





'''simple docstring'''







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_   (	SCREAMING_SNAKE_CASE__   :      Optional[Any],      SCREAMING_SNAKE_CASE__   :      Optional[int]=256 )			->						str:
						UpperCAmelCase_      :   str      =	min(img.size )

						if s < target_image_size:
												raise ValueError(F"""min dim for image {s} < {target_image_size}""" )

						UpperCAmelCase_      :   Any      =	target_image_size / s
						UpperCAmelCase_      :   Union[str, Any]      =	(round(r * img.size[1] ), round(r * img.size[0] ))
						UpperCAmelCase_      :   Any      =	TF.resize(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      interpolation=PIL.Image.LANCZOS )
						UpperCAmelCase_      :   Optional[Any]      =	TF.center_crop(SCREAMING_SNAKE_CASE__,      output_size=2 * [target_image_size] )
						UpperCAmelCase_      :   int      =	torch.unsqueeze(T.ToTensor()(SCREAMING_SNAKE_CASE__ ),      0 )
						return img





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      int )			->						Dict:
						UpperCAmelCase_      :   str      =	2.0 * x - 1.0
						return x





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Tuple,      SCREAMING_SNAKE_CASE__   :      Optional[int]=True,      SCREAMING_SNAKE_CASE__   :      int="RGB" )			->						Optional[Any]:
						UpperCAmelCase_      :   Optional[Any]      =	x.detach().cpu()
						if process:
												UpperCAmelCase_      :   Tuple      =	post_process_tensor(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   str      =	x.numpy()
						if process:
												UpperCAmelCase_      :   int      =	(255 * x).astype(np.uinta )
						UpperCAmelCase_      :   Optional[int]      =	Image.fromarray(SCREAMING_SNAKE_CASE__ )
						if not x.mode == mode:
												UpperCAmelCase_      :   Any      =	x.convert(SCREAMING_SNAKE_CASE__ )
						return x





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      int )			->						Optional[Any]:
						UpperCAmelCase_      :   List[str]      =	torch.clamp(SCREAMING_SNAKE_CASE__,      -1.0,      1.0 )
						UpperCAmelCase_      :   List[Any]      =	(x + 1.0) / 2.0
						UpperCAmelCase_      :   List[Any]      =	x.permute(1,      2,      0 )
						return x





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Union[str, Any] )			->						Optional[Any]:
						UpperCAmelCase_      :   str      =	post_process_tensor(x.squeeze() )
						return x.permute(2,      0,      1 ).unsqueeze(0 )










from typing import Callable





def     __lowerCamelCase							(   __magic_name__						:	Callable    ):

							def wrapper(__magic_name__						:	Optional[Any]    ):
														a__:							List[Any]									=function_with_docstring.__doc__
														return target_function

							return wrapper





