

import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 							a__							( UpperCAmelCase							):







  """simple docstring"""




  UpperCAmelCase__   :			int      							=(KDPMaAncestralDiscreteScheduler,)
  UpperCAmelCase__   :			List[Any]      							=1_0


  def 			_lowercase   (      self     :			Optional[Any]					,					**UpperCAmelCase__     :			Union[str, Any]		)       ->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Optional[int]	  = {
            """num_train_timesteps""": 1_1_0_0,
            """beta_start""": 0.00_01,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
        }

        config.update(**UpperCAmelCase__		)
        return config


  def 			_lowercase   (      self     :			Tuple		)       ->Union[str, Any]:
        """simple docstring"""







        for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
              self.check_over_configs(num_train_timesteps=UpperCAmelCase__		)


  def 			_lowercase   (      self     :			int		)       ->Dict:
        """simple docstring"""







        for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					,					[0.00_02, 0.0_02, 0.02]		):
              self.check_over_configs(beta_start=UpperCAmelCase__					,					beta_end=UpperCAmelCase__		)


  def 			_lowercase   (      self     :			Optional[Any]		)       ->Optional[Any]:
        """simple docstring"""







        for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=UpperCAmelCase__		)


  def 			_lowercase   (      self     :			Dict		)       ->Union[str, Any]:
        """simple docstring"""







        if torch_device == "mps":
              return
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = self.get_scheduler_config()
        SCREAMING_SNAKE_CASE			:   int	  = scheduler_class(**UpperCAmelCase__		)

        scheduler.set_timesteps(self.num_inference_steps		)

        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = torch.manual_seed(0		)

        SCREAMING_SNAKE_CASE			:   Any	  = self.dummy_model()
        SCREAMING_SNAKE_CASE			:   List[Any]	  = self.dummy_sample_deter * scheduler.init_noise_sigma
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = sample.to(UpperCAmelCase__		)

        for i, t in enumerate(scheduler.timesteps		):
              SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = scheduler.scale_model_input(UpperCAmelCase__					,					UpperCAmelCase__		)

              SCREAMING_SNAKE_CASE			:   Any	  = model(UpperCAmelCase__					,					UpperCAmelCase__		)

              SCREAMING_SNAKE_CASE			:   List[str]	  = scheduler.step(UpperCAmelCase__					,					UpperCAmelCase__					,					UpperCAmelCase__					,					generator=UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE			:   int	  = output.prev_sample

        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = torch.sum(torch.abs(UpperCAmelCase__		)		)
        SCREAMING_SNAKE_CASE			:   List[Any]	  = torch.mean(torch.abs(UpperCAmelCase__		)		)

        assert abs(result_sum.item() - 1_38_49.38_77		) < 1e-2
        assert abs(result_mean.item() - 18.03_31		) < 5e-3


  def 			_lowercase   (      self     :			Optional[int]		)       ->Tuple:
        """simple docstring"""







        for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=UpperCAmelCase__		)


  def 			_lowercase   (      self     :			Tuple		)       ->Tuple:
        """simple docstring"""







        if torch_device == "mps":
              return
        SCREAMING_SNAKE_CASE			:   List[Any]	  = self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE			:   int	  = self.get_scheduler_config(prediction_type="""v_prediction"""		)
        SCREAMING_SNAKE_CASE			:   Tuple	  = scheduler_class(**UpperCAmelCase__		)

        scheduler.set_timesteps(self.num_inference_steps		)

        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = self.dummy_model()
        SCREAMING_SNAKE_CASE			:   int	  = self.dummy_sample_deter * scheduler.init_noise_sigma
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = sample.to(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = torch.manual_seed(0		)

        for i, t in enumerate(scheduler.timesteps		):
              SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = scheduler.scale_model_input(UpperCAmelCase__					,					UpperCAmelCase__		)

              SCREAMING_SNAKE_CASE			:   Dict	  = model(UpperCAmelCase__					,					UpperCAmelCase__		)

              SCREAMING_SNAKE_CASE			:   List[Any]	  = scheduler.step(UpperCAmelCase__					,					UpperCAmelCase__					,					UpperCAmelCase__					,					generator=UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE			:   List[Any]	  = output.prev_sample

        SCREAMING_SNAKE_CASE			:   Dict	  = torch.sum(torch.abs(UpperCAmelCase__		)		)
        SCREAMING_SNAKE_CASE			:   int	  = torch.mean(torch.abs(UpperCAmelCase__		)		)

        assert abs(result_sum.item() - 3_28.99_70		) < 1e-2
        assert abs(result_mean.item() - 0.42_84		) < 1e-3




  def 			_lowercase   (      self     :			Optional[int]		)       ->Optional[Any]:
        """simple docstring"""







        if torch_device == "mps":
              return
        SCREAMING_SNAKE_CASE			:   int	  = self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = self.get_scheduler_config()
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = scheduler_class(**UpperCAmelCase__		)

        scheduler.set_timesteps(self.num_inference_steps					,					device=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = torch.manual_seed(0		)

        SCREAMING_SNAKE_CASE			:   Any	  = self.dummy_model()
        SCREAMING_SNAKE_CASE			:   List[Any]	  = self.dummy_sample_deter.to(UpperCAmelCase__		) * scheduler.init_noise_sigma

        for t in scheduler.timesteps:
              SCREAMING_SNAKE_CASE			:   List[Any]	  = scheduler.scale_model_input(UpperCAmelCase__					,					UpperCAmelCase__		)

              SCREAMING_SNAKE_CASE			:   List[str]	  = model(UpperCAmelCase__					,					UpperCAmelCase__		)

              SCREAMING_SNAKE_CASE			:   List[str]	  = scheduler.step(UpperCAmelCase__					,					UpperCAmelCase__					,					UpperCAmelCase__					,					generator=UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE			:   Optional[Any]	  = output.prev_sample

        SCREAMING_SNAKE_CASE			:   List[Any]	  = torch.sum(torch.abs(UpperCAmelCase__		)		)
        SCREAMING_SNAKE_CASE			:   Dict	  = torch.mean(torch.abs(UpperCAmelCase__		)		)

        assert abs(result_sum.item() - 1_38_49.38_18		) < 1e-1
        assert abs(result_mean.item() - 18.03_31		) < 1e-3







import itertools






def        __lowercase   (  _A = 1000000      )  ->    int:
      SCREAMING_SNAKE_CASE			:   Tuple	  = [1]

      for i in itertools.count(len(_A      )      ):
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = 0
            for j in itertools.count(1      ):
                  SCREAMING_SNAKE_CASE			:   str	  = -1 if j % 2 == 0 else +1
                  SCREAMING_SNAKE_CASE			:   Any	  = (j * j * 3 - j) // 2
                  if index > i:
                        break
                  item += partitions[i - index] * sign
                  item %= number
                  index += j
                  if index > i:
                        break
                  item += partitions[i - index] * sign
                  item %= number

            if item == 0:
                  return i
            partitions.append(_A      )

      return 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(F"""{solution() = }""")




