

'''simple docstring'''





from typing import Dict, Optional, Tuple

import torch


def 				_UpperCAmelCase							(	a__):

			'''simple docstring'''



			a_	:	Any          =   boundaries[1] - boundaries[0]
			a_	:	str          =   boundaries + step / 2
			a_	:	List[str]          =   torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1)]     ,  dim=0)
			return bin_centers


def 				_UpperCAmelCase							(	a__     ,  a__     ,  ):

			'''simple docstring'''



			a_	:	int          =   _calculate_bin_centers(a__)
			return (
			    torch.sum(aligned_distance_error_probs * bin_centers     ,  dim=-1),
			    bin_centers[-1],
			)


def 				_UpperCAmelCase							(	a__     ,  a__ = 3_1     ,  a__ = 6_4     ,  **a__     ,  ):

			'''simple docstring'''



			a_	:	int          =   torch.linspace(0     ,  a__     ,  steps=(no_bins - 1)     ,  device=logits.device)

			a_	:	str          =   torch.nn.functional.softmax(a__     ,  dim=-1)
			a_	:	List[Any]          =   _calculate_expected_aligned_error(
			    alignment_confidence_breaks=a__     ,  aligned_distance_error_probs=a__     ,  )

			return {
			    "aligned_confidence_probs": aligned_confidence_probs,
			    "predicted_aligned_error": predicted_aligned_error,
			    "max_predicted_aligned_error": max_predicted_aligned_error,
			}



def 				_UpperCAmelCase							(	a__     ,  a__ = None     ,  a__ = 3_1     ,  a__ = 6_4     ,  a__ = 1e-8     ,  **a__     ,  ):

			'''simple docstring'''



			if residue_weights is None:
						a_	:	Dict          =   logits.new_ones(logits.shape[-2])

			a_	:	Tuple          =   torch.linspace(0     ,  a__     ,  steps=(no_bins - 1)     ,  device=logits.device)

			a_	:	Dict          =   _calculate_bin_centers(a__)
			torch.sum(a__)
			a_	:	List[str]          =   logits.shape[-2]
			a_	:	Optional[Any]          =   max(a__     ,  1_9)

			a_	:	Optional[int]          =   1.24 * (clipped_n - 1_5) ** (1.0 / 3) - 1.8

			a_	:	Optional[int]          =   torch.nn.functional.softmax(a__     ,  dim=-1)

			a_	:	Union[str, Any]          =   1.0 / (1 + (bin_centers**2) / (da**2))
			a_	:	List[str]          =   torch.sum(probs * tm_per_bin     ,  dim=-1)

			a_	:	str          =   residue_weights / (eps + residue_weights.sum())
			a_	:	Optional[int]          =   torch.sum(predicted_tm_term * normed_residue_mask     ,  dim=-1)

			a_	:	Dict          =   per_alignment * residue_weights

			a_	:	Union[str, Any]          =   (weighted == torch.max(a__)).nonzero()[0]
			return per_alignment[tuple(a__)]





from cva import destroyAllWindows, imread, imshow, waitKey


def 				_UpperCAmelCase							(	a__     ,  a__):

			'''simple docstring'''



			a_       ,				a_	:	Optional[int]          =   img.shape[0], img.shape[1]

			def to_grayscale(a__     ,  a__     ,  a__):
						return 0.2126 * red + 0.587 * green + 0.114 * blue

			def normalize(a__):
						return min(a__     ,  2_5_5)

			for i in range(a__):
						for j in range(a__):
									a_	:	Optional[Any]          =   int(to_grayscale(*img[i][j]))
									a_	:	Union[str, Any]          =   [
									    normalize(a__),
									    normalize(greyscale + factor),
									    normalize(greyscale + 2 * factor),
									]

			return img


if __name__ == "__main__":
	# read original image
	__snake_case							:			Dict      		= {
	    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()


