





'''simple docstring'''




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    (a__					,		a__					,		unittest.TestCase							):
     lowerCAmelCase					=       VQModel
     lowerCAmelCase					=       '''sample'''


     @property
     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase=(32, 32)):

          '''simple docstring'''






          __A						: List[str]      =       4
          __A						: int      =       3

          __A						: int      =       floats_tensor((batch_size, num_channels) + sizes).to(_UpperCAmelCase)

          return {"sample": image}


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return (3, 32, 32)


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return (3, 32, 32)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[str]      =       {
              'block_out_channels': [32, 64],
              'in_channels': 3,
              'out_channels': 3,
              'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
              'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
              'latent_channels': 3,
          }
          __A						: Optional[int]      =       self.dummy_input
          return init_dict, inputs_dict


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A ,__A						: Union[str, Any]      =       VQModel.from_pretrained('fusing/vqgan-dummy'						,	output_loading_info=_UpperCAmelCase)
          self.assertIsNotNone(_UpperCAmelCase)
          self.assertEqual(len(loading_info['missing_keys'])						,	0)

          model.to(_UpperCAmelCase)
          __A						: str      =       model(**self.dummy_input)

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[Any]      =       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)

          __A						: Dict      =       torch.randn(1						,	model.config.in_channels						,	model.config.sample_size						,	model.config.sample_size)
          __A						: Any      =       image.to(_UpperCAmelCase)
          with torch.no_grad():
               __A						: List[str]      =       model(_UpperCAmelCase).sample

          __A						: Union[str, Any]      =       output[0, -1, -3:, -3:].flatten().cpu()
          # fmt: off
          __A						: int      =       torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143])
          # fmt: on
          self.assertTrue(torch.allclose(_UpperCAmelCase						,	_UpperCAmelCase						,	atol=1e-3))






'''simple docstring'''




from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros






def        _lowerCAmelCase			(  __snake_case  : Optional[Any] ,							__snake_case  : Optional[int]			)     ->     List[str]:
     __A						: List[str]      =       k_size // 2
     __A ,__A						: List[Any]      =       mgrid[0 - center : k_size - center, 0 - center : k_size - center]
     __A						: List[Any]      =       1 / (2 * pi * sigma) * exp(-(square(__snake_case			) + square(__snake_case			)) / (2 * square(__snake_case			))			)
     return g






def        _lowerCAmelCase			(  __snake_case  : List[str] ,							__snake_case  : List[str] ,							__snake_case  : int			)     ->     Union[str, Any]:
     __A ,__A						: Tuple      =       image.shape[0], image.shape[1]
     # dst image height and width
     __A						: Tuple      =       height - k_size + 1
     __A						: Optional[Any]      =       width - k_size + 1

     # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
     __A						: str      =       zeros((dst_height * dst_width, k_size * k_size)			)
     __A						: Optional[Any]      =       0
     for i, j in product(range(__snake_case			) ,							range(__snake_case			)			):
          __A						: int      =       ravel(image[i : i + k_size, j : j + k_size]			)
          __A						: List[str]      =       window
          row += 1

     #  turn the kernel into shape(k*k, 1)
     __A						: List[Any]      =       gen_gaussian_kernel(__snake_case ,							__snake_case			)
     __A						: Any      =       ravel(__snake_case			)

     # reshape and get the dst image
     __A						: Dict      =       dot(__snake_case ,							__snake_case			).reshape(__snake_case ,							__snake_case			).astype(__snake_case			)

     return dst


if __name__ == "__main__":
  # read original image
  lowercase__    :      List[Any]						=    imread(r'''../image_data/lena.jpg''')
  # turn image in gray scale value
  lowercase__    :      Dict						=    cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  lowercase__    :      Any						=    gaussian_filter(gray, 3, sigma=1)
  lowercase__    :      str						=    gaussian_filter(gray, 5, sigma=0.8)

  # show result images
  imshow('''gaussian filter with 3x3 mask''', gaussianaxa)
  imshow('''gaussian filter with 5x5 mask''', gaussianaxa)
  waitKey()