

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 	_A						(					__magic_name__):
  SCREAMING_SNAKE_CASE      : Tuple    =					(LMSDiscreteScheduler,)
  SCREAMING_SNAKE_CASE      : Union[str, Any]    =					10

  def 	UpperCAmelCase      (			self	,   **_SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					str          =       {
            'num_train_timesteps': 1100,
            'beta_start': 0.0001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
        }

        config.update(**_SCREAMING_SNAKE_CASE   )
        return config

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        for timesteps in [10, 50, 100, 1000]:
              self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE   )

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]	,   [0.0002, 0.002, 0.02]   ):
              self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE	,   beta_end=_SCREAMING_SNAKE_CASE   )

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







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

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







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

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        for t in [0, 500, 800]:
              self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE   )

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					int          =       self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       self.get_scheduler_config()
        SCREAMING_SNAKE_CASE_			:					int          =       scheduler_class(**_SCREAMING_SNAKE_CASE   )

        scheduler.set_timesteps(self.num_inference_steps   )

        SCREAMING_SNAKE_CASE_			:					Dict          =       self.dummy_model()
        SCREAMING_SNAKE_CASE_			:					Any          =       self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps   ):
              SCREAMING_SNAKE_CASE_			:					List[str]          =       scheduler.scale_model_input(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )

              SCREAMING_SNAKE_CASE_			:					int          =       model(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )

              SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       scheduler.step(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )
              SCREAMING_SNAKE_CASE_			:					Any          =       output.prev_sample

        SCREAMING_SNAKE_CASE_			:					str          =       torch.sum(torch.abs(_SCREAMING_SNAKE_CASE   )   )
        SCREAMING_SNAKE_CASE_			:					Any          =       torch.mean(torch.abs(_SCREAMING_SNAKE_CASE   )   )

        assert abs(result_sum.item() - 1006.388   ) < 1e-2
        assert abs(result_mean.item() - 1.31   ) < 1e-3

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					List[str]          =       self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       self.get_scheduler_config(prediction_type='v_prediction'   )
        SCREAMING_SNAKE_CASE_			:					Dict          =       scheduler_class(**_SCREAMING_SNAKE_CASE   )

        scheduler.set_timesteps(self.num_inference_steps   )

        SCREAMING_SNAKE_CASE_			:					Tuple          =       self.dummy_model()
        SCREAMING_SNAKE_CASE_			:					str          =       self.dummy_sample_deter * scheduler.init_noise_sigma

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

              SCREAMING_SNAKE_CASE_			:					Dict          =       model(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )

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

        SCREAMING_SNAKE_CASE_			:					int          =       torch.sum(torch.abs(_SCREAMING_SNAKE_CASE   )   )
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       torch.mean(torch.abs(_SCREAMING_SNAKE_CASE   )   )

        assert abs(result_sum.item() - 0.0017   ) < 1e-2
        assert abs(result_mean.item() - 2.2_676e-06   ) < 1e-3

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					str          =       self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       self.get_scheduler_config()
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       scheduler_class(**_SCREAMING_SNAKE_CASE   )

        scheduler.set_timesteps(self.num_inference_steps	,   device=_SCREAMING_SNAKE_CASE   )

        SCREAMING_SNAKE_CASE_			:					Tuple          =       self.dummy_model()
        SCREAMING_SNAKE_CASE_			:					Dict          =       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       sample.to(_SCREAMING_SNAKE_CASE   )

        for i, t in enumerate(scheduler.timesteps   ):
              SCREAMING_SNAKE_CASE_			:					Tuple          =       scheduler.scale_model_input(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )

              SCREAMING_SNAKE_CASE_			:					Dict          =       model(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )

              SCREAMING_SNAKE_CASE_			:					List[Any]          =       scheduler.step(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )
              SCREAMING_SNAKE_CASE_			:					List[str]          =       output.prev_sample

        SCREAMING_SNAKE_CASE_			:					Dict          =       torch.sum(torch.abs(_SCREAMING_SNAKE_CASE   )   )
        SCREAMING_SNAKE_CASE_			:					Any          =       torch.mean(torch.abs(_SCREAMING_SNAKE_CASE   )   )

        assert abs(result_sum.item() - 1006.388   ) < 1e-2
        assert abs(result_mean.item() - 1.31   ) < 1e-3



  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					List[str]          =       self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       self.get_scheduler_config()
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       scheduler_class(**_SCREAMING_SNAKE_CASE	,   use_karras_sigmas=_SCREAMING_SNAKE_CASE   )

        scheduler.set_timesteps(self.num_inference_steps	,   device=_SCREAMING_SNAKE_CASE   )

        SCREAMING_SNAKE_CASE_			:					List[str]          =       self.dummy_model()
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       self.dummy_sample_deter.to(_SCREAMING_SNAKE_CASE   ) * scheduler.init_noise_sigma
        SCREAMING_SNAKE_CASE_			:					List[str]          =       sample.to(_SCREAMING_SNAKE_CASE   )

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

              SCREAMING_SNAKE_CASE_			:					List[Any]          =       model(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )

              SCREAMING_SNAKE_CASE_			:					List[Any]          =       scheduler.step(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )
              SCREAMING_SNAKE_CASE_			:					List[str]          =       output.prev_sample

        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       torch.sum(torch.abs(_SCREAMING_SNAKE_CASE   )   )
        SCREAMING_SNAKE_CASE_			:					Any          =       torch.mean(torch.abs(_SCREAMING_SNAKE_CASE   )   )

        assert abs(result_sum.item() - 3812.9927   ) < 2e-2
        assert abs(result_mean.item() - 4.9648   ) < 1e-3









from math import sqrt

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					List[str]          =       [1]
      for i in range(2   ,				int(sqrt(a					)					) + 1   ,				1					):
            if number % i == 0:
                  values.append(a					)
                  if int(number // i					) != i:
                        values.append(int(number // i					)					)
      return sorted(a					)

def 				A_					(							a					):

      """simple docstring"""



      return sum(factors(a					)					) > n

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       factors(a					)
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       len(a					)
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       [[0 for i in range(number + 1					)] for j in range(r + 1					)]
      for i in range(r + 1					):
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       True

      for i in range(1   ,				number + 1					):
            SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       False

      for i in range(1   ,				r + 1					):
            for j in range(1   ,				number + 1					):
                  if j < values[i - 1]:
                        SCREAMING_SNAKE_CASE_			:					Dict          =       subset[i - 1][j]
                  else:
                        SCREAMING_SNAKE_CASE_			:					List[str]          =       subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

      return subset[r][number] != 0

def 				A_					(							a					):

      """simple docstring"""



      return abundant(a					) and not semi_perfect(a					)


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
    for number in (69, 70, 71):
        print(F'{number} is {"" if weird(number) else "not "}weird.')






