Kobart.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Choigapju/NLP-study/blob/main/Kobart.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://github.com/SKT-AI/KoBART"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Llw0Sjs4Hq0O",
        "outputId": "1b689b7b-ff8a-468e-ea93-bb951ddac5cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://github.com/SKT-AI/KoBART\n",
            "  Cloning https://github.com/SKT-AI/KoBART to /tmp/pip-req-build-710n7evs\n",
            "  Running command git clone -q https://github.com/SKT-AI/KoBART /tmp/pip-req-build-710n7evs\n",
            "Collecting boto3\n",
            "  Downloading boto3-1.26.16-py3-none-any.whl (132 kB)\n",
            "\u001b[K     |████████████████████████████████| 132 kB 27.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from kobart==0.5.1) (1.3.5)\n",
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            "  Downloading pytorch_lightning-1.2.1-py3-none-any.whl (814 kB)\n",
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            "\u001b[?25hCollecting torch==1.7.1\n",
            "  Downloading torch-1.7.1-cp37-cp37m-manylinux1_x86_64.whl (776.8 MB)\n",
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            "Collecting future>=0.17.1\n",
            "  Downloading future-0.18.2.tar.gz (829 kB)\n",
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            "  Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
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            "Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.2.1->kobart==0.5.1) (1.50.0)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch-lightning==1.2.1->kobart==0.5.1) (1.8.1)\n",
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            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers==4.3.3->kobart==0.5.1) (3.10.0)\n",
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            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.3.3->kobart==0.5.1) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.3.3->kobart==0.5.1) (1.24.3)\n",
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            "Collecting jmespath<2.0.0,>=0.7.1\n",
            "  Downloading jmespath-1.0.1-py3-none-any.whl (20 kB)\n",
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            "Collecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1\n",
            "  Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)\n",
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            "Building wheels for collected packages: kobart, future, sacremoses\n",
            "  Building wheel for kobart (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kobart: filename=kobart-0.5.1-py3-none-any.whl size=9584 sha256=2738a0dc3af4e179b7ac72802b1f774d295509b1b2e35278fd908d6e3d6d4e7f\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-aoft0_gk/wheels/6e/55/c4/bd4fede223bc304089ac8da2a2099a69db3fcd4b0e853383f5\n",
            "  Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for future: filename=future-0.18.2-py3-none-any.whl size=491071 sha256=b08be676e490108aecdc1e8a5a19ddb202e60a6dd2c10cc79cf09f8616de0ac2\n",
            "  Stored in directory: /root/.cache/pip/wheels/56/b0/fe/4410d17b32f1f0c3cf54cdfb2bc04d7b4b8f4ae377e2229ba0\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.53-py3-none-any.whl size=895260 sha256=e44f7b1e5cd424912c921f1518f48fbd308b05a53c9c6f6543163b71077faa89\n",
            "  Stored in directory: /root/.cache/pip/wheels/87/39/dd/a83eeef36d0bf98e7a4d1933a4ad2d660295a40613079bafc9\n",
            "Successfully built kobart future sacremoses\n",
            "Installing collected packages: urllib3, jmespath, botocore, torch, tokenizers, sacremoses, s3transfer, future, transformers, pytorch-lightning, boto3, kobart\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 1.12.1+cu113\n",
            "    Uninstalling torch-1.12.1+cu113:\n",
            "      Successfully uninstalled torch-1.12.1+cu113\n",
            "  Attempting uninstall: future\n",
            "    Found existing installation: future 0.16.0\n",
            "    Uninstalling future-0.16.0:\n",
            "      Successfully uninstalled future-0.16.0\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",
            "torchvision 0.13.1+cu113 requires torch==1.12.1, but you have torch 1.7.1 which is incompatible.\n",
            "torchtext 0.13.1 requires torch==1.12.1, but you have torch 1.7.1 which is incompatible.\n",
            "torchaudio 0.12.1+cu113 requires torch==1.12.1, but you have torch 1.7.1 which is incompatible.\u001b[0m\n",
            "Successfully installed boto3-1.26.16 botocore-1.29.16 future-0.18.2 jmespath-1.0.1 kobart-0.5.1 pytorch-lightning-1.2.1 s3transfer-0.6.0 sacremoses-0.0.53 tokenizers-0.10.3 torch-1.7.1 transformers-4.3.3 urllib3-1.25.11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from kobart import get_kobart_tokenizer\n",
        "kobart_tokenizer = get_kobart_tokenizer()\n",
        "kobart_tokenizer.tokenize(\"안녕하세요. SKT AI팀에서 발표한 KoBART를 소개 합니다.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oQdHfB9THzxx",
        "outputId": "b3ed8b14-89f7-4016-a8a0-4d6affafedf8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/.cache/kobart_base_tokenizer_cased_cf74400bce.zip[██████████████████████████████████████████████████]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['▁안녕하',\n",
              " '세요.',\n",
              " '▁SK',\n",
              " 'T',\n",
              " '▁AI',\n",
              " '팀',\n",
              " '에서',\n",
              " '▁발표한',\n",
              " '▁K',\n",
              " 'o',\n",
              " 'B',\n",
              " 'A',\n",
              " 'R',\n",
              " 'T',\n",
              " '를',\n",
              " '▁소개',\n",
              " '▁합니다.']"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BartModel\n",
        "from kobart import get_pytorch_kobart_model, get_kobart_tokenizer\n",
        "kobart_tokenizer = get_kobart_tokenizer()\n",
        "model = BartModel.from_pretrained(get_pytorch_kobart_model())\n",
        "\n",
        "def count_parameters(model):\n",
        "  return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
        "\n",
        "print(f'모델 파라미터의 수 : {count_parameters(model):,}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YCKowftsI1wD",
        "outputId": "109f804c-ba82-49b7-aabd-c096d25ed280"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "using cached model. /content/.cache/kobart_base_tokenizer_cased_cf74400bce.zip\n",
            "/content/.cache/kobart_base_cased_ff4bda5738.zip[██████████████████████████████████████████████████]\n",
            "모델 파라미터의 수 : 123,859,968\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import PreTrainedTokenizerFast\n",
        "from transformers import BartForConditionalGeneration\n",
        "\n",
        "tokenizer = PreTrainedTokenizerFast.from_pretrained('digit82/kobart-summarization')\n",
        "model = BartForConditionalGeneration.from_pretrained('digit82/kobart-summarization')\n",
        "\n",
        "text = \"\"\"\n",
        "1일 오후 9시까지 최소 20만3220명이 코로나19에 신규 확진됐다. 또다시 동시간대 최다 기록으로, 사상 처음 20만명대에 진입했다.\n",
        "방역 당국과 서울시 등 각 지방자치단체에 따르면 이날 0시부터 오후 9시까지 전국 신규 확진자는 총 20만3220명으로 집계됐다.\n",
        "국내 신규 확진자 수가 20만명대를 넘어선 것은 이번이 처음이다.\n",
        "동시간대 최다 기록은 지난 23일 오후 9시 기준 16만1389명이었는데, 이를 무려 4만1831명이나 웃돌았다. 전날 같은 시간 기록한 13만3481명보다도 6만9739명 많다.\n",
        "확진자 폭증은 3시간 전인 오후 6시 집계에서도 예견됐다.\n",
        "오후 6시까지 최소 17만8603명이 신규 확진돼 동시간대 최다 기록(24일 13만8419명)을 갈아치운 데 이어 이미 직전 0시 기준 역대 최다 기록도 넘어섰다. 역대 최다 기록은 지난 23일 0시 기준 17만1451명이었다.\n",
        "17개 지자체별로 보면 서울 4만6938명, 경기 6만7322명, 인천 1만985명 등 수도권이 12만5245명으로 전체의 61.6%를 차지했다. 서울과 경기는 모두 동시간대 기준 최다로, 처음으로 각각 4만명과 6만명을 넘어섰다.\n",
        "비수도권에서는 7만7975명(38.3%)이 발생했다. 제주를 제외한 나머지 지역에서 모두 동시간대 최다를 새로 썼다.\n",
        "부산 1만890명, 경남 9909명, 대구 6900명, 경북 6977명, 충남 5900명, 대전 5292명, 전북 5150명, 울산 5141명, 광주 5130명, 전남 4996명, 강원 4932명, 충북 3845명, 제주 1513명, 세종 1400명이다.\n",
        "집계를 마감하는 자정까지 시간이 남아있는 만큼 2일 0시 기준으로 발표될 신규 확진자 수는 이보다 더 늘어날 수 있다. 이에 따라 최종 집계되는 확진자 수는 21만명 안팎을 기록할 수 있을 전망이다.\n",
        "한편 전날 하루 선별진료소에서 이뤄진 검사는 70만8763건으로 검사 양성률은 40.5%다. 양성률이 40%를 넘은 것은 이번이 처음이다. 확산세가 계속 거세질 수 있다는 얘기다.\n",
        "이날 0시 기준 신규 확진자는 13만8993명이었다. 이틀 연속 13만명대를 이어갔다.\n",
        "\"\"\"\n",
        "\n",
        "text = text.replace('\\n', ' ')\n",
        "\n",
        "raw_input_ids = tokenizer.encode(text)\n",
        "input_ids = [tokenizer.bos_token_id] + raw_input_ids + [tokenizer.eos_token_id]\n",
        "\n",
        "summary_ids = model.generate(torch.tensor([input_ids]),  num_beams=4,  max_length=512,  eos_token_id=1)\n",
        "output = tokenizer.decode(summary_ids.squeeze().tolist(), skip_special_tokens=True)\n",
        "\n",
        "print(output)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195,
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          "text": [
            "1일 0 9시까지 최소 20만3220명이 코로나19에 신규 확진되어 역대 최다 기록을 갈아치웠다.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"\"\"\n",
        "아픈 아들을 자신의 집으로 데려와 키우기 위해 별거 중인 남편의 도장을 위조한 뒤 전입 신고를 했다면 법적 처벌을 받아야 할까.\n",
        "\n",
        "대법원 3부(주심 노정희 대법관)는 사인위조 등 혐의로 기소된 A씨에 대해 무죄를 선고한 원심을 확정했다고 27일 밝혔다.\n",
        "\n",
        "A씨는 2015년 이혼소송 중인 남편의 도장을 위조해 자신의 아들 전입 신고에 사용한 혐의로 재판에 넘겨졌다. A씨는 자녀들의 주소지를 이전하는 과정에서 남편 동의를 받지 않고 도장을 위조한 뒤, 이를 찍은 전입신고서를 주민센터에 제출했다.\n",
        "\n",
        "1심은 \"A씨는 남편과 연락이 가능한 상황이었으므로 승낙을 받는 게 불가능하지 않았다\"며 징역 4개월에 집행유예 1년을 선고했다.\n",
        "\n",
        "반면 2심은 A씨에게 무죄를 선고했다. 30개월 아이가 감기에 걸려 아픈 상황에서 돌볼 목적으로 데려갔고, 일하는 시간 동안 어린이집에 보내야 해 전입 신고를 하려고 한 것일뿐 불순한 의도가 없었다는 취지다. A씨는 남편에게 전입 신고를 동의해달라고 수십 차례에 걸쳐 메시지를 보냈지만 답장을 받지 못한 것으로 알려졌다.\n",
        "\n",
        "2심 재판부는 \"남편의 인장이 위조됐다는 법익 침해가 있기는 했지만, 반대 보호이익으로서 자녀의 복리가 우선적으로 고려돼야 한다\"며 \"이러한 점에서 A씨의 남편 인장 위조·사용 행위는 사회윤리 내지 통념에 비춰 용인될 수 있는 행위\"라고 밝혔다. 그러면서 \"남편은 A씨가 아이를 데려간 것을 알고도 곧바로 이의를 제기하지 않았고, 전입 신고 후 수일이 지난 후에야 내용증명을 보냈다\"며 \"A씨로서는 당시 남편이 상황을 방치 또는 묵인했다고 받아들일 여지가 있었다\"고 판단했다.\n",
        "\n",
        "대법원도 \"원심 판단에 법리를 오해한 잘못이 없다\"며 판결을 확정했다.\n",
        "\"\"\"\n",
        "\n",
        "text = text.replace('\\n', ' ')\n",
        "\n",
        "raw_input_ids = tokenizer.encode(text)\n",
        "input_ids = [tokenizer.bos_token_id] + raw_input_ids + [tokenizer.eos_token_id]\n",
        "\n",
        "summary_ids = model.generate(torch.tensor([input_ids]),  num_beams=4,  max_length=512,  eos_token_id=1)\n",
        "output = tokenizer.decode(summary_ids.squeeze().tolist(), skip_special_tokens=True)\n",
        "\n",
        "print(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vSM3NyuRY5J0",
        "outputId": "df23f68b-5520-4315-f5ab-a97fa303abe4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "대법원 3부는 이혼소송 중인 남편의 도장을 위조해 자신의 아들 전입 신고에 사용한 혐의로 재판에 넘겨진 A씨에게 징역 4개월에 집행유예 1년을 선고한 원심을 확정했다.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"\"\"\n",
        "계약금 10%·중도금무이자융자·6개월 후 전매가능 기자 아파트 분양시장이 실수요자 중심으로 바뀌면서 초역세권 입지와 변화하는 라이프스타일에 맞춘 혁신평면이 아파트 선택에 미치는 영향력이 커지고 있다. 태왕이 지난 22일 공개한 ‘성당 태왕아너스 메트로’ 모델하우스를 찾은 방문객들은 초역세권 입지에다 혁신평면에 다시 한번 호평을 보냈다. 오픈 당일 문을 열기 전부터 관람객의 줄이 이어지면서 꽃샘추위의 궂은 날씨에도 주말까지 관람객의 발길이 끊이지 않았다. 이 단지는 서부정류장역 초역세권인데다 단지방향으로 지하철 출입구가 추가설치 될 계획이다. 또 평면에서는 도심역세권에서 만나기 어려운 공간 활용성이 탁월한 와이드 84㎡라는 점에서 주목받았다. 특히 주방 팬트리와 안방 워크인드레스룸 등 수납특화에 큰 관심을 보였다. 분양관계자는 “성당동 유일의 초역세권아파트인데다 실수요자들이 혁신평면에 매우 만족하고 있다. 또 합리적인 분양가와 중도금무이자 등의 분양조건도 실수요자에게 유리해 높은 청약경쟁률을 기대한다”고 밝혔다. LG U+ 기반의 첨단ICT 솔루션 AI스마트홈 시스템을 적용한 인공지능아파트로 음성인식이 가능한 AI스피커를 제공해 인공지능 ICT플랫폼을 기반으로 한 다양한 첨단서비스를 누릴 수 있는 점도 장점이다. 또 지하주차장 주차차량 위치인식, 공동현관 자동문열림, 홈네트워크 연동 무인택배시스템, 공동현관 E/V 자동호출 등 원패스카드 하나로 다 누리는 아너스 원패스시스템도 적용된다. 분양관계자는 “성당동 유일의 초역세권아파트인데다 실수요자들이 혁신평면에 매우 만족하고 있다. 합리적인 분양가와 중도금무이자 등의 분양조건도 실수요자에게 유리해 높은 청약경쟁률을 기대한다”고 전망했다. 아파트의 경우 27일 특별공급을 시작으로 28일 1순위, 29일에 2순위 청약접수가 진행된다. 오피스텔은 3월27일부터 4월1일까지 청약을 한다. 아파트 1순위 청약자격은 청약통장 가입기간 6개월 이상과 청약예치금 250만 원 이상이며 대구 및 경북 6개월 이상 거주자에게 우선 공급된다. 더불어 계약금 10%, 중도금 무이자 혜택이 주어지며 6개월 후 전매가 가능하다. 모델하우스는 달서구 장기동 119-8번지에서 공개 중이다. 태왕이 지난 22일 공개한 ‘성당 태왕아너스 메트로’ 모델하우스를 찾은 방문객의 행렬이 이어진 모습.\n",
        "\"\"\"\n",
        "\n",
        "text = text.replace('\\n', ' ')\n",
        "\n",
        "raw_input_ids = tokenizer.encode(text)\n",
        "input_ids = [tokenizer.bos_token_id] + raw_input_ids + [tokenizer.eos_token_id]\n",
        "\n",
        "summary_ids = model.generate(torch.tensor([input_ids]),  num_beams=4,  max_length=512,  eos_token_id=1)\n",
        "output = tokenizer.decode(summary_ids.squeeze().tolist(), skip_special_tokens=True)\n",
        "\n",
        "print(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "98uTfMGAZFjf",
        "outputId": "8d9354d4-9aca-4291-9c28-92d46a0b5a50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "태왕이 지난 22일 공개한 ‘성당 태왕아너스 메트로’ 모델하우스를 찾은 방문객들은 초역세권 입지와 변화하는 라이프스타일에 맞춘 혁신평면에 호평을 보냈으며 합리적인 분양가와 중도금무이자 등의 분양조건도 실수요자에게 유리해 높은 청약경쟁률을 기대한다고 밝혔다.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import shutil\n",
        "from zipfile import ZipFile\n",
        "from transformers import PreTrainedTokenizerFast\n",
        "\n",
        "from kobart import download\n",
        "\n",
        "\n",
        "def get_pytorch_kobart_model(ctx=\"cpu\", cachedir=\".cache\"):\n",
        "    pytorch_kobart = {\n",
        "        \"url\": \"s3://skt-lsl-nlp-model/KoBART/models/kobart_base_cased_ff4bda5738.zip\",\n",
        "        \"chksum\": \"ff4bda5738\",\n",
        "    }\n",
        "    model_zip, is_cached = download(\n",
        "        pytorch_kobart[\"url\"], pytorch_kobart[\"chksum\"], cachedir=cachedir\n",
        "    )\n",
        "    cachedir_full = os.path.join(os.getcwd(), cachedir)\n",
        "    model_path = os.path.join(cachedir_full, \"kobart_from_pretrained\")\n",
        "    if not os.path.exists(model_path) or not is_cached:\n",
        "        if not is_cached:\n",
        "            shutil.rmtree(model_path, ignore_errors=True)\n",
        "        zipf = ZipFile(os.path.expanduser(model_zip))\n",
        "        zipf.extractall(path=cachedir_full)\n",
        "    return model_path\n",
        "\n",
        "\n",
        "def get_kobart_tokenizer(cachedir=\".cache\"):\n",
        "    \"\"\"Get KoGPT2 Tokenizer file path after downloading\"\"\"\n",
        "    tokenizer = {\n",
        "        \"url\": \"s3://skt-lsl-nlp-model/KoBART/tokenizers/kobart_base_tokenizer_cased_cf74400bce.zip\",\n",
        "        \"chksum\": \"cf74400bce\",\n",
        "    }\n",
        "    file_path, is_cached = download(\n",
        "        tokenizer[\"url\"], tokenizer[\"chksum\"], cachedir=cachedir\n",
        "    )\n",
        "    cachedir_full = os.path.expanduser(cachedir)\n",
        "    if (\n",
        "        not os.path.exists(os.path.join(cachedir_full, \"emji_tokenizer\"))\n",
        "        or not is_cached\n",
        "    ):\n",
        "        if not is_cached:\n",
        "            shutil.rmtree(\n",
        "                os.path.join(cachedir_full, \"emji_tokenizer\"), ignore_errors=True\n",
        "            )\n",
        "        zipf = ZipFile(os.path.expanduser(file_path))\n",
        "        zipf.extractall(path=cachedir_full)\n",
        "    tok_path = os.path.join(cachedir_full, \"emji_tokenizer/model.json\")\n",
        "    tokenizer_obj = PreTrainedTokenizerFast(\n",
        "        tokenizer_file=tok_path,\n",
        "        bos_token=\"<s>\",\n",
        "        eos_token=\"</s>\",\n",
        "        unk_token=\"<unk>\",\n",
        "        pad_token=\"<pad>\",\n",
        "        mask_token=\"<mask>\",\n",
        "    )\n",
        "    return tokenizer_obj\n",
        "\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    # pip install git+https://github.com/SKT-AI/KoBART#egg=kobart\n",
        "    from transformers import BartModel\n",
        "    from kobart import get_pytorch_kobart_model, get_kobart_tokenizer\n",
        "\n",
        "    kobart_tokenizer = get_kobart_tokenizer()\n",
        "    print(kobart_tokenizer.tokenize(\"안녕하세요. 한국어 BART 입니다.🤣:)l^o\"))\n",
        "\n",
        "    model = BartModel.from_pretrained(get_pytorch_kobart_model())\n",
        "    inputs = kobart_tokenizer([\"안녕하세요.\"], return_tensors=\"pt\")\n",
        "    print(model(inputs[\"input_ids\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0iVJLWzoZW36",
        "outputId": "e056b550-77c5-49ad-828b-a2478849712e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "using cached model. /content/.cache/kobart_base_tokenizer_cased_cf74400bce.zip\n",
            "['▁안녕하', '세요.', '▁한국어', '▁B', 'A', 'R', 'T', '▁입', '니다.', '🤣', ':)', 'l^o']\n",
            "using cached model. /content/.cache/kobart_base_cased_ff4bda5738.zip\n",
            "Seq2SeqModelOutput(last_hidden_state=tensor([[[-0.4418, -4.3673,  3.2404,  ...,  5.8832,  4.0629,  3.5540],\n",
            "         [-0.1316, -4.6446,  2.5955,  ...,  6.0093,  2.7467,  3.0007]]],\n",
            "       grad_fn=<NativeLayerNormBackward>), past_key_values=((tensor([[[[-9.7980e-02, -6.6584e-01, -1.8089e+00,  ...,  9.6023e-01,\n",
            "           -1.8818e-01, -1.3252e+00],\n",
            "          [-6.2507e-01,  5.1009e-01, -7.4878e-01,  ...,  8.6230e-01,\n",
            "            1.5722e-01, -6.0267e-01]],\n",
            "\n",
            "         [[ 5.4597e-01, -2.3990e-01,  1.5901e+00,  ...,  4.3655e-01,\n",
            "            7.9514e-01,  8.9880e-02],\n",
            "          [-1.7327e-01, -6.3167e-01,  4.5152e-02,  ..., -1.4111e-01,\n",
            "            1.8678e-01, -1.2081e-01]],\n",
            "\n",
            "         [[ 1.4621e+00,  1.8980e+00, -7.6696e-01,  ...,  1.5695e+00,\n",
            "            6.7921e-02, -3.9372e-01],\n",
            "          [-4.1204e-02,  1.7132e+00, -1.1863e+00,  ..., -2.2272e-01,\n",
            "            9.8309e-02,  8.1729e-01]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[ 4.8868e-01,  1.2633e+00, -1.1658e-01,  ..., -3.1989e-01,\n",
            "            1.2202e+00, -7.9021e-02],\n",
            "          [-8.4946e-01,  8.9379e-02, -1.0224e+00,  ...,  3.3125e-01,\n",
            "           -2.5262e-01,  5.0875e-01]],\n",
            "\n",
            "         [[ 1.5854e+00,  3.2461e-01,  3.0826e+00,  ..., -1.6728e+00,\n",
            "            1.2071e+00, -3.5671e-01],\n",
            "          [ 5.5400e-02, -9.2782e-01, -2.3055e-03,  ..., -6.1646e-01,\n",
            "            1.0880e+00,  2.8645e-01]],\n",
            "\n",
            "         [[ 8.7081e-01, -4.6088e-01, -2.8388e+00,  ...,  1.6038e+00,\n",
            "           -1.0963e+00, -1.8732e-01],\n",
            "          [ 4.5471e-01, -3.1087e-02, -2.4484e+00,  ...,  1.9392e+00,\n",
            "           -4.0694e-01, -1.9906e-01]]]], grad_fn=<CopyBackwards>), tensor([[[[ 0.2187,  1.3322, -0.0016,  ..., -0.1200, -0.0395,  0.0971],\n",
            "          [-0.2722, -0.0590,  0.4620,  ...,  0.1822, -0.0171, -0.2176]],\n",
            "\n",
            "         [[ 0.1439,  0.0074,  0.0249,  ...,  0.2148, -0.5016,  0.1263],\n",
            "          [ 0.3625, -0.3192, -0.2254,  ...,  0.6312,  0.1061,  0.3506]],\n",
            "\n",
            "         [[-0.0862, -0.0073, -0.0479,  ...,  0.0110,  0.0198,  0.1909],\n",
            "          [-0.4622, -0.4537, -0.6060,  ...,  0.5620,  1.3319,  0.0989]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[-0.0868, -0.1329,  0.1862,  ..., -0.0489, -0.0084,  0.0151],\n",
            "          [-0.4065,  0.4082,  0.7682,  ..., -0.0909,  0.1701,  0.0145]],\n",
            "\n",
            "         [[ 0.1256,  0.1170,  0.0912,  ...,  0.0623, -0.0574,  0.0611],\n",
            "          [-0.2664, -0.4485,  0.1439,  ...,  0.2324,  0.3832, -1.0943]],\n",
            "\n",
            "         [[ 0.0702,  0.0227,  0.0311,  ..., -0.0203,  0.0602,  0.0033],\n",
            "          [-0.3197,  0.4879,  0.1972,  ..., -0.3241,  0.1762,  0.5655]]]],\n",
            "       grad_fn=<CopyBackwards>), tensor([[[[-1.0648, -2.5643, -1.2208,  ...,  1.4609,  1.0088,  0.8616],\n",
            "          [-1.3031, -2.4746, -1.2874,  ...,  1.4641,  0.6007,  0.5663]],\n",
            "\n",
            "         [[-0.9957, -1.4633, -0.6104,  ..., -0.0807,  0.6639,  1.1657],\n",
            "          [-0.8524, -2.1023, -0.3089,  ...,  0.1747,  0.9409,  0.8996]],\n",
            "\n",
            "         [[ 1.7265, -0.0293, -0.3755,  ..., -0.6951,  0.4730,  0.3141],\n",
            "          [ 1.5839, -0.6262, -0.5320,  ...,  0.2295,  0.5922,  0.6125]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[-0.1965, -0.4015,  1.1312,  ..., -0.5016, -0.1073,  0.0549],\n",
            "          [-0.5866, -0.6739,  1.0961,  ..., -0.3082, -0.1296,  0.0126]],\n",
            "\n",
            "         [[-0.0225,  0.6815, -1.6006,  ...,  1.7747, -0.4324, -1.2341],\n",
            "          [ 0.2012,  0.2133, -2.0224,  ...,  1.0445, -0.3429, -1.1153]],\n",
            "\n",
            "         [[-0.4898, -0.2916,  0.1036,  ..., -1.1657, -2.0047, -0.5035],\n",
            "          [ 0.2014,  0.0172, -0.2052,  ..., -1.1187, -2.9760, -0.2174]]]],\n",
            "       grad_fn=<CopyBackwards>), tensor([[[[ 0.2822, -0.1429, -0.0214,  ...,  0.0815,  0.1900, -0.2706],\n",
            "          [ 0.0435, -0.3802, -0.3064,  ...,  0.5159,  0.1527, -0.2039]],\n",
            "\n",
            "         [[-0.8001,  0.0466,  1.0855,  ..., -0.2635, -0.2926,  0.3927],\n",
            "          [-0.3446, -0.6481,  0.5623,  ...,  0.3295,  0.6792,  0.4060]],\n",
            "\n",
            "         [[-0.8864, -0.0462, -0.4937,  ..., -1.2180,  0.5027,  0.1991],\n",
            "          [-0.8240, -0.4198,  0.1187,  ..., -0.3042,  0.6595, -0.1653]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[-0.3499,  0.7639,  0.0130,  ...,  0.3620,  0.4275, -0.6412],\n",
            "          [-0.1910,  0.5114, -0.0352,  ...,  0.3060,  0.2019, -0.4844]],\n",
            "\n",
            "         [[ 0.2417,  0.1567,  0.2082,  ...,  0.1640, -0.1767, -0.1738],\n",
            "          [ 0.1304, -0.0716, -0.1840,  ...,  0.3307,  0.1039,  0.1808]],\n",
            "\n",
            "         [[-0.4749, -0.9211,  1.0276,  ..., -0.3349, -0.2668, -0.2143],\n",
            "          [ 0.0051, -0.7191,  0.6315,  ..., -0.2618,  0.0611, -0.1337]]]],\n",
            "       grad_fn=<CopyBackwards>)), (tensor([[[[ 0.0568,  0.1593,  0.3356,  ..., -0.0771, -0.1446, -0.1562],\n",
            "          [ 1.6254, -1.5403,  1.2852,  ...,  0.3466, -1.0204, -1.6762]],\n",
            "\n",
            "         [[ 0.2014, -0.2892, -0.4829,  ...,  0.2817, -0.1413,  1.5805],\n",
            "          [ 0.6554,  1.6124,  0.4492,  ...,  0.3675,  1.0584, -3.6320]],\n",
            "\n",
            "         [[-0.1167,  0.6492, -0.5715,  ...,  0.0990, -1.3751, -0.3157],\n",
            "          [ 0.5295, -2.8552,  1.0669,  ...,  0.8807,  2.6310, -0.9037]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[ 0.0606,  0.1003,  0.1334,  ...,  0.0648,  0.0886,  1.1766],\n",
            "          [ 0.9016, -1.1139,  1.2164,  ...,  0.0885,  0.5449, -1.5874]],\n",
            "\n",
            "         [[ 0.1461,  0.1861,  0.0330,  ..., -0.0966, -0.3876,  0.2000],\n",
            "          [-0.4877,  0.6808,  0.8141,  ...,  1.1511, -1.0541, -0.7516]],\n",
            "\n",
            "         [[ 0.4198,  0.1532,  0.4183,  ..., -0.1995, -0.0194,  0.3649],\n",
            "          [ 1.9049,  0.4632, -1.9892,  ...,  0.2458,  0.5797, -0.5118]]]],\n",
            "       grad_fn=<CopyBackwards>), tensor([[[[ 1.5379e-02,  4.6779e-02,  2.3209e-02,  ..., -3.6808e-03,\n",
            "           -6.6380e-02,  8.8301e-04],\n",
            "          [ 3.3722e-01,  5.9773e-01, -9.7631e-01,  ..., -6.6999e-01,\n",
            "           -8.6936e-01, -1.0744e+00]],\n",
            "\n",
            "         [[-8.2674e-03, -2.8380e-02, -3.8986e-02,  ...,  1.9672e-02,\n",
            "            2.6157e-03,  2.0187e-03],\n",
            "          [ 4.2856e-01,  1.3611e+00,  4.8732e-01,  ..., -5.9339e-02,\n",
            "           -1.1926e-01, -4.2579e-02]],\n",
            "\n",
            "         [[ 6.0655e-02, -1.8254e-02, -1.5229e-02,  ..., -1.6663e-02,\n",
            "            8.4880e-03, -4.8301e-02],\n",
            "          [-1.0497e-01, -8.1324e-02,  3.3243e-01,  ...,  3.5059e-01,\n",
            "           -7.1202e-02,  1.6473e-02]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[ 3.6912e-02, -6.7970e-02, -8.0440e-02,  ..., -9.4137e-02,\n",
            "            3.8143e-03,  7.8197e-02],\n",
            "          [-2.4057e-01,  2.6768e-03,  7.9480e-01,  ...,  2.9896e-01,\n",
            "            7.9629e-01,  9.2801e-01]],\n",
            "\n",
            "         [[ 1.9845e-02,  4.9133e-03, -3.3145e-02,  ...,  7.2793e-02,\n",
            "            5.9300e-02, -5.7760e-02],\n",
            "          [-3.6504e-01,  5.4651e-01, -4.3529e-02,  ..., -3.2015e-01,\n",
            "            7.9082e-01, -9.7559e-02]],\n",
            "\n",
            "         [[ 3.4483e-02, -1.2532e-02,  1.5040e-02,  ...,  1.7609e-02,\n",
            "           -2.3566e-02, -2.2959e-02],\n",
            "          [-1.1245e+00, -6.8237e-01,  1.0025e-01,  ..., -2.7405e-01,\n",
            "           -5.1046e-01, -1.9639e-01]]]], grad_fn=<CopyBackwards>), tensor([[[[ 2.3286e+00, -4.2951e-02,  3.0110e+00,  ...,  1.0627e+00,\n",
            "           -9.1236e-01,  2.6603e+00],\n",
            "          [ 3.0073e+00, -1.5674e-01,  2.8979e+00,  ...,  9.7826e-01,\n",
            "           -5.3163e-01,  3.0233e+00]],\n",
            "\n",
            "         [[ 1.7130e+00, -2.9926e+00,  1.8776e+00,  ...,  7.5416e-01,\n",
            "           -8.7124e-01, -4.8129e-01],\n",
            "          [ 2.5121e+00, -3.2266e+00,  2.2682e+00,  ...,  4.4046e-01,\n",
            "           -5.1476e-01, -5.2341e-01]],\n",
            "\n",
            "         [[-8.1486e-01, -9.5213e-01, -8.3259e-04,  ...,  1.5024e+00,\n",
            "           -5.7569e-01,  1.9130e-01],\n",
            "          [-8.1358e-01, -1.2385e+00,  9.3572e-01,  ...,  1.1406e+00,\n",
            "           -9.5044e-01, -4.8016e-01]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[ 8.1763e-01, -9.2276e-01,  1.5260e+00,  ...,  2.9562e-02,\n",
            "            1.5474e-02, -8.2440e-01],\n",
            "          [ 1.4520e-02, -1.5225e+00,  1.9413e+00,  ..., -3.6908e-01,\n",
            "            5.3651e-01, -2.1469e-01]],\n",
            "\n",
            "         [[-1.0017e+00, -5.5066e-01, -1.8923e+00,  ..., -5.8160e-01,\n",
            "            4.2548e-01, -6.7959e-01],\n",
            "          [-5.8291e-01, -8.2588e-01, -2.0550e+00,  ..., -2.2966e-01,\n",
            "            2.4397e-02, -2.6139e-01]],\n",
            "\n",
            "         [[-1.4829e+00, -8.8995e-01,  9.4026e-01,  ...,  1.4175e+00,\n",
            "           -3.9160e-01, -9.2237e-01],\n",
            "          [-1.0111e+00, -1.1450e+00,  1.1993e+00,  ...,  2.0617e+00,\n",
            "           -5.9144e-01, -1.3860e+00]]]], grad_fn=<CopyBackwards>), tensor([[[[ 0.1935, -0.1730,  0.3934,  ..., -0.0840,  0.2339,  0.1091],\n",
            "          [-0.0569, -0.3551,  0.3823,  ...,  0.0197, -0.0043, -0.0436]],\n",
            "\n",
            "         [[ 0.2364,  0.4606,  0.5622,  ...,  0.4820,  0.1865,  0.6264],\n",
            "          [ 0.5973,  0.2811,  0.2784,  ...,  0.1489, -0.4533,  0.4468]],\n",
            "\n",
            "         [[ 0.4116,  0.4325, -0.3306,  ..., -0.7509,  0.2645, -0.1624],\n",
            "          [ 0.8045,  0.3654, -0.4785,  ..., -0.7986, -0.0484, -0.1889]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[ 0.1706, -1.0074,  0.2970,  ...,  0.4921,  0.5458,  0.1225],\n",
            "          [ 0.3865, -1.1976, -0.2150,  ...,  0.6561,  0.4510,  0.1755]],\n",
            "\n",
            "         [[ 0.0047, -0.7317,  0.3213,  ...,  0.5871, -0.2654, -0.4047],\n",
            "          [-0.1921, -0.4592,  0.0943,  ...,  0.3975, -0.2417, -0.3156]],\n",
            "\n",
            "         [[-0.1002, -0.3230,  0.6674,  ..., -0.2355, -0.6963, -0.3525],\n",
            "          [-0.1739, -0.4883,  0.4101,  ..., -0.2514, -0.6257, -0.6858]]]],\n",
            "       grad_fn=<CopyBackwards>)), (tensor([[[[-0.1980, -0.8728,  0.6663,  ...,  0.2215,  0.3865, -0.0239],\n",
            "          [ 0.7417,  3.5312, -0.8480,  ..., -0.0536, -0.7790, -0.2229]],\n",
            "\n",
            "         [[ 0.3768, -0.2689, -0.0428,  ...,  0.1046, -0.1428, -0.9039],\n",
            "          [ 0.0478,  2.5229, -0.2297,  ..., -0.2463,  3.8656,  4.1838]],\n",
            "\n",
            "         [[-0.0777,  0.5079,  0.0822,  ...,  0.0193, -0.5585,  0.1365],\n",
            "          [ 0.6972, -2.6046,  1.8231,  ..., -1.2138,  2.4488,  0.6540]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[-0.4664,  0.2169,  0.0277,  ...,  1.1934,  0.6856,  0.1670],\n",
            "          [ 0.6041,  0.9994,  0.1424,  ..., -1.8193, -1.1613, -0.5548]],\n",
            "\n",
            "         [[ 0.0759,  0.1255, -0.0151,  ...,  0.0307,  0.1052, -0.6455],\n",
            "          [ 0.6701,  0.7716,  0.2974,  ..., -0.7394,  0.5873,  3.0509]],\n",
            "\n",
            "         [[ 0.1543,  0.2131, -0.0718,  ...,  0.0975, -0.0069, -0.3904],\n",
            "          [-0.5077, -1.8357,  2.1244,  ...,  0.3969,  1.3626, -0.0225]]]],\n",
            "       grad_fn=<CopyBackwards>), tensor([[[[ 2.9269e-02,  1.1764e-01, -1.2732e-02,  ..., -8.6454e-02,\n",
            "            1.2252e-01,  6.7036e-03],\n",
            "          [ 3.9763e-01, -2.9862e-01, -4.3627e-01,  ..., -5.6594e-02,\n",
            "           -2.9131e-01, -6.3776e-01]],\n",
            "\n",
            "         [[ 1.6335e-02, -2.4424e-02, -4.8432e-03,  ..., -5.6251e-02,\n",
            "           -6.8835e-02,  1.2608e-02],\n",
            "          [ 3.5408e-01,  6.1424e-01,  3.5653e-01,  ..., -3.0891e-02,\n",
            "            7.4476e-02,  2.0799e-02]],\n",
            "\n",
            "         [[ 2.5243e-02,  1.0407e-02, -4.8344e-02,  ...,  6.4945e-03,\n",
            "            3.9794e-04,  1.2100e-01],\n",
            "          [ 7.1725e-01, -1.8262e-01, -3.0229e-01,  ..., -4.4057e-01,\n",
            "           -3.8583e-02, -2.3157e-01]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[ 9.2560e-03,  1.4655e-02,  3.4054e-02,  ...,  9.6428e-03,\n",
            "           -6.3913e-03,  2.7569e-02],\n",
            "          [ 4.2214e-01,  3.7137e-01,  2.3890e-01,  ..., -1.7631e-01,\n",
            "            9.0582e-01,  1.0196e-01]],\n",
            "\n",
            "         [[ 3.1055e-03,  1.0484e-02, -4.7095e-03,  ...,  4.8638e-02,\n",
            "           -1.5250e-02, -7.0406e-02],\n",
            "          [ 9.7427e-02,  2.6014e-01,  3.1176e-01,  ..., -4.8579e-01,\n",
            "            9.3071e-01, -1.1125e-01]],\n",
            "\n",
            "         [[ 2.8621e-02, -2.8968e-03, -3.1328e-02,  ...,  2.5224e-02,\n",
            "            1.8680e-02, -4.3578e-02],\n",
            "          [ 3.0150e-01, -1.9468e-01, -1.7805e-01,  ...,  5.1854e-01,\n",
            "           -3.4874e-02,  9.8429e-02]]]], grad_fn=<CopyBackwards>), tensor([[[[-1.7399,  1.5544,  0.8420,  ...,  0.1949,  0.3739, -0.4898],\n",
            "          [-1.6240,  1.3219,  0.9978,  ..., -0.4160, -0.1783,  0.3793]],\n",
            "\n",
            "         [[-0.2877, -0.3362, -0.3745,  ...,  0.3365,  0.1496, -1.2037],\n",
            "          [-0.2730, -0.1071, -0.1413,  ...,  0.3996, -0.1407, -1.1605]],\n",
            "\n",
            "         [[ 1.4108, -0.6566,  1.7061,  ...,  1.4992,  0.5674, -0.9317],\n",
            "          [ 1.7853, -1.0445,  1.7326,  ...,  1.7643,  0.5169, -1.0316]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[-1.7319,  3.2136,  1.1013,  ..., -0.8693, -0.2972, -1.2679],\n",
            "          [-2.0636,  3.6187,  0.9261,  ..., -0.9856, -0.3694, -1.0883]],\n",
            "\n",
            "         [[-0.9236,  0.7800, -0.0765,  ...,  1.6758,  1.2948,  0.3508],\n",
            "          [-0.6022,  0.7458, -0.1418,  ...,  2.1779,  1.1909,  0.1315]],\n",
            "\n",
            "         [[ 0.4240,  0.0391, -1.3894,  ..., -1.0823, -0.4126,  0.9783],\n",
            "          [ 0.5731, -0.2723, -1.8803,  ..., -1.2079, -1.0119,  0.5113]]]],\n",
            "       grad_fn=<CopyBackwards>), tensor([[[[ 0.0985, -0.6352, -0.0868,  ...,  0.0363,  0.0543, -0.3238],\n",
            "          [-0.2378, -0.1665, -0.3516,  ...,  0.3921, -0.2744, -0.3039]],\n",
            "\n",
            "         [[ 0.8450,  0.2317,  0.6335,  ...,  0.5318, -0.0949,  0.1750],\n",
            "          [ 0.5121,  0.0876,  0.5792,  ..., -0.0148, -0.4605,  0.2020]],\n",
            "\n",
            "         [[-0.2443, -0.0378,  0.2317,  ...,  0.0219, -0.1216,  0.0395],\n",
            "          [-0.4156,  0.0576,  0.1678,  ...,  0.0501, -0.3476, -0.2093]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[ 0.1073,  0.2830,  0.1495,  ..., -1.0262,  0.5416,  0.5043],\n",
            "          [ 0.2258,  0.0643, -0.1481,  ..., -1.0380,  0.3420,  0.5458]],\n",
            "\n",
            "         [[-0.1796, -0.4815,  0.2412,  ..., -0.1129,  0.5764,  0.0777],\n",
            "          [-0.2803, -0.4700,  0.0848,  ...,  0.2658,  0.4753,  0.1039]],\n",
            "\n",
            "         [[ 0.5494,  0.6024, -0.6296,  ...,  0.1007,  0.8262, -0.1736],\n",
            "          [ 0.3254,  0.5532, -0.1605,  ..., -0.2396,  0.2754,  0.1460]]]],\n",
            "       grad_fn=<CopyBackwards>)), (tensor([[[[ 0.8659,  1.2230, -0.0036,  ..., -0.1768,  1.2656,  1.3975],\n",
            "          [-2.4391, -1.6445,  1.7410,  ..., -1.8302, -2.0103, -1.7548]],\n",
            "\n",
            "         [[ 0.4183,  0.5536,  0.7881,  ..., -0.2234,  0.4811, -0.0375],\n",
            "          [-1.6890, -0.3787, -1.0577,  ..., -0.8861, -1.5151, -0.2259]],\n",
            "\n",
            "         [[-0.5553, -1.0096,  0.1065,  ..., -0.4831,  1.4737, -0.0735],\n",
            "          [ 0.7138,  0.3943,  0.3157,  ...,  0.2931, -0.2829,  0.3843]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[-0.6875,  0.4129, -0.7369,  ..., -1.1018, -0.7937,  0.5081],\n",
            "          [ 1.8270,  0.4463, -1.1159,  ...,  0.7450,  1.0975,  0.1329]],\n",
            "\n",
            "         [[ 0.2528,  0.2051,  0.2412,  ...,  0.1542,  0.5065, -0.0168],\n",
            "          [ 0.0560,  1.4332,  0.3786,  ...,  0.1777, -0.5803,  0.0421]],\n",
            "\n",
            "         [[-0.2057,  0.4577,  0.8795,  ..., -0.3249, -0.0460,  0.9419],\n",
            "          [-1.3328,  0.4214, -0.5938,  ..., -0.5810,  0.0759, -0.4099]]]],\n",
            "       grad_fn=<CopyBackwards>), tensor([[[[-4.0518e-02, -5.9250e-02, -1.6183e-02,  ..., -8.3794e-04,\n",
            "           -3.3078e-02,  7.6099e-02],\n",
            "          [-4.3446e-01, -1.9747e-01, -4.3507e-01,  ..., -4.8681e-01,\n",
            "           -2.7235e-01, -8.4327e-02]],\n",
            "\n",
            "         [[-1.4299e-01,  1.0976e-01, -3.2061e-02,  ..., -7.9136e-02,\n",
            "           -6.2019e-02,  1.1654e-03],\n",
            "          [ 5.5592e-01, -4.1033e-02,  1.5684e-01,  ...,  4.7759e-01,\n",
            "            4.6287e-01,  1.3250e-01]],\n",
            "\n",
            "         [[-1.0311e-02, -3.4038e-02, -1.5415e-01,  ...,  2.3261e-02,\n",
            "           -1.4703e-02,  2.4091e-02],\n",
            "          [-8.1498e-01, -3.5524e-01,  3.3233e-02,  ...,  2.5945e-01,\n",
            "            5.5901e-02,  1.0831e+00]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[-1.8605e-02,  5.5119e-02,  2.7359e-03,  ..., -2.4560e-02,\n",
            "            6.2882e-03,  5.5262e-03],\n",
            "          [ 1.2290e-01, -4.1711e-01, -3.5411e-02,  ..., -2.1633e-01,\n",
            "           -4.8053e-01,  9.0854e-02]],\n",
            "\n",
            "         [[-8.3878e-04,  6.2181e-02, -3.9761e-02,  ...,  5.1470e-02,\n",
            "            7.0819e-02,  8.0597e-03],\n",
            "          [ 5.9444e-02,  5.7503e-01, -7.6640e-01,  ...,  4.2504e-03,\n",
            "           -7.7810e-02,  9.4069e-02]],\n",
            "\n",
            "         [[ 7.7253e-02,  7.0009e-02, -1.6194e-02,  ...,  2.5476e-02,\n",
            "           -8.1933e-03,  2.2595e-02],\n",
            "          [ 1.1499e-01, -3.9242e-01,  7.6922e-01,  ...,  2.9296e-01,\n",
            "            8.2074e-01,  6.4681e-01]]]], grad_fn=<CopyBackwards>), tensor([[[[ 2.0849, -0.0636,  0.5000,  ...,  0.9966,  1.7627,  1.5514],\n",
            "          [ 1.7876, -0.7635,  1.6988,  ...,  2.0722,  1.4739,  1.1660]],\n",
            "\n",
            "         [[-0.8371,  0.9337,  0.7837,  ...,  0.0498,  1.6516,  0.1707],\n",
            "          [-0.5368,  1.0880,  0.5653,  ..., -0.0634,  1.3486, -0.6582]],\n",
            "\n",
            "         [[-0.0912,  0.6096, -0.6076,  ...,  0.7558, -0.0126, -1.5131],\n",
            "          [-0.2230,  0.5867, -0.7267,  ...,  0.8867, -0.1216, -1.8096]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[-0.6766,  0.6220,  1.5134,  ..., -0.1829,  0.0215,  1.1120],\n",
            "          [-0.8275,  0.6928,  1.6118,  ..., -0.2364,  0.0025,  1.4436]],\n",
            "\n",
            "         [[ 1.1364,  0.0790, -0.3696,  ...,  0.2541,  0.9125, -0.5037],\n",
            "          [ 1.5442,  0.0934, -0.4613,  ...,  0.0754,  0.0034, -0.9217]],\n",
            "\n",
            "         [[ 1.2640, -0.4548, -0.5475,  ...,  0.5674, -0.4214, -1.0081],\n",
            "          [ 1.2149, -0.7560, -0.2841,  ...,  0.4172, -0.3426, -0.9019]]]],\n",
            "       grad_fn=<CopyBackwards>), tensor([[[[-0.0386, -0.1903,  0.1470,  ..., -0.3462, -0.1094, -0.1254],\n",
            "          [-0.0886,  0.0395,  0.0272,  ...,  0.0271, -0.3885,  0.0461]],\n",
            "\n",
            "         [[ 0.1720, -0.3733, -0.0356,  ...,  0.0848,  0.4297,  0.5137],\n",
            "          [ 0.2865, -0.2505, -0.2307,  ..., -0.2456,  0.3889,  0.3922]],\n",
            "\n",
            "         [[-0.5288, -0.7186,  0.3537,  ..., -0.7664,  0.2124,  0.3075],\n",
            "          [-0.3642, -0.0657,  0.0399,  ..., -0.5451,  0.2110,  0.1761]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[ 0.1834, -0.7011,  0.8361,  ...,  0.3994, -0.3654, -0.3145],\n",
            "          [ 0.2181, -0.7129,  0.8148,  ...,  0.1566,  0.0218,  0.0515]],\n",
            "\n",
            "         [[-0.3867, -0.7703, -0.4015,  ..., -0.3819, -0.8259,  0.0101],\n",
            "          [ 0.2124,  0.1236, -0.8350,  ...,  0.3556, -1.1067, -0.0330]],\n",
            "\n",
            "         [[-0.1518, -0.0204,  0.3507,  ..., -0.0252, -0.2174,  0.3534],\n",
            "          [ 0.2348, -0.1401,  0.4585,  ...,  0.2866, -0.0423,  0.0067]]]],\n",
            "       grad_fn=<CopyBackwards>)), (tensor([[[[-0.2997,  1.0674, -0.2439,  ..., -0.2289, -0.6333, -0.6078],\n",
            "          [ 0.9438, -1.8757, -0.5755,  ...,  0.1229,  1.7929, -0.3130]],\n",
            "\n",
            "         [[ 0.3514,  1.5137,  0.4524,  ...,  1.1948, -1.3977,  0.3308],\n",
            "          [ 0.1387, -1.1616, -0.6226,  ..., -2.1536, -0.1766, -1.1653]],\n",
            "\n",
            "         [[-1.3330, -0.6003,  0.2709,  ...,  1.1001, -0.1418, -0.6230],\n",
            "          [-1.5638,  1.9531, -0.2184,  ..., -0.3042, -2.2814,  1.5291]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[-0.4103, -0.2594, -0.7356,  ...,  0.4831,  0.0690,  0.3132],\n",
            "          [-0.5332,  0.1774,  1.8028,  ...,  0.1322,  0.1915, -0.9951]],\n",
            "\n",
            "         [[-0.0831, -0.3862, -0.7452,  ..., -0.2725, -0.0534, -0.2660],\n",
            "          [-1.4382, -1.8134,  2.8000,  ..., -0.8421, -0.6358,  0.1198]],\n",
            "\n",
            "         [[-1.0351, -0.4564, -1.0894,  ...,  0.2800, -0.1461, -0.3104],\n",
            "          [ 0.2128, -0.7712, -0.1874,  ..., -0.7119, -0.5132, -1.6355]]]],\n",
            "       grad_fn=<CopyBackwards>), tensor([[[[-3.3177e-02, -1.8028e-04,  4.5838e-04,  ...,  1.3248e-02,\n",
            "            1.9023e-02,  3.2562e-02],\n",
            "          [-3.2168e-02, -1.5418e-01,  1.4814e-01,  ..., -2.6376e-01,\n",
            "            1.0807e-02, -6.4121e-01]],\n",
            "\n",
            "         [[-1.0983e-02, -2.1240e-02, -4.1562e-02,  ..., -2.0584e-02,\n",
            "           -5.9072e-02, -6.3754e-02],\n",
            "          [-7.4979e-01,  4.7896e-01,  3.1803e-01,  ..., -2.9700e-01,\n",
            "           -1.4955e-01, -2.6639e-01]],\n",
            "\n",
            "         [[ 5.7588e-02,  8.7107e-02, -3.4391e-02,  ..., -1.3255e-02,\n",
            "            2.8237e-02,  1.2374e-01],\n",
            "          [-6.9403e-01,  5.8305e-01, -4.0446e-03,  ...,  2.2897e-02,\n",
            "           -6.2816e-01,  3.7408e-01]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[-3.4895e-02,  7.3233e-03,  3.9967e-02,  ...,  4.8722e-02,\n",
            "           -6.5669e-02, -5.9926e-02],\n",
            "          [-1.0827e+00, -5.7699e-01,  4.4333e-01,  ...,  2.7596e-01,\n",
            "            9.1006e-01, -4.2223e-01]],\n",
            "\n",
            "         [[-2.7116e-02,  1.4112e-02, -1.0806e-01,  ..., -2.1597e-02,\n",
            "            1.6286e-02,  6.7958e-03],\n",
            "          [ 2.9614e-01,  6.9683e-01,  2.1899e-01,  ...,  1.0726e-02,\n",
            "            3.8513e-02,  1.0382e-02]],\n",
            "\n",
            "         [[ 5.4608e-03,  4.4768e-02, -1.2635e-02,  ...,  1.2815e-03,\n",
            "            3.0699e-02, -1.0643e-01],\n",
            "          [-3.3885e-01, -4.5145e-02,  4.8048e-02,  ..., -4.6988e-01,\n",
            "            2.6005e-01, -7.2162e-01]]]], grad_fn=<CopyBackwards>), tensor([[[[ 1.0374,  0.3909, -0.6754,  ...,  0.2131, -0.7792,  0.1073],\n",
            "          [ 0.6857,  0.6310, -0.8816,  ..., -0.0665,  0.0525, -0.0755]],\n",
            "\n",
            "         [[ 0.5946, -0.2358, -1.0036,  ..., -0.7404, -1.1777, -2.7705],\n",
            "          [ 0.9685, -0.8442, -1.0034,  ..., -0.5312, -1.2558, -2.5045]],\n",
            "\n",
            "         [[-0.8795, -0.7718, -1.1451,  ...,  0.3562,  0.6508, -0.3904],\n",
            "          [-0.6928, -1.3858, -1.1689,  ...,  0.9893,  0.1329, -0.7229]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[-2.6684, -0.8720,  0.7093,  ...,  1.3669,  1.9079,  0.6156],\n",
            "          [-3.0017, -0.5988,  0.8993,  ...,  1.0432,  1.9338, -0.0553]],\n",
            "\n",
            "         [[-3.6058,  2.1486,  2.1966,  ...,  1.6174, -2.3318, -0.6465],\n",
            "          [-3.9024,  2.2764,  2.3965,  ...,  0.1705, -2.1405, -0.2133]],\n",
            "\n",
            "         [[-0.7480,  1.9170,  1.3470,  ...,  0.4011, -0.4394, -1.1463],\n",
            "          [-0.3069,  1.8317,  1.8609,  ...,  0.0209, -0.2684, -0.9492]]]],\n",
            "       grad_fn=<CopyBackwards>), tensor([[[[ 0.7396, -0.1129,  0.1192,  ..., -0.0200, -0.2694, -0.1733],\n",
            "          [ 0.9639, -0.3486, -0.0451,  ..., -0.7172, -0.3156,  0.1609]],\n",
            "\n",
            "         [[ 0.0073, -0.7114, -0.4992,  ..., -0.3623, -0.2582,  0.3008],\n",
            "          [ 0.2753, -0.3529, -0.4996,  ..., -0.3469, -0.0021,  0.2831]],\n",
            "\n",
            "         [[ 0.1104,  0.1524, -0.2274,  ..., -0.2116,  0.0372,  0.3177],\n",
            "          [ 0.2157,  0.3495, -0.1770,  ...,  0.2640, -0.0096,  0.4679]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[ 0.2203, -0.0463,  0.1329,  ...,  0.0979, -0.3664,  0.3431],\n",
            "          [-0.0973, -0.2557,  0.4457,  ...,  0.1551, -0.3278,  0.6508]],\n",
            "\n",
            "         [[ 0.5532, -0.3937,  0.4316,  ..., -0.0912,  0.6116, -0.1449],\n",
            "          [ 0.6388, -0.4841,  0.4255,  ..., -0.0629,  0.5913, -0.1810]],\n",
            "\n",
            "         [[ 0.4216, -0.3658, -0.3054,  ...,  0.1030,  0.0099, -0.1975],\n",
            "          [ 0.3812, -0.3219, -0.4682,  ..., -0.3154,  0.0673, -0.2982]]]],\n",
            "       grad_fn=<CopyBackwards>)), (tensor([[[[-0.0638, -0.3080,  0.1955,  ..., -0.2980,  0.1688,  0.0431],\n",
            "          [ 0.5026, -0.5220,  3.4995,  ..., -1.0278,  0.1422,  2.3666]],\n",
            "\n",
            "         [[ 0.4591,  0.1555, -0.1794,  ..., -0.2225, -0.4867,  0.2342],\n",
            "          [-0.4296, -0.2702, -0.8925,  ..., -1.1856,  0.8598, -0.2742]],\n",
            "\n",
            "         [[-0.3287, -0.1999, -0.2862,  ..., -0.5182,  0.0972,  0.6838],\n",
            "          [ 2.6562,  1.6259, -1.6430,  ...,  0.5375,  1.0974, -3.0638]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[-0.1022, -0.1004,  0.0337,  ..., -0.4126,  0.0917,  0.2785],\n",
            "          [-0.3013,  0.2233, -0.8632,  ..., -2.7654,  0.0675,  2.4467]],\n",
            "\n",
            "         [[ 0.3300,  0.2506,  0.0510,  ..., -0.0276, -0.1510, -0.1070],\n",
            "          [-3.6227,  0.0169, -1.4157,  ..., -0.7226, -1.0749,  1.1248]],\n",
            "\n",
            "         [[-0.0671, -0.4478, -0.0450,  ..., -0.1774, -0.1607, -0.0955],\n",
            "          [-4.6108,  0.6886,  0.8102,  ..., -0.4719,  0.8873, -2.4897]]]],\n",
            "       grad_fn=<CopyBackwards>), tensor([[[[-0.0404, -0.0917,  0.0642,  ..., -0.0035, -0.0486,  0.0240],\n",
            "          [-0.2731,  0.0407,  0.5085,  ..., -0.1216, -0.2275, -0.7928]],\n",
            "\n",
            "         [[ 0.0031, -0.1010,  0.0053,  ...,  0.0069, -0.0100,  0.0144],\n",
            "          [-0.1159, -0.6017,  0.1824,  ..., -0.1040,  0.0132,  0.0196]],\n",
            "\n",
            "         [[ 0.1149, -0.0396, -0.0644,  ..., -0.1404,  0.0345,  0.0140],\n",
            "          [ 0.4707,  0.5479,  0.1769,  ..., -1.8493, -0.6008, -0.0666]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[ 0.0385, -0.0342, -0.0351,  ...,  0.0089, -0.0578,  0.0940],\n",
            "          [ 0.3749,  0.0586, -0.0326,  ..., -0.1291, -0.1929,  0.6304]],\n",
            "\n",
            "         [[-0.0040,  0.0416,  0.0358,  ...,  0.0166,  0.0453,  0.0051],\n",
            "          [ 0.0923, -0.0818,  0.3751,  ...,  0.5956, -0.0576, -0.0671]],\n",
            "\n",
            "         [[ 0.0988,  0.0028,  0.0458,  ...,  0.1539,  0.0561,  0.0554],\n",
            "          [ 0.0944, -0.1632,  0.0327,  ...,  0.9541,  0.3676,  0.2072]]]],\n",
            "       grad_fn=<CopyBackwards>), tensor([[[[ 0.4540, -0.9974, -2.6459,  ...,  1.1923, -0.3397,  3.7741],\n",
            "          [ 0.5595, -0.9436, -1.4177,  ...,  0.9573, -0.9510,  3.5285]],\n",
            "\n",
            "         [[ 0.5767, -1.4281, -0.0372,  ...,  0.2962,  1.1243,  1.1418],\n",
            "          [-0.3780, -1.4567,  1.4320,  ..., -0.2319,  0.4736,  0.6123]],\n",
            "\n",
            "         [[ 0.4139, -1.4614, -0.8103,  ..., -3.3714,  0.1478,  0.2090],\n",
            "          [-0.1716, -1.2067, -0.2029,  ..., -2.4051, -0.5142,  0.5125]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[-3.4578, -0.9712,  0.9847,  ..., -0.2011, -1.6939,  0.2227],\n",
            "          [-1.9282, -0.9421,  0.6625,  ..., -0.3203, -2.4944,  0.4157]],\n",
            "\n",
            "         [[-1.9077,  2.6054,  0.7046,  ...,  3.3153,  0.8114, -1.1166],\n",
            "          [-0.5560,  2.4935,  1.0945,  ...,  3.5782,  0.8835, -1.7877]],\n",
            "\n",
            "         [[-0.7894, -0.6044, -0.4530,  ...,  2.1209, -1.0290,  1.3274],\n",
            "          [-1.5356,  0.6271, -1.3167,  ...,  1.9269, -1.3602,  2.0155]]]],\n",
            "       grad_fn=<CopyBackwards>), tensor([[[[ 0.2468,  0.4531, -0.2456,  ..., -0.2639, -0.4747,  0.2349],\n",
            "          [ 0.6563,  0.5046, -0.2421,  ...,  0.0345, -0.6680,  0.2661]],\n",
            "\n",
            "         [[ 0.1554,  0.0104,  0.0888,  ...,  0.5652,  0.1155,  0.2966],\n",
            "          [ 0.2705, -0.4564,  0.2788,  ...,  0.3807,  0.1667,  0.0607]],\n",
            "\n",
            "         [[-0.5218, -0.1890,  0.0533,  ..., -0.1139,  0.2080, -0.0636],\n",
            "          [-0.3670, -0.1278, -0.1607,  ...,  0.1446, -0.0988, -0.1946]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[-0.0757,  0.8512,  0.1290,  ...,  0.5617,  0.4623, -0.4476],\n",
            "          [-0.1185,  0.9600,  0.4644,  ...,  0.6976,  0.4706,  0.1709]],\n",
            "\n",
            "         [[ 0.0076, -0.1633,  0.0613,  ..., -0.4322, -0.1017,  0.0051],\n",
            "          [ 0.0926, -0.3790,  0.3583,  ..., -0.1947, -0.1139, -0.2700]],\n",
            "\n",
            "         [[ 0.8125,  0.0627,  0.4241,  ...,  0.3436,  0.0131, -0.7254],\n",
            "          [ 0.4459,  0.2707,  0.4448,  ...,  0.2226,  0.4030, -0.7320]]]],\n",
            "       grad_fn=<CopyBackwards>))), decoder_hidden_states=None, decoder_attentions=None, cross_attentions=None, encoder_last_hidden_state=tensor([[[ 0.4624, -0.2475,  0.0902,  ...,  0.1127,  0.6529,  0.2203],\n",
            "         [ 0.4538, -0.2948,  0.2556,  ..., -0.0442,  0.6858,  0.4372]]],\n",
            "       grad_fn=<NativeLayerNormBackward>), encoder_hidden_states=None, encoder_attentions=None)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import boto3\n",
        "import os\n",
        "import sys\n",
        "from botocore import UNSIGNED\n",
        "from botocore.client import Config\n",
        "\n",
        "\n",
        "class AwsS3Downloader(object):\n",
        "    def __init__(\n",
        "        self,\n",
        "        aws_access_key_id=None,\n",
        "        aws_secret_access_key=None,\n",
        "    ):\n",
        "        self.resource = boto3.Session(\n",
        "            aws_access_key_id=aws_access_key_id,\n",
        "            aws_secret_access_key=aws_secret_access_key,\n",
        "        ).resource(\"s3\")\n",
        "        self.client = boto3.client(\n",
        "            \"s3\",\n",
        "            aws_access_key_id=aws_access_key_id,\n",
        "            aws_secret_access_key=aws_secret_access_key,\n",
        "            config=Config(signature_version=UNSIGNED),\n",
        "        )\n",
        "\n",
        "    def __split_url(self, url: str):\n",
        "        if url.startswith(\"s3://\"):\n",
        "            url = url.replace(\"s3://\", \"\")\n",
        "        bucket, key = url.split(\"/\", maxsplit=1)\n",
        "        return bucket, key\n",
        "\n",
        "    def download(self, url: str, local_dir: str):\n",
        "        bucket, key = self.__split_url(url)\n",
        "        filename = os.path.basename(key)\n",
        "        file_path = os.path.join(local_dir, filename)\n",
        "\n",
        "        os.makedirs(os.path.dirname(file_path), exist_ok=True)\n",
        "        meta_data = self.client.head_object(Bucket=bucket, Key=key)\n",
        "        total_length = int(meta_data.get(\"ContentLength\", 0))\n",
        "\n",
        "        downloaded = 0\n",
        "\n",
        "        def progress(chunk):\n",
        "            nonlocal downloaded\n",
        "            downloaded += chunk\n",
        "            done = int(50 * downloaded / total_length)\n",
        "            sys.stdout.write(\n",
        "                \"\\r{}[{}{}]\".format(file_path, \"█\" * done, \".\" * (50 - done))\n",
        "            )\n",
        "            sys.stdout.flush()\n",
        "\n",
        "        try:\n",
        "            with open(file_path, \"wb\") as f:\n",
        "                self.client.download_fileobj(bucket, key, f, Callback=progress)\n",
        "            sys.stdout.write(\"\\n\")\n",
        "            sys.stdout.flush()\n",
        "        except:\n",
        "            raise Exception(f\"downloading file is failed. {url}\")\n",
        "        return file_path\n",
        "\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    s3 = AwsS3Downloader()\n",
        "\n",
        "    s3.download(\n",
        "        url=\"s3://skt-lsl-nlp-model/KoBART/models/kobart_base_cased_ff4bda5738.zip\",\n",
        "        local_dir=\".cache\",\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_fetad3fZt0f",
        "outputId": "ebff2798-bdb1-44e9-a3a5-a0a8473cb571"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".cache/kobart_base_cased_ff4bda5738.zip[██████████████████████████████████████████████████]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BartModel\n",
        "from kobart import get_pytorch_kobart_model, get_kobart_tokenizer\n",
        "kobart_tokenizer = get_kobart_tokenizer()\n",
        "model = BartModel.from_pretrained(get_pytorch_kobart_model())\n",
        "inputs = kobart_tokenizer(['안녕하세요.'], return_tensors='pt')\n",
        "model(inputs['input_ids'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mmqm996ZbiMw",
        "outputId": "96e37983-1c63-487c-b301-a2a32d147bc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "using cached model. /content/.cache/kobart_base_tokenizer_cased_cf74400bce.zip\n",
            "using cached model. /content/.cache/kobart_base_cased_ff4bda5738.zip\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Seq2SeqModelOutput(last_hidden_state=tensor([[[-0.4418, -4.3673,  3.2404,  ...,  5.8832,  4.0629,  3.5540],\n",
              "         [-0.1316, -4.6446,  2.5955,  ...,  6.0093,  2.7467,  3.0007]]],\n",
              "       grad_fn=<NativeLayerNormBackward>), past_key_values=((tensor([[[[-9.7980e-02, -6.6584e-01, -1.8089e+00,  ...,  9.6023e-01,\n",
              "           -1.8818e-01, -1.3252e+00],\n",
              "          [-6.2507e-01,  5.1009e-01, -7.4878e-01,  ...,  8.6230e-01,\n",
              "            1.5722e-01, -6.0267e-01]],\n",
              "\n",
              "         [[ 5.4597e-01, -2.3990e-01,  1.5901e+00,  ...,  4.3655e-01,\n",
              "            7.9514e-01,  8.9880e-02],\n",
              "          [-1.7327e-01, -6.3167e-01,  4.5152e-02,  ..., -1.4111e-01,\n",
              "            1.8678e-01, -1.2081e-01]],\n",
              "\n",
              "         [[ 1.4621e+00,  1.8980e+00, -7.6696e-01,  ...,  1.5695e+00,\n",
              "            6.7921e-02, -3.9372e-01],\n",
              "          [-4.1204e-02,  1.7132e+00, -1.1863e+00,  ..., -2.2272e-01,\n",
              "            9.8309e-02,  8.1729e-01]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[ 4.8868e-01,  1.2633e+00, -1.1658e-01,  ..., -3.1989e-01,\n",
              "            1.2202e+00, -7.9021e-02],\n",
              "          [-8.4946e-01,  8.9379e-02, -1.0224e+00,  ...,  3.3125e-01,\n",
              "           -2.5262e-01,  5.0875e-01]],\n",
              "\n",
              "         [[ 1.5854e+00,  3.2461e-01,  3.0826e+00,  ..., -1.6728e+00,\n",
              "            1.2071e+00, -3.5671e-01],\n",
              "          [ 5.5400e-02, -9.2782e-01, -2.3055e-03,  ..., -6.1646e-01,\n",
              "            1.0880e+00,  2.8645e-01]],\n",
              "\n",
              "         [[ 8.7081e-01, -4.6088e-01, -2.8388e+00,  ...,  1.6038e+00,\n",
              "           -1.0963e+00, -1.8732e-01],\n",
              "          [ 4.5471e-01, -3.1087e-02, -2.4484e+00,  ...,  1.9392e+00,\n",
              "           -4.0694e-01, -1.9906e-01]]]], grad_fn=<CopyBackwards>), tensor([[[[ 0.2187,  1.3322, -0.0016,  ..., -0.1200, -0.0395,  0.0971],\n",
              "          [-0.2722, -0.0590,  0.4620,  ...,  0.1822, -0.0171, -0.2176]],\n",
              "\n",
              "         [[ 0.1439,  0.0074,  0.0249,  ...,  0.2148, -0.5016,  0.1263],\n",
              "          [ 0.3625, -0.3192, -0.2254,  ...,  0.6312,  0.1061,  0.3506]],\n",
              "\n",
              "         [[-0.0862, -0.0073, -0.0479,  ...,  0.0110,  0.0198,  0.1909],\n",
              "          [-0.4622, -0.4537, -0.6060,  ...,  0.5620,  1.3319,  0.0989]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[-0.0868, -0.1329,  0.1862,  ..., -0.0489, -0.0084,  0.0151],\n",
              "          [-0.4065,  0.4082,  0.7682,  ..., -0.0909,  0.1701,  0.0145]],\n",
              "\n",
              "         [[ 0.1256,  0.1170,  0.0912,  ...,  0.0623, -0.0574,  0.0611],\n",
              "          [-0.2664, -0.4485,  0.1439,  ...,  0.2324,  0.3832, -1.0943]],\n",
              "\n",
              "         [[ 0.0702,  0.0227,  0.0311,  ..., -0.0203,  0.0602,  0.0033],\n",
              "          [-0.3197,  0.4879,  0.1972,  ..., -0.3241,  0.1762,  0.5655]]]],\n",
              "       grad_fn=<CopyBackwards>), tensor([[[[-1.0648, -2.5643, -1.2208,  ...,  1.4609,  1.0088,  0.8616],\n",
              "          [-1.3031, -2.4746, -1.2874,  ...,  1.4641,  0.6007,  0.5663]],\n",
              "\n",
              "         [[-0.9957, -1.4633, -0.6104,  ..., -0.0807,  0.6639,  1.1657],\n",
              "          [-0.8524, -2.1023, -0.3089,  ...,  0.1747,  0.9409,  0.8996]],\n",
              "\n",
              "         [[ 1.7265, -0.0293, -0.3755,  ..., -0.6951,  0.4730,  0.3141],\n",
              "          [ 1.5839, -0.6262, -0.5320,  ...,  0.2295,  0.5922,  0.6125]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[-0.1965, -0.4015,  1.1312,  ..., -0.5016, -0.1073,  0.0549],\n",
              "          [-0.5866, -0.6739,  1.0961,  ..., -0.3082, -0.1296,  0.0126]],\n",
              "\n",
              "         [[-0.0225,  0.6815, -1.6006,  ...,  1.7747, -0.4324, -1.2341],\n",
              "          [ 0.2012,  0.2133, -2.0224,  ...,  1.0445, -0.3429, -1.1153]],\n",
              "\n",
              "         [[-0.4898, -0.2916,  0.1036,  ..., -1.1657, -2.0047, -0.5035],\n",
              "          [ 0.2014,  0.0172, -0.2052,  ..., -1.1187, -2.9760, -0.2174]]]],\n",
              "       grad_fn=<CopyBackwards>), tensor([[[[ 0.2822, -0.1429, -0.0214,  ...,  0.0815,  0.1900, -0.2706],\n",
              "          [ 0.0435, -0.3802, -0.3064,  ...,  0.5159,  0.1527, -0.2039]],\n",
              "\n",
              "         [[-0.8001,  0.0466,  1.0855,  ..., -0.2635, -0.2926,  0.3927],\n",
              "          [-0.3446, -0.6481,  0.5623,  ...,  0.3295,  0.6792,  0.4060]],\n",
              "\n",
              "         [[-0.8864, -0.0462, -0.4937,  ..., -1.2180,  0.5027,  0.1991],\n",
              "          [-0.8240, -0.4198,  0.1187,  ..., -0.3042,  0.6595, -0.1653]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[-0.3499,  0.7639,  0.0130,  ...,  0.3620,  0.4275, -0.6412],\n",
              "          [-0.1910,  0.5114, -0.0352,  ...,  0.3060,  0.2019, -0.4844]],\n",
              "\n",
              "         [[ 0.2417,  0.1567,  0.2082,  ...,  0.1640, -0.1767, -0.1738],\n",
              "          [ 0.1304, -0.0716, -0.1840,  ...,  0.3307,  0.1039,  0.1808]],\n",
              "\n",
              "         [[-0.4749, -0.9211,  1.0276,  ..., -0.3349, -0.2668, -0.2143],\n",
              "          [ 0.0051, -0.7191,  0.6315,  ..., -0.2618,  0.0611, -0.1337]]]],\n",
              "       grad_fn=<CopyBackwards>)), (tensor([[[[ 0.0568,  0.1593,  0.3356,  ..., -0.0771, -0.1446, -0.1562],\n",
              "          [ 1.6254, -1.5403,  1.2852,  ...,  0.3466, -1.0204, -1.6762]],\n",
              "\n",
              "         [[ 0.2014, -0.2892, -0.4829,  ...,  0.2817, -0.1413,  1.5805],\n",
              "          [ 0.6554,  1.6124,  0.4492,  ...,  0.3675,  1.0584, -3.6320]],\n",
              "\n",
              "         [[-0.1167,  0.6492, -0.5715,  ...,  0.0990, -1.3751, -0.3157],\n",
              "          [ 0.5295, -2.8552,  1.0669,  ...,  0.8807,  2.6310, -0.9037]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[ 0.0606,  0.1003,  0.1334,  ...,  0.0648,  0.0886,  1.1766],\n",
              "          [ 0.9016, -1.1139,  1.2164,  ...,  0.0885,  0.5449, -1.5874]],\n",
              "\n",
              "         [[ 0.1461,  0.1861,  0.0330,  ..., -0.0966, -0.3876,  0.2000],\n",
              "          [-0.4877,  0.6808,  0.8141,  ...,  1.1511, -1.0541, -0.7516]],\n",
              "\n",
              "         [[ 0.4198,  0.1532,  0.4183,  ..., -0.1995, -0.0194,  0.3649],\n",
              "          [ 1.9049,  0.4632, -1.9892,  ...,  0.2458,  0.5797, -0.5118]]]],\n",
              "       grad_fn=<CopyBackwards>), tensor([[[[ 1.5379e-02,  4.6779e-02,  2.3209e-02,  ..., -3.6808e-03,\n",
              "           -6.6380e-02,  8.8301e-04],\n",
              "          [ 3.3722e-01,  5.9773e-01, -9.7631e-01,  ..., -6.6999e-01,\n",
              "           -8.6936e-01, -1.0744e+00]],\n",
              "\n",
              "         [[-8.2674e-03, -2.8380e-02, -3.8986e-02,  ...,  1.9672e-02,\n",
              "            2.6157e-03,  2.0187e-03],\n",
              "          [ 4.2856e-01,  1.3611e+00,  4.8732e-01,  ..., -5.9339e-02,\n",
              "           -1.1926e-01, -4.2579e-02]],\n",
              "\n",
              "         [[ 6.0655e-02, -1.8254e-02, -1.5229e-02,  ..., -1.6663e-02,\n",
              "            8.4880e-03, -4.8301e-02],\n",
              "          [-1.0497e-01, -8.1324e-02,  3.3243e-01,  ...,  3.5059e-01,\n",
              "           -7.1202e-02,  1.6473e-02]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[ 3.6912e-02, -6.7970e-02, -8.0440e-02,  ..., -9.4137e-02,\n",
              "            3.8143e-03,  7.8197e-02],\n",
              "          [-2.4057e-01,  2.6768e-03,  7.9480e-01,  ...,  2.9896e-01,\n",
              "            7.9629e-01,  9.2801e-01]],\n",
              "\n",
              "         [[ 1.9845e-02,  4.9133e-03, -3.3145e-02,  ...,  7.2793e-02,\n",
              "            5.9300e-02, -5.7760e-02],\n",
              "          [-3.6504e-01,  5.4651e-01, -4.3529e-02,  ..., -3.2015e-01,\n",
              "            7.9082e-01, -9.7559e-02]],\n",
              "\n",
              "         [[ 3.4483e-02, -1.2532e-02,  1.5040e-02,  ...,  1.7609e-02,\n",
              "           -2.3566e-02, -2.2959e-02],\n",
              "          [-1.1245e+00, -6.8237e-01,  1.0025e-01,  ..., -2.7405e-01,\n",
              "           -5.1046e-01, -1.9639e-01]]]], grad_fn=<CopyBackwards>), tensor([[[[ 2.3286e+00, -4.2951e-02,  3.0110e+00,  ...,  1.0627e+00,\n",
              "           -9.1236e-01,  2.6603e+00],\n",
              "          [ 3.0073e+00, -1.5674e-01,  2.8979e+00,  ...,  9.7826e-01,\n",
              "           -5.3163e-01,  3.0233e+00]],\n",
              "\n",
              "         [[ 1.7130e+00, -2.9926e+00,  1.8776e+00,  ...,  7.5416e-01,\n",
              "           -8.7124e-01, -4.8129e-01],\n",
              "          [ 2.5121e+00, -3.2266e+00,  2.2682e+00,  ...,  4.4046e-01,\n",
              "           -5.1476e-01, -5.2341e-01]],\n",
              "\n",
              "         [[-8.1486e-01, -9.5213e-01, -8.3259e-04,  ...,  1.5024e+00,\n",
              "           -5.7569e-01,  1.9130e-01],\n",
              "          [-8.1358e-01, -1.2385e+00,  9.3572e-01,  ...,  1.1406e+00,\n",
              "           -9.5044e-01, -4.8016e-01]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[ 8.1763e-01, -9.2276e-01,  1.5260e+00,  ...,  2.9562e-02,\n",
              "            1.5474e-02, -8.2440e-01],\n",
              "          [ 1.4520e-02, -1.5225e+00,  1.9413e+00,  ..., -3.6908e-01,\n",
              "            5.3651e-01, -2.1469e-01]],\n",
              "\n",
              "         [[-1.0017e+00, -5.5066e-01, -1.8923e+00,  ..., -5.8160e-01,\n",
              "            4.2548e-01, -6.7959e-01],\n",
              "          [-5.8291e-01, -8.2588e-01, -2.0550e+00,  ..., -2.2966e-01,\n",
              "            2.4397e-02, -2.6139e-01]],\n",
              "\n",
              "         [[-1.4829e+00, -8.8995e-01,  9.4026e-01,  ...,  1.4175e+00,\n",
              "           -3.9160e-01, -9.2237e-01],\n",
              "          [-1.0111e+00, -1.1450e+00,  1.1993e+00,  ...,  2.0617e+00,\n",
              "           -5.9144e-01, -1.3860e+00]]]], grad_fn=<CopyBackwards>), tensor([[[[ 0.1935, -0.1730,  0.3934,  ..., -0.0840,  0.2339,  0.1091],\n",
              "          [-0.0569, -0.3551,  0.3823,  ...,  0.0197, -0.0043, -0.0436]],\n",
              "\n",
              "         [[ 0.2364,  0.4606,  0.5622,  ...,  0.4820,  0.1865,  0.6264],\n",
              "          [ 0.5973,  0.2811,  0.2784,  ...,  0.1489, -0.4533,  0.4468]],\n",
              "\n",
              "         [[ 0.4116,  0.4325, -0.3306,  ..., -0.7509,  0.2645, -0.1624],\n",
              "          [ 0.8045,  0.3654, -0.4785,  ..., -0.7986, -0.0484, -0.1889]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[ 0.1706, -1.0074,  0.2970,  ...,  0.4921,  0.5458,  0.1225],\n",
              "          [ 0.3865, -1.1976, -0.2150,  ...,  0.6561,  0.4510,  0.1755]],\n",
              "\n",
              "         [[ 0.0047, -0.7317,  0.3213,  ...,  0.5871, -0.2654, -0.4047],\n",
              "          [-0.1921, -0.4592,  0.0943,  ...,  0.3975, -0.2417, -0.3156]],\n",
              "\n",
              "         [[-0.1002, -0.3230,  0.6674,  ..., -0.2355, -0.6963, -0.3525],\n",
              "          [-0.1739, -0.4883,  0.4101,  ..., -0.2514, -0.6257, -0.6858]]]],\n",
              "       grad_fn=<CopyBackwards>)), (tensor([[[[-0.1980, -0.8728,  0.6663,  ...,  0.2215,  0.3865, -0.0239],\n",
              "          [ 0.7417,  3.5312, -0.8480,  ..., -0.0536, -0.7790, -0.2229]],\n",
              "\n",
              "         [[ 0.3768, -0.2689, -0.0428,  ...,  0.1046, -0.1428, -0.9039],\n",
              "          [ 0.0478,  2.5229, -0.2297,  ..., -0.2463,  3.8656,  4.1838]],\n",
              "\n",
              "         [[-0.0777,  0.5079,  0.0822,  ...,  0.0193, -0.5585,  0.1365],\n",
              "          [ 0.6972, -2.6046,  1.8231,  ..., -1.2138,  2.4488,  0.6540]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[-0.4664,  0.2169,  0.0277,  ...,  1.1934,  0.6856,  0.1670],\n",
              "          [ 0.6041,  0.9994,  0.1424,  ..., -1.8193, -1.1613, -0.5548]],\n",
              "\n",
              "         [[ 0.0759,  0.1255, -0.0151,  ...,  0.0307,  0.1052, -0.6455],\n",
              "          [ 0.6701,  0.7716,  0.2974,  ..., -0.7394,  0.5873,  3.0509]],\n",
              "\n",
              "         [[ 0.1543,  0.2131, -0.0718,  ...,  0.0975, -0.0069, -0.3904],\n",
              "          [-0.5077, -1.8357,  2.1244,  ...,  0.3969,  1.3626, -0.0225]]]],\n",
              "       grad_fn=<CopyBackwards>), tensor([[[[ 2.9269e-02,  1.1764e-01, -1.2732e-02,  ..., -8.6454e-02,\n",
              "            1.2252e-01,  6.7036e-03],\n",
              "          [ 3.9763e-01, -2.9862e-01, -4.3627e-01,  ..., -5.6594e-02,\n",
              "           -2.9131e-01, -6.3776e-01]],\n",
              "\n",
              "         [[ 1.6335e-02, -2.4424e-02, -4.8432e-03,  ..., -5.6251e-02,\n",
              "           -6.8835e-02,  1.2608e-02],\n",
              "          [ 3.5408e-01,  6.1424e-01,  3.5653e-01,  ..., -3.0891e-02,\n",
              "            7.4476e-02,  2.0799e-02]],\n",
              "\n",
              "         [[ 2.5243e-02,  1.0407e-02, -4.8344e-02,  ...,  6.4945e-03,\n",
              "            3.9794e-04,  1.2100e-01],\n",
              "          [ 7.1725e-01, -1.8262e-01, -3.0229e-01,  ..., -4.4057e-01,\n",
              "           -3.8583e-02, -2.3157e-01]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[ 9.2560e-03,  1.4655e-02,  3.4054e-02,  ...,  9.6428e-03,\n",
              "           -6.3913e-03,  2.7569e-02],\n",
              "          [ 4.2214e-01,  3.7137e-01,  2.3890e-01,  ..., -1.7631e-01,\n",
              "            9.0582e-01,  1.0196e-01]],\n",
              "\n",
              "         [[ 3.1055e-03,  1.0484e-02, -4.7095e-03,  ...,  4.8638e-02,\n",
              "           -1.5250e-02, -7.0406e-02],\n",
              "          [ 9.7427e-02,  2.6014e-01,  3.1176e-01,  ..., -4.8579e-01,\n",
              "            9.3071e-01, -1.1125e-01]],\n",
              "\n",
              "         [[ 2.8621e-02, -2.8968e-03, -3.1328e-02,  ...,  2.5224e-02,\n",
              "            1.8680e-02, -4.3578e-02],\n",
              "          [ 3.0150e-01, -1.9468e-01, -1.7805e-01,  ...,  5.1854e-01,\n",
              "           -3.4874e-02,  9.8429e-02]]]], grad_fn=<CopyBackwards>), tensor([[[[-1.7399,  1.5544,  0.8420,  ...,  0.1949,  0.3739, -0.4898],\n",
              "          [-1.6240,  1.3219,  0.9978,  ..., -0.4160, -0.1783,  0.3793]],\n",
              "\n",
              "         [[-0.2877, -0.3362, -0.3745,  ...,  0.3365,  0.1496, -1.2037],\n",
              "          [-0.2730, -0.1071, -0.1413,  ...,  0.3996, -0.1407, -1.1605]],\n",
              "\n",
              "         [[ 1.4108, -0.6566,  1.7061,  ...,  1.4992,  0.5674, -0.9317],\n",
              "          [ 1.7853, -1.0445,  1.7326,  ...,  1.7643,  0.5169, -1.0316]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[-1.7319,  3.2136,  1.1013,  ..., -0.8693, -0.2972, -1.2679],\n",
              "          [-2.0636,  3.6187,  0.9261,  ..., -0.9856, -0.3694, -1.0883]],\n",
              "\n",
              "         [[-0.9236,  0.7800, -0.0765,  ...,  1.6758,  1.2948,  0.3508],\n",
              "          [-0.6022,  0.7458, -0.1418,  ...,  2.1779,  1.1909,  0.1315]],\n",
              "\n",
              "         [[ 0.4240,  0.0391, -1.3894,  ..., -1.0823, -0.4126,  0.9783],\n",
              "          [ 0.5731, -0.2723, -1.8803,  ..., -1.2079, -1.0119,  0.5113]]]],\n",
              "       grad_fn=<CopyBackwards>), tensor([[[[ 0.0985, -0.6352, -0.0868,  ...,  0.0363,  0.0543, -0.3238],\n",
              "          [-0.2378, -0.1665, -0.3516,  ...,  0.3921, -0.2744, -0.3039]],\n",
              "\n",
              "         [[ 0.8450,  0.2317,  0.6335,  ...,  0.5318, -0.0949,  0.1750],\n",
              "          [ 0.5121,  0.0876,  0.5792,  ..., -0.0148, -0.4605,  0.2020]],\n",
              "\n",
              "         [[-0.2443, -0.0378,  0.2317,  ...,  0.0219, -0.1216,  0.0395],\n",
              "          [-0.4156,  0.0576,  0.1678,  ...,  0.0501, -0.3476, -0.2093]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[ 0.1073,  0.2830,  0.1495,  ..., -1.0262,  0.5416,  0.5043],\n",
              "          [ 0.2258,  0.0643, -0.1481,  ..., -1.0380,  0.3420,  0.5458]],\n",
              "\n",
              "         [[-0.1796, -0.4815,  0.2412,  ..., -0.1129,  0.5764,  0.0777],\n",
              "          [-0.2803, -0.4700,  0.0848,  ...,  0.2658,  0.4753,  0.1039]],\n",
              "\n",
              "         [[ 0.5494,  0.6024, -0.6296,  ...,  0.1007,  0.8262, -0.1736],\n",
              "          [ 0.3254,  0.5532, -0.1605,  ..., -0.2396,  0.2754,  0.1460]]]],\n",
              "       grad_fn=<CopyBackwards>)), (tensor([[[[ 0.8659,  1.2230, -0.0036,  ..., -0.1768,  1.2656,  1.3975],\n",
              "          [-2.4391, -1.6445,  1.7410,  ..., -1.8302, -2.0103, -1.7548]],\n",
              "\n",
              "         [[ 0.4183,  0.5536,  0.7881,  ..., -0.2234,  0.4811, -0.0375],\n",
              "          [-1.6890, -0.3787, -1.0577,  ..., -0.8861, -1.5151, -0.2259]],\n",
              "\n",
              "         [[-0.5553, -1.0096,  0.1065,  ..., -0.4831,  1.4737, -0.0735],\n",
              "          [ 0.7138,  0.3943,  0.3157,  ...,  0.2931, -0.2829,  0.3843]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[-0.6875,  0.4129, -0.7369,  ..., -1.1018, -0.7937,  0.5081],\n",
              "          [ 1.8270,  0.4463, -1.1159,  ...,  0.7450,  1.0975,  0.1329]],\n",
              "\n",
              "         [[ 0.2528,  0.2051,  0.2412,  ...,  0.1542,  0.5065, -0.0168],\n",
              "          [ 0.0560,  1.4332,  0.3786,  ...,  0.1777, -0.5803,  0.0421]],\n",
              "\n",
              "         [[-0.2057,  0.4577,  0.8795,  ..., -0.3249, -0.0460,  0.9419],\n",
              "          [-1.3328,  0.4214, -0.5938,  ..., -0.5810,  0.0759, -0.4099]]]],\n",
              "       grad_fn=<CopyBackwards>), tensor([[[[-4.0518e-02, -5.9250e-02, -1.6183e-02,  ..., -8.3794e-04,\n",
              "           -3.3078e-02,  7.6099e-02],\n",
              "          [-4.3446e-01, -1.9747e-01, -4.3507e-01,  ..., -4.8681e-01,\n",
              "           -2.7235e-01, -8.4327e-02]],\n",
              "\n",
              "         [[-1.4299e-01,  1.0976e-01, -3.2061e-02,  ..., -7.9136e-02,\n",
              "           -6.2019e-02,  1.1654e-03],\n",
              "          [ 5.5592e-01, -4.1033e-02,  1.5684e-01,  ...,  4.7759e-01,\n",
              "            4.6287e-01,  1.3250e-01]],\n",
              "\n",
              "         [[-1.0311e-02, -3.4038e-02, -1.5415e-01,  ...,  2.3261e-02,\n",
              "           -1.4703e-02,  2.4091e-02],\n",
              "          [-8.1498e-01, -3.5524e-01,  3.3233e-02,  ...,  2.5945e-01,\n",
              "            5.5901e-02,  1.0831e+00]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[-1.8605e-02,  5.5119e-02,  2.7359e-03,  ..., -2.4560e-02,\n",
              "            6.2882e-03,  5.5262e-03],\n",
              "          [ 1.2290e-01, -4.1711e-01, -3.5411e-02,  ..., -2.1633e-01,\n",
              "           -4.8053e-01,  9.0854e-02]],\n",
              "\n",
              "         [[-8.3878e-04,  6.2181e-02, -3.9761e-02,  ...,  5.1470e-02,\n",
              "            7.0819e-02,  8.0597e-03],\n",
              "          [ 5.9444e-02,  5.7503e-01, -7.6640e-01,  ...,  4.2504e-03,\n",
              "           -7.7810e-02,  9.4069e-02]],\n",
              "\n",
              "         [[ 7.7253e-02,  7.0009e-02, -1.6194e-02,  ...,  2.5476e-02,\n",
              "           -8.1933e-03,  2.2595e-02],\n",
              "          [ 1.1499e-01, -3.9242e-01,  7.6922e-01,  ...,  2.9296e-01,\n",
              "            8.2074e-01,  6.4681e-01]]]], grad_fn=<CopyBackwards>), tensor([[[[ 2.0849, -0.0636,  0.5000,  ...,  0.9966,  1.7627,  1.5514],\n",
              "          [ 1.7876, -0.7635,  1.6988,  ...,  2.0722,  1.4739,  1.1660]],\n",
              "\n",
              "         [[-0.8371,  0.9337,  0.7837,  ...,  0.0498,  1.6516,  0.1707],\n",
              "          [-0.5368,  1.0880,  0.5653,  ..., -0.0634,  1.3486, -0.6582]],\n",
              "\n",
              "         [[-0.0912,  0.6096, -0.6076,  ...,  0.7558, -0.0126, -1.5131],\n",
              "          [-0.2230,  0.5867, -0.7267,  ...,  0.8867, -0.1216, -1.8096]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[-0.6766,  0.6220,  1.5134,  ..., -0.1829,  0.0215,  1.1120],\n",
              "          [-0.8275,  0.6928,  1.6118,  ..., -0.2364,  0.0025,  1.4436]],\n",
              "\n",
              "         [[ 1.1364,  0.0790, -0.3696,  ...,  0.2541,  0.9125, -0.5037],\n",
              "          [ 1.5442,  0.0934, -0.4613,  ...,  0.0754,  0.0034, -0.9217]],\n",
              "\n",
              "         [[ 1.2640, -0.4548, -0.5475,  ...,  0.5674, -0.4214, -1.0081],\n",
              "          [ 1.2149, -0.7560, -0.2841,  ...,  0.4172, -0.3426, -0.9019]]]],\n",
              "       grad_fn=<CopyBackwards>), tensor([[[[-0.0386, -0.1903,  0.1470,  ..., -0.3462, -0.1094, -0.1254],\n",
              "          [-0.0886,  0.0395,  0.0272,  ...,  0.0271, -0.3885,  0.0461]],\n",
              "\n",
              "         [[ 0.1720, -0.3733, -0.0356,  ...,  0.0848,  0.4297,  0.5137],\n",
              "          [ 0.2865, -0.2505, -0.2307,  ..., -0.2456,  0.3889,  0.3922]],\n",
              "\n",
              "         [[-0.5288, -0.7186,  0.3537,  ..., -0.7664,  0.2124,  0.3075],\n",
              "          [-0.3642, -0.0657,  0.0399,  ..., -0.5451,  0.2110,  0.1761]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[ 0.1834, -0.7011,  0.8361,  ...,  0.3994, -0.3654, -0.3145],\n",
              "          [ 0.2181, -0.7129,  0.8148,  ...,  0.1566,  0.0218,  0.0515]],\n",
              "\n",
              "         [[-0.3867, -0.7703, -0.4015,  ..., -0.3819, -0.8259,  0.0101],\n",
              "          [ 0.2124,  0.1236, -0.8350,  ...,  0.3556, -1.1067, -0.0330]],\n",
              "\n",
              "         [[-0.1518, -0.0204,  0.3507,  ..., -0.0252, -0.2174,  0.3534],\n",
              "          [ 0.2348, -0.1401,  0.4585,  ...,  0.2866, -0.0423,  0.0067]]]],\n",
              "       grad_fn=<CopyBackwards>)), (tensor([[[[-0.2997,  1.0674, -0.2439,  ..., -0.2289, -0.6333, -0.6078],\n",
              "          [ 0.9438, -1.8757, -0.5755,  ...,  0.1229,  1.7929, -0.3130]],\n",
              "\n",
              "         [[ 0.3514,  1.5137,  0.4524,  ...,  1.1948, -1.3977,  0.3308],\n",
              "          [ 0.1387, -1.1616, -0.6226,  ..., -2.1536, -0.1766, -1.1653]],\n",
              "\n",
              "         [[-1.3330, -0.6003,  0.2709,  ...,  1.1001, -0.1418, -0.6230],\n",
              "          [-1.5638,  1.9531, -0.2184,  ..., -0.3042, -2.2814,  1.5291]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[-0.4103, -0.2594, -0.7356,  ...,  0.4831,  0.0690,  0.3132],\n",
              "          [-0.5332,  0.1774,  1.8028,  ...,  0.1322,  0.1915, -0.9951]],\n",
              "\n",
              "         [[-0.0831, -0.3862, -0.7452,  ..., -0.2725, -0.0534, -0.2660],\n",
              "          [-1.4382, -1.8134,  2.8000,  ..., -0.8421, -0.6358,  0.1198]],\n",
              "\n",
              "         [[-1.0351, -0.4564, -1.0894,  ...,  0.2800, -0.1461, -0.3104],\n",
              "          [ 0.2128, -0.7712, -0.1874,  ..., -0.7119, -0.5132, -1.6355]]]],\n",
              "       grad_fn=<CopyBackwards>), tensor([[[[-3.3177e-02, -1.8028e-04,  4.5838e-04,  ...,  1.3248e-02,\n",
              "            1.9023e-02,  3.2562e-02],\n",
              "          [-3.2168e-02, -1.5418e-01,  1.4814e-01,  ..., -2.6376e-01,\n",
              "            1.0807e-02, -6.4121e-01]],\n",
              "\n",
              "         [[-1.0983e-02, -2.1240e-02, -4.1562e-02,  ..., -2.0584e-02,\n",
              "           -5.9072e-02, -6.3754e-02],\n",
              "          [-7.4979e-01,  4.7896e-01,  3.1803e-01,  ..., -2.9700e-01,\n",
              "           -1.4955e-01, -2.6639e-01]],\n",
              "\n",
              "         [[ 5.7588e-02,  8.7107e-02, -3.4391e-02,  ..., -1.3255e-02,\n",
              "            2.8237e-02,  1.2374e-01],\n",
              "          [-6.9403e-01,  5.8305e-01, -4.0446e-03,  ...,  2.2897e-02,\n",
              "           -6.2816e-01,  3.7408e-01]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[-3.4895e-02,  7.3233e-03,  3.9967e-02,  ...,  4.8722e-02,\n",
              "           -6.5669e-02, -5.9926e-02],\n",
              "          [-1.0827e+00, -5.7699e-01,  4.4333e-01,  ...,  2.7596e-01,\n",
              "            9.1006e-01, -4.2223e-01]],\n",
              "\n",
              "         [[-2.7116e-02,  1.4112e-02, -1.0806e-01,  ..., -2.1597e-02,\n",
              "            1.6286e-02,  6.7958e-03],\n",
              "          [ 2.9614e-01,  6.9683e-01,  2.1899e-01,  ...,  1.0726e-02,\n",
              "            3.8513e-02,  1.0382e-02]],\n",
              "\n",
              "         [[ 5.4608e-03,  4.4768e-02, -1.2635e-02,  ...,  1.2815e-03,\n",
              "            3.0699e-02, -1.0643e-01],\n",
              "          [-3.3885e-01, -4.5145e-02,  4.8048e-02,  ..., -4.6988e-01,\n",
              "            2.6005e-01, -7.2162e-01]]]], grad_fn=<CopyBackwards>), tensor([[[[ 1.0374,  0.3909, -0.6754,  ...,  0.2131, -0.7792,  0.1073],\n",
              "          [ 0.6857,  0.6310, -0.8816,  ..., -0.0665,  0.0525, -0.0755]],\n",
              "\n",
              "         [[ 0.5946, -0.2358, -1.0036,  ..., -0.7404, -1.1777, -2.7705],\n",
              "          [ 0.9685, -0.8442, -1.0034,  ..., -0.5312, -1.2558, -2.5045]],\n",
              "\n",
              "         [[-0.8795, -0.7718, -1.1451,  ...,  0.3562,  0.6508, -0.3904],\n",
              "          [-0.6928, -1.3858, -1.1689,  ...,  0.9893,  0.1329, -0.7229]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[-2.6684, -0.8720,  0.7093,  ...,  1.3669,  1.9079,  0.6156],\n",
              "          [-3.0017, -0.5988,  0.8993,  ...,  1.0432,  1.9338, -0.0553]],\n",
              "\n",
              "         [[-3.6058,  2.1486,  2.1966,  ...,  1.6174, -2.3318, -0.6465],\n",
              "          [-3.9024,  2.2764,  2.3965,  ...,  0.1705, -2.1405, -0.2133]],\n",
              "\n",
              "         [[-0.7480,  1.9170,  1.3470,  ...,  0.4011, -0.4394, -1.1463],\n",
              "          [-0.3069,  1.8317,  1.8609,  ...,  0.0209, -0.2684, -0.9492]]]],\n",
              "       grad_fn=<CopyBackwards>), tensor([[[[ 0.7396, -0.1129,  0.1192,  ..., -0.0200, -0.2694, -0.1733],\n",
              "          [ 0.9639, -0.3486, -0.0451,  ..., -0.7172, -0.3156,  0.1609]],\n",
              "\n",
              "         [[ 0.0073, -0.7114, -0.4992,  ..., -0.3623, -0.2582,  0.3008],\n",
              "          [ 0.2753, -0.3529, -0.4996,  ..., -0.3469, -0.0021,  0.2831]],\n",
              "\n",
              "         [[ 0.1104,  0.1524, -0.2274,  ..., -0.2116,  0.0372,  0.3177],\n",
              "          [ 0.2157,  0.3495, -0.1770,  ...,  0.2640, -0.0096,  0.4679]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[ 0.2203, -0.0463,  0.1329,  ...,  0.0979, -0.3664,  0.3431],\n",
              "          [-0.0973, -0.2557,  0.4457,  ...,  0.1551, -0.3278,  0.6508]],\n",
              "\n",
              "         [[ 0.5532, -0.3937,  0.4316,  ..., -0.0912,  0.6116, -0.1449],\n",
              "          [ 0.6388, -0.4841,  0.4255,  ..., -0.0629,  0.5913, -0.1810]],\n",
              "\n",
              "         [[ 0.4216, -0.3658, -0.3054,  ...,  0.1030,  0.0099, -0.1975],\n",
              "          [ 0.3812, -0.3219, -0.4682,  ..., -0.3154,  0.0673, -0.2982]]]],\n",
              "       grad_fn=<CopyBackwards>)), (tensor([[[[-0.0638, -0.3080,  0.1955,  ..., -0.2980,  0.1688,  0.0431],\n",
              "          [ 0.5026, -0.5220,  3.4995,  ..., -1.0278,  0.1422,  2.3666]],\n",
              "\n",
              "         [[ 0.4591,  0.1555, -0.1794,  ..., -0.2225, -0.4867,  0.2342],\n",
              "          [-0.4296, -0.2702, -0.8925,  ..., -1.1856,  0.8598, -0.2742]],\n",
              "\n",
              "         [[-0.3287, -0.1999, -0.2862,  ..., -0.5182,  0.0972,  0.6838],\n",
              "          [ 2.6562,  1.6259, -1.6430,  ...,  0.5375,  1.0974, -3.0638]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[-0.1022, -0.1004,  0.0337,  ..., -0.4126,  0.0917,  0.2785],\n",
              "          [-0.3013,  0.2233, -0.8632,  ..., -2.7654,  0.0675,  2.4467]],\n",
              "\n",
              "         [[ 0.3300,  0.2506,  0.0510,  ..., -0.0276, -0.1510, -0.1070],\n",
              "          [-3.6227,  0.0169, -1.4157,  ..., -0.7226, -1.0749,  1.1248]],\n",
              "\n",
              "         [[-0.0671, -0.4478, -0.0450,  ..., -0.1774, -0.1607, -0.0955],\n",
              "          [-4.6108,  0.6886,  0.8102,  ..., -0.4719,  0.8873, -2.4897]]]],\n",
              "       grad_fn=<CopyBackwards>), tensor([[[[-0.0404, -0.0917,  0.0642,  ..., -0.0035, -0.0486,  0.0240],\n",
              "          [-0.2731,  0.0407,  0.5085,  ..., -0.1216, -0.2275, -0.7928]],\n",
              "\n",
              "         [[ 0.0031, -0.1010,  0.0053,  ...,  0.0069, -0.0100,  0.0144],\n",
              "          [-0.1159, -0.6017,  0.1824,  ..., -0.1040,  0.0132,  0.0196]],\n",
              "\n",
              "         [[ 0.1149, -0.0396, -0.0644,  ..., -0.1404,  0.0345,  0.0140],\n",
              "          [ 0.4707,  0.5479,  0.1769,  ..., -1.8493, -0.6008, -0.0666]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[ 0.0385, -0.0342, -0.0351,  ...,  0.0089, -0.0578,  0.0940],\n",
              "          [ 0.3749,  0.0586, -0.0326,  ..., -0.1291, -0.1929,  0.6304]],\n",
              "\n",
              "         [[-0.0040,  0.0416,  0.0358,  ...,  0.0166,  0.0453,  0.0051],\n",
              "          [ 0.0923, -0.0818,  0.3751,  ...,  0.5956, -0.0576, -0.0671]],\n",
              "\n",
              "         [[ 0.0988,  0.0028,  0.0458,  ...,  0.1539,  0.0561,  0.0554],\n",
              "          [ 0.0944, -0.1632,  0.0327,  ...,  0.9541,  0.3676,  0.2072]]]],\n",
              "       grad_fn=<CopyBackwards>), tensor([[[[ 0.4540, -0.9974, -2.6459,  ...,  1.1923, -0.3397,  3.7741],\n",
              "          [ 0.5595, -0.9436, -1.4177,  ...,  0.9573, -0.9510,  3.5285]],\n",
              "\n",
              "         [[ 0.5767, -1.4281, -0.0372,  ...,  0.2962,  1.1243,  1.1418],\n",
              "          [-0.3780, -1.4567,  1.4320,  ..., -0.2319,  0.4736,  0.6123]],\n",
              "\n",
              "         [[ 0.4139, -1.4614, -0.8103,  ..., -3.3714,  0.1478,  0.2090],\n",
              "          [-0.1716, -1.2067, -0.2029,  ..., -2.4051, -0.5142,  0.5125]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[-3.4578, -0.9712,  0.9847,  ..., -0.2011, -1.6939,  0.2227],\n",
              "          [-1.9282, -0.9421,  0.6625,  ..., -0.3203, -2.4944,  0.4157]],\n",
              "\n",
              "         [[-1.9077,  2.6054,  0.7046,  ...,  3.3153,  0.8114, -1.1166],\n",
              "          [-0.5560,  2.4935,  1.0945,  ...,  3.5782,  0.8835, -1.7877]],\n",
              "\n",
              "         [[-0.7894, -0.6044, -0.4530,  ...,  2.1209, -1.0290,  1.3274],\n",
              "          [-1.5356,  0.6271, -1.3167,  ...,  1.9269, -1.3602,  2.0155]]]],\n",
              "       grad_fn=<CopyBackwards>), tensor([[[[ 0.2468,  0.4531, -0.2456,  ..., -0.2639, -0.4747,  0.2349],\n",
              "          [ 0.6563,  0.5046, -0.2421,  ...,  0.0345, -0.6680,  0.2661]],\n",
              "\n",
              "         [[ 0.1554,  0.0104,  0.0888,  ...,  0.5652,  0.1155,  0.2966],\n",
              "          [ 0.2705, -0.4564,  0.2788,  ...,  0.3807,  0.1667,  0.0607]],\n",
              "\n",
              "         [[-0.5218, -0.1890,  0.0533,  ..., -0.1139,  0.2080, -0.0636],\n",
              "          [-0.3670, -0.1278, -0.1607,  ...,  0.1446, -0.0988, -0.1946]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[-0.0757,  0.8512,  0.1290,  ...,  0.5617,  0.4623, -0.4476],\n",
              "          [-0.1185,  0.9600,  0.4644,  ...,  0.6976,  0.4706,  0.1709]],\n",
              "\n",
              "         [[ 0.0076, -0.1633,  0.0613,  ..., -0.4322, -0.1017,  0.0051],\n",
              "          [ 0.0926, -0.3790,  0.3583,  ..., -0.1947, -0.1139, -0.2700]],\n",
              "\n",
              "         [[ 0.8125,  0.0627,  0.4241,  ...,  0.3436,  0.0131, -0.7254],\n",
              "          [ 0.4459,  0.2707,  0.4448,  ...,  0.2226,  0.4030, -0.7320]]]],\n",
              "       grad_fn=<CopyBackwards>))), decoder_hidden_states=None, decoder_attentions=None, cross_attentions=None, encoder_last_hidden_state=tensor([[[ 0.4624, -0.2475,  0.0902,  ...,  0.1127,  0.6529,  0.2203],\n",
              "         [ 0.4538, -0.2948,  0.2556,  ..., -0.0442,  0.6858,  0.4372]]],\n",
              "       grad_fn=<NativeLayerNormBackward>), encoder_hidden_states=None, encoder_attentions=None)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pytorch_lightning"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1vOHkrhMcf4B",
        "outputId": "02c08801-95b9-487c-8f44-b5e6720bc01c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pytorch_lightning in /usr/local/lib/python3.7/dist-packages (1.2.1)\n",
            "Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.7/dist-packages (from pytorch_lightning) (4.64.1)\n",
            "Requirement already satisfied: future>=0.17.1 in /usr/local/lib/python3.7/dist-packages (from pytorch_lightning) (0.18.2)\n",
            "Requirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.7/dist-packages (from pytorch_lightning) (1.21.6)\n",
            "Requirement already satisfied: torch>=1.4 in /usr/local/lib/python3.7/dist-packages (from pytorch_lightning) (1.7.1)\n",
            "Requirement already satisfied: fsspec[http]>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from pytorch_lightning) (2022.11.0)\n",
            "Requirement already satisfied: tensorboard>=2.2.0 in /usr/local/lib/python3.7/dist-packages (from pytorch_lightning) (2.9.1)\n",
            "Requirement already satisfied: PyYAML!=5.4.*,>=5.1 in /usr/local/lib/python3.7/dist-packages (from pytorch_lightning) (6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from fsspec[http]>=0.8.1->pytorch_lightning) (2.23.0)\n",
            "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /usr/local/lib/python3.7/dist-packages (from fsspec[http]>=0.8.1->pytorch_lightning) (3.8.3)\n",
            "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]>=0.8.1->pytorch_lightning) (2.1.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]>=0.8.1->pytorch_lightning) (4.1.1)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]>=0.8.1->pytorch_lightning) (4.0.2)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]>=0.8.1->pytorch_lightning) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]>=0.8.1->pytorch_lightning) (22.1.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]>=0.8.1->pytorch_lightning) (6.0.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]>=0.8.1->pytorch_lightning) (1.3.3)\n",
            "Requirement already satisfied: asynctest==0.13.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]>=0.8.1->pytorch_lightning) (0.13.0)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]>=0.8.1->pytorch_lightning) (1.8.1)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->pytorch_lightning) (0.6.1)\n",
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            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard>=2.2.0->pytorch_lightning) (4.13.0)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->fsspec[http]>=0.8.1->pytorch_lightning) (2022.9.24)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->fsspec[http]>=0.8.1->pytorch_lightning) (3.0.4)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0->pytorch_lightning) (3.2.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import PreTrainedTokenizerFast, BartModel\n",
        "\n",
        "tokenizer = PreTrainedTokenizerFast.from_pretrained('gogamza/kobart-base-v1')\n",
        "model = BartModel.from_pretrained('gogamza/kobart-base-v1')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
            "cefbcdf0c0014a60b6a3b6ca88192774",
            "ce30994c1e0c463480a5002bcf63102f",
            "1f004f8772094a42a197407de71dbd76",
            "657ea55eed3d416ebed5b808fcb1812c",
            "bf1b24f43a7e4b6e9dee9ff35d7c8874",
            "bd5c89efd6b44546bd7e44ea56b37b3c",
            "3a4b9aadefa64a9b93a6908d72b3a6f4",
            "c633e692d216449db758a45e66288c19",
            "a19c46106f80495eb97d247ff0a61129",
            "1b37b59a1161415db15850325905ced7",
            "d9e70955c5f04f67b88f5a7696c3ec41",
            "1177190a372e47d0bae0168498d7598a",
            "757b648cce3c474b9084db7a2756ab3b",
            "637fba7a0e3e4280a2532ac064e5232a",
            "92289a5af30e4587921b179e0ad3c7f5",
            "6c4c39a01a8145a0823763d8c1df3945",
            "ef2b4f2763654cc3b81cf2e614b8bf24",
            "d2062290cd6044059c517fecd48509ac",
            "ecca1646790d4d00831d8d8fadac5a92",
            "bf37a7c6480c4ea79e117e69dbc59883",
            "52cad73fe94a468ab799864867758995",
            "7d92dd433be34925b875ef03c22aa32c",
            "a7d994f04a9e4f0683b178f912944a41",
            "16d4b4746f464d5591353aea38d50d1f",
            "b9b775cb5bb54d9e9a695ff679f1b527",
            "1fe58b8cc4004e2cbcab4cd0a8312c6b",
            "f7c86d5acb89419ba72943381ccff24d",
            "80ff3f783ed24e10acafff2cf27db345",
            "1afac63dd34d451cb0c48a6bf7298af6",
            "e037f4aa9e4c43689b8b205f8e84bdf9",
            "5fea78afe48c4e578d575bad5bff961c",
            "ef075e20c61d475bb0788c22816f5009",
            "73e07444ac6c499bbe72e65461316f43",
            "a900587f3d484028a1899acb23da9492",
            "d1af03253baf46e2aac24bfcfee56850",
            "c164f60590724101bb941325e18eee61",
            "48e91e3c4b594142a58cc6d02f04066b",
            "4bc70098aff74414a7fa143e9f97163a",
            "3a0ab3ca93004cd4b738e38b493bfa90",
            "31ae89ed6a2a48b7ba8aed4b9e098bb8",
            "dc31a607cd7c4303a917709d26775368",
            "094ad3cf36c44acda628bb830e0173b9",
            "4fa4e41de9914f98a3d9f61145fb6ac3",
            "f33042b0447643abb001efc7a9bff83e",
            "fb85bbcfb0c6443c91eaa00598bbc223",
            "148fa499414c4e56998349b54758360a",
            "3b8dfaf1216a4af0a68af63b947fba33",
            "201046a745aa419da685a2d24c8b22d0",
            "9ed4e522719e467e8ad94e31647d72e5",
            "091988afbe304cdebdb9dda2edf4e9d1",
            "dc7b0893403846569febcad3cdd5fd91",
            "219266f530804248a75c9f3e63f9b99f",
            "ac8205ececfb4e4e9b12d91e071b5295",
            "58d389e743ac437eacc030162b9a22d8",
            "7d6dab5dfc05477283fa50c6d5cbb1b4"
          ]
        },
        "id": "KSY__SiReco_",
        "outputId": "e3305a23-e691-4db8-8571-df055b249a49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cefbcdf0c0014a60b6a3b6ca88192774"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1177190a372e47d0bae0168498d7598a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a7d994f04a9e4f0683b178f912944a41"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a900587f3d484028a1899acb23da9492"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "fb85bbcfb0c6443c91eaa00598bbc223"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install streamlit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l-iJwHPbn-MO",
        "outputId": "2f459888-0eb7-439e-b9bf-adc93a53c97f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting streamlit\n",
            "  Downloading streamlit-1.15.1-py2.py3-none-any.whl (10.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 10.3 MB 20.5 MB/s \n",
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            "Collecting blinker>=1.0.0\n",
            "  Downloading blinker-1.5-py2.py3-none-any.whl (12 kB)\n",
            "Requirement already satisfied: tornado>=5.0 in /usr/local/lib/python3.7/dist-packages (from streamlit) (6.0.4)\n",
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            "Collecting rich>=10.11.0\n",
            "  Downloading rich-12.6.0-py3-none-any.whl (237 kB)\n",
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            "Collecting pympler>=0.9\n",
            "  Downloading Pympler-1.0.1-py3-none-any.whl (164 kB)\n",
            "\u001b[K     |████████████████████████████████| 164 kB 65.7 MB/s \n",
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            "Requirement already satisfied: altair>=3.2.0 in /usr/local/lib/python3.7/dist-packages (from streamlit) (4.2.0)\n",
            "Requirement already satisfied: protobuf<4,>=3.12 in /usr/local/lib/python3.7/dist-packages (from streamlit) (3.19.6)\n",
            "Collecting semver\n",
            "  Downloading semver-2.13.0-py2.py3-none-any.whl (12 kB)\n",
            "Requirement already satisfied: typing-extensions>=3.10.0.0 in /usr/local/lib/python3.7/dist-packages (from streamlit) (4.1.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from streamlit) (7.1.2)\n",
            "Collecting watchdog\n",
            "  Downloading watchdog-2.1.9-py3-none-manylinux2014_x86_64.whl (78 kB)\n",
            "\u001b[K     |████████████████████████████████| 78 kB 8.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tzlocal>=1.1 in /usr/local/lib/python3.7/dist-packages (from streamlit) (1.5.1)\n",
            "Collecting gitpython!=3.1.19\n",
            "  Downloading GitPython-3.1.29-py3-none-any.whl (182 kB)\n",
            "\u001b[K     |████████████████████████████████| 182 kB 71.2 MB/s \n",
            "\u001b[?25hCollecting validators>=0.2\n",
            "  Downloading validators-0.20.0.tar.gz (30 kB)\n",
            "Collecting pydeck>=0.1.dev5\n",
            "  Downloading pydeck-0.8.0-py2.py3-none-any.whl (4.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.7 MB 51.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests>=2.4 in /usr/local/lib/python3.7/dist-packages (from streamlit) (2.23.0)\n",
            "Requirement already satisfied: toolz in /usr/local/lib/python3.7/dist-packages (from altair>=3.2.0->streamlit) (0.12.0)\n",
            "Requirement already satisfied: entrypoints in /usr/local/lib/python3.7/dist-packages (from altair>=3.2.0->streamlit) (0.4)\n",
            "Requirement already satisfied: jsonschema>=3.0 in /usr/local/lib/python3.7/dist-packages (from altair>=3.2.0->streamlit) (4.3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from altair>=3.2.0->streamlit) (2.11.3)\n",
            "Collecting gitdb<5,>=4.0.1\n",
            "  Downloading gitdb-4.0.10-py3-none-any.whl (62 kB)\n",
            "\u001b[K     |████████████████████████████████| 62 kB 1.4 MB/s \n",
            "\u001b[?25hCollecting smmap<6,>=3.0.1\n",
            "  Downloading smmap-5.0.0-py3-none-any.whl (24 kB)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=1.4->streamlit) (3.10.0)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=3.0->altair>=3.2.0->streamlit) (22.1.0)\n",
            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=3.0->altair>=3.2.0->streamlit) (0.19.2)\n",
            "Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=3.0->altair>=3.2.0->streamlit) (5.10.0)\n",
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            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.21.0->streamlit) (2022.6)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil->streamlit) (1.15.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.4->streamlit) (2022.9.24)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.4->streamlit) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.4->streamlit) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.4->streamlit) (1.25.11)\n",
            "Collecting commonmark<0.10.0,>=0.9.0\n",
            "  Downloading commonmark-0.9.1-py2.py3-none-any.whl (51 kB)\n",
            "\u001b[K     |████████████████████████████████| 51 kB 8.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pygments<3.0.0,>=2.6.0 in /usr/local/lib/python3.7/dist-packages (from rich>=10.11.0->streamlit) (2.6.1)\n",
            "Requirement already satisfied: decorator>=3.4.0 in /usr/local/lib/python3.7/dist-packages (from validators>=0.2->streamlit) (4.4.2)\n",
            "Building wheels for collected packages: validators\n",
            "  Building wheel for validators (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for validators: filename=validators-0.20.0-py3-none-any.whl size=19581 sha256=3a04f9122936542aff65d8b7165f02d65431e3165170e3b678f9b31ac63c698b\n",
            "  Stored in directory: /root/.cache/pip/wheels/5f/55/ab/36a76989f7f88d9ca7b1f68da6d94252bb6a8d6ad4f18e04e9\n",
            "Successfully built validators\n",
            "Installing collected packages: smmap, gitdb, commonmark, watchdog, validators, semver, rich, pympler, pydeck, gitpython, blinker, streamlit\n",
            "Successfully installed blinker-1.5 commonmark-0.9.1 gitdb-4.0.10 gitpython-3.1.29 pydeck-0.8.0 pympler-1.0.1 rich-12.6.0 semver-2.13.0 smmap-5.0.0 streamlit-1.15.1 validators-0.20.0 watchdog-2.1.9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def solution(array):\n",
        "    array.sort()\n",
        "    n = len(array) // 2\n",
        "    return array[n]\n",
        "\n",
        "print(solution([9, -1, 0]))"
      ],
      "metadata": {
        "id": "extRBxVBm-ak",
        "outputId": "2eadcf03-bc5e-4efe-e6be-2f9b03752d11",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gUZKQ_Yz4zwf"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}