



'''simple docstring'''





import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

__a  			=						imread(R"digital_image_processing/image_data/lena_small.jpg")
__a  			=						cvtColor(img, COLOR_BGR2GRAY)





def 		__snake_case(							)						->					List[Any]:
      snake_case__    :       Optional[Any]       		= cn.convert_to_negative(_lowerCAmelCase						)
      # assert negative_img array for at least one True
      assert negative_img.any()





def 		__snake_case(							)						->					Dict:
      with Image.open("""digital_image_processing/image_data/lena_small.jpg"""						) as img:
            # Work around assertion for response
            assert str(cc.change_contrast(_lowerCAmelCase	,  110						)						).startswith(
                """<PIL.Image.Image image mode=RGB size=100x100 at"""						)





def 		__snake_case(							)						->					int:
      snake_case__    :       Optional[Any]       		= canny.gen_gaussian_kernel(9	,  sigma=1.4						)
      # Assert ambiguous array
      assert resp.all()





def 		__snake_case(							)						->					List[Any]:
      snake_case__    :       Any       		= imread("""digital_image_processing/image_data/lena_small.jpg"""	,  0						)
      # assert ambiguous array for all == True
      assert canny_img.all()
      snake_case__    :       List[str]       		= canny.canny(_lowerCAmelCase						)
      # assert canny array for at least one True
      assert canny_array.any()





def 		__snake_case(							)						->					List[str]:
      assert gg.gaussian_filter(_lowerCAmelCase	,  5	,  sigma=0.9						).all()





def 		__snake_case(							)						->					Tuple:
      # laplace diagonals
      snake_case__    :       Optional[Any]       		= array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]						)
      snake_case__    :       Optional[int]       		= conv.img_convolve(_lowerCAmelCase	,  _lowerCAmelCase						).astype(_lowerCAmelCase						)
      assert res.any()





def 		__snake_case(							)						->					List[str]:
      assert med.median_filter(_lowerCAmelCase	,  3						).any()





def 		__snake_case(							)						->					Optional[int]:
      snake_case__		,							snake_case__    :       str       		= sob.sobel_filter(_lowerCAmelCase						)
      assert grad.any() and theta.any()





def 		__snake_case(							)						->					Tuple:
      snake_case__    :       Tuple       		= sp.make_sepia(_lowerCAmelCase	,  20						)
      assert sepia.all()





def 		__snake_case(							_lowerCAmelCase = "digital_image_processing/image_data/lena_small.jpg"						)						->					Union[str, Any]:
      snake_case__    :       Any       		= bs.Burkes(imread(_lowerCAmelCase	,  1						)	,  120						)
      burkes.process()
      assert burkes.output_img.any()





def 		__snake_case(							_lowerCAmelCase = "digital_image_processing/image_data/lena_small.jpg"	,  )						->					int:
      snake_case__    :       str       		= rs.NearestNeighbour(imread(_lowerCAmelCase	,  1						)	,  400	,  200						)
      nn.process()
      assert nn.output.any()





def 		__snake_case(							)						->					Union[str, Any]:
      snake_case__    :       str       		= """digital_image_processing/image_data/lena.jpg"""

      # Reading the image and converting it to grayscale.
      snake_case__    :       List[Any]       		= imread(_lowerCAmelCase	,  0						)

      # Test for get_neighbors_pixel function() return not None
      snake_case__    :       Optional[int]       		= 0
      snake_case__    :       Union[str, Any]       		= 0
      snake_case__    :       Union[str, Any]       		= image[x_coordinate][y_coordinate]

      snake_case__    :       Dict       		= lbp.get_neighbors_pixel(
          _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)

      assert neighbors_pixels is not None

      # Test for local_binary_pattern function()
      # Create a numpy array as the same height and width of read image
      snake_case__    :       Tuple       		= np.zeros((image.shape[0], image.shape[1])						)

      # Iterating through the image and calculating the local binary pattern value
      # for each pixel.
      for i in range(0	,  image.shape[0]						):
            for j in range(0	,  image.shape[1]						):
                  snake_case__    :       Dict       		= lbp.local_binary_value(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)

      assert lbp_image.any()





'''simple docstring'''





import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation







class        UpperCAmelCase_			(			unittest.TestCase      ):
    """simple docstring"""







    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__    :       int       		= get_activation("""swish"""		)

          self.assertIsInstance(snake_case_					,	nn.SiLU		)

          self.assertEqual(act(torch.tensor(-100					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertNotEqual(act(torch.tensor(-1					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(0					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(20					,	dtype=torch.floataa		)		).item()					,	20		)




    def lowerCamelCase			(       self   :	Optional[Any]		):
          snake_case__    :       int       		= get_activation("""silu"""		)

          self.assertIsInstance(snake_case_					,	nn.SiLU		)

          self.assertEqual(act(torch.tensor(-100					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertNotEqual(act(torch.tensor(-1					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(0					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(20					,	dtype=torch.floataa		)		).item()					,	20		)




    def lowerCamelCase			(       self   :	Dict		):
          snake_case__    :       Union[str, Any]       		= get_activation("""mish"""		)

          self.assertIsInstance(snake_case_					,	nn.Mish		)

          self.assertEqual(act(torch.tensor(-200					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertNotEqual(act(torch.tensor(-1					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(0					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(20					,	dtype=torch.floataa		)		).item()					,	20		)




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__    :       int       		= get_activation("""gelu"""		)

          self.assertIsInstance(snake_case_					,	nn.GELU		)

          self.assertEqual(act(torch.tensor(-100					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertNotEqual(act(torch.tensor(-1					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(0					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(20					,	dtype=torch.floataa		)		).item()					,	20		)
