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	# 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.
	"""PsyQA dataset."""


	import json
	import os
	import datasets



	_DESCRIPTION = """ FutureWarning
	"""
	_CITATION = """ null """
	_URLs = {
	"train": "https://huggingface.co/datasets/siyangliu/PsyQA/resolve/main/train.json",
	"valid": "https://huggingface.co/datasets/siyangliu/PsyQA/resolve/main/valid.json",
	"test": "https://huggingface.co/datasets/siyangliu/PsyQA/resolve/main/test.json",
	"train_translated": "https://huggingface.co/datasets/siyangliu/PsyQA/resolve/main/train_translated.json",
	"valid_translated": "https://huggingface.co/datasets/siyangliu/PsyQA/resolve/main/valid_translated.json",
	"test_translated": "https://huggingface.co/datasets/siyangliu/PsyQA/resolve/main/test_translated.json"
	}

	_STRATEGY={"Approval and Reassurance": "[AR]",
	"Interpretation": "[IN]",
	"Self-disclosure": "[SELF]",
	"Direct Guidance": "[DG]",
	"Others": "[OT]",
	"Restatement": "[RES]",
	"Information": "[INFO]"}


	class PsyQA(datasets.GeneratorBasedBuilder):
	"""PsyQA dataset."""

	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="wo strategy",
	description="",
	version=VERSION,
	),
	datasets.BuilderConfig(
	name="w strategy",
	description="",
	version=VERSION,
	),
	datasets.BuilderConfig(
	name="translated",
	description="",
	version=VERSION,
	)
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"question": datasets.Value("string"),
	"questionID": datasets.Value("int16"),
	"description": datasets.Value("string"),
	"keywords": datasets.Value("string"),
	"answer": datasets.Value("string"),
	"has_label": datasets.Value("bool"),
	"reference":datasets.features.Sequence(datasets.Value("string"))
	# "labels_sequence":datasets.features.Sequence(
	# {
	# "start": datasets.Value("int16"),
	# "end": datasets.Value("int16"),
	# "type": datasets.Value("string"),
	# }
	# ),
	}
	),
	supervised_keys=None,
	homepage="https://huggingface.co/datasets/siyangliu/PsyQA",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	data_dir = dl_manager.download_and_extract(_URLs)
	if self.config.name != "translated":

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": data_dir["train"],
	"strategy": self.config.name == "w strategy"
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": data_dir["test"],
	"strategy": self.config.name == "w strategy"
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": data_dir["valid"],
	"strategy": self.config.name == "w strategy"
	},
	),
	]
	else:
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": data_dir["train_translated"]
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": data_dir["test_translated"]
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": data_dir["valid_translated"]
	},
	),
	]


	def _generate_examples(self, filepath, label_filepath=None, strategy=False):
	"""Yields examples."""
	with open(filepath, encoding="utf-8") as input_file:
	dataset = json.load(input_file)
	idx = 0
	for meta_data in dataset:
	reference = [ans["answer_text"] for ans in meta_data["answers"]]
	for ans in meta_data["answers"]:
	if strategy and ans["labels_sequence"] is None:
	continue
	elif strategy and ans["labels_sequence"] is not None:
	pieces = []
	for label in ans["labels_sequence"]:
	pieces.append(_STRATEGY[label["type"]]+ans["answer_text"][label["start"]:label["end"]])
	ans_w_strategy = "".join(pieces)
	yield idx, {"question": meta_data["question"], "description": meta_data["description"], "keywords": meta_data["keywords"], "answer": ans_w_strategy, \
	"questionID": meta_data["questionID"], "has_label": ans["has_label"], "reference": reference}
	else:
	yield idx, {"question": meta_data["question"], "description": meta_data["description"], "keywords": meta_data["keywords"], "answer": ans["answer_text"], \
	"questionID": meta_data["questionID"], "has_label": ans["has_label"], "reference":reference}
	idx += 1