code: 
  
  
  import warnings
  from contextlib import contextmanager
  
  from ...processing_utils import ProcessorMixin
  
  
  
  
  class 					_UpperCamelCase (      lowerCamelCase__	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  ["image_processor", "tokenizer"]
  							_A  				=  "AutoImageProcessor"
  							_A  				=  "AutoTokenizer"
  
  
  
  
  
  
  
  							def __init__(		self  :				List[Any]		,				SCREAMING_SNAKE_CASE_  :				List[Any]=None		,				SCREAMING_SNAKE_CASE_  :				str=None		,				**SCREAMING_SNAKE_CASE_  :				Union[str, Any]		):
  													_a     =			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_		,				)
  																			_a     =			kwargs.pop('feature_extractor'		)
  
  													_a     =			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_		)
  													_a     =			self.image_processor
  													_a     =			False
  
  
  
  
  
  
  
  							def __call__(		self  :				Any		,				*SCREAMING_SNAKE_CASE_  :				Union[str, Any]		,				**SCREAMING_SNAKE_CASE_  :				Union[str, Any]		):
  													# For backward compatibility
  													if self._in_target_context_manager:
  																			return self.current_processor(*SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		)
  
  													_a     =			kwargs.pop('images'		,				SCREAMING_SNAKE_CASE_		)
  													_a     =			kwargs.pop('text'		,				SCREAMING_SNAKE_CASE_		)
  													if len(SCREAMING_SNAKE_CASE_		) > 0:
  																			_a     =			args[0]
  																			_a     =			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:
  																			_a     =			self.image_processor(SCREAMING_SNAKE_CASE_		,				*SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		)
  													if text is not None:
  																			_a     =			self.tokenizer(SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		)
  
  													if text is None:
  																			return inputs
  													elif images is None:
  																			return encodings
  													else:
  																			_a     =			encodings['input_ids']
  																			return inputs
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				str		,				*SCREAMING_SNAKE_CASE_  :				List[str]		,				**SCREAMING_SNAKE_CASE_  :				Union[str, Any]		):
  													return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Optional[int]		,				*SCREAMING_SNAKE_CASE_  :				Optional[Any]		,				**SCREAMING_SNAKE_CASE_  :				Optional[Any]		):
  													return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							@contextmanager
  							def 		_UpperCAmelCase	(		self  :				Optional[int]		):
  													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.'		)
  													_a     =			True
  													_a     =			self.tokenizer
  													yield
  													_a     =			self.image_processor
  													_a     =			False
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				Any		):
  													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 		_UpperCAmelCase	(		self  :				Union[str, Any]		):
  													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: 
  
  
  import numpy as np
  import PIL
  import torch
  import torchvision.transforms as T
  import torchvision.transforms.functional as TF
  from PIL import Image
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=256				)    ->  Dict:
  						_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(_UpperCAmelCase							,     _UpperCAmelCase							,     interpolation=PIL.Image.LANCZOS				)
  						_a     =			TF.center_crop(_UpperCAmelCase							,     output_size=2 * [target_image_size]				)
  						_a     =			torch.unsqueeze(T.ToTensor()(_UpperCAmelCase				)							,     0				)
  						return img
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  List[Any]:
  						_a     =			2.0 * x - 1.0
  						return x
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=True							,     _UpperCAmelCase="RGB"				)    ->  Optional[Any]:
  						_a     =			x.detach().cpu()
  						if process:
  												_a     =			post_process_tensor(_UpperCAmelCase				)
  						_a     =			x.numpy()
  						if process:
  												_a     =			(255 * x).astype(np.uinta				)
  						_a     =			Image.fromarray(_UpperCAmelCase				)
  						if not x.mode == mode:
  												_a     =			x.convert(_UpperCAmelCase				)
  						return x
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  Optional[Any]:
  						_a     =			torch.clamp(_UpperCAmelCase							,     -1.0							,     1.0				)
  						_a     =			(x + 1.0) / 2.0
  						_a     =			x.permute(1							,     2							,     0				)
  						return x
  
  
  
  
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  str:
  						_a     =			post_process_tensor(x.squeeze()				)
  						return x.permute(2							,     0							,     1				).unsqueeze(0				)
  
  
  