
'''simple docstring'''
a_											=						"Alexander Joslin"

import operator as op

from .stack import Stack

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[str]     			=						{"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

    snake_case_ :    Stack[int]     			=						Stack()
    snake_case_ :    Stack[str]     			=						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
            snake_case_ :    List[Any]     			=						operator_stack.peek()
            operator_stack.pop()
            snake_case_ :    Union[str, Any]     			=						operand_stack.peek()
            operand_stack.pop()
            snake_case_ :    List[str]     			=						operand_stack.peek()
            operand_stack.pop()

            snake_case_ :    List[Any]     			=						operators[opr](__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
            operand_stack.push(__SCREAMING_SNAKE_CASE       )

    # RULE 5
    return operand_stack.peek()


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






'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						"""ClapFeatureExtractor"""
 UpperCAmelCase_		    =						("""RobertaTokenizer""", """RobertaTokenizerFast""")





 def __init__(    self ,       lowercase_ ,       lowercase_):
     super().__init__(lowercase_ ,       lowercase_)





 def __call__(    self ,       lowercase_=None ,       lowercase_=None ,       lowercase_=None ,       **lowercase_):
     snake_case_ :    Any     			=						kwargs.pop("sampling_rate" ,       lowercase_)

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

     if text is not None:
         snake_case_ :    Optional[int]     			=						self.tokenizer(lowercase_ ,       return_tensors=lowercase_ ,       **lowercase_)

     if audios is not None:
         snake_case_ :    Any     			=						self.feature_extractor(
             lowercase_ ,       sampling_rate=lowercase_ ,       return_tensors=lowercase_ ,       **lowercase_)

     if text is not None and audios is not None:
         snake_case_ :    Dict     			=						audio_features.input_features
         return encoding
     elif text is not None:
         return encoding
     else:
         return BatchEncoding(data=dict(**lowercase_) ,       tensor_type=lowercase_)





 def        snake_case__		(    self ,       *lowercase_ ,       **lowercase_):
     return self.tokenizer.batch_decode(*lowercase_ ,       **lowercase_)





 def        snake_case__		(    self ,       *lowercase_ ,       **lowercase_):
     return self.tokenizer.decode(*lowercase_ ,       **lowercase_)





 @property
 def        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						self.tokenizer.model_input_names
     snake_case_ :    int     			=						self.feature_extractor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))




