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       (		lowerCamelCase_	):
   lowerCAmelCase_														=							"Wav2Vec2FeatureExtractor"
   lowerCAmelCase_														=							"AutoTokenizer"




   def __init__(					self    :       Dict ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       str   ):



     """simple docstring"""






     super().__init__(_lowercase ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.feature_extractor
     SCREAMING_SNAKE_CASE__    							=   False




   @classmethod
   def 			__a		(					cls    :       Any ,			_lowercase    :       Dict ,			**_lowercase    :       Optional[int]   ):



     """simple docstring"""






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

       SCREAMING_SNAKE_CASE__    							=   WavaVecaFeatureExtractor.from_pretrained(_lowercase ,			**_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   WavaVecaCTCTokenizer.from_pretrained(_lowercase ,			**_lowercase   )

       return cls(feature_extractor=_lowercase ,			tokenizer=_lowercase   )




   def __call__(					self    :       Any ,			*_lowercase    :       Optional[Any] ,			**_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."""   )
       SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""raw_speech"""   )
     else:
       SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""audio""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""sampling_rate""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""text""" ,			_lowercase   )
     if len(_lowercase   ) > 0:
       SCREAMING_SNAKE_CASE__    							=   args[0]
       SCREAMING_SNAKE_CASE__    							=   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:
       SCREAMING_SNAKE_CASE__    							=   self.feature_extractor(_lowercase ,			*_lowercase ,			sampling_rate=_lowercase ,			**_lowercase   )
     if text is not None:
       SCREAMING_SNAKE_CASE__    							=   self.tokenizer(_lowercase ,			**_lowercase   )

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




   def 			__a		(					self    :       Tuple ,			*_lowercase    :       Tuple ,			**_lowercase    :       int   ):



     """simple docstring"""






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

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

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

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




   def 			__a		(					self    :       Any ,			*_lowercase    :       int ,			**_lowercase    :       int   ):



     """simple docstring"""






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




   def 			__a		(					self    :       int ,			*_lowercase    :       str ,			**_lowercase    :       Tuple   ):



     """simple docstring"""






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




   @contextmanager
   def 			__a		(					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."""   )
     SCREAMING_SNAKE_CASE__    							=   True
     SCREAMING_SNAKE_CASE__    							=   self.tokenizer
     yield
     SCREAMING_SNAKE_CASE__    							=   self.feature_extractor
     SCREAMING_SNAKE_CASE__    							=   False

from __future__ import annotations



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: list			)							->      float:







  """simple docstring"""





  if not nums:
    raise ValueError("""List is empty"""			)
  return sum(__UpperCamelCase			) / len(__UpperCamelCase			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
