'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class        a_		(    _snake_case       ):
  lowercase  	=   ['image_processor', 'tokenizer']
  lowercase  	=   'CLIPImageProcessor'
  lowercase  	=   ('CLIPTokenizer', 'CLIPTokenizerFast')

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=None    ,	**_SCREAMING_SNAKE_CASE   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      None
        if "feature_extractor" in kwargs:
              warnings.warn(
                  """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                  """ instead."""    ,	_SCREAMING_SNAKE_CASE    ,	)
              UpperCamelCase       						=      kwargs.pop("""feature_extractor"""   )

        UpperCamelCase       						=      image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
              raise ValueError("""You need to specify an `image_processor`."""   )
        if tokenizer is None:
              raise ValueError("""You need to specify a `tokenizer`."""   )

        super().__init__(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

  def __call__(   self    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=None    ,	**_SCREAMING_SNAKE_CASE   )						->   Union[str, Any]:



        """simple docstring"""






        if text is None and images is None:
              raise ValueError("""You have to specify either text or images. Both cannot be none."""   )

        if text is not None:
              UpperCamelCase       						=      self.tokenizer(_SCREAMING_SNAKE_CASE    ,	return_tensors=_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )

        if images is not None:
              UpperCamelCase       						=      self.image_processor(_SCREAMING_SNAKE_CASE    ,	return_tensors=_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )

        if text is not None and images is not None:
              UpperCamelCase       						=      image_features.pixel_values
              return encoding
        elif text is not None:
              return encoding
        else:
              return BatchEncoding(data=dict(**_SCREAMING_SNAKE_CASE   )    ,	tensor_type=_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self    ,	*_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )						->   Optional[int]:



        """simple docstring"""






        return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self    ,	*_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )						->   Tuple:



        """simple docstring"""






        return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )

  @property
  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.tokenizer.model_input_names
        UpperCamelCase       						=      self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )

  @property
  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""    ,	_SCREAMING_SNAKE_CASE    ,	)
        return self.image_processor_class


  @property
  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""    ,	_SCREAMING_SNAKE_CASE    ,	)
        return self.image_processor








'''simple docstring'''

from math import factorial







def 					lowercase__				(   __UpperCamelCase = 20					)->				int:
      UpperCamelCase       						=      2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
      # 2, 3,...
      UpperCamelCase       						=      n // 2

      return int(factorial(__UpperCamelCase					) / (factorial(__UpperCamelCase					) * factorial(n - k					))					)


if __name__ == "__main__":
     import sys

     if len(sys.argv) == 1:
          print(solution(2_0))
     else:
          try:
               SCREAMING_SNAKE_CASE__											=     int(sys.argv[1])
               print(solution(n))
          except ValueError:
               print('Invalid entry - please enter a number.')
