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	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
	#
	# 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.

	# Lint as: python3
	"""SQUAD: The Stanford Question Answering Dataset."""
	import csv
	import json

	import datasets
	from datasets import TextClassification


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """
	@article{2016arXiv160605250R,
	author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
	Konstantin and {Liang}, Percy},
	title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
	journal = {arXiv e-prints},
	year = 2016,
	eid = {arXiv:1606.05250},
	pages = {arXiv:1606.05250},
	archivePrefix = {arXiv},
	eprint = {1606.05250},
	}
	"""

	_DESCRIPTION = """
	Stanford Question Answering Dataset (SQuAD) is a reading comprehension
	dataset, consisting of questions posed by crowdworkers on a set of Wikipedia
	articles, where the answer to every question is a segment of text, or span,
	from the corresponding reading passage, or the question might be unanswerable.
	"""

	train_url = "https://raw.githubusercontent.com/Sampson2016/test/master/train.csv?token=GHSAT0AAAAAABR4XKTH73T5VNFVZ3KS33FYYVQLQAA"

	_URLS = {
	"train": train_url,
	"test": train_url,
	}


	class Demo2Config(datasets.BuilderConfig):

	def __init__(self, **kwargs):
	super(Demo2Config, self).__init__(**kwargs)


	class Demo2(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIGS = [
	Demo2Config(
	name="plain_text",
	version=datasets.Version("1.0.0", ""),
	description="Plain text",
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"text": datasets.Value("string"),
	"label": datasets.features.ClassLabel(names=['0', '1'])
	}
	),
	# No default supervised_keys (as we have to pass both question
	# and context as input).
	supervised_keys=None,
	homepage="https://rajpurkar.github.io/SQuAD-explorer/",
	citation=_CITATION,
	task_templates=[
	TextClassification(
	text_column="text", label_column="label"
	)
	],
	)

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

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
	]

	def _generate_examples(self, filepath):
	logger.info("generating examples from = %s", filepath)
	with open(filepath, encoding="utf-8") as f:
	demo2 = csv.DictReader(f)
	for key, row in enumerate(demo2):
	yield key, {
	"text": row['text'],
	"label": row['label'],
	}