code: 
  
  
  
  
  import warnings
  from contextlib import contextmanager
  
  from ...processing_utils import ProcessorMixin
  class 							lowerCamelCase		( _UpperCAmelCase				):
     lowercase       :  Tuple      =				['image_processor', 'tokenizer']
     lowercase       :  Any      =				'AutoImageProcessor'
     lowercase       :  Optional[int]      =				'AutoTokenizer'
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      **SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Optional[Any]          =							None
         if "feature_extractor" in kwargs:
             warnings.warn(
                 """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                 """ instead."""    ,      SCREAMING_SNAKE_CASE_    ,      )
             UpperCamelCase				:   Optional[int]          =							kwargs.pop("""feature_extractor""" )
  
         UpperCamelCase				:   Union[str, Any]          =							image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
             raise ValueError("""You need to specify an `image_processor`.""" )
         if tokenizer is None:
             raise ValueError("""You need to specify a `tokenizer`.""" )
  
         super().__init__(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Tuple          =							self.image_processor
         UpperCamelCase				:   List[Any]          =							False
  
  
  
  
  
  
  
     def __call__(							self    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
         # For backward compatibility
         if self._in_target_context_manager:
             return self.current_processor(*SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   List[str]          =							kwargs.pop("""images"""    ,      SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   List[str]          =							kwargs.pop("""text"""    ,      SCREAMING_SNAKE_CASE_ )
         if len(SCREAMING_SNAKE_CASE_ ) > 0:
             UpperCamelCase				:   Dict          =							args[0]
             UpperCamelCase				:   Tuple          =							args[1:]
  
         if images is None and text is None:
             raise ValueError("""You need to specify either an `images` or `text` input to process.""" )
  
         if images is not None:
             UpperCamelCase				:   List[str]          =							self.image_processor(SCREAMING_SNAKE_CASE_    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
         if text is not None:
             UpperCamelCase				:   Dict          =							self.tokenizer(SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
  
         if text is None:
             return inputs
         elif images is None:
             return encodings
         else:
             UpperCamelCase				:   Tuple          =							encodings["""input_ids"""]
             return inputs
  
  
  
  
  
  
  
     def       a_							(							self    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
         return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
         return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     @contextmanager
     def       a_							(							self ):
         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 images inputs, or in a separate call.""" )
         UpperCamelCase				:   int          =							True
         UpperCamelCase				:   Union[str, Any]          =							self.tokenizer
         yield
         UpperCamelCase				:   Dict          =							self.image_processor
         UpperCamelCase				:   Any          =							False
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         warnings.warn(
             """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""    ,      SCREAMING_SNAKE_CASE_    ,      )
         return self.image_processor_class
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         warnings.warn(
             """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""    ,      SCREAMING_SNAKE_CASE_    ,      )
         return self.image_processor
  
  
  
  
  
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 					A_			(		snake_case_     :       List[str]						,snake_case_     :       Union[str, Any]=2_5_6    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Optional[int]          =							min(img.size    )
  
      if s < target_image_size:
          raise ValueError(f'min dim for image {s} < {target_image_size}'    )
  
      UpperCamelCase				:   Dict          =							target_image_size / s
      UpperCamelCase				:   Any          =							(round(r * img.size[1]    ), round(r * img.size[0]    ))
      UpperCamelCase				:   Union[str, Any]          =							TF.resize(snake_case_						,snake_case_						,interpolation=PIL.Image.LANCZOS    )
      UpperCamelCase				:   Optional[int]          =							TF.center_crop(snake_case_						,output_size=2 * [target_image_size]    )
      UpperCamelCase				:   Optional[Any]          =							torch.unsqueeze(T.ToTensor()(snake_case_    )						,0    )
      return img
  def 					A_			(		snake_case_     :       List[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Any          =							2.0 * x - 1.0
      return x
  def 					A_			(		snake_case_     :       Tuple						,snake_case_     :       Optional[Any]=True						,snake_case_     :       Dict="RGB"    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Dict          =							x.detach().cpu()
      if process:
          UpperCamelCase				:   str          =							post_process_tensor(snake_case_    )
      UpperCamelCase				:   List[Any]          =							x.numpy()
      if process:
          UpperCamelCase				:   Optional[int]          =							(2_5_5 * x).astype(np.uinta    )
      UpperCamelCase				:   List[Any]          =							Image.fromarray(snake_case_    )
      if not x.mode == mode:
          UpperCamelCase				:   Any          =							x.convert(snake_case_    )
      return x
  def 					A_			(		snake_case_     :       int    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   int          =							torch.clamp(snake_case_						,-1.0						,1.0    )
      UpperCamelCase				:   Optional[Any]          =							(x + 1.0) / 2.0
      UpperCamelCase				:   Dict          =							x.permute(1						,2						,0    )
      return x
  
  
  def 					A_			(		snake_case_     :       int    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Optional[Any]          =							post_process_tensor(x.squeeze()    )
      return x.permute(2						,0						,1    ).unsqueeze(0    )
  
  
  
  
  