import os import pyarrow as pa import pyarrow.parquet as pq import datasets # Meta infomation _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} } """ ################################################################################################ # Config metadata _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": 3, "train/medium/java": 5, "train/medium/javascript": 2, "train/medium/php": 3, "train/medium/python": 7, "train/medium/ruby": 1, "train/medium/rust": 1, "train/full/c": 6, "train/full/c_sharp": 10, "train/full/cpp": 6, "train/full/go": 12, "train/full/java": 20, "train/full/javascript": 5, "train/full/php": 12, "train/full/python": 25, "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] # Convert to text name 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 = _LANG_CONFIGS[1:] # train_split_files = [] 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) # if load_full_train and "train" in split: # train_split_files.extend(split_files) # else: generators.append( datasets.SplitGenerator( name="train" if split == "train/full" else split.replace("/", "_"), gen_kwargs={ "files": split_files, }, ), ) # if load_full_train and train_split_files: # generators = [datasets.SplitGenerator(name="train", gen_kwargs={"files": train_split_files})] + generators 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