






"""simple docstring"""

import torch
from torch import autograd

class     A_				(   autograd.Function	):







				@staticmethod
				def      _snake_case       (				__lowerCamelCase				:    List[str]    ,     __lowerCamelCase				:    torch.tensor    ,     __lowerCamelCase				:    float    ,     __lowerCamelCase				:    bool						)					->       int:
										__magic_name__      =  inputs.numel()
										__magic_name__      =  int(0.005 * nb_elems						) + 1
										if sigmoid:
																__magic_name__      =  (torch.sigmoid(__lowerCamelCase						) > threshold).type(inputs.type()						)
										else:
																__magic_name__      =  (inputs > threshold).type(inputs.type()						)
										if mask.sum() < nb_min:
																# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
																__magic_name__      =  inputs.flatten().kthvalue(max(nb_elems - nb_min    ,     1						)						).values
																__magic_name__      =  (inputs > k_threshold).type(inputs.type()						)
										return mask







				@staticmethod
				def      _snake_case       (				__lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    int						)					->       Tuple:
										return gradOutput, None, None





class     A_				(   autograd.Function	):







				@staticmethod
				def      _snake_case       (				__lowerCamelCase				:    Any    ,     __lowerCamelCase				:    torch.tensor    ,     __lowerCamelCase				:    float						)					->       Union[str, Any]:
										__magic_name__      =  inputs.clone()
										__magic_name__   ,     __magic_name__      =  inputs.flatten().sort(descending=__lowerCamelCase						)
										__magic_name__      =  int(threshold * inputs.numel()						)

										# flat_out and mask access the same memory.
										__magic_name__      =  mask.flatten()
										__magic_name__      =  0
										__magic_name__      =  1
										return mask







				@staticmethod
				def      _snake_case       (				__lowerCamelCase				:    Dict    ,     __lowerCamelCase				:    Union[str, Any]						)					->       Any:
										return gradOutput, None





class     A_				(   snake_case_	):







				@staticmethod
				def      _snake_case       (				__lowerCamelCase				:    torch.tensor    ,     __lowerCamelCase				:    float						)					->       int:
										__magic_name__      =  inputs.clone()
										__magic_name__   ,     __magic_name__      =  inputs.abs().flatten().sort(descending=__lowerCamelCase						)
										__magic_name__      =  int(threshold * inputs.numel()						)

										# flat_out and mask access the same memory.
										__magic_name__      =  mask.flatten()
										__magic_name__      =  0
										__magic_name__      =  1
										return mask











"""simple docstring"""

from cva import destroyAllWindows, imread, imshow, waitKey






def _lowerCAmelCase						(	__lowerCamelCase:Tuple			,    __lowerCamelCase:int ):

						'''simple docstring'''






						__magic_name__   ,     __magic_name__      =  img.shape[0], img.shape[1]

						def to_grayscale(__lowerCamelCase:List[str]			,    __lowerCamelCase:Optional[Any]			,    __lowerCamelCase:int ):
												return 0.2_1_2_6 * red + 0.5_8_7 * green + 0.1_1_4 * blue

						def normalize(__lowerCamelCase:Union[str, Any] ):
												return min(__lowerCamelCase			,    2_5_5 )

						for i in range(__lowerCamelCase ):
												for j in range(__lowerCamelCase ):
																		__magic_name__      =  int(to_grayscale(*img[i][j] ) )
																		__magic_name__      =  [
																		    normalize(__lowerCamelCase ),
																		    normalize(greyscale + factor ),
																		    normalize(greyscale + 2 * factor ),
																		]

						return img


if __name__ == "__main__":
			# read original image
			lowercase           =							{
			    percentage: imread('''image_data/lena.jpg''', 1) for percentage in (10, 20, 30, 40)
			}

			for percentage, img in images.items():
						make_sepia(img, percentage)

			for percentage, img in images.items():
						imshow(f'''Original image with sepia (factor: {percentage})''', img)

			waitKey(0)
			destroyAllWindows()



