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 			__lowercase     (				a__							,     a__=2_56     )       ->				int:
  							__SCREAMING_SNAKE_CASE								=      min(img.size     )
  
  							if s < target_image_size:
  														raise ValueError(f"""min dim for image {s} < {target_image_size}"""     )
  
  							__SCREAMING_SNAKE_CASE								=      target_image_size / s
  							__SCREAMING_SNAKE_CASE								=      (round(r * img.size[1]     ), round(r * img.size[0]     ))
  							__SCREAMING_SNAKE_CASE								=      TF.resize(a__							,     a__							,     interpolation=PIL.Image.LANCZOS     )
  							__SCREAMING_SNAKE_CASE								=      TF.center_crop(a__							,     output_size=2 * [target_image_size]     )
  							__SCREAMING_SNAKE_CASE								=      torch.unsqueeze(T.ToTensor()(a__     )							,     0     )
  							return img
  
  
  
  
  
  
  def 			__lowercase     (				a__     )       ->				Any:
  							__SCREAMING_SNAKE_CASE								=      2.0 * x - 1.0
  							return x
  
  
  
  
  
  
  def 			__lowercase     (				a__							,     a__=True							,     a__="RGB"     )       ->				str:
  							__SCREAMING_SNAKE_CASE								=      x.detach().cpu()
  							if process:
  														__SCREAMING_SNAKE_CASE								=      post_process_tensor(a__     )
  							__SCREAMING_SNAKE_CASE								=      x.numpy()
  							if process:
  														__SCREAMING_SNAKE_CASE								=      (2_55 * x).astype(np.uinta     )
  							__SCREAMING_SNAKE_CASE								=      Image.fromarray(a__     )
  							if not x.mode == mode:
  														__SCREAMING_SNAKE_CASE								=      x.convert(a__     )
  							return x
  
  
  
  
  
  
  def 			__lowercase     (				a__     )       ->				Any:
  							__SCREAMING_SNAKE_CASE								=      torch.clamp(a__							,     -1.0							,     1.0     )
  							__SCREAMING_SNAKE_CASE								=      (x + 1.0) / 2.0
  							__SCREAMING_SNAKE_CASE								=      x.permute(1							,     2							,     0     )
  							return x
  
  
  
  
  
  
  def 			__lowercase     (				a__     )       ->				Dict:
  							__SCREAMING_SNAKE_CASE								=      post_process_tensor(x.squeeze()     )
  							return x.permute(2							,     0							,     1     ).unsqueeze(0     )
  
  
  
  
  
  
  
style_context: 
  
  
  
  from __future__ import annotations
  
  class      UpperCAmelCase_						:
  
  						'''simple docstring'''
  
  
  
  
  						def __init__(					self   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      value
  													__SCREAMING_SNAKE_CASE								=      None
  													__SCREAMING_SNAKE_CASE								=      None
  
  
  
  
  
  
  
  class      UpperCAmelCase_						:
  
  						'''simple docstring'''
  
  
  
  
  						UpperCamelCase__    :				int
  
  
  
  
  
  
  						def __init__(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      0
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													if node is None:
  																				return
  
  													if path_sum == self.target:
  																				self.paths += 1
  
  													if node.left:
  																				self.depth_first_search(node.left   ,							path_sum + node.left.value	)
  													if node.right:
  																				self.depth_first_search(node.right   ,							path_sum + node.right.value	)
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A = None	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													if node is None:
  																				return 0
  													if target is not None:
  																				__SCREAMING_SNAKE_CASE								=      target
  
  													self.depth_first_search(_A   ,							node.value	)
  													self.path_sum(node.left	)
  													self.path_sum(node.right	)
  
  													return self.paths
  
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  
  
  
  
  
  
  