GPTRick.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gauravlochab/dailoGPT/blob/main/GPTRick.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4hyTyW7Le2Sb"
      },
      "source": [
        "## First dialogue with DialoGPT"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sgL1yLLS09mP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f3ce3a42-4d74-418f-ded1-02031bb4c1ee"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers==3.0.2"
      ],
      "metadata": {
        "id": "uhnKcnaEvHSI",
        "outputId": "222a34fa-63fe-417d-d23d-195f9ab19c1b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers==3.0.2\n",
            "  Downloading transformers-3.0.2-py3-none-any.whl (769 kB)\n",
            "\u001b[K     |████████████████████████████████| 769 kB 11.6 MB/s \n",
            "\u001b[?25hCollecting sacremoses\n",
            "  Downloading sacremoses-0.0.53.tar.gz (880 kB)\n",
            "\u001b[K     |████████████████████████████████| 880 kB 50.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from transformers==3.0.2) (1.21.6)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers==3.0.2) (2022.6.2)\n",
            "Collecting sentencepiece!=0.1.92\n",
            "  Downloading sentencepiece-0.1.97-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.3 MB 53.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers==3.0.2) (4.64.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers==3.0.2) (3.8.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers==3.0.2) (2.23.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers==3.0.2) (21.3)\n",
            "Collecting tokenizers==0.8.1.rc1\n",
            "  Downloading tokenizers-0.8.1rc1-cp37-cp37m-manylinux1_x86_64.whl (3.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.0 MB 55.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers==3.0.2) (3.0.9)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==3.0.2) (2022.9.24)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==3.0.2) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==3.0.2) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==3.0.2) (3.0.4)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==3.0.2) (1.15.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==3.0.2) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==3.0.2) (1.2.0)\n",
            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.53-py3-none-any.whl size=895259 sha256=911585ca25d2fd1d605b2da43712c88ec5c5e9060ac7a358c1363499e53362bc\n",
            "  Stored in directory: /root/.cache/pip/wheels/87/39/dd/a83eeef36d0bf98e7a4d1933a4ad2d660295a40613079bafc9\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: tokenizers, sentencepiece, sacremoses, transformers\n",
            "  Attempting uninstall: tokenizers\n",
            "    Found existing installation: tokenizers 0.13.2\n",
            "    Uninstalling tokenizers-0.13.2:\n",
            "      Successfully uninstalled tokenizers-0.13.2\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.24.0\n",
            "    Uninstalling transformers-4.24.0:\n",
            "      Successfully uninstalled transformers-4.24.0\n",
            "Successfully installed sacremoses-0.0.53 sentencepiece-0.1.97 tokenizers-0.8.1rc1 transformers-3.0.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d7Y1XcsqgXBQ"
      },
      "source": [
        "Let's move to the desired folder in which we will store all our data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OHNacD0k1HI3"
      },
      "source": [
        "import os\n",
        "os.chdir(\"/content\")"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zVSTlysV2jaM"
      },
      "source": [
        "Try to chat with DialoGPT without fine-tuning."
      ]
    },
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        "id": "w6qrl7_SvPKg",
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            "31910d79b4bd499bb469c3f366622e87",
            "c80cbacb26294ce7a244494690900e72",
            "d667621bedb64541b394aad4d9bce073",
            "45ea16175b724c82ade7a511724f7185",
            "cf64acb98c7547cda569c8002a6c300c",
            "bb1900cd4bae44a19babd69d14c893a5",
            "f667209fdb2149abaaa1e91ec1cff773",
            "d6bcf849f95f4fcbaba9e6c1af400c90",
            "01df70af3422401fbcf63d375443c6c7",
            "5c5b50dce0c742a2ad18c5332efb3172",
            "c4b454a2643046c3b11b5f5b165f161f",
            "938991b5c7cc4aebb58d2219130dee78",
            "fecbdf0a61834a9fa2a99118290d8915",
            "4ed0cd626c9f485094505dad91b9c81b",
            "c8129432d6994edd9b704deef0f939fc"
          ]
        },
        "outputId": "0a4ba036-5cfc-45c5-b4f8-ba994103b551"
      },
      "source": [
        "from transformers import AutoModelWithLMHead, AutoTokenizer\n",
        "import torch\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/DialoGPT-small\")\n",
        "model = AutoModelWithLMHead.from_pretrained(\"microsoft/DialoGPT-small\")"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/641 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9121ac8d86ac4eb4bc87c361c07187c2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e033b62725024ff0b74bbccb35fde401"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cdd8a214bb3c46fb9d716dddec68bec5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/modeling_auto.py:798: FutureWarning: The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use `AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and `AutoModelForSeq2SeqLM` for encoder-decoder models.\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/351M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cf64acb98c7547cda569c8002a6c300c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:transformers.modeling_utils:Some weights of GPT2LMHeadModel were not initialized from the model checkpoint at microsoft/DialoGPT-small and are newly initialized: ['transformer.h.0.attn.masked_bias', 'transformer.h.1.attn.masked_bias', 'transformer.h.2.attn.masked_bias', 'transformer.h.3.attn.masked_bias', 'transformer.h.4.attn.masked_bias', 'transformer.h.5.attn.masked_bias', 'transformer.h.6.attn.masked_bias', 'transformer.h.7.attn.masked_bias', 'transformer.h.8.attn.masked_bias', 'transformer.h.9.attn.masked_bias', 'transformer.h.10.attn.masked_bias', 'transformer.h.11.attn.masked_bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CjZaN5ilgd-z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "903b7768-747f-4c1e-b024-674fafc25e4b"
      },
      "source": [
        "# Let's chat for 5 lines\n",
        "for step in range(5):\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",
        "\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=1000,\n",
        "    pad_token_id=tokenizer.eos_token_id\n",
        "    )\n",
        "\n",
        "    # pretty print last ouput tokens from bot\n",
        "    print(\"DialoGPT: {}\".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">> User:Hi Rick\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DialoGPT: I'm not your buddy, friend.\n",
            ">> User:How are you?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DialoGPT: Hi there!\n",
            ">> User:who is morty?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DialoGPT: Hello there!\n",
            ">> User:rick and morty?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DialoGPT: I'm not your guy, guy.\n",
            ">> User:who are you?/\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DialoGPT: I'm not your buddy, guy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IuzSROqxjUKM"
      },
      "source": [
        "## Model initial configuration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TC3qNlfp30aU"
      },
      "source": [
        "\n",
        "For start, we will need basic configuration and a dataset.\n",
        "Configuration and training scripts are mostly based on this [script](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py) from Huggingface and great [tutorial](https://nathancooper.io/i-am-a-nerd/chatbot/deep-learning/gpt2/2020/05/12/chatbot-part-1.html) from Nathan Cooper."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g91QzdqU2haO"
      },
      "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",
        "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 pandas as pd\n",
        "import numpy as np\n",
        "import torch\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",
        "    AutoModelWithLMHead,\n",
        "    AutoTokenizer,\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\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)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "utprDGf06OVt"
      },
      "source": [
        "# Args to allow for easy convertion of python script to notebook\n",
        "class Args():\n",
        "    def __init__(self):\n",
        "        self.output_dir = 'output-small'\n",
        "        self.model_type = 'gpt2'\n",
        "        self.model_name_or_path = 'microsoft/DialoGPT-small'\n",
        "        self.config_name = 'microsoft/DialoGPT-small'\n",
        "        self.tokenizer_name = 'microsoft/DialoGPT-small'\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 = 3\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()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X_qYqlTe9yx2"
      },
      "source": [
        "## Prepare Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zbB-56mmm-rL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "796efced-6387-4ef8-ccda-35c3e226adf7"
      },
      "source": [
        "!pip install kaggle"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (1.5.12)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.64.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle) (6.1.2)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle) (2022.9.24)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (3.0.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1c6L4u8_neZf"
      },
      "source": [
        "#!mkdir ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/kaggle.json"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r-HczUyTnyzh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cacac8fc-e6ea-4b80-e147-f34012d20026"
      },
      "source": [
        "!kaggle datasets download andradaolteanu/rickmorty-scripts -f RickAndMortyScripts.csv "
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'\n",
            "Downloading RickAndMortyScripts.csv to /content\n",
            "100% 205k/205k [00:00<00:00, 357kB/s]\n",
            "100% 205k/205k [00:00<00:00, 356kB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TxHdyv67dptm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "42555d39-4b47-4b3e-a65d-919b763b8944"
      },
      "source": [
        "# Let's look at original dataset\n",
        "all_rick = pd.read_csv('RickAndMortyScripts.csv')\n",
        "all_rick.head(10)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   index  season no.  episode no. episode name   name  \\\n",
              "0      0           1            1        Pilot   Rick   \n",
              "1      1           1            1        Pilot  Morty   \n",
              "2      2           1            1        Pilot   Rick   \n",
              "3      3           1            1        Pilot  Morty   \n",
              "4      4           1            1        Pilot   Rick   \n",
              "5      5           1            1        Pilot  Morty   \n",
              "6      6           1            1        Pilot   Rick   \n",
              "7      7           1            1        Pilot   Rick   \n",
              "8      8           1            1        Pilot  Morty   \n",
              "9      9           1            1        Pilot   Rick   \n",
              "\n",
              "                                                line  \n",
              "0  Morty! You gotta come on. Jus'... you gotta co...  \n",
              "1                       What, Rick? What’s going on?  \n",
              "2                   I got a surprise for you, Morty.  \n",
              "3  It's the middle of the night. What are you tal...  \n",
              "4  Come on, I got a surprise for you.  Come on, h...  \n",
              "5                Ow! Ow! You're tugging me too hard!  \n",
              "6  We gotta go, gotta get outta here, come on. Go...  \n",
              "7  What do you think of this... flying vehicle, M...  \n",
              "8  Yeah, Rick... I-it's great. Is this the surprise?  \n",
              "9  Morty. I had to... I had to do it. I had— I ha...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d0115953-e689-42a5-919b-f38bb96b1646\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>index</th>\n",
              "      <th>season no.</th>\n",
              "      <th>episode no.</th>\n",
              "      <th>episode name</th>\n",
              "      <th>name</th>\n",
              "      <th>line</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Pilot</td>\n",
              "      <td>Rick</td>\n",
              "      <td>Morty! You gotta come on. Jus'... you gotta co...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Pilot</td>\n",
              "      <td>Morty</td>\n",
              "      <td>What, Rick? What’s going on?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Pilot</td>\n",
              "      <td>Rick</td>\n",
              "      <td>I got a surprise for you, Morty.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Pilot</td>\n",
              "      <td>Morty</td>\n",
              "      <td>It's the middle of the night. What are you tal...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Pilot</td>\n",
              "      <td>Rick</td>\n",
              "      <td>Come on, I got a surprise for you.  Come on, h...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Pilot</td>\n",
              "      <td>Morty</td>\n",
              "      <td>Ow! Ow! You're tugging me too hard!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Pilot</td>\n",
              "      <td>Rick</td>\n",
              "      <td>We gotta go, gotta get outta here, come on. Go...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Pilot</td>\n",
              "      <td>Rick</td>\n",
              "      <td>What do you think of this... flying vehicle, M...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Pilot</td>\n",
              "      <td>Morty</td>\n",
              "      <td>Yeah, Rick... I-it's great. Is this the surprise?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Pilot</td>\n",
              "      <td>Rick</td>\n",
              "      <td>Morty. I had to... I had to do it. I had— I ha...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d0115953-e689-42a5-919b-f38bb96b1646')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-d0115953-e689-42a5-919b-f38bb96b1646 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d0115953-e689-42a5-919b-f38bb96b1646');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n51hsl2mAG8v"
      },
      "source": [
        "We will convert this dataset in a way that every responce row will contain **n** previous responces as a context. For our purposes seven previous responces will be enough."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WyL7Dx1bbLRC"
      },
      "source": [
        "contexted = []\n",
        "\n",
        "n = 7\n",
        "\n",
        "for i in range(n, len(all_rick['line'])):\n",
        "  row = []\n",
        "  prev = i - 1 - n # we additionally substract 1, so row will contain current responce and 7 previous responces  \n",
        "  for j in range(i, prev, -1):\n",
        "    row.append(all_rick['line'][j])\n",
        "  contexted.append(row)  "
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R_cwbBolVi14",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b21b2b86-680a-4b78-adcf-5616cbaf7379"
      },
      "source": [
        "len(contexted)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1898"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gNkYLty-VhTO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb6a91d6-addb-44de-8e6f-f2d0e1409cf7"
      },
      "source": [
        "columns = ['response', 'context'] \n",
        "columns = columns + ['context/'+str(i) for i in range(n-1)]\n",
        "columns"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['response',\n",
              " 'context',\n",
              " 'context/0',\n",
              " 'context/1',\n",
              " 'context/2',\n",
              " 'context/3',\n",
              " 'context/4',\n",
              " 'context/5']"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kPafxqIYgurW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "7b0469b9-2066-4e13-cc2f-abf968c48e5b"
      },
      "source": [
        "df = pd.DataFrame.from_records(contexted, columns=columns)\n",
        "df.head(5)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            response  \\\n",
              "0  What do you think of this... flying vehicle, M...   \n",
              "1  Yeah, Rick... I-it's great. Is this the surprise?   \n",
              "2  Morty. I had to... I had to do it. I had— I ha...   \n",
              "3                                    What?! A bomb?!   \n",
              "4  We're gonna drop it down there just get a whol...   \n",
              "\n",
              "                                             context  \\\n",
              "0  We gotta go, gotta get outta here, come on. Go...   \n",
              "1  What do you think of this... flying vehicle, M...   \n",
              "2  Yeah, Rick... I-it's great. Is this the surprise?   \n",
              "3  Morty. I had to... I had to do it. I had— I ha...   \n",
              "4                                    What?! A bomb?!   \n",
              "\n",
              "                                           context/0  \\\n",
              "0                Ow! Ow! You're tugging me too hard!   \n",
              "1  We gotta go, gotta get outta here, come on. Go...   \n",
              "2  What do you think of this... flying vehicle, M...   \n",
              "3  Yeah, Rick... I-it's great. Is this the surprise?   \n",
              "4  Morty. I had to... I had to do it. I had— I ha...   \n",
              "\n",
              "                                           context/1  \\\n",
              "0  Come on, I got a surprise for you.  Come on, h...   \n",
              "1                Ow! Ow! You're tugging me too hard!   \n",
              "2  We gotta go, gotta get outta here, come on. Go...   \n",
              "3  What do you think of this... flying vehicle, M...   \n",
              "4  Yeah, Rick... I-it's great. Is this the surprise?   \n",
              "\n",
              "                                           context/2  \\\n",
              "0  It's the middle of the night. What are you tal...   \n",
              "1  Come on, I got a surprise for you.  Come on, h...   \n",
              "2                Ow! Ow! You're tugging me too hard!   \n",
              "3  We gotta go, gotta get outta here, come on. Go...   \n",
              "4  What do you think of this... flying vehicle, M...   \n",
              "\n",
              "                                           context/3  \\\n",
              "0                   I got a surprise for you, Morty.   \n",
              "1  It's the middle of the night. What are you tal...   \n",
              "2  Come on, I got a surprise for you.  Come on, h...   \n",
              "3                Ow! Ow! You're tugging me too hard!   \n",
              "4  We gotta go, gotta get outta here, come on. Go...   \n",
              "\n",
              "                                           context/4  \\\n",
              "0                       What, Rick? What’s going on?   \n",
              "1                   I got a surprise for you, Morty.   \n",
              "2  It's the middle of the night. What are you tal...   \n",
              "3  Come on, I got a surprise for you.  Come on, h...   \n",
              "4                Ow! Ow! You're tugging me too hard!   \n",
              "\n",
              "                                           context/5  \n",
              "0  Morty! You gotta come on. Jus'... you gotta co...  \n",
              "1                       What, Rick? What’s going on?  \n",
              "2                   I got a surprise for you, Morty.  \n",
              "3  It's the middle of the night. What are you tal...  \n",
              "4  Come on, I got a surprise for you.  Come on, h...  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>response</th>\n",
              "      <th>context</th>\n",
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              "      <td>What do you think of this... flying vehicle, M...</td>\n",
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              "      <td>Ow! Ow! You're tugging me too hard!</td>\n",
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              "      <td>Yeah, Rick... I-it's great. Is this the surprise?</td>\n",
              "      <td>What do you think of this... flying vehicle, M...</td>\n",
              "      <td>We gotta go, gotta get outta here, come on. Go...</td>\n",
              "      <td>Ow! Ow! You're tugging me too hard!</td>\n",
              "      <td>Come on, I got a surprise for you.  Come on, h...</td>\n",
              "      <td>It's the middle of the night. What are you tal...</td>\n",
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              "      <th>2</th>\n",
              "      <td>Morty. I had to... I had to do it. I had— I ha...</td>\n",
              "      <td>Yeah, Rick... I-it's great. Is this the surprise?</td>\n",
              "      <td>What do you think of this... flying vehicle, M...</td>\n",
              "      <td>We gotta go, gotta get outta here, come on. Go...</td>\n",
              "      <td>Ow! Ow! You're tugging me too hard!</td>\n",
              "      <td>Come on, I got a surprise for you.  Come on, h...</td>\n",
              "      <td>It's the middle of the night. What are you tal...</td>\n",
              "      <td>I got a surprise for you, Morty.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>What?! A bomb?!</td>\n",
              "      <td>Morty. I had to... I had to do it. I had— I ha...</td>\n",
              "      <td>Yeah, Rick... I-it's great. Is this the surprise?</td>\n",
              "      <td>What do you think of this... flying vehicle, M...</td>\n",
              "      <td>We gotta go, gotta get outta here, come on. Go...</td>\n",
              "      <td>Ow! Ow! You're tugging me too hard!</td>\n",
              "      <td>Come on, I got a surprise for you.  Come on, h...</td>\n",
              "      <td>It's the middle of the night. What are you tal...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>We're gonna drop it down there just get a whol...</td>\n",
              "      <td>What?! A bomb?!</td>\n",
              "      <td>Morty. I had to... I had to do it. I had— I ha...</td>\n",
              "      <td>Yeah, Rick... I-it's great. Is this the surprise?</td>\n",
              "      <td>What do you think of this... flying vehicle, M...</td>\n",
              "      <td>We gotta go, gotta get outta here, come on. Go...</td>\n",
              "      <td>Ow! Ow! You're tugging me too hard!</td>\n",
              "      <td>Come on, I got a surprise for you.  Come on, h...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-abe7a061-2e0e-4d60-9a3a-2811766d3b1c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-abe7a061-2e0e-4d60-9a3a-2811766d3b1c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-abe7a061-2e0e-4d60-9a3a-2811766d3b1c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aBeM8pvEjigq"
      },
      "source": [
        "Split our dataset into a training and test parts."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g1CeutVVlL85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "outputId": "38b86b1d-bed0-4e63-d041-f70a595259b5"
      },
      "source": [
        "trn_df, val_df = train_test_split(df, test_size = 0.1)\n",
        "trn_df.head()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               response  \\\n",
              "1790  CALL ME A SIDEKICK ONE MORE TIME! CALL ME A SI...   \n",
              "593                                     Are those cats?   \n",
              "928   Jerry and Beth, your family is a golden exampl...   \n",
              "1811                 I hope they see me as their Morty.   \n",
              "1171  Operating an unregistered portal gun, radicali...   \n",
              "\n",
              "                                                context  \\\n",
              "1790  Well, maybe the uniform makes a big difference...   \n",
              "593                                         Oh, my God!   \n",
              "928                                Oh God! We’re up?!?!   \n",
              "1811  Gosh. We moved around so much it’s hard to rem...   \n",
              "1171  Because Ricks hate themselves the most. And ou...   \n",
              "\n",
              "                                              context/0  \\\n",
              "1790       He’s not my Rick. He’s my partner. Aw, geez.   \n",
              "593             We're not on any timeline, dummy. Look.   \n",
              "928   That was Chunky Tunk with “Full Ming Mong, Emp...   \n",
              "1811                                               Yes?   \n",
              "1171  But if every Rick hates the government, why wo...   \n",
              "\n",
              "                                              context/1  \\\n",
              "1790  Ah geez man, that sucks that your Rick’s makin...   \n",
              "593        But I thought there were infinite timelines.   \n",
              "928   Morty, suppose you could retrieve your family ...   \n",
              "1811  I-I-I want a portal gun. Unregistered, untrace...   \n",
              "1171  It's the Citadel of Ricks. All the different R...   \n",
              "\n",
              "                                              context/2  \\\n",
              "1790  I hear that, ah geez, I guess I’m supposed to ...   \n",
              "593   Our time is fractured. You two somehow created...   \n",
              "928                       What’s that? Who’s that baby?   \n",
              "1811  Your life is a lie, man.  All your lives are l...   \n",
              "1171                                A city of Grandpas?   \n",
              "\n",
              "                                              context/3  \\\n",
              "1790  Aw geez, man, nothin’ man, we’re just hangin’ ...   \n",
              "593                               What? English please?   \n",
              "928   And as a result, he has the power to save or d...   \n",
              "1811                  I know.  That’s why you’re fired.   \n",
              "1171     It's a trap! Abort! We never left his --   No!   \n",
              "\n",
              "                                              context/4  \\\n",
              "1790             Aw geez, hey, what’s goin’ on, fellas?   \n",
              "593                                   What? In English?   \n",
              "928   Tammy… gross. Birdperson, you always stick up ...   \n",
              "1811  Holy shit.  I don’t believe it! I-I can’t beli...   \n",
              "1171  Okay, have fun in what's left of my brain. I'm...   \n",
              "\n",
              "                                              context/5  \n",
              "1790                       Lemme turn over a few rocks.  \n",
              "593                                   What do you mean?  \n",
              "928                              Don’t be gross, Tammy.  \n",
              "1811  But I’ve got a message for them, from the Rick...  \n",
              "1171           Mission accomplished, boys. Pull me out.  "
            ],
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              "      <th></th>\n",
              "      <th>response</th>\n",
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              "      <th>1790</th>\n",
              "      <td>CALL ME A SIDEKICK ONE MORE TIME! CALL ME A SI...</td>\n",
              "      <td>Well, maybe the uniform makes a big difference...</td>\n",
              "      <td>He’s not my Rick. He’s my partner. Aw, geez.</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>593</th>\n",
              "      <td>Are those cats?</td>\n",
              "      <td>Oh, my God!</td>\n",
              "      <td>We're not on any timeline, dummy. Look.</td>\n",
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              "      <th>928</th>\n",
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              "      <td>Don’t be gross, Tammy.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1811</th>\n",
              "      <td>I hope they see me as their Morty.</td>\n",
              "      <td>Gosh. We moved around so much it’s hard to rem...</td>\n",
              "      <td>Yes?</td>\n",
              "      <td>I-I-I want a portal gun. Unregistered, untrace...</td>\n",
              "      <td>Your life is a lie, man.  All your lives are l...</td>\n",
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              "    <tr>\n",
              "      <th>1171</th>\n",
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              "      <td>But if every Rick hates the government, why wo...</td>\n",
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              "      <td>It's a trap! Abort! We never left his --   No!</td>\n",
              "      <td>Okay, have fun in what's left of my brain. I'm...</td>\n",
              "      <td>Mission accomplished, boys. Pull me out.</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "86F3WhnFO4H8"
      },
      "source": [
        "Now will convert our dataset in a format suitable for our model. Basically we will concatenate responses in one string for each row (additionally we will add special 'end of string' token between responses, so the model will understand end of each response in a string).  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PX7jeWpYmOe_"
      },
      "source": [
        "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.max_len - 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)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "naaRHoXgnStq"
      },
      "source": [
        "# Cacheing 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)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pkvMNnrnVHQw"
      },
      "source": [
        "## Training and Evaluating\n",
        "\n",
        "There will be quite a lot of code needed for training our model but don’t worry, everything should work as is, the main thing is to give the model the dataset in the right format.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tXzKlXHeu0Mb"
      },
      "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"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-MGD6bFXV4Z-"
      },
      "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 = AutoModelWithLMHead.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 = AutoModelWithLMHead.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 = AutoModelWithLMHead.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"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UZEHDzR0Vjs7"
      },
      "source": [
        "It is time to train our model!\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "__iqR8YFV-Ex",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464,
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        },
        "outputId": "d7296f8b-ffec-4c2d-a3c8-522ef8df9b11"
      },
      "source": [
        "main(trn_df, val_df)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:__main__:Process rank: -1, device: cuda, n_gpu: 1, distributed training: False, 16-bits training: False\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/641 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3f8c304a4b9f41f4b046ca4ed87e1784"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4654534de0384c73a8811e659a8767e8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5eac9f6fb4d94ed6ab6c8a6a54a069b6"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/modeling_auto.py:798: FutureWarning: The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use `AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and `AutoModelForSeq2SeqLM` for encoder-decoder models.\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/351M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "77ab36fc3f534735a9a52e4b1c24c722"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:transformers.modeling_utils:Some weights of GPT2LMHeadModel were not initialized from the model checkpoint at microsoft/DialoGPT-small and are newly initialized: ['transformer.h.0.attn.masked_bias', 'transformer.h.1.attn.masked_bias', 'transformer.h.2.attn.masked_bias', 'transformer.h.3.attn.masked_bias', 'transformer.h.4.attn.masked_bias', 'transformer.h.5.attn.masked_bias', 'transformer.h.6.attn.masked_bias', 'transformer.h.7.attn.masked_bias', 'transformer.h.8.attn.masked_bias', 'transformer.h.9.attn.masked_bias', 'transformer.h.10.attn.masked_bias', 'transformer.h.11.attn.masked_bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c40973e13579474a9b4038fd0818b1c1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Iteration:   0%|          | 0/427 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d26fcc4bf9f84acdb4176c8c88d80b01"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Iteration:   0%|          | 0/427 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9d5add46b21449eb8651dc9e050a33df"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Iteration:   0%|          | 0/427 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "99fedcdb36174555b422d26c06808bf6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py:249: UserWarning: To get the last learning rate computed by the scheduler, please use `get_last_lr()`.\n",
            "  warnings.warn(\"To get the last learning rate computed by the scheduler, \"\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating:   0%|          | 0/47 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2fe16425b98e4e14976a73b4b9b3485f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'perplexity_': tensor(3.8139)}"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6eDkPEuvbD47"
      },
      "source": [
        "## Chatting with  Rick"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rjVqotI05gOS"
      },
      "source": [
        "The model is ready, so it's time to chat with Rick. But don't forget that Rick can be rude, I warned you.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nIeqMwZktv7N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39eaef1b-a4f3-42d6-eeee-f407ea776c6e"
      },
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained('microsoft/DialoGPT-small')\n",
        "model = AutoModelWithLMHead.from_pretrained('output-small')\n",
        "\n",
        "# Let's chat for 5 lines\n",
        "for step in range(5):\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(\"RickBot: {}\".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">> User:How are you Rick?\n",
            "RickBot: It's fine, Morty. You're all right.\n",
            ">> User:where is morty\n",
            "RickBot: Where is morgan?\n",
            ">> User:i dont understand\n",
            "RickBot: Whoa, whoa, hold on a second. You don't know me!  I'm just saying!\n",
            ">> User:i hate you rick\n",
            "RickBot: I hate you too, Jessica!\n",
            ">> User:i am not jessica rick\n",
            "RickBot: !!!!!!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1jAMH0XPuE-f"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gjd1pCZxukWh"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ofg7iJNFyjBs"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}