



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



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					) ->    List[str]:

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

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

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

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

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

									# fill kernel
									lowercase__   					=			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
							lowercase_						=       imread("""../image_data/lena.jpg""")
							# turn image in gray scale value
							lowercase_						=       cvtColor(img, COLOR_BGR2GRAY)

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

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

							waitKey(0)









from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
							if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ...utils.dummy_torch_and_transformers_objects import (
							    VersatileDiffusionDualGuidedPipeline,
							    VersatileDiffusionImageVariationPipeline,
							    VersatileDiffusionPipeline,
							    VersatileDiffusionTextToImagePipeline,
							)
else:
							from .modeling_text_unet import UNetFlatConditionModel
							from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
							from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
							from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
							from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline




