Datasets:
neural_code_search

	# 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.
	"""Neural-Code-Search-Evaluation-Dataset presents an evaluation dataset consisting of natural language query and code snippet pairs"""


	import json
	from itertools import chain

	import datasets


	_CITATION = """\
	@InProceedings{huggingface:dataset,
	title = {Neural Code Search Evaluation Dataset},
	authors = {Hongyu Li, Seohyun Kim and Satish Chandra},
	journal = {arXiv e-prints},
	year = 2018,
	eid = {arXiv:1908.09804 [cs.SE]},
	pages = {arXiv:1908.09804 [cs.SE]},
	archivePrefix = {arXiv},
	eprint = {1908.09804},
	}
	"""

	_DESCRIPTION = """\
	Neural-Code-Search-Evaluation-Dataset presents an evaluation dataset \
	consisting of natural language query and code snippet pairs and a search corpus \
	consisting of code snippets collected from the most popular Android repositories \
	on GitHub.
	"""

	_HOMEPAGE = "https://github.com/facebookresearch/Neural-Code-Search-Evaluation-Dataset/tree/master/data"

	_LICENSE = "CC-BY-NC 4.0 (Attr Non-Commercial Inter.)"

	_BASE_URL = "https://raw.githubusercontent.com/facebookresearch/Neural-Code-Search-Evaluation-Dataset/master/data/"
	_URLs = {
	"evaluation_dataset": _BASE_URL + "287_android_questions.json",
	"search_corpus_1": _BASE_URL + "search_corpus_1.tar.gz",
	"search_corpus_2": _BASE_URL + "search_corpus_2.tar.gz",
	}


	class NeuralCodeSearch(datasets.GeneratorBasedBuilder):
	"""Neural Code Search Evaluation Dataset"""

	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="evaluation_dataset",
	version=VERSION,
	description="The evaluation dataset is composed of \
	287 Stack Overflow question and answer pairs",
	),
	datasets.BuilderConfig(
	name="search_corpus",
	version=VERSION,
	description="The search corpus is indexed using all \
	method bodies parsed from the 24,549 GitHub repositories.",
	),
	]

	FILENAME_MAP = {
	"evaluation_dataset": "287_android_questions.json",
	"search_corpus": "search_corpus_1.jsonl",
	}

	def _info(self):
	if self.config.name == "evaluation_dataset":
	features = datasets.Features(
	{
	"stackoverflow_id": datasets.Value("int32"),
	"question": datasets.Value("string"),
	"question_url": datasets.Value("string"),
	"question_author": datasets.Value("string"),
	"question_author_url": datasets.Value("string"),
	"answer": datasets.Value("string"),
	"answer_url": datasets.Value("string"),
	"answer_author": datasets.Value("string"),
	"answer_author_url": datasets.Value("string"),
	"examples": datasets.features.Sequence(datasets.Value("int32")),
	"examples_url": datasets.features.Sequence(datasets.Value("string")),
	}
	)
	else:
	features = datasets.Features(
	{
	"id": datasets.Value("int32"),
	"filepath": datasets.Value("string"),
	"method_name": datasets.Value("string"),
	"start_line": datasets.Value("int32"),
	"end_line": datasets.Value("int32"),
	"url": datasets.Value("string"),
	}
	)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	if self.config.name == "evaluation_dataset":
	filepath = dl_manager.download_and_extract(_URLs[self.config.name])
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": filepath},
	),
	]
	else:
	my_urls = [url for config, url in _URLs.items() if config.startswith(self.config.name)]
	archives = dl_manager.download(my_urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"files": chain(*(dl_manager.iter_archive(archive) for archive in archives)),
	},
	),
	]

	def _generate_examples(self, filepath=None, files=None):
	"""Yields examples."""
	id_ = 0
	if self.config.name == "evaluation_dataset":
	with open(filepath, encoding="utf-8") as f:
	data = json.load(f)
	for row in data:
	yield id_, {
	"stackoverflow_id": row["stackoverflow_id"],
	"question": row["question"],
	"question_url": row["question_url"],
	"question_author": row["question_author"],
	"question_author_url": row["question_author_url"],
	"answer": row["answer"],
	"answer_url": row["answer_url"],
	"answer_author": row["answer_author"],
	"answer_author_url": row["answer_author_url"],
	"examples": row["examples"],
	"examples_url": row["examples_url"],
	}
	id_ += 1
	else:
	for _, f in files:
	for row in f:
	data_dict = json.loads(row.decode("utf-8"))
	yield id_, {
	"id": data_dict["id"],
	"filepath": data_dict["filepath"],
	"method_name": data_dict["method_name"],
	"start_line": data_dict["start_line"],
	"end_line": data_dict["end_line"],
	"url": data_dict["url"],
	}
	id_ += 1