






'''simple docstring'''



from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			['''image_processor''', '''tokenizer''']
      UpperCamelCase_								=			'''AutoImageProcessor'''
      UpperCamelCase_								=			'''AutoTokenizer'''






      def __init__( self			:		Dict     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		List[str]		)  ->  str:


       '''simple docstring'''

       super().__init__(UpperCAmelCase     ,      UpperCAmelCase		)
       lowercase					:   Union[str, Any]				  =self.image_processor






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


       '''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:
        lowercase					:   Optional[int]				  =self.tokenizer(UpperCAmelCase     ,      return_tensors=UpperCAmelCase     ,      **UpperCAmelCase		)

       if images is not None:
        lowercase					:   Dict				  =self.image_processor(UpperCAmelCase     ,      return_tensors=UpperCAmelCase     ,      **UpperCAmelCase		)

       if text is not None and images is not None:
        lowercase					:   Optional[int]				  =image_features.pixel_values
        return encoding
       elif text is not None:
        return encoding
       else:
        return BatchEncoding(data=dict(**UpperCAmelCase		)     ,      tensor_type=UpperCAmelCase		)






      def 				A__      ( self			:		Union[str, Any]     ,      *UpperCAmelCase			:		List[str]     ,      **UpperCAmelCase			:		int		)  ->  List[str]:


       '''simple docstring'''

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






      def 				A__      ( self			:		Union[str, Any]     ,      *UpperCAmelCase			:		Any     ,      **UpperCAmelCase			:		str		)  ->  List[Any]:


       '''simple docstring'''

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






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


       '''simple docstring'''

       return ["input_ids", "attention_mask", "pixel_values"]











'''simple docstring'''



SCREAMING_SNAKE_CASE						=	'Alexander Joslin'

import operator as op

from .stack import Stack


def 							lowercase_				(    __A :       str			)       ->      int:



 """simple docstring"""




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

 lowercase					:   Stack[int]				  =Stack()
 lowercase					:   Stack[str]				  =Stack()

 for i in equation:
  if i.isdigit():
   # RULE 1
   operand_stack.push(int(__A			)			)
  elif i in operators:
   # RULE 2
   operator_stack.push(__A			)
  elif i == ")":
   # RULE 4
   lowercase					:   Optional[Any]				  =operator_stack.peek()
   operator_stack.pop()
   lowercase					:   Optional[Any]				  =operand_stack.peek()
   operand_stack.pop()
   lowercase					:   Optional[Any]				  =operand_stack.peek()
   operand_stack.pop()

   lowercase					:   List[str]				  =operators[opr](__A					,      __A			)
   operand_stack.push(__A			)

    # RULE 5
 return operand_stack.peek()


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE						=	'(5 + ((4 * 2) * (2 + 3)))'
      # answer = 45
      print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")



