code: 
  
  
  import tempfile
  
  import torch
  
  from diffusers import (
      DEISMultistepScheduler,
      DPMSolverMultistepScheduler,
      DPMSolverSinglestepScheduler,
      UniPCMultistepScheduler,
  )
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  	UpperCAmelCase   :							Union[str, Any]         = (DPMSolverMultistepScheduler,)
  	UpperCAmelCase   :							Any         = (("""num_inference_steps""", 25),)
  
  
  
  
  
  	def     A				(  self				,    **UpperCamelCase_							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Dict			=	{
  								    """num_train_timesteps""": 1000,
  								    """beta_start""": 0.0001,
  								    """beta_end""": 0.02,
  								    """beta_schedule""": """linear""",
  								    """solver_order""": 2,
  								    """prediction_type""": """epsilon""",
  								    """thresholding""": False,
  								    """sample_max_value""": 1.0,
  								    """algorithm_type""": """dpmsolver++""",
  								    """solver_type""": """midpoint""",
  								    """lower_order_final""": False,
  								    """lambda_min_clipped""": -float("""inf"""							),
  								    """variance_type""": None,
  								}
  
  								config.update(**UpperCamelCase_							)
  								return config
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_=0				,    **UpperCamelCase_							)		->			Tuple:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   List[str]			=	dict(self.forward_default_kwargs							)
  								a_       :   str			=	kwargs.pop("""num_inference_steps"""				,    UpperCamelCase_							)
  								a_       :   Optional[Any]			=	self.dummy_sample
  								a_       :   Optional[int]			=	0.1 * sample
  								a_       :   str			=	[residual + 0.2, residual + 0.15, residual + 0.10]
  
  								for scheduler_class in self.scheduler_classes:
  															a_       :   Tuple			=	self.get_scheduler_config(**UpperCamelCase_							)
  															a_       :   Tuple			=	scheduler_class(**UpperCamelCase_							)
  															scheduler.set_timesteps(UpperCamelCase_							)
  															# copy over dummy past residuals
  															a_       :   int			=	dummy_past_residuals[: scheduler.config.solver_order]
  
  															with tempfile.TemporaryDirectory() as tmpdirname:
  																						scheduler.save_config(UpperCamelCase_							)
  																						a_       :   Optional[Any]			=	scheduler_class.from_pretrained(UpperCamelCase_							)
  																						new_scheduler.set_timesteps(UpperCamelCase_							)
  																						# copy over dummy past residuals
  																						a_       :   int			=	dummy_past_residuals[: new_scheduler.config.solver_order]
  
  															a_		,				a_       :   Optional[int]			=	sample, sample
  															for t in range(UpperCamelCase_				,    time_step + scheduler.config.solver_order + 1							):
  																						a_       :   Dict			=	scheduler.step(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    **UpperCamelCase_							).prev_sample
  																						a_       :   List[Any]			=	new_scheduler.step(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    **UpperCamelCase_							).prev_sample
  
  																						assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"
  
  
  
  
  
  	def     A				(  self							)		->			Dict:
  								"""simple docstring"""
  
  
  
  
  
  
  								pass
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_=0				,    **UpperCamelCase_							)		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   List[str]			=	dict(self.forward_default_kwargs							)
  								a_       :   List[str]			=	kwargs.pop("""num_inference_steps"""				,    UpperCamelCase_							)
  								a_       :   Any			=	self.dummy_sample
  								a_       :   Dict			=	0.1 * sample
  								a_       :   Optional[int]			=	[residual + 0.2, residual + 0.15, residual + 0.10]
  
  								for scheduler_class in self.scheduler_classes:
  															a_       :   Any			=	self.get_scheduler_config()
  															a_       :   Dict			=	scheduler_class(**UpperCamelCase_							)
  															scheduler.set_timesteps(UpperCamelCase_							)
  
  															# copy over dummy past residuals (must be after setting timesteps)
  															a_       :   int			=	dummy_past_residuals[: scheduler.config.solver_order]
  
  															with tempfile.TemporaryDirectory() as tmpdirname:
  																						scheduler.save_config(UpperCamelCase_							)
  																						a_       :   Optional[Any]			=	scheduler_class.from_pretrained(UpperCamelCase_							)
  																						# copy over dummy past residuals
  																						new_scheduler.set_timesteps(UpperCamelCase_							)
  
  																						# copy over dummy past residual (must be after setting timesteps)
  																						a_       :   int			=	dummy_past_residuals[: new_scheduler.config.solver_order]
  
  															a_       :   Union[str, Any]			=	scheduler.step(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    **UpperCamelCase_							).prev_sample
  															a_       :   Dict			=	new_scheduler.step(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    **UpperCamelCase_							).prev_sample
  
  															assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_=None				,    **UpperCamelCase_							)		->			Optional[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if scheduler is None:
  															a_       :   str			=	self.scheduler_classes[0]
  															a_       :   Any			=	self.get_scheduler_config(**UpperCamelCase_							)
  															a_       :   Optional[int]			=	scheduler_class(**UpperCamelCase_							)
  
  								a_       :   int			=	10
  								a_       :   Union[str, Any]			=	self.dummy_model()
  								a_       :   List[str]			=	self.dummy_sample_deter
  								scheduler.set_timesteps(UpperCamelCase_							)
  
  								for i, t in enumerate(scheduler.timesteps							):
  															a_       :   Any			=	model(UpperCamelCase_				,    UpperCamelCase_							)
  															a_       :   int			=	scheduler.step(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							).prev_sample
  
  								return sample
  
  
  
  
  
  	def     A				(  self							)		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Union[str, Any]			=	dict(self.forward_default_kwargs							)
  
  								a_       :   Union[str, Any]			=	kwargs.pop("""num_inference_steps"""				,    UpperCamelCase_							)
  
  								for scheduler_class in self.scheduler_classes:
  															a_       :   str			=	self.get_scheduler_config()
  															a_       :   List[str]			=	scheduler_class(**UpperCamelCase_							)
  
  															a_       :   int			=	self.dummy_sample
  															a_       :   Any			=	0.1 * sample
  
  															if num_inference_steps is not None and hasattr(UpperCamelCase_				,    """set_timesteps"""							):
  																						scheduler.set_timesteps(UpperCamelCase_							)
  															elif num_inference_steps is not None and not hasattr(UpperCamelCase_				,    """set_timesteps"""							):
  																						a_       :   Tuple			=	num_inference_steps
  
  															# copy over dummy past residuals (must be done after set_timesteps)
  															a_       :   Tuple			=	[residual + 0.2, residual + 0.15, residual + 0.10]
  															a_       :   Optional[int]			=	dummy_past_residuals[: scheduler.config.solver_order]
  
  															a_       :   Union[str, Any]			=	scheduler.timesteps[5]
  															a_       :   Union[str, Any]			=	scheduler.timesteps[6]
  
  															a_       :   Any			=	scheduler.step(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    **UpperCamelCase_							).prev_sample
  															a_       :   Tuple			=	scheduler.step(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    **UpperCamelCase_							).prev_sample
  
  															self.assertEqual(output_a.shape				,    sample.shape							)
  															self.assertEqual(output_a.shape				,    output_a.shape							)
  
  
  
  
  
  	def     A				(  self							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								for timesteps in [25, 50, 100, 999, 1000]:
  															self.check_over_configs(num_train_timesteps=UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self							)		->			Optional[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								self.check_over_configs(thresholding=UpperCamelCase_							)
  								for order in [1, 2, 3]:
  															for solver_type in ["midpoint", "heun"]:
  																						for threshold in [0.5, 1.0, 2.0]:
  																													for prediction_type in ["epsilon", "sample"]:
  																																				self.check_over_configs(
  																																				    thresholding=UpperCamelCase_				,    prediction_type=UpperCamelCase_				,    sample_max_value=UpperCamelCase_				,    algorithm_type="""dpmsolver++"""				,    solver_order=UpperCamelCase_				,    solver_type=UpperCamelCase_				,    )
  
  
  
  
  
  	def     A				(  self							)		->			Tuple:
  								"""simple docstring"""
  
  
  
  
  
  
  								for prediction_type in ["epsilon", "v_prediction"]:
  															self.check_over_configs(prediction_type=UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self							)		->			Dict:
  								"""simple docstring"""
  
  
  
  
  
  
  								for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
  															for solver_type in ["midpoint", "heun"]:
  																						for order in [1, 2, 3]:
  																													for prediction_type in ["epsilon", "sample"]:
  																																				if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
  																																											if order == 3:
  																																																		continue
  																																				else:
  																																											self.check_over_configs(
  																																											    solver_order=UpperCamelCase_				,    solver_type=UpperCamelCase_				,    prediction_type=UpperCamelCase_				,    algorithm_type=UpperCamelCase_				,    )
  																																				a_       :   str			=	self.full_loop(
  																																				    solver_order=UpperCamelCase_				,    solver_type=UpperCamelCase_				,    prediction_type=UpperCamelCase_				,    algorithm_type=UpperCamelCase_				,    )
  																																				assert not torch.isnan(UpperCamelCase_							).any(), "Samples have nan numbers"
  
  
  
  
  
  	def     A				(  self							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								self.check_over_configs(lower_order_final=UpperCamelCase_							)
  								self.check_over_configs(lower_order_final=UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self							)		->			Any:
  								"""simple docstring"""
  
  
  
  
  
  
  								self.check_over_configs(lambda_min_clipped=-float("""inf"""							)							)
  								self.check_over_configs(lambda_min_clipped=-5.1							)
  
  
  
  
  
  	def     A				(  self							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								self.check_over_configs(variance_type=UpperCamelCase_							)
  								self.check_over_configs(variance_type="""learned_range"""							)
  
  
  
  
  
  	def     A				(  self							)		->			Tuple:
  								"""simple docstring"""
  
  
  
  
  
  
  								for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
  															self.check_over_forward(num_inference_steps=UpperCamelCase_				,    time_step=0							)
  
  
  
  
  
  	def     A				(  self							)		->			Any:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Union[str, Any]			=	self.full_loop()
  								a_       :   Any			=	torch.mean(torch.abs(UpperCamelCase_							)							)
  
  								assert abs(result_mean.item() - 0.3301							) < 1e-3
  
  
  
  
  
  	def     A				(  self							)		->			Any:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   str			=	self.full_loop(thresholding=UpperCamelCase_				,    dynamic_thresholding_ratio=0.87				,    sample_max_value=0.5							)
  								a_       :   int			=	torch.mean(torch.abs(UpperCamelCase_							)							)
  
  								assert abs(result_mean.item() - 1.1364							) < 1e-3
  
  
  
  
  
  	def     A				(  self							)		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   List[Any]			=	self.full_loop(prediction_type="""v_prediction"""							)
  								a_       :   List[Any]			=	torch.mean(torch.abs(UpperCamelCase_							)							)
  
  								assert abs(result_mean.item() - 0.2251							) < 1e-3
  
  
  
  
  
  	def     A				(  self							)		->			Optional[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Any			=	self.full_loop(prediction_type="""v_prediction"""				,    use_karras_sigmas=UpperCamelCase_							)
  								a_       :   Optional[Any]			=	torch.mean(torch.abs(UpperCamelCase_							)							)
  
  								assert abs(result_mean.item() - 0.2096							) < 1e-3
  
  
  
  
  
  	def     A				(  self							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Dict			=	DPMSolverMultistepScheduler(**self.get_scheduler_config()							)
  								a_       :   Any			=	self.full_loop(scheduler=UpperCamelCase_							)
  								a_       :   Dict			=	torch.mean(torch.abs(UpperCamelCase_							)							)
  
  								assert abs(result_mean.item() - 0.3301							) < 1e-3
  
  								a_       :   Optional[int]			=	DPMSolverSinglestepScheduler.from_config(scheduler.config							)
  								a_       :   int			=	UniPCMultistepScheduler.from_config(scheduler.config							)
  								a_       :   List[Any]			=	DEISMultistepScheduler.from_config(scheduler.config							)
  								a_       :   Optional[int]			=	DPMSolverMultistepScheduler.from_config(scheduler.config							)
  
  								a_       :   Union[str, Any]			=	self.full_loop(scheduler=UpperCamelCase_							)
  								a_       :   Optional[int]			=	torch.mean(torch.abs(UpperCamelCase_							)							)
  
  								assert abs(result_mean.item() - 0.3301							) < 1e-3
  
  
  
  
  
  	def     A				(  self							)		->			Tuple:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Union[str, Any]			=	self.scheduler_classes[0]
  								a_       :   Tuple			=	self.get_scheduler_config(thresholding=UpperCamelCase_				,    dynamic_thresholding_ratio=0							)
  								a_       :   List[str]			=	scheduler_class(**UpperCamelCase_							)
  
  								a_       :   Optional[int]			=	10
  								a_       :   Tuple			=	self.dummy_model()
  								a_       :   Tuple			=	self.dummy_sample_deter.half()
  								scheduler.set_timesteps(UpperCamelCase_							)
  
  								for i, t in enumerate(scheduler.timesteps							):
  															a_       :   str			=	model(UpperCamelCase_				,    UpperCamelCase_							)
  															a_       :   int			=	scheduler.step(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							).prev_sample
  
  								assert sample.dtype == torch.floataa
  
  
  
  
  
  	def     A				(  self				,    **UpperCamelCase_							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								for scheduler_class in self.scheduler_classes:
  															a_       :   List[str]			=	self.get_scheduler_config(**UpperCamelCase_							)
  															a_       :   Any			=	scheduler_class(**UpperCamelCase_							)
  
  															scheduler.set_timesteps(scheduler.config.num_train_timesteps							)
  															assert len(scheduler.timesteps.unique()							) == scheduler.num_inference_steps
  
  
  
style_context: 
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: list[int]      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   List[Any]			=	len(SCREAMING_SNAKE_CASE_      )
  							if n in (0, 1):
  														return 0
  							return sum(query * (n - i - 1) for i, query in enumerate(sorted(SCREAMING_SNAKE_CASE_      )      )      )
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  
  