





'''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  lowercase__(		_UpperCamelCase  : Tuple ,   _UpperCamelCase  : List[str]=256				)->   Dict:






					"""simple docstring"""

					_UpperCamelCase											=   min(img.size				)

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

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



def  lowercase__(		_UpperCamelCase  : Any				)->   Dict:






					"""simple docstring"""

					_UpperCamelCase											=   2.0 * x - 1.0
					return x



def  lowercase__(		_UpperCamelCase  : Union[str, Any] ,   _UpperCamelCase  : Union[str, Any]=True ,   _UpperCamelCase  : str="RGB"				)->   Optional[Any]:






					"""simple docstring"""

					_UpperCamelCase											=   x.detach().cpu()
					if process:
										_UpperCamelCase											=   post_process_tensor(_UpperCamelCase				)
					_UpperCamelCase											=   x.numpy()
					if process:
										_UpperCamelCase											=   (255 * x).astype(np.uinta				)
					_UpperCamelCase											=   Image.fromarray(_UpperCamelCase				)
					if not x.mode == mode:
										_UpperCamelCase											=   x.convert(_UpperCamelCase				)
					return x



def  lowercase__(		_UpperCamelCase  : Union[str, Any]				)->   Optional[int]:






					"""simple docstring"""

					_UpperCamelCase											=   torch.clamp(_UpperCamelCase ,   -1.0 ,   1.0				)
					_UpperCamelCase											=   (x + 1.0) / 2.0
					_UpperCamelCase											=   x.permute(1 ,   2 ,   0				)
					return x



def  lowercase__(		_UpperCamelCase  : Union[str, Any]				)->   Union[str, Any]:






					"""simple docstring"""

					_UpperCamelCase											=   post_process_tensor(x.squeeze()				)
					return x.permute(2 ,   0 ,   1				).unsqueeze(0				)










'''simple docstring'''



from __future__ import annotations



def  lowercase__(		_UpperCamelCase  : list[list[int]] ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int | float				)->   None:






					"""simple docstring"""

					_UpperCamelCase				,	_UpperCamelCase											=   m - 1, 0
					while i >= 0 and j < n:
										if key == mat[i][j]:
															print(f"Key {key} found at row- {i + 1} column- {j + 1}"				)
															return
										if key < mat[i][j]:
															i -= 1
										else:
															j += 1
					print(f"Key {key} not found"				)



def  lowercase__(		)->   None:






					"""simple docstring"""

					_UpperCamelCase											=   [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
					_UpperCamelCase											=   int(input("Enter the element to be searched:"				)				)
					print(_UpperCamelCase				)
					search_in_a_sorted_matrix(_UpperCamelCase ,   len(_UpperCamelCase				) ,   len(mat[0]				) ,   _UpperCamelCase				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



