






"""simple docstring"""


def     snake_case						(A_   :int   ):







  '''simple docstring'''

  if number < 0:
    raise ValueError('number must not be negative'   )
  return number & (number - 1) == 0


if __name__ == "__main__":
   import doctest

   doctest.testmod()












"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer






class 		snake_case						(						UpperCAmelCase ):
    __magic_name__					  =  '''Wav2Vec2FeatureExtractor'''
    __magic_name__					  =  '''AutoTokenizer'''





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


      '''simple docstring'''




      super().__init__(A       ,	A    )
      a  :    int       =     self.feature_extractor
      a  :    List[Any]       =     False





    @classmethod
    def        lowerCamelCase__   (							cls			:      Optional[Any]       ,	A			:      str       ,	**A			:      Union[str, Any]    ):


      '''simple docstring'''




      try:
        return super().from_pretrained(A       ,	**A    )
      except OSError:
        warnings.warn(
            F'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
            ' include a `tokenizer_class` attribute is deprecated and will be '
            'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
            ' attribute to either your `config.json` or `tokenizer_config.json` '
            'file to suppress this warning: '       ,	A       ,	)

        a  :    int       =     WavaVecaFeatureExtractor.from_pretrained(A       ,	**A    )
        a  :    Dict       =     WavaVecaCTCTokenizer.from_pretrained(A       ,	**A    )

        return cls(feature_extractor=A       ,	tokenizer=A    )





    def __call__(							self			:      str       ,	*A			:      int       ,	**A			:      Union[str, Any]    ):


      '''simple docstring'''




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

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

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





    def        lowerCamelCase__   (							self			:      Optional[int]       ,	*A			:      Tuple       ,	**A			:      List[Any]    ):


      '''simple docstring'''




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

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

      if input_features is not None:
        a  :    str       =     self.feature_extractor.pad(A       ,	*A       ,	**A    )
      if labels is not None:
        a  :    Optional[Any]       =     self.tokenizer.pad(A       ,	**A    )

      if labels is None:
        return input_features
      elif input_features is None:
        return labels
      else:
        a  :    str       =     labels['input_ids']
        return input_features





    def        lowerCamelCase__   (							self			:      Union[str, Any]       ,	*A			:      Optional[Any]       ,	**A			:      Tuple    ):


      '''simple docstring'''




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





    def        lowerCamelCase__   (							self			:      Dict       ,	*A			:      Optional[Any]       ,	**A			:      str    ):


      '''simple docstring'''




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





    @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  :    List[Any]       =     True
      a  :    Optional[Any]       =     self.tokenizer
      yield
      a  :    Union[str, Any]       =     self.feature_extractor
      a  :    List[Any]       =     False




