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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.

	"""
	CoQAR is a corpus containing 4.5K conversations from the open-source dataset [Conversational Question-Answering dataset CoQA](https://stanfordnlp.github.io/coqa/), for a total of 53K follow-up question-answer pairs.
	In CoQAR each original question was manually annotated with at least 2 at most 3 out-of-context rewritings.
	COQAR can be used for (at least) three NLP tasks: question paraphrasing, question rewriting and conversational question answering.

	We annotated each original question of CoQA with at least 2 at most 3 out-of-context rewritings.

	![image](https://user-images.githubusercontent.com/52821991/165952155-822ce743-791d-46c8-8705-0937a69df933.png)

	The annotations are published under the licence CC-BY-SA 4.0.
	The original content of the dataset CoQA is under the distinct licences described below.

	The corpus CoQA contains passages from seven domains, which are public under the following licenses:
	- Literature and Wikipedia passages are shared under CC BY-SA 4.0 license.
	- Children's stories are collected from MCTest which comes with MSR-LA license.
	- Middle/High school exam passages are collected from RACE which comes with its own license.
	- News passages are collected from the DeepMind CNN dataset which comes with Apache license (see [K. M. Hermann, T. Kočiský and E. Grefenstette, L. Espeholt, W. Kay, M. Suleyman, P. Blunsom, Teaching Machines to Read and Comprehend. Advances in Neural Information Processing Systems (NIPS), 2015](http://arxiv.org/abs/1506.03340)).
	"""


	import csv
	import json
	import os

	import datasets

	_CITATION = """\
	@inproceedings{brabant-etal-2022-coqar,
	title = "{C}o{QAR}: Question Rewriting on {C}o{QA}",
	author = "Brabant, Quentin and
	Lecorv{\'e}, Gw{\'e}nol{\'e} and
	Rojas Barahona, Lina M.",
	booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
	month = jun,
	year = "2022",
	address = "Marseille, France",
	publisher = "European Language Resources Association",
	url = "https://aclanthology.org/2022.lrec-1.13",
	pages = "119--126"
	}
	"""

	_DESCRIPTION = """\
	CoQAR is a corpus containing 4.5K conversations from the open-source dataset [Conversational Question-Answering dataset CoQA](https://stanfordnlp.github.io/coqa/), for a total of 53K follow-up question-answer pairs.
	In CoQAR each original question was manually annotated with at least 2 at most 3 out-of-context rewritings.
	COQAR can be used for (at least) three NLP tasks: question paraphrasing, question rewriting and conversational question answering.

	We annotated each original question of CoQA with at least 2 at most 3 out-of-context rewritings.

	![image](https://user-images.githubusercontent.com/52821991/165952155-822ce743-791d-46c8-8705-0937a69df933.png)

	The annotations are published under the licence CC-BY-SA 4.0.
	The original content of the dataset CoQA is under the distinct licences described below.

	The corpus CoQA contains passages from seven domains, which are public under the following licenses:
	- Literature and Wikipedia passages are shared under CC BY-SA 4.0 license.
	- Children's stories are collected from MCTest which comes with MSR-LA license.
	- Middle/High school exam passages are collected from RACE which comes with its own license.
	- News passages are collected from the DeepMind CNN dataset which comes with Apache license (see [K. M. Hermann, T. Kočiský and E. Grefenstette, L. Espeholt, W. Kay, M. Suleyman, P. Blunsom, Teaching Machines to Read and Comprehend. Advances in Neural Information Processing Systems (NIPS), 2015](http://arxiv.org/abs/1506.03340)).
	"""

	_HOMEPAGE = "https://github.com/Orange-OpenSource/COQAR/"

	_LICENSE = """
	- Annotations, litterature and Wikipedia passages: licence CC-BY-SA 4.0.
	- Children's stories are from MCTest (MSR-LA license).
	- Exam passages come from RACE which has its own license.
	- News passages are from the DeepMind CNN dataset (Apache license).
	"""

	_URLS = {
	"train": "https://raw.githubusercontent.com/Orange-OpenSource/COQAR/master/data/CoQAR/train/coqar-train-v1.0.json",
	"dev": "https://raw.githubusercontent.com/Orange-OpenSource/COQAR/master/data/CoQAR/dev/coqar-dev-v1.0.json"
	}


	class CoQAR(datasets.GeneratorBasedBuilder):
	"""
	CoQAR is a corpus containing 4.5K conversations from the open-source dataset [Conversational Question-Answering dataset CoQA](https://stanfordnlp.github.io/coqa/), for a total of 53K follow-up question-answer pairs.
	In CoQAR each original question was manually annotated with at least 2 at most 3 out-of-context rewritings.
	COQAR can be used for (at least) three NLP tasks: question paraphrasing, question rewriting and conversational question answering.
	"""

	VERSION = datasets.Version("1.1.0")

	def _info(self):
	features = datasets.Features(
	{
	'conversation_id' : datasets.Value("string"),
	'turn_id': datasets.Value("int16"),
	'original_question' : datasets.Value("string"),
	'question_paraphrases' : datasets.Sequence(feature=datasets.Value("string")),
	'answer' : datasets.Value("string"),
	'answer_span_start' : datasets.Value("int32"),
	'answer_span_end' : datasets.Value("int32"),
	'answer_span_text' : datasets.Value("string"),
	'conversation_history' : datasets.Sequence(feature=datasets.Value("string")),
	'file_name' : datasets.Value("string"),
	'story': datasets.Value("string"),
	'name': datasets.Value("string"),
	}
	)

	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features,
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	data_dir = dl_manager.download_and_extract(_URLS)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": data_dir['train'],
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": data_dir['dev'],
	"split": "dev",
	},
	)
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath, split):
	with open(filepath, 'r') as f:
	dic = json.load(f)
	i = 0
	for datum in dic['data']:
	history = []
	for question, answer in zip(datum['questions'], datum['answers']):
	yield i, {
	'conversation_id' : datum['id'],
	'turn_id': question['turn_id'],
	'original_question' :question['input_text'],
	'question_paraphrases' : question['paraphrase'],
	'answer' : answer['input_text'],
	'answer_span_start' : answer['span_start'],
	'answer_span_end' : answer['span_end'],
	'answer_span_text' : answer['span_text'],
	'conversation_history' : list(history),
	'file_name' : datum['filename'],
	'story': datum['story'],
	'name': datum['name']
	}
	history.append(question['input_text'])
	history.append(answer['input_text'])
	i+=1