




# 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   ):



 '''simple docstring'''


 UpperCamelCase						   =						1

 @register_to_config
 def __init__(				self					:  Dict						,							A_					:  Union[str, Any]=2000						,							A_					:  str=0.1						,							A_					:  Optional[Any]=20						,							A_					:  int=1E-3  )   ->	Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_		     =    None
       lowerCamelCase_		     =    None
       lowerCamelCase_		     =    None

 def 				a__ (				self					:  Optional[int]						,							A_					:  Optional[Any]						,							A_					:  Union[str, torch.device] = None  )   ->	str:

       """simple docstring"""






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

 def 				a__ (				self					:  Optional[Any]						,							A_					:  Optional[Any]						,							A_					:  Any						,							A_					:  Any						,							A_					:  List[Any]=None  )   ->	Union[str, Any]:

       """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_		     =    (
           -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
       )
       lowerCamelCase_		     =    torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff  )  )
       lowerCamelCase_		     =    std.flatten()
       while len(std.shape  ) < len(score.shape  ):
             lowerCamelCase_		     =    std.unsqueeze(-1  )
       lowerCamelCase_		     =    -score / std

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

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

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

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

       return x, x_mean





 def __len__(				self					:  Any  )   ->	Optional[int]:

       """simple docstring"""






       return self.config.num_train_timesteps









def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       int	,							lowercase	:       int     ):


      '''simple docstring'''



      return int((input_a, input_a).count(1     ) != 0     )

def 			_SCREAMING_SNAKE_CASE			(							):


      '''simple docstring'''



      assert or_gate(0	,							0     ) == 0
      assert or_gate(0	,							1     ) == 1
      assert or_gate(1	,							0     ) == 1
      assert or_gate(1	,							1     ) == 1


if __name__ == "__main__":
 print(or_gate(0, 1))
 print(or_gate(1, 0))
 print(or_gate(0, 0))
 print(or_gate(1, 1))



