



def        a						(      _UpperCAmelCase							:					int						):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	abs(_UpperCAmelCase						)
		__UpperCAmelCase    :     Tuple													=	0
		while n > 0:
				res += n % 10
				n //= 10
		return res

def        a						(      _UpperCAmelCase							:					int						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[Any]													=	abs(_UpperCAmelCase						)
		return n if n < 10 else n % 10 + sum_of_digits(n // 10						)

def        a						(      _UpperCAmelCase							:					int						):







		'''simple docstring'''





		return sum(int(_UpperCAmelCase						) for c in str(abs(_UpperCAmelCase						)						)						)

def        a						(      ):







		'''simple docstring'''





		from collections.abc import Callable
		from timeit import timeit

		def benchmark_a_function(_UpperCAmelCase							:					Callable ,   _UpperCAmelCase							:					int						) -> None:
				__UpperCAmelCase    :     Tuple													=	f'{func.__name__}({value})'
				__UpperCAmelCase    :     Any													=	timeit(f'__main__.{call}' ,   setup='''import __main__'''						)
				print(f'{call:56} = {func(_UpperCAmelCase						)} -- {timing:.4f} seconds'						)

		for value in (26_21_44, 11_25_89_99_06_84_26_24, 1_26_76_50_60_02_28_22_94_01_49_67_03_20_53_76):
				for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
						benchmark_a_function(_UpperCAmelCase ,   _UpperCAmelCase						)
				print()


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				benchmark()










from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros

def        a						(      _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Optional[int]						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	k_size // 2
		__UpperCAmelCase							,					__UpperCAmelCase    :     Optional[Any]													=	mgrid[0 - center : k_size - center, 0 - center : k_size - center]
		__UpperCAmelCase    :     Any													=	1 / (2 * pi * sigma) * exp(-(square(_UpperCAmelCase						) + square(_UpperCAmelCase						)) / (2 * square(_UpperCAmelCase						))						)
		return g

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	image.shape[0], image.shape[1]
		# dst image height and width
		__UpperCAmelCase    :     str													=	height - k_size + 1
		__UpperCAmelCase    :     Optional[int]													=	width - k_size + 1

		# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
		__UpperCAmelCase    :     str													=	zeros((dst_height * dst_width, k_size * k_size)						)
		__UpperCAmelCase    :     Optional[Any]													=	0
		for i, j in product(range(_UpperCAmelCase						) ,   range(_UpperCAmelCase						)						):
				__UpperCAmelCase    :     int													=	ravel(image[i : i + k_size, j : j + k_size]						)
				__UpperCAmelCase    :     Union[str, Any]													=	window
				row += 1

		#  turn the kernel into shape(k*k, 1)
		__UpperCAmelCase    :     Tuple													=	gen_gaussian_kernel(_UpperCAmelCase ,   _UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	ravel(_UpperCAmelCase						)

		# reshape and get the dst image
		__UpperCAmelCase    :     Optional[Any]													=	dot(_UpperCAmelCase ,   _UpperCAmelCase						).reshape(_UpperCAmelCase ,   _UpperCAmelCase						).astype(_UpperCAmelCase						)

		return dst


if __name__ == "__main__":
				# read original image
				__A   			=imread(R"../image_data/lena.jpg")
				# turn image in gray scale value
				__A   			=cvtColor(img, COLOR_BGR2GRAY)

				# get values with two different mask size
				__A   			=gaussian_filter(gray, 3, sigma=1)
				__A   			=gaussian_filter(gray, 5, sigma=0.8)

				# show result images
				imshow("gaussian filter with 3x3 mask", gaussianaxa)
				imshow("gaussian filter with 5x5 mask", gaussianaxa)
				waitKey()





