Eval2_NEARCoder_3b.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1jUkmrJoAp2C",
        "outputId": "fbe2ac34-5e54-48d1-cf44-39ea676f3192"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-2.19.2-py3-none-any.whl (542 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.1/542.1 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.14.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.25.2)\n",
            "Requirement already satisfied: pyarrow>=12.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (16.1.0)\n",
            "Collecting pyarrow-hotfix (from datasets)\n",
            "  Downloading pyarrow_hotfix-0.6-py3-none-any.whl (7.9 kB)\n",
            "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
            "  Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.0.3)\n",
            "Collecting requests>=2.32.1 (from datasets)\n",
            "  Downloading requests-2.32.3-py3-none-any.whl (64 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.4)\n",
            "Collecting xxhash (from datasets)\n",
            "  Downloading xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m17.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting fsspec[http]<=2024.3.1,>=2023.1.0 (from datasets)\n",
            "  Downloading fsspec-2024.3.1-py3-none-any.whl (171 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m172.0/172.0 kB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting aiohttp (from datasets)\n",
            "  Downloading aiohttp-3.9.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m20.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: huggingface-hub>=0.21.2 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.23.2)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.1)\n",
            "Collecting aiosignal>=1.1.2 (from aiohttp->datasets)\n",
            "  Downloading aiosignal-1.3.1-py3-none-any.whl (7.6 kB)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.2.0)\n",
            "Collecting frozenlist>=1.1.1 (from aiohttp->datasets)\n",
            "  Downloading frozenlist-1.4.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (239 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m239.5/239.5 kB\u001b[0m \u001b[31m21.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting multidict<7.0,>=4.5 (from aiohttp->datasets)\n",
            "  Downloading multidict-6.0.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (124 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m124.3/124.3 kB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting yarl<2.0,>=1.0 (from aiohttp->datasets)\n",
            "  Downloading yarl-1.9.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (301 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m301.6/301.6 kB\u001b[0m \u001b[31m26.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting async-timeout<5.0,>=4.0 (from aiohttp->datasets)\n",
            "  Downloading async_timeout-4.0.3-py3-none-any.whl (5.7 kB)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.2->datasets) (4.12.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.1->datasets) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.1->datasets) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.1->datasets) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.1->datasets) (2024.6.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.9.0.post0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
            "Installing collected packages: xxhash, requests, pyarrow-hotfix, multidict, fsspec, frozenlist, dill, async-timeout, yarl, multiprocess, aiosignal, aiohttp, datasets\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.31.0\n",
            "    Uninstalling requests-2.31.0:\n",
            "      Successfully uninstalled requests-2.31.0\n",
            "  Attempting uninstall: fsspec\n",
            "    Found existing installation: fsspec 2024.5.0\n",
            "    Uninstalling fsspec-2024.5.0:\n",
            "      Successfully uninstalled fsspec-2024.5.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed aiohttp-3.9.5 aiosignal-1.3.1 async-timeout-4.0.3 datasets-2.19.2 dill-0.3.8 frozenlist-1.4.1 fsspec-2024.3.1 multidict-6.0.5 multiprocess-0.70.16 pyarrow-hotfix-0.6 requests-2.32.3 xxhash-3.4.1 yarl-1.9.4\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "d675eb6bc9eb4429b7ee770bb6719ce0",
            "705bdd1c3dc54be0bb7fbe85e4cf3297",
            "ca46cb6d023d414e92865f4dde10f33d",
            "e477ce85fe4d4d33a0d0277a0de14d50",
            "5579f4ca943f4995aafa957dfef264ff",
            "b2891a6f8c404bd091a19cd737316225",
            "0ecc5d3b29e34318b6a5b4c6603ba9c6",
            "f173a5cb6b1142398155c764018395fe",
            "c3807bc82fb844e6aeab4d09c29f1837",
            "9dbd5fe77ae24fa8ae3121946eb2a265",
            "38d428b1dfc548669a0f1055edd6a904",
            "eaa01304d7844e03b0460e1558f734b6",
            "f3a9142437824f9197f5b730a938c2ab",
            "49c83980d558454b837bf7dee4bd212b",
            "f34a4826d03c4d9ea5b208012acecfc2",
            "83c15e1b80984ba7a39da45200ab4c09",
            "afadff6b69694559bb9603f35767d2f8",
            "73c3b573315b451b8a7d0d11e75c39e5",
            "8ab591c6f7ff4e16b790054fdbe0f793",
            "57b248b2e6fd4c55b5dff43a710e04aa",
            "c13fb31293414751a262d107d8693c64",
            "93ba56e867964615b55bd07ebd0b0f61",
            "1afd577049ff434497fa9b111ad97ff3",
            "e9218f9eecad4648b309878efaa93532",
            "974e964caa4c405cb870410a976dff27",
            "da8920811ff34f2eab5f811a9bae2b5f",
            "d5ff03562fb443fe921493a23f7df23f",
            "0395f281ebda4b089949758412b1c863",
            "13f7766fad7d46a2b474dc1aec4244f0",
            "5af4950cb08542f284d9615ccb203885",
            "d2948ee829d5441c902144b4224d009e",
            "64a6b9fe07d64da4b6931f75120825d8",
            "6ca7d3967f75450ba9e0bac2f6f13de7",
            "6bb8a7d7fd4c49919b915c52b0f2db57",
            "26b9edd852e74020bd67723cd6652cf0",
            "12d68a451541473b914281630d8476c3",
            "47d44536fcf44717b21f234989f010f7",
            "824a9665f9c54c2088ffe6ca320e9b59",
            "5bba298a62e14111b3d7aeb78f47c2d2",
            "1fa8d4a52dc54c2d8dd9a09b6487d62f",
            "7153a4ebb0824a9b905bd784ad285262",
            "298c99bee794492a89a19a98d8c328fe",
            "f3718214e19f4ce8b42fc5f7db93051f",
            "2223b2cd384847a29d3950cd1921c919",
            "00061b41cdca4b739a01b6a229ec1278",
            "5daae55d90434834a59aaab63915e075",
            "614ff9e99b224b92825c6c94495da77b",
            "657ed04daa6e4ca28620bb58f21694c3",
            "98c755ef50dd445c81051105bdcbe709",
            "eef0192a008140deb2eefb6dad0468ae",
            "75838d22e40246c2b65fa797248cc189",
            "955462276f6145b78311e6b9a5036a4a",
            "04601a13ad9442c0b96d82c151424ab0",
            "4ebfc07f55754d94ac7c36c6f3161de0",
            "3d80cefcca074fc5814467e2c440ff3f",
            "3d205bec0ff64b3abb72011dffa4fcd6",
            "2cd6cab6a53f44d0807057656529c4b6",
            "097b790f1aa94f9c9017aa172fbf06f3",
            "920ea221313e4e2da23f8af115356509",
            "2dd6fcef47ff409ab7ca29e9b05d5e60",
            "76d47cfbc39c4966aa75d5755634533a",
            "9e3eae0a218e4b769b43c631eb9b2c06",
            "2287868e0e7f41b7a66147f7eb9330ca",
            "fa2280e1b2c04d86bb6998bcfd97ea10",
            "7cf7ff011af048ad8426b41670bbad9f",
            "a169b5c0a96f460da7b0519a873c987b",
            "8f6d7895c0a943a8a8b866db0d8487ae",
            "0c3053449e5e4b389d235359821d4b84",
            "9a09a833adbb4923b72add5acb112072",
            "427a1ed4c5ac4638b29010355c76d9ed",
            "c29e9e6afc6b49aa809405bb303fbde1",
            "99c554b2ea92435d940d1d01565a162e",
            "d2cd1a24609842ca8131699e97da2fb4",
            "64a516e9d2854cd19a4739893dd18afb",
            "52008b4173dd46b1a3c4e09cc5f20388",
            "c8a6fe4dbe5a4fd09c55256917e12609",
            "93da7bc98c0044bf888953975c5b3c4b",
            "6b1455c9256e42398a59aff0e1441998",
            "2f0892ee1280450996bc4397cf12ad15",
            "75261b484f6b4d18a4942bde9882718a",
            "e57258abf667436fbcd5d92816b6b00b",
            "89a68e9c10b540b8bbb6470e7a552d9c",
            "2ee4e729ffe9415487072fab3dc0441b",
            "47a0143bd32c470e921775782aaa3289",
            "40b3f9bfa20047fc97cc761f611f8ab4",
            "3f74b0728e834e70aacab015538b4bbe",
            "b9d28eb1b117435fb98e5666b36a20a3",
            "b1c3882e82c945df8475f359ba4f619c",
            "245b668091224491ac4be5dfe53311e4",
            "daa75dfeaa7f44c386b2146831be771d",
            "153f641003d84a918503987e01a92af7",
            "f207788b3b9c43ea863f3a6a8f853742",
            "d4015b12d86448eeab101c0d5185bba0",
            "a1005eb60a4c4ed9a8310baadbec8301",
            "6506cb9be28a46a49c992a51b777b38e",
            "eb1384fc14ef4463a670ed3e47e369bb",
            "5f1365e062584a7f99d5bbc6a63989b6",
            "5d07de5508334fbf826c26f7d12c8a10",
            "a063634d27344d21a70eb65b56f70d4b",
            "bc12173618474805ba8d77696f2536f1",
            "846258fc402f4701b3956dda5c464eba",
            "135dcb9e30e44807a345363f71244a62",
            "fca6d6d901904313bc319359021efa11",
            "fa8619f1adf7480bbefc03779fa75a8e",
            "0395df2ef1ce4490a7af964d7da37551",
            "2389302be42447728a9574b6dcb0630a",
            "ada2232e6607460c9f0640d027074ec6",
            "c051bd1fae5e4337a3fff816af10b46a",
            "cd626013012b4132854f6520219a403b",
            "193cb0baee854b968eea108b84aebeb9",
            "7a86d66bab5b4c54a6b1914465106167",
            "1c20fbedcd7d41b78f698ab3d3245661",
            "cb104e8b23d24b759c2959183d68a176",
            "8c55fca9fb7841e2bd5f908216a0b6ec",
            "87c2399531204b17b11c9a41fce17a44",
            "1de1d95f460c42a297107173edcf3d3a",
            "5611bbcce7624abab139935975c1a64a",
            "5ab0e886d41e495a802da12095f0695a",
            "42eca246c3bd4e6fa90877243b4adb64",
            "4b509db4c94d4c7b8b5fab3d6c448891",
            "2f8a2a5c3afb4d42bbb54d732c4faac9",
            "624b6fb6274f4a339243915c371498fa",
            "5e22536293914017bba62370ec2426b1",
            "7e97354c5bea4b96aa9a7e58075232a8",
            "26ade4c51ef74261a0ec0a9424458d21",
            "59618700975c476ba8576ceaee2f8fe3",
            "4227e8433ac74ac1804a6dbde8f19818",
            "1d5efed044a34e01a099f6637e0a1730",
            "16d6a6d8ee754f5d86ddcb33facd74f2",
            "f19d42bb68954d14a6faa4ec9735f7aa",
            "f4d34295332c496c98f03a391bd1f200",
            "7d5f249e7eac4c878cffda7ad4b46e62"
          ]
        },
        "id": "_Je202BFI-Wy",
        "outputId": "5bca86d6-60a3-4001-af8c-5ced97833829"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluating jcarbonnell/NEARCoder-3b\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/8.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d675eb6bc9eb4429b7ee770bb6719ce0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/777k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eaa01304d7844e03b0460e1558f734b6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/442k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1afd577049ff434497fa9b111ad97ff3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.06M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6bb8a7d7fd4c49919b915c52b0f2db57"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/1.45k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "00061b41cdca4b739a01b6a229ec1278"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/784 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3d205bec0ff64b3abb72011dffa4fcd6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/39.0k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8f6d7895c0a943a8a8b866db0d8487ae"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6b1455c9256e42398a59aff0e1441998"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00001-of-00002.safetensors:   0%|          | 0.00/4.95G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "245b668091224491ac4be5dfe53311e4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00002-of-00002.safetensors:   0%|          | 0.00/1.11G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bc12173618474805ba8d77696f2536f1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7a86d66bab5b4c54a6b1914465106167"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/111 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "624b6fb6274f4a339243915c371498fa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: from typing import List\n",
            "\n",
            "\n",
            "def has_close_elements(numbers: List[float], threshold: float) -> bool:\n",
            "    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n",
            "    given threshold.\n",
            "    >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n",
            "    False\n",
            "    >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n",
            "    True\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     for idx, elem in enumerate(numbers):\n",
            "        for idx2, elem2 in enumerate(numbers):\n",
            "            if idx != idx2:\n",
            "                distance = abs(elem - elem2)\n",
            "                if distance < threshold:\n",
            "                    return True\n",
            "\n",
            "    return False\n",
            "\n",
            "Generated Output: from typing import List\n",
            "\n",
            "\n",
            "def has_close_elements(numbers: List[float], threshold: float) -> bool:\n",
            "    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n",
            "    given threshold.\n",
            "    >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n",
            "    False\n",
            "    >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n",
            "    True\n",
            "    \"\"\"\n",
            "\n",
            "def get_close_elements(numbers: List[float]\n",
            "----------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: from typing import List\n",
            "\n",
            "\n",
            "def separate_paren_groups(paren_string: str) -> List[str]:\n",
            "    \"\"\" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to\n",
            "    separate those group into separate strings and return the list of those.\n",
            "    Separate groups are balanced (each open brace is properly closed) and not nested within each other\n",
            "    Ignore any spaces in the input string.\n",
            "    >>> separate_paren_groups('( ) (( )) (( )( ))')\n",
            "    ['()', '(())', '(()())']\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     result = []\n",
            "    current_string = []\n",
            "    current_depth = 0\n",
            "\n",
            "    for c in paren_string:\n",
            "        if c == '(':\n",
            "            current_depth += 1\n",
            "            current_string.append(c)\n",
            "        elif c == ')':\n",
            "            current_depth -= 1\n",
            "            current_string.append(c)\n",
            "\n",
            "            if current_depth == 0:\n",
            "                result.append(''.join(current_string))\n",
            "                current_string.clear()\n",
            "\n",
            "    return result\n",
            "\n",
            "Generated Output: from typing import List\n",
            "\n",
            "\n",
            "def separate_paren_groups(paren_string: str) -> List[str]:\n",
            "    \"\"\" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to\n",
            "    separate those group into separate strings and return the list of those.\n",
            "    Separate groups are balanced (each open brace is properly closed) and not nested within each other\n",
            "    Ignore any spaces in the input string.\n",
            "    >>> separate_paren_groups('( ) (( )) (( )( ))')\n",
            "    ['()', '(())', '(()())']\n",
            "    \"\"\"\n",
            "\n",
            "def separate_paren_groups_recursive(paren_string: str) -> List[str]:\n",
            "    \"\"\" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to\n",
            "    separate those group into separate strings and return the list of those.\n",
            "    Separate groups are balanced (each open brace is properly closed) and not nested within each other\n",
            "    Ignore any spaces in the input string.\n",
            "    >>> separate_paren_groups_recursive('( ) (( )) (( )( ))')\n",
            "    ['()'\n",
            "----------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: \n",
            "\n",
            "def truncate_number(number: float) -> float:\n",
            "    \"\"\" Given a positive floating point number, it can be decomposed into\n",
            "    and integer part (largest integer smaller than given number) and decimals\n",
            "    (leftover part always smaller than 1).\n",
            "\n",
            "    Return the decimal part of the number.\n",
            "    >>> truncate_number(3.5)\n",
            "    0.5\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     return number % 1.0\n",
            "\n",
            "Generated Output: \n",
            "\n",
            "def truncate_number(number: float) -> float:\n",
            "    \"\"\" Given a positive floating point number, it can be decomposed into\n",
            "    and integer part (largest integer smaller than given number) and decimals\n",
            "    (leftover part always smaller than 1).\n",
            "\n",
            "    Return the decimal part of the number.\n",
            "    >>> truncate_number(3.5)\n",
            "    0.5\n",
            "    \"\"\"\n",
            "\n",
            "def round_number(number: float\n",
            "----------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: from typing import List\n",
            "\n",
            "\n",
            "def below_zero(operations: List[int]) -> bool:\n",
            "    \"\"\" You're given a list of deposit and withdrawal operations on a bank account that starts with\n",
            "    zero balance. Your task is to detect if at any point the balance of account fallls below zero, and\n",
            "    at that point function should return True. Otherwise it should return False.\n",
            "    >>> below_zero([1, 2, 3])\n",
            "    False\n",
            "    >>> below_zero([1, 2, -4, 5])\n",
            "    True\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     balance = 0\n",
            "\n",
            "    for op in operations:\n",
            "        balance += op\n",
            "        if balance < 0:\n",
            "            return True\n",
            "\n",
            "    return False\n",
            "\n",
            "Generated Output: from typing import List\n",
            "\n",
            "\n",
            "def below_zero(operations: List[int]) -> bool:\n",
            "    \"\"\" You're given a list of deposit and withdrawal operations on a bank account that starts with\n",
            "    zero balance. Your task is to detect if at any point the balance of account fallls below zero, and\n",
            "    at that point function should return True. Otherwise it should return False.\n",
            "    >>> below_zero([1, 2, 3])\n",
            "    False\n",
            "    >>> below_zero([1, 2, -4, 5])\n",
            "    True\n",
            "    \"\"\"\n",
            "\n",
            "def below_zero_v2(operations: List[int]) -> bool:\n",
            "    \"\"\" You're given a list of deposit and withdrawal operations on a bank account that starts with\n",
            "    zero balance. Your task is to detect if at any point the balance of account fallls below zero\n",
            "----------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: from typing import List\n",
            "\n",
            "\n",
            "def mean_absolute_deviation(numbers: List[float]) -> float:\n",
            "    \"\"\" For a given list of input numbers, calculate Mean Absolute Deviation\n",
            "    around the mean of this dataset.\n",
            "    Mean Absolute Deviation is the average absolute difference between each\n",
            "    element and a centerpoint (mean in this case):\n",
            "    MAD = average | x - x_mean |\n",
            "    >>> mean_absolute_deviation([1.0, 2.0, 3.0, 4.0])\n",
            "    1.0\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     mean = sum(numbers) / len(numbers)\n",
            "    return sum(abs(x - mean) for x in numbers) / len(numbers)\n",
            "\n",
            "Generated Output: from typing import List\n",
            "\n",
            "\n",
            "def mean_absolute_deviation(numbers: List[float]) -> float:\n",
            "    \"\"\" For a given list of input numbers, calculate Mean Absolute Deviation\n",
            "    around the mean of this dataset.\n",
            "    Mean Absolute Deviation is the average absolute difference between each\n",
            "    element and a centerpoint (mean in this case):\n",
            "    MAD = average | x - x_mean |\n",
            "    >>> mean_absolute_deviation([1.0, 2.0, 3.0, 4.0])\n",
            "    1.0\n",
            "    \"\"\"\n",
            "\n",
            "def median_absolute_deviation(numbers: List[float]) -> float:\n",
            "    \"\"\" For a given list of input numbers\n",
            "----------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: from typing import List\n",
            "\n",
            "\n",
            "def intersperse(numbers: List[int], delimeter: int) -> List[int]:\n",
            "    \"\"\" Insert a number 'delimeter' between every two consecutive elements of input list `numbers'\n",
            "    >>> intersperse([], 4)\n",
            "    []\n",
            "    >>> intersperse([1, 2, 3], 4)\n",
            "    [1, 4, 2, 4, 3]\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     if not numbers:\n",
            "        return []\n",
            "\n",
            "    result = []\n",
            "\n",
            "    for n in numbers[:-1]:\n",
            "        result.append(n)\n",
            "        result.append(delimeter)\n",
            "\n",
            "    result.append(numbers[-1])\n",
            "\n",
            "    return result\n",
            "\n",
            "Generated Output: from typing import List\n",
            "\n",
            "\n",
            "def intersperse(numbers: List[int], delimeter: int) -> List[int]:\n",
            "    \"\"\" Insert a number 'delimeter' between every two consecutive elements of input list `numbers'\n",
            "    >>> intersperse([], 4)\n",
            "    []\n",
            "    >>> intersperse([1, 2, 3], 4)\n",
            "    [1, 4, 2, 4, 3]\n",
            "    \"\"\"\n",
            "\n",
            "def intersperse_with_index(numbers: List[int]\n",
            "----------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: from typing import List\n",
            "\n",
            "\n",
            "def parse_nested_parens(paren_string: str) -> List[int]:\n",
            "    \"\"\" Input to this function is a string represented multiple groups for nested parentheses separated by spaces.\n",
            "    For each of the group, output the deepest level of nesting of parentheses.\n",
            "    E.g. (()()) has maximum two levels of nesting while ((())) has three.\n",
            "\n",
            "    >>> parse_nested_parens('(()()) ((())) () ((())()())')\n",
            "    [2, 3, 1, 3]\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     def parse_paren_group(s):\n",
            "        depth = 0\n",
            "        max_depth = 0\n",
            "        for c in s:\n",
            "            if c == '(':\n",
            "                depth += 1\n",
            "                max_depth = max(depth, max_depth)\n",
            "            else:\n",
            "                depth -= 1\n",
            "\n",
            "        return max_depth\n",
            "\n",
            "    return [parse_paren_group(x) for x in paren_string.split(' ') if x]\n",
            "\n",
            "Generated Output: from typing import List\n",
            "\n",
            "\n",
            "def parse_nested_parens(paren_string: str) -> List[int]:\n",
            "    \"\"\" Input to this function is a string represented multiple groups for nested parentheses separated by spaces.\n",
            "    For each of the group, output the deepest level of nesting of parentheses.\n",
            "    E.g. (()()) has maximum two levels of nesting while ((())) has three.\n",
            "\n",
            "    >>> parse_nested_parens('(()()) ((())) () ((())()())')\n",
            "    [2, 3, 1, 3]\n",
            "    \"\"\"\n",
            "\n",
            "def parse_nested_parens_recursive(paren_string: str) -> List[int]:\n",
            "    \"\"\" Input to this function is a string represented multiple groups for nested parentheses separated by spaces.\n",
            "    For each of the group\n",
            "----------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: from typing import List\n",
            "\n",
            "\n",
            "def filter_by_substring(strings: List[str], substring: str) -> List[str]:\n",
            "    \"\"\" Filter an input list of strings only for ones that contain given substring\n",
            "    >>> filter_by_substring([], 'a')\n",
            "    []\n",
            "    >>> filter_by_substring(['abc', 'bacd', 'cde', 'array'], 'a')\n",
            "    ['abc', 'bacd', 'array']\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     return [x for x in strings if substring in x]\n",
            "\n",
            "Generated Output: from typing import List\n",
            "\n",
            "\n",
            "def filter_by_substring(strings: List[str], substring: str) -> List[str]:\n",
            "    \"\"\" Filter an input list of strings only for ones that contain given substring\n",
            "    >>> filter_by_substring([], 'a')\n",
            "    []\n",
            "    >>> filter_by_substring(['abc', 'bacd', 'cde', 'array'], 'a')\n",
            "    ['abc', 'bacd', 'array']\n",
            "    \"\"\"\n",
            "\n",
            "def filter_by_substring_case_insensitive(strings: List[str]\n",
            "----------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: from typing import List, Tuple\n",
            "\n",
            "\n",
            "def sum_product(numbers: List[int]) -> Tuple[int, int]:\n",
            "    \"\"\" For a given list of integers, return a tuple consisting of a sum and a product of all the integers in a list.\n",
            "    Empty sum should be equal to 0 and empty product should be equal to 1.\n",
            "    >>> sum_product([])\n",
            "    (0, 1)\n",
            "    >>> sum_product([1, 2, 3, 4])\n",
            "    (10, 24)\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     sum_value = 0\n",
            "    prod_value = 1\n",
            "\n",
            "    for n in numbers:\n",
            "        sum_value += n\n",
            "        prod_value *= n\n",
            "    return sum_value, prod_value\n",
            "\n",
            "Generated Output: from typing import List, Tuple\n",
            "\n",
            "\n",
            "def sum_product(numbers: List[int]) -> Tuple[int, int]:\n",
            "    \"\"\" For a given list of integers, return a tuple consisting of a sum and a product of all the integers in a list.\n",
            "    Empty sum should be equal to 0 and empty product should be equal to 1.\n",
            "    >>> sum_product([])\n",
            "    (0, 1)\n",
            "    >>> sum_product([1, 2, 3, 4])\n",
            "    (10, 24)\n",
            "    \"\"\"\n",
            "\n",
            "def sum_product_v2(numbers: List[int]) -> Tuple[int\n",
            "----------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: from typing import List, Tuple\n",
            "\n",
            "\n",
            "def rolling_max(numbers: List[int]) -> List[int]:\n",
            "    \"\"\" From a given list of integers, generate a list of rolling maximum element found until given moment\n",
            "    in the sequence.\n",
            "    >>> rolling_max([1, 2, 3, 2, 3, 4, 2])\n",
            "    [1, 2, 3, 3, 3, 4, 4]\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     running_max = None\n",
            "    result = []\n",
            "\n",
            "    for n in numbers:\n",
            "        if running_max is None:\n",
            "            running_max = n\n",
            "        else:\n",
            "            running_max = max(running_max, n)\n",
            "\n",
            "        result.append(running_max)\n",
            "\n",
            "    return result\n",
            "\n",
            "Generated Output: from typing import List, Tuple\n",
            "\n",
            "\n",
            "def rolling_max(numbers: List[int]) -> List[int]:\n",
            "    \"\"\" From a given list of integers, generate a list of rolling maximum element found until given moment\n",
            "    in the sequence.\n",
            "    >>> rolling_max([1, 2, 3, 2, 3, 4, 2])\n",
            "    [1, 2, 3, 3, 3, 4, 4]\n",
            "    \"\"\"\n",
            "\n",
            "def rolling_min(numbers: List[int]) -> List[int]:\n",
            "    \"\"\" From a given list of integers\n",
            "----------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: \n",
            "\n",
            "def is_palindrome(string: str) -> bool:\n",
            "    \"\"\" Test if given string is a palindrome \"\"\"\n",
            "    return string == string[::-1]\n",
            "\n",
            "\n",
            "def make_palindrome(string: str) -> str:\n",
            "    \"\"\" Find the shortest palindrome that begins with a supplied string.\n",
            "    Algorithm idea is simple:\n",
            "    - Find the longest postfix of supplied string that is a palindrome.\n",
            "    - Append to the end of the string reverse of a string prefix that comes before the palindromic suffix.\n",
            "    >>> make_palindrome('')\n",
            "    ''\n",
            "    >>> make_palindrome('cat')\n",
            "    'catac'\n",
            "    >>> make_palindrome('cata')\n",
            "    'catac'\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     if not string:\n",
            "        return ''\n",
            "\n",
            "    beginning_of_suffix = 0\n",
            "\n",
            "    while not is_palindrome(string[beginning_of_suffix:]):\n",
            "        beginning_of_suffix += 1\n",
            "\n",
            "    return string + string[:beginning_of_suffix][::-1]\n",
            "\n",
            "Generated Output: \n",
            "\n",
            "def is_palindrome(string: str) -> bool:\n",
            "    \"\"\" Test if given string is a palindrome \"\"\"\n",
            "    return string == string[::-1]\n",
            "\n",
            "\n",
            "def make_palindrome(string: str) -> str:\n",
            "    \"\"\" Find the shortest palindrome that begins with a supplied string.\n",
            "    Algorithm idea is simple:\n",
            "    - Find the longest postfix of supplied string that is a palindrome.\n",
            "    - Append to the end of the string reverse of a string prefix that comes before the palindromic suffix.\n",
            "    >>> make_palindrome('')\n",
            "    ''\n",
            "    >>> make_palindrome('cat')\n",
            "    'catac'\n",
            "    >>> make_palindrome('cata')\n",
            "    'catac'\n",
            "    \"\"\"\n",
            "#     if is_palindrome(string):\n",
            "#         return string\n",
            "#     else:\n",
            "#         return string + make_palindrome(string[1:])[::-1]\n",
            "    if is_palindrome(string):\n",
            "        return string\n",
            "    else:\n",
            "        return string + make_palindrome(string[1:])[::-1]\n",
            "\n",
            "\n",
            "def make_palindrome_recursive(string: str) -> str:\n",
            "    \"\"\" Find the shortest palindrome that begins with a supplied string.\n",
            "    Algorithm idea is simple:\n",
            "    - Find the longest postfix of supplied string that is a palindrome.\n",
            "    - Append to the end of the string reverse of a string prefix that comes before the palindromic suffix.\n",
            "    >>> make_palindrome_recursive('')\n",
            "    ''\n",
            "    >>> make_palindrome_recursive('cat')\n",
            "    'catac'\n",
            "    >>> make_palindrome_recursive('cata')\n",
            "    'catac'\n",
            "    \"\"\"\n",
            "    if is_palindrome(string):\n",
            "        return string\n",
            "    else:\n",
            "        return string + make_palindrome_recursive(string[1:])[::-1]\n",
            "\n",
            "\n",
            "def make_palindrome_iterative(string: str) -> str:\n",
            "    \"\"\" Find the shortest palindrome that begins with a supplied string.\n",
            "    Algorithm idea is simple:\n",
            "    - Find the longest postfix of supplied string that is a palindrome.\n",
            "    - Append to the end of the string reverse of a string prefix that comes before the palindromic suffix.\n",
            "    >>> make_palindrome_iterative('')\n",
            "    ''\n",
            "    >>> make_palindrome_iterative('cat')\n",
            "    'catac'\n",
            "    >>> make_palindrome_iterative('cata')\n",
            "    'catac'\n",
            "    \"\"\"\n",
            "    if is_palindrome(string):\n",
            "        return string\n",
            "    else:\n",
            "        for i in range(len(string)\n",
            "----------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: from typing import List\n",
            "\n",
            "\n",
            "def string_xor(a: str, b: str) -> str:\n",
            "    \"\"\" Input are two strings a and b consisting only of 1s and 0s.\n",
            "    Perform binary XOR on these inputs and return result also as a string.\n",
            "    >>> string_xor('010', '110')\n",
            "    '100'\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     def xor(i, j):\n",
            "        if i == j:\n",
            "            return '0'\n",
            "        else:\n",
            "            return '1'\n",
            "\n",
            "    return ''.join(xor(x, y) for x, y in zip(a, b))\n",
            "\n",
            "Generated Output: from typing import List\n",
            "\n",
            "\n",
            "def string_xor(a: str, b: str) -> str:\n",
            "    \"\"\" Input are two strings a and b consisting only of 1s and 0s.\n",
            "    Perform binary XOR on these inputs and return result also as a string.\n",
            "    >>> string_xor('010', '110')\n",
            "    '100'\n",
            "    \"\"\"\n",
            "\n",
            "def string_xor_list(a: List[str]\n",
            "----------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: from typing import List, Optional\n",
            "\n",
            "\n",
            "def longest(strings: List[str]) -> Optional[str]:\n",
            "    \"\"\" Out of list of strings, return the longest one. Return the first one in case of multiple\n",
            "    strings of the same length. Return None in case the input list is empty.\n",
            "    >>> longest([])\n",
            "\n",
            "    >>> longest(['a', 'b', 'c'])\n",
            "    'a'\n",
            "    >>> longest(['a', 'bb', 'ccc'])\n",
            "    'ccc'\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     if not strings:\n",
            "        return None\n",
            "\n",
            "    maxlen = max(len(x) for x in strings)\n",
            "    for s in strings:\n",
            "        if len(s) == maxlen:\n",
            "            return s\n",
            "\n",
            "Generated Output: from typing import List, Optional\n",
            "\n",
            "\n",
            "def longest(strings: List[str]) -> Optional[str]:\n",
            "    \"\"\" Out of list of strings, return the longest one. Return the first one in case of multiple\n",
            "    strings of the same length. Return None in case the input list is empty.\n",
            "    >>> longest([])\n",
            "\n",
            "    >>> longest(['a', 'b', 'c'])\n",
            "    'a'\n",
            "    >>> longest(['a', 'bb', 'ccc'])\n",
            "    'ccc'\n",
            "    \"\"\"\n",
            "\n",
            "def longest_with_index(strings: List[str]) -> Optional[Tuple[str\n",
            "----------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: \n",
            "\n",
            "def greatest_common_divisor(a: int, b: int) -> int:\n",
            "    \"\"\" Return a greatest common divisor of two integers a and b\n",
            "    >>> greatest_common_divisor(3, 5)\n",
            "    1\n",
            "    >>> greatest_common_divisor(25, 15)\n",
            "    5\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     while b:\n",
            "        a, b = b, a % b\n",
            "    return a\n",
            "\n",
            "Generated Output: \n",
            "\n",
            "def greatest_common_divisor(a: int, b: int) -> int:\n",
            "    \"\"\" Return a greatest common divisor of two integers a and b\n",
            "    >>> greatest_common_divisor(3, 5)\n",
            "    1\n",
            "    >>> greatest_common_divisor(25, 15)\n",
            "    5\n",
            "    \"\"\"\n",
            "\n",
            "def least_common_multiple(a: int\n",
            "----------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: from typing import List\n",
            "\n",
            "\n",
            "def all_prefixes(string: str) -> List[str]:\n",
            "    \"\"\" Return list of all prefixes from shortest to longest of the input string\n",
            "    >>> all_prefixes('abc')\n",
            "    ['a', 'ab', 'abc']\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     result = []\n",
            "\n",
            "    for i in range(len(string)):\n",
            "        result.append(string[:i+1])\n",
            "    return result\n",
            "\n",
            "Generated Output: from typing import List\n",
            "\n",
            "\n",
            "def all_prefixes(string: str) -> List[str]:\n",
            "    \"\"\" Return list of all prefixes from shortest to longest of the input string\n",
            "    >>> all_prefixes('abc')\n",
            "    ['a', 'ab', 'abc']\n",
            "    \"\"\"\n",
            "\n",
            "def all_suffixes(string: str) -> List[str]:\n",
            "    \"\"\" Return list of all suffixes from shortest to longest of the input string\n",
            "    >>> all_suffixes('abc')\n",
            "    ['bc'\n",
            "----------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: \n",
            "\n",
            "def string_sequence(n: int) -> str:\n",
            "    \"\"\" Return a string containing space-delimited numbers starting from 0 upto n inclusive.\n",
            "    >>> string_sequence(0)\n",
            "    '0'\n",
            "    >>> string_sequence(5)\n",
            "    '0 1 2 3 4 5'\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     return ' '.join([str(x) for x in range(n + 1)])\n",
            "\n",
            "Generated Output: \n",
            "\n",
            "def string_sequence(n: int) -> str:\n",
            "    \"\"\" Return a string containing space-delimited numbers starting from 0 upto n inclusive.\n",
            "    >>> string_sequence(0)\n",
            "    '0'\n",
            "    >>> string_sequence(5)\n",
            "    '0 1 2 3 4 5'\n",
            "    \"\"\"\n",
            "\n",
            "def string_sequence_with_separator(n: int\n",
            "----------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: \n",
            "\n",
            "def count_distinct_characters(string: str) -> int:\n",
            "    \"\"\" Given a string, find out how many distinct characters (regardless of case) does it consist of\n",
            "    >>> count_distinct_characters('xyzXYZ')\n",
            "    3\n",
            "    >>> count_distinct_characters('Jerry')\n",
            "    4\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     return len(set(string.lower()))\n",
            "\n",
            "Generated Output: \n",
            "\n",
            "def count_distinct_characters(string: str) -> int:\n",
            "    \"\"\" Given a string, find out how many distinct characters (regardless of case) does it consist of\n",
            "    >>> count_distinct_characters('xyzXYZ')\n",
            "    3\n",
            "    >>> count_distinct_characters('Jerry')\n",
            "    4\n",
            "    \"\"\"\n",
            "\n",
            "def count_distinct_characters_with_case(string: str) -> int:\n",
            "    \"\"\" Given a string\n",
            "----------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: from typing import List\n",
            "\n",
            "\n",
            "def parse_music(music_string: str) -> List[int]:\n",
            "    \"\"\" Input to this function is a string representing musical notes in a special ASCII format.\n",
            "    Your task is to parse this string and return list of integers corresponding to how many beats does each\n",
            "    not last.\n",
            "\n",
            "    Here is a legend:\n",
            "    'o' - whole note, lasts four beats\n",
            "    'o|' - half note, lasts two beats\n",
            "    '.|' - quater note, lasts one beat\n",
            "\n",
            "    >>> parse_music('o o| .| o| o| .| .| .| .| o o')\n",
            "    [4, 2, 1, 2, 2, 1, 1, 1, 1, 4, 4]\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     note_map = {'o': 4, 'o|': 2, '.|': 1}\n",
            "    return [note_map[x] for x in music_string.split(' ') if x]\n",
            "\n",
            "Generated Output: from typing import List\n",
            "\n",
            "\n",
            "def parse_music(music_string: str) -> List[int]:\n",
            "    \"\"\" Input to this function is a string representing musical notes in a special ASCII format.\n",
            "    Your task is to parse this string and return list of integers corresponding to how many beats does each\n",
            "    not last.\n",
            "\n",
            "    Here is a legend:\n",
            "    'o' - whole note, lasts four beats\n",
            "    'o|' - half note, lasts two beats\n",
            "    '.|' - quater note, lasts one beat\n",
            "\n",
            "    >>> parse_music('o o|.| o| o|.|.|.|.| o o')\n",
            "    [4, 2, 1, 2, 2, 1, 1, 1, 1, 4, 4]\n",
            "    \"\"\"\n",
            "\n",
            "def parse_music_with_rests(music_string: str) -> List[int]:\n",
            "    \"\"\" Input to this function is a string representing musical notes in a special ASCII format.\n",
            "    Your task is to parse this string and return list of integers corresponding to how many beats does each\n",
            "    not last.\n",
            "\n",
            "    Here is a legend:\n",
            "    'o' - whole note\n",
            "----------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: \n",
            "\n",
            "def how_many_times(string: str, substring: str) -> int:\n",
            "    \"\"\" Find how many times a given substring can be found in the original string. Count overlaping cases.\n",
            "    >>> how_many_times('', 'a')\n",
            "    0\n",
            "    >>> how_many_times('aaa', 'a')\n",
            "    3\n",
            "    >>> how_many_times('aaaa', 'aa')\n",
            "    3\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     times = 0\n",
            "\n",
            "    for i in range(len(string) - len(substring) + 1):\n",
            "        if string[i:i+len(substring)] == substring:\n",
            "            times += 1\n",
            "\n",
            "    return times\n",
            "\n",
            "Generated Output: \n",
            "\n",
            "def how_many_times(string: str, substring: str) -> int:\n",
            "    \"\"\" Find how many times a given substring can be found in the original string. Count overlaping cases.\n",
            "    >>> how_many_times('', 'a')\n",
            "    0\n",
            "    >>> how_many_times('aaa', 'a')\n",
            "    3\n",
            "    >>> how_many_times('aaaa', 'aa')\n",
            "    3\n",
            "    \"\"\"\n",
            "\n",
            "def how_many_times_2(string: str\n",
            "----------------------------------------------------\n",
            "Prompt: from typing import List\n",
            "\n",
            "\n",
            "def sort_numbers(numbers: str) -> str:\n",
            "    \"\"\" Input is a space-delimited string of numberals from 'zero' to 'nine'.\n",
            "    Valid choices are 'zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight' and 'nine'.\n",
            "    Return the string with numbers sorted from smallest to largest\n",
            "    >>> sort_numbers('three one five')\n",
            "    'one three five'\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     value_map = {\n",
            "        'zero': 0,\n",
            "        'one': 1,\n",
            "        'two': 2,\n",
            "        'three': 3,\n",
            "        'four': 4,\n",
            "        'five': 5,\n",
            "        'six': 6,\n",
            "        'seven': 7,\n",
            "        'eight': 8,\n",
            "        'nine': 9\n",
            "    }\n",
            "    return ' '.join(sorted([x for x in numbers.split(' ') if x], key=lambda x: value_map[x]))\n",
            "\n",
            "Generated Output: from typing import List\n",
            "\n",
            "\n",
            "def sort_numbers(numbers: str) -> str:\n",
            "    \"\"\" Input is a space-delimited string of numberals from 'zero' to 'nine'.\n",
            "    Valid choices are 'zero', 'one', 'two', 'three', 'four', 'five','six','seven', 'eight' and 'nine'.\n",
            "    Return the string with numbers sorted from smallest to largest\n",
            "    >>> sort_numbers('three one five')\n",
            "    'one three five'\n",
            "    \"\"\"\n",
            "\n",
            "def sort_numbers_with_list(numbers: List[str]) -> List[str]:\n",
            "    \"\"\" Input is a list of numberals from 'zero' to 'nine'.\n",
            "    Valid choices are 'zero'\n",
            "----------------------------------------------------\n",
            "Accuracy: 0.0%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Question: What is the Nightshade Sharding?\n",
            "Expected Answer: Nightshade Sharding is a method used by the NEAR protocol for scaling.\n",
            "Generated Answer: What is the Nightshade Sharding?\n",
            "\n",
            "The Nightshade Sharding is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is a sharding algorithm that is used to shard data across multiple shards.\n",
            "\n",
            "The Nightshade Sharding algorithm is\n",
            "----------------------------------------------------\n",
            "Accuracy: 0.0%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAIjCAYAAAAQgZNYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABJWElEQVR4nO3deXxN1/7/8fdJZECawZRUpIm5QszzUFR6Y7xokaINuaXtbYs2aCk1dYhSQ29ruNpb9N4qLVptuUiDalWrZlXzfBGzhKggWb8//JyvI8E5kYgdr+fjcR4PZ5219/6cfeJ4W1l7bZsxxggAAACwILe8LgAAAADILsIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsANwlYWFh6tmzZ16XcVM9e/ZUWFhYnh3/Xj8/eclms2nEiBF5XQZwTyLMAhYwY8YM2Ww2eXt76/Dhw5leb9asmapUqeLQFhYWJpvNluWjZcuWWR7n1Vdflc1mU3R0dJav79+/32E/bm5uKlKkiFq1aqXVq1c79V5WrFjhsA8PDw+VKVNGMTEx2rt3r1P7cNbkyZM1Y8aMHN3n3XCzz81ms+n555/P6/LuyM8//6wRI0bo7NmzeV2K3bW/X9ceBQoUUHBwsHr27Jnl37d7wb14HoG8UiCvCwDgvLS0NI0ePVoffPCBU/2rV6+u/v37Z2ovWbJkpjZjjD7//HOFhYXp22+/1blz5/TAAw9kud+uXbuqdevWSk9P186dOzV58mQ1b95cv/32myIiIpyqrW/fvqpTp44uX76s9evXa9q0aVq4cKG2bNmSZX3ZMXnyZBUrVsySo32PPfaYYmJiMrVXqFAhD6rJOT///LNGjhypnj17yt/f3+G1HTt2yM0t78ZYRo0apdKlS+vixYv65ZdfNGPGDP3000/6/fff5e3tnWd1ZeVW5xG43xBmAQupXr26PvroIw0ePNipwBccHKynnnrKqX2vWLFC//vf/7Rs2TJFRUVp/vz56tGjR5Z9a9as6bDfJk2aqFWrVpoyZYomT57s1PGaNGmiTp06SZJiY2NVoUIF9e3bVzNnztTgwYOz3CY1NVWFCxd2av9WV6FCBac/u/zCy8srT4/fqlUr1a5dW5LUq1cvFStWTO+++66++eYbdenSJU9rA3BzTDMALOT1119Xenq6Ro8eneP7/uyzzxQeHq7mzZsrMjJSn332mdPbNmnSRJK0Z8+ebB//0UcflSTt27dPkjRixAjZbDb98ccf6tatmwICAtS4cWNJ0pUrV/Tmm2+qbNmy8vLyUlhYmF5//XWlpaXZ9xcWFqatW7fqhx9+sP/6uFmzZvbX9+7dq86dO6tIkSIqVKiQ6tevr4ULF2aq6+LFixoxYoQqVKggb29vPfjgg3r88ccd3mtqaqr69++vkJAQeXl5qWLFinrvvfdkjMn2+bidl156ST4+Prpw4UKm17p27aqgoCClp6dLkhYsWKA2bdqoZMmS8vLyUtmyZfXmm2/aX7+Za1NCVqxY4dB+bbrJ9VM4Nm/erJ49e6pMmTLy9vZWUFCQ/va3v+nUqVP2PiNGjNDAgQMlSaVLl7Z/Lvv375eU9ZxZZz6na3V+8cUXevvtt1WqVCl5e3urRYsW2r179y3f463c7Od6+/bt6tSpk4oUKSJvb2/Vrl1b33zzjUOfy5cva+TIkSpfvry8vb1VtGhRNW7cWAkJCfY+zZo1c/iZvOZ2c5dvdx4TEhLUuHFj+fv7y8fHRxUrVtTrr7+ejTMAWAMjs4CFlC5dWjExMfroo480aNCg247OXr58WSdPnszUXrhwYRUsWND+PC0tTfPmzbNPSejatatiY2OVlJSkoKCg29Z17R/RgIAAF96No2uBoWjRog7tnTt3Vvny5fXOO+/Yw2GvXr00c+ZMderUSf3799evv/6q+Ph4bdu2TV999ZUkaeLEierTp498fHw0ZMgQSVJgYKAk6dixY2rYsKEuXLigvn37qmjRopo5c6b++te/au7cuerYsaMkKT09XW3btlViYqKefPJJ9evXT+fOnVNCQoJ+//13lS1bVsYY/fWvf9Xy5cv1zDPPqHr16lqyZIkGDhyow4cPa8KECdk6HxcvXszys/P19ZWnp6eio6M1adIkLVy4UJ07d7a/fuHCBX377bfq2bOn3N3dJV2dE+rj46O4uDj5+Pho2bJlGjZsmFJSUjR27Nhs1XejhIQE7d27V7GxsQoKCtLWrVs1bdo0bd26Vb/88otsNpsef/xx7dy5U59//rkmTJigYsWKSZKKFy+e5T6d/ZyuGT16tNzc3DRgwAAlJydrzJgx6t69u3799ddsvaesfq63bt2qRo0aKTg4WIMGDVLhwoX1xRdfqEOHDpo3b569phEjRig+Pl69evVS3bp1lZKSorVr12r9+vV67LHHslXPNbc6j1u3blXbtm1VtWpVjRo1Sl5eXtq9e7dWrVp1R8cE7mkGwD1v+vTpRpL57bffzJ49e0yBAgVM37597a83bdrUVK5c2WGb0NBQIynLR3x8vEPfuXPnGklm165dxhhjUlJSjLe3t5kwYYJDv3379hlJZuTIkebEiRMmKSnJ/Pjjj6ZOnTpGkvnyyy9v+16WL19uJJlPPvnEnDhxwhw5csQsXLjQhIWFGZvNZn777TdjjDHDhw83kkzXrl0dtt+4caORZHr16uXQPmDAACPJLFu2zN5WuXJl07Rp00w1vPzyy0aS+fHHH+1t586dM6VLlzZhYWEmPT3dGGPMJ598YiSZ8ePHZ9pHRkaGMcaYr7/+2kgyb731lsPrnTp1MjabzezevdveFhoaanr06HHbc3Szz02S+fzzz+3HDw4ONk888YTDtl988YWRZFauXGlvu3DhQqZjPPfcc6ZQoULm4sWL9rYePXqY0NBQ+/Nrn9Xy5csdtr32czB9+vRbHuPzzz/PVMvYsWONJLNv375M/W88P85+TtfqrFSpkklLS7P3ff/9940ks2XLlkzHut61v1/ff/+9OXHihDl06JCZO3euKV68uPHy8jKHDh2y923RooWJiIhwOG8ZGRmmYcOGpnz58va2atWqmTZt2tzyuE2bNs3y5/PGz8GYqz8Tw4cPtz+/2XmcMGGCkWROnDhxy2MD+QnTDACLKVOmjJ5++mlNmzZNR48evWXfevXqKSEhIdOja9euDv0+++wz1a5dW+XKlZMkPfDAA2rTps1NpxoMHz5cxYsXV1BQkJo0aaJt27Zp3Lhx9jmwzvjb3/6m4sWLq2TJkmrTpo1SU1M1c+ZM+5zFa268en/RokWSpLi4OIf2a6PKWU0VuNGiRYtUt25d+7QFSfLx8dGzzz6r/fv3648//pAkzZs3T8WKFVOfPn0y7cNms9n35e7urr59+2aqxxij//73v7etJyvt27fP8rNr3ry5/fidO3fWokWLdP78eft2c+bMUXBwsMN7u34U/ty5czp58qSaNGmiCxcuaPv27dmq70bXH+PaqHL9+vUlSevXr8/WPp39nK6JjY2Vp6en/fm1aQLOrpIRGRmp4sWLKyQkRJ06dVLhwoX1zTffqFSpUpKk06dPa9myZerSpYv9PJ48eVKnTp1SVFSUdu3aZV/9wN/fX1u3btWuXbuy9d6z69rFYAsWLFBGRsZdPTaQVwizgAUNHTpUV65cue3c2WLFiikyMjLTIzQ01N7n7NmzWrRokZo2bardu3fbH40aNdLatWu1c+fOTPt99tlnlZCQoG+//VavvPKK/vzzz9vOv7zRsGHDlJCQoGXLlmnz5s06cuSInn766Uz9Spcu7fD8wIEDcnNzswfva4KCguTv768DBw7c9tgHDhxQxYoVM7VXqlTJ/rp0depDxYoVVaDAzWdkHThwQCVLlsy08sON+3JVqVKlsvzsrk2VkKTo6Gj9+eef9vma58+f16JFi9S5c2d72Jau/mq8Y8eO8vPzk6+vr4oXL26/uCw5OTlb9d3o9OnT6tevnwIDA1WwYEEVL17c/tll9xjOfk7XPPTQQw7Pr00POHPmjFPHmzRpkhISEjR37ly1bt1aJ0+edLgobffu3TLG6I033lDx4sUdHsOHD5ckHT9+XNLVlRHOnj2rChUqKCIiQgMHDtTmzZudfOfZFx0drUaNGqlXr14KDAzUk08+qS+++IJgi3yNObOABZUpU0ZPPfWUpk2bpkGDBt3Rvr788kulpaVp3LhxGjduXKbXP/vsM40cOdKhrXz58oqMjJQktW3bVu7u7ho0aJCaN2+eaWT1ZiIiIuz7uJXrR/yud31Yu1/Vr19fYWFh+uKLL9StWzd9++23+vPPPx3WCT579qyaNm0qX19fjRo1SmXLlpW3t7fWr1+v11577ZYh52bnOKv/uHTp0kU///yzBg4cqOrVq8vHx0cZGRlq2bLlXQtS1+YI38g4eSFe3bp17T+/HTp0UOPGjdWtWzft2LHD/n4kacCAAYqKispyH9f+k/XII49oz549WrBggZYuXaqPP/5YEyZM0NSpU9WrVy9JV89vVrW5+h/D6xUsWFArV67U8uXLtXDhQi1evFhz5szRo48+qqVLl970HAFWxsgsYFHXRmfffffdO9rPZ599pipVqujLL7/M9IiMjNSsWbNuu48hQ4bogQce0NChQ++oFmeEhoYqIyMj069vjx07prNnzzqMOt8sjIWGhmrHjh2Z2q/9yv3aPsqWLasdO3bo8uXLt6znyJEjOnfu3C33lVu6dOmixYsXKyUlRXPmzFFYWJj91/vS1Sv9T506pRkzZqhfv35q27atIiMjnbpY71qfGxfmv3FE9MyZM0pMTNSgQYM0cuRIdezYUY899pjKlCmTaZ+u/CfE2c8pN7i7uys+Pl5HjhzRhx9+KEn29+Ph4ZHlqHlkZKTDCH2RIkUUGxurzz//XIcOHVLVqlUd7uIVEBCQ5U0PnBnNv9V5dHNzU4sWLTR+/Hj98ccfevvtt7Vs2TItX77cyXcPWAthFrCosmXL6qmnntI///lPJSUlZWsfhw4d0sqVK9WlSxd16tQp0yM2Nla7d+++7dXg/v7+eu6557RkyRJt3LgxW7U4q3Xr1pKurlZwvfHjx0uS2rRpY28rXLhwlmGhdevWWrNmjcNdy1JTUzVt2jSFhYUpPDxckvTEE0/o5MmT9jBzvWsjatduHnFjnwkTJshms6lVq1auv0kXREdHKy0tTTNnztTixYszrYd6bSTu+hHAS5cuObUecGhoqNzd3bVy5UqH9hu3zeoYUubPSJJ9nWBn7lzl7OeUW5o1a6a6detq4sSJunjxokqUKKFmzZrpn//8Z5bz1U+cOGH/8/VLkklX5/qWK1fOYfm4smXLavv27Q7bbdq0yamVB252Hk+fPp2pb/Xq1SXJ4dhAfsI0A8DChgwZon//+9/asWOHKleunOn1w4cP6z//+U+mdh8fH3Xo0EGzZs2yLy2VldatW6tAgQL67LPPVK9evVvW0q9fP02cOFGjR4/W7Nmzs/eGnFCtWjX16NFD06ZNs/8Kfc2aNZo5c6Y6dOhgv0BKkmrVqqUpU6borbfeUrly5VSiRAk9+uijGjRokD7//HO1atVKffv2VZEiRTRz5kzt27dP8+bNs9+FKiYmRp9++qni4uK0Zs0aNWnSRKmpqfr+++/1wgsvqH379mrXrp2aN2+uIUOGaP/+/apWrZqWLl2qBQsW6OWXX1bZsmWz9T537tyZ5WcXGBjosLRTzZo1Va5cOQ0ZMkRpaWmZbkXcsGFDBQQEqEePHurbt69sNpv+/e9/O/Wrdz8/P3Xu3FkffPCBbDabypYtq++++84+L/QaX19fPfLIIxozZowuX76s4OBgLV261L5m8PVq1aol6erP7pNPPikPDw+1a9cuy5thOPs55aaBAweqc+fOmjFjhp5//nlNmjRJjRs3VkREhHr37q0yZcro2LFjWr16tf73v/9p06ZNkqTw8HA1a9ZMtWrVUpEiRbR27VrNnTtXL730kn3ff/vb3zR+/HhFRUXpmWee0fHjxzV16lRVrlxZKSkpt6zrZudx1KhRWrlypdq0aaPQ0FAdP35ckydPVqlSpRwupAPylTxbRwGA065fmutGPXr0MJJcWprr2rI/ERER5qGHHrrlsZs1a2ZKlChhLl++bF+SaezYsVn27dmzp3F3d3dYjupG15ZRut0yXteW5spqiaHLly+bkSNHmtKlSxsPDw8TEhJiBg8e7LBckjHGJCUlmTZt2pgHHnjASHJYBmnPnj2mU6dOxt/f33h7e5u6deua7777LtOxLly4YIYMGWI/VlBQkOnUqZPZs2ePvc+5c+fMK6+8YkqWLGk8PDxM+fLlzdixY+3Ld12TE0tzZbWU05AhQ4wkU65cuSz3t2rVKlO/fn1TsGBBU7JkSfPqq6+aJUuWZFp2K6sloU6cOGGeeOIJU6hQIRMQEGCee+458/vvv2damut///uf6dixo/H39zd+fn6mc+fO5siRI5mWlDLGmDfffNMEBwcbNzc3h+Wlsjo/znxON/uZymoJsazc6u9Xenq6KVu2rClbtqy5cuWKvaaYmBgTFBRkPDw8THBwsGnbtq2ZO3eufbu33nrL1K1b1/j7+5uCBQuahx9+2Lz99tvm0qVLDvv/z3/+Y8qUKWM8PT1N9erVzZIlS5xamutm5zExMdG0b9/elCxZ0nh6epqSJUuarl27mp07d97yHABWZjMmF29RAwAAAOQi5swCAADAsgizAAAAsCzCLAAAACyLMAsAAADLIswCAADAsgizAAAAsKz77qYJGRkZOnLkiB544AHu7Q4AAHAPMsbo3LlzKlmy5G1vkHLfhdkjR44oJCQkr8sAAADAbRw6dEilSpW6ZZ/7Lsw+8MADkq6eHF9f3zyuBgAAADdKSUlRSEiIPbfdyn0XZq9NLfD19SXMAgAA3MOcmRLKBWAAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMvK0zC7cuVKtWvXTiVLlpTNZtPXX399221WrFihmjVrysvLS+XKldOMGTNyvU4AAADcm/I0zKampqpatWqaNGmSU/337dunNm3aqHnz5tq4caNefvll9erVS0uWLMnlSgEAAHAvKpCXB2/VqpVatWrldP+pU6eqdOnSGjdunCSpUqVK+umnnzRhwgRFRUXlVpkAAAC4R1lqzuzq1asVGRnp0BYVFaXVq1ffdJu0tDSlpKQ4PAAAAJA/5OnIrKuSkpIUGBjo0BYYGKiUlBT9+eefKliwYKZt4uPjNXLkyLtVIgDcn2y2vK4AQG4zJq8ryJKlRmazY/DgwUpOTrY/Dh06lNclAQAAIIdYamQ2KChIx44dc2g7duyYfH19sxyVlSQvLy95eXndjfIAAABwl1lqZLZBgwZKTEx0aEtISFCDBg3yqCIAAADkpTwNs+fPn9fGjRu1ceNGSVeX3tq4caMOHjwo6eoUgZiYGHv/559/Xnv37tWrr76q7du3a/Lkyfriiy/0yiuv5EX5AAAAyGN5GmbXrl2rGjVqqEaNGpKkuLg41ahRQ8OGDZMkHT161B5sJal06dJauHChEhISVK1aNY0bN04ff/wxy3IBAADcp2zG3KOXpuWSlJQU+fn5KTk5Wb6+vnldDgDkD6xmAOR/dzEyupLXLDVnFgAAALgeYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFhWnofZSZMmKSwsTN7e3qpXr57WrFlzy/4TJ05UxYoVVbBgQYWEhOiVV17RxYsX71K1AAAAuJfkaZidM2eO4uLiNHz4cK1fv17VqlVTVFSUjh8/nmX/WbNmadCgQRo+fLi2bdumf/3rX5ozZ45ef/31u1w5AAAA7gV5GmbHjx+v3r17KzY2VuHh4Zo6daoKFSqkTz75JMv+P//8sxo1aqRu3bopLCxMf/nLX9S1a9fbjuYCAAAgf8qzMHvp0iWtW7dOkZGR/1eMm5siIyO1evXqLLdp2LCh1q1bZw+ve/fu1aJFi9S6deubHictLU0pKSkODwAAAOQPBfLqwCdPnlR6eroCAwMd2gMDA7V9+/Yst+nWrZtOnjypxo0byxijK1eu6Pnnn7/lNIP4+HiNHDkyR2sHAADAvSHPLwBzxYoVK/TOO+9o8uTJWr9+vebPn6+FCxfqzTffvOk2gwcPVnJysv1x6NChu1gxAAAAclOejcwWK1ZM7u7uOnbsmEP7sWPHFBQUlOU2b7zxhp5++mn16tVLkhQREaHU1FQ9++yzGjJkiNzcMmdzLy8veXl55fwbAAAAQJ7Ls5FZT09P1apVS4mJifa2jIwMJSYmqkGDBlluc+HChUyB1d3dXZJkjMm9YgEAAHBPyrORWUmKi4tTjx49VLt2bdWtW1cTJ05UamqqYmNjJUkxMTEKDg5WfHy8JKldu3YaP368atSooXr16mn37t1644031K5dO3uoBQAAwP0jT8NsdHS0Tpw4oWHDhikpKUnVq1fX4sWL7ReFHTx40GEkdujQobLZbBo6dKgOHz6s4sWLq127dnr77bfz6i0AAAAgD9nMffb7+ZSUFPn5+Sk5OVm+vr55XQ4A5A82W15XACC33cXI6Epes9RqBgAAAMD1CLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALMvlMLt48WL99NNP9ueTJk1S9erV1a1bN505cyZHiwMAAABuxeUwO3DgQKWkpEiStmzZov79+6t169bat2+f4uLicrxAAAAA4GYKuLrBvn37FB4eLkmaN2+e2rZtq3feeUfr169X69atc7xAAAAA4GZcHpn19PTUhQsXJEnff/+9/vKXv0iSihQpYh+xBQAAAO4Gl0dmGzdurLi4ODVq1Ehr1qzRnDlzJEk7d+5UqVKlcrxAAAAA4GZcHpn98MMPVaBAAc2dO1dTpkxRcHCwJOm///2vWrZsmeMFAgAAADdjM8aYvC7ibkpJSZGfn5+Sk5Pl6+ub1+UAQP5gs+V1BQBy212MjK7ktWytM7tnzx4NHTpUXbt21fHjxyVdHZndunVrdnYHAAAAZIvLYfaHH35QRESEfv31V82fP1/nz5+XJG3atEnDhw/P8QIBAACAm3E5zA4aNEhvvfWWEhIS5OnpaW9/9NFH9csvv+RocQAAAMCtuBxmt2zZoo4dO2ZqL1GihE6ePJkjRQEAAADOcDnM+vv76+jRo5naN2zYYF/ZAAAAALgbXA6zTz75pF577TUlJSXJZrMpIyNDq1at0oABAxQTE5MbNQIAAABZcjnMvvPOO3r44YcVEhKi8+fPKzw8XI888ogaNmyooUOH5kaNAAAAQJayvc7swYMH9fvvv+v8+fOqUaOGypcvn9O15QrWmQWAXMA6s0D+d4+uM+vy7Wyveeihh/TQQw9ld3MAAADgjjkVZuPi4pze4fjx47NdDAAAAOAKp8Lshg0bnNqZjV8zAQAA4C5yKswuX748t+sAAAAAXObyagbXO3TokA4dOpRTtQAAAAAucTnMXrlyRW+88Yb8/PwUFhamsLAw+fn5aejQobp8+XJu1AgAAABkyeXVDPr06aP58+drzJgxatCggSRp9erVGjFihE6dOqUpU6bkeJEAAABAVlxeZ9bPz0+zZ89Wq1atHNoXLVqkrl27Kjk5OUcLzGmsMwsAuYALgIH87x5dZ9blaQZeXl4KCwvL1F66dGl5enq6ujsAAAAg21wOsy+99JLefPNNpaWl2dvS0tL09ttv66WXXsrR4gAAAIBbcXnO7IYNG5SYmKhSpUqpWrVqkqRNmzbp0qVLatGihR5//HF73/nz5+dcpQAAAMANXA6z/v7+euKJJxzaQkJCcqwgAAAAwFkuh9np06fnRh0AAACAy+7opgkAAABAXnJ5ZPbUqVMaNmyYli9fruPHjysjI8Ph9dOnT+dYcQAAAMCtuBxmn376ae3evVvPPPOMAgMDZWNtQQAAAOQRl8Psjz/+qJ9++sm+kgEAAACQV1yeM/vwww/rzz//zI1aAAAAAJe4HGYnT56sIUOG6IcfftCpU6eUkpLi8AAAAADuFpfDrL+/v1JSUvToo4+qRIkSCggIUEBAgPz9/RUQEOByAZMmTVJYWJi8vb1Vr149rVmz5pb9z549qxdffFEPPvigvLy8VKFCBS1atMjl4wIAAMD6XJ4z2717d3l4eGjWrFl3fAHYnDlzFBcXp6lTp6pevXqaOHGioqKitGPHDpUoUSJT/0uXLumxxx5TiRIlNHfuXAUHB+vAgQPy9/fPdg0AAACwLpsxxriyQaFChbRhwwZVrFjxjg9er1491alTRx9++KEkKSMjQyEhIerTp48GDRqUqf/UqVM1duxYbd++XR4eHtk6ZkpKivz8/JScnCxfX987qh8A8P+xsg2Q/7kWGe+IK3nN5WkGtWvX1qFDh7Jd3DWXLl3SunXrFBkZ+X/FuLkpMjJSq1evznKbb775Rg0aNNCLL76owMBAValSRe+8847S09Nvepy0tDTm9QIAAORTLk8z6NOnj/r166eBAwcqIiIi0whp1apVndrPyZMnlZ6ersDAQIf2wMBAbd++Pctt9u7dq2XLlql79+5atGiRdu/erRdeeEGXL1/W8OHDs9wmPj5eI0eOdKomAAAAWIvL0wzc3DIP5tpsNhljZLPZbjlKer0jR44oODhYP//8sxo0aGBvf/XVV/XDDz/o119/zbRNhQoVdPHiRe3bt0/u7u6SpPHjx2vs2LE6evRolsdJS0tTWlqa/XlKSopCQkKYZgAAOYlpBkD+d49OM3B5ZHbfvn3ZLux6xYoVk7u7u44dO+bQfuzYMQUFBWW5zYMPPigPDw97kJWkSpUqKSkpSZcuXZKnp2embby8vOTl5ZUjNQMAAODe4nKYDQ0NzZEDe3p6qlatWkpMTFSHDh0kXb0ALDExUS+99FKW2zRq1EizZs1SRkaGfYR4586devDBB7MMsgAAAMjfXA6z1/zxxx86ePCgLl265ND+17/+1el9xMXFqUePHqpdu7bq1q2riRMnKjU1VbGxsZKkmJgYBQcHKz4+XpL097//XR9++KH69eunPn36aNeuXXrnnXfUt2/f7L4NAAAAWJjLYXbv3r3q2LGjtmzZYp8rK8m+3qyzc2YlKTo6WidOnNCwYcOUlJSk6tWra/HixfaLwg4ePOgwRzckJERLlizRK6+8oqpVqyo4OFj9+vXTa6+95urbAAAAQD7g8gVg7dq1k7u7uz7++GOVLl1aa9as0alTp9S/f3+99957atKkSW7VmiNYZxYAcgEXgAH5X365AGz16tVatmyZihUrJjc3N7m5ualx48aKj49X3759tWHDhmwXDgAAALjC5ZsmpKen64EHHpB0dUWCI0eOSLp6YdiOHTtytjoAAADgFlwema1SpYo2bdqk0qVLq169ehozZow8PT01bdo0lSlTJjdqBAAAALLkcpgdOnSoUlNTJUmjRo1S27Zt1aRJExUtWlRz5szJ8QIBAACAm3H5ArCsnD59WgEBAfYVDe5lXAAGALnAAt//AO7QPXoBmMtzZk+cOJGprUiRIrLZbNqyZYuruwMAAACyzeUwGxERoYULF2Zqf++991S3bt0cKQoAAABwhsthNi4uTk888YT+/ve/688//9Thw4fVokULjRkzRrNmzcqNGgEAAIAsuRxmX331Va1evVo//vijqlatqqpVq8rLy0ubN29Wx44dc6NGAAAAIEsuh1lJKleunKpUqaL9+/crJSVF0dHRCgoKyunaAAAAgFtyOcyuWrVKVatW1a5du7R582ZNmTJFffr0UXR0tM6cOZMbNQIAAABZcjnMPvroo4qOjtYvv/yiSpUqqVevXtqwYYMOHjyoiIiI3KgRAAAAyJLLN01YunSpmjZt6tBWtmxZrVq1Sm+//XaOFQYAAADcTo7cNMFKuGkCAOQCbpoA5H9Wv2lC69atlZycbH8+evRonT171v781KlTCg8Pd71aAAAAIJucDrNLlixRWlqa/fk777yj06dP259fuXJFO3bsyNnqAAAAgFtwOszeOBvhPpudAAAAgHtQttaZBQAAAO4FTodZm80m2w0T/G98DgAAANxNTi/NZYxRz5495eXlJUm6ePGinn/+eRUuXFiSHObTAgAAAHeD02G2R48eDs+feuqpTH1iYmLuvCIAAADASU6H2enTp+dmHQAAAIDLuAAMAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYllNhtmbNmjpz5owkadSoUbpw4UKuFgUAAAA4w6kwu23bNqWmpkqSRo4cqfPnz+dqUQAAAIAznFqaq3r16oqNjVXjxo1ljNF7770nHx+fLPsOGzYsRwsEAAAAbsZmjDG367Rjxw4NHz5ce/bs0fr16xUeHq4CBTLnYJvNpvXr1+dKoTklJSVFfn5+Sk5Olq+vb16XAwD5A7c3B/K/20fGHONKXnMqzF7Pzc1NSUlJKlGixB0VmVcIswCQCwizQP53j4ZZp+8Adk1GRka2CwMAAAByksthVpL27NmjiRMnatu2bZKk8PBw9evXT2XLls3R4gAAAIBbcXmd2SVLlig8PFxr1qxR1apVVbVqVf3666+qXLmyEhIScqNGAAAAIEsuz5mtUaOGoqKiNHr0aIf2QYMGaenSpVwABgD3I+bMAvnfPTpn1uWR2W3btumZZ57J1P63v/1Nf/zxh6u7AwAAALLN5TBbvHhxbdy4MVP7xo0bLbvCAQAAAKzJ5QvAevfurWeffVZ79+5Vw4YNJUmrVq3Su+++q7i4uBwvEAAAALgZl+fMGmM0ceJEjRs3TkeOHJEklSxZUgMHDlTfvn1lu8fnTTFnFgBywT3+3Q8gB9yjc2ZdDrPXO3funCTpgQceyO4u7jrCLADkAsIskP/do2E2W+vMXmOlEAsAAID8x+ULwAAAAIB7BWEWAAAAlkWYBQAAgGW5FGYvX76sFi1aaNeuXblVDwAAAOA0l8Ksh4eHNm/enFu1AAAAAC5xeZrBU089pX/961+5UQsAAADgEpeX5rpy5Yo++eQTff/996pVq5YKFy7s8Pr48eNzrDgAAADgVlwOs7///rtq1qwpSdq5c6fDa/f63b8AAACQv7gcZpcvX54bdQAAAAAuy/bSXLt379aSJUv0559/SpLu4K64AAAAQLa4HGZPnTqlFi1aqEKFCmrdurWOHj0qSXrmmWfUv3//HC8QAAAAuBmXw+wrr7wiDw8PHTx4UIUKFbK3R0dHa/HixTlaHAAAAHArLs+ZXbp0qZYsWaJSpUo5tJcvX14HDhzIscIAAACA23F5ZDY1NdVhRPaa06dPy8vLK0eKAgAAAJzhcpht0qSJPv30U/tzm82mjIwMjRkzRs2bN8/R4gAAAIBbcXmawZgxY9SiRQutXbtWly5d0quvvqqtW7fq9OnTWrVqVW7UCAAAAGTJ5ZHZKlWqaOfOnWrcuLHat2+v1NRUPf7449qwYYPKli2bGzUCAAAAWbKZ+2yB2JSUFPn5+Sk5OVm+vr55XQ4A5A/cARLI/+5iZHQlr7k8zUCSzpw5o3/961/atm2bJCk8PFyxsbEqUqRIdnYHAAAAZIvL0wxWrlypsLAw/eMf/9CZM2d05swZ/eMf/1Dp0qW1cuXK3KgRAAAAyJLL0wwiIiLUoEEDTZkyRe7u7pKk9PR0vfDCC/r555+1ZcuWXCk0pzDNAAByAdMMgPzvHp1m4PLI7O7du9W/f397kJUkd3d3xcXFaffu3a5XCwAAAGSTy2G2Zs2a9rmy19u2bZuqVauWI0UBAAAAznDqArDNmzfb/9y3b1/169dPu3fvVv369SVJv/zyiyZNmqTRo0fnTpUAAABAFpyaM+vm5iabzabbdbXZbEpPT8+x4nIDc2YBIBcwZxbI/+7RObNOjczu27cvRwoDAAAAcpJTYTY0NDS36wAAAABclq2bJhw5ckQ//fSTjh8/royMDIfX+vbtmyOFAQAAALfjcpidMWOGnnvuOXl6eqpo0aKyXTdPymazEWYBAABw17gcZt944w0NGzZMgwcPlpubyyt7AQAAADnG5TR64cIFPfnkkwRZAAAA5DmXE+kzzzyjL7/8MjdqAQAAAFzi1Dqz10tPT1fbtm31559/KiIiQh4eHg6vjx8/PkcLzGmsMwsAuYB1ZoH8z8rrzF4vPj5eS5YsUcWKFSUp0wVgAAAAwN3icpgdN26cPvnkE/Xs2TMXygEAAACc5/KcWS8vLzVq1Cg3agEAAABc4nKY7devnz744IMcLWLSpEkKCwuTt7e36tWrpzVr1ji13ezZs2Wz2dShQ4ccrQcAAADW4PI0gzVr1mjZsmX67rvvVLly5UwXgM2fP9+l/c2ZM0dxcXGaOnWq6tWrp4kTJyoqKko7duxQiRIlbrrd/v37NWDAADVp0sTVtwAAAIB8wuWRWX9/fz3++ONq2rSpihUrJj8/P4eHq8aPH6/evXsrNjZW4eHhmjp1qgoVKqRPPvnkptukp6ere/fuGjlypMqUKePyMQEAAJA/uDwyO3369Bw7+KVLl7Ru3ToNHjzY3ubm5qbIyEitXr36ptuNGjVKJUqU0DPPPKMff/zxlsdIS0tTWlqa/XlKSsqdFw4AAIB7Qp7exuvkyZNKT09XYGCgQ3tgYKCSkpKy3Oann37Sv/71L3300UdOHSM+Pt5h5DgkJOSO6wYAAMC9weWR2dKlS99yPdm9e/feUUG3cu7cOT399NP66KOPVKxYMae2GTx4sOLi4uzPU1JSCLQAAAD5hMth9uWXX3Z4fvnyZW3YsEGLFy/WwIEDXdpXsWLF5O7urmPHjjm0Hzt2TEFBQZn679mzR/v371e7du3sbRkZGZKkAgUKaMeOHSpbtqzDNl5eXvLy8nKpLgAAAFiDy2G2X79+WbZPmjRJa9eudWlfnp6eqlWrlhITE+3La2VkZCgxMVEvvfRSpv4PP/ywtmzZ4tA2dOhQnTt3Tu+//z4jrgAAAPcZl8PszbRq1UqDBw92+QKxuLg49ejRQ7Vr11bdunU1ceJEpaamKjY2VpIUExOj4OBgxcfHy9vbW1WqVHHY3t/fX5IytQMAACD/y7EwO3fuXBUpUsTl7aKjo3XixAkNGzZMSUlJql69uhYvXmy/KOzgwYNyc8vT69QAAABwj7IZY4wrG9SoUcPhAjBjjJKSknTixAlNnjxZzz77bI4XmZNSUlLk5+en5ORk+fr65nU5AJA/3OLCYAD5hGuR8Y64ktdcHpm98daxbm5uKl68uJo1a6aHH37Y1d0BAAAA2ebyyKzVMTILALmAkVkg/7tHR2aZjAoAAADLcnqagZub2y1vliBJNptNV65cueOiAAAAAGc4HWa/+uqrm762evVq/eMf/7DfwAAAAAC4G5wOs+3bt8/UtmPHDg0aNEjffvutunfvrlGjRuVocQAAAMCtZGvO7JEjR9S7d29FREToypUr2rhxo2bOnKnQ0NCcrg8AAAC4KZfCbHJysl577TWVK1dOW7duVWJior799lvuvgUAAIA84fQ0gzFjxujdd99VUFCQPv/88yynHQAAAAB3k9PrzLq5ualgwYKKjIyUu7v7TfvNnz8/x4rLDawzCwC5gHVmgfzvHl1n1umR2ZiYmNsuzQUAAADcTU6H2RkzZuRiGQAAAIDruAMYAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALOueCLOTJk1SWFiYvL29Va9ePa1Zs+amfT/66CM1adJEAQEBCggIUGRk5C37AwAAIP/K8zA7Z84cxcXFafjw4Vq/fr2qVaumqKgoHT9+PMv+K1asUNeuXbV8+XKtXr1aISEh+stf/qLDhw/f5coBAACQ12zGGJOXBdSrV0916tTRhx9+KEnKyMhQSEiI+vTpo0GDBt12+/T0dAUEBOjDDz9UTEzMbfunpKTIz89PycnJ8vX1veP6AQCSbLa8rgBAbruLkdGVvJanI7OXLl3SunXrFBkZaW9zc3NTZGSkVq9e7dQ+Lly4oMuXL6tIkSJZvp6WlqaUlBSHBwAAAPKHPA2zJ0+eVHp6ugIDAx3aAwMDlZSU5NQ+XnvtNZUsWdIhEF8vPj5efn5+9kdISMgd1w0AAIB7Q57Pmb0To0eP1uzZs/XVV1/J29s7yz6DBw9WcnKy/XHo0KG7XCUAAAByS4G8PHixYsXk7u6uY8eOObQfO3ZMQUFBt9z2vffe0+jRo/X999+ratWqN+3n5eUlLy+vHKkXAAAA95Y8HZn19PRUrVq1lJiYaG/LyMhQYmKiGjRocNPtxowZozfffFOLFy9W7dq170apAAAAuAfl6cisJMXFxalHjx6qXbu26tatq4kTJyo1NVWxsbGSpJiYGAUHBys+Pl6S9O6772rYsGGaNWuWwsLC7HNrfXx85OPjk2fvAwAAAHdfnofZ6OhonThxQsOGDVNSUpKqV6+uxYsX2y8KO3jwoNzc/m8AecqUKbp06ZI6derksJ/hw4drxIgRd7N0AAAA5LE8X2f2bmOdWQDIBawzC+R/rDMLAAAA5CzCLAAAACyLMAsAAADLIswCAADAsgizAAAAsCzCLAAAACyLMAsAAADLIswCAADAsgizAAAAsCzCLAAAACyLMAsAAADLIswCAADAsgizAAAAsCzCLAAAACyLMAsAAADLIswCAADAsgizAAAAsCzCLAAAACyLMAsAAADLIswCAADAsgizAAAAsCzCLAAAACyLMAsAAADLIswCAADAsgizAAAAsCzCLAAAACyLMAsAAADLIswCAADAsgizAAAAsCzCLAAAACyLMAsAAADLIswCAADAsgizAAAAsCzCLAAAACyLMAsAAADLIswCAADAsgizAAAAsCzCLAAAACyLMAsAAADLIswCAADAsgizAAAAsCzCLAAAACyLMAsAAADLIswCAADAsgizAAAAsCzCLAAAACyLMAsAAADLIswCAADAsgizAAAAsCzCLAAAACyLMAsAAADLIswCAADAsgizAAAAsCzCLAAAACyLMAsAAADLIswCAADAsgizAAAAsCzCLAAAACyLMAsAAADLIswCAADAsgizAAAAsCzCLAAAACyLMAsAAADLIswCAADAsgizAAAAsCzCLAAAACyLMAsAAADLIswCAADAsgizAAAAsCzCLAAAACyLMAsAAADLIswCAADAsgizAAAAsCzCLAAAACyLMAsAAADLIswCAADAsgizAAAAsKx7IsxOmjRJYWFh8vb2Vr169bRmzZpb9v/yyy/18MMPy9vbWxEREVq0aNFdqhQAAAD3kjwPs3PmzFFcXJyGDx+u9evXq1q1aoqKitLx48ez7P/zzz+ra9eueuaZZ7RhwwZ16NBBHTp00O+//36XKwcAAEBesxljTF4WUK9ePdWpU0cffvihJCkjI0MhISHq06ePBg0alKl/dHS0UlNT9d1339nb6tevr+rVq2vq1Km3PV5KSor8/PyUnJwsX1/fnHsjAHA/s9nyugIAue0uRkZX8lqBu1RTli5duqR169Zp8ODB9jY3NzdFRkZq9erVWW6zevVqxcXFObRFRUXp66+/zrJ/Wlqa0tLS7M+Tk5MlXT1JAAAAcNJdzE7XcpozY655GmZPnjyp9PR0BQYGOrQHBgZq+/btWW6TlJSUZf+kpKQs+8fHx2vkyJGZ2kNCQrJZNQAAwH3Iz++uH/LcuXPyu81x8zTM3g2DBw92GMnNyMjQ6dOnVbRoUdn4tRhySUpKikJCQnTo0CGmswDId/iOQ24zxujcuXMqWbLkbfvmaZgtVqyY3N3ddezYMYf2Y8eOKSgoKMttgoKCXOrv5eUlLy8vhzZ/f//sFw24wNfXly96APkW33HITbcbkb0mT1cz8PT0VK1atZSYmGhvy8jIUGJioho0aJDlNg0aNHDoL0kJCQk37Q8AAID8K8+nGcTFxalHjx6qXbu26tatq4kTJyo1NVWxsbGSpJiYGAUHBys+Pl6S1K9fPzVt2lTjxo1TmzZtNHv2bK1du1bTpk3Ly7cBAACAPJDnYTY6OlonTpzQsGHDlJSUpOrVq2vx4sX2i7wOHjwoN7f/G0Bu2LChZs2apaFDh+r1119X+fLl9fXXX6tKlSp59RaATLy8vDR8+PBMU1wAID/gOw73kjxfZxYAAADIrjy/AxgAAACQXYRZAAAAWBZhFgAAAJZFmAUAAIBlEWZxX0tKSlKfPn1UpkwZeXl5KSQkRO3atcu0lvG9YMaMGdzwA0AmPXv2VIcOHfK6jFzRrFkzvfzyy3ldBu5xeb40F5BX9u/fr0aNGsnf319jx45VRESELl++rCVLlujFF1/U9u3bXd7npUuX5Onpman98uXL8vDwyImyAeCektX3Xnp6umw2m8PSmkBu4acM960XXnhBNptNa9as0RNPPKEKFSqocuXKiouL0y+//CLp6jrH7du3l4+Pj3x9fdWlSxeH2ymPGDFC1atX18cff6zSpUvL29tbkmSz2TRlyhT99a9/VeHChfX2229LkhYsWKCaNWvK29tbZcqU0ciRI3XlyhX7/s6ePavnnntOgYGB8vb2VpUqVfTdd99pxYoVio2NVXJysmw2m2w2m0aMGHH3ThYAS2jWrJn69u2rV199VUWKFFFQUFCm74qbfc9cM2/ePFWuXFleXl4KCwvTuHHjHLYPCwvTm2++qZiYGPn6+urZZ5+1/+bom2++UXh4uLy8vHTw4EGlpaVpwIABCg4OVuHChVWvXj2tWLHCYX+rVq1Ss2bNVKhQIQUEBCgqKkpnzpxRz5499cMPP+j999+3f+/t378/l84cLM0A96FTp04Zm81m3nnnnZv2SU9PN9WrVzeNGzc2a9euNb/88oupVauWadq0qb3P8OHDTeHChU3Lli3N+vXrzaZNm4wxxkgyJUqUMJ988onZs2ePOXDggFm5cqXx9fU1M2bMMHv27DFLly41YWFhZsSIEfbj1a9f31SuXNksXbrU7Nmzx3z77bdm0aJFJi0tzUycONH4+vqao0ePmqNHj5pz587l6jkCYA09evQw7du3N8YY07RpU+Pr62tGjBhhdu7caWbOnGlsNptZunSpMebW3zPGGLN27Vrj5uZmRo0aZXbs2GGmT59uChYsaKZPn24/XmhoqPH19TXvvfee2b17t9m9e7eZPn268fDwMA0bNjSrVq0y27dvN6mpqaZXr16mYcOGZuXKlWb37t1m7NixxsvLy+zcudMYY8yGDRuMl5eX+fvf/242btxofv/9d/PBBx+YEydOmLNnz5oGDRqY3r1727/3rly5clfPLayBMIv70q+//mokmfnz59+0z9KlS427u7s5ePCgvW3r1q1GklmzZo0x5mqY9fDwMMePH3fYVpJ5+eWXHdpatGiRKTz/+9//Ng8++KAxxpglS5YYNzc3s2PHjizrmT59uvHz83P6PQK4P9wYZhs3buzwep06dcxrr71mjLn990y3bt3MY4895tA2cOBAEx4ebn8eGhpqOnTo4NBn+vTpRpLZuHGjve3AgQPG3d3dHD582KFvixYtzODBg40xxnTt2tU0atTopu+tadOmpl+/fjd9HTDGGObM4r5knLjx3bZt2xQSEqKQkBB7W3h4uPz9/bVt2zbVqVNHkhQaGqrixYtn2r527doOzzdt2qRVq1bZpxxIV+eVXbx4URcuXNDGjRtVqlQpVahQIbtvCwBUtWpVh+cPPvigjh8/Lkm3/Z7Ztm2b2rdv79DWqFEjTZw4Uenp6XJ3d5eU+ftNkjw9PR2OvWXLFqWnp2c6VlpamooWLWqvp3Pnzi6+Q8ARYRb3pfLly8tms2XrIq8bFS5c2Kn28+fPa+TIkXr88ccz9fX29lbBggXvuBYAuPFiU5vNpoyMDEnKse+ZrL73ChYsKJvNZn9+/vx5ubu7a926dfYQfI2Pj0+O1oP7GxeA4b5UpEgRRUVFadKkSUpNTc30+tmzZ1WpUiUdOnRIhw4dsrf/8ccfOnv2rMLDw10+Zs2aNbVjxw6VK1cu08PNzU1Vq1bV//73P+3cuTPL7T09PZWenu7ycQHgmtt9z1SqVEmrVq1yaFu1apUqVKiQKZDeTo0aNZSenq7jx49n+s4LCgqy13OrpRD53oMzCLO4b02aNEnp6emqW7eu5s2bp127dmnbtm36xz/+oQYNGigyMlIRERHq3r271q9frzVr1igmJkZNmzbN8ldstzNs2DB9+umnGjlypLZu3apt27Zp9uzZGjp0qCSpadOmeuSRR/TEE08oISFB+/bt03//+18tXrxY0tUriM+fP6/ExESdPHlSFy5cyNHzASD/u933TP/+/ZWYmKg333xTO3fu1MyZM/Xhhx9qwIABLh+rQoUK6t69u2JiYjR//nzt27dPa9asUXx8vBYuXChJGjx4sH777Te98MIL2rx5s7Zv364pU6bo5MmTkq5+7/3666/av3+/Tp48aR9hBq5HmMV9q0yZMlq/fr2aN2+u/v37q0qVKnrssceUmJioKVOmyGazacGCBQoICNAjjzyiyMhIlSlTRnPmzMnW8aKiovTdd99p6dKlqlOnjurXr68JEyYoNDTU3mfevHmqU6eOunbtqvDwcL366qv2UYmGDRvq+eefV3R0tIoXL64xY8bkyHkAcH+51fdMzZo19cUXX2j27NmqUqWKhg0bplGjRqlnz57ZOtb06dMVExOj/v37q2LFiurQoYN+++03PfTQQ5KuBt6lS5dq06ZNqlu3rho0aKAFCxaoQIGrsyAHDBggd3d3hYeHq3jx4jp48GCOnAPkLzbjzJUwAAAAwD2IkVkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAuAP79++XzWbTxo0bc/1YM2bMkL+/f64f514wYsQIVa9ePa/LAGABhFkA+VbPnj1ls9kyPVq2bJnXpd1WWFiYJk6c6NAWHR2tnTt35vqxmzVrZj9X3t7eqlChguLj45WXN4zs2bOnOnTokGfHB3DvKpDXBQBAbmrZsqWmT5/u0Obl5ZVH1dyZggULqmDBgnflWL1799aoUaOUlpamZcuW6dlnn5W/v7/+/ve/35XjA4CzGJkFkK95eXkpKCjI4REQECBJ6tatm6Kjox36X758WcWKFdOnn34qSVq8eLEaN24sf39/FS1aVG3bttWePXtueryspgJ8/fXXstls9ud79uxR+/btFRgYKB8fH9WpU0fff/+9/fVmzZrpwIEDeuWVV+wjpDfb95QpU1S2bFl5enqqYsWK+ve//+3wus1m08cff6yOHTuqUKFCKl++vL755pvbnrdChQopKChIoaGhio2NVdWqVZWQkGB/PS0tTQMGDFBwcLAKFy6sevXqacWKFfbXDxw4oHbt2ikgIECFCxdW5cqVtWjRIqfP0fVGjBihmTNnasGCBfbzcf2xANzfCLMA7lvdu3fXt99+q/Pnz9vblixZogsXLqhjx46SpNTUVMXFxWnt2rVKTEyUm5ubOnbsqIyMjGwf9/z582rdurUSExO1YcMGtWzZUu3atdPBgwclSfPnz1epUqU0atQoHT16VEePHs1yP1999ZX69eun/v376/fff9dzzz2n2NhYLV++3KHfyJEj1aVLF23evFmtW7dW9+7ddfr0aadqNcboxx9/1Pbt2+Xp6Wlvf+mll7R69WrNnj1bmzdvVufOndWyZUvt2rVLkvTiiy8qLS1NK1eu1JYtW/Tuu+/Kx8cnO6dLAwYMUJcuXdSyZUv7+WjYsGG29gUgHzIAkE/16NHDuLu7m8KFCzs83n77bWOMMZcvXzbFihUzn376qX2brl27mujo6Jvu88SJE0aS2bJlizHGmH379hlJZsOGDcYYY6ZPn278/Pwctvnqq6/M7b5uK1eubD744AP789DQUDNhwgSHPjfuu2HDhqZ3794OfTp37mxat25tfy7JDB061P78/PnzRpL573//e9NamjZtajw8PEzhwoWNh4eHkWS8vb3NqlWrjDHGHDhwwLi7u5vDhw87bNeiRQszePBgY4wxERERZsSIEVnu35lzNHz4cFOtWjX78x49epj27dvftGYA9y9GZgHka82bN9fGjRsdHs8//7wkqUCBAurSpYs+++wzSVdHYRcsWKDu3bvbt9+1a5e6du2qMmXKyNfXV2FhYZJkH0XNjvPnz2vAgAGqVKmS/P395ePjo23btrm8z23btqlRo0YObY0aNdK2bdsc2qpWrWr/c+HCheXr66vjx4/fct/du3fXxo0btWrVKrVq1UpDhgyxj4Zu2bJF6enpqlChgnx8fOyPH374wT4Fo2/fvnrrrbfUqFEjDR8+XJs3b3bpvQGAs7gADEC+VrhwYZUrV+6mr3fv3l1NmzbV8ePHlZCQoIIFCzqsdtCuXTuFhobqo48+UsmSJZWRkaEqVaro0qVLWe7Pzc0t01X/ly9fdng+YMAAJSQk6L333lO5cuVUsGBBderU6ab7vFMeHh4Oz202222nSfj5+dnP2xdffKFy5cqpfv36ioyM1Pnz5+Xu7q5169bJ3d3dYbtrUwl69eqlqKgoLVy4UEuXLlV8fLzGjRunPn36OHWOAMBZjMwCuK81bNhQISEhmjNnjj777DN17tzZHv5OnTqlHTt2aOjQoWrRooUqVaqkM2fO3HJ/xYsX17lz55Sammpvu3EN2lWrVqlnz57q2LGjIiIiFBQUpP379zv08fT0VHp6+i2PValSJa1atSrTvsPDw2/zrl3j4+Ojfv36acCAATLGqEaNGkpPT9fx48dVrlw5h0dQUJB9u5CQED3//POaP3+++vfvr48++kiSc+foRs6cDwD3J8IsgHwtLS1NSUlJDo+TJ0869OnWrZumTp2qhIQEhykGAQEBKlq0qKZNm6bdu3dr2bJliouLu+Xx6tWrp0KFCun111/Xnj17NGvWLM2YMcOhT/ny5TV//nxt3LhRmzZtUrdu3TKNlIaFhWnlypU6fPhwpnqvGThwoGbMmKEpU6Zo165dGj9+vObPn68BAwa4cIac89xzz2nnzp2aN2+eKlSooO7duysmJkbz58/Xvn37tGbNGsXHx2vhwoWSpJdffllLlizRvn37tH79ei1fvlyVKlVy+hzdKCwsTJs3b9aOHTt08uRJRnIB2BFmAeRrixcv1oMPPujwaNy4sUOf7t27648//lBwcLDDHFQ3NzfNnj1b69atU5UqVfTKK69o7NixtzxekSJF9J///EeLFi1SRESEPv/8c40YMcKhz/jx4xUQEKCGDRuqXbt2ioqKUs2aNR36jBo1Svv371fZsmVVvHjxLI/VoUMHvf/++3rvvfdUuXJl/fOf/9T06dPVrFkz50+Qk4oUKaKYmBiNGDFCGRkZmj59umJiYtS/f39VrFhRHTp00G+//aaHHnpIkpSenq4XX3xRlSpVUsuWLVWhQgVNnjzZ6XN0o969e6tixYqqXbu2ihcvnmlEGsD9y2ZunLgEAAAAWAQjswAAALAswiwAAAAsizALAAAAyyLMAgAAwLIIswAAALAswiwAAAAsizALAAAAyyLMAgAAwLIIswAAALAswiwAAAAsizALAAAAy/p/iqNTX/NXSNwAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: Generate a smart contract for a simple token on NEAR protocol with appropriate comments.\n",
            "Expected Code: Expected code with comments\n",
            "Generated Code: Generate a smart contract for a simple token on NEAR protocol with appropriate comments.\n",
            "\n",
            "## How to use\n",
            "\n",
            "```bash\n",
            "npm install\n",
            "npm run build\n",
            "npm run deploy\n",
            "```\n",
            "\n",
            "## License\n",
            "\n",
            "[MIT](LICENSE)\n",
            "----------------------------------------------------\n",
            "Accuracy: 0.0%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAIjCAYAAAAQgZNYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABIL0lEQVR4nO3deVxV1f7/8fcB5YAg4AgOJA45oDgrjmFpYaZlWZp6A63MSk1DM+2aUyXllJXTbcK66VUzs1uaqKQNZpmzlrM43BRxBMUChfX7w5/n2wlQjoKw8fV8PM7j4V5n7b0/Z4und4u117YZY4wAAAAAC3Ir6AIAAACA60WYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBW4xY8eOlc1mK+gykI8OHjwom82mOXPmFMj558yZI5vNpoMHDxbI+Qsz/v0BeY8wC8AhIyNDFStWlM1m09dff11gdQQHB8tms13zVVBhLbeuBJecXomJiQVd4g2ZMGGClixZUtBlOPn7z463t7eaN2+ujz/+uKBLy1FhvI6AlRQr6AIAFB7ffPONjh07puDgYM2dO1f33ntvgdQxbdo0nT9/3rG9bNky/ec//9Gbb76psmXLOtpbtWpVEOW5bNasWfLx8cnS7u/vf/OLyUMTJkzQww8/rK5duzq1P/bYY3r00Udlt9sLpK6GDRtq6NChkqRjx47p/fffV1RUlNLS0tSvX78CqelqcrqOAHKHMAvA4ZNPPlHjxo0VFRWll156SampqfL29r7pdfz9P+qJiYn6z3/+o65duyo4ODjH/Qqq3mt5+OGHnUJ4Uefu7i53d/cCO3+lSpX0j3/8w7Hdp08fVatWTW+++WahDLMAbgzTDIAi7IcfflCzZs3k6emp6tWr61//+leOff/44w99/vnnevTRR9W9e3f98ccf+uKLL7L069Onj3x8fHTgwAFFRETI29tbFStW1Pjx42WMcfS7Mm9z8uTJevPNN1WlShV5eXkpPDxcO3bsuOHPdqWO/fv3q1OnTipZsqR69+4tScrMzNS0adNUt25deXp6KiAgQP3799eZM2eyHOfrr79W27Zt5e3trZIlS+q+++7Tr7/+6tQnMTFRffv2VeXKlWW321WhQgU98MADeTIn9Pjx4ypWrJjGjRuX5b3du3fLZrNp+vTpkqTTp09r2LBhCg0NlY+Pj3x9fXXvvfdq69at1zxPu3bt1K5duyztffr0yfI/CJMnT1arVq1UpkwZeXl5qUmTJlq0aJFTH5vNptTUVH300UeOX+n36dNHUs5zZmfOnKm6devKbrerYsWKGjBggM6ePZulznr16um3337TnXfeqRIlSqhSpUqaOHHiNT9jTsqVK6fatWtr//79Tu25/TnZsGGDIiIiVLZsWXl5ealq1ap6/PHHHe+vWbNGNptNa9ascdovN3OXr3Ydz507pyFDhig4OFh2u13ly5fX3XffrU2bNl33tQCKIkZmgSJq+/btuueee1SuXDmNHTtWly5d0pgxYxQQEJBt///+9786f/68Hn30UQUGBqpdu3aaO3euevXqlaVvRkaGOnbsqBYtWmjixIlavny5xowZo0uXLmn8+PFOfT/++GOdO3dOAwYM0J9//qm33npLd911l7Zv355jLbl16dIlRUREqE2bNpo8ebJKlCghSerfv7/mzJmjvn376rnnnlNCQoKmT5+uzZs3a+3atSpevLgk6d///reioqIUERGhN954QxcuXNCsWbPUpk0bbd682RHyunXrpl9//VWDBg1ScHCwkpKStHLlSh0+fPiqI8VXnD59OktbsWLF5O/vr4CAAIWHh2vhwoUaM2aMU58FCxbI3d1djzzyiCTpwIEDWrJkiR555BFVrVpVx48f17/+9S+Fh4frt99+U8WKFW/gav6ft956S/fff7969+6t9PR0zZ8/X4888oi++uor3XfffZIuX7snn3xSzZs311NPPSVJql69eo7HHDt2rMaNG6cOHTromWee0e7duzVr1iz98ssvTn8nknTmzBl17NhRDz30kLp3765FixbpxRdfVGho6HVNfbl06ZL+97//qVSpUk7tufk5SUpKcvw7GjFihPz9/XXw4EEtXrzY5Tqyc7Xr+PTTT2vRokUaOHCgQkJCdOrUKf3www/auXOnGjdunCfnB4oEA6BI6tq1q/H09DSHDh1ytP3222/G3d3dZPdPv3PnzqZ169aO7XfffdcUK1bMJCUlOfWLiooyksygQYMcbZmZmea+++4zHh4e5sSJE8YYYxISEowk4+XlZf73v/85+v78889Gknn++edz/VkmTZpkJJmEhIQsdYwYMcKp7/fff28kmblz5zq1L1++3Kn93Llzxt/f3/Tr18+pX2JiovHz83O0nzlzxkgykyZNynW9V4wZM8ZIyvZVq1YtR79//etfRpLZvn270/4hISHmrrvucmz/+eefJiMjw6lPQkKCsdvtZvz48U5tkkxsbKyjLTw83ISHh2epMSoqylSpUsWp7cKFC07b6enppl69ek61GGOMt7e3iYqKynLM2NhYp7+vpKQk4+HhYe655x6n+qdPn24kmQ8//NCpTknm448/drSlpaWZwMBA061btyzn+rsqVaqYe+65x5w4ccKcOHHCbN++3Tz22GNGkhkwYICjX25/Tj7//HMjyfzyyy85nnP16tVGklm9erVTe3Z/D1d+Jv4qp+vo5+fnVDOA7DHNACiCMjIyFBcXp65du+q2225ztNepU0cRERFZ+p86dUpxcXHq2bOno61bt26y2WxauHBhtucYOHCg4882m00DBw5Uenq6Vq1a5dSva9euqlSpkmO7efPmCgsL07Jly6778/3VM88847T96aefys/PT3fffbdOnjzpeDVp0kQ+Pj5avXq1JGnlypU6e/asevbs6dTP3d1dYWFhjn5eXl7y8PDQmjVrsp2mkBufffaZVq5c6fSKjY11vP/QQw+pWLFiWrBggaNtx44d+u2339SjRw9Hm91ul5vb5a/tjIwMnTp1Sj4+PqpVq1ae/urZy8vL8eczZ84oOTlZbdu2ve5zrFq1Sunp6RoyZIijfknq16+ffH19tXTpUqf+Pj4+TnNePTw81Lx5cx04cCBX51uxYoXKlSuncuXKKTQ0VP/+97/Vt29fTZo0ydEntz8nV27S++qrr3Tx4sXr+vzXy9/fXz///LOOHj16U88LWA3TDIAi6MSJE/rjjz90++23Z3mvVq1aWYLkggULdPHiRTVq1Ej79u1ztIeFhWnu3LkaMGCAU383NzdVq1bNqa1mzZqSlGWeZHY11KxZM8eQ7IpixYqpcuXKTm179+5VcnKyypcvn+0+SUlJjn6SdNddd2Xbz9fXV9LlAPnGG29o6NChCggIUIsWLdS5c2dFRkYqMDAwV3XecccdV70BrGzZsmrfvr0WLlyoV155RdLlv5NixYrpoYcecvTLzMzUW2+9pZkzZyohIUEZGRmO98qUKZOrWnLjq6++0quvvqotW7YoLS3N0X6966MeOnRI0uWfvb/y8PBQtWrVHO9fUbly5SznKlWqlLZt25ar84WFhenVV19VRkaGduzYoVdffVVnzpyRh4eHo09uf07Cw8PVrVs3jRs3Tm+++abatWunrl27qlevXvm+WsPEiRMVFRWloKAgNWnSRJ06dVJkZGSWf3vArY4wC0Bz586VJLVu3Trb9w8cOFAo/wP615HKKzIzM1W+fHnHZ/q7cuXKOfpJl+csZhdKixX7v6/HIUOGqEuXLlqyZIni4uL08ssvKyYmRt98840aNWqUJ5/l0UcfVd++fbVlyxY1bNhQCxcuVPv27Z1C8IQJE/Tyyy/r8ccf1yuvvKLSpUvLzc1NQ4YMcXyenNhsNqcb9K74ayCWpO+//17333+/7rjjDs2cOVMVKlRQ8eLFFRsbq3nz5uXJZ72WnFZCyK7+7JQtW1YdOnSQJEVERKh27drq3Lmz3nrrLUVHR0vK/c+JzWbTokWL9NNPP+nLL79UXFycHn/8cU2ZMkU//fSTfHx8cgz5f7+2rurevbvatm2rzz//XCtWrNCkSZP0xhtvaPHixQW2bB5QGBFmgSKoXLly8vLycow+/tXu3budthMSEvTjjz9q4MCBCg8Pd3ovMzNTjz32mObNm6dRo0Y5tR84cMAxGitJe/bskaQsN0RlV8OePXtydePU9ahevbpWrVql1q1bO/26PLt+klS+fHlH8LnWcYcOHaqhQ4dq7969atiwoaZMmaJPPvkkT+ru2rWr+vfv75hqsGfPHo0cOdKpz6JFi3TnnXfqgw8+cGo/e/bsNZf+KlWqVLa/pv/7qOhnn30mT09PxcXFOY08/nVaxBW5HamtUqWKpMs/e3/9n6L09HQlJCTk6vrfiPvuu0/h4eGaMGGC+vfvL29v71z/nFzRokULtWjRQq+99prmzZun3r17a/78+XryyScdN5b9fWWGv1/bnFztOlaoUEHPPvusnn32WSUlJalx48Z67bXXCLPAXzBnFiiC3N3dFRERoSVLlujw4cOO9p07dyouLs6p75WRqeHDh+vhhx92enXv3l3h4eHZjl5dWS5KujxiNn36dBUvXlzt27d36rdkyRL9/vvvju3169fr559/zrf/GHfv3l0ZGRmOX9f/1aVLlxyBIyIiQr6+vpowYUK2cyFPnDghSbpw4YL+/PNPp/eqV6+ukiVLOv0K/kb5+/srIiJCCxcu1Pz58+Xh4ZFlvV13d/cso5Offvqp0/XNSfXq1bVr1y7H55KkrVu3au3atVnOYbPZnEYVDx48mO0Tqry9vbMEuOx06NBBHh4eevvtt53q/+CDD5ScnOxYISE/vfjiizp16pTee+89Sbn/OTlz5kyWa96wYUNJcvz9V6lSRe7u7vruu++c+s2cOTNXtWV3HTMyMpScnOzUVr58eVWsWDFPf+6AooCRWaCIGjdunJYvX662bdvq2Wef1aVLl/TOO++obt26TnMP586dq4YNGyooKCjb49x///0aNGiQNm3a5FgOyNPTU8uXL1dUVJTCwsL09ddfa+nSpXrppZccv569okaNGmrTpo2eeeYZpaWladq0aSpTpoyGDx+eL587PDxc/fv3V0xMjLZs2aJ77rlHxYsX1969e/Xpp5/qrbfe0sMPPyxfX1/NmjVLjz32mBo3bqxHH31U5cqV0+HDh7V06VK1bt1a06dP1549e9S+fXt1795dISEhKlasmD7//HMdP35cjz76aK5qWrRoUbZPALv77rudlifr0aOH/vGPf2jmzJmKiIjI8oSwzp07a/z48erbt69atWql7du3a+7cubmaAvL4449r6tSpioiI0BNPPKGkpCTNnj1bdevWVUpKiqPffffdp6lTp6pjx47q1auXkpKSNGPGDNWoUSPLnNUmTZpo1apVmjp1qipWrKiqVasqLCwsy7nLlSunkSNHaty4cerYsaPuv/9+7d69WzNnzlSzZs2cbvbKL/fee6/q1aunqVOnasCAAbn+Ofnoo480c+ZMPfjgg6pevbrOnTun9957T76+vurUqZMkyc/PT4888ojeeecd2Ww2Va9eXV999ZVj3u21ZHcda9WqpcqVK+vhhx9WgwYN5OPjo1WrVumXX37RlClT8vNSAdZTkEspAMhf3377rWnSpInx8PAw1apVM7Nnz3ZaGmjjxo1Gknn55ZdzPMbBgwedltKKiooy3t7eZv/+/eaee+4xJUqUMAEBAWbMmDFOyy5dWZZo0qRJZsqUKSYoKMjY7XbTtm1bs3XrVpc+R05Lc3l7e+e4z7vvvmuaNGlivLy8TMmSJU1oaKgZPny4OXr0qFO/1atXm4iICOPn52c8PT1N9erVTZ8+fcyGDRuMMcacPHnSDBgwwNSuXdt4e3sbPz8/ExYWZhYuXHjNuq+2NJeyWcopJSXFeHl5GUnmk08+yXK8P//80wwdOtRUqFDBeHl5mdatW5t169ZlWXYruyWhjDHmk08+MdWqVTMeHh6mYcOGJi4uLtuluT744ANz++23G7vdbmrXrm1iY2OzXVJq165d5o477nDUfGV5qb8vzXXF9OnTTe3atU3x4sVNQECAeeaZZ8yZM2ec+oSHh5u6detm+ezZ1ZmdKlWqmPvuuy/b9+bMmZPlulzr52TTpk2mZ8+e5rbbbjN2u92UL1/edO7c2fHzccWJEydMt27dTIkSJUypUqVM//79zY4dO3K1NFd21zEtLc288MILpkGDBqZkyZLG29vbNGjQwMycOfOa1wC41diMyeWMegDQ5SdGLVq0SOfPn79qv4MHD6pq1aqaNGmShg0bdpOqAwDcapgzCwAAAMsizAIAAMCyCLMAAACwLObMAgAAwLIYmQUAAIBlEWYBAABgWbfcQxMyMzN19OhRlSxZMtePYgQAAMDNY4zRuXPnVLFiRbm5XX3s9ZYLs0ePHs3xSUcAAAAoPI4cOaLKlStftc8tF2ZLliwp6fLF8fX1LeBqAAAA8HcpKSkKCgpy5LarueXC7JWpBb6+voRZAACAQiw3U0K5AQwAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFkFGma/++47denSRRUrVpTNZtOSJUuuuc+aNWvUuHFj2e121ahRQ3PmzMn3OgEAAFA4FWiYTU1NVYMGDTRjxoxc9U9ISNB9992nO++8U1u2bNGQIUP05JNPKi4uLp8rBQAAQGFUrCBPfu+99+ree+/Ndf/Zs2eratWqmjJliiSpTp06+uGHH/Tmm28qIiIiv8oEAABAIWWpObPr1q1Thw4dnNoiIiK0bt26HPdJS0tTSkqK0wsAAABFQ4GOzLoqMTFRAQEBTm0BAQFKSUnRH3/8IS8vryz7xMTEaNy4cTerRAC4NdlsBV0BgPxmTEFXkC1Ljcxej5EjRyo5OdnxOnLkSEGXBAAAgDxiqZHZwMBAHT9+3Knt+PHj8vX1zXZUVpLsdrvsdvvNKA8AAAA3maVGZlu2bKn4+HintpUrV6ply5YFVBEAAAAKUoGG2fPnz2vLli3asmWLpMtLb23ZskWHDx+WdHmKQGRkpKP/008/rQMHDmj48OHatWuXZs6cqYULF+r5558viPIBAABQwAo0zG7YsEGNGjVSo0aNJEnR0dFq1KiRRo8eLUk6duyYI9hKUtWqVbV06VKtXLlSDRo00JQpU/T++++zLBcAAMAtymZMIb01LZ+kpKTIz89PycnJ8vX1LehyAKBoYDUDoOi7iZHRlbxmqTmzAAAAwF8RZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGUVeJidMWOGgoOD5enpqbCwMK1fv/6q/adNm6ZatWrJy8tLQUFBev755/Xnn3/epGoBAABQmBRomF2wYIGio6M1ZswYbdq0SQ0aNFBERISSkpKy7T9v3jyNGDFCY8aM0c6dO/XBBx9owYIFeumll25y5QAAACgMCjTMTp06Vf369VPfvn0VEhKi2bNnq0SJEvrwww+z7f/jjz+qdevW6tWrl4KDg3XPPfeoZ8+e1xzNBQAAQNFUYGE2PT1dGzduVIcOHf6vGDc3dejQQevWrct2n1atWmnjxo2O8HrgwAEtW7ZMnTp1yvE8aWlpSklJcXoBAACgaChWUCc+efKkMjIyFBAQ4NQeEBCgXbt2ZbtPr169dPLkSbVp00bGGF26dElPP/30VacZxMTEaNy4cXlaOwAAAAqHAr8BzBVr1qzRhAkTNHPmTG3atEmLFy/W0qVL9corr+S4z8iRI5WcnOx4HTly5CZWDAAAgPxUYCOzZcuWlbu7u44fP+7Ufvz4cQUGBma7z8svv6zHHntMTz75pCQpNDRUqampeuqpp/TPf/5Tbm5Zs7ndbpfdbs/7DwAAAIACV2Ajsx4eHmrSpIni4+MdbZmZmYqPj1fLli2z3efChQtZAqu7u7skyRiTf8UCAACgUCqwkVlJio6OVlRUlJo2barmzZtr2rRpSk1NVd++fSVJkZGRqlSpkmJiYiRJXbp00dSpU9WoUSOFhYVp3759evnll9WlSxdHqAUAAMCto0DDbI8ePXTixAmNHj1aiYmJatiwoZYvX+64Kezw4cNOI7GjRo2SzWbTqFGj9Pvvv6tcuXLq0qWLXnvttYL6CAAAAChANnOL/X4+JSVFfn5+Sk5Olq+vb0GXAwBFg81W0BUAyG83MTK6ktcstZoBAAAA8FeEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACW5XKYXb58uX744QfH9owZM9SwYUP16tVLZ86cydPiAAAAgKtxOcy+8MILSklJkSRt375dQ4cOVadOnZSQkKDo6Og8LxAAAADISTFXd0hISFBISIgk6bPPPlPnzp01YcIEbdq0SZ06dcrzAgEAAICcuDwy6+HhoQsXLkiSVq1apXvuuUeSVLp0aceILQAAAHAzuDwy26ZNG0VHR6t169Zav369FixYIEnas2ePKleunOcFAgAAADlxeWR2+vTpKlasmBYtWqRZs2apUqVKkqSvv/5aHTt2zPMCAQAAgJzYjDGmoIu4mVJSUuTn56fk5GT5+voWdDkAUDTYbAVdAYD8dhMjoyt57brWmd2/f79GjRqlnj17KikpSdLlkdlff/31eg4HAAAAXBeXw+y3336r0NBQ/fzzz1q8eLHOnz8vSdq6davGjBmT5wUCAAAAOXE5zI4YMUKvvvqqVq5cKQ8PD0f7XXfdpZ9++ilPiwMAAACuxuUwu337dj344INZ2suXL6+TJ0/mSVEAAABAbrgcZv39/XXs2LEs7Zs3b3asbAAAAADcDC6H2UcffVQvvviiEhMTZbPZlJmZqbVr12rYsGGKjIzMjxoBAACAbLkcZidMmKDatWsrKChI58+fV0hIiO644w61atVKo0aNyo8aAQAAgGxd9zqzhw8f1o4dO3T+/Hk1atRIt99+e17Xli9YZxYA8gHrzAJFXyFdZ9blx9lecdttt+m222673t0BAACAG5arMBsdHZ3rA06dOvW6iwEAAABckaswu3nz5lwdzMavmQAAAHAT5SrMrl69Or/rAAAAAFzm8moGf3XkyBEdOXIkr2oBAAAAXOJymL106ZJefvll+fn5KTg4WMHBwfLz89OoUaN08eLF/KgRAAAAyJbLqxkMGjRIixcv1sSJE9WyZUtJ0rp16zR27FidOnVKs2bNyvMiAQAAgOy4vM6sn5+f5s+fr3vvvdepfdmyZerZs6eSk5PztMC8xjqzAJAPuAEYKPoK6TqzLk8zsNvtCg4OztJetWpVeXh4uHo4AAAA4Lq5HGYHDhyoV155RWlpaY62tLQ0vfbaaxo4cGCeFgcAAABcjctzZjdv3qz4+HhVrlxZDRo0kCRt3bpV6enpat++vR566CFH38WLF+ddpQAAAMDfuBxm/f391a1bN6e2oKCgPCsIAAAAyC2Xw2xsbGx+1AEAAAC47IYemgAAAAAUJJdHZk+dOqXRo0dr9erVSkpKUmZmptP7p0+fzrPiAAAAgKtxOcw+9thj2rdvn5544gkFBATIxtqCAAAAKCAuh9nvv/9eP/zwg2MlAwAAAKCguDxntnbt2vrjjz/yoxYAAADAJS6H2ZkzZ+qf//ynvv32W506dUopKSlOLwAAAOBmcTnM+vv7KyUlRXfddZfKly+vUqVKqVSpUvL391epUqVcLmDGjBkKDg6Wp6enwsLCtH79+qv2P3v2rAYMGKAKFSrIbrerZs2aWrZsmcvnBQAAgPW5PGe2d+/eKl68uObNm3fDN4AtWLBA0dHRmj17tsLCwjRt2jRFRERo9+7dKl++fJb+6enpuvvuu1W+fHktWrRIlSpV0qFDh+Tv73/dNQAAAMC6bMYY48oOJUqU0ObNm1WrVq0bPnlYWJiaNWum6dOnS5IyMzMVFBSkQYMGacSIEVn6z549W5MmTdKuXbtUvHjx6zpnSkqK/Pz8lJycLF9f3xuqHwDw/7GyDVD0uRYZb4grec3laQZNmzbVkSNHrru4K9LT07Vx40Z16NDh/4pxc1OHDh20bt26bPf573//q5YtW2rAgAEKCAhQvXr1NGHCBGVkZOR4nrS0NOb1AgAAFFEuTzMYNGiQBg8erBdeeEGhoaFZRkjr16+fq+OcPHlSGRkZCggIcGoPCAjQrl27st3nwIED+uabb9S7d28tW7ZM+/bt07PPPquLFy9qzJgx2e4TExOjcePG5aomAAAAWIvL0wzc3LIO5tpsNhljZLPZrjpK+ldHjx5VpUqV9OOPP6ply5aO9uHDh+vbb7/Vzz//nGWfmjVr6s8//1RCQoLc3d0lSVOnTtWkSZN07NixbM+TlpamtLQ0x3ZKSoqCgoKYZgAAeYlpBkDRV0inGbg8MpuQkHDdhf1V2bJl5e7uruPHjzu1Hz9+XIGBgdnuU6FCBRUvXtwRZCWpTp06SkxMVHp6ujw8PLLsY7fbZbfb86RmAAAAFC4uh9kqVarkyYk9PDzUpEkTxcfHq2vXrpIu3wAWHx+vgQMHZrtP69atNW/ePGVmZjpGiPfs2aMKFSpkG2QBAABQtLkcZq/47bffdPjwYaWnpzu133///bk+RnR0tKKiotS0aVM1b95c06ZNU2pqqvr27StJioyMVKVKlRQTEyNJeuaZZzR9+nQNHjxYgwYN0t69ezVhwgQ999xz1/sxAAAAYGEuh9kDBw7owQcf1Pbt2x1zZSU51pvN7ZxZSerRo4dOnDih0aNHKzExUQ0bNtTy5csdN4UdPnzYaY5uUFCQ4uLi9Pzzz6t+/fqqVKmSBg8erBdffNHVjwEAAIAiwOUbwLp06SJ3d3e9//77qlq1qtavX69Tp05p6NChmjx5stq2bZtfteYJ1pkFgHzADWBA0VdUbgBbt26dvvnmG5UtW1Zubm5yc3NTmzZtFBMTo+eee06bN2++7sIBAAAAV7j80ISMjAyVLFlS0uUVCY4ePSrp8o1hu3fvztvqAAAAgKtweWS2Xr162rp1q6pWraqwsDBNnDhRHh4eevfdd1WtWrX8qBEAAADIlsthdtSoUUpNTZUkjR8/Xp07d1bbtm1VpkwZLViwIM8LBAAAAHLi8g1g2Tl9+rRKlSrlWNGgMOMGMADIBxb4/gdwgwrpDWAuz5k9ceJElrbSpUvLZrNp+/btrh4OAAAAuG4uh9nQ0FAtXbo0S/vkyZPVvHnzPCkKAAAAyA2Xw2x0dLS6deumZ555Rn/88Yd+//13tW/fXhMnTtS8efPyo0YAAAAgWy6H2eHDh2vdunX6/vvvVb9+fdWvX192u13btm3Tgw8+mB81AgAAANlyOcxKUo0aNVSvXj0dPHhQKSkp6tGjhwIDA/O6NgAAAOCqXA6za9euVf369bV3715t27ZNs2bN0qBBg9SjRw+dOXMmP2oEAAAAsuVymL3rrrvUo0cP/fTTT6pTp46efPJJbd68WYcPH1ZoaGh+1AgAAABky+WHJqxYsULh4eFObdWrV9fatWv12muv5VlhAAAAwLXkyUMTrISHJgBAPuChCUDRZ/WHJnTq1EnJycmO7ddff11nz551bJ86dUohISGuVwsAAABcp1yH2bi4OKWlpTm2J0yYoNOnTzu2L126pN27d+dtdQAAAMBV5DrM/n02wi02OwEAAACF0HWtMwsAAAAUBrkOszabTba/TfD/+zYAAABwM+V6aS5jjPr06SO73S5J+vPPP/X000/L29tbkpzm0wIAAAA3Q67DbFRUlNP2P/7xjyx9IiMjb7wiAAAAIJdyHWZjY2Pzsw4AAADAZdwABgAAAMsizAIAAMCyCLMAAACwLMIsAAAALCtXYbZx48Y6c+aMJGn8+PG6cOFCvhYFAAAA5EauwuzOnTuVmpoqSRo3bpzOnz+fr0UBAAAAuZGrpbkaNmyovn37qk2bNjLGaPLkyfLx8cm27+jRo/O0QAAAACAnNmOMuVan3bt3a8yYMdq/f782bdqkkJAQFSuWNQfbbDZt2rQpXwrNKykpKfLz81NycrJ8fX0LuhwAKBp4vDlQ9F07MuYZV/JarsLsX7m5uSkxMVHly5e/oSILCmEWAPIBYRYo+gppmM31E8CuyMzMvO7CAAAAgLzkcpiVpP3792vatGnauXOnJCkkJESDBw9W9erV87Q4AAAA4GpcXmc2Li5OISEhWr9+verXr6/69evr559/Vt26dbVy5cr8qBEAAADIlstzZhs1aqSIiAi9/vrrTu0jRozQihUruAEMAG5FzJkFir5COmfW5ZHZnTt36oknnsjS/vjjj+u3335z9XAAAADAdXM5zJYrV05btmzJ0r5lyxbLrnAAAAAAa3L5BrB+/frpqaee0oEDB9SqVStJ0tq1a/XGG28oOjo6zwsEAAAAcuLynFljjKZNm6YpU6bo6NGjkqSKFSvqhRde0HPPPSdbIZ83xZxZAMgHhfy7H0AeKKRzZl0Os3917tw5SVLJkiWv9xA3HWEWAPIBYRYo+gppmL2udWavsFKIBQAAQNHj8g1gAAAAQGFBmAUAAIBlEWYBAABgWS6F2YsXL6p9+/bau3dvftUDAAAA5JpLYbZ48eLatm1bftUCAAAAuMTlaQb/+Mc/9MEHH+RHLQAAAIBLXF6a69KlS/rwww+1atUqNWnSRN7e3k7vT506Nc+KAwAAAK7G5TC7Y8cONW7cWJK0Z88ep/cK+9O/AAAAULS4HGZXr16dH3UAAAAALrvupbn27dunuLg4/fHHH5KkG3gqLgAAAHBdXA6zp06dUvv27VWzZk116tRJx44dkyQ98cQTGjp0aJ4XCAAAAOTE5TD7/PPPq3jx4jp8+LBKlCjhaO/Ro4eWL1+ep8UBAAAAV+PynNkVK1YoLi5OlStXdmq//fbbdejQoTwrDAAAALgWl0dmU1NTnUZkrzh9+rTsdnueFAUAAADkhsthtm3btvr4448d2zabTZmZmZo4caLuvPPOPC0OAAAAuBqXpxlMnDhR7du314YNG5Senq7hw4fr119/1enTp7V27dr8qBEAAADIlssjs/Xq1dOePXvUpk0bPfDAA0pNTdVDDz2kzZs3q3r16vlRIwAAAJAtm7nFFohNSUmRn5+fkpOT5evrW9DlAEDRwBMggaLvJkZGV/Kay9MMJOnMmTP64IMPtHPnTklSSEiI+vbtq9KlS1/P4QAAAIDr4vI0g++++07BwcF6++23debMGZ05c0Zvv/22qlatqu+++y4/agQAAACy5fI0g9DQULVs2VKzZs2Su7u7JCkjI0PPPvusfvzxR23fvj1fCs0rTDMAgHzANAOg6Cuk0wxcHpndt2+fhg4d6giykuTu7q7o6Gjt27fP9WoBAACA6+RymG3cuLFjruxf7dy5Uw0aNMiTogAAAIDcyNUNYNu2bXP8+bnnntPgwYO1b98+tWjRQpL0008/acaMGXr99dfzp0oAAAAgG7maM+vm5iabzaZrdbXZbMrIyMiz4vIDc2YBIB8wZxYo+grpnNlcjcwmJCTkSWEAAABAXspVmK1SpUp+1wEAAAC47LoemnD06FH98MMPSkpKUmZmptN7zz33XJ4UBgAAAFyLy2F2zpw56t+/vzw8PFSmTBnZ/jJPymazEWYBAABw07gcZl9++WWNHj1aI0eOlJubyyt7AQAAAHnG5TR64cIFPfroowRZAAAAFDiXE+kTTzyhTz/9ND9qAQAAAFySq3Vm/yojI0OdO3fWH3/8odDQUBUvXtzp/alTp+ZpgXmNdWYBIB+wzixQ9Fl5ndm/iomJUVxcnGrVqiVJWW4AAwAAAG4Wl8PslClT9OGHH6pPnz75UA4AAACQey7PmbXb7WrdunV+1AIAAAC4xOUwO3jwYL3zzjt5WsSMGTMUHBwsT09PhYWFaf369bnab/78+bLZbOratWue1gMAAABrcHmawfr16/XNN9/oq6++Ut26dbPcALZ48WKXjrdgwQJFR0dr9uzZCgsL07Rp0xQREaHdu3erfPnyOe538OBBDRs2TG3btnX1IwAAAKCIcHlk1t/fXw899JDCw8NVtmxZ+fn5Ob1cNXXqVPXr1099+/ZVSEiIZs+erRIlSujDDz/McZ+MjAz17t1b48aNU7Vq1Vw+JwAAAIoGl0dmY2Nj8+zk6enp2rhxo0aOHOloc3NzU4cOHbRu3boc9xs/frzKly+vJ554Qt9///1Vz5GWlqa0tDTHdkpKyo0XDgAAgEKhQB/jdfLkSWVkZCggIMCpPSAgQImJidnu88MPP+iDDz7Qe++9l6tzxMTEOI0cBwUF3XDdAAAAKBxcHpmtWrXqVdeTPXDgwA0VdDXnzp3TY489pvfee09ly5bN1T4jR45UdHS0YzslJYVACwAAUES4HGaHDBnitH3x4kVt3rxZy5cv1wsvvODSscqWLSt3d3cdP37cqf348eMKDAzM0n///v06ePCgunTp4mjLzMyUJBUrVky7d+9W9erVnfax2+2y2+0u1QUAAABrcDnMDh48ONv2GTNmaMOGDS4dy8PDQ02aNFF8fLxjea3MzEzFx8dr4MCBWfrXrl1b27dvd2obNWqUzp07p7feeosRVwAAgFuMy2E2J/fee69Gjhzp8g1i0dHRioqKUtOmTdW8eXNNmzZNqamp6tu3ryQpMjJSlSpVUkxMjDw9PVWvXj2n/f39/SUpSzsAAACKvjwLs4sWLVLp0qVd3q9Hjx46ceKERo8ercTERDVs2FDLly933BR2+PBhubkV6H1qAAAAKKRsxhjjyg6NGjVyugHMGKPExESdOHFCM2fO1FNPPZXnReallJQU+fn5KTk5Wb6+vgVdDgAUDVe5MRhAEeFaZLwhruQ1l0dm//7oWDc3N5UrV07t2rVT7dq1XT0cAAAAcN1cHpm1OkZmASAfMDILFH2FdGSWyagAAACwrFxPM3Bzc7vqwxIkyWaz6dKlSzdcFAAAAJAbuQ6zn3/+eY7vrVu3Tm+//bbjAQYAAADAzZDrMPvAAw9kadu9e7dGjBihL7/8Ur1799b48ePztDgAAADgaq5rzuzRo0fVr18/hYaG6tKlS9qyZYs++ugjValSJa/rAwAAAHLkUphNTk7Wiy++qBo1aujXX39VfHy8vvzyS56+BQAAgAKR62kGEydO1BtvvKHAwED95z//yXbaAQAAAHAz5XqdWTc3N3l5ealDhw5yd3fPsd/ixYvzrLj8wDqzAJAPWGcWKPoK6TqzuR6ZjYyMvObSXAAAAMDNlOswO2fOnHwsAwAAAHAdTwADAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRWKMDtjxgwFBwfL09NTYWFhWr9+fY5933vvPbVt21alSpVSqVKl1KFDh6v2BwAAQNFV4GF2wYIFio6O1pgxY7Rp0yY1aNBAERERSkpKyrb/mjVr1LNnT61evVrr1q1TUFCQ7rnnHv3+++83uXIAAAAUNJsxxhRkAWFhYWrWrJmmT58uScrMzFRQUJAGDRqkESNGXHP/jIwMlSpVStOnT1dkZOQ1+6ekpMjPz0/Jycny9fW94foBAJJstoKuAEB+u4mR0ZW8VqAjs+np6dq4caM6dOjgaHNzc1OHDh20bt26XB3jwoULunjxokqXLp3t+2lpaUpJSXF6AQAAoGgo0DB78uRJZWRkKCAgwKk9ICBAiYmJuTrGiy++qIoVKzoF4r+KiYmRn5+f4xUUFHTDdQMAAKBwKPA5szfi9ddf1/z58/X555/L09Mz2z4jR45UcnKy43XkyJGbXCUAAADyS7GCPHnZsmXl7u6u48ePO7UfP35cgYGBV9138uTJev3117Vq1SrVr18/x352u112uz1P6gUAAEDhUqAjsx4eHmrSpIni4+MdbZmZmYqPj1fLli1z3G/ixIl65ZVXtHz5cjVt2vRmlAoAAIBCqEBHZiUpOjpaUVFRatq0qZo3b65p06YpNTVVffv2lSRFRkaqUqVKiomJkSS98cYbGj16tObNm6fg4GDH3FofHx/5+PgU2OcAAADAzVfgYbZHjx46ceKERo8ercTERDVs2FDLly933BR2+PBhubn93wDyrFmzlJ6erocfftjpOGPGjNHYsWNvZukAAAAoYAW+zuzNxjqzAJAPWGcWKPpYZxYAAADIW4RZAAAAWBZhFgAAAJZFmAUAAIBlEWYBAABgWYRZAAAAWBZhFgAAAJZFmAUAAIBlEWYBAABgWYRZAAAAWBZhFgAAAJZFmAUAAIBlEWYBAABgWYRZAAAAWBZhFgAAAJZFmAUAAIBlEWYBAABgWYRZAAAAWBZhFgAAAJZFmAUAAIBlEWYBAABgWYRZAAAAWBZhFgAAAJZFmAUAAIBlEWYBAABgWYRZAAAAWBZhFgAAAJZFmAUAAIBlEWYBAABgWYRZAAAAWBZhFgAAAJZFmAUAAIBlEWYBAABgWYRZAAAAWBZhFgAAAJZFmAUAAIBlEWYBAABgWYRZAAAAWBZhFgAAAJZFmAUAAIBlEWYBAABgWYRZAAAAWBZhFgAAAJZFmAUAAIBlEWYBAABgWYRZAAAAWBZhFgAAAJZFmAUAAIBlEWYBAABgWYRZAAAAWBZhFgAAAJZFmAUAAIBlEWYBAABgWYRZAAAAWBZhFgAAAJZFmAUAAIBlEWYBAABgWYRZAAAAWBZhFgAAAJZFmAUAAIBlEWYBAABgWYRZAAAAWBZhFgAAAJZFmAUAAIBlEWYBAABgWYRZAAAAWBZhFgAAAJZFmAUAAIBlEWYBAABgWYRZAAAAWBZhFgAAAJZFmAUAAIBlEWYBAABgWYRZAAAAWBZhFgAAAJZFmAUAAIBlEWYBAABgWYUizM6YMUPBwcHy9PRUWFiY1q9ff9X+n376qWrXri1PT0+FhoZq2bJlN6lSAAAAFCYFHmYXLFig6OhojRkzRps2bVKDBg0UERGhpKSkbPv/+OOP6tmzp5544glt3rxZXbt2VdeuXbVjx46bXDkAAAAKms0YYwqygLCwMDVr1kzTp0+XJGVmZiooKEiDBg3SiBEjsvTv0aOHUlNT9dVXXznaWrRooYYNG2r27NnXPF9KSor8/PyUnJwsX1/fvPsgAHArs9kKugIA+e0mRkZX8lqxm1RTttLT07Vx40aNHDnS0ebm5qYOHTpo3bp12e6zbt06RUdHO7VFRERoyZIl2fZPS0tTWlqaYzs5OVnS5YsEAACAXLqJ2elKTsvNmGuBhtmTJ08qIyNDAQEBTu0BAQHatWtXtvskJiZm2z8xMTHb/jExMRo3blyW9qCgoOusGgAA4Bbk53fTT3nu3Dn5XeO8BRpmb4aRI0c6jeRmZmbq9OnTKlOmjGz8Wgz5JCUlRUFBQTpy5AjTWQAUOXzHIb8ZY3Tu3DlVrFjxmn0LNMyWLVtW7u7uOn78uFP78ePHFRgYmO0+gYGBLvW32+2y2+1Obf7+/tdfNOACX19fvugBFFl8xyE/XWtE9ooCXc3Aw8NDTZo0UXx8vKMtMzNT8fHxatmyZbb7tGzZ0qm/JK1cuTLH/gAAACi6CnyaQXR0tKKiotS0aVM1b95c06ZNU2pqqvr27StJioyMVKVKlRQTEyNJGjx4sMLDwzVlyhTdd999mj9/vjZs2KB33323ID8GAAAACkCBh9kePXroxIkTGj16tBITE9WwYUMtX77ccZPX4cOH5eb2fwPIrVq10rx58zRq1Ci99NJLuv3227VkyRLVq1evoD4CkIXdbteYMWOyTHEBgKKA7zgUJgW+ziwAAABwvQr8CWAAAADA9SLMAgAAwLIIswAAALAswiwAAAAsizCLW1piYqIGDRqkatWqyW63KygoSF26dMmylnFhMGfOHB74ASCLPn36qGvXrgVdRr5o166dhgwZUtBloJAr8KW5gIJy8OBBtW7dWv7+/po0aZJCQ0N18eJFxcXFacCAAdq1a5fLx0xPT5eHh0eW9osXL6p48eJ5UTYAFCrZfe9lZGTIZrM5La0J5Bd+ynDLevbZZ2Wz2bR+/Xp169ZNNWvWVN26dRUdHa2ffvpJ0uV1jh944AH5+PjI19dX3bt3d3qc8tixY9WwYUO9//77qlq1qjw9PSVJNptNs2bN0v333y9vb2+99tprkqQvvvhCjRs3lqenp6pVq6Zx48bp0qVLjuOdPXtW/fv3V0BAgDw9PVWvXj199dVXWrNmjfr27avk5GTZbDbZbDaNHTv25l0sAJbQrl07Pffccxo+fLhKly6twMDALN8VOX3PXPHZZ5+pbt26stvtCg4O1pQpU5z2Dw4O1iuvvKLIyEj5+vrqqaeecvzm6L///a9CQkJkt9t1+PBhpaWladiwYapUqZK8vb0VFhamNWvWOB1v7dq1ateunUqUKKFSpUopIiJCZ86cUZ8+ffTtt9/qrbfecnzvHTx4MJ+uHCzNALegU6dOGZvNZiZMmJBjn4yMDNOwYUPTpk0bs2HDBvPTTz+ZJk2amPDwcEefMWPGGG9vb9OxY0ezadMms3XrVmOMMZJM+fLlzYcffmj2799vDh06ZL777jvj6+tr5syZY/bv329WrFhhgoODzdixYx3na9Gihalbt65ZsWKF2b9/v/nyyy/NsmXLTFpampk2bZrx9fU1x44dM8eOHTPnzp3L12sEwBqioqLMAw88YIwxJjw83Pj6+pqxY8eaPXv2mI8++sjYbDazYsUKY8zVv2eMMWbDhg3Gzc3NjB8/3uzevdvExsYaLy8vExsb6zhflSpVjK+vr5k8ebLZt2+f2bdvn4mNjTXFixc3rVq1MmvXrjW7du0yqamp5sknnzStWrUy3333ndm3b5+ZNGmSsdvtZs+ePcYYYzZv3mzsdrt55plnzJYtW8yOHTvMO++8Y06cOGHOnj1rWrZsafr16+f43rt06dJNvbawBsIsbkk///yzkWQWL16cY58VK1YYd3d3c/jwYUfbr7/+aiSZ9evXG2Muh9nixYubpKQkp30lmSFDhji1tW/fPkt4/ve//20qVKhgjDEmLi7OuLm5md27d2dbT2xsrPHz88v1ZwRwa/h7mG3Tpo3T+82aNTMvvviiMeba3zO9evUyd999t1PbCy+8YEJCQhzbVapUMV27dnXqExsbaySZLVu2ONoOHTpk3N3dze+//+7Ut3379mbkyJHGGGN69uxpWrduneNnCw8PN4MHD87xfcAYY5gzi1uSycWD73bu3KmgoCAFBQU52kJCQuTv76+dO3eqWbNmkqQqVaqoXLlyWfZv2rSp0/bWrVu1du1ax5QD6fK8sj///FMXLlzQli1bVLlyZdWsWfN6PxYAqH79+k7bFSpUUFJSkiRd83tm586deuCBB5zaWrdurWnTpikjI0Pu7u6Ssn6/SZKHh4fTubdv366MjIws50pLS1OZMmUc9TzyyCMufkLAGWEWt6Tbb79dNpvtum7y+jtvb+9ctZ8/f17jxo3TQw89lKWvp6envLy8brgWAPj7zaY2m02ZmZmSlGffM9l973l5eclmszm2z58/L3d3d23cuNERgq/w8fHJ03pwa+MGMNySSpcurYiICM2YMUOpqalZ3j979qzq1KmjI0eO6MiRI4723377TWfPnlVISIjL52zcuLF2796tGjVqZHm5ubmpfv36+t///qc9e/Zku7+Hh4cyMjJcPi8AXHGt75k6depo7dq1Tm1r165VzZo1swTSa2nUqJEyMjKUlJSU5TsvMDDQUc/VlkLkew+5QZjFLWvGjBnKyMhQ8+bN9dlnn2nv3r3auXOn3n77bbVs2VIdOnRQaGioevfurU2bNmn9+vWKjIxUeHh4tr9iu5bRo0fr448/1rhx4/Trr79q586dmj9/vkaNGiVJCg8P1x133KFu3bpp5cqVSkhI0Ndff63ly5dLunwH8fnz5xUfH6+TJ0/qwoULeXo9ABR91/qeGTp0qOLj4/XKK69oz549+uijjzR9+nQNGzbM5XPVrFlTvXv3VmRkpBYvXqyEhAStX79eMTExWrp0qSRp5MiR+uWXX/Tss89q27Zt2rVrl2bNmqWTJ09Kuvy99/PPP+vgwYM6efKkY4QZ+CvCLG5Z1apV06ZNm3TnnXdq6NChqlevnu6++27Fx8dr1qxZstls+uKLL1SqVCndcccd6tChg6pVq6YFCxZc1/kiIiL01VdfacWKFWrWrJlatGihN998U1WqVHH0+eyzz9SsWTP17NlTISEhGj58uGNUolWrVnr66afVo0cPlStXThMnTsyT6wDg1nK175nGjRtr4cKFmj9/vurVq6fRo0dr/Pjx6tOnz3WdKzY2VpGRkRo6dKhq1aqlrl276pdfftFtt90m6XLgXbFihbZu3armzZurZcuW+uKLL1Ss2OVZkMOGDZO7u7tCQkJUrlw5HT58OE+uAYoWm8nNnTAAAABAIcTILAAAACyLMAsAAADLIswCAADAsgizAAAAsCzCLAAAACyLMAsAAADLIswCAADAsgizAAAAsCzCLADcgIMHD8pms2nLli35fq45c+bI398/389TGIwdO1YNGzYs6DIAWABhFkCR1adPH9lstiyvjh07FnRp1xQcHKxp06Y5tfXo0UN79uzJ93O3a9fOca08PT1Vs2ZNxcTEqCAfGNmnTx917dq1wM4PoPAqVtAFAEB+6tixo2JjY53a7HZ7AVVzY7y8vOTl5XVTztWvXz+NHz9eaWlp+uabb/TUU0/J399fzzzzzE05PwDkFiOzAIo0u92uwMBAp1epUqUkSb169VKPHj2c+l+8eFFly5bVxx9/LElavny52rRpI39/f5UpU0adO3fW/v37czxfdlMBlixZIpvN5tjev3+/HnjgAQUEBMjHx0fNmjXTqlWrHO+3a9dOhw4d0vPPP+8YIc3p2LNmzVL16tXl4eGhWrVq6d///rfT+zabTe+//74efPBBlShRQrfffrv++9//XvO6lShRQoGBgapSpYr69u2r+vXra+XKlY7309LSNGzYMFWqVEne3t4KCwvTmjVrHO8fOnRIXbp0UalSpeTt7a26detq2bJlub5GfzV27Fh99NFH+uKLLxzX46/nAnBrI8wCuGX17t1bX375pc6fP+9oi4uL04ULF/Tggw9KklJTUxUdHa0NGzYoPj5ebm5uevDBB5WZmXnd5z1//rw6deqk+Ph4bd68WR07dlSXLl10+PBhSdLixYtVuXJljR8/XseOHdOxY8eyPc7nn3+uwYMHa+jQodqxY4f69++vvn37avXq1U79xo0bp+7du2vbtm3q1KmTevfurdOnT+eqVmOMvv/+e+3atUseHh6O9oEDB2rdunWaP3++tm3bpkceeUQdO3bU3r17JUkDBgxQWlqavvvuO23fvl1vvPGGfHx8rudyadiwYerevbs6duzouB6tWrW6rmMBKIIMABRRUVFRxt3d3Xh7ezu9XnvtNWOMMRcvXjRly5Y1H3/8sWOfnj17mh49euR4zBMnThhJZvv27cYYYxISEowks3nzZmOMMbGxscbPz89pn88//9xc6+u2bt265p133nFsV6lSxbz55ptOff5+7FatWpl+/fo59XnkkUdMp06dHNuSzKhRoxzb58+fN5LM119/nWMt4eHhpnjx4sbb29sUL17cSDKenp5m7dq1xhhjDh06ZNzd3c3vv//utF/79u3NyJEjjTHGhIaGmrFjx2Z7/NxcozFjxpgGDRo4tqOioswDDzyQY80Abl2MzAIo0u68805t2bLF6fX0009LkooVK6bu3btr7ty5ki6Pwn7xxRfq3bu3Y/+9e/eqZ8+eqlatmnx9fRUcHCxJjlHU63H+/HkNGzZMderUkb+/v3x8fLRz506Xj7lz5061bt3aqa1169bauXOnU1v9+vUdf/b29pavr6+SkpKueuzevXtry5YtWrt2re69917985//dIyGbt++XRkZGapZs6Z8fHwcr2+//dYxBeO5557Tq6++qtatW2vMmDHatm2bS58NAHKLG8AAFGne3t6qUaNGju/37t1b4eHhSkpK0sqVK+Xl5eW02kGXLl1UpUoVvffee6pYsaIyMzNVr149paenZ3s8Nze3LHf9X7x40Wl72LBhWrlypSZPnqwaNWrIy8tLDz/8cI7HvFHFixd32rbZbNecJuHn5+e4bgsXLlSNGjXUokULdejQQefPn5e7u7s2btwod3d3p/2uTCV48sknFRERoaVLl2rFihWKiYnRlClTNGjQoFxdIwDILUZmAdzSWrVqpaCgIC1YsEBz587VI4884gh/p06d0u7duzVq1Ci1b99ederU0ZkzZ656vHLlyuncuXNKTU11tP19Ddq1a9eqT58+evDBBxUaGqrAwEAdPHjQqY+Hh4cyMjKueq46depo7dq1WY4dEhJyjU/tGh8fHw0ePFjDhg2TMUaNGjVSRkaGkpKSVKNGDadXYGCgY7+goCA9/fTTWrx4sYYOHar33ntPUu6u0d/l5noAuDURZgEUaWlpaUpMTHR6nTx50qlPr169NHv2bK1cudJpikGpUqVUpkwZvfvuu9q3b5+++eYbRUdHX/V8YWFhKlGihF566SXt379f8+bN05w5c5z63H777Vq8eLG2bNmirVu3qlevXllGSoODg/Xdd9/p999/z1LvFS+88ILmzJmjWbNmae/evZo6daoWL16sYcOGuXCFcqd///7as2ePPvvsM9WsWVO9e/dWZGSkFi9erISEBK1fv14xMTFaunSpJGnIkCGKi4tTQkKCNm3apNWrV6tOnTq5vkZ/FxwcrG3btmn37t06efIkI7kAHAizAIq05cuXq0KFCk6vNm3aOPXp3bu3fvvtN1WqVMlpDqqbm5vmz5+vjRs3ql69enr++ec1adKkq56vdOnS+uSTT7Rs2TKFhobqP//5j8aOHevUZ+rUqSpVqpRatWqlLl26KCIiQo0bN3bqM378eB08eFDVq1dXuXLlsj1X165d9dZbb2ny5MmqW7eu/vWvfyk2Nlbt2rXL/QXKpdKlSysyMlJjx45VZmamYmNjFRkZqaFDh6pWrVrq2rWrfvnlF912222SpIyMDA0YMEB16tRRx44dVbNmTc2cOTPX1+jv+vXrp1q1aqlp06YqV65clhFpALcum/n7xCUAAADAIhiZBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABY1v8DcKQPPdJXaecAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Task: Create a basic calendar dApp on NEAR.\n",
            "Expected Output: Expected calendar dApp code\n",
            "Generated Output: Create a basic calendar dApp on NEAR.\n",
            "\n",
            "## Getting started\n",
            "\n",
            "### Prerequisites\n",
            "\n",
            "- [Node.js](https://nodejs.org/)\n",
            "- [Yarn](https://yarnpkg.com/)\n",
            "- [NEAR CLI](https://github.com/near/near-cli)\n",
            "\n",
            "### Installation\n",
            "\n",
            "Clone the repository:\n",
            "\n",
            "```bash\n",
            "git clone https://github.com/near-examples/calendar-dapp.git\n",
            "cd calendar-dapp\n",
            "```\n",
            "\n",
            "Install dependencies:\n",
            "\n",
            "```bash\n",
            "yarn install\n",
            "```\n",
            "\n",
            "### Configuration\n",
            "\n",
            "Copy the `.env.example` file to `.env` and fill in the values:\n",
            "\n",
            "```bash\n",
            "cp.env.example.env\n",
            "```\n",
            "\n",
            "### Compile and deploy\n",
            "\n",
            "Compile and deploy the contract:\n",
            "\n",
            "```bash\n",
            "yarn deploy\n",
            "```\n",
            "\n",
            "### Run the app\n",
            "\n",
            "Start the app:\n",
            "\n",
            "```bash\n",
            "yarn start\n",
            "```\n",
            "\n",
            "Open [http://localhost:3000](http://localhost:3000) to view it in the browser.\n",
            "\n",
            "## License\n",
            "\n",
            "[MIT](LICENSE)\n",
            "\n",
            "----------------------------------------------------\n",
            "Task: Create a simple snake game dApp on NEAR.\n",
            "Expected Output: Expected snake game code\n",
            "Generated Output: Create a simple snake game dApp on NEAR.\n",
            "\n",
            "## Getting started\n",
            "\n",
            "### Prerequisites\n",
            "\n",
            "- [Node.js](https://nodejs.org/)\n",
            "- [Yarn](https://yarnpkg.com/)\n",
            "- [NEAR CLI](https://github.com/near/near-cli)\n",
            "\n",
            "### Installation\n",
            "\n",
            "Clone the repository:\n",
            "\n",
            "```bash\n",
            "git clone https://github.com/near-examples/snake-game.git\n",
            "cd snake-game\n",
            "```\n",
            "\n",
            "Install dependencies:\n",
            "\n",
            "```bash\n",
            "yarn install\n",
            "```\n",
            "\n",
            "### Compile and deploy\n",
            "\n",
            "Compile and deploy the contract:\n",
            "\n",
            "```bash\n",
            "yarn deploy\n",
            "```\n",
            "\n",
            "### Run the client\n",
            "\n",
            "Start the client:\n",
            "\n",
            "```bash\n",
            "yarn start\n",
            "```\n",
            "\n",
            "Open [http://localhost:3000](http://localhost:3000) to view it in the browser.\n",
            "\n",
            "## License\n",
            "\n",
            "[MIT](LICENSE)\n",
            "\n",
            "## Acknowledgements\n",
            "\n",
            "- [NEAR](https://near.org/)\n",
            "- [NEAR Developer Portal](https://docs.near.org/)\n",
            "- [NEAR Developer Tools](https://github.com/near/near-cli)\n",
            "- [NEAR React Starter Kit](https://github.com/near/near-react-starter-kit)\n",
            "- [NEAR React Snake Game](https://github.com/near/near-react-snake-game)\n",
            "- [NEAR React Snake Game Tutorial](https://docs.near.org/docs/tutorials/react/snake-game)\n",
            "- [NEAR React Snake Game Tutorial - Part 2](https://docs.near.org/docs/tutorials/react/snake-game-tutorial-part-2)\n",
            "- [NEAR React Snake Game Tutorial - Part 3](https://docs.near.org/docs/tutorials/react/snake-game-tutorial-part-3)\n",
            "- [NEAR React Snake Game Tutorial - Part 4](https://docs.near.org/docs/tutorials/react/snake-game-tutorial-part-4)\n",
            "- [NEAR React Snake Game Tutorial - Part 5](https://docs.near.org/docs/tutorials/react/snake-game-tutorial-part-5)\n",
            "- [NEAR React Snake Game Tutorial - Part 6](https://docs.near.org/docs/tutorials/react/snake-game-tutorial-part-6)\n",
            "- [NEAR React Snake Game Tutorial - Part 7](https://docs.near.org/docs/tutorials/react/snake-game-tutorial-part-7)\n",
            "- [NEAR React Snake Game Tutorial - Part 8](https://docs.near.org/docs/tutorials/react/snake-game-tutorial-part-8)\n",
            "- [NEAR React Snake Game Tutorial - Part 9](https://docs.near.org/docs/tutorials/react/snake-game-tutorial-part-9)\n",
            "- [NEAR React Snake Game Tutorial - Part 10](https://docs.near.org/docs/tutorials/react/snake-game-tutorial-part-10)\n",
            "- [NEAR React Snake Game Tutorial - Part 11](https://docs.near.org/docs/tutorials/react/snake-game-tutorial-part-11)\n",
            "- [NEAR React Snake Game Tutorial - Part 12](https://docs.near.org/docs/tutorials/react/snake-game-tutorial-part-12)\n",
            "- [NEAR React Snake Game Tutorial - Part 13](https://docs.near.org/docs/tutorials/react/snake-game-tutorial-part-13)\n",
            "- [NEAR React Snake Game Tutorial - Part 14](https://docs.near.org/docs/tutorials/react/snake-game-tutorial-part-14)\n",
            "- [NEAR React Snake Game Tutorial - Part 15](https://docs.near.org/docs/tutorials/react/snake-game-tutorial-part-15)\n",
            "- [NEAR React Snake Game Tutorial - Part 16](https://docs.near.org/docs/tutorials/react/snake-game-tutorial-part-16)\n",
            "- [NEAR React Snake Game Tutorial - Part 17](https://docs.near.org/docs/tutorials/react/snake-game-tutorial-part-17)\n",
            "- [NEAR React Snake Game Tutorial - Part 18](https://docs.near.org/docs/tutorials/react/snake-game-tutorial-part-18)\n",
            "- [\n",
            "----------------------------------------------------\n",
            "Accuracy: 0.0%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "from datasets import load_dataset\n",
        "\n",
        "# Function to load model and tokenizer\n",
        "def load_model(model_name):\n",
        "    tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "    model = AutoModelForCausalLM.from_pretrained(model_name)\n",
        "    return tokenizer, model\n",
        "\n",
        "# Function to generate completion\n",
        "def generate_completion(model, tokenizer, prompt, max_length=1000, temperature=0.7):\n",
        "    inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
        "    input_ids = inputs[\"input_ids\"]\n",
        "    attention_mask = inputs[\"attention_mask\"]\n",
        "\n",
        "    with torch.no_grad():\n",
        "        outputs = model.generate(input_ids=input_ids, attention_mask=attention_mask,\n",
        "                                 max_length=max_length, temperature=temperature)\n",
        "\n",
        "    completion = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "    return completion\n",
        "\n",
        "# Evaluation functions for each task\n",
        "def evaluate_human_eval(model, tokenizer, dataset, num_examples=50):\n",
        "    correct = 0\n",
        "    results = []\n",
        "    for i, example in enumerate(dataset[\"test\"]):\n",
        "        if i >= num_examples:\n",
        "            break\n",
        "\n",
        "        prompt = example[\"prompt\"]\n",
        "        expected_output = example[\"canonical_solution\"]\n",
        "\n",
        "        generated_output = generate_completion(model, tokenizer, prompt)\n",
        "\n",
        "        print(f\"Prompt: {prompt}\")\n",
        "        print(f\"Expected Output: {expected_output}\")\n",
        "        print(f\"Generated Output: {generated_output}\")\n",
        "        print(\"----------------------------------------------------\")\n",
        "\n",
        "        is_correct = expected_output in generated_output\n",
        "        results.append(is_correct)\n",
        "        if is_correct:\n",
        "            correct += 1\n",
        "\n",
        "    accuracy = correct / num_examples\n",
        "    print(f\"Accuracy: {accuracy * 100}%\")\n",
        "\n",
        "    return results\n",
        "\n",
        "def evaluate_near_protocol(model, tokenizer, questions):\n",
        "    results = []\n",
        "    for question, expected_answer in questions:\n",
        "        generated_answer = generate_completion(model, tokenizer, question)\n",
        "\n",
        "        print(f\"Question: {question}\")\n",
        "        print(f\"Expected Answer: {expected_answer}\")\n",
        "        print(f\"Generated Answer: {generated_answer}\")\n",
        "        print(\"----------------------------------------------------\")\n",
        "\n",
        "        is_correct = expected_answer in generated_answer\n",
        "        results.append(is_correct)\n",
        "\n",
        "    accuracy = sum(results) / len(questions)\n",
        "    print(f\"Accuracy: {accuracy * 100}%\")\n",
        "\n",
        "    return results\n",
        "\n",
        "def evaluate_dapp_trees(model, tokenizer, prompts):\n",
        "    results = []\n",
        "    for prompt, expected_code in prompts:\n",
        "        generated_code = generate_completion(model, tokenizer, prompt)\n",
        "\n",
        "        print(f\"Prompt: {prompt}\")\n",
        "        print(f\"Expected Code: {expected_code}\")\n",
        "        print(f\"Generated Code: {generated_code}\")\n",
        "        print(\"----------------------------------------------------\")\n",
        "\n",
        "        is_correct = expected_code in generated_code\n",
        "        results.append(is_correct)\n",
        "\n",
        "    accuracy = sum(results) / len(prompts)\n",
        "    print(f\"Accuracy: {accuracy * 100}%\")\n",
        "\n",
        "    return results\n",
        "\n",
        "def evaluate_build_near_dapp(model, tokenizer, tasks):\n",
        "    results = []\n",
        "    for task, expected_output in tasks:\n",
        "        generated_output = generate_completion(model, tokenizer, task)\n",
        "\n",
        "        print(f\"Task: {task}\")\n",
        "        print(f\"Expected Output: {expected_output}\")\n",
        "        print(f\"Generated Output: {generated_output}\")\n",
        "        print(\"----------------------------------------------------\")\n",
        "\n",
        "        is_correct = expected_output in generated_output\n",
        "        results.append(is_correct)\n",
        "\n",
        "    accuracy = sum(results) / len(tasks)\n",
        "    print(f\"Accuracy: {accuracy * 100}%\")\n",
        "\n",
        "    return results\n",
        "\n",
        "# Plotting function\n",
        "def plot_results(task, results):\n",
        "    labels = ['Correct', 'Incorrect']\n",
        "    counts = [sum(results), len(results) - sum(results)]\n",
        "\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.bar(labels, counts, color=['green', 'red'])\n",
        "    plt.xlabel('Evaluation Result')\n",
        "    plt.ylabel('Number of Examples')\n",
        "    plt.title(f'{task} Evaluation Results')\n",
        "    plt.show()\n",
        "\n",
        "# Main evaluation routine\n",
        "def evaluate_models(model_names, human_eval_dataset, near_questions, dapp_prompts, dapp_tasks):\n",
        "    for model_name in model_names:\n",
        "        print(f\"Evaluating {model_name}\")\n",
        "        tokenizer, model = load_model(model_name)\n",
        "\n",
        "        # HumanEval\n",
        "        human_eval_results = evaluate_human_eval(model, tokenizer, human_eval_dataset)\n",
        "        plot_results('HumanEval', human_eval_results)\n",
        "\n",
        "        # NEAR Protocol\n",
        "        near_results = evaluate_near_protocol(model, tokenizer, near_questions)\n",
        "        plot_results('NEAR Protocol', near_results)\n",
        "\n",
        "        # dApp Trees\n",
        "        dapp_trees_results = evaluate_dapp_trees(model, tokenizer, dapp_prompts)\n",
        "        plot_results('dApp Trees', dapp_trees_results)\n",
        "\n",
        "        # Building NEAR dApp\n",
        "        dapp_build_results = evaluate_build_near_dapp(model, tokenizer, dapp_tasks)\n",
        "        plot_results('Building NEAR dApp', dapp_build_results)\n",
        "\n",
        "# Load HumanEval dataset\n",
        "human_eval_dataset = load_dataset(\"openai_humaneval\")\n",
        "\n",
        "# Example questions, prompts, and tasks\n",
        "near_questions = [\n",
        "    (\"What is the Nightshade Sharding?\", \"Nightshade Sharding is a method used by the NEAR protocol for scaling.\"),\n",
        "    # Add more questions here...\n",
        "]\n",
        "\n",
        "dapp_prompts = [\n",
        "    (\"Generate a smart contract for a simple token on NEAR protocol with appropriate comments.\", \"Expected code with comments\"),\n",
        "    # Add more prompts here...\n",
        "]\n",
        "\n",
        "dapp_tasks = [\n",
        "    (\"Create a basic calendar dApp on NEAR.\", \"Expected calendar dApp code\"),\n",
        "    (\"Create a simple snake game dApp on NEAR.\", \"Expected snake game code\"),\n",
        "    # Add more tasks here...\n",
        "]\n",
        "\n",
        "# List of models to evaluate\n",
        "model_names = [\n",
        "    \"jcarbonnell/NEARCoder-3b\",\n",
        "    #\"bigcode/starcoder2-3b\",\n",
        "    # Add more model names here...\"jcarbonnell/NEAR-preTrainedStarCoder2\", \"jcarbonnell/NEAR-structTunedStarcoder2\", \"jcarbonnell/NEARCoder-3b\"\n",
        "]\n",
        "\n",
        "# Run the evaluation\n",
        "evaluate_models(model_names, human_eval_dataset, near_questions, dapp_prompts, dapp_tasks)\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "gpuType": "V28",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "d675eb6bc9eb4429b7ee770bb6719ce0": {
          "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_705bdd1c3dc54be0bb7fbe85e4cf3297",
              "IPY_MODEL_ca46cb6d023d414e92865f4dde10f33d",
              "IPY_MODEL_e477ce85fe4d4d33a0d0277a0de14d50"
            ],
            "layout": "IPY_MODEL_5579f4ca943f4995aafa957dfef264ff"
          }
        },
        "705bdd1c3dc54be0bb7fbe85e4cf3297": {
          "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_b2891a6f8c404bd091a19cd737316225",
            "placeholder": "​",
            "style": "IPY_MODEL_0ecc5d3b29e34318b6a5b4c6603ba9c6",
            "value": "tokenizer_config.json: 100%"
          }
        },
        "ca46cb6d023d414e92865f4dde10f33d": {
          "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_f173a5cb6b1142398155c764018395fe",
            "max": 8199,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_c3807bc82fb844e6aeab4d09c29f1837",
            "value": 8199
          }
        },
        "e477ce85fe4d4d33a0d0277a0de14d50": {
          "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_9dbd5fe77ae24fa8ae3121946eb2a265",
            "placeholder": "​",
            "style": "IPY_MODEL_38d428b1dfc548669a0f1055edd6a904",
            "value": " 8.20k/8.20k [00:00&lt;00:00, 572kB/s]"
          }
        },
        "5579f4ca943f4995aafa957dfef264ff": {
          "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
          }
        },
        "b2891a6f8c404bd091a19cd737316225": {
          "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
          }
        },
        "0ecc5d3b29e34318b6a5b4c6603ba9c6": {
          "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": ""
          }
        },
        "f173a5cb6b1142398155c764018395fe": {
          "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
          }
        },
        "c3807bc82fb844e6aeab4d09c29f1837": {
          "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": ""
          }
        },
        "9dbd5fe77ae24fa8ae3121946eb2a265": {
          "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
          }
        },
        "38d428b1dfc548669a0f1055edd6a904": {
          "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": ""
          }
        },
        "eaa01304d7844e03b0460e1558f734b6": {
          "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_f3a9142437824f9197f5b730a938c2ab",
              "IPY_MODEL_49c83980d558454b837bf7dee4bd212b",
              "IPY_MODEL_f34a4826d03c4d9ea5b208012acecfc2"
            ],
            "layout": "IPY_MODEL_83c15e1b80984ba7a39da45200ab4c09"
          }
        },
        "f3a9142437824f9197f5b730a938c2ab": {
          "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_afadff6b69694559bb9603f35767d2f8",
            "placeholder": "​",
            "style": "IPY_MODEL_73c3b573315b451b8a7d0d11e75c39e5",
            "value": "vocab.json: 100%"
          }
        },
        "49c83980d558454b837bf7dee4bd212b": {
          "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_8ab591c6f7ff4e16b790054fdbe0f793",
            "max": 777202,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_57b248b2e6fd4c55b5dff43a710e04aa",
            "value": 777202
          }
        },
        "f34a4826d03c4d9ea5b208012acecfc2": {
          "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_c13fb31293414751a262d107d8693c64",
            "placeholder": "​",
            "style": "IPY_MODEL_93ba56e867964615b55bd07ebd0b0f61",
            "value": " 777k/777k [00:00&lt;00:00, 13.5MB/s]"
          }
        },
        "83c15e1b80984ba7a39da45200ab4c09": {
          "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
          }
        },
        "afadff6b69694559bb9603f35767d2f8": {
          "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
          }
        },
        "73c3b573315b451b8a7d0d11e75c39e5": {
          "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": ""
          }
        },
        "8ab591c6f7ff4e16b790054fdbe0f793": {
          "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
          }
        },
        "57b248b2e6fd4c55b5dff43a710e04aa": {
          "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": ""
          }
        },
        "c13fb31293414751a262d107d8693c64": {
          "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
          }
        },
        "93ba56e867964615b55bd07ebd0b0f61": {
          "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": ""
          }
        },
        "1afd577049ff434497fa9b111ad97ff3": {
          "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_e9218f9eecad4648b309878efaa93532",
              "IPY_MODEL_974e964caa4c405cb870410a976dff27",
              "IPY_MODEL_da8920811ff34f2eab5f811a9bae2b5f"
            ],
            "layout": "IPY_MODEL_d5ff03562fb443fe921493a23f7df23f"
          }
        },
        "e9218f9eecad4648b309878efaa93532": {
          "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_0395f281ebda4b089949758412b1c863",
            "placeholder": "​",
            "style": "IPY_MODEL_13f7766fad7d46a2b474dc1aec4244f0",
            "value": "merges.txt: 100%"
          }
        },
        "974e964caa4c405cb870410a976dff27": {
          "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_5af4950cb08542f284d9615ccb203885",
            "max": 441705,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_d2948ee829d5441c902144b4224d009e",
            "value": 441705
          }
        },
        "da8920811ff34f2eab5f811a9bae2b5f": {
          "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_64a6b9fe07d64da4b6931f75120825d8",
            "placeholder": "​",
            "style": "IPY_MODEL_6ca7d3967f75450ba9e0bac2f6f13de7",
            "value": " 442k/442k [00:00&lt;00:00, 31.5MB/s]"
          }
        },
        "d5ff03562fb443fe921493a23f7df23f": {
          "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
          }
        },
        "0395f281ebda4b089949758412b1c863": {
          "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
          }
        },
        "13f7766fad7d46a2b474dc1aec4244f0": {
          "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": ""
          }
        },
        "5af4950cb08542f284d9615ccb203885": {
          "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
          }
        },
        "d2948ee829d5441c902144b4224d009e": {
          "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": ""
          }
        },
        "64a6b9fe07d64da4b6931f75120825d8": {
          "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
          }
        },
        "6ca7d3967f75450ba9e0bac2f6f13de7": {
          "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": ""
          }
        },
        "6bb8a7d7fd4c49919b915c52b0f2db57": {
          "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_26b9edd852e74020bd67723cd6652cf0",
              "IPY_MODEL_12d68a451541473b914281630d8476c3",
              "IPY_MODEL_47d44536fcf44717b21f234989f010f7"
            ],
            "layout": "IPY_MODEL_824a9665f9c54c2088ffe6ca320e9b59"
          }
        },
        "26b9edd852e74020bd67723cd6652cf0": {
          "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_5bba298a62e14111b3d7aeb78f47c2d2",
            "placeholder": "​",
            "style": "IPY_MODEL_1fa8d4a52dc54c2d8dd9a09b6487d62f",
            "value": "tokenizer.json: 100%"
          }
        },
        "12d68a451541473b914281630d8476c3": {
          "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_7153a4ebb0824a9b905bd784ad285262",
            "max": 2060947,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_298c99bee794492a89a19a98d8c328fe",
            "value": 2060947
          }
        },
        "47d44536fcf44717b21f234989f010f7": {
          "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_f3718214e19f4ce8b42fc5f7db93051f",
            "placeholder": "​",
            "style": "IPY_MODEL_2223b2cd384847a29d3950cd1921c919",
            "value": " 2.06M/2.06M [00:00&lt;00:00, 27.0MB/s]"
          }
        },
        "824a9665f9c54c2088ffe6ca320e9b59": {
          "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
          }
        },
        "5bba298a62e14111b3d7aeb78f47c2d2": {
          "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
          }
        },
        "1fa8d4a52dc54c2d8dd9a09b6487d62f": {
          "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": ""
          }
        },
        "7153a4ebb0824a9b905bd784ad285262": {
          "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
          }
        },
        "298c99bee794492a89a19a98d8c328fe": {
          "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": ""
          }
        },
        "f3718214e19f4ce8b42fc5f7db93051f": {
          "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
          }
        },
        "2223b2cd384847a29d3950cd1921c919": {
          "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": ""
          }
        },
        "00061b41cdca4b739a01b6a229ec1278": {
          "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_5daae55d90434834a59aaab63915e075",
              "IPY_MODEL_614ff9e99b224b92825c6c94495da77b",
              "IPY_MODEL_657ed04daa6e4ca28620bb58f21694c3"
            ],
            "layout": "IPY_MODEL_98c755ef50dd445c81051105bdcbe709"
          }
        },
        "5daae55d90434834a59aaab63915e075": {
          "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_eef0192a008140deb2eefb6dad0468ae",
            "placeholder": "​",
            "style": "IPY_MODEL_75838d22e40246c2b65fa797248cc189",
            "value": "special_tokens_map.json: 100%"
          }
        },
        "614ff9e99b224b92825c6c94495da77b": {
          "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_955462276f6145b78311e6b9a5036a4a",
            "max": 1446,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_04601a13ad9442c0b96d82c151424ab0",
            "value": 1446
          }
        },
        "657ed04daa6e4ca28620bb58f21694c3": {
          "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_4ebfc07f55754d94ac7c36c6f3161de0",
            "placeholder": "​",
            "style": "IPY_MODEL_3d80cefcca074fc5814467e2c440ff3f",
            "value": " 1.45k/1.45k [00:00&lt;00:00, 114kB/s]"
          }
        },
        "98c755ef50dd445c81051105bdcbe709": {
          "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
          }
        },
        "eef0192a008140deb2eefb6dad0468ae": {
          "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
          }
        },
        "75838d22e40246c2b65fa797248cc189": {
          "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": ""
          }
        },
        "955462276f6145b78311e6b9a5036a4a": {
          "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
          }
        },
        "04601a13ad9442c0b96d82c151424ab0": {
          "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": ""
          }
        },
        "4ebfc07f55754d94ac7c36c6f3161de0": {
          "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
          }
        },
        "3d80cefcca074fc5814467e2c440ff3f": {
          "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": ""
          }
        },
        "3d205bec0ff64b3abb72011dffa4fcd6": {
          "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_2cd6cab6a53f44d0807057656529c4b6",
              "IPY_MODEL_097b790f1aa94f9c9017aa172fbf06f3",
              "IPY_MODEL_920ea221313e4e2da23f8af115356509"
            ],
            "layout": "IPY_MODEL_2dd6fcef47ff409ab7ca29e9b05d5e60"
          }
        },
        "2cd6cab6a53f44d0807057656529c4b6": {
          "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_76d47cfbc39c4966aa75d5755634533a",
            "placeholder": "​",
            "style": "IPY_MODEL_9e3eae0a218e4b769b43c631eb9b2c06",
            "value": "config.json: 100%"
          }
        },
        "097b790f1aa94f9c9017aa172fbf06f3": {
          "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_2287868e0e7f41b7a66147f7eb9330ca",
            "max": 784,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_fa2280e1b2c04d86bb6998bcfd97ea10",
            "value": 784
          }
        },
        "920ea221313e4e2da23f8af115356509": {
          "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_7cf7ff011af048ad8426b41670bbad9f",
            "placeholder": "​",
            "style": "IPY_MODEL_a169b5c0a96f460da7b0519a873c987b",
            "value": " 784/784 [00:00&lt;00:00, 62.1kB/s]"
          }
        },
        "2dd6fcef47ff409ab7ca29e9b05d5e60": {
          "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
          }
        },
        "76d47cfbc39c4966aa75d5755634533a": {
          "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
          }
        },
        "9e3eae0a218e4b769b43c631eb9b2c06": {
          "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": ""
          }
        },
        "2287868e0e7f41b7a66147f7eb9330ca": {
          "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
          }
        },
        "fa2280e1b2c04d86bb6998bcfd97ea10": {
          "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": ""
          }
        },
        "7cf7ff011af048ad8426b41670bbad9f": {
          "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
          }
        },
        "a169b5c0a96f460da7b0519a873c987b": {
          "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": ""
          }
        },
        "8f6d7895c0a943a8a8b866db0d8487ae": {
          "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_0c3053449e5e4b389d235359821d4b84",
              "IPY_MODEL_9a09a833adbb4923b72add5acb112072",
              "IPY_MODEL_427a1ed4c5ac4638b29010355c76d9ed"
            ],
            "layout": "IPY_MODEL_c29e9e6afc6b49aa809405bb303fbde1"
          }
        },
        "0c3053449e5e4b389d235359821d4b84": {
          "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_99c554b2ea92435d940d1d01565a162e",
            "placeholder": "​",
            "style": "IPY_MODEL_d2cd1a24609842ca8131699e97da2fb4",
            "value": "model.safetensors.index.json: 100%"
          }
        },
        "9a09a833adbb4923b72add5acb112072": {
          "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_64a516e9d2854cd19a4739893dd18afb",
            "max": 39044,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_52008b4173dd46b1a3c4e09cc5f20388",
            "value": 39044
          }
        },
        "427a1ed4c5ac4638b29010355c76d9ed": {
          "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_c8a6fe4dbe5a4fd09c55256917e12609",
            "placeholder": "​",
            "style": "IPY_MODEL_93da7bc98c0044bf888953975c5b3c4b",
            "value": " 39.0k/39.0k [00:00&lt;00:00, 2.81MB/s]"
          }
        },
        "c29e9e6afc6b49aa809405bb303fbde1": {
          "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
          }
        },
        "99c554b2ea92435d940d1d01565a162e": {
          "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
          }
        },
        "d2cd1a24609842ca8131699e97da2fb4": {
          "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": ""
          }
        },
        "64a516e9d2854cd19a4739893dd18afb": {
          "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
          }
        },
        "52008b4173dd46b1a3c4e09cc5f20388": {
          "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": ""
          }
        },
        "c8a6fe4dbe5a4fd09c55256917e12609": {
          "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
          }
        },
        "93da7bc98c0044bf888953975c5b3c4b": {
          "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": ""
          }
        },
        "6b1455c9256e42398a59aff0e1441998": {
          "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_2f0892ee1280450996bc4397cf12ad15",
              "IPY_MODEL_75261b484f6b4d18a4942bde9882718a",
              "IPY_MODEL_e57258abf667436fbcd5d92816b6b00b"
            ],
            "layout": "IPY_MODEL_89a68e9c10b540b8bbb6470e7a552d9c"
          }
        },
        "2f0892ee1280450996bc4397cf12ad15": {
          "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_2ee4e729ffe9415487072fab3dc0441b",
            "placeholder": "​",
            "style": "IPY_MODEL_47a0143bd32c470e921775782aaa3289",
            "value": "Downloading shards: 100%"
          }
        },
        "75261b484f6b4d18a4942bde9882718a": {
          "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_40b3f9bfa20047fc97cc761f611f8ab4",
            "max": 2,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_3f74b0728e834e70aacab015538b4bbe",
            "value": 2
          }
        },
        "e57258abf667436fbcd5d92816b6b00b": {
          "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_b9d28eb1b117435fb98e5666b36a20a3",
            "placeholder": "​",
            "style": "IPY_MODEL_b1c3882e82c945df8475f359ba4f619c",
            "value": " 2/2 [02:21&lt;00:00, 62.74s/it]"
          }
        },
        "89a68e9c10b540b8bbb6470e7a552d9c": {
          "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
          }
        },
        "2ee4e729ffe9415487072fab3dc0441b": {
          "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
          }
        },
        "47a0143bd32c470e921775782aaa3289": {
          "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": ""
          }
        },
        "40b3f9bfa20047fc97cc761f611f8ab4": {
          "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
          }
        },
        "3f74b0728e834e70aacab015538b4bbe": {
          "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": ""
          }
        },
        "b9d28eb1b117435fb98e5666b36a20a3": {
          "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
          }
        },
        "b1c3882e82c945df8475f359ba4f619c": {
          "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": ""
          }
        },
        "245b668091224491ac4be5dfe53311e4": {
          "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_daa75dfeaa7f44c386b2146831be771d",
              "IPY_MODEL_153f641003d84a918503987e01a92af7",
              "IPY_MODEL_f207788b3b9c43ea863f3a6a8f853742"
            ],
            "layout": "IPY_MODEL_d4015b12d86448eeab101c0d5185bba0"
          }
        },
        "daa75dfeaa7f44c386b2146831be771d": {
          "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_a1005eb60a4c4ed9a8310baadbec8301",
            "placeholder": "​",
            "style": "IPY_MODEL_6506cb9be28a46a49c992a51b777b38e",
            "value": "model-00001-of-00002.safetensors: 100%"
          }
        },
        "153f641003d84a918503987e01a92af7": {
          "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_eb1384fc14ef4463a670ed3e47e369bb",
            "max": 4949933808,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_5f1365e062584a7f99d5bbc6a63989b6",
            "value": 4949933808
          }
        },
        "f207788b3b9c43ea863f3a6a8f853742": {
          "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_5d07de5508334fbf826c26f7d12c8a10",
            "placeholder": "​",
            "style": "IPY_MODEL_a063634d27344d21a70eb65b56f70d4b",
            "value": " 4.95G/4.95G [01:55&lt;00:00, 42.9MB/s]"
          }
        },
        "d4015b12d86448eeab101c0d5185bba0": {
          "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
          }
        },
        "a1005eb60a4c4ed9a8310baadbec8301": {
          "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
          }
        },
        "6506cb9be28a46a49c992a51b777b38e": {
          "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": ""
          }
        },
        "eb1384fc14ef4463a670ed3e47e369bb": {
          "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
          }
        },
        "5f1365e062584a7f99d5bbc6a63989b6": {
          "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": ""
          }
        },
        "5d07de5508334fbf826c26f7d12c8a10": {
          "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
          }
        },
        "a063634d27344d21a70eb65b56f70d4b": {
          "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": ""
          }
        },
        "bc12173618474805ba8d77696f2536f1": {
          "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_846258fc402f4701b3956dda5c464eba",
              "IPY_MODEL_135dcb9e30e44807a345363f71244a62",
              "IPY_MODEL_fca6d6d901904313bc319359021efa11"
            ],
            "layout": "IPY_MODEL_fa8619f1adf7480bbefc03779fa75a8e"
          }
        },
        "846258fc402f4701b3956dda5c464eba": {
          "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_0395df2ef1ce4490a7af964d7da37551",
            "placeholder": "​",
            "style": "IPY_MODEL_2389302be42447728a9574b6dcb0630a",
            "value": "model-00002-of-00002.safetensors: 100%"
          }
        },
        "135dcb9e30e44807a345363f71244a62": {
          "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_ada2232e6607460c9f0640d027074ec6",
            "max": 1110862480,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_c051bd1fae5e4337a3fff816af10b46a",
            "value": 1110862480
          }
        },
        "fca6d6d901904313bc319359021efa11": {
          "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_cd626013012b4132854f6520219a403b",
            "placeholder": "​",
            "style": "IPY_MODEL_193cb0baee854b968eea108b84aebeb9",
            "value": " 1.11G/1.11G [00:25&lt;00:00, 35.3MB/s]"
          }
        },
        "fa8619f1adf7480bbefc03779fa75a8e": {
          "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
          }
        },
        "0395df2ef1ce4490a7af964d7da37551": {
          "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
          }
        },
        "2389302be42447728a9574b6dcb0630a": {
          "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": ""
          }
        },
        "ada2232e6607460c9f0640d027074ec6": {
          "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
          }
        },
        "c051bd1fae5e4337a3fff816af10b46a": {
          "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": ""
          }
        },
        "cd626013012b4132854f6520219a403b": {
          "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
          }
        },
        "193cb0baee854b968eea108b84aebeb9": {
          "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": ""
          }
        },
        "7a86d66bab5b4c54a6b1914465106167": {
          "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_1c20fbedcd7d41b78f698ab3d3245661",
              "IPY_MODEL_cb104e8b23d24b759c2959183d68a176",
              "IPY_MODEL_8c55fca9fb7841e2bd5f908216a0b6ec"
            ],
            "layout": "IPY_MODEL_87c2399531204b17b11c9a41fce17a44"
          }
        },
        "1c20fbedcd7d41b78f698ab3d3245661": {
          "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_1de1d95f460c42a297107173edcf3d3a",
            "placeholder": "​",
            "style": "IPY_MODEL_5611bbcce7624abab139935975c1a64a",
            "value": "Loading checkpoint shards: 100%"
          }
        },
        "cb104e8b23d24b759c2959183d68a176": {
          "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_5ab0e886d41e495a802da12095f0695a",
            "max": 2,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_42eca246c3bd4e6fa90877243b4adb64",
            "value": 2
          }
        },
        "8c55fca9fb7841e2bd5f908216a0b6ec": {
          "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_4b509db4c94d4c7b8b5fab3d6c448891",
            "placeholder": "​",
            "style": "IPY_MODEL_2f8a2a5c3afb4d42bbb54d732c4faac9",
            "value": " 2/2 [00:01&lt;00:00,  1.23it/s]"
          }
        },
        "87c2399531204b17b11c9a41fce17a44": {
          "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
          }
        },
        "1de1d95f460c42a297107173edcf3d3a": {
          "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
          }
        },
        "5611bbcce7624abab139935975c1a64a": {
          "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": ""
          }
        },
        "5ab0e886d41e495a802da12095f0695a": {
          "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
          }
        },
        "42eca246c3bd4e6fa90877243b4adb64": {
          "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": ""
          }
        },
        "4b509db4c94d4c7b8b5fab3d6c448891": {
          "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
          }
        },
        "2f8a2a5c3afb4d42bbb54d732c4faac9": {
          "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": ""
          }
        },
        "624b6fb6274f4a339243915c371498fa": {
          "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_5e22536293914017bba62370ec2426b1",
              "IPY_MODEL_7e97354c5bea4b96aa9a7e58075232a8",
              "IPY_MODEL_26ade4c51ef74261a0ec0a9424458d21"
            ],
            "layout": "IPY_MODEL_59618700975c476ba8576ceaee2f8fe3"
          }
        },
        "5e22536293914017bba62370ec2426b1": {
          "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_4227e8433ac74ac1804a6dbde8f19818",
            "placeholder": "​",
            "style": "IPY_MODEL_1d5efed044a34e01a099f6637e0a1730",
            "value": "generation_config.json: 100%"
          }
        },
        "7e97354c5bea4b96aa9a7e58075232a8": {
          "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_16d6a6d8ee754f5d86ddcb33facd74f2",
            "max": 111,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_f19d42bb68954d14a6faa4ec9735f7aa",
            "value": 111
          }
        },
        "26ade4c51ef74261a0ec0a9424458d21": {
          "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_f4d34295332c496c98f03a391bd1f200",
            "placeholder": "​",
            "style": "IPY_MODEL_7d5f249e7eac4c878cffda7ad4b46e62",
            "value": " 111/111 [00:00&lt;00:00, 12.0kB/s]"
          }
        },
        "59618700975c476ba8576ceaee2f8fe3": {
          "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
          }
        },
        "4227e8433ac74ac1804a6dbde8f19818": {
          "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
          }
        },
        "1d5efed044a34e01a099f6637e0a1730": {
          "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": ""
          }
        },
        "16d6a6d8ee754f5d86ddcb33facd74f2": {
          "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
          }
        },
        "f19d42bb68954d14a6faa4ec9735f7aa": {
          "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": ""
          }
        },
        "f4d34295332c496c98f03a391bd1f200": {
          "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
          }
        },
        "7d5f249e7eac4c878cffda7ad4b46e62": {
          "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": ""
          }
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}