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  "cells": [
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Junseokee/Deep_Learning/blob/main/220921_chat_bot.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 라이브러리 호출"
      ],
      "metadata": {
        "id": "LZG8V0aXtEtB"
      }
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    {
      "cell_type": "code",
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        "!pip install pytorch_lightning==1.7.6\n",
        "!pip install transformers"
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        "id": "xlMG88GVtLZf"
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    },
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      "outputs": [
        {
          "output_type": "stream",
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          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moving 0 files to the new cache system\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
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      ],
      "source": [
        "import argparse\n",
        "import logging\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import torch\n",
        "import pytorch_lightning as pl\n",
        "from pytorch_lightning.callbacks import ModelCheckpoint\n",
        "from pytorch_lightning.core.lightning import LightningModule\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "from transformers.optimization import AdamW, get_cosine_schedule_with_warmup\n",
        "from transformers import PreTrainedTokenizerFast, GPT2LMHeadModel"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "parser = argparse.ArgumentParser(description='Simsimi based on KoGPT-2')"
      ],
      "metadata": {
        "id": "1tMsuXdQtHYY"
      },
      "execution_count": 3,
      "outputs": []
    },
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      "source": [
        "parser.add_argument('--chat',\n",
        "                    action='store_true',\n",
        "                    default=False,\n",
        "                    help='response generation on given user input')\n",
        "\n",
        "parser.add_argument('--sentiment',\n",
        "                    type=str,\n",
        "                    default='0',\n",
        "                    help='sentiment for system. 0 is neutral, 1 is negative, 2 is positive.')\n",
        "\n",
        "parser.add_argument('--model_params',\n",
        "                    type=str,\n",
        "                    default='model_chp/model_-last.ckpt',\n",
        "                    help='model binary for starting chat')\n",
        "\n",
        "parser.add_argument('--train',\n",
        "                    action='store_true',\n",
        "                    default=False,\n",
        "                    help='for training')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Lq6RR8uu08X",
        "outputId": "68fd3278-efcf-40da-d4f6-bb99dfa94926"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "_StoreTrueAction(option_strings=['--train'], dest='train', nargs=0, const=True, default=False, type=None, choices=None, help='for training', metavar=None)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
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    {
      "cell_type": "code",
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        "logger = logging.getLogger()\n",
        "logger.setLevel(logging.INFO)\n"
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      "metadata": {
        "id": "lLj0iB0Uu2EX"
      },
      "execution_count": 5,
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    },
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        "U_TKN = '<usr>'\n",
        "S_TKN = '<sys>'\n",
        "BOS = '</s>'\n",
        "EOS = '</s>'\n",
        "MASK = '<unused0>'\n",
        "SENT = '<unused1>'\n",
        "PAD = '<pad>'"
      ],
      "metadata": {
        "id": "oAhXfc6Iu4nt"
      },
      "execution_count": 6,
      "outputs": []
    },
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        "TOKENIZER = PreTrainedTokenizerFast.from_pretrained(\"skt/kogpt2-base-v2\",\n",
        "            bos_token=BOS, eos_token=EOS, unk_token='<unk>',\n",
        "            pad_token=PAD, mask_token=MASK) "
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        "outputId": "e5ebf32b-50ee-40db-93d1-7d0350ef41a7"
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      "execution_count": 7,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/2.83M [00:00<?, ?B/s]"
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        {
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          "text": [
            "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
            "The tokenizer class you load from this checkpoint is 'GPT2Tokenizer'. \n",
            "The class this function is called from is 'PreTrainedTokenizerFast'.\n"
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    },
    {
      "cell_type": "code",
      "source": [
        "class CharDataset(Dataset):\n",
        "    def __init__(self, chats, max_len=32):\n",
        "        self._data = chats\n",
        "        self.first = True\n",
        "        self.q_token = U_TKN\n",
        "        self.a_token = S_TKN\n",
        "        self.sent_token = SENT\n",
        "        self.bos = BOS\n",
        "        self.eos = EOS\n",
        "        self.mask = MASK\n",
        "        self.pad = PAD\n",
        "        self.max_len = max_len\n",
        "        self.tokenizer = TOKENIZER \n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self._data)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        turn = self._data.iloc[idx]\n",
        "        q = turn['Q']\n",
        "        a = turn['A']\n",
        "        sentiment = str(turn['label'])\n",
        "        q_toked = self.tokenizer.tokenize(self.q_token + q + \\\n",
        "                                          self.sent_token + sentiment)   \n",
        "        q_len = len(q_toked)\n",
        "        a_toked = self.tokenizer.tokenize(self.a_token + a + self.eos)\n",
        "        a_len = len(a_toked)\n",
        "        if q_len + a_len > self.max_len:\n",
        "            a_len = self.max_len - q_len\n",
        "            if a_len <= 0:\n",
        "                q_toked = q_toked[-(int(self.max_len/2)):]\n",
        "                q_len = len(q_toked)\n",
        "                a_len = self.max_len - q_len\n",
        "                assert a_len > 0\n",
        "            a_toked = a_toked[:a_len]\n",
        "            a_len = len(a_toked)\n",
        "            assert a_len == len(a_toked), f'{a_len} ==? {len(a_toked)}'\n",
        "        # [mask, mask, ...., mask, ..., <bos>,..A.. <eos>, <pad>....]\n",
        "        labels = [\n",
        "            self.mask,\n",
        "        ] * q_len + a_toked[1:]\n",
        "        if self.first:\n",
        "            logging.info(\"contexts : {}\".format(q))\n",
        "            logging.info(\"toked ctx: {}\".format(q_toked))\n",
        "            logging.info(\"response : {}\".format(a))\n",
        "            logging.info(\"toked response : {}\".format(a_toked))\n",
        "            logging.info('labels {}'.format(labels))\n",
        "            self.first = False\n",
        "        mask = [0] * q_len + [1] * a_len + [0] * (self.max_len - q_len - a_len)\n",
        "        self.max_len\n",
        "        labels_ids = self.tokenizer.convert_tokens_to_ids(labels)\n",
        "        while len(labels_ids) < self.max_len:\n",
        "            labels_ids += [self.tokenizer.pad_token_id]\n",
        "        token_ids = self.tokenizer.convert_tokens_to_ids(q_toked + a_toked)\n",
        "        while len(token_ids) < self.max_len:\n",
        "            token_ids += [self.tokenizer.pad_token_id]\n",
        "        return(token_ids, np.array(mask),\n",
        "               labels_ids)"
      ],
      "metadata": {
        "id": "SVgQtikJu73L"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class KoGPT2Chat(LightningModule):\n",
        "    def __init__(self, hparams, **kwargs):\n",
        "        super(KoGPT2Chat, self).__init__()\n",
        "        self.hparams = hparams\n",
        "        self.neg = -1e18\n",
        "        self.kogpt2 = GPT2LMHeadModel.from_pretrained('skt/kogpt2-base-v2')\n",
        "        self.loss_function = torch.nn.CrossEntropyLoss(reduction='none')\n",
        "\n",
        "    @staticmethod\n",
        "    def add_model_specific_args(parent_parser):\n",
        "        # add model specific args\n",
        "        parser = argparse.ArgumentParser(parents=[parent_parser], add_help=False)\n",
        "        parser.add_argument('--max-len',\n",
        "                            type=int,\n",
        "                            default=32,\n",
        "                            help='max sentence length on input (default: 32)')\n",
        "\n",
        "        parser.add_argument('--batch-size',\n",
        "                            type=int,\n",
        "                            default=96,\n",
        "                            help='batch size for training (default: 96)')\n",
        "        parser.add_argument('--lr',\n",
        "                            type=float,\n",
        "                            default=5e-5,\n",
        "                            help='The initial learning rate')\n",
        "        parser.add_argument('--warmup_ratio',\n",
        "                            type=float,\n",
        "                            default=0.1,\n",
        "                            help='warmup ratio')\n",
        "        return parser\n",
        "\n",
        "    def forward(self, inputs):\n",
        "        # (batch, seq_len, hiddens)\n",
        "        output = self.kogpt2(inputs, return_dict=True)\n",
        "        return output.logits\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        token_ids, mask, label = batch\n",
        "        out = self(token_ids)\n",
        "        mask_3d = mask.unsqueeze(dim=2).repeat_interleave(repeats=out.shape[2], dim=2)\n",
        "        mask_out = torch.where(mask_3d == 1, out, self.neg * torch.ones_like(out))\n",
        "        loss = self.loss_function(mask_out.transpose(2, 1), label)\n",
        "        loss_avg = loss.sum() / mask.sum()\n",
        "        self.log('train_loss', loss_avg)\n",
        "        return loss_avg\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        # Prepare optimizer\n",
        "        param_optimizer = list(self.named_parameters())\n",
        "        no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']\n",
        "        optimizer_grouped_parameters = [\n",
        "            {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},\n",
        "            {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n",
        "        ]\n",
        "        optimizer = AdamW(optimizer_grouped_parameters,\n",
        "                          lr=self.hparams.lr, correct_bias=False)\n",
        "        # warm up lr\n",
        "        num_train_steps = len(self.train_dataloader()) * self.hparams.max_epochs\n",
        "        num_warmup_steps = int(num_train_steps * self.hparams.warmup_ratio)\n",
        "        scheduler = get_cosine_schedule_with_warmup(\n",
        "            optimizer,\n",
        "            num_warmup_steps=num_warmup_steps, num_training_steps=num_train_steps)\n",
        "        lr_scheduler = {'scheduler': scheduler, 'name': 'cosine_schedule_with_warmup',\n",
        "                        'monitor': 'loss', 'interval': 'step',\n",
        "                        'frequency': 1}\n",
        "        return [optimizer], [lr_scheduler]\n",
        "\n",
        "    def _collate_fn(self, batch):\n",
        "        data = [item[0] for item in batch]\n",
        "        mask = [item[1] for item in batch]\n",
        "        label = [item[2] for item in batch]\n",
        "        return torch.LongTensor(data), torch.LongTensor(mask), torch.LongTensor(label)\n",
        "\n",
        "    def train_dataloader(self):\n",
        "        data = pd.read_csv('Chatbot_data/ChatbotData.csv')\n",
        "        self.train_set = CharDataset(data, max_len=self.hparams.max_len)\n",
        "        train_dataloader = DataLoader(\n",
        "            self.train_set, batch_size=self.hparams.batch_size, num_workers=2,\n",
        "            shuffle=True, collate_fn=self._collate_fn)\n",
        "        return train_dataloader\n",
        "\n",
        "    def chat(self, sent='0'):\n",
        "        tok = TOKENIZER\n",
        "        sent_tokens = tok.tokenize(sent)\n",
        "        with torch.no_grad():\n",
        "            while 1:\n",
        "                q = input('user > ').strip()\n",
        "                if q == 'quit':\n",
        "                    break\n",
        "                a = ''\n",
        "                while 1:\n",
        "                    input_ids = torch.LongTensor(tok.encode(U_TKN + q + SENT + sent + S_TKN + a)).unsqueeze(dim=0)\n",
        "                    pred = self(input_ids)\n",
        "                    gen = tok.convert_ids_to_tokens(\n",
        "                        torch.argmax(\n",
        "                            pred,\n",
        "                            dim=-1).squeeze().numpy().tolist())[-1]\n",
        "                    if gen == EOS:\n",
        "                        break\n",
        "                    a += gen.replace('▁', ' ')\n",
        "                print(\"Simsimi > {}\".format(a.strip()))\n",
        "\n",
        "parser = KoGPT2Chat.add_model_specific_args(parser)\n",
        "parser = pl.Trainer.add_argparse_args(parser)\n",
        "args = parser.parse_args()\n",
        "logging.info(args)\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    if args.train:\n",
        "        checkpoint_callback = ModelCheckpoint(\n",
        "            dirpath='model_chp',\n",
        "            filename='{epoch:02d}-{train_loss:.2f}',\n",
        "            verbose=True,\n",
        "            save_last=True,\n",
        "            monitor='train_loss',\n",
        "            mode='min',\n",
        "            prefix='model_'\n",
        "        )\n",
        "        # python train_torch.py --train --gpus 1 --max_epochs 3\n",
        "        model = KoGPT2Chat(args)\n",
        "        model.train()\n",
        "        trainer = pl.Trainer.from_argparse_args(\n",
        "            args,\n",
        "            checkpoint_callback=checkpoint_callback, gradient_clip_val=1.0)\n",
        "        trainer.fit(model)\n",
        "        logging.info('best model path {}'.format(checkpoint_callback.best_model_path))\n",
        "    if args.chat:\n",
        "        model = KoGPT2Chat.load_from_checkpoint(args.model_params)\n",
        "        model.chat()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        },
        "id": "7LXTjyU7vBX0",
        "outputId": "5892556a-c95c-48d8-d325-0982f17cdb19"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ArgumentError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mArgumentError\u001b[0m                             Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-b3c9c031e9b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    101\u001b[0m                 \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Simsimi > {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKoGPT2Chat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_model_specific_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparser\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    104\u001b[0m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_argparse_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparser\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-16-b3c9c031e9b6>\u001b[0m in \u001b[0;36madd_model_specific_args\u001b[0;34m(parent_parser)\u001b[0m\n\u001b[1;32m     14\u001b[0m                             \u001b[0mtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m                             \u001b[0mdefault\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m                             help='max sentence length on input (default: 32)')\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m         parser.add_argument('--batch-size',\n",
            "\u001b[0;32m/usr/lib/python3.7/argparse.py\u001b[0m in \u001b[0;36madd_argument\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1380\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"length of metavar tuple does not match nargs\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1381\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1382\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_action\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1384\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0madd_argument_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/argparse.py\u001b[0m in \u001b[0;36m_add_action\u001b[0;34m(self, action)\u001b[0m\n\u001b[1;32m   1743\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_add_action\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1744\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moption_strings\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1745\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_optionals\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_action\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1746\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1747\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_positionals\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_action\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/argparse.py\u001b[0m in \u001b[0;36m_add_action\u001b[0;34m(self, action)\u001b[0m\n\u001b[1;32m   1584\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1585\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_add_action\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1586\u001b[0;31m         \u001b[0maction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ArgumentGroup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_action\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1587\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_group_actions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1588\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/argparse.py\u001b[0m in \u001b[0;36m_add_action\u001b[0;34m(self, action)\u001b[0m\n\u001b[1;32m   1394\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_add_action\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1395\u001b[0m         \u001b[0;31m# resolve any conflicts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1396\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_conflict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1397\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1398\u001b[0m         \u001b[0;31m# add to actions list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/argparse.py\u001b[0m in \u001b[0;36m_check_conflict\u001b[0;34m(self, action)\u001b[0m\n\u001b[1;32m   1533\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mconfl_optionals\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1534\u001b[0m             \u001b[0mconflict_handler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_handler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1535\u001b[0;31m             \u001b[0mconflict_handler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfl_optionals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1536\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1537\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_handle_conflict_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconflicting_actions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/argparse.py\u001b[0m in \u001b[0;36m_handle_conflict_error\u001b[0;34m(self, action, conflicting_actions)\u001b[0m\n\u001b[1;32m   1542\u001b[0m                                      \u001b[0;32mfor\u001b[0m \u001b[0moption_string\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1543\u001b[0m                                      in conflicting_actions])\n\u001b[0;32m-> 1544\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mArgumentError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mconflict_string\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1545\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1546\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_handle_conflict_resolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconflicting_actions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mArgumentError\u001b[0m: argument --max-len: conflicting option string: --max-len"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "avVcjSFrwsSx"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}