"""simple docstring"""





from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like





def     a_					(				_lowerCAmelCase  :			Optional[int]       ,			_lowerCAmelCase  :			List[str]=3					):





       '''simple docstring'''

       lowercase__      :		Dict          =     int(mask / 2					)
       # copy image size
       lowercase__      :		Dict          =     zeros_like(_lowerCAmelCase					)
       for i in range(_lowerCAmelCase       ,			gray_img.shape[0] - bd					):
              for j in range(_lowerCAmelCase       ,			gray_img.shape[1] - bd					):
                     # get mask according with mask
                     lowercase__      :		List[Any]          =     ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]					)
                     # calculate mask median
                     lowercase__      :		int          =     sort(_lowerCAmelCase					)[inta(divide((multiply(_lowerCAmelCase       ,			_lowerCAmelCase					))       ,			2					) + 1					)]
                     lowercase__      :		Dict          =     median
       return median_img


if __name__ == "__main__":
       # read original image
       _UpperCamelCase       :						int     =   imread("../image_data/lena.jpg")
       # turn image in gray scale value
       _UpperCamelCase       :						List[str]     =   cvtColor(img, COLOR_BGR2GRAY)

       # get values with two different mask size
       _UpperCamelCase       :						Dict     =   median_filter(gray, 3)
       _UpperCamelCase       :						List[Any]     =   median_filter(gray, 5)

       # show result images
       imshow("median filter with 3x3 mask", medianaxa)
       imshow("median filter with 5x5 mask", medianaxa)
       waitKey(0)


"""simple docstring"""





import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig


class 					UpperCAmelCase_     (   _a):
      lowerCamelCase__		:   List[str]			  =  CustomConfig


      def __init__(       self		,      a							)	-> Optional[int]:
             super().__init__(a							)
             lowercase__      :		Any          =     torch.nn.Linear(config.hidden_size		,      config.hidden_size							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Union[str, Any]:
             return self.linear(a							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Tuple:
             pass





class 					UpperCAmelCase_     (   _a):
      lowerCamelCase__		:   Optional[int]			  =  NoSuperInitConfig


      def __init__(       self		,      a							)	-> int:
             super().__init__(a							)
             lowercase__      :		List[Any]          =     torch.nn.Linear(config.attribute		,      config.attribute							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Optional[int]:
             return self.linear(a							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Optional[Any]:
             pass

