pdf_translate_fugumt.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "448d422cf60f4f459058689095dba4cd": {
          "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_6b51755c36f940219d1a409a2acc870b",
              "IPY_MODEL_fb14c021ae64412bae30268a5d83a402",
              "IPY_MODEL_7bedb16e5fdd492292fdd54f1b2f0a30"
            ],
            "layout": "IPY_MODEL_4bc15d05ef7b48089882100db4024f00"
          }
        },
        "6b51755c36f940219d1a409a2acc870b": {
          "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_533a0ebbb8a4483793f3b6fe0c424181",
            "placeholder": "​",
            "style": "IPY_MODEL_d0f7988971264b63bcab908ecbf3cf8c",
            "value": "Downloading (…)lve/main/config.json: 100%"
          }
        },
        "fb14c021ae64412bae30268a5d83a402": {
          "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_ec4feda6a6d842099ed01897ca4fb65e",
            "max": 1020,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_a8b7366ed6e14e2b805fc6ae02ac6ef6",
            "value": 1020
          }
        },
        "7bedb16e5fdd492292fdd54f1b2f0a30": {
          "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_e6f488c03ce542fcad881951646bec02",
            "placeholder": "​",
            "style": "IPY_MODEL_2e87382e953a4b6d8e95976f6c5f7a8a",
            "value": " 1.02k/1.02k [00:00&lt;00:00, 15.8kB/s]"
          }
        },
        "4bc15d05ef7b48089882100db4024f00": {
          "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
          }
        },
        "533a0ebbb8a4483793f3b6fe0c424181": {
          "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
          }
        },
        "d0f7988971264b63bcab908ecbf3cf8c": {
          "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": ""
          }
        },
        "ec4feda6a6d842099ed01897ca4fb65e": {
          "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
          }
        },
        "a8b7366ed6e14e2b805fc6ae02ac6ef6": {
          "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": ""
          }
        },
        "e6f488c03ce542fcad881951646bec02": {
          "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
          }
        },
        "2e87382e953a4b6d8e95976f6c5f7a8a": {
          "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": ""
          }
        },
        "2f7aab00d62b4a8a979b855be9752542": {
          "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_e8969eaf85d949e5932c76a78882f618",
              "IPY_MODEL_011b364b3c654400affb016336cfb071",
              "IPY_MODEL_38a900e770834ba59d8803f47dd98a34"
            ],
            "layout": "IPY_MODEL_896fa865a108449da726ebd35e89469d"
          }
        },
        "e8969eaf85d949e5932c76a78882f618": {
          "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_cb1470ce52a84326a8f688fc54903b60",
            "placeholder": "​",
            "style": "IPY_MODEL_24a8f0ff798047cebd71341acd214d27",
            "value": "Downloading (…)&quot;pytorch_model.bin&quot;;: 100%"
          }
        },
        "011b364b3c654400affb016336cfb071": {
          "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_692cdf9cb4434cfcb8727adca1ff99b9",
            "max": 153960131,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_2a4cb481e35b4e7db593db70672369a3",
            "value": 153960131
          }
        },
        "38a900e770834ba59d8803f47dd98a34": {
          "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_e8650e22cf1d446795294fc14da69781",
            "placeholder": "​",
            "style": "IPY_MODEL_4e3a7bc757004e6f8abd6a2dbdf3891f",
            "value": " 154M/154M [00:02&lt;00:00, 95.1MB/s]"
          }
        },
        "896fa865a108449da726ebd35e89469d": {
          "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
          }
        },
        "cb1470ce52a84326a8f688fc54903b60": {
          "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
          }
        },
        "24a8f0ff798047cebd71341acd214d27": {
          "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": ""
          }
        },
        "692cdf9cb4434cfcb8727adca1ff99b9": {
          "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
          }
        },
        "2a4cb481e35b4e7db593db70672369a3": {
          "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": ""
          }
        },
        "e8650e22cf1d446795294fc14da69781": {
          "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
          }
        },
        "4e3a7bc757004e6f8abd6a2dbdf3891f": {
          "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": ""
          }
        },
        "3d9ffaa7d52d4fd19d3b3344505ade9f": {
          "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_4796d5872a6d41a5aecc90122dfe89e2",
              "IPY_MODEL_5bcaf6bb3cb9435ea9089aeb4765f202",
              "IPY_MODEL_218078f3844445fe8a856748c6eb5889"
            ],
            "layout": "IPY_MODEL_3a22687df79e476fb0bfbb4d59bd84aa"
          }
        },
        "4796d5872a6d41a5aecc90122dfe89e2": {
          "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_05cd31473aad41a3b25af2552f2fba2d",
            "placeholder": "​",
            "style": "IPY_MODEL_c6220f4aa76748ef9e3b1b9754f07e89",
            "value": "Downloading (…)okenizer_config.json: 100%"
          }
        },
        "5bcaf6bb3cb9435ea9089aeb4765f202": {
          "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_7fbf9e39755f485aa4e9fb28447f4eb2",
            "max": 42,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_12fad3e7d0224686b7380f5335a23b3b",
            "value": 42
          }
        },
        "218078f3844445fe8a856748c6eb5889": {
          "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_7673a685fce04f7ebc6517948a8fdc68",
            "placeholder": "​",
            "style": "IPY_MODEL_ba04cc50329d44af9e9d838edf243d8a",
            "value": " 42.0/42.0 [00:00&lt;00:00, 1.48kB/s]"
          }
        },
        "3a22687df79e476fb0bfbb4d59bd84aa": {
          "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
          }
        },
        "05cd31473aad41a3b25af2552f2fba2d": {
          "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
          }
        },
        "c6220f4aa76748ef9e3b1b9754f07e89": {
          "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": ""
          }
        },
        "7fbf9e39755f485aa4e9fb28447f4eb2": {
          "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
          }
        },
        "12fad3e7d0224686b7380f5335a23b3b": {
          "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": ""
          }
        },
        "7673a685fce04f7ebc6517948a8fdc68": {
          "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
          }
        },
        "ba04cc50329d44af9e9d838edf243d8a": {
          "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": ""
          }
        },
        "33ee1f32e1eb46b7bfe8e409a21ff205": {
          "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_7c6ea734a1c1465ebf1cef64db706177",
              "IPY_MODEL_ff79766126084d998f1ebacf86a4deaf",
              "IPY_MODEL_a93eb2dd00a74019927244ca70013c7f"
            ],
            "layout": "IPY_MODEL_d83a9b7d6648437babe6f482e4dd438c"
          }
        },
        "7c6ea734a1c1465ebf1cef64db706177": {
          "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_eaf6fcd9b6054783bed8f8828f2412c6",
            "placeholder": "​",
            "style": "IPY_MODEL_9c5ad61d04ff4d63b94126c9de784d60",
            "value": "Downloading (…)olve/main/source.spm: 100%"
          }
        },
        "ff79766126084d998f1ebacf86a4deaf": {
          "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_601d53d691e64319aa53bbb4b2316ac5",
            "max": 768297,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_4fc43562b3f5498cb431868588a25a17",
            "value": 768297
          }
        },
        "a93eb2dd00a74019927244ca70013c7f": {
          "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_d80b6f0191d743c291dde49e4c0b4c1e",
            "placeholder": "​",
            "style": "IPY_MODEL_15d8114592394251b197f8b2721759f9",
            "value": " 768k/768k [00:00&lt;00:00, 22.2MB/s]"
          }
        },
        "d83a9b7d6648437babe6f482e4dd438c": {
          "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
          }
        },
        "eaf6fcd9b6054783bed8f8828f2412c6": {
          "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
          }
        },
        "9c5ad61d04ff4d63b94126c9de784d60": {
          "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": ""
          }
        },
        "601d53d691e64319aa53bbb4b2316ac5": {
          "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
          }
        },
        "4fc43562b3f5498cb431868588a25a17": {
          "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": ""
          }
        },
        "d80b6f0191d743c291dde49e4c0b4c1e": {
          "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
          }
        },
        "15d8114592394251b197f8b2721759f9": {
          "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": ""
          }
        },
        "66916cd944ff499991ba6c1ce4e9e61f": {
          "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_5d2637149f044886913a1d2597c12eea",
              "IPY_MODEL_a2f07523e2e54f92a335953e3e15fd30",
              "IPY_MODEL_4b5a98260cf44fdbb541c5141f02e9fd"
            ],
            "layout": "IPY_MODEL_da76b69ee2254fdfab7cfa1a10777586"
          }
        },
        "5d2637149f044886913a1d2597c12eea": {
          "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_86463d8f0e614794a81c0d77fdb260f7",
            "placeholder": "​",
            "style": "IPY_MODEL_f013347f9fd64c71b283da3d8c9deb03",
            "value": "Downloading (…)olve/main/vocab.json: 100%"
          }
        },
        "a2f07523e2e54f92a335953e3e15fd30": {
          "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_c3079b1c79504eecb37515e2fb60bbfc",
            "max": 791091,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_e37bb8ca8dd94e59be1f5626709d9520",
            "value": 791091
          }
        },
        "4b5a98260cf44fdbb541c5141f02e9fd": {
          "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_4ac3e622ceca4bc180de59a685385c41",
            "placeholder": "​",
            "style": "IPY_MODEL_3fda80395b354376b3209f3d29786899",
            "value": " 791k/791k [00:00&lt;00:00, 15.7MB/s]"
          }
        },
        "da76b69ee2254fdfab7cfa1a10777586": {
          "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
          }
        },
        "86463d8f0e614794a81c0d77fdb260f7": {
          "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
          }
        },
        "f013347f9fd64c71b283da3d8c9deb03": {
          "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": ""
          }
        },
        "c3079b1c79504eecb37515e2fb60bbfc": {
          "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
          }
        },
        "e37bb8ca8dd94e59be1f5626709d9520": {
          "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": ""
          }
        },
        "4ac3e622ceca4bc180de59a685385c41": {
          "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
          }
        },
        "3fda80395b354376b3209f3d29786899": {
          "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": ""
          }
        },
        "1890a9210447495e80b673ff89db8610": {
          "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_4c2cf1367612493290e72f856ccb13db",
              "IPY_MODEL_71376ddeb9074a7dbfdc3341d7206d9b",
              "IPY_MODEL_b88960ca714843d5839faecdddfc5179"
            ],
            "layout": "IPY_MODEL_15987f963261463dabfaabd6dc11a9ce"
          }
        },
        "4c2cf1367612493290e72f856ccb13db": {
          "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_1aaa7adef01c4ce8b88b2bafdd417b34",
            "placeholder": "​",
            "style": "IPY_MODEL_b028a2c50c1341d7aad21eeffd9f096d",
            "value": "Downloading (…)cial_tokens_map.json: 100%"
          }
        },
        "71376ddeb9074a7dbfdc3341d7206d9b": {
          "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_d32f8a984ef54d388b07a651a8c3f239",
            "max": 65,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_53534d7dffc641a58d87b4dae88525a0",
            "value": 65
          }
        },
        "b88960ca714843d5839faecdddfc5179": {
          "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_c250daf4faef4ad4adcc32b3b620b935",
            "placeholder": "​",
            "style": "IPY_MODEL_96cfd26e53fa4077822186e26dec1708",
            "value": " 65.0/65.0 [00:00&lt;00:00, 3.87kB/s]"
          }
        },
        "15987f963261463dabfaabd6dc11a9ce": {
          "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
          }
        },
        "1aaa7adef01c4ce8b88b2bafdd417b34": {
          "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
          }
        },
        "b028a2c50c1341d7aad21eeffd9f096d": {
          "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": ""
          }
        },
        "d32f8a984ef54d388b07a651a8c3f239": {
          "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
          }
        },
        "53534d7dffc641a58d87b4dae88525a0": {
          "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": ""
          }
        },
        "c250daf4faef4ad4adcc32b3b620b935": {
          "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
          }
        },
        "96cfd26e53fa4077822186e26dec1708": {
          "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": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ykitaguchi77/Colab_Scripts/blob/master/pdf_translate_fugumt.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 概要\n",
        "\n",
        "pdf形式の英語論文を翻訳するやつ\n",
        "\n",
        "## 使い方や説明など\n",
        "\n",
        "https://qiita.com/sakasegawa/items/f50aae7f3acf475411aa\n",
        "\n",
        "## 注意\n",
        "\n",
        "- pipのlayoutparserインストールのとこでruntime restartボタンが出るから初回実行時はrestart一回かける必要あり\n",
        "\n",
        "## memo\n",
        "\n",
        "DPI = 72 くらいでいいかも\n",
        "\n",
        "by https://twitter.com/gyakuse"
      ],
      "metadata": {
        "id": "vRf6T1aOt4o-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 準備"
      ],
      "metadata": {
        "id": "RuaQDKPeuKMb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Install libraries\n",
        "!pip install layoutparser --q # Install the base layoutparser library with\n",
        "!pip install layoutparser torchvision -- q && pip install --q \"git+https://github.com/facebookresearch/detectron2.git@v0.5#egg=detectron2\"\n",
        "!pip install \"layoutparser[layoutmodels]\" --q # Install DL layout model toolkit \n",
        "!apt-get install poppler-utils --q\n",
        "!pip install reportlab --q\n",
        "!pip install pypdf --q\n",
        "!pip install transformers --q\n",
        "!pip install sentencepiece --q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dq4RvRMtuQnq",
        "outputId": "bfd5546e-feed-4b9e-844d-64b91e0b223f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: layoutparser in /usr/local/lib/python3.8/dist-packages (0.3.4)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.8/dist-packages (0.14.1+cu116)\n",
            "Requirement already satisfied: pdf2image in /usr/local/lib/python3.8/dist-packages (from layoutparser) (1.16.2)\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.8/dist-packages (from layoutparser) (4.6.0.66)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.8/dist-packages (from layoutparser) (9.4.0)\n",
            "Requirement already satisfied: pdfplumber in /usr/local/lib/python3.8/dist-packages (from layoutparser) (0.7.6)\n",
            "Requirement already satisfied: iopath in /usr/local/lib/python3.8/dist-packages (from layoutparser) (0.1.10)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from layoutparser) (6.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from layoutparser) (1.7.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from layoutparser) (1.21.6)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (from layoutparser) (1.3.5)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from torchvision) (2.25.1)\n",
            "Requirement already satisfied: torch==1.13.1 in /usr/local/lib/python3.8/dist-packages (from torchvision) (1.13.1+cu116)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torchvision) (4.4.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from iopath->layoutparser) (4.64.1)\n",
            "Requirement already satisfied: portalocker in /usr/local/lib/python3.8/dist-packages (from iopath->layoutparser) (2.7.0)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->layoutparser) (2022.7.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->layoutparser) (2.8.2)\n",
            "Requirement already satisfied: Wand>=0.6.10 in /usr/local/lib/python3.8/dist-packages (from pdfplumber->layoutparser) (0.6.11)\n",
            "Requirement already satisfied: pdfminer.six==20221105 in /usr/local/lib/python3.8/dist-packages (from pdfplumber->layoutparser) (20221105)\n",
            "Requirement already satisfied: charset-normalizer>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from pdfminer.six==20221105->pdfplumber->layoutparser) (2.1.1)\n",
            "Requirement already satisfied: cryptography>=36.0.0 in /usr/local/lib/python3.8/dist-packages (from pdfminer.six==20221105->pdfplumber->layoutparser) (39.0.1)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->torchvision) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->torchvision) (2022.12.7)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->torchvision) (4.0.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->torchvision) (1.24.3)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->layoutparser) (1.15.0)\n",
            "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.8/dist-packages (from cryptography>=36.0.0->pdfminer.six==20221105->pdfplumber->layoutparser) (1.15.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.8/dist-packages (from cffi>=1.12->cryptography>=36.0.0->pdfminer.six==20221105->pdfplumber->layoutparser) (2.21)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 79
        },
        "id": "L5WPyMY-nTqg",
        "outputId": "500bdeb6-2f63-4d17-bf78-5c28cce3de35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-3e5ca630-92db-426c-9b3b-5fb6472ce690\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-3e5ca630-92db-426c-9b3b-5fb6472ce690\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving atten.pdf to atten.pdf\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pdf_file_name = list(uploaded.keys())[0]\n",
        "target_pdf_file_path = '/content/' + pdf_file_name\n",
        "is_mihiraki = True"
      ],
      "metadata": {
        "id": "2WoTZkIJn4w3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# フォントをダウンロード\n",
        "font_name = 'BIZUDGothic'\n",
        "font_ttf = 'BIZUDGothic-Regular.ttf'\n",
        "font_url = f'https://github.com/googlefonts/morisawa-biz-ud-gothic/raw/main/fonts/ttf/{font_ttf}'\n",
        "\n",
        "!wget https://github.com/googlefonts/morisawa-biz-ud-gothic/raw/main/fonts/ttf/BIZUDGothic-Regular.ttf"
      ],
      "metadata": {
        "id": "fi_9YO3D4TDB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43d7c550-d259-4f1f-e160-59a1e8093403"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-02-10 11:13:29--  https://github.com/googlefonts/morisawa-biz-ud-gothic/raw/main/fonts/ttf/BIZUDGothic-Regular.ttf\n",
            "Resolving github.com (github.com)... 140.82.121.4\n",
            "Connecting to github.com (github.com)|140.82.121.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/googlefonts/morisawa-biz-ud-gothic/main/fonts/ttf/BIZUDGothic-Regular.ttf [following]\n",
            "--2023-02-10 11:13:30--  https://raw.githubusercontent.com/googlefonts/morisawa-biz-ud-gothic/main/fonts/ttf/BIZUDGothic-Regular.ttf\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4667376 (4.5M) [application/octet-stream]\n",
            "Saving to: ‘BIZUDGothic-Regular.ttf’\n",
            "\n",
            "BIZUDGothic-Regular 100%[===================>]   4.45M  --.-KB/s    in 0.02s   \n",
            "\n",
            "2023-02-10 11:13:30 (277 MB/s) - ‘BIZUDGothic-Regular.ttf’ saved [4667376/4667376]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WdSlCwyPqxDO",
        "outputId": "2b7ee61e-c32b-42ec-dd11-7e17e4ea04e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "atten.pdf  BIZUDGothic-Regular.ttf  \u001b[0m\u001b[01;34msample_data\u001b[0m/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 実行"
      ],
      "metadata": {
        "id": "bInDDGzDved7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "DPI = 72"
      ],
      "metadata": {
        "id": "zkKUK3o-xJWj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# pdfを画像として読み込む\n",
        "import pdf2image\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "def pdf_to_image(file_path, page_num):\n",
        "    return np.asarray(pdf2image.convert_from_path(file_path, dpi = DPI)[page_num])"
      ],
      "metadata": {
        "id": "HYy5JK53w19F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 特定のtext_blockがparagraph_blockに含まれているかチェック\n",
        "def is_inside(paragraph_block, text_block):\n",
        "    paragraph_width = paragraph_block.block.x_2 - paragraph_block.block.x_1\n",
        "    paragraph_height = paragraph_block.block.y_2 - paragraph_block.block.y_1\n",
        "    if paragraph_width > 300:\n",
        "        allowable_error_pixel = 10\n",
        "        return (text_block.block.x_1 >= paragraph_block.block.x_1 - allowable_error_pixel and text_block.block.y_1 >= paragraph_block.block.y_1 and\n",
        "                text_block.block.x_2 <= paragraph_block.block.x_2 + allowable_error_pixel and text_block.block.y_2 <= paragraph_block.block.y_2 + allowable_error_pixel)\n",
        "    else:\n",
        "        allowable_error_pixel = 3\n",
        "        return (text_block.block.x_1 >= paragraph_block.block.x_1 - allowable_error_pixel and text_block.block.y_1 >= paragraph_block.block.y_1 and\n",
        "                text_block.block.x_2 <= paragraph_block.block.x_2 + allowable_error_pixel and text_block.block.y_2 <= paragraph_block.block.y_2 + allowable_error_pixel)"
      ],
      "metadata": {
        "id": "H6utU-XL2WgK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def fill_cover(canvas, x, y, width, height):\n",
        "    canvas.setFillColorRGB(1, 1, 1)\n",
        "    # でかいパラグラフは検出精度悪いので補正する\n",
        "    if width > 300:\n",
        "        canvas.rect(\n",
        "            x - 5,\n",
        "            y,\n",
        "            width + 10,\n",
        "            height + 10,\n",
        "            stroke=0,\n",
        "            fill=1\n",
        "        )\n",
        "    else:\n",
        "        canvas.rect(\n",
        "            x,\n",
        "            y,\n",
        "            width,\n",
        "            height,\n",
        "            stroke=0,\n",
        "            fill=1\n",
        "        )"
      ],
      "metadata": {
        "id": "9YaNsFzd6nlP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calc_fontsize(paragraph_width, paragraph_height, translated_text):\n",
        "    return int(numpy.sqrt((paragraph_width) * (paragraph_height) / len(translated_text)))"
      ],
      "metadata": {
        "id": "M1hojIWKgLQ-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# pdfを読み込む\n",
        "import cv2\n",
        "import layoutparser as lp\n",
        "\n",
        "pdf_pages, _ = lp.load_pdf(target_pdf_file_path, load_images=True, dpi=DPI)"
      ],
      "metadata": {
        "id": "cir_eUVmubT5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# レイアウト(物体)検出モデルを準備\n",
        "import layoutparser as lp\n",
        "model = lp.Detectron2LayoutModel('lp://PubLayNet/mask_rcnn_X_101_32x8d_FPN_3x/config',\n",
        "                                extra_config=[\"MODEL.ROI_HEADS.SCORE_THRESH_TEST\", 0.5],\n",
        "                                label_map={0: \"Text\", 1: \"Title\", 2: \"List\", 3:\"Table\", 4:\"Figure\"})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eZhqMlxe0kHi",
        "outputId": "18e8a283-6f21-470b-d4f3-0fa368c1946d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "config.yaml?dl=1: 8.19kB [00:01, 7.82kB/s]\n",
            "model_final.pth?dl=1: 856MB [01:15, 11.3MB/s]                           \n",
            "WARNING:fvcore.common.checkpoint:The checkpoint state_dict contains keys that are not used by the model:\n",
            "  proposal_generator.anchor_generator.cell_anchors.{0, 1, 2, 3, 4}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image, ImageDraw, ImageFont\n",
        "\n",
        "def get_max_font_size(paragraph_width, paragraph_height, translated_text, font_face=\"./BIZUDGothic-Regular.ttf\", max_font_size=100):\n",
        "    \"\"\"\n",
        "    指定された領域内で最大のフォントサイズを求める。\n",
        "    :param text: 描画する文字列。\n",
        "    :param font_face: フォント名。\n",
        "    :param rectangle: 描画領域を表すタプル (x0, y0, x1, y1)。\n",
        "    :param max_font_size: 最大フォントサイズ。デフォルトは 100。\n",
        "    :return: 最大フォントサイズ。\n",
        "    \"\"\"\n",
        "    for font_size in range(max_font_size, 0, -1):\n",
        "        font = ImageFont.truetype(font_face, font_size)\n",
        "        # 描画する文字列のサイズを求める\n",
        "        text_width, text_height = font.getsize(translated_text)\n",
        "        if text_width <= paragraph_width and text_height <= paragraph_height:\n",
        "            return font_size\n",
        "    return 0"
      ],
      "metadata": {
        "id": "qwrMPrEimoWz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# フォント登録\n",
        "from reportlab.pdfbase import pdfmetrics\n",
        "from reportlab.pdfbase.ttfonts import TTFont\n",
        "\n",
        "pdfmetrics.registerFont(TTFont(font_name, font_ttf))"
      ],
      "metadata": {
        "id": "PIItDfXL4cgq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 翻訳モデル: fugumt\n",
        "from transformers import pipeline\n",
        "\n",
        "translator = pipeline('translation', model='staka/fugumt-en-ja')"
      ],
      "metadata": {
        "id": "pRTEM3Ahr0BJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432,
          "referenced_widgets": [
            "448d422cf60f4f459058689095dba4cd",
            "6b51755c36f940219d1a409a2acc870b",
            "fb14c021ae64412bae30268a5d83a402",
            "7bedb16e5fdd492292fdd54f1b2f0a30",
            "4bc15d05ef7b48089882100db4024f00",
            "533a0ebbb8a4483793f3b6fe0c424181",
            "d0f7988971264b63bcab908ecbf3cf8c",
            "ec4feda6a6d842099ed01897ca4fb65e",
            "a8b7366ed6e14e2b805fc6ae02ac6ef6",
            "e6f488c03ce542fcad881951646bec02",
            "2e87382e953a4b6d8e95976f6c5f7a8a",
            "2f7aab00d62b4a8a979b855be9752542",
            "e8969eaf85d949e5932c76a78882f618",
            "011b364b3c654400affb016336cfb071",
            "38a900e770834ba59d8803f47dd98a34",
            "896fa865a108449da726ebd35e89469d",
            "cb1470ce52a84326a8f688fc54903b60",
            "24a8f0ff798047cebd71341acd214d27",
            "692cdf9cb4434cfcb8727adca1ff99b9",
            "2a4cb481e35b4e7db593db70672369a3",
            "e8650e22cf1d446795294fc14da69781",
            "4e3a7bc757004e6f8abd6a2dbdf3891f",
            "3d9ffaa7d52d4fd19d3b3344505ade9f",
            "4796d5872a6d41a5aecc90122dfe89e2",
            "5bcaf6bb3cb9435ea9089aeb4765f202",
            "218078f3844445fe8a856748c6eb5889",
            "3a22687df79e476fb0bfbb4d59bd84aa",
            "05cd31473aad41a3b25af2552f2fba2d",
            "c6220f4aa76748ef9e3b1b9754f07e89",
            "7fbf9e39755f485aa4e9fb28447f4eb2",
            "12fad3e7d0224686b7380f5335a23b3b",
            "7673a685fce04f7ebc6517948a8fdc68",
            "ba04cc50329d44af9e9d838edf243d8a",
            "33ee1f32e1eb46b7bfe8e409a21ff205",
            "7c6ea734a1c1465ebf1cef64db706177",
            "ff79766126084d998f1ebacf86a4deaf",
            "a93eb2dd00a74019927244ca70013c7f",
            "d83a9b7d6648437babe6f482e4dd438c",
            "eaf6fcd9b6054783bed8f8828f2412c6",
            "9c5ad61d04ff4d63b94126c9de784d60",
            "601d53d691e64319aa53bbb4b2316ac5",
            "4fc43562b3f5498cb431868588a25a17",
            "d80b6f0191d743c291dde49e4c0b4c1e",
            "15d8114592394251b197f8b2721759f9",
            "66916cd944ff499991ba6c1ce4e9e61f",
            "5d2637149f044886913a1d2597c12eea",
            "a2f07523e2e54f92a335953e3e15fd30",
            "4b5a98260cf44fdbb541c5141f02e9fd",
            "da76b69ee2254fdfab7cfa1a10777586",
            "86463d8f0e614794a81c0d77fdb260f7",
            "f013347f9fd64c71b283da3d8c9deb03",
            "c3079b1c79504eecb37515e2fb60bbfc",
            "e37bb8ca8dd94e59be1f5626709d9520",
            "4ac3e622ceca4bc180de59a685385c41",
            "3fda80395b354376b3209f3d29786899",
            "1890a9210447495e80b673ff89db8610",
            "4c2cf1367612493290e72f856ccb13db",
            "71376ddeb9074a7dbfdc3341d7206d9b",
            "b88960ca714843d5839faecdddfc5179",
            "15987f963261463dabfaabd6dc11a9ce",
            "1aaa7adef01c4ce8b88b2bafdd417b34",
            "b028a2c50c1341d7aad21eeffd9f096d",
            "d32f8a984ef54d388b07a651a8c3f239",
            "53534d7dffc641a58d87b4dae88525a0",
            "c250daf4faef4ad4adcc32b3b620b935",
            "96cfd26e53fa4077822186e26dec1708"
          ]
        },
        "outputId": "a57d76ad-5c28-4060-8720-12d5ed993dfc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.02k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "448d422cf60f4f459058689095dba4cd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)\"pytorch_model.bin\";:   0%|          | 0.00/154M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2f7aab00d62b4a8a979b855be9752542"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/42.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3d9ffaa7d52d4fd19d3b3344505ade9f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/source.spm:   0%|          | 0.00/768k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "33ee1f32e1eb46b7bfe8e409a21ff205"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/791k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "66916cd944ff499991ba6c1ce4e9e61f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/65.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1890a9210447495e80b673ff89db8610"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/transformers/models/marian/tokenization_marian.py:194: UserWarning: Recommended: pip install sacremoses.\n",
            "  warnings.warn(\"Recommended: pip install sacremoses.\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pypdf import PdfWriter, PdfReader\n",
        "import io\n",
        "from reportlab.platypus import BaseDocTemplate, PageTemplate\n",
        "from reportlab.platypus import Paragraph, PageBreak, FrameBreak, KeepInFrame\n",
        "from reportlab.platypus.flowables import Spacer\n",
        "from reportlab.lib.styles import ParagraphStyle\n",
        "from reportlab.lib.pagesizes import A4, mm\n",
        "from reportlab.pdfbase import pdfmetrics\n",
        "from reportlab.pdfbase import cidfonts\n",
        "from reportlab.platypus.frames import Frame\n",
        "from reportlab.pdfgen import canvas\n",
        "from reportlab.lib.pagesizes import letter\n",
        "import numpy\n",
        "\n",
        "# reportlab用の座標取る\n",
        "base_pdf = PdfReader(open(target_pdf_file_path, \"rb\"))\n",
        "_, _, base_width, base_height = base_pdf.pages[0].mediabox\n",
        "\n",
        "output = PdfWriter()\n",
        "\n",
        "for page_index, pdf_page in enumerate(pdf_pages):\n",
        "    print(\"■%s ページ目\" % page_index)\n",
        "    # テキストブロックを取得\n",
        "    text_blocks = pdf_page.get_homogeneous_blocks()\n",
        "    # pdfを画像として取得\n",
        "    pdf_image = pdf_to_image(target_pdf_file_path, page_index)\n",
        "    # 座標取る\n",
        "    height, width, channel = pdf_image.shape\n",
        "    print(height, width)\n",
        "    plt.imshow(pdf_image)\n",
        "    # レイアウトを取得\n",
        "    pdf_layout = model.detect(pdf_image)\n",
        "\n",
        "    # 段落ブロックの処理\n",
        "    # 段落ブロックを抽出\n",
        "    paragraph_blocks = lp.Layout([b for b in pdf_layout if b.type=='Text'])\n",
        "\n",
        "    cover_packet = io.BytesIO()\n",
        "    cover_canvas = canvas.Canvas(cover_packet, pagesize=(int(base_width), int(base_height)), bottomup=True)\n",
        "\n",
        "    text_packet = io.BytesIO()\n",
        "    text_canvas = canvas.Canvas(text_packet, pagesize=(int(base_width), int(base_height)), bottomup=True)\n",
        "    for paragraph_block in paragraph_blocks:\n",
        "        # 段落中のテキストブロックを抽出\n",
        "        inner_text_blocks = list(filter(lambda x: is_inside(paragraph_block, x), text_blocks))\n",
        "        print(len(inner_text_blocks))\n",
        "        if len(inner_text_blocks) == 0:\n",
        "            continue\n",
        "        # 段落中のテキストブロックからテキストを抽出\n",
        "        text = \" \".join(list(map(lambda x: x.text, inner_text_blocks)))\n",
        "        print(text)\n",
        "        # テキストを翻訳\n",
        "        result = translator(text)\n",
        "        translated_text = result[0]['translation_text']\n",
        "        print(translated_text)\n",
        "        paragraph_x = (paragraph_block.block.x_1 / width) * base_width\n",
        "        paragraph_y = (paragraph_block.block.y_2 / height) * base_height\n",
        "        paragraph_width = ((paragraph_block.block.x_2 - paragraph_block.block.x_1) / width) * base_width\n",
        "        paragraph_height = ((paragraph_block.block.y_2 - paragraph_block.block.y_1) / height) * base_height\n",
        "\n",
        "        # カバーフレームの追加\n",
        "        fill_cover(cover_canvas, paragraph_x, height - paragraph_y, paragraph_width, paragraph_height)\n",
        "\n",
        "        # テキストフレームの追加\n",
        "        frame = Frame(paragraph_x, height - paragraph_y, paragraph_width, paragraph_height,\n",
        "                            showBoundary=0, leftPadding=0, rightPadding=0, topPadding=0, bottomPadding=0)\n",
        "        # テキスト実態の追加\n",
        "        fontsize = calc_fontsize(paragraph_width, paragraph_height, translated_text)\n",
        "        style = ParagraphStyle(name='Normal', fontName=font_name, fontSize=fontsize, leading=fontsize)\n",
        "        paragraph = Paragraph(translated_text, style)\n",
        "        story = [paragraph]\n",
        "        story_inframe = KeepInFrame(paragraph_width * 1.5, paragraph_height * 1.5, story)\n",
        "        frame.addFromList([story_inframe], text_canvas)\n",
        "\n",
        "\n",
        "    # カバーをpdfページにする\n",
        "    cover_canvas.save()\n",
        "\n",
        "    cover_packet.seek(0)\n",
        "    cover_pdf = PdfReader(cover_packet)\n",
        "\n",
        "    # テキストをpdfページにする\n",
        "\n",
        "    text_canvas.save()\n",
        "\n",
        "    text_packet.seek(0)\n",
        "    text_pdf = PdfReader(text_packet)\n",
        "\n",
        "    # pdfをマージ\n",
        "    base_pdf = PdfReader(open(target_pdf_file_path, \"rb\"))\n",
        "    base_page = base_pdf.pages[page_index]\n",
        "    # 見開き用\n",
        "    if is_mihiraki:\n",
        "        output.add_page(base_page)\n",
        "    try:\n",
        "        base_page.merge_page(cover_pdf.pages[0])\n",
        "        base_page.merge_page(text_pdf.pages[0])\n",
        "    except Exception as e:\n",
        "        print(\"error: %s\" % e)\n",
        "    \n",
        "\n",
        "    output.add_page(base_page)\n",
        "\n",
        "# 保存\n",
        "# pdfの出力パス\n",
        "output_filepath = \"translated_\" + pdf_file_name\n",
        "print(output_filepath)\n",
        "outputStream = open(output_filepath, \"wb\")\n",
        "output.write(outputStream)\n",
        "outputStream.close()\n",
        "files.download(output_filepath)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1c9JNisilmTX",
        "outputId": "b1fe9fe4-bdef-495c-fa24-0c3d10064985"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "■0 ページ目\n",
            "792 612\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "170\n",
            "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring signiﬁcantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English- to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.\n",
            "支配的なシーケンス変換モデルは、エンコーダとデコーダを含む複雑なリカレントまたは畳み込みニューラルネットワークに基づいている。最高のパフォーマンスモデルは、エンコーダとデコーダをアテンション機構を通じて接続する。我々は、アテンション機構のみに基づく新しい単純なネットワークアーキテクチャであるTransformerを提案し、リカレンスと畳み込みを完全にディスペンシングする。2つの機械翻訳タスクの実験は、これらのモデルが品質に優れており、より並列化可能であり、トレーニングに要する時間が著しく少ないことを示す。本モデルは、WMT 2014の英語-ドイツ語翻訳タスクで28.4 BLEUを達成し、アンサンブルを含む既存の最良の結果を2 BLEUで改善する。WMT 2014の英語-フランス語翻訳タスクでは、8つのGPUで3.5日間トレーニングした後、新しい単一モデルのBLEUスコアを41.8に設定し、トレーニングのコストをわずかに削減する。\n",
            "130\n",
            "∗ Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the ﬁrst Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efﬁcient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research. † Work performed while at Google Brain.\n",
            "リストの順序はランダムです。Jakob氏はRNNを自己注意で置き換えることを提案し、このアイデアを評価する努力を始めました。Illia氏と共に、最初のTransformerモデルを設計および実装し、この作業のあらゆる側面に非常に深く関与しています。Noam氏は、ドットプロダクトの注意、マルチヘッドの注意、パラメータのない位置表現を提案し、ほぼすべての詳細に関わる他の人物になりました。Niki氏は、元のコードベースとテンソルテンソルで無数のモデル変種を設計し、調整し、評価しました。Llion氏は、新しいモデル変種を実験し、私たちの最初のコードベースと効率的な推論と視覚化を担当しました。Lukasz氏とAidan氏は、長年にわたって、テンソルテンソルテンソルのさまざまな部分を設計し、実装し、以前のコードベースを置き換え、その結果を大幅に改善し、研究を加速させました。\n",
            "29\n",
            "Recurrent neural networks, long short-term memory [13] and gated recurrent [7] neural networks in particular, have been ﬁrmly established as state of the art approaches in sequence modeling and\n",
            "リカレントニューラルネットワーク、特に長い短期記憶[13]とゲートリカレント[7]ニューラルネットワークは、シーケンスモデリングにおける最先端のアプローチとしてしっかりと確立されている。\n",
            "6\n",
            "Work performed while at Google Brain.\n",
            "Google Brainでの仕事。\n",
            "7\n",
            "‡ Work performed while at Google Research.\n",
            "Google Researchでの仕事。\n",
            "5\n",
            "Llion Jones Google Research llion@google.com\n",
            "Llion Jones Google Research llion@google.com\n",
            "3\n",
            "Google Research usz@google.com\n",
            "Google Research usz@google.com\n",
            "1\n",
            "illia.polosukhin@gmail.com\n",
            "illia.polosukhin@gmail.com\n",
            "■1 ページ目\n",
            "792 612\n",
            "110\n",
            "Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states h t , as a function of the previous hidden state h t − 1 and the input for position t . This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples. Recent work has achieved signiﬁcant improvements in computational efﬁciency through factorization tricks [21] and conditional computation [32], while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains.\n",
            "リカレントモデルは典型的には、入力シーケンスと出力シーケンスのシンボル位置に沿って因子計算を行う。計算時間内のステップに位置を調整すると、前の隠れ状態 ht の関数として隠れた状態 ht と位置 t の入力列を生成する。\n",
            "93\n",
            "Most competitive neural sequence transduction models have an encoder-decoder structure [5, 2, 35]. Here, the encoder maps an input sequence of symbol representations ( x 1 , ..., x n ) to a sequence of continuous representations z = ( z 1 , ..., z n ) . Given z , the decoder then generates an output sequence ( y 1 , ..., y m ) of symbols one element at a time. At each step the model is auto-regressive [10], consuming the previously generated symbols as additional input when generating the next.\n",
            "ほとんどの競合するニューラルシーケンス変換モデルはエンコーダデコーダ構造[5, 2, 35]を持っている。ここでは、エンコーダはシンボル表現(x1, ..., xn)の入力シーケンスを連続表現z = (z1, ..., zn)のシーケンスにマッピングする。\n",
            "123\n",
            "The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU [16], ByteNet [18] and ConvS2S [9], all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difﬁcult to learn dependencies between distant positions [12]. In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as described in section 3.2.\n",
            "シーケンシャルな計算を減らすという目標は、拡張ニューラルGPU[16]、ByteNet[18]、ConvS2S[9]の基盤も形成し、これらはすべて畳み込みニューラルネットワークを基本的なビルディングブロックとして使用し、すべての入出力位置に対して隠れた表現を並列に計算する。これらのモデルでは、2つの任意の入力または出力位置からの信号を関連付けるのに必要な操作の数が、ConvS2SとByteNetの対数的に、位置間の依存関係を学習するのがより困難である[12]。これは、注意重み付けされた位置の平均化による効果的な解像度の削減のコストにもかかわらず、第3条2。\n",
            "54\n",
            "Attention mechanisms have become an integral part of compelling sequence modeling and transduc- tion models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences [2, 19]. In all but a few cases [27], however, such attention mechanisms are used in conjunction with a recurrent network.\n",
            "注意機構は様々なタスクにおいて説得力のあるシーケンスモデリングと変換モデルの一部となり、入力シーケンスと出力シーケンスの距離に関係なく依存関係のモデリングを可能にする[2,19]。\n",
            "50\n",
            "Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations [4, 27, 28, 22].\n",
            "自己アテンション(self-attention)は、シーケンスの表現を計算するために、単一のシーケンスの異なる位置に関連する注意機構である。自己アテンションは、読解、抽象要約、テキストエンタテインメント、学習タスクに依存しない文表現を含む様々なタスクで成功している[4, 27, 28, 22]。\n",
            "60\n",
            "In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for signiﬁcantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.\n",
            "本研究では,入力と出力間のグローバルな依存関係を描画するアテンション機構に完全に依存するモデルアーキテクチャであるTransformerを提案する。Transformerは,8つのP100 GPU上でわずか12時間トレーニングした後,翻訳品質の新たな状態に到達できる。\n",
            "31\n",
            "The Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure 1, respectively.\n",
            "Transformerは、図1の左半分と右半分に示されているエンコーダとデコーダの両方に対して、スタックされた自己注意層とポイントワイズ層を使用して、この全体的なアーキテクチャに従います。\n",
            "56\n",
            "To the best of our knowledge, however, the Transformer is the ﬁrst transduction model relying entirely on self-attention to compute representations of its input and output without using sequence- aligned RNNs or convolution. In the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as [17, 18] and [9].\n",
            "しかし、我々の知る限りでは、TransformerはシーケンスアライメントRNNや畳み込みを使わずに入力と出力の表現を計算するために完全に自己アテンションに依存する最初のトランスダクションモデルである。次のセクションでは、Transformerを説明し、自己アテンションを動機付け、[17,18]や[9]などのモデルに対する利点について論じる。\n",
            "31\n",
            "End-to-end memory networks are based on a recurrent attention mechanism instead of sequence- aligned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks [34].\n",
            "エンドツーエンドメモリネットワークはシーケンスアライメントリカレンスの代わりにリカレントアテンション機構に基づいており、単純な言語質問応答と言語モデリングタスクでうまく機能することが示されている[34]。\n",
            "31\n",
            "transduction problems such as language modeling and machine translation [35, 2, 5]. Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures [38, 24, 15].\n",
            "言語モデリングや機械翻訳などの変換問題[35, 2, 5]。その後、リカレント言語モデルとエンコーダデコーダアーキテクチャの境界を押し広げてきた[38, 24, 15]。\n",
            "■2 ページ目\n",
            "792 612\n",
            "111\n",
            "Decoder: The decoder is also composed of a stack of N = 6 identical layers. In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack. Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization. We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions. This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position i can depend only on the known outputs at positions less than i .\n",
            "デコーダはまた、N = 6つの同一層からなる。各エンコーダ層内の2つのサブ層に加えて、デコーダはエンコーダスタックの出力に対してマルチヘッドアテンションを行う第3のサブ層を挿入する。エンコーダと同様に、各サブ層の周りに残差接続を採用し、次にレイヤ正規化を行う。また、デコーダスタック内の自己アテンションサブ層を変更して、位置がその後の位置に出席するのを防ぐ。このマスキングは、出力埋め込みが1つの位置でオフセットされているという事実と組み合わせて、位置iの予測がiよりも少ない位置で既知の出力にのみ依存できることを保証する。\n",
            "107\n",
            "Encoder: The encoder is composed of a stack of N = 6 identical layers. Each layer has two sub-layers. The ﬁrst is a multi-head self-attention mechanism, and the second is a simple, position- wise fully connected feed-forward network. We employ a residual connection [11] around each of the two sub-layers, followed by layer normalization [1]. That is, the output of each sub-layer is LayerNorm( x + Sublayer( x )) , where Sublayer( x ) is the function implemented by the sub-layer itself. To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension d model = 512 .\n",
            "エンコーダ: エンコーダはN = 6の同一層からなる。各層には2つのサブレイヤがある。第1はマルチヘッド自己アテンション機構であり、第2は単純で位置的に完全に接続されたフィードフォワードネットワークである。我々は2つのサブレイヤのそれぞれの周りに残差接続[11]を使用し、次に層正規化[1]。つまり、各サブレイヤの出力はLayerNorm(x + Sublayer(x))であり、Sublayer(x)はサブレイヤ自体によって実装される関数である。これらの残差接続を容易にするために、モデル内のすべてのサブレイヤと埋め込み層は次元dModel = 512の出力を生成する。\n",
            "60\n",
            "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is computed as a weighted sum of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key.\n",
            "アテンション関数は、クエリとキー値ペアのセットを、クエリ、キー、値、出力がすべてベクトルである出力にマッピングするものとして記述することができる。出力は、値の重み付き和として計算され、各値に割り当てられた重みは、対応するキーとのクエリの互換性関数によって計算される。\n",
            "7\n",
            "Figure 1: The Transformer - model architecture.\n",
            "図 1: Transformer モデルアーキテクチャ。\n",
            "translated_atten.pdf\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}