




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     ):
    """simple docstring"""



    SCREAMING_SNAKE_CASE_ :							Dict											=      'Wav2Vec2FeatureExtractor'
    SCREAMING_SNAKE_CASE_ :							Any											=      'AutoTokenizer'





    def __init__(   self       :  str      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  List[str]							)      ->  Tuple:
       super().__init__(__lowerCamelCase      ,   __lowerCamelCase							)
       a					=    self.feature_extractor
       a					=    False





    @classmethod
    def 							__UpperCAmelCase							(   cls       :  int      ,   __lowerCamelCase       :  List[Any]      ,   **__lowerCamelCase       :  int							)      ->  Dict:







       try:
          return super().from_pretrained(__lowerCamelCase      ,   **__lowerCamelCase							)
       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: "      ,   __lowerCamelCase      ,   )

          a					=    WavaVecaFeatureExtractor.from_pretrained(__lowerCamelCase      ,   **__lowerCamelCase							)
          a					=    WavaVecaCTCTokenizer.from_pretrained(__lowerCamelCase      ,   **__lowerCamelCase							)

          return cls(feature_extractor=__lowerCamelCase      ,   tokenizer=__lowerCamelCase							)





    def __call__(   self       :  Dict      ,   *__lowerCamelCase       :  Optional[Any]      ,   **__lowerCamelCase       :  Optional[Any]							)      ->  List[Any]:







       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."							)
          a					=    kwargs.pop("raw_speech"							)
       else:
          a					=    kwargs.pop("audio"      ,   __lowerCamelCase							)
       a					=    kwargs.pop("sampling_rate"      ,   __lowerCamelCase							)
       a					=    kwargs.pop("text"      ,   __lowerCamelCase							)
       if len(__lowerCamelCase							) > 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(__lowerCamelCase      ,   *__lowerCamelCase      ,   sampling_rate=__lowerCamelCase      ,   **__lowerCamelCase							)
       if text is not None:
          a					=    self.tokenizer(__lowerCamelCase      ,   **__lowerCamelCase							)

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





    def 							__UpperCAmelCase							(   self       :  Any      ,   *__lowerCamelCase       :  Tuple      ,   **__lowerCamelCase       :  Any							)      ->  Optional[int]:







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

       a					=    kwargs.pop("input_features"      ,   __lowerCamelCase							)
       a					=    kwargs.pop("labels"      ,   __lowerCamelCase							)
       if len(__lowerCamelCase							) > 0:
          a					=    args[0]
          a					=    args[1:]

       if input_features is not None:
          a					=    self.feature_extractor.pad(__lowerCamelCase      ,   *__lowerCamelCase      ,   **__lowerCamelCase							)
       if labels is not None:
          a					=    self.tokenizer.pad(__lowerCamelCase      ,   **__lowerCamelCase							)

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





    def 							__UpperCAmelCase							(   self       :  Any      ,   *__lowerCamelCase       :  Optional[Any]      ,   **__lowerCamelCase       :  Optional[Any]							)      ->  Any:
       return self.tokenizer.batch_decode(*__lowerCamelCase      ,   **__lowerCamelCase							)





    def 							__UpperCAmelCase							(   self       :  Union[str, Any]      ,   *__lowerCamelCase       :  str      ,   **__lowerCamelCase       :  str							)      ->  int:
       return self.tokenizer.decode(*__lowerCamelCase      ,   **__lowerCamelCase							)





    @contextmanager
    def 							__UpperCAmelCase							(   self       :  Dict							)      ->  Any:
       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     __magic_name__       (				A	:      int,						A	:      int,						A	:      int     ):






			'''simple docstring'''







			if exponent == 1:
						return base
			if exponent % 2 == 0:
						a					=    _modexpt(A,						exponent // 2,						A     ) % modulo_value
						return (x * x) % modulo_value
			else:
						return (base * _modexpt(A,						exponent - 1,						A     )) % modulo_value




def     __magic_name__       (				A	:      int = 1777,						A	:      int = 1855,						A	:      int = 8     ):






			'''simple docstring'''







			a					=    base
			for _ in range(1,						A     ):
						a					=    _modexpt(A,						A,						10**digits     )

			return result


if __name__ == "__main__":
		print(F'''{solution() = }''')






