{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VTze-VbeU1c0"
   },
   "source": [
    "# Fine-tune a DialoGPT model\n",
    "\n",
    "Adapted from a notebook adapted from the notebook in [this Medium post](https://towardsdatascience.com/make-your-own-rick-sanchez-bot-with-transformers-and-dialogpt-fine-tuning-f85e6d1f4e30?gi=e4a72d1510f0)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y17kuzFNUSrZ"
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dnv5kT-mLsB-",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# all the imports\n",
    "\n",
    "import glob\n",
    "import logging\n",
    "import os\n",
    "import pickle\n",
    "import random\n",
    "import re\n",
    "import shutil\n",
    "from typing import Dict, List, Tuple\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from torch.nn.utils.rnn import pad_sequence\n",
    "from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler\n",
    "from torch.utils.data.distributed import DistributedSampler\n",
    "from tqdm.notebook import tqdm, trange\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "from transformers import (\n",
    "    MODEL_WITH_LM_HEAD_MAPPING,\n",
    "    WEIGHTS_NAME,\n",
    "    AdamW,\n",
    "    AutoConfig,\n",
    "    PreTrainedModel,\n",
    "    PreTrainedTokenizer,\n",
    "    get_linear_schedule_with_warmup,\n",
    ")\n",
    "\n",
    "\n",
    "try:\n",
    "    from torch.utils.tensorboard import SummaryWriter\n",
    "except ImportError:\n",
    "    from tensorboardX import SummaryWriter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BmrbGB8aUmBm"
   },
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-18T05:27:13.388169Z",
     "iopub.status.busy": "2022-10-18T05:27:13.387823Z",
     "iopub.status.idle": "2022-10-18T05:27:13.451208Z",
     "shell.execute_reply": "2022-10-18T05:27:13.450527Z",
     "shell.execute_reply.started": "2022-10-18T05:27:13.388152Z"
    },
    "id": "RXdJTSVwWGHj",
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "execution": {
     "iopub.execute_input": "2022-10-18T05:27:13.452257Z",
     "iopub.status.busy": "2022-10-18T05:27:13.451996Z",
     "iopub.status.idle": "2022-10-18T05:27:13.465447Z",
     "shell.execute_reply": "2022-10-18T05:27:13.464448Z",
     "shell.execute_reply.started": "2022-10-18T05:27:13.452239Z"
    },
    "id": "h6kGx-9eG7qA",
    "outputId": "bd2efe43-1e50-4716-81a2-bf15a3dd03bd",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>line</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14152</th>\n",
       "      <td>Cartman</td>\n",
       "      <td>I...  told your mom you got an F on that socia...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10447</th>\n",
       "      <td>Kyle</td>\n",
       "      <td>It's that ring. Somehow, putting on that ring ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65079</th>\n",
       "      <td>Kyle</td>\n",
       "      <td>Hey Look!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14862</th>\n",
       "      <td>Benedict XVI</td>\n",
       "      <td>Tom, Tom! The gingers are claiming they have M...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6060</th>\n",
       "      <td>Squirrelly Squirrel</td>\n",
       "      <td>Now come on y'all. We can't waste time arguing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63693</th>\n",
       "      <td>Cartman</td>\n",
       "      <td>How could you be so stupid!</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      name                                               line\n",
       "14152              Cartman  I...  told your mom you got an F on that socia...\n",
       "10447                 Kyle  It's that ring. Somehow, putting on that ring ...\n",
       "65079                 Kyle                                          Hey Look!\n",
       "14862         Benedict XVI  Tom, Tom! The gingers are claiming they have M...\n",
       "6060   Squirrelly Squirrel  Now come on y'all. We can't waste time arguing...\n",
       "63693              Cartman                        How could you be so stupid!"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sample(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-18T05:27:13.466439Z",
     "iopub.status.busy": "2022-10-18T05:27:13.466236Z",
     "iopub.status.idle": "2022-10-18T05:27:13.469713Z",
     "shell.execute_reply": "2022-10-18T05:27:13.468950Z",
     "shell.execute_reply.started": "2022-10-18T05:27:13.466421Z"
    },
    "id": "PG8v6--qWUwj",
    "tags": []
   },
   "outputs": [],
   "source": [
    "CHARACTER_NAME = 'Cartman'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-18T05:27:13.470543Z",
     "iopub.status.busy": "2022-10-18T05:27:13.470349Z",
     "iopub.status.idle": "2022-10-18T05:27:13.643993Z",
     "shell.execute_reply": "2022-10-18T05:27:13.643224Z",
     "shell.execute_reply.started": "2022-10-18T05:27:13.470528Z"
    },
    "id": "GZUcEMd2WLDT",
    "tags": []
   },
   "outputs": [],
   "source": [
    "contexted = []\n",
    "\n",
    "# context window of size 7\n",
    "n = 1\n",
    "\n",
    "for i in data[data.name == CHARACTER_NAME].index:\n",
    "    if i < n:\n",
    "        continue\n",
    "    row = []\n",
    "    prev = i - 1 - n # we additionally substract 1, so row will contain current response and 7 previous responses  \n",
    "    for j in range(i, prev, -1):\n",
    "        row.append(data.line[j])\n",
    "        contexted.append(row)\n",
    "\n",
    "columns = ['response', 'context'] \n",
    "columns = columns + ['context/' + str(i) for i in range(n - 1)]\n",
    "\n",
    "df = pd.DataFrame.from_records(contexted, columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 446
    },
    "execution": {
     "iopub.execute_input": "2022-10-18T05:27:13.644878Z",
     "iopub.status.busy": "2022-10-18T05:27:13.644713Z",
     "iopub.status.idle": "2022-10-18T05:27:13.652020Z",
     "shell.execute_reply": "2022-10-18T05:27:13.651291Z",
     "shell.execute_reply.started": "2022-10-18T05:27:13.644862Z"
    },
    "id": "4T5OlNZHUxij",
    "outputId": "895603a6-ca02-4301-c4b0-5bccbee8a3b8",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>response</th>\n",
       "      <th>context</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13726</th>\n",
       "      <td>Yeah, we're gonna use him to help raise money ...</td>\n",
       "      <td>Yahahah!!!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6269</th>\n",
       "      <td>My password is uloveboobs!</td>\n",
       "      <td>How did you know that?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2703</th>\n",
       "      <td>God, you are such a pussy, Stan! You're such a...</td>\n",
       "      <td>Dude, there's nothing I can do about it.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5788</th>\n",
       "      <td>Wow, that makes sense. Don't think anyone can ...</td>\n",
       "      <td>It's a complicated political issue, my son. An...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9795</th>\n",
       "      <td>Ugh. Uunnh.  Ey, give me that!  Ahhhh...</td>\n",
       "      <td>It'll make your itches go away.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5086</th>\n",
       "      <td>Heh! You're crazy! It can't be done!</td>\n",
       "      <td>Alright Eric, here's the deal: This school can...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                response  \\\n",
       "13726  Yeah, we're gonna use him to help raise money ...   \n",
       "6269                          My password is uloveboobs!   \n",
       "2703   God, you are such a pussy, Stan! You're such a...   \n",
       "5788   Wow, that makes sense. Don't think anyone can ...   \n",
       "9795            Ugh. Uunnh.  Ey, give me that!  Ahhhh...   \n",
       "5086                Heh! You're crazy! It can't be done!   \n",
       "\n",
       "                                                 context  \n",
       "13726                                         Yahahah!!!  \n",
       "6269                              How did you know that?  \n",
       "2703            Dude, there's nothing I can do about it.  \n",
       "5788   It's a complicated political issue, my son. An...  \n",
       "9795                     It'll make your itches go away.  \n",
       "5086   Alright Eric, here's the deal: This school can...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 380
    },
    "execution": {
     "iopub.execute_input": "2022-10-18T05:27:13.654567Z",
     "iopub.status.busy": "2022-10-18T05:27:13.653983Z",
     "iopub.status.idle": "2022-10-18T05:27:13.669974Z",
     "shell.execute_reply": "2022-10-18T05:27:13.669272Z",
     "shell.execute_reply.started": "2022-10-18T05:27:13.654539Z"
    },
    "id": "NGy0MxMQVIAP",
    "outputId": "08b7f0eb-6a38-4b83-efdc-e53778d7547a",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>response</th>\n",
       "      <th>context</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8751</th>\n",
       "      <td>Gimme that cake!</td>\n",
       "      <td>Who the hell are you?!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10518</th>\n",
       "      <td>Aww, screw you guys anyway!</td>\n",
       "      <td>Cartman, will you shut the hell up and get som...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7483</th>\n",
       "      <td>You wanna take it out for a spin?</td>\n",
       "      <td>It can fly like a quarter mile away from whoev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16857</th>\n",
       "      <td>Well, I guess we can go back to playing laundr...</td>\n",
       "      <td>Son of a bitch stupid FBI!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6644</th>\n",
       "      <td>Hey Token? How are you doing?</td>\n",
       "      <td>No! Nonono!  Nooo!</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                response  \\\n",
       "8751                                    Gimme that cake!   \n",
       "10518                        Aww, screw you guys anyway!   \n",
       "7483                   You wanna take it out for a spin?   \n",
       "16857  Well, I guess we can go back to playing laundr...   \n",
       "6644                       Hey Token? How are you doing?   \n",
       "\n",
       "                                                 context  \n",
       "8751                              Who the hell are you?!  \n",
       "10518  Cartman, will you shut the hell up and get som...  \n",
       "7483   It can fly like a quarter mile away from whoev...  \n",
       "16857                         Son of a bitch stupid FBI!  \n",
       "6644                                  No! Nonono!  Nooo!  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trn_df, val_df = train_test_split(df, test_size=0.1)\n",
    "trn_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-18T05:27:13.671526Z",
     "iopub.status.busy": "2022-10-18T05:27:13.671152Z",
     "iopub.status.idle": "2022-10-18T05:27:13.682715Z",
     "shell.execute_reply": "2022-10-18T05:27:13.681858Z",
     "shell.execute_reply.started": "2022-10-18T05:27:13.671496Z"
    },
    "id": "aEeJQlAKWtiJ",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# create dataset suitable for our model\n",
    "def construct_conv(row, tokenizer, eos = True):\n",
    "    flatten = lambda l: [item for sublist in l for item in sublist]\n",
    "    conv = list(reversed([tokenizer.encode(x) + [tokenizer.eos_token_id] for x in row]))\n",
    "    conv = flatten(conv)\n",
    "    return conv\n",
    "\n",
    "class ConversationDataset(Dataset):\n",
    "    def __init__(self, tokenizer: PreTrainedTokenizer, args, df, block_size=512):\n",
    "\n",
    "        block_size = block_size - (tokenizer.model_max_length - tokenizer.max_len_single_sentence)\n",
    "\n",
    "        directory = args.cache_dir\n",
    "        cached_features_file = os.path.join(\n",
    "            directory, args.model_type + \"_cached_lm_\" + str(block_size)\n",
    "        )\n",
    "\n",
    "        if os.path.exists(cached_features_file) and not args.overwrite_cache:\n",
    "            logger.info(\"Loading features from cached file %s\", cached_features_file)\n",
    "            with open(cached_features_file, \"rb\") as handle:\n",
    "                self.examples = pickle.load(handle)\n",
    "        else:\n",
    "            logger.info(\"Creating features from dataset file at %s\", directory)\n",
    "\n",
    "            self.examples = []\n",
    "            for _, row in df.iterrows():\n",
    "                conv = construct_conv(row, tokenizer)\n",
    "                self.examples.append(conv)\n",
    "\n",
    "            logger.info(\"Saving features into cached file %s\", cached_features_file)\n",
    "            with open(cached_features_file, \"wb\") as handle:\n",
    "                pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.examples)\n",
    "\n",
    "    def __getitem__(self, item):\n",
    "        return torch.tensor(self.examples[item], dtype=torch.long)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-18T05:27:13.684175Z",
     "iopub.status.busy": "2022-10-18T05:27:13.683859Z",
     "iopub.status.idle": "2022-10-18T05:27:13.693632Z",
     "shell.execute_reply": "2022-10-18T05:27:13.692937Z",
     "shell.execute_reply.started": "2022-10-18T05:27:13.684147Z"
    },
    "id": "-3iHwoKlWyrs",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Caching and storing of data/checkpoints\n",
    "\n",
    "def load_and_cache_examples(args, tokenizer, df_trn, df_val, evaluate=False):\n",
    "    return ConversationDataset(tokenizer, args, df_val if evaluate else df_trn)\n",
    "\n",
    "\n",
    "def set_seed(args):\n",
    "    random.seed(args.seed)\n",
    "    np.random.seed(args.seed)\n",
    "    torch.manual_seed(args.seed)\n",
    "    if args.n_gpu > 0:\n",
    "        torch.cuda.manual_seed_all(args.seed)\n",
    "\n",
    "\n",
    "def _sorted_checkpoints(args, checkpoint_prefix=\"checkpoint\", use_mtime=False) -> List[str]:\n",
    "    ordering_and_checkpoint_path = []\n",
    "\n",
    "    glob_checkpoints = glob.glob(os.path.join(args.output_dir, \"{}-*\".format(checkpoint_prefix)))\n",
    "\n",
    "    for path in glob_checkpoints:\n",
    "        if use_mtime:\n",
    "            ordering_and_checkpoint_path.append((os.path.getmtime(path), path))\n",
    "        else:\n",
    "            regex_match = re.match(\".*{}-([0-9]+)\".format(checkpoint_prefix), path)\n",
    "            if regex_match and regex_match.groups():\n",
    "                ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))\n",
    "\n",
    "    checkpoints_sorted = sorted(ordering_and_checkpoint_path)\n",
    "    checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]\n",
    "    return checkpoints_sorted\n",
    "\n",
    "\n",
    "def _rotate_checkpoints(args, checkpoint_prefix=\"checkpoint\", use_mtime=False) -> None:\n",
    "    if not args.save_total_limit:\n",
    "        return\n",
    "    if args.save_total_limit <= 0:\n",
    "        return\n",
    "\n",
    "    # Check if we should delete older checkpoint(s)\n",
    "    checkpoints_sorted = _sorted_checkpoints(args, checkpoint_prefix, use_mtime)\n",
    "    if len(checkpoints_sorted) <= args.save_total_limit:\n",
    "        return\n",
    "\n",
    "    number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - args.save_total_limit)\n",
    "    checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]\n",
    "    for checkpoint in checkpoints_to_be_deleted:\n",
    "        logger.info(\"Deleting older checkpoint [{}] due to args.save_total_limit\".format(checkpoint))\n",
    "        shutil.rmtree(checkpoint)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EEDdTJTqUwZJ"
   },
   "source": [
    "## Build Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-10-18T05:27:13.694533Z",
     "iopub.status.busy": "2022-10-18T05:27:13.694353Z",
     "iopub.status.idle": "2022-10-18T05:27:21.231192Z",
     "shell.execute_reply": "2022-10-18T05:27:21.230658Z",
     "shell.execute_reply.started": "2022-10-18T05:27:13.694517Z"
    },
    "id": "r2cE0fY5UHpz",
    "outputId": "e4f382cd-57d9-49b7-9da4-4b44fe57df5b",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import AutoModelWithLMHead, AutoModelForCausalLM, AutoTokenizer\n",
    "import torch\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/DialoGPT-large\")\n",
    "model = AutoModelForCausalLM.from_pretrained(\"microsoft/DialoGPT-large\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-18T05:27:21.232063Z",
     "iopub.status.busy": "2022-10-18T05:27:21.231898Z",
     "iopub.status.idle": "2022-10-18T05:27:21.259427Z",
     "shell.execute_reply": "2022-10-18T05:27:21.258757Z",
     "shell.execute_reply.started": "2022-10-18T05:27:21.232048Z"
    },
    "id": "ra2vsRp-UMXo",
    "tags": []
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, BERT, RoBERTa).\n",
    "GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned\n",
    "using a masked language modeling (MLM) loss.\n",
    "\"\"\"\n",
    "\n",
    "# Configs\n",
    "logger = logging.getLogger(__name__)\n",
    "\n",
    "MODEL_CONFIG_CLASSES = list(MODEL_WITH_LM_HEAD_MAPPING.keys())\n",
    "MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-18T05:27:21.260380Z",
     "iopub.status.busy": "2022-10-18T05:27:21.260189Z",
     "iopub.status.idle": "2022-10-18T05:27:21.265817Z",
     "shell.execute_reply": "2022-10-18T05:27:21.265203Z",
     "shell.execute_reply.started": "2022-10-18T05:27:21.260362Z"
    },
    "id": "2OnASqJjUNJa",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Args to allow for easy conversion of python script to notebook\n",
    "class Args():\n",
    "    def __init__(self):\n",
    "        self.output_dir = 'models/output-medium-oneline'\n",
    "        self.model_type = 'gpt2'\n",
    "        self.model_name_or_path = 'microsoft/DialoGPT-medium'\n",
    "        self.config_name = 'microsoft/DialoGPT-medium'\n",
    "        self.tokenizer_name = 'microsoft/DialoGPT-medium'\n",
    "        self.cache_dir = 'cached'\n",
    "        self.block_size = 512\n",
    "        self.do_train = True\n",
    "        self.do_eval = True\n",
    "        self.evaluate_during_training = False\n",
    "        self.per_gpu_train_batch_size = 4\n",
    "        self.per_gpu_eval_batch_size = 4\n",
    "        self.gradient_accumulation_steps = 1\n",
    "        self.learning_rate = 5e-5\n",
    "        self.weight_decay = 0.0\n",
    "        self.adam_epsilon = 1e-8\n",
    "        self.max_grad_norm = 1.0\n",
    "        self.num_train_epochs = 4\n",
    "        self.max_steps = -1\n",
    "        self.warmup_steps = 0\n",
    "        self.logging_steps = 1000\n",
    "        self.save_steps = 3500\n",
    "        self.save_total_limit = None\n",
    "        self.eval_all_checkpoints = False\n",
    "        self.no_cuda = False\n",
    "        self.overwrite_output_dir = True\n",
    "        self.overwrite_cache = True\n",
    "        self.should_continue = False\n",
    "        self.seed = 42\n",
    "        self.local_rank = -1\n",
    "        self.fp16 = False\n",
    "        self.fp16_opt_level = 'O1'\n",
    "\n",
    "args = Args()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9Q1dTFXxW9NE"
   },
   "source": [
    "## Train and Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-18T05:27:21.266896Z",
     "iopub.status.busy": "2022-10-18T05:27:21.266734Z",
     "iopub.status.idle": "2022-10-18T05:27:21.302284Z",
     "shell.execute_reply": "2022-10-18T05:27:21.301566Z",
     "shell.execute_reply.started": "2022-10-18T05:27:21.266881Z"
    },
    "id": "PaarIDZrW81h",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def train(args, train_dataset, model: PreTrainedModel, tokenizer: PreTrainedTokenizer) -> Tuple[int, float]:\n",
    "    \"\"\" Train the model \"\"\"\n",
    "    if args.local_rank in [-1, 0]:\n",
    "        tb_writer = SummaryWriter()\n",
    "\n",
    "    args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)\n",
    "\n",
    "    def collate(examples: List[torch.Tensor]):\n",
    "        if tokenizer._pad_token is None:\n",
    "            return pad_sequence(examples, batch_first=True)\n",
    "        return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)\n",
    "\n",
    "    train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)\n",
    "    train_dataloader = DataLoader(\n",
    "        train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, collate_fn=collate, drop_last = True\n",
    "    )\n",
    "\n",
    "    if args.max_steps > 0:\n",
    "        t_total = args.max_steps\n",
    "        args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1\n",
    "    else:\n",
    "        t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs\n",
    "\n",
    "    model = model.module if hasattr(model, \"module\") else model  # Take care of distributed/parallel training\n",
    "    model.resize_token_embeddings(len(tokenizer))\n",
    "    # add_special_tokens_(model, tokenizer)\n",
    "\n",
    "\n",
    "    # Prepare optimizer and schedule (linear warmup and decay)\n",
    "    no_decay = [\"bias\", \"LayerNorm.weight\"]\n",
    "    optimizer_grouped_parameters = [\n",
    "        {\n",
    "            \"params\": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],\n",
    "            \"weight_decay\": args.weight_decay,\n",
    "        },\n",
    "        {\"params\": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], \"weight_decay\": 0.0},\n",
    "    ]\n",
    "    optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)\n",
    "    scheduler = get_linear_schedule_with_warmup(\n",
    "        optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total\n",
    "    )\n",
    "\n",
    "    # Check if saved optimizer or scheduler states exist\n",
    "    if (\n",
    "        args.model_name_or_path\n",
    "        and os.path.isfile(os.path.join(args.model_name_or_path, \"optimizer.pt\"))\n",
    "        and os.path.isfile(os.path.join(args.model_name_or_path, \"scheduler.pt\"))\n",
    "    ):\n",
    "        # Load in optimizer and scheduler states\n",
    "        optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, \"optimizer.pt\")))\n",
    "        scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, \"scheduler.pt\")))\n",
    "\n",
    "    if args.fp16:\n",
    "        try:\n",
    "            from apex import amp\n",
    "        except ImportError:\n",
    "            raise ImportError(\"Please install apex from https://www.github.com/nvidia/apex to use fp16 training.\")\n",
    "        model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)\n",
    "\n",
    "    # multi-gpu training (should be after apex fp16 initialization)\n",
    "    if args.n_gpu > 1:\n",
    "        model = torch.nn.DataParallel(model)\n",
    "\n",
    "    # Distributed training (should be after apex fp16 initialization)\n",
    "    if args.local_rank != -1:\n",
    "        model = torch.nn.parallel.DistributedDataParallel(\n",
    "            model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True\n",
    "        )\n",
    "\n",
    "    # Train!\n",
    "    logger.info(\"***** Running training *****\")\n",
    "    logger.info(\"  Num examples = %d\", len(train_dataset))\n",
    "    logger.info(\"  Num Epochs = %d\", args.num_train_epochs)\n",
    "    logger.info(\"  Instantaneous batch size per GPU = %d\", args.per_gpu_train_batch_size)\n",
    "    logger.info(\n",
    "        \"  Total train batch size (w. parallel, distributed & accumulation) = %d\",\n",
    "        args.train_batch_size\n",
    "        * args.gradient_accumulation_steps\n",
    "        * (torch.distributed.get_world_size() if args.local_rank != -1 else 1),\n",
    "    )\n",
    "    logger.info(\"  Gradient Accumulation steps = %d\", args.gradient_accumulation_steps)\n",
    "    logger.info(\"  Total optimization steps = %d\", t_total)\n",
    "\n",
    "    global_step = 0\n",
    "    epochs_trained = 0\n",
    "    steps_trained_in_current_epoch = 0\n",
    "    # Check if continuing training from a checkpoint\n",
    "    if args.model_name_or_path and os.path.exists(args.model_name_or_path):\n",
    "        try:\n",
    "            # set global_step to gobal_step of last saved checkpoint from model path\n",
    "            checkpoint_suffix = args.model_name_or_path.split(\"-\")[-1].split(\"/\")[0]\n",
    "            global_step = int(checkpoint_suffix)\n",
    "            epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)\n",
    "            steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)\n",
    "\n",
    "            logger.info(\"  Continuing training from checkpoint, will skip to saved global_step\")\n",
    "            logger.info(\"  Continuing training from epoch %d\", epochs_trained)\n",
    "            logger.info(\"  Continuing training from global step %d\", global_step)\n",
    "            logger.info(\"  Will skip the first %d steps in the first epoch\", steps_trained_in_current_epoch)\n",
    "        except ValueError:\n",
    "            logger.info(\"  Starting fine-tuning.\")\n",
    "\n",
    "    tr_loss, logging_loss = 0.0, 0.0\n",
    "\n",
    "    model.zero_grad()\n",
    "    train_iterator = trange(\n",
    "        epochs_trained, int(args.num_train_epochs), desc=\"Epoch\", disable=args.local_rank not in [-1, 0]\n",
    "    )\n",
    "    set_seed(args)  # Added here for reproducibility\n",
    "    for _ in train_iterator:\n",
    "        epoch_iterator = tqdm(train_dataloader, desc=\"Iteration\", disable=args.local_rank not in [-1, 0])\n",
    "        for step, batch in enumerate(epoch_iterator):\n",
    "\n",
    "            # Skip past any already trained steps if resuming training\n",
    "            if steps_trained_in_current_epoch > 0:\n",
    "                steps_trained_in_current_epoch -= 1\n",
    "                continue\n",
    "\n",
    "            inputs, labels = (batch, batch)\n",
    "            if inputs.shape[1] > 1024: continue\n",
    "            inputs = inputs.to(args.device)\n",
    "            labels = labels.to(args.device)\n",
    "            model.train()\n",
    "            outputs = model(inputs, labels=labels)\n",
    "            loss = outputs[0]  # model outputs are always tuple in transformers (see doc)\n",
    "\n",
    "            if args.n_gpu > 1:\n",
    "                loss = loss.mean()  # mean() to average on multi-gpu parallel training\n",
    "            if args.gradient_accumulation_steps > 1:\n",
    "                loss = loss / args.gradient_accumulation_steps\n",
    "\n",
    "            if args.fp16:\n",
    "                with amp.scale_loss(loss, optimizer) as scaled_loss:\n",
    "                    scaled_loss.backward()\n",
    "            else:\n",
    "                loss.backward()\n",
    "\n",
    "            tr_loss += loss.item()\n",
    "            if (step + 1) % args.gradient_accumulation_steps == 0:\n",
    "                if args.fp16:\n",
    "                    torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)\n",
    "                else:\n",
    "                    torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)\n",
    "                optimizer.step()\n",
    "                scheduler.step()  # Update learning rate schedule\n",
    "                model.zero_grad()\n",
    "                global_step += 1\n",
    "\n",
    "                if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:\n",
    "                    # Log metrics\n",
    "                    if (\n",
    "                        args.local_rank == -1 and args.evaluate_during_training\n",
    "                    ):  # Only evaluate when single GPU otherwise metrics may not average well\n",
    "                        results = evaluate(args, model, tokenizer)\n",
    "                        for key, value in results.items():\n",
    "                            tb_writer.add_scalar(\"eval_{}\".format(key), value, global_step)\n",
    "                    tb_writer.add_scalar(\"lr\", scheduler.get_lr()[0], global_step)\n",
    "                    tb_writer.add_scalar(\"loss\", (tr_loss - logging_loss) / args.logging_steps, global_step)\n",
    "                    logging_loss = tr_loss\n",
    "\n",
    "                if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:\n",
    "                    checkpoint_prefix = \"checkpoint\"\n",
    "                    # Save model checkpoint\n",
    "                    output_dir = os.path.join(args.output_dir, \"{}-{}\".format(checkpoint_prefix, global_step))\n",
    "                    os.makedirs(output_dir, exist_ok=True)\n",
    "                    model_to_save = (\n",
    "                        model.module if hasattr(model, \"module\") else model\n",
    "                    )  # Take care of distributed/parallel training\n",
    "                    model_to_save.save_pretrained(output_dir)\n",
    "                    tokenizer.save_pretrained(output_dir)\n",
    "\n",
    "                    torch.save(args, os.path.join(output_dir, \"training_args.bin\"))\n",
    "                    logger.info(\"Saving model checkpoint to %s\", output_dir)\n",
    "\n",
    "                    _rotate_checkpoints(args, checkpoint_prefix)\n",
    "\n",
    "                    torch.save(optimizer.state_dict(), os.path.join(output_dir, \"optimizer.pt\"))\n",
    "                    torch.save(scheduler.state_dict(), os.path.join(output_dir, \"scheduler.pt\"))\n",
    "                    logger.info(\"Saving optimizer and scheduler states to %s\", output_dir)\n",
    "\n",
    "            if args.max_steps > 0 and global_step > args.max_steps:\n",
    "                epoch_iterator.close()\n",
    "                break\n",
    "        if args.max_steps > 0 and global_step > args.max_steps:\n",
    "            train_iterator.close()\n",
    "            break\n",
    "\n",
    "    if args.local_rank in [-1, 0]:\n",
    "        tb_writer.close()\n",
    "\n",
    "    return global_step, tr_loss / global_step\n",
    "\n",
    "# Evaluation of some model\n",
    "\n",
    "def evaluate(args, model: PreTrainedModel, tokenizer: PreTrainedTokenizer, df_trn, df_val, prefix=\"\") -> Dict:\n",
    "    # Loop to handle MNLI double evaluation (matched, mis-matched)\n",
    "    eval_output_dir = args.output_dir\n",
    "\n",
    "    eval_dataset = load_and_cache_examples(args, tokenizer, df_trn, df_val, evaluate=True)\n",
    "    os.makedirs(eval_output_dir, exist_ok=True)\n",
    "    args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)\n",
    "    # Note that DistributedSampler samples randomly\n",
    "\n",
    "    def collate(examples: List[torch.Tensor]):\n",
    "        if tokenizer._pad_token is None:\n",
    "            return pad_sequence(examples, batch_first=True)\n",
    "        return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)\n",
    "\n",
    "    eval_sampler = SequentialSampler(eval_dataset)\n",
    "    eval_dataloader = DataLoader(\n",
    "        eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=collate, drop_last = True\n",
    "    )\n",
    "\n",
    "    # multi-gpu evaluate\n",
    "    if args.n_gpu > 1:\n",
    "        model = torch.nn.DataParallel(model)\n",
    "\n",
    "    # Eval!\n",
    "    logger.info(\"***** Running evaluation {} *****\".format(prefix))\n",
    "    logger.info(\"  Num examples = %d\", len(eval_dataset))\n",
    "    logger.info(\"  Batch size = %d\", args.eval_batch_size)\n",
    "    eval_loss = 0.0\n",
    "    nb_eval_steps = 0\n",
    "    model.eval()\n",
    "\n",
    "    for batch in tqdm(eval_dataloader, desc=\"Evaluating\"):\n",
    "        inputs, labels = (batch, batch)\n",
    "        inputs = inputs.to(args.device)\n",
    "        labels = labels.to(args.device)\n",
    "\n",
    "        with torch.no_grad():\n",
    "            outputs = model(inputs, labels=labels)\n",
    "            lm_loss = outputs[0]\n",
    "            eval_loss += lm_loss.mean().item()\n",
    "        nb_eval_steps += 1\n",
    "\n",
    "    eval_loss = eval_loss / nb_eval_steps\n",
    "    perplexity = torch.exp(torch.tensor(eval_loss))\n",
    "\n",
    "    result = {\"perplexity\": perplexity}\n",
    "\n",
    "    output_eval_file = os.path.join(eval_output_dir, prefix, \"eval_results.txt\")\n",
    "    with open(output_eval_file, \"w\") as writer:\n",
    "        logger.info(\"***** Eval results {} *****\".format(prefix))\n",
    "        for key in sorted(result.keys()):\n",
    "            logger.info(\"  %s = %s\", key, str(result[key]))\n",
    "            writer.write(\"%s = %s\\n\" % (key, str(result[key])))\n",
    "\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-18T05:27:21.303584Z",
     "iopub.status.busy": "2022-10-18T05:27:21.303400Z",
     "iopub.status.idle": "2022-10-18T05:27:21.314912Z",
     "shell.execute_reply": "2022-10-18T05:27:21.314269Z",
     "shell.execute_reply.started": "2022-10-18T05:27:21.303568Z"
    },
    "id": "SCnGAJWbXD9C",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Main runner\n",
    "\n",
    "def main(df_trn, df_val):\n",
    "    args = Args()\n",
    "    \n",
    "    if args.should_continue:\n",
    "        sorted_checkpoints = _sorted_checkpoints(args)\n",
    "        if len(sorted_checkpoints) == 0:\n",
    "            raise ValueError(\"Used --should_continue but no checkpoint was found in --output_dir.\")\n",
    "        else:\n",
    "            args.model_name_or_path = sorted_checkpoints[-1]\n",
    "\n",
    "    if (\n",
    "        os.path.exists(args.output_dir)\n",
    "        and os.listdir(args.output_dir)\n",
    "        and args.do_train\n",
    "        and not args.overwrite_output_dir\n",
    "        and not args.should_continue\n",
    "    ):\n",
    "        raise ValueError(\n",
    "            \"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.\".format(\n",
    "                args.output_dir\n",
    "            )\n",
    "        )\n",
    "\n",
    "    # Setup CUDA, GPU & distributed training\n",
    "    device = torch.device(\"cuda\")\n",
    "    args.n_gpu = torch.cuda.device_count()\n",
    "    args.device = device\n",
    "\n",
    "    # Setup logging\n",
    "    logging.basicConfig(\n",
    "        format=\"%(asctime)s - %(levelname)s - %(name)s -   %(message)s\",\n",
    "        datefmt=\"%m/%d/%Y %H:%M:%S\",\n",
    "        level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,\n",
    "    )\n",
    "    logger.warning(\n",
    "        \"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s\",\n",
    "        args.local_rank,\n",
    "        device,\n",
    "        args.n_gpu,\n",
    "        bool(args.local_rank != -1),\n",
    "        args.fp16,\n",
    "    )\n",
    "\n",
    "    # Set seed\n",
    "    set_seed(args)\n",
    "\n",
    "    config = AutoConfig.from_pretrained(args.config_name, cache_dir=args.cache_dir)\n",
    "    tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, cache_dir=args.cache_dir)\n",
    "    model = AutoModelForCausalLM.from_pretrained(\n",
    "        args.model_name_or_path,\n",
    "        from_tf=False,\n",
    "        config=config,\n",
    "        cache_dir=args.cache_dir,\n",
    "    )\n",
    "    model.to(args.device)\n",
    "    \n",
    "    logger.info(\"Training/evaluation parameters %s\", args)\n",
    "\n",
    "    # Training\n",
    "    if args.do_train:\n",
    "        train_dataset = load_and_cache_examples(args, tokenizer, df_trn, df_val, evaluate=False)\n",
    "\n",
    "        global_step, tr_loss = train(args, train_dataset, model, tokenizer)\n",
    "        logger.info(\" global_step = %s, average loss = %s\", global_step, tr_loss)\n",
    "\n",
    "    # Saving best-practices: if you use save_pretrained for the model and tokenizer, you can reload them using from_pretrained()\n",
    "    if args.do_train:\n",
    "        # Create output directory if needed\n",
    "        os.makedirs(args.output_dir, exist_ok=True)\n",
    "\n",
    "        logger.info(\"Saving model checkpoint to %s\", args.output_dir)\n",
    "        # Save a trained model, configuration and tokenizer using `save_pretrained()`.\n",
    "        # They can then be reloaded using `from_pretrained()`\n",
    "        model_to_save = (\n",
    "            model.module if hasattr(model, \"module\") else model\n",
    "        )  # Take care of distributed/parallel training\n",
    "        model_to_save.save_pretrained(args.output_dir)\n",
    "        tokenizer.save_pretrained(args.output_dir)\n",
    "\n",
    "        # Good practice: save your training arguments together with the trained model\n",
    "        torch.save(args, os.path.join(args.output_dir, \"training_args.bin\"))\n",
    "\n",
    "        # Load a trained model and vocabulary that you have fine-tuned\n",
    "        model = AutoModelForCausalLM.from_pretrained(args.output_dir)\n",
    "        tokenizer = AutoTokenizer.from_pretrained(args.output_dir)\n",
    "        model.to(args.device)\n",
    "\n",
    "    # Evaluation\n",
    "    results = {}\n",
    "    if args.do_eval and args.local_rank in [-1, 0]:\n",
    "        checkpoints = [args.output_dir]\n",
    "        if args.eval_all_checkpoints:\n",
    "            checkpoints = list(\n",
    "                os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + \"/**/\" + WEIGHTS_NAME, recursive=True))\n",
    "            )\n",
    "            logging.getLogger(\"transformers.modeling_utils\").setLevel(logging.WARN)  # Reduce logging\n",
    "        logger.info(\"Evaluate the following checkpoints: %s\", checkpoints)\n",
    "        for checkpoint in checkpoints:\n",
    "            global_step = checkpoint.split(\"-\")[-1] if len(checkpoints) > 1 else \"\"\n",
    "            prefix = checkpoint.split(\"/\")[-1] if checkpoint.find(\"checkpoint\") != -1 else \"\"\n",
    "\n",
    "            model = AutoModelForCausalLM.from_pretrained(checkpoint)\n",
    "            model.to(args.device)\n",
    "            result = evaluate(args, model, tokenizer, df_trn, df_val, prefix=prefix)\n",
    "            result = dict((k + \"_{}\".format(global_step), v) for k, v in result.items())\n",
    "            results.update(result)\n",
    "\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7NWvkdR-XHeB"
   },
   "source": [
    "## Train it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 780,
     "referenced_widgets": [
      "1d7f4c82687540f1ad69eb54ac3c25b4",
      "e7b9f3fc77a24259a87ef0dc735dfecb",
      "f3bf54733c2d4d9daa1cc9a7746ccb14",
      "aa40eb6346b54e7dac98e0b068cd4927",
      "021b771a270f479aa3b9e2b5f17e3d97",
      "450b0e7fd7a347c7beb78b7d72f64385",
      "9391d7abf6ed4400903995f56d7a1260",
      "ea6b919964d24c2f9de1c64c9cefaf23",
      "2fa1fa2407384cb98d79a912de2d5b8f",
      "dc27e2caf1ea4a4ab9ae3708fb06952f",
      "e38fb98fd7b3413392dc39c93a107a35",
      "855ca0a6125a4d698416214a9425ad98",
      "4699416338ae40a5b6abf19e45089aec",
      "43fdb31d3f314624ba07a15718b0c8f3",
      "de252cd193114c40ad5f5e9622b7abc7",
      "5e48b617cc3f41c3945efc28fc5e0c75",
      "68a9dc52819c48fb97259f318f9b5c6a",
      "b4e00059cf3a49929978ed780aae8358",
      "0ff5f4e3506b493a98d72008a467f35f",
      "77b97fa3271b48ac9f93665a102b4fd1",
      "a937f1dfeee5432ba31b3016fd30e9e2",
      "3c6d446f491c48fcae03e0034bfaaae9",
      "a193bb3a0b5b4cbba587e2460075a445",
      "75f8aebc30304fe198b5a2898a53a92d",
      "8b8a7c771d234f6c9d758a1f07f75a90",
      "c6518c4a721745bf97ee682f2ebe4635",
      "29cffa2b4f234e12802344eb53838641",
      "96243b7b227f465f83a289481680b925",
      "8c016a54f0a24fcdacf369baa9d24f1e",
      "7fe5b457ca0f417f90a20d235e9cec07",
      "fdffb26b99c24c978580f1cf97359fea",
      "8e3f1740c82f47949eefc2eb53052eae",
      "9cccd43f6acc4e25b4876fd0ae7a2ad6",
      "175e94deab7f4d20b99b419bea33583b",
      "41f26f7210e540479814e5d68de13ddb",
      "cf5cd281fa3b453093e210650bf81e9e",
      "e1fbe239c2394cbf973ac5b95e1e1491",
      "810ac22adad344b7bf8b556ded990122",
      "8b3a41c1900b45ebb9c56601deca0e84",
      "002f56aac3d64b33a0e799c0baf1e6b9",
      "a0f2a9a279734aa5bf146f0a5b33c43b",
      "850b5411122e4d608511fe26818bea68",
      "0663fb4bd85f4d87a7d61910b995be14",
      "cb7f52610fcf49bda46a14b296ff5bb5",
      "0ca29b4a62e04d9c937189ea19b25de8",
      "f871b83632974e0088bae65e78efaf28",
      "4cacf7fc20754a7ca7fe08c8ec187a81",
      "8bcc625c0f284398bbd287fe45021b17"
     ]
    },
    "id": "e61zo2JtXGNX",
    "outputId": "22d4916e-7169-44b5-f9d8-79b9c43fab2e",
    "tags": []
   },
   "outputs": [],
   "source": [
    "main(trn_df, val_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YRpQ_n2zXQj-"
   },
   "source": [
    "## Test it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.status.busy": "2022-10-18T05:27:32.954246Z",
     "iopub.status.idle": "2022-10-18T05:27:32.954450Z",
     "shell.execute_reply": "2022-10-18T05:27:32.954353Z",
     "shell.execute_reply.started": "2022-10-18T05:27:32.954342Z"
    },
    "id": "HGw3qgfaXQHX",
    "outputId": "93e84cfd-9718-42e5-bd11-418112c91d71",
    "tags": []
   },
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained('microsoft/DialoGPT-large')\n",
    "model = AutoModelForCausalLM.from_pretrained('models/output-medium-oneline')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.status.busy": "2022-10-18T05:27:32.955300Z",
     "iopub.status.idle": "2022-10-18T05:27:32.955497Z",
     "shell.execute_reply": "2022-10-18T05:27:32.955404Z",
     "shell.execute_reply.started": "2022-10-18T05:27:32.955394Z"
    },
    "id": "lAWsiAvNXbxd",
    "outputId": "0fd2541e-ee68-4976-b098-8483efe38d5e",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Let's chat for 4 lines\n",
    "for step in range(4):\n",
    "    # encode the new user input, add the eos_token and return a tensor in Pytorch\n",
    "    new_user_input_ids = tokenizer.encode(input(\">> User:\") + tokenizer.eos_token, return_tensors='pt')\n",
    "    # print(new_user_input_ids)\n",
    "\n",
    "    # append the new user input tokens to the chat history\n",
    "    bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids\n",
    "\n",
    "    # generated a response while limiting the total chat history to 1000 tokens, \n",
    "    chat_history_ids = model.generate(\n",
    "        bot_input_ids, max_length= 200,\n",
    "        pad_token_id=tokenizer.eos_token_id,  \n",
    "        no_repeat_ngram_size=3,       \n",
    "        do_sample=True, \n",
    "        top_k=100, \n",
    "        top_p=0.7,\n",
    "        temperature=0.8\n",
    "    )\n",
    "    \n",
    "    # pretty print last ouput tokens from bot\n",
    "    print(\"Cartman: {}\".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "model_train_upload_workflow.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "002f56aac3d64b33a0e799c0baf1e6b9": {
     "model_module": "@jupyter-widgets/base",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "021b771a270f479aa3b9e2b5f17e3d97": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": "initial"
     }
    },
    "0663fb4bd85f4d87a7d61910b995be14": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "Evaluating: 100%",
      "description_tooltip": null,
      "layout": "IPY_MODEL_f871b83632974e0088bae65e78efaf28",
      "max": 21,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_0ca29b4a62e04d9c937189ea19b25de8",
      "value": 21
     }
    },
    "0ca29b4a62e04d9c937189ea19b25de8": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": "initial"
     }
    },
    "0ff5f4e3506b493a98d72008a467f35f": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "Iteration: 100%",
      "description_tooltip": null,
      "layout": "IPY_MODEL_3c6d446f491c48fcae03e0034bfaaae9",
      "max": 195,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_a937f1dfeee5432ba31b3016fd30e9e2",
      "value": 195
     }
    },
    "175e94deab7f4d20b99b419bea33583b": {
     "model_module": "@jupyter-widgets/base",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "1d7f4c82687540f1ad69eb54ac3c25b4": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_f3bf54733c2d4d9daa1cc9a7746ccb14",
       "IPY_MODEL_aa40eb6346b54e7dac98e0b068cd4927"
      ],
      "layout": "IPY_MODEL_e7b9f3fc77a24259a87ef0dc735dfecb"
     }
    },
    "29cffa2b4f234e12802344eb53838641": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "Iteration: 100%",
      "description_tooltip": null,
      "layout": "IPY_MODEL_7fe5b457ca0f417f90a20d235e9cec07",
      "max": 195,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_8c016a54f0a24fcdacf369baa9d24f1e",
      "value": 195
     }
    },
    "2fa1fa2407384cb98d79a912de2d5b8f": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_e38fb98fd7b3413392dc39c93a107a35",
       "IPY_MODEL_855ca0a6125a4d698416214a9425ad98"
      ],
      "layout": "IPY_MODEL_dc27e2caf1ea4a4ab9ae3708fb06952f"
     }
    },
    "3c6d446f491c48fcae03e0034bfaaae9": {
     "model_module": "@jupyter-widgets/base",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "41f26f7210e540479814e5d68de13ddb": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "Iteration: 100%",
      "description_tooltip": null,
      "layout": "IPY_MODEL_810ac22adad344b7bf8b556ded990122",
      "max": 195,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_e1fbe239c2394cbf973ac5b95e1e1491",
      "value": 195
     }
    },
    "43fdb31d3f314624ba07a15718b0c8f3": {
     "model_module": "@jupyter-widgets/base",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "450b0e7fd7a347c7beb78b7d72f64385": {
     "model_module": "@jupyter-widgets/base",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "4699416338ae40a5b6abf19e45089aec": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": "initial"
     }
    },
    "4cacf7fc20754a7ca7fe08c8ec187a81": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "5e48b617cc3f41c3945efc28fc5e0c75": {
     "model_module": "@jupyter-widgets/base",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "68a9dc52819c48fb97259f318f9b5c6a": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_0ff5f4e3506b493a98d72008a467f35f",
       "IPY_MODEL_77b97fa3271b48ac9f93665a102b4fd1"
      ],
      "layout": "IPY_MODEL_b4e00059cf3a49929978ed780aae8358"
     }
    },
    "75f8aebc30304fe198b5a2898a53a92d": {
     "model_module": "@jupyter-widgets/base",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "77b97fa3271b48ac9f93665a102b4fd1": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_75f8aebc30304fe198b5a2898a53a92d",
      "placeholder": "​",
      "style": "IPY_MODEL_a193bb3a0b5b4cbba587e2460075a445",
      "value": " 195/195 [00:35&lt;00:00,  5.45it/s]"
     }
    },
    "7fe5b457ca0f417f90a20d235e9cec07": {
     "model_module": "@jupyter-widgets/base",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "810ac22adad344b7bf8b556ded990122": {
     "model_module": "@jupyter-widgets/base",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "850b5411122e4d608511fe26818bea68": {
     "model_module": "@jupyter-widgets/base",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "855ca0a6125a4d698416214a9425ad98": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_5e48b617cc3f41c3945efc28fc5e0c75",
      "placeholder": "​",
      "style": "IPY_MODEL_de252cd193114c40ad5f5e9622b7abc7",
      "value": " 195/195 [00:44&lt;00:00,  4.39it/s]"
     }
    },
    "8b3a41c1900b45ebb9c56601deca0e84": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "8b8a7c771d234f6c9d758a1f07f75a90": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_29cffa2b4f234e12802344eb53838641",
       "IPY_MODEL_96243b7b227f465f83a289481680b925"
      ],
      "layout": "IPY_MODEL_c6518c4a721745bf97ee682f2ebe4635"
     }
    },
    "8bcc625c0f284398bbd287fe45021b17": {
     "model_module": "@jupyter-widgets/base",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "8c016a54f0a24fcdacf369baa9d24f1e": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": "initial"
     }
    },
    "8e3f1740c82f47949eefc2eb53052eae": {
     "model_module": "@jupyter-widgets/base",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "9391d7abf6ed4400903995f56d7a1260": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "96243b7b227f465f83a289481680b925": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_8e3f1740c82f47949eefc2eb53052eae",
      "placeholder": "​",
      "style": "IPY_MODEL_fdffb26b99c24c978580f1cf97359fea",
      "value": " 195/195 [01:17&lt;00:00,  2.53it/s]"
     }
    },
    "9cccd43f6acc4e25b4876fd0ae7a2ad6": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_41f26f7210e540479814e5d68de13ddb",
       "IPY_MODEL_cf5cd281fa3b453093e210650bf81e9e"
      ],
      "layout": "IPY_MODEL_175e94deab7f4d20b99b419bea33583b"
     }
    },
    "a0f2a9a279734aa5bf146f0a5b33c43b": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_0663fb4bd85f4d87a7d61910b995be14",
       "IPY_MODEL_cb7f52610fcf49bda46a14b296ff5bb5"
      ],
      "layout": "IPY_MODEL_850b5411122e4d608511fe26818bea68"
     }
    },
    "a193bb3a0b5b4cbba587e2460075a445": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "a937f1dfeee5432ba31b3016fd30e9e2": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": "initial"
     }
    },
    "aa40eb6346b54e7dac98e0b068cd4927": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_ea6b919964d24c2f9de1c64c9cefaf23",
      "placeholder": "​",
      "style": "IPY_MODEL_9391d7abf6ed4400903995f56d7a1260",
      "value": " 4/4 [02:23&lt;00:00, 36.00s/it]"
     }
    },
    "b4e00059cf3a49929978ed780aae8358": {
     "model_module": "@jupyter-widgets/base",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "c6518c4a721745bf97ee682f2ebe4635": {
     "model_module": "@jupyter-widgets/base",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "cb7f52610fcf49bda46a14b296ff5bb5": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_8bcc625c0f284398bbd287fe45021b17",
      "placeholder": "​",
      "style": "IPY_MODEL_4cacf7fc20754a7ca7fe08c8ec187a81",
      "value": " 21/21 [00:01&lt;00:00, 10.78it/s]"
     }
    },
    "cf5cd281fa3b453093e210650bf81e9e": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_002f56aac3d64b33a0e799c0baf1e6b9",
      "placeholder": "​",
      "style": "IPY_MODEL_8b3a41c1900b45ebb9c56601deca0e84",
      "value": " 195/195 [00:40&lt;00:00,  4.84it/s]"
     }
    },
    "dc27e2caf1ea4a4ab9ae3708fb06952f": {
     "model_module": "@jupyter-widgets/base",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "de252cd193114c40ad5f5e9622b7abc7": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "e1fbe239c2394cbf973ac5b95e1e1491": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": "initial"
     }
    },
    "e38fb98fd7b3413392dc39c93a107a35": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "Iteration: 100%",
      "description_tooltip": null,
      "layout": "IPY_MODEL_43fdb31d3f314624ba07a15718b0c8f3",
      "max": 195,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_4699416338ae40a5b6abf19e45089aec",
      "value": 195
     }
    },
    "e7b9f3fc77a24259a87ef0dc735dfecb": {
     "model_module": "@jupyter-widgets/base",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "ea6b919964d24c2f9de1c64c9cefaf23": {
     "model_module": "@jupyter-widgets/base",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "f3bf54733c2d4d9daa1cc9a7746ccb14": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "Epoch: 100%",
      "description_tooltip": null,
      "layout": "IPY_MODEL_450b0e7fd7a347c7beb78b7d72f64385",
      "max": 4,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_021b771a270f479aa3b9e2b5f17e3d97",
      "value": 4
     }
    },
    "f871b83632974e0088bae65e78efaf28": {
     "model_module": "@jupyter-widgets/base",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "fdffb26b99c24c978580f1cf97359fea": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}