



'''simple docstring'''






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


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int  )      ->   np.ndarray:

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

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

																					# degree to radiant
																					UpperCAmelCase :       Any 							=		theta / 180 * np.pi
																					UpperCAmelCase :       str 							=		np.cos(_theta  )
																					UpperCAmelCase :       str 							=		np.sin(_theta  )

																					# get kernel x
																					UpperCAmelCase :       Union[str, Any] 							=		cos_theta * px + sin_theta * py

																					# get kernel y
																					UpperCAmelCase :       int 							=		-sin_theta * px + cos_theta * py

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

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

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

						waitKey(0)










'''simple docstring'''






from typing import List, Optional, Tuple, Union

import torch

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

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       42
	lowerCamelCase__			 =       42







	def __init__(						self			:     Union[str, Any]		,			__snake_case			:     UNetaDModel		,			__snake_case			:     ScoreSdeVeScheduler		)			->		int:
								super().__init__()
								self.register_modules(unet=__snake_case		,			scheduler=__snake_case		)







	@torch.no_grad()
	def __call__(						self			:     Optional[int]		,			__snake_case			:     int = 1		,			__snake_case			:     int = 2000		,			__snake_case			:     Optional[Union[torch.Generator, List[torch.Generator]]] = None		,			__snake_case			:     Optional[str] = "pil"		,			__snake_case			:     bool = True		,			**__snake_case			:     Optional[int]		,			)			->		Union[ImagePipelineOutput, Tuple]:
								UpperCAmelCase :       str 							=		self.unet.config.sample_size
								UpperCAmelCase :       Union[str, Any] 							=		(batch_size, 3, img_size, img_size)

								UpperCAmelCase :       int 							=		self.unet

								UpperCAmelCase :       Any 							=		randn_tensor(__snake_case		,			generator=__snake_case		) * self.scheduler.init_noise_sigma
								UpperCAmelCase :       List[Any] 							=		sample.to(self.device		)

								self.scheduler.set_timesteps(__snake_case		)
								self.scheduler.set_sigmas(__snake_case		)

								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
															UpperCAmelCase :       Any 							=		self.scheduler.sigmas[i] * torch.ones(shape[0]		,			device=self.device		)

															# correction step
															for _ in range(self.scheduler.config.correct_steps		):
																						UpperCAmelCase :       Union[str, Any] 							=		self.unet(__snake_case		,			__snake_case		).sample
																						UpperCAmelCase :       Optional[Any] 							=		self.scheduler.step_correct(__snake_case		,			__snake_case		,			generator=__snake_case		).prev_sample

															# prediction step
															UpperCAmelCase :       Optional[Any] 							=		model(__snake_case		,			__snake_case		).sample
															UpperCAmelCase :       List[str] 							=		self.scheduler.step_pred(__snake_case		,			__snake_case		,			__snake_case		,			generator=__snake_case		)

															UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		output.prev_sample, output.prev_sample_mean

								UpperCAmelCase :       int 							=		sample_mean.clamp(0		,			1		)
								UpperCAmelCase :       Union[str, Any] 							=		sample.cpu().permute(0		,			2		,			3		,			1		).numpy()
								if output_type == "pil":
															UpperCAmelCase :       Optional[Any] 							=		self.numpy_to_pil(__snake_case		)

								if not return_dict:
															return (sample,)

								return ImagePipelineOutput(images=__snake_case		)





