code: import numpy as np
  import PIL
  import torch
  import torchvision.transforms as T
  import torchvision.transforms.functional as TF
  from PIL import Image
  
  
  def 					__lowerCAmelCase			(  _A ,_A=256					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	min(img.size					)
  
  							if s < target_image_size:
  														raise ValueError(f'''min dim for image {s} < {target_image_size}'''					)
  
  							_lowercase				=	target_image_size / s
  							_lowercase				=	(round(r * img.size[1]					), round(r * img.size[0]					))
  							_lowercase				=	TF.resize(snake_case_ ,snake_case_ ,interpolation=PIL.Image.LANCZOS					)
  							_lowercase				=	TF.center_crop(snake_case_ ,output_size=2 * [target_image_size]					)
  							_lowercase				=	torch.unsqueeze(T.ToTensor()(snake_case_					) ,0					)
  							return img
  
  
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	2.0 * x - 1.0
  							return x
  
  
  def 					__lowerCAmelCase			(  _A ,_A=True ,_A="RGB"					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	x.detach().cpu()
  							if process:
  														_lowercase				=	post_process_tensor(snake_case_					)
  							_lowercase				=	x.numpy()
  							if process:
  														_lowercase				=	(255 * x).astype(np.uinta					)
  							_lowercase				=	Image.fromarray(snake_case_					)
  							if not x.mode == mode:
  														_lowercase				=	x.convert(snake_case_					)
  							return x
  
  
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	torch.clamp(snake_case_ ,-1.0 ,1.0					)
  							_lowercase				=	(x + 1.0) / 2.0
  							_lowercase				=	x.permute(1 ,2 ,0					)
  							return x
  
  
  
  
  
  
  
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	post_process_tensor(x.squeeze()					)
  							return x.permute(2 ,0 ,1					).unsqueeze(0					)
  
  
  
style_context: import warnings
  
  from ...utils import logging
  from .image_processing_mobilenet_va import MobileNetVaImageProcessor
  
  
  A_:      Tuple        =			logging.get_logger(__name__)
  
  
  class _lowercase					(       _UpperCAmelCase						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							def __init__(    self     ,   *UpperCAmelCase     ,   **UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														warnings.warn(
  														    """The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
  														    """ Please use MobileNetV2ImageProcessor instead."""     ,   UpperCAmelCase     ,   )
  														super().__init__(*UpperCAmelCase     ,   **UpperCAmelCase  )
  
  
  