Generate_Python_Code_Dataset_.ipynb

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   "cell_type": "markdown",
   "metadata": {
    "id": "jQg6hsMhbuod"
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   "source": [
    "### REQUIRED PIP INSTALLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AagX97p36_7z",
    "outputId": "6ea54073-1385-44d3-81f3-0ac9abba190c"
   },
   "outputs": [],
   "source": [
    "!pip install transformers kaggle datasets tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K9sCPQzIb278"
   },
   "source": [
    "### DOWLOAD THE DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Im-RfeRablcJ",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import kaggle\n",
    "from pathlib import Path\n",
    "\n",
    "def download_dataset_from_kaggle(path=\"data\"):\n",
    "    \"\"\"\n",
    "    Download the CodeSearchNet dataset from Kaggle.\n",
    "    Make sure to have the Kaggle API token in ~/.kaggle/kaggle.json\n",
    "\n",
    "    Returns:\n",
    "        str: Path to the downloaded dataset.\n",
    "    \"\"\"\n",
    "    path = Path(path)\n",
    "    path.parent.mkdir(parents=True, exist_ok=True)\n",
    "    kaggle.api.authenticate()\n",
    "    kaggle.api.dataset_download_files(\n",
    "        \"omduggineni/codesearchnet\", path=path, unzip=True\n",
    "    )\n",
    "    \n",
    "#download_dataset_from_kaggle()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tDzmSa0loVOC"
   },
   "source": [
    "### LOAD THE DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 338
    },
    "id": "Z3GQ3tGYnM_z",
    "outputId": "fd76a782-2919-4dff-a1f4-d1ecef3b48b3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset json (/home/nando/.cache/huggingface/datasets/json/default-dea67a2e36b13223/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4)\n"
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    },
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       "  0%|          | 0/3 [00:00<?, ?it/s]"
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   ],
   "source": [
    "import glob\n",
    "\n",
    "from datasets import load_dataset\n",
    "from pathlib import Path\n",
    "\n",
    "LANG = 'python'\n",
    "\n",
    "def load_local_dataset(lang=\"all\", path=\"data\"):\n",
    "    \"\"\"\n",
    "    Load a local dataset from the downloaded Kaggle dataset.\n",
    "\n",
    "    Args:\n",
    "        lang (str): The language to be used for the dataset.\n",
    "        path (str, optional): Path to the downloaded dataset. Defaults to \"data\".\n",
    "\n",
    "    Returns:\n",
    "        Dataset: dataset loaded from local files\n",
    "    \"\"\"\n",
    "    path = Path(path)\n",
    "\n",
    "    if lang != \"all\":\n",
    "        # Read the downloaded dataset\n",
    "        path = path / lang / lang / \"final/jsonl\"\n",
    "        dataset = load_dataset(\n",
    "            \"json\",\n",
    "            data_files={\n",
    "                \"train\": glob.glob(path.as_posix() + \"/train/*.jsonl\"),\n",
    "                \"validation\": glob.glob(path.as_posix() + \"/valid/*.jsonl\"),\n",
    "                \"test\": glob.glob(path.as_posix() + \"/test/*.jsonl\"),\n",
    "            },\n",
    "        )\n",
    "    else:\n",
    "        train_files = glob.glob(path.as_posix() + \"/**/train/*.jsonl\", recursive=True)\n",
    "        valid_files = glob.glob(path.as_posix() + \"/**/valid/*.jsonl\", recursive=True)\n",
    "        test_files = glob.glob(path.as_posix() + \"/**/test/*.jsonl\", recursive=True)\n",
    "        dataset = load_dataset(\n",
    "            \"json\",\n",
    "            data_files={\n",
    "                \"train\": train_files,\n",
    "                \"validation\": valid_files,\n",
    "                \"test\": test_files,\n",
    "            },\n",
    "        )\n",
    "\n",
    "    return dataset\n",
    "\n",
    "dataset = load_local_dataset(LANG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iRzz39jDb8AT"
   },
   "source": [
    "### LOAD THE MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 737
    },
    "id": "KkTFNYCi6-PO",
    "outputId": "0720883a-203a-4f84-e949-ceb18f97c29d"
   },
   "outputs": [],
   "source": [
    "from transformers import RobertaTokenizer, T5ForConditionalGeneration\n",
    "tokenizer = RobertaTokenizer.from_pretrained('Salesforce/codet5-base', truncation_side='right')\n",
    "#tokenizer = RobertaTokenizer.from_pretrained('Salesforce/codet5-base')\n",
    "model = T5ForConditionalGeneration.from_pretrained(f'Salesforce/codet5-base-codexglue-sum-{LANG}').to(\"cuda\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3MxfnNxX2n0m"
   },
   "source": [
    "### GENERATE THE SUMMARIES AND ANOTATE THE DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    },
    "id": "LtmFMCPVuf3j",
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   ],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "from tqdm.auto import tqdm\n",
    "import json\n",
    "\n",
    "BATCH_SIZE = 32\n",
    "PARTITION = 'train'\n",
    "#all_summaries = []\n",
    "\n",
    "def tokenization_collator(batch_sample):\n",
    "  code = list(map(lambda x: x['original_string'], batch_sample))\n",
    "  return tokenizer(code, \n",
    "                   return_tensors='pt', \n",
    "                   padding='longest', \n",
    "                   truncation=True).input_ids.to('cuda')\n",
    "\n",
    "\n",
    "for PARTITION in ['train', 'test', 'validation']:\n",
    "  dataloader = DataLoader(dataset[PARTITION], \n",
    "                          batch_size=BATCH_SIZE, \n",
    "                          collate_fn=tokenization_collator)\n",
    "\n",
    "  with open(PARTITION + f'-{LANG}.jsonl', 'w') as f:\n",
    "    for batch_num, batch_data in tqdm(enumerate(iter(dataloader)), \n",
    "                                      total=(len(dataset[PARTITION]) // BATCH_SIZE) + 1):\n",
    "      \n",
    "      generated_ids = model.generate(batch_data, max_length=1024)\n",
    "      summaries = tokenizer.batch_decode(generated_ids.squeeze(), \n",
    "                                         skip_special_tokens=True)\n",
    "      \n",
    "      #all_summaries.extend(summaries)\n",
    "      for summary in summaries:\n",
    "        f.write(json.dumps({'summary': summary}))\n",
    "        f.write('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gzip, json\n",
    "\n",
    "with gzip.open(f'dataset_{LANG}.jsonl.gz', 'w') as w:\n",
    "  for PARTITION in ['train', 'test', 'validation']:\n",
    "    with open(PARTITION + f'-{LANG}.jsonl') as f:\n",
    "      for line_pos, line in enumerate(f.readlines()):\n",
    "        summary = json.loads(line)\n",
    "        if len(summary['summary']) > 350:\n",
    "            continue\n",
    "            \n",
    "        d = dataset[PARTITION][line_pos].copy()\n",
    "        d.update(summary)\n",
    "        b = json.dumps(d)\n",
    "        w.write(b.encode('UTF-8'))\n",
    "        w.write('\\n'.encode('UTF-8'))\n"
   ]
  },
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   "execution_count": 5,
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       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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     "text": [
      "Downloading and preparing dataset json/default to /home/nando/.cache/huggingface/datasets/json/default-8d86563eb7816a96/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4...\n"
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      "text/plain": [
       "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
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     },
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       "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
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     },
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      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset json downloaded and prepared to /home/nando/.cache/huggingface/datasets/json/default-8d86563eb7816a96/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4. Subsequent calls will reuse this data.\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Pushing split train to the Hub.\n"
     ]
    },
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   ],
   "source": [
    "processed_dataset = load_dataset('json', data_files=f'./dataset_{LANG}.jsonl.gz')\n",
    "processed_dataset.push_to_hub(f'Nan-Do/code-search-net-{LANG}')"
   ]
  },
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