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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "# Set up"
      ],
      "metadata": {
        "id": "290ItwXUQeoJ"
      }
    },
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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "7KxCeOBQYonI"
      },
      "outputs": [],
      "source": [
        "# Install necessary library\n",
        "\n",
        "!pip install transformers &> /dev/null\n",
        "!pip install gensim &> /dev/null"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!spacy download en_core_web_md"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "pctUo5mph5OO",
        "outputId": "1a60c0e0-c59b-492c-ecc2-0a505a4c551f"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting en_core_web_md==2.2.5\n",
            "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_md-2.2.5/en_core_web_md-2.2.5.tar.gz (96.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 96.4 MB 1.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: spacy>=2.2.2 in /usr/local/lib/python3.7/dist-packages (from en_core_web_md==2.2.5) (2.2.4)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_md==2.2.5) (2.0.6)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_md==2.2.5) (1.0.6)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_md==2.2.5) (1.0.5)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_md==2.2.5) (2.23.0)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_md==2.2.5) (1.1.3)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_md==2.2.5) (4.62.3)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_md==2.2.5) (0.8.2)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_md==2.2.5) (1.19.5)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_md==2.2.5) (7.4.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_md==2.2.5) (57.4.0)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_md==2.2.5) (0.4.1)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_md==2.2.5) (1.0.0)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_md==2.2.5) (3.0.6)\n",
            "Requirement already satisfied: importlib-metadata>=0.20 in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_md==2.2.5) (4.8.2)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_md==2.2.5) (3.10.0.2)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_md==2.2.5) (3.6.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_md==2.2.5) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_md==2.2.5) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_md==2.2.5) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_md==2.2.5) (2021.10.8)\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the model via spacy.load('en_core_web_md')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import GPT2Tokenizer, GPT2LMHeadModel\n",
        "import numpy as np\n",
        "import json\n",
        "import gensim.downloader as api\n",
        "import gensim\n",
        "import spacy\n",
        "import en_core_web_md\n",
        "import os"
      ],
      "metadata": {
        "id": "_PMaFl_KayWh"
      },
      "execution_count": 61,
      "outputs": []
    },
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        "device = 'cpu'\n",
        "if torch.cuda.is_available():\n",
        "    device = 'cuda'\n",
        "tokenizer = GPT2Tokenizer.from_pretrained('gpt2-medium')\n",
        "model = GPT2LMHeadModel.from_pretrained('gpt2-medium')\n",
        "model = model.to(device)\n"
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        {
          "output_type": "display_data",
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              "model_id": "8ab0865dbd0d4958b9e3294b61ec8c3c",
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            "text/plain": [
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          "metadata": {}
        },
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          "output_type": "display_data",
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              "model_id": "f9a802cb17554d33adcf2698d2f0daa3",
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          "output_type": "display_data",
          "data": {
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[==================================================] 100.0% 104.8/104.8MB downloaded\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similarity_function = en_core_web_md.load()"
      ],
      "metadata": {
        "id": "FXB0klMHjDya"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "word2vectors = gensim.downloader.load('glove-twitter-100')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PM4y86hIaKJk",
        "outputId": "1b25e433-33a9-4b1f-8334-26306d454047"
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      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[==================================================] 100.0% 387.1/387.1MB downloaded\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BgZFFzE7Y_XP",
        "outputId": "e42f9eb3-243e-4028-ffb7-14203133048e"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model link:\n",
        "# https://drive.google.com/drive/folders/1j6WISBalKI6icL_GmSlLSv-6CI71KE8X?usp=sharing\n",
        "# you can load the model folder once you have added a shortcut to the root directory of your google drive"
      ],
      "metadata": {
        "id": "2-S-rPv1ZJnL"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = '/content/drive/MyDrive/trained_models' \n",
        "person_list = ['ArianaGrande', 'ddlovato', 'cnnbrk', 'katyperry', 'shakira', 'twitter', 'cristiano', 'theellenshow', 'justinbieber', 'rihanna', 'YouTube', 'instagram', 'selenagomez', 'BarackObama', 'jtimberlake', 'KimKardashian', 'britneyspears', 'taylorswift', 'jimmyfallon', 'ladygaga', 'donaldtrump']\n",
        "for idx, i in enumerate(person_list):\n",
        "    person_list[idx] = person_list[idx].lower()"
      ],
      "metadata": {
        "id": "LTVWhBP-aRhn"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "word2vectors.similarity('queen', 'woman')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ScONKW-MhQJN",
        "outputId": "5b9b7b8b-922b-47f5-d5d0-ded45343883d"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7844197"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Function definition"
      ],
      "metadata": {
        "id": "dFiWHSzZcKrT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Select the word in word_list that is closest to the input word using word2vec\n",
        "def return_closest_word(word, word_list):\n",
        "    score_list = []\n",
        "    for i in word_list:\n",
        "        try:\n",
        "            score_list.append(word2vectors.similarity(word, i))\n",
        "        except KeyError:\n",
        "            score_list.append(np.random.rand())\n",
        "    return word_list[np.argmax(score_list)]"
      ],
      "metadata": {
        "id": "9qVUJHxKRiKp"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def choose_from_top(probs, n=5):\n",
        "    ind = np.argpartition(probs, -n)[-n:]\n",
        "    top_prob = probs[ind]\n",
        "    top_prob = top_prob / np.sum(top_prob) # Normalize\n",
        "    choice = np.random.choice(n, 1, p = top_prob)\n",
        "    token_id = ind[choice][0]\n",
        "    return int(token_id)"
      ],
      "metadata": {
        "id": "h5l3WqjThxAw"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "def beam_sampling_strategy(model, cur_ids, beam_width, end_of_text):\n",
        "    outputs = model(cur_ids, labels=cur_ids)\n",
        "    loss, logits = outputs[:2]\n",
        "    softmax_logits = torch.softmax(logits[0,-1], dim=0).to('cpu') #Take the first(from only one in this case) batch and the last predicted embedding\n",
        "    beams = [(cur_ids, softmax_logits, 0)]\n",
        "    finished = []\n",
        "    for ii in range(100):\n",
        "        new_beams = []\n",
        "        # each beam\n",
        "        for beam in beams:\n",
        "            # print(beam)\n",
        "            # sample\n",
        "            samples = torch.multinomial(beam[1], beam_width, replacement=True)\n",
        "            # create beam for each sample\n",
        "            for sample in samples:\n",
        "                cur = ((torch.cat([beam[0], torch.ones((1,1)).long().to(device) * int(sample)], dim = 1),None,(beam[2] + torch.log(beam[1][sample]))* (1.+math.log(len(beam[0])+1.))))\n",
        "                if sample in end_of_text:\n",
        "                    finished.append(cur)\n",
        "                else:\n",
        "                    new_beams.append(cur)\n",
        "        new_beams = sorted(new_beams, reverse=True, key=lambda x:x[2])\n",
        "        new_beams = new_beams[:beam_width]\n",
        "        for i, new_beam in enumerate(new_beams):\n",
        "            outputs = model(new_beam[0], labels=new_beam[0])\n",
        "            loss, logits = outputs[:2]\n",
        "            softmax_logits = torch.softmax(logits[0,-1], dim=0).to('cpu')\n",
        "            new_beams[i] = (new_beam[0], softmax_logits, new_beam[2])\n",
        "        beams = new_beams\n",
        "\n",
        "    return sorted(finished, reverse=True, key=lambda x:x[2])[0][0]"
      ],
      "metadata": {
        "id": "E8P2UyX7twxA"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_text(username, model_choice, output_file=True, method=0, model=model, file_path=file_path):\n",
        "    model_path = os.path.join(file_path, f\"gpt2_medium_{model_choice}_final.pt\")\n",
        "    model.load_state_dict(torch.load(model_path))\n",
        "\n",
        "    output_file_path = f'generated_tweet_{username}.txt'\n",
        "\n",
        "    model.eval()\n",
        "    \n",
        "    tweet_num = 0\n",
        "    with torch.no_grad():\n",
        "        while True:\n",
        "        \n",
        "            tweet_finished = False\n",
        "\n",
        "            cur_ids = torch.tensor(tokenizer.encode(f'{username}:')).unsqueeze(0).to(device)\n",
        "\n",
        "            if (method == 0):\n",
        "                for i in range(100):\n",
        "                    outputs = model(cur_ids, labels=cur_ids)\n",
        "                    loss, logits = outputs[:2]\n",
        "                    softmax_logits = torch.softmax(logits[0,-1], dim=0) #Take the first(from only one in this case) batch and the last predicted embedding\n",
        "                    if i < 3:\n",
        "                        n = 20\n",
        "                    else:\n",
        "                        n = 10\n",
        "                    next_token_id = choose_from_top(softmax_logits.to('cpu').numpy(), n=n) #Randomly(from the topN probability distribution) select the next word\n",
        "                    cur_ids = torch.cat([cur_ids, torch.ones((1,1)).long().to(device) * next_token_id], dim = 1) # Add the last word to the running sequence\n",
        "\n",
        "                    if next_token_id in tokenizer.encode('<|endoftext|>'):\n",
        "                        tweet_finished = True\n",
        "                        break\n",
        "\n",
        "                if tweet_finished:\n",
        "                    \n",
        "                    tweet_num = tweet_num + 1\n",
        "                    output_list = list(cur_ids.squeeze().to('cpu').numpy())\n",
        "                    output_text = tokenizer.decode(output_list)\n",
        "                    output_text = output_text.replace('<|endoftext|>', '')\n",
        "                    output_text = output_text.replace('\\n', ' ')\n",
        "                    if (len(output_text) < 50):\n",
        "                        tweet_num = tweet_num - 1\n",
        "                    else:\n",
        "                        print(f\"{tweet_num}: {output_text} \\n\")\n",
        "                        if output_file:\n",
        "                            with open(output_file_path, 'w') as f:\n",
        "                                f.write(output_text)\n",
        "            else:\n",
        "                cur_ids = beam_sampling_strategy(model, cur_ids, 15, tokenizer.encode('<|endoftext|>'))\n",
        "                tweet_num = tweet_num + 1\n",
        "                \n",
        "                output_list = list(cur_ids.squeeze().to('cpu').numpy())\n",
        "                output_text = tokenizer.decode(output_list)\n",
        "                output_text = output_text.replace('<|endoftext|>', '')\n",
        "                output_text = output_text.replace('\\n', ' ')\n",
        "                print(f\"{tweet_num}:{output_text} \\n\")\n",
        "            \n",
        "                if output_file:\n",
        "                    with open(output_file_path, 'w') as f:\n",
        "                        f.write(output_text)\n",
        "            \n",
        "            if tweet_num >= 10:\n",
        "                break\n"
      ],
      "metadata": {
        "id": "nH9LwrMDlLA1"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Start"
      ],
      "metadata": {
        "id": "VUbDL-TqRScK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# User input\n",
        "user_input = input('Please enter the twitter account name: you can choose from the name in person_list or some general english word.\\t')\n",
        "\n",
        "predicting_type = int(input('Please enter your prefer way of generating tweets: \\n \\t 0 is random sampling using distribution, 1 is beam search.\\t'))\n",
        "\n",
        "output_file = bool(input('Do you want an output file?\\n\\t 1 is Yes, 0 is No.\\t'))\n",
        "\n",
        "generate_text(user_input, return_closest_word(user_input, person_list), \n",
        "              output_file=output_file, method=predicting_type)"
      ],
      "metadata": {
        "id": "NkUvncKzRQuH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "yqgn4AHdt1Yo"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}