code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding
  
  
  
  
  
  
  class 						lowercase     (   a_):
        __lowercase 		=  ['image_processor', 'tokenizer']
        __lowercase 		=  'EfficientNetImageProcessor'
        __lowercase 		=  ('BertTokenizer', 'BertTokenizerFast')
  
  
  
  
  
  
        def __init__(self ,  a__ ,  a__					)       ->   Tuple:
           super().__init__(a__ ,  a__					)
  
  
  
  
  
  
        def __call__(self ,  a__=None ,  a__=None ,  a__="max_length" ,  a__=6_4 ,  a__=None ,  **a__					)       ->   List[Any]:
           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:
              SCREAMING_SNAKE_CASE__			:				int       						= self.tokenizer(
                  a__ ,  padding=a__ ,  max_length=a__ ,  return_tensors=a__ ,  **a__					)
  
           if images is not None:
              SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= self.image_processor(a__ ,  return_tensors=a__ ,  **a__					)
  
           if text is not None and images is not None:
              SCREAMING_SNAKE_CASE__			:				Dict       						= image_features.pixel_values
              return encoding
           elif text is not None:
              return encoding
           else:
              return BatchEncoding(data=dict(**a__					) ,  tensor_type=a__					)
  
  
  
  
  
  
        def    A__(self ,  *a__ ,  **a__					)       ->   List[str]:
           return self.tokenizer.batch_decode(*a__ ,  **a__					)
  
  
  
  
  
  
        def    A__(self ,  *a__ ,  **a__					)       ->   Any:
           return self.tokenizer.decode(*a__ ,  **a__					)
  
  
  
  
  
  
        @property
        def    A__(self					)       ->   Optional[Any]:
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= self.tokenizer.model_input_names
           SCREAMING_SNAKE_CASE__			:				str       						= self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  lowercase__			=	{
      '''+''': lambda x, y: x + y,
      '''-''': lambda x, y: x - y,
      '''*''': lambda x, y: x * y,
      '''/''': lambda x, y: x / y,
  }
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     return c.isdigit()
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= []
  
     # iterate over the string in reverse order
     for c in expression.split()[::-1]:
        # push operand to stack
        if is_operand(snake_case   ):
           stack.append(int(snake_case   )   )
  
        else:
           # pop values from stack can calculate the result
           # push the result onto the stack again
           SCREAMING_SNAKE_CASE__			:				Dict       						= stack.pop()
           SCREAMING_SNAKE_CASE__			:				Any       						= stack.pop()
           stack.append(calc[c](snake_case	,     snake_case   )   )
  
     return stack.pop()
  
  
  # Driver code
  if __name__ == "__main__":
         lowercase__			=	'''+ 9 * 2 6'''
         print(evaluate(test_expression))
  
         lowercase__			=	'''/ * 10 2 + 4 1 '''
         print(evaluate(test_expression))
  
  