wodehouse_generator_gpu.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "gpt2_huggingface_GPU_working.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shikha-aggarwal/nlp/blob/master/wodehouse_generator_gpu.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uicio9FLPv5j"
      },
      "source": [
        "!git clone https://github.com/huggingface/transformers\n",
        "\n",
        "import os\n",
        "os.chdir('/content/transformers')\n",
        "\n",
        "# # Use language modeling version as of April 21st.\n",
        "!git checkout b1ff0b2ae7d368b7db3a8a8472a29cc195d278d8\n",
        "\n",
        "!pip install .\n",
        "!pip install -r ./examples/requirements.txt\n",
        "!pip install urllib3==1.25.10"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LiZqyDhMH043"
      },
      "source": [
        "### If you see errors during the above installation step, restarting the runtime might help."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7iHm5X-u1ePl"
      },
      "source": [
        "os.chdir('/content/transformers/examples/')\n",
        "# !pip install dict_to_obj"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "weh0BoPfk1zc",
        "outputId": "69745ae6-5445-418d-a50c-b3fcc1a756b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "import torch\n",
        "import run_language_modeling\n",
        "import run_generation\n",
        "import collections\n",
        "import random\n",
        "import numpy as np\n",
        "\n",
        "from transformers import AutoConfig\n",
        "from transformers import AutoTokenizer\n",
        "from transformers import AutoModelWithLMHead\n",
        "\n",
        "# Text processing\n",
        "import json\n",
        "from pathlib import Path\n",
        "from glob import glob\n",
        "import os\n",
        "from concurrent.futures import ProcessPoolExecutor\n",
        "from itertools import chain\n",
        "import nltk\n",
        "import re\n",
        "nltk.download('punkt')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QHQJYzKzZiIF",
        "outputId": "ba99beee-cb43-4ee6-fea8-c53d43fb544b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "import os"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IOwa3bCysC2V"
      },
      "source": [
        "# all_data_txt = '/content/drive/My Drive/Colab Notebooks/wodehouse_generator/data/all_novels/train.txt'\n",
        "\n",
        "# with open(all_data_txt, \"w\") as f:\n",
        "#     f.write(training_text)\n",
        "#     f.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "65MIoc4RXT0k"
      },
      "source": [
        "### Text Preprocessing\n",
        "\n",
        "##### Using sliding window of 8 sentences"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WA_0gVDRTBKY"
      },
      "source": [
        "sentence_tokenizer = nltk.data.load('tokenizers/punkt/polish.pickle')\n",
        "sent_tokenize = sentence_tokenizer.tokenize\n",
        "\n",
        "def flatten(iterable):\n",
        "    return chain.from_iterable(iterable)\n",
        "\n",
        "def preprocess_book(book_txt):\n",
        "    if \"404 Not Found\" in book_txt:\n",
        "        return \"\"\n",
        "    \n",
        "    start_idx = book_txt.index(\"START OF THIS PROJECT GUTENBERG\") + 100\n",
        "    end_idx = book_txt.index(\"END OF THIS PROJECT\") - 20\n",
        "    txt =  book_txt[start_idx: end_idx]\n",
        "    return re.sub(\"\\s+\", \" \", txt)\n",
        "\n",
        "\n",
        "def process_book(book_path):\n",
        "    try:\n",
        "        txt = preprocess_book(Path(book_path).read_text(\"utf-8\"))\n",
        "        sentences = [s for s in sent_tokenize(txt) if len(s) >= 16]\n",
        "        windowed_sentences = []\n",
        "        for snt in range(len(sentences)):\n",
        "            windowed_sentences.append(\" \".join(sentences[snt: snt + 8]))\n",
        "        return windowed_sentences\n",
        "    except:\n",
        "        print(f\"Could not parse \\n{book_path}\\n\")\n",
        "        return []"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TJMdA9R1bWkG",
        "outputId": "58eea446-5817-4576-afad-2765e690ffba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "train_data_directory = '/content/drive/My Drive/Colab Notebooks/wodehouse_generator/data/all_novels/'\n",
        "sliding_train_data = '/content/drive/My Drive/Colab Notebooks/wodehouse_generator/data/train_sliding.txt'\n",
        "\n",
        "books = []\n",
        "for filename in os.listdir(train_data_directory):\n",
        "  file_path = os.path.join(train_data_directory, filename)\n",
        "  books.append(file_path)\n",
        "\n",
        "buffer, BUFFER_SIZE = [], 100000\n",
        "with open(sliding_train_data, \"w\") as file:\n",
        "  for i, sentence in enumerate(flatten(process_book(f) for f in books)):\n",
        "    if len(buffer) >= BUFFER_SIZE:\n",
        "      file.write(\"\\n\".join(buffer))\n",
        "      buffer.clear()\n",
        "      print(i, end=\"\\r\")\n",
        "    buffer.append(sentence)\n",
        "  if len(buffer) > 0:\n",
        "    file.write(\"\\n\".join(buffer))\n",
        "    buffer.clear()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Could not parse \n",
            "/content/drive/My Drive/Colab Notebooks/wodehouse_generator/data/all_novels/pg59254.txt\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TbNQ-dOgdUWu",
        "outputId": "067a2b8a-db7e-4409-9951-14a2d7cd58ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        }
      },
      "source": [
        "!head /content/drive/My\\ Drive/Colab\\ Notebooks/wodehouse_generator/data/train_sliding.txt"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "uck Greif and the Online Distributed Proofreading Team INDISCRETIONS OF ARCHIE By P. G. Wodehouse It wasn't Archie's fault really. Its true he went to America and fell in love with Lucille, the daughter of a millionaire hotel proprietor and if he did marry her--well, what else was there to do? From his point of view, the whole thing was a thoroughly good egg; but Mr. Brewster, his father-in-law, thought differently, Archie had neither money nor occupation, which was distasteful in the eyes of the industrious Mr. Brewster; but the real bar was the fact that he had once adversely criticised one of his hotels. Archie does his best to heal the breach; but, being something of an ass, genus priceless, he finds it almost beyond his powers to placate “the man-eating fish” whom Providence has given him as a father-in-law P. G. Wodehouse AUTHOR OF “THE LITTLE WARRIOR,” “A DAMSEL IN DISTRESS,” “UNEASY MONEY,” ETC. NEW YORK GEORGE H. DORAN COMPANY COPYRIGHT,1921, BY GEORGE H, DORAN COMPANY COPYRIGHT, 1920, BY INTERNATIONAL MAGAZINE COMPANY (COSMOPOLITAN MAGAZINE) PRINTED IN THE UNITED STATES OF AMERICA DEDICATION TO B. W. KING-HALL My dear Buddy,-- We have been friends for eighteen years. A considerable proportion of my books were written under your hospitable roof. And yet I have never dedicated one to you.\n",
            "G. Wodehouse It wasn't Archie's fault really. Its true he went to America and fell in love with Lucille, the daughter of a millionaire hotel proprietor and if he did marry her--well, what else was there to do? From his point of view, the whole thing was a thoroughly good egg; but Mr. Brewster, his father-in-law, thought differently, Archie had neither money nor occupation, which was distasteful in the eyes of the industrious Mr. Brewster; but the real bar was the fact that he had once adversely criticised one of his hotels. Archie does his best to heal the breach; but, being something of an ass, genus priceless, he finds it almost beyond his powers to placate “the man-eating fish” whom Providence has given him as a father-in-law P. G. Wodehouse AUTHOR OF “THE LITTLE WARRIOR,” “A DAMSEL IN DISTRESS,” “UNEASY MONEY,” ETC. NEW YORK GEORGE H. DORAN COMPANY COPYRIGHT,1921, BY GEORGE H, DORAN COMPANY COPYRIGHT, 1920, BY INTERNATIONAL MAGAZINE COMPANY (COSMOPOLITAN MAGAZINE) PRINTED IN THE UNITED STATES OF AMERICA DEDICATION TO B. W. KING-HALL My dear Buddy,-- We have been friends for eighteen years. A considerable proportion of my books were written under your hospitable roof. And yet I have never dedicated one to you. What will be the verdict of Posterity on this?\n",
            "Its true he went to America and fell in love with Lucille, the daughter of a millionaire hotel proprietor and if he did marry her--well, what else was there to do? From his point of view, the whole thing was a thoroughly good egg; but Mr. Brewster, his father-in-law, thought differently, Archie had neither money nor occupation, which was distasteful in the eyes of the industrious Mr. Brewster; but the real bar was the fact that he had once adversely criticised one of his hotels. Archie does his best to heal the breach; but, being something of an ass, genus priceless, he finds it almost beyond his powers to placate “the man-eating fish” whom Providence has given him as a father-in-law P. G. Wodehouse AUTHOR OF “THE LITTLE WARRIOR,” “A DAMSEL IN DISTRESS,” “UNEASY MONEY,” ETC. NEW YORK GEORGE H. DORAN COMPANY COPYRIGHT,1921, BY GEORGE H, DORAN COMPANY COPYRIGHT, 1920, BY INTERNATIONAL MAGAZINE COMPANY (COSMOPOLITAN MAGAZINE) PRINTED IN THE UNITED STATES OF AMERICA DEDICATION TO B. W. KING-HALL My dear Buddy,-- We have been friends for eighteen years. A considerable proportion of my books were written under your hospitable roof. And yet I have never dedicated one to you. What will be the verdict of Posterity on this? The fact is, I have become rather superstitious about dedications.\n",
            "From his point of view, the whole thing was a thoroughly good egg; but Mr. Brewster, his father-in-law, thought differently, Archie had neither money nor occupation, which was distasteful in the eyes of the industrious Mr. Brewster; but the real bar was the fact that he had once adversely criticised one of his hotels. Archie does his best to heal the breach; but, being something of an ass, genus priceless, he finds it almost beyond his powers to placate “the man-eating fish” whom Providence has given him as a father-in-law P. G. Wodehouse AUTHOR OF “THE LITTLE WARRIOR,” “A DAMSEL IN DISTRESS,” “UNEASY MONEY,” ETC. NEW YORK GEORGE H. DORAN COMPANY COPYRIGHT,1921, BY GEORGE H, DORAN COMPANY COPYRIGHT, 1920, BY INTERNATIONAL MAGAZINE COMPANY (COSMOPOLITAN MAGAZINE) PRINTED IN THE UNITED STATES OF AMERICA DEDICATION TO B. W. KING-HALL My dear Buddy,-- We have been friends for eighteen years. A considerable proportion of my books were written under your hospitable roof. And yet I have never dedicated one to you. What will be the verdict of Posterity on this? The fact is, I have become rather superstitious about dedications. No sooner do you label a book with the legend-- TO MY BEST FRIEND X than X cuts you in Piccadilly, or you bring a lawsuit against him.\n",
            "Archie does his best to heal the breach; but, being something of an ass, genus priceless, he finds it almost beyond his powers to placate “the man-eating fish” whom Providence has given him as a father-in-law P. G. Wodehouse AUTHOR OF “THE LITTLE WARRIOR,” “A DAMSEL IN DISTRESS,” “UNEASY MONEY,” ETC. NEW YORK GEORGE H. DORAN COMPANY COPYRIGHT,1921, BY GEORGE H, DORAN COMPANY COPYRIGHT, 1920, BY INTERNATIONAL MAGAZINE COMPANY (COSMOPOLITAN MAGAZINE) PRINTED IN THE UNITED STATES OF AMERICA DEDICATION TO B. W. KING-HALL My dear Buddy,-- We have been friends for eighteen years. A considerable proportion of my books were written under your hospitable roof. And yet I have never dedicated one to you. What will be the verdict of Posterity on this? The fact is, I have become rather superstitious about dedications. No sooner do you label a book with the legend-- TO MY BEST FRIEND X than X cuts you in Piccadilly, or you bring a lawsuit against him. There is a fatality about it.\n",
            "G. Wodehouse AUTHOR OF “THE LITTLE WARRIOR,” “A DAMSEL IN DISTRESS,” “UNEASY MONEY,” ETC. NEW YORK GEORGE H. DORAN COMPANY COPYRIGHT,1921, BY GEORGE H, DORAN COMPANY COPYRIGHT, 1920, BY INTERNATIONAL MAGAZINE COMPANY (COSMOPOLITAN MAGAZINE) PRINTED IN THE UNITED STATES OF AMERICA DEDICATION TO B. W. KING-HALL My dear Buddy,-- We have been friends for eighteen years. A considerable proportion of my books were written under your hospitable roof. And yet I have never dedicated one to you. What will be the verdict of Posterity on this? The fact is, I have become rather superstitious about dedications. No sooner do you label a book with the legend-- TO MY BEST FRIEND X than X cuts you in Piccadilly, or you bring a lawsuit against him. There is a fatality about it. However, I can't imagine anyone quarrelling with you, and I am getting more attractive all the time, so let's take a chance.\n",
            "A considerable proportion of my books were written under your hospitable roof. And yet I have never dedicated one to you. What will be the verdict of Posterity on this? The fact is, I have become rather superstitious about dedications. No sooner do you label a book with the legend-- TO MY BEST FRIEND X than X cuts you in Piccadilly, or you bring a lawsuit against him. There is a fatality about it. However, I can't imagine anyone quarrelling with you, and I am getting more attractive all the time, so let's take a chance. CONTENTS I DISTRESSING SCENE IN A HOTEL II A SHOCK FOR MR. BREWSTER III MR. BREWSTER DELIVERS SENTENCE IV WORK WANTED V STRANGE EXPERIENCE OF AN ARTIST'S MODEL VI THE BOMB VII MR. ROSCOE SHERRIFF HAS AN IDEA VIII A DISTURBED NIGHT FOR DEAR OLD SQUIFFY IX A LETTER FROM PARKER X DOING FATHER A BIT OF GOOD XI SALVATORE CHOOSES THE WRONG MOMENT XII BRIGHT EYES-AND A FLY XIII RALLYING ROUND PERCY XIV THE SAD CASE OF LOONEY BIDDLE XV SUMMER STORMS XVI ARCHIE ACCEPTS A SITUATION XVII BROTHER BILL'S ROMANCE XVIII THE SAUSAGE CHAPPIE XIX REGGIE COMES TO LIFE XX THE SAUSAGE CHAPPIE CLICKS XXI THE-GROWING BOY XXII WASHY STEPS INTO THE HALL OF FAME XXIII MOTHER'S-KNEE XXIV THE MELTING OF MR. CONNOLLY XXV THE WIGMORE VENUS XXVI A TALE OF A GRANDFATHER CHAPTER I. DISTRESSING SCENE “I say, laddie!” said Archie.\n",
            "And yet I have never dedicated one to you. What will be the verdict of Posterity on this? The fact is, I have become rather superstitious about dedications. No sooner do you label a book with the legend-- TO MY BEST FRIEND X than X cuts you in Piccadilly, or you bring a lawsuit against him. There is a fatality about it. However, I can't imagine anyone quarrelling with you, and I am getting more attractive all the time, so let's take a chance. CONTENTS I DISTRESSING SCENE IN A HOTEL II A SHOCK FOR MR. BREWSTER III MR. BREWSTER DELIVERS SENTENCE IV WORK WANTED V STRANGE EXPERIENCE OF AN ARTIST'S MODEL VI THE BOMB VII MR. ROSCOE SHERRIFF HAS AN IDEA VIII A DISTURBED NIGHT FOR DEAR OLD SQUIFFY IX A LETTER FROM PARKER X DOING FATHER A BIT OF GOOD XI SALVATORE CHOOSES THE WRONG MOMENT XII BRIGHT EYES-AND A FLY XIII RALLYING ROUND PERCY XIV THE SAD CASE OF LOONEY BIDDLE XV SUMMER STORMS XVI ARCHIE ACCEPTS A SITUATION XVII BROTHER BILL'S ROMANCE XVIII THE SAUSAGE CHAPPIE XIX REGGIE COMES TO LIFE XX THE SAUSAGE CHAPPIE CLICKS XXI THE-GROWING BOY XXII WASHY STEPS INTO THE HALL OF FAME XXIII MOTHER'S-KNEE XXIV THE MELTING OF MR. CONNOLLY XXV THE WIGMORE VENUS XXVI A TALE OF A GRANDFATHER CHAPTER I. DISTRESSING SCENE “I say, laddie!” said Archie. “Sir?” replied the desk-clerk alertly.\n",
            "What will be the verdict of Posterity on this? The fact is, I have become rather superstitious about dedications. No sooner do you label a book with the legend-- TO MY BEST FRIEND X than X cuts you in Piccadilly, or you bring a lawsuit against him. There is a fatality about it. However, I can't imagine anyone quarrelling with you, and I am getting more attractive all the time, so let's take a chance. CONTENTS I DISTRESSING SCENE IN A HOTEL II A SHOCK FOR MR. BREWSTER III MR. BREWSTER DELIVERS SENTENCE IV WORK WANTED V STRANGE EXPERIENCE OF AN ARTIST'S MODEL VI THE BOMB VII MR. ROSCOE SHERRIFF HAS AN IDEA VIII A DISTURBED NIGHT FOR DEAR OLD SQUIFFY IX A LETTER FROM PARKER X DOING FATHER A BIT OF GOOD XI SALVATORE CHOOSES THE WRONG MOMENT XII BRIGHT EYES-AND A FLY XIII RALLYING ROUND PERCY XIV THE SAD CASE OF LOONEY BIDDLE XV SUMMER STORMS XVI ARCHIE ACCEPTS A SITUATION XVII BROTHER BILL'S ROMANCE XVIII THE SAUSAGE CHAPPIE XIX REGGIE COMES TO LIFE XX THE SAUSAGE CHAPPIE CLICKS XXI THE-GROWING BOY XXII WASHY STEPS INTO THE HALL OF FAME XXIII MOTHER'S-KNEE XXIV THE MELTING OF MR. CONNOLLY XXV THE WIGMORE VENUS XXVI A TALE OF A GRANDFATHER CHAPTER I. DISTRESSING SCENE “I say, laddie!” said Archie. “Sir?” replied the desk-clerk alertly. All the employes of the Hotel Cosmopolis were alert.\n",
            "The fact is, I have become rather superstitious about dedications. No sooner do you label a book with the legend-- TO MY BEST FRIEND X than X cuts you in Piccadilly, or you bring a lawsuit against him. There is a fatality about it. However, I can't imagine anyone quarrelling with you, and I am getting more attractive all the time, so let's take a chance. CONTENTS I DISTRESSING SCENE IN A HOTEL II A SHOCK FOR MR. BREWSTER III MR. BREWSTER DELIVERS SENTENCE IV WORK WANTED V STRANGE EXPERIENCE OF AN ARTIST'S MODEL VI THE BOMB VII MR. ROSCOE SHERRIFF HAS AN IDEA VIII A DISTURBED NIGHT FOR DEAR OLD SQUIFFY IX A LETTER FROM PARKER X DOING FATHER A BIT OF GOOD XI SALVATORE CHOOSES THE WRONG MOMENT XII BRIGHT EYES-AND A FLY XIII RALLYING ROUND PERCY XIV THE SAD CASE OF LOONEY BIDDLE XV SUMMER STORMS XVI ARCHIE ACCEPTS A SITUATION XVII BROTHER BILL'S ROMANCE XVIII THE SAUSAGE CHAPPIE XIX REGGIE COMES TO LIFE XX THE SAUSAGE CHAPPIE CLICKS XXI THE-GROWING BOY XXII WASHY STEPS INTO THE HALL OF FAME XXIII MOTHER'S-KNEE XXIV THE MELTING OF MR. CONNOLLY XXV THE WIGMORE VENUS XXVI A TALE OF A GRANDFATHER CHAPTER I. DISTRESSING SCENE “I say, laddie!” said Archie. “Sir?” replied the desk-clerk alertly. All the employes of the Hotel Cosmopolis were alert. It was one of the things on which Mr. Daniel Brewster, the proprietor, insisted.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j3Ilz24pd-i1",
        "outputId": "89144d3f-1f2f-47a4-c575-910916272f55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "\n",
        "\n",
        "# run_language_modeling_with_tokenizers.py -- it's the version with support for fast tokenizers, see above\n",
        "!python run_language_modeling.py \\\n",
        "    --train_data_file=/content/drive/My\\ Drive/Colab\\ Notebooks/wodehouse_generator/data/train_sliding.txt \\\n",
        "    --eval_data_file=/content/drive/My\\ Drive/Colab\\ Notebooks/wodehouse_generator/data/all_novels/validate.txt \\\n",
        "    --output_dir='/content/drive/My Drive/finetuned_models/sliding/wodehouse' \\\n",
        "    --model_type=gpt2 \\\n",
        "    --model_name_or_path=gpt2-medium \\\n",
        "    --mlm \\\n",
        "    --do_train \\\n",
        "    --do_eval \\\n",
        "    --evaluate_during_training \\\n",
        "    --line_by_line \\\n",
        "    --learning_rate=1e-5 \\\n",
        "    --num_train_epochs=5 \\\n",
        "    --save_total_limit=2 \\\n",
        "    --save_steps=5000 \\\n",
        "    --per_gpu_train_batch_size=8 \\\n",
        "    --warmup_steps=10000 \\\n",
        "    --logging_steps=500 \\\n",
        "    --gradient_accumulation_steps=4 \\\n",
        "    --seed=666 --block_size=512"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-10-21 23:57:28.057317: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "10/21/2020 23:57:29 - WARNING - __main__ -   Process rank: -1, device: cuda, n_gpu: 1, distributed training: False, 16-bits training: False\n",
            "10/21/2020 23:57:29 - INFO - transformers.configuration_utils -   loading configuration file https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-config.json from cache at /root/.cache/torch/transformers/98aa65385e18b0efd17acd8bf64dcdf21406bb0c99c801c2d3c9f6bfd1f48f29.250d6dc755ccb17d19c7c1a7677636683aa35f0f6cb5461b3c0587bc091551a0\n",
            "10/21/2020 23:57:29 - INFO - transformers.configuration_utils -   Model config GPT2Config {\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 50256,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 50256,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 1024,\n",
            "  \"n_head\": 16,\n",
            "  \"n_layer\": 24,\n",
            "  \"n_positions\": 1024,\n",
            "  \"n_special\": 0,\n",
            "  \"predict_special_tokens\": true,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50\n",
            "    }\n",
            "  },\n",
            "  \"vocab_size\": 50257\n",
            "}\n",
            "\n",
            "10/21/2020 23:57:29 - INFO - transformers.configuration_utils -   loading configuration file https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-config.json from cache at /root/.cache/torch/transformers/98aa65385e18b0efd17acd8bf64dcdf21406bb0c99c801c2d3c9f6bfd1f48f29.250d6dc755ccb17d19c7c1a7677636683aa35f0f6cb5461b3c0587bc091551a0\n",
            "10/21/2020 23:57:29 - INFO - transformers.configuration_utils -   Model config GPT2Config {\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 50256,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 50256,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 1024,\n",
            "  \"n_head\": 16,\n",
            "  \"n_layer\": 24,\n",
            "  \"n_positions\": 1024,\n",
            "  \"n_special\": 0,\n",
            "  \"predict_special_tokens\": true,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50\n",
            "    }\n",
            "  },\n",
            "  \"vocab_size\": 50257\n",
            "}\n",
            "\n",
            "10/21/2020 23:57:29 - INFO - transformers.tokenization_utils -   loading file https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-vocab.json from cache at /root/.cache/torch/transformers/f20f05d3ae37c4e3cd56764d48e566ea5adeba153dcee6eb82a18822c9c731ec.1512018be4ba4e8726e41b9145129dc30651ea4fec86aa61f4b9f40bf94eac71\n",
            "10/21/2020 23:57:29 - INFO - transformers.tokenization_utils -   loading file https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-merges.txt from cache at /root/.cache/torch/transformers/6d882670c55563617571fe0c97df88626fb5033927b40fc18a8acf98dafd4946.70bec105b4158ed9a1747fea67a43f5dee97855c64d62b6ec3742f4cfdb5feda\n",
            "10/21/2020 23:57:29 - INFO - transformers.modeling_utils -   loading weights file https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-pytorch_model.bin from cache at /root/.cache/torch/transformers/4b337a4f3b7d3e1518f799e238af607498c02938a3390152aaec7d4dabca5a02.8769029be4f66a5ae1055eefdd1d11621b901d510654266b8681719fff492d6e\n",
            "10/21/2020 23:58:23 - INFO - transformers.modeling_utils -   Weights of GPT2LMHeadModel not initialized from pretrained model: ['h.0.attn.masked_bias', 'h.1.attn.masked_bias', 'h.2.attn.masked_bias', 'h.3.attn.masked_bias', 'h.4.attn.masked_bias', 'h.5.attn.masked_bias', 'h.6.attn.masked_bias', 'h.7.attn.masked_bias', 'h.8.attn.masked_bias', 'h.9.attn.masked_bias', 'h.10.attn.masked_bias', 'h.11.attn.masked_bias', 'h.12.attn.masked_bias', 'h.13.attn.masked_bias', 'h.14.attn.masked_bias', 'h.15.attn.masked_bias', 'h.16.attn.masked_bias', 'h.17.attn.masked_bias', 'h.18.attn.masked_bias', 'h.19.attn.masked_bias', 'h.20.attn.masked_bias', 'h.21.attn.masked_bias', 'h.22.attn.masked_bias', 'h.23.attn.masked_bias', 'lm_head.weight']\n",
            "10/21/2020 23:58:29 - INFO - __main__ -   Training/evaluation parameters Namespace(adam_epsilon=1e-08, block_size=512, cache_dir=None, config_name=None, device=device(type='cuda'), do_eval=True, do_train=True, eval_all_checkpoints=False, eval_data_file='/content/drive/My Drive/Colab Notebooks/wodehouse_generator/data/all_novels/validate.txt', evaluate_during_training=True, fp16=False, fp16_opt_level='O1', gradient_accumulation_steps=4, learning_rate=1e-05, line_by_line=True, local_rank=-1, logging_steps=500, max_grad_norm=1.0, max_steps=-1, mlm=True, mlm_probability=0.15, model_name_or_path='gpt2-medium', model_type='gpt2', n_gpu=1, no_cuda=False, num_train_epochs=5.0, output_dir='/content/drive/My Drive/finetuned_models/sliding/wodehouse', overwrite_cache=False, overwrite_output_dir=False, per_gpu_eval_batch_size=4, per_gpu_train_batch_size=8, save_steps=5000, save_total_limit=2, seed=666, server_ip='', server_port='', should_continue=False, tokenizer_name=None, train_data_file='/content/drive/My Drive/Colab Notebooks/wodehouse_generator/data/train_sliding.txt', warmup_steps=10000, weight_decay=0.0)\n",
            "10/21/2020 23:58:29 - INFO - __main__ -   Creating features from dataset file at /content/drive/My Drive/Colab Notebooks/wodehouse_generator/data/train_sliding.txt\n",
            "10/21/2020 23:59:50 - INFO - __main__ -   ***** Running training *****\n",
            "10/21/2020 23:59:50 - INFO - __main__ -     Num examples = 170037\n",
            "10/21/2020 23:59:50 - INFO - __main__ -     Num Epochs = 5\n",
            "10/21/2020 23:59:50 - INFO - __main__ -     Instantaneous batch size per GPU = 8\n",
            "10/21/2020 23:59:50 - INFO - __main__ -     Total train batch size (w. parallel, distributed & accumulation) = 32\n",
            "10/21/2020 23:59:50 - INFO - __main__ -     Gradient Accumulation steps = 4\n",
            "10/21/2020 23:59:50 - INFO - __main__ -     Total optimization steps = 26565\n",
            "Epoch:   0% 0/5 [00:00<?, ?it/s]\n",
            "Iteration:   0% 0/21255 [00:00<?, ?it/s]\u001b[A10/21/2020 23:59:50 - ERROR - transformers.tokenization_utils -   Using mask_token, but it is not set yet.\n",
            "Traceback (most recent call last):\n",
            "  File \"run_language_modeling.py\", line 787, in <module>\n",
            "    main()\n",
            "  File \"run_language_modeling.py\", line 737, in main\n",
            "    global_step, tr_loss = train(args, train_dataset, model, tokenizer)\n",
            "  File \"run_language_modeling.py\", line 333, in train\n",
            "    inputs, labels = mask_tokens(batch, tokenizer, args) if args.mlm else (batch, batch)\n",
            "  File \"run_language_modeling.py\", line 184, in mask_tokens\n",
            "    \"This tokenizer does not have a mask token which is necessary for masked language modeling. Remove the --mlm flag if you want to use this tokenizer.\"\n",
            "ValueError: This tokenizer does not have a mask token which is necessary for masked language modeling. Remove the --mlm flag if you want to use this tokenizer.\n",
            "\n",
            "Epoch:   0% 0/5 [00:00<?, ?it/s]\n",
            "Iteration:   0% 0/21255 [00:00<?, ?it/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C33GutF1QVEV",
        "outputId": "75ea9f19-e1a5-4536-c8b0-95f2a22a9289",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!python run_language_modeling.py \\\n",
        "    --output_dir='/content/drive/My Drive/finetuned_models/wodehouse' \\\n",
        "    --model_type=gpt2 \\\n",
        "    --model_name_or_path=gpt2-medium \\\n",
        "    --save_total_limit=5 \\\n",
        "    --num_train_epochs=1.0 \\\n",
        "    --do_train \\\n",
        "    --evaluate_during_training \\\n",
        "    --logging_steps=500 \\\n",
        "    --save_steps=1500 \\\n",
        "    --train_data_file=/content/drive/My\\ Drive/Colab\\ Notebooks/wodehouse_generator/data/train_sliding.txt \\\n",
        "    --do_eval \\\n",
        "    --eval_data_file=/content/drive/My\\ Drive/Colab\\ Notebooks/wodehouse_generator/data/all_novels/validate.txt \\\n",
        "    --per_gpu_train_batch_size=2 \\\n",
        "    --per_gpu_eval_batch_size=2 \\\n",
        "    --block_size=128 \\\n",
        "    --gradient_accumulation_steps=5 \\\n",
        "    --overwrite_output_dir # too lazy to delete previous failed run"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Evaluating:  23% 158/695 [00:10<00:36, 14.89it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  23% 160/695 [00:10<00:36, 14.84it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  23% 162/695 [00:10<00:35, 15.00it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  24% 164/695 [00:11<00:35, 14.77it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  24% 166/695 [00:11<00:35, 14.89it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  24% 168/695 [00:11<00:35, 14.87it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  24% 170/695 [00:11<00:35, 14.86it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  25% 172/695 [00:11<00:35, 14.78it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  25% 174/695 [00:11<00:35, 14.76it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  25% 176/695 [00:11<00:34, 14.93it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  26% 178/695 [00:11<00:35, 14.71it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  26% 180/695 [00:12<00:34, 14.81it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  26% 182/695 [00:12<00:34, 15.01it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  26% 184/695 [00:12<00:34, 14.79it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  27% 186/695 [00:12<00:34, 14.94it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  27% 188/695 [00:12<00:34, 14.83it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  27% 190/695 [00:12<00:34, 14.84it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  28% 192/695 [00:12<00:33, 14.85it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  28% 194/695 [00:13<00:33, 14.86it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  28% 196/695 [00:13<00:33, 14.90it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  28% 198/695 [00:13<00:33, 14.77it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  29% 200/695 [00:13<00:33, 14.69it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  29% 202/695 [00:13<00:33, 14.61it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  29% 204/695 [00:13<00:33, 14.69it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  30% 206/695 [00:13<00:33, 14.77it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  30% 208/695 [00:14<00:33, 14.74it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  30% 210/695 [00:14<00:32, 14.84it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  31% 212/695 [00:14<00:32, 14.96it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  31% 214/695 [00:14<00:32, 14.76it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  31% 216/695 [00:14<00:32, 14.76it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  31% 218/695 [00:14<00:31, 14.94it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  32% 220/695 [00:14<00:32, 14.83it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  32% 222/695 [00:14<00:31, 14.89it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  32% 224/695 [00:15<00:31, 14.98it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  33% 226/695 [00:15<00:31, 14.92it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  33% 228/695 [00:15<00:31, 14.87it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  33% 230/695 [00:15<00:31, 14.86it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  33% 232/695 [00:15<00:31, 14.79it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  34% 234/695 [00:15<00:30, 14.92it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  34% 236/695 [00:15<00:30, 14.99it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  34% 238/695 [00:16<00:30, 15.06it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  35% 240/695 [00:16<00:30, 15.00it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  35% 242/695 [00:16<00:30, 14.82it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  35% 244/695 [00:16<00:30, 14.94it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  35% 246/695 [00:16<00:30, 14.91it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  36% 248/695 [00:16<00:30, 14.81it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  36% 250/695 [00:16<00:29, 14.86it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  36% 252/695 [00:16<00:29, 14.92it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  37% 254/695 [00:17<00:29, 14.98it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  37% 256/695 [00:17<00:29, 14.89it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  37% 258/695 [00:17<00:29, 14.81it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  37% 260/695 [00:17<00:29, 14.74it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  38% 262/695 [00:17<00:29, 14.86it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  38% 264/695 [00:17<00:29, 14.86it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  38% 266/695 [00:17<00:28, 14.81it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  39% 268/695 [00:18<00:28, 14.99it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  39% 270/695 [00:18<00:28, 14.97it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  39% 272/695 [00:18<00:28, 14.88it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  39% 274/695 [00:18<00:28, 14.79it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  40% 276/695 [00:18<00:28, 14.70it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  40% 278/695 [00:18<00:28, 14.80it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  40% 280/695 [00:18<00:27, 14.92it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  41% 282/695 [00:18<00:27, 14.80it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  41% 284/695 [00:19<00:27, 14.88it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  41% 286/695 [00:19<00:27, 14.99it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  41% 288/695 [00:19<00:27, 14.90it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  42% 290/695 [00:19<00:26, 15.01it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  42% 292/695 [00:19<00:27, 14.92it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  42% 294/695 [00:19<00:26, 15.06it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  43% 296/695 [00:19<00:26, 15.11it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  43% 298/695 [00:20<00:26, 15.20it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  43% 300/695 [00:20<00:26, 14.86it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  43% 302/695 [00:20<00:26, 14.74it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  44% 304/695 [00:20<00:26, 14.75it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  44% 306/695 [00:20<00:26, 14.82it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  44% 308/695 [00:20<00:26, 14.88it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  45% 310/695 [00:20<00:25, 14.88it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  45% 312/695 [00:20<00:25, 14.91it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  45% 314/695 [00:21<00:25, 14.73it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  45% 316/695 [00:21<00:25, 14.74it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  46% 318/695 [00:21<00:25, 14.86it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  46% 320/695 [00:21<00:25, 14.85it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  46% 322/695 [00:21<00:25, 14.78it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  47% 324/695 [00:21<00:24, 14.84it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  47% 326/695 [00:21<00:24, 14.95it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  47% 328/695 [00:22<00:24, 14.78it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  47% 330/695 [00:22<00:24, 14.85it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  48% 332/695 [00:22<00:24, 14.85it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  48% 334/695 [00:22<00:24, 14.74it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  48% 336/695 [00:22<00:24, 14.80it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  49% 338/695 [00:22<00:23, 14.90it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  49% 340/695 [00:22<00:23, 14.86it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  49% 342/695 [00:23<00:23, 14.94it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  49% 344/695 [00:23<00:23, 14.85it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  50% 346/695 [00:23<00:23, 14.82it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  50% 348/695 [00:23<00:23, 14.81it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  50% 350/695 [00:23<00:23, 14.93it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  51% 352/695 [00:23<00:22, 15.01it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  51% 354/695 [00:23<00:22, 15.00it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  51% 356/695 [00:23<00:22, 14.95it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  52% 358/695 [00:24<00:22, 15.06it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  52% 360/695 [00:24<00:22, 14.90it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  52% 362/695 [00:24<00:22, 14.86it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  52% 364/695 [00:24<00:22, 14.89it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  53% 366/695 [00:24<00:22, 14.91it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  53% 368/695 [00:24<00:21, 14.87it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  53% 370/695 [00:24<00:21, 14.90it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  54% 372/695 [00:25<00:21, 14.97it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  54% 374/695 [00:25<00:21, 15.03it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  54% 376/695 [00:25<00:21, 14.95it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  54% 378/695 [00:25<00:21, 14.81it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  55% 380/695 [00:25<00:21, 14.98it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  55% 382/695 [00:25<00:21, 14.87it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  55% 384/695 [00:25<00:20, 14.92it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  56% 386/695 [00:25<00:20, 14.98it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  56% 388/695 [00:26<00:20, 14.90it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  56% 390/695 [00:26<00:20, 14.96it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  56% 392/695 [00:26<00:20, 14.86it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  57% 394/695 [00:26<00:20, 14.80it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  57% 396/695 [00:26<00:20, 14.80it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  57% 398/695 [00:26<00:19, 14.94it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  58% 400/695 [00:26<00:19, 14.88it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  58% 402/695 [00:27<00:19, 14.90it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  58% 404/695 [00:27<00:19, 14.91it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  58% 406/695 [00:27<00:19, 14.85it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  59% 408/695 [00:27<00:19, 14.97it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  59% 410/695 [00:27<00:19, 14.94it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  59% 412/695 [00:27<00:18, 15.02it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  60% 414/695 [00:27<00:18, 14.96it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  60% 416/695 [00:27<00:18, 14.82it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  60% 418/695 [00:28<00:18, 14.91it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  60% 420/695 [00:28<00:18, 14.73it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  61% 422/695 [00:28<00:18, 14.83it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  61% 424/695 [00:28<00:18, 14.99it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  61% 426/695 [00:28<00:17, 14.95it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  62% 428/695 [00:28<00:18, 14.74it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  62% 430/695 [00:28<00:17, 14.95it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  62% 432/695 [00:29<00:17, 14.89it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  62% 434/695 [00:29<00:17, 14.81it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  63% 436/695 [00:29<00:17, 14.80it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  63% 438/695 [00:29<00:17, 14.91it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  63% 440/695 [00:29<00:17, 14.95it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  64% 442/695 [00:29<00:16, 15.01it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  64% 444/695 [00:29<00:16, 14.97it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  64% 446/695 [00:29<00:16, 15.05it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  64% 448/695 [00:30<00:16, 14.95it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  65% 450/695 [00:30<00:16, 14.92it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  65% 452/695 [00:30<00:16, 15.01it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  65% 454/695 [00:30<00:16, 14.92it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  66% 456/695 [00:30<00:15, 15.10it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  66% 458/695 [00:30<00:15, 14.94it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  66% 460/695 [00:30<00:15, 14.80it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  66% 462/695 [00:31<00:15, 14.93it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  67% 464/695 [00:31<00:15, 14.82it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  67% 466/695 [00:31<00:15, 14.80it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  67% 468/695 [00:31<00:15, 14.83it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  68% 470/695 [00:31<00:15, 14.90it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  68% 472/695 [00:31<00:14, 15.04it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  68% 474/695 [00:31<00:14, 14.94it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  68% 476/695 [00:32<00:14, 14.90it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  69% 478/695 [00:32<00:14, 14.68it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  69% 480/695 [00:32<00:14, 14.86it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  69% 482/695 [00:32<00:14, 14.78it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  70% 484/695 [00:32<00:14, 14.81it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  70% 486/695 [00:32<00:14, 14.83it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluating:  70% 488/695 [00:32<00:13, 14.86it/s]\u001b[A\u001b[A\n",
            "\n",
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            "10/21/2020 20:31:16 - INFO - __main__ -   ***** Eval results  *****\n",
            "10/21/2020 20:31:16 - INFO - __main__ -     perplexity = tensor(16.3520)\n",
            "\n",
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            "Iteration: 100% 14628/14628 [1:02:43<00:00,  3.89it/s]\n",
            "Epoch: 100% 1/1 [1:02:43<00:00, 3763.44s/it]\n",
            "10/21/2020 20:39:37 - INFO - __main__ -    global_step = 2925, average loss = 2.93447317159074\n",
            "10/21/2020 20:39:37 - INFO - __main__ -   Saving model checkpoint to /content/drive/My Drive/finetuned_models/wodehouse\n",
            "10/21/2020 20:39:37 - INFO - transformers.configuration_utils -   Configuration saved in /content/drive/My Drive/finetuned_models/wodehouse/config.json\n",
            "10/21/2020 20:39:44 - INFO - transformers.modeling_utils -   Model weights saved in /content/drive/My Drive/finetuned_models/wodehouse/pytorch_model.bin\n",
            "10/21/2020 20:39:44 - INFO - transformers.configuration_utils -   loading configuration file /content/drive/My Drive/finetuned_models/wodehouse/config.json\n",
            "10/21/2020 20:39:44 - INFO - transformers.configuration_utils -   Model config GPT2Config {\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 50256,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 50256,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 1024,\n",
            "  \"n_head\": 16,\n",
            "  \"n_layer\": 24,\n",
            "  \"n_positions\": 1024,\n",
            "  \"n_special\": 0,\n",
            "  \"predict_special_tokens\": true,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50\n",
            "    }\n",
            "  },\n",
            "  \"vocab_size\": 50257\n",
            "}\n",
            "\n",
            "10/21/2020 20:39:44 - INFO - transformers.modeling_utils -   loading weights file /content/drive/My Drive/finetuned_models/wodehouse/pytorch_model.bin\n",
            "10/21/2020 20:40:03 - INFO - transformers.configuration_utils -   loading configuration file /content/drive/My Drive/finetuned_models/wodehouse/config.json\n",
            "10/21/2020 20:40:03 - INFO - transformers.configuration_utils -   Model config GPT2Config {\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 50256,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 50256,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 1024,\n",
            "  \"n_head\": 16,\n",
            "  \"n_layer\": 24,\n",
            "  \"n_positions\": 1024,\n",
            "  \"n_special\": 0,\n",
            "  \"predict_special_tokens\": true,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50\n",
            "    }\n",
            "  },\n",
            "  \"vocab_size\": 50257\n",
            "}\n",
            "\n",
            "10/21/2020 20:40:03 - INFO - transformers.tokenization_utils -   Model name '/content/drive/My Drive/finetuned_models/wodehouse' not found in model shortcut name list (gpt2, gpt2-medium, gpt2-large, gpt2-xl, distilgpt2). Assuming '/content/drive/My Drive/finetuned_models/wodehouse' is a path, a model identifier, or url to a directory containing tokenizer files.\n",
            "10/21/2020 20:40:03 - INFO - transformers.tokenization_utils -   Didn't find file /content/drive/My Drive/finetuned_models/wodehouse/added_tokens.json. We won't load it.\n",
            "10/21/2020 20:40:03 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/finetuned_models/wodehouse/vocab.json\n",
            "10/21/2020 20:40:03 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/finetuned_models/wodehouse/merges.txt\n",
            "10/21/2020 20:40:03 - INFO - transformers.tokenization_utils -   loading file None\n",
            "10/21/2020 20:40:03 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/finetuned_models/wodehouse/special_tokens_map.json\n",
            "10/21/2020 20:40:03 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/finetuned_models/wodehouse/tokenizer_config.json\n",
            "10/21/2020 20:40:04 - INFO - __main__ -   Evaluate the following checkpoints: ['/content/drive/My Drive/finetuned_models/wodehouse']\n",
            "10/21/2020 20:40:04 - INFO - transformers.configuration_utils -   loading configuration file /content/drive/My Drive/finetuned_models/wodehouse/config.json\n",
            "10/21/2020 20:40:04 - INFO - transformers.configuration_utils -   Model config GPT2Config {\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 50256,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 50256,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 1024,\n",
            "  \"n_head\": 16,\n",
            "  \"n_layer\": 24,\n",
            "  \"n_positions\": 1024,\n",
            "  \"n_special\": 0,\n",
            "  \"predict_special_tokens\": true,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50\n",
            "    }\n",
            "  },\n",
            "  \"vocab_size\": 50257\n",
            "}\n",
            "\n",
            "10/21/2020 20:40:04 - INFO - transformers.modeling_utils -   loading weights file /content/drive/My Drive/finetuned_models/wodehouse/pytorch_model.bin\n",
            "10/21/2020 20:40:22 - INFO - __main__ -   Loading features from cached file /content/drive/My Drive/Colab Notebooks/wodehouse_generator/data/all_novels/gpt2_cached_lm_128_validate.txt\n",
            "10/21/2020 20:40:22 - INFO - __main__ -   ***** Running evaluation  *****\n",
            "10/21/2020 20:40:22 - INFO - __main__ -     Num examples = 1389\n",
            "10/21/2020 20:40:22 - INFO - __main__ -     Batch size = 2\n",
            "Evaluating: 100% 695/695 [00:47<00:00, 14.55it/s]\n",
            "10/21/2020 20:41:10 - INFO - __main__ -   ***** Eval results  *****\n",
            "10/21/2020 20:41:10 - INFO - __main__ -     perplexity = tensor(16.2201)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q9mAiosB2wBm"
      },
      "source": [
        "### Compute perplexity of a dataset.\n",
        "This section shows how to compute perplexity of a dataset according to either the pre-trained or your fine-tuned language model. While this is possible to do by calling `run_language_modeling.py` on the command-line as above, we'll instead call the Python functions directly."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eiqSgGhtcDNd"
      },
      "source": [
        "#### Look at what checkpoints are available\n",
        "Run `ls` to look at what checkpoints saved been saved. You'll want to set `CHECKPOINT_PATH` below to one of these in order to evaluate the model weights saved in that checkpoint."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zk_qHytBIETo",
        "outputId": "92f53937-3ff4-44a4-b383-4ac16822d493",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "!ls '/content/drive/My Drive/finetuned_models/wodehouse'"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "checkpoint-1500   Final_model  pytorch_model.bin\ttraining_args.bin\n",
            "config.json\t  merges.txt   special_tokens_map.json\tvocab.json\n",
            "eval_results.txt  output       tokenizer_config.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TRFwV1Ry3Evk"
      },
      "source": [
        "#### Helper functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sc2VCFBG3pFf"
      },
      "source": [
        "def load_model(args):\n",
        "  \"\"\"Creates a model and loads in weights for it.\"\"\"\n",
        "  config = AutoConfig.from_pretrained(args.model_name_or_path, cache_dir=None)\n",
        "\n",
        "  model = AutoModelWithLMHead.from_pretrained(\n",
        "      args.model_name_or_path,\n",
        "      from_tf=bool(\".ckpt\" in args.model_name_or_path),\n",
        "      config=config,\n",
        "      cache_dir=None\n",
        "  )\n",
        "  \n",
        "  model.to(args.device)\n",
        "  return model\n",
        "\n",
        "def set_seed(seed):\n",
        "  \"\"\"Set the random seed.\"\"\"\n",
        "  random.seed(seed)\n",
        "  np.random.seed(seed)\n",
        "  torch.manual_seed(seed)\n",
        "  if args.n_gpu > 0:\n",
        "    torch.cuda.manual_seed_all(args.seed)\n",
        "\n",
        "def do_perplexity_eval(args, model, data_file_path):\n",
        "  \"\"\"Computes the perplexity of the text in data_file_path according to the provided model.\"\"\"\n",
        "  set_seed(args.seed)\n",
        "\n",
        "  args.eval_data_file=data_file_path\n",
        "\n",
        "  tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, cache_dir=None)\n",
        "\n",
        "  args.block_size = min(args.block_size, tokenizer.max_len)\n",
        "\n",
        "  result = run_language_modeling.evaluate(args, model, tokenizer, prefix=\"\")\n",
        "  return result"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kClE2Px-j9bb"
      },
      "source": [
        "#### Compute it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HKsGfPtzJo6b"
      },
      "source": [
        "class DictToObject(object):\n",
        "\n",
        "    def __init__(self, dictionary):\n",
        "        def _traverse(key, element):\n",
        "            if isinstance(element, dict):\n",
        "                return key, DictToObject(element)\n",
        "            else:\n",
        "                return key, element\n",
        "\n",
        "        objd = dict(_traverse(k, v) for k, v in dictionary.items())\n",
        "        self.__dict__.update(objd)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ERCKSncEBYgJ",
        "outputId": "c7eac947-84c4-4d90-ade9-b00ae6fa3798",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Set this to the checkpoint you want to evalute, or to \"gpt2-medium\" to\n",
        "# evaluate the pre-trained model without finetuning.\n",
        "CHECKPOINT_PATH = '/content/drive/My Drive/finetuned_models/wodehouse/'\n",
        "OUTPUT_PATH = '/content/drive/My Drive/finetuned_models/wodehouse/output'\n",
        "\n",
        "# Set this to the list of text files you want to evaluate the perplexity of.\n",
        "DATA_PATHS = [\"/content/drive/My Drive/Colab Notebooks/wodehouse_generator/data/validate.txt\",\n",
        "              \"/content/drive/My Drive/Colab Notebooks/wodehouse_generator/data/test.txt\"]\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "n_gpu = torch.cuda.device_count()\n",
        "print(\"Running on device: \", device)\n",
        "\n",
        "args = collections.defaultdict(\n",
        "  model_name_or_path=CHECKPOINT_PATH,\n",
        "  output_dir=OUTPUT_PATH,\n",
        "  block_size = 128,\n",
        "  local_rank=-1,\n",
        "  eval_batch_size=2,\n",
        "  per_gpu_eval_batch_size=2,\n",
        "  n_gpu=n_gpu,\n",
        "  mlm=False,\n",
        "  device=device,\n",
        "  line_by_line=False,\n",
        "  overwrite_cache=None,\n",
        "  model_type='gpt2',\n",
        "  seed=42,\n",
        ")\n",
        "args = DictToObject(args)\n",
        "\n",
        "model = load_model(args)\n",
        "\n",
        "# for data_path in DATA_PATHS:\n",
        "#   eval_results = do_perplexity_eval(args, model, data_path)\n",
        "#   perplexity = eval_results['perplexity']\n",
        "#   print('{} is the perplexity of {} according to {}'.format(\n",
        "#       perplexity, data_path, CHECKPOINT_PATH))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10/21/2020 21:14:06 - INFO - transformers.configuration_utils -   loading configuration file /content/drive/My Drive/finetuned_models/wodehouse/config.json\n",
            "10/21/2020 21:14:06 - INFO - transformers.configuration_utils -   Model config GPT2Config {\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 50256,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 50256,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 1024,\n",
            "  \"n_head\": 16,\n",
            "  \"n_layer\": 24,\n",
            "  \"n_positions\": 1024,\n",
            "  \"n_special\": 0,\n",
            "  \"predict_special_tokens\": true,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50\n",
            "    }\n",
            "  },\n",
            "  \"vocab_size\": 50257\n",
            "}\n",
            "\n",
            "10/21/2020 21:14:06 - INFO - transformers.modeling_utils -   loading weights file /content/drive/My Drive/finetuned_models/wodehouse/pytorch_model.bin\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Running on device:  cuda\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "10/21/2020 21:15:11 - INFO - transformers.configuration_utils -   loading configuration file /content/drive/My Drive/finetuned_models/wodehouse/config.json\n",
            "10/21/2020 21:15:11 - INFO - transformers.configuration_utils -   Model config GPT2Config {\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 50256,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 50256,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 1024,\n",
            "  \"n_head\": 16,\n",
            "  \"n_layer\": 24,\n",
            "  \"n_positions\": 1024,\n",
            "  \"n_special\": 0,\n",
            "  \"predict_special_tokens\": true,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50\n",
            "    }\n",
            "  },\n",
            "  \"vocab_size\": 50257\n",
            "}\n",
            "\n",
            "10/21/2020 21:15:11 - INFO - transformers.tokenization_utils -   Model name '/content/drive/My Drive/finetuned_models/wodehouse/' not found in model shortcut name list (gpt2, gpt2-medium, gpt2-large, gpt2-xl, distilgpt2). Assuming '/content/drive/My Drive/finetuned_models/wodehouse/' is a path, a model identifier, or url to a directory containing tokenizer files.\n",
            "10/21/2020 21:15:11 - INFO - transformers.tokenization_utils -   Didn't find file /content/drive/My Drive/finetuned_models/wodehouse/added_tokens.json. We won't load it.\n",
            "10/21/2020 21:15:11 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/finetuned_models/wodehouse/vocab.json\n",
            "10/21/2020 21:15:11 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/finetuned_models/wodehouse/merges.txt\n",
            "10/21/2020 21:15:11 - INFO - transformers.tokenization_utils -   loading file None\n",
            "10/21/2020 21:15:11 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/finetuned_models/wodehouse/special_tokens_map.json\n",
            "10/21/2020 21:15:11 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/finetuned_models/wodehouse/tokenizer_config.json\n",
            "10/21/2020 21:15:12 - INFO - run_language_modeling -   Loading features from cached file /content/drive/My Drive/Colab Notebooks/wodehouse_generator/data/gpt2_cached_lm_128_validate.txt\n",
            "10/21/2020 21:15:12 - INFO - run_language_modeling -   ***** Running evaluation  *****\n",
            "10/21/2020 21:15:12 - INFO - run_language_modeling -     Num examples = 580\n",
            "10/21/2020 21:15:12 - INFO - run_language_modeling -     Batch size = 2\n",
            "Evaluating: 100%|██████████| 290/290 [00:17<00:00, 16.77it/s]\n",
            "10/21/2020 21:15:29 - INFO - run_language_modeling -   ***** Eval results  *****\n",
            "10/21/2020 21:15:29 - INFO - run_language_modeling -     perplexity = tensor(19.8150)\n",
            "10/21/2020 21:15:29 - INFO - transformers.configuration_utils -   loading configuration file /content/drive/My Drive/finetuned_models/wodehouse/config.json\n",
            "10/21/2020 21:15:29 - INFO - transformers.configuration_utils -   Model config GPT2Config {\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 50256,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 50256,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 1024,\n",
            "  \"n_head\": 16,\n",
            "  \"n_layer\": 24,\n",
            "  \"n_positions\": 1024,\n",
            "  \"n_special\": 0,\n",
            "  \"predict_special_tokens\": true,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50\n",
            "    }\n",
            "  },\n",
            "  \"vocab_size\": 50257\n",
            "}\n",
            "\n",
            "10/21/2020 21:15:29 - INFO - transformers.tokenization_utils -   Model name '/content/drive/My Drive/finetuned_models/wodehouse/' not found in model shortcut name list (gpt2, gpt2-medium, gpt2-large, gpt2-xl, distilgpt2). Assuming '/content/drive/My Drive/finetuned_models/wodehouse/' is a path, a model identifier, or url to a directory containing tokenizer files.\n",
            "10/21/2020 21:15:29 - INFO - transformers.tokenization_utils -   Didn't find file /content/drive/My Drive/finetuned_models/wodehouse/added_tokens.json. We won't load it.\n",
            "10/21/2020 21:15:29 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/finetuned_models/wodehouse/vocab.json\n",
            "10/21/2020 21:15:29 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/finetuned_models/wodehouse/merges.txt\n",
            "10/21/2020 21:15:29 - INFO - transformers.tokenization_utils -   loading file None\n",
            "10/21/2020 21:15:29 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/finetuned_models/wodehouse/special_tokens_map.json\n",
            "10/21/2020 21:15:29 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/finetuned_models/wodehouse/tokenizer_config.json\n",
            "10/21/2020 21:15:29 - INFO - run_language_modeling -   Loading features from cached file /content/drive/My Drive/Colab Notebooks/wodehouse_generator/data/gpt2_cached_lm_128_test.txt\n",
            "10/21/2020 21:15:29 - INFO - run_language_modeling -   ***** Running evaluation  *****\n",
            "10/21/2020 21:15:29 - INFO - run_language_modeling -     Num examples = 959\n",
            "10/21/2020 21:15:29 - INFO - run_language_modeling -     Batch size = 2\n",
            "Evaluating:   0%|          | 0/480 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "19.814990997314453 is the perplexity of /content/drive/My Drive/Colab Notebooks/wodehouse_generator/data/validate.txt according to /content/drive/My Drive/finetuned_models/wodehouse/\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Evaluating: 100%|██████████| 480/480 [00:28<00:00, 16.75it/s]\n",
            "10/21/2020 21:15:58 - INFO - run_language_modeling -   ***** Eval results  *****\n",
            "10/21/2020 21:15:58 - INFO - run_language_modeling -     perplexity = tensor(14.7571)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "14.757075309753418 is the perplexity of /content/drive/My Drive/Colab Notebooks/wodehouse_generator/data/test.txt according to /content/drive/My Drive/finetuned_models/wodehouse/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r5o7v2hmhMTO"
      },
      "source": [
        "### Generate samples\n",
        "The following code generates text samples that are are continuations of a provided prompt."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XcvySe_wrCWh"
      },
      "source": [
        "def generate_samples(args, model, prompt_text):\n",
        "  \"\"\"Generating sampling for the provided prompt using the provided model.\"\"\"\n",
        "  set_seed(args.seed)\n",
        "\n",
        "  tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, cache_dir=None)\n",
        "\n",
        "  requires_preprocessing = args.model_type in run_generation.PREPROCESSING_FUNCTIONS.keys()\n",
        "  encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False, return_tensors=\"pt\")\n",
        "  encoded_prompt = encoded_prompt.to(args.device)\n",
        "\n",
        "  output_sequences = model.generate(\n",
        "      input_ids=encoded_prompt,\n",
        "      max_length=args.length + len(encoded_prompt[0]),\n",
        "      temperature=args.temperature,\n",
        "      top_k=args.k,\n",
        "      top_p=args.p,\n",
        "      repetition_penalty=args.repetition_penalty,\n",
        "      do_sample=True,\n",
        "      num_return_sequences=args.num_return_sequences,\n",
        "  )\n",
        "\n",
        "  # Remove the batch dimension when returning multiple sequences\n",
        "  if len(output_sequences.shape) > 2:\n",
        "    output_sequences.squeeze_()\n",
        "\n",
        "  generated_sequences = []\n",
        "\n",
        "  for generated_sequence_idx, generated_sequence in enumerate(output_sequences):\n",
        "    generated_sequence = generated_sequence.tolist()\n",
        "\n",
        "    # Decode text\n",
        "    text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)\n",
        "\n",
        "    # Remove all text after the stop token\n",
        "    text = text[: text.find(args.stop_token) if args.stop_token else None]\n",
        "\n",
        "    # Remove the excess text that was used for pre-processing\n",
        "    text = text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :]\n",
        "\n",
        "    # Add the prompt at the beginning of the sequence.\n",
        "    total_sequence = prompt_text + text\n",
        "\n",
        "    generated_sequences.append(total_sequence)\n",
        "\n",
        "  return generated_sequences"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s3LKo9VVjHw0"
      },
      "source": [
        "# Set this to the checkpoint you want to use for generation, or to \"gpt2-medium\"\n",
        "# to generate with the pre-trained model without finetuning.\n",
        "\n",
        "def generate_wodehouse_samples(prompt):\n",
        "\n",
        "  # You should try out other prompts as well as no prompt at all.\n",
        "  PROMPT = prompt\n",
        "  device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "  n_gpu = torch.cuda.device_count()\n",
        "  print(\"Running on device: \", device)\n",
        "\n",
        "  args = collections.defaultdict(\n",
        "    model_name_or_path=CHECKPOINT_PATH,\n",
        "    output_dir=OUTPUT_PATH,\n",
        "    n_gpu=n_gpu,\n",
        "    mlm=False,\n",
        "    device=device,\n",
        "    model_type='gpt2',\n",
        "    seed=42,\n",
        "    stop_token=None, # Set this if your dataset has a special word that indicates the end of a text.\n",
        "    temperature=1.0,  # temperature sampling. Set this to temperature=1.0 to not use temperature.\n",
        "    k=50,  # k for top-k sampling. Set this to k=0 to not use top-k.\n",
        "    p=1.0,  # p for nucleus sampling. Set this to p=1.0 to not use nucleus sampling.\n",
        "    repetition_penalty=None,\n",
        "    length=900,  # Number of tokens to generate.\n",
        "    num_return_sequences=3,  # Number of independently computed samples to generate.\n",
        "  )\n",
        "  args = DictToObject(dict(args))\n",
        "\n",
        "  model = load_model(args)\n",
        "  sequences = generate_samples(args, model, PROMPT)\n",
        "\n",
        "  return sequences\n"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qGFeDb23LNJO",
        "outputId": "0b92a69c-f71c-40d8-c79c-17bb5b50c557",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "sequences = generate_wodehouse_samples(\"Seated with his wife at breakfast on the veranda which overlooked the rolling lawns and leafy woods of his charming Sussex home, Geoffrey Windlebird, the great financier, was enjoying the morning sun to the full. \")\n",
        "\n",
        "for idx, sequence in enumerate(sequences):\n",
        "  print('\\n====== GENERATION {} ======'.format(idx))\n",
        "  print(sequence)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10/21/2020 21:17:54 - INFO - transformers.configuration_utils -   loading configuration file /content/drive/My Drive/finetuned_models/wodehouse/config.json\n",
            "10/21/2020 21:17:54 - INFO - transformers.configuration_utils -   Model config GPT2Config {\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 50256,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 50256,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 1024,\n",
            "  \"n_head\": 16,\n",
            "  \"n_layer\": 24,\n",
            "  \"n_positions\": 1024,\n",
            "  \"n_special\": 0,\n",
            "  \"predict_special_tokens\": true,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50\n",
            "    }\n",
            "  },\n",
            "  \"vocab_size\": 50257\n",
            "}\n",
            "\n",
            "10/21/2020 21:17:54 - INFO - transformers.modeling_utils -   loading weights file /content/drive/My Drive/finetuned_models/wodehouse/pytorch_model.bin\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Running on device:  cuda\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "10/21/2020 21:18:07 - INFO - transformers.configuration_utils -   loading configuration file /content/drive/My Drive/finetuned_models/wodehouse/config.json\n",
            "10/21/2020 21:18:07 - INFO - transformers.configuration_utils -   Model config GPT2Config {\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 50256,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 50256,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 1024,\n",
            "  \"n_head\": 16,\n",
            "  \"n_layer\": 24,\n",
            "  \"n_positions\": 1024,\n",
            "  \"n_special\": 0,\n",
            "  \"predict_special_tokens\": true,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50\n",
            "    }\n",
            "  },\n",
            "  \"vocab_size\": 50257\n",
            "}\n",
            "\n",
            "10/21/2020 21:18:07 - INFO - transformers.tokenization_utils -   Model name '/content/drive/My Drive/finetuned_models/wodehouse' not found in model shortcut name list (gpt2, gpt2-medium, gpt2-large, gpt2-xl, distilgpt2). Assuming '/content/drive/My Drive/finetuned_models/wodehouse' is a path, a model identifier, or url to a directory containing tokenizer files.\n",
            "10/21/2020 21:18:07 - INFO - transformers.tokenization_utils -   Didn't find file /content/drive/My Drive/finetuned_models/wodehouse/added_tokens.json. We won't load it.\n",
            "10/21/2020 21:18:07 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/finetuned_models/wodehouse/vocab.json\n",
            "10/21/2020 21:18:07 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/finetuned_models/wodehouse/merges.txt\n",
            "10/21/2020 21:18:07 - INFO - transformers.tokenization_utils -   loading file None\n",
            "10/21/2020 21:18:07 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/finetuned_models/wodehouse/special_tokens_map.json\n",
            "10/21/2020 21:18:07 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/finetuned_models/wodehouse/tokenizer_config.json\n",
            "10/21/2020 21:18:08 - WARNING - transformers.modeling_utils -   Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "====== GENERATION 0 ======\n",
            "Seated with his wife at breakfast on the veranda which overlooked the rolling lawns and leafy woods of his charming Sussex home, Geoffrey Windlebird, the great financier, was enjoying the morning sun to the full.  He was\n",
            "being a pleasant fellow, and had not expected his visitor to have a\n",
            "familiar name. The only thing which made him so different from the other\n",
            "well-known figures in the New York financial establishment--and he felt\n",
            "uncomfortable--was that he thought of him as an old friend. In his\n",
            "habit of collecting things at random, Geoffrey always found in his\n",
            "own collection one that was either a gold coin or a hundred dollars\n",
            "in gold. He did not believe in collecting, but he was grateful that\n",
            "he had this new toy in the form of himself. On the terrace he had a\n",
            "stash of letters. A few were dated on the front page, and a few had pages\n",
            "which looked to have been stamped on to them by hand. To Geoffrey, on reading a\n",
            "dozen, there seemed to arise an impression that he was meeting the\n",
            "new boss once more. He was amused to see the face of another financial\n",
            "manager. He was sure it was the same.\n",
            "\n",
            "'I hope you don't mind having a tea?' asked the man.\n",
            "\n",
            "'Not really. I wish to sit down with a nice, quiet cigar.'\n",
            "\n",
            "'All right,' said the man, as they crossed the road.\n",
            "\n",
            "'What's the matter?'\n",
            "\n",
            "'I see that my room is a little too big. I know it; I know of a place, I suppose,\n",
            "near Fifth Avenue.'\n",
            "\n",
            "'Fifth Avenue. This is it?'\n",
            "\n",
            "'Well, I should take a bit more rooms on that. That way, if I am going\n",
            "to stay here long, I suppose it's going to be one or two of the rest of the\n",
            "house?'\n",
            "\n",
            "'Certainly, sir. It beats all--this is a long way away. I've the whole lot packed, that is\n",
            "to say. I hope it's all right.'\n",
            "\n",
            "On the terrace were about forty-five new pallets stacked\n",
            "with things.\n",
            "\n",
            "'Well, what have you got on?'\n",
            "\n",
            "'A cigarette case, sir.'\n",
            "\n",
            "Mr Windlebird made a little gesture with his hand\n",
            "to indicate the contents. He took a second glance\n",
            "at the first fifteen and saw that the man with the\n",
            "money was looking decidedly unimpressed. He looked about him\n",
            "thoughtfully for the other.\n",
            "\n",
            "'You can have a drink of it,' he said. 'Sit down and smoke. I am not so much\n",
            "a smoker as I used to be. Good day.'\n",
            "\n",
            "He left without a word.\n",
            "\n",
            "In the sitting-room and opposite the smoking-room Mr Windlebird observed the\n",
            "trouble of a young gentleman who had been waiting, while the\n",
            "first secretary went off and made his way upstairs,\n",
            "to his room to be taken by the first secretary's lady, for breakfast, and\n",
            "to sleep after the meeting to-morrow. This first secretary\n",
            "had brought his papers with him when the meal was finished, and as\n",
            "such\n",
            "there was nothing particularly to say to him on this new visit. He seemed to have a\n",
            "nearly\n",
            "simple idea of his role in the scheme of affairs. With a good deal of\n",
            "effort, indeed, he had come to the conclusion that it was Mr\n",
            "Windlebird who had arranged for the opening of the letters.\n",
            "\n",
            "'What are you doing, sir?' asked Mr Windlebird.\n",
            "\n",
            "'I am the owner of the firm, sir. Just as you wish.'\n",
            "\n",
            "'But, the letters, are they?'\n",
            "\n",
            "'No. What are you doing in New York, then?'\n",
            "\n",
            "'No business, sir. It's the scheme that interests me, sir.'\n",
            "\n",
            "Mr Windlebird raised his head.\n",
            "\n",
            "'Gather round, what are you doing?'\n",
            "\n",
            "Somebody had been leaning over to the door that led in, peering in out\n",
            "through the window. The hand of the person whose watch was\n",
            "coming fell on a silver bracelet, which indicated that this was the\n",
            "sir of the firm.\n",
            "\n",
            "'Mr Windlebird! This man was called Windlebird.'\n",
            "\n",
            "The latter looked pale. His eyes, like the first secretary's had been, wore an expression\n",
            "of almost shock.\n",
            "\n",
            "'Mr Windlebird!'\n",
            "\n",
            "====== GENERATION 1 ======\n",
            "Seated with his wife at breakfast on the veranda which overlooked the rolling lawns and leafy woods of his charming Sussex home, Geoffrey Windlebird, the great financier, was enjoying the morning sun to the full. \n",
            "\n",
            "As his wife did not enter the room, she was unable to\n",
            "understand his attitude, but she did see from a glance that he was\n",
            "looking about him anxiously. She felt that he had, indeed, become\n",
            "excited.\n",
            "\n",
            "\"So they are saying! We must certainly start at once.\"\n",
            "\n",
            "\"I've always hoped that the time would come,\" said Mr. Windlebird, smiling, \"that\n",
            "the time might come. I don't want you wasting any time talking to the\n",
            "little ass. Why, I'll go and look in here and ask him what you want with. I've\n",
            "thought of it for a moment. Tell him, don't you know, you know, that I have something\n",
            "to think of before we go, and he must be thinking about it.\"\n",
            "\n",
            "Mr. Windlebird's manner did not appear to have stimulated a desire to\n",
            "discuss the subject of Mr. Bickersdyke's troubles, as did that of his\n",
            "dear friend, who was still gesticulating wildly in his manner of address. But it\n",
            "seemed the best thing in the world.\n",
            "\n",
            "\"I'll go and fetch you something,\" he said, as they retired for the\n",
            "desert. \"You don't want any trouble.\"\n",
            "\n",
            "\"Right-o,\" said Mr. Windlebird.\n",
            "\n",
            "His companion followed him, disinterested. Windlebird could walk by anyone, a quiet,\n",
            "quiet man. He did not care a jot who knew what the things in his life\n",
            "were.\n",
            "\n",
            "\"It's on your left,\" he said, coming in.\n",
            "\n",
            "Mr. Windlebird looked down the veranda. The house-front was clear of foliage.\n",
            "Mr. Windlebird was one of those men, even by the rules of nature, whose\n",
            "sight is almost always right, as it were. The trees that covered his\n",
            "front lawn seemed to take him through the air; the street, just now,\n",
            "had the air of being full of a vast collection of birds, all moving in a rapid\n",
            "sort of way about themselves.\n",
            "\n",
            "Windlebird glanced again down the veranda. With an exclamation, he jumped over\n",
            "fences, dashed back into the kitchen, and made for\n",
            "sitting-room.\n",
            "\n",
            "\"I can read through that tableclerk window, Mr. Windlebird,\" said Mr. Windlebird, when\n",
            "Biddy had brought the news of his absence to\n",
            "him in the cab. \"I was wondering whether you might be up.\"\n",
            "\n",
            "\"Up and down,\" said Mr. Windlebird. \"Biff a chair to you and see the maid. I feel\n",
            "really hot with pleasure.\"\n",
            "\n",
            "Biddy, turning the page, returned Mr. Windlebird's gaze with the air of one who\n",
            "strenuously endeavouring to find\n",
            "just the right words to make\n",
            "it clear that there was nothing of the kind to be got out of him.\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "12\n",
            "\n",
            "\n",
            "\n",
            "And then, after the preliminaries of this interview had been done, there arrived in the\n",
            "driving-cab and the backseat a voice on a high heel. It was Lord Dreever,\n",
            "standing on a chair beside him, bearing two large papers. When he sat down,\n",
            "he was staring at his wife. There was a certain sadness about\n",
            "the look in her eyes. There was something in her gentle form which, but\n",
            "for that expression--a kind of grim determination--had\n",
            "sensed that this great change in her was about to take place at last.\n",
            "\n",
            "\"Mr. Windlebird,\" she said, \"please allow me, for the second time in two days, to sit this one\n",
            "day, to-day! And to-morrow, we shall walk to Aldershot.\"\n",
            "\n",
            "The sight of Lord Dreever, whose presence was always accompanied by its chief\n",
            "attacker, Mr. Windlebird, was too terrible to be endured.\n",
            "He sat down and looked at his wife.\n",
            "\n",
            "\"Windlebird,\" he cried in a voice too deep for words, \"I am not--I am not a man to\n",
            "look at! I have been too busy.\"\n",
            "\n",
            "He eyed her in silence. Mr. Windlebird, who for days had\n",
            "\n",
            "====== GENERATION 2 ======\n",
            "Seated with his wife at breakfast on the veranda which overlooked the rolling lawns and leafy woods of his charming Sussex home, Geoffrey Windlebird, the great financier, was enjoying the morning sun to the full.  He\n",
            "was in his normal condition, and with the warmth of sun-soaked cheeks was\n",
            "conspicuous in the midst of the group. The first remark he heard as he\n",
            "began to make a move at breakfast one morning was: \"Mr. Waring,\n",
            "I am glad to see you. I am glad I saw you, too, as the old man.\"\n",
            "\n",
            "\"Thanks,\" said Mr. Windlebird. \"I have to get my bicycle to Waterloo to\n",
            "visit the old man.\"\n",
            "\n",
            "It must have been early. By the clock in the next minute he had already\n",
            "started off the second half hour, and was still in his morning clothes when\n",
            "Miss Peavey, coming into the room, appeared beside him.\n",
            "\n",
            "\"What is up, Mr. Windlebird?\" she inquired.\n",
            "\n",
            "Windlebird shook her hand.\n",
            "\n",
            "\"Mr. Windlebird, have I said any good to you for the past few days?\" he said.\n",
            "\n",
            "Miss Peavey made no answer.\n",
            "\n",
            "For the moment Mr. Windlebird, at the request of Miss Peavey, leaned back and meditated\n",
            "in silence. It was not until he saw Miss Peavey take out her card that his mind\n",
            "woke up.\n",
            "\n",
            "\"Why, there's a letter, you see, from _Mr. Waring_ on the subject of your\n",
            "soul and soul-time. What are you--?\"\n",
            "\n",
            "The thought of his young master, Mr. Peavey's boyhood friend, in prison and unable\n",
            "to write sent him wild with interest. He seized it, and read.\n",
            "\n",
            "It read:\n",
            "\n",
            "\"The great Mr. Waring of the Family.  _Your father----\"\n",
            "\n",
            "\"Oh, I know him all right,\" protested Miss Peavey.\n",
            "\n",
            "\"Of course--of course,\" agreed Windlebird, shaking his hand.\n",
            "\n",
            "\"What is the matter, Mr. Windlebird?\" asked Miss Peavey. \"I've been waiting\n",
            "for him for so long!\"\n",
            "\n",
            "Windlebird's eye, like the other's of his house, was drawn from the card to his\n",
            "mind. It had not been for the first time since the last\n",
            "day of the first summer holidays when the old\n",
            "boy's father had put up his pen--he had gone on vacation a\n",
            "week after returning from New York for a week\n",
            "and a half-term at college. His sister, though not the last\n",
            "time his parents had taken him to the house,\n",
            "had come when he arrived, and he always looked as if he had got\n",
            "into a slump. His brother was his new employer, and all the\n",
            "friends and acquaintances whom he had known through his school days had\n",
            "disloped at a moment when nobody could have expected\n",
            "his attitude of attitude to change.\n",
            "\n",
            "Windlebird was not surprised. He had always liked his old college friend\n",
            "hardly at all. He had not yet noticed it in himself, but he now,\n",
            "though he had come to think of him in the old days as Mr. Windlebird,\n",
            "could understand, Mr. Windlebird, that that the only real friend one had\n",
            "in this world--the only one--was himself. The young Mr. Windlebird\n",
            "stood by his father's side, silent, unafraid, and very careful of what she\n",
            "sought for, always a little uneasy because of a thought which, for\n",
            "the moment now in her mind, seemed to be coming. She knew that, if she\n",
            "wished to be taken seriously, and not merely in the vein of its\n",
            "companion, he must come in to help her. She had to go on being his mother.\n",
            "\n",
            "\"What did they do?\" she asked. The conversation was not long enough for her to\n",
            "have answered any one-sidedly. She spoke almost as if she wanted to make a\n",
            "point. She was feeling uneasy and nervous. Mr. Windlebird's feelings and\n",
            "thoughts were mixed up. It was his duty to listen.\n",
            "\n",
            "Miss Peavey's eyes came back fixedly to the card.\n",
            "\n",
            "\"Ah!\" she said. \"Why?\"\n",
            "\n",
            "\"I have decided that you must come to Waterloo, and be his special adviser\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IkWgdXIZ-shY",
        "outputId": "ce1b8890-65d8-45e2-cad5-b07992370f68",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "sequences = generate_wodehouse_samples(\"It was in Oxford Street at the hour when women come up from the suburbs to shop; and he was standing among the dogs and commissionaires outside Selfridge’s.\")\n",
        "\n",
        "for idx, sequence in enumerate(sequences):\n",
        "  print('\\n====== GENERATION {} ======'.format(idx))\n",
        "  print(sequence)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10/21/2020 20:53:14 - INFO - transformers.configuration_utils -   loading configuration file /content/drive/My Drive/finetuned_models/wodehouse/config.json\n",
            "10/21/2020 20:53:14 - INFO - transformers.configuration_utils -   Model config GPT2Config {\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 50256,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 50256,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 1024,\n",
            "  \"n_head\": 16,\n",
            "  \"n_layer\": 24,\n",
            "  \"n_positions\": 1024,\n",
            "  \"n_special\": 0,\n",
            "  \"predict_special_tokens\": true,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50\n",
            "    }\n",
            "  },\n",
            "  \"vocab_size\": 50257\n",
            "}\n",
            "\n",
            "10/21/2020 20:53:14 - INFO - transformers.modeling_utils -   loading weights file /content/drive/My Drive/finetuned_models/wodehouse/pytorch_model.bin\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Running on device:  cuda\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "10/21/2020 20:53:54 - INFO - transformers.configuration_utils -   loading configuration file /content/drive/My Drive/finetuned_models/wodehouse/config.json\n",
            "10/21/2020 20:53:54 - INFO - transformers.configuration_utils -   Model config GPT2Config {\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 50256,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 50256,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 1024,\n",
            "  \"n_head\": 16,\n",
            "  \"n_layer\": 24,\n",
            "  \"n_positions\": 1024,\n",
            "  \"n_special\": 0,\n",
            "  \"predict_special_tokens\": true,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50\n",
            "    }\n",
            "  },\n",
            "  \"vocab_size\": 50257\n",
            "}\n",
            "\n",
            "10/21/2020 20:53:54 - INFO - transformers.tokenization_utils -   Model name '/content/drive/My Drive/finetuned_models/wodehouse' not found in model shortcut name list (gpt2, gpt2-medium, gpt2-large, gpt2-xl, distilgpt2). Assuming '/content/drive/My Drive/finetuned_models/wodehouse' is a path, a model identifier, or url to a directory containing tokenizer files.\n",
            "10/21/2020 20:53:54 - INFO - transformers.tokenization_utils -   Didn't find file /content/drive/My Drive/finetuned_models/wodehouse/added_tokens.json. We won't load it.\n",
            "10/21/2020 20:53:54 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/finetuned_models/wodehouse/vocab.json\n",
            "10/21/2020 20:53:54 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/finetuned_models/wodehouse/merges.txt\n",
            "10/21/2020 20:53:54 - INFO - transformers.tokenization_utils -   loading file None\n",
            "10/21/2020 20:53:54 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/finetuned_models/wodehouse/special_tokens_map.json\n",
            "10/21/2020 20:53:54 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/finetuned_models/wodehouse/tokenizer_config.json\n",
            "10/21/2020 20:53:54 - WARNING - transformers.modeling_utils -   Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "====== GENERATION 0 ======\n",
            "It was in Oxford Street at the hour when women come up from the suburbs to shop; and he was standing among the dogs and commissionaires outside Selfridge’s. He was\n",
            "still in the habit of sitting up and listening to them speak quietly and\n",
            "frequently, when he was offered to talk, and this year there had not\n",
            "been a single dog in the neighbourhood. The nearest Mr Perceval\n",
            "had came to talking was from Mr Perceval, a dog in a neighbourhood so\n",
            "small and isolated--it consisted of only six streets in each--that it\n",
            "may not even be possible for a man of his position to listen\n",
            "peacefully.\n",
            "\n",
            "He was aware that, for a moment, he was compelled to look over his shoulder, but\n",
            "he had done so only to see a slight gurgle of what might have been\n",
            "a sheep.\n",
            "\n",
            "“That’s not worth the candle.”\n",
            "\n",
            "“Why, what on earth is your name going to be?”\n",
            "\n",
            "“Yes, yes! I’ve just come in to the office,” said Mr Perceval, starting\n",
            "down the street.\n",
            "\n",
            "Lord Emsworth’s head rose from his chair. His face was pallid and his forehead\n",
            "was flushed.\n",
            "\n",
            "“But what am I to do now?” He had gone, and in a quarter of an hour’s\n",
            "time the door of his flat was locked against the entry of his\n",
            "fellow-labourer. The sight of Lord Emsworth, who was now leaning\n",
            "against the window and looking on the sidewalk, was\n",
            "exhausting. The sight of that gentleman in his suit of green\n",
            "collared, untucked trousers, and a green flannel suit, was all\n",
            "his soul. He would have accepted the terms of his engagement\n",
            "on the day, but a thing as trivial as to go in as a professional\n",
            "sitting-room attendant, and find it on the floor half a dozen\n",
            "years later, did not comfort him. The sight of what awaited\n",
            "him at Mr Perceval’s seemed to keep him on his guard again. This\n",
            "caught his eye, and he drew himself up as low as he could. He made a\n",
            "diamond or two\n",
            "with his left hand. He took a step towards him, with the intention of\n",
            "laying his right hand on the back of Lord Emsworth’s\n",
            "head and raising it to greet him.\n",
            "\n",
            "“Mr Pickering,” he said, “’is my name,” and turned to Mr Perceval, “is\n",
            "our address, Mr Perceval is Mr Perceval, ’and I want you\n",
            "just now to show me an advertisement of the _Cosy Moments_,\n",
            "if you like.”\n",
            "\n",
            "“What the devil is this?”\n",
            "\n",
            "“What, ‘Mr Pickering’s name?’\n",
            "\n",
            "“My father,” said Mr Perceval placidly.\n",
            "\n",
            "“And the _Cosy Moments_,” said Lord Emsworth, “has just published the fourth\n",
            "issue,” and began to read the page in which the advertisement had given\n",
            "His lordship a description of Lord Marshmoreton,\n",
            "the\n",
            "Pugsy of England. He was reading with an air of concentration. He had an impression\n",
            "of himself looking into some sort of a mirror.\n",
            "\n",
            "“The third edition,” said Mr Perceval, “has had three more issues, and it's in\n",
            "the hands of the best,” he said, moving towards a chair, “of a dozen or more\n",
            "dozen authors, who would not give them this or any of their stuff for a paper of their\n",
            "likes which\n",
            "would be worth having in most shops.”\n",
            "\n",
            "Lord Emsworth smiled. He was always inclined to smile.\n",
            "\n",
            "“Good Lord,” he moaned, as the advertisement came to a close, “you’re a\n",
            "good man to have put yourself through. It is a wonderful thing. If, like a boy in a\n",
            "paper, I was allowed to keep up my good cheer, what with all the coldness and\n",
            "stiffness of Mr Perceval, I should have to find it all in vain to continue. But\n",
            "\n",
            "\n",
            "====== GENERATION 1 ======\n",
            "It was in Oxford Street at the hour when women come up from the suburbs to shop; and he was standing among the dogs and commissionaires outside Selfridge’s.\n",
            "\n",
            "“What did you do?”\n",
            "\n",
            "“No news, no news, no news!”\n",
            "\n",
            "“Don’t lie to her.”\n",
            "\n",
            "“When I told her to get down here I was telling her to come in\n",
            "and sit down so you could look all right, she wasn’t there, and now she has\n",
            "found out so, what?”\n",
            "\n",
            "“She got up and I went and stood in front of her. She came over. Why’s\n",
            "she behaving like that?”\n",
            "\n",
            "“Why is she behaving like that? Why, she’s doing a hundred\n",
            "brains a day like that. Well, I’m getting a kick if she’s going to\n",
            "look like that.” He turned to Mrs. Ukridge, who had stopped on her\n",
            "foot under the counter for a moment to admire the roses she had collected\n",
            "for sale, as\n",
            "dismissively as he could manage; “How are you getting on?”\n",
            "\n",
            "“What’s the matter, Ukridge?” asked the voice of the child who had been playing\n",
            "the piano.\n",
            "\n",
            "A voice spoke.\n",
            "\n",
            "“I know it beats me, Mr. Ukridge.”\n",
            "\n",
            "Mr. Ukridge turned to the speaker.\n",
            "\n",
            "“I will just go along a foot,” he said weakly, as a hand passed over a\n",
            "handkerchief-covered plate, “to try to calm her down. You know, Miss Ukridge.\n",
            "You have had a shock just now, and I would like to think that all this\n",
            "is over. You’ll have a shock after this, and I’ll have a shock just\n",
            "as good as yours! You ought to find a nice home, don’t you think.”\n",
            "\n",
            "“Of course, a nice home, Mrs. Ukridge.”\n",
            "\n",
            "“And I had to have that old man send a few flowers down here.” There\n",
            "was a touch of melancholy in her voice, but she did not rise.”\n",
            "\n",
            "The face of Ukridge, who had made the request.\n",
            "\n",
            "“Of course I’ve had a shock.”\n",
            "\n",
            "“The kid has told me that, after seeing you in a bar, he brought\n",
            "himself to see me right across the way, and all this while he was\n",
            "going to a place where they gave a quarter of a cent in the window\n",
            "to a lady which was a very pretty girl and you had not seen.\n",
            "When he came back I was thinking,” said Ukridge,”’\n",
            "so what? I wish you would not think that, really----”\n",
            "\n",
            "“He could have a better job than yours in the country. He seemed so bright and clever all\n",
            "the time. It’s enough to give a poor lad a chance. You’ll be sure that\n",
            "he starts a little by himself at Stapleton and ends\n",
            "up doing something on a high scale.”\n",
            "\n",
            "He sat on the counter, his back to Ukridge, for his eyes met Mrs. Ukridge’s.\n",
            "\n",
            "“What is it?” his voice boomed. “Go on.’I will let you, but why am I saying\n",
            "this?”\n",
            "\n",
            "Ukridge listened coldly. His face shone with pain. His arms hung at his sides, and his\n",
            "mind was made up. There was another hand gently caressing, a hand which moved\n",
            "away from the glass window. There was a short, thin movement of the wrist and\n",
            "a quick flash of the hair in front of his eye which said’Yes, do, you are.”\n",
            "\n",
            "Mr. Ukridge sighed deeply. He regarded the face of Mrs. Ukridge with a frown.\n",
            "\n",
            "“Thank heavens, Mrs. Ukridge.”\n",
            "\n",
            "Ukridge stood and gazed at her in a sad smile which seemed to fall upon him as\n",
            "he spoke a word. His fingers closed on the handle. Mr. Ukridge\n",
            "stopped upon his feet and stared at him for a very\n",
            "\n",
            "====== GENERATION 2 ======\n",
            "It was in Oxford Street at the hour when women come up from the suburbs to shop; and he was standing among the dogs and commissionaires outside Selfridge’s. He\n",
            "felt the rush of a woman and the sensation of a crowd gathering on\n",
            "his shoulder. And when a woman came with a man who looked as if she\n",
            "was saying, “It’s as warm as this!” he shook his head.\n",
            "\n",
            "“No! No.” It’s not, it’s not. It isn't!” The words came to him at once.\n",
            "“I must be mistaken. I am not mistaken. Why, it is as bright\n",
            "as a sun. I see it more clearly than a sunbeam. We are a sort\n",
            "of hive-of-stars. I cannot see a second of our existence without\n",
            "seeing a bright, shining city of stars. What a great city of stars it\n",
            "is! And it gives me the awful sensation that it is as\n",
            "welterly warm as this!”\n",
            "\n",
            "“It isn’t,” said Sally sympathetically.\n",
            "\n",
            "“When it isn’t, of course, it seems as if the suns would stop. But...”\n",
            "\n",
            "And she broke off, almost regretfully. It was her way to stop, but she\n",
            "felt that she had only begun.\n",
            "\n",
            "“I’m worried about those children of Mervo,” she murmured. “And,” she went on\n",
            "till it was night, “I don’t suppose there’s time for their future.\n",
            "It’s not like this for them. They’re working hard now, but they’re\n",
            "not going to be ready at a time like this. We’re bound to lose\n",
            "them all.”\n",
            "\n",
            "“That’s a frightful thing for a world of starving children to think!”\n",
            "\n",
            "“My dear dear, it’s a horrible pity. I mean to say, they’re a dear old\n",
            "lot of fun. And they’re a lot too strong for this world. And, you know\n",
            "what, I can’t see what I’m going to do about it. We’re all on the right\n",
            "note,” said Sally. “I think I was right there. I’m not so sure they’re\n",
            "ready\n",
            "when I get back to Paris tomorrow, I think.”\n",
            "\n",
            "“The time is there. The time seems almost past.”\n",
            "\n",
            "“Yes?”\n",
            "\n",
            "“The time is always on the clock somewhere in the future.”\n",
            "\n",
            "“But’s this all the time?”\n",
            "\n",
            "Sally was not convinced. There was a pause. Then a sudden and unexpected\n",
            "chatter of laughter came from somewhere in the crowd. The\n",
            "pugilists of B. and B. were doing something about\n",
            "it in their corner. Sally and Bill laughed joyfully.\n",
            "\n",
            "“I don’t think I’ve ever been so happy--that I felt joy so\n",
            "broad and strong.”\n",
            "\n",
            "“Why, that’s right. How does she do it?”\n",
            "\n",
            "“She goes to some of these people and tells them she is there.” She was not alone in\n",
            "the thing, though Bill was the first. Jill, too, was there. Her face was\n",
            "an expression\n",
            "of surprise and admiration, but she seemed less than animated. She was the quiet girl\n",
            "with the strong mouth, who came across and broke her silence to the\n",
            "wondering crowd.\n",
            "\n",
            "“I can’t even think of anything, laddie!” she said.\n",
            "\n",
            "“Nothing!”\n",
            "\n",
            "“What’s that?” cried Sally. She spoke almost as if she wanted to make a remark.\n",
            "\n",
            "There was an instant of silence. Then a voice, a sort of whisper behind their backs,\n",
            "said at intervals, “Ginger! Ginger, why no? Ginger, why no?”\n",
            "\n",
            "“Come on!” said Bill.\n",
            "\n",
            "The voices stopped. Sally looked over her shoulder. Bill seemed to be listening. Bill looked\n"
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