



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_					( UpperCamelCase__     ,      UpperCamelCase__):
       lowerCamelCase__         =	1







       @register_to_config
       def __init__(     self,    __a=2000,    __a=0.1,    __a=20,    __a=1E-3):

              '''simple docstring'''

              _lowerCAmelCase  :							List[str]				     =					None
              _lowerCAmelCase  :							List[Any]				     =					None
              _lowerCAmelCase  :							Optional[int]				     =					None







       def      snake_case__   (     self,    __a,    __a = None):

              '''simple docstring'''

              _lowerCAmelCase  :							Tuple				     =					torch.linspace(1,    self.config.sampling_eps,    __a,    device=__a)







       def      snake_case__   (     self,    __a,    __a,    __a,    __a=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
              _lowerCAmelCase  :							Dict				     =					(
                  -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
              )
              _lowerCAmelCase  :							Any				     =					torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff))
              _lowerCAmelCase  :							str				     =					std.flatten()
              while len(std.shape) < len(score.shape):
                     _lowerCAmelCase  :							Any				     =					std.unsqueeze(-1)
              _lowerCAmelCase  :							Optional[int]				     =					-score / std

              # compute
              _lowerCAmelCase  :							Optional[Any]				     =					-1.0 / len(self.timesteps)

              _lowerCAmelCase  :							Union[str, Any]				     =					self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
              _lowerCAmelCase  :							Any				     =					beta_t.flatten()
              while len(beta_t.shape) < len(x.shape):
                     _lowerCAmelCase  :							Any				     =					beta_t.unsqueeze(-1)
              _lowerCAmelCase  :							Optional[Any]				     =					-0.5 * beta_t * x

              _lowerCAmelCase  :							Optional[int]				     =					torch.sqrt(__a)
              _lowerCAmelCase  :							Dict				     =					drift - diffusion**2 * score
              _lowerCAmelCase  :							List[Any]				     =					x + drift * dt

              # add noise
              _lowerCAmelCase  :							Tuple				     =					randn_tensor(x.shape,    layout=x.layout,    generator=__a,    device=x.device,    dtype=x.dtype)
              _lowerCAmelCase  :							Dict				     =					x_mean + diffusion * math.sqrt(-dt) * noise

              return x, x_mean







       def __len__(     self):

              '''simple docstring'''

              return self.config.num_train_timesteps





import baseaa






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							return baseaa.aaaencode(string.encode("utf-8"						)						)






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							return baseaa.aaadecode(_lowerCamelCase						).decode("utf-8"						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
