

import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 						UpperCAmelCase_					( __lowerCamelCase):







							def __init__(     self,    __a,    __a,    __a):

														'''simple docstring'''

														super().__init__()
														self.register_modules(vqvae=a_,    unet=a_,    scheduler=a_)







							@torch.no_grad()
							def __call__(     self,    __a = 1,    __a = None,    __a = 0.0,    __a = 50,    __a = "pil",    __a = True,    **__a,    ):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					randn_tensor(
														    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),    generator=a_,    )
														_lowerCAmelCase  :							Optional[int]				     =					latents.to(self.device)

														# scale the initial noise by the standard deviation required by the scheduler
														_lowerCAmelCase  :							Optional[int]				     =					latents * self.scheduler.init_noise_sigma

														self.scheduler.set_timesteps(a_)

														# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
														_lowerCAmelCase  :							Any				     =					"eta" in set(inspect.signature(self.scheduler.step).parameters.keys())

														_lowerCAmelCase  :							Optional[int]				     =					{}
														if accepts_eta:
																					_lowerCAmelCase  :							Optional[int]				     =					eta

														for t in self.progress_bar(self.scheduler.timesteps):
																					_lowerCAmelCase  :							Dict				     =					self.scheduler.scale_model_input(a_,    a_)
																					# predict the noise residual
																					_lowerCAmelCase  :							Optional[Any]				     =					self.unet(a_,    a_).sample
																					# compute the previous noisy sample x_t -> x_t-1
																					_lowerCAmelCase  :							List[Any]				     =					self.scheduler.step(a_,    a_,    a_,    **a_).prev_sample

														# decode the image latents with the VAE
														_lowerCAmelCase  :							str				     =					self.vqvae.decode(a_).sample

														_lowerCAmelCase  :							Optional[Any]				     =					(image / 2 + 0.5).clamp(0,    1)
														_lowerCAmelCase  :							Union[str, Any]				     =					image.cpu().permute(0,    2,    3,    1).numpy()
														if output_type == "pil":
																					_lowerCAmelCase  :							Optional[Any]				     =					self.numpy_to_pil(a_)

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=a_)





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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							if (ksize % 2) == 0:
														_lowerCAmelCase  :							str				     =					ksize + 1
							_lowerCAmelCase  :							List[str]				     =					np.zeros((ksize, ksize)       ,				dtype=np.floataa						)

							# each value
							for y in range(_lowerCamelCase						):
														for x in range(_lowerCamelCase						):
																					# distance from center
																					_lowerCAmelCase  :							int				     =					x - ksize // 2
																					_lowerCAmelCase  :							Dict				     =					y - ksize // 2

																					# degree to radiant
																					_lowerCAmelCase  :							List[Any]				     =					theta / 180 * np.pi
																					_lowerCAmelCase  :							int				     =					np.cos(_theta						)
																					_lowerCAmelCase  :							Optional[int]				     =					np.sin(_theta						)

																					# get kernel x
																					_lowerCAmelCase  :							int				     =					cos_theta * px + sin_theta * py

																					# get kernel y
																					_lowerCAmelCase  :							str				     =					-sin_theta * px + cos_theta * py

																					# fill kernel
																					_lowerCAmelCase  :							Union[str, Any]				     =					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
						_snake_case	=						imread("../image_data/lena.jpg")
						# turn image in gray scale value
						_snake_case	=						cvtColor(img, COLOR_BGR2GRAY)

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

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

						waitKey(0)
