

import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()




class 					_SCREAMING_SNAKE_CASE  (      lowercase_       ,					lowercase_       ,					unittest.TestCase ):
      _A						:		Optional[int]    	=     VQModel
      _A						:		str    	=     '''sample'''
      @property
      def A_			(			self		,	lowerCamelCase=(32, 32) ):
          snake_case__						=				4
          snake_case__						=				3

          snake_case__						=				floats_tensor((batch_size, num_channels) + sizes ).to(UpperCamelCase__ )

          return {"sample": image}
      @property
      def A_			(			self ):
          return (3, 32, 32)
      @property
      def A_			(			self ):
          return (3, 32, 32)
      def A_			(			self ):
          snake_case__						=				{
              "block_out_channels": [32, 64],
              "in_channels": 3,
              "out_channels": 3,
              "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
              "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
              "latent_channels": 3,
          }
          snake_case__						=				self.dummy_input
          return init_dict, inputs_dict
      def A_			(			self ):
          pass
      def A_			(			self ):
          pass
      def A_			(			self ):
          snake_case__						,					snake_case__						=				VQModel.from_pretrained("fusing/vqgan-dummy"		,	output_loading_info=UpperCamelCase__ )
          self.assertIsNotNone(UpperCamelCase__ )
          self.assertEqual(len(loading_info["missing_keys"] )		,	0 )

          model.to(UpperCamelCase__ )
          snake_case__						=				model(**self.dummy_input )

          assert image is not None, "Make sure output is not None"



      def A_			(			self ):
          snake_case__						=				VQModel.from_pretrained("fusing/vqgan-dummy" )
          model.to(UpperCamelCase__ ).eval()

          torch.manual_seed(0 )
          if torch.cuda.is_available():
              torch.cuda.manual_seed_all(0 )

          snake_case__						=				torch.randn(1		,	model.config.in_channels		,	model.config.sample_size		,	model.config.sample_size )
          snake_case__						=				image.to(UpperCamelCase__ )
          with torch.no_grad():
              snake_case__						=				model(UpperCamelCase__ ).sample

          snake_case__						=				output[0, -1, -3:, -3:].flatten().cpu()
          # fmt: off
          snake_case__						=				torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3] )
          # fmt: on
          self.assertTrue(torch.allclose(UpperCamelCase__		,	UpperCamelCase__		,	atol=1e-3 ) )





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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):

    # prepare kernel
    # the kernel size have to be odd
    if (ksize % 2) == 0:
        snake_case__						=				ksize + 1
    snake_case__						=				np.zeros((ksize, ksize)    ,		dtype=np.floataa							)

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

            # degree to radiant
            snake_case__						=				theta / 180 * np.pi
            snake_case__						=				np.cos(_theta							)
            snake_case__						=				np.sin(_theta							)

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

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

            # fill kernel
            snake_case__						=				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
     __magic_name__            =  imread('''../image_data/lena.jpg''')
     # turn image in gray scale value
     __magic_name__            =  cvtColor(img, COLOR_BGR2GRAY)

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

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

     waitKey(0)



