

"""simple docstring"""



import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput



@dataclass
class        a    (							_lowerCamelCase					):



    """simple docstring"""



    UpperCAmelCase    			=       42



class        a    (							unittest.TestCase					):



    """simple docstring"""



    def 					UpperCamelCase						(  self:			Dict							):






         """simple docstring"""


         A__				      =	CustomOutput(images=np.random.rand(1     ,    3     ,    4     ,    4							)							)

         # check every way of getting the attribute
         assert isinstance(outputs.images     ,    np.ndarray							)
         assert outputs.images.shape == (1, 3, 4, 4)
         assert isinstance(outputs["""images"""]     ,    np.ndarray							)
         assert outputs["images"].shape == (1, 3, 4, 4)
         assert isinstance(outputs[0]     ,    np.ndarray							)
         assert outputs[0].shape == (1, 3, 4, 4)

         # test with a non-tensor attribute
         A__				      =	CustomOutput(images=[PIL.Image.new("""RGB"""     ,    (4, 4)							)]							)

         # check every way of getting the attribute
         assert isinstance(outputs.images     ,    UpperCamelCase							)
         assert isinstance(outputs.images[0]     ,    PIL.Image.Image							)
         assert isinstance(outputs["""images"""]     ,    UpperCamelCase							)
         assert isinstance(outputs["""images"""][0]     ,    PIL.Image.Image							)
         assert isinstance(outputs[0]     ,    UpperCamelCase							)
         assert isinstance(outputs[0][0]     ,    PIL.Image.Image							)





    def 					UpperCamelCase						(  self:			Tuple							):






         """simple docstring"""


         A__				      =	CustomOutput({"""images""": np.random.rand(1     ,    3     ,    4     ,    4							)}							)

         # check every way of getting the attribute
         assert isinstance(outputs.images     ,    np.ndarray							)
         assert outputs.images.shape == (1, 3, 4, 4)
         assert isinstance(outputs["""images"""]     ,    np.ndarray							)
         assert outputs["images"].shape == (1, 3, 4, 4)
         assert isinstance(outputs[0]     ,    np.ndarray							)
         assert outputs[0].shape == (1, 3, 4, 4)

         # test with a non-tensor attribute
         A__				      =	CustomOutput({"""images""": [PIL.Image.new("""RGB"""     ,    (4, 4)							)]}							)

         # check every way of getting the attribute
         assert isinstance(outputs.images     ,    UpperCamelCase							)
         assert isinstance(outputs.images[0]     ,    PIL.Image.Image							)
         assert isinstance(outputs["""images"""]     ,    UpperCamelCase							)
         assert isinstance(outputs["""images"""][0]     ,    PIL.Image.Image							)
         assert isinstance(outputs[0]     ,    UpperCamelCase							)
         assert isinstance(outputs[0][0]     ,    PIL.Image.Image							)









"""simple docstring"""



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





def 			_snake_case  (     UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       List[Any]=3      ):
     A__				      =	int(mask / 2      )
     # copy image size
     A__				      =	zeros_like(UpperCAmelCase_      )
     for i in range(UpperCAmelCase_       , gray_img.shape[0] - bd      ):
          for j in range(UpperCAmelCase_       , gray_img.shape[1] - bd      ):
               # get mask according with mask
               A__				      =	ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]      )
               # calculate mask median
               A__				      =	sort(UpperCAmelCase_      )[inta(divide((multiply(UpperCAmelCase_       , UpperCAmelCase_      ))       , 2      ) + 1      )]
               A__				      =	median
     return median_img


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

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

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






