






'''simple docstring'''





import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation
class 				lowercase       (							unittest.TestCase      ):


    '''simple docstring'''

    def    a__				(					self     :     Optional[int]     )							->							Tuple:

     '''simple docstring'''




     lowerCamelCase__  =     get_activation("swish"     )

     self.assertIsInstance(__a				,     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    a__				(					self     :     Dict     )							->							int:

     '''simple docstring'''




     lowerCamelCase__  =     get_activation("silu"     )

     self.assertIsInstance(__a				,     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    a__				(					self     :     Any     )							->							Union[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     get_activation("mish"     )

     self.assertIsInstance(__a				,     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    a__				(					self     :     Tuple     )							->							Union[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     get_activation("gelu"     )

     self.assertIsInstance(__a				,     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     )









'''simple docstring'''





from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     cva.getAffineTransform(lowercase__				,     lowercase__)
 return cva.warpAffine(lowercase__				,     lowercase__				,     (rows, cols))


if __name__ == "__main__":
 # read original image
 __A							:							Any  							=		cva.imread(
     str(Path(__file__).resolve().parent.parent / """image_data""" / """lena.jpg""")
 )
 # turn image in gray scale value
 __A							:							int  							=		cva.cvtColor(image, cva.COLOR_BGR2GRAY)
 # get image shape
 __A							,			__A							:							Dict  							=		gray_img.shape

 # set different points to rotate image
 __A							:							int  							=		np.array([[50, 50], [2_00, 50], [50, 2_00]], np.floataa)
 __A							:							Any  							=		np.array([[10, 1_00], [2_00, 50], [1_00, 2_50]], np.floataa)
 __A							:							str  							=		np.array([[50, 50], [1_50, 50], [1_20, 2_00]], np.floataa)
 __A							:							Optional[int]  							=		np.array([[10, 1_00], [80, 50], [1_80, 2_50]], np.floataa)

 # add all rotated images in a list
 __A							:							List[Any]  							=		[
     gray_img,
     get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
     get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
     get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
 ]

 # plot different image rotations
 __A							:							int  							=		plt.figure(1)
 __A							:							int  							=		["""Original""", """Rotation 1""", """Rotation 2""", """Rotation 3"""]
 for i, image in enumerate(images):
  plt.subplot(2, 2, i + 1), plt.imshow(image, """gray""")
  plt.title(titles[i])
  plt.axis("""off""")
  plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
 plt.show()

