| | import os |
| |
|
| | import pyarrow as pa |
| | import pyarrow.parquet as pq |
| | import datasets |
| |
|
| |
|
| | |
| | _REPO_NAME = 'Fsoft-AIC/the-vault-function' |
| |
|
| | _DESCRIPTION = """The Vault is a multilingual code-text dataset with over 40 million pairs covering 10 popular programming languages. |
| | It is the largest corpus containing parallel code-text data. By building upon The Stack, a massive raw code sample collection, |
| | the Vault offers a comprehensive and clean resource for advancing research in code understanding and generation. It provides a |
| | high-quality dataset that includes code-text pairs at multiple levels, such as class and inline-level, in addition to the function level. |
| | The Vault can serve many purposes at multiple levels.""" |
| |
|
| | _HOMEPAGE = "https://huggingface.co/Fsoft-AIC" |
| | _LICENSE = "MIT License" |
| | _CITATION = """ |
| | @article{manh2023vault, |
| | title={The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation}, |
| | 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}, |
| | journal={arXiv preprint arXiv:2305.06156}, |
| | year={2023} |
| | } |
| | """ |
| | |
| |
|
| | |
| | _LANG_TO_TEXT = { |
| | "python": "python", |
| | "c": "c", |
| | "c#": "c_sharp", |
| | "c++": "cpp", |
| | "go": "go", |
| | "java": "java", |
| | "javascript": "javascript", |
| | "php": "php", |
| | "ruby": "ruby", |
| | "rust": "rust", |
| | } |
| | _LANG_CONFIGS = ["all"] + list(_LANG_TO_TEXT.keys()) |
| |
|
| | _TEXT_TO_LANG = {} |
| | for lang in _LANG_TO_TEXT: |
| | _TEXT_TO_LANG[_LANG_TO_TEXT[lang]] = lang |
| |
|
| | num_shard_split = { |
| | "train/small/ruby": 1, |
| | "train/small/c": 1, |
| | "train/small/c_sharp": 1, |
| | "train/small/cpp": 1, |
| | "train/small/go": 1, |
| | "train/small/java": 2, |
| | "train/small/javascript": 1, |
| | "train/small/php": 1, |
| | "train/small/python": 2, |
| | "train/small/rust": 1, |
| |
|
| | "train/medium/c": 2, |
| | "train/medium/c_sharp": 3, |
| | "train/medium/cpp": 2, |
| | "train/medium/go": 4, |
| | "train/medium/java": 6, |
| | "train/medium/javascript": 2, |
| | "train/medium/php": 4, |
| | "train/medium/python": 9, |
| | "train/medium/ruby": 1, |
| | "train/medium/rust": 1, |
| |
|
| | "train/full/c": 7, |
| | "train/full/c_sharp": 13, |
| | "train/full/cpp": 7, |
| | "train/full/go": 14, |
| | "train/full/java": 25, |
| | "train/full/javascript": 6, |
| | "train/full/php": 15, |
| | "train/full/python": 33, |
| | "train/full/ruby": 2, |
| | "train/full/rust": 3, |
| |
|
| | "validation/ruby": 1, |
| | "validation/c": 1, |
| | "validation/c_sharp": 1, |
| | "validation/cpp": 1, |
| | "validation/go": 1, |
| | "validation/java": 1, |
| | "validation/javascript": 1, |
| | "validation/php": 1, |
| | "validation/python": 1, |
| | "validation/rust": 1, |
| |
|
| | "test/ruby": 1, |
| | "test/c": 1, |
| | "test/c_sharp": 1, |
| | "test/cpp": 1, |
| | "test/go": 1, |
| | "test/java": 1, |
| | "test/javascript": 1, |
| | "test/php": 1, |
| | "test/python": 1, |
| | "test/rust": 1 |
| |
|
| | } |
| | _SPLIT_CONFIGS = ["all", "train", "train/small", "train/medium", "train/full", "validation", "test"] |
| |
|
| | |
| |
|
| | class TheVaultFunctionConfig(datasets.BuilderConfig): |
| | """BuilderConfig for The Vault dataset.""" |
| |
|
| | def __init__(self, *args, languages=["all"], split_set= ["all"], **kwargs): |
| | """BuilderConfig for the The Vault dataset. |
| | Args: |
| | split_set (:obj:`List[str]`): List of split set to load. |
| | languages (:obj:`List[str]`): List of languages to load. |
| | **kwargs: keyword arguments forwarded to super. |
| | """ |
| | super().__init__( |
| | *args, |
| | 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]), |
| | **kwargs, |
| | ) |
| | |
| | languages = set([lang.lower() for lang in languages]) |
| | split_set = set([split.lower() for split in split_set]) |
| | |
| | assert all([language in _LANG_CONFIGS for language in languages]), f"languages {languages} contains language not in {_LANG_CONFIGS}." |
| | assert all([split in _SPLIT_CONFIGS for split in split_set]), f"split_set {split_set} contains element not in {_SPLIT_CONFIGS}." |
| |
|
| | if "all" in split_set: |
| | assert len(split_set)==1, f"Passed 'all' together with other split sets. {split_set}" |
| | if "train" in split_set and "train/full" in split_set: |
| | print("WARNING - Split set 'train' and 'train/full' are similar. Force to only train/full.") |
| | split_set.remove("train") |
| | if "train" in split_set or "train/full" in split_set: |
| | for split in split_set: |
| | if "train" in split and (split != "train" and split != "train/full"): |
| | raise ValueError(f"Split set 'train' (or 'train/full) already contains '{split}'. Please only include one.") |
| |
|
| | if "all" in languages: |
| | assert len(languages)==1, f"Passed 'all' together with other languages. {languages}" |
| | else: |
| | languages = [_LANG_TO_TEXT[lang] for lang in languages] |
| | |
| | self.languages = list(languages) |
| | self.split_set= list(split_set) |
| |
|
| |
|
| | class TheVaultFunction(datasets.GeneratorBasedBuilder): |
| | """The Vault dataset.""" |
| |
|
| | VERSION = datasets.Version("1.0.0") |
| | |
| | BUILDER_CONFIG_CLASS = TheVaultFunctionConfig |
| | BUILDER_CONFIGS = [TheVaultFunctionConfig(languages=[lang], split_set=[spl]) for lang in _LANG_CONFIGS for spl in _SPLIT_CONFIGS] |
| | DEFAULT_CONFIG_NAME = "all-all" |
| |
|
| | |
| | def _info(self): |
| | return datasets.DatasetInfo( |
| | description=_DESCRIPTION, |
| | features=datasets.Features({ |
| | "hexsha": datasets.Value("string"), |
| | "repo": datasets.Value("string"), |
| | "path": datasets.Value("string"), |
| | "license": datasets.Sequence(datasets.Value("string")), |
| | "language": datasets.Value("string"), |
| | "identifier": datasets.Value("string"), |
| | "return_type": datasets.Value("string"), |
| | "original_string": datasets.Value("string"), |
| | "original_docstring": datasets.Value("string"), |
| | "docstring": datasets.Value("string"), |
| | "docstring_tokens": datasets.Sequence(datasets.Value("string")), |
| | "code": datasets.Value("string"), |
| | "code_tokens": datasets.Sequence(datasets.Value("string")), |
| | "short_docstring": datasets.Value("string"), |
| | "short_docstring_tokens": datasets.Sequence(datasets.Value("string")), |
| | "comment": datasets.Sequence(datasets.Value("string")), |
| | "parameters": [ |
| | { |
| | "param": datasets.Value("string"), |
| | "type": datasets.Value("string"), |
| | } |
| | ], |
| | "docstring_params": |
| | { |
| | "returns": [ |
| | { |
| | "docstring": datasets.Value("string"), |
| | "docstring_tokens": datasets.Sequence(datasets.Value("string")), |
| | "type": datasets.Value("string") |
| | } |
| | ], |
| | "raises": [ |
| | { |
| | "docstring": datasets.Value("string"), |
| | "docstring_tokens": datasets.Sequence(datasets.Value("string")), |
| | "type": datasets.Value("string") |
| | } |
| | ], |
| | "params": [ |
| | { |
| | "identifier": datasets.Value("string"), |
| | "type": datasets.Value("string"), |
| | "docstring": datasets.Value("string"), |
| | "docstring_tokens": datasets.Sequence(datasets.Value("string")), |
| | "default": datasets.Value("string"), |
| | "is_optional": datasets.Value("bool") |
| | } |
| | ], |
| | "outlier_params": [ |
| | { |
| | "identifier": datasets.Value("string"), |
| | "type": datasets.Value("string"), |
| | "docstring": datasets.Value("string"), |
| | "docstring_tokens": datasets.Sequence(datasets.Value("string")), |
| | "default": datasets.Value("string"), |
| | "is_optional": datasets.Value("bool") |
| | } |
| | ], |
| | "others": [ |
| | { |
| | "identifier": datasets.Value("string"), |
| | "docstring": datasets.Value("string"), |
| | "docstring_tokens": datasets.Sequence(datasets.Value("string")) |
| | } |
| | ] |
| | }, |
| | }), |
| | supervised_keys=None, |
| | homepage=_HOMEPAGE, |
| | license=_LICENSE, |
| | citation=_CITATION, |
| | |
| | ) |
| |
|
| | def _split_generators(self, dl_manager): |
| | generators = [] |
| | split_set = self.config.split_set |
| | languages = self.config.languages |
| | |
| | if "all" in split_set: |
| | split_set = ["train/full", "validation", "test"] |
| |
|
| | if "train" in split_set: |
| | split_set.remove('train') |
| | split_set = ["train/full"] + split_set |
| | |
| | if "all" in languages: |
| | languages = list(_LANG_TO_TEXT.values()) |
| |
|
| | |
| | for split in split_set: |
| | split_files = [] |
| | for language in languages: |
| | num_shards = num_shard_split[f"{split}/{language}"] |
| | data_files = [ |
| | f"data/{split}/{language}-{_index:05d}-of-{num_shards:05d}.parquet" |
| | for _index in range(num_shards) |
| | ] |
| | files = dl_manager.download(data_files) |
| | split_files.extend(files) |
| |
|
| | |
| | |
| | |
| |
|
| | generators.append( |
| | datasets.SplitGenerator( |
| | name="train" if split == "train/full" else split.replace("/", "_"), |
| | gen_kwargs={ |
| | "files": split_files, |
| | }, |
| | ), |
| | ) |
| | |
| | |
| | |
| |
|
| |
|
| | return generators |
| |
|
| | def _generate_examples(self, files): |
| | key = 0 |
| | for file_idx, file in enumerate(files): |
| | with open(file, "rb") as f: |
| | parquet_file = pq.ParquetFile(f) |
| | for batch_idx, record_batch in enumerate(parquet_file.iter_batches(batch_size=10_000)): |
| | pa_table = pa.Table.from_batches([record_batch]) |
| | for row_index in range(pa_table.num_rows): |
| | row = pa_table.slice(row_index, 1).to_pydict() |
| | |
| | yield key, { |
| | "hexsha": row['hexsha'][0], |
| | "repo": row['repo'][0], |
| | "path": row['path'][0], |
| | "license": row['license'][0], |
| | "language": row['language'][0], |
| | "identifier": row['identifier'][0], |
| | "return_type": row['return_type'][0], |
| | "original_string": row['original_string'][0], |
| | "original_docstring": row['original_docstring'][0], |
| | "docstring": row['docstring'][0], |
| | "docstring_tokens": row['docstring_tokens'][0], |
| | "code": row['code'][0], |
| | "code_tokens": row['code_tokens'][0], |
| | "short_docstring": row['short_docstring'][0], |
| | "short_docstring_tokens": row['short_docstring_tokens'][0], |
| | "comment": row['comment'][0], |
| | "parameters": row['parameters'][0], |
| | "docstring_params": row['docstring_params'][0], |
| | } |
| | key += 1 |
