code: 
  
  
  
  
  
  
  from dataclasses import dataclass
  from typing import Callable, Optional
  
  import datasets
  
  
  
  @dataclass
  class 							_A (    datasets.BuilderConfig							):
      """simple docstring"""
  
      lowerCamelCase  :       Optional[Callable]   =   None
      lowerCamelCase  :       Optional[dict]   =   None
      lowerCamelCase  :       Optional[datasets.Features]   =   None
  
  
  
      def _a       (							self			:    Dict			)			->       List[Any]:
             assert self.generator is not None, "generator must be specified"
  
             if self.gen_kwargs is None:
                    __UpperCAmelCase    							={}
  
  
  
  
  class 							_A (    datasets.GeneratorBasedBuilder							):
      """simple docstring"""
  
      lowerCamelCase  :       List[Any]   =   GeneratorConfig
  
  
  
      def _a       (							self			:    Optional[int]			)			->       Optional[Any]:
             return datasets.DatasetInfo(features=self.config.features			)
  
  
  
      def _a       (							self			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]			)			->       List[str]:
             return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,    gen_kwargs=self.config.gen_kwargs			)]
  
  
  
      def _a       (							self			:    List[str]   ,    **__SCREAMING_SNAKE_CASE			:    List[str]			)			->       Union[str, Any]:
             for idx, ex in enumerate(self.config.generator(**__SCREAMING_SNAKE_CASE			)			):
                    yield idx, ex
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
  from numpy import divide, inta, multiply, ravel, sort, zeros_like
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	int     ,			A_:	Any=3		)  ->							Dict:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=int(mask / 2		)
         # copy image size
         __UpperCAmelCase    							=zeros_like(A_		)
         for i in range(A_     ,			gray_img.shape[0] - bd		):
                for j in range(A_     ,			gray_img.shape[1] - bd		):
                       # get mask according with mask
                       __UpperCAmelCase    							=ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]		)
                       # calculate mask median
                       __UpperCAmelCase    							=sort(A_		)[inta(divide((multiply(A_     ,			A_		))     ,			2		) + 1		)]
                       __UpperCAmelCase    							=median
         return median_img
  
  
  if __name__ == "__main__":
     # read original image
     __A  							=  imread("../image_data/lena.jpg")
     # turn image in gray scale value
     __A  							=  cvtColor(img, COLOR_BGR2GRAY)
  
     # get values with two different mask size
     __A  							=  median_filter(gray, 3)
     __A  							=  median_filter(gray, 5)
  
     # show result images
     imshow("median filter with 3x3 mask", medianaxa)
     imshow("median filter with 5x5 mask", medianaxa)
     waitKey(0)
  
  
  
  
  