

from ...processing_utils import ProcessorMixin

class    _lowercase				(					__lowerCamelCase			):
   _lowercase     :       int  					=    'SpeechT5FeatureExtractor'
   _lowercase     :       Optional[Any]  					=    'SpeechT5Tokenizer'

   def __init__(				self	:    Union[str, Any]			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    List[Any]       ) ->			Optional[Any]:
         """simple docstring"""





         super().__init__(lowerCamelCase__			,						lowerCamelCase__       )

   def __call__(				self	:    List[str]			,						*lowerCamelCase__	:    int			,						**lowerCamelCase__	:    List[str]       ) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		kwargs.pop('''audio'''			,						lowerCamelCase__       )
         A_							       =		kwargs.pop('''text'''			,						lowerCamelCase__       )
         A_							       =		kwargs.pop('''text_target'''			,						lowerCamelCase__       )
         A_							       =		kwargs.pop('''audio_target'''			,						lowerCamelCase__       )
         A_							       =		kwargs.pop('''sampling_rate'''			,						lowerCamelCase__       )

         if audio is not None and text is not None:
               raise ValueError(
                   '''Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'''       )
         if audio_target is not None and text_target is not None:
               raise ValueError(
                   '''Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'''       )
         if audio is None and audio_target is None and text is None and text_target is None:
               raise ValueError(
                   '''You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'''       )

         if audio is not None:
               A_							       =		self.feature_extractor(lowerCamelCase__			,						*lowerCamelCase__			,						sampling_rate=lowerCamelCase__			,						**lowerCamelCase__       )
         elif text is not None:
               A_							       =		self.tokenizer(lowerCamelCase__			,						**lowerCamelCase__       )
         else:
               A_							       =		None

         if audio_target is not None:
               A_							       =		self.feature_extractor(audio_target=lowerCamelCase__			,						*lowerCamelCase__			,						sampling_rate=lowerCamelCase__			,						**lowerCamelCase__       )
               A_							       =		targets['''input_values''']
         elif text_target is not None:
               A_							       =		self.tokenizer(lowerCamelCase__			,						**lowerCamelCase__       )
               A_							       =		targets['''input_ids''']
         else:
               A_							       =		None

         if inputs is None:
               return targets

         if targets is not None:
               A_							       =		labels

               A_							       =		targets.get('''attention_mask'''       )
               if decoder_attention_mask is not None:
                     A_							       =		decoder_attention_mask

         return inputs

   def     UpperCamelCase					(				self	:    str			,						*lowerCamelCase__	:    Union[str, Any]			,						**lowerCamelCase__	:    int       ) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		kwargs.pop('''input_values'''			,						lowerCamelCase__       )
         A_							       =		kwargs.pop('''input_ids'''			,						lowerCamelCase__       )
         A_							       =		kwargs.pop('''labels'''			,						lowerCamelCase__       )

         if input_values is not None and input_ids is not None:
               raise ValueError('''Cannot process both `input_values` and `input_ids` inputs.'''       )
         if input_values is None and input_ids is None and labels is None:
               raise ValueError(
                   '''You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'''       )

         if input_values is not None:
               A_							       =		self.feature_extractor.pad(lowerCamelCase__			,						*lowerCamelCase__			,						**lowerCamelCase__       )
         elif input_ids is not None:
               A_							       =		self.tokenizer.pad(lowerCamelCase__			,						**lowerCamelCase__       )
         else:
               A_							       =		None

         if labels is not None:
               if "input_ids" in labels or (isinstance(lowerCamelCase__			,						lowerCamelCase__       ) and "input_ids" in labels[0]):
                     A_							       =		self.tokenizer.pad(lowerCamelCase__			,						**lowerCamelCase__       )
                     A_							       =		targets['''input_ids''']
               else:
                     A_							       =		self.feature_extractor.feature_size
                     A_							       =		self.feature_extractor.num_mel_bins
                     A_							       =		self.feature_extractor.pad(lowerCamelCase__			,						*lowerCamelCase__			,						**lowerCamelCase__       )
                     A_							       =		feature_size_hack
                     A_							       =		targets['''input_values''']
         else:
               A_							       =		None

         if inputs is None:
               return targets

         if targets is not None:
               A_							       =		labels

               A_							       =		targets.get('''attention_mask'''       )
               if decoder_attention_mask is not None:
                     A_							       =		decoder_attention_mask

         return inputs

   def     UpperCamelCase					(				self	:    Dict			,						*lowerCamelCase__	:    Optional[Any]			,						**lowerCamelCase__	:    Optional[Any]       ) ->			List[Any]:
         """simple docstring"""





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





   def     UpperCamelCase					(				self	:    Any			,						*lowerCamelCase__	:    Union[str, Any]			,						**lowerCamelCase__	:    List[Any]       ) ->			Optional[int]:
         """simple docstring"""





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








__lowercase				      =						"""Alexander Joslin"""

import operator as op

from .stack import Stack







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      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(SCREAMING_SNAKE_CASE   )   )
            elif i in operators:
                  # RULE 2
                  operator_stack.push(SCREAMING_SNAKE_CASE   )
            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](SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )
                  operand_stack.push(SCREAMING_SNAKE_CASE   )

    # RULE 5
      return operand_stack.peek()


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





