# 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. # Lint as: python3 """CodeSearchNet corpus: proxy dataset for semantic code search""" # TODO: add licensing info in the examples # TODO: log richer informations (especially while extracting the jsonl.gz files) # TODO: enable custom configs; such as: "java+python" # TODO: enable fetching examples with a given license, eg: "java_MIT" import json import os import datasets _CITATION = """\ @article{husain2019codesearchnet, title={{CodeSearchNet} challenge: Evaluating the state of semantic code search}, author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc}, journal={arXiv preprint arXiv:1909.09436}, year={2019} } """ _DESCRIPTION = """\ CodeSearchNet corpus contains about 6 million functions from open-source code \ spanning six programming languages (Go, Java, JavaScript, PHP, Python, and Ruby). \ The CodeSearchNet Corpus also contains automatically generated query-like \ natural language for 2 million functions, obtained from mechanically scraping \ and preprocessing associated function documentation. """ _HOMEPAGE = "https://github.com/github/CodeSearchNet" _LICENSE = "Various" _DATA_DIR_URL = "data/" _AVAILABLE_LANGUAGES = ["python", "java", "javascript", "go", "ruby", "php"] _URLs = {language: _DATA_DIR_URL + f"{language}.zip" for language in _AVAILABLE_LANGUAGES} # URLs for "all" are just the concatenation of URLs for all languages _URLs["all"] = _URLs.copy() class CodeSearchNet(datasets.GeneratorBasedBuilder): """ "CodeSearchNet corpus: proxy dataset for semantic code search.""" VERSION = datasets.Version("1.0.0", "Add CodeSearchNet corpus dataset") BUILDER_CONFIGS = [ datasets.BuilderConfig( name="all", version=VERSION, description="All available languages: Java, Go, Javascript, Python, PHP, Ruby", ), datasets.BuilderConfig( name="java", version=VERSION, description="Java language", ), datasets.BuilderConfig( name="go", version=VERSION, description="Go language", ), datasets.BuilderConfig( name="python", version=VERSION, description="Pyhton language", ), datasets.BuilderConfig( name="javascript", version=VERSION, description="Javascript language", ), datasets.BuilderConfig( name="ruby", version=VERSION, description="Ruby language", ), datasets.BuilderConfig( name="php", version=VERSION, description="PHP language", ), ] DEFAULT_CONFIG_NAME = "all" def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "repository_name": datasets.Value("string"), "func_path_in_repository": datasets.Value("string"), "func_name": datasets.Value("string"), "whole_func_string": datasets.Value("string"), "language": datasets.Value("string"), "func_code_string": datasets.Value("string"), "func_code_tokens": datasets.Sequence(datasets.Value("string")), "func_documentation_string": datasets.Value("string"), "func_documentation_tokens": datasets.Sequence(datasets.Value("string")), "split_name": datasets.Value("string"), "func_code_url": datasets.Value("string"), # TODO - add licensing info in the examples } ), # No default supervised keys supervised_keys=None, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators. Note: The original data is stored in S3, and follows this unusual directory structure: ``` . ├── # e.g. python │   └── final │   └── jsonl │   ├── test │   │   └── _test_0.jsonl.gz │   ├── train │   │   ├── _train_0.jsonl.gz │   │   ├── _train_1.jsonl.gz │   │   ├── ... │   │   └── _train_n.jsonl.gz │   └── valid │   └── _valid_0.jsonl.gz ├── _dedupe_definitions_v2.pkl └── _licenses.pkl ``` """ data_urls = _URLs[self.config.name] if isinstance(data_urls, str): data_urls = {self.config.name: data_urls} # Download & extract the language archives data_dirs = [ os.path.join(directory, lang, "final", "jsonl") for lang, directory in dl_manager.download_and_extract(data_urls).items() ] split2dirs = { split_name: [os.path.join(directory, split_name) for directory in data_dirs] for split_name in ["train", "test", "valid"] } split2paths = dl_manager.extract( { split_name: [ os.path.join(directory, entry_name) for directory in split_dirs for entry_name in os.listdir(directory) ] for split_name, split_dirs in split2dirs.items() } ) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepaths": split2paths["train"], }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepaths": split2paths["test"], }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepaths": split2paths["valid"], }, ), ] def _generate_examples(self, filepaths): """Yields the examples by iterating through the available jsonl files.""" for file_id_, filepath in enumerate(filepaths): with open(filepath, encoding="utf-8") as f: for row_id_, row in enumerate(f): # Key of the example = file_id + row_id, # to ensure all examples have a distinct key id_ = f"{file_id_}_{row_id_}" data = json.loads(row) yield id_, { "repository_name": data["repo"], "func_path_in_repository": data["path"], "func_name": data["func_name"], "whole_func_string": data["original_string"], "language": data["language"], "func_code_string": data["code"], "func_code_tokens": data["code_tokens"], "func_documentation_string": data["docstring"], "func_documentation_tokens": data["docstring_tokens"], "split_name": data["partition"], "func_code_url": data["url"], }