


'''simple docstring'''


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 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE  , __SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							1






  @register_to_config
  def __init__(							self :       str	,_a :       Dict=2000	,_a :       Optional[Any]=0.1	,_a :       List[str]=20	,_a :       Tuple=1e-3 ):

        '''simple docstring'''




        A_						:  List[str]          =				None
        A_						:  Tuple          =				None
        A_						:  Any          =				None






  def        _a							(							self :       int	,_a :       Optional[int]	,_a :       Union[str, torch.device] = None ):

        '''simple docstring'''




        A_						:  int          =				torch.linspace(1	,self.config.sampling_eps	,_a	,device=_a )






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

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

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

        A_						:  List[str]          =				torch.sqrt(_a )
        A_						:  Any          =				drift - diffusion**2 * score
        A_						:  Optional[int]          =				x + drift * dt

        # add noise
        A_						:  Optional[Any]          =				randn_tensor(x.shape	,layout=x.layout	,generator=_a	,device=x.device	,dtype=x.dtype )
        A_						:  Dict          =				x_mean + diffusion * math.sqrt(-dt ) * noise

        return x, x_mean






  def __len__(							self :       List[Any] ):

        '''simple docstring'''




        return self.config.num_train_timesteps




'''simple docstring'''


import baseaa




def      lowerCamelCase					(      lowerCamelCase  :      str):
      return baseaa.aaaencode(string.encode("""utf-8"""))




def      lowerCamelCase					(      lowerCamelCase  :      bytes):
      return baseaa.aaadecode(lowerCamelCase).decode("""utf-8""")


if __name__ == "__main__":
     import doctest

     doctest.testmod()
