






import math
import sys

import cva
import numpy as np
def 		_a       ( a					:Union[str, Any]      ,				a					:Union[str, Any]					)				->     np.ndarray:
 a						    = math.sqrt(a					)
 a						    = 1 / (sigma * math.sqrt(2 * math.pi					))
 return cons * np.exp(-((img / sigma) ** 2) * 0.5					)
def 		_a       ( a					:Tuple      ,				a					:Tuple      ,				a					:Union[str, Any]      ,				a					:Optional[Any]					)				->     np.ndarray:
 a						    = kernel_size // 2
 return img[x - half : x + half + 1, y - half : y + half + 1]
def 		_a       ( a					:Any      ,				a					:str					)				->     np.ndarray:
 a						    = np.zeros((kernel_size, kernel_size)					)
 for i in range(0      ,				a					):
  for j in range(0      ,				a					):
   a						    = math.sqrt(
       abs(i - kernel_size // 2					) ** 2 + abs(j - kernel_size // 2					) ** 2					)
 return vec_gaussian(a      ,				a					)
def 		_a       ( a					:Optional[int]      ,				a					:List[str]      ,				a					:Optional[Any]      ,				a					:Tuple      ,				)				->     np.ndarray:
 a						    = np.zeros(img.shape					)
 a						    = get_gauss_kernel(a      ,				a					)
 a    ,	a						    = img.shape
 for i in range(kernel_size // 2      ,				size_x - kernel_size // 2					):
  for j in range(kernel_size // 2      ,				size_y - kernel_size // 2					):
   a						    = get_slice(a      ,				a      ,				a      ,				a					)
   a						    = img_s - img_s[kernel_size // 2, kernel_size // 2]
   a						    = vec_gaussian(a      ,				a					)
   a						    = np.multiply(a      ,				a					)
   a						    = np.multiply(a      ,				a					)
   a						    = np.sum(a					) / np.sum(a					)
   a						    = val
 return imga
def 		_a       ( a					:Tuple					)				->     tuple:
 a						    = args[1] if args[1:] else '''../image_data/lena.jpg'''
 a						    = float(args[2]					) if args[2:] else 1.0
 a						    = float(args[3]					) if args[3:] else 1.0
 if args[4:]:
  a						    = int(args[4]					)
  a						    = kernel_size + abs(kernel_size % 2 - 1					)
 else:
  a						    = 5
 return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
     UpperCAmelCase__							  =						parse_args(sys.argv)
     UpperCAmelCase__							  =						cva.imread(filename, 0)
     cva.imshow("input image", img)

     UpperCAmelCase__							  =						img / 255
     UpperCAmelCase__							  =						out.astype("float32")
     UpperCAmelCase__							  =						bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
     UpperCAmelCase__							  =						out * 255
     UpperCAmelCase__							  =						np.uinta(out)
     cva.imshow("output image", out)
     cva.waitKey(0)
     cva.destroyAllWindows()








import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
				import jax



@require_flax
class       a_       (     SCREAMING_SNAKE_CASE__  ,    unittest.TestCase     ):
	A    							= FlaxAutoencoderKL





	@property
	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						4
				SCREAMING_SNAKE_CASE_								=						3
				SCREAMING_SNAKE_CASE_								=						(32, 32)

				SCREAMING_SNAKE_CASE_								=						jax.random.PRNGKey(0      )
				SCREAMING_SNAKE_CASE_								=						jax.random.uniform(SCREAMING_SNAKE_CASE      ,     ((batch_size, num_channels) + sizes)      )

				return {"sample": image, "prng_key": prng_key}





	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_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': 4,
				}
				SCREAMING_SNAKE_CASE_								=						self.dummy_input
				return init_dict, inputs_dict
