diff --git "a/content/Alzheimer_Classification_with_Resnet50.ipynb" "b/content/Alzheimer_Classification_with_Resnet50.ipynb" new file mode 100644--- /dev/null +++ "b/content/Alzheimer_Classification_with_Resnet50.ipynb" @@ -0,0 +1,2629 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU", + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "d29410a4da974ff39765ace7edb25057": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_cd2e630097b94f7aaf170c932c6ae31d", + "IPY_MODEL_739b789f7004406091470b68a1e15ff2", + "IPY_MODEL_d19b9e85a4ff45c8adda76402d91671e" + ], + "layout": "IPY_MODEL_5024fb0ba90941809a50b6ca49326bb4" + } + }, + "cd2e630097b94f7aaf170c932c6ae31d": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_226db94c6f7045a6b3895f4f1ac420ed", + "placeholder": "​", + "style": "IPY_MODEL_731035c3b49f4bba9c803b3e3885ca47", + "value": "Downloading readme: 100%" + } + }, + "739b789f7004406091470b68a1e15ff2": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_48aec22d6da94c81a75bed91a3d8dd3b", + "max": 2130, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_428ce444dabc42efbcdb75f82114b159", + "value": 2130 + } + }, + "d19b9e85a4ff45c8adda76402d91671e": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_8440e34a4a554c8b8b9a39b84692f45c", + "placeholder": "​", + "style": "IPY_MODEL_dab510f38b7b47b4b495c68c84672ec8", + "value": " 2.13k/2.13k [00:00<00:00, 154kB/s]" + } + }, + "5024fb0ba90941809a50b6ca49326bb4": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "226db94c6f7045a6b3895f4f1ac420ed": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "731035c3b49f4bba9c803b3e3885ca47": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "48aec22d6da94c81a75bed91a3d8dd3b": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "428ce444dabc42efbcdb75f82114b159": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "8440e34a4a554c8b8b9a39b84692f45c": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "dab510f38b7b47b4b495c68c84672ec8": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "09e802aa7c454ceabc3309c829c610b8": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_cbcf6165297749b6a1f25dd576920287", + "IPY_MODEL_ba6ec2c5f48949d3aa6c1ccf46c3321d", + "IPY_MODEL_0b6480abe5104c72b011c19cbdf39c27" + ], + "layout": "IPY_MODEL_507e4bd91efb46819a77fb0c0a47faa8" + } + }, + "cbcf6165297749b6a1f25dd576920287": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_136088b1287c4848bf3189d110e34a6a", + "placeholder": "​", + "style": "IPY_MODEL_f894ae380a564ec09e16e07025b2330b", + "value": "Downloading data: 100%" + } + }, + "ba6ec2c5f48949d3aa6c1ccf46c3321d": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_5bd7f251ea8d42308ddfb33cbaa3bb89", + "max": 22643887, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_c1e682b0f430485ea91691950a19ce28", + "value": 22643887 + } + }, + "0b6480abe5104c72b011c19cbdf39c27": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_36996152bfac4251829ddbaa3a269334", + "placeholder": "​", + "style": "IPY_MODEL_554edf8ef73d48549f231269e125c505", + "value": " 22.6M/22.6M [00:00<00:00, 39.8MB/s]" + } + }, + "507e4bd91efb46819a77fb0c0a47faa8": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "136088b1287c4848bf3189d110e34a6a": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "f894ae380a564ec09e16e07025b2330b": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "5bd7f251ea8d42308ddfb33cbaa3bb89": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "c1e682b0f430485ea91691950a19ce28": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "36996152bfac4251829ddbaa3a269334": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "554edf8ef73d48549f231269e125c505": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "94e6de8ee2694484a816c250a616c34b": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_d59ac6e9a82349098ce9e48372f70441", + "IPY_MODEL_c2b07e18cd3a47e7a0d589f65e5f173b", + "IPY_MODEL_2156ae6daf904436960848e63509dbbc" + ], + "layout": "IPY_MODEL_cfd94c448e16487392d87f75ee19ef54" + } + }, + "d59ac6e9a82349098ce9e48372f70441": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_fcc8e7b61f3e47169d8d86a9881f72dc", + "placeholder": "​", + "style": "IPY_MODEL_77ea5483c3b34c538758a50ee5e94f62", + "value": "Downloading data: 100%" + } + }, + "c2b07e18cd3a47e7a0d589f65e5f173b": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_2d6daad1765b4cf78ed9e11c730217c2", + "max": 5645961, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_91bcc0dfd91d4d56aa1fd6583be2bdc7", + "value": 5645961 + } + }, + "2156ae6daf904436960848e63509dbbc": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_e5e32eea618d4361a55718fb40f68bbf", + "placeholder": "​", + "style": "IPY_MODEL_b8d0816fb5ac42a89d2f245f976ffa39", + "value": " 5.65M/5.65M [00:00<00:00, 43.2MB/s]" + } + }, + "cfd94c448e16487392d87f75ee19ef54": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "fcc8e7b61f3e47169d8d86a9881f72dc": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "77ea5483c3b34c538758a50ee5e94f62": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "2d6daad1765b4cf78ed9e11c730217c2": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "91bcc0dfd91d4d56aa1fd6583be2bdc7": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "e5e32eea618d4361a55718fb40f68bbf": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "b8d0816fb5ac42a89d2f245f976ffa39": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "a8a534d4c77e48e4adb0d4f20b711cd9": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_75776a644e984af1a9e33d8409160531", + "IPY_MODEL_b899073529e641928eb4033fcd7d55eb", + "IPY_MODEL_2216f244e45b4e4ca791f4540dba3e75" + ], + "layout": "IPY_MODEL_06e393417cad48478c416ff26f9892fc" + } + }, + "75776a644e984af1a9e33d8409160531": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_20dbca70db394c3b910e83277212364c", + "placeholder": "​", + "style": "IPY_MODEL_49ce11753be04892beeee9e3bb6b657e", + "value": "Generating train split: 100%" + } + }, + "b899073529e641928eb4033fcd7d55eb": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_999e63afc0b242c9a70d47e7499f5876", + "max": 5120, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_c19ac80ad03045a680147f70309a880b", + "value": 5120 + } + }, + "2216f244e45b4e4ca791f4540dba3e75": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_85ba26a2d2c2461fa43ae0854e06ea48", + "placeholder": "​", + "style": "IPY_MODEL_609c6dccef484cf68465ec8db980c46b", + "value": " 5120/5120 [00:00<00:00, 39633.54 examples/s]" + } + }, + "06e393417cad48478c416ff26f9892fc": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "20dbca70db394c3b910e83277212364c": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "49ce11753be04892beeee9e3bb6b657e": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "999e63afc0b242c9a70d47e7499f5876": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "c19ac80ad03045a680147f70309a880b": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "85ba26a2d2c2461fa43ae0854e06ea48": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "609c6dccef484cf68465ec8db980c46b": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "40989fbe8ba7448a9f892144ee9e9202": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_23d9fa4a88a4451aa1a61ae1f02b8723", + "IPY_MODEL_ef20d4d168554ebd8cde45011e7730f7", + "IPY_MODEL_b0b70dbe51e0446f838f52da59d9b416" + ], + "layout": "IPY_MODEL_1dcfb594bb534f85a7ab80a24267f809" + } + }, + "23d9fa4a88a4451aa1a61ae1f02b8723": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_557e077615a24c39a82cf7e4f46c5769", + "placeholder": "​", + "style": "IPY_MODEL_a1baf56b311b4fedb16e3bc06a9bed34", + "value": "Generating test split: 100%" + } + }, + "ef20d4d168554ebd8cde45011e7730f7": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_c28882f718bc485a9ff695239208bfe4", + "max": 1280, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_39e07a9883934cbcbdf924fb3b641da5", + "value": 1280 + } + }, + "b0b70dbe51e0446f838f52da59d9b416": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_64e771ab068e4488a756ef091176135b", + "placeholder": "​", + "style": "IPY_MODEL_a4a90ce80e014ab1a7a8db8186c5d21c", + "value": " 1280/1280 [00:00<00:00, 24939.07 examples/s]" + } + }, + "1dcfb594bb534f85a7ab80a24267f809": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "557e077615a24c39a82cf7e4f46c5769": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "a1baf56b311b4fedb16e3bc06a9bed34": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "c28882f718bc485a9ff695239208bfe4": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "39e07a9883934cbcbdf924fb3b641da5": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "64e771ab068e4488a756ef091176135b": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "a4a90ce80e014ab1a7a8db8186c5d21c": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + } + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tCsdssVupYzB", + "outputId": "bbd49b8a-1b7e-4bf4-cdb4-9f8acbfc0170" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.41.2)\n", + "Collecting datasets\n", + " Downloading datasets-2.20.0-py3-none-any.whl (547 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.8/547.8 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.3.0+cu121)\n", + "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.18.0+cu121)\n", + "Collecting gradio\n", + " Downloading gradio-4.36.1-py3-none-any.whl (12.3 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.3/12.3 MB\u001b[0m \u001b[31m37.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.14.0)\n", + "Requirement already satisfied: huggingface-hub<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.23.3)\n", + "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.25.2)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n", + "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n", + "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.5.15)\n", + "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n", + "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n", + "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.3)\n", + "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.4)\n", + "Collecting pyarrow>=15.0.0 (from datasets)\n", + " Downloading pyarrow-16.1.0-cp310-cp310-manylinux_2_28_x86_64.whl (40.8 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.8/40.8 MB\u001b[0m \u001b[31m16.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets) (0.6)\n", + "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n", + " Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.0.3)\n", + "Collecting requests (from transformers)\n", + " Downloading requests-2.32.3-py3-none-any.whl (64 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hCollecting xxhash (from datasets)\n", + " Downloading xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m12.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hCollecting multiprocess (from datasets)\n", + " Downloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: fsspec[http]<=2024.5.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n", + "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.9.5)\n", + "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.12.2)\n", + "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12.1)\n", + "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.3)\n", + "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n", + "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch)\n", + " Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n", + "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch)\n", + " Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n", + "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch)\n", + " Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n", + "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch)\n", + " Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n", + "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch)\n", + " Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n", + "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch)\n", + " Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n", + "Collecting nvidia-curand-cu12==10.3.2.106 (from torch)\n", + " Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n", + "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch)\n", + " Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n", + "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch)\n", + " Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n", + "Collecting nvidia-nccl-cu12==2.20.5 (from torch)\n", + " Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n", + "Collecting nvidia-nvtx-cu12==12.1.105 (from torch)\n", + " Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n", + "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.3.0)\n", + "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch)\n", + " Downloading nvidia_nvjitlink_cu12-12.5.40-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m37.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (9.4.0)\n", + "Collecting aiofiles<24.0,>=22.0 (from gradio)\n", + " Downloading aiofiles-23.2.1-py3-none-any.whl (15 kB)\n", + "Requirement already satisfied: altair<6.0,>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (4.2.2)\n", + "Collecting fastapi (from gradio)\n", + " Downloading fastapi-0.111.0-py3-none-any.whl (91 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.0/92.0 kB\u001b[0m \u001b[31m15.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hCollecting ffmpy (from gradio)\n", + " Downloading ffmpy-0.3.2.tar.gz (5.5 kB)\n", + " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "Collecting gradio-client==1.0.1 (from gradio)\n", + " Downloading gradio_client-1.0.1-py3-none-any.whl (318 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m318.1/318.1 kB\u001b[0m \u001b[31m38.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hCollecting httpx>=0.24.1 (from gradio)\n", + " Downloading httpx-0.27.0-py3-none-any.whl (75 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.6/75.6 kB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: importlib-resources<7.0,>=1.3 in /usr/local/lib/python3.10/dist-packages (from gradio) (6.4.0)\n", + "Requirement already satisfied: markupsafe~=2.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (2.1.5)\n", + "Requirement already satisfied: matplotlib~=3.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (3.7.1)\n", + "Collecting orjson~=3.0 (from gradio)\n", + " Downloading orjson-3.10.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (144 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m145.0/145.0 kB\u001b[0m \u001b[31m21.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: pydantic>=2.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (2.7.3)\n", + "Collecting pydub (from gradio)\n", + " Downloading pydub-0.25.1-py2.py3-none-any.whl (32 kB)\n", + "Collecting python-multipart>=0.0.9 (from gradio)\n", + " Downloading python_multipart-0.0.9-py3-none-any.whl (22 kB)\n", + "Collecting ruff>=0.2.2 (from gradio)\n", + " Downloading ruff-0.4.9-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (8.8 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.8/8.8 MB\u001b[0m \u001b[31m88.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hCollecting semantic-version~=2.0 (from gradio)\n", + " Downloading semantic_version-2.10.0-py2.py3-none-any.whl (15 kB)\n", + "Collecting tomlkit==0.12.0 (from gradio)\n", + " Downloading tomlkit-0.12.0-py3-none-any.whl (37 kB)\n", + "Requirement already satisfied: typer<1.0,>=0.12 in /usr/local/lib/python3.10/dist-packages (from gradio) (0.12.3)\n", + "Requirement already satisfied: urllib3~=2.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (2.0.7)\n", + "Collecting uvicorn>=0.14.0 (from gradio)\n", + " Downloading uvicorn-0.30.1-py3-none-any.whl (62 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.4/62.4 kB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hCollecting websockets<12.0,>=10.0 (from gradio-client==1.0.1->gradio)\n", + " Downloading websockets-11.0.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (129 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.9/129.9 kB\u001b[0m \u001b[31m14.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: entrypoints in /usr/local/lib/python3.10/dist-packages (from altair<6.0,>=4.2.0->gradio) (0.4)\n", + "Requirement already satisfied: jsonschema>=3.0 in /usr/local/lib/python3.10/dist-packages (from altair<6.0,>=4.2.0->gradio) (4.19.2)\n", + "Requirement already satisfied: toolz in /usr/local/lib/python3.10/dist-packages (from altair<6.0,>=4.2.0->gradio) (0.12.1)\n", + "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n", + "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.2.0)\n", + "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.1)\n", + "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.5)\n", + "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.4)\n", + "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n", + "Requirement already satisfied: anyio in /usr/local/lib/python3.10/dist-packages (from httpx>=0.24.1->gradio) (3.7.1)\n", + "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx>=0.24.1->gradio) (2024.6.2)\n", + "Collecting httpcore==1.* (from httpx>=0.24.1->gradio)\n", + " Downloading httpcore-1.0.5-py3-none-any.whl (77 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: idna in /usr/local/lib/python3.10/dist-packages (from httpx>=0.24.1->gradio) (3.7)\n", + "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from httpx>=0.24.1->gradio) (1.3.1)\n", + "Collecting h11<0.15,>=0.13 (from httpcore==1.*->httpx>=0.24.1->gradio)\n", + " Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio) (1.2.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio) (4.53.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio) (1.4.5)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio) (3.1.2)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.4)\n", + "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n", + "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2.0->gradio) (0.7.0)\n", + "Requirement already satisfied: pydantic-core==2.18.4 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2.0->gradio) (2.18.4)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n", + "Requirement already satisfied: click>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from typer<1.0,>=0.12->gradio) (8.1.7)\n", + "Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from typer<1.0,>=0.12->gradio) (1.5.4)\n", + "Requirement already satisfied: rich>=10.11.0 in /usr/local/lib/python3.10/dist-packages (from typer<1.0,>=0.12->gradio) (13.7.1)\n", + "Collecting starlette<0.38.0,>=0.37.2 (from fastapi->gradio)\n", + " Downloading starlette-0.37.2-py3-none-any.whl (71 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.9/71.9 kB\u001b[0m \u001b[31m11.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hCollecting fastapi-cli>=0.0.2 (from fastapi->gradio)\n", + " Downloading fastapi_cli-0.0.4-py3-none-any.whl (9.5 kB)\n", + "Collecting ujson!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0,>=4.0.1 (from fastapi->gradio)\n", + " Downloading ujson-5.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (53 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.6/53.6 kB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hCollecting email_validator>=2.0.0 (from fastapi->gradio)\n", + " Downloading email_validator-2.1.1-py3-none-any.whl (30 kB)\n", + "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n", + "Collecting dnspython>=2.0.0 (from email_validator>=2.0.0->fastapi->gradio)\n", + " Downloading dnspython-2.6.1-py3-none-any.whl (307 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m307.7/307.7 kB\u001b[0m \u001b[31m36.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio) (2023.12.1)\n", + "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio) (0.35.1)\n", + "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio) (0.18.1)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib~=3.0->gradio) (1.16.0)\n", + "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio) (3.0.0)\n", + "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio) (2.16.1)\n", + "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx>=0.24.1->gradio) (1.2.1)\n", + "Collecting httptools>=0.5.0 (from uvicorn>=0.14.0->gradio)\n", + " Downloading httptools-0.6.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (341 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m341.4/341.4 kB\u001b[0m \u001b[31m33.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hCollecting python-dotenv>=0.13 (from uvicorn>=0.14.0->gradio)\n", + " Downloading python_dotenv-1.0.1-py3-none-any.whl (19 kB)\n", + "Collecting uvloop!=0.15.0,!=0.15.1,>=0.14.0 (from uvicorn>=0.14.0->gradio)\n", + " Downloading uvloop-0.19.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.4 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m94.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hCollecting watchfiles>=0.13 (from uvicorn>=0.14.0->gradio)\n", + " Downloading watchfiles-0.22.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m80.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer<1.0,>=0.12->gradio) (0.1.2)\n", + "Building wheels for collected packages: ffmpy\n", + " Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for ffmpy: filename=ffmpy-0.3.2-py3-none-any.whl size=5584 sha256=690078bca4c69a49d017de3aaa952a109976f0301098acbdbbf506605e96beaf\n", + " Stored in directory: /root/.cache/pip/wheels/bd/65/9a/671fc6dcde07d4418df0c592f8df512b26d7a0029c2a23dd81\n", + "Successfully built ffmpy\n", + "Installing collected packages: pydub, ffmpy, xxhash, websockets, uvloop, ujson, tomlkit, semantic-version, ruff, requests, python-multipart, python-dotenv, pyarrow, orjson, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, httptools, h11, dnspython, dill, aiofiles, watchfiles, uvicorn, starlette, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, httpcore, email_validator, nvidia-cusolver-cu12, httpx, gradio-client, fastapi-cli, datasets, fastapi, gradio\n", + " Attempting uninstall: requests\n", + " Found existing installation: requests 2.31.0\n", + " Uninstalling requests-2.31.0:\n", + " Successfully uninstalled requests-2.31.0\n", + " Attempting uninstall: pyarrow\n", + " Found existing installation: pyarrow 14.0.2\n", + " Uninstalling pyarrow-14.0.2:\n", + " Successfully uninstalled pyarrow-14.0.2\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", + "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 which is incompatible.\n", + "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n", + "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 16.1.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed aiofiles-23.2.1 datasets-2.20.0 dill-0.3.8 dnspython-2.6.1 email_validator-2.1.1 fastapi-0.111.0 fastapi-cli-0.0.4 ffmpy-0.3.2 gradio-4.36.1 gradio-client-1.0.1 h11-0.14.0 httpcore-1.0.5 httptools-0.6.1 httpx-0.27.0 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 orjson-3.10.5 pyarrow-16.1.0 pydub-0.25.1 python-dotenv-1.0.1 python-multipart-0.0.9 requests-2.32.3 ruff-0.4.9 semantic-version-2.10.0 starlette-0.37.2 tomlkit-0.12.0 ujson-5.10.0 uvicorn-0.30.1 uvloop-0.19.0 watchfiles-0.22.0 websockets-11.0.3 xxhash-3.4.1\n" + ] + } + ], + "source": [ + "pip install transformers datasets torch torchvision gradio\n" + ] + }, + { + "cell_type": "code", + "source": [ + "from datasets import load_dataset\n", + "\n", + "# Load the dataset\n", + "dataset = load_dataset(\"Falah/Alzheimer_MRI\")\n", + "\n", + "# Inspect the dataset\n", + "print(dataset)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 489, + "referenced_widgets": [ + "d29410a4da974ff39765ace7edb25057", + "cd2e630097b94f7aaf170c932c6ae31d", + "739b789f7004406091470b68a1e15ff2", + "d19b9e85a4ff45c8adda76402d91671e", + "5024fb0ba90941809a50b6ca49326bb4", + "226db94c6f7045a6b3895f4f1ac420ed", + "731035c3b49f4bba9c803b3e3885ca47", + "48aec22d6da94c81a75bed91a3d8dd3b", + "428ce444dabc42efbcdb75f82114b159", + "8440e34a4a554c8b8b9a39b84692f45c", + "dab510f38b7b47b4b495c68c84672ec8", + "09e802aa7c454ceabc3309c829c610b8", + "cbcf6165297749b6a1f25dd576920287", + "ba6ec2c5f48949d3aa6c1ccf46c3321d", + "0b6480abe5104c72b011c19cbdf39c27", + "507e4bd91efb46819a77fb0c0a47faa8", + "136088b1287c4848bf3189d110e34a6a", + "f894ae380a564ec09e16e07025b2330b", + "5bd7f251ea8d42308ddfb33cbaa3bb89", + "c1e682b0f430485ea91691950a19ce28", + "36996152bfac4251829ddbaa3a269334", + "554edf8ef73d48549f231269e125c505", + "94e6de8ee2694484a816c250a616c34b", + "d59ac6e9a82349098ce9e48372f70441", + "c2b07e18cd3a47e7a0d589f65e5f173b", + "2156ae6daf904436960848e63509dbbc", + "cfd94c448e16487392d87f75ee19ef54", + "fcc8e7b61f3e47169d8d86a9881f72dc", + "77ea5483c3b34c538758a50ee5e94f62", + "2d6daad1765b4cf78ed9e11c730217c2", + "91bcc0dfd91d4d56aa1fd6583be2bdc7", + "e5e32eea618d4361a55718fb40f68bbf", + "b8d0816fb5ac42a89d2f245f976ffa39", + "a8a534d4c77e48e4adb0d4f20b711cd9", + "75776a644e984af1a9e33d8409160531", + "b899073529e641928eb4033fcd7d55eb", + "2216f244e45b4e4ca791f4540dba3e75", + "06e393417cad48478c416ff26f9892fc", + "20dbca70db394c3b910e83277212364c", + "49ce11753be04892beeee9e3bb6b657e", + "999e63afc0b242c9a70d47e7499f5876", + "c19ac80ad03045a680147f70309a880b", + "85ba26a2d2c2461fa43ae0854e06ea48", + "609c6dccef484cf68465ec8db980c46b", + "40989fbe8ba7448a9f892144ee9e9202", + "23d9fa4a88a4451aa1a61ae1f02b8723", + "ef20d4d168554ebd8cde45011e7730f7", + "b0b70dbe51e0446f838f52da59d9b416", + "1dcfb594bb534f85a7ab80a24267f809", + "557e077615a24c39a82cf7e4f46c5769", + "a1baf56b311b4fedb16e3bc06a9bed34", + "c28882f718bc485a9ff695239208bfe4", + "39e07a9883934cbcbdf924fb3b641da5", + "64e771ab068e4488a756ef091176135b", + "a4a90ce80e014ab1a7a8db8186c5d21c" + ] + }, + "id": "_2ZkW62GpfD2", + "outputId": "66602c95-e714-4d31-e7b3-7911e59a7606" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading readme: 0%| | 0.00/2.13k [00:00" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "from collections import Counter\n", + "from datasets import load_dataset\n", + "import seaborn as sns\n", + "\n", + "# Get the train dataset\n", + "train_dataset = dataset['train']\n", + "\n", + "# Count the number of samples per class\n", + "labels = [sample['label'] for sample in train_dataset]\n", + "label_counter = Counter(labels)\n", + "class_names = train_dataset.features['label'].names\n", + "\n", + "# Create a color palette\n", + "colors = sns.color_palette(\"viridis\", len(class_names))\n", + "\n", + "# Plot the class distribution\n", + "plt.figure(figsize=(12, 6))\n", + "bars = plt.bar(class_names, [label_counter[i] for i in range(len(class_names))], color=colors)\n", + "plt.xlabel('Class', fontsize=14)\n", + "plt.ylabel('Number of Samples', fontsize=14)\n", + "plt.title('Class Distribution in Train Dataset', fontsize=16, fontweight='bold')\n", + "plt.xticks(rotation=45, fontsize=12)\n", + "plt.yticks(fontsize=12)\n", + "plt.grid(axis='y', linestyle='--', alpha=0.7)\n", + "\n", + "# Add value labels on top of the bars\n", + "for bar in bars:\n", + " height = bar.get_height()\n", + " plt.text(bar.get_x() + bar.get_width() / 2.0, height, f'{height}', ha='center', va='bottom', fontsize=12)\n", + "\n", + "# Add a tight layout for better spacing\n", + "plt.tight_layout()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 384 + }, + "id": "0HJrwEeRpp4o", + "outputId": "c5d1ea82-2499-488b-c50e-96c627777f6a" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAABKUAAAJOCAYAAABm7rQwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADU5ElEQVR4nOzdeVhU5fvH8c8Zhk0QFBdEwV1yRSvRNBcsNdNSy1zKStv85lr6NddyK7O0PfPbri1muVRmmpamuWeWmqWmmbu4oiAiCDPn94c/TgyLwoiD4Pt1XV4693nOmfuegePMPc95xjBN0xQAAAAAAADgQbaCTgAAAAAAAADXHppSAAAAAAAA8DiaUgAAAAAAAPA4mlIAAAAAAADwOJpSAAAAAAAA8DiaUgAAAAAAAPA4mlIAAAAAAADwOJpSAAAAAAAA8DiaUgAAAAAAAPA4mlIAgKvO/v37NX78eN1yyy0qX768/Pz85Ofnp/DwcN1222168cUXtX//fpd9YmJiZBiG9Wfv3r0Fk/wVlLlGwzDk7e2t4sWLKyIiQjfddJMeeeQRffnll0pLS8vxOOPGjXM5xowZMzxXRB5lzLNy5cou22bMmOGyfdy4cQWSY25crI6rWUE9xpUrV87ys57bP5743S/on73MNdtsNvn6+qpEiRKqWrWqWrVqpcGDB2v9+vUezQsAgLyiKQUAuGqkpKToiSeeULVq1TRu3DgtX75csbGxSklJUUpKig4dOqTvv/9eI0aMUIMGDQo63atCWlqaEhMTdfDgQf3888/68MMP1aVLF1WpUkWLFy/2WB4rVqxweZPcu3dvj913QbkWa8bVyTRNnT9/XvHx8dqzZ49WrFih1157TU2aNFGTJk30999/5/t9FnRj7kopqnUBwNXKXtAJAAAgScnJyWrTpo1Wr17tEi9evLgaNmyowMBAHTt2TFu2bFFycrKcTmcBZXr1aNiwoSpVqqTExET9/fff2r17t7Xt4MGDat++vd58803179/fZb/atWurS5cu1u2reeZOxjzLli1bgJlcnsJaR+XKlV1yr127tkfut3379jp27JhLbNu2bdq+fbt1u1KlSmrYsGGWfQMCAq54fgX1uOTk9ttvl7+/v+Lj4/X777/r+PHj1rb169frhhtu0Pfff6+bbrqpALMEACArmlIAgKvCgAEDXBpShmFozJgxGjFihPz8/Kz4uXPnNGvWLL322msFkOXVpX///i6zczZv3qzHH39cP//8s6QLsycGDRqkunXrqmXLlta4bt26qVu3bp5O1y1z584t6BTyRWGtIyYmRjExMR6/32nTpmWJjRs3TuPHj7dux8TEFNilpwX1uORk2rRpVnPZNE3Nnz9fffv21ZEjRyRJZ86cUadOnbRt2zaVKlWqADMFAMAVl+8BAArcH3/8oenTp7vExo8fr3Hjxrk0pCTJ399fDz/8sH755Zc83cebb76pXr166YYbblB4eLgCAgLk6+ur0NBQtWzZUpMnT9aZM2ey3Xf79u3q27ev6tSpo+LFi8tut6tUqVK67rrr1LlzZz333HNZLo85efKkxo0bp8aNGyskJETe3t4KCgpS1apVdeutt2r48OFauXJlnmq4lAYNGmj58uWqW7euFXM6nRo+fLjLuEutKZWX3NMvYWvVqpXLMT766KMcL23LvL7S+fPnNXnyZEVFRSkgIECGYeQ49lKOHz+ugQMHqlKlSvL19VVERIQGDhzoMnMkXe/evV2Ov2LFCpftOV2elx81Z+fAgQMaNWqUoqOjVbJkSXl7e6tUqVK6+eabNXHiRJ04cSLb/TIf2+l06v3339dNN92kwMBABQYGqnnz5vruu+8u+fhldqlLmbJby+3HH39Uhw4dFBISIj8/P9WpU0evvvqqTNPM8/3nR7579uxR7969VaFCBdntdut5OXnypJ599ll16dJFderUUbly5eTr66tixYqpYsWK6tixo2bOnJntrMyr+XExDEOdO3fW8uXLXWaNHTt2TFOmTHEZu3r1ag0ePFitWrVStWrVVLJkSdntdgUHB6tevXrq27evtmzZkm3tDz30kEt8/PjxOT4mnj7/Sheac99++626deumypUry9/fX8WKFdN1112nvn37aseOHZddFwAgH5gAABSwp59+2pRk/SlTpoyZnJycp2O0bNnS5Rh79uxx2R4QEOCyPbs/lSpVMvfv3++y36pVq0w/P79L7vvmm29a+xw/ftysVKnSJffp0qXLZdU4ffr0bMd99dVXWe5r9+7d1vaxY8fmeJy85r58+fJLjpVk9urVy7qPjPGwsDDz1ltvzTI+u7GVKlVyqXP69Oku23v27GmWL18+2/uvWLFilp+JXr16uYxZvny5y/bMtaXXcLk1Z67DNE1z5syZl/wZLV26tLl06dIs+2YcExoaarZt2zbb/Q3DML/88susPzAXkfkxHjt2rMv2zD+TDz74YI75P/HEE3m678wy/9xmfHxzyrdjx45mUFBQtvv98ssvuXoeb7vtNvP8+fNX1eOS+RiZf7bTPfHEEy7jIiIiXLb379//kvV7eXmZH3zwQY615/Qn42PiyfOvaZpmQkKCefvtt190H29vb/Ptt9++rLoAAJePy/cAAAVuzZo1LrdvvfVW+fr65vv9FC9eXJGRkSpZsqQCAgJ05swZbdmyRSdPnpQk7du3TwMHDtTXX39t7fPss88qOTnZun399dcrIiJCp0+f1uHDh7Vnzx45HA6X+3nvvfe0b98+63blypVVt25da7H2PXv26Ny5c/leX7q2bdvKy8vLJa/Vq1eratWql9w3r7mXKVNGXbp00fHjx11mfmVe7yc6Ojrb+4uNjVVsbKwCAgJ0ww03yM/PL8+z4NLNnDlTNptNjRo1kp+fn37++WelpKRIuvCNjvfff3+WNcvccbk1Z7ZixQo9+OCDLs9XlSpVFBkZqa1bt+rw4cOSpBMnTqhTp0769ddfdd1112V7rKNHj+r7779XWFiY6tatq02bNlkzrEzT1PDhw3XXXXfluebc+vjjjxUYGKhGjRpp//79LjNY3nzzTf33v/9VRETEFbv/zL755htJUnh4uOrVq6eTJ0/Ky8vLZUy5cuVUqVIllSxZUj4+Pjpx4oQ2bdpk/ZwvWbJEb731lp588km38yiox6V9+/Z6/fXXrdsHDhzQ/v37VbFiRStms9kUGRmpMmXKqGTJkkpNTdXevXut9bscDof69++v22+/XWFhYdZ6Wvv27dPGjRut49SqVctlba3M62x56vwrSffee6/LzMAyZcroxhtvVEpKitasWaPz588rNTVVffv2VcWKFXX77be7XRcA4DIVdFcMAIDatWu7fBI9YsSIPB/jUjOlNm3aZKalpWXZLyUlxWzatKm1n91uN8+cOWNtr1GjhrXt4YcfzrL/qVOnzDlz5pjr1q2zYo899pi1T2RkZJb7TUlJMZctW2bOnTv3smrMaaaUaZpmaGioy9jJkydb2y42U8rd3HOaVZQdZZp50KBBA/PgwYPW9oyz5DKOu9RMKUnmggULrO2//fab6e/v77L9p59+sra7O1PqcmvOXMdNN93ksr1v376mw+EwTdM0z507Z3bo0MFle48ePS76eLZr185MSkoyTdM0jxw5YpYtW9Zl+759+3LMM7O8zgiqVKmSuXfvXtM0TTM1NTXLLLiPPvoo1/edmTszpSSZw4cPtx5P0/z35+v06dPmzp07s72vI0eOuMzuady48VX1uGSuMaeZUtu3b88ydsOGDdb2Xbt2madPn85236lTp7rs97///S9Pj0FGnjz/Ll261CWvjh07mikpKdb2v/76ywwMDLS2161b1+26AACXj5lSAICrjnkF1p4JDw/X888/r++//147d+7U6dOndf78+Szj0tLS9Pfff6tBgwaSLsx+2bVrlyRp8eLFmjx5smrXrq1q1aqpWrVqKlGihO655x6XY1SqVMn69549ezRq1Cg1bNhQ1apVU2RkpAIDA3XLLbfke40ZZV4HJ+M6TRdTELm/+eabqlChgnXb3Vlyt956q+644w7r9vXXX6+ePXvq/ffft2I//PCDWrRo4X6y+ezYsWPWwvSS5OPjo0mTJslmu7Dsp5+fnyZPnqyFCxdaYxYtWiSn02mNyezVV1+Vv7+/JCk0NFSNGzfWggULrO2HDh1ymSmTn0aMGGH9DNntdrVv317Lli1zuW9PioyM1MSJE10eq/Sfr+DgYB08eFCDBg3SqlWrtHfvXiUmJiotLS3LcTKvP5RXBfW4ZLceVsZzQdWqVTV37lx98cUX2rx5s44cOaJz585lew6+nMfAk+ffr776yuX2iRMndN9997nEvL29rX//8ccf2rt371X9LaQAUJTRlAIAFLjQ0FBt27bNur137958Pf6OHTvUsmXLLF8xn5P4+Hjr308//bRWrVqllJQUHT582GXRcB8fH914442677771KdPH/n4+EiSHnvsMb377rvav3+/UlNTNXnyZGsfwzBUs2ZNderUSUOGDFGZMmXyqcp/JSYmKi4uziUWGhqaq309nbuPj4+aNm162ceRpKioqCyxjIu+S3K5NPFqsG/fPpcGQMWKFRUcHOwyplatWvLx8bHexCckJOjkyZPZPv6BgYGqWbOmSyzz8dIvabwSMl+y6Mn7zk7z5s2zXK6Xbvbs2erZs2e2TajMMp4T3FFQj0t2P+/p5wLTNNWlSxeXy+Uuxt3HwNPn3z179rgcb+3atZe8zz179tCUAoACQlMKAFDgbr75Zi1fvty6vWzZMqWkpOTbulJDhw51eUPk7+9vfbOcYRjauHGjy5u3jE2Cli1b6vfff9e0adO0bNky/fXXX0pNTZUknT9/XuvWrdO6dev0448/6ssvv5QklS1bVps3b9b//vc/LVy4UFu2bNHZs2etY2/fvl3bt2/X559/ri1btigoKChf6ky3ZMmSLOus3Hzzzbna19O5h4aG5jjjx9MyNyeOHj16xe8z84yU3M5oy0mpUqWyxHJqylwJme/fk/ednfLly2cbP3/+vPr27evynJcpU0Y33HCDAgMDJUnfffedkpKS8iWPgnpcFi1a5HI7IiLCWrtq3rx5WRpS9erVU5UqVeTt7Z1lzTR3Z7B6+vzrjvRzHADA866OV4EAgGta9+7dXRoTJ06ccJmhk528zCxYtWqV9W9fX1/t2LFDy5cv17x58zR37twcF41OFxkZqddee01bt25VUlKS9u/frwULFqhOnTrWmK+++splhlfJkiU1atQorVmzRmfOnNHRo0e1atUql0Wm9+7de1lvpLJz9uxZjRkzxiXWuHHjXC1yns6d3N1tpuRnQ2rr1q1ZYn/++afL7YyXJ6bPrEiXvuByuow/N9m53AaSpCyzM/bv36+EhASX2I4dO1wudSpevHi2zSdkldPP159//ukym7BBgwY6cOCAFi9erLlz5+rzzz/3VIpXzLZt2/Thhx+6xDJexpb55/vFF1/U77//rvnz52vu3Ll6/PHHL3r83P78e/r8W6VKFZf9P//8c5mmedE/GS/7zY/fawBA7tGUAgAUuLp166p3794usbFjx2r8+PEu37wkSefOndMHH3yQ6282k2R9si5deJOavt6OdOHNzNKlS3Pcd8aMGVq0aJHVBLPb7YqIiNAdd9yh+vXru4w9cuSIJGn58uX65JNPrDe9hmGobNmyatasmW6//fZs98kPmzZtUqtWrVwuhfTy8rpkgy8jd3PP+JhKnl87SLowwy7jN25t2bJFM2fOdBnTunVr69+ZZ9FMnz7dav4sWrRIH3zwwUXvLz9qLlu2rBo1amTdTklJ0ahRo6y1gFJSUjRixAiXfdq3b3/VzC4rrDKeE6QLDcr0dYacTqdGjhyZb7OkPM00TX311Vdq1aqVSw2hoaF66qmnrNuZH4NixYpZ/z5y5Iiee+65i95Pbn/+PX3+7dixo0v8mWeeyXJJX3q+b731lgYOHOhWXQCA/MHlewCAq8LUqVO1c+dOrV69WtKFN1bjxo3Tyy+/rOjoaAUGBurYsWPavHmzkpOTs6zJcjE33XSTdXnguXPnVKtWLTVu3FhHjhzRb7/9dtFPxr/++mvNnz9fxYoVU61atVSuXDl5eXnp77//dmn+2O121ahRQ9KFZsjgwYPl5eWlyMhIVaxYUf7+/jpy5Ih++eUXl+PXqlUr13Vk9tZbb+nbb79VUlKSdu3a5fI189KFN4Bvvvlmnhb2djf3GjVqyGazWc2UpUuXqkmTJtYC5iNHjtSNN97obqm5kj7jITo6Wn5+flq/fr3LjLqmTZuqZcuW1u02bdpo/Pjx1u0lS5aodOnSKlasWK4u3cuvmidNmqQ2bdpYx3nrrbf03XffKTIyUlu3bnV5U1ysWDGNHTv2ksfExdWtW1eBgYFKTEyUJG3YsEGRkZGqWbOmtm3bpj179sgwjCvypQtXQr9+/eTv76+EhARt2bJFx48fd9keHBysb775xmWG3U033aT//e9/1u0nnnhCs2fPlq+vr9avX3/JS9oyr102ffp0/f3339Z9vPrqq4qIiPD4+bdt27Zq06aNfvjhB0nSrl27VKNGDd1www0KCwtTUlKS/v77b2tmVcZzQl7qAgDkD5pSAICrgr+/v5YuXaqnnnpK06ZNs9ZEOnPmjH788ccs4/MyU+SFF15Qy5YtrVlXJ0+etNZaadSokSpVqqQ5c+Zc9BhJSUn69ddfc9z+7LPPZrmkyuFwWGswZad9+/a68847c11HZhs3btTGjRuz3RYREaEPPvhAbdq0cevYec29ZMmS6tKli8vjuH79euvfmWfCXQmdO3fWqlWrXL7NLl14eLg+/fRTl9jNN9+sTp06af78+VbszJkzOnPmjLy9vfXII4/o7bffzvH+8qvmW265RTNmzNB//vMfnTt3TpL0zz//6J9//nEZFxISolmzZl1WIxMXFCtWTM8//7wGDRpkxXbv3q3du3dLkgYMGKAFCxZcdQvj5yTjDMHMmjZtqk8++STLJbz33nuvpk2bZv2+OJ1O61I7f39/TZgwQc8880yOx42KilKjRo20YcMGSRfOGStWrLC2jxs3ThEREQVy/p07d666deumJUuWWLllbqqns9td3w7lti4AQP5g7jcA4Krh6+urN954Q7t379bYsWPVsmVLlStXTr6+vvLx8VGFChXUpk0bTZo0SZs3b871cRs1aqR169apY8eOKlGihHx9fVWjRg0988wz+umnn1wuW8ns6aef1rPPPqv27durRo0aCgkJkZeXl4oVK6bIyEjdf//9WrFihcslVnfffbdef/119ejRQ3Xq1FFoaKi8vb3l6+uriIgIdejQQR999JG++eaby74MKz2XChUqKDo6Wr1799bcuXP1zz//uNWQupzcP/zwQ/33v/9VtWrVsqzX5An169fXli1b9Oijj6pChQry9vZWhQoV1K9fP23cuDHLWjOS9MUXX+iZZ55RtWrV5O3trdKlS+uee+7Rr7/+qu7du1/yPvOr5gceeEDbt2/X8OHDdeONNyo4OFh2u10lS5bUTTfdpPHjx2v79u1q27at2/cBVwMHDtTcuXN10003yd/fX4GBgWrUqJGmT5+uN998s6DTyzO73a6goCBVrlxZLVq00KBBg7R27VqtWbMm2zXlvL29tWzZMg0bNkyVK1eWt7e3ypQpo3vuuUe//PKLmjVrdsn7XLBggR577DFFRERkae6k8/T5V5KCgoK0ePFiLVy4UPfdd5+qVaumYsWKycvLSyVLltT111+vRx55RJ9//rm++eYbt+oCAOQPwyws85IBAAAAAABQZDBTCgAAAAAAAB5HUwoAAAAAAAAeR1MKAAAAAAAAHkdTCgAAAAAAAB5HUwoAAAAAAAAeR1MKAAAAAAAAHmcv6ASKIqfTqcOHD6t48eIyDKOg0wEAAAAAAPAY0zR15swZlS9fXjZbzvOhaEpdAYcPH1ZERERBpwEAAAAAAFBgDhw4oPDw8By305S6AooXLy7pwoMfFBRUwNkAAACgMPn11181a9YsrVq1Svv371dISIgaNmyoZ555RtWrV7fG9e3bV5999lmW/WvUqKGNGzdmif/zzz+aOHGiVqxYocTERJUvX1533XWXxowZ4zLO6XTqww8/1IwZM7Rr1y75+/urbt26mjRpkurVq5f/BQMAipyEhARFRERY/ZGc0JS6AtIv2QsKCqIpBQAAgDx56623tGbNGnXt2lVRUVE6cuSIpk6dqhYtWmj9+vWqW7euJMnb21u+vr56//33XfYPDg7O8hp08+bNiomJUYUKFTR06FCVKlVK+/fvz/ZD1N69e2vmzJl68MEHNWjQIJ09e1abNm1SUlISr20BAHlyqSWNDNM0TQ/lcs1ISEhQcHCw4uPj+Y8bAAAAebJ27Vo1bNhQPj4+VmzXrl2qV6+e7rnnHn366aeSLjSP5s6dq8TExIsez+l0qn79+goICNDy5cvl7++f49jZs2ere/fu+vLLL3XXXXflT0EAgGtObvsizJQCAAAAriJNmzbNEqtRo4bq1Kmj7du3Z9nmcDh09uzZHF/0f//99/rjjz+0aNEi+fv7KykpSb6+vvLy8soy9pVXXlGjRo101113yel06ty5cwoICLj8ogAAyEbOS6ADAAAAuCqYpqmjR4+qdOnSLvH0S+qCg4MVEhKi/v37Z5k5tXTpUkmSr6+vGjZsqICAABUrVkw9evRQXFycNS4hIUEbNmxQdHS0Ro0apeDgYAUGBqpq1aqaPXv2lS8SAHDNYaYUAAAAcJWbOXOmDh06pAkTJlixsLAwDRs2TDfccIOcTqcWL16sadOmacuWLVqxYoXs9gsv9Xft2iVJ6tatm9q1a6eRI0dqy5YtmjRpkg4cOKDVq1fLMAzt3r1bpmnq888/l91u1+TJkxUcHKzXX39dPXr0UFBQkNq1a1cg9QMAiibWlLoCWFMKAAAA+WXHjh1q3Lix6tSpo1WrVmV72V26559/XqNHj9asWbPUo0cPSdKtt96qH3/8Ue3atdN3331njX3hhRc0cuRI/fDDD2rdurVWrVqlFi1aSJLWr1+vxo0bS5ISExNVpUoVXXfddVq9evUVrBQAUFTkti/C5XsAAADAVerIkSPq0KGDgoODNXfu3Is2pCRp8ODBstls1iV7kqyFze+9916Xsffdd5+kCwurZxxXpUoVqyElSYGBgbrzzju1YcMGpaWlXX5RAAD8P5pSAAAAwFUoPj5et99+u06fPq3FixerfPnyl9zH399fpUqVclkrKn2/0NBQl7Fly5aVJJ06deqi49LHpqam6uzZs+4VAwBANmhKAQAAAFeZ5ORk3Xnnndq5c6e+/fZb1a5dO1f7nTlzRidOnFCZMmWs2I033ihJOnTokMvYw4cPS5I1tnz58ipXrlyWcelj/fz8VLx4cbfqAQAgOzSlAAAAgKuIw+FQ9+7dtW7dOs2ZM0dNmjTJMiY5OVlnzpzJEn/22WdlmqbLguSdOnWSr6+vpk+fLqfTacXff/99SVKbNm2sWPfu3XXgwAH98MMPVuzEiROaP3++brnlFtlsvH0AAOQfFjq/AljoHAAAAO568skn9frrr+vOO+9Ut27dsmy///77tXfvXl1//fW69957VbNmTUnSkiVLtGjRIrVr104LFy50aSA9++yzGjNmjNq0aaPOnTtry5Yteu+999SjRw999tln1rijR4/q+uuvV2JiooYMGaLg4GC9/fbbOnDggNatW6f69etf+QcAAFDo5bYvQlPqCqApBQAAAHfFxMTop59+ynG7aZo6ffq0Bg4cqPXr1+vw4cNyOByqXr26evbsqaFDh8rb2zvLPm+99ZbefPNN7dmzR+XKlVOvXr00ZsyYLGP/+ecfDR06VMuWLVNqaqqaNGmiF154QdHR0VekXgBA0UNTqgDRlAIAAAAAANeq3PZFuCgcAAAAAAAAHkdTCgAAAAAAAB5HUwoAAAAAAAAeZy/oBAAAAFB4VftiYkGnABR6u7uPLugUAKBAMFMKAAAAAAAAHkdTCgAAAAAAAB531TWlfvnlFw0YMEB16tRRQECAKlasqG7dumnnzp0u43r37i3DMLL8qVmzZpZjOp1OTZ48WVWqVJGfn5+ioqI0a9asbO9/+/btateunQIDAxUSEqIHHnhAx48fvyK1AgAAAAAAXKuuujWlXnzxRa1Zs0Zdu3ZVVFSUjhw5oqlTp+qGG27Q+vXrVbduXWusr6+v3n//fZf9g4ODsxxz9OjReuGFF/TYY48pOjpa8+fP13333SfDMNSjRw9r3MGDB9WiRQsFBwfr+eefV2Jiol566SVt3bpVGzZskI+Pz5UrHAAAAAAA4BpimKZpFnQSGa1du1YNGzZ0aQDt2rVL9erV0z333KNPP/1U0oWZUnPnzlViYuJFj3fo0CFVqVJFffr00dSpUyVJpmmqZcuW2rNnj/bu3SsvLy9JUr9+/TRjxgzt2LFDFStWlCQtXbpUbdq00TvvvKM+ffrkqoaEhAQFBwcrPj5eQUFBeX4MAAAACgsWOgcuHwudAyhqctsXueou32vatGmWGUk1atRQnTp1tH379izjHQ6HEhIScjze/PnzlZqaqn79+lkxwzDUt29fHTx4UOvWrbPi8+bN0x133GE1pCSpdevWioyM1OzZsy+nLAAAAAAAAGRw1TWlsmOapo4eParSpUu7xJOSkhQUFKTg4GCFhISof//+WWZObdq0SQEBAapVq5ZLvFGjRtZ26cKMqmPHjqlhw4ZZ7r9Ro0bWOAAAAAAAAFy+q25NqezMnDlThw4d0oQJE6xYWFiYhg0bphtuuEFOp1OLFy/WtGnTtGXLFq1YsUJ2+4XSYmNjFRoaKsMwXI4ZFhYmSTp8+LA1LmM889i4uDilpKTI19c3y/aUlBSlpKRYt9NnbqWlpSktLU2SZLPZZLPZ5HQ65XQ6rbHpcYfDoYxXUuYU9/LykmEY1nEzxqULM8dyE7fb7TJN0yVuGIa8vLyy5JhTnJqoiZqoiZqoiZqoyVsXXmOZktJkyibJS/++7sop7pTkkCkvGS6fkjpkyinJLkNGLuJpMmVKVh6XiqfKlPH/x7lUnJqoyVM1FeVzBDVREzVdmzVlHpOTq74ptWPHDvXv319NmjRRr169rPikSZNcxvXo0UORkZEaPXq05s6day1gfu7cuWwbSX5+ftb2jH9famx22ydNmqTx48dniafP0pKkMmXKqFq1atqzZ4/Lt/mFh4crPDxcO3fuVHx8vBWvWrWqypYtqz/++MPKTZJq1qypEiVKaNOmTS5PeFRUlHx8fLRx40aXHBo2bKjz58/r999/t2JeXl6Kjo5WfHy8duzYYcX9/f1Vv359nThxQv/8848VDw4OVq1atXT48GEdPHjQilMTNVETNVETNVETNT3oHSFJOm2mal5arKrbAtXcK8Qaf8hM1uK0Y2pgC9b1Xv9+Ic1OZ6JWOeLU1KukIm2BVnyTI16/OePV2l5GFQw/K77KEaedzkR1spdTCcPbii9OO6ZDZrLu9Q53aQLMS43VWaVZ+aX7OPWAAmRXF+9/P4hMlamPUw+ovOGndvayVpyaqMlTNRXlcwQ1URM1XZs1nT17Vrlx1S10ntGRI0d08803KzU1VevXr1f58uUvOv7cuXMKDAzUQw89ZH0r3x133KHt27dr9+7dLmOTkpIUEBCgESNGaNKkSdq4caOio6P18ccf64EHHnAZO2zYME2ZMkXJycm5nikVERGhkydPWgt60W2lJmqiJmqiJmqipqJYU915kyUVjdkqRXEGDjUVjpr+7jaqyJ4jqImaqOnarCkhIUGlSpW65ELnV+1Mqfj4eN1+++06ffq0Vq1adcmGlHShW1iqVCnFxcVZsbCwMC1fvlymabpcwpd+uV76cdMv20uPZxQbG6uQkJBsG1LShdlV2W2z2+3WZYTp0p/czNKfxNzGMx/XnbhhGNnGc8oxr3Fqoqac4tRETRI15ZRjXuPURE1SwdaUKtfPN52SnJliF4s7ZMqRJXrhjX12copnzuNicTOPcWqipovF86OmonyOyG2O1ERNOeWY1zg1XR015TQmS065GuVhycnJuvPOO7Vz5059++23ql27dq72O3PmjE6cOKEyZcpYsQYNGigpKSnLN/f9/PPP1nZJqlChgsqUKZNlCp0kbdiwwRoHAAAAAACAy3fVNaUcDoe6d++udevWac6cOWrSpEmWMcnJyTpz5kyW+LPPPivTNNWuXTsr1qlTJ3l7e2vatGlWzDRNvf3226pQoYKaNm1qxbt06aJvv/1WBw4csGLLli3Tzp071bVr1/wqEQAAAAAA4Jp31V2+99///lfffPON7rzzTsXFxenTTz912X7//ffryJEjuv7663XvvfeqZs2akqQlS5Zo0aJFateunTp16mSNDw8P15NPPqkpU6YoNTVV0dHR+vrrr7Vq1SrNnDnTZTraqFGjNGfOHLVq1UpPPPGEEhMTNWXKFNWrV08PPfSQZx4AAAAAAACAa8BVt9B5TEyMfvrppxy3m6ap06dPa+DAgVq/fr0OHz4sh8Oh6tWrq2fPnho6dKi8vb1d9nE6nXrxxRf1zjvvKDY2VjVq1NDIkSPVs2fPLMf/888/NWTIEK1evVo+Pj7q0KGDXn75ZYWGhua6hoSEBAUHB19yQS8AAIDCrtoXEws6BaDQ2919dEGnAAD5Krd9kauuKVUU0JQCAADXCppSwOWjKQWgqMltX+SqW1MKAAAAAAAARR9NKQAAAAAAAHgcTSkAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHgcTSkAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHgcTSkAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHgcTSkAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHgcTSkAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHgcTSkAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHgcTSkAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHgcTSkAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHgcTSkAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHgcTSkAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHgcTSkAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHgcTSkAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHgcTSkAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHgcTSkAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHgcTSkAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHgcTSkAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHgcTSkAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHgcTSkAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHjcVdeU+uWXXzRgwADVqVNHAQEBqlixorp166adO3dmGbt9+3a1a9dOgYGBCgkJ0QMPPKDjx49nGed0OjV58mRVqVJFfn5+ioqK0qxZs7K9/9weEwAAAAAAAO6zF3QCmb344otas2aNunbtqqioKB05ckRTp07VDTfcoPXr16tu3bqSpIMHD6pFixYKDg7W888/r8TERL300kvaunWrNmzYIB8fH+uYo0eP1gsvvKDHHntM0dHRmj9/vu677z4ZhqEePXpY4/JyTAAAAAAAALjPME3TzOtOBw4c0K5du3TTTTepWLFiki7MRpoyZYq++eYb+fv7a/DgwerQoUOeE1q7dq0aNmzo0gDatWuX6tWrp3vuuUeffvqpJKlfv36aMWOGduzYoYoVK0qSli5dqjZt2uidd95Rnz59JEmHDh1SlSpV1KdPH02dOlWSZJqmWrZsqT179mjv3r3y8vLK0zEvJSEhQcHBwYqPj1dQUFCeHwMAAIDCotoXEws6BaDQ2919dEGnAAD5Krd9Ebcu33vmmWfUtWtXeXt7W7GJEydq5MiRWrdunX788Ud17txZv/zyS56P3bRp0ywzkmrUqKE6depo+/btVmzevHm64447rOaRJLVu3VqRkZGaPXu2FZs/f75SU1PVr18/K2YYhvr27auDBw9q3bp1eT4mAAAAAAAALo9bTak1a9aodevWVlPKNE1NnTpVNWvW1P79+7VhwwYFBARoypQp+ZKkaZo6evSoSpcuLenC7Kdjx46pYcOGWcY2atRImzZtsm5v2rRJAQEBqlWrVpZx6dvzekwAAAAAAABcHrfWlDp27JgqVapk3d68ebOOHz+ucePGKTw8XOHh4ercubN++umnfEly5syZOnTokCZMmCBJio2NlSSFhYVlGRsWFqa4uDilpKTI19dXsbGxCg0NlWEYWcZJ0uHDh/N8zMxSUlKUkpJi3U5ISJAkpaWlKS0tTZJks9lks9nkdDrldDqtselxh8OhjFdS5hT38vKSYRjWcTPGJcnhcOQqbrfbZZqmS9wwDHl5eWXJMac4NVETNVETNVETNVGTty68xjIlpcmUTZKX/n3dlVPcKckhU14yXD4ldciUU5JdhoxcxNNkypSsPC4VT5Up4/+Pc6k4NVGTp2oqyucIaqImaro2a8o8JiduNaUyF7JixQoZhqFbbrnFilWoUEFHjhxx5/AuduzYof79+6tJkybq1auXJOncuXOSlG2DyM/Pzxrj6+tr/X2xcXk9ZmaTJk3S+PHjs8TTZ2lJUpkyZVStWjXt2bPH5dv80pt4O3fuVHx8vBWvWrWqypYtqz/++MPKTZJq1qypEiVKaNOmTS5PeFRUlHx8fLRx40aXHBo2bKjz58/r999/t2JeXl6Kjo5WfHy8duzYYcX9/f1Vv359nThxQv/8848VDw4OVq1atXT48GEdPHjQilMTNVETNVETNVETNT3oHSFJOm2mal5arKrbAtXcK8Qaf8hM1uK0Y2pgC9b1XsFWfKczUasccWrqVVKRtkArvskRr9+c8WptL6MKhp8VX+WI005nojrZy6mE8e8SEovTjumQmax7vcNdmgDzUmN1VmlWfuk+Tj2gANnVxfvfDyJTZerj1AMqb/ipnb2sFacmavJUTUX5HEFN1ERN12ZNZ8+eVW64tdB5nTp1VKpUKa1cuVKS1LJlS+3evdul6EceeUSLFi2yZiC548iRI7r55puVmpqq9evXq3z58pKkjRs3Kjo6Wh9//LEeeOABl32GDRumKVOmKDk5Wb6+vrrjjju0fft27d6922VcUlKSAgICNGLECE2aNClPx8wsu5lSEREROnnypLWgF91WaqImaqImaqImaiqKNdWdN1lS0ZitUhRn4FBT4ajp726jiuw5gpqoiZquzZoSEhJUqlSpSy507tZMqS5dumjixIm655575Ofnp9WrV2vAgAEuY7Zt26aqVau6c3hJUnx8vG6//XadPn1aq1atshpS0r+X2GXX8IqNjVVISIjVPAoLC9Py5ctlmqbLJXzp+6YfNy/HzMzX1zfbbXa7XXa760Oc/uRmlv4k5jae+bjuxA3DyDaeU455jVMTNeUUpyZqkqgppxzzGqcmapIKtqZUuX6+6ZTkzBS7WNwhU44s0Qtv7LOTUzxzHheLm3mMUxM1XSyeHzUV5XNEbnOkJmrKKce8xqnp6qgppzFZcsrVqEyGDh2q6Ohoffnll/rss89Ur149jRs3ztq+b98+bdiwQTExMe4cXsnJybrzzju1c+dOffvtt6pdu7bL9goVKqhMmTJZprtJ0oYNG9SgQQPrdoMGDZSUlOTyzX2S9PPPP1vb83pMAAAAAAAAXB63mlJBQUFav369fv/9d/3+++/69ddfVbJkSZcxX375pfr165fnYzscDnXv3l3r1q3TnDlz1KRJk2zHdenSRd9++60OHDhgxZYtW6adO3eqa9euVqxTp07y9vbWtGnTrJhpmnr77bdVoUIFNW3aNM/HBAAAAAAAwOVxa02pK+nJJ5/U66+/rjvvvFPdunXLsv3++++XJB04cEDXX3+9SpQooSeeeEKJiYmaMmWKwsPD9csvv7hcTpe+JlSfPn0UHR2tr7/+WgsXLtTMmTN13333WePycsyLSUhIUHBw8CWvnQQAACjsqn0xsaBTAAq93d1HF3QKAJCvctsXuaym1JEjR/Tll19qx44dSkpK0vvvvy9JOn78uPbs2aN69erJ398/T8eMiYnRTz/9lOP2jOn++eefGjJkiFavXi0fHx916NBBL7/8skJDQ132cTqdevHFF/XOO+8oNjZWNWrU0MiRI9WzZ88sx8/tMS+GphQAALhW0JQCLh9NKQBFzRVvSk2bNk3//e9/rW+dMwzDWmn9zz//VFRUlN5++2099thj7hy+UKMpBQAArhU0pYDLR1MKQFGT276IW2tKLViwQAMGDFC9evX0zTffqG/fvi7b69Spo6ioKH399dfuHB4AAAAAAABFXO6+oy+TKVOmqGLFilq+fLkCAgL066+/ZhlTr149rVq16rITBAAAAAAAQNHj1kypzZs3q0OHDgoICMhxTIUKFXT06FG3EwMAAAAAAEDR5VZTyul0ytvb+6Jjjh07lutvqwMAAAAAAMC1xa2m1HXXXXfRS/PS0tK0cuVK1atXz+3EAAAAAAAAUHS51ZTq2bOnNm3apPHjx2fZ5nA4NHToUP3zzz968MEHLztBAAAAAACuZYmJiRo7dqzatWunkJAQGYahGTNmZBlnGEaOf9q0aeMyNjY2Vn369FGVKlXk7++vatWqaciQITp58qTLuA0bNqhfv3668cYb5e3tLcMwrmSpuMa4tdD5wIEDtWDBAk2YMEEzZ86Un5+fJKlbt27auHGj9u7dq7Zt2+qRRx7J12QBAAAAALjWnDhxQhMmTFDFihVVv359rVixIttxn3zySZbYxo0b9frrr6tt27ZWLDExUU2aNNHZs2fVr18/RUREaMuWLZo6daqWL1+uX3/9VTbbhTksixYt0vvvv6+oqChVrVpVO3fuvCI14trkVlPK29tbS5Ys0fjx4/X222/r1KlTkqS5c+cqKChIw4cP1/jx4+mgAgAAAABwmcLCwhQbG6ty5cpp48aNio6Oznbc/fffnyW2YsUKGYahe++914p988032rdvn7799lt16NDBioeEhGjChAnasmWLrr/+eklS3759NXz4cPn7+2vAgAE0pZCv3GpKSZKPj48mTpyo5557Tn/99Zfi4uIUFBSkWrVqycvLKz9zBAAAAADgmuXr66ty5crleb+UlBTNmzdPLVu2VHh4uBVPSEiQJIWGhrqMDwsLkyT5+/tbscxjgPzkdlMqnWEYqlmzZn7kAgAAAAAA8smiRYt0+vRp9ezZ0yXeokUL2Ww2PfHEE3r55ZcVHh6u33//XRMnTlTnzp15jw+PcWuhcwAAAAAAcHWbOXOmfH19dc8997jEa9eurXfffVfbtm1TkyZNFBERoQ4dOujWW2/VnDlzCihbXItyNVPqlltucevghmFo2bJlbu0LAAAAAADck5CQoIULF6p9+/YqUaJElu0VKlRQo0aN1L59e1WqVEmrVq3SG2+8odKlS+ull17yfMK4JuWqKZXTyv6XwkLnAAAAAAB43rx585ScnJzl0j1JWrNmje644w6tX79eDRs2lCR17txZQUFBGj9+vB5++GHVrl3b0ynjGpSry/ecTqdbfxwOx5XOHwAAAAAAZDJz5kwFBwfrjjvuyLLtnXfeUWhoqNWQStexY0eZpqm1a9d6Kk1c41hTCgAAAACAIiQ2NlbLly9Xly5d5Ovrm2X70aNHs51EkpqaKklKS0u74jkCUj41pdLS0nTq1Cl+cAEAAAAAKGCff/65nE5ntpfuSVJkZKSOHj2aZameWbNmSZKuv/76K50iICmXa0plx+Fw6I033tCMGTP0559/yjRNGYahunXrqnfv3howYIDsdrcPDwAAAAAA/t/UqVN1+vRpHT58WJK0YMECHTx4UJI0cOBABQcHW2Nnzpyp8uXLKyYmJttjDRgwQNOnT9edd96pgQMHqlKlSvrpp580a9YstWnTRo0bN7bG7tu3T5988okkaePGjZKk5557TpJUqVIlPfDAA/leK64dhmmaZl53SkxM1G233ab169fLZrMpIiJCoaGhOnr0qA4cOCCn06kmTZpoyZIlCggIuBJ5X9USEhIUHBys+Ph4BQUFFXQ6AAAAV0y1LyYWdApAobe7++iCTgGFQOXKlbVv375st+3Zs0eVK1eWJP3111+qWbOmhgwZopdffjnH4/311196+umn9fPPP+vIkSMqX768unbtqvHjx6tYsWLWuBUrVqhVq1bZHqNly5ZufzEairbc9kXcmso0ZswYrVu3Tvfdd5+ef/55VaxY0dq2f/9+jRw5UrNmzdKYMWMu+ksAAAAAAAAube/evbkad9111yk3c0+uu+46zZkz55LjYmJicnU8wB1uzZQKDw9X+fLltWHDhhzHREdHKzY21ppOeC1hphQAALhWMFMKuHzMlAJQ1OS2L+LWQucnT55U69atLzqmdevWiouLc+fwAAAAAAAAKOLcakrVqFFDx44du+iY48ePq3r16m4lBQAAAAAAgKLNrTWlnnjiCT355JMaPHiw6tSpk2X71q1b9fnnn+uNN9647AQBAAAAAIXH6N/vLugUgEJtYtSXBZ2Cx7jVlKpRo4ZuueUWNWzYUL169VKzZs2sb99btWqVPv74Y912222qXr26Vq5c6bJvixYt8iVxAAAAAAAAFF5uNaViYmJkGIZM09S7776r9957z9qWvm76ggULtGDBgiz7OhwON1MFAAAAAABAUeFWU2rMmDEyDCO/cwEAAAAAAMA1wq2m1Lhx4/I5DQAAAAAAAFxL3Pr2PQAAAAAAAOByuDVTKiOn06mjR48qNTU12+0VK1a83LsAAAAAAABAEeN2U+rTTz/VSy+9pG3btuW4eLlhGEpLS3M7OQAAAAAAABRNbjWlXnrpJQ0fPlze3t5q0aKFwsLCZLdf9qQrAAAAAAAAXCPc6iS9+eabqlChgtauXavw8PD8zgkAAAAAAABFnFsLnR8/flxdunShIQUAAAAAAAC3uNWUioyM1KlTp/I7FwAAAAAAAFwj3GpKDR48WPPnz9e+ffvyOx8AAAAAAABcA9xaU6pXr146duyYmjZtqn79+ql+/foKCgrKdmyLFi0uK0EAAAAAAAAUPW5/ZV5CQoLi4+M1ZsyYi45zOBzu3gUAAAAAAACKKLeaUmPGjNHzzz+vMmXKqEePHgoLC5Pd7nZ/CwAAAAAAANcYtzpJH374oSIjI/XLL78oMDAwv3MCAAAAAABAEefWQuenTp1Shw4daEgBAAAAAADALW41perVq6fY2Nj8zgUAAAAAAADXCLeaUqNHj9bXX3+t3377Lb/zAQAAAAAAwDXArTWlTp06pTZt2qhp06Z64IEHVL9+fQUFBWU79sEHH7ysBAEAAAAAAFD0uNWU6t27twzDkGma+uCDDyRJhmG4jDFNU4Zh0JQCAAAAAABAFm41paZPn57feQAAAAAAAOAa4lZTqlevXvmdBwAAAAAAAK4hbi10DgAAAAAAAFwOt2ZKZeRwOHTixAmlpKRku71ixYqXexcAAAAAAAAoYtxuSv36668aNWqUVq5cqfPnz2c7xjAMpaWluZ0cAAAAAAAAiia3mlKbN29W8+bNZbfb1bZtWy1YsED169dXuXLl9Ntvv+n48eOKiYlRpUqV8jtfAAAAAAAAFAFurSn17LPPSpJ+/vlnzZ8/X5J011136bvvvtPevXv1+OOP648//tDYsWPzL1MAAAAAAAAUGW41pVavXq2OHTuqVq1aVsw0TUmSv7+/pk6dqvLly2vUqFH5kyUAAAAAAACKFLeaUvHx8apatap129vbW4mJif8e1GZTTEyMli1bdvkZAgAAAAAAoMhxqylVtmxZnTp1yrpdrlw57dq1y2VMcnKykpKSLi87AAAAAAAAFEluNaVq166tv/76y7p988036/vvv9e6deskSdu3b9fs2bNVs2bN/MkSAAAAAAAARYpbTakOHTpo5cqVio2NlSQNHz5cpmmqWbNmKlOmjOrVq6fTp0+7taZUYmKixo4dq3bt2ikkJESGYWjGjBlZxvXu3VuGYWT5k10jzOl0avLkyapSpYr8/PwUFRWlWbNmZXv/27dvV7t27RQYGKiQkBA98MADOn78eJ7rAAAAAAAAQM7s7uz0+OOPq1u3bipZsqQkqX79+lq2bJkmTpyof/75RzfeeKMGDhyoDh065PnYJ06c0IQJE1SxYkXVr19fK1asyHGsr6+v3n//fZdYcHBwlnGjR4/WCy+8oMcee0zR0dGaP3++7rvvPhmGoR49eljjDh48qBYtWig4OFjPP/+8EhMT9dJLL2nr1q3asGGDfHx88lwPAAAAAAAAsnKrKeXt7a3Q0FCXWNOmTbVw4cLLTigsLEyxsbEqV66cNm7cqOjo6BzH2u123X///Rc93qFDh/Tyyy+rf//+mjp1qiTp0UcfVcuWLfXUU0+pa9eu8vLykiQ9//zzOnv2rH799VdVrFhRktSoUSO1adNGM2bMUJ8+fS67PgAAAAAAALh5+d6V5Ovrq3LlyuV6vMPhUEJCQo7b58+fr9TUVPXr18+KGYahvn376uDBg9Y6WJI0b9483XHHHVZDSpJat26tyMhIzZ49O4+VAAAAAAAAICd5ako5nc5s46dPn9bgwYNVv3591a9fXwMGDNCxY8fyJcGLSUpKUlBQkIKDgxUSEqL+/fsrMTHRZcymTZsUEBCgWrVqucQbNWpkbZcuzKg6duyYGjZsmOV+GjVqZI0DAAAAAADA5cv15XtvvPGGBg8erMWLF6tNmzZWPDk5WS1atNCff/4p0zQlSVu3btWSJUv022+/qXjx4vmftS5c5jds2DDdcMMNcjqdWrx4saZNm6YtW7ZoxYoVstsvlBYbG6vQ0FAZhpFlf0k6fPiwNS5jPPPYuLg4paSkyNfXN8v2lJQUpaSkWLfTZ26lpaUpLS1NkmSz2WSz2eR0Ol2ae+lxh8NhPX4Xi3t5eckwDOu4GePShZljuYnb7XaZpukSNwxDXl5eWXLMKU5N1ERN1ERN1ERN1OStC6+xTElpMmWT5KV/X3flFHdKcsiUlwyXT0kdMuWUZJchIxfxNJkyJSuPS8VTZcr4/+NcKk5N1OSpmoraOUKSZBqymf8+wqZMmTZnjnHDNGRkjBumTMMpw7TJMDM8r4ZTpmHKcNpcnhGn4ZQuErc5vVzScxoXcraZuYzbHNRETR6rKePvYGF9HZF5TE5y3ZT66aefVLZsWZeGlCS99957+uOPP1SnTh29+eabCgwM1OTJkzVv3jy98cYbGj16dG7vIk8mTZrkcrtHjx6KjIzU6NGjNXfuXGsB83PnzmXbSPLz87O2Z/z7UmOz2z5p0iSNHz8+Szx9lpYklSlTRtWqVdOePXtcvs0vPDxc4eHh2rlzp+Lj46141apVVbZsWf3xxx9WbpJUs2ZNlShRQps2bXJ5wqOiouTj46ONGze65NCwYUOdP39ev//+uxXz8vJSdHS04uPjtWPHDivu7++v+vXr68SJE/rnn3+seHBwsGrVqqXDhw/r4MGDVpyaqImaqImaqImaqOlB7whJ0mkzVfPSYlXdFqjmXiHW+ENmshanHVMDW7Cu9/r3C2l2OhO1yhGnpl4lFWkLtOKbHPH6zRmv1vYyqmD4WfFVjjjtdCaqk72cShjeVnxx2jEdMpN1r3e4SxNgXmqszirNyi/dx6kHFCC7unj/+0Fkqkx9nHpA5Q0/tbOXteLURE2eqqmonSPkKxU7X1IVTjWw4uftZ7Wv9M8KOhem0IR/vzE9yTdOh0puVsjZygpJrGLFE/wP62jwDpVNiFTQufJWPC5wj04G7lH5+CgVS/n3+T4atEMJxQ6rYly0fNICrPihkpuV5BunKieauTQH9pX+WWm2ZFU71tKlpt1lf5Ld6adKJxpbMafNod1lf6ImavJYTem/a4X5dcTZs2eVG4aZse11ETVq1NCNN96ozz//3CXerFkzrVu3Tps2bVJUVJSkC92xKlWqKCwsTD///HOuEslO+kLn06dPV+/evS85/ty5cwoMDNRDDz1kfSvfHXfcoe3bt2v37t0uY5OSkhQQEKARI0Zo0qRJ1n19/PHHeuCBB1zGDhs2TFOmTFFycnKuZ0pFRETo5MmTCgoKknT1fXpRWLut1ERN1ERN1ERN1HR11VR33mRJRWO2SlGcgUNNhaOmv7uNKlLniLHbuhWJ2Sou8SIyA4eaCkdNY+vOsuKF9XVEQkKCSpUqpfj4eKsvkp1cz5Q6fvy4qlWr5hJLTU3Vxo0bVb16dashlZ7Qbbfdprlz5+b28PnC399fpUqVUlxcnBULCwvT8uXLZZqmyyV86ZfrlS9f3hqXMZ5RbGysQkJCsm1ISRdmV2W3zW63W5cRpkt/cjNLfxJzG898XHfihmFkG88px7zGqYmacopTEzVJ1JRTjnmNUxM1SQVbU6pcP990SnJmil0s7pCpbC78UVo2Yy8Wz5zHxeJmHuPURE0Xi+dHTUXxHCHDtN5k5yZ+oRGQXdwp08gSlmnL7lnNOe60ZfdsK/scc4pTEzXJMzVl/p0qjOeInMZkySlXo3Rh7ajk5GSX2NatW3X+/HnddNNNWcaHhoYqKSkpt4fPF2fOnNGJEydUpkwZK9agQQMlJSVp+/btLmPTZ3A1aNBAklShQgWVKVMmy5RUSdqwYYM1DgAAAAAAAJcv102psLAwbdu2zSW2du1aGYZhfZNdRulTta6E5ORknTlzJkv82WeflWmaateunRXr1KmTvL29NW3aNCtmmqbefvttVahQQU2bNrXiXbp00bfffqsDBw5YsWXLlmnnzp3q2rXrFakFAAAAAADgWpTry/datGihzz77TCtXrlSLFi107tw5vffee5Lk0gRKt2XLFoWHh7uV1NSpU3X69Gnrm/EWLFhgLdI1cOBAnTp1Stdff73uvfde1ax5YaGyJUuWaNGiRWrXrp06depkHSs8PFxPPvmkpkyZotTUVEVHR+vrr7/WqlWrNHPmTJfpaKNGjdKcOXPUqlUrPfHEE0pMTNSUKVNUr149PfTQQ27VAgAAAAAAgKxyvdD5n3/+qRtvvFFOp1N169ZVbGysjh49qo4dO+rrr792GXvy5EmFhYVpwIABeuWVV/KcVOXKlbVv375st+3Zs0clSpTQwIEDtX79eh0+fFgOh0PVq1dXz549NXToUHl7e7vs43Q69eKLL+qdd95RbGysatSooZEjR6pnz57Z1jlkyBCtXr1aPj4+6tChg15++WWFhobmOv+EhAQFBwdfckEvAACAwq7aFxMLOgWg0Nvd/cp8Y3lBGf373QWdAlCoTYz6sqBTuGy57YvkeqZUnTp1tGDBAvXt21ebN2+W3W7X3XffrXfffTfL2HfeeUdpaWm67bbb3Ep+7969lxzzySef5Pp4NptNI0eO1MiRIy85tk6dOlqyZEmujw0AAAAAAIC8y3VTSpLatGmjv//+W8ePH1dwcLB8fHyyHffkk0+qf//+zBICAAAAAABAtvLUlEqX8dvtslOsWDG3kgEAAAAAAMC1IdffvgcAAAAAAADkF5pSAAAAAAAA8DiaUgAAAAAAAPA4mlIAAAAAAADwOJpSAAAAAAAA8LhcNaXuvvtuzZ4927q9cuVK7d+//4olBQAAAAAAgKItV02pr7/+Wjt27LBut2rVSjNmzLhSOQEAAAAAAKCIy1VTqkSJEkpISLBum6Z5xRICAAAAAABA0WfPzaDatWtr1qxZio6OVlhYmCRp7969Wrly5SX3bdGixeVlCAAAAAAAgCInV02pMWPGqHPnzrrvvvus2EcffaSPPvrokvs6HA73swMAAAAAAECRlKumVNu2bbV9+3YtXbpUhw4d0rhx49SyZUu1bNnySucHAAAAAACAIihXTSlJqlSpkh555BFJ0rhx4xQTE6MxY8ZcscQAAAAAAABQdOW6KZXRnj17VKJEiXxOBQAAAAAAANcKt5pSlSpVsv6dlpamv/76SwkJCQoKCtJ1110nu92twwIAAAAAAOAaYXN3x7i4OD322GMKDg5WVFSUmjVrpqioKJUoUUJ9+vTRyZMn8zNPAAAAAAAAFCFuTWmKi4vTTTfdpL///lshISFq3ry5wsLCdOTIEW3cuFHvv/++fvrpJ61bt04hISH5nTMAAAAAAAAKObdmSj377LP6+++/9dRTT2nfvn1avHixpk+fru+++0779u3T8OHDtWvXLk2cODG/8wUAAAAAAEAR4FZTav78+YqJidGLL76ogIAAl23FihXTpEmTFBMTo6+++ipfkgQAAAAAAEDR4lZT6vDhw2rSpMlFxzRp0kSHDx92KykAAAAAAAAUbW41pYKDg7Vv376Ljtm3b5+Cg4PdSgoAAAAAAABFm1tNqZYtW2rOnDlaunRpttuXLVumOXPmKCYm5nJyAwAAAAAAQBHl1rfvjR07VgsXLtRtt92m9u3bq2XLlgoNDdXRo0e1YsUKfffddypWrJjGjBmT3/kCAAAAAACgCHCrKVWnTh0tWbJEvXv31sKFC7Vw4UIZhiHTNCVJ1apV04wZM1SnTp18TRYAAAAAAABFg1tNKUlq1qyZdu3apTVr1mjTpk1KSEhQUFCQrr/+et18880yDCM/8wQAAAAAAEAR4nZTSpIMw1CzZs3UrFmz/MoHAAAAAAAA1wC3FjoHAAAAAAAALgdNKQAAAAAAAHgcTSkAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHgcTSkAAAAAAAB4nFtNKS8vL/Xs2TO/cwEAAAAAAMA1wq2mVFBQkCIiIvI7FwAAAAAAAFwj3GpKNWrUSFu2bMnvXAAAAAAAAHCNcKspNW7cOP3444/6+OOP8zsfAAAAAAAAXAPs7uz0ww8/KCYmRg899JDefPNNRUdHKzQ0VIZhuIwzDEPPPPNMviQKAAAAAACAosOtptS4ceOsf//666/69ddfsx1HUwoAAAAAAADZcasptXz58vzOAwAAAAAAANcQt5pSLVu2zO88AAAAAAAAcA1xa6FzAAAAAAAA4HK43ZRKS0vTq6++qkaNGikoKEh2+7+TrjZv3qx+/fpp586d+ZIkAAAAAAAAiha3Lt87d+6c2rZtq7Vr16p06dIKCgrS2bNnre1VqlTR9OnTFRISoueeey7fkgUAAAAAAEDR4NZMqeeff15r1qzRpEmTdOTIET366KMu24ODg9WyZUstWbIkX5IEAAAAAABA0eJWU+qLL75Qq1atNGzYMBmGIcMwsoypWrWq9u/ff9kJAgAAAAAAoOhxqym1f/9+NWzY8KJjihcvrvj4eLeSAgAAAAAAQNHmVlOqePHiOnbs2EXH7N69W2XKlHErKQAAAAAAABRtbjWlbrrpJi1YsECnT5/OdvuBAwe0aNEitWjR4nJyAwAAAAAAQBHlVlPqqaee0qlTp3TrrbdqzZo1SktLkyQlJSVp2bJluu2225SWlqYhQ4bka7IAAAAAAAAoGuzu7NSiRQtNnTpVTzzxhMtsqOLFi0uSvLy8NG3aNN144435kyUAAAAAAACKFLeaUpLUt29fxcTE6O2339bPP/+suLg4BQUFqXHjxurXr5/q1KmTn3kCAAAAAACgCHG7KSVJtWrV0uuvv55fuQAAAAAAAOAa4daaUgAAAAAAAMDluKym1FdffaVOnTqpYsWKCg4OVsWKFdWpUyd9/fXX+ZQeAAAAAAAAiiK3Lt9LS0vTfffdp3nz5sk0TdntdpUqVUpHjhzRggUL9O2336pLly767LPPZLdf1hWCAAAAAAAAKILcmik1adIkzZ07V82bN9eqVauUnJys2NhYJScna+XKlWrWrJnmzZunF154Ic/HTkxM1NixY9WuXTuFhITIMAzNmDEj27Hbt29Xu3btFBgYqJCQED3wwAM6fvx4lnFOp1OTJ09WlSpV5Ofnp6ioKM2aNeuyjgkAAAAAAAD3udWUmj59umrWrKmlS5fq5ptvls124TA2m03NmjXT0qVLFRkZqQ8//DDPxz5x4oQmTJig7du3q379+jmOO3jwoFq0aKG///5bzz//vIYOHaqFCxeqTZs2On/+vMvY0aNHa/jw4WrTpo3efPNNVaxYUffdd58+//xzt48JAAAAAAAA97l1bV1sbKwGDRqU46V53t7euvPOO/Xmm2/m+dhhYWGKjY1VuXLltHHjRkVHR2c77vnnn9fZs2f166+/qmLFipKkRo0aqU2bNpoxY4b69OkjSTp06JBefvll9e/fX1OnTpUkPfroo2rZsqWeeuopde3aVV5eXnk6JgAAAAAAAC6PWzOlIiIilJiYeNExZ8+etRo7eeHr66ty5cpdcty8efN0xx13uNxH69atFRkZqdmzZ1ux+fPnKzU1Vf369bNihmGob9++OnjwoNatW5fnYwIAAAAAAODyuNWUevTRRzV79mzFxsZmu/3QoUP64osv9Oijj15Wcjk5dOiQjh07poYNG2bZ1qhRI23atMm6vWnTJgUEBKhWrVpZxqVvz+sxAQAAAAAAcHlydfne/v37XW5369ZNa9as0fXXX68nn3xSzZo1U2hoqI4ePapVq1bp9ddfV7NmzdS1a9crknR6MywsLCzLtrCwMMXFxSklJUW+vr6KjY1VaGioDMPIMk6SDh8+nOdjZpaSkqKUlBTrdkJCgqQL31KYlpYm6cJ6WzabTU6nU06n0xqbHnc4HDJN85JxLy8vGYZhHTdjXJIcDkeu4na7XaZpusQNw5CXl1eWHHOKUxM1URM1URM1URM1eevCayxTUppM2SR56d/XXTnFnZIcMuUlw+VTUodMOSXZZcjIRTxNpkzJyuNS8VSZMv7/OJeKUxM1eaqmonaOkCSZhmzmv4+wKVOmzZlj3DANGRnjhinTcMowbTLMDM+r4ZRpmDKcNpdnxGk4pYvEbU4vl/ScxoWcbWYu4zYHNVGTx2rK+DtYWF9HZB6Tk1w1pSpXrpylqSNJpmlq9OjR2ca/+eYbffvtt7lOJC/OnTsnSdk2iPz8/Kwxvr6+1t8XG5fXY2Y2adIkjR8/Pks8fZaWJJUpU0bVqlXTnj17XL7NLzw8XOHh4dq5c6fi4+OteNWqVVW2bFn98ccfVm6SVLNmTZUoUUKbNm1yecKjoqLk4+OjjRs3uuTQsGFDnT9/Xr///rsV8/LyUnR0tOLj47Vjxw4r7u/vr/r16+vEiRP6559/rHhwcLBq1aqlw4cP6+DBg1acmqiJmqiJmqiJmqjpQe8ISdJpM1Xz0mJV3Rao5l4h1vhDZrIWpx1TA1uwrvcKtuI7nYla5YhTU6+SirQFWvFNjnj95oxXa3sZVTD8rPgqR5x2OhPVyV5OJQxvK7447ZgOmcm61zvcpQkwLzVWZ5Vm5Zfu49QDCpBdXbz//SAyVaY+Tj2g8oaf2tnLWnFqoiZP1VTUzhHylYqdL6kKpxpY8fP2s9pX+mcFnQtTaEJNK57kG6dDJTcr5GxlhSRWseIJ/od1NHiHyiZEKuhceSseF7hHJwP3qHx8lIql/Pt8Hw3aoYRih1UxLlo+aQFW/FDJzUryjVOVE81cmgP7Sv+sNFuyqh1r6VLT7rI/ye70U6UTja2Y0+bQ7rI/URM1eaym9N+1wvw64uzZs8oNw8zY9spB7969s21K5cb06dPd2k+StdD59OnT1bt37yzxjz/+WA888IDLPsOGDdOUKVOUnJwsX19f3XHHHdq+fbt2797tMi4pKUkBAQEaMWKEJk2alKdjZpbdTKmIiAidPHlSQUFBkq6+Ty8Ka7eVmqiJmqiJmqiJmq6umurOmyypaMxWKYozcKipcNT0d7dRReocMXZbtyIxW8UlXkRm4FBT4ahpbN1ZVrywvo5ISEhQqVKlFB8fb/VFspOrmVIzZszIzTCPSb/ELrs1rWJjYxUSEmI1j8LCwrR8+XKZpunSWEvft3z58nk+Zma+vr7ZbrPb7Vm+oTD9yc0s/UnMbTynbz7MS9wwjGzjOeWY1zg1UVNOcWqiJomacsoxr3FqoiapYGtKlevnm05Jzkyxi8UdMpXNhT9Ky2bsxeKZ87hY3MxjnJqo6WLx/KipKJ4jZJjWm+zcxC80ArKLO2UaWcIybdk9qznHnbbsnm1ln2NOcWqiJnmmpsy/U4XxHJHTmCw55WrUVaZChQoqU6ZMlumjkrRhwwY1aNDAut2gQQMlJSVp+/btLuN+/vlna3tejwkAAAAAAIDLUyibUpLUpUsXffvttzpw4IAVW7ZsmXbu3OmywHqnTp3k7e2tadOmWTHTNPX222+rQoUKatq0aZ6PCQAAAAAAgMuTu/lU2Vi9erVeeuklbdmyRYcPH852QfPsrjXMjalTp+r06dPWN+MtWLDAWqRr4MCBCg4O1qhRozRnzhy1atVKTzzxhBITEzVlyhTVq1dPDz30kHWs8PBwPfnkk5oyZYpSU1MVHR2tr7/+WqtWrdLMmTNdpqPl9pgAAAAAAAC4PLla6DyzTz75RL1795ZpmqpatarCwsJyvF5w+fLleU6qcuXK2rdvX7bb9uzZo8qVK0uS/vzzTw0ZMkSrV6+Wj4+POnTooJdfflmhoaEu+zidTr344ot65513FBsbqxo1amjkyJHq2bNnluPn9pgXk5CQoODg4Esu6AUAAFDYVftiYkGnABR6u7tn/Ubzwmz073cXdApAoTYx6suCTuGy5bYv4lZTKjIyUnFxcVq0aJEaNWp0WYkWRTSlAADAtYKmFHD5aEoByOhaakq5tabUgQMH1KNHDxpSAAAAAAAAcItbTalKlSrp/Pnz+Z0LAAAAAAAArhFuNaUee+wxffvtt4qLi8vvfAAAAAAAAHANcOvb9/773//qn3/+0c0336ynn35a9evXz/EawYoVK15WggAAAAAAACh63GpKSdINN9ygzz77TA8++GCOYwzDUFpamrt3AQAAAAAAgCLKrabUm2++qSeffFLe3t5q1aqVwsLCZLe73d8CAAAAAADANcatTtKrr76qChUqaO3atQoPD8/vnAAAAAAAAFDEubXQ+ZEjR9SlSxcaUgAAAAAAAHCLW02p6tWr6/Tp0/mcCgAAAAAAAK4VbjWlBg8erPnz52vfvn35nQ8AAAAAAACuAW6tKVWtWjW1bNlSDRs21JNPPqn69esrKCgo27EtWrS4rAQBAAAAAABQ9LjVlIqJiZFhGDJNU88884wMw8hxrMPhcDs5AAAAAAAAFE1uNaXGjBlz0UYUAAAAAAAAcDFuNaXGjRuXz2kA17bevXvro48+ynH7wYMHVbJkSU2fPl3z58/X1q1blZiYqOrVq6tPnz7q06ePvLy8ctx/5syZuv/++xUQEKDExMQrUQIAAAAAAHniVlMKQP76z3/+o9atW7vETNPU448/rsqVK6tChQr6448/NHDgQN16660aMmSIgoKCtGTJEvXr10/r16/PsamVmJioYcOGKSAgwBOlAAAAAACQKzSlgKtAkyZN1KRJE5fY6tWrlZSUpJ49e0qSypUrp61bt6pOnTrWmP/85z96+OGHNX36dD3zzDOqXr16lmM/99xzKl68uFq1aqWvv/76itYBAAAAAEBu2dzayWaTl5fXJf/Y7fS8AHd99tlnMgxD9913nySpdOnSLg2pdHfddZckafv27Vm27dq1S6+++qpeeeUVfh8BAAAAAFcVt96ltmjRItuFzuPj47Vr1y6dPXtW9evXV4kSJS43P+CalJqaqtmzZ6tp06aqXLnyRcceOXJE0oWmVWZPPvmkWrVqpfbt22v27NlXIlUAAAAAANziVlNqxYoVOW5LSkrSiBEjtHjxYv3www/u5gVc05YsWaKTJ09al+7l5Pz583rttddUpUoVRUdHu2xbuHChvv/+e23ZsuVKpgoAAAAAgFvcunzvYooVK6Y33nhDwcHBeuqpp/L78MA14bPPPpO3t7e6det20XEDBgzQtm3bNHXqVJfL886fP6/Bgwfr8ccfV+3ata90ugAAAAAA5Fm+N6XSNW/eXAsXLrxShweKrMTERM2fP1+33XabSpUqleO4KVOm6L333tOzzz6r9u3bu2x79dVXdeLECY0fP/5KpwsAAAAAgFuu2MrHx48fV2Ji4pU6PFBkff311y7fupedGTNmaPjw4Xr88cf19NNPu2yLj4/Xc889p379+ikhIUEJCQmSLjS7TNPU3r17VaxYMZUtW/aK1gEAAAAAwMXke1PK6XRq5syZ+uKLL9SwYcP8PjxQ5M2cOVOBgYHq2LFjttvnz5+vRx99VHfffbfeeuutLNtPnTqlxMRETZ48WZMnT86yvUqVKurUqZO+/vrr/E4dAAAAAIBcc6spVbVq1WzjaWlpOnbsmFJTU+Xt7a1JkyZdVnLAteb48eNaunSp7r33XhUrVizL9pUrV6pHjx5q0aKFZs6cKZst6xW4ZcuW1VdffZUl/sYbb2jdunWaNWuWwsLCrkj+AAAAAADklltNKafTKcMwssS9vb1Vt25dRUdHa8CAAapTp85lJwhcS7744gulpaVle+nevn371LFjRxmGoXvuuUdz5sxx2R4VFaWoqCgVK1ZMnTt3zrL/119/rQ0bNmS7DQAAAAAAT3OrKbV37958TgOAdOHSvbJly6p169ZZtu3Zs0fx8fGSpP79+2fZPnbsWEVFRV3xHAEAAAAAyA9XbKFzAHm3bt26HLfFxMTINE23jz1jxgzNmDHD7f0BAAAAAMhPWRekAQAAAAAAAK6wXM+Uevjhh/N8cMMw9MEHH+R5PwAAAAAAABRtuW5K5eWyH8MwZJomTSkAAAAAAABkK9dNqYutdZPR33//rXHjxmn37t1uJ4Wrxy1tXijoFIBC7ccfRhR0CgAAAABwVcp1U6px48YX3X7ixAmNHz9e7733ns6fP69mzZrpxRdfvOwEAQAAAAAAUPRc9rfvJSUl6aWXXtLLL7+sM2fOqE6dOnr++ed155135kd+AAAAAAAAKILcbko5HA698847evbZZ3X06FGFh4frtddeU69evWSz8aV+AAAAAAAAyJlbTak5c+bo6aef1t9//63g4GC98MILGjRokPz8/PI7PwAAAAAAABRBeWpKrVixQsOHD9fGjRvl4+Oj//73vxo1apRKlChxhdIDAAAAAABAUZTrptTtt9+u77//XjabTb169dKECRMUHh5+JXMDAAAAAABAEZXrptSSJUtkGIYqVqyoI0eOqE+fPpfcxzAMLVy48LISBAAAAAAAQNGTp8v3TNPUnj17tGfPnlyNNwzDraQAAAAAAABQtOW6KZXbRhQAAAAAAABwKbluSlWqVOlK5gEAAAAAAIBriK2gEwAAAAAAAMC1h6YUAAAAAAAAPI6mFAAAAAAAADyOphQAAAAAAAA8jqYUAAAAAAAAPI6mFAAAAAAAADyOphQAAAAAAAA8jqYUAAAAAAAAPI6mFAAAAAAAADyOphQAAAAAAAA8jqYUAAAAAAAAPI6mFAAAAAAAADyOphQAAAAAAAA8jqYUAAAAAAAAPK7QNqVWrFghwzCy/bN+/XqXsWvXrlWzZs1UrFgxlStXToMGDVJiYmKWY6akpGj48OEqX768/P391bhxY/3www+eKgkAAAAAAOCaYS/oBC7XoEGDFB0d7RKrXr269e/Nmzfr1ltvVa1atfTKK6/o4MGDeumll7Rr1y599913Lvv17t1bc+fO1ZNPPqkaNWpoxowZat++vZYvX65mzZp5pB4AAAAAAIBrQaFvSjVv3lz33HNPjttHjRqlkiVLasWKFQoKCpIkVa5cWY899pi+//57tW3bVpK0YcMGff7555oyZYqGDh0qSXrwwQdVt25dDRs2TGvXrr3yxQAAAAAAAFwjCu3lexmdOXNGaWlpWeIJCQn64YcfdP/991sNKelCsykwMFCzZ8+2YnPnzpWXl5f69Oljxfz8/PTII49o3bp1OnDgwJUtAgAAAAAA4BpS6JtSDz30kIKCguTn56dWrVpp48aN1ratW7cqLS1NDRs2dNnHx8dHDRo00KZNm6zYpk2bFBkZ6dK8kqRGjRpJunAZIAAAAAAAAPJHob18z8fHR126dFH79u1VunRpbdu2TS+99JKaN2+utWvX6vrrr1dsbKwkKSwsLMv+YWFhWrVqlXU7NjY2x3GSdPjw4RxzSUlJUUpKinU7ISFBkpSWlmbN4LLZbLLZbHI6nXI6ndbY9LjD4ZBpmpeMe3l5yTCMLDPDvLy8JEkOhyNXcbvdLtM0XeKGYcjLy8slR7vdkGlKDocpm02y2QxrvNNpyulUjnEvL0PGv2E5HKZMM+e43Z4hKCktzbRyyG3cMC4cP1167jnFqYmarnRNmX/ns/s9kwrvOYKaqImaqMlbF859pqQ0mbJJ8lKG82EOcackh0x5yXD5lNQhU05JdhkychFPkylTsvK4VDxVpoz/P86l4tRETZ6qqaidIyRJpiGb+e8jbMqUaXPmGDdMQ0bGuGHKNJwyTJsMM8PzajhlGqYMp83lGXEaTukicZvTyyU9p3EhZ5uZy7jNQU3U5LGaMv4OFtbXEdldzZadQtuUatq0qZo2bWrd7tixo+655x5FRUVp5MiRWrx4sc6dOydJ8vX1zbK/n5+ftV2Szp07l+O49O05mTRpksaPH58lvmnTJgUEBEiSypQpo2rVqmnPnj06fvy4NSY8PFzh4eHauXOn4uPjrXjVqlVVtmxZ/fHHHy73XbNmTZUoUUKbNm1yecKjoqLk4+PjMlNMkho2bKjz58/r999/t2JeXl6Kjo5WfHy8duzYYcX9/f1Vv359nThxQv/8848k6fbbKuj48WSt33BCNaoHKbLGvzPJ9h84qy2/n1K9uiVVMSLAiu/claC/diYo+sZSKlPGz4pv+f2U9h84qxbNQhUY+O+P3voNJ3T8eLLa3Fre5c3+ip+O6FyyQ7ffVsGlpu+WHJK/n5diWpazYmlppr5bckilS/vppkalrXhiYpqW/3REEeEBqh9V0opTEzV5qqaMv0+SFBwcrFq1aunw4cM6ePCgFS+s5whqoiZqoqYHvSMkSafNVM1Li1V1W6Cae4VY4w+ZyVqcdkwNbMG63ivYiu90JmqVI05NvUoq0hZoxTc54vWbM16t7WVUwfj3XL7KEaedzkR1spdTCcPbii9OO6ZDZrLu9Q53aQLMS43VWaVZ+aX7OPWAAmRXF+9/P4xMlamPUw+ovOGndvayVpyaqMlTNRW1c4R8pWLnS6rCqQZW/Lz9rPaV/llB58IUmlDTiif5xulQyc0KOVtZIYlVrHiC/2EdDd6hsgmRCjpX3orHBe7RycA9Kh8fpWIp/z7fR4N2KKHYYVWMi5ZP2r+vDw+V3Kwk3zhVOdHMpTmwr/TPSrMlq9qxli417S77k+xOP1U60diKOW0O7S77EzVRk8dqSv9dK8yvI86ePavcMMyMba8i4N5779WXX36ppKQkffXVV+ratatWrlyp5s2bu4zr1q2bVq1aZc2mqlu3rkJDQ7Vs2TKXcdu2bVOdOnX09ttv6z//+U+295ndTKmIiAidPHnSuhzwavv0Irfd1vZ3vlwkZqsUxRk41FQ4alq6ZNhV++nFxeKF9RMZaqImavJ8TXXnTZZUNGarFMUZONRUOGr6u9uoInWOGLutW5GYreISLyIzcKipcNQ0tu4sK15YX0ckJCSoVKlSio+Pz7JMUkaFdqZUTiIiInT+/HmdPXvWuvQuvfGUUWxsrMqX/7frGRYWpkOHDmU7TpLL2Mx8fX2znWVlt9tlt7s+xOlPbmbpT2Ju45mP607cMIxs4xlzTH8TLklO54U3/ZnlFHc4su935hTPeF/uxk0zb3FqoqaccsxrPKfcc/qdz2v8aj1HXE6cmqgppzg1Fa6aUuV67nNKciqbc3YOcYdMZXPhj9KyGXuxeOY8LhY38xinJmq6WDw/aiqK5wgZpvUmOzfxC42A7OJOmUaWsExbds9qznGnLbtnW9nnmFOcmqhJnqkp8+9UYTxH5DQmS065GlWI/PPPP/Lz81NgYKDq1q0ru92eZZrp+fPntXnzZjVo0MCKNWjQQDt37rTWg0r3888/W9sBAAAAAACQPwptUyrjdZDptmzZom+++UZt27aVzWZTcHCwWrdurU8//VRnzpyxxn3yySdKTExU165drdg999wjh8Ohd99914qlpKRo+vTpaty4sSIiXK8JBwAAAAAAgPsK7eV73bt3l7+/v5o2baqyZctq27Ztevfdd1WsWDG98MIL1riJEyeqadOmatmypfr06aODBw/q5ZdfVtu2bdWuXTtrXOPGjdW1a1eNHDlSx44dU/Xq1fXRRx9p7969+uCDDwqiRAAAAAAAgCKr0M6U6ty5s06cOKFXXnlF/fr10xdffKG7775bGzduVK1ataxxN9xwg5YuXSp/f38NHjxY7777rh555BHNnTs3yzE//vhjPfnkk/rkk080aNAgpaam6ttvv1WLFi08WRoAAAAAAECRV2hnSg0aNEiDBg3K1dhmzZppzZo1lxzn5+enKVOmaMqUKZebHgAAAAAAAC6i0M6UAgAAAAAAQOFFUwoAAAAAAAAeR1MKAAAAAAAAHkdTCgAAAAAAAB5HUwoAAAAAAAAeR1MKAAAAAAAAHkdTCgAAAAAAAB5HUwoAAAAAAAAeR1MKAAAAAAAAHkdTCgAAAAAAAB5HUwoAAAAAAAAeR1MKAAAAAAAAHkdTCgAAAAAAAB5HUwoAAAAAAAAeR1MKAAAAAAAAHkdTCgAAAAAAAB5HUwoAAAAAAAAeR1MKAAAAAAAAHkdTCgAAAAAAAB5HUwoAAAAAAAAeR1MKAAAAAAAAHkdTCgAAAAAAAB5HUwoAAAAAAAAeR1MKAAAAAAAAHkdTCgAAAAAAAB5HUwoAAAAAAAAeR1MKAAAAAAAAHkdTCgAAAAAAAB5HUwoAAAAAAAAeR1MKAAAAAAAAHkdTCgAAAAAAAB5HUwoAAAAAAAAeR1MKAAAAAAAAHkdTCgAAAAAAAB5HUwoAAAAAAAAeR1MKAAAAAAAAHkdTCgAAAAAAAB5HUwoAAAAAAAAeR1MKAIBCbOLEiTIMQ3Xr1nWJp6amavz48apatap8fX1VtWpVPffcc0pLSyugTAEAAABX9oJOAAAAuOfgwYN6/vnnFRAQkGXb/fffrzlz5ujhhx9Ww4YNtX79ej3zzDPav3+/3n333QLIFgAAAHBFUwoAgEJq6NChuummm+RwOHTixAkr/ssvv2j27Nl65plnNGHCBEnS448/rtKlS+uVV17RgAEDFBUVVVBpAwAAAJK4fA8AgEJp5cqVmjt3rl577bUs21atWiVJ6tGjh0u8R48eMk1TX3zxhSdSBAAAAC6KphQAAIWMw+HQwIED9eijj6pevXpZtqekpEiS/P39XeLFihWTJP36669XPkkAAADgErh8DwCAQubtt9/Wvn37tHTp0my3X3fddZKkNWvWqEqVKlY8fQbVoUOHrnySAAAAwCXQlAIAoBA5efKkxowZo2eeeUZlypTJdkz79u1VqVIlDR06VMWKFdONN96on3/+WaNHj5bdbte5c+c8nDUAAACQFZfvAQBQiDz99NMKCQnRwIEDcxzj5+enhQsXqlSpUurSpYsqV66sBx98UGPGjFFISIgCAwM9mDEAAACQPWZKAQBQSOzatUvvvvuuXnvtNR0+fNiKJycnKzU1VXv37lVQUJBCQkJUp04d/fHHH9q2bZtOnTql2rVry9/fX4MHD1bLli0LsAoAAADgAppSAAAUEocOHZLT6dSgQYM0aNCgLNurVKmiJ554wvpGPsMwVKdOHWv7okWL5HQ61bp1a0+lDAAAAOSIphQAAIVE3bp19dVXX2WJP/300zpz5oxef/11VatWLdt9z507p2eeeUZhYWG69957r3SqAAAAwCXRlAIAoJAoXbq0OnfunCWePjMq47Zu3bqpfPnyql27thISEvThhx/qn3/+0cKFC1W8eHHPJAwAAABcBE0pAACKoIYNG2r69Ol655135O/vr+bNm+uzzz5TgwYNCjo1AAAAQBJNKQAACr0VK1ZkiQ0bNkzDhg3zfDIAAABALtkKOgEAAAAAAABce2hKAQAAAAAAwONoSgEAAAAAAMDjWFMKAJAndUa+WtApAIXan5MGF3QKAAAAVwVmSmWSkpKi4cOHq3z58vL391fjxo31ww8/FHRaAAAAAAAARQpNqUx69+6tV155RT179tTrr78uLy8vtW/fXqtXry7o1AAAAAAAAIoMLt/LYMOGDfr88881ZcoUDR06VJL04IMPqm7duho2bJjWrl1bwBkCAAAAAAAUDcyUymDu3Lny8vJSnz59rJifn58eeeQRrVu3TgcOHCjA7AAAAAAAAIoOmlIZbNq0SZGRkQoKCnKJN2rUSJK0efPmAsgKAAAAAACg6OHyvQxiY2MVFhaWJZ4eO3z4cLb7paSkKCUlxbodHx8vSYqLi1NaWpokyWazyWazyel0yul0WmPT4w6HQ6ZpXjLu5eUlwzCs42aMS5LD4chV3G63yzRNl7hhGPLy8sqUY4pMU3I4TNlsks1mWOOdTlNOp3KMe3kZMv4Ny+EwZZo5x+32DEFJaWnm/+ea+7hhXDh+uvTcc4pTEzVd6ZpOnz7t8juf/e9Z4TpHmCnJ8rK51ppmmrIZkleGB9hpmnKYkpch2TLEHaYppynZjUzPh9OUU1njaU5TpiRvW6bHPYd4qtOUIcmei7iVu0RN1OSxmuLi4nI8FxTGc4Qt6cJrIFOSQxeeJ5syPE85xJ2SnDJlk+HyKalTF54PLxkychF36MLzYZfr455TPE0Xng+vXMSpiZo8VVN8fHwuXpcXnnNESmKqZBqymf8+wqZMmTZnjnHDNGRkjBumTMMpw7TJMDM8r4ZTpmHKcNpcnhGn4ZQuErc5vVxydBoXcraZuYzbHNRETR6rKS4uzorn/r371XWOSEhIuPC4Zdg/OzSlMjh37px8fX2zxP38/Kzt2Zk0aZLGjx+fJV6lSpX8TRBAoVOyZNZzA4BrW6mXRxZ0CgCuMiUefq6gUwBwFZmiUgWdQr45c+aMgoODc9xOUyoDf39/lxlP6ZKTk63t2Rk5cqSGDBli3XY6nYqLi1OpUqVkGEa2+wD5ISEhQRERETpw4ECWy04BXHs4JwDIjPMCgMw4L8ATTNPUmTNnVL58+YuOoymVQVhYmA4dOpQlHhsbK0k5Ppi+vr5ZZliVKFEi3/MDchIUFMR/KAAsnBMAZMZ5AUBmnBdwpV1shlQ6FjrPoEGDBtq5c6d17WO6n3/+2doOAAAAAACAy0dTKoN77rlHDodD7777rhVLSUnR9OnT1bhxY0VERBRgdgAAAAAAAEUHl+9l0LhxY3Xt2lUjR47UsWPHVL16dX300Ufau3evPvjgg4JOD8jC19dXY8eOzXaBfgDXHs4JADLjvAAgM84LuJoY5qW+n+8ak5ycrGeeeUaffvqpTp06paioKD377LO67bbbCjo1AAAAAACAIoOmFAAAAAAAADyONaUAAAAAAADgcTSlAAAAAAAA4HE0pQAAAAAAQKHndDoLOgXkEU0pAFcE/yEAAAAA8IQ5c+Zo48aNstlsvA8pZGhKAch3s2bN0ty5c5WSklLQqQAoItasWaMTJ04UdBoA8lF8fHxBpwCgCNi9e7eGDRumLl26aNOmTTSmChmaUgDyVXJysiZOnKi+ffvqu+++ozEF4LJt27ZNzZs314gRI3Ty5MmCTgdAPvjwww/Vs2dPbd++vaBTAVDIVatWTePHj1epUqXUo0cPGlOFDE0pAPnKz89Py5YtU82aNdW/f38tWrSIxhSAy1KlShW9/PLL+uyzz/T000/TmAKKAG9vby1atEgTJkzQjh07CjodAIVUeuPpwQcf1H//+1/5+Pioe/fu+u2332hMFRKGaZpmQScBoOg5evSoOnbsqH379mnatGm6/fbb5e/vX9BpASikzp07p3fffVdDhw7Vww8/rIkTJ6p06dIFnRaAyzB79mzdf//96ty5syZMmKCaNWsWdEoACqGUlBT5+vpKkmbOnKmXXnpJSUlJmjt3rurVqyen0ymbjfk4Vyt7QScAoGhJS0uT3W5XaGioFi1apObNm2vcuHEyTVMdOnSQn59fQacIoJBxOBzy9/fX448/rhIlSuiRRx5R8eLF9dRTTyk0NLSg0wPgBofDoW7dusnhcKh3797y8vLS6NGjVbdu3YJODUAh4nA4rIbUiy++qF27dunkyZM6ePCgunXrps8//1z169enMXUV41kBkG8cDofs9gu97ieeeEIDBgyQzWbTH3/8oaeffpo1pgDkmcPhkJeXl6QLa9Ds3LlToaGheuWVVzR58mQWPwcKobS0NOv3OiYmRk899ZS++OILvf7669q2bVsBZwegMEk/l3Tu3FlvvPGGAgMD9cwzz6hXr17av3+/unXrxqV8Vzku3wOQ77p06aK1a9eqV69eatmypdatW6c5c+YoMTFRb7zxhtq3b299ogEAudG+fXtt375dUVFRqlmzphYvXqytW7fq8ccf14QJE7iUDygkMs5W6NGjh7Zu3aqgoCBt3bpVSUlJ6t69u8aOHculfABybfHixerYsaNefPFF9e3b17oy491339Urr7wi0zQ1e/ZsZkxdpbh8D0C+Wr16tX744QeNGDFCgwcPlr+/v1q3bq37779f9957rwYNGiTTNNW+fXsu5QOQK++8845+/PFHvfvuu7r77rsVGBiovn37aubMmRo7dqwkafz48SpTpkwBZwrgUtLfDPbp00dLlizR5MmT1alTJ+3fv1/ff/+9xo4dK6fTqfHjx9OYApArR48eVVpamrVUSPoaU3369JHD4dCAAQPUvXt3zZ49W1FRUTSmrjI0pQDkq7NnzyoxMVHVqlWTv7+/HA6HvL29FRkZqU8//VQdOnTQmDFjZBiG2rVrx+LnAC7p8OHDstvtaty4sQIDAyVJlStX1sCBA+VwODRu3DgFBARo6NChrDEFFAIJCQlauXKlWrdurd69e8vb21tly5ZVw4YNFRYWpj59+shut2vUqFGqU6dOQacL4CpXrlw5SdLKlSsVGRkpX19f6/L/vn37atmyZfryyy/VsWNHffHFF2rcuHEBZ4yMaA8CyFelS5eW3W7Xvn37JEmGYSj9KuFq1aqpdevW2rZtm4YNG6ZvvvmGa7sBXJJhGEpLS7Mu+01NTZUkBQUFqVu3bgoLC9PLL7+s5557TsePHy/IVAFcgtPpVGJioo4cOaKQkBB5e3u7rDeZ/rXus2bN0tSpU/XHH38UYLYAriY5vW+oUaOGQkJCNGvWLGtdOi8vL+v1Qrly5XTdddfJbrdrx44dHssXuUNTCoBbcvpPITIyUm3bttWECRO0bt062Ww2ORwOmaYpHx8f1a5dWzExMbLZbIqPj2fqLABL5vNKekO7devW8vX11fDhw+V0OuXt7a3z589LkmrWrKmYmBjVq1dPb731lrZs2eLxvAHkjmmastlsKl++vBo1aqTly5fL6XTK19dXaWlpki68kWzdurV8fHz0zjvvaNiwYTp16lQBZw6goKWlpVnvG/bv369ffvlFJ0+eVGJioqpWrapXXnlFK1as0KRJk7R161ZJkre3t44ePaqjR4/qscce0+LFi9WrV6+CLAPZ4PI9AHmWlpZmfcveunXrlJCQIJvNpjZt2qh48eL6z3/+o82bN+vBBx/URx99pKZNm0qSjhw5ojVr1uiGG27QDz/8YH1bBgBkPK8cP35caWlp8vHxUalSpdSgQQN16tRJ8+bN04gRIzR58mT5+PhIkvbu3atdu3Zp4MCBatWqlapVq1aQZQDIIOO3Z0oXZj1KF5pT9913nx555BHdf//9+uyzz2S3263xPj4+aty4se6++27ZbDaVLFmyoEoAcBXI+A3fDz74oJYvX65Dhw6pXLlyatmypaZMmaIHH3xQsbGxGjVqlHbt2qW7775bkZGRWrRokRYsWKD7779f1atXl3ThHJR+PkLBoykFIE8y/qfQvXt3rVmzRrGxsfLy8lLz5s01evRo3XnnnTp27JgmTJigVq1aadCgQQoODtbOnTu1YMECvfvuu9aLVP5TAJDxvNKvXz+tX79eBw8eVEhIiIYPH66HHnpIr7/+ug4dOqS33npLO3bs0JgxY3Ty5EmtWLFCe/bsUZUqVayGFAuYAgUvY0Pqww8/1K5du5ScnKw777xTt9xyi7p27apVq1bps88+U7du3TR9+nQFBAToyJEjWrBggZKTk/XQQw8pKChIEq8XgGtZ+rnk7rvv1rp16/Twww/rlltu0Xfffadp06bpl19+0W+//abhw4erRo0aGjJkiEaMGCHpwqX+zz33nDp16mQdj3PJ1cUw0+fGA0AedO/eXT/99JOeeuopNWjQQElJSXrggQdUsWJFff7556pdu7YWLlyozz//XF9++aXOnz+vcuXKafDgwRoyZEhBpw/gKnTnnXdq/fr16tKli8qWLatt27bpyy+/1H//+19NmTJFJ0+e1KhRozR//nwdO3ZMkuTn56cxY8ZYLz4BFLyMjeH032tvb2/5+vpq3759euyxx/T0008rICBAw4cP18cff6yyZcvquuuuU3x8vH799Ve9+uqreuKJJwq4EgBXi0WLFunRRx/Vs88+q65duyooKEjLly9Xu3bt1LNnT02aNMn6spPY2FjFxsYqISFBpUuXVt26dSXxodXViplSAPJs5cqVWrt2rcaPH6+ePXsqMDBQK1eu1Llz59S4cWOVKFFCktShQwe1b99eY8aMsdaRSJ82y38KADJ67733tHHjRk2dOlUdO3aUv7+/vvrqK3355ZdKTU1VXFycSpUqpddee01DhgzRihUrFBQUpLCwMMXExEjivAJcLdJ/D/v376/ffvtNL7/8sm6//XYFBQXp0Ucf1XvvvaeYmBjde++9eumll9S+fXvNmjVLv//+u8LDw/Xuu+/q0UcflcQMKQAXbNu2TefPn9ddd92loKAgLV26VJ06dVLXrl01YcIEhYaGyjRNJSUlKSwsTGFhYS778xrh6kVTCsAlZX5BeODAAZ0+fVq33XabAgMDtXz5cnXo0EHdunXTuHHjVL58eWusYRiqUaNGluPxnwKAjLZt26YyZcqobdu28vf3108//aSePXvqgQce0JAhQxQSEiJJ8vf313XXXafrrrvOZX9ebAJXl7i4OK1cuVL33XefOnfubM1qmD9/vh544AHdfPPNMk1TwcHBuuuuu3TXXXcpJSVFNptN3t7ekvi9Bq4Vmd9rZPzdT01Nlbe3t7W+XEhIiH788Ud16tRJd911l6ZMmWI1oKZPn65Dhw5p5MiR1rIA6TiXXL14ZgC4SL+iN+O3YGX3CWVaWppKliypjRs3qkOHDrr77rs1efJkVahQQZI0adIk9e/fP9v74BNP4NqSeaWA9K9ozujQoUMKDAxUyZIltWrVKrVv31533323XnjhBVWsWFGSNHHiRK1evTrb++DFJlCw0r89L93hw4e1Y8cOtWvXzprVcMcdd6hjx47W77VhGPrrr7+sfXx8fKw3knyABVw7DMPQ+fPntWDBAu3YscP63e/du7dWrVolSapVq5Z2796t0aNHq3PnzurcubNLQ+qPP/7Q66+/rrNnz+b4LeG4OnGmB+Ai/T+FOXPmaO7cuVb8rrvu0vjx4yVJdevWVfny5dWrVy/FxMSoa9eumjRpkvWfwq5du/TTTz/p+PHjOnPmTIHUAeDqYRiGUlJStGrVKu3bt8+aBTFixAirydSkSRPFxsZqypQpateundWQSj+vbNmyRR9++KGWLl0qh8NRYLUA+FdqaqoSEhIkyWom/fLLL5KkKlWqqEyZMvrll1+0bt06derUKcubyJkzZ6px48bau3evpAvnivQPrvgAC7i2xMbG6rXXXlPPnj31999/q127dpo1a5YcDoecTqfatWunHj16aNKkSYqKitIbb7xhnUsOHTqkL774QnFxcWrRooX1Db0oHGhKAcji1KlTmjVrlgYMGKCPPvpI7dq105o1a3TjjTdKkurXr6+YmBh98803qlatmkaOHKmIiAhJFz4Z/eijj7RlyxZ1795dxYsXL8hSAFwlfvvtNw0cOFBjx45VbGys2rRpo08//dT6Rp327dsrISFBw4cPV/PmzfXqq68qPDxc0oUXqh9//LFsNptiYmJcvmIeQMFwOBz6/PPPNWzYMB08eFCS1LZtWw0dOlRHjhyRYRhq0aKF/ve//6lNmza666679MYbb1gLEe/du1crVqxQ3bp1mREFQKGhoRoyZIgOHz6sJk2aaOPGjZozZ45atWolm80mwzD0+OOPq02bNtq4caPef/99bdiwQT/88IPGjRunF198UU888YTat29f0KUgr0wAyMYvv/xi1qxZ0wwICDBLlChhfv/996bT6XQZc/vtt5uGYZh33HGH+f3335vTp083e/XqZdrtdnPKlCkFlDmAq1X//v1NwzDMChUqmGXKlDGXLFlinjt3ztr+ww8/mP7+/ma9evXM6dOnm6ZpmitXrjSHDh1qent7m6+//noBZQ4gO6+99prp6+trtmvXzmzWrJlZtmxZ86uvvjKTk5NN07zwWiI4ONj09fU1//e//1n77d271xwzZowZFBRkfvDBBwWVPoCrTFpamlm/fn3TMAzzuuuuM3/99dcsY9auXWs+8MADpmEYpmEYps1mM6tXr+7yGsHhcHgybVwmw/y/9u47sOZ78f/482QLYtaOrbSoURSltWqrJEZRrVKamrFHkGGEELtma1Rbas/aW12zVtWqUlTEXgky378//M65idBv7+1tTsjr8U/dcz7n3fen1+f9eX9en/cwTy30ICJpnvn/iw1WqFCB48ePkzlzZoYNG8bnn38OQHR0NK6ursCTnXXWr1/P77//jsVi4Y033qBTp0506dIF0CKlIpK0HciUKROPHz+mevXqTJ06leLFixMfH4/FYsHBwYGNGzfSrl07bt68iZubG/Hx8WTLlo2ePXvSt29fQLtxiaQmgYGBjBkzBmMMEyZMoHPnzsCTNaacnJzYvXs3TZs2xdHRkRo1apArVy6OHz/OwYMHCQgIYODAgYCuaxGBP/74g2nTpmGMYe7cuRQqVIhZs2ZRunTpZMceOHCA8PBwPDw88PT0tG2spGePF49CKRFJxtoxDA4OxsXFhXnz5hEdHc2QIUNsWzQ/fvwYNzc34MkN5Ny5c2TPnh0PDw/bosS6KYiIVVxcHHfu3KFOnTpkz56d7du30759ewYPHkzhwoUxxpCQkICjoyMXLlzg0KFDHD16lJIlS1K4cGEqV64MqF0RSS2soVOXLl346quvcHFxoUqVKsyaNYtChQrZ1n5zdHTkyJEjzJgxg507d3Lv3j2qVq1Ks2bNaNOmDaDrWiStio+PTzYlPzo6mujoaHbs2MFnn31GwYIF+eqrryhZsqQtuLbuyPc0hdsvJoVSIgI8+6ZgtXfvXtq3b8+jR48YOnSoLZgyxhAbG/vMxQR1UxCRZ7Ur1o5k9+7dmTp1Ku3atSMgIIBChQr9n+2G2hUR+3v6Orxw4QIRERGsWrWKGTNm8NZbbzFlyhReffVV4uLicHBwwMHBwdYe3L9/n/Tp09vaBgVSImlT4j7C5s2biYqKokqVKmTJkgUXFxciIyPZtGkTXbt2pUCBAsyePZuSJUsSGRnJggULyJ8/P++9957WmXwJKJQSkSQ3hblz52KMIWvWrHh5edmO2blzJ59//jmPHj2yjZiKjIxk3rx5XLp0iTFjxtip9iKSGllHUQDs37+fTJkykTlzZnLlymU7pkuXLsyYMSNJMHXnzh22bNlC9uzZqVmzpr2qLyLPkPi6BoiJiUnyYsrf359p06bx1ltvMXnyZIoXLw7A/fv3uXbtmm16jTXYUtAsIk2bNmX9+vXExcWRL18+Pv74Y/r160emTJmIiopi48aNdO3alYIFC9KuXTsuX77MqFGj+OKLL2zLhciLTaGUiNg0atSITZs2AU+Cqnbt2hEaGsorr7yCxWJh165d+Pr6cv/+fXx8fHB3d2fs2LGMGTPGttaLiEjiB80WLVqwceNGHBwcyJcvH9OnT6d69eq2Y63BVJs2bWjTpg3Hjh0jKCiIuXPn2qb2iIj9JX6BNWDAAM6fP4+7uzs9evSw7c4LSYOpmTNnkidPHqZMmcLXX3/NihUrKFKkiL1OQURSgcRtSUBAAHPmzKFnz55UrFiRsLAwjhw5Qp06dZgwYQJZsmQhKiqKbdu20aNHDy5evEiGDBkYPHgwAwYMsPOZyP+KQimRNCzxTWHixIlMmjSJgIAAChcuzP79+xk5ciRvv/02X3zxBYUKFcJisfCvf/0Lf39/Dh8+jIuLCwMGDKBfv352PhMRSS0ST8Xp0qULy5cvp2PHjjg4OPDDDz9w4sQJvvnmG1q2bGn7Tc+ePZk+fbrtd/7+/gwdOtQu9ReRP9ekSRO2b99Onjx5ePjwIdevX2fmzJm0bNmS9OnTAzBkyBCmT5+Ou7s7b7zxBuvXr2fYsGEMGTLEzrUXkdTi6NGjzJ8/n4wZMzJo0CDc3NyIiYmhT58+rFixglq1ajFp0iSyZMmCMYZ79+6xY8cOcuXKpXUmXzIKpUSEU6dOsWDBAq5cucKUKVNInz49jx49YuPGjXTo0IG33nqLqVOn2oKpS5cuERUVxaNHjyhfvjygm4KIJF9rpnXr1pQpU4Y+ffrg7OzMkSNHCAkJYdWqVcyfP59WrVrZjl28eDH37t0jV65cNGnSBFC7IpIaJH6BtW3bNrp160ZgYCD169fn8uXLhIWF8d133zFu3Dg6dOhAhgwZAJg0aRJr1qzh9u3bdOzY0TbNRlP2RKRDhw5s2LABi8XCjBkzaNKkiW0TpdjYWHr37m0LpiZPnkzmzJmTlaE+wsvD6f8+REReZoGBgQwfPpxChQrRu3dv21vOdOnS0aRJE+bMmUOHDh3o2rUr06ZNo1ChQrbd9ax0UxARwPag2bx5c37++WcyZsxI7969bTvklCtXjuHDh2OxWPj4448BbMFU4pFToHZFJLWwBlJhYWG4u7uTJ08emjRpgru7O5kyZSIsLIx06dLRp08fAFsw5efnh6+vL48fP7Y9UOq6FhGAmjVrsn79eiIiIjhx4gRNmjSxBVLOzs5MmDABi8XC6tWr6dKlC1OnTiVLlixJylBb8vLQ/5MiaVz9+vXx8fHhwoUL7NmzhwcPHti+c3R0pEmTJsydO5effvqJTp068dtvvyUrQzcFEbGKjIykWLFiPHz4kBMnTnDp0iUA2/bwJUqUYNiwYXh5edGhQwcWLFjwzHLUroikHtu3b6d///707NkTZ2dn3N3dbd9lz56dESNG0LFjR/r06cO8efOIiooCwM3NzRZIGWN0XYukQYknZsXFxQHw0UcfMXPmTDJlysSkSZNYuXIlAM7OzsTGxuLk5MT48eNp2LAhq1at4uDBg/aouqQQ3RlE0pCEhIRkn1WpUoW+ffvSuHFjVq5cyQ8//JDke0dHRxo3bsyMGTPYtm0bP//8c0pVV0ReQBkyZKBXr1507tyZdOnSMX36dKKjo3F0dEwSTA0fPpx69erRtm1bzp49a+dai8ifefPNN5kzZw4FChTg8OHDHD58OMn32bJlY8SIEfj6+tKjRw+mTp1qu96tNGVPJO2Jj49Pcu0nfvn9/vvv8+WXXxIfH09gYCCrV68GkgZTkyZNYt26ddStWzfF6y4pR2tKiaQRibdxvn37No8fP8bV1ZVs2bIBcOjQIQYOHMj+/fuZM2cOLVq0SPL7+Ph4Ll68SOHChVO87iKSOiVea8bKul5MREQEc+bMITg4mLp167JixQqcnJyS/OaXX37h4sWLNGzY0B7VF5FneNZ1DU/6DqtWraJv37688cYbfP3118mm89+8eZPevXtToUIFevTokVJVFpFUKHFb0r9/f/71r39x/vx5atWqRfPmzWnatCkWi4UlS5bQtWtXcuXKxciRI23rSsbExODi4mIrT9N/X14KpUTSgMQ3hc6dO7N//37Onz9PwYIF6dixI926dQPg8OHDDBgwgH379j0zmLLSTUFEErcro0aN4sSJE9y7d49y5crRuXNn8uTJw/Xr15k9ezZBQUG89957rFy5MlkwZaV2RcT+Er/AOnjwIFevXiVLliyUK1eODBky8ODBA5YuXUrv3r0pX748c+fOTRZMWRcrFpG0K/GGBo0bN+bgwYNUrVqVwoULs2zZMiIjI/H19WXEiBFYLBZWrFiBr68v+fLlIyAgAC8vL/uegKQohVIiaUiTJk3Yt28f3t7e5MuXjyNHjrBq1Sr69OnDqFGjcHJy4qeffmLgwIEcOnSIGTNm8MEHH9i72iKSyiTubDZs2JC9e/fy6quvkpCQwKVLlzDGsHnzZsqUKcOtW7eYNWsWQUFBNGjQgKVLl9oeekUk9UgcDLdo0YJdu3Zx48YN0qVLR/78+Zk7dy6VK1cmKiqKxYsX24KpefPm4enpmaw87bInIuPGjWP8+PGEhYXRpEkTMmTIwPLly2nevDlDhw6ld+/eZMqUCYAVK1bQvn17PDw82LFjh2ZnpCF6JSmSRnz11VccOHCAL774ggkTJhAQEGALnB48eEBkZCTwZN2I0aNHU6ZMGVq3bs25c+fsWW0RSYWsD5r+/v4cPnyYL7/8ku3bt3Pw4EE6derEzZs3WbZsGTExMWTLlg1fX1+GDRvG6tWrqV27NnofJpL6WAOpVq1asWfPHvr378+xY8eYPXs29+7d4/333+f06dOkT5+eFi1aMH78eI4fP86HH37I77//nqw8BVIicuDAAYoVK0bjxo3JkCEDmzdv5pNPPqFNmzZ06NCBTJkyERMTA4C3tzczZsxgyJAhCqTSGL2qFEkjTp48Sa5cuWjUqBHp06dnx44dfPrpp3z88ccMHjzYtjsO/DuYCg8Pp2jRovartIikWgkJCRw+fJjatWtTr1493N3d2bNnDxMmTODTTz+lQ4cOtrUgsmbNSvv27YmKiuKVV17Rw6pIKnXw4EF2797NoEGD+Pjjj8mUKRPXrl3j3r17NG/enIwZMwJPNjRo1qwZCQkJdOrUicOHD1OwYEH7Vl5EUo2EhATi4uIIDw+nZMmSZMyYkc2bN+Pl5YW3tzdhYWHkypULgDVr1lCkSBHKli1Lq1atbGVotGXaoZFSImnErVu3cHFxIUOGDOzevZtGjRrh7e3NqFGjbMPux40bx/LlywGoXLkyPj4+wLN37RORtO3BgwecOHGCIkWKkDFjRnbs2EHdunXx8vJi2LBhtgfU77//nps3b5IjRw78/f3p3r07gEZLiaRC58+f58aNG7Ro0YJMmTKxdetWmjZtire3NyEhIeTNmxeAhw8f4uHhQfPmzTlx4oStvyAiaVPie7oxBgcHB1xcXChVqhTr169n/fr1+Pj44OXlxdixY22B1J49e+jYsSOnTp1KVqYCqbRDoZTIS+bpBz3rlsyvvvoq165dY9KkSdSvXx8fHx9CQ0PJnTs38GSR8/nz53PmzBni4uKSlKHFh0UksYSEBFxcXPD09OTSpUusW7eOhg0b4u3tzdixY23typYtWwgODubQoUMASRY/VmdTJPXJmjUrbm5uREZGsnv3bt5//33bdW0NpKZPn06rVq2Ijo4mU6ZMvP7664BeYImkVXFxcUnu6Yn/3Lx5c+Li4mjUqBF169Zl3rx55MyZE4ArV66watUqcubMSZEiRVK83pJ66ElT5CXy9E0BsO1w1aFDB2JiYujVqxfvvvsuoaGhtg7m1atXWbhwIZGRkVSpUkWLEIuIjTXYTszBwYF06dLRunVr5s+fT+PGjfHy8mLcuHHkyZMHeNLZXLp0KenSpXvmIsgiYj/PC5CyZcuGxWJh0KBBNGrUCB8fnyRB8+nTp1m9ejWZM2e2rQNjpRdYImlPfHy87blhxIgRdO7cmSlTpnD8+HEAqlWrRqtWrciSJQtXrlzh2rVrODg48PPPPzNp0iQmT55M165dqVSpkj1PQ+xMT54iL4mEhATbTaFXr15cuHABd3d3+vXrR7FixcidOzdff/017du358qVK2zfvp3WrVuzb98+Vq5cycSJEwkLC6NGjRr2PRERSTXi4+Ntwfa0adOIj48nT548NGvWDHgSdp88eZJZs2ZRrFgxHj16BDxZw27RokV88803jB8/npIlS9rtHEQkqbi4OFt/ITw8nISEBPLlywdA+fLl6d27N8HBwZQoUYLevXvbAqnw8HAWLFjA4cOHmTp1qm19KRFJu6x9BC8vL1avXk2WLFm4c+cOhQsXZuLEiTRu3JgRI0bg6OjI3LlzKVSoEAUKFCAqKorY2FiGDx+eZFq/RlGnTRajRR1EXire3t5s2rSJvHnzcufOHSwWCyNGjKBly5ZkzpyZTZs20aFDB8LDw3FycsLBwYFs2bLRu3dv+vTpA+imICJJNW7cmHXr1uHo6Eh8fDxt27YlODiYQoUKcfr0aUJCQvj2228pVKgQuXPnJjw8nFu3buHv78+AAQMAtSsiqUHi67BVq1bs2LGDqKgo2rdvj6+vLyVLluTRo0f079+fqVOn0rx5c7y9vQFYt24dixYtYsSIEfTv39+epyEiqYC1Pdm8eTO9evViwIAB+Pj4sHz5csaNG8fZs2f55ptvaNasGTExMRw9epRNmzbx22+/8frrr1OhQgVq1qwJPHm5rtGWaZdGSom8RM6cOcMff/zBd999R+XKlbl79y49e/bEz8+Pe/fu0bFjR+rWrcuRI0fYsGEDv//+O8WKFaNw4cK2YbO6KYhIYkuWLOG3335j/vz55MuXj3/961+EhIQQERHBxIkTef311/nqq6+oXbs269atIyIighYtWvD222/z/vvvA2pXRFILayD1+eefs2vXLurVq0dCQgJfffUVx48fJygoiBo1ajBx4kRy5crFxIkTWbp0KQBlypRh0qRJdO7cGdB1LZJWWUdRW9uThw8fUqhQIZo3b066dOn46KOPyJ49O0OHDqVt27ZYLBZ8fHyoVKnSM6fpqS0RjZQSeYElnloD8PPPP+Pt7c2ePXtsiwhGRkby4YcfsnHjRkaMGMHHH39Mjhw5nlmeRjKIyNPtyuzZs/nuu+9YtWoVGTNmJCoqitWrV+Pr68tbb73FhAkTKFWqlO23xpgk69Kpsylif4nv748fP7Zty96xY0ccHR1Zu3Ytbdu2pWjRoowZM4ZatWoB8Ntvv/Ho0SMcHR3JkiWLbccsXdciaVPiPsKsWbNwcHDgyJEjXL16leXLlyeZHrx+/XqGDh3KiRMnWLx4Me+//z7GGFt7pGcOsVIoJfKCSnxTmDBhAn/88QeOjo6cPXuWlStXJjnGGkxt2rSJkSNH0q5dO7Jly2bH2otIapS4Xdm0aROPHj3i9OnTALZpeACxsbEsXboUX19fKleuTFhYGG+88YZd6iwify7xQ+Ljx495/PgxH3/8MaNGjaJkyZK2gGnbtm34+PhQrFgxQkJCeO+9955Znl5giUjjxo3ZuHEjCQkJODs7kzlzZnbt2sWrr76apM3ZsGEDQUFBHDhwgEWLFtGiRQs711xSI03fE3lBWR8cmzRpwrp163B3d+fRo0e2YfjWt5/x8fFkyJCBBQsW8NFHH9G3b1+ioqLo378/rq6udj4LEUlNEi9YumbNGqzvrfLnz4+XlxfFixcHwNnZmebNm2OxWOjatSt+fn5MmDCBsmXL2qvqIvIMiTdB8fX15ciRI9y/f5/bt29z5coVSpYsiYODA8YYatWqxfLly/Hx8SEgIADgmcGUAimRtCdx0DRv3jx+/fVXZsyYQdmyZVm4cCGzZ8+2zczImjWr7fj69euTkJCAn58f165ds/NZSGqlcbciL5jE27MfOnSI8+fPs2DBAvbv38+6devIkCEDISEhtjUgrMFU+vTpmT9/PjVq1CBLliwKpETEJnG7Mm3aNI4cOcL48eOZM2cOn3zyCZcuXWLmzJlcvnzZdpyzszPNmjVj8uTJ7Ny5kwsXLtij6iLyHMYY2xS75s2bs2TJEtzc3MiVKxc3b95k4sSJnDlzBngSNFmDqRUrVnDs2DG6deuW5JoXkbTLGkj98MMP3L59m7Jly9K6dWvefPNNhg4dSkhICOfOnaNevXrcvn0bJycn4uLiAGjYsCFbtmyhW7du9jwFScU0UkrkBWMdyTBq1CgiIiLImTMnDRs2JGPGjLz++uvs2rWLd955h4EDBwJPOqKJR0xt2rQpyXovIiLWdmXr1q38/PPPNG7cmK5du+Lk5ESLFi1wd3dn4sSJODo60qNHDzw9PYEnwVTLli2pWLEir776qj1PQUQSSTwV9+bNmzx69Ihp06bRoEEDMmbMSP/+/Rk/fjzZs2dnyJAhvPrqq7ZgqmbNmqxYsYLz58/brnURkTlz5tCxY0eyZMnCJ598gru7O8YYMmXKRLt27QDw9/enXr16bNq0iSxZshAbG4uzszMFChQANP1Xnk1PpiIvoMWLFzN48GCKFStGzZo1yZgxI/Hx8SQkJFC2bFl27dpF9erVGThwIBaLhWbNmtk6p9ZASjcFEUmsS5cuzJgxg1KlSjFkyBDbW8706dPzxRdfADBu3DgA/Pz8yJcvH/AkmLIGUlr8WCR1sN7zO3TowM2bNzl27BhTpkwhU6ZMAISFheHk5MSYMWMAkgVT9erVs5Wl/oKIANSsWZOBAwcyadIk9uzZQ3h4OHny5AHA3d3dFkwFBgZSs2ZNtm7dmmwNW7Ul8izqOYq8gFq2bElQUBC//vor3377LYcOHcLR0dH2EFm2bFl2797N7du36dWrFwsWLEhWhm4KIpJYYGAgr776KidOnGDZsmXExsbi5OREQkICAFOmTKFLly5MmjSJ8ePHc/HixWRlKJASST3u3r3L7t272bt3L+7u7radd6OjowEYPXo0/fv359tvvyUkJCTJVL7E1F8QSXus9/7EChUqxKeffkrXrl05ePAgEydO5NGjR7bvrcFUQEAAx48fZ/PmzSlZZXmBqfcokso966YAEBAQwIgRI3j8+DEDBw7k+PHjWCwWHB0dbcHU1q1b+eOPP4iJiUnhWotIavZ0u2KMIWfOnPzrX/+iRIkSLF++nFmzZhEXF4eDgwPx8fFYLBamTJlChw4dmDhxotaaEUnF4uPjyZw5M7t376ZUqVKcO3eOAQMGYIzB1dXV1i8YPXo0AwcOZP78+QwaNIi7d+/at+IiYnfWez/AhQsXOHr0KOfOnQOgSJEidOvWje7duxMWFsawYcOSBVPt27fn2LFjtGrVyi71lxePxVi31hGRVCfxThcHDx7k1q1bZMuWjcKFC9uGwwYEBDB+/HiqVavG2LFjKV26NMYY4uPjcXJy4ubNm2TPnt2epyEiqUjiduXOnTtERUWRJ08eWwf0xo0bvPXWW9y7d4+RI0fSsWNH24gp6y5dO3fupEaNGnY8CxFJLPEaUk+LiIigefPmnDhxgp49exIYGIjFYiEmJgYXFxcAevToQYECBejTp09KVltEUpnEbcnHH3/Mnj17uHDhAjly5KBOnTpMnTqVTJky8ccffzBu3DgmTZrEgAEDCAgIIF26dMnK07R++SsUSomkUokb8VatWrFjxw6uX7+Og4MDDRs2pG3btrRs2RKA4OBgxo4dS/Xq1Rk7diylSpUi8aVtsVh0UxCRJJ3Nbt26sWPHDq5cuUKhQoXo3bs31atXp0CBAty4cYPKlStz9+5dRo4cSadOnWwbJiR+8FW7ImJ/ia/L2bNnc+HCBe7du0fz5s0pUaIEOXPmJDw8nObNm3PmzBm6d+9uC6aio6OT7carNaRExNvbm71799KhQwfKly/PkSNHGDVqFLVq1WLx4sVkzZqVy5cvM378eKZOnUrPnj0JCgrC3d3d3lWXF5AWOhdJpawPei1btmTXrl3079+fihUrcvv2bVq0aMHJkyfJly8fVatWtXUux48fj5+fH2FhYZQrV+6Z5YlI2mV9cG3SpAn79+/H29ubAgUKsGfPHj7++GN8fX0JCgoiZ86c7N+/n7feeovAwEDi4+Px9fVNtnOn2hUR+0pISEhyXe/btw8XFxccHByYN28e3t7eDBw4kNdff50VK1bQrFkzpkyZgoODA0OGDMHV1TVZuKxASiRtW716NXv37iU0NBRvb288PDxwc3PD0dERT09P4uLiAPD09KRXr17ExcURFhaGl5cXVatWtXPt5UWk3qRIKrZz504OHjxIUFAQvr6+VK9eneLFi5OQkEDDhg0pXLiwbW2YgIAAunfvzvbt25+5ALGICMCXX37J0aNHmThxIpMmTcLf35/BgwcDkDVrVtzc3IiLiyN79uzs378fFxcXunfvzqlTp+xccxF5mjVM6tKlC4cOHWLixIns27ePy5cvU716db777jv27t1LdHQ0OXPmZNmyZZQqVYqxY8cycODAJGWIiACcPXsWi8ViC6S2bdvGBx98QOvWrRk+fLht0wSA/Pnz07t3b3bs2KFASv5ruguJpCJPz6a9fPky169f59133yV9+vRs27aN8uXL07JlSwYMGECuXLlwcHAgPDwcgOHDh7N37168vLzsUHsReREcO3aMzJkz89577+Hm5sbWrVupW7curVu3xtfXl0yZMuHo6Eh0dDTZs2fnp59+Ys6cOZQuXdreVReRZ4iIiGDPnj20b98eLy8vPD092bdvHz/++CPt27enbt26uLq6EhsbS86cOfn+++8pWLAgRYoUsXfVRSSFPW/lnsQboFgsFiwWCx4eHvz44480adIEb29vQkNDyZcvHwDjx49n6NChwJNd+d55551k5Yj8VQqlROzkWTcF65B567DYhw8f4u7uzmuvvcb+/ftp3LgxPj4+jBs3jrx58wIwY8YMpk+fTlRUFABvvfUWoJuCiCRl3W3r4sWLFChQgFdeeYVt27bx/vvv4+XlRVhYGPnz5weerEvz888/k5CQQI4cOfjkk08AtSsi9mLtMzyr73Dv3j3Onz9P+fLlbS+watWqRZMmTRg+fDienp4AnDhxggcPHpA7d24OHDiAr69vip6DiNhXQkICFouFuLg47t+/z8aNG9m0aRNnzpzh8ePHtuPy5MlDZGQkgwYNon79+jRr1ozQ0FBy584NwM8//8yKFSuIiopK8jvQyEv57+hvjYgdWG8KMTExXL58ma+//pqZM2eyZs0abt26ZVu3pW7duiQkJNCgQQPq1KlDixYtCAkJsd0Uzp07x/z58585XU83BZG0xRoYWXffPHLkCL/88gvXr18HsO2y9d5777F3717GjRtH06ZNbW8/re3KsWPH8Pf3Z/v27cnWllG7IpKyrCGU9Vp8ViiVLl06nJycuHPnDqdPn6Zx48Z4e3sTFhZmu64XLFhAx44duXnzpu03zytPRF4+8fHxODg4cPv2bfz9/alUqRINGjSgfv36lC1blk8++YR169YB0Lp1aypVqkRoaCjly5cnMDDQ9jL86tWrLFy4kN9//53atWvj5uZmz9OSl4QWOhdJYdZdcm7dusXgwYPZuHFjklDJ09OTyZMnU6dOHfLmzUvbtm356quvyJcvH9OmTSN9+vTAk6l98+bN4/z58/Tr18/2uYikPdaFiu/evcuUKVNYs2YNhw8fxhhD0aJFadKkCWPGjMHBwYGqVauSI0cO+vfvT61atZg/f74tbLpy5QrfffcdHh4eVKpUSQsei9iR9bqOioriwIEDrF27lnPnzuHp6ckbb7zBZ599BjxZ06VZs2b07t2bmJgY2rZtS3BwsC2QunTpElu3biVdunRJds8ELWoukhZYnz2uXbtG/fr1iY2NpUKFCkyePJlff/2Vw4cPM3fuXHbs2MGwYcP4/PPPmTVrFh9//DHHjh3j22+/pVWrVly8eJHVq1czc+ZMRo8eTaNGjex9avKyMCKSYuLi4owxxly9etWULl3alC1b1nTp0sXs3bvX7Nu3z/Tu3dsUKFDApEuXzowcOdI8fvzYXL582bz//vvGycnJvP/++2b9+vVmzpw5pk2bNsbZ2dmMGTPGzmclIvZkbVciIiJM5cqVTfHixU29evXMqFGjjL+/vylcuLCxWCymZs2a5vTp08YYY7766ivj4eFhihUrZpYsWWKioqLMjz/+aHr27GlcXFzMxIkT7XlKImme9bq+fv268fLyMrly5TLp0qUznp6exmKxGIvFYho0aGAOHTpkjDFm27Zt5vXXXzceHh5m4cKFtnIuXLhgAgICjIeHh5k9e7ZdzkVE7Cfxs0e+fPlM5cqVzdq1a5Md9/3335sMGTIYV1dXM2/ePGOMMZcuXTINGjQwDg4OtnanSJEiSfoI8fHxKXMi8lJTKCWSQhLfFPLkyWPeeusts2nTJhMbG5vkuM2bN5v33nvPuLi42AKnCxcumP79+5u8efMaFxcX4+rqasqVK2emTp1q+51uCiJpT+J2xdPT01SuXNksW7YsSXtw/vx506tXL+Pu7m4qVqxofv/9d2OMMTNmzDAlS5Y0FovF5MiRw3h4eJh8+fKZsLAw228TEhJS9oREJMl1XaxYMVOpUiUzduxYEx0dbWJjY83JkydN586dTcaMGc0bb7xhtm7daowx5ttvvzWvvvqqsVgs5sMPPzSffPKJqVatmnFzczOjR4+2la/rWiRtiYiIMJ6enqZKlSrm4MGDtjbg6WeQ1atXGxcXF5M/f36za9cu2+e7d+82y5YtMzt27LC93DJGzx7yv2MxRpPJRVLK9evXqVy5Mrly5WLmzJm89tprODk5YZ4ExLYpNAcOHGDAgAEcPHiQH374gXfffZdHjx4RFRXFoUOHyJUrF1myZKFAgQLAv4f4i0jaYYzBYrFw7do1KleuTL58+Rg/fjzly5fH0dHRtn6ExWLh5s2bzJgxg+HDh1OjRg02btwIPFms9PTp0+zfv59SpUpRokQJKleuDKhdEbGHxNNsypcvT/78+QkNDaVq1ao4OTkRGxuLs7Mzt27dYtmyZQwZMoS8efOybNkyChcuzNatW9mwYQOrV68mNjaWihUr0rRpU9q0aQPouhZJa2JiYvjwww9ZtmwZ3377ra0tsPYhnv7zvHnz6NChA/369SM0NPS55Sb+jcjfpVBKJIUkJCTw/vvvs27dOvr06cPYsWOBJzvtWRc2T9zAr1y5ktatW1OmTBk2b95MxowZn1mubgoiadejR4/w9PTk9u3bLFq0iBYtWgDPfvC8fv06gwYNYu7cuYwdO5Y+ffo8t1y1KyL2c+PGDSpXroyLiwurV6+maNGiWCyWZNflgwcP+Prrr+nTpw/vv/8+S5YssX139+5dnJ2dcXJywtXVFVAgJZIWxcTEsGrVKoKDg4mPj2fx4sWULl062XHWSCAmJoZ69epx+vRpzpw5Q6ZMmVK6ypIG6c4kkkIcHBwYPXo0ZcqUYcGCBUyZMgUAJycn265Z1k4ngJeXF/Xr1+fs2bPcunXrueXqwVEk7UqXLp1tW/elS5dy5swZIPkuecYYcuTIwYABA0iXLh379u2ztTtAkj+D2hURe1q3bh0XLlwgU6ZM3Llz55mBFEDGjBlp1qwZdevWZdmyZWzevBl4cj1nzpyZ9OnT4+zsDJBkNLaIpB0uLi54eXkRGhpKTEwMLVq04OjRo8mOs1gsWCwWXF1dKVasGJGRkcTHx6d8hSVN0t1JJIXEx8dTqlQpFixYQJYsWRg1ahQTJ04EnjxAJg6mYmJiAKhYsSIPHz60bekuImJlbTNGjhxJUFAQS5YsYejQoZw8eTLZsRaLhbi4OF599VXKlSvHr7/+SmRkpO17PayKpB7t2rVj/PjxnDx5kkGDBnHgwIHnBsW5c+eme/fuAERERABJr2frnxU0i6Rdzs7O1K1bl0mTJhEbG0vr1q2fGUxZX4wbY0ifPr1tJofIP029UJEU4ujoSEJCAq+99hpLliwha9asjBkz5pnBlIuLCwDXrl0jffr05M2b117VFpFUKnGbERAQQFBQEEuXLiU4OPiZwZS1cxkfH4+rq6ttBIWIpB7WkQk9e/YkICAgyRqTT7Ne/6+88grwpM8A/36wFBGxcnZ2pl69ekyaNImYmJhnBlMWi4XDhw+za9cu2rRpg4eHR7KR1CL/BIVSIinI+hD5V4Kp48ePs3XrVnx8fMidO7c6mSKSzLOCqSVLljw3mNqzZw9nz56lefPmpEuXTu2KSCpjfYEF0LdvX4KCgjh06BD9+/dPEkwlXh/ql19+AaBatWqARkWJyLM9K5g6cuSI7fvr16/z/fffEx8fT5MmTQCNpJaUob9lIinsecHUhAkTbN9fu3aN77//ngcPHuDl5WXbQUtE5Gl/FkxZH1YBrl69ypIlS8iZM6ceXkVSscTXdJ8+fZ4ZTFkfFH/77TcWLVpEzZo1KVy4sN3qLCIvhqeDqTZt2nDixAnu3LnD7NmzmTRpEj169KBWrVr2rqqkIQqlROzgWcHU2LFjmTJlCg8ePODbb78lLCyMnj170qhRI3tXV0RSuecFU8OGDePXX38lJiaGuXPnMmvWLLp160aVKlXsXGMR+TN/JZi6desW3377Lfv27ePTTz8lR44c9qyyiLwgng6mmjdvztChQxkyZAgBAQH4+fkBmgosKcdi9LdN5H/i9u3bZM2a9T/6jXX4/alTp2jRogV3797ljTfeYMOGDQwbNowhQ4YkOU5E5M8kbiuGDRtGUFAQPj4+5M+fn4kTJzJ8+HAGDx4M8MzdvEQkdUl8TY8bN46goCAqVaqEv78/v/zyCz179mT06NH0798f0HUtkpbMnj2b48ePM2nSpP/q97GxsWzevJlevXrx66+/MnLkSAYNGgTo2UNSlkIpkf+Bb775hu+++46QkBDKly//H/3W2uifPn2apk2b8uuvvxIaGkq/fv2SfC8i8lckbjNGjBhBQEAAACEhIQwcODDZMSKSuiW+XsPCwhg5ciQZMmTgypUrBAcHM3To0GTHicjL7fHjxwQGBjJ27FgGDBjAqFGj/qtyYmJiWLNmDfHx8bRs2RJQWyIpT/s8ivwPhIeHs2vXLkaNGsXgwYMpW7bsX/6tdYh+iRIlWLFiBYcPH6Zt27aAbgoi8p+ztikODg4MGTKEuLg43NzcFEiJvKASX9N9+/bFGMOAAQMYO3Ysffr0AXRdi6Q1bm5u9OrVC3d3d4KDg0lISCA0NPQ/LsfFxQVvb29b+6G2ROxBI6VE/kcmTZpEcHAwNWrUICAg4D8KpiD5TUA3BRH5O57VhqhdEXlxJb5+jx49autn6LoWSbtu3LjBlClTGDVqFL179/6vgikRe9NIKZG/ydoZ9PPzIyEhgeHDh2OxWBg8ePB/NJXv6Q6lg4OD1oYQScPi4+NxdHT8j7+zeroNMcbowVXEzp537f6V+33iEVMKpETSttjYWJydnXnllVdo1qwZN2/eZOzYsWTKlAl/f///qsy/0rcQ+SdopJTI3xQXF4eT05N8NzY2lg4dOrB9+3beeustAgMDeeONN/7jMi9evEiBAgX+11UVkRdE4o5haGgov/zyC/fv36d8+fJ06dKF7Nmz/8cPo48fP8bNze2fqrKI/B8S9xe2bdtGdHQ0FStWJEuWLDg6Ov7XAZNeYImkLYnbivbt23P27Flu377NmTNnABg0aBAjR478j8ocOnQoN2/eZOrUqQq6JcXpb5zI35CQkGDrYPr4+PD2229z4MABAFasWEFAQADHjx//j8ocPXo0nTp14vLly//z+opI6meMsQVSDRs2ZNSoUZw6dYqLFy8yZcoUypQpw6lTp/6jTuO0adP44osviI6O/qeqLSL/B2t/wcvLiwYNGtCoUSMqV65MSEgIjx49wsHBgfj4+L9c3pYtW7h586YCKZE0xnr/b9u2LWvXrqVNmzasXbuWtWvX8sEHHzBq1CjbOpJ/xejRoxk5ciT58uVD41XEHhRKifwN1pvC559/zrZt2/D19WXDhg3s3r2bAQMGsHbtWgIDA/9yMDVq1Cj8/f1p2LAhefLk+SerLiKplPUBc+DAgRw+fJgvv/ySnTt3cuTIETp27MjVq1dZvHjxX354HTVqFN26dSNLliw4Ozv/k1UXkWdIfK0OGjSIQ4cOERwczIYNGyhUqBCzZs2iZ8+ePHz40DZi6v8yfPhwPvroI3755Zd/suoiYmc7d+7k9OnTyT4PDw9ny5Yt+Pj40KFDB4oUKULDhg0ZPXo0ffr0YcyYMbadOf9MSEgIgwcPZty4cfTv31/T98Q+jIj8JcuXLzcXL15M9vm1a9dMyZIljY+Pj4mJiUny3ejRo43FYjFNmzY1x44d+9PyR44caRwcHMz48eOTlSMiaUtcXJx57733TNu2bc29e/eMMcb8+OOPxt3d3XTs2NFcuHAhyfHx8fHPLGfkyJHG0dHRjBs3Tu2KiJ39/PPPxs/PzwwbNsw8evTIGGPMgwcPTKdOnUzevHlNx44dTVRUlDHm+de0McaEhIQYBwcHM2bMGBMbG5sidReRlPfTTz8ZBwcH07JlS3P27Fnb5wkJCebkyZPGYrGYKVOmGGOMefz4se373377zTRs2NBYLBYTEhLy3PKtzx7qI4i9KZQS+QsuXbpksmXLZooXL24uX76c5LsbN26YAgUKmLZt29o+S9xJ9PPzMxaLxbRs2dIcPHjwmeXrpiCStiQkJCT5309f97du3TK5cuUywcHBxhhjtm/fbtzd3U2bNm1MeHi47bhFixaZu3fvPvPfoXZFJPVo166dyZ8/v8mdO7dZt26dMcbYgqmoqKhnBlNxcXHJytF1LfLyS9xHGDRokHnllVdM27ZtzZkzZ2yfR0dHm+LFi5uaNWua6OhoY0zSNmPhwoXGYrEYi8ViunbtmuzfERISopdWkmpo+p7IX/DKK68wZ84cEhISaNiwYZL1ntzd3cmZMyeHDh3i2rVrwJN1I2JjYwFo1aoVmTJlYseOHQwaNIibN28mKTs0NJShQ4cyduxYunfvruk1ImmAxWIhJiaG8PBwHj9+bLvu169fz927d3F3d8fT05NLly6xfv16GjZsiLe3N2PHjiV37twAbN68mcDAQNs6domNHj1a7YpIKlK1alUePXpERESEbSqOm5sbsbGxuLu7M2nSJBo1asTmzZvp0qULkZGRyabRhISE6LoWSQOuX79u+3NISAidO3dm7dq1DB8+nLNnzwJP+hG1a9dm3759jBs3jujoaBwdHW3PHy4uLpQuXRo/Pz+KFy+epPwpU6YwePBgxowZo7ZEUgd7p2IiqdmJEyfMzZs3jTFP3kisXbvWFCxY0JQuXdpcunTJdtyqVauMi4uL6d69e7Iytm7dasqVK2f8/PzMtGnTknw3fPhwY7FYzIQJE/SWQiSNWbRokWnatKnZuXOnMcaYt99+27z55pu2acLjxo2zveVs3bq1iYiIsP32jz/+MJ999pkpX768OXnyZJJyBw8ebCwWi6YCi9hJ4lEOiUdOL1682KRPn97kyZPHNlrKmH+PlHz48KHp0KGDcXd3N5s3b05SZkhIiHF2dtaoBpGX3Pr1643FYjHr169P8nlAQIDJnDmzadu2rTl16pQxxpg7d+6Y0qVLm4wZM5rg4GDz8OFDY4wxV69eNT179jTNmjUz169ft5WRkJBgoqKizLBhw8zMmTPVlkiqoVBK5Dm2bNliLBaLCQgIsA2xj4mJeWYwFRERYZum16VLF/Pbb78ZY548OPbt29e8++67SYbUWjus69atM2PHjtVNQSQNOnz4sMmbN6957bXXzJtvvmly5cplVq5caetU3r5923Tq1MlYLBYzbNgwW1h18uRJExAQYNzd3c3MmTOTlBkdHW3atGljhg8frnZFxA6ennKXeJ0XY4z59ttvTdasWc2bb75pNmzYYPs8cTC1devWJL9Zs2aNsVgsCqRE0oBt27aZEiVK2NaCShxsJw6mfvnlF2PMk+n+5cuXN25ubqZ8+fKmS5cuplatWsbBwSHZy3CrqKioP123TiSlWYzRvo8iz1OpUiV8fHySbKsaHR3Nli1b6NatGxkzZuSHH37A09OT3377jenTp/PFF1+QOXNmihUrRmRkJMeOHSMsLIzevXsnKdsYg8Visf1TRNKeo0ePUrFiRZycnBg4cCB9+/Ylffr0tnbh+PHjhIaGsnDhQooWLUqePHm4fPkyN2/exN/fnwEDBgAkaUdiY2NxcHDQDjoiKSw+Pt523YWEhPDTTz9x+fJl6tatS8OGDalatSoA8+fPp1evXhQsWJCQkBDq1asHQExMDC4uLrbyEl/XGzZsoHbt2ppmI5IGXL58GU9PTwBOnDhBqVKlbN8FBgYyefJkGjduzMCBAylZsiT3798nNDSUbdu2cfLkSQoXLkzHjh3p2rUrgJ41JNVTKCWSiLVDGRUVRfr06ZN8t2rVKqpVq0a2bNmSBFMZMmRg3bp1eHp6cvv2bX7++WfGjBnD7du3yZQpEy1atODTTz8FdFMQkaS+++47unXrhpOTE9mzZ2fq1KlUr149yYNnVFQU33//PevWrSMiIoIqVapQvXp1mjZtCkBCQgIODloiUsSeEt/fGzduzN69e8mfPz9OTk4cPnwYT09P/Pz86NWrF/DvYKpIkSIEBwfToEGDZ5abOOgSkZeP9RqPjo4GwNXV1fbd4MGDGTVqFNu2baNGjRq2zxMHU4MGDeL111+39QVu3LiBm5sbGTNmBNRHkBeEfQZoiaQ+1il1169fN15eXmbRokW274KDg43FYjFhYWHm9u3bxpgnQ/KtU/lKlSqVZI2p6OhoExcXZyIjI22faZisiDzt1q1b5syZM2bXrl2mcOHCpkSJEmbjxo3P3ebdusOOldoVkZT39HWXeMresGHDTObMmc0333xjoqKiTFxcnPnpp59MkSJFTPbs2c2kSZNsx3733XcmQ4YMpkiRIrbpuSKS9ly9etWUKVPGfPnll0nu/1u2bDHvvvuucXFxMdu2bUvym8RT+U6fPv3Mcp/e6VcktdJIKZFEIiMjKVWqFJcuXaJKlSr079/fNhqhUaNGbN++nWHDhvHpp5+SJUuW507lM09NzTMaISWS5v1fIx72799P69atcXV1ZeLEidSuXRsnJydu377NH3/8gaenJ1myZEnBGovI89y8eZOFCxfSvXt34N/Xd5MmTbh27Rq7d+/G1dWV2NhYnJ2dOX/+PLVr18bFxYWlS5dSunRpAObOnUtMTAy+vr72PB0RsZPY2FhatmzJqlWr8PDwYMKECXz00Uc4OTkB8OOPPzJ48GD+9a9/sXHjRmrVqmX7bVBQENOmTaNmzZoEBQXx2muv2es0RP4WjeUTSSRDhgy27dbDw8MZMWIEy5cvB+CHH36gRo0aDBkyhNmzZ3Pnzh1cXV2pU6cOX3zxBY8ePaJu3bpcvHjRFkA9/U8RSZsSB1LTpk2jR48e9OjRg02bNvHo0SMAKlasyPfff09MTAw9e/Zk69atXLhwgS+//JKmTZty8eJFe56CiPx/CQkJ9O7dGz8/P0aMGAGAo6Mj9+/fJzw8HCcnJ1xcXIiPj8fZ2Zn4+HgKFy7M/Pnz+fXXX9myZYutrPbt29sCKb0nFkl74uLiKFSoEDlz5qRo0aL4+voyZ84c4uLiAKhWrRojR46katWq1KtXj23bttl+GxQUxGeffcaSJUu4dOmSvU5B5O+z5zAtkdTEOvx++fLlpmrVqubDDz80OXLkMG+88YZZsWKF7bgGDRoYV1dXM3bs2CRT+VavXm0yZ85svv32W3tUX0RSqcRTfRo3bmwyZsxoPD09TZYsWUzevHlNjx49zIMHD2zHHjhwwBQrVsw4Ozub119/3Tg7O5vBgwfbq/oi8gxHjx41devWNZkzZzZBQUG2z319fY2bm5ttOo11Kk5CQoK5c+eOyZs3r2nZsqUxJvlOfSKSNv3888/G3d3d+Pr6mq5duxonJycza9asJFP5du/ebd555x3j5OSUbIfOvXv3pnSVRf6nNFJK5P+zjmIoV64c9+/f57XXXmP+/Pn88ccfBAYGsnLlSgDWrVtHrVq1ko2Yeu+99zhy5AgffvihHc9CRFIb6wKjn376KUeOHGHq1KkcPnyY27dvU7JkSb7++mt69uxJZGQkDg4OVKxYke3bt9O8eXPKli3LzJkzbaMxEhIS7HkqIsKT67BMmTKMHz+e8uXLM3HiRAICAgDo2rUrHh4etGjRgsjISNsUHIvFws2bN7FYLBQuXBhAC5iLpDEm0WhI6/08ISGBUqVK0bt3b3bv3k2tWrXw8fGha9euzJs375kjpho1apRkxGXlypWTlCnyolEoJWlSfHw8kLTxTkhIICEhgYIFC9K3b1+Cg4MpWrQoCxYs4MqVKwQFBSULpoKCgpg1axa3b9/Gzc2NggULJitXRGTr1q3s2LGDwYMH07RpU7Jnz87BgwfZvXs3r7zyCitWrKB3795ERUUBkDdvXhYsWMCcOXNo3749oB10ROzB2l9IzBhDQkICJUuWZPLkyZQvX57JkyczcuRISpcuTWBgIGfOnKFKlSocPHiQqKgoLly4wNy5c7l9+zZVqlSxw5mIiD3Fx8djsVhsU/at93PrP99++20eP35MTEwMoaGh1K9f/5nBVEhICOXKlaNu3br88ccfSYIu9RHkhWXnkVoiKc66E8XVq1dNmzZtzJw5c8yFCxeSHHPp0iXz7rvvmi5duhhjjFm4cKHJnj27KVOmTJKpfHXq1DEWi8X89NNPKVV9EXkBPD0t5+DBg6Zq1armzJkzxhhjjh07Ztzd3U27du3MpUuXTL169YzFYjG+vr7m/v379qiyiDwlcX+ha9euZs2aNea3335Ldtzx48dNrVq1TMaMGc3YsWONMcbMmDHDeHp6GovFYgoVKmQKFixo3NzczKhRo1L0HEQk9bh+/bqpVq2a8fHxMadPn7YtA2L1wQcfmBIlSpj4+Hhz/vx54+XlZVxcXJLtyrdt2zazZMmSlK6+yD9GoZSkSVFRUeaNN94wFovFFC1a1Hh6epqZM2cm2VI1KCjIeHh4mIiICGOMMQsWLLAFUytXrrQdl/jPIiKJt2CuW7euWb16tTHmSWfUGGOuXLliihUrZho1amR7wN23b5/Jnj27yZ8/v2nRooWJiYlJ+YqLSDKJ+ws5cuQwWbJkMb6+vmb27Nnm4cOH5tGjR8YYY06cOGFq165t3N3dTWhoqDHGmIsXLxp/f3/j4+Nj/Pz8krzUSrzWnIi8/B4/fmxKly5tLBaL7fmjRYsWZuPGjbZjjh8/bvLnz29mzJhhjHmy1pS3t7dxcXExs2fPfmbfQG2JvAw0xk/SpLi4OGrXrk3GjBnJlCkTjRs3pl+/frRv357+/fsTExPDgAEDeP311+nVqxcArVu3ZurUqVy7do1BgwaxePFiAJo2bQpoyp6IPGkHrLttBgcHs2fPHi5fvkx8fDzZsmUD4NixYzx48IDOnTvb1pY5d+4crq6ulC9fnrp16+Ls7Gy3cxCRf7P2Fzw8PHB2dsbLy4vVq1fTsWNHSpQoQZs2bdi0aRO5c+fmm2++oWrVqoSFhREcHEz+/PkZOXIkS5cuZeLEiXh5eQGaiiuSFrm6uuLr60v27Nl55513KFWqFO7u7tSvX5+2bduycOFCSpcuTcmSJVmzZg0ApUqVIjg4mKZNm9KxY0dmzZqVbJdOtSXyMtDfYkmTPDw8CAwMpGvXrhw5cgRPT0+WL19OjRo1mDt3LmXKlMHf358SJUoQERHBsWPHAGjZsiUTJkzg7NmztvndVropiIi1Hfjtt984duwY3bp1o23btjg6Otq+i4yM5MaNG7a1aq5evcrBgwdp3LgxS5YsoWPHjoC2hxdJDRL3F65evUrRokXZtGkTO3bs4O233+b48ePUr1+fEiVKMHnyZMqVK4enpyczZ860LX5uDaqt1F8QSVus9/OuXbsSHBzMqVOnuH37Ns2bN2f79u1cvnwZPz8/WrVqRdmyZVm3bh3fffcdAKVLlyYgIIA6deokefEl8jKxGPV6JQ27f/8+wcHBTJgwgcDAQPr06UNMTAxDhw7l5MmT7Ny5E4CFCxfywQcf2H537tw5ihYtaq9qi0gq1r59ezZu3EjWrFmZPHkytWrVIj4+3rbT1qFDh2jZsiXZsmWjRo0a3Lhxg++//55x48bRtWtX4EkHVh1PkdTj3r17DBs2jAkTJtCvXz9CQ0MBePToEatWrWLXrl0sWbIEZ2dnIiIibL87cOAAFSpUsFe1RSSVSDxCcsaMGQwaNIhXX32V+fPnU6hQIfbt28fAgQMJDw/n0qVL+Pv74+/vj7u7OwC3b98ma9as9jwFkX+MQilJ8xIHU35+fkyYMAFjDOHh4SxdupTjx4/Tvn17qlWrZruhWB8YNQRfRBIzxrBq1Sr69+/PuXPn6N27NyNHjsTV1TVJ0LR06VLGjRvHmTNnyJ49O127dsXPz8/OtReRP3P//n2GDRvG+PHj6dmzJyNGjLA9MAJERERw4MAB1q1bx6ZNm+jVqxfdu3e3Y41FJDVJ/Nwwa9Ys+vbty+uvv05YWBjVqlUDYNmyZYSHh1O+fHnefvvtZGXopZW8jBRKifCkozl8+HDGjRtHz549GTlyJOnSpbN95+HhYecaikhq9KzOYXR0NDt37qRz5844OTkxc+ZM3nnnHRwcHJKMmLp06RIJCQnExcXZRl4q6BZJ3RL3F/r06UNgYCAZMmRIdu3+8ccf5MuXD9B1LSL/9nQw1b9/f1577TVGjBhB7dq1kx0jkhYolBL5/57X0bTSmwkRSSxxwBQdHU1cXBxubm44OjoSGxvLli1b6NChA56enkyZMoVKlSr96QhLtTEiL4an+wvBwcG2EVOJ2wXQdS0iyT0vmBo1ahQ1atQA1HZI2qIIVuT/8/DwYOjQofTp04dx48YxYsQIHj58aPteNwYRsUr84Dlo0CAaN27MO++8g4+PD7/88guOjo40aNCAOXPmcOnSJXr06MGBAwcwxtimAD9NbYzIi+Hp/sKwYcNs/YXEgRTouhaR5BwcHGy7dn/22WeMGTOGU6dOMXToUHbs2AGo7ZC0RSOlRJ5y//59Ro4cydixY+ncuTOTJk3CycnJ3tUSkVSoUaNG7N27l2rVquHu7s6xY8e4du0aEyZMoE2bNjg6OrJp0yY++eQTChcuzPjx43nrrbfU2RR5Cai/ICJ/R+IRU1999RXdu3encOHCbNq0ibx589q5diIpR3dOkad4eHgwePBgIiMjKV68uDqYIgIkH0o/evRojhw5wqxZs3j//fdxcXFhzpw5dOzYkQsXLhAbG4uzszN169bl66+/pnXr1nzyySfs3LmTnDlz2vFMROR/Qf0FEUksLi7uP2oHrCOmHBwc6NixI48ePcLJyUmBlKQ5Gikl8hwxMTG4uLjYuxoiYkcrVqzA1dWVhg0bAkmDqQ8++IBbt26xePFismbNys6dO2nYsCEtWrQgODiYAgUK2Mp5/PgxmzZt4tq1a3Tq1Mku5yIi/wz1F0QksSlTptCyZUty5Mjxl0ZGP2utSa0pJWmJXumIPIc6mCJplzGGu3fv0qxZM0qUKIGzszPvvfceFouF+Ph4EhIS+OWXX6hatSpZs2Zlx44dNGrUCG9vb0aNGkXu3LkBmDZtGh9//DEZMmSgSZMmtg6mOpsiLw/1F0TEavHixfj5+VGlShVy5sz5l3bSUyAlaZ0WOhcREXmKxWIhS5YsHDlyhGvXrjFgwAA2bdoEPFnI2NnZmWrVqvHzzz/z9ddf2wKp0NBQWyC1f/9+Bg4cyPfff28rM3H5IiIi8nKpV68epUqVIigoiIcPH/6fgdSzREVF/QM1E0m9FEqJiIg8Q3x8PGXKlGHbtm2cO3eOfv36sWHDBtv37777Lvv376d9+/bUrVuXadOm2daBuHLlCt9++y358+enbNmydjoDERERSUmZMmWibt267N+/nxMnTgDYdtr7KyZMmED//v0VTEmaolBKRETkGaxLLpYpU4bDhw/z66+/MnbsWNauXQtA69atCQoKAp6MnrJ2Pg8fPsy0adP48ssv6dKlCxUqVLBL/UVEROSfERcX99zP/P39cXZ25osvvgCST897nlGjRtGnTx9KlSqFm5vb/66yIqmc1pQSERF5Snx8vG0HnT59+pA+fXqyZcvG9u3biY6OxsHBgYYNGxIQEEBMTAyjRo1i48aN5M+fnxs3bmCMITg4mC5dugBaH0JERORlYu0jjB07lqJFi+Lt7W37zMPDg06dOjF9+nR27NhBjRo1/s/yQkJCCAgIICwsjE6dOuHo6PhPVl8kVdHueyIiIs/h5eXFwYMHad68OSVLluTEiRPMnj2b4sWLExISQv369QFYs2YNR48e5ciRI7z11luUL1+e9957D3j2rjoiIiLy4omLi8PJyQljDKtXr8bb2xs3NzcaNmzIRx99RN26dUmXLh1Hjx6levXqdOvWjVGjRv1pXyAkJIShQ4cyduxYunfvjrOzcwqflYh9KZQSERF5hjVr1tC6dWuGDRvG559/jru7OwA//vgjDRo04NVXX2X48OE0bNjwuWUokBIREXk5JB71XKdOHdq3b0+xYsU4cuQIY8aM4d69exQqVIjhw4fz7rvvMmXKFIYNG8b+/fspWbLkM8scPXo0gwcPViAlaZp6yiIiIs/w8OFDHj58SJkyZXB3d8cYQ0JCAtWqVWPt2rWcOnWK0aNHs27dOttvnl7MVIGUiIjIiy8+Pt4WSH3xxRccPnyYR48eUalSJXx9fTlx4gSBgYGkT58eLy8v6tevz/Hjx8mTJw+LFi0iLi6Op8eC+Pv74+/vT1hYmAIpSdPUWxYREXkGa6B05coV4EmH1MHBgfj4eKpVq8b777/Pnj17GDlypG3xc4VQIiIiLx/rGk/79+9n//79NG/enJYtWwJPpvSlS5eObt26sWPHDr744guyZ8/OokWLOHfuHKtWreLx48dYLJYkwVRkZCRBQUF069ZNgZSkaeo9i4hImva8rZobNWpEuXLlCA0NJTIyEicnJ2JjY3F0dMTR0ZFChQpRpkwZ9u7dy927d1O20iIiIpKi/Pz8qFKlCj/99BM1a9bEw8MjycYo1pFUHTt2ZN68eezdu5dmzZpx5swZhg8fbjvGGkxNnjyZwYMHK5CSNE+hlIiIpFlxcXG20U2///47Z8+e5eLFiwC4u7vTrVs3zp8/T82aNbl3756t4xgeHs7Jkyfx8/Pj4cOHtG3b1m7nICIiIv+8Hj16ULFiRU6fPs2iRYu4f/8+jo6OSUY/Wf+cIUMGKlSowPTp02nYsCFbt27l4cOHQNJgSrvsiSiUEhGRNCrx201fX1/q169PuXLlKF++PP7+/pw+fZp27doRHBzMyZMnefPNNwkNDWXmzJkEBgayefNmnJyccHNzA54/4kpEREReLM+6pxcpUoTFixdTqVIltmzZwqJFi5JNy7OOlrJYLMTHx5M9e3b8/Pw4fPgwe/bssZVlPU5EwMneFRAREUlJ1t1zrG8nvb29+fHHH2nWrBm5c+fm+PHjjBkzhp07dzJhwgT69u1LgQIFmDZtGv7+/jg4OJA1a1aGDRvGhx9+aCtX60mJiIi8+OLi4mwvrW7fvk1MTAweHh64u7tToEABvv/+e5o0aUJAQADu7u40a9YMNze3JLvzwb9HQT169AgPDw8ePHhgl/MRSe0s5ultAERERF4i8fHxODo6Ehsba5t+Z/1syZIldO7cmZCQED7++GPbqKdFixbRunVrqlevzty5cylcuDAJCQns2rULd3d3XF1dKVOmDPDkbaoCKRERkRdP4nu4dZdda5jUo0cPfvzxRyIiIsiRIweBgYFUrVqVnDlz8vvvv9OkSRNu3brFmDFjaNGiBa6ursmCqfv379O7d2/mzp3L+fPnKVCggF3OUyQ1Uy9aREReWsYYHB0duXHjBi1btmTp0qXAv99eXr58maioKKpUqWILpIwxfPDBB3z77bfs3r2bJUuWAE9GQtWoUYNKlSopkBIREXnBGWNwcHDgxo0bHD58OMko6iZNmrBw4ULKly9P+/btyZMnDy1atGDixIncunWLggULsmbNGrJly4a/v3+SqXxPq1ChAseOHVMgJfIcmr4nIiIvLYvFQmRkJBUrVuTSpUtcv34dZ2dnmjZtCjx5g5mQkGDrRFqH7CckJODj40P16tWZM2cOn376KdmzZ09WvgIpERGRF5PFYiEmJgYvLy8iIiI4fPgwmTJlYvr06Rw5coQpU6bg7e2Nq6sr69evZ8OGDQC2UdcFCxZk7dq11K1bl88//5y33nqL4sWLJ/l3eHh48Nlnn6m/IPIndHWIiMhLLUOGDOTOnRt4smveiBEjWL16NQDNmjXDxcWFoKAgAJycnIiJicHBwQE3NzcKFChAdHQ0rq6u9qq+iIiI/ENcXFwoXbo04eHh7N27F4AjR47wyiuvULt2bVxdXdm+fTvNmzenbdu2dO7cGQ8PD9vvCxQowPr165k1a1ayQMpKgZTIn9MVIiIiL634+HgA+vfvT5UqVXj77be5dOkSQ4cOZf369ZQuXZpPPvmElStX0q1bN+BJBxXg6tWrREREUKRIEeLj49ESjCIiIi8P6319xIgRZM+enWnTpgFP7v85c+bklVdeYefOnTRu3Bhvb29CQ0PJnz8/AJMmTeL06dMAFC5cmLZt2wLaiVfkv6FQSkREXlrWtSHKlSvH/fv3ee2115g/fz6XL1+mf//+/Pjjj4wcOZKmTZsybdo0GjVqxJYtW1i1ahVhYWFs27aN1q1bkzlzZm3fLCIi8hKxWCwYY8iYMSM+Pj788MMPbN26lVq1anHq1CnCwsJo1KgR3t7ejB492jbq+tixY4wYMYKVK1cme2GlUVEi/zntviciIi8F6456iRcft76xdHBw4Ouvv6ZTp06cOnWKc+fO0aZNG/LkycOsWbMoVaoUI0aMYN68edy4cQNHR0deeeUVevXqRb9+/QCS7agjIiIiLwbrI+/z7uNHjx6lUqVK+Pr60q5dOxo1asSNGzeoV68ey5cvJ126dMCTZQDGjRvH6tWrmT17Nu+8806KnYPIy0qhlIiIvPCsgVFERAR9+vShTp061KxZk4IFC9qOuXz5Mh999BElS5Zk6tSpfP/993Tr1o3cuXMzdepU3nnnHa5cucK6devIkycPOXPmpEKFCoB22RMREXlRWe/hMTExtin6iT+39iH69OnDlClTOHPmDCdOnKBp06aULVuWAQMG8MEHH7Bnzx5WrlzJ5MmTGTt2LD169LDjWYm8PBRKiYjIS+Hhw4dUqVKFn3/+mSJFihAdHc2QIUN49913bYuPBgcHM378eM6ePUvOnDlZuHAh3bt3J0+ePIwePZqGDRsmK1eBlIiIyIvt1q1bVK1alSZNmtC0aVOqV69u+84aSm3atAkvLy/atGnDV199xbx58wgICCA8PJzs2bMTFRVFpkyZ6NmzJ3379k3yWxH57ymUEhGRl8L9+/cJCgpi9uzZFCtWjEqVKvHdd99RsmRJqlWrxogRI0hISKBmzZoUKlSIBQsWALB48WL8/PzIkiULI0eOxNvb285nIiIiIv9Lv/32G+3atePIkSO4urrSsGFDhg4dSu7cuZPspvfhhx/yww8/cOzYMQoUKMChQ4f49ddf2bdvH2XLlqV48eJUrVoV0Esrkf8VhVIiIvLSuHfvHqGhoYSGhjJixAgqVarE1q1b+fLLL8mePTsNGjTgzp07XLx4kQkTJlCmTBkAvv/+e9q2bcv8+fNp06aNnc9CRERE/tfi4uI4cuQII0eOZPv27Tg5OVGrVi169+5NpUqVcHR05MiRI1SvXp1mzZrx9ddfP7csjZAS+d9RKCUiIi+V+/fvExwczIQJEwgMDKRPnz7ExMQwdOhQTp48yc6dOwFYuHAhH3zwge13586do2jRovaqtoiIiKSA+/fvc/78ecaPH8+aNWu4d+8eH374IV5eXjRr1oxGjRpx9uxZVqxYQalSpUhISMBisSiEEvmHKJQSEZGXTuJgys/PjwkTJmCMITw8nKVLl3L8+HHat29PtWrVki10quH4IiIiacOePXtYt24dU6dO5f79+3zyySdkzpyZiRMnEhISwsCBA+1dRZGXnkIpERF5Kd2/f5/hw4czbtw4evbsyciRI21bOt+/fz/JGhIiIiKSdjz9Auro0aOsXLmSWbNmERkZSWRkJOnTp+fUqVPkzZtXo6RE/kEKpURE5KWVOJjq06cPgYGBZMiQwfa91oQQERERq7t37zJ69Gi2bdtGmzZt6Nmzp72rJPLSUyglIiIvtcTBVP/+/QkICMDd3d3e1RIREZFUJPHoqevXr5MjR45kn4vI/56TvSsgIiLyT/Lw8GDo0KE4ODgwZswYHjx4wKRJk3By0i1QREREnki8vqQ1kDLGKJAS+YepRy4iIi89Dw8PBg8eTGRkJMWLF1cgJSIiIsk8PaVfU/xF/nmaviciImlGTEwMLi4u9q6GiIiIiIigUEpEREREREREROxAE2RFRERERERERCTFKZQSEREREREREZEUp1BKRERERERERERSnEIpERERERERERFJcQqlREREREREREQkxSmUEhERERERERGRFKdQSkREREREREREUpxCKREREZEXRFBQEBaLhR07dti7KiIiIiJ/m0IpERERETv76aef+PTTTylWrBjp06cnXbp0FClShI8++ojNmzfbu3oiIiIi/wiFUiIiIiJ2kpCQQO/evalQoQLz58+ncOHCfP755/j5+fHmm2/yww8/ULduXYYPH27vqoqIiIj8zznZuwIiIiIiadWQIUOYMGECZcuWZenSpRQpUiTJ948ePeKLL77g1q1bdqqhiIiIyD9HI6VERERE7ODcuXOMGTOGbNmysWHDhmSBFEC6dOno168fwcHBf1rWnDlzaNq0KQULFsTNzY2sWbNSr149tm/f/szjly1bxrvvvkuOHDlwc3MjT5481KlTh2XLliU5bvv27TRo0IA8efLg6upKzpw5qV69OrNmzfrvT1xERETk/9NIKRERERE7mDdvHvHx8fj6+pIzZ84/PdbV1fVPv+/atStlypShTp06vPLKK1y5coWVK1dSp04dli9fTtOmTW3HTp8+nS5dupA7d268vb3Jli0bERERHDhwgBUrVtCsWTMAfvjhB5o0aULmzJlp2rQpuXPn5saNGxw7doxvvvmGzz777O//RxAREZE0TaGUiIiIiB3s2bMHgFq1av3tsk6ePEmhQoWSfHb16lUqVKhAv379koRSX331FS4uLhw9epQcOXIk+U3iaYJz5szBGMP27dspU6bMc48TERER+W9p+p6IiIiIHURERACQL1++v13W04EUQO7cuWnWrBm//vorFy9eTPKds7Mzzs7OyX6TLVu2ZJ+lS5fuLx0nIiIi8p9SKCUiIiLygjt//jydOnWiSJEiuLm5YbFYsFgsTJkyBYDw8HDbsa1atSIqKopSpUrRr18/1q1bx/3795OV2apVKwAqV65Mt27dWLFiBTdv3kyZExIREZE0QaGUiIiIiB3kypULgCtXrvytcs6dO0eFChWYO3cuhQsX5vPPP2fo0KEEBgby7rvvAhAdHW07vm/fvsyePZs8efIwbtw4GjVqRLZs2fDy8uLChQu241q0aMHKlSspXbo0M2bMwMfHhxw5clC7dm2OHj36t+osIiIiAgqlREREROzi7bffBmDr1q1/q5wJEyZw584d5s2bx+bNm5k4cSLDhg0jKCiIEiVKJDveYrHQoUMHDh48yI0bN1ixYgU+Pj6sWrWKxo0bEx8fbzu2adOm7Ny5kzt37rB+/Xo6duzIjh07qF+/Pnfv3v1b9RYRERFRKCUiIiJiB5988gmOjo7MmjWLGzdu/OmxiUc6Pe23334DSLKYOYAxxraY+vNYR0gtWrSIWrVqcfLkSc6dO5fsuIwZM1K/fn1mzZrFJ598wrVr19i/f/+fli0iIiLyf1EoJSIiImIHRYsWpX///ty8eZMGDRokmTpn9fjxY8aPH09QUNBzyylQoAAAP/74Y5LPR48ezYkTJ5Idv2PHDowxST6LjY3l9u3bALi5uQGwa9euJKOmrK5fv57kOBEREZH/lpO9KyAiIiKSVo0YMYLHjx8zYcIEihcvTq1atShVqhTOzs5cuHCBLVu2cOvWLUaMGPHcMj7//HPmzp1Ls2bNaNmyJdmyZWPfvn0cPnyYRo0a8cMPPyQ53svLCw8PDypXrkyBAgWIjY1l8+bNnDx5kubNm9tCrh49ehAeHk61atUoWLAgFouFH3/8kQMHDlC5cmWqVav2j/63ERERkZefQikRERERO3FwcGD8+PG0adOG6dOns2vXLnbt2kVCQgK5c+emXr16tG/fnjp16jy3jHLlyrFp0yaGDBnC8uXLcXR0pGrVquzZs4fVq1cnC6VGjRrFhg0bOHDgAGvWrCF9+vQUKVKE6dOn8+mnn9qOGzRoEMuXL+enn35i48aNODs7U7BgQUJDQ+nSpQuOjo7/2H8XERERSRss5unx2yIiIiIiIiIiIv8wrSklIiIiIiIiIiIpTqGUiIiIiIiIiIikOIVSIiIiIiIiIiKS4hRKiYiIiIiIiIhIilMoJSIiIiIiIiIiKU6hlIiIiIiIiIiIpDiFUiIiIiIiIiIikuIUSomIiIiIiIiISIpTKCUiIiIiIiIiIilOoZSIiIiIiIiIiKQ4hVIiIiIiIiIiIpLiFEqJiIiIiIiIiEiKUyglIiIiIiIiIiIp7v8BoxW0bhybsL0AAAAASUVORK5CYII=\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "from torch.utils.data import DataLoader, Dataset\n", + "import torchvision.transforms as transforms\n", + "from torchvision import models\n", + "from datasets import load_dataset\n", + "\n", + "# Custom dataset class\n", + "class AlzheimerDataset(Dataset):\n", + " def __init__(self, dataset_split, transform=None):\n", + " self.dataset_split = dataset_split\n", + " self.transform = transform\n", + "\n", + " def __len__(self):\n", + " return len(self.dataset_split)\n", + "\n", + " def __getitem__(self, idx):\n", + " image = self.dataset_split[idx]['image']\n", + " label = self.dataset_split[idx]['label']\n", + " if self.transform:\n", + " image = self.transform(image)\n", + " return image, label\n", + "\n", + "# Split dataset into training and validation sets\n", + "train_dataset = dataset['train']\n", + "val_dataset = dataset['test']\n", + "\n", + "train_data = AlzheimerDataset(train_dataset, transform=transform)\n", + "val_data = AlzheimerDataset(val_dataset, transform=transform)\n", + "\n", + "train_loader = DataLoader(train_data, batch_size=16, shuffle=True)\n", + "val_loader = DataLoader(val_data, batch_size=16, shuffle=False)\n", + "\n", + "# Load pretrained model and modify the final layer\n", + "model = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V1)\n", + "model.fc = nn.Linear(model.fc.in_features, 4) # 4 classes\n", + "\n", + "# Training parameters\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "model.to(device)\n", + "\n", + "optimizer = optim.Adam(model.parameters(), lr=0.002, betas=(0.9, 0.999), eps=1e-08)\n", + "criterion = nn.CrossEntropyLoss()\n", + "num_epochs = 50\n", + "\n", + "# Lists to store metrics\n", + "train_losses = []\n", + "val_losses = []\n", + "val_accuracies = []\n", + "\n", + "# Training loop\n", + "for epoch in range(num_epochs):\n", + " model.train()\n", + " running_loss = 0.0\n", + " for images, labels in train_loader:\n", + " images, labels = images.to(device), labels.to(device)\n", + "\n", + " optimizer.zero_grad()\n", + " outputs = model(images)\n", + " loss = criterion(outputs, labels)\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " running_loss += loss.item()\n", + "\n", + " avg_train_loss = running_loss / len(train_loader)\n", + " train_losses.append(avg_train_loss)\n", + " print(f\"Epoch [{epoch+1}/{num_epochs}], Loss: {avg_train_loss}\")\n", + "\n", + " # Validation\n", + " model.eval()\n", + " val_loss = 0.0\n", + " correct = 0\n", + " total = 0\n", + " with torch.no_grad():\n", + " for images, labels in val_loader:\n", + " images, labels = images.to(device), labels.to(device)\n", + " outputs = model(images)\n", + " loss = criterion(outputs, labels)\n", + " val_loss += loss.item()\n", + " _, predicted = torch.max(outputs.data, 1)\n", + " total += labels.size(0)\n", + " correct += (predicted == labels).sum().item()\n", + "\n", + " avg_val_loss = val_loss / len(val_loader)\n", + " val_accuracy = correct / total\n", + " val_losses.append(avg_val_loss)\n", + " val_accuracies.append(val_accuracy * 100)\n", + " print(f\"Validation Loss: {avg_val_loss}, Accuracy: {100 * correct / total}%\")\n", + "\n", + "# Save the model\n", + "torch.save(model.state_dict(), \"alzheimer_model.pth\")\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0GLVQN-VpuI6", + "outputId": "a076a7eb-cfdd-4703-d3d3-fe14de04bd1a" + }, + "execution_count": 6, + "outputs": [ + { + "metadata": { + "tags": null + }, + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n", + "100%|██████████| 97.8M/97.8M [00:00<00:00, 182MB/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch [1/50], Loss: 0.9666981162503362\n", + "Validation Loss: 0.9650966212153435, Accuracy: 52.578125%\n", + "Epoch [2/50], Loss: 0.8875503182411194\n", + "Validation Loss: 0.964703431352973, Accuracy: 56.71875%\n", + "Epoch [3/50], Loss: 0.8438583416864276\n", + "Validation Loss: 1.6287580162286759, Accuracy: 51.015625%\n", + "Epoch [4/50], Loss: 0.7919109098613262\n", + "Validation Loss: 0.8394970461726189, Accuracy: 60.625%\n", + "Epoch [5/50], Loss: 0.7594592651352287\n", + "Validation Loss: 0.7985197100788355, Accuracy: 64.53125%\n", + "Epoch [6/50], Loss: 0.6926557298749685\n", + "Validation Loss: 0.7380028367042542, Accuracy: 67.1875%\n", + "Epoch [7/50], Loss: 0.5833855544682592\n", + "Validation Loss: 0.6458866974338889, Accuracy: 71.5625%\n", + "Epoch [8/50], Loss: 0.5087373699992895\n", + "Validation Loss: 0.6277932519093156, Accuracy: 74.21875%\n", + "Epoch [9/50], Loss: 0.44258568226359785\n", + "Validation Loss: 0.5151352984830737, Accuracy: 79.765625%\n", + "Epoch [10/50], Loss: 0.34885500750970094\n", + "Validation Loss: 0.453957705758512, Accuracy: 82.890625%\n", + "Epoch [11/50], Loss: 0.2559841972659342\n", + "Validation Loss: 0.32305632419884206, Accuracy: 87.890625%\n", + "Epoch [12/50], Loss: 0.2304643289127853\n", + "Validation Loss: 0.4162524829618633, Accuracy: 82.421875%\n", + "Epoch [13/50], Loss: 0.21909499398898333\n", + "Validation Loss: 1.381332490593195, Accuracy: 65.390625%\n", + "Epoch [14/50], Loss: 0.14471084828255698\n", + "Validation Loss: 0.27995605163741855, Accuracy: 90.703125%\n", + "Epoch [15/50], Loss: 0.08878722377121448\n", + "Validation Loss: 0.27826691993395797, Accuracy: 91.171875%\n", + "Epoch [16/50], Loss: 0.13227807867879166\n", + "Validation Loss: 0.3056281531462446, Accuracy: 88.59375%\n", + "Epoch [17/50], Loss: 0.10038681486112182\n", + "Validation Loss: 0.31708151729835665, Accuracy: 91.171875%\n", + "Epoch [18/50], Loss: 0.11154956809332361\n", + "Validation Loss: 0.2509289276553318, Accuracy: 91.71875%\n", + "Epoch [19/50], Loss: 0.08695360383644583\n", + "Validation Loss: 0.2998581437917892, Accuracy: 89.921875%\n", + "Epoch [20/50], Loss: 0.06050011310653645\n", + "Validation Loss: 0.2610326875816099, Accuracy: 93.828125%\n", + "Epoch [21/50], Loss: 0.08265193927290966\n", + "Validation Loss: 0.36029749492954577, Accuracy: 91.796875%\n", + "Epoch [22/50], Loss: 0.053944889996819255\n", + "Validation Loss: 0.18217028171056882, Accuracy: 93.984375%\n", + "Epoch [23/50], Loss: 0.05374233725124213\n", + "Validation Loss: 0.25408602944517045, Accuracy: 92.890625%\n", + "Epoch [24/50], Loss: 0.06821585582447369\n", + "Validation Loss: 0.2837550931493752, Accuracy: 92.34375%\n", + "Epoch [25/50], Loss: 0.06546100183372801\n", + "Validation Loss: 0.40590114874066785, Accuracy: 89.53125%\n", + "Epoch [26/50], Loss: 0.06918905273414566\n", + "Validation Loss: 0.3061864467952546, Accuracy: 91.25%\n", + "Epoch [27/50], Loss: 0.06599215954170176\n", + "Validation Loss: 0.2490427941927919, Accuracy: 92.578125%\n", + "Epoch [28/50], Loss: 0.03927733796354005\n", + "Validation Loss: 0.39097568443976344, Accuracy: 88.046875%\n", + "Epoch [29/50], Loss: 0.010857281496043925\n", + "Validation Loss: 0.13141321224102284, Accuracy: 96.015625%\n", + "Epoch [30/50], Loss: 0.015075708252254572\n", + "Validation Loss: 0.24722284730814864, Accuracy: 92.265625%\n", + "Epoch [31/50], Loss: 0.09887518676623586\n", + "Validation Loss: 0.2379806660435861, Accuracy: 93.28125%\n", + "Epoch [32/50], Loss: 0.025910123788571583\n", + "Validation Loss: 0.4488462015782716, Accuracy: 90.234375%\n", + "Epoch [33/50], Loss: 0.048598142486389405\n", + "Validation Loss: 0.1567992424563272, Accuracy: 96.015625%\n", + "Epoch [34/50], Loss: 0.007354617485850668\n", + "Validation Loss: 0.15541992863554696, Accuracy: 96.953125%\n", + "Epoch [35/50], Loss: 0.06545311848897199\n", + "Validation Loss: 0.33815595179912633, Accuracy: 88.984375%\n", + "Epoch [36/50], Loss: 0.04291429588724895\n", + "Validation Loss: 0.10488697934706578, Accuracy: 97.1875%\n", + "Epoch [37/50], Loss: 0.015658567650314127\n", + "Validation Loss: 0.13757365790297627, Accuracy: 95.859375%\n", + "Epoch [38/50], Loss: 0.01987580951654877\n", + "Validation Loss: 0.4331179558095755, Accuracy: 91.640625%\n", + "Epoch [39/50], Loss: 0.046722228391763564\n", + "Validation Loss: 0.4023116291849874, Accuracy: 90.703125%\n", + "Epoch [40/50], Loss: 0.051056481100340535\n", + "Validation Loss: 0.13100997004621603, Accuracy: 96.796875%\n", + "Epoch [41/50], Loss: 0.009764916591643668\n", + "Validation Loss: 0.1416727503110451, Accuracy: 96.71875%\n", + "Epoch [42/50], Loss: 0.01807436700426024\n", + "Validation Loss: 0.4186861822527135, Accuracy: 88.59375%\n", + "Epoch [43/50], Loss: 0.034246843548828565\n", + "Validation Loss: 0.3134862938139122, Accuracy: 91.40625%\n", + "Epoch [44/50], Loss: 0.03032314967235834\n", + "Validation Loss: 0.16844148109607887, Accuracy: 95.78125%\n", + "Epoch [45/50], Loss: 0.03955402145510334\n", + "Validation Loss: 0.18183696664927992, Accuracy: 95.3125%\n", + "Epoch [46/50], Loss: 0.02179776818800292\n", + "Validation Loss: 0.17941529625804833, Accuracy: 95.546875%\n", + "Epoch [47/50], Loss: 0.030901480437665895\n", + "Validation Loss: 0.17204297400603535, Accuracy: 94.609375%\n", + "Epoch [48/50], Loss: 0.03137923947844854\n", + "Validation Loss: 0.3414109962031944, Accuracy: 91.875%\n", + "Epoch [49/50], Loss: 0.018533342322200497\n", + "Validation Loss: 0.156633417601779, Accuracy: 96.640625%\n", + "Epoch [50/50], Loss: 0.011366857101631923\n", + "Validation Loss: 0.20709561281710193, Accuracy: 95.9375%\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "model = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V1)\n", + "model.fc = nn.Linear(model.fc.in_features, 4)\n", + "model.load_state_dict(torch.load(\"/content/alzheimer_model.pth\"))\n", + "model.to(device)\n", + "model.eval()\n", + "\n", + "# Prepare the test dataset\n", + "test_dataset = dataset['test']\n", + "test_data = AlzheimerDataset(test_dataset, transform=transform)\n", + "test_loader = DataLoader(test_data, batch_size=16, shuffle=False)\n", + "\n", + "# Evaluate the model on test data\n", + "correct = 0\n", + "total = 0\n", + "test_loss = 0.0\n", + "with torch.no_grad():\n", + " for images, labels in test_loader:\n", + " images, labels = images.to(device), labels.to(device)\n", + " outputs = model(images)\n", + " loss = criterion(outputs, labels)\n", + " test_loss += loss.item()\n", + " _, predicted = torch.max(outputs.data, 1)\n", + " total += labels.size(0)\n", + " correct += (predicted == labels).sum().item()\n", + "\n", + "avg_test_loss = test_loss / len(test_loader)\n", + "test_accuracy = correct / total * 100\n", + "print(f\"Test Loss: {avg_test_loss}, Test Accuracy: {test_accuracy}%\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JGEKAT-6qe6t", + "outputId": "3050a33d-701d-45da-d431-4200b5de2a5a" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Test Loss: 0.20709561281710193, Test Accuracy: 95.9375%\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Plot the training loss, validation loss, and validation accuracy\n", + "epochs = range(1, num_epochs + 1)\n", + "\n", + "plt.figure(figsize=(12, 5))\n", + "\n", + "# Loss plot\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(epochs, train_losses, 'bo-', label='Training loss')\n", + "plt.plot(epochs, val_losses, 'ro-', label='Validation loss')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Loss')\n", + "plt.title('Training and Validation Loss')\n", + "plt.legend()" + ], + "metadata": { + "id": "bC7gsX_4w37_", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 505 + }, + "outputId": "8a7bdbe3-76d0-436b-f4a8-05682e6a7594" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 8 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Accuracy plot\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(epochs, val_accuracies, 'go-', label='Validation accuracy')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Accuracy (%)')\n", + "plt.title('Validation Accuracy')\n", + "plt.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 487 + }, + "id": "znMpDj-5HFRr", + "outputId": "f26bbbb4-cf2e-4d9f-8989-b17c3a81a763" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "2Pn-yQTtHIuL" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file