





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      ):

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

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

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

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

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

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

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

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

     waitKey(0)







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

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




class 		__A   (      UpperCamelCase__       ,    UpperCamelCase__      ):
						UpperCamelCase      =       1

						@register_to_config
						def __init__(		self :Any     ,			__snake_case :Tuple=20_00     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :Any=20     ,			__snake_case :Optional[int]=1E-3			):
										'''simple docstring'''

										__magic_name__						:						Dict										=None
										__magic_name__						:						List[str]										=None
										__magic_name__						:						str										=None

						def      A__    (		self :Dict     ,			__snake_case :Optional[int]     ,			__snake_case :Union[str, torch.device] = None			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=torch.linspace(1     ,			self.config.sampling_eps     ,			__snake_case     ,			device=__snake_case			)

						def      A__    (		self :List[str]     ,			__snake_case :List[str]     ,			__snake_case :int     ,			__snake_case :int     ,			__snake_case :List[str]=None			):
										'''simple docstring'''

										if self.timesteps is None:
														raise ValueError(
														    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""			)

										# TODO(Patrick) better comments + non-PyTorch
										# postprocess model score
										__magic_name__						:						int										=(
										    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
										)
										__magic_name__						:						Optional[int]										=torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff			)			)
										__magic_name__						:						str										=std.flatten()
										while len(std.shape			) < len(score.shape			):
														__magic_name__						:						List[str]										=std.unsqueeze(-1			)
										__magic_name__						:						Union[str, Any]										=-score / std

										# compute
										__magic_name__						:						Tuple										=-1.0 / len(self.timesteps			)

										__magic_name__						:						int										=self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
										__magic_name__						:						Dict										=beta_t.flatten()
										while len(beta_t.shape			) < len(x.shape			):
														__magic_name__						:						Any										=beta_t.unsqueeze(-1			)
										__magic_name__						:						Dict										=-0.5 * beta_t * x

										__magic_name__						:						Optional[int]										=torch.sqrt(__snake_case			)
										__magic_name__						:						int										=drift - diffusion**2 * score
										__magic_name__						:						List[str]										=x + drift * dt

										# add noise
										__magic_name__						:						Optional[int]										=randn_tensor(x.shape     ,			layout=x.layout     ,			generator=__snake_case     ,			device=x.device     ,			dtype=x.dtype			)
										__magic_name__						:						Optional[Any]										=x_mean + diffusion * math.sqrt(-dt			) * noise

										return x, x_mean


						def __len__(		self :List[Any]			):
										'''simple docstring'''

										return self.config.num_train_timesteps




