



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     )











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()






