





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
UpperCamelCase__ :			List[str]       	=			"""\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n"""

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

# TODO: Add a link to an official homepage for the dataset here
UpperCamelCase__ :			int       	=			""""""

# TODO: Add the licence for the dataset here if you can find it
UpperCamelCase__ :			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)
UpperCamelCase__ :			Any       	=			{
    """first_domain""": """https://huggingface.co/great-new-dataset-first_domain.zip""",
    """second_domain""": """https://huggingface.co/great-new-dataset-second_domain.zip""",
}







class 	_UpperCamelCase	(							datasets.GeneratorBasedBuilder	):







					'''simple docstring'''

					lowerCamelCase  :  Tuple           =							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')
					lowerCamelCase  :  Union[str, Any]           =							[
					    datasets.BuilderConfig(name='first_domain'    ,							version=SCREAMING_SNAKE_CASE_    ,							description='This part of my dataset covers a first domain'	),
					    datasets.BuilderConfig(name='second_domain'    ,							version=SCREAMING_SNAKE_CASE_    ,							description='This part of my dataset covers a second domain'	),
					]

					lowerCamelCase  :  List[str]           =							"""first_domain"""  # It's not mandatory to have a default configuration. Just use one if it make sense.





					def 							SCREAMING_SNAKE_CASE				(    self     :	Dict		):






						'''simple docstring'''







						if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
							UpperCAmelCase_		    =  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"
							UpperCAmelCase_		    =  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=__UpperCAmelCase			,       homepage=_HOMEPAGE			,       license=_LICENSE			,       citation=_CITATION			,       )





					def 							SCREAMING_SNAKE_CASE				(    self     :	str			,       __lowercase     :	Dict		):






						'''simple docstring'''







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





					def 							SCREAMING_SNAKE_CASE				(    self     :	Tuple			,       __lowercase     :	List[Any]			,       __lowercase     :	Optional[int]		):






						'''simple docstring'''







						with open(__UpperCAmelCase			,       encoding="""utf-8"""		) as f:
							for key, row in enumerate(__UpperCAmelCase		):
								UpperCAmelCase_		    =  json.loads(__UpperCAmelCase		)
								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"],
									}




"""simple docstring"""






def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase = 1_000     )						->str:

   """simple docstring"""



   if (not isinstance(UpperCAmelCase ,   UpperCAmelCase     )) or (digit_position <= 0):
      raise ValueError("Digit position must be a positive integer"     )
   elif (not isinstance(UpperCAmelCase ,   UpperCAmelCase     )) or (precision < 0):
      raise ValueError("Precision must be a nonnegative integer"     )

   # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
   # accurate
   a_						   =     (
       4 * _subsum(UpperCAmelCase ,   1 ,   UpperCAmelCase     )
       - 2 * _subsum(UpperCAmelCase ,   4 ,   UpperCAmelCase     )
       - _subsum(UpperCAmelCase ,   5 ,   UpperCAmelCase     )
       - _subsum(UpperCAmelCase ,   6 ,   UpperCAmelCase     )
   )

   # return the first hex digit of the fractional part of the result
   return hex(int((sum_result % 1) * 16     )     )[2:]
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )						->float:

   """simple docstring"""



   a_						   =     0.0
   for sum_index in range(digit_pos_to_extract + precision     ):
      a_						   =     8 * sum_index + denominator_addend
      if sum_index < digit_pos_to_extract:
         # if the exponential term is an integer and we mod it by the denominator
         # before dividing, only the integer part of the sum will change;
         # the fractional part will not
         a_						   =     pow(
             16 ,   digit_pos_to_extract - 1 - sum_index ,   UpperCAmelCase     )
      else:
         a_						   =     pow(16 ,   digit_pos_to_extract - 1 - sum_index     )
      total += exponential_term / denominator
   return total


if __name__ == "__main__":
      import doctest

      doctest.testmod()