{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!git clone https://huggingface.co/datasets/muellerzr/RAG-accelerate"
      ],
      "metadata": {
        "id": "L6pZLIWYSsSq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd RAG-accelerate"
      ],
      "metadata": {
        "id": "Ntgk_YKiSyIq",
        "outputId": "14d9ffc4-0b42-4283-adeb-4b981ad2ee57",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/RAG-accelerate\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re"
      ],
      "metadata": {
        "id": "ERy5RO58TBcu"
      },
      "execution_count": 222,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def process_file(content, filename):\n",
        "    # Declare constants\n",
        "    func = ''\n",
        "    tab  = ''\n",
        "    brackets = {'(':0, '[':0, '{':0}\n",
        "    close = {')':'(', ']':'[', '}':'{'}\n",
        "    string=''\n",
        "    tab_f=''\n",
        "    c1=''\n",
        "    c2=''\n",
        "    multiline=False\n",
        "    check=False\n",
        "    in_class = False\n",
        "    in_function = False\n",
        "    decorator = None\n",
        "    class_name = \"\"\n",
        "    func_list=[]\n",
        "    for line in content.split('\\n'):\n",
        "        tab = re.findall(r'^\\s*',line)[0]\n",
        "        if re.findall(r'^\\s*@', line) and not func:\n",
        "            decorator = line\n",
        "        if (len(line.lstrip(\" \")) - len(line)) == 0:\n",
        "            in_class = False\n",
        "        if (re.findall(r'^\\s*def ', line) or re.findall(r'^\\s*class ', line)) and not string and not multiline:\n",
        "            if re.findall(r'^\\s*class ', line):\n",
        "                in_class = True\n",
        "                class_name = re.findall(r'class\\s+(\\w+)\\s*[\\(:]', line)[0]\n",
        "            func += line + '\\n'\n",
        "            tab_f = tab\n",
        "            check=True\n",
        "        if func:\n",
        "            if not check:\n",
        "                if sum(brackets.values()) == 0 and not string and not multiline:\n",
        "                    if len(tab) <= len(tab_f):\n",
        "                        # Append the metadata\n",
        "                        metadata = f'File: {filename}'\n",
        "                        if in_class:\n",
        "                            metadata += f'\\nClass: {class_name}'\n",
        "                        if decorator is not None:\n",
        "                            func = f'{decorator}\\n{func}'\n",
        "                            decorator = None\n",
        "                        func = f'{metadata}\\nSource:\\n{func}'\n",
        "                        func_list.append(func)\n",
        "                        func=''\n",
        "                        c1=''\n",
        "                        c2=''\n",
        "                        continue\n",
        "                func += line + '\\n'\n",
        "            check = False\n",
        "        for c0 in line:\n",
        "            if c0 == '#' and not string and not multiline:\n",
        "                break\n",
        "            if c1 != '\\\\':\n",
        "                if c0 in ['\"', \"'\"]:\n",
        "                    if c2 == c1 == c0 == '\"' and string != \"'\":\n",
        "                        multiline = not multiline\n",
        "                        string = ''\n",
        "                        continue\n",
        "                    if not multiline:\n",
        "                        if c0 in string:\n",
        "                            string = ''\n",
        "                        else:\n",
        "                            if not string:\n",
        "                                string = c0\n",
        "                if not string and not multiline:\n",
        "                    if c0 in brackets:\n",
        "                        brackets[c0] += 1\n",
        "                    if c0 in close:\n",
        "                        b = close[c0]\n",
        "                        brackets[b] -= 1\n",
        "            c2=c1\n",
        "            c1=c0\n",
        "    return func_list"
      ],
      "metadata": {
        "id": "yTaa5oufYwVj"
      },
      "execution_count": 223,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path"
      ],
      "metadata": {
        "id": "SObDuDAPYj1u"
      },
      "execution_count": 224,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "src_path = Path(\"src\")"
      ],
      "metadata": {
        "id": "EBVypAmNYsnR"
      },
      "execution_count": 225,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results = []\n",
        "for f in src_path.glob('**/*'):\n",
        "    if f.is_file():\n",
        "        content = f.read_text()\n",
        "        output = process_file(content, f.name)\n",
        "        results += output"
      ],
      "metadata": {
        "id": "J0c0eNPYcAoX"
      },
      "execution_count": 226,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(results)"
      ],
      "metadata": {
        "id": "nuZS65CzdAMm",
        "outputId": "2601f995-fce6-49d5-b2dd-71e8150746a1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "536"
            ]
          },
          "metadata": {},
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "eFinZFPTf6Ac"
      },
      "execution_count": 232,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array(results)"
      ],
      "metadata": {
        "id": "Hg6FiWfAgQcA"
      },
      "execution_count": 234,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.save(\"processed_chunks.npy\", arr)"
      ],
      "metadata": {
        "id": "OVEMm3gsgRW-"
      },
      "execution_count": 235,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4f3fivFAgxmg"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "scratchpad",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}