


"""simple docstring"""





import math
import sys

import cva
import numpy as np


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


def      _snake_case						(						_snake_case				:			np.ndarray ,   _snake_case				:			int ,   _snake_case				:			int ,   _snake_case				:			int			):
      lowerCAmelCase					:	int       =   kernel_size // 2
      return img[x - half : x + half + 1, y - half : y + half + 1]


def      _snake_case						(						_snake_case				:			int ,   _snake_case				:			float			):
      # Creates a gaussian kernel of given dimension.
      lowerCAmelCase					:	Optional[Any]       =   np.zeros((kernel_size, kernel_size)			)
      for i in range(0 ,   _snake_case			):
            for j in range(0 ,   _snake_case			):
                  lowerCAmelCase					:	Tuple       =   math.sqrt(
                      abs(i - kernel_size // 2			) ** 2 + abs(j - kernel_size // 2			) ** 2			)
      return vec_gaussian(_snake_case ,   _snake_case			)


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


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


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

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






"""simple docstring"""





from torch import nn



class 		snake_case_(       nn.Module			):






     def __init__(	self  :					int					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					int				):
           super().__init__()
           lowerCAmelCase					:	str       =   class_size
           lowerCAmelCase					:	Dict       =   embed_size
           # self.mlp1 = nn.Linear(embed_size, embed_size)
           # self.mlp2 = (nn.Linear(embed_size, class_size))
           lowerCAmelCase					:	Any       =   nn.Linear(UpperCamelCase_					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					List[Any]					,				UpperCamelCase_  :					Tuple				):
           # hidden_state = nn.functional.relu(self.mlp1(hidden_state))
           # hidden_state = self.mlp2(hidden_state)
           lowerCAmelCase					:	int       =   self.mlp(UpperCamelCase_				)
           return logits


