



'''simple docstring'''





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      Tuple				=      (DDPMScheduler,)




  def 							UpperCAmelCase_	(    self  ,**__UpperCAmelCase      )    ->		Tuple:
      lowerCAmelCase__ :  Dict							      =					{
          """num_train_timesteps""": 1000,
          """beta_start""": 0.0_0_0_1,
          """beta_end""": 0.0_2,
          """beta_schedule""": """linear""",
          """variance_type""": """fixed_small""",
          """clip_sample""": True,
      }

      config.update(**__UpperCAmelCase      )
      return config




  def 							UpperCAmelCase_	(    self      )    ->		str:
      for timesteps in [1, 5, 100, 1000]:
          self.check_over_configs(num_train_timesteps=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]  ,[0.0_0_2, 0.0_2, 0.2, 2]      ):
          self.check_over_configs(beta_start=__UpperCAmelCase  ,beta_end=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		int:
      for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		int:
      for variance in ["fixed_small", "fixed_large", "other"]:
          self.check_over_configs(variance_type=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      for clip_sample in [True, False]:
          self.check_over_configs(clip_sample=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      self.check_over_configs(thresholding=__UpperCAmelCase      )
      for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(
                  thresholding=__UpperCAmelCase  ,prediction_type=__UpperCAmelCase  ,sample_max_value=__UpperCAmelCase  ,)




  def 							UpperCAmelCase_	(    self      )    ->		int:
      for prediction_type in ["epsilon", "sample", "v_prediction"]:
          self.check_over_configs(prediction_type=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      for t in [0, 500, 999]:
          self.check_over_forward(time_step=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      lowerCAmelCase__ :  List[Any]							      =					self.scheduler_classes[0]
      lowerCAmelCase__ :  Optional[Any]							      =					self.get_scheduler_config()
      lowerCAmelCase__ :  Dict							      =					scheduler_class(**__UpperCAmelCase      )

      assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 0.0      )      ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(487      ) - 0.0_0_9_7_9      )      ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.0_2      )      ) < 1E-5




  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      lowerCAmelCase__ :  List[str]							      =					self.scheduler_classes[0]
      lowerCAmelCase__ :  int							      =					self.get_scheduler_config()
      lowerCAmelCase__ :  List[Any]							      =					scheduler_class(**__UpperCAmelCase      )

      lowerCAmelCase__ :  Union[str, Any]							      =					len(__UpperCAmelCase      )

      lowerCAmelCase__ :  List[Any]							      =					self.dummy_model()
      lowerCAmelCase__ :  str							      =					self.dummy_sample_deter
      lowerCAmelCase__ :  List[str]							      =					torch.manual_seed(0      )

      for t in reversed(range(__UpperCAmelCase      )      ):
          # 1. predict noise residual
          lowerCAmelCase__ :  int							      =					model(__UpperCAmelCase  ,__UpperCAmelCase      )

          # 2. predict previous mean of sample x_t-1
          lowerCAmelCase__ :  Union[str, Any]							      =					scheduler.step(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,generator=__UpperCAmelCase      ).prev_sample

          # if t > 0:
          #     noise = self.dummy_sample_deter
          #     variance = scheduler.get_variance(t) ** (0.5) * noise
          #
          # sample = pred_prev_sample + variance
          lowerCAmelCase__ :  List[Any]							      =					pred_prev_sample

      lowerCAmelCase__ :  List[Any]							      =					torch.sum(torch.abs(__UpperCAmelCase      )      )
      lowerCAmelCase__ :  List[Any]							      =					torch.mean(torch.abs(__UpperCAmelCase      )      )

      assert abs(result_sum.item() - 2_5_8.9_6_0_6      ) < 1E-2
      assert abs(result_mean.item() - 0.3_3_7_2      ) < 1E-3




  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      lowerCAmelCase__ :  int							      =					self.scheduler_classes[0]
      lowerCAmelCase__ :  List[str]							      =					self.get_scheduler_config(prediction_type="""v_prediction"""      )
      lowerCAmelCase__ :  List[str]							      =					scheduler_class(**__UpperCAmelCase      )

      lowerCAmelCase__ :  List[str]							      =					len(__UpperCAmelCase      )

      lowerCAmelCase__ :  List[Any]							      =					self.dummy_model()
      lowerCAmelCase__ :  Tuple							      =					self.dummy_sample_deter
      lowerCAmelCase__ :  Union[str, Any]							      =					torch.manual_seed(0      )

      for t in reversed(range(__UpperCAmelCase      )      ):
          # 1. predict noise residual
          lowerCAmelCase__ :  List[Any]							      =					model(__UpperCAmelCase  ,__UpperCAmelCase      )

          # 2. predict previous mean of sample x_t-1
          lowerCAmelCase__ :  List[str]							      =					scheduler.step(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,generator=__UpperCAmelCase      ).prev_sample

          # if t > 0:
          #     noise = self.dummy_sample_deter
          #     variance = scheduler.get_variance(t) ** (0.5) * noise
          #
          # sample = pred_prev_sample + variance
          lowerCAmelCase__ :  str							      =					pred_prev_sample

      lowerCAmelCase__ :  List[Any]							      =					torch.sum(torch.abs(__UpperCAmelCase      )      )
      lowerCAmelCase__ :  Any							      =					torch.mean(torch.abs(__UpperCAmelCase      )      )

      assert abs(result_sum.item() - 2_0_2.0_2_9_6      ) < 1E-2
      assert abs(result_mean.item() - 0.2_6_3_1      ) < 1E-3




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      lowerCAmelCase__ :  Optional[int]							      =					self.scheduler_classes[0]
      lowerCAmelCase__ :  List[Any]							      =					self.get_scheduler_config()
      lowerCAmelCase__ :  Optional[Any]							      =					scheduler_class(**__UpperCAmelCase      )

      lowerCAmelCase__ :  str							      =					[100, 87, 50, 1, 0]

      scheduler.set_timesteps(timesteps=__UpperCAmelCase      )

      lowerCAmelCase__ :  str							      =					scheduler.timesteps

      for i, timestep in enumerate(__UpperCAmelCase      ):
          if i == len(__UpperCAmelCase      ) - 1:
              lowerCAmelCase__ :  Union[str, Any]							      =					-1
          else:
              lowerCAmelCase__ :  List[str]							      =					timesteps[i + 1]

          lowerCAmelCase__ :  str							      =					scheduler.previous_timestep(__UpperCAmelCase      )
          lowerCAmelCase__ :  Dict							      =					prev_t.item()

          self.assertEqual(__UpperCAmelCase  ,__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      lowerCAmelCase__ :  Optional[int]							      =					self.scheduler_classes[0]
      lowerCAmelCase__ :  Optional[int]							      =					self.get_scheduler_config()
      lowerCAmelCase__ :  Any							      =					scheduler_class(**__UpperCAmelCase      )

      lowerCAmelCase__ :  Tuple							      =					[100, 87, 50, 51, 0]

      with self.assertRaises(__UpperCAmelCase  ,msg="""`custom_timesteps` must be in descending order."""      ):
          scheduler.set_timesteps(timesteps=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      lowerCAmelCase__ :  Dict							      =					self.scheduler_classes[0]
      lowerCAmelCase__ :  List[str]							      =					self.get_scheduler_config()
      lowerCAmelCase__ :  Tuple							      =					scheduler_class(**__UpperCAmelCase      )

      lowerCAmelCase__ :  List[Any]							      =					[100, 87, 50, 1, 0]
      lowerCAmelCase__ :  Union[str, Any]							      =					len(__UpperCAmelCase      )

      with self.assertRaises(__UpperCAmelCase  ,msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""      ):
          scheduler.set_timesteps(num_inference_steps=__UpperCAmelCase  ,timesteps=__UpperCAmelCase      )






  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      lowerCAmelCase__ :  List[Any]							      =					self.scheduler_classes[0]
      lowerCAmelCase__ :  int							      =					self.get_scheduler_config()
      lowerCAmelCase__ :  Tuple							      =					scheduler_class(**__UpperCAmelCase      )

      lowerCAmelCase__ :  List[str]							      =					[scheduler.config.num_train_timesteps]

      with self.assertRaises(
          __UpperCAmelCase  ,msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""  ,):
          scheduler.set_timesteps(timesteps=__UpperCAmelCase      )






def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			List[Any]:







 """simple docstring"""


 if b == 0:
  return 1
 if (b % 2) == 0:
  return actual_power(__snake_case     ,						int(b / 2						)						) * actual_power(__snake_case     ,						int(b / 2						)						)
 else:
  return a * actual_power(__snake_case     ,						int(b / 2						)						) * actual_power(__snake_case     ,						int(b / 2						)						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			float:







 """simple docstring"""


 if b < 0:
  return 1 / actual_power(__snake_case     ,						__snake_case						)
 return actual_power(__snake_case     ,						__snake_case						)


if __name__ == "__main__":
  print(power(-2, -3))

