


"""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 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       a  :    List[Any]       ="Wav2Vec2FeatureExtractor"
       a  :    str       ="AutoTokenizer"

       def __init__(						self		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         super().__init__(snake_case__		,					snake_case__						)
         lowerCAmelCase					:					Tuple								=							self.feature_extractor
         lowerCAmelCase					:					Dict								=							False

       @classmethod
       def 	lowercase__							(						cls		,					snake_case__		,					**snake_case__						):






         """simple docstring"""





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

           lowerCAmelCase					:					List[Any]								=							WavaVecaFeatureExtractor.from_pretrained(snake_case__		,					**snake_case__						)
           lowerCAmelCase					:					str								=							WavaVecaCTCTokenizer.from_pretrained(snake_case__		,					**snake_case__						)

           return cls(feature_extractor=snake_case__		,					tokenizer=snake_case__						)

       def __call__(						self		,					*snake_case__		,					**snake_case__						):






         """simple docstring"""





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

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

         if text is None:
           return inputs
         elif audio is None:
           return encodings
         else:
           lowerCAmelCase					:					Union[str, Any]								=							encodings["input_ids"]
           return inputs

       def 	lowercase__							(						self		,					*snake_case__		,					**snake_case__						):






         """simple docstring"""





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

         lowerCAmelCase					:					int								=							kwargs.pop("input_features"		,					snake_case__						)
         lowerCAmelCase					:					Any								=							kwargs.pop("labels"		,					snake_case__						)
         if len(snake_case__						) > 0:
           lowerCAmelCase					:					List[Any]								=							args[0]
           lowerCAmelCase					:					Union[str, Any]								=							args[1:]

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

         if labels is None:
           return input_features
         elif input_features is None:
           return labels
         else:
           lowerCAmelCase					:					Optional[Any]								=							labels["input_ids"]
           return input_features

       def 	lowercase__							(						self		,					*snake_case__		,					**snake_case__						):






         """simple docstring"""





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

       def 	lowercase__							(						self		,					*snake_case__		,					**snake_case__						):






         """simple docstring"""





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







       @contextmanager
       def 	lowercase__							(						self						):






         """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					:					List[str]								=							True
         lowerCAmelCase					:					Any								=							self.tokenizer
         yield
         lowerCAmelCase					:					Optional[Any]								=							self.feature_extractor
         lowerCAmelCase					:					List[Any]								=							False









"""simple docstring"""







import baseaa
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ):


  '''simple docstring'''







  return baseaa.baaencode(string.encode("utf-8"    )    )
def  a__      (    SCREAMING_SNAKE_CASE   :   bytes    ):


  '''simple docstring'''







  return baseaa.baadecode(SCREAMING_SNAKE_CASE    ).decode("utf-8"    )


if __name__ == "__main__":
     lowerCAmelCase__				    =       '''Hello World!'''
     lowerCAmelCase__				    =       baseaa_encode(test)
     print(encoded)

     lowerCAmelCase__				    =       baseaa_decode(encoded)
     print(decoded)





