
"""simple docstring"""







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

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

import torch

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



@dataclass
class 		UpperCAmelCase_						(lowerCamelCase_    ):
 """simple docstring"""




 UpperCamelCase_    :  torch.FloatTensor
 UpperCamelCase_    :  torch.FloatTensor



class 		UpperCAmelCase_						(lowerCamelCase_	,			lowerCamelCase_    ):
 """simple docstring"""




 UpperCamelCase_    :  Any					 =     1




 @register_to_config
 def __init__(			self							:			str  , a_							:			int = 20_00  , a_							:			float = 0.15  , a_							:			float = 0.01  , a_							:			float = 1348.0  , a_							:			float = 1E-5  , a_							:			int = 1  , )->       Tuple:



      """simple docstring"""

      UpperCAmelCase_    :     Any            =	sigma_max

      # setable values
      UpperCAmelCase_    :     Tuple            =	None

      self.set_sigmas(a_  , a_  , a_  , a_			)




 def 	a   (			self							:			str  , a_							:			torch.FloatTensor  , a_							:			Optional[int] = None			)->       torch.FloatTensor:



      """simple docstring"""

      return sample




 def 	a   (			self							:			Tuple  , a_							:			int  , a_							:			float = None  , a_							:			Union[str, torch.device] = None			)->       int:



      """simple docstring"""

      UpperCAmelCase_    :     List[str]            =	sampling_eps if sampling_eps is not None else self.config.sampling_eps

      UpperCAmelCase_    :     Union[str, Any]            =	torch.linspace(1  , a_  , a_  , device=a_			)




 def 	a   (			self							:			List[str]  , a_							:			int  , a_							:			float = None  , a_							:			float = None  , a_							:			float = None			)->       str:



      """simple docstring"""

      UpperCAmelCase_    :     str            =	sigma_min if sigma_min is not None else self.config.sigma_min
      UpperCAmelCase_    :     Optional[Any]            =	sigma_max if sigma_max is not None else self.config.sigma_max
      UpperCAmelCase_    :     Union[str, Any]            =	sampling_eps if sampling_eps is not None else self.config.sampling_eps
      if self.timesteps is None:
           self.set_timesteps(a_  , a_			)

      UpperCAmelCase_    :     Optional[int]            =	sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
      UpperCAmelCase_    :     Optional[int]            =	torch.exp(torch.linspace(math.log(a_			)  , math.log(a_			)  , a_			)			)
      UpperCAmelCase_    :     Optional[int]            =	torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]			)




 def 	a   (			self							:			Union[str, Any]  , a_							:			Union[str, Any]  , a_							:			Union[str, Any]			)->       Dict:



      """simple docstring"""

      return torch.where(
          timesteps == 0  , torch.zeros_like(t.to(timesteps.device			)			)  , self.discrete_sigmas[timesteps - 1].to(timesteps.device			)  , )




 def 	a   (			self							:			Optional[int]  , a_							:			torch.FloatTensor  , a_							:			int  , a_							:			torch.FloatTensor  , a_							:			Optional[torch.Generator] = None  , a_							:			bool = True  , )->       Union[SdeVeOutput, Tuple]:



      """simple docstring"""

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

      UpperCAmelCase_    :     Any            =	timestep * torch.ones(
          sample.shape[0]  , device=sample.device			)  # torch.repeat_interleave(timestep, sample.shape[0])
      UpperCAmelCase_    :     int            =	(timestep * (len(self.timesteps			) - 1)).long()

      # mps requires indices to be in the same device, so we use cpu as is the default with cuda
      UpperCAmelCase_    :     List[str]            =	timesteps.to(self.discrete_sigmas.device			)

      UpperCAmelCase_    :     Optional[Any]            =	self.discrete_sigmas[timesteps].to(sample.device			)
      UpperCAmelCase_    :     Optional[int]            =	self.get_adjacent_sigma(a_  , a_			).to(sample.device			)
      UpperCAmelCase_    :     List[Any]            =	torch.zeros_like(a_			)
      UpperCAmelCase_    :     Optional[int]            =	(sigma**2 - adjacent_sigma**2) ** 0.5

      # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
      # also equation 47 shows the analog from SDE models to ancestral sampling methods
      UpperCAmelCase_    :     str            =	diffusion.flatten()
      while len(diffusion.shape			) < len(sample.shape			):
           UpperCAmelCase_    :     Union[str, Any]            =	diffusion.unsqueeze(-1			)
      UpperCAmelCase_    :     Dict            =	drift - diffusion**2 * model_output

      #  equation 6: sample noise for the diffusion term of
      UpperCAmelCase_    :     Optional[Any]            =	randn_tensor(
          sample.shape  , layout=sample.layout  , generator=a_  , device=sample.device  , dtype=sample.dtype			)
      UpperCAmelCase_    :     Optional[int]            =	sample - drift  # subtract because `dt` is a small negative timestep
      # TODO is the variable diffusion the correct scaling term for the noise?
      UpperCAmelCase_    :     int            =	prev_sample_mean + diffusion * noise  # add impact of diffusion field g

      if not return_dict:
           return (prev_sample, prev_sample_mean)

      return SdeVeOutput(prev_sample=a_  , prev_sample_mean=a_			)




 def 	a   (			self							:			int  , a_							:			torch.FloatTensor  , a_							:			torch.FloatTensor  , a_							:			Optional[torch.Generator] = None  , a_							:			bool = True  , )->       Union[SchedulerOutput, Tuple]:



      """simple docstring"""

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

      # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
      # sample noise for correction
      UpperCAmelCase_    :     List[str]            =	randn_tensor(sample.shape  , layout=sample.layout  , generator=a_			).to(sample.device			)

      # compute step size from the model_output, the noise, and the snr
      UpperCAmelCase_    :     Optional[int]            =	torch.norm(model_output.reshape(model_output.shape[0]  , -1			)  , dim=-1			).mean()
      UpperCAmelCase_    :     List[Any]            =	torch.norm(noise.reshape(noise.shape[0]  , -1			)  , dim=-1			).mean()
      UpperCAmelCase_    :     Optional[Any]            =	(self.config.snr * noise_norm / grad_norm) ** 2 * 2
      UpperCAmelCase_    :     Tuple            =	step_size * torch.ones(sample.shape[0]			).to(sample.device			)
      # self.repeat_scalar(step_size, sample.shape[0])

      # compute corrected sample: model_output term and noise term
      UpperCAmelCase_    :     int            =	step_size.flatten()
      while len(step_size.shape			) < len(sample.shape			):
           UpperCAmelCase_    :     Dict            =	step_size.unsqueeze(-1			)
      UpperCAmelCase_    :     Union[str, Any]            =	sample + step_size * model_output
      UpperCAmelCase_    :     Union[str, Any]            =	prev_sample_mean + ((step_size * 2) ** 0.5) * noise

      if not return_dict:
           return (prev_sample,)

      return SchedulerOutput(prev_sample=a_			)




 def 	a   (			self							:			Tuple  , a_							:			torch.FloatTensor  , a_							:			torch.FloatTensor  , a_							:			torch.FloatTensor  , )->       torch.FloatTensor:



      """simple docstring"""

      UpperCAmelCase_    :     str            =	timesteps.to(original_samples.device			)
      UpperCAmelCase_    :     List[Any]            =	self.discrete_sigmas.to(original_samples.device			)[timesteps]
      UpperCAmelCase_    :     List[Any]            =	(
          noise * sigmas[:, None, None, None]
          if noise is not None
          else torch.randn_like(a_			) * sigmas[:, None, None, None]
      )
      UpperCAmelCase_    :     List[Any]            =	noise + original_samples
      return noisy_samples






 def __len__(			self							:			Tuple			)->       Tuple:



      """simple docstring"""

      return self.config.num_train_timesteps



"""simple docstring"""







def       A_      (       lowercase    ,			lowercase   )  ->				int:


     """simple docstring"""







     return number | (1 << position)




def       A_      (       lowercase    ,			lowercase   )  ->				int:


     """simple docstring"""







     return number & ~(1 << position)




def       A_      (       lowercase    ,			lowercase   )  ->				int:


     """simple docstring"""







     return number ^ (1 << position)




def       A_      (       lowercase    ,			lowercase   )  ->				bool:


     """simple docstring"""







     return ((number >> position) & 1) == 1




def       A_      (       lowercase    ,			lowercase   )  ->				int:


     """simple docstring"""







     return int((number & (1 << position)) != 0   )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

