code: 
  
  
  
  
  
  '''simple docstring'''
  
  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_			       =				'\\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
  lowercase_			       =				'\\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
  lowercase_			       =				''
  
  # TODO: Add the licence for the dataset here if you can find it
  lowercase_			       =				''
  
  # 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_			       =				{
      'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
      'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
  }
  
  
  class       a_  (       datasets.GeneratorBasedBuilder ):
  
      '''simple docstring'''
      UpperCamelCase									=				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')
      UpperCamelCase									=				[
          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''' ),
      ]
  
      UpperCamelCase									=				'''first_domain'''  # It's not mandatory to have a default configuration. Just use one if it make sense.
  
  
      def     snake_case_(	self )	->     List[str]:
          # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
          if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
              _SCREAMING_SNAKE_CASE             =    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"
              _SCREAMING_SNAKE_CASE             =    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     snake_case_(	self				,	A )	->     int:
          # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
          # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
  
          # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
          # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
          # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
          _SCREAMING_SNAKE_CASE             =    _URLS[self.config.name]
          _SCREAMING_SNAKE_CASE             =    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     snake_case_(	self				,	A				,	A )	->     List[str]:
          # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
          # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
          with open(_lowerCAmelCase				,	encoding="""utf-8""" ) as f:
              for key, row in enumerate(_lowerCAmelCase ):
                  _SCREAMING_SNAKE_CASE             =    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"],
                      }
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  def lowerCamelCase		(  __lowerCamelCase				:					int = 10**6					)		->int:
  
      if n <= 0:
          raise ValueError("""Please enter an integer greater than 0"""					)
  
      _SCREAMING_SNAKE_CASE             =    list(range(n + 1					)					)
      for number in range(2      , n + 1					):
          if phi[number] == number:
              phi[number] -= 1
              for multiple in range(number * 2      , n + 1      , __lowerCamelCase					):
                  _SCREAMING_SNAKE_CASE             =    (phi[multiple] // number) * (number - 1)
  
      _SCREAMING_SNAKE_CASE             =    1
      for number in range(1      , n + 1					):
          if (answer / phi[answer]) < (number / phi[number]):
              _SCREAMING_SNAKE_CASE             =    number
  
      return answer
  
  
  if __name__ == "__main__":
        print(solution())
  
  
  
  