





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest

class   __snake_case				(			_a   ):






    '''simple docstring'''
    _lowerCAmelCase     =	(VQDiffusionScheduler,)





    def 	A		(   self						:		str			,						**snake_case						:		str							):

      """simple docstring"""





      __lowerCAmelCase			=				{
          '''num_vec_classes''': 4_097,
          '''num_train_timesteps''': 100,
      }

      config.update(**snake_case							)
      return config





    def 	A		(   self						:		List[str]			,						snake_case						:		int							):

      """simple docstring"""





      __lowerCAmelCase			=				4
      __lowerCAmelCase			=				8
      __lowerCAmelCase			=				8

      __lowerCAmelCase			=				torch.randint(0			,						snake_case			,						(batch_size, height * width)							)

      return sample





    @property
    def 	A		(   self						:		Dict							):

      """simple docstring"""





      assert False





    def 	A		(   self						:		List[str]			,						snake_case						:		Optional[int]							):

      """simple docstring"""





      def model(snake_case						:		str			,						snake_case						:		List[str]			,						*snake_case						:		Union[str, Any]							):
        __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						:		str							):

      """simple docstring"""





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





    def 	A		(   self						:		List[Any]							):

      """simple docstring"""





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





    def 	A		(   self						:		Any							):

      """simple docstring"""





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





    def 	A		(   self						:		int							):

      """simple docstring"""





      pass







def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		list):



  '''simple docstring'''






  if len(__lowercase) <= 1:
    return data
  else:
    return [
        *quick_sort([e for e in data[1:] if e <= data[0]]),
        data[0],
        *quick_sort([e for e in data[1:] if e > data[0]]),
    ]


if __name__ == "__main__":
      import doctest

      doctest.testmod()
