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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.
	#
	# Custom DROP dataset that, unlike HF, keeps all question-answer pairs
	# even if there are multiple types of answers for the same question.
	"""DROP dataset."""


	import json
	import os

	import datasets


	_CITATION = """\
	@misc{dua2019drop,
	title={DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs},
	author={Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
	year={2019},
	eprint={1903.00161},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""

	_DESCRIPTION = """\
	DROP is a QA dataset which tests comprehensive understanding of paragraphs. In
	this crowdsourced, adversarially-created, 96k question-answering benchmark, a
	system must resolve multiple references in a question, map them onto a paragraph,
	and perform discrete operations over them (such as addition, counting, or sorting).
	"""

	_HOMEPAGE = "https://allenai.org/data/drop"

	# License declared at https://allenai.org/data/drop
	_LICENSE = "CC BY 4.0"

	_URLS = {
	"drop": "https://s3-us-west-2.amazonaws.com/allennlp/datasets/drop/drop_dataset.zip",
	}

	_EMPTY_VALIDATED_ANSWER = [
	{
	"number": "",
	"date": {
	"day": "",
	"month": "",
	"year": "",
	},
	"spans": [],
	"worker_id": "",
	"hit_id": "",
	}
	]


	class Drop(datasets.GeneratorBasedBuilder):
	"""DROP is a QA dataset which tests comprehensive understanding of paragraphs."""

	VERSION = datasets.Version("0.0.1")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="drop", version=VERSION, description="The DROP dataset."
	),
	]

	def _info(self):
	features = datasets.Features(
	{
	"section_id": datasets.Value("string"),
	"passage": datasets.Value("string"),
	"question": datasets.Value("string"),
	"query_id": datasets.Value("string"),
	"answer": {
	"number": datasets.Value("string"),
	"date": {
	"day": datasets.Value("string"),
	"month": datasets.Value("string"),
	"year": datasets.Value("string"),
	},
	"spans": datasets.features.Sequence(datasets.Value("string")),
	"worker_id": datasets.Value("string"),
	"hit_id": datasets.Value("string"),
	},
	"validated_answers": datasets.features.Sequence(
	{
	"number": datasets.Value("string"),
	"date": {
	"day": datasets.Value("string"),
	"month": datasets.Value("string"),
	"year": datasets.Value("string"),
	},
	"spans": datasets.features.Sequence(datasets.Value("string")),
	"worker_id": datasets.Value("string"),
	"hit_id": datasets.Value("string"),
	}
	),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	urls = _URLS[self.config.name]
	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={
	"filepath": os.path.join(
	data_dir, "drop_dataset", "drop_dataset_train.json"
	),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(
	data_dir, "drop_dataset", "drop_dataset_dev.json"
	),
	"split": "validation",
	},
	),
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath, split):
	with open(filepath, encoding="utf-8") as f:
	data = json.load(f)
	key = 0
	for section_id, example in data.items():
	# Each example (passage) has multiple sub-question-answer pairs.
	for qa in example["qa_pairs"]:
	# Build answer.
	answer = qa["answer"]
	answer = {
	"number": answer["number"],
	"date": {
	"day": answer["date"].get("day", ""),
	"month": answer["date"].get("month", ""),
	"year": answer["date"].get("year", ""),
	},
	"spans": answer["spans"],
	"worker_id": answer.get("worker_id", ""),
	"hit_id": answer.get("hit_id", ""),
	}
	validated_answers = []
	if "validated_answers" in qa:
	for validated_answer in qa["validated_answers"]:
	va = {
	"number": validated_answer.get("number", ""),
	"date": {
	"day": validated_answer["date"].get("day", ""),
	"month": validated_answer["date"].get("month", ""),
	"year": validated_answer["date"].get("year", ""),
	},
	"spans": validated_answer.get("spans", ""),
	"worker_id": validated_answer.get("worker_id", ""),
	"hit_id": validated_answer.get("hit_id", ""),
	}
	validated_answers.append(va)
	else:
	validated_answers = _EMPTY_VALIDATED_ANSWER
	yield key, {
	"section_id": section_id,
	"passage": example["passage"],
	"question": qa["question"],
	"query_id": qa["query_id"],
	"answer": answer,
	"validated_answers": validated_answers,
	}
	key += 1