




from ...processing_utils import ProcessorMixin




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				'''WhisperFeatureExtractor'''
    lowercase__						 =				'''WhisperTokenizer'''




    def __init__(   self						:	Tuple     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Any				)				->							str:




     '''simple docstring'''

     super().__init__(snake_case_     ,			snake_case_				)
     A__	=    self.feature_extractor
     A__	=    False




    def 							__magic_name__							(   self						:	List[str]     ,			snake_case_						:	Any=None     ,			snake_case_						:	Optional[Any]=None     ,			snake_case_						:	Optional[int]=True				)				->							Union[str, Any]:




     '''simple docstring'''

     return self.tokenizer.get_decoder_prompt_ids(task=snake_case_     ,			language=snake_case_     ,			no_timestamps=snake_case_				)




    def __call__(   self						:	int     ,			*snake_case_						:	Union[str, Any]     ,			**snake_case_						:	int				)				->							int:




     '''simple docstring'''

     if self._in_target_context_manager:
      return self.current_processor(*snake_case_     ,			**snake_case_				)

     A__	=    kwargs.pop("audio"     ,			snake_case_				)
     A__	=    kwargs.pop("sampling_rate"     ,			snake_case_				)
     A__	=    kwargs.pop("text"     ,			snake_case_				)
     if len(snake_case_				) > 0:
      A__	=    args[0]
      A__	=    args[1:]

     if audio is None and text is None:
      raise ValueError("You need to specify either an `audio` or `text` input to process."				)

     if audio is not None:
      A__	=    self.feature_extractor(snake_case_     ,			*snake_case_     ,			sampling_rate=snake_case_     ,			**snake_case_				)
     if text is not None:
      A__	=    self.tokenizer(snake_case_     ,			**snake_case_				)

     if text is None:
      return inputs

     elif audio is None:
      return encodings
     else:
      A__	=    encodings["input_ids"]
      return inputs




    def 							__magic_name__							(   self						:	Optional[int]     ,			*snake_case_						:	Dict     ,			**snake_case_						:	List[Any]				)				->							Dict:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	Union[str, Any]     ,			*snake_case_						:	Any     ,			**snake_case_						:	List[Any]				)				->							Union[str, Any]:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	int     ,			snake_case_						:	str     ,			snake_case_						:	Union[str, Any]="np"				)				->							Union[str, Any]:




     '''simple docstring'''

     return self.tokenizer.get_prompt_ids(snake_case_     ,			return_tensors=snake_case_				)











"""simple docstring"""



SCREAMING_SNAKE_CASE    =	"Alexander Joslin"

import operator as op

from .stack import Stack







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		int:
 A__	=    {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

 A__	=    Stack()
 A__	=    Stack()

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

   A__	=    operators[opr](lowercase_      ,			lowercase_						)
   operand_stack.push(lowercase_						)

    # 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)}')





