# coding=utf-8 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """GitHub Code clean dataset.""" import os import pyarrow as pa import pyarrow.parquet as pq import datasets _REPO_NAME = "codeparrot/github-code-clean" _LANG_TO_EXTENSION = { "Assembly": [".asm"], "Batchfile": [".bat", ".cmd"], "C": [".c", ".h"], "C#": [".cs"], "C++": [".cpp", ".hpp", ".c++", ".h++", ".cc", ".hh", ".C", ".H"], "CMake": [".cmake"], "CSS": [".css"], "Dockerfile": [".dockerfile", "Dockerfile"], "FORTRAN": ['.f90', '.f', '.f03', '.f08', '.f77', '.f95', '.for', '.fpp'], "GO": [".go"], "Haskell": [".hs"], "HTML":[".html"], "Java": [".java"], "JavaScript": [".js"], "Julia": [".jl"], "Lua": [".lua"], "Makefile": ["Makefile"], "Markdown": [".md", ".markdown"], "PHP": [".php", ".php3", ".php4", ".php5", ".phps", ".phpt"], "Perl": [".pl", ".pm", ".pod", ".perl"], "PowerShell": ['.ps1', '.psd1', '.psm1'], "Python": [".py"], "Ruby": [".rb"], "Rust": [".rs"], "SQL": [".sql"], "Scala": [".scala"], "Shell": [".sh", ".bash", ".command", ".zsh"], "TypeScript": [".ts", ".tsx"], "TeX": [".tex"], "Visual Basic": [".vb"] } _LICENSES = ['mit', 'apache-2.0', 'gpl-3.0', 'gpl-2.0', 'bsd-3-clause', 'agpl-3.0', 'lgpl-3.0', 'lgpl-2.1', 'bsd-2-clause', 'cc0-1.0', 'epl-1.0', 'mpl-2.0', 'unlicense', 'isc', 'artistic-2.0'] _DESCRIPTION = """\ The GitHub Code clean dataset in a more filtered version of codeparrot/github-code dataset, it consists of 115M code files from GitHub in 32 programming \ languages with 60 extensions totaling in almost 1TB of text data. """ _HOMEPAGE = "https://cloud.google.com/blog/topics/public-datasets/github-on-bigquery-analyze-all-the-open-source-code/" _EXTENSION_TO_LANG = {} for lang in _LANG_TO_EXTENSION: for extension in _LANG_TO_EXTENSION[lang]: _EXTENSION_TO_LANG[extension] = lang _LANG_CONFIGS = ["all"] + list(_LANG_TO_EXTENSION.keys()) _LICENSE_CONFIGS = ["all"] + _LICENSES class GithubCodeConfig(datasets.BuilderConfig): """BuilderConfig for the GitHub Code dataset.""" def __init__(self, *args, languages=["all"], licenses=["all"], max_samples=None, **kwargs): """BuilderConfig for the GitHub Code dataset. Args: languages (:obj:`List[str]`): List of languages to load. licenses (:obj:`List[str]`): List of licenses to load. max_samples (:obj:`int`, optional): Maximum number of samples to generate (for early stopping). **kwargs: keyword arguments forwarded to super. """ super().__init__( *args, name="+".join(languages)+"-"+"+".join(licenses), **kwargs, ) languages = set(languages) licenses = set(licenses) assert all([language in _LANG_CONFIGS for language in languages]), f"Language not in {_LANG_CONFIGS}." assert all([license in _LICENSE_CONFIGS for license in licenses]), f"License not in {_LICENSE_CONFIGS}." if "all" in languages: assert len(languages)==1, "Passed 'all' together with other languages." self.filter_languages = False else: self.filter_languages = True if "all" in licenses: assert len(licenses)==1, "Passed 'all' together with other licenses." self.filter_licenses = False else: self.filter_licenses = True self.languages = set(languages) self.licenses = set(licenses) self.max_samples = max_samples class GithubCode(datasets.GeneratorBasedBuilder): """GitHub Code dataset.""" VERSION = datasets.Version("1.0.0") BUILDER_CONFIG_CLASS = GithubCodeConfig BUILDER_CONFIGS = [GithubCodeConfig(languages=[lang], licenses=[license]) for lang in _LANG_CONFIGS for license in _LICENSE_CONFIGS] DEFAULT_CONFIG_NAME = "all-all" def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features({"code": datasets.Value("string"), "repo_name": datasets.Value("string"), "path": datasets.Value("string"), "language": datasets.Value("string"), "license": datasets.Value("string"), "size": datasets.Value("int32")}), supervised_keys=None, homepage=_HOMEPAGE, license="Multiple: see the 'license' field of each sample.", ) def _split_generators(self, dl_manager): num_shards = 880 data_files = [ f"data/train-{_index:05d}-of-{num_shards:05d}.parquet" for _index in range(num_shards) ] files = dl_manager.download(data_files) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "files": files, }, ), ] def _generate_examples(self, files): key = 0 yielded_count = 0 max_samples = self.config.max_samples for file_idx, file in enumerate(files): # Early stopping at file level if max_samples is not None and yielded_count >= max_samples: return parquet_file = pq.ParquetFile(file) # Process each row group separately (Parquet internal chunking) for rg_idx in range(parquet_file.num_row_groups): # Early stopping at row group level if max_samples is not None and yielded_count >= max_samples: return # PASS 1: Read ONLY filter columns from this row group filter_table = parquet_file.read_row_group(rg_idx, columns=['path', 'license']) paths = filter_table['path'].to_pylist() licenses = filter_table['license'].to_pylist() # Find matching indices within this row group matching_indices = [] matching_langs = [] for row_index in range(len(paths)): if max_samples is not None and yielded_count + len(matching_indices) >= max_samples: break lang = lang_from_name(paths[row_index]) license = licenses[row_index] if self.config.filter_languages and lang not in self.config.languages: continue if self.config.filter_licenses and license not in self.config.licenses: continue matching_indices.append(row_index) matching_langs.append(lang) # PASS 2: Read full row group ONLY if there are matches if matching_indices: # Now read ALL columns for this row group full_table = parquet_file.read_row_group(rg_idx) # Extract only matching rows filtered_table = full_table.take(matching_indices) batch_dict = filtered_table.to_pydict() # Yield all matching rows for i in range(len(matching_indices)): yield key, { "code": batch_dict['code'][i], "repo_name": batch_dict['repo_name'][i], "path": batch_dict['path'][i], "license": batch_dict['license'][i], "language": matching_langs[i], "size": int(batch_dict['size'][i]) } key += 1 yielded_count += 1 if max_samples is not None and yielded_count >= max_samples: return def lang_from_name(name): for extension in _EXTENSION_TO_LANG: if name.endswith(extension): return _EXTENSION_TO_LANG[extension] return None