

'''simple docstring'''


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




def 	_a       (				__lowerCAmelCase		:      int   ,							__lowerCAmelCase		:      int   ,							__lowerCAmelCase		:      int   ,							__lowerCAmelCase		:      int   ,							__lowerCAmelCase		:      int   ,							__lowerCAmelCase		:      int     ):

		"""simple docstring"""

		if (ksize % 2) == 0:
				snake_case__						:       str       						=  ksize + 1
		snake_case__						:       Any       						=  np.zeros((ksize, ksize)   ,							dtype=np.floataa     )

		# each value
		for y in range(__lowerCAmelCase     ):
				for x in range(__lowerCAmelCase     ):
						# distance from center
						snake_case__						:       Optional[int]       						=  x - ksize // 2
						snake_case__						:       List[Any]       						=  y - ksize // 2

						# degree to radiant
						snake_case__						:       List[str]       						=  theta / 1_80 * np.pi
						snake_case__						:       Dict       						=  np.cos(_theta     )
						snake_case__						:       List[Any]       						=  np.sin(_theta     )

						# get kernel x
						snake_case__						:       Any       						=  cos_theta * px + sin_theta * py

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

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

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

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

			waitKey(0)






'''simple docstring'''


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin




@dataclass
class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    42
						__UpperCAmelCase			     =    42
						__UpperCAmelCase			     =    None





class     a			(				SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    2







						@register_to_config
						def __init__(							self					:      int			,			snake_case_					:      float = 0.0_2			,			snake_case_					:      float = 1_0_0			,			snake_case_					:      float = 1.0_0_7			,			snake_case_					:      float = 8_0			,			snake_case_					:      float = 0.0_5			,			snake_case_					:      float = 5_0			,			):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  sigma_max

								# setable values
								snake_case__						:       int       						=  None
								snake_case__						:       np.IntTensor       						=  None
								snake_case__						:       torch.FloatTensor       						=  None  # sigma(t_i)







						def       __magic_name__							(							self					:      Any			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      Optional[int] = None  ):



								'''simple docstring'''



								return sample







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      int			,			snake_case_					:      Union[str, torch.device] = None  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  num_inference_steps
								snake_case__						:       Tuple       						=  np.arange(0			,			self.num_inference_steps  )[::-1].copy()
								snake_case__						:       int       						=  torch.from_numpy(snake_case_  ).to(snake_case_  )
								snake_case__						:       List[str]       						=  [
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in self.timesteps
								]
								snake_case__						:       Union[str, Any]       						=  torch.tensor(snake_case_			,			dtype=torch.floataa			,			device=snake_case_  )







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      float			,			snake_case_					:      Optional[torch.Generator] = None  ):



								'''simple docstring'''



								if self.config.s_min <= sigma <= self.config.s_max:
										snake_case__						:       List[Any]       						=  min(self.config.s_churn / self.num_inference_steps			,			2**0.5 - 1  )
								else:
										snake_case__						:       List[Any]       						=  0

								# sample eps ~ N(0, S_noise^2 * I)
								snake_case__						:       Any       						=  self.config.s_noise * randn_tensor(sample.shape			,			generator=snake_case_  ).to(sample.device  )
								snake_case__						:       Tuple       						=  sigma + gamma * sigma
								snake_case__						:       Dict       						=  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      float			,			snake_case_					:      float			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      bool = True			,			):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  sample_hat + sigma_hat * model_output
								snake_case__						:       List[Any]       						=  (sample_hat - pred_original_sample) / sigma_hat
								snake_case__						:       Any       						=  sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
										return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=snake_case_			,			derivative=snake_case_			,			pred_original_sample=snake_case_  )







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      float			,			snake_case_					:      float			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      bool = True			,			):



								'''simple docstring'''



								snake_case__						:       str       						=  sample_prev + sigma_prev * model_output
								snake_case__						:       List[Any]       						=  (sample_prev - pred_original_sample) / sigma_prev
								snake_case__						:       Any       						=  sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
										return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=snake_case_			,			derivative=snake_case_			,			pred_original_sample=snake_case_  )







						def       __magic_name__							(							self					:      str			,			snake_case_					:      List[Any]			,			snake_case_					:      Optional[int]			,			snake_case_					:      Tuple  ):



								'''simple docstring'''



								raise NotImplementedError()



