

import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class  __a				(	_SCREAMING_SNAKE_CASE					):





       """simple docstring"""


       __UpperCamelCase				:			List[str]          =	(VQDiffusionScheduler,)






       def  SCREAMING_SNAKE_CASE_  (      self ,				**snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							{
            "num_vec_classes": 4_097,
            "num_train_timesteps": 100,
        }

        config.update(**__A      )
        return config






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Any    =							4
        lowerCAmelCase__      :							str    =							8
        lowerCAmelCase__      :							List[Any]    =							8

        lowerCAmelCase__      :							Optional[Any]    =							torch.randint(0 ,				__A ,				(batch_size, height * width)      )

        return sample






       @property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        assert False






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        def model(snake_case ,				snake_case ,				*snake_case      ):
         lowerCAmelCase__ ,	lowerCAmelCase__      :							List[str]    =							sample.shape
         lowerCAmelCase__      :							str    =							torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)      )
         lowerCAmelCase__      :							Tuple    =							F.log_softmax(logits.double() ,				dim=1      ).float()
         return return_value

        return model






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        for timesteps in [2, 5, 100, 1_000]:
         self.check_over_configs(num_train_timesteps=__A      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        for num_vec_classes in [5, 100, 1_000, 4_000]:
         self.check_over_configs(num_vec_classes=__A      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        for t in [0, 50, 99]:
         self.check_over_forward(time_step=__A      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass









"""simple docstring"""

def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	int:
 return 1 if input_a == 0 else 0



def 			SCREAMING_SNAKE_CASE       (   ) ->	None:
 assert not_gate(0      ) == 1
 assert not_gate(1      ) == 0


if __name__ == "__main__":
    print(not_gate(0))
    print(not_gate(1))






