

"""simple docstring"""





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

import numpy as np
import torch

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


__UpperCamelCase						:   str					   =	logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class        SCREAMING_SNAKE_CASE      (	SCREAMING_SNAKE_CASE__ ):






					"""simple docstring"""



					lowercase__       				= 42
					lowercase__       				= None

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_=0.999		,  A_="cosine"		,  ):







	if alpha_transform_type == "cosine":

		def alpha_bar_fn(A_				):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(A_				):
			return math.exp(t * -12.0				)

	else:
		raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'				)

	lowerCAmelCase__			:		List[str]						       =			[]
	for i in range(A_				):
		lowerCAmelCase__			:		Union[str, Any]						       =			i / num_diffusion_timesteps
		lowerCAmelCase__			:		Union[str, Any]						       =			(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(A_				) / alpha_bar_fn(A_				)		,  A_				)				)
	return torch.tensor(A_		,  dtype=torch.floataa				)







class        SCREAMING_SNAKE_CASE      (	SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__ ):






					"""simple docstring"""



					lowercase__       				= [e.name for e in KarrasDiffusionSchedulers]
					lowercase__       				= 1







					@register_to_config
					def __init__( self    :       str     ,lowercase_    :       Tuple = 1_0_0_0     ,lowercase_    :       Dict = 0.0001     ,lowercase_    :       Dict = 0.02     ,lowercase_    :       Any = "linear"     ,lowercase_    :       Dict = None     ,lowercase_    :       List[str] = "epsilon"     ,lowercase_    :       Tuple = "linear"     ,lowercase_    :       Optional[Any] = False     ,lowercase_    :       Union[str, Any] = "linspace"     ,lowercase_    :       int = 0     ,):



						if trained_betas is not None:
							lowerCAmelCase__			:		List[Any]						       =			torch.tensor(lowercase_     ,dtype=torch.floataa	)
						elif beta_schedule == "linear":
							lowerCAmelCase__			:		str						       =			torch.linspace(lowercase_     ,lowercase_     ,lowercase_     ,dtype=torch.floataa	)
						elif beta_schedule == "scaled_linear":
							# this schedule is very specific to the latent diffusion model.
							lowerCAmelCase__			:		Optional[Any]						       =			(
							    torch.linspace(beta_start**0.5     ,beta_end**0.5     ,lowercase_     ,dtype=torch.floataa	) ** 2
							)
						elif beta_schedule == "squaredcos_cap_v2":
							# Glide cosine schedule
							lowerCAmelCase__			:		Optional[Any]						       =			betas_for_alpha_bar(lowercase_	)
						else:
							raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'	)

						lowerCAmelCase__			:		List[str]						       =			1.0 - self.betas
						lowerCAmelCase__			:		int						       =			torch.cumprod(self.alphas     ,dim=0	)

						lowerCAmelCase__			:		Union[str, Any]						       =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
						lowerCAmelCase__			:		int						       =			np.concatenate([sigmas[::-1], [0.0]]	).astype(np.floataa	)
						lowerCAmelCase__			:		Dict						       =			torch.from_numpy(lowercase_	)

						# setable values
						lowerCAmelCase__			:		Optional[int]						       =			None
						lowerCAmelCase__			:		List[Any]						       =			np.linspace(0     ,num_train_timesteps - 1     ,lowercase_     ,dtype=lowercase_	)[::-1].copy()
						lowerCAmelCase__			:		int						       =			torch.from_numpy(lowercase_	)
						lowerCAmelCase__			:		List[Any]						       =			False
						lowerCAmelCase__			:		List[Any]						       =			use_karras_sigmas







					@property
					def  __lowerCAmelCase				( self    :       Tuple	):



						if self.config.timestep_spacing in ["linspace", "trailing"]:
							return self.sigmas.max()

						return (self.sigmas.max() ** 2 + 1) ** 0.5







					def  __lowerCAmelCase				( self    :       Union[str, Any]     ,lowercase_    :       str     ,lowercase_    :       int	):



						if isinstance(lowercase_     ,torch.Tensor	):
							lowerCAmelCase__			:		Union[str, Any]						       =			timestep.to(self.timesteps.device	)
						lowerCAmelCase__			:		str						       =			(self.timesteps == timestep).nonzero().item()
						lowerCAmelCase__			:		Tuple						       =			self.sigmas[step_index]

						lowerCAmelCase__			:		Any						       =			sample / ((sigma**2 + 1) ** 0.5)

						lowerCAmelCase__			:		List[Any]						       =			True
						return sample







					def  __lowerCAmelCase				( self    :       List[str]     ,lowercase_    :       Tuple     ,lowercase_    :       List[str] = None	):
						lowerCAmelCase__			:		str						       =			num_inference_steps

						# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
						if self.config.timestep_spacing == "linspace":
							lowerCAmelCase__			:		Tuple						       =			np.linspace(0     ,self.config.num_train_timesteps - 1     ,lowercase_     ,dtype=lowercase_	)[
							    ::-1
							].copy()
						elif self.config.timestep_spacing == "leading":
							lowerCAmelCase__			:		str						       =			self.config.num_train_timesteps // self.num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# casting to int to avoid issues when num_inference_step is power of 3
							lowerCAmelCase__			:		Any						       =			(np.arange(0     ,lowercase_	) * step_ratio).round()[::-1].copy().astype(lowercase_	)
							timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
							lowerCAmelCase__			:		int						       =			self.config.num_train_timesteps / self.num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# casting to int to avoid issues when num_inference_step is power of 3
							lowerCAmelCase__			:		int						       =			(np.arange(self.config.num_train_timesteps     ,0     ,-step_ratio	)).round().copy().astype(lowercase_	)
							timesteps -= 1
						else:
							raise ValueError(
							    F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'	)

						lowerCAmelCase__			:		Optional[Any]						       =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
						lowerCAmelCase__			:		Optional[Any]						       =			np.log(lowercase_	)

						if self.config.interpolation_type == "linear":
							lowerCAmelCase__			:		Optional[int]						       =			np.interp(lowercase_     ,np.arange(0     ,len(lowercase_	)	)     ,lowercase_	)
						elif self.config.interpolation_type == "log_linear":
							lowerCAmelCase__			:		Optional[int]						       =			torch.linspace(np.log(sigmas[-1]	)     ,np.log(sigmas[0]	)     ,num_inference_steps + 1	).exp()
						else:
							raise ValueError(
							    F'{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'
							    ''' \'linear\' or \'log_linear\''''	)

						if self.use_karras_sigmas:
							lowerCAmelCase__			:		Dict						       =			self._convert_to_karras(in_sigmas=lowercase_     ,num_inference_steps=self.num_inference_steps	)
							lowerCAmelCase__			:		Tuple						       =			np.array([self._sigma_to_t(lowercase_     ,lowercase_	) for sigma in sigmas]	)

						lowerCAmelCase__			:		Dict						       =			np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
						lowerCAmelCase__			:		Any						       =			torch.from_numpy(lowercase_	).to(device=lowercase_	)
						if str(lowercase_	).startswith('''mps'''	):
							# mps does not support float64
							lowerCAmelCase__			:		List[str]						       =			torch.from_numpy(lowercase_	).to(lowercase_     ,dtype=torch.floataa	)
						else:
							lowerCAmelCase__			:		Optional[int]						       =			torch.from_numpy(lowercase_	).to(device=lowercase_	)







					def  __lowerCAmelCase				( self    :       List[str]     ,lowercase_    :       Dict     ,lowercase_    :       Optional[int]	):
						lowerCAmelCase__			:		int						       =			np.log(lowercase_	)

						# get distribution
						lowerCAmelCase__			:		int						       =			log_sigma - log_sigmas[:, np.newaxis]

						# get sigmas range
						lowerCAmelCase__			:		Union[str, Any]						       =			np.cumsum((dists >= 0)     ,axis=0	).argmax(axis=0	).clip(max=log_sigmas.shape[0] - 2	)
						lowerCAmelCase__			:		List[str]						       =			low_idx + 1

						lowerCAmelCase__			:		int						       =			log_sigmas[low_idx]
						lowerCAmelCase__			:		Optional[Any]						       =			log_sigmas[high_idx]

						# interpolate sigmas
						lowerCAmelCase__			:		List[Any]						       =			(low - log_sigma) / (low - high)
						lowerCAmelCase__			:		str						       =			np.clip(lowercase_     ,0     ,1	)

						# transform interpolation to time range
						lowerCAmelCase__			:		Dict						       =			(1 - w) * low_idx + w * high_idx
						lowerCAmelCase__			:		Tuple						       =			t.reshape(sigma.shape	)
						return t







					def  __lowerCAmelCase				( self    :       Optional[int]     ,lowercase_    :       Tuple     ,lowercase_    :       List[str]	):
						lowerCAmelCase__			:		str						       =			in_sigmas[-1].item()
						lowerCAmelCase__			:		Dict						       =			in_sigmas[0].item()

						lowerCAmelCase__			:		Optional[Any]						       =			7.0  # 7.0 is the value used in the paper
						lowerCAmelCase__			:		Tuple						       =			np.linspace(0     ,1     ,lowercase_	)
						lowerCAmelCase__			:		Tuple						       =			sigma_min ** (1 / rho)
						lowerCAmelCase__			:		List[Any]						       =			sigma_max ** (1 / rho)
						lowerCAmelCase__			:		Union[str, Any]						       =			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
						return sigmas







					def  __lowerCAmelCase				( self    :       Union[str, Any]     ,lowercase_    :       Optional[int]     ,lowercase_    :       Dict     ,lowercase_    :       Dict     ,lowercase_    :       List[Any] = 0.0     ,lowercase_    :       Union[str, Any] = 0.0     ,lowercase_    :       List[Any] = float('''inf'''	)     ,lowercase_    :       Optional[int] = 1.0     ,lowercase_    :       List[str] = None     ,lowercase_    :       Optional[int] = True     ,):



						if (
						    isinstance(lowercase_     ,lowercase_	)
						    or isinstance(lowercase_     ,torch.IntTensor	)
						    or isinstance(lowercase_     ,torch.LongTensor	)
						):
							raise ValueError(
							    (
							        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
							        ''' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'''
							        ''' one of the `scheduler.timesteps` as a timestep.'''
							    )     ,)

						if not self.is_scale_input_called:
							logger.warning(
							    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
							    '''See `StableDiffusionPipeline` for a usage example.'''	)

						if isinstance(lowercase_     ,torch.Tensor	):
							lowerCAmelCase__			:		Union[str, Any]						       =			timestep.to(self.timesteps.device	)

						lowerCAmelCase__			:		Tuple						       =			(self.timesteps == timestep).nonzero().item()
						lowerCAmelCase__			:		List[Any]						       =			self.sigmas[step_index]

						lowerCAmelCase__			:		Any						       =			min(s_churn / (len(self.sigmas	) - 1)     ,2**0.5 - 1	) if s_tmin <= sigma <= s_tmax else 0.0

						lowerCAmelCase__			:		Any						       =			randn_tensor(
						    model_output.shape     ,dtype=model_output.dtype     ,device=model_output.device     ,generator=lowercase_	)

						lowerCAmelCase__			:		Union[str, Any]						       =			noise * s_noise
						lowerCAmelCase__			:		Union[str, Any]						       =			sigma * (gamma + 1)

						if gamma > 0:
							lowerCAmelCase__			:		Optional[Any]						       =			sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						# NOTE: "original_sample" should not be an expected prediction_type but is left in for
						# backwards compatibility
						if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
							lowerCAmelCase__			:		Optional[Any]						       =			model_output
						elif self.config.prediction_type == "epsilon":
							lowerCAmelCase__			:		Any						       =			sample - sigma_hat * model_output
						elif self.config.prediction_type == "v_prediction":
							# * c_out + input * c_skip
							lowerCAmelCase__			:		Optional[int]						       =			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
						else:
							raise ValueError(
							    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'	)

						# 2. Convert to an ODE derivative
						lowerCAmelCase__			:		Any						       =			(sample - pred_original_sample) / sigma_hat

						lowerCAmelCase__			:		Tuple						       =			self.sigmas[step_index + 1] - sigma_hat

						lowerCAmelCase__			:		Union[str, Any]						       =			sample + derivative * dt

						if not return_dict:
							return (prev_sample,)

						return EulerDiscreteSchedulerOutput(prev_sample=lowercase_     ,pred_original_sample=lowercase_	)







					def  __lowerCAmelCase				( self    :       Optional[int]     ,lowercase_    :       Dict     ,lowercase_    :       Optional[Any]     ,lowercase_    :       str     ,):
						lowerCAmelCase__			:		List[Any]						       =			self.sigmas.to(device=original_samples.device     ,dtype=original_samples.dtype	)
						if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_	):
							# mps does not support float64
							lowerCAmelCase__			:		List[Any]						       =			self.timesteps.to(original_samples.device     ,dtype=torch.floataa	)
							lowerCAmelCase__			:		List[Any]						       =			timesteps.to(original_samples.device     ,dtype=torch.floataa	)
						else:
							lowerCAmelCase__			:		Any						       =			self.timesteps.to(original_samples.device	)
							lowerCAmelCase__			:		Dict						       =			timesteps.to(original_samples.device	)

						lowerCAmelCase__			:		List[Any]						       =			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

						lowerCAmelCase__			:		List[str]						       =			sigmas[step_indices].flatten()
						while len(sigma.shape	) < len(original_samples.shape	):
							lowerCAmelCase__			:		str						       =			sigma.unsqueeze(-1	)

						lowerCAmelCase__			:		Optional[Any]						       =			original_samples + noise * sigma
						return noisy_samples







					def __len__( self    :       int	):
						return self.config.num_train_timesteps







"""simple docstring"""





from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_=3				):
	lowerCAmelCase__			:		Union[str, Any]						       =			int(mask / 2				)
	# copy image size
	lowerCAmelCase__			:		Dict						       =			zeros_like(A_				)
	for i in range(A_		,  gray_img.shape[0] - bd				):
		for j in range(A_		,  gray_img.shape[1] - bd				):
			# get mask according with mask
			lowerCAmelCase__			:		str						       =			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]				)
			# calculate mask median
			lowerCAmelCase__			:		str						       =			sort(A_				)[inta(divide((multiply(A_		,  A_				))		,  2				) + 1				)]
			lowerCAmelCase__			:		List[str]						       =			median
	return median_img


if __name__ == "__main__":
							# read original image
							__UpperCamelCase						:   Dict							   =	imread('''../image_data/lena.jpg''')
							# turn image in gray scale value
							__UpperCamelCase						:   Optional[Any]							   =	cvtColor(img, COLOR_BGR2GRAY)

							# get values with two different mask size
							__UpperCamelCase						:   int							   =	median_filter(gray, 3)
							__UpperCamelCase						:   Tuple							   =	median_filter(gray, 5)

							# show result images
							imshow('''median filter with 3x3 mask''', medianaxa)
							imshow('''median filter with 5x5 mask''', medianaxa)
							waitKey(0)




