code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  import tempfile
  
  import torch
  
  from diffusers import (
      DEISMultistepScheduler,
      DPMSolverMultistepScheduler,
      DPMSolverSinglestepScheduler,
      UniPCMultistepScheduler,
  )
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  
  class 							__snake_case    (				__magic_name__       ):
  	__lowerCAmelCase						=     (DPMSolverMultistepScheduler,)
  	__lowerCAmelCase						=     (('''num_inference_steps''', 25),)
  
  
  
  
  
  	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		Optional[Any]:
  		snake_case__  					=      {
  		    'num_train_timesteps': 1000,
  		    'beta_start': 0.0_0_0_1,
  		    'beta_end': 0.0_2,
  		    '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      _snake_case      (		self			,					UpperCamelCase_=0			,					**UpperCamelCase_		)			->		Any:
  		snake_case__  					=      dict(self.forward_default_kwargs		)
  		snake_case__  					=      kwargs.pop('num_inference_steps'			,					UpperCamelCase_		)
  		snake_case__  					=      self.dummy_sample
  		snake_case__  					=      0.1 * sample
  		snake_case__  					=      [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
  
  		for scheduler_class in self.scheduler_classes:
  			snake_case__  					=      self.get_scheduler_config(**UpperCamelCase_		)
  			snake_case__  					=      scheduler_class(**UpperCamelCase_		)
  			scheduler.set_timesteps(UpperCamelCase_		)
  			# copy over dummy past residuals
  			snake_case__  					=      dummy_past_residuals[: scheduler.config.solver_order]
  
  			with tempfile.TemporaryDirectory() as tmpdirname:
  				scheduler.save_config(UpperCamelCase_		)
  				snake_case__  					=      scheduler_class.from_pretrained(UpperCamelCase_		)
  				new_scheduler.set_timesteps(UpperCamelCase_		)
  				# copy over dummy past residuals
  				snake_case__  					=      dummy_past_residuals[: new_scheduler.config.solver_order]
  
  			snake_case__					,     snake_case__  					=      sample, sample
  			for t in range(UpperCamelCase_			,					time_step + scheduler.config.solver_order + 1		):
  				snake_case__  					=      scheduler.step(UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					**UpperCamelCase_		).prev_sample
  				snake_case__  					=      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      _snake_case      (		self		)			->		Dict:
  		pass
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_=0			,					**UpperCamelCase_		)			->		Union[str, Any]:
  		snake_case__  					=      dict(self.forward_default_kwargs		)
  		snake_case__  					=      kwargs.pop('num_inference_steps'			,					UpperCamelCase_		)
  		snake_case__  					=      self.dummy_sample
  		snake_case__  					=      0.1 * sample
  		snake_case__  					=      [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
  
  		for scheduler_class in self.scheduler_classes:
  			snake_case__  					=      self.get_scheduler_config()
  			snake_case__  					=      scheduler_class(**UpperCamelCase_		)
  			scheduler.set_timesteps(UpperCamelCase_		)
  
  			# copy over dummy past residuals (must be after setting timesteps)
  			snake_case__  					=      dummy_past_residuals[: scheduler.config.solver_order]
  
  			with tempfile.TemporaryDirectory() as tmpdirname:
  				scheduler.save_config(UpperCamelCase_		)
  				snake_case__  					=      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)
  				snake_case__  					=      dummy_past_residuals[: new_scheduler.config.solver_order]
  
  			snake_case__  					=      scheduler.step(UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					**UpperCamelCase_		).prev_sample
  			snake_case__  					=      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      _snake_case      (		self			,					UpperCamelCase_=None			,					**UpperCamelCase_		)			->		Any:
  		if scheduler is None:
  			snake_case__  					=      self.scheduler_classes[0]
  			snake_case__  					=      self.get_scheduler_config(**UpperCamelCase_		)
  			snake_case__  					=      scheduler_class(**UpperCamelCase_		)
  
  		snake_case__  					=      10
  		snake_case__  					=      self.dummy_model()
  		snake_case__  					=      self.dummy_sample_deter
  		scheduler.set_timesteps(UpperCamelCase_		)
  
  		for i, t in enumerate(scheduler.timesteps		):
  			snake_case__  					=      model(UpperCamelCase_			,					UpperCamelCase_		)
  			snake_case__  					=      scheduler.step(UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		).prev_sample
  
  		return sample
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Any:
  		snake_case__  					=      dict(self.forward_default_kwargs		)
  
  		snake_case__  					=      kwargs.pop('num_inference_steps'			,					UpperCamelCase_		)
  
  		for scheduler_class in self.scheduler_classes:
  			snake_case__  					=      self.get_scheduler_config()
  			snake_case__  					=      scheduler_class(**UpperCamelCase_		)
  
  			snake_case__  					=      self.dummy_sample
  			snake_case__  					=      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'		):
  				snake_case__  					=      num_inference_steps
  
  			# copy over dummy past residuals (must be done after set_timesteps)
  			snake_case__  					=      [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
  			snake_case__  					=      dummy_past_residuals[: scheduler.config.solver_order]
  
  			snake_case__  					=      scheduler.timesteps[5]
  			snake_case__  					=      scheduler.timesteps[6]
  
  			snake_case__  					=      scheduler.step(UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					**UpperCamelCase_		).prev_sample
  			snake_case__  					=      scheduler.step(UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					**UpperCamelCase_		).prev_sample
  
  			self.assertEqual(output_a.shape			,					sample.shape		)
  			self.assertEqual(output_a.shape			,					output_a.shape		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Optional[int]:
  		for timesteps in [25, 50, 100, 999, 1000]:
  			self.check_over_configs(num_train_timesteps=UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Optional[int]:
  		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      _snake_case      (		self		)			->		List[str]:
  		for prediction_type in ["epsilon", "v_prediction"]:
  			self.check_over_configs(prediction_type=UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Tuple:
  		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_			,					)
  						snake_case__  					=      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      _snake_case      (		self		)			->		List[str]:
  		self.check_over_configs(lower_order_final=UpperCamelCase_		)
  		self.check_over_configs(lower_order_final=UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[str]:
  		self.check_over_configs(lambda_min_clipped=-float('inf'		)		)
  		self.check_over_configs(lambda_min_clipped=-5.1		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Optional[Any]:
  		self.check_over_configs(variance_type=UpperCamelCase_		)
  		self.check_over_configs(variance_type='learned_range'		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Optional[int]:
  		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      _snake_case      (		self		)			->		int:
  		snake_case__  					=      self.full_loop()
  		snake_case__  					=      torch.mean(torch.abs(UpperCamelCase_		)		)
  
  		assert abs(result_mean.item() - 0.3_3_0_1		) < 1E-3
  
  
  
  
  
  	def      _snake_case      (		self		)			->		int:
  		snake_case__  					=      self.full_loop(thresholding=UpperCamelCase_			,					dynamic_thresholding_ratio=0.8_7			,					sample_max_value=0.5		)
  		snake_case__  					=      torch.mean(torch.abs(UpperCamelCase_		)		)
  
  		assert abs(result_mean.item() - 1.1_3_6_4		) < 1E-3
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Tuple:
  		snake_case__  					=      self.full_loop(prediction_type='v_prediction'		)
  		snake_case__  					=      torch.mean(torch.abs(UpperCamelCase_		)		)
  
  		assert abs(result_mean.item() - 0.2_2_5_1		) < 1E-3
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Dict:
  		snake_case__  					=      self.full_loop(prediction_type='v_prediction'			,					use_karras_sigmas=UpperCamelCase_		)
  		snake_case__  					=      torch.mean(torch.abs(UpperCamelCase_		)		)
  
  		assert abs(result_mean.item() - 0.2_0_9_6		) < 1E-3
  
  
  
  
  
  	def      _snake_case      (		self		)			->		str:
  		# make sure that iterating over schedulers with same config names gives same results
  		# for defaults
  		snake_case__  					=      DPMSolverMultistepScheduler(**self.get_scheduler_config()		)
  		snake_case__  					=      self.full_loop(scheduler=UpperCamelCase_		)
  		snake_case__  					=      torch.mean(torch.abs(UpperCamelCase_		)		)
  
  		assert abs(result_mean.item() - 0.3_3_0_1		) < 1E-3
  
  		snake_case__  					=      DPMSolverSinglestepScheduler.from_config(scheduler.config		)
  		snake_case__  					=      UniPCMultistepScheduler.from_config(scheduler.config		)
  		snake_case__  					=      DEISMultistepScheduler.from_config(scheduler.config		)
  		snake_case__  					=      DPMSolverMultistepScheduler.from_config(scheduler.config		)
  
  		snake_case__  					=      self.full_loop(scheduler=UpperCamelCase_		)
  		snake_case__  					=      torch.mean(torch.abs(UpperCamelCase_		)		)
  
  		assert abs(result_mean.item() - 0.3_3_0_1		) < 1E-3
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Any:
  		snake_case__  					=      self.scheduler_classes[0]
  		snake_case__  					=      self.get_scheduler_config(thresholding=UpperCamelCase_			,					dynamic_thresholding_ratio=0		)
  		snake_case__  					=      scheduler_class(**UpperCamelCase_		)
  
  		snake_case__  					=      10
  		snake_case__  					=      self.dummy_model()
  		snake_case__  					=      self.dummy_sample_deter.half()
  		scheduler.set_timesteps(UpperCamelCase_		)
  
  		for i, t in enumerate(scheduler.timesteps		):
  			snake_case__  					=      model(UpperCamelCase_			,					UpperCamelCase_		)
  			snake_case__  					=      scheduler.step(UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		).prev_sample
  
  		assert sample.dtype == torch.floataa
  
  
  
  
  
  	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		int:
  		for scheduler_class in self.scheduler_classes:
  			snake_case__  					=      self.get_scheduler_config(**UpperCamelCase_		)
  			snake_case__  					=      scheduler_class(**UpperCamelCase_		)
  
  			scheduler.set_timesteps(scheduler.config.num_train_timesteps		)
  			assert len(scheduler.timesteps.unique()		) == scheduler.num_inference_steps
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  from typing import Callable
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_       )      ->str:
  
  	def wrapper(UpperCAmelCase_       ):
  		snake_case__  					=      function_with_docstring.__doc__
  		return target_function
  
  	return wrapper
  
  
  
  