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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-inline' |
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_DESCRIPTION = """The Vault is a multilingual code-text dataset with over 34 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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"ruby": 3, |
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"c": 29, |
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"c_sharp": 25, |
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"cpp": 39, |
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"go": 15, |
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"java": 75, |
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"javascript": 6, |
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"php": 21, |
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"python": 48, |
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"rust": 10, |
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} |
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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"], **kwargs): |
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"""BuilderConfig for the The Vault dataset. |
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Args: |
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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([_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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assert all([language in _LANG_CONFIGS for language in languages]), f"languages {languages} contains language not in {_LANG_CONFIGS}." |
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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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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]) for lang in _LANG_CONFIGS] |
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DEFAULT_CONFIG_NAME = "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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"code": datasets.Value("string"), |
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"code_tokens": datasets.Sequence(datasets.Value("string")), |
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"original_comment": datasets.Value("string"), |
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"comment": datasets.Value("string"), |
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"comment_tokens": datasets.Sequence(datasets.Value("string")), |
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"start_point": datasets.Sequence(datasets.Value("int32")), |
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"end_point": datasets.Sequence(datasets.Value("int32")), |
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"prev_context": |
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{ |
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"code": datasets.Value("string"), |
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"start_point": datasets.Sequence(datasets.Value("int32")), |
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"end_point": datasets.Sequence(datasets.Value("int32")), |
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}, |
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"next_context": |
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{ |
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"code": datasets.Value("string"), |
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"start_point": datasets.Sequence(datasets.Value("int32")), |
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"end_point": datasets.Sequence(datasets.Value("int32")), |
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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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languages = self.config.languages |
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if "all" in languages: |
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languages = list(_LANG_TO_TEXT.values()) |
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split_files = [] |
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for language in languages: |
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num_shards = num_shard_split[language] |
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data_files = [ |
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f"data/train/{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", |
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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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"code": row['code'][0], |
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"code_tokens": row['code_tokens'][0], |
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"original_comment": row['original_comment'][0], |
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"comment": row['comment'][0], |
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"comment_tokens": row['comment_tokens'][0], |
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"start_point": row['start_point'][0], |
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"end_point": row['end_point'][0], |
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"prev_context": row['prev_context'][0], |
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"next_context": row['next_context'][0] |
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} |
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key += 1 |
