






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        __UpperCamelCase    (     __snake_case   ):
	__snake_case	:torch.FloatTensor
	__snake_case	:torch.FloatTensor
	__snake_case	:Optional[torch.FloatTensor]   =       None


class        __UpperCamelCase    (     __snake_case   ,			__snake_case   ):
	__snake_case	:int   =       2







	@register_to_config
	def __init__(  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							List[str] = 0.02       ,				_lowerCAmelCase      :							Any = 100       ,				_lowerCAmelCase      :							Dict = 1.007       ,				_lowerCAmelCase      :							str = 80       ,				_lowerCAmelCase      :							int = 0.05       ,				_lowerCAmelCase      :							Tuple = 50       ,				)		-> List[Any]:
		"""simple docstring"""







		__lowercase					       =  sigma_max

		# setable values
		__lowercase					       =  None
		__lowercase					       =  None
		__lowercase					       =  None  # sigma(t_i)







	def  _a   (  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							List[Any]       ,				_lowerCAmelCase      :							str = None			)		-> List[str]:
		"""simple docstring"""







		return sample







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							Union[str, Any] = None			)		-> List[Any]:
		"""simple docstring"""







		__lowercase					       =  num_inference_steps
		__lowercase					       =  np.arange(0       ,				self.num_inference_steps			)[::-1].copy()
		__lowercase					       =  torch.from_numpy(A_			).to(A_			)
		__lowercase					       =  [
		    (
		        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
		]
		__lowercase					       =  torch.tensor(A_       ,				dtype=torch.floataa       ,				device=A_			)







	def  _a   (  self      :							str       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							int = None			)		-> Dict:
		"""simple docstring"""







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

		# sample eps ~ N(0, S_noise^2 * I)
		__lowercase					       =  self.config.s_noise * randn_tensor(sample.shape       ,				generator=A_			).to(sample.device			)
		__lowercase					       =  sigma + gamma * sigma
		__lowercase					       =  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

		return sample_hat, sigma_hat







	def  _a   (  self      :							Optional[Any]       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							Optional[Any]       ,				_lowerCAmelCase      :							Union[str, Any] = True       ,				)		-> Optional[int]:
		"""simple docstring"""







		__lowercase					       =  sample_hat + sigma_hat * model_output
		__lowercase					       =  (sample_hat - pred_original_sample) / sigma_hat
		__lowercase					       =  sample_hat + (sigma_prev - sigma_hat) * derivative

		if not return_dict:
			return (sample_prev, derivative)

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







	def  _a   (  self      :							Any       ,				_lowerCAmelCase      :							Union[str, Any]       ,				_lowerCAmelCase      :							Tuple       ,				_lowerCAmelCase      :							Tuple       ,				_lowerCAmelCase      :							Optional[Any]       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							Union[str, Any]       ,				_lowerCAmelCase      :							Union[str, Any] = True       ,				)		-> List[Any]:
		"""simple docstring"""







		__lowercase					       =  sample_prev + sigma_prev * model_output
		__lowercase					       =  (sample_prev - pred_original_sample) / sigma_prev
		__lowercase					       =  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=A_       ,				derivative=A_       ,				pred_original_sample=A_			)







	def  _a   (  self      :							Optional[int]       ,				_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							Tuple			)		-> Optional[Any]:
		"""simple docstring"""







		raise NotImplementedError()









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





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







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

	# each value
	for y in range(lowerCamelCase					):
		for x in range(lowerCamelCase					):
			# 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
						__UpperCamelCase      :      List[Any]     						=						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      :      Union[str, Any]     						=						np.zeros(gray.shape[:2])
						for theta in [0, 30, 60, 90, 120, 150]:
												__UpperCamelCase      :      Tuple     						=						gabor_filter_kernel(10, 8, theta, 10, 0, 0)
												out += filteraD(gray, CV_8UC3, kernel_aa)
						__UpperCamelCase      :      List[str]     						=						out / out.max() * 255
						__UpperCamelCase      :      List[str]     						=						out.astype(np.uinta)

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

						waitKey(0)

