Datasets:

tianyang
/

repobench-r

	# 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.

	"""RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems"""

	import gzip
	import pickle
	import textwrap
	import datasets

	_CITATION = """\
	@misc{liu2023repobench,
	title={RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems},
	author={Tianyang Liu and Canwen Xu and Julian McAuley},
	year={2023},
	eprint={2306.03091},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""

	_DESCRIPTION = """\
	RepoBench is a dataset that benchmarks repository-level code auto-completion systems.

	RepoBench-R denotes RepoBench for Retrieval, which is a sub-task of RepoBench,
	aiming to evaluate the ability of code auto-completion systems to retrieve
	relevant code snippets for next-line code completion.
	"""

	_HOMEPAGE = "https://github.com/Leolty/repobench"

	_LICENSE = "Apache License 2.0"

	_URLs = {
	"java_cff": "https://huggingface.co/datasets/tianyang/repobench-r/resolve/main/data/java_cff.gz",
	"java_cfr": "https://huggingface.co/datasets/tianyang/repobench-r/resolve/main/data/java_cfr.gz",
	"python_cff": "https://huggingface.co/datasets/tianyang/repobench-r/resolve/main/data/python_cff.gz",
	"python_cfr": "https://huggingface.co/datasets/tianyang/repobench-r/resolve/main/data/python_cfr.gz"
	}

	class RepoBenchR(datasets.GeneratorBasedBuilder):
	"""RepoBench"""

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="python_cff",
	description=textwrap.dedent(
	"""
	cff: cross_file_first -> mask the the line that a cross-file module is first used
	"""
	)
	),
	datasets.BuilderConfig(
	name="python_cfr",
	description=textwrap.dedent(
	"""
	cfr: cross_file_random -> mask a random line that a cross-file module is used (not the first time)
	"""
	)
	),
	datasets.BuilderConfig(
	name="java_cff",
	description=textwrap.dedent(
	"""
	cff: cross_file_first -> mask the the line that a cross-file module is first used
	"""
	)
	),
	datasets.BuilderConfig(
	name="java_cfr",
	description=textwrap.dedent(
	"""
	cfr: cross_file_random -> mask a random line that a cross-file module is used (not the first time)
	"""
	)
	)
	]

	def _info(self):
	features = datasets.Features(
	{
	"repo_name": datasets.Value("string"),
	"file_path": datasets.Value("string"),
	"context": [datasets.Value("string")],
	"import_statement": datasets.Value("string"),
	"code": datasets.Value("string"),
	"next_line": datasets.Value("string"),
	"gold_snippet_index": datasets.Value("int32")
	}
	)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	config_urls = _URLs[self.config.name]
	data_dir = dl_manager.download(config_urls)

	return [
	datasets.SplitGenerator(
	name=datasets.Split("train_easy"),
	gen_kwargs={"data_dir": data_dir, "split": "train_easy"},
	),
	datasets.SplitGenerator(
	name=datasets.Split("train_hard"),
	gen_kwargs={"data_dir": data_dir, "split": "train_hard"},
	),
	datasets.SplitGenerator(
	name=datasets.Split("test_easy"),
	gen_kwargs={"data_dir": data_dir, "split": "test_easy"},
	),
	datasets.SplitGenerator(
	name=datasets.Split("test_hard"),
	gen_kwargs={"data_dir": data_dir, "split": "test_hard"},
	)
	]

	def _generate_examples(self, data_dir, split):
	""" Yields examples. """
	with gzip.open(data_dir, "rb") as f:
	data = pickle.load(f)

	subset, level = split.split("_")

	for i, example in enumerate(data[subset][level]):
	yield i, {
	"repo_name": example["repo_name"],
	"file_path": example["file_path"],
	"context": example["context"],
	"import_statement": example["import_statement"],
	"code": example["code"],
	"next_line": example["next_line"],
	"gold_snippet_index": example["golden_snippet_index"]
	}