






"""simple docstring"""





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class    __lowerCamelCase    (  _UpperCamelCase  ):
   a      :				Any 						=["image_processor", "tokenizer"]
   a      :				Tuple 						="CLIPImageProcessor"
   a      :				Optional[int] 						=("CLIPTokenizer", "CLIPTokenizerFast")

   def __init__(					self ,				snake_case_=None ,				snake_case_=None ,				**snake_case_      )		->       Optional[int]:

       '''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.' ,				__a ,				)
           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__(__a ,				__a      )

   def __call__(					self ,				snake_case_=None ,				snake_case_=None ,				snake_case_=None ,				**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(__a ,				return_tensors=__a ,				**__a      )

       if images is not None:
           UpperCamelCase__         =    self.image_processor(__a ,				return_tensors=__a ,				**__a      )

       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(**__a      ) ,				tensor_type=__a      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				*snake_case_ ,				**snake_case_      )		->       int:

       '''simple docstring'''





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

   def 						SCREAMING_SNAKE_CASE__ (					self ,				*snake_case_ ,				**snake_case_      )		->       int:

       '''simple docstring'''





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

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:

       '''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 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:

       '''simple docstring'''





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







   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:

       '''simple docstring'''





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












"""simple docstring"""





A__					: Tuple=	"""Alexander Joslin"""

import operator as op

from .stack import Stack







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

    UpperCamelCase__         =    Stack()
    UpperCamelCase__         =    Stack()

    for i in equation:
        if i.isdigit():
            # RULE 1
            operand_stack.push(int(SCREAMING_SNAKE_CASE					)					)
        elif i in operators:
            # RULE 2
            operator_stack.push(SCREAMING_SNAKE_CASE					)
        elif i == ")":
            # RULE 4
            UpperCamelCase__         =    operator_stack.peek()
            operator_stack.pop()
            UpperCamelCase__         =    operand_stack.peek()
            operand_stack.pop()
            UpperCamelCase__         =    operand_stack.peek()
            operand_stack.pop()

            UpperCamelCase__         =    operators[opr](SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
            operand_stack.push(SCREAMING_SNAKE_CASE					)

    # RULE 5
    return operand_stack.peek()


if __name__ == "__main__":
  A__					: int=	"""(5 + ((4 * 2) * (2 + 3)))"""
  # answer = 45
  print(F"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")




