





# 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 UpperCAmelCase_				( _SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE ):
  __lowerCAmelCase   :						str							=   1


  @register_to_config
  def __init__(							self ,__snake_case=2_0_0_0 ,__snake_case=0.1 ,__snake_case=2_0 ,__snake_case=1E-3 ):



       """simple docstring"""





       A_          =       None
       A_          =       None
       A_          =       None


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case = None ):



       """simple docstring"""





       A_          =       torch.linspace(1 ,self.config.sampling_eps ,__snake_case ,device=__snake_case )


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case=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
       A_          =       (
           -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
       )
       A_          =       torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) )
       A_          =       std.flatten()
       while len(std.shape ) < len(score.shape ):
            A_          =       std.unsqueeze(-1 )
       A_          =       -score / std

       # compute
       A_          =       -1.0 / len(self.timesteps )

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

       A_          =       torch.sqrt(__snake_case )
       A_          =       drift - diffusion**2 * score
       A_          =       x + drift * dt

       # add noise
       A_          =       randn_tensor(x.shape ,layout=x.layout ,generator=__snake_case ,device=x.device ,dtype=x.dtype )
       A_          =       x_mean + diffusion * math.sqrt(-dt ) * noise

       return x, x_mean




  def __len__(							self ):



       """simple docstring"""





       return self.config.num_train_timesteps










def      UpperCAmelCase_    (    _UpperCAmelCase							:Any  ,						_UpperCAmelCase							:Dict				)	->				Union[str, Any]:
     '''simple docstring'''


     A_          =       [1]
     for i in range(2  ,						_UpperCAmelCase				):
          factorials.append(factorials[-1] * i				)
     assert 0 <= k < factorials[-1] * n, "k out of bounds"

     A_          =       []
     A_          =       list(range(_UpperCAmelCase				)				)

     # Find permutation
     while factorials:
          A_          =       factorials.pop()
          A_   ,    A_          =       divmod(_UpperCAmelCase  ,						_UpperCAmelCase				)
          permutation.append(elements[number]				)
          elements.remove(elements[number]				)
     permutation.append(elements[0]				)

     return permutation


if __name__ == "__main__":
      import doctest

      doctest.testmod()



