code: 
  
  
  import torch
  import torch.nn.functional as F
  
  from diffusers import VQDiffusionScheduler
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  
  
  class 					__lowercase					(       lowercase_     ):
  
  
  
    """simple docstring"""
  
    UpperCamelCase    :				List[str] 					=		(VQDiffusionScheduler,)
  
  
  
  
  
  
  
    def 				__A      (   self		,			**A  )     ->   Optional[Any]:
  
  
  
  
  
  
  
       '''simple docstring'''
  
  
  
  
  
  
  
       lowerCamelCase								=     {
           """num_vec_classes""": 40_97,
           """num_train_timesteps""": 1_00,
       }
  
       config.update(**A  )
       return config
  
  
  
  
  
  
  
    def 				__A      (   self		,			A  )     ->   Optional[Any]:
  
  
  
  
  
  
  
       '''simple docstring'''
  
  
  
  
  
  
  
       lowerCamelCase								=     4
       lowerCamelCase								=     8
       lowerCamelCase								=     8
  
       lowerCamelCase								=     torch.randint(0		,			A		,			(batch_size, height * width)  )
  
       return sample
  
  
  
  
  
  
  
    @property
    def 				__A      (   self  )     ->   int:
  
  
  
  
  
  
  
       '''simple docstring'''
  
  
  
  
  
  
  
       assert False
  
  
  
  
  
  
  
    def 				__A      (   self		,			A  )     ->   Optional[Any]:
  
  
  
  
  
  
  
       '''simple docstring'''
  
  
  
  
  
  
  
       def model(A		,			A		,			*A  ):
          lowerCamelCase		,		lowerCamelCase								=     sample.shape
          lowerCamelCase								=     torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)  )
          lowerCamelCase								=     F.log_softmax(logits.double()		,			dim=1  ).float()
          return return_value
  
       return model
  
  
  
  
  
  
  
    def 				__A      (   self  )     ->   List[str]:
  
  
  
  
  
  
  
       '''simple docstring'''
  
  
  
  
  
  
  
       for timesteps in [2, 5, 1_00, 10_00]:
          self.check_over_configs(num_train_timesteps=A  )
  
  
  
  
  
  
  
    def 				__A      (   self  )     ->   Union[str, Any]:
  
  
  
  
  
  
  
       '''simple docstring'''
  
  
  
  
  
  
  
       for num_vec_classes in [5, 1_00, 10_00, 40_00]:
          self.check_over_configs(num_vec_classes=A  )
  
  
  
  
  
  
  
    def 				__A      (   self  )     ->   int:
  
  
  
  
  
  
  
       '''simple docstring'''
  
  
  
  
  
  
  
       for t in [0, 50, 99]:
          self.check_over_forward(time_step=A  )
  
  
  
  
  
  
  
    def 				__A      (   self  )     ->   Dict:
  
  
  
  
  
  
  
       '''simple docstring'''
  
  
  
  
  
  
  
       pass
  
  
style_context: 
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	int = 100     ,       lowerCamelCase__		:	int = 100			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			return max(
  			    sum(int(lowerCamelCase__			) for x in str(base**power			)			)
  			    for base in range(lowerCamelCase__			)
  			    for power in range(lowerCamelCase__			)			)
  
  
  # Tests
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  