"""simple docstring"""
import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin




class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		"""MCTCTFeatureExtractor"""
      a__				=		"""AutoTokenizer"""



      def __init__(						self   ,      lowercase   ,      lowercase)     ->      List[Any]:





        '''simple docstring'''





        super().__init__(lowercase   ,      lowercase)
        a__:					str      							=     self.feature_extractor
        a__:					Union[str, Any]      							=     False



      def __call__(						self   ,      *lowercase   ,      **lowercase)     ->      Dict:





        '''simple docstring'''





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

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

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



      def   lowerCamelCase_       (						self   ,      *lowercase   ,      **lowercase)     ->      str:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self   ,      *lowercase   ,      **lowercase)     ->      Optional[Any]:





        '''simple docstring'''





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

        a__:					Optional[int]      							=     kwargs.pop('input_features'   ,      lowercase)
        a__:					List[Any]      							=     kwargs.pop('labels'   ,      lowercase)
        if len(lowercase) > 0:
          a__:					Dict      							=     args[0]
          a__:					List[str]      							=     args[1:]

        if input_features is not None:
          a__:					Optional[int]      							=     self.feature_extractor.pad(lowercase   ,      *lowercase   ,      **lowercase)
        if labels is not None:
          a__:					Any      							=     self.tokenizer.pad(lowercase   ,      **lowercase)

        if labels is None:
          return input_features
        elif input_features is None:
          return labels
        else:
          a__:					Optional[int]      							=     labels['input_ids']
          return input_features



      def   lowerCamelCase_       (						self   ,      *lowercase   ,      **lowercase)     ->      Optional[int]:





        '''simple docstring'''





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



      @contextmanager
      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''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__:					int      							=     True
        a__:					List[Any]      							=     self.tokenizer
        yield
        a__:					Optional[int]      							=     self.feature_extractor
        a__:					Dict      							=     False



"""simple docstring"""
from __future__ import annotations




class        __snake_case	:



      def __init__(						self   ,      lowercase=None)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					int      							=     data
        a__:					str      							=     None



      def __repr__(						self)     ->      List[str]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     []
        a__:					Union[str, Any]      							=     self
        while temp:
          string_rep.append(f'{temp.data}')
          a__:					Tuple      							=     temp.next
        return "->".join(lowercase)







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->str:
  if not elements_list:
    raise Exception('The Elements List is empty'       )

  a__:					Any      							=     Node(elements_list[0]       )
  for i in range(1		,					len(_SCREAMING_SNAKE_CASE       )       ):
    a__:					Optional[Any]      							=     Node(elements_list[i]       )
    a__:					Tuple      							=     current.next
  return head







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->None:
  if head_node is not None and isinstance(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       ):
    print_reverse(head_node.next       )
    print(head_node.data       )







def 						__a       (     )     ->Optional[Any]:
  from doctest import testmod

  testmod()

  a__:					Tuple      							=     make_linked_list([14, 52, 14, 12, 43]       )
  print('Linked List:'       )
  print(_SCREAMING_SNAKE_CASE       )
  print('Elements in Reverse:'       )
  print_reverse(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
 main()


