


'''simple docstring'''



def     UpperCAmelCase__      (			UpperCAmelCase_					:       list							)  ->	list:
     __lowerCamelCase     :			Dict              =	len(UpperCAmelCase_							)

     # Set the gap value to be decreased by a factor of 1.3
     # after each iteration
     __lowerCamelCase     :			int              =	1.3

     # Continue sorting until the gap is 1
     while gap > 1:
          # Decrease the gap value
          __lowerCamelCase     :			str              =	int(gap / shrink							)

          # Sort the elements using insertion sort
          for i in range(UpperCAmelCase_   ,					len(UpperCAmelCase_							)							):
               __lowerCamelCase     :			str              =	collection[i]
               __lowerCamelCase     :			Optional[int]              =	i
               while j >= gap and collection[j - gap] > temp:
                    __lowerCamelCase     :			Any              =	collection[j - gap]
                    j -= gap
               __lowerCamelCase     :			Any              =	temp

     return collection


if __name__ == "__main__":
      import doctest

      doctest.testmod()



'''simple docstring'''



import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class      UpperCAmelCase_						(_UpperCAmelCase						):





    """simple docstring"""






    lowerCamelCase						:      Optional[Any]							  =    (KDPMaAncestralDiscreteScheduler,)
    lowerCamelCase						:      Tuple							  =    1_0







    def    lowercase_       (							self    ,  **SCREAMING_SNAKE_CASE_			)       ->  Tuple:
         __lowerCamelCase     :			Union[str, Any]              =	{
             'num_train_timesteps': 11_00,
             'beta_start': 0.0_0_0_1,
             'beta_end': 0.0_2,
             'beta_schedule': 'linear',
         }

         config.update(**SCREAMING_SNAKE_CASE_			)
         return config







    def    lowercase_       (							self			)       ->  Union[str, Any]:
         for timesteps in [10, 50, 1_00, 10_00]:
              self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  Dict:
         for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]    ,  [0.0_0_0_2, 0.0_0_2, 0.0_2]			):
              self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_    ,  beta_end=SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  List[str]:
         for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  int:
         if torch_device == "mps":
              return
         __lowerCamelCase     :			Union[str, Any]              =	self.scheduler_classes[0]
         __lowerCamelCase     :			Optional[Any]              =	self.get_scheduler_config()
         __lowerCamelCase     :			Optional[int]              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)

         scheduler.set_timesteps(self.num_inference_steps			)

         __lowerCamelCase     :			int              =	torch.manual_seed(0			)

         __lowerCamelCase     :			int              =	self.dummy_model()
         __lowerCamelCase     :			List[Any]              =	self.dummy_sample_deter * scheduler.init_noise_sigma
         __lowerCamelCase     :			Any              =	sample.to(SCREAMING_SNAKE_CASE_			)

         for i, t in enumerate(scheduler.timesteps			):
              __lowerCamelCase     :			Dict              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			Dict              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			Union[str, Any]              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
              __lowerCamelCase     :			Any              =	output.prev_sample

         __lowerCamelCase     :			Tuple              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
         __lowerCamelCase     :			Optional[int]              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

         assert abs(result_sum.item() - 1_3_8_4_9.3_8_7_7			) < 1E-2
         assert abs(result_mean.item() - 1_8.0_3_3_1			) < 5E-3







    def    lowercase_       (							self			)       ->  List[str]:
         for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  List[str]:
         if torch_device == "mps":
              return
         __lowerCamelCase     :			Union[str, Any]              =	self.scheduler_classes[0]
         __lowerCamelCase     :			Optional[Any]              =	self.get_scheduler_config(prediction_type='v_prediction'			)
         __lowerCamelCase     :			str              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)

         scheduler.set_timesteps(self.num_inference_steps			)

         __lowerCamelCase     :			int              =	self.dummy_model()
         __lowerCamelCase     :			int              =	self.dummy_sample_deter * scheduler.init_noise_sigma
         __lowerCamelCase     :			Dict              =	sample.to(SCREAMING_SNAKE_CASE_			)

         __lowerCamelCase     :			int              =	torch.manual_seed(0			)

         for i, t in enumerate(scheduler.timesteps			):
              __lowerCamelCase     :			Any              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			List[str]              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			str              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
              __lowerCamelCase     :			Tuple              =	output.prev_sample

         __lowerCamelCase     :			int              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
         __lowerCamelCase     :			int              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

         assert abs(result_sum.item() - 3_2_8.9_9_7_0			) < 1E-2
         assert abs(result_mean.item() - 0.4_2_8_4			) < 1E-3







    def    lowercase_       (							self			)       ->  Dict:
         if torch_device == "mps":
              return
         __lowerCamelCase     :			Optional[int]              =	self.scheduler_classes[0]
         __lowerCamelCase     :			str              =	self.get_scheduler_config()
         __lowerCamelCase     :			Union[str, Any]              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)

         scheduler.set_timesteps(self.num_inference_steps    ,  device=SCREAMING_SNAKE_CASE_			)
         __lowerCamelCase     :			List[Any]              =	torch.manual_seed(0			)

         __lowerCamelCase     :			Optional[Any]              =	self.dummy_model()
         __lowerCamelCase     :			Dict              =	self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_			) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
              __lowerCamelCase     :			Any              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			Optional[Any]              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			Dict              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
              __lowerCamelCase     :			Union[str, Any]              =	output.prev_sample

         __lowerCamelCase     :			int              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
         __lowerCamelCase     :			Dict              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

         assert abs(result_sum.item() - 1_3_8_4_9.3_8_1_8			) < 1E-1
         assert abs(result_mean.item() - 1_8.0_3_3_1			) < 1E-3






