| 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 |
