import os import pyarrow as pa import pyarrow.parquet as pq import datasets _REPO_NAME = 'Fsoft-AIC/the-vault' _LANG_TO_TEXT = { "python": "python", "c": "c", "c#": "c_sharp", "c++": "cpp", "go": "go", "Java": "java", "javascript": "javascript", "php": "php", "ruby": "ruby", "rust": "rust", } _DESCRIPTION = """The Vault""" _HOMEPAGE = "https://huggingface.co/Fsoft-AIC" _TEXT_TO_LANG = {} for lang in _LANG_TO_TEXT: _TEXT_TO_LANG[_LANG_TO_TEXT[lang]] = lang _LANG_CONFIGS = ["all"] + list(_TEXT_TO_LANG.keys()) num_shard_split = { 'train/small/python': 1, 'train/medium/python': 1, 'train/small/c': 1, 'train/medium/c': 1 } _SPLIT_CONFIGS = ["all", "train/small", "train/medium"] class TheVaultFunctionConfig(datasets.BuilderConfig): """BuilderConfig for The Vault dataset.""" def __init__(self, *args, languages=["all"], split_set= ["all"], **kwargs): """BuilderConfig for the GitHub Code 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(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}" elif "train" in split_set: for split in split_set: if "train" in split and split != "train": raise f"Split set 'train' already contains '{split}'. Please only include one." if "all" in languages: assert len(languages)==1, f"Passed 'all' together with other languages. {languages}" # self.filter_languages = False # else: # self.filter_languages = True self.languages = set(languages) self.split_set= 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({ # "original_string": datasets.Value("string"), "original_docstring": datasets.Value("string"), "code": datasets.Value("string"), "docstring": datasets.Value("string"), "code_tokens": datasets.Value("string"), "docstring_tokens": datasets.Value("string"), "short_docstring": datasets.Value("string"), "short_docstring_tokens": datasets.Value("string"), "comment": datasets.Value("string"), "return_type": datasets.Value("string"), "identifier": datasets.Value("string"), "repo": datasets.Value("string"), "path": datasets.Value("string"), "language": datasets.Value("string"), }), supervised_keys=None, homepage=_HOMEPAGE, ) def _split_generators(self, dl_manager): print(self.config.split_set) generators = [] split_set = list(self.config.split_set) languages = list(self.config.languages) if "all" in split_set: split_set = _SPLIT_CONFIGS[1:] if "train" in split_set: split_set.remove('train') split_set.extend(["train/small", "train/medium"]) if "all" in languages: languages = _LANG_CONFIGS[1:] for split in split_set: 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) generators.append( datasets.SplitGenerator( name=split.replace("/", "_"), gen_kwargs={ "files": 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() # lang = row['language'][0] # if self.config.filter_languages and not lang in self.config.languages: # continue yield key, { "repo": row['repo'][0], "path": row['path'][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] } key += 1 # def lang_from_name(name): # for extension in _EXTENSION_TO_LANG: # if name.endswith(extension): # return _EXTENSION_TO_LANG[extension]