code: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding
  class 							lowerCamelCase		( _UpperCAmelCase				):
     lowercase       :  List[Any]      =				['image_processor', 'tokenizer']
     lowercase       :  Union[str, Any]      =				'EfficientNetImageProcessor'
     lowercase       :  Optional[Any]      =				('BertTokenizer', 'BertTokenizerFast')
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         super().__init__(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def __call__(							self    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_="max_length"    ,      SCREAMING_SNAKE_CASE_=64    ,      SCREAMING_SNAKE_CASE_=None    ,      **SCREAMING_SNAKE_CASE_ ):
         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				:   Tuple          =							self.tokenizer(
                 SCREAMING_SNAKE_CASE_    ,      padding=SCREAMING_SNAKE_CASE_    ,      max_length=SCREAMING_SNAKE_CASE_    ,      return_tensors=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
  
         if images is not None:
             UpperCamelCase				:   Dict          =							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				:   Dict          =							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_ ):
         return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
         return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         UpperCamelCase				:   Dict          =							self.tokenizer.model_input_names
         UpperCamelCase				:   Any          =							self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  def 					A_			(		snake_case_     :       Tuple						,snake_case_     :       Optional[Any]						,snake_case_     :       Optional[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      assert b != 0, "This cannot accept modulo that is == 0"
      if n == 0:
          return 1
  
      if n % 2 == 1:
          return (bin_exp_mod(snake_case_						,n - 1						,snake_case_    ) * a) % b
  
      UpperCamelCase				:   List[Any]          =							bin_exp_mod(snake_case_						,n / 2						,snake_case_    )
      return (r * r) % b
  
  
  if __name__ == "__main__":
    try:
      __A :		Any      							=    int(input('''Enter Base : ''').strip())
      __A :		Any      							=    int(input('''Enter Power : ''').strip())
      __A :		List[Any]      							=    int(input('''Enter Modulo : ''').strip())
    except ValueError:
      print('''Invalid literal for integer''')
  
    print(bin_exp_mod(BASE, POWER, MODULO))
  
  
  
  
  