



"""simple docstring"""






import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin






class        __snake_case       (	_lowercase):
  snake_case__				:   List[Any]  			=						"MCTCTFeatureExtractor"
  snake_case__				:   Optional[int]  			=						"AutoTokenizer"




  def __init__( self     :						Optional[int]						,					__lowerCAmelCase     :						List[Any]						,					__lowerCAmelCase     :						Optional[Any]		):






         """simple docstring"""






         super().__init__(__lowerCAmelCase						,					__lowerCAmelCase		)
         _lowerCamelCase						:				Optional[Any]    			= self.feature_extractor
         _lowerCamelCase						:				Optional[Any]    			= False




  def __call__( self     :						Optional[int]						,					*__lowerCAmelCase     :						Dict						,					**__lowerCAmelCase     :						str		):






         """simple docstring"""






         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.'''		)
                _lowerCamelCase						:				str    			= kwargs.pop('''raw_speech'''		)
         else:
                _lowerCamelCase						:				str    			= kwargs.pop('''audio'''						,					__lowerCAmelCase		)
         _lowerCamelCase						:				Dict    			= kwargs.pop('''sampling_rate'''						,					__lowerCAmelCase		)
         _lowerCamelCase						:				List[str]    			= kwargs.pop('''text'''						,					__lowerCAmelCase		)
         if len(__lowerCAmelCase		) > 0:
                _lowerCamelCase						:				Optional[Any]    			= args[0]
                _lowerCamelCase						:				str    			= 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:
                _lowerCamelCase						:				str    			= self.feature_extractor(__lowerCAmelCase						,					*__lowerCAmelCase						,					sampling_rate=__lowerCAmelCase						,					**__lowerCAmelCase		)
         if text is not None:
                _lowerCamelCase						:				Union[str, Any]    			= self.tokenizer(__lowerCAmelCase						,					**__lowerCAmelCase		)

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




  def 						SCREAMING_SNAKE_CASE				( self     :						Dict						,					*__lowerCAmelCase     :						List[Any]						,					**__lowerCAmelCase     :						int		):






         """simple docstring"""






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




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[Any]						,					*__lowerCAmelCase     :						Union[str, Any]						,					**__lowerCAmelCase     :						Optional[int]		):






         """simple docstring"""






         if self._in_target_context_manager:
                return self.current_processor.pad(*__lowerCAmelCase						,					**__lowerCAmelCase		)

         _lowerCamelCase						:				Tuple    			= kwargs.pop('''input_features'''						,					__lowerCAmelCase		)
         _lowerCamelCase						:				int    			= kwargs.pop('''labels'''						,					__lowerCAmelCase		)
         if len(__lowerCAmelCase		) > 0:
                _lowerCamelCase						:				List[str]    			= args[0]
                _lowerCamelCase						:				Optional[int]    			= args[1:]

         if input_features is not None:
                _lowerCamelCase						:				int    			= self.feature_extractor.pad(__lowerCAmelCase						,					*__lowerCAmelCase						,					**__lowerCAmelCase		)
         if labels is not None:
                _lowerCamelCase						:				Tuple    			= self.tokenizer.pad(__lowerCAmelCase						,					**__lowerCAmelCase		)

         if labels is None:
                return input_features
         elif input_features is None:
                return labels
         else:
                _lowerCamelCase						:				Any    			= labels['''input_ids''']
                return input_features




  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]						,					*__lowerCAmelCase     :						Any						,					**__lowerCAmelCase     :						Union[str, Any]		):






         """simple docstring"""






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




  @contextmanager
  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]		):






         """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.'''		)
         _lowerCamelCase						:				Tuple    			= True
         _lowerCamelCase						:				str    			= self.tokenizer
         yield
         _lowerCamelCase						:				Union[str, Any]    			= self.feature_extractor
         _lowerCamelCase						:				Optional[Any]    			= False










"""simple docstring"""


import re



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  return [char.split() for char in re.split(r'''[^ a-z A-Z 0-9 \s]'''		,       str_ )]



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  __A									= split_input(str_ )
  return "".join(
      [''''''.join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  try:
    __A									= split_input(__UpperCamelCase )
    if upper:
      __A									= ''''''.join(
          [
              separator.join([char.upper() for char in sub_str] )
              for sub_str in string_split
          ] )
    else:
      __A									= ''''''.join(
          [
              separator.join([char.lower() for char in sub_str] )
              for sub_str in string_split
          ] )
    return res_str
  except IndexError:
    return "not valid string"



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  return to_simple_case(__UpperCamelCase )



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  try:
    __A									= to_simple_case(__UpperCamelCase )
    return res_str[0].lower() + res_str[1:]
  except IndexError:
    return "not valid string"



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  return to_complex_case(__UpperCamelCase		,       __UpperCamelCase		,       '''_''' )



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  return to_complex_case(__UpperCamelCase		,       __UpperCamelCase		,       '''-''' )


if __name__ == "__main__":
       __import__('doctest').testmod()




