


'''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 							_UpperCamelCase (						UpperCAmelCase__,UpperCAmelCase__						):

   '''simple docstring'''







   a_				:							Tuple    = 1

   @register_to_config
   def __init__(  self  : Tuple       ,  _lowerCamelCase  : List[str]=2_0_0_0       ,  _lowerCamelCase  : List[Any]=0.1       ,  _lowerCamelCase  : List[str]=2_0       ,  _lowerCamelCase  : Any=1E-3							):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						None
         __lowerCamelCase     :      Tuple       						=						None
         __lowerCamelCase     :      Union[str, Any]       						=						None

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






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						torch.linspace(1       ,  self.config.sampling_eps       ,  _lowerCamelCase       ,  device=_lowerCamelCase							)

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

         # compute
         __lowerCamelCase     :      Union[str, Any]       						=						-1.0 / len(self.timesteps							)

         __lowerCamelCase     :      Optional[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     :      List[str]       						=						beta_t.unsqueeze(-1							)
         __lowerCamelCase     :      Union[str, Any]       						=						-0.5 * beta_t * x

         __lowerCamelCase     :      Tuple       						=						torch.sqrt(_lowerCamelCase							)
         __lowerCamelCase     :      str       						=						drift - diffusion**2 * score
         __lowerCamelCase     :      Tuple       						=						x + drift * dt

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

         return x, x_mean


   def __len__(  self  : Tuple							):






         '''simple docstring'''



         return self.config.num_train_timesteps







def   _UpperCAmelCase       (     UpperCAmelCase      :			str      ):
      """simple docstring"""




      __lowerCamelCase     :      List[Any]       						=						hex_num.strip()
      if not hex_num:
            raise ValueError("""No value was passed to the function"""      )

      __lowerCamelCase     :      Dict       						=						hex_num[0] == """-"""
      if is_negative:
            __lowerCamelCase     :      Optional[Any]       						=						hex_num[1:]

      try:
            __lowerCamelCase     :      Any       						=						int(UpperCAmelCase						,	16      )
      except ValueError:
            raise ValueError("""Invalid value was passed to the function"""      )

      __lowerCamelCase     :      List[str]       						=						""""""
      while int_num > 0:
            __lowerCamelCase     :      Dict       						=						str(int_num % 2      ) + bin_str
            int_num >>= 1

      return int(("""-""" + bin_str) if is_negative else bin_str      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()



