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	'''
	Author: Xiang Pan
	Date: 2021-09-20 01:38:55
	LastEditTime: 2021-09-20 01:45:46
	LastEditors: Xiang Pan
	Description:
	FilePath: /SNLI_OOD/cached_datasets/snli_break/snli_break.py
	xiangpan@nyu.edu
	'''
	import csv
	import os

	import datasets

	_DESCRIPTION = """\
	The SNLI corpus (version 1.0) is a collection of 570k human-written English
	sentence pairs manually labeled for balanced classification with the labels
	entailment, contradiction, and neutral, supporting the task of natural language
	inference (NLI), also known as recognizing textual entailment (RTE).
	"""

	# _DATA_URL = "https://nlp.stanford.edu/projects/snli/snli_1.0.zip"


	class snli_break(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="plain_text",
	version=datasets.Version("1.0.0", ""),
	description="Plain text import of SNLI",
	)
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	# features=datasets.Features(
	# {
	# "premise": datasets.Value("string"),
	# "hypothesis": datasets.Value("string"),
	# "label": datasets.features.ClassLabel(names=["entailment", "neutral", "contradiction"]),
	# }
	# ),
	# # No default supervised_keys (as we have to pass both premise
	# # and hypothesis as input).
	# supervised_keys=None,
	# homepage="https://nlp.stanford.edu/projects/snli/",
	# # citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	# dl_dir = dl_manager.download_and_extract(_DATA_URL)
	data_dir = ("./")
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "dataset.jsonl")}
	),
	# datasets.SplitGenerator(
	# name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_dev.txt")}
	# ),
	# datasets.SplitGenerator(
	# name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_train.txt")}
	# ),
	]

	def _generate_examples(self, filepath):
	"""This function returns the examples in the raw (text) form."""
	with open(filepath, encoding="utf-8") as f:
	reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
	for idx, row in enumerate(reader):
	label = -1 if row["gold_label"] == "-" else row["gold_label"]
	yield idx, {
	"premise": row["sentence1"],
	"hypothesis": row["sentence2"],
	"label": label,
	}