code: 
  
  
  
  
  
  '''simple docstring'''
  
  import warnings
  from contextlib import contextmanager
  
  from ...processing_utils import ProcessorMixin
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   __lowercase     	=       """AutoFeatureExtractor"""
   __lowercase     	=       """Speech2Text2Tokenizer"""
   def __init__(       self   :Tuple		, lowercase_   :str		, lowercase_   :Optional[Any]							)->							Any:
       super().__init__(lowercase_		, lowercase_							)
       A__       =    self.feature_extractor
       A__       =    False
   def __call__(       self   :Optional[int]		, *lowercase_   :List[str]		, **lowercase_   :List[str]							)->							Dict:
       # For backward compatibility
       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."							)
           A__       =    kwargs.pop("raw_speech"							)
       else:
           A__       =    kwargs.pop("audio"		, lowercase_							)
       A__       =    kwargs.pop("sampling_rate"		, lowercase_							)
       A__       =    kwargs.pop("text"		, lowercase_							)
       if len(lowercase_							) > 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(lowercase_		, *lowercase_		, sampling_rate=lowercase_		, **lowercase_							)
       if text is not None:
           A__       =    self.tokenizer(lowercase_		, **lowercase_							)
  
       if text is None:
           return inputs
       elif audio is None:
           return encodings
       else:
           A__       =    encodings["input_ids"]
           return inputs
   def   UpperCAmelCase_							(       self   :List[str]		, *lowercase_   :List[Any]		, **lowercase_   :List[str]							)->							Any:
       return self.tokenizer.batch_decode(*lowercase_		, **lowercase_							)
   def   UpperCAmelCase_							(       self   :Union[str, Any]		, *lowercase_   :List[str]		, **lowercase_   :List[str]							)->							Any:
       return self.tokenizer.decode(*lowercase_		, **lowercase_							)
  
  
  
   @contextmanager
   def   UpperCAmelCase_							(       self   :Tuple							)->							List[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
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import numpy as np
  import PIL
  import torch
  import torchvision.transforms as T
  import torchvision.transforms.functional as TF
  from PIL import Image
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      Union[str, Any]=2_56 ):
      A__       =    min(img.size )
  
      if s < target_image_size:
          raise ValueError(F"min dim for image {s} < {target_image_size}" )
  
      A__       =    target_image_size / s
      A__       =    (round(r * img.size[1] ), round(r * img.size[0] ))
      A__       =    TF.resize(_lowerCamelCase		, _lowerCamelCase		, interpolation=PIL.Image.LANCZOS )
      A__       =    TF.center_crop(_lowerCamelCase		, output_size=2 * [target_image_size] )
      A__       =    torch.unsqueeze(T.ToTensor()(_lowerCamelCase )		, 0 )
      return img
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      int ):
      A__       =    2.0 * x - 1.0
      return x
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      Tuple=True		, _lowerCamelCase      :      int="RGB" ):
      A__       =    x.detach().cpu()
      if process:
          A__       =    post_process_tensor(_lowerCamelCase )
      A__       =    x.numpy()
      if process:
          A__       =    (2_55 * x).astype(np.uinta )
      A__       =    Image.fromarray(_lowerCamelCase )
      if not x.mode == mode:
          A__       =    x.convert(_lowerCamelCase )
      return x
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Any ):
      A__       =    torch.clamp(_lowerCamelCase		, -1.0		, 1.0 )
      A__       =    (x + 1.0) / 2.0
      A__       =    x.permute(1		, 2		, 0 )
      return x
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      List[Any] ):
      A__       =    post_process_tensor(x.squeeze() )
      return x.permute(2		, 0		, 1 ).unsqueeze(0 )
  
  
  
  