1tJpVIEAgG/kcHGMjaA3L59O0eOHGFmZgbP82htbaWvr4+GhoZl4wjcRgxbL7uDr+y+WO4UiMYYP9VBzt2qAasNVsfHx5mfn6elpaVqwGkDVDctIBgM0tvb63dx2jXdbfArcjHZysO2bFpubmjlvHzutCM2bcUdMAWnA0cb6EYiETZs2MDU1BQzMzP+PKxwekUUdyCMe7VtuV1FbsBsu6eqJfiLWKlUivn5eb9RwZZfe5K1k6/X19ev966KrIlNnYrH4+f0uspR+LbebGho8Mu/zfG2+ae2zu3o6GDnzp28+OKL5HI5f774zZs309LSsmwp18qBtG7dbOtst7fOXZgmlUqp4e48nXXpnHw+z6uvvsqWLVvo6OioWoDcqRvcvEG7NNn09LTfDbrWfBSRt8rtnnErmWqj7m0Ztt3vbre8W7ZtJeVup7GxkaamJpLJJOFw2B+saAcn2m4nt5vI3m8DWbs/7vQnbiCrAVeykmQyyfT0tH+7skVHLfNyNalsDHAbKYBldbh9TiAQIBqNsmPHDiYnJ5mZmWFsbIzFxUWWlpZobW2lsbGR5uZmf8pON2h1U7rsHNuZTMav8wuFAul02j8naMaO87NqwGpP2keOHGF8fHzVg1w5oa4xxl/LN5lM0t7eTktLy7IRrCIXi9ut7nYVVXbD2+dWJsS7t93y6pZxy05FZSstm2BfuRa7DYTd7bnbt/tqr8btQCyR1dgZJgYGBvylKW35LxaLjI+Pc+LECbXQyxWt2qAmW+/a+t5tDHAbAWxjRVdXF/v37+fo0aOMjIwwOzvL3Nwc0WiU5uZmenp6/JZfd+55Oz4ik8kwNzdHKpViaWkJwF9py8ZBiUTiEh6VK8uqZ0Obf9fd3U1vby+NjY1VKz17wrWjqm2uYHNzMzfeeCPZbJZ9+/axbds2f9CLyKVgV0uxAaR7Ze2O5LQVmb2gcgdrud3ygF8B5vN5IpEIXV1ddHV1MTEx4X9nKnOY7HfDbtO2+Nog2nbhGmP897brwtvcVpFqduzYQS6X45vf/Cb33nsvbW1tfnldWFjgiSee4PHHH2fbtm3rvasiF5Wb2uXe59bHlbdtl71tuNixYwednZ0cP36co0ePMjo6yuzsLDMzMwwODpJKpWhtbfXHPth8WTsft9so0tDQ4E9lGI/H2bp1K4lEourgdTm7VSPHlpYWstkst912G5s3b15xpSubN2WvWmxAGg6H+chHPsK9995LU1MTTU1NRKPRi7KmtUilyjwmm89qA85cLrcsaR5O52275biy295ejdtKJxaL0dTU5Oc52fd2pz+B06tt2ZHcgL9dd+CV/T7ZQBlYtoKKiCsWi3HttdfS09PD//gf/4P5+Xk2bNjA4uIiY2Nj7N+/n8985jN0d3ev966KXDTuVFNwunXVDVBt3V6tLrV1dzAYpKWlheuuu47du3eztLTExMQEY2NjzMzM+PFLJpMhk8mQzWZJp9OkUimam5vp7u72B3Y1NDT4g7ZsKoE7uFfOjdFJUERERERqmcJ8EREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKYpYBURERGRmqaAVURERERqmgJWEREREalpClhFREREpKZdtgGrMearxpjPlv9+hzHm8Hlu50vGmN+4sHt3aZztcxtj+owxi8aY4KXcL7n4VP5lPVxp5c4Yc7sx5ki5nvygMabLGPOkMWbBGPPfjDH/wRjzlfXeT6kNV1r5v9xc1IDVGNNvjEmVK4Ox8j87caHfx/O8pzzPu2YN+/PLxpgfVLz2Y57n/acLvU+XQuXnLh/v9ziPn/Q8L+F5XmF99vDqpvIv60Hl7pz8JvBQuZ78K+CfA5NAo+d5/8bzvN/yPO+fXeg3NcbcZYx5/EJvV1T+K977M8aYP7nY73OpXIoW1ns8z0sANwI3A79e+QRjTOgS7IfIelD5l/Wgcrc2m4HXKm6/7nmed7HeUMf9klD5vwJdspQAz/NOAX8LXAtgjPGMMR83xhwBjpTv+yljzMvGmFljzA+NMQfs640xNxhjXix31XwdiDmP3WWMGXJubzLGPGKMmTDGTBljHjLG7AG+BNxWvvKaLT/Xb+Iv3/4VY8xRY8y0MeZRY0yv85hnjPlYuQtp1hjze8YYs5bPX77S+YYx5uvlz/CiMeY65/E9xpjHy9t9zRhzr/PY+40xr5dfd8oY8+nKz22M+RrQBzxW/ny/ZozZUt7nUPk5veXPNF3+jL9SsX9/YYz5/8rv85ox5ua1fDY5O5V/EzXGPGiMGS7/PGiMibr7b4z5N8aYcWPMiDHmo+dznGU5lTvzdmPM0+XXjZT3KVJ+7BiwjdN15sPAA8CvlW+/x1S0UBlj7igfo1ljzKAx5pfL90eNMb9tjDlpSq16XzLG1LnHyRjzb40xo8AfVeyjMcZ8oVz2540xB40x167l88nqrubyb4y5G/gPwP3l936lfP+KcUDN8zzvov0A/cB7yn9vonQl+5/Ktz3gu0ArUAfcAIwDtwBBShVHPxAFIsAA8EkgDPwckAM+W97WXcBQ+e8g8ArwBaCeUgG7o/zYLwM/qNjHrzrbeRel7qAby+/7u8CTznM94G+AZkrB4QRwd/mxPmAW6FvhWHymvM8/V/4MnwZOlP8OA0cpFa5IeT8WgGvKrx0B3lH+uwW4sfJzVx7v8u0t5X0OlW8/CXyxfEyuL+//u5z9SwPvLx/DzwE/upjl40r/Uflf9j6/CfwI6AQ6gB86x+IuIF9+TrhcBpNAy3r/Dy/HH5W7Ze9zE3ArEKJUHx4CPlHtWFXuV/n2Z4A/Kf+9mVK9/Ivl49EGXF9+7AvAo+Xj2gA8Bnyuonz/l/Lnq6vYx/cBL5Q/nwH2AD3rXY4u1x+V/2Xv45df574V44Ba/7kUBWexfEAHygepzvknvMt57n+3hcq57zDwTuBOYBgwzmM/XKHg3Fb+B4Sq7FXWAOsAACAASURBVM/ZCs4fAP/VeSxRLqBbnH2+w3n8L4B/t8Zj8RmcAJBS6/YI8I7yzygQcB5/GPhM+e+TwL+glFflbtP/3JVf1PLtLeV9DlH64haABufxzwFfdfbve85je4HUehfQy/lH5X/Z+xwD3u/cfh/Q7+x/yt1nSieRW9f7f3g5/qjcrXpsPgH8r4pjtdaA9d+7r3WeY4AlYLtz323ACec4ZYHYCvv0LuBNSoF14Fw/k35U/lc5Fn75Ld9eNQ6o9Z9LkcPxQc/zvrfCY4PO35uBB4wx/8q5LwL0UvqHnfLKR7dsYIVtbgIGPM/Ln8e+9gIv2hue5y0aY6aADZS+BFAKLK0kpcK1Vv7n9TyvWO5OsE3/g57nFZ3nDpTfF+BnKeXgfN4Y8yqlwvr0Obwv5feZ9jxvoeI93G7/ys8WM8aEzvNYSonK/+ltu/s8wOmyDzBVsc/n+t2S5VTuAGPMLuB3KNVzcUoX7y+cxz5C6TMeq3J/R3nbLzg9tYZSq5s14XleutpGPc/7vjHmIeD3gM3GmEeAT3ueN3+e+ykq/6u919nigJq13tNauQVhEPjPnuc1Oz9xz/MeptQSuaEib6NvhW0OAn2mekK1V+U+1zClAgyAMaaeUrfPqbN9kDXa5Gw7AGwsv+cwsKl8n9Vn39fzvOc8z/tpSt2pf0XpCqua1T7fMNBqjGmo9h6yLq6m8r9s25T2f/gCbFfO3dVU7v478Aaw0/O8RkppV2vKf61iENhe5f5JSj0E+5xj2OSVBv1Yqx4Dz/P+X8/zbqLUs7UL+NXz3Ec5u6up/Fe+92UdB6x3wOr6MvAxY8wt5ST0emPMB8oH9mlKOUD/2hgTNsbcB7x9he08S6mgfb68jZgx5vbyY2PARpt0X8XDwEeNMdeb0oCQ3wKe8Tyv/wJ9xpuMMfeVC/UngAylvL5nKF01/Vr5890F3AP8uTEmYoz5sDGmyfO8HDAPFFfY/hilQQRn8DxvkFJ3xufKx+QA8E+BK2bKi8vclV7+HwZ+3RjTYYxpB/5vVPZqwZVe7hoo1ZmLxpjdwL98C9v6U+A9xpifN8aEjDFtxpjryz1jXwa+YIzpBDDGbDDGvG8tGzXGvK18/MOUUgvSrFzHy4V1pZf/MWCLbQy73OOAmglYPc97HvgV4CFghtIgpF8uP5YF7ivfngbuBx5ZYTsFSsHeDkq5n0Pl5wN8n1IC9qgxZrLKa78H/Abwl5QK33bgF9ay/+b0JP0rXYEB/HV5X2aAjwD3eZ6XK3++e4CfpHS1/kXgn3ie90b5dR8B+o0x88DHgA+vsP3PUQoKZk15JoEKv0gpr3UY+F/Af1yl20Quoaug/H8WeB54FThIqQvssys8Vy6Rq6DcfRr4JUqDpb4MfH0t263G87yTlAYE/htKx+NlwM708m8pHbsflevp7wFnnaOzrLG8bzOUumengP/nfPdT1u4qKP//s/x7yhhj0w4u2zjALE/PkIvFGPMZYIfnef94vfdFRERE5HJSMy2sIiIiIiLVKGAVERERkZqmlAARERERqWlqYRURERGRmna2hQMuu+bXv/u7v+Ohhx7ij//4j2lsbMTzPOw0asFg0P97vTz44IN88pOfXNd9uNQ8z1uPg37Zld0rged5FAqFVR8HKBQKZLNZHn/8cfbu3Ut3dzfxeJxAoOauoVV2z+L73/8+f/M3f0OxWOTBBx9c7925KD772c/yG7/xG+u9G+fkUte7+XzeCwaDFAoFisXSrFyBQIBgsLR+wqlTp3j11Vd55plnWFhYYPPmzdxwww1s27aNzs5OQqHVwxFjjL/iUbFY9M/tdvv5fH7ZNowx/mvsbyjVPXY7f/EXf8FHPvKRi3E45DytVm4vxUpXl1RLSws7d+5kcHCQffv2rXuAKrJezhY4Vn43bCVu//72t7/NG2+8wQMPPEBXV9ea39dup1rw6V48zs/P88orr7B3717/pCOXH8/z6OjoYMuWLeu9K7KOgsEgxWLR/94XCgXy+bx/cfriiy/y+OOPMz4+TjqdJplMYowhl8sRjUapq6sjFAoRCoX8ustuyw1W3fvcgNT+XSgU/PrEbsetX2wdFAgECIfDl+bgyAVxxQWskUiERCLB9PS0f1+1k7PIlcwNPNf6HFvhp1Ip/u7v/o7BwUFuuukm6uvrL+h+2fdLpVJkMpma6PmoJfYke671lm15euyxx3jXu95FQ0PDqq+/UBcJN910E7t27aKuru6CbE8uT4VCgUKh4AePgUAAz/PIZrNMTExw/PhxlpaWAOju7qa7u5vm5mbq6uowxhAOh5cFo/a3W4btduF0vWVv23rEfY3dB/e7VIO9OLJGV1zA6nkeuVyOhYXTS+XqZChy2mqBUCaT4cSJE5w4cYIDBw6wd+9eYrHYOW3fPWG4wbArn8/z2muv0dLS4rdyVAbXtlvR3e9q27qQPM9jcXGRI0eOMD4+zvvf//6L9l6r7YP7+1xel8lkePLJJ2lra+PGG28kHo+f8ZwLffyam5tpbm6+oNuUy49t/ay8AC4Wi4yPjzMyMsLMzAwbN27k+uuvZ9OmTTQ0NPh1gHvhWhm4uttzb7stq5Yb0Lp1kW39VTxw+briAtZiscji4iIDAwNqWRU5B6lUilOnTvH666/T3d3NTTfdRFNT0wVpias80RQKBX784x+zf/9+6urqqp5ILkTAerbXuHWE53kkk0mOHz/OU089hTFmXQLWlZytPjPGUCwWOXHiBCMjI+RyuTNe627j+PHjJJNJmpub6erq8vP/qgXKZ3vfWnX48GGWlpbo6Ohg48aNNb2vVwK3ZRRONyANDAxw6tQppqenuf322zlw4AC9vb0Eg0FCoRDhcNjvynf/R5XfX7e1tPKieLVUpMrnqRxcni55wJrP50kmkyQSiYvSNB8MBvE8j5deeumCb1vkcuVW/JWBC0A6nWZoaIgf//jHTExMcO+999Lc3HxGl9y5vme1k4/neeTzeSYnJ9m3b9+Kg60qX38++7HW19gT6/DwMC+99BInT57kQx/60Dm/34VSLWhcKfiuPImn02na29sJhULLtuMGrYVCgccee4xUKsUNN9xAPB73UwjctI3K96v23sFgsGa7Wb/97W9z6NAhbr/9dn7hF35BOYsXkZt36rauZjIZ3nzzTQYHBykUCvT09NDW1uanAkQiET/PNRKJ+Odwt0xXDriq1gILpYvcYrF4RmqA/dsdDKag9fJzyWuZ0dFRHn30USYmJsjlcuRyOfL5/Io/9jm5XO6MFpdqmpub2bt3L4FAYNVBJwDZbPZCfSyRy0JlMAKlSn5wcJAXXniBkZER3ve+99HT03POXdJnY08a+Xye8fFx4vE4TU1N/kloNXZUcLFYJJ/Pn/GZqqm8335298fK5XKcOnWKgwcP0t/fz65du7j++uvP85NeOO6+VjvBVut+jUajhMNh/5i6XaL2fzA/P8/TTz/NzTffzNvf/nb/mLojqe3zrcqu18thDu+enh7m5uYYHh4mk8ms9+5c0WwOqr0gst/ZXC5HNpulWCzS0NBAe3s7kUjEf10+nycQCFBXV7dsG3YGADdNIBQKLSvr7o99TmUagN1W5UXV5VB+ZblLHrDW1dURi8X4+Mc/7ido2wJXrTI81xaeaDRKfX094+PjZ1SwrmQyyS/90i+dNagVuRK43WeVAZ/tAi8Wi3zwgx9k8+bNZ7RQnCu3NdU9edlBGIcPH+aWW24hFov5J6GVtlM5kMMdSOG2GtqTmPudtvvhBm+VrTb5fJ5UKsVTTz3F//7f/5tIJMIDDzywbq2GK+XpVbYSVXvcDjq1F/xuPep+HtuqnUqlCIVCtLS0LNsHdzoiy/4fbarBWy0jl8I999zD2972NgCGh4fXeW+ubNUuaGzL6cTEBNFolI0bN/oDrcLhMKFQyG+hr2wRta+1Zdneb3+7r3HrBjtbQeVMA67Kizm5PJw1JaBYLDI0NMT4+Djz8/MUCgWampq46aabql61nE1DQwO7d+8mmUzy8MMP86EPfYhYLLasxaNaIVprwYrH43R3d7O4uEgul1txwEg4HOaDH/xgzXZliVwolTlfboA4MTHBU089RUdHBwcOHKCrq+uCfCfctIPKFo9kMkl/fz8/8zM/QyQSWTWnrFod47bAuM9zf7v7YP+2LYbVPt/jjz/O4OAgt9xyC+9973spFApnnRfyYqtWH7rdodXYk3gqlVo26rpym8FgkHg8TjabJZPJEI1GlwXxNlitNpjlcqozC4WCPwDXbdWTC89eWNrfUCqvCwsLjI+PA7B582YikYjfqmoHW9keUVv2bCALp7+7sLzHoVqrqi2bdmosO7VWNBpddvGqmUkuT2etkf/gD/6Aw4cPk81maWlpIR6Pc/jwYYLBIL/9279NU1PTub1hKMSmTZv4tV/7Nb74xS/y7ne/m66urjMKULVcqbWoq6ujvb2dXC7H5OQkGzZsqFoww+Ew99xzjwqtXPFWCuLm5+f57ne/S2NjI3v27KG3t3fF3C63hbTy/vn5eQ4ePMjU1BTd3d3ccssty7bhjtpdWlqiv78fYwyZTIbnnnuOTCZDb28vGzZsoLGxser+V7aInK173L1t73Pvd3M5n332WU6ePMn27du5/vrraWtrW7V35mKrVve53aLVuC3nW7Zs4ZlnnuGGG26gvr5+xe01NjYyNTXF5OTkGcfdHVFt//fue+fzef/EX8v5gOFwmGg0SiAQOOfZLuT8uBc+hULBb5G3A6Lz+TzBYNC/oHLLTjqd9gNWt5XfTTWw7+E+VtnzYO+H0y29nuedkdctl5ezXiofO3aMm2++mXvuuYf3vve9vOtd7+Kee+4B4Mtf/jKZTMa/krHN8LaL3+aeHj58mLGxMbLZLMYYotEoe/fuZdeuXXz1q19lbGxsWfdVNedSIQYCAfL5PI899tiquXHnGmzL1amyu7nyKj+ZTDI2Nsbo6Oh67+qaeJ5HOp3mBz/4Afl8nt27d9PT01N1eqlqf9vPXigUWFpa4oUXXmBubo6Ojg7/AtF9rlUsFpmamuLgwYMsLi7y2GOP8Z3vfIcnnniCv/zLv+TVV189Yz/t62DlwUbu39XyU63K7utiscjhw4c5ePAg+Xye3t5eOjo6lv2f14N972oDTly2/Lmtpp7nsXXrVoaHh8lms1WPh93eNddcw+joKIODg/5ja+kqdbtja509Byn169KxFzE24IzFYiQSCf9xd1BWZQu+DVTdC+TKuqRylSv3Ys7NbXVzV93W18q0Irl8nDVgve6667j99tu5/fbbufnmmzlw4AB33XUX73znO3n88cfJ5XJnFJJisUg2m2VpaYmnn36axcXFZYFjIBCgoaGB97znPTz//PN873vfY3R09IIUHmNKExA3NTXxzDPPLJvaReR8uHmY7k86naa/v5/XX3+dgYEBf1Lslbbh/tiLvMXFRWZnZ5mZmWFmZoa5ubkzgqWVKtazDYCptg3bEnf06FFOnDjBjh072LRpkz/p+0oBX+V9nlea8/Ppp5/m2LFjNDU1sWvXLnp6elb83EtLSwwPD3Ps2DFmZmYIBAJs3LiRxsZG+vv7GRkZWfbayi59d3tujlpl4LbS8XcVi0XGxsZ46aWXOHXqFH19fWzcuPGMeUvXQ+WAkbOpPDFv376d+fl5ksnksrJUmZ6xc+dOFhYWGBsb89Mlql0EVMtTtcFCracHrPYZ5MJzL2TcC6loNOq3nLopSbYutfWrW6Yq6yI3T7Wyx8Ruq7L8ut8hN02pMq1ALg+rpgQMDAxw9913E4/Hl+U0xeNx7rrrLr7yla+ccRKzLU6HDx9mdnaWb3zjG3ziE5+gubnZzwkzxhAKhbj55pvZunUr3/rWt2hubiYej9Pc3PyWKsFAIEAikWDXrl0cOXLE/yKczzZtYDE3N8f09DSJRIKWlhY/36va8+3ns8dicHCQTZs2aenJS+xswUsulyOdTvtdhqv9fyqDtWKx6Adfb7zxht+lvdJKP/l8nnQ67Y+WdRe2WFhYIJlM+l2s0WiU/fv309TUtGLXtrtfld3c1XIW3cAvm80yPj7OK6+8Qnd3N/v37/e7St0TROVJofK4FgoFBgYGePLJJ9m1axd9fX20t7dXDWxs0DQ8PMyRI0fIZDJ0dXXxkz/5k3R0dPDcc88xPDy84nfKbWXM5/NkMhkWFxfJZDLU1dWRSCT8nLdq+2tnIvE8z++KzGQyvPLKK/8/e3ceHNd1H/j+e3tDoxuNfWmsBECABAGCBDdzM0VKIimLWmzJ1jJlyU7K8bx4LDtx3iR5ZTvJTOLJJJ5MrJqZKFa9PJfLizSWrMWLrNFKUqJFizu4gCQIEiD2HQ2g0fvy/gDP0W0Q3GRJbFK/TxWKBHq7ffv0vb97zu/8Dm1tbWRlZem6kOZZyNeLefhTnaDN5va+mlmtVmprawkEAszMzBCNRuftEbVYLFRWVhKPx/H5fITDYf3e56uHqV7XXBg+3YNVmN0fKi1COi8+XKqdqSBR5Y+qnn6bzZZSqULdx5xfPt9xeO4Fl7kNmo9ZkUhEjzJA6spbKi3kUqM/4sZw2YD1X/7lX/jKV76C0+lMSYA2DIPMzMyLepTC4TBTU1OcOnWKb37zm/T29mK327n77rspKSnB4XCkHAStVivf+MY3+M//+T/z5ptvYrPZ2Lx5M263+6KeFUCfdC63BrBhGLjdblatWsXx48eJRqN6NuJ8V3/zHZRVz42qFfnGG2/w0ksvsXr1anbs2MGiRYt04vilJJNJpqam+N73vsff/u3f6uUt5Yruo2Eu02P+jOPxOOFwmKGhIfr7+ykpKaGysjJlyOpS1IExGo1y/Phx3n77bQoKCtiwYQMLFiwgIyNj3sdNTEzQ29vL4OAgY2NjDA8P6zxOt9tNMBjUAVh+fj4ul0uXVJrv4Go+4JsDubl5enN7zBKJhG7PsViM+++/X096Mj/GHIjM19umhvZ//etfs3jxYm6//XaKiorm3V9q0kUwGOTIkSMcO3aM+vp6HnroIdxuN+FwmKNHj5KRkXHRTHXzdqsUo6mpKQYGBjh9+rS+4Fi2bBnV1dW4XK6UfEt1EhsfH2diYoJ4PK5X1pmenmbPnj34/X4eeeQRSktL02YihtrX7+ci12KxUFBQQCKRIBgM6nXa556oLRYLXq8Xq9WKz+djcnISl8t1yVSI+S5E5n630pHdbqeoqIiJiQmGhoaoqKi47P3NvXLzXSyIS5ubbwqzx9tQKEQkEsHpdOoLd3MKoHmIHlJrqar/zz3fmydcweyiJ8FgUPfWquN8LBbD4XCQn5+vKxOYL/jk872xXDZgHRoa4sSJE3g8npQeH5htKOYTdDQaZf/+/bz22mv4fD76+/t14Petb32Lv/mbv+Huu+8mMzMzpaGWlZXxjW98g+eff549e/YQj8fZtm2bDkpV76iaHHHq1Cnq6urYsGHDvAGCOeAcHh7ml7/8JbfddpsOBNRzTUxMYLPZLlpSUB2wEokEHR0dPP744+zcuZNQKMTRo0cZHx/nz/7szyguLtY9IHN7vdRw6e7duzl79izT09M4HI7rPuv448j8GcViMcbHx9m5cye5ubnccsstOJ3OKxYTVwe3eDzO6Ogov/3tbzlz5gy33347TU1NV/xcX3jhBQ4fPkwymSQzM5OGhgb+3b/7dxQVFZGRkUFfXx+//e1v8fl8bN68mSVLlujAwTwEFg6HCQQCOJ1OHA6HPtj6/X46OjpwOBzz1g5VB/6RkRGOHz/O0NAQjz322FVNQjFPclLPMz4+zlNPPYXH4+G+++7TBcABXSfVbrdjGAZ+v58nn3yS8fFx4vE4CxYs4MEHHyQrK4tkMklfXx/nzp1jzZo1LFy48KLXV9//wcFBDh48SE9PD2VlZaxZs4bs7Gzdy5uZmUlVVZUeUkwkEvh8Pg4cOEBbW5vuPVXf7b6+Prq7u/mjP/ojFixYoIM1dYFrPoFeD+ZJJKqsj8Vi0Rf98/UwmUfBsrKyCAaD8wa+5qDMbrczPT1Nb2+vrhBhPonPl9NqbhNza7WmG8MwaGpq4uDBgxw5coRVq1bp2+YOH0ciEaampujv7ycYDFJRUUFxcbEsNnCV5ub3q/aakZGha+CWlJToEVvVbqLR6EWTrMzD++bA1Rxkqu/FzMwMJ06cYGxsjGAwmDJa4Pf7CQaDfOITn2D58uUkEgl9vJZg9cZz2TNtf38/hYWFKV9YdbBTuXYAo6OjvP766/h8PtatW0dubi7f+MY3iMVivPvuuzz55JMMDAwwPj6uJ2WYZ5/W1tbyB3/wBzz//PP86Ec/or29na9//esXBcgrVqwgGo3y3HPP0dzcPG/AOjU1xeHDh3nuuecIhULs2rWLF198kYKCAj7/+c+zdetWxsfH+elPf0pzczO33XbbRQfjRCLB4OAg//N//k/+5E/+hMbGRsrLy3nppZc4cuQIv/zlL/n85z/PD3/4Qx588EE9w9bc8xUMBjl06BAul4vp6WlycnKu+0nw40gdnGZmZjh37hwHDx7krrvuIj8/PyVF5XLUsFZvby+vvvoq8Xicr3zlK1itVj3sZbfbL3lie+SRR3jooYf0wVfVCUwmkxw4cIADBw6wePFi1q9fj8vl0jNZzYHyyZMn2bt3Lx0dHTQ0NOiybJOTk/zmN7+hrq6OtWvXpryuOX8rEAjQ1tbGiRMnuOuuu3Rv5HzvfW7Aor7z4XCYgYEB3njjDaxWK48++mhK7qsKbsxF6+PxOJmZmQSDQZYtW8a2bdtwuVw6TeLQoUPU19ezcuVKvF7vRdsSDAZpbW1l586dJJNJVq5cycaNG3E6nUSjUfLy8nRRchXYh0Ihzp07x1tvvYXT6eShhx4iJyeH6elpjh8/zu7du+nr66O5uZmmpibdy6zetzqBXu/RkFdeeYU333yT8+fPY7PZqK6u5r777iM3N5fKykp90aIuEsy9Rh6Ph3feeUfXvTT3Xqn3qepiHj9+nNbWVl2v1Hwf86pDMPs9UkPs8y1MEI1G6ezs5Lvf/S73338/a9eu1SumARcFxFdrbGyMsbExampq9HOFQiFdrqi9vZ3+/n62b99+0WMXLlxIZ2cnZ86cSfm7OQe6ra2N1157jQMHDlBSUsLg4CD19fV8+tOfZsGCBbLIzFUyp7GY05QyMjJ0qTUVrFqtVv0D702Wnq/OrznnVPXQhsNhxsbG6Ojo4N1339XtQV0Mx+NxgsEgExMTnDhxgkceeYSmpiY8Ho9uN9f7Oy6uzWUD1nXr1vHqq69SVlaGy+VKOfk98cQTfOYzn8Fut/P444/jcDjYtGkTK1eu1PlksViMrVu3Mjo6qieolJeXXzQEmZGRQWFhITt27MDlcvHCCy9w4sQJnnzyyYuCAPU8Bw8e5I477rhom+12O/X19fzVX/0Vjz32GF/96lfxer04HA48Hg/wXgmYs2fPctttt6U8Ph6P09nZyZNPPsmf/umf4vV6+cIXvoDdbmfVqlUcPnyYoaEhLBYLOTk5fO1rX+O///f/Tl5env4iqqt1v9+PzWZj37595OfnS1mV6yCRSDA8PExnZyd+v18Hq/BekHWlGc+xWIxTp05x6NAhAB544AEyMzOxWq06uLnc483pNOr/sViM1tZW9u7dS0tLC83NzTptJBaLMTQ0xL59+8jMzCQQCBCJRPB6vaxbtw6Hw8GBAwc4fvw4x44do6SkhJqamotyaKPRqD4JnD17lt7eXnJzc6mtrSUUCumTwdjYGGfPnsXv9+u81rm9xslkkoGBAQ4fPszMzAz33XdfygXj3EkQKqAJhUKMjIywcOFCli9frlMHYrEYv/jFL8jPz2flypUXpRSo13v77bd5++23KS0tZePGjSxduhS73a4Do4MHD7J06VJyc3P1AgA9PT288847RCIR7rnnHrKzs3U96TNnzuiKBp/73OdSAm71Pmw2m+7RvB7C4TCPP/44bW1tLF68mC1btmCxWHjjjTf4x3/8R4qLi7FaraxatYpbb72VBQsWpPSi2mw2li9fztGjRxkdHdVlhNT7Mw/lb9y4kfHxcfr6+nQ9Vrh43XW1L8yT0ubm0EYiEbq6uviHf/gHduzYwRtvvIHNZmPFihU6aH2/+/TMmTPs3buXL33pS/ozU0HQyy+/TDwev+hYrrbf6XTS0NBwUcqJOb3lt7/9LR0dHfzlX/4lJSUl+iLgf/yP/8GOHTsum/4l3qMuts29rOoYFAwGKSoq0m3AHIhaLBY9gVsdV1W+ufqcLBaLzoft7+/n9OnT+kLF5/MxNTWF2+2mvLycvLw8PB4PmZmZTE5OcuLECfbu3YvD4WDZsmX6Qkw+1xvLZQPW0tJS9u/fz8zMjG5YU1NTHDhwgJGREb7whS/gcDiYmJhg/fr1LFmyRB/Q1IFf/Zudna3LSM29coLZhl5aWsq2bdsoLCzkxz/+MX/7t3/LV77yFYqLi7HZbNjtdvLy8igvL6e7u3vebXY6nXi9Xtxutx6iKCgoSJlYowKHzs7Oi/KwTp48yTPPPMPQ0BBlZWV6UQNAfwHUsMLKlSv5+c9/TmdnJ5mZmTqoHxkZYdeuXaxbt469e/eyc+dO3fMsPhrqM+vr66O9vR2r1UpTUxN5eXm6V+pqTU5O0tPTQzgcZtOmTRQUFADvVaS4VD60Yj5Rq9VbVK/AJz7xCRYvXkx+fr4+aPt8Pt58801GRkbw+/2sX7+e6upqcnJydLH3UCjEu+++i9Vqpbq6mqKiopSgRfVMwuxF3KlTp5iamqKgoIDW1lZ8Ph8jIyPA7HevrKyM2tpaCgoKdO+IeUQgFosxMDDA8PAwq1atoqio6KIAde7/Y7EYv/vd7/D7/WzYsIHq6mp9IXvixAmcTid1dXWUlJSk5Mmr/fTSSy9x7NgxcnJyWLt2rQ60Ozo6aGtrI5lMUldXR11dHRkZGfh8Ptrb2zlw++FQAAAAIABJREFU4ACJRILS0lI92TMYDDI2NkZfXx8A9957L4WFhbqX3NyrqIYmr9dQ97PPPktXVxef/vSnaWxsJCcnh4GBAaxWK9nZ2Xg8HpYuXUpbW5sO3ioqKlLa9Sc/+UlaW1uZmZlJCVjNn2sikaCoqIjs7GwmJycvKiWk3r+5bKE5pUvdpu43Pj7Ok08+SUtLC8uXL8fr9dLZ2Ulvb6++SFSPSSQSvP3222RmZhKLxQgEAkxPT9Pd3c3g4CDf/OY39WQ6AJ/Px7lz55iZmcHpdOqRh9OnTxONRvF6vfMeX1XnSXV1NeXl5Sm3qfd5+vRpenp6yM7Oprq6WncsqPNeRkaGLDpwFea2LRWkTk5OEgqFdJqfuaSU+hzNaS3mlBPzvAGLxUI4HKatrY3W1lbOnj3L0NAQU1NTJJNJ3G431dXVNDQ0UFFRoYPVZDLJ7373O9rb2ykuLsbj8ejbxY3lsgFrYWEhk5OTRKNRPfu/o6OD/fv3s2PHDqqrq/VJ1u12k5WVlbIcWiAQ4NChQ3R2dnLLLbdQUlJyyddSwwalpaW43W6mp6d55ZVXeOqpp9ixYwc1NTU4nU48Hg81NTUEAoF5n8dqteq8mcrKSkZGRlKGV2H2BF5YWMixY8cuerwabqivr08ZcguHw5w8eZL+/n5KS0t1/q3T6eTVV1+luLiY8vJyRkZGOHjwIKdOneLhhx9menqaF154gUAgcNVlasQHI5FI0NbWRiKRoLa2VvdMmScrXY3Ozk76+vr0AdEcGFzL88BsEOfz+WhtbaWsrIwlS5booEqtynPq1Cm6u7sZHh6mpaWFmpoaKioq9DBWPB5nfHycUCjEpk2bKC0tnTc9pru7m5GREaamphgcHNQToEZGRnA4HOTm5uLxePB4PFRVVVFcXJwyQccc1A8MDDA4OIjD4aCuri6l50PtB/O/8Xicrq4uWltbWbp0KXV1dXrJ0LGxMdra2igpKaGoqEgHIOb9mEwmOX/+POfPn2flypWMjo7i9/sJhUK6d3jBggUsXLiQnJwcnfJw6NAhBgYGqKqqora2VuesDw4O0tvbqys6LFq0KCUlZG7P3/WsM/rmm2+yZMkSWlpaKC0tJZlMEggEWLJkCWvXruXgwYO0tLTg9/sZHBzk/PnzeL1encZgs9moqqrCMAxGR0fx+XwUFxfr5zcP8btcLlwuFzMzM5w5c4bGxsaLJt11dnZy+vRpALZu3ZqygpY5HcDv99Pa2spDDz1EUVERxcXFjI2N6dxg1fuWSCQ4fPgwu3btYvPmzbpDISMjg6mpKXbu3ElPTw/19fU6gFFpaMePH2fTpk1YrVbC4TAHDhwgLy+PqqqqeXPJ1XF/vkmVqu2qYD4QCDA2NkZRUZGugrFx48aLSrWJSzNf8KiRRnXuy8jISDmmmPP0zX83Px7eSzWJRCL09fVx8OBBurq6GBsbIxQK6Xa/YMECbrnlFkpLS3XHRE5Ojh5RGRwcpKurC7fbjcvlorS0NK3zr8XFLhuwlpWV6cLLIyMjjIyMMDg4iMfj4a677tIHrcLCQgYHB9m/f7/ucbJYLIyPj7N//37cbjdVVVXzzgKee6IzDIPc3Fzuvfdepqam2L17N1lZWVgsFqqrqykoKGDFihWEQqF5t9ncY9vS0sKZM2d0zpt6PYfDwYIFC/D5fBcFGw6HQ5/QzBNthoeHOXLkCPF4nHXr1mEYBk6nkyVLlrBnzx5WrVpFKBSit7eXs2fPkpubS2lpKS0tLbzwwguMjIxQVVX1Pj4i8X6ok1xPT49uN+Yg7FoOVAMDA/h8Pp3WMTf309y7eDnqAN7V1UUgEGDLli06lQRme0V7e3s5ceIEsViMzMxM1q5dq4MRmA14+/r6GB4eZtmyZSxfvhyXy3XR+1G1iCcmJujo6CCZTFJUVERmZiYZGRl4vV6ampooKCggMzPzkkshqudVr5mbm6tzIucOL5sD3ZmZGQ4cOEAkEmHt2rX6Im9qaorOzk6Gh4dpbGzUqRXz1Z6NRqM4HA594Tw+Po7VaqWgoIC6ujpqamrIyMggFArR3t7Onj17OH/+PKWlpVRWVlJRUUEiMbsS1+nTp+nr68PlctHY2KgnYM4XmM69wP2odXd388ADD6SsUpWdnc2aNWvwer288847FBYWsnbtWvbs2UNHR4d+v+r9ZGZm4vF46O/vp6+vj4KCgpQJaYrNZtMjX/v372fJkiX6uK4usM+ePctrr72mh9ZramouKhWo8gUjkQjV1dV6kmltba0e7TJPqHnllVd0IFFXV6ePtdXV1Rw4cICOjg79+cLs6JbL5WLPnj1s3LhRB+Pnzp1j48aNFBUVXXNngPqcy8vLqaur4+jRo+zatYva2lrKy8sJBAJs3LgxpWqNuDzzAg3m3HeLxaKPU+Z9Obf2qrnOtToOqbzW8+fPc+TIEdrb24lGo/rYEQgEyMnJobm5mRUrVug8WdXOKysrWbp0Kbt27eLs2bPE43EqKyv1SJm4cVw2YK2trSUajRIMBjl9+jQjIyOUl5ezbdu2lOG7LVu2sGvXLvbu3UsymdS5UOFwmEWLFvHlL3+Z8vLylBPifMx/t9vtPPDAA0SjUd5880299nVtbe1lr3jVicZut7NixQp++tOf8uCDD+J2u1MmG9TU1Mwb9E5NTTE8PMytt95KX1+frk6gVuNqaGhgwYIF+rW++MUvcuDAAXbu3Mm+fftwuVwsWLCAlStXkpGRoWcgHz58+IolVcQHRx30VK9Nb28vgUAAu92O2+3G4XDoXsIrUQc/NdMVUpf9u9JzmCfGqEmB69evTxkmjUajDA4OcvjwYYaHh8nJyWHbtm26Z1XdZ2RkhFdffZWZmRluu+22i2bcmrd548aNrFy5kpmZGYqLi1mzZg1lZWV6m8w513Nnnqv7qP+rsjHZ2dk6mFQze829ITAbVJ8/f54DBw6wZs0aXQ0hHA4zPDzMyZMnKSgooKGhQacNzRcghkIhli9fzq233orVasXj8ZCdna1f02azEY1G6e7u5le/+hUdHR0UFRWxePFi1q1bh9Pp1D2HJ06cYGpqivr6epYuXZrSu2quO6p6sK8nwzD0JE2VvuRyuaiurtZ1VQ3DYPHixbS3t9PW1sbOnTtTqi9YrVbq6+s5d+4cXV1dNDY2pqQFqDatemNLS0s5fvw48F6gqu7j9/sJBAK4XC727dvHwoULiUQiOBwOnRKghvVV6pb6jGpqalKqG6jesj179vDoo4/q70A0GiUQCBCNRqmpqWFgYCAlsK6oqKCpqYlXX31Vr1zV2tqKx+OhoKAgZaLftexngKKiIrZu3Up2djY/+9nPyM3N5c4779QTe1U7F5c3MzNDOBxOmcAYCoWYnp4mmZyt3+7xeFKOOeqiUY18mSdcAfq4Ozw8zN69ezl69CiRSISSkhK8Xi/BYJC2tjaam5tZt26dnkyqntflclFcXMyqVat48cUXOXbsGMFgkBUrVlBTU3Pd9pV4fy4bsKrZ7+oDVjlF5skjANu2bWPDhg2Mjo5y/vx5uru7qampYenSpbon41I1Bed2/6uAd3x8nFdffVXXrlSNsra29qremMViYd26dTz++OP4/f6UWeEWi0UP7849SU5OTrJr1y7eeustDMPQj/vSl77EqlWrLlpzOz8/n89+9rO8+OKL9PT0sGnTJu655x7KyspIJBK4XC7uvvtunnnmGRYuXCjFqz9CNpuNDRs2sG/fPp599lkmJycpLS2lrq6OpqYmPZSuAthLUZOhfD4fwWBQ593NnXR1qcBVnUj9fj+nT58mmUzS2NiI3W4nFosRj8cZHh6mra2Nrq4uFi5cyObNm/F6vTpoicfjjI2NcejQIQ4dOsT27dvJzc29bK1ONXnIXGBfBekqIDFv39zg2xyElpWV6Ylb58+f1+k9yWRSP6c6SU1MTPDcc8+Rm5vL7bffrvNzx8bG6OrqIpFIsGXLFn1sUI+d+16i0ShbtmyhtLRU31fVWVRVBvbt28fTTz9Nb28vd9xxB5/5zGeoqKjAZrMxMzPDrl276OjoIBaL4Xa7yc/Pp6ioSF9UqxEXc6CqJnqqGqYftXg8rmtHq+1KJmeX01W9VapXfPv27bqk4Jo1a1i6dKl+H7fffju9vb2MjIykrCSknldNhqmvr2dsbIyf/OQnTE5O4vF49MVeMpnU9S0LCws5d+6c3mfqPKDSwvx+v96n6rM057yqduf3+/H7/ZSUlBCJRBgZGWF4eJijR49y4MABJicn+fa3v50S2FRUVLB27Vp+9atf0dbWxqJFi/jVr37FAw88oIPia61bq/aBw+HA6/Vy9913c+utt3LmzBm++93vEg6H+eY3v0lDQ8N1v4i5ERw7doxz587pY6SaS6KOmzMzM3i9Xt0mzOd8VeVCtU01shsKhRgaGuKll17S8wjKy8tZtWoVNTU1WK1WNmzYwOLFi/Xo11yq/rSqCdvf38/AwEBKpSNxY7hswGq1WsnLy+PEiROUlpZSW1ubUsdMsdvteqiwrq7usi9oPgmqk2NbWxtHjx7Vk0wGBweZmZnB4XCwfPlyPv/5z1NWVqaHrq5EnQBdLherV6/m6NGj5Obm6pOsCkSfeOKJix67fft2Nm7cqHO/ampqLurFmtsTfN9993HHHXfoPNy5CyR89rOf5dChQ+zevZv29vareg/i96NmmVZUVFBRUcGnPvUp+vv7OXbsGPv27eOtt95ienqaO++8k9tuu+2yvd9Lliyhu7ub8+fP4/P5cDqdOjcSuKg+4HxUDvjExISulBEKhUgkEkxMTPDmm29y9OhR6urq2LFjh84HVz0Pqvd137591NTUcN9996X0CqrhN/MohppN73Q6GRsbo7+/X190qgO7+p6Z8xLns2jRIvx+P2+88QZPPPEE0WiUoqIiXC4XDQ0NNDc3U1paytTUFHv27GFwcJA///M/Jzs7WwdLqsblzMwMRUVFuqi3edKTOehQpcJU2SrDMIhEIoyOjjI4OEg8HufVV1/F5/PpYMPr9eog6/nnn8disfDQQw+xc+dOXWnB3GNmLo1kHjJPJpPXbaKNy+UiEAhgsVj0sTYejzMyMsLPf/5zvvzlL+NwOPQkrJaWFgKBAG+99RYNDQ26d0ktNdvf3097ezstLS2Ew2G9z5PJJE888YQ+HqoAf8OGDbr9+Xw+xsbGdJWNf/3Xf9W9pHMXnVCTctUFlsrLhvd660OhEN/73vcYHR3ln/7pn3RgYrFYKC4u5rbbbtOVHeauKFhQUMC2bdv4m7/5G77zne9w8uTJlF67WCx2TbWuzRdsqiPB4/HQ0tLCD3/4Q77//e/zT//0T3zlK1+RlICrcPDgQc6dO6eDw5KSEsrKyhgdHSUSiZCXl4fT6Uz5/iUSCb1CleqBVz/qAvf06dN0dnYyPT1Nbm4ua9asobm5mYKCgpS8aJXWFIlEUgJRp9PJokWLdE71zMwM7e3tNDc3S1mrG8wVA9aSkhLa29tZuXIl9fX1v/fQiLqKMlu6dClLlixJmaVqruk4d5Wqq+VwOPja177G3r17CYfDKSf0S1EnATW0drnXVUGKygObL5dRPf4v/uIvePnll+ed6CU+eOaLBlWXcuHChdTU1HDrrbfS1dXFU089dVXlxnJzcyksLOT06dO8+eabPPjgg7pdqoPr3GFxM9X7qtpxW1sbAIODg7pQ/fDwMNXV1ezYsQOPx6MDqGQyycmTJ/VELK/Xy6OPPorb7dYlWcy9lPNtu9vtZmBggPz8fGpqanQP3tDQEK+88grbtm3D6/Xq79x8J32Hw8Hq1atpaWnRwY7qSVM/FouF4eFhzp49y7333qvzVtX3eWhoCJ/Px+LFi1O+1/OVllG57L/4xS8Ih8MUFRXp3FiLxUJVVRWbNm3C5/ORm5vL6tWrqaysJBgMMjAwwL59+4DZC9CMjAwmJyeprq6mvr5ev646yc1NCYD36o9eD9u3b+f//J//Q1FREVVVVYyNjXH06FGOHj3Kww8/rAv8x2IxJicn6ejo0Au8BAKBlLbR3NzM4cOHeffdd3Wvvvn9NTY2smfPHvbs2UN/fz///M//zNNPP63bQkFBAU6nk/Ly8pQ0gLnlr5xOp65U0dfXx6JFiy7qWY3H4/qY/M4779Dc3MyWLVuoqqoiKytLr0RkDtQVwzAoLCzkzjvv5JlnnuFHP/qRvmAy1/y8Fuq7q/aHeh3184UvfIFgMMjJkyc5efLk+/koP1ZycnL0apeBQIBQKMTk5KReETM7O1unfqiLEXOvqnmUY3BwkKNHj3L8+HFGR0cZHR2luLiYzs5OfaEPqbXPo9Eo4XA45W/J5OxiLYsWLeLrX/86P/3pT3X+K8jKkzeaK16O7tixg6effpqpqSkgtXblB8VcPPj3NTddobS0lK1bt5KVlXVNr3E170990a5UAsdqtZKZmclnPvMZent72b1791Vvh3h/5rZR85C9Wuhi+/btNDY2XnFZVpvNRlNTE8FgkD179hAMBqmsrKSyspLp6Wn8fj/FxcXU1dWl5KWaWSwW8vLyWLFihQ421Mx8t9vNoUOHOHPmDDt37qSpqUkvZ9jb20skEtHBQklJid5eFXyYJ7SYRSIR7HY7mzdv5pVXXmHPnj2cPn2a4uJivWzhtm3byM3Nnbegu+p5UsHo5RZHUNxuN4sWLaKlpUUHReZ8SYDp6Wn6+/txuVxkZWXNO+xutVp56KGHOHv2LB0dHYRCIcrLy1mwYAEFBQW4XC496QJmU3lULc2JiQmcTif333+/Dv5dLpcO3lVvrc1mIxKJpBQ6N7/365W3eMcdd/Dtb39bD5HPzMxQXl7OQw89RHl5ud6uoaEhXn/9dU6ePEkgEKCzs5O///u/57HHHtMVMRoaGjh79iw9PT2EQqGUC7lEIkFLSwtVVVWcOXOGl19+maNHj/K5z32O4uJi8vLycDgcJBIJDhw4wPPPP097ezvLli276ALNZrORl5fHsmXL+Od//mf++q//muLi4pT9qnrQc3Nzeeyxx/jhD3/Ihg0byMnJITs7W18A2u12fVGlqIA0Ozub5uZmWltbWb9+vS4z+H4myXV3d1NcXKx7ilUbVaMTeXl5bN26ld/85jcXLTogLvbJT34Sr9fLwYMH6evr00Hr+Pg4MJtauHv3bkpKSigtLaWkpIT8/PyLJl2OjY1x+PBhjh07xsjICIlEAq/XS3V1Nbm5uSk1deem2KlREdUW1OhNXl4eTU1NlJeX6xHciYmJi5aXF+ntigGryrucmZm5bjld18p84LLZbJcMIj6o17rSgdKc3D+3UoL4cJjzMtWBMBwO09nZybFjx1i0aBHNzc1XzAOF2eCpqKiIlpYWbDYbfr9fH4Tz8/MpKSkhLy/vksOR6vO32WwUFBSwbNkyPVTv8Xj0rFa11KiqXVpWVqZzLtVwuioT43K5dPBhngA23+uWlZWxYcMGiouLdb3NrKwsFixYQGVlZcpqRnMnHl1r0JaTk8OaNWt0wGR+jkWLFuk8MzWRSE1qmdtDbLFYKC8vx+l0UlFRQSQSIScnR1c1gNmAvKCggO7ubl5//XU9tLxw4UJWrlypc5RVT6KqHWvuzbnc5Lnr1cPq9Xq588479SINapZzYWEh8N5M7CNHjtDW1sbg4CDFxcVs2rSJUCjEiy++yG233UZVVRWFhYVkZ2dz7Ngx9u/fz6233poSRKq6rm63W68IZLFYaGlp0TP91QS36elpnn76aUZGRli7dq3uEYXZtpaXl8fdd9/Nrl27+P73v09ZWZm+yFDly3Jyckgmk6xYsYK3336bt956i+7ubpqammhoaNCjHWr1OHN1DJW+ceedd3L06FGqqqpSKilcq56eHs6fP099fX3KYiLqIs0wDGpra8nIyJCVrq6Cyk/NyclhbGyM8fFxurq69CIeIyMjepJkXl4eXq+X8vJyiouLyc/P1xeT7e3ttLe361J8JSUluq6vmnClAlPzBC9Af24qRcQ8ClRYWEh9fT0TExP4fD6Gh4d1R5y4MVwxYM3LyyORSOiSJapmohDpzNyLpGY69/T0cPz4cex2O2vWrEnJab4cFVypAGB4eFiXWCorKyM3N1ef3C+1LSogy8jIoLy8XPcmqQCqurpap6IMDQ3pgLK2tpbCwkJ8Ph8ul4vx8XHGx8f1ZBG1ffMFq2rmtOohrqys1PVMs7KyqL5QJF3N9J7v4utae67Ufpov37u8vJyCggI9m3zuqMfc4FiV31L1EtU+VDmSFotFV1sIBAI4HA5KS0tpamrSlQDU/ZcsWaJz3MxldMxpI3Pf9/XicDjYvn07MzMzDA4O6pXW+vv7dYdBPB7nwIED9Pb26jJ9W7duZWpqitdee413330XwzAoLS2ltLSUnJwcDh8+zObNm3WuoDqZqxzWxsZGHA4Hhw4d4lOf+hTw3n6pqKhg69atPPfcc4yMjHDixAlyc3PJycmhsLBQD+mvWbMGp9PJ7t27mZqa0j3bqp15PB7i8TjZ2dls376d/fv3c/78efx+PwMDA7p2pqprrXpAVZqAzWZj3bp13HrrrTQ3N+ue2bkja1fD5XJx8OBBJiYmqK+v199LNQlItZMPcgTwZqZS4xYvXkwgENALk7S2thKNRvF4POTk5DA+Pq4Dxr6+PgoLC6mtrdXHY1XvPBwOU1hYSHNzM6tXr9YVAMwF/83pI+pCWH13zRfM6nGNjY2cO3eOsbExfD4f09PTH+UuEr+nywashmHoFZymp6f1DFIp8SHSnblMis/n0/Vxp6enueeee8jPz9c1g68UnKghY7vdjsvl0j1dV8t8lW9et17lvqohfa/XS0lJie4BNOemulwufbD3+/0pw6CXCjbNAbTadq/Xe9H2XctElatxqZO7ClKvlIKhmPPM5gbAKhi/6667WL9+PTCbjqB66MzvyTAMPRlL/a72r3m4Ol04HA6Kiop4+OGHOXXqFAcPHmT//v2EQiGdXhGLxcjLyyMnJ0eXfAIoLi7mgQce4Ac/+AFtbW0633f58uXs27ePcDisJw2a94HdbqesrIyamhr27dunJ7jAe/u/pqaGb3zjG/T09HDo0CE8Ho9ebEJdfOTl5bF582Y2bNjAxMQEwWBQt391waAu3LZs2UJTUxNHjx7lxIkT7N+/n6mpKUpKSlixYgUZGRnk5+frhWBUekJmZiZ/9md/pkdHzL1q16KxsZH29naOHDnC4OAgLS0tuic4HA7jcrno7OwkHA5fMRVGvLfqmRrVUMG/0+nE6XSyevVqvF4vZ86cYXh4mHA4zMTEBGNjYwSDQUZGRjAMg/b2diYnJ/VF9YoVK/SSruaLa/PomfkYC+/1vKpjqEo1aWhooKuri46ODqxW6yXruYv0dMWAFWZ7Rjo7Ozl+/DiVlZUfyYYJ8ftQM0WHhoY4dOgQPp+P2tpa7r33Xj3UqIZWP+yh30sFQ+rgejUBo81m0yfwj4ur2S8OhyNlffubgTruulwuVqxYQUtLC7FYjFgsRiQS0WWDVI+Tuk2t/pednc1/+A//gV//+te0t7dTU1NDcXExAwMDnDlzhubm5pReatX+c3Nz+ff//t/zh3/4h4yMjOj8Qkid/NrQ0EBjY6PeXhUwmi9UrFbrJetlm9PKsrOzWbhwIffdd99V7RcVlHwQq08ZhsFdd91FZWUlR44c4ZlnniEUCrF06VKdY/76669TU1NDTU0NR44c+b1f82ZmvvhXFxeqN9Xj8bB69WqKiopobm7WHWBq9Smn08nAwADnzp1jcnISi2V2Jcn6+vqUtBNz5Q6V96pK3amLIRWgmidzqkooqhJFfX090WhUUgJuMFc8I9jtdu6//35+/OMf097ezrZt22RdZXFDOHLkCN3d3SxevJj6+npdP3Vuj1069a4JAegUE5XSospYqV4mlVM5txddnbjj8Th33303b7/9Nl1dXXrC3jPPPENLS4t+rLmXym63U1NTQ0FBAU899RR/8id/ovOAzRPwzL2ZN3q5p6ysLNatW8eqVavw+/309vbyzjvvEIvFeOONN3jggQdYvHixXmJbXJoKFNXoUSgU0hdRzc3Nuqa56pEvLS1lyZIleiTs9OnTtLe3k0wm9UiMYRhMT0/riaIqRWNu9R6r1UosFtM9u+oizlwOTy0RaxgGZWVlWK1Wzp49e712l3gfrmossKysjMzMTCKRyA1/gBIfDz/72c9Yu3Ytzc3NKUPLamjSXNJI2rRIN6rCgwoqFRUQmHtXVRCp6lmqkQOXy8WqVat47bXXOHbsGOvWreMHP/gBU1NTuN3ulOF09RqZmZk8/PDDPPfcc7r+LaCHepX3OykvnZhTU9QESK/Xy8qVK4HUZUOvVPpOvHcBpAJXlQuclZXFqlWrdMqOuY3C7IVSKBSiuLhY14xWQ/+q2L/f79cXbUBKSpSiFhRR823C4bB+nUgkwqlTpxgcHGR6elqX1pK66DeWqwpYVUMJBoOMjo7q5R2FSFff+ta39GpAV3Ijn3TFh+v06dMf+WuqXn9z76fqPVJ1SlXunbmnSc2MjsfjepJTVlYWCxcupLu7m1deeQWbzcapU6dYunSprtgwt6bl3Xffzc9//nN+/etf8+lPfzplEp1ys35nzKkNMtHq2qhFPlRFh0AgoAPW8vLylNq9c1cHVPnP1dXVDA8PMzo6yv79++nu7qaxsZG8vDzy8vKYmZlJWV1Nrf4Gs2Xe1GfW29tLX1+froridrsZHx9nYmICh8NBMBikoqIiZQKXSH9XFbBarVZqa2vp6Ohg37593HPPPfJlFmltaGjoem+CEO+LuZg6vFeizTyhxDz8b56Ipkp7qUknNpuN+vp6XVaop6eHX/7ylyxatEjnGZpXGFOl12prazl48CDr16+noqIiZWKf6kWTajHCTAWi8XicYDDI2NgYgUCAhoYGsrOzU5b7NZeTMy/WsXTpUvr7++nu7tasG9T4AAAgAElEQVTlp6amppienqa8vJxQKKRfIxwOEwqF9N9gtid1amqKyclJXQdW1WHOzMyksrKSoqIiMjIyqKiooLW19brtL3HtrjpgXbx4MePj45w9e1ZWhxBCiA/R3IDQHCyaF2GYG0gCKSXTLBYL2dnZLF68mFtuuQW/309ra2tKDqy6n7ks0J133snLL7+sh3bn2xbz70KopXHNCwHk5+ezZs0aPB5PSr1neC/n1XxxtXDhQlavXo3D4aCzs5PJyUkGBgaIRqP09PToJYvj8bhe1letZqVqZE9NTWGxWMjMzMTpdGK328nPz6e+vj5l4RGPx0NfX9913mviWlx1PZvq6mrOnDnDiRMn5l1KUQghxO/PnLM6d4KTuZyPedlbc51aeC9VQN03JyeH9evXEw6H+bd/+zeGhobIzc3VS2Say3sZhsGnPvUpgsGgXm51buqBEHOZRwJsNpteOnfZsmW43W4sFou+UDIHrqot2+12CgoKdMCanZ1Nf38/IyMjBAIB/H6/7k1Vga7dbsfpdOqeVZX7XVRUpFfScrvdeL1eGhoayM/P14uVADKB/AZzVQGrKgjsdrsJhUJMTU1ddS1FIYQQV0+VmzIvLKEmj2RkZOilfeG9vFVVSUDlYJonE6og1u12s3nzZp599ll2795NTU1NynHcnLtpt9v5whe+8FG+bXGDM/egZmdn09jYiMVi0cGq+T7meqqqtrBa7CQ3N5c1a9awaNEienp6OHHiBL29vQwODup61NFoVC8HnJOTw9TUFOPj4xQWFlJWVsayZcvwer24XC6cTidut5toNIrb7QbQueAfdA1q8eG6qk9LNaqGhgbi8Tjnzp2TiVdCCPEhUEOl5hnRKs80GAySkZGRMtNa1alUeX3qZKweo4IItQTwI488wk9+8hM+97nPfazq+ooPlzk1xWazkZ2drQNV1fuqRgXmjgiYqUUisrOzKSkpoaGhgWAwiN/v10taO51O3VOaSCTo6+tjaGiIhQsX6p5du92uX0NVJ1CTF+dOahQ3BkM+MCGEEEIIkc5uztokQgghhBDipiEBqxBCCCGESGsSsAohhBBCiLQmAasQQgghhEhrErAKIYQQQoi0JgGrEEIIIYRIaxKwCiGEEEKItCYBqxBCCCGESGsSsAohhBBCiLQmAasQQgghhEhrErAKIYQQQoi0JgGrEEIIIYRIaxKwCiGEEEKItCYBqxBCCCGESGsSsAohhBBCiLQmAasQQgghhEhrErAKIYQQQoi0JgGrEEIIIYRIaxKwCiGEEEKItCYBqxBCCCGESGsSsAohhBBCiLQmAasQQgghhEhrErAKIYQQQoi0JgGrEEIIIYRIaxKwCiGEEEKItCYBqxBCCCGESGsSsAohhBBCiLQmAasQQgghhEhrErAKIYQQQoi0JgGrEEIIIYRIaxKwCiGEEEKItCYBqxBCCCGESGsSsAohhBBCiLQmAasQQgghhEhrErAKIYQQQoi0JgGrEEIIIYRIaxKwCiGEEEKItCYBqxBCCCGESGsSsAohhBBCiLQmAasQQgghhEhrErAKIYQQQoi0JgGrEEIIIYRIaxKwCiGEEEKItCYBqxBCCCGESGsSsAohhBBCiLQmAasQQgghhEhrErAKIYQQQoi0JgGrEEIIIYRIaxKwCiGEEEKItCYBqxBCCCGESGsSsAohhBBCiLQmAasQQgghhEhrErAKIYQQQoi0JgGrEEIIIYRIaxKwCiGEEEKItCYBqxBCCCGESGsSsAohhBBCiLQmAasQQgghhEhrErAKIYQQQoi0JgGrEEIIIYRIaxKwCiGEEEKItCYBqxBCCCGESGsSsAohhBBCiLQmAasQQgghhEhrErAKIYQQQoi0JgGrEEIIIYRIaxKwCiGEEEKItCYBqxBCCCGESGsSsAohhBBCiLQmAasQQgghhEhrErAKIYQQQoi0JgGrEEIIIYRIaxKwCiGEEEKItCYBqxBCCCGESGsSsAohhBBCiLQmAasQQgghhEhrErAKIYQQQoi0JgGrEEIIIYRIaxKwCiGEEEKItCYBqxBCCCGESGsSsAohhBBCiLQmAasQQgghhEhrErAKIYQQQoi0JgGrEEIIIYRIaxKwCiGEEEKItCYBqxBCCCGESGsSsAohhBBCiLQmAasQQgghhEhrErAKIYQQQoi0JgGrEEIIIYRIaxKwCiGEEEKItCYBqxBCCCGESGsSsAohhBBCiLQmAasQQgghhEhrErAKIYQQQoi0JgGrEEIIIYRIaxKwCiGEEEKItCYBqxBCCCGESGsSsAohhBBCiLQmAasQQgghhEhrErAKIYQQQoi0JgGrEEIIIYRIaxKwCiGEEEKItCYBqxBCCCGESGsSsAohhBBCiLQmAasQQgghhEhrErAKIYQQQoi0JgGrEEIIIYRIaxKwCiGEEEKItCYBqxBCCCGESGsSsAohhBBCiLQmAasQQgghhEhrErAKIYQQQoi0JgGrEEIIIYRIaxKwCiGEEEKItCYBqxBCCCGESGsSsAohhBBCiLT2sQtYDcP4oWEY37nw/02GYZx+n8/zfcMw/uqD3bp5X+c/GYbxkw/7dcSN5UZrx0JcjrRnke6kjV5/aRmwGobRZRhG0DAMv2EYQxcaStYH/TrJZPLtZDK5+Cq25w8Mw9gz57F/nEwm/+6D3qZrdWFfbb3e2yEuJu1Y3EykPYt0J2305paWAesF9ySTySxgJbAa+PbcOxiGYfvIt0qIayPtWNxMpD2LdCdt9CaVzgErAMlksg94GVgKYBhG0jCMrxqGcQY4c+FvdxuGccQwDJ9hGO8YhrFMPd4wjBWGYRwyDGPaMIyfAU7TbVsMw+g1/V5pGMbzhmGMGIYxZhjG/zIMYwnwfWD9has234X76uGBC79/2TCMDsMwxg3D+KVhGGWm25KGYfyxYRhnLmzjvxiGYVzDbnAahvGzC+/hkGEYyy8874+BKuBXF7btLy78fd2F/eAzDKPVMIwt1/Ba4kPwcW/HhmFkGIbxuGEY/Rd+HjcMI8O8/YZh/N+GYQwbhjFgGMYfvp/9LD4aH/f2LNLfx72N3ozH3LQPWA3DqAR2AIdNf/4MsBZoNAxjBfAD4P8CCoAngV9e+LAcwIvAj4F84Fngs5d4HSvwa+A8UA2UA/87mUyeBP4Y2JtMJrOSyWTuPI+9DfivwINA6YXn+N9z7nY3sAZYduF+d1x4bNWFhlh1md3w6Qvbng88BbxoGIY9mUw+CnRz4YoymUx+1zCMcuAl4DsX7v8fgecMwyi6zPOLD5m0Y74FrANagOXAJ0jt+fACORe290vAvxiGkXeJ5xLXmbRnke6kjd6Ex9xkMpl2P0AX4Ad8zH6ATwCZF25LAreZ7vuvwN/NefxpYDNwC9APGKbb3gG+c+H/W4DeC/9fD4wAtnm25w+APXP+9kPT8/x/wHdNt2UBUaDatM2fNN3+DPD/XOW++E/A70y/W4ABYJNpX2013f6XwI/nPMcrwBev9+f6cfuRdpzyOmeBHabf7wC6TNsfNG8zMAysu96fofxIe5afG+dH2mjK69x0x9x0zuP4TDKZfP0St/WY/r8A+KJhGF8z/c0BlDH7YfclL3waF5y/xHNWAueTyWTsfWxrGXBI/ZJMJv2GYYwxe+XSdeHPg6b7B5htmFdLv99kMpm4MBRRdon7LgAeMAzjHtPf7MDOa3g98cGRdvzec5u3+TypbXhszjZf63dEfDSkPYt0J230vee+qY65aZ8ScAnmRtQD/JdkMplr+nElk8mnme2JLJ+T83Gp7vMeoMqYPxk7Oc/fzPqZbfwAGIbhZnaIoe9Kb+QqVZqe2wJUXHjN+bath9keVvP+cCeTyX/4gLZFfHA+Tu045bmZ3f7+S9xX3Jg+Tu1Z3Jg+Tm30pjvm3qgBq9n/C/yxYRhrjVluwzDuMgzDA+wFYsDXDcOwG4ZxP7N5HPPZx2wj/YcLz+E0DGPjhduGgIoLeS3zeRr4Q8MwWi4kNf898G4ymez6gN7jKsMw7r/whfhTIAz8zrRttab7/gS4xzCMOwzDsF54H1sMw6j4gLZFfDhu9nb8NPBtwzCKDMMoBP6a2bYqbk43dXu+cEy9UjAi0ttN3Ua5CY+5N3zAmkwmDwBfBv4XMAF0MJs3QjKZjAD3X/h9HHgIeP4SzxMH7gHqmJ3I1Hvh/gBvAieAQcMwRud57OvAXwHPMdtwFwIPX832X0ic9l8huf8XF7ZlAngUuD+ZTEYv3PZfmW2UPsMw/mMymexhdpLWN5nNq+kB/pyb4LO+mX0M2vF3gAPAUeAYs8Ng37nEfcUN7mPQniuZzWkUN6iPQRu96Y65RmqKhhBCCCEuxzCMfwOeTSaTr1zvbRHi40ICViGEEEIIkdZkmFgIIYQQQqQ1CViFEEIIIURak4BVCCGEEEKktSstHPB7J7jGYjHmy5NNJBJYLBai0Shf/OIXsdvtfO5zn2PHjh1YrVZisdl6tgUFBQSDwd93M8R1lEwmr8f63JKc/RFLJpOMjY1x/PhxwuEwbW1t/NEf/RGZmZnXe9PeN5vN9pG33Wg0mtJ2LRYLFouFUChEX18fra2t7N+/n0AgQGZmJoWFhZSXl1NVVUVVVRX5+fk4nU6SySSPPvooP/vZzz7qtyDSwEd93J2enk4mk0kMwyCZTJJIJEgkEsBsG7bZbNhssyFHOBwmHo/rv8NsrGC1WolGo/o5VOxgsViIxWLEYjGmp6fp6upieHiYsbExBgYGqKioYGhoiCVLltDQ0EBhYSEulwubzYYqpZpIJMjLS++VRwUAl2y3lw1YI5EIFotFNxrDMHRDUrcbhsGPfvQjurq6WL9+Pffee++lt+JCwzE36mAwyNjYGE6nk0gkohup1WpNeYwQIr3F43FGR0d59913qaysxO128+qrr3L77beTlZXWC6ikFXWSV8dZ9XskEmFiYoKBgQEmJycJBoMEg0Gi0SjhcJhAIEAgEKCuro6ioiIcjkuVfhTig6cCTXVut1qt2Gw2AoGAbovqHG8YBna7XZ/f4/E4gH6cOViNx+PMzMxw9OhR2tramJqaYnh4WHdqhUIhent7yc3NxeFwcP78ebKyssjKysIwDCwWC4lEQgfG4sZ1xU9QNZq5vaSqYU1MTLBz5056enooLy+/5HOYr7oslvcyEQKBAPF4nEWLFlFRUUEymSQej2MYhg5ahRA3hnA4zODgIAUFBaxbt459+/bpE4u4eolEIuWYm0gkdA+TClDNP4FAgOnpaQKBABkZGTgcDnJzc+cd3RLiw2Cz2YjH4/ocn0wmicViZGRk6HO5xWIhHo/rc7zFYtHn+lgspm+LxWKEQiEmJiY4e/YsHR0djI2N0dnZCcwGqR6Ph9LSUvLy8sjIyKCxsZHq6mrsdjs5OTkYhpESCIsb32UDVnPvpvn/qiFarVbeffddurq6sNvtuFyuq34eFcT29/cTiUSoq6ujsrIy5TYhxI3DMAwcDgd5eXk4nU7Ky8upq6uTnr73ydzTqk64TqcTj8eD3W4nEAgQiUQIhUIEg0ECgQDRaJT8/HxycnJwOp36OYT4sKlAVQWj6kd1UKlOLnW7+pu5J3V6eprx8XFGRkYYGRlheHiY7u5u+vv7cTgcOBwOvF4vxcXFFBcXU1RURFZWFna7nbKyMp0GYO5NVa8hQeuN76p6WOdrXNFolFgsxssvv8zExASbNm1i4cKF8z7H3ODTPNR17NgxYrEYxcXF5Obm6vube2GFEDcGh8NBfn6+Pnk1NDSQkZFxvTfrhmI+0ScSCX38tFgseDwevF4vBQUF+hiselnVkGxPTw+FhYXk5uZK77b4yJhHT1XcYBiGbsPmQNb8mGQySSQSYXR0lK6uLnp7exkcHGR0dJTp6WmSyST5+fl4vV7y8/OprKyksrKS/Px8HA6HzoO1Wq0pKQnqO6TiFrl4u/FdMWBNJBJYrdaLhqji8TiTk5Ps3r2bcDjM6tWraWpqmvc5zDmw6nc14eq3v/0tiURCDxuoCQbSwyrEjUd9b7u7u5mZmaGoqOiGSe0JBoMkEgmcTud13WZzwGoOAACys7OpqqpifHwcmD0+q2HUSCRCIBBgYGCAoqIiSkpKiEajl3wdIT5IamKUChLNw/F2u123VRVQxmIxIpEIMzMzTExM0NrayvHjx3XbVkP7FRUVLF26lKqqKjwejw5SzfFERkZGSmA6NzA2b4+4cV0xYFUHbpUInUgkiEQiADrxubq6mtLS0kumBKiGZB4mUFf+HR0d+sCsZg2q15KgVYgbTyQS4cyZMym5azeCI0eOMDMzQ0tLC4WFhddtOy6VigWzJ/G8vDwWL16cEqiGQiESiQShUAifz6eHU8Ph8Ee9+eJjSnVuqe+86uSy2Ww6aI1EIrrqj9/vp7u7m9bWVk6fPo1hGExNTeF2u2lqaqKpqYmKigqysrJ0D6qayKVGH2KxmO69VX+be5FnDpg/jswdjeb0iPlSJOaOppvzjM0T5FSno/n+c59H3fZBxnFXlcOqNl4l/asE6SNHjgDw2GOPsW7dukvmqpmH99Ubs1qthMNhhoeH2bRpE8XFxbpL39z4hBA3BsMwKCwsZP369bz11lt0dnbi8XhuiNm58Xic1tZWfD4fdXV11zVgnXuCmfs3h8NBcXExk5OTjI6OMjIyQjKZJBqN6sorPp+P/v5+AoHAR7794uNJna/n9m6qSVTJZBK/309nZ6eeSNXb20s8HsdqteL1etm+fTtLlizB4/FcNOJq/l0FTSpmUL275jQAtS0wO0nrZkszVBeo4XBYv081Gm4Yhk4ZikajhEIhpqam8Pv99Pf309nZyfT0NNFoVE+Uy8jIICsri3g8js/nY3h4mPLycpYsWUJFRQX19fUYhsELL7zA4OAgGzduJC8vD7/fz+TkJFNTUxiGgc1mo7Kykry8PPLy8sjMzLxoxEjFimqinQqO4/E4Xq/3ku/5qlICzDtD7RCA733ve1RWVlJXV4fL5Zo3wFS9qebeU5htxBMTEyQSCZqamigoKNBlLlQvruS+ifdrvuGfZDLJxMQE/+2//Tempqb4u7/7O3Jzcy95IEskEnrmtRp2Ugn+6qA4tzfsZjsoXgvzpCuHw0FbWxvNzc3Xe7OuysTEhO75UT1C12uEx9z7MbcepbnSisvlwu1243A4UoY8VRmgoaEhCVjFRyYjIyOl5qn6Uef/kydPsnfvXrq6unRwk5ubS01NDdXV1SxdupScnJx5h/wBHRuo76ZKMTAHtEBKWgLMfp8zMjL0yLCyb98+Dhw4QEZGBmvXriUrKwuLxYLP50sJAg3DoKioiKqqqo9kP14LtX/NZUBV5+LQ0BAjIyMMDAxw7tw5pqamSCaTTE5OEgqFiEQiOli02Wzk5OToygujo6NEo1E6Ojro6+ujoKCA4uJi1q9fz/79+5mZmWFkZIRIJKID30gkQiKRwOVyUVBQgNVqJTc3l6qqKhYuXEhDQwPZ2dkpNXpVzzm8Nynvci4bsKpuX/Pv8Xgcv9/P66+/TigU4qtf/aruHZ3vZK0OrnNvUw0DoLKyEo/Ho++jTnzSwyrer/mGOyKRiP4Ce71eHA5HSl3h+fzmN79hdHRUX7g5nU4yMzNZvnw5FRUVZGdn66v8+Zhzv1Uv2OjoKOfOnePMmTO6RJHNZtOBh8VioaamhpaWFt3T8FEHT9casKkTiEr1KSsrY3Jy8n3PzP2oA0bzySwYDF7XdCTVS6GYZ1ED+rbs7Gxyc3PJzs4mMzOTcDisj6GJREL3uArxUTAHlNFolEQiQSAQoL29nX379tHX10cwGCQej1NVVcWiRYuor6+noKBAt2E13G/Og507vGyezAXoDi7z8VON0JqHtueOAI+OjtLR0UFGRoYOftXIhGK1WvF4PCxatIhHHnkEmE1lgPe+h+o11HdP5Y2bOzDMlRHi8bieIKm23zyvR3XyRSIR/a/P58PtdhOJRBgeHiaRSOB2u/F6vZSUlOjXnZmZYWZmhs7OTjo7OxkcHGRiYgK/309mZqbuFCwtLaW8vJz8/HyysrJwu91kZmbqKgt1dXWMj49z+vRpzpw5Q29vLyMjI0xNTTE+Ps709DSjo6PAbG+pqnsbDodJJBL09/cTi8Ww2+10dXVx6tQpzp49yyc/+UlKSkr056z2XyKR0B1Bl3NVVQLMJ1w15PTss8/idDppbGzUBXovZW7ys/rQzp49C0BRURF2u/3/b+9MY9s6s/P/kBLFnbwUKS6iKEoUtdqS5UWOk3EycZymcaZ1MijQTAdt2mmLfmmAaT8F6JeigwJFB2jRb20xBQoMBkg6SZOgycx4MhnYSRTFcSxrtyVTohaKIinu+3ZJ/j/4f95cyrLsLONkkvcHCLYlmcvlve897znPeQ7bKUgDVw7n86JQKMDn80GhUODIkSN33c3JZDIcPnwYiUQChUKBWQetrq7C7/fD5XLhyJEj8Hq9MBgM+56vdDHG43EEAgEEg0GEQiEkk0l2kZvNZmasT8/l9/uxtLQEp9OJEydOwG63f6LSOi2KKpXqkx2kPa/9k1AqlVAoFKBSqaBUKhEIBD5Ro0M2m0UkEkEwGMT6+jo8Hg8efPDBpmk190q1WkU2m0UoFEKtVoMgCDCZTNBqtfsuiuRdmkwmEQ6H0d/f/4me7/NEapwuveFKtf0ymYxlWOlGBHyccSGrK95owrlfUNm/0WigVCphd3cXN2/exNzcHCKRCGq1GtxuN9xuN2w2G+v6b2trY4FUtVplldi9kkQ6l6U/kzoDSBvEKfCT6mn3Qq8xn88jkUiwAFGhUEClUqG9vZ19ud3upv9L5XZ6XaStBT4OTikQlXooZ7NZlMtlhEIhlskVRRGZTAZqtRq1Wo1lhKUDQyKRCBvCkEqlWIVlbGyMZYfj8ThmZ2eRTCYRi8VQLBZRLpehUCgwPDwMl8sFQRBQq9Wg1+uh1WrZWt3W1ta0vrS1tcFoNDIHh5WVFRb4lstlppk3Go0YGBjAxMQEG/4kTQxtb2+zADeZTGJ3dxejo6M4fPgwzGYzq2ZRpvxufGJxWblcRjAYxMLCAp566in2pHe6oewNVqUB65UrV6BUKqHVapl+QRpE8ICV83lBG61AIIBqtYqhoaF7aggaGBhggVi5XEYul4NWq0UoFEIul8P8/DxEUWSZgv2ed2NjAz6fD1tbW8hms1Cr1XC5XHC73TAYDDAYDNBoNBBFEalUCrlcDplMBjdv3mRNiZTRvdtrrtfryOfz8Pl8yGaz6O3tZdnkT3vMSA5xp4CZSul0s1GpVDCbzVhfX2dB892u5UKhgBs3bmB2dhbb29vY2NhgAa/FYkFnZycMBgMLzPajWq0imUxiZ2cHiUQCmUwGyWQShUIBJpMJdrsdFosFRqMRNputKZhXqVTM23R3d/cLlQRQdkg62hK4XXKiUCjYzZ5uNtImrEKhwG2tOPcNyqwmk0lsb2/D7/djfX0doVAIJpMJXV1d8Hq96OnpYd3+0mZuaZBHGce9DUJ0Xe61rZKOc5fKZvZu+va+3lKpxIK7rq4uuN1u2O12qNVqmEwmtLe3Q6/XN63t+1lkURmevl+tVlEsFlnGM5fLoVAoIJ1Oo1AoIBaLseREpVJBLpeDTqdjcgalUskC4nq9jkwmg3K5zI6xXC6HTqdDZ2cnywoHg0HMzs6iVCrBZDLB5XJBo9FAo9Ggs7MTVqsVer0ewK3s8F43hUajgXw+j2KxiFgshkgkgkgkglwuxyQEtLZLs8xWqxXHjh2DTqdjutp0Oo1oNIrNzU0Eg0EkEgnE43FMT08jHo+jWCzi6NGjsNvtbLMizZrfibtKAoBmHVUqlcLly5chiiK+973vQaPR3HVhl54s9PdKpYKpqSlYrVYolcrbLFzuVqrlcD4JVJ4Kh8PIZrOw2+33FJCQWbXBYABw69wcHBxELpeD3+/H1atXcePGDajV6n0D1nQ6jY8++girq6tQKBTo6enB6OgoXC4X1Gr1bbtKu93OFqqRkRH84he/wOzsLAsabTbbga+bSkbvvPMOW3jOnDmD9vb2T3jEbr12v9+PSqUCu92Ojo4OaLXapt/J5/MIBALQ6/UwGAxQqVQoFotwu93Y2NhAoVCAVqs9MNCu1WpYX1/H1NQUlpaWYDQaYbVaUS6XcfHiRRiNRoyPj2N4eBg2m23fxyqVSgiHw7hx4wYWFhZYBttoNCKbzTLrHK1WC0EQ4PF40NXVBZPJxDIxLS0tKJVKSCQSX7hLyZ26eaU3aMrqSPsCKCPCA1bO/UYmk6FYLGJ5eRlzc3MIh8MAbsmDhoaGMDIywqZSSfsASOO6V89IwZP0HKaMqiiKLBPZ2toKtVoN4Nb6R9eGVFKwH1qtFnq9HsFgEPl8HsPDwzh9+jQsFgsrWZNUS7pZl1YtSD9LiYJSqYRSqYRsNotUKoV4PI5YLIZkMolsNsuyrQqFgjWjUSAqiiLy+TwLfCmYpcC8UqnAYrEw1wRBEKBWqxGJRLC4uIhYLIZKpQKr1YrDhw+jr68PBoOh6bjS50SBMK0XlJChJk6/34/NzU0WKFcqFdhsNvT390OpVCISiaBUKqFaraJaraKtrQ1qtRotLS3sXujxeDA6OopoNIqNjQ0sLi7i5s2bWFlZYRaCR44cYRuElpaWu9rw3VPTldRCIhQK4dVXX4XVaoXX62Uf2t2CS2nwS5mkra0t/NEf/RH0ej1UKtU9PQ6H82moVqtMk9No3DKi/jQNUqQvFAQBR48ehdVqxYcffohcLgdRFG/LZM7MzGBtbQ1KpRInTpxoKnHfaSGlxbG9vR0dHR0IBAKYmZkBAJw7d+7AbGm9XkcymUStVoPH48Hs7CyOHTv2iQJWWswuX76MX//61ygWixgZGcHp06cxNjbGfqdQKOD69eu4efMmjh8/Dr1ej0wmg3w+D0EQkMvlEIvFIAjCvkEmXevFYhHvvLFNNBAAACAASURBVPMOFhcX0dvbi+eeew4GgwH5fB7vvvsu3n//fUSjUZTLZTz00ENswAghiiK2t7dx6dIlrKysQKfTYXh4GGfOnIHZbGaLfTKZRCgUgs/nw09+8hM88MADeOKJJ6DVaqFQKFhWI5lM7vtZ3i+k2VVpllU6SIDOQ/qiLmm6QVcqFaYn43DuB1SduHbtGgKBANra2uB2u3HixAkMDQ2xjaE0YJJ+Ty6XMx1qNpvF5uYmVlZWkEqloNFokEwmWYawUqnAYDDAbrfDZDKho6OD2ejt9VGWZm6l9PX14dChQ1hcXGTNoh0dHSyRIO1sl94rpJlAqf/x7u4uk0FkMhkUi0XkcjkUi0UW9La1tcFisUCj0aCtrQ3VahXpdBqRSAQymQzlchnFYpE1RNGmVKvVwmQywePxoL+/HxqNBgaDAfF4HDMzM/D7/TCbzThy5AgeeOABCIJwm/ct2eAVCgUWdGezWZYNj8VizLWBJEVSL9xTp07B4/EgnU5jcnISxWIRpVIJ+XyeSRsIknhqtVqW4R0ZGcG1a9fw85//HNFoFL/85S8Ri8Vw6tQpVvG8W/x3zxpWsqQIBoPIZrP4t3/7N7S2trJs0J2aruhASW/QlUoFfr8f1WoVTz/9NARBuE06wDOsnM8T2oXSDGqpX9+nhWycOjs7AdwS8dPficuXLyOTyeCRRx7BxMREU0n7oCweLdqTk5Noa2tDLBbDwsICJiYm4HA47qiXpabIVCoFmUyGfD7fdHO4l6whXXdTU1NQKBSseYquZeDWQvfSSy9BqVTi3LlzsFqtmJmZwcWLF1EqlWCxWNDS0oK5uTk4nU7W+SuFFv1AIIC1tTVoNBqMjo4ynVUul8Pu7i4AsJ27tIxPrymRSODll1/G+vo6hoeH8Xu/93tsM03vl0aaulwujIyMoL+/H2+++Sa2t7fx7LPPQhAE2Gw2CILAOpgpi3O/oazF3tKjtIGV9HoajQaCIECn07EsDf2cKlcczv2gXC7jf//3f5FOp9Ha2oq+vj6cPn0anZ2dTGdNwZDUr1XqIlSpVNBoNLC1tYUrV65gbm4OhUKBlbClzUhqtRrt7e0QBAHVahV2ux0nT56EyWRio4lpQ0cNs1J0Oh1MJhNaW1tRqVTw3nvv4cyZM016cbrOpOsmWUGVy2Wmu5+ZmcH169eRSqWQzWabnk8mk2FwcBADAwMoFApIJBJYW1tDKpVCsVhktlONRgOCIDDdaWdnJ7xeL4aGhuB0OqFSqVivhCiKiMViLEg2mUw4deoUxsbGmB++VF5BxyydTuPKlStYXV1FPB5HoVBg1ZhisYjW1lbodDpYrVYMDg7C6/XCZrPBYDAwi0JqOp6amkK9XkexWMTKygqzNqXPkvSpAFiW9/jx49DpdHj33Xfh8/kwOTmJSqUCnU4Hu91+V839XVdjaZfb7OwsXnvtNQiCgMOHD6PRaDSJ/fez9ZHaYtEJkE6n8eMf/xgKhQIOh4MJpaXNVnTz5UEr57NSrVaxu7uL1dVViKKIgYGBfW2pPilUpm9paUGxWGRaTinxeBydnZ1wOBxMqyP9/9Lze+9rkcvlUKvVsFgsrOT9wQcf4Omnn77NeomurdbWVthsNqTTaaytreEv/uIvYDab7ylYlTY3JJNJtLW1YXZ2FgMDAzh69Ch6e3tRKBQQDAZx4cIFdHV14fTp02zDSSUwm82G69evw2AwYG1tDblcrklSIf2zWq1iZmYG0WgUFosFiUQCP/3pT7G4uIhcLofDhw/jz//8z9Hb28scE6SUSiW88sor2NnZwWOPPYbHH38c7e3t+75XOgZ6vR7j4+NQKpX4v//7P7z99ts4e/Ysy2iTzsxoNB54vH5TSLufaQGX2vxIp1vRDYTsrSjLsVcuwOH8pvnZz37GGqYOHTqEY8eOwel0smYqOjeVSiU7f+maVKvVqNfrbNAF6bLJvaO3t5dJdsjdhXyIU6kUnE4nrly5gmvXrkEul+PkyZM4cuQI3G432tvbWYAppa2tDYIgwG63o1AoYHt7G7lcjgWaUg/YYrHIGmMrlQp2dnawuLiI+fl5Jn2Sy+UolUqw2WxM40mZ1UAggM3NTRb/UM8CbSxJUnXixAn09PTAbDZDq9WyTSc1SpF+lbxTqbFUJpMxSRbwcfNluVxGPB7H8vIyJicn2X2Eegv0ej37MhgMOHnyJPr6+iAIAnMMoGNAjbxyuRyHDh2Cz+djTapTU1OYmJho8tCn+JAqPnK5HBaLBWq1Guvr61CpVNja2sJHH32EUqmE733ve7dJzvZyYMAqfWIAzHD2G9/4BvNIld4Y7nSToANI/67ValheXkZ3d3eTd5tUNgDgU3UHczh7iUajCIfD7KKnCW2fV8mXNEX7PR55YpZKJVbiuRcXjHq9DrVaje7ubmxtbSEYDLKMKZWJpdcNyWx8Ph8uXbqE5eVlKJVKdHV1MX3XQdTrdeRyOYTDYaysrODChQuoVCrIZDLY2dnB3Nwc1tfXsbW1hVwuh2eeeQYDAwPQaDTsPXV2dsJut2NpaQnnz5/Hz3/+c5YBpQUQ+Nh+JRQKMe1qOBxGLpdDS0sLBgYG8N3vfhcOhwN6vZ51se6XLVxYWMDS0hIOHTqEsbGxA311CZlMBqVSieHhYWxtbWFycpJ1rbpcLqTTaQQCgS8sYN27CQFwWwWLzgG6cVGTyn6NJRzO/YDWikajAbPZDJvNxrSqUguovR3hFIBRdUAmk8Fut+Oxxx7D+Pg4AMBsNrPznDKeUjP8Wq0Gv9+PyclJqFQqrK2tIRaLwel0YmxsDD09PbcFQ3K5HB0dHRgeHsYHH3wAURRx8eJFnDlzBkajkQXNAJo2fsFgEBcvXsT6+jrK5TKzlpLJZAiHw0ilUmyzSCV4er8mkwlOpxN2ux1OpxMmk4llTqn0L21wpceh4yad2KXVatFoNJBIJFCtVvHyyy/jr//6r5mfLAXVS0tLiMfjTFoxODgIs9kMu90Ou90OQRCabK1IHgWADR6gNZMkF3TMotEo08r7/X5W2pdahVEihCadaTQaPPXUU3jllVdgMBhYED03N4eJiYkDz7G7Nl3RgVpfX8fq6iq0Wi3OnTvXlNKvVCq3ZY8IqfUDdZAFAgFEo1H87d/+LSt/0Ycj7QDkcD4rtVoNwWAQsVgMFosFer2eXcRHjx5tWkiBT+ZMQb9L2cj9ApyHH34YS0tLuHnzJhOi0+u6U/ZLWsZxOp24evUqEokEayAqFossg1atVvHRRx9hY2ODdV+aTCZ861vfwtTUFBYWFiAIAjNy3ptprVQqiMfjmJubw40bNxCNRtHa2orTp0/D7Xbj6tWrWFhYgM/ng0ajgU6nw9mzZ+H1eqHX65ser7u7G9/85jehVCqxvr6ORqOBYDCIX/ziF/B4PNDpdMjn89jZ2UE6nWavYWxsDKIoIpFIQKlUYmhoCL29vWwRv1N2uNFoYHJyEuVymZXNpMf0bpKLtrY2jI+PIxqNQqlUsgaJRCKBqakpjIyM3O0U+I1AWSrKrtL7l3qsUjKhXC4jn8+zm4ZUTkVrLodzP2hpaUEmk2Ed8nTuSZufqtXqbdfoXmcAWqeMRiMLnNRqNfNmpyBIq9XCaDSy+KGrqwsOhwPLy8sIBoPY2tpCJBLB9vY2XC4XxsbGYLFY2HXU0tKCjo4OjI2N4fLly1AoFJifn8exY8dgMBjY7+3VsPp8PoTDYbS2tsLtdmNwcBAdHR0AbkmlqBmJAnEaQqBWq6HX66HRaKBSqZoyqHQfkslkUCgUzMeWgnjgVjWJAsFqtQqFQsFst4LBIBQKBS5cuACdTgeXywWfz4eNjQ3UajWMjIywbDK5rVCQKm2CAz7eDO91a5C6IGi1WgwNDSEcDiOdTqNer2NmZgYDAwNMskDvh44hea0Ct6xMR0dHsbu7i1QqBZ1Ohw8//JBtUO7EPWlY6/U6pqencf36dZjNZuZReC+Z0KtXryIQCLCpK+RBRg0b6XQadrsdjz76KJskQQeKl7M4n5VwOIydnR2oVCqcPHkSlUoFi4uLeO+999DR0QGr1couVulm6V43TrVaDdlslnVs7uXEiRMoFAqIRCJMz9rR0cEM3+k5yPy6Wq0ik8mwjvfd3V3YbDaUy2VkMhmsrKzg9ddfZ5ZGlUoFwWAQoihCp9PB7XbD4/Ew/VY4HMalS5fgcDiYrkuv1yOVSiGRSLAOWdopU1B9/PhxmM1mtLe3w+PxsM5WQRAwMjLCFnRpoK/VauHxeCCXyxEIBJBKpbCxsYGrV68ilUrBZrMxKxur1Qqz2QyNRgO32w29Xo+ZmRn2HsvlMsbGxmAymZpKc1IqlQq2trZYNlomk7GpZNLO1b3ZR1p8qaSYTqfZDau1tRVOp5M1H3wRSLM60gzpXt01TZkh3ZxUDtDW1sZKehzO/UAQBOZQkU6nkc1moVKpWCOV1PJJen+n9U+6KaMsnTTGoJ4Z2sxJh60AYL6iGo0GRqMRq6urCIVCiEajCAQC2N3dxenTp5sCUa1Wi87OTlgsFraebGxswGw2s7VqLxRIUvJAq9U2SR/26s5J/kCNVvutZQqFoilABD4O9AuFAtsEUCAM3JJWeL1enDp1CtPT0yiVSpienoZSqcTy8jKrJLpcLrhcLnR3dzOtu7Q5k6p/lAChz4o2FxRsSr3GG40Gent74fP52Doql8uxvr6Ovr6+25xNpJtu+gy7u7thNpsRiUSYnePm5ibrP9iPu65m9KaWlpaQTCZx5swZpuVgD3LAovjGG29gaWkJkUiEmefGYjHIZDLMz89jfn4eo6OjzGdSahPBswOcz0KpVMLKygoKhQJcLhfGx8eRy+UwOjqK5eVlTE1NobOzk02voqYA0grRjljqdbl3oYlGowDAbDn20tXVhfHxcSwsLCAUCuHKlStwOBzo6elBR0cHK23lcjmWKYtGo8wKxGQy4eTJk3A4HPD5fKzcVa/X2dxns9mMzs5O9PT0wOVywWAwsAWISuZUHVGpVDAajUgmk4jH48hkMjCbzbBYLDh8+DB6enrQ09MDjUaDWq2G/v5+eL3epsBUehz2SoK0Wi36+/tZttPv9yMajUIURej1enR3d7POXurgl8vlMBgMUCqVmJ2dxdLSEvx+PwqFAssIUPBJx7lUKmFzc5PpTZeXl5mRtUqlgiiKrHOYmqeq1SoKhQIymQzi8Th2dnawtbWFRCKBUqkEt9uN/v5+dHd3M2eCL2ocI7kAAM1aZ6m+lQzPydCbAlqFQgG9Xo+Ojo7PNDiCw/kkuFwullWMRCLw+XzI5/NwOBxNwScAtrmSJrukAQ1lWimrKs3+UTWXrgtpRrCtrY3p0GkjT8EzNaBKaW1thcFgwPDwMMvMLiwswGq1sglOe+MQanqNRCLM7slqtTJ5lHSIAD1fpVJh3xNFkWUhqRlKGkPR0JNyucwq0OQcQH7RKpUKnZ2dcDqdaG1thUqlgt/vZ0Nq1tbWIIoijEYjWyeoikRrBx0zGqtKGwEKaMklgHqMLBZLU8BNCQyVSsWqVLOzs+jr62P/hz5TiumkwbggCHC73QgEAtje3obVasXc3BzOnj17x3PsnrbfOzs7iMfj6O3txeOPP85OonvptF5YWEClUoHX64XFYkEmk8HFixdhNptx/vx5FAoFdkOW2lzQB8c1WJxPSywWw82bN9HT04OBgQFWAvmzP/sz/OhHP8KlS5fYTl+n00GtVqNUKqGnpwfRaJQFL2TuT+MDqbmlUqng+vXrrMN8v3NVLpdjZGQEbrcbPp8P77//PtbX1xGNRmEymaBQKNgErGw2C+DW4tbT04PHH38cx48fh1qtxvb2Nh544AF0d3cjmUwikUigvb2dzWumsYa0+Le0tKCnpwdWqxUymYyN18tms8x02mKx4IknnsDY2BiMRmPTWERpKedeoYWdFtWzZ89CFEVMT0+jv78fx44dg8fjYdkE2rkDYJ6oXq8XU1NTWFlZwauvvop6vQ6PxwO9Xg+73Q6HwwGdTodEIoHJyUnm1/jqq68yBwCPx8PKbpSVKRaLiMfj2NzcZKWycrkMvV6PU6dO4ciRI3A4HFCpVAiFQrh+/Tqmp6e/kICVSvlS3b80+0S6Mtp0JBIJ5PN5AGCm42Qcvje5wOH8pujr64PD4UChUMD6+jrS6TRMJhOOHz8Om80GvV4PhULBNp1S6RMh9RKmoJW+T0EPZTEp4KLv5fN5pFIp3LhxA/Pz86y5SKlUQqVSsQSBFFoLH3roISwsLDBnE71eD7VaDa/Xe1ulo7u7mznN7O7uYm5uDi0tLXA6ndBqtWxcN5XaG40GcxQAbiVSZDIZc3PJZrNMJyqTyRCPx1mpnJJ85MdKa6vRaMTv/u7vYmBgAA6HA2azGWNjY0ilUrhy5QpmZ2fZVCq/34/d3V1m3aXX6xGNRtkmlzLGAJjVFnmiSjPbGo2G6WsbjQbzT3W73VhZWUFLSwt2dnZY0L5fEz3wcQVJo9Ggv78fm5ub2NraQktLCxYWFg48x2QHBYTVarUhiiL+67/+C6+99hrGx8fxwgsvQK/XN/mUEftZWyWTSfaC0+k03n77bfzLv/wL/u7v/g7f+ta3WJdco9FgB4col8uw2Wz7dl9zfntoNBr3XZCcy+Uar7/+OvL5PB566CEMDg6y3Tt5iIZCISQSCUQiEbYLT6VSrDFLLpczayWar3zy5EkMDg6yDIJKpcKpU6fYFKq95z9dpNJsQLFYxLVr19hCFI/HoVQq2XxnhUIBq9XaZF8kbb4h9mtW3OtCQH/STp02mdQgRtnSvSW5OzVC3ul7excl+lmtVsPOzg50Oh3LQEiv8b1NmXR8tre3sbCwgKtXr2J3dxcKhQI6nQ56vR6CIMDlcsFoNEKr1eKDDz7A5uYmC8bJ1iabzeLIkSPIZrPMxqVUKkGn0+HMmTM4fvw4XC4Xm8RFr6VcLmNrawsvv/wy/v7v//6+n7vFYrGxnxyBMiDlchmpVArT09OYnZ1lmrpSqYTW1lbY7XYcO3YMDz74IH74wx/iV7/61f1+C5wvAfd73Q2FQo2VlRW8+eabrGqRSqXQaDTY1Kiuri709vaip6eHWUpJG4pqtRpUKlXTOkJyKamNJvmEUrUkmUwyZxFqwqpWq9BoNGzd9nq9GB0dZdd5NptFLpeDTCZDJpPBf/zHf2BnZ4c1XJ4/fx5nz56FIAhNPQpbW1vY2dnBhQsX4Pf7IQgCbt68yaZ20oCVwcFBOBwOFItFBINBpNNppNNpZDIZ1jBGwSu9V1EUoVKpmsrx5BajUChgt9uZ3RUlU/7/Z816hCqVCsLhMObn57GwsNAkb5I6O1H2VOp/S8G5VAJG46y1Wi1bu5VKJTo6OtgAnkAgwJxavF4va6yTDnqgvg3S7ZbLZZZ4eP3115HNZmGxWPDaa6/d8bw9MMMqiiIKhQImJyeRTCbR3t7OxqiShY70prg3gKWDQoTDYSwvL0On0+Hhhx9m/meUEpeemBzOZ2FychKzs7P40z/9U/T19TVpoRqNBnQ6HdNik2hfWuqmHSdZmNDou+npaXz44YfY3d3FuXPn8Mgjj7AN3H7sJxNQKpV47LHHPvf3fBAHjTT9vKCgfO8mmKQ++/0u8HEpkBZMvV6P4eFhDA0N4ZlnnmG+hPT70lF+MpkMExMT7LOZm5tDKBTC5uYmRFFEOBzG+fPnMTw8DKvVio6ODuY8QM+9F5VKhf7+fvzVX/3Vb+IwfWoo25LJZLC1tYW1tTXs7OywCTp0MyADdMqaczj3g0ajgaGhIdjtdqysrLCxnLOzswiHw/D7/VheXmYZQrJhojWZZtcbjUZmfE/VEfIKlcvliMVi7N+lUgmiKCKXy7GSuVwuR3d3N8bGxjAyMsKCKrLDIuh6AW5l+5577jm8+OKLbPNbKpVYBlI6gamtrQ12ux1/+Id/iJ2dHayursLpdOLNN99kMqO2tjZcu3aNddxTgiCVSjG7LmnsQ7K0er0Ou90OjUYDl8uFrq4u1ixM1RO9Xn+b0wKV6Ok9aTQaeDwePP300xBFkelU0+l003sh6RF5XNNaTNlVksNRzEeZV9L30ho8NjbGAlQKiqWWqACa5AXkCkFZ7JMnT2J2dhbPPvvsgefYXW2taKzYt7/9bfzBH/xBUwcrHYAf/OAHePTRR/HYY49BEISmRZLKp/V6HfPz8/j1r38Nm83GSpjSEiRlgpLJJN577z2k0+kvrPGB89tNMBjEd77zHbjd7tvspqSyE2D/oJJmU+v1egwMDMDr9bJdIonGaTQgd7S4hVR3SXzWQJlKiAcdY4VCgb6+Prjdbjz99NO3rRlS3dV+DQ93wm63f6bX/mmhxV7qw0plRWpku3LlClZWVpgdGGmWBUGA1+uF1+tljSMczv2Agju1Wo2RkREMDAxAFEV8+9vfRiwWw/z8PG7cuIFwOIxgMIhAIADgYx0rxRb0b2k1hsaXtra2suAL+Lj7HLjVeX748GGMjo6is7MTJpOJNcLul1Cg0jU1RNntdvzJn/wJjh49inQ6jeHhYQiC0GQzRa+XpARkrl+pVPDII48gFovh+vXrWF1dZTKvfD7PdKc9PT0YGRlhDa4klSC9Lr1HChRJVkAJQnqvVJWmiVd7Pbn3Ik0sSn++t0q2l3tdKz/tPbDRaMDlcuHBBx9ErVa763p1YMBar9fx0UcfoVAoMPsEqbat0WjgP//zP/HBBx/A5/Mhk8ngO9/5TpNuioJQmuWt0+nw5JNPQqlUMmsG+hB2d3cxNTWFl19+GeFwuElbweF8Ev7xH/8ROp2O7bg/DXfSaN/p+zxw/epx+fLl+/6c0psw/b1arSKVSmFzc5PN5CbnFQCsmc7r9WJgYACdnZ1sMg2Hcz+QVlxpvGm9XofZbEZHRwe6urrwwAMPIJ1OI5FIsObSeDyOcrnMTPar1SpyuRyq1SoLVKlMTtZ6BoMBVqsVNpuNNVoaDAaYTCaWuZVWbfZLStDzSa3gyCeVpAl75UIAmMML0Cxp6urqgiiKmJiYYE1SUh06ld41Gg0LQGkzvZ9LjfRrb0AqjcP2vr7fpvsQHZd7XacO/K1arYZ3330X+XyeNZtIfdNqtRqWlpag1WoRDoextrbGNGLSx6jX61hcXMTKygoEQWByAHrB9Xoda2truHTpEt566y0sLi6iXq/j0KFDn+FQcL7ObGxsfNEvgcP51Eizq6IoIp/PIxwOY3V1FTdv3kQoFGJdzzSxp6urC0NDQ2y6D8/+c+4nL7zwAoDb/Y/JOUDakU4a1Fwux2bbU1a1VquhUqncZjpPOle5XM7sq/R6PZMVSDWZewM4SjDcyxAVzhfLv//7v9/xZwcGrOVyGTdu3EBXVxesVuttFhQA4PF4MDIygnfeeQeRSATBYBAWi4U9BpUJ5ufnsb6+DofDga6uLrbzEUURq6ureOutt/D2229jdXUVBoMBmUwGTz75JC5dusTtrTgcztcG6fQq0p8lk0lsbGxgdXUVgUAAmUwG9XodbW1t0Ov1cDgc6O/vR39/P+x2O7Ra7b42bBzOb4of//jHX/RL4HwFOChgPbBWSh1tjzzyCPPW2vv1zDPP4Nlnn0V3dzd2dnawtLR02+PQ2K5cLger1do0NSEej+NnP/sZXn/9dYTDYfT09MDpdMLpdOL3f//3+fAADofztYICVvJfjMfj2Nrags/nYyMnyUBcp9PBarWit7eX2adRHwEPVjkczleJAwPWQCCAcrmM3t5eCILAUvXSaQ5DQ0NMNExmvVJoIkShUIDT6cTx48fZTFpRFDE5OYlXX30V+XweTz75JJ566ilEIhE8//zzMJvNfNHlcDhfO6T2VRsbG6xhZXd3l3k26nQ62O12eDweDA0Nob+//zarIO5jzeFwviocKAn47//+b1SrVWZMTtlOKlORDpVGGhoMBthstqbHkMvluHz5MsLhMMbHx/Hoo49CFEWUy2WUSiX8wz/8A1paWvD9738fm5ubePHFF/HEE0/g7NmzfEoLh8P52kH2frFYDH6/HwsLC1hcXEQkEmmawEYNVl6vFz09PSxxINX+cTgczleFAwPW999/H11dXczIt1wus6CVjF+z2SyWl5cRCoWwuLiIt956C8899xx7DFEUce3aNWaOrlaroVQqUSqV8JOf/ASlUgkvvPACfvWrX+HGjRs4deoUvv/970MQhNu63zgcDuerTrVaRTqdxubmJhYWFnD9+nUEg0HU63UIggC73Q6n04mhoSF4PB44HA7mW0nwtZPD4XzVODBgLRaLbEQZ+Z+Fw2HMzMzg8uXL2NzcRCwWQ7FYRCqVQnd3N06ePNn0GKIoYnt7Gx0dHXA6nU1zav/nf/4HMpkMP/rRj1Cv13H+/Hn88R//MQuQpTYZHA6H83WA1lm/34+1tTWEw2EAgNFohMvlwvDwMAYHB+F0OmEymaDRaJgpOgWtdxrkwuFwOL+t3NX8ymKxQC6Xo1gs4r333sNPf/pTRCIRpFIpeDweHDt2DBcvXoRKpcKJEyfw6KOPNv1/Gi7gdDrR2dnJxmOKooh4PA5RFNHR0YHf+Z3fwTe/+U10dnbe0diWw+FwvupkMhlsbGxgY2MDyWQScrkcFosF3d3dGBoawvDwMJxOJzM1l/o4ElIPRw6Hw/kqcGDAqtPpUCgUmN50Y2MDCwsLOHToECYmJtDf34/Z2VkAwMmTJ3HmzBm4XK6mxyDzXzLbzefzCIVCePfdd1ngOjY2hm984xvo7e1lo7totjmHw+F8ndjc3GQjV2nUqsPhgNfrhcfjgcvlQnt7e9NQjL2VKB6scjicrxoHBqxjY2MIBALIZrMwm82o1+vQ6/U4ffo0RkdHEY1GceXKFdhsNpw7dw4nTpy4zYaKRriWSiVsbW1hd3cXi4uLeOONNzA8PIz19XUAt0ZparVaCIKAfD6PVCoFg8HAPVg5HM7Xio2NDWQyGajVahiNT+74wQAAAcpJREFURjgcDng8HnR1dcFsNsNoNDKZFg1ekY5e5MEqh8P5KnJgwPqXf/mX+MEPfoBSqcQ8/9RqNTY2NrCysoILFy5Ao9Hg+eefx8TExL4zvxUKBfR6PWZmZrC8vIxKpYJ0Oo2hoSE8//zz+Nd//Ve88cYbeP/99/Hwww9jZGQEPp8P09PTbBYxh8PhfF0oFoswGo2wWCxsVGRXV1dTVpXmmVOgKh3owgNWDofzVUR2UFOTKIqNQqHA5gKHQiFcuHAB//RP/4R6vQ63240f/vCHzEmgpaUFCoUCGo2GPUYsFsMrr7yCV155BYFAAOPj4/ibv/kbuN1uKJVKpFIp/PKXv8SLL74In8/H/AVHR0fxz//8zxgbG0OpVLofx4LzG6LRaNz3u6dMJuPdepzPzBdx7r700ksNg8EAQRAgCAL0ej00Gg3zr5bJZGhrawOAJvkUVbekwep3v/tdvPTSS/f7LXC+BNzvc5evuZzPg4PO2wMDVg6Hw+FwOBwO54uGe55wOBwOh8PhcL7U8ICVw+FwOBwOh/OlhgesHA6Hw+FwOJwvNTxg5XA4HA6Hw+F8qeEBK4fD4XA4HA7nSw0PWDkcDofD4XA4X2r+H83XM6APHJjuAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x864 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}