Datasets:

THUDM
/

humaneval-x

	# 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.
	"""HumanEval-X dataset."""


	import json
	import datasets



	_DESCRIPTION = """
	HumanEval-X is a benchmark for the evaluation of the multilingual ability of code generative models. \
	It consists of 820 high-quality human-crafted data samples (each with test cases) in Python, C++, Java, JavaScript, and Go, and can be used for various tasks.
	"""

	_HOMEPAGE = "https://github.com/THUDM/CodeGeeX"

	def get_url(name):
	url = f"data/{name}/data/humaneval.jsonl"
	return url

	def split_generator(dl_manager, name):
	downloaded_files = dl_manager.download(get_url(name))
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": downloaded_files,
	},
	)
	]

	class HumanEvalXConfig(datasets.BuilderConfig):
	"""BuilderConfig """

	def __init__(self, name, description, features, **kwargs):
	super(HumanEvalXConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
	self.name = name
	self.description = description
	self.features = features


	class HumanEvalX(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIGS = [
	HumanEvalXConfig(
	name="python",
	description="Python HumanEval",
	features=["task_id", "prompt", "declaration", "canonical_solution", "test", "example_test"]
	),
	HumanEvalXConfig(
	name="cpp",
	description="C++ HumanEval",
	features=["task_id", "prompt", "declaration", "canonical_solution", "test", "example_test"]
	),

	HumanEvalXConfig(
	name="go",
	description="Go HumanEval",
	features=["task_id", "prompt", "declaration", "canonical_solution", "test", "example_test"]
	),
	HumanEvalXConfig(
	name="java",
	description="Java HumanEval",
	features=["task_id", "prompt", "declaration", "canonical_solution", "test", "example_test"]
	),

	HumanEvalXConfig(
	name="js",
	description="JavaScript HumanEval",
	features=["task_id", "prompt", "declaration", "canonical_solution", "test", "example_test"]
	),
	]
	DEFAULT_CONFIG_NAME = "python"

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features({"task_id": datasets.Value("string"),
	"prompt": datasets.Value("string"),
	"declaration": datasets.Value("string"),
	"canonical_solution": datasets.Value("string"),
	"test": datasets.Value("string"),
	"example_test": datasets.Value("string"),
	}),
	homepage=_HOMEPAGE,
	)

	def _split_generators(self, dl_manager):
	if self.config.name == "python":
	return split_generator(dl_manager, self.config.name)

	elif self.config.name == "cpp":
	return split_generator(dl_manager, self.config.name)

	elif self.config.name == "go":
	return split_generator(dl_manager, self.config.name)

	elif self.config.name == "java":
	return split_generator(dl_manager, self.config.name)

	elif self.config.name == "js":
	return split_generator(dl_manager, self.config.name)

	def _generate_examples(self, filepath):
	key = 0
	with open(filepath) as f:
	for line in f:
	row = json.loads(line)
	key += 1
	yield key, {
	"task_id": row["task_id"],
	"prompt": row["prompt"],
	"declaration": row["declaration"],
	"canonical_solution": row["canonical_solution"],
	"test": row["test"],
	"example_test": row["example_test"],

	}
	key += 1