




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class 						_A					(  a__      ):
  _UpperCamelCase   :			Any =			'''Wav2Vec2FeatureExtractor'''
  _UpperCamelCase   :			Union[str, Any] =			'''AutoTokenizer'''






  def __init__(  self		:	List[Any]      ,						_A		:	str      ,						_A		:	int      )		->    Any:






       """simple docstring"""







       super().__init__(UpperCamelCase_      ,						UpperCamelCase_      )
       lowercase					:   List[str]								= self.feature_extractor
       lowercase					:   List[str]								= False






  @classmethod
  def 					__a			(  cls		:	Tuple      ,						_A		:	Union[str, Any]      ,						**_A		:	List[str]      )		->    Dict:






       """simple docstring"""







       try:
            return super().from_pretrained(UpperCamelCase_      ,						**UpperCamelCase_      )
       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: '''      ,						UpperCamelCase_      ,						)

            lowercase					:   int								= WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase_      ,						**UpperCamelCase_      )
            lowercase					:   Union[str, Any]								= WavaVecaCTCTokenizer.from_pretrained(UpperCamelCase_      ,						**UpperCamelCase_      )

            return cls(feature_extractor=UpperCamelCase_      ,						tokenizer=UpperCamelCase_      )






  def __call__(  self		:	Any      ,						*_A		:	int      ,						**_A		:	Union[str, Any]      )		->    List[Any]:






       """simple docstring"""







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

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

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






  def 					__a			(  self		:	str      ,						*_A		:	List[str]      ,						**_A		:	Tuple      )		->    Tuple:






       """simple docstring"""







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

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

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

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






  def 					__a			(  self		:	Union[str, Any]      ,						*_A		:	List[str]      ,						**_A		:	Any      )		->    Dict:






       """simple docstring"""







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






  def 					__a			(  self		:	Optional[Any]      ,						*_A		:	Dict      ,						**_A		:	Union[str, Any]      )		->    int:






       """simple docstring"""







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






  @contextmanager
  def 					__a			(  self		:	Optional[int]      )		->    Optional[int]:






       """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.'''      )
       lowercase					:   Union[str, Any]								= True
       lowercase					:   List[str]								= self.tokenizer
       yield
       lowercase					:   Optional[Any]								= self.feature_extractor
       lowercase					:   Union[str, Any]								= False



"""simple docstring"""





from datetime import datetime

import matplotlib.pyplot as plt
import torch


def    _snake_case  (  _snake_case :	int	):
      for param in module.parameters():
            lowerCAmelCase    :		Optional[int]				=      False


def    _snake_case  (  ):
      lowerCAmelCase    :		List[str]				=      '''cuda''' if torch.cuda.is_available() else '''cpu'''
      if torch.backends.mps.is_available() and torch.backends.mps.is_built():
            lowerCAmelCase    :		Any				=      '''mps'''
      if device == "mps":
            print(
                '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
                ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
                ''' with generations.'''	)
      return device


def    _snake_case  (  _snake_case :	Dict	):
      lowerCAmelCase    :		Optional[int]				=      plt.imshow(_snake_case	)
      fig.axes.get_xaxis().set_visible(_snake_case	)
      fig.axes.get_yaxis().set_visible(_snake_case	)
      plt.show()






def    _snake_case  (  ):
      lowerCAmelCase    :		List[str]				=      datetime.now()
      lowerCAmelCase    :		Union[str, Any]				=      current_time.strftime('''%H:%M:%S'''	)
      return timestamp


