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code
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repobench-p / repobench-p.py
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# 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-P denotes RepoBench for pipeline,
which is subtask of RepoBench including both relevant code retrieval and next-line code prediction.
"""
_HOMEPAGE = "https://github.com/Leolty/repobench"
_LICENSE = "Apache License 2.0"
_URLs = {
"python": "https://raw.githubusercontent.com/Leolty/repobench/main/data/pipeline/python/",
"java": "https://raw.githubusercontent.com/Leolty/repobench/main/data/pipeline/java/"
}
class RepoBenchP(datasets.GeneratorBasedBuilder):
"""RepoBench"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="python",
description=textwrap.dedent(
"""
This part of RepoBench-P is for python language.
"""
)
),
datasets.BuilderConfig(
name="java",
description=textwrap.dedent(
"""
This part of RepoBench-P is for java language.
"""
)
)
]
def _info(self):
features = datasets.Features(
{
"repo_name": datasets.Value("string"),
"file_path": datasets.Value("string"),
"context": [{
"path": datasets.Value("string"),
"identifier": datasets.Value("string"),
"snippet": 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({
"cff": config_urls + "cross_file_first.gz",
"cfr": config_urls + "cross_file_random.gz",
"if": config_urls + "in_file.gz"
}
)
return [
datasets.SplitGenerator(
name=datasets.Split("cff"),
gen_kwargs={"data_dir": data_dir, "split": "cff"}
),
datasets.SplitGenerator(
name=datasets.Split("cfr"),
gen_kwargs={"data_dir": data_dir, "split": "cfr"}
),
datasets.SplitGenerator(
name=datasets.Split("if"),
gen_kwargs={"data_dir": data_dir, "split": "if"}
)
]
def _generate_examples(self, data_dir, split):
""" Yields examples. """
with gzip.open(data_dir[split], "rb") as f:
data = pickle.load(f)
if split == "cff" or split == "cfr":
for i, example in enumerate(data):
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["gold_snippet_index"]
}
elif split == "if":
for i, example in enumerate(data):
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"],
# in-file data has no gold_snippet_index
"gold_snippet_index": -1
}