code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import warnings
  
  from ...utils import logging
  from .image_processing_vit import ViTImageProcessor
  
  
  lowercase_								=    logging.get_logger(__name__)
  
  
  
  
  class 					_UpperCamelCase (      lowerCamelCase__	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  
  
  
  
  
  
  
  							def __init__(		self  :				Dict		,				*SCREAMING_SNAKE_CASE_  :				str		,				**SCREAMING_SNAKE_CASE_  :				List[Any]		):
  													warnings.warn(
  													    'The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
  													    ' use ViTImageProcessor instead.'		,				SCREAMING_SNAKE_CASE_		,				)
  													super().__init__(*SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		)
  
  
  
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				)
  
  
  