



import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_lowercase				:					Tuple     					="""\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_lowercase				:					str     					="""\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_lowercase				:					List[str]     					=""""""

# TODO: Add the licence for the dataset here if you can find it
_lowercase				:					List[Any]     					=""""""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_lowercase				:					Dict     					={
    """first_domain""": """https://huggingface.co/great-new-dataset-first_domain.zip""",
    """second_domain""": """https://huggingface.co/great-new-dataset-second_domain.zip""",
}
class lowerCAmelCase_       (   datasets.GeneratorBasedBuilder			):







	'''simple docstring'''




	A_       :     Any									=		datasets.Version('1.1.0'			)

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	A_       :     str									=		[
	    datasets.BuilderConfig(name='first_domain' ,version=A_ ,description='This part of my dataset covers a first domain'			),
	    datasets.BuilderConfig(name='second_domain' ,version=A_ ,description='This part of my dataset covers a second domain'			),
	]

	A_       :     str									=		'first_domain'  # It's not mandatory to have a default configuration. Just use one if it make sense.

	def 				_A			(	self       ):



				'''simple docstring'''


				if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
							a__					=  datasets.Features(
							    {
							        """sentence""": datasets.Value("""string"""       ),
							        """option1""": datasets.Value("""string"""       ),
							        """answer""": datasets.Value("""string"""       )
							        # These are the features of your dataset like images, labels ...
							    }       )
				else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
							a__					=  datasets.Features(
							    {
							        """sentence""": datasets.Value("""string"""       ),
							        """option2""": datasets.Value("""string"""       ),
							        """second_domain_answer""": datasets.Value("""string"""       )
							        # These are the features of your dataset like images, labels ...
							    }       )
				return datasets.DatasetInfo(
				    # This is the description that will appear on the datasets page.
				    description=_DESCRIPTION   ,       features=lowerCamelCase   ,       homepage=_HOMEPAGE   ,       license=_LICENSE   ,       citation=_CITATION   ,       )

	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				a__					=  _URLS[self.config.name]
				a__					=  dl_manager.download_and_extract(lowerCamelCase       )
				return [
				    datasets.SplitGenerator(
				        name=datasets.Split.TRAIN   ,       gen_kwargs={
				            """filepath""": os.path.join(lowerCamelCase   ,       """train.jsonl"""       ),
				            """split""": """train""",
				        }   ,       ),
				    datasets.SplitGenerator(
				        name=datasets.Split.VALIDATION   ,       gen_kwargs={
				            """filepath""": os.path.join(lowerCamelCase   ,       """dev.jsonl"""       ),
				            """split""": """dev""",
				        }   ,       ),
				    datasets.SplitGenerator(
				        name=datasets.Split.TEST   ,       gen_kwargs={
				            """filepath""": os.path.join(lowerCamelCase   ,       """test.jsonl"""       ),
				            """split""": """test"""
				        }   ,       ),
				]







	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase       ):



				'''simple docstring'''


				with open(lowerCamelCase   ,       encoding="""utf-8"""       ) as f:
							for key, row in enumerate(lowerCamelCase       ):
										a__					=  json.loads(lowerCamelCase       )
										if self.config.name == "first_domain":
													# Yields examples as (key, example) tuples
													yield key, {
													    "sentence": data["sentence"],
													    "option1": data["option1"],
													    "answer": "" if split == "test" else data["answer"],
													}
										else:
													yield key, {
													    "sentence": data["sentence"],
													    "option2": data["option2"],
													    "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
													}






from .prime_check import Test

Test()

