


'''simple docstring'''







import math
import sys

import cva
import numpy as np






def 				lowerCAmelCase__	(    lowerCamelCase					:      np.ndarray  ,lowerCamelCase					:      float       ):
       # For applying gaussian function for each element in matrix.
       _A					:     List[Any]     = math.sqrt(lowerCamelCase       )
       _A					:     Optional[Any]     = 1 / (sigma * math.sqrt(2 * math.pi       ))
       return cons * np.exp(-((img / sigma) ** 2) * 0.5       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      np.ndarray  ,lowerCamelCase					:      int  ,lowerCamelCase					:      int  ,lowerCamelCase					:      int       ):
       _A					:     str     = kernel_size // 2
       return img[x - half : x + half + 1, y - half : y + half + 1]






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      float       ):
       # Creates a gaussian kernel of given dimension.
       _A					:     Dict     = np.zeros((kernel_size, kernel_size)       )
       for i in range(0  ,lowerCamelCase       ):
              for j in range(0  ,lowerCamelCase       ):
                     _A					:     Dict     = math.sqrt(
                         abs(i - kernel_size // 2       ) ** 2 + abs(j - kernel_size // 2       ) ** 2       )
       return vec_gaussian(lowerCamelCase  ,lowerCamelCase       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      np.ndarray  ,lowerCamelCase					:      float  ,lowerCamelCase					:      float  ,lowerCamelCase					:      int  ,):
       _A					:     Tuple     = np.zeros(img.shape       )
       _A					:     str     = get_gauss_kernel(lowerCamelCase  ,lowerCamelCase       )
       _A  ,			_A					:     Optional[int]     = 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					:     Dict     = get_slice(lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase       )
                     _A					:     Dict     = img_s - img_s[kernel_size // 2, kernel_size // 2]
                     _A					:     str     = vec_gaussian(lowerCamelCase  ,lowerCamelCase       )
                     _A					:     Tuple     = np.multiply(lowerCamelCase  ,lowerCamelCase       )
                     _A					:     Any     = np.multiply(lowerCamelCase  ,lowerCamelCase       )
                     _A					:     List[str]     = np.sum(lowerCamelCase       ) / np.sum(lowerCamelCase       )
                     _A					:     str     = val
       return imga






def 				lowerCAmelCase__	(    lowerCamelCase					:      list       ):
       _A					:     int     = args[1] if args[1:] else '../image_data/lena.jpg'
       _A					:     List[str]     = float(args[2]       ) if args[2:] else 1.0
       _A					:     Dict     = float(args[3]       ) if args[3:] else 1.0
       if args[4:]:
              _A					:     List[str]     = int(args[4]       )
              _A					:     Union[str, Any]     = kernel_size + abs(kernel_size % 2 - 1       )
       else:
              _A					:     Optional[Any]     = 5
       return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
   A						, A						, A						, A				:					List[Any]    						=			parse_args(sys.argv)
   A				:					Optional[Any]    						=			cva.imread(filename, 0)
   cva.imshow('''input image''', img)

   A				:					Optional[Any]    						=			img / 255
   A				:					Optional[Any]    						=			out.astype('''float32''')
   A				:					Tuple    						=			bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
   A				:					List[Any]    						=			out * 255
   A				:					Tuple    						=			np.uinta(out)
   cva.imshow('''output image''', out)
   cva.waitKey(0)
   cva.destroyAllWindows()










'''simple docstring'''







def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]  ,lowerCamelCase					:      int       ):

       def count_of_possible_combinations(lowerCamelCase					:      int       ) -> int:
              if target < 0:
                     return 0
              if target == 0:
                     return 1
              return sum(count_of_possible_combinations(target - item       ) for item in array       )

       return count_of_possible_combinations(lowerCamelCase       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]  ,lowerCamelCase					:      int       ):

       def count_of_possible_combinations_with_dp_array(
           lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]       ) -> int:
              if target < 0:
                     return 0
              if target == 0:
                     return 1
              if dp_array[target] != -1:
                     return dp_array[target]
              _A					:     Optional[Any]     = sum(
                  count_of_possible_combinations_with_dp_array(target - item  ,lowerCamelCase       )
                  for item in array       )
              _A					:     List[str]     = answer
              return answer

       _A					:     Optional[int]     = [-1] * (target + 1)
       return count_of_possible_combinations_with_dp_array(lowerCamelCase  ,lowerCamelCase       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]  ,lowerCamelCase					:      int       ):
       _A					:     Dict     = [0] * (target + 1)
       _A					:     List[str]     = 1

       for i in range(1  ,target + 1       ):
              for j in range(lowerCamelCase       ):
                     if i - array[j] >= 0:
                            dp_array[i] += dp_array[i - array[j]]

       return dp_array[target]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   A				:					Dict    						=			3
   A				:					Union[str, Any]    						=			5
   A				:					Union[str, Any]    						=			[1, 2, 5]
   print(combination_sum_iv(n, array, target))






