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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CodeT Code Generation Datasets\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/LAION-AI/Open-Assistant/blob/main/notebooks/data-augmentation/codet-data/Augment_CodeT_codegen.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook contains code to parse CodeT code generation prompt and solution data and modify to `(prompt, solution)` pairs outputted in a `.jsonl` file.\n",
    "\n",
    "Requirements: `requests`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from pathlib import Path\n",
    "import requests\n",
    "from typing import Dict, List, Tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_FILES: List[str] = [\n",
    "    \"HumanEval_for_code_generation.jsonl\",\n",
    "    \"mbpp_sanitized_for_code_generation.jsonl\",\n",
    "]\n",
    "\n",
    "OUT_FILES: List[str] = [\n",
    "    \"HumanEval_codegen.jsonl\",\n",
    "    \"mbpp_codegen.jsonl\",\n",
    "]\n",
    "\n",
    "FILE_PATHS: List[Path] = [Path(f\"data/{data_file}\") for data_file in DATA_FILES]\n",
    "\n",
    "OUT_PATHS: List[Path] = [Path(f\"data/augmented/{out_file}\") for out_file in OUT_FILES]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def download_file(filename: str):\n",
    "    url = f\"https://raw.githubusercontent.com/microsoft/CodeT/main/CodeT/data/dataset/{filename}\"\n",
    "    response = requests.get(url)\n",
    "    with open(f\"data/{filename}\", \"wb\") as f:\n",
    "        f.write(response.content)\n",
    "\n",
    "\n",
    "for filename in DATA_FILES:\n",
    "    download_file(filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can find the docstring, use its contents as the instruction (prefixed with \"Write a function corresponding to the docstring:\") and then use the content prior to the docstring and the canonical solution as the response."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_docstring_indices(prompt_lines: List[str]) -> Tuple[int, int]:\n",
    "    docstring_start, docstring_end = None, None\n",
    "\n",
    "    for i, line in enumerate(prompt_lines):\n",
    "        if not (line.strip().startswith('\"\"\"') or line.strip().startswith(\"'''\")):\n",
    "            continue\n",
    "        if docstring_start:\n",
    "            docstring_end = i\n",
    "            break\n",
    "        docstring_start = i\n",
    "\n",
    "    if docstring_end:\n",
    "        return docstring_start, docstring_end\n",
    "    raise ValueError(f\"No complete docstring found!\\n{prompt_lines}\")\n",
    "\n",
    "\n",
    "def get_before(prompt_lines: List[str], before: int) -> List[str]:\n",
    "    before_lines = prompt_lines[:before]\n",
    "    return before_lines\n",
    "\n",
    "\n",
    "def get_between(prompt_lines: List[str], start: int, end: int) -> List[str]:\n",
    "    between_lines = prompt_lines[start:end]\n",
    "    return between_lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_request_and_solution(sample: dict) -> Tuple[List[str], List[str]]:\n",
    "    prompt = sample[\"prompt\"]\n",
    "    prompt_lines = prompt.splitlines()\n",
    "\n",
    "    docstring_start, docstring_end = get_docstring_indices(prompt_lines)\n",
    "\n",
    "    # Extract prompt\n",
    "    in_docstring = get_between(prompt_lines, docstring_start, docstring_end)\n",
    "    if '\"\"\"' in in_docstring[0] or \"'''\" in in_docstring[0]:\n",
    "        in_docstring[0] = in_docstring[0].replace('\"\"\"', \"\").replace(\"...\", \"\").strip()\n",
    "    request = \"Write a Python function corresponding to the docstring: \" + \" \".join([p.strip() for p in in_docstring])\n",
    "\n",
    "    # Extract solution\n",
    "    before_docstring = get_before(prompt_lines, docstring_start)\n",
    "    after_docstring = sample[\"canonical_solution\"].splitlines()\n",
    "    solution = before_docstring + after_docstring\n",
    "    # Gets rid of consecutive empty lines\n",
    "    solution = [v for i, v in enumerate(solution) if v != \"\" or v != solution[i - 1]]\n",
    "    solution = \"\\n\".join(solution)\n",
    "\n",
    "    return request, solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_file(file_path: Path, out_path: Path):\n",
    "    lines = file_path.read_text().splitlines()\n",
    "    samples = list(map(json.loads, lines))\n",
    "\n",
    "    output = []\n",
    "    for sample in samples:\n",
    "        prompt, solution = get_request_and_solution(sample)\n",
    "        output.append({\"prompt\": prompt, \"solution\": solution})\n",
    "\n",
    "    with open(out_path, \"w\") as f:\n",
    "        for sample in output:\n",
    "            f.write(json.dumps(sample))\n",
    "            f.write(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "for file_path, out_path in zip(FILE_PATHS, OUT_PATHS):\n",
    "    process_file(file_path, out_path)"
   ]
  }
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