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import os |
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import pyarrow as pa |
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import pyarrow.parquet as pq |
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import datasets |
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_REPO_NAME = 'Fsoft-AIC/the-vault-function' |
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_DESCRIPTION = """The Vault is a multilingual code-text dataset with over 40 million pairs covering 10 popular programming languages. |
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It is the largest corpus containing parallel code-text data. By building upon The Stack, a massive raw code sample collection, |
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the Vault offers a comprehensive and clean resource for advancing research in code understanding and generation. It provides a |
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high-quality dataset that includes code-text pairs at multiple levels, such as class and inline-level, in addition to the function level. |
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The Vault can serve many purposes at multiple levels.""" |
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_HOMEPAGE = "https://huggingface.co/Fsoft-AIC" |
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_LICENSE = "MIT License" |
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_CITATION = """ |
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@article{manh2023vault, |
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title={The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation}, |
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author={Manh, Dung Nguyen and Hai, Nam Le and Dau, Anh TV and Nguyen, Anh Minh and Nghiem, Khanh and Guo, Jin and Bui, Nghi DQ}, |
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journal={arXiv preprint arXiv:2305.06156}, |
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year={2023} |
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} |
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""" |
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_LANG_TO_TEXT = { |
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"python": "python", |
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"c": "c", |
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"c#": "c_sharp", |
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"c++": "cpp", |
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"go": "go", |
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"java": "java", |
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"javascript": "javascript", |
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"php": "php", |
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"ruby": "ruby", |
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"rust": "rust", |
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} |
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_LANG_CONFIGS = ["all"] + list(_LANG_TO_TEXT.keys()) |
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_TEXT_TO_LANG = {} |
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for lang in _LANG_TO_TEXT: |
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_TEXT_TO_LANG[_LANG_TO_TEXT[lang]] = lang |
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num_shard_split = { |
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"train/small/ruby": 1, |
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"train/small/c": 1, |
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"train/small/c_sharp": 1, |
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"train/small/cpp": 1, |
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"train/small/go": 1, |
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"train/small/java": 2, |
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"train/small/javascript": 1, |
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"train/small/php": 1, |
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"train/small/python": 2, |
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"train/small/rust": 1, |
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"train/medium/c": 2, |
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"train/medium/c_sharp": 3, |
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"train/medium/cpp": 2, |
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"train/medium/go": 3, |
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"train/medium/java": 5, |
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"train/medium/javascript": 2, |
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"train/medium/php": 3, |
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"train/medium/python": 7, |
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"train/medium/ruby": 1, |
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"train/medium/rust": 1, |
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"train/full/c": 6, |
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"train/full/c_sharp": 10, |
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"train/full/cpp": 6, |
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"train/full/go": 12, |
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"train/full/java": 20, |
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"train/full/javascript": 5, |
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"train/full/php": 12, |
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"train/full/python": 25, |
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"train/full/ruby": 2, |
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"train/full/rust": 3, |
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"validation/ruby": 1, |
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"validation/c": 1, |
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"validation/c_sharp": 1, |
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"validation/cpp": 1, |
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"validation/go": 1, |
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"validation/java": 1, |
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"validation/javascript": 1, |
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"validation/php": 1, |
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"validation/python": 1, |
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"validation/rust": 1, |
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"test/ruby": 1, |
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"test/c": 1, |
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"test/c_sharp": 1, |
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"test/cpp": 1, |
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"test/go": 1, |
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"test/java": 1, |
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"test/javascript": 1, |
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"test/php": 1, |
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"test/python": 1, |
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"test/rust": 1 |
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} |
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_SPLIT_CONFIGS = ["all", "train", "train/small", "train/medium", "train/full", "validation", "test"] |
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class TheVaultFunctionConfig(datasets.BuilderConfig): |
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"""BuilderConfig for The Vault dataset.""" |
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def __init__(self, *args, languages=["all"], split_set= ["all"], **kwargs): |
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"""BuilderConfig for the The Vault dataset. |
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Args: |
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split_set (:obj:`List[str]`): List of split set to load. |
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languages (:obj:`List[str]`): List of languages to load. |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super().__init__( |
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*args, |
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name= "+".join([split.replace("/", "_") for split in split_set]) + "-" + "+".join([_LANG_TO_TEXT[lang] if lang in _LANG_TO_TEXT else lang for lang in languages]), |
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**kwargs, |
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) |
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languages = set([lang.lower() for lang in languages]) |
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split_set = set([split.lower() for split in split_set]) |
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assert all([language in _LANG_CONFIGS for language in languages]), f"languages {languages} contains language not in {_LANG_CONFIGS}." |
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assert all([split in _SPLIT_CONFIGS for split in split_set]), f"split_set {split_set} contains element not in {_SPLIT_CONFIGS}." |
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if "all" in split_set: |
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assert len(split_set)==1, f"Passed 'all' together with other split sets. {split_set}" |
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if "train" in split_set and "train/full" in split_set: |
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print("WARNING - Split set 'train' and 'train/full' are similar. Force to only train/full.") |
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split_set.remove("train") |
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if "train" in split_set or "train/full" in split_set: |
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for split in split_set: |
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if "train" in split and (split != "train" and split != "train/full"): |
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raise ValueError(f"Split set 'train' (or 'train/full) already contains '{split}'. Please only include one.") |
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if "all" in languages: |
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assert len(languages)==1, f"Passed 'all' together with other languages. {languages}" |
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else: |
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languages = [_LANG_TO_TEXT[lang] for lang in languages] |
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self.languages = list(languages) |
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self.split_set= list(split_set) |
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class TheVaultFunction(datasets.GeneratorBasedBuilder): |
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"""The Vault dataset.""" |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIG_CLASS = TheVaultFunctionConfig |
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BUILDER_CONFIGS = [TheVaultFunctionConfig(languages=[lang], split_set=[spl]) for lang in _LANG_CONFIGS for spl in _SPLIT_CONFIGS] |
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DEFAULT_CONFIG_NAME = "all-all" |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features({ |
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"hexsha": datasets.Value("string"), |
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"repo": datasets.Value("string"), |
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"path": datasets.Value("string"), |
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"license": datasets.Sequence(datasets.Value("string")), |
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"language": datasets.Value("string"), |
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"identifier": datasets.Value("string"), |
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"return_type": datasets.Value("string"), |
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"original_string": datasets.Value("string"), |
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"original_docstring": datasets.Value("string"), |
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"docstring": datasets.Value("string"), |
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"docstring_tokens": datasets.Sequence(datasets.Value("string")), |
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"code": datasets.Value("string"), |
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"code_tokens": datasets.Sequence(datasets.Value("string")), |
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"short_docstring": datasets.Value("string"), |
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"short_docstring_tokens": datasets.Sequence(datasets.Value("string")), |
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"comment": datasets.Sequence(datasets.Value("string")), |
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"parameters": [ |
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{ |
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"param": datasets.Value("string"), |
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"type": datasets.Value("string"), |
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} |
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], |
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"docstring_params": |
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{ |
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"returns": [ |
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{ |
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"docstring": datasets.Value("string"), |
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"docstring_tokens": datasets.Sequence(datasets.Value("string")), |
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"type": datasets.Value("string") |
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} |
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], |
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"raises": [ |
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{ |
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"docstring": datasets.Value("string"), |
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"docstring_tokens": datasets.Sequence(datasets.Value("string")), |
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"type": datasets.Value("string") |
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} |
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], |
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"params": [ |
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{ |
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"identifier": datasets.Value("string"), |
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"type": datasets.Value("string"), |
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"docstring": datasets.Value("string"), |
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"docstring_tokens": datasets.Sequence(datasets.Value("string")), |
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"default": datasets.Value("string"), |
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"is_optional": datasets.Value("bool") |
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} |
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], |
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"outlier_params": [ |
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{ |
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"identifier": datasets.Value("string"), |
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"type": datasets.Value("string"), |
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"docstring": datasets.Value("string"), |
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"docstring_tokens": datasets.Sequence(datasets.Value("string")), |
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"default": datasets.Value("string"), |
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"is_optional": datasets.Value("bool") |
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} |
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], |
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"others": [ |
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{ |
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"identifier": datasets.Value("string"), |
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"docstring": datasets.Value("string"), |
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"docstring_tokens": datasets.Sequence(datasets.Value("string")) |
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} |
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] |
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}, |
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}), |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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generators = [] |
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split_set = self.config.split_set |
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languages = self.config.languages |
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if "all" in split_set: |
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split_set = ["train/full", "validation", "test"] |
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if "train" in split_set: |
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split_set.remove('train') |
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split_set = ["train/full"] + split_set |
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if "all" in languages: |
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languages = _LANG_CONFIGS[1:] |
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for split in split_set: |
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split_files = [] |
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for language in languages: |
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num_shards = num_shard_split[f"{split}/{language}"] |
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data_files = [ |
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f"data/{split}/{language}-{_index:05d}-of-{num_shards:05d}.parquet" |
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for _index in range(num_shards) |
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] |
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files = dl_manager.download(data_files) |
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split_files.extend(files) |
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generators.append( |
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datasets.SplitGenerator( |
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name="train" if split == "train/full" else split.replace("/", "_"), |
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gen_kwargs={ |
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"files": split_files, |
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}, |
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), |
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) |
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return generators |
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def _generate_examples(self, files): |
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key = 0 |
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for file_idx, file in enumerate(files): |
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with open(file, "rb") as f: |
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parquet_file = pq.ParquetFile(f) |
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for batch_idx, record_batch in enumerate(parquet_file.iter_batches(batch_size=10_000)): |
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pa_table = pa.Table.from_batches([record_batch]) |
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for row_index in range(pa_table.num_rows): |
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row = pa_table.slice(row_index, 1).to_pydict() |
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yield key, { |
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"hexsha": row['hexsha'][0], |
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"repo": row['repo'][0], |
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"path": row['path'][0], |
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"license": row['license'][0], |
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"language": row['language'][0], |
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"identifier": row['identifier'][0], |
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"return_type": row['return_type'][0], |
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"original_string": row['original_string'][0], |
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"original_docstring": row['original_docstring'][0], |
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"docstring": row['docstring'][0], |
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"docstring_tokens": row['docstring_tokens'][0], |
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"code": row['code'][0], |
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"code_tokens": row['code_tokens'][0], |
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"short_docstring": row['short_docstring'][0], |
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"short_docstring_tokens": row['short_docstring_tokens'][0], |
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"comment": row['comment'][0], |
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"parameters": row['parameters'][0], |
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"docstring_params": row['docstring_params'][0], |
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} |
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key += 1 |
