






import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase=256     )     ->		Dict:







							"""simple docstring"""





							_a  :    Optional[int]			=						min(img.size     )

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

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






def    UpperCamelCase__  (   UpperCAmelCase     )     ->		Union[str, Any]:







							"""simple docstring"""





							_a  :    Any			=						2.0 * x - 1.0
							return x






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase=True    ,	UpperCAmelCase="RGB"     )     ->		List[str]:







							"""simple docstring"""





							_a  :    Dict			=						x.detach().cpu()
							if process:
														_a  :    Dict			=						post_process_tensor(UpperCAmelCase     )
							_a  :    str			=						x.numpy()
							if process:
														_a  :    int			=						(255 * x).astype(np.uinta     )
							_a  :    Optional[int]			=						Image.fromarray(UpperCAmelCase     )
							if not x.mode == mode:
														_a  :    Any			=						x.convert(UpperCAmelCase     )
							return x






def    UpperCamelCase__  (   UpperCAmelCase     )     ->		Dict:







							"""simple docstring"""





							_a  :    Tuple			=						torch.clamp(UpperCAmelCase    ,	-1.0    ,	1.0     )
							_a  :    str			=						(x + 1.0) / 2.0
							_a  :    Optional[int]			=						x.permute(1    ,	2    ,	0     )
							return x







def    UpperCamelCase__  (   UpperCAmelCase     )     ->		Union[str, Any]:







							"""simple docstring"""





							_a  :    Tuple			=						post_process_tensor(x.squeeze()     )
							return x.permute(2    ,	0    ,	1     ).unsqueeze(0     )







def    UpperCamelCase__  (   UpperCAmelCase     )     ->		tuple[int, int]:







							"""simple docstring"""





							if len(UpperCAmelCase     ) == 1:
														return a[0], a[0]
							else:
														# split A into half.
														_a  :    Union[str, Any]			=						a[: len(UpperCAmelCase     ) // 2]
														_a  :    Optional[Any]			=						a[len(UpperCAmelCase     ) // 2 :]

														# 2 sub problems, 1/2 of original size.
														_a		,					_a  :    Union[str, Any]			=						max_difference(UpperCAmelCase     )
														_a		,					_a  :    List[Any]			=						max_difference(UpperCAmelCase     )

														# get min of first and max of second
														# linear time
														_a  :    Union[str, Any]			=						min(UpperCAmelCase     )
														_a  :    Any			=						max(UpperCAmelCase     )

														# 3 cases, either (small1, big1),
														# (min_first, max_second), (small2, big2)
														# constant comparisons
														if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
																					return smalla, biga
														elif biga - smalla > max_second - min_first:
																					return smalla, biga
														else:
																					return min_first, max_second


if __name__ == "__main__":
						import doctest

						doctest.testmod()