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 (     SCREAMING_SNAKE_CASE__						,			SCREAMING_SNAKE_CASE__				):
  SCREAMING_SNAKE_CASE_						:						int    =1






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





       '''simple docstring'''





       UpperCamelCase__					    =   None
       UpperCamelCase__					    =   None
       UpperCamelCase__					    =   None






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ = None						)		->   Tuple:





       '''simple docstring'''





       UpperCamelCase__					    =   torch.linspace(1 ,					self.config.sampling_eps ,					__lowerCamelCase ,					device=__lowerCamelCase						)






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_=None						)		->   Tuple:





       '''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(__lowerCamelCase						)
       UpperCamelCase__					    =   drift - diffusion**2 * score
       UpperCamelCase__					    =   x + drift * dt

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

       return x, x_mean






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





       '''simple docstring'''





       return self.config.num_train_timesteps










"""simple docstring"""



def 						__lowercase	(  snake_case_    :    int							,snake_case_    :    int		)  ->int:



    '''simple docstring'''







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







def 						__lowercase	(  )  ->None:



    '''simple docstring'''







    assert and_gate(0							,0		) == 0
    assert and_gate(0							,1		) == 0
    assert and_gate(1							,0		) == 0
    assert and_gate(1							,1		) == 1


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




