import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						np.ndarray:




       '''simple docstring'''






       if (ksize % 2) == 0:
              _A=	ksize + 1
       _A=	np.zeros((ksize, ksize)       ,					dtype=np.floataa   )

       # each value
       for y in range(lowerCAmelCase_   ):
              for x in range(lowerCAmelCase_   ):
                     # distance from center
                     _A=	x - ksize // 2
                     _A=	y - ksize // 2

                     # degree to radiant
                     _A=	theta / 1_80 * np.pi
                     _A=	np.cos(_theta   )
                     _A=	np.sin(_theta   )

                     # get kernel x
                     _A=	cos_theta * px + sin_theta * py

                     # get kernel y
                     _A=	-sin_theta * px + cos_theta * py

                     # fill kernel
                     _A=	np.exp(
                         -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)   ) * np.cos(2 * np.pi * _x / lambd + psi   )

       return gabor


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      # read original image
      UpperCAmelCase_								=							imread('''../image_data/lena.jpg''')
      # turn image in gray scale value
      UpperCAmelCase_								=							cvtColor(img, COLOR_BGR2GRAY)

      # Apply multiple Kernel to detect edges
      UpperCAmelCase_								=							np.zeros(gray.shape[:2])
      for theta in [0, 30, 60, 90, 120, 150]:
            UpperCAmelCase_								=							gabor_filter_kernel(10, 8, theta, 10, 0, 0)
            out += filteraD(gray, CV_8UC3, kernel_aa)
      UpperCAmelCase_								=							out / out.max() * 255
      UpperCAmelCase_								=							out.astype(np.uinta)

      imshow('''Original''', gray)
      imshow('''Gabor filter with 20x20 mask and 6 directions''', out)

      waitKey(0)
import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu





@require_cpu
class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	torch.nn.Linear(10				,      10       )
            _A=	torch.optim.SGD(model.parameters()				,      0.1       )
            _A=	Accelerator()
            _A=	accelerator.prepare(lowerCAmelCase__       )
            try:
                   pickle.loads(pickle.dumps(lowerCAmelCase__       )       )
            except Exception as e:
                   self.fail(f"Accelerated optimizer pickling failed with {e}"       )
            AcceleratorState._reset_state()