# 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 					A							(    UpperCAmelCase_		,    UpperCAmelCase_					):
   __UpperCAmelCase			:			Optional[Any]              =      1


   @register_to_config
   def __init__(self					:						List[str]	,  __UpperCAmelCase					:						Union[str, Any]=2_0_0_0	,  __UpperCAmelCase					:						str=0.1	,  __UpperCAmelCase					:						Optional[Any]=2_0	,  __UpperCAmelCase					:						str=1E-3		)						->    Dict:
         """simple docstring"""



         UpperCAmelCase__								=  None
         UpperCAmelCase__								=  None
         UpperCAmelCase__								=  None


   def       lowercase_				(self					:						Optional[int]	,  __UpperCAmelCase					:						List[str]	,  __UpperCAmelCase					:						Optional[Any] = None		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  torch.linspace(1	,  self.config.sampling_eps	,  _a	,  device=_a		)


   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						Optional[Any]	,  __UpperCAmelCase					:						Optional[int]	,  __UpperCAmelCase					:						str	,  __UpperCAmelCase					:						Optional[Any]=None		)						->    str:
         """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
         UpperCAmelCase__								=  (
             -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
         )
         UpperCAmelCase__								=  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff		)		)
         UpperCAmelCase__								=  std.flatten()
         while len(std.shape		) < len(score.shape		):
               UpperCAmelCase__								=  std.unsqueeze(-1		)
         UpperCAmelCase__								=  -score / std

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

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

         UpperCAmelCase__								=  torch.sqrt(_a		)
         UpperCAmelCase__								=  drift - diffusion**2 * score
         UpperCAmelCase__								=  x + drift * dt

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

         return x, x_mean





   def __len__(self					:						Dict		)						->    Tuple:
         """simple docstring"""



         return self.config.num_train_timesteps




def        lowerCAmelCase_			(					__A							)					->    str:







      '''simple docstring'''


      return " ".join(input_str.split()[::-1]							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



