



"""simple docstring"""




_UpperCamelCase:				Optional[Any]    							=  {
    '+': lambda x, y: x + y,
    '-': lambda x, y: x - y,
    '*': lambda x, y: x * y,
    '/': lambda x, y: x / y,
}


def   lowercase__     (			_UpperCAmelCase				)     ->			str:






      '''simple docstring'''


      return c.isdigit()


def   lowercase__     (			_UpperCAmelCase				)     ->			int:






      '''simple docstring'''


      lowercase  :      Dict					=   []

      # iterate over the string in reverse order
      for c in expression.split()[::-1]:
            # push operand to stack
            if is_operand(_UpperCAmelCase				):
                  stack.append(int(_UpperCAmelCase				)				)

            else:
                  # pop values from stack can calculate the result
                  # push the result onto the stack again
                  lowercase  :      Any					=   stack.pop()
                  lowercase  :      Dict					=   stack.pop()
                  stack.append(calc[c](_UpperCAmelCase	,      _UpperCAmelCase				)				)

      return stack.pop()


# Driver code
if __name__ == "__main__":
     _UpperCamelCase:				int    							=  '+ 9 * 2 6'
     print(evaluate(test_expression))

     _UpperCamelCase:				str    							=  '/ * 10 2 + 4 1 '
     print(evaluate(test_expression))





"""simple docstring"""




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				'AutoFeatureExtractor'
   _lowerCamelCase												=				'Speech2Text2Tokenizer'


   def __init__(							self      :							str,     lowerCAmelCase      :							List[str],     lowerCAmelCase      :							Optional[Any]			)    ->       Dict:
         super().__init__(lowerCAmelCase,     lowerCAmelCase			)
         lowercase  :      Tuple					=   self.feature_extractor
         lowercase  :      Any					=   False


   def __call__(							self      :							Tuple,     *lowerCAmelCase      :							Tuple,     **lowerCAmelCase      :							Tuple			)    ->       Dict:
         # For backward compatibility
         if self._in_target_context_manager:
               return self.current_processor(*lowerCAmelCase,     **lowerCAmelCase			)

         if "raw_speech" in kwargs:
               warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'			)
               lowercase  :      str					=   kwargs.pop('raw_speech'			)
         else:
               lowercase  :      str					=   kwargs.pop('audio',     lowerCAmelCase			)
         lowercase  :      Dict					=   kwargs.pop('sampling_rate',     lowerCAmelCase			)
         lowercase  :      Optional[Any]					=   kwargs.pop('text',     lowerCAmelCase			)
         if len(lowerCAmelCase			) > 0:
               lowercase  :      Any					=   args[0]
               lowercase  :      int					=   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:
               lowercase  :      List[Any]					=   self.feature_extractor(lowerCAmelCase,     *lowerCAmelCase,     sampling_rate=lowerCAmelCase,     **lowerCAmelCase			)
         if text is not None:
               lowercase  :      str					=   self.tokenizer(lowerCAmelCase,     **lowerCAmelCase			)

         if text is None:
               return inputs
         elif audio is None:
               return encodings
         else:
               lowercase  :      Tuple					=   encodings['input_ids']
               return inputs


   def        lowercase				(							self      :							str,     *lowerCAmelCase      :							List[str],     **lowerCAmelCase      :							Union[str, Any]			)    ->       str:
         return self.tokenizer.batch_decode(*lowerCAmelCase,     **lowerCAmelCase			)


   def        lowercase				(							self      :							Tuple,     *lowerCAmelCase      :							Union[str, Any],     **lowerCAmelCase      :							int			)    ->       Dict:
         return self.tokenizer.decode(*lowerCAmelCase,     **lowerCAmelCase			)






   @contextmanager
   def        lowercase				(							self      :							str			)    ->       Any:
         warnings.warn(
             '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
             'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
             'your audio inputs, or in a separate call.'			)
         lowercase  :      Optional[Any]					=   True
         lowercase  :      Any					=   self.tokenizer
         yield
         lowercase  :      Any					=   self.feature_extractor
         lowercase  :      Any					=   False
