



"""simple docstring"""

import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput




class SCREAMING_SNAKE_CASE__					(		__snake_case     ,					__snake_case      ):
						_lowerCAmelCase							=					1



						@register_to_config
						def __init__(self						,     _lowercase = 1000						,     _lowercase = None							):





													'''simple docstring'''

													self.set_timesteps(_lowercase							)

													# standard deviation of the initial noise distribution
													__a		:		Union[str, Any]									=       1.0

													# For now we only support F-PNDM, i.e. the runge-kutta method
													# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
													# mainly at formula (9), (12), (13) and the Algorithm 2.
													__a		:		Union[str, Any]									=       4

													# running values
													__a		:		int									=       []



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase = None							):





													'''simple docstring'''

													__a		:		Dict									=       num_inference_steps
													__a		:		List[str]									=       torch.linspace(1						,     0						,     num_inference_steps + 1							)[:-1]
													__a		:		Dict									=       torch.cat([steps, torch.tensor([0.0]							)]							)

													if self.config.trained_betas is not None:
																				__a		:		Any									=       torch.tensor(self.config.trained_betas						,     dtype=torch.floataa							)
													else:
																				__a		:		Optional[Any]									=       torch.sin(steps * math.pi / 2							) ** 2

													__a		:		Any									=       (1.0 - self.betas**2) ** 0.5

													__a		:		Optional[int]									=       (torch.atana(self.betas						,     self.alphas							) / math.pi * 2)[:-1]
													__a		:		List[str]									=       timesteps.to(_lowercase							)

													__a		:		Union[str, Any]									=       []



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase = True						,     ):





													'''simple docstring'''

													if self.num_inference_steps is None:
																				raise ValueError(
																				    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""							)

													__a		:		Dict									=       (self.timesteps == timestep).nonzero().item()
													__a		:		Union[str, Any]									=       timestep_index + 1

													__a		:		Any									=       sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
													self.ets.append(_lowercase							)

													if len(self.ets							) == 1:
																				__a		:		Dict									=       self.ets[-1]
													elif len(self.ets							) == 2:
																				__a		:		Any									=       (3 * self.ets[-1] - self.ets[-2]) / 2
													elif len(self.ets							) == 3:
																				__a		:		Optional[int]									=       (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
													else:
																				__a		:		int									=       (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

													__a		:		Dict									=       self._get_prev_sample(_lowercase						,     _lowercase						,     _lowercase						,     _lowercase							)

													if not return_dict:
																				return (prev_sample,)

													return SchedulerOutput(prev_sample=_lowercase							)



						def     lowerCAmelCase__(self						,     _lowercase						,     *_lowercase						,     **_lowercase							):





													'''simple docstring'''

													return sample



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Any									=       self.alphas[timestep_index]
													__a		:		Dict									=       self.betas[timestep_index]

													__a		:		Any									=       self.alphas[prev_timestep_index]
													__a		:		Any									=       self.betas[prev_timestep_index]

													__a		:		Dict									=       (sample - sigma * ets) / max(_lowercase						,     1e-8							)
													__a		:		List[str]									=       next_alpha * pred + ets * next_sigma

													return prev_sample





						def __len__(self							):





													'''simple docstring'''

													return self.config.num_train_timesteps






"""simple docstring"""

import math
import sys

import cva
import numpy as np





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: float					):
							# For applying gaussian function for each element in matrix.
							__a		:		int									=       math.sqrt(_lowerCamelCase					)
							__a		:		Any									=       1 / (sigma * math.sqrt(2 * math.pi					))
							return cons * np.exp(-((img / sigma) ** 2) * 0.5					)





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int					):
							__a		:		Any									=       kernel_size // 2
							return img[x - half : x + half + 1, y - half : y + half + 1]





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: float					):
							# Creates a gaussian kernel of given dimension.
							__a		:		int									=       np.zeros((kernel_size, kernel_size)					)
							for i in range(0  ,       _lowerCamelCase					):
														for j in range(0  ,       _lowerCamelCase					):
																					__a		:		Any									=       math.sqrt(
																					    abs(i - kernel_size // 2					) ** 2 + abs(j - kernel_size // 2					) ** 2					)
							return vec_gaussian(_lowerCamelCase  ,       _lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: int  ,       ):
							__a		:		Tuple									=       np.zeros(img.shape					)
							__a		:		Optional[int]									=       get_gauss_kernel(_lowerCamelCase  ,       _lowerCamelCase					)
							__a							,     __a		:		int									=       img.shape
							for i in range(kernel_size // 2  ,       size_x - kernel_size // 2					):
														for j in range(kernel_size // 2  ,       size_y - kernel_size // 2					):
																					__a		:		List[str]									=       get_slice(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Any									=       img_s - img_s[kernel_size // 2, kernel_size // 2]
																					__a		:		Optional[Any]									=       vec_gaussian(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Optional[Any]									=       np.multiply(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Any									=       np.multiply(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Tuple									=       np.sum(_lowerCamelCase					) / np.sum(_lowerCamelCase					)
																					__a		:		Optional[Any]									=       val
							return imga





def       __magic_name__    (   _lowerCamelCase				: list					):
							__a		:		Optional[Any]									=       args[1] if args[1:] else """../image_data/lena.jpg"""
							__a		:		Union[str, Any]									=       float(args[2]					) if args[2:] else 1.0
							__a		:		Optional[int]									=       float(args[3]					) if args[3:] else 1.0
							if args[4:]:
														__a		:		Any									=       int(args[4]					)
														__a		:		Any									=       kernel_size + abs(kernel_size % 2 - 1					)
							else:
														__a		:		Optional[int]									=       5
							return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
	lowercase__   ,   lowercase__   ,   lowercase__   ,   lowercase__			   =  parse_args(sys.argv)
	lowercase__			   =  cva.imread(filename, 0)
	cva.imshow("input image", img)

	lowercase__			   =  img / 255
	lowercase__			   =  out.astype("float32")
	lowercase__			   =  bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
	lowercase__			   =  out * 255
	lowercase__			   =  np.uinta(out)
	cva.imshow("output image", out)
	cva.waitKey(0)
	cva.destroyAllWindows()

