




import math
import sys

import cva
import numpy as np



def 							_A       (						lowerCAmelCase_					: np.ndarray       ,    lowerCAmelCase_					: float      ):
     """simple docstring"""

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



def 							_A       (						lowerCAmelCase_					: np.ndarray       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

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



def 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: float      ):
     """simple docstring"""

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



def 							_A       (						lowerCAmelCase_					: np.ndarray       ,    lowerCAmelCase_					: float       ,    lowerCAmelCase_					: float       ,    lowerCAmelCase_					: int       ,    ):
     """simple docstring"""

     lowerCAmelCase__          =			np.zeros(img.shape      )
     lowerCAmelCase__          =			get_gauss_kernel(lowerCAmelCase_       ,    lowerCAmelCase_      )
     lowerCAmelCase__     ,		lowerCAmelCase__          =			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__          =			get_slice(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )
               lowerCAmelCase__          =			img_s - img_s[kernel_size // 2, kernel_size // 2]
               lowerCAmelCase__          =			vec_gaussian(lowerCAmelCase_       ,    lowerCAmelCase_      )
               lowerCAmelCase__          =			np.multiply(lowerCAmelCase_       ,    lowerCAmelCase_      )
               lowerCAmelCase__          =			np.multiply(lowerCAmelCase_       ,    lowerCAmelCase_      )
               lowerCAmelCase__          =			np.sum(lowerCAmelCase_      ) / np.sum(lowerCAmelCase_      )
               lowerCAmelCase__          =			val
     return imga



def 							_A       (						lowerCAmelCase_					: list      ):
     """simple docstring"""

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


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

     UpperCamelCase								=					img / 255
     UpperCamelCase								=					out.astype('float32')
     UpperCamelCase								=					bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
     UpperCamelCase								=					out * 255
     UpperCamelCase								=					np.uinta(out)
     cva.imshow('output image', out)
     cva.waitKey(0)
     cva.destroyAllWindows()






import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer



def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: str       ,    **lowerCAmelCase_					: str      ):
     """simple docstring"""

     lowerCAmelCase__          =			AutoConfig.from_pretrained(lowerCAmelCase_       ,    **lowerCAmelCase_      )
     lowerCAmelCase__          =			AutoModelForSeqaSeqLM.from_config(lowerCAmelCase_      )
     model.save_pretrained(lowerCAmelCase_      )
     AutoTokenizer.from_pretrained(lowerCAmelCase_      ).save_pretrained(lowerCAmelCase_      )
     return model


if __name__ == "__main__":
     fire.Fire(save_randomly_initialized_version)
