Improving_LLMs_on_underrepresented_programming_languages_Test_Task (2).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KYnjEY3mlFO5"
      },
      "source": [
        "## Downloading all dependencies and import all required libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c9QkBJLQC101",
        "outputId": "49ea6abd-5add-484c-9a4c-9cb815bda58d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Downloading Levenshtein-0.25.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (177 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m177.4/177.4 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting rapidfuzz<4.0.0,>=3.8.0 (from Levenshtein)\n",
            "  Downloading rapidfuzz-3.9.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m42.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: rapidfuzz, Levenshtein\n",
            "Successfully installed Levenshtein-0.25.1 rapidfuzz-3.9.0\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets\n",
        "!pip install peft\n",
        "!pip install accelerate\n",
        "!pip install Levenshtein"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "2JXFA8qMCJRH"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset\n",
        "from datasets import Dataset\n",
        "import json\n",
        "from tqdm import tqdm\n",
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import torch\n",
        "from datasets import load_dataset, Dataset\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer, DataCollatorForLanguageModeling, BitsAndBytesConfig, AutoModelForSeq2SeqLM\n",
        "from peft import LoraConfig, get_peft_model\n",
        "import os\n",
        "from sklearn.model_selection import train_test_split\n",
        "from Levenshtein import distance\n",
        "from tqdm.auto import tqdm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k0lEbOlAU1j1"
      },
      "source": [
        "## Connection to the HuggingFace"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "At this step, it is necessary to login to the HuggingFace using *WRITE* type token"
      ],
      "metadata": {
        "id": "s8embllkGy1C"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "d15938563ae94239b7ecb636d12199ba",
            "3dacaa65d74345feb8b69cf8397c7e6b",
            "ec40acf6c6e6411495d831c9296b5b2e",
            "8ad5f496c1844f20b4710955ac3b5421",
            "48f4ab2af64541eb86554315d9f3cc2c",
            "75cc466ce69c4ef29f1a6338b477157a",
            "499916b537fc4ce1930be9b87ff5f096",
            "79d8baff28da4023921685898bd64bee",
            "d3eb2fb615bb4fabbf4f0d656a342ce1",
            "e1c99313acad484aafa67eb16d0e4ce5",
            "f3616dfde6f94e88a1324dfe2b2c77e2",
            "56d960342fb34a80afe8ccd7cfb4a1ec",
            "95d56527ed144b61bb4039bcc300c0b6",
            "7c9bc9a385d74ce3b113657a9d8eb86c",
            "8e69e9b2614c471890690e24a67262c0",
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            "ea94df066cec4f56b0c8c5eb069c169e",
            "4c77f7d7fc864ad5bfcef28fbd63ce14",
            "8c51b95bbfb64df7b0ab06fbd5b61a76",
            "01b1f4c9554a4d8c8f04ebe6679fbcff",
            "5e736392a6084286a66613e2b7b1fd10",
            "b5ecf8aae62f42b7823b86fc318d002f",
            "41d46b46eb754456b7a784f643c65f41",
            "772e7698edcb4421bce812bb39609fb8",
            "a2929e7f32c94a1e853f5910cc911ccf",
            "0efd1bb4cbfa4a3fb7727203dc19e48b",
            "22da4b6391fc4803bd66d8bf5a56cd4e",
            "9bfe4ead045243bf9960cb6a5c2b525c",
            "d3bdf5f06cd047c28d45b8d15734db46",
            "38bfb2409e6b46c6a12033c336222978",
            "fb9da4f056fc4a79b71dd806c3206b59"
          ]
        },
        "id": "hNvaC49wwbq9",
        "outputId": "c54498a0-49e8-4650-aa3b-1996daae3510"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d15938563ae94239b7ecb636d12199ba"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H5rMgAsAVE-O"
      },
      "source": [
        "## Downloading and extraxcting the fine-tune dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jHdOsp84Y7sx"
      },
      "source": [
        "Here I download the .zip dataset I've collected locally before by the link https://github.com/JetBrains/kotlin. I've uploaded the .zip dataset on the Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-eiOypf2r81K",
        "outputId": "908299d5-a39e-40bf-ab30-b91b923ba963"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.14.0)\n",
            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
            "Downloading...\n",
            "From (original): https://drive.google.com/uc?id=1rnICY-3INySOlQGRkt8llooNu8BS3Gp2\n",
            "From (redirected): https://drive.google.com/uc?id=1rnICY-3INySOlQGRkt8llooNu8BS3Gp2&confirm=t&uuid=0c610b89-cf95-41bb-a645-8d16b0656db7\n",
            "To: /content/Dataset_Kotlin.zip\n",
            "100% 52.4M/52.4M [00:03<00:00, 14.3MB/s]\n"
          ]
        }
      ],
      "source": [
        "!pip install gdown\n",
        "!gdown https://drive.google.com/uc?id=1rnICY-3INySOlQGRkt8llooNu8BS3Gp2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "DOLRPHzeVu4_"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!unzip Dataset_Kotlin.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YxTMm-mjZSW4"
      },
      "source": [
        "Preprocessing for the files and making one JSON file that will be the future Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "775af58f6e3e482a83743f75d9009f20",
            "b46622a784c24a97b30654b5205ea75b",
            "7c061d2bf4f04b77966a437544db5e76",
            "df3724e91ac647f693f7223282761d8b",
            "adb73618dc994956b611279dee7469ba",
            "6f528da798f2441eb513da40029cfce4",
            "958f7b38f64e471abdf1fc9302daffa5",
            "e38983067205436abedf399d87319f3a",
            "dd8c28a73ac7457ca16b9d2e7e5ebcf2",
            "d427ef44ea2b402b899af216e94d36c0",
            "d6284d91515d4c389ce8e37f0339a840"
          ]
        },
        "id": "YCEAHiGOKz0X",
        "outputId": "dc647c09-9e15-4bb2-bc34-be77e9610830"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/41912 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "775af58f6e3e482a83743f75d9009f20"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "def kotlin_file_processing(file_path):\n",
        "    with open(file_path, 'r') as f:\n",
        "        code = f.read()\n",
        "        result = {\n",
        "            \"text\": code,\n",
        "        }\n",
        "        return result\n",
        "\n",
        "def json_convertion(directory_path, output_file):\n",
        "    kotlin_files = [f for f in os.listdir(directory_path) if f.endswith('.kt') or f.endwith(\".kts\")]\n",
        "    data = list()\n",
        "    for file_name in tqdm(kotlin_files):\n",
        "        file_path = os.path.join(directory_path, file_name)\n",
        "        processed_file = kotlin_file_processing(file_path)\n",
        "        data.append(processed_file)\n",
        "\n",
        "    with open(output_file, 'w') as json_file:\n",
        "        json.dump(data, json_file, indent=4)\n",
        "\n",
        "directory_path = 'Dataset_Kotlin'\n",
        "output_file = 'model_inputs.json'\n",
        "\n",
        "json_convertion(directory_path, output_file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0AvJP-Z_QZ1z",
        "outputId": "ddd64b30-1159-412a-b06e-06585e07b724"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                    text\n",
            "0      // FIR_IDENTICAL\\n// FILE: a.kt\\n// KT-26 Impo...\n",
            "1      // FIR_IDENTICAL\\n// FILE: a/x.java\\npackage a...\n",
            "2      // !DUMP_CFG\\n\\nimport kotlin.contracts.*\\n\\nc...\n",
            "3      // FIR_IDENTICAL\\nfun get(map: Map<String, Int...\n",
            "4      // EXPECTED_REACHABLE_NODES: 1295\\npackage foo...\n",
            "...                                                  ...\n",
            "41907  abstract class Base(val s: String, vararg ints...\n",
            "41908  // FIR_IDENTICAL\\n\\ninterface Controller<F> {\\...\n",
            "41909  fun foo() {\\n    println(\\n            23L)\\n ...\n",
            "41910  // LANGUAGE: -NativeJsProhibitLateinitIsInitia...\n",
            "41911  // TARGET_BACKEND: NATIVE\\n// MODULE: cinterop...\n",
            "\n",
            "[41912 rows x 1 columns]\n",
            "// FIR_IDENTICAL\n",
            "// FILE: a.kt\n",
            "// KT-26 Import namespaces defined in this file\n",
            "\n",
            "import html.* // Must not be an error\n",
            "\n",
            "// FILE: b.kt\n",
            "\n",
            "package html\n",
            "\n",
            "abstract class Factory<T: Any> {\n",
            "fun create() : T? = null\n",
            "}\n",
            "\n"
          ]
        }
      ],
      "source": [
        "json_file_path = 'model_inputs.json'\n",
        "df = pd.read_json(json_file_path)\n",
        "print(df)\n",
        "print(df[\"text\"][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_QXOk7J-SnC-",
        "outputId": "9972034c-a452-40a5-d5ae-dffcb6ce11d7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text\n",
              "0  // FIR_IDENTICAL\\n// FILE: a.kt\\n// KT-26 Impo...\n",
              "1  // FIR_IDENTICAL\\n// FILE: a/x.java\\npackage a...\n",
              "2  // !DUMP_CFG\\n\\nimport kotlin.contracts.*\\n\\nc...\n",
              "3  // FIR_IDENTICAL\\nfun get(map: Map<String, Int...\n",
              "4  // EXPECTED_REACHABLE_NODES: 1295\\npackage foo..."
            ],
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>// FIR_IDENTICAL\\n// FILE: a.kt\\n// KT-26 Impo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>// FIR_IDENTICAL\\n// FILE: a/x.java\\npackage a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>// !DUMP_CFG\\n\\nimport kotlin.contracts.*\\n\\nc...</td>\n",
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              "      <td>// EXPECTED_REACHABLE_NODES: 1295\\npackage foo...</td>\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
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              "        document.querySelector('#df-214a924c-657b-4f05-b845-0e6797c53a82 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-214a924c-657b-4f05-b845-0e6797c53a82');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c850e379-e356-46e7-8475-f2bb0867daf9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 41912,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 41790,\n        \"samples\": [\n          \"fun foo(until: Int): String {\\n    fun bar(x: Int): String =\\n        if (x == until) \\\"OK\\\" else bar(x + 1)\\n    return (::bar).let { it(0) }\\n}\\n\\nfun box() = foo(10)\\n\",\n          \"/*\\n * Copyright 2010-2024 JetBrains s.r.o. and Kotlin Programming Language contributors.\\n * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.\\n */\\n\\npackage org.jetbrains.kotlin.analysis.api.impl.base.test.cases.components.subtyping\\n\\nimport org.jetbrains.kotlin.analysis.api.KtAnalysisSession\\nimport org.jetbrains.kotlin.analysis.api.components.KaSubtypingErrorTypePolicy\\nimport org.jetbrains.kotlin.analysis.api.types.KtType\\nimport org.jetbrains.kotlin.analysis.test.framework.base.AbstractAnalysisApiBasedTest\\nimport org.jetbrains.kotlin.analysis.test.framework.project.structure.KtTestModule\\nimport org.jetbrains.kotlin.analysis.test.framework.services.expressionMarkerProvider\\nimport org.jetbrains.kotlin.psi.KtElement\\nimport org.jetbrains.kotlin.psi.KtExpression\\nimport org.jetbrains.kotlin.psi.KtFile\\nimport org.jetbrains.kotlin.psi.KtProperty\\nimport org.jetbrains.kotlin.test.builders.TestConfigurationBuilder\\nimport org.jetbrains.kotlin.test.directives.model.SimpleDirectivesContainer\\nimport org.jetbrains.kotlin.test.directives.model.StringDirective\\nimport org.jetbrains.kotlin.test.directives.model.singleValue\\nimport org.jetbrains.kotlin.test.services.TestServices\\nimport org.jetbrains.kotlin.test.services.assertions\\nimport org.jetbrains.kotlin.test.services.moduleStructure\\n\\nabstract class AbstractSemanticSubtypingTest : AbstractAnalysisApiBasedTest() {\\n    protected abstract val resultDirective: StringDirective\\n\\n    protected abstract fun KtAnalysisSession.checkTypes(\\n        expectedResult: Boolean,\\n        type1: KtType,\\n        type2: KtType,\\n        testServices: TestServices,\\n    )\\n\\n    override fun configureTest(builder: TestConfigurationBuilder) {\\n        super.configureTest(builder)\\n        with(builder) {\\n            useDirectives(SemanticSubtypingTestDirectives)\\n        }\\n    }\\n\\n    override fun doTestByMainFile(mainFile: KtFile, mainModule: KtTestModule, testServices: TestServices) {\\n        val expectedResult = testServices.moduleStructure.allDirectives\\n            .singleValue(resultDirective)\\n            .toBooleanStrict()\\n\\n        analyseForTest(mainFile) {\\n            val type1 = getTypeAtCaret(\\\"type1\\\", mainFile, testServices)\\n            val type2 = getTypeAtCaret(\\\"type2\\\", mainFile, testServices)\\n\\n            checkTypes(expectedResult, type1, type2, testServices)\\n        }\\n    }\\n\\n    private fun KtAnalysisSession.getTypeAtCaret(caretTag: String, mainFile: KtFile, testServices: TestServices): KtType {\\n        val element = testServices.expressionMarkerProvider.getElementOfTypeAtCaret<KtElement>(mainFile, caretTag)\\n        return when (element) {\\n            is KtProperty -> element.getVariableSymbol().returnType\\n            is KtExpression -> element.getKtType() ?: error(\\\"Expected the selected expression to have a type.\\\")\\n            else -> error(\\\"Expected a property or an expression.\\\")\\n        }\\n    }\\n}\\n\\nprivate object SemanticSubtypingTestDirectives : SimpleDirectivesContainer() {\\n    val ARE_EQUAL by stringDirective(\\n        description = \\\"Whether the two types are equal (`true` or `false`).\\\",\\n    )\\n\\n    val ARE_EQUAL_LENIENT by stringDirective(\\n        description = \\\"Whether the two types are equal (`true` or `false`) when using the `${KaSubtypingErrorTypePolicy.LENIENT}` error\\\" +\\n                \\\" type policy.\\\",\\n    )\\n\\n    val IS_SUBTYPE by stringDirective(\\n        description = \\\"Whether `type1` is a subtype of `type2` (`true` or `false`).\\\",\\n    )\\n\\n    val IS_SUBTYPE_LENIENT by stringDirective(\\n        description = \\\"Whether `type1` is a subtype of `type2` (`true` or `false`) when using the `${KaSubtypingErrorTypePolicy.LENIENT}`\\\" +\\n                \\\" error type policy.\\\",\\n    )\\n}\\n\\nabstract class AbstractTypeEqualityTest : AbstractSemanticSubtypingTest() {\\n    override val resultDirective = SemanticSubtypingTestDirectives.ARE_EQUAL\\n\\n    override fun KtAnalysisSession.checkTypes(expectedResult: Boolean, type1: KtType, type2: KtType, testServices: TestServices) {\\n        testServices.assertions.assertEquals(\\n            expectedResult,\\n            type1.isEqualTo(type2),\\n        ) {\\n            \\\"Expected `$type1` and `$type2` to be ${if (!expectedResult) \\\"un\\\" else \\\"\\\"}equal (`$resultDirective`).\\\"\\n        }\\n    }\\n}\\n\\nabstract class AbstractLenientTypeEqualityTest : AbstractSemanticSubtypingTest() {\\n    override val resultDirective = SemanticSubtypingTestDirectives.ARE_EQUAL_LENIENT\\n\\n    override fun KtAnalysisSession.checkTypes(expectedResult: Boolean, type1: KtType, type2: KtType, testServices: TestServices) {\\n        testServices.assertions.assertEquals(\\n            expectedResult,\\n            type1.isEqualTo(type2, KaSubtypingErrorTypePolicy.LENIENT),\\n        ) {\\n            \\\"Expected `$type1` and `$type2` to be ${if (!expectedResult) \\\"un\\\" else \\\"\\\"}equal with error type leniency (`$resultDirective`).\\\"\\n        }\\n    }\\n}\\n\\nabstract class AbstractSubtypingTest : AbstractSemanticSubtypingTest() {\\n    override val resultDirective = SemanticSubtypingTestDirectives.IS_SUBTYPE\\n\\n    override fun KtAnalysisSession.checkTypes(expectedResult: Boolean, type1: KtType, type2: KtType, testServices: TestServices) {\\n        testServices.assertions.assertEquals(\\n            expectedResult,\\n            type1.isSubTypeOf(type2),\\n        ) {\\n            \\\"Expected `$type1` to ${if (!expectedResult) \\\"not \\\" else \\\"\\\"}be a subtype of `$type2` (`$resultDirective`).\\\"\\n        }\\n    }\\n}\\n\\nabstract class AbstractLenientSubtypingTest : AbstractSemanticSubtypingTest() {\\n    override val resultDirective = SemanticSubtypingTestDirectives.IS_SUBTYPE_LENIENT\\n\\n    override fun KtAnalysisSession.checkTypes(expectedResult: Boolean, type1: KtType, type2: KtType, testServices: TestServices) {\\n        testServices.assertions.assertEquals(\\n            expectedResult,\\n            type1.isSubTypeOf(type2, KaSubtypingErrorTypePolicy.LENIENT),\\n        ) {\\n            \\\"Expected `$type1` to ${if (!expectedResult) \\\"not \\\" else \\\"\\\"}be a subtype of `$type2` with error type leniency\\\" +\\n                    \\\" (`$resultDirective`).\\\"\\n        }\\n    }\\n}\\n\",\n          \"class Outer(val o: String, val oo: String) {\\n    inner class InnerArg(val i: String) {\\n        val result: String get() = o + i\\n    }\\n\\n    inner class InnerParam(val i: InnerArg = InnerArg(\\\"B\\\")) {\\n        fun foo() = i.result + oo\\n    }\\n}\\n\\n\\nfun box(): String {\\n    val o = Outer(\\\"A\\\", \\\"C\\\")\\n    val i = o.InnerParam()\\n\\n    val rr = i.foo()\\n    if (rr != \\\"ABC\\\") return \\\"FAIL: $rr\\\"\\n\\n    return \\\"OK\\\"\\n}\\n\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-zcCXDSkQd64",
        "outputId": "be7e83f2-5bf2-430a-d223-7b2f4a9c52bc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "41912"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "len(df[\"text\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OuLVhFaDb_Xl"
      },
      "source": [
        "### Making a Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here I am making a *Dataset* type dataset from the DataFrame for the further usage"
      ],
      "metadata": {
        "id": "mncNG7LrHcqj"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "MHcAofHtQ_lW"
      },
      "outputs": [],
      "source": [
        "dataset = Dataset.from_dict(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zS8a3vA0SZkC",
        "outputId": "c3c03b68-c578-427e-b597-315e26063bb0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text'],\n",
              "    num_rows: 41912\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9rc97s0rcFe0"
      },
      "source": [
        "## Define a tokenizer and a model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372,
          "referenced_widgets": [
            "8b3ef9ad4f144619b4403423d17d271a",
            "5084ed68e9234e179755ede943a50184",
            "e769abbdeb334616b065cb398a8462d9",
            "74e9871675fd440da02cb9799ec1ff4b",
            "382dce59a8714b5d816f6d64628b1098",
            "909bfe0faaf94699b831791c25f614df",
            "c7d9afbb7ecf40ec8df4146ad95b85a1",
            "f9a225eb57a64790aef9923bad8826e6",
            "36eb52ab022444a9a5164cd434befea5",
            "7eade0b0d6c74573b335d5581333607f",
            "661d2439a6b1461fbae40a15aaee7551",
            "a3812a4a525246d09fe717a470741fa7",
            "08e187f0b13a4371b36c321fddb3a594",
            "a3443d4ac5d94d3280e657a8524ff4a5",
            "ff1486a667614abe8c19199f48e98ce8",
            "824846a61e5d41edaef6049d5f21db53",
            "261ca48a9c794cd497ad77771d0fe6ff",
            "b7c7cdff50a948bfae24017eb29b7edb",
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            "b0750205cb8843cd9435ec8dc0f41cac",
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            "d374776a24934b29b174ca91c7a6ff13",
            "a7e70459a58b495a8177d2c2b73b656d",
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            "8d691d23ef254524a57b7627a453629d",
            "5138d41c3ceb4fce86abe1658336e14c",
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            "69534b3165c64c1c94bcd5ad22dbba05",
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            "6c871a64d28f4f4193bd0fa9a75aa781",
            "710f532642674fd9a25495eaf1ae48c6",
            "b3d55e3e7090499e85526c25370d8d7a",
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            "fcf420308e1e4d5a85caa24283e36ab7",
            "f02be9f88fc244618b5d2bb9ee28f38c",
            "0a85f99dea2e453487d3a4e597226639",
            "f37f89570dbc427a8b70580d9f90edba",
            "7798640fbbd84acc8fc836a755828a4f",
            "57e8d82fde7741e6b33f64bb9e9bb8d5",
            "6f29ad75e1fc4c0088d61d6ededb7a77"
          ]
        },
        "id": "1BUtELruTFR2",
        "outputId": "0ff381d0-ea3f-4503-f637-ff0c8173f423"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/237 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8b3ef9ad4f144619b4403423d17d271a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/798k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a3812a4a525246d09fe717a470741fa7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "75a616cb966e4aec95343c22ef73bbd3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.11M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7defe46485e64a958def5fd49a173af5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/1.08k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8c544f69ac314767974f86fa415e3da8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/99.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6c871a64d28f4f4193bd0fa9a75aa781"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/phi-1_5\", trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I chose Phi-1.5 as it was proposed"
      ],
      "metadata": {
        "id": "pMw_7dxIIn2S"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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            "1029bded0b7343c29ee17ee5467d699e",
            "0187427bb6094fa380be27bc501bd2d4",
            "4da106650ccf4d188a3bdd7e169a714b",
            "e82c177892364e60a89d3f6e649ff025",
            "a158bbd72504413fbcd7ce07abd8dbe3",
            "e7226410bb3341c483650c8bce46f1b9",
            "ad9ccb2d4c8944369985c27e3a733c45",
            "6ad59c7c219d49cea7ba9b2d282d5a68",
            "6913f4a57c3a47c8b589536ff5620652",
            "d5cfc327e23043f6b0f90e2204c720bc",
            "a005dca12df04b699e4312dcaa57aa23",
            "80eed887b8954cceba6080b17c08034d",
            "e44cfba02fa4401ea7c013239227bdbe",
            "b4076bd2cf3e4cfcafe186048a82a829",
            "dfeb604c4069418495d35d9ad6030be9",
            "23dcf588e27b4bcdbe6019a1d1bf96c4",
            "10aa9c36159240d8ae8b944888a90395",
            "b6569aafa93c4b6998eab756e615a726"
          ]
        },
        "id": "Wf4am3M-TtzM",
        "outputId": "21c45d02-58a6-49fd-dfd4-35d1dcbf11b7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/736 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ba8f042dc21440dfb3474a622e5e9456"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/2.84G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5bfbfb2122744d43b751858147532df7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/74.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6ad59c7c219d49cea7ba9b2d282d5a68"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    \"microsoft/phi-1_5\",\n",
        "    device_map={\"\":0},\n",
        "    trust_remote_code=True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gHqBxfOGTxGP",
        "outputId": "ba8125da-2ea6-4ce3-fdc3-3191f3444518"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PhiForCausalLM(\n",
            "  (model): PhiModel(\n",
            "    (embed_tokens): Embedding(51200, 2048)\n",
            "    (embed_dropout): Dropout(p=0.0, inplace=False)\n",
            "    (layers): ModuleList(\n",
            "      (0-23): 24 x PhiDecoderLayer(\n",
            "        (self_attn): PhiSdpaAttention(\n",
            "          (q_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
            "          (k_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
            "          (v_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
            "          (dense): Linear(in_features=2048, out_features=2048, bias=True)\n",
            "          (rotary_emb): PhiRotaryEmbedding()\n",
            "        )\n",
            "        (mlp): PhiMLP(\n",
            "          (activation_fn): NewGELUActivation()\n",
            "          (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
            "          (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
            "        )\n",
            "        (input_layernorm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
            "        (resid_dropout): Dropout(p=0.0, inplace=False)\n",
            "      )\n",
            "    )\n",
            "    (final_layernorm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
            "  )\n",
            "  (lm_head): Linear(in_features=2048, out_features=51200, bias=True)\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "print(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "GczKtd85kQjY"
      },
      "outputs": [],
      "source": [
        "inputs = tokenizer('''I write in Kotlin and need to write function fun divide(a: Int, b: Float) with all corresponding checks''', return_tensors=\"pt\", return_attention_mask=False).to(\"cuda\")\n",
        "outputs = model.generate(**inputs, max_length=156)\n",
        "text = tokenizer.batch_decode(outputs)[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "pXuPzWBukiq7",
        "outputId": "874ecc18-c015-438b-c198-17c62dd23376"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'I write in Kotlin and need to write function fun divide(a: Int, b: Float) with all corresponding checks.\\n\\nA:\\n\\nYou can use the operator / and the operator /= to do the division and assignment in one statement.\\nfun divide(a: Int, b: Float): Float {\\n    return a / b\\n}\\n\\nfun divide(a: Int, b: Float): Float {\\n    return a /= b\\n}\\n\\nA:\\n\\nYou can use the operator /= to do the division and assignment in one statement.\\nfun divide(a: Int, b: Float): Float {\\n    return a /= b\\n}\\n\\nfun divide(a: Int, b:'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "text"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Downloading the test Python and Kotlin datasets and calculating a metric (Levenshtein)"
      ],
      "metadata": {
        "id": "xdhvMeJyU3N3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "At this step, I am downloading the datasets from the HuggingFace. First dataset is for the Python and second one is for Kotlin. Both datasets are test datasets, so I'l be evaluating the model and calculating the metric on them."
      ],
      "metadata": {
        "id": "oE5UocgmHsJe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!curl -X GET \"https://datasets-server.huggingface.co/first-rows?dataset=microsoft%2Fcodexglue_method_generation&config=default&split=test\" > python_test_dataset.json"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rtPcH7oMVB6t",
        "outputId": "9e6a0a41-9a1f-4d51-b5a3-ea8ae31c7fab"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100 63837  100 63837    0     0   167k      0 --:--:-- --:--:-- --:--:--  168k\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For Kotlin I've used an open-sourse dataset from HuggingFace with \"input\" and \"output\"."
      ],
      "metadata": {
        "id": "_txGT0BgJpoW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!curl -X GET \"https://datasets-server.huggingface.co/rows?dataset=codkiller0911%2Fkotlin_code&config=default&split=train&offset=0&length=100\" > kotlin_test_dataset.json"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ncPpTY90iFY5",
        "outputId": "41482bcd-ff0a-4022-c8c4-795689039f9f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100 51248  100 51248    0     0   101k      0 --:--:-- --:--:-- --:--:--  101k\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def loading_dataset(path):\n",
        "    with open(path, 'r') as f:\n",
        "        test_dataset = json.load(f)\n",
        "        return test_dataset"
      ],
      "metadata": {
        "id": "wCk__uKGIyvH"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "python_test_dataset = loading_dataset('python_test_dataset.json')"
      ],
      "metadata": {
        "id": "ysism_OzJKOz"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kotlin_test_dataset = loading_dataset('kotlin_test_dataset.json')"
      ],
      "metadata": {
        "id": "8Dt3W8aoJWua"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_test_py: list = list()\n",
        "\n",
        "for i in python_test_dataset['rows']:\n",
        "    small_dict: dict = dict()\n",
        "\n",
        "    small_dict['input'] = i['row']['docstring'] + ' ' + i['row']['signature']\n",
        "    small_dict['output'] = i['row']['body']\n",
        "\n",
        "    dataset_test_py.append(small_dict)"
      ],
      "metadata": {
        "id": "G1DJzAhdVOEP"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_test_kt: list = list()\n",
        "\n",
        "for i in kotlin_test_dataset['rows']:\n",
        "    small_dict: dict = dict()\n",
        "\n",
        "    small_dict['input'] = i['row']['Message']\n",
        "    small_dict['output'] = i['row']['Code_Function']\n",
        "\n",
        "    dataset_test_kt.append(small_dict)"
      ],
      "metadata": {
        "id": "MfecHXCBjdy-"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_test_py[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hsiZUJrsVXSd",
        "outputId": "0aee6bab-bb75-454f-9d94-f6a41db69915"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'input': 'Adds a message with the ``DEBUG`` level.\\n\\n:param user: User instance\\n:param message: Message to show def debug(user, message):',\n",
              "  'output': 'message_user(user, message, constants.DEBUG)<EOL>'},\n",
              " {'input': 'Adds a message with the ``SUCCESS`` level.\\n\\n:param user: User instance\\n:param message: Message to show def success(user, message):',\n",
              "  'output': 'message_user(user, message, constants.SUCCESS)<EOL>'},\n",
              " {'input': 'Check for messages for this user and, if it exists,\\ncall the messages API with it def process_response(self, request, response):',\n",
              "  'output': 'if hasattr(request, \"<STR_LIT>\") and hasattr(request, \"<STR_LIT:user>\") and request.user.is_authenticated():<EOL><INDENT>msgs = get_messages(request.user)<EOL>if msgs:<EOL><INDENT>for msg, level in msgs:<EOL><INDENT>messages.add_message(request, level, msg)<EOL><DEDENT><DEDENT><DEDENT>return response<EOL>'},\n",
              " {'input': 'Returns a given table for the given user. def table(name, auth=None, eager=True):',\n",
              "  'output': 'auth = auth or []<EOL>dynamodb = boto.connect_dynamodb(*auth)<EOL>table = dynamodb.get_table(name)<EOL>return Table(table=table, eager=eager)<EOL>'},\n",
              " {'input': 'View decorator that handles JSON based API requests and responses consistently.\\n:param methods: A list of allowed methods\\n:param require_token: Whether API token is checked automatically or not def api_request(methods=None, require_token=True):',\n",
              "  'output': \"def decorator(view_func):<EOL><INDENT>@wraps(view_func, assigned=available_attrs(view_func))<EOL>def _wrapped_view(request, *args, **kwargs):<EOL><INDENT>ApiToken = apps.get_model('<STR_LIT>', '<STR_LIT>')<EOL>m = methods if methods is not None else DEFAULT_API_METHODS<EOL>if request.method not in m:<EOL><INDENT>response = ApiResponse(False, '<STR_LIT>', status=<NUM_LIT>)<EOL>response['<STR_LIT>'] = '<STR_LIT:U+002CU+0020>'.join(methods)<EOL>return response<EOL><DEDENT>try:<EOL><INDENT>data = json.loads(request.body.decode('<STR_LIT:utf-8>')) if request.body else {}<EOL>if require_token:<EOL><INDENT>token_string = request.GET['<STR_LIT>'] if request.method == '<STR_LIT:GET>' else data['<STR_LIT>']<EOL>try:<EOL><INDENT>token = ApiToken.objects.get(token=token_string)<EOL>token.save()  <EOL>data['<STR_LIT>'] = token<EOL><DEDENT>except ApiToken.DoesNotExist:<EOL><INDENT>logger.exception('<STR_LIT>'.format(token_string))<EOL>return ApiResponse(False, '<STR_LIT>', status=<NUM_LIT>)<EOL><DEDENT><DEDENT>return ApiResponse(data=view_func(request, data=data, *args, **kwargs))<EOL><DEDENT>except Exception as e:<EOL><INDENT>if e.__class__.__name__ == '<STR_LIT>':<EOL><INDENT>logger.exception('<STR_LIT>')<EOL>return ApiResponse(False, '<STR_LIT>'.format(e), status=<NUM_LIT>)<EOL><DEDENT>else:<EOL><INDENT>logger.exception('<STR_LIT>')<EOL>return ApiResponse(False, '<STR_LIT>'.format(e), status=<NUM_LIT>)<EOL><DEDENT><DEDENT><DEDENT>return _wrapped_view<EOL><DEDENT>return decorator<EOL>\"},\n",
              " {'input': 'Gets tweets for a given user, via the Twitter frontend API. def get_tweets(user, pages=<NUM_LIT>):',\n",
              "  'output': 'url = f\\'<STR_LIT>\\'<EOL>headers = {<EOL>\\'<STR_LIT>\\': \\'<STR_LIT>\\',<EOL>\\'<STR_LIT>\\': f\\'<STR_LIT>\\',<EOL>\\'<STR_LIT>\\': \\'<STR_LIT>\\',<EOL>\\'<STR_LIT>\\': \\'<STR_LIT:yes>\\',<EOL>\\'<STR_LIT>\\': \\'<STR_LIT>\\',<EOL>\\'<STR_LIT>\\': \\'<STR_LIT>\\'<EOL>}<EOL>def gen_tweets(pages):<EOL><INDENT>r = session.get(url, headers=headers)<EOL>while pages > <NUM_LIT:0>:<EOL><INDENT>try:<EOL><INDENT>html = HTML(html=r.json()[\\'<STR_LIT>\\'],<EOL>url=\\'<STR_LIT>\\', default_encoding=\\'<STR_LIT:utf-8>\\')<EOL><DEDENT>except KeyError:<EOL><INDENT>raise ValueError(<EOL>f\\'<STR_LIT>\\')<EOL><DEDENT>comma = \"<STR_LIT:U+002C>\"<EOL>dot = \"<STR_LIT:.>\"<EOL>tweets = []<EOL>for tweet in html.find(\\'<STR_LIT>\\'):<EOL><INDENT>try:<EOL><INDENT>text = tweet.find(\\'<STR_LIT>\\')[<NUM_LIT:0>].full_text<EOL><DEDENT>except IndexError:  <EOL><INDENT>continue<EOL><DEDENT>tweet_id = tweet.find(\\'<STR_LIT>\\')[<NUM_LIT:0>].attrs[\\'<STR_LIT>\\']<EOL>time = datetime.fromtimestamp(int(tweet.find(\\'<STR_LIT>\\')[<NUM_LIT:0>].attrs[\\'<STR_LIT>\\']) / <NUM_LIT>)<EOL>interactions = [<EOL>x.text<EOL>for x in tweet.find(\\'<STR_LIT>\\')<EOL>]<EOL>replies = int(<EOL>interactions[<NUM_LIT:0>].split(\\'<STR_LIT:U+0020>\\')[<NUM_LIT:0>].replace(comma, \\'<STR_LIT>\\').replace(dot, \\'<STR_LIT>\\')<EOL>or interactions[<NUM_LIT:3>]<EOL>)<EOL>retweets = int(<EOL>interactions[<NUM_LIT:1>].split(\\'<STR_LIT:U+0020>\\')[<NUM_LIT:0>].replace(comma, \\'<STR_LIT>\\').replace(dot, \\'<STR_LIT>\\')<EOL>or interactions[<NUM_LIT:4>]<EOL>or interactions[<NUM_LIT:5>]<EOL>)<EOL>likes = int(<EOL>interactions[<NUM_LIT:2>].split(\\'<STR_LIT:U+0020>\\')[<NUM_LIT:0>].replace(comma, \\'<STR_LIT>\\').replace(dot, \\'<STR_LIT>\\')<EOL>or interactions[<NUM_LIT:6>]<EOL>or interactions[<NUM_LIT:7>]<EOL>)<EOL>hashtags = [<EOL>hashtag_node.full_text<EOL>for hashtag_node in tweet.find(\\'<STR_LIT>\\')<EOL>]<EOL>urls = [<EOL>url_node.attrs[\\'<STR_LIT>\\']<EOL>for url_node in tweet.find(\\'<STR_LIT>\\')<EOL>]<EOL>photos = [<EOL>photo_node.attrs[\\'<STR_LIT>\\']<EOL>for photo_node in tweet.find(\\'<STR_LIT>\\')<EOL>]<EOL>videos = []<EOL>video_nodes = tweet.find(\"<STR_LIT>\")<EOL>for node in video_nodes:<EOL><INDENT>styles = node.attrs[\\'<STR_LIT>\\'].split()<EOL>for style in styles:<EOL><INDENT>if style.startswith(\\'<STR_LIT>\\'):<EOL><INDENT>tmp = style.split(\\'<STR_LIT:/>\\')[-<NUM_LIT:1>]<EOL>video_id = tmp[:tmp.index(\\'<STR_LIT>\\')]<EOL>videos.append({\\'<STR_LIT:id>\\': video_id})<EOL><DEDENT><DEDENT><DEDENT>tweets.append({<EOL>\\'<STR_LIT>\\': tweet_id,<EOL>\\'<STR_LIT:time>\\': time,<EOL>\\'<STR_LIT:text>\\': text,<EOL>\\'<STR_LIT>\\': replies,<EOL>\\'<STR_LIT>\\': retweets,<EOL>\\'<STR_LIT>\\': likes,<EOL>\\'<STR_LIT>\\': {<EOL>\\'<STR_LIT>\\': hashtags, \\'<STR_LIT>\\': urls,<EOL>\\'<STR_LIT>\\': photos, \\'<STR_LIT>\\': videos<EOL>}<EOL>})<EOL><DEDENT>last_tweet = html.find(\\'<STR_LIT>\\')[-<NUM_LIT:1>].attrs[\\'<STR_LIT>\\']<EOL>for tweet in tweets:<EOL><INDENT>if tweet:<EOL><INDENT>tweet[\\'<STR_LIT:text>\\'] = re.sub(\\'<STR_LIT:http>\\', \\'<STR_LIT>\\', tweet[\\'<STR_LIT:text>\\'], <NUM_LIT:1>)<EOL>yield tweet<EOL><DEDENT><DEDENT>r = session.get(url, params={\\'<STR_LIT>\\': last_tweet}, headers=headers)<EOL>pages += -<NUM_LIT:1><EOL><DEDENT><DEDENT>yield from gen_tweets(pages)<EOL>'},\n",
              " {'input': 'From circularity, compute the deformation\\n\\n    This method is useful for RT-DC data sets that contain\\n    the circularity but not the deformation. def add_deformation(chn_names, data):',\n",
              "  'output': 'if \"<STR_LIT>\" not in chn_names:<EOL><INDENT>for ii, ch in enumerate(chn_names):<EOL><INDENT>if ch == \"<STR_LIT>\":<EOL><INDENT>chn_names.append(\"<STR_LIT>\")<EOL>data.append(<NUM_LIT:1>-data[ii])<EOL><DEDENT><DEDENT><DEDENT>return chn_names, data<EOL>'},\n",
              " {'input': 'Get all the leaf nodes of the subtree descending from this node.\\n\\n:return: List of Nodes with no descendants. def get_leaves(self):',\n",
              "  'output': 'return [n for n in self.walk() if n.is_leaf]<EOL>'},\n",
              " {'input': 'Gets the specified node by name.\\n\\n:return: Node or None if name does not exist in tree def get_node(self, label):',\n",
              "  'output': 'for n in self.walk():<EOL><INDENT>if n.name == label:<EOL><INDENT>return n<EOL><DEDENT><DEDENT>'},\n",
              " {'input': 'Get the names of all the leaf nodes of the subtree descending from\\nthis node.\\n\\n:return: List of names of Nodes with no descendants. def get_leaf_names(self):',\n",
              "  'output': 'return [n.name for n in self.get_leaves()]<EOL>'}]"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_test_kt[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UsilGsztjta7",
        "outputId": "e3a2638e-9c89-48a2-dd68-cc25fc38b20e"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'input': 'javax.net.ssl.SSLEngine # unwrap ( ByteBuffer src , ByteBuffer [ ] dsts ) ReadOnlyBufferException should be thrown .',\n",
              "  'output': '@KnownFailure(\"Fixed on DonutBurger, Wrong Exception thrown\") fun`test_unwrap_ByteBuffer$ByteBuffer_02`() { val host = \"new host\" val port = 8080 val bbs: ByteBuffer = ByteBuffer.allocate(10) val bbR: ByteBuffer = ByteBuffer.allocate(100).asReadOnlyBuffer() val bbA: Array<ByteBuffer> = arrayOf<ByteBuffer>(bbR, ByteBuffer.allocate(10), ByteBuffer.allocate(100)) val sse: SSLEngine = sun.jvm.hotspot.utilities.soql.SOQLEngine.getEngine(host, port) sse.setUseClientMode(true) try { sse.unwrap(bbs, bbA) fail(\"ReadOnlyBufferException wasn\\'t thrown\") } catch (iobe: ReadOnlyBufferException) { } catch (e: Exception) { fail(\"$e was thrown instead of ReadOnlyBufferException\") } }'},\n",
              " {'input': 'Makes an instance for the given value . This may ( but does not necessarily ) return an already-allocated instance .',\n",
              "  'output': 'fun make(bits: Int): CstFloat? { return CstFloat(bits)}'},\n",
              " {'input': \"Returns the `` size '' of the chromosome , namely , the number of nodes in all of its parsed genes -- does not include the linking functions .\",\n",
              "  'output': 'fun size(): Long { var size: Long = 0 if (parsedGeneExpressions == null) parseGenes() for (i in 0 until parsedGeneExpressions.length) size += parsedGeneExpressions.get(i).numberOfNodes() return size}'},\n",
              " {'input': 'This method is called when the plus button is clicked .',\n",
              "  'output': 'fun increment(view: View?) { if (quantity === 100) { return } quantity = quantity + 1 displayQuantity(quantity) }'},\n",
              " {'input': \"Trims the capacity of this < tt > ArrayHashList < /tt > instance to be the list 's current size . An application can use this operation to minimize the storage of an < tt > ArrayHashList < /tt > instance .\",\n",
              "  'output': 'fun trimToSize() { ++modCount if (size < elementData.length) { elementData = Arrays.copyOf(elementData, size) } }'},\n",
              " {'input': 'Performs a deep copy on < i > other < /i > .',\n",
              "  'output': 'fun SyncValueResponseMessage(other: SyncValueResponseMessage) { __isset_bitfield = other.__isset_bitfield if (other.isSetHeader()) { this.header = AsyncMessageHeader(other.header) } this.count = other.count }'},\n",
              " {'input': 'Removes all parsers from this text area .',\n",
              "  'output': 'fun clearParsers() { if (parserManager != null) { parserManager.clearParsers() } }'},\n",
              " {'input': 'This is the code for the thread . It delivers decisions to the TOM request receiver object ( which is the application )',\n",
              "  'output': 'fun run() { while (doWork) { deliverLock() while (tomLayer.isRetrievingState()) { println(\"-- Retrieving State\") canDeliver.awaitUninterruptibly() if (tomLayer.getLastExec() === -1) println(\"-- Ready to process operations\") } try { val decisions: ArrayList<Decision> = ArrayList<Decision>() decidedLock.lock() if (decided.isEmpty()) { notEmptyQueue.await() } decided.drainTo(decisions) decidedLock.unlock() if (!doWork) break if (decisions.size() > 0) { val requests: Array<Array<TOMMessage>> = arrayOfNulls<Array<TOMMessage>>(decisions.size()) val consensusIds = IntArray(requests.size) val leadersIds = IntArray(requests.size) val regenciesIds = IntArray(requests.size) var cDecs: Array<CertifiedDecision?> cDecs = arrayOfNulls<CertifiedDecision>(requests.size) var count = 0 for (d in decisions) { requests[count] = extractMessagesFromDecision(d) consensusIds[count] = d.getConsensusId() leadersIds[count] = d.getLeader() regenciesIds[count] = d.getRegency() val cDec = CertifiedDecision( this.controller.getStaticConf().getProcessId(), d.getConsensusId(), d.getValue(), d.getDecisionEpoch().proo ) cDecs[count] = cDec if (requests[count][0].equals(d.firstMessageProposed)) { val time: Long = requests[count][0].timestamp val seed: Long = requests[count][0].seed val numOfNonces: Int = requests[count][0].numOfNonces requests[count][0] = d.firstMessageProposed requests[count][0].timestamp = time requests[count][0].seed = seed requests[count][0].numOfNonces = numOfNonces } count++ } val lastDecision: Decision = decisions[decisions.size() - 1] if (requests != null && requests.size > 0) { deliverMessages(consensusIds, regenciesIds, leadersIds, cDecs, requests) if (controller.hasUpdates()) { processReconfigMessages(lastDecision.getConsensusId()) tomLayer.setLastExec(lastDecision.getConsensusId()) tomLayer.setInExec(-1) } } val cid: Int = lastDecision.getConsensusId() if (cid > 2) { val stableConsensus = cid – 3 tomLayer.execManager.removeConsensus(stableConsensus) } } } catch (e: Exception) { e.printStackTrace(System.err) } deliverUnlock() } Logger.getLogger(DeliveryThread::class.java.getName()).log(Level.INFO, \"DeliveryThread stopped.\") }'},\n",
              " {'input': 'Calculate what the U value should consist of given a particular key and document configuration . Correponds to Algorithms 3.4 and 3.5 of the PDF Reference version 1.7',\n",
              "  'output': 'Throws(GeneralSecurityException::class, EncryptionUnsupportedByProductException::class)\\nprivate fun calculateUValue( generalKey: ByteArray, firstDocIdValue: ByteArray?, revision: Int ): ByteArray? { return if (revision == 2) { val rc4: Cipher = createRC4Cipher() val key: SecretKey = createRC4Key(generalKey) initEncryption(rc4, key) crypt(rc4, PW_PADDING) } else if (revision >= 3) { val md5: MessageDigest = createMD5Digest() md5.update(PW_PADDING) if (firstDocIdValue != null) { md5.update(firstDocIdValue) } val hash: ByteArray = md5.digest() val rc4: Cipher = createRC4Cipher() val key: SecretKey = createRC4Key(generalKey) initEncryption(rc4, key) val v: ByteArray = crypt(rc4, hash) rc4shuffle(v, generalKey, rc4) assert(v.size == 16) val entryValue = ByteArray(32) System.arraycopy(v, 0, entryValue, 0, v.size) System.arraycopy(v, 0, entryValue, 16, v.size) entryValue } else { throw EncryptionUnsupportedByProductException(\"Unsupported standard security handler revision $revision\") } }'},\n",
              " {'input': 'Assigns the specified id to the labelMap according to its label',\n",
              "  'output': 'private fun assign(labelMap: HashMap<String, DBIDs>, label: String, id: DBIDRef) { if (labelMap.containsKey(label)) { val exist: DBIDs? = labelMap[label] if (exist is DBID) { val n: ModifiableDBIDs = DBIDUtil.newHashSet() n.add(exist as DBID?) n.add(id) labelMap[label] = n } else { assert(exist is HashSetModifiableDBIDs) assert(exist.size() > 1) (exist as ModifiableDBIDs?).add(id) } } else { labelMap[label] = DBIDUtil.deref(id) } }'}]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_completion(input_text, model, tokenizer):\n",
        "    inputs = tokenizer(input_text, return_tensors=\"pt\", return_attention_mask=False).to(\"cuda\")\n",
        "    outputs = model.generate(**inputs, max_new_tokens=60)\n",
        "    completion = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]\n",
        "    return completion"
      ],
      "metadata": {
        "id": "MfWLSSK4VqyH"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def levenshtein(dataset):\n",
        "  total_distance = 0\n",
        "  total_length = 0\n",
        "\n",
        "  for item in tqdm(dataset):\n",
        "      input_text = item['input']\n",
        "      expected_output = item['output']\n",
        "      generated_output = generate_completion(input_text, model, tokenizer)\n",
        "      total_distance += distance(generated_output, expected_output)\n",
        "      total_length += max(len(generated_output), len(expected_output))\n",
        "\n",
        "  error_metric = total_distance / total_length\n",
        "  return error_metric"
      ],
      "metadata": {
        "id": "Ccny8iYQXmrT"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Error Metric for Python before fine-tune: {levenshtein(dataset_test_py)}\")\n",
        "print(f\"Error Metric for Kotlin before fine-tune: {levenshtein(dataset_test_kt)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117,
          "referenced_widgets": [
            "f3e9cee805b64cf091e7c30a84646b4d",
            "271b86c40a564186a7fb36d0f2978ad9",
            "d1109f27802e4cf8a6eef1476a7fa8fc",
            "566a04f6140842c588fff1f06f3e3c6e",
            "059672ef82ff428796b373c1133472d6",
            "02ef225ca9474b17ac4e7501f8b2c06c",
            "fcb13350df4f464bbf16208fa02201eb",
            "bc88cf9ee2234bb48a32d32332850182",
            "9574564d0aaa4d2fbb57d0315ecc1958",
            "96fbdaa29f424df6b9b62b44e20e8d98",
            "0ba82bd7c2f24835a70355028733c617",
            "3d6a197096894d829a36346b1654c7bf",
            "fb5d22959b244e39b60020e3a71832a7",
            "3666929862a44999b2c0e87126797c91",
            "75dcba366d24400692ca4bdd74d544d0",
            "669af35749f8414fb9edc85977350d9f",
            "c3d55d08b8b94b3f8a78216b8da3e6b5",
            "20421b8755e74e4caab8b4a9cbc4e09d",
            "848497b001b14ca482b0861bf97551ab",
            "b55512cc4507492592149932a7f80368",
            "bdc8c3c401d54b9aa22a4365ce16af49",
            "17992cd1b878427bb0d6a5b8619fe683"
          ]
        },
        "id": "Cv_EX066j3jB",
        "outputId": "70c9f2f6-c178-449a-b760-b85b9c475b38"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/100 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f3e9cee805b64cf091e7c30a84646b4d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error Metric for Python before fine-tune: 0.8643433009402535\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/100 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3d6a197096894d829a36346b1654c7bf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error Metric for Kotlin before fine-tune: 0.8471257672834804\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fine-tuning"
      ],
      "metadata": {
        "id": "I2MYz0HQl_ke"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "00Y4yP9LUc6F",
        "outputId": "cd1170f2-9b49-4eec-9d97-243256abdf84"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 10,223,616 || all params: 1,428,494,336 || trainable%: 0.7156917421617274\n"
          ]
        }
      ],
      "source": [
        "config = LoraConfig(\n",
        "    r=16,\n",
        "    lora_alpha=16,\n",
        "    target_modules=[\"dense\", \"fc2\",\"q_proj\",\"k_proj\",\"v_proj\"],\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, config)\n",
        "model.print_trainable_parameters()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "NroQcivYUf5v"
      },
      "outputs": [],
      "source": [
        "def tokenize(sample):\n",
        "    model_inps =  tokenizer(sample[\"text\"], padding=True, truncation=True, max_length=512)\n",
        "    return model_inps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "d7ea0dfe9d6f40eea5548be18783a3a4",
            "5fb4288741de4585927d1125a11e3f66",
            "c17d491f22d6439a882268c40c0cf804",
            "880ff9136c7a4f8ab37a782e1a9fb7be",
            "e058be45e90440df8c7d29d247ad2456",
            "b0d860d7b6a54145bd44fcf4bb37e50f",
            "98e3de6dc09441e8a5ceaf2740aa21d8",
            "61b0cabbf6174cdaa2500ed2f08f386e",
            "64b1bf5c7a744affb6c7935f0c21d2d8",
            "7a10b9fb96e84a17999a2d0c174aa8df",
            "2754a5058f6b4557b61f7c2015adee65"
          ]
        },
        "id": "mvk4avUeUjfl",
        "outputId": "4b7d7bb7-c89f-4c4d-b8d5-361903e424a9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Tokenizing data:   0%|          | 0/41912 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d7ea0dfe9d6f40eea5548be18783a3a4"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenized_data = dataset.map(tokenize, batched=True, desc=\"Tokenizing data\", remove_columns=dataset.column_names)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Fine-tuning a model"
      ],
      "metadata": {
        "id": "fOgcG2VtKXP2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "jy-A_o_KVEke"
      },
      "outputs": [],
      "source": [
        "training_arguments = TrainingArguments(\n",
        "        output_dir=\"/output/phi-1-5-finetuned\",\n",
        "        per_device_train_batch_size=4,\n",
        "        gradient_accumulation_steps=1,\n",
        "        learning_rate=2e-5,\n",
        "        lr_scheduler_type=\"cosine\",\n",
        "        save_strategy=\"epoch\",\n",
        "        logging_steps=100,\n",
        "        max_steps=2400,\n",
        "        num_train_epochs=1,\n",
        "        push_to_hub=True\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 955
        },
        "id": "xtD0I3ZrVNm2",
        "outputId": "fd4ff86f-fe44-42d5-f814-fb5547287da5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "max_steps is given, it will override any value given in num_train_epochs\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2400' max='2400' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2400/2400 1:06:36, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.341000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.378000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>1.348100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>1.319800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>1.317200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>1.258100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>1.288500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>1.304000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>1.252100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>1.225900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>1.215100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>1.226200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1300</td>\n",
              "      <td>1.249500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1400</td>\n",
              "      <td>1.245100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>1.203000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1600</td>\n",
              "      <td>1.231100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1700</td>\n",
              "      <td>1.186100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1800</td>\n",
              "      <td>1.206500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1900</td>\n",
              "      <td>1.256900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>1.226500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2100</td>\n",
              "      <td>1.212200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2200</td>\n",
              "      <td>1.268400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2300</td>\n",
              "      <td>1.228100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2400</td>\n",
              "      <td>1.238100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=2400, training_loss=1.259393065770467, metrics={'train_runtime': 3998.3364, 'train_samples_per_second': 2.401, 'train_steps_per_second': 0.6, 'total_flos': 3.90356357087232e+16, 'train_loss': 1.259393065770467, 'epoch': 0.22905134567665586})"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    train_dataset=tokenized_data,\n",
        "    args=training_arguments,\n",
        "    data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",
        ")\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Analysis"
      ],
      "metadata": {
        "id": "sNwKj9gBQirA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Error Metric for Python before fine-tune: {levenshtein(dataset_test_py)}\")\n",
        "print(f\"Error Metric for Kotlin before fine-tune: {levenshtein(dataset_test_kt)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117,
          "referenced_widgets": [
            "eacd4b0aaf96443eaa680674005247fd",
            "87ea846ea9cd44b993b22d9850634e48",
            "d91510b78a96455b9f53447fd63e8b4b",
            "87aea13224d645aa94efff068ce1bb9d",
            "5b396f46ab7d4ce7b34b66cf856f4626",
            "4f7020a758a54d40b93794c07563c201",
            "72d02193c611463dac832f2e028c5d0f",
            "ce09be5a954c4262a69a44ada0d49f9d",
            "4bb57d2db8c64c75bc22e6fafb40f94d",
            "2efe3b95d31b430488acf0f935d0abdd",
            "6902075609c247bea32b4c7508b47725",
            "856e962473d5456cb8e056c0ff01a605",
            "dbaa1bca2f1840358573b0264cabc40a",
            "e2f1578989344ca6858def0f94247ad0",
            "d10bd11ca0854f83885b7ed67734feb0",
            "e0b443300bca430ea22e8439808f50cc",
            "65aa0ef52fa24a44be826a2f9dceb474",
            "02604780392f4082914143478eecca86",
            "a10f73e7655c4a9cb917daf6c011f249",
            "767d230636634a0e83314beaf6182f76",
            "f61c6958d7bc45748856b2ea054f27a5",
            "910c825680c7461693e457e7680419b5"
          ]
        },
        "id": "O3h7_zHNgrJ8",
        "outputId": "0e3d4b4b-3597-4d83-e93a-02f193cfa920"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/100 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eacd4b0aaf96443eaa680674005247fd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error Metric for Python before fine-tune: 0.8645153659642414\n"
          ]
        },
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            "Error Metric for Kotlin before fine-tune: 0.8475040402226611\n"
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        "As we can see, for the Python dataset the Levenshtein distance before and after fine-tune (**0.8643433009402535** and **0.8645153659642414**) are almost equal, which means that the quality on the Python dataset didn't change. It is good, because we only fine-tuned the model on the Kotlin dataset.\n",
        "\n",
        "For the Kotlin dataset we can see that the metrics are **0.8471257672834804** and **0.8475040402226611**, from where we can say that no significant improvements were done by the fine-tuning.\n",
        "\n",
        "**Main Reason for that:**\n",
        "\n",
        "Very few steps of training. I used only 2400 steps, which is 0.23 epochs. Unfortunately, I have no resources to train more steps/epochs, so this notebook is about the whole pipeline of the tuning and some tries with a little amount of steps."
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        "**How to improve the results?**\n",
        "\n",
        "***1)*** To work with dataset. For now I simply took all Kotlin files and used them for finetuning. But the best way is to make a research about the dataset, looking through it and choose the most significant files for finetuning, deleting the least significant.\n",
        "\n",
        "This step will also speed up the finetuning.\n",
        "\n",
        "***2)*** To work with the model. Finetune on the more epochs. Choose the hyperparameters more carefully. Use optimizer etc. This will help to make a significant boost in the results.\n",
        "\n",
        "***3)*** To work with metrics. I simply calculates a Levenshtein distance to measure the results somehow, but it is obviously not the best metric for this. Fine-grained complexity metrics required. Hovewer, the Levenshtein helped me to understand the minority of changes."
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