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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.
	"""MuTual dataset."""


	import json
	import os
	from pathlib import Path

	import datasets


	_CITATION = """\
	@inproceedings{mutual,
	title = "MuTual: A Dataset for Multi-Turn Dialogue Reasoning",
	author = "Cui, Leyang and Wu, Yu and Liu, Shujie and Zhang, Yue and Zhou, Ming" ,
	booktitle = "Proceedings of the 58th Conference of the Association for Computational Linguistics",
	year = "2020",
	publisher = "Association for Computational Linguistics",
	}
	"""

	_DESCRIPTION = """\
	MuTual is a retrieval-based dataset for multi-turn dialogue reasoning, which is
	modified from Chinese high school English listening comprehension test data.
	"""

	_HOMEPAGE = "https://github.com/Nealcly/MuTual"

	_LICENSE = "No license found"

	_URLS = "https://github.com/Nealcly/MuTual/archive/master.zip"


	class Mutual(datasets.GeneratorBasedBuilder):
	"""MuTual: A Dataset for Multi-Turn Dialogue Reasoning"""

	VERSION = datasets.Version("0.0.1")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="mutual", version=VERSION, description="The MuTual dataset."
	),
	datasets.BuilderConfig(
	name="mutual_plus",
	version=VERSION,
	description="MuTualPlus is a more difficult MuTual that replaces positive responses with a safe responses.",
	),
	]

	def _info(self):
	features = datasets.Features(
	{
	"answers": datasets.Value("string"),
	"options": datasets.features.Sequence(datasets.Value("string")),
	"article": datasets.Value("string"),
	"id": datasets.Value("string"),
	}
	)
	return datasets.DatasetInfo(
	description=f"{_DESCRIPTION}\n{self.config.description}",
	features=features,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	urls = _URLS
	data_dir = dl_manager.download_and_extract(urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"basepath": os.path.join(
	data_dir, "MuTual-master", "data", self.config.name, "train"
	),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"basepath": os.path.join(
	data_dir, "MuTual-master", "data", self.config.name, "test"
	),
	"split": "test",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"basepath": os.path.join(
	data_dir, "MuTual-master", "data", self.config.name, "dev"
	),
	"split": "dev",
	},
	),
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, basepath, split):
	# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
	key = 0
	for file in sorted(Path(basepath).iterdir()):
	if file.suffix != ".txt":
	continue
	with open(file, "r", encoding="utf-8") as f:
	data_str = f.read()
	# Ignore the occasional empty file.
	if not data_str:
	continue
	data = json.loads(data_str)
	yield key, {
	"answers": data["answers"],
	"options": data["options"],
	"article": data["article"],
	"id": data["id"],
	}
	key += 1