



import math
import sys

import cva
import numpy as np






def     a(      A       :    np.ndarray ,     A       :    float       )		->       np.ndarray:
					"""simple docstring"""





					a					     =   math.sqrt(A       )
					a					     =   1 / (sigma * math.sqrt(2 * math.pi       ))
					return cons * np.exp(-((img / sigma) ** 2) * 0.5       )






def     a(      A       :    np.ndarray ,     A       :    int ,     A       :    int ,     A       :    int       )		->       np.ndarray:
					"""simple docstring"""





					a					     =   kernel_size // 2
					return img[x - half : x + half + 1, y - half : y + half + 1]






def     a(      A       :    int ,     A       :    float       )		->       np.ndarray:
					"""simple docstring"""





					a					     =   np.zeros((kernel_size, kernel_size)       )
					for i in range(0 ,     A       ):
										for j in range(0 ,     A       ):
															a					     =   math.sqrt(
															    abs(i - kernel_size // 2       ) ** 2 + abs(j - kernel_size // 2       ) ** 2       )
					return vec_gaussian(A ,     A       )






def     a(      A       :    np.ndarray ,     A       :    float ,     A       :    float ,     A       :    int ,     )		->       np.ndarray:
					"""simple docstring"""





					a					     =   np.zeros(img.shape       )
					a					     =   get_gauss_kernel(A ,     A       )
					a     ,				a					     =   img.shape
					for i in range(kernel_size // 2 ,     size_x - kernel_size // 2       ):
										for j in range(kernel_size // 2 ,     size_y - kernel_size // 2       ):
															a					     =   get_slice(A ,     A ,     A ,     A       )
															a					     =   img_s - img_s[kernel_size // 2, kernel_size // 2]
															a					     =   vec_gaussian(A ,     A       )
															a					     =   np.multiply(A ,     A       )
															a					     =   np.multiply(A ,     A       )
															a					     =   np.sum(A       ) / np.sum(A       )
															a					     =   val
					return imga






def     a(      A       :    list       )		->       tuple:
					"""simple docstring"""





					a					     =   args[1] if args[1:] else "../image_data/lena.jpg"
					a					     =   float(args[2]       ) if args[2:] else 1.0
					a					     =   float(args[3]       ) if args[3:] else 1.0
					if args[4:]:
										a					     =   int(args[4]       )
										a					     =   kernel_size + abs(kernel_size % 2 - 1       )
					else:
										a					     =   5
					return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
				_lowercase			,				_lowercase			,				_lowercase			,				_lowercase: Tuple      			=		parse_args(sys.argv)
				_lowercase: Any      			=		cva.imread(filename, 0)
				cva.imshow("input image", img)

				_lowercase: Any      			=		img / 255
				_lowercase: List[str]      			=		out.astype("float32")
				_lowercase: Union[str, Any]      			=		bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
				_lowercase: Any      			=		out * 255
				_lowercase: Dict      			=		np.uinta(out)
				cva.imshow("output image", out)
				cva.waitKey(0)
				cva.destroyAllWindows()





def     a(      A       :    int       )		->       str:
					"""simple docstring"""





					if number > 0:
										raise ValueError("input must be a negative integer"       )
					a					     =   len(bin(A       )[3:]       )
					a					     =   bin(abs(A       ) - (1 << binary_number_length)       )[3:]
					a					     =   (
					    (
					        "1"
					        + "0" * (binary_number_length - len(A       ))
					        + twos_complement_number
					    )
					    if number < 0
					    else "0"
					)
					return "0b" + twos_complement_number


if __name__ == "__main__":
				import doctest

				doctest.testmod()
