






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

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 							A_      (  __lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     torch.FloatTensor
 _SCREAMING_SNAKE_CASE							:     Optional[torch.FloatTensor]	=   None



def        _lowerCamelCase		(  __A      :							List[str]						,						__A      :							Optional[int]=0.999						,						__A      :							List[Any]="cosine"						,						) ->   Tuple:
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(__A      :							Union[str, Any]	):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2	) ** 2

    elif alpha_transform_type == "exp":

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

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

    _UpperCAmelCase			: Optional[int]     					=			[]
    for i in range(__A	):
        _UpperCAmelCase			: Union[str, Any]     					=			i / num_diffusion_timesteps
        _UpperCAmelCase			: List[str]     					=			(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 							A_      (  __lowercase	,				__lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     str	=   [e.name for e in KarrasDiffusionSchedulers]
 _SCREAMING_SNAKE_CASE							:     int	=   1


 @register_to_config
 def __init__(		self							,					_A = 1000							,					_A = 0.0001							,					_A = 0.02							,					_A = "linear"							,					_A = None							,					_A = False							,					_A = "epsilon"							,					_A = "linspace"							,					_A = 0							,					)	->						Union[str, Any]:

     """simple docstring"""



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

     _UpperCAmelCase			: Union[str, Any]     					=			1.0 - self.betas
     _UpperCAmelCase			: int     					=			torch.cumprod(self.alphas							,					dim=0)

     _UpperCAmelCase			: Optional[Any]     					=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
     _UpperCAmelCase			: Any     					=			np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
     _UpperCAmelCase			: str     					=			torch.from_numpy(_A)

     # setable values
     _UpperCAmelCase			: int     					=			None
     _UpperCAmelCase			: List[Any]     					=			use_karras_sigmas
     self.set_timesteps(_A							,					_A)
     _UpperCAmelCase			: List[Any]     					=			[]
     _UpperCAmelCase			: Union[str, Any]     					=			False


 @property
 def 	snake_case__			(		self)	->						Optional[int]:

     """simple docstring"""



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

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


 def 	snake_case__			(		self							,					_A							,					_A)	->						torch.FloatTensor:

     """simple docstring"""



     if isinstance(_A							,					torch.Tensor):
         _UpperCAmelCase			: Optional[Any]     					=			timestep.to(self.timesteps.device)
     _UpperCAmelCase			: Any     					=			(self.timesteps == timestep).nonzero().item()
     _UpperCAmelCase			: Tuple     					=			self.sigmas[step_index]
     _UpperCAmelCase			: List[str]     					=			sample / ((sigma**2 + 1) ** 0.5)
     _UpperCAmelCase			: Optional[int]     					=			True
     return sample


 def 	snake_case__			(		self							,					_A							,					_A							,					_A)	->						List[Any]:

     """simple docstring"""



     def lms_derivative(_A):
         _UpperCAmelCase			: List[Any]     					=			1.0
         for k in range(_A):
             if current_order == k:
                 continue
             prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
         return prod

     _UpperCAmelCase			: str     					=			integrate.quad(_A							,					self.sigmas[t]							,					self.sigmas[t + 1]							,					epsrel=1e-4)[0]

     return integrated_coeff


 def 	snake_case__			(		self							,					_A							,					_A = None)	->						List[Any]:

     """simple docstring"""



     _UpperCAmelCase			: Optional[Any]     					=			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":
         _UpperCAmelCase			: List[Any]     					=			np.linspace(0							,					self.config.num_train_timesteps - 1							,					_A							,					dtype=_A)[
             ::-1
         ].copy()
     elif self.config.timestep_spacing == "leading":
         _UpperCAmelCase			: Tuple     					=			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
         _UpperCAmelCase			: Union[str, Any]     					=			(np.arange(0							,					_A) * step_ratio).round()[::-1].copy().astype(_A)
         timesteps += self.config.steps_offset
     elif self.config.timestep_spacing == "trailing":
         _UpperCAmelCase			: Union[str, Any]     					=			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
         _UpperCAmelCase			: Union[str, Any]     					=			(np.arange(self.config.num_train_timesteps							,					0							,					-step_ratio)).round().copy().astype(_A)
         timesteps -= 1
     else:
         raise ValueError(
             f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''')

     _UpperCAmelCase			: Optional[int]     					=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
     _UpperCAmelCase			: str     					=			np.log(_A)
     _UpperCAmelCase			: Optional[Any]     					=			np.interp(_A							,					np.arange(0							,					len(_A))							,					_A)

     if self.use_karras_sigmas:
         _UpperCAmelCase			: str     					=			self._convert_to_karras(in_sigmas=_A)
         _UpperCAmelCase			: List[Any]     					=			np.array([self._sigma_to_t(_A							,					_A) for sigma in sigmas])

     _UpperCAmelCase			: str     					=			np.concatenate([sigmas, [0.0]]).astype(np.floataa)

     _UpperCAmelCase			: List[Any]     					=			torch.from_numpy(_A).to(device=_A)
     if str(_A).startswith('''mps'''):
         # mps does not support float64
         _UpperCAmelCase			: Tuple     					=			torch.from_numpy(_A).to(_A							,					dtype=torch.floataa)
     else:
         _UpperCAmelCase			: Dict     					=			torch.from_numpy(_A).to(device=_A)

     _UpperCAmelCase			: List[str]     					=			[]


 def 	snake_case__			(		self							,					_A							,					_A)	->						Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase			: Optional[int]     					=			np.log(_A)

     # get distribution
     _UpperCAmelCase			: str     					=			log_sigma - log_sigmas[:, np.newaxis]

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

     _UpperCAmelCase			: List[Any]     					=			log_sigmas[low_idx]
     _UpperCAmelCase			: str     					=			log_sigmas[high_idx]

     # interpolate sigmas
     _UpperCAmelCase			: Tuple     					=			(low - log_sigma) / (low - high)
     _UpperCAmelCase			: Optional[int]     					=			np.clip(_A							,					0							,					1)

     # transform interpolation to time range
     _UpperCAmelCase			: List[Any]     					=			(1 - w) * low_idx + w * high_idx
     _UpperCAmelCase			: int     					=			t.reshape(sigma.shape)
     return t


 def 	snake_case__			(		self							,					_A)	->						torch.FloatTensor:

     """simple docstring"""



     _UpperCAmelCase			: float     					=			in_sigmas[-1].item()
     _UpperCAmelCase			: float     					=			in_sigmas[0].item()

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


 def 	snake_case__			(		self							,					_A							,					_A							,					_A							,					_A = 4							,					_A = True							,					)	->						Union[LMSDiscreteSchedulerOutput, Tuple]:

     """simple docstring"""



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

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

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
         _UpperCAmelCase			: Dict     					=			sample - sigma * model_output
     elif self.config.prediction_type == "v_prediction":
         # * c_out + input * c_skip
         _UpperCAmelCase			: Union[str, Any]     					=			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
     elif self.config.prediction_type == "sample":
         _UpperCAmelCase			: int     					=			model_output
     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
     _UpperCAmelCase			: List[str]     					=			(sample - pred_original_sample) / sigma
     self.derivatives.append(_A)
     if len(self.derivatives) > order:
         self.derivatives.pop(0)

     # 3. Compute linear multistep coefficients
     _UpperCAmelCase			: str     					=			min(step_index + 1							,					_A)
     _UpperCAmelCase			: Dict     					=			[self.get_lms_coefficient(_A							,					_A							,					_A) for curr_order in range(_A)]

     # 4. Compute previous sample based on the derivatives path
     _UpperCAmelCase			: Optional[int]     					=			sample + sum(
         coeff * derivative for coeff, derivative in zip(_A							,					reversed(self.derivatives)))

     if not return_dict:
         return (prev_sample,)

     return LMSDiscreteSchedulerOutput(prev_sample=_A							,					pred_original_sample=_A)


 def 	snake_case__			(		self							,					_A							,					_A							,					_A							,					)	->						torch.FloatTensor:

     """simple docstring"""



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

     _UpperCAmelCase			: List[str]     					=			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

     _UpperCAmelCase			: List[Any]     					=			original_samples + noise * sigma
     return noisy_samples


 def __len__(		self)	->						str:

     """simple docstring"""



     return self.config.num_train_timesteps












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

def        _lowerCamelCase		(  __A      :							str						,						__A      :							List[str]=3	) ->   List[Any]:
    _UpperCAmelCase			: Tuple     					=			int(mask / 2	)
    # copy image size
    _UpperCAmelCase			: Any     					=			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
            _UpperCAmelCase			: Any     					=			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]	)
            # calculate mask median
            _UpperCAmelCase			: List[str]     					=			sort(__A	)[inta(divide((multiply(__A						,						__A	))						,						2	) + 1	)]
            _UpperCAmelCase			: int     					=			median
    return median_img


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

  # get values with two different mask size
  SCREAMING_SNAKE_CASE								=  median_filter(gray, 3)
  SCREAMING_SNAKE_CASE								=  median_filter(gray, 5)

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




