




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				'''Speech2TextFeatureExtractor'''
    UpperCAmelCase__							  =				'''Speech2TextTokenizer'''


    def __init__(      self				:     int ,			UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     str)						->Optional[Any]:





           '''simple docstring'''

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


    def __call__(      self				:     Tuple ,			*UpperCAmelCase__				:     int ,			**UpperCAmelCase__				:     Optional[Any])						->int:





           '''simple docstring'''

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

           if "raw_speech" in kwargs:
                  warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.''')
                  A__							=   kwargs.pop('''raw_speech''')
           else:
                  A__							=   kwargs.pop('''audio''' ,			_SCREAMING_SNAKE_CASE)
           A__							=   kwargs.pop('''sampling_rate''' ,			_SCREAMING_SNAKE_CASE)
           A__							=   kwargs.pop('''text''' ,			_SCREAMING_SNAKE_CASE)
           if len(_SCREAMING_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(_SCREAMING_SNAKE_CASE ,			*_SCREAMING_SNAKE_CASE ,			sampling_rate=_SCREAMING_SNAKE_CASE ,			**_SCREAMING_SNAKE_CASE)
           if text is not None:
                  A__							=   self.tokenizer(_SCREAMING_SNAKE_CASE ,			**_SCREAMING_SNAKE_CASE)

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			*UpperCAmelCase__				:     Optional[Any] ,			**UpperCAmelCase__				:     Union[str, Any])						->List[str]:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			*UpperCAmelCase__				:     int ,			**UpperCAmelCase__				:     int)						->Tuple:





           '''simple docstring'''

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





    @contextmanager
    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Tuple:





           '''simple docstring'''

           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.''')
           A__							=   True
           A__							=   self.tokenizer
           yield
           A__							=   self.feature_extractor
           A__							=   False








def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	str:



       """simple docstring"""

       return " ".join(
           ''''''.join(word[::-1]						) if len(lowercase_						) > 4 else word for word in sentence.split()						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(reverse_long_words("""Hey wollef sroirraw"""))


