


'''simple docstring'''



import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class        UpperCamelCase__(   _UpperCamelCase       ):
				__magic_name__				:      Tuple  		=       (DPMSolverMultistepScheduler,)
				__magic_name__				:      Tuple  		=       (("num_inference_steps", 25),)




				def      a__(		self			: Optional[int]      ,    **lowerCAmelCase			: Tuple      )->     Tuple:

										"""simple docstring"""




										UpperCAmelCase     				=	{
										    "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(**__a      )
										return config




				def      a__(		self			: Any      ,    lowerCAmelCase			: Dict=0      ,    **lowerCAmelCase			: Any      )->     Optional[int]:

										"""simple docstring"""




										UpperCAmelCase     				=	dict(self.forward_default_kwargs      )
										UpperCAmelCase     				=	kwargs.pop('''num_inference_steps'''      ,    __a      )
										UpperCAmelCase     				=	self.dummy_sample
										UpperCAmelCase     				=	0.1 * sample
										UpperCAmelCase     				=	[residual + 0.2, residual + 0.15, residual + 0.10]

										for scheduler_class in self.scheduler_classes:
																UpperCAmelCase     				=	self.get_scheduler_config(**__a      )
																UpperCAmelCase     				=	scheduler_class(**__a      )
																scheduler.set_timesteps(__a      )
																# copy over dummy past residuals
																UpperCAmelCase     				=	dummy_past_residuals[: scheduler.config.solver_order]

																with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(__a      )
																						UpperCAmelCase     				=	scheduler_class.from_pretrained(__a      )
																						new_scheduler.set_timesteps(__a      )
																						# copy over dummy past residuals
																						UpperCAmelCase     				=	dummy_past_residuals[: new_scheduler.config.solver_order]

																UpperCAmelCase     				=	sample, sample
																for t in range(__a      ,    time_step + scheduler.config.solver_order + 1      ):
																						UpperCAmelCase     				=	scheduler.step(__a      ,    __a      ,    __a      ,    **__a      ).prev_sample
																						UpperCAmelCase     				=	new_scheduler.step(__a      ,    __a      ,    __a      ,    **__a      ).prev_sample

																						assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"




				def      a__(		self			: Optional[int]      )->     str:

										"""simple docstring"""




										pass




				def      a__(		self			: Any      ,    lowerCAmelCase			: Union[str, Any]=0      ,    **lowerCAmelCase			: Union[str, Any]      )->     Any:

										"""simple docstring"""




										UpperCAmelCase     				=	dict(self.forward_default_kwargs      )
										UpperCAmelCase     				=	kwargs.pop('''num_inference_steps'''      ,    __a      )
										UpperCAmelCase     				=	self.dummy_sample
										UpperCAmelCase     				=	0.1 * sample
										UpperCAmelCase     				=	[residual + 0.2, residual + 0.15, residual + 0.10]

										for scheduler_class in self.scheduler_classes:
																UpperCAmelCase     				=	self.get_scheduler_config()
																UpperCAmelCase     				=	scheduler_class(**__a      )
																scheduler.set_timesteps(__a      )

																# copy over dummy past residuals (must be after setting timesteps)
																UpperCAmelCase     				=	dummy_past_residuals[: scheduler.config.solver_order]

																with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(__a      )
																						UpperCAmelCase     				=	scheduler_class.from_pretrained(__a      )
																						# copy over dummy past residuals
																						new_scheduler.set_timesteps(__a      )

																						# copy over dummy past residual (must be after setting timesteps)
																						UpperCAmelCase     				=	dummy_past_residuals[: new_scheduler.config.solver_order]

																UpperCAmelCase     				=	scheduler.step(__a      ,    __a      ,    __a      ,    **__a      ).prev_sample
																UpperCAmelCase     				=	new_scheduler.step(__a      ,    __a      ,    __a      ,    **__a      ).prev_sample

																assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"




				def      a__(		self			: str      ,    lowerCAmelCase			: Optional[Any]=None      ,    **lowerCAmelCase			: int      )->     List[Any]:

										"""simple docstring"""




										if scheduler is None:
																UpperCAmelCase     				=	self.scheduler_classes[0]
																UpperCAmelCase     				=	self.get_scheduler_config(**__a      )
																UpperCAmelCase     				=	scheduler_class(**__a      )

										UpperCAmelCase     				=	10
										UpperCAmelCase     				=	self.dummy_model()
										UpperCAmelCase     				=	self.dummy_sample_deter
										scheduler.set_timesteps(__a      )

										for i, t in enumerate(scheduler.timesteps      ):
																UpperCAmelCase     				=	model(__a      ,    __a      )
																UpperCAmelCase     				=	scheduler.step(__a      ,    __a      ,    __a      ).prev_sample

										return sample




				def      a__(		self			: Dict      )->     Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase     				=	dict(self.forward_default_kwargs      )

										UpperCAmelCase     				=	kwargs.pop('''num_inference_steps'''      ,    __a      )

										for scheduler_class in self.scheduler_classes:
																UpperCAmelCase     				=	self.get_scheduler_config()
																UpperCAmelCase     				=	scheduler_class(**__a      )

																UpperCAmelCase     				=	self.dummy_sample
																UpperCAmelCase     				=	0.1 * sample

																if num_inference_steps is not None and hasattr(__a      ,    '''set_timesteps'''      ):
																						scheduler.set_timesteps(__a      )
																elif num_inference_steps is not None and not hasattr(__a      ,    '''set_timesteps'''      ):
																						UpperCAmelCase     				=	num_inference_steps

																# copy over dummy past residuals (must be done after set_timesteps)
																UpperCAmelCase     				=	[residual + 0.2, residual + 0.15, residual + 0.10]
																UpperCAmelCase     				=	dummy_past_residuals[: scheduler.config.solver_order]

																UpperCAmelCase     				=	scheduler.timesteps[5]
																UpperCAmelCase     				=	scheduler.timesteps[6]

																UpperCAmelCase     				=	scheduler.step(__a      ,    __a      ,    __a      ,    **__a      ).prev_sample
																UpperCAmelCase     				=	scheduler.step(__a      ,    __a      ,    __a      ,    **__a      ).prev_sample

																self.assertEqual(output_a.shape      ,    sample.shape      )
																self.assertEqual(output_a.shape      ,    output_a.shape      )




				def      a__(		self			: str      )->     List[Any]:

										"""simple docstring"""




										for timesteps in [25, 50, 100, 999, 1000]:
																self.check_over_configs(num_train_timesteps=__a      )




				def      a__(		self			: Optional[int]      )->     Tuple:

										"""simple docstring"""




										self.check_over_configs(thresholding=__a      )
										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=__a      ,    prediction_type=__a      ,    sample_max_value=__a      ,    algorithm_type='''dpmsolver++'''      ,    solver_order=__a      ,    solver_type=__a      ,    )




				def      a__(		self			: List[str]      )->     str:

										"""simple docstring"""




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




				def      a__(		self			: Dict      )->     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=__a      ,    solver_type=__a      ,    prediction_type=__a      ,    algorithm_type=__a      ,    )
																																		UpperCAmelCase     				=	self.full_loop(
																																		    solver_order=__a      ,    solver_type=__a      ,    prediction_type=__a      ,    algorithm_type=__a      ,    )
																																		assert not torch.isnan(__a      ).any(), "Samples have nan numbers"




				def      a__(		self			: Tuple      )->     List[Any]:

										"""simple docstring"""




										self.check_over_configs(lower_order_final=__a      )
										self.check_over_configs(lower_order_final=__a      )




				def      a__(		self			: List[Any]      )->     int:

										"""simple docstring"""




										self.check_over_configs(lambda_min_clipped=-float('''inf'''      )      )
										self.check_over_configs(lambda_min_clipped=-5.1      )




				def      a__(		self			: Dict      )->     Dict:

										"""simple docstring"""




										self.check_over_configs(variance_type=__a      )
										self.check_over_configs(variance_type='''learned_range'''      )




				def      a__(		self			: Dict      )->     Optional[Any]:

										"""simple docstring"""




										for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
																self.check_over_forward(num_inference_steps=__a      ,    time_step=0      )




				def      a__(		self			: Dict      )->     Dict:

										"""simple docstring"""




										UpperCAmelCase     				=	self.full_loop()
										UpperCAmelCase     				=	torch.mean(torch.abs(__a      )      )

										assert abs(result_mean.item() - 0.3301      ) < 1E-3




				def      a__(		self			: List[Any]      )->     str:

										"""simple docstring"""




										UpperCAmelCase     				=	self.full_loop(thresholding=__a      ,    dynamic_thresholding_ratio=0.87      ,    sample_max_value=0.5      )
										UpperCAmelCase     				=	torch.mean(torch.abs(__a      )      )

										assert abs(result_mean.item() - 1.1364      ) < 1E-3




				def      a__(		self			: Dict      )->     Tuple:

										"""simple docstring"""




										UpperCAmelCase     				=	self.full_loop(prediction_type='''v_prediction'''      )
										UpperCAmelCase     				=	torch.mean(torch.abs(__a      )      )

										assert abs(result_mean.item() - 0.2251      ) < 1E-3




				def      a__(		self			: Dict      )->     Optional[int]:

										"""simple docstring"""




										UpperCAmelCase     				=	self.full_loop(prediction_type='''v_prediction'''      ,    use_karras_sigmas=__a      )
										UpperCAmelCase     				=	torch.mean(torch.abs(__a      )      )

										assert abs(result_mean.item() - 0.2096      ) < 1E-3




				def      a__(		self			: int      )->     Any:

										"""simple docstring"""




										UpperCAmelCase     				=	DPMSolverMultistepScheduler(**self.get_scheduler_config()      )
										UpperCAmelCase     				=	self.full_loop(scheduler=__a      )
										UpperCAmelCase     				=	torch.mean(torch.abs(__a      )      )

										assert abs(result_mean.item() - 0.3301      ) < 1E-3

										UpperCAmelCase     				=	DPMSolverSinglestepScheduler.from_config(scheduler.config      )
										UpperCAmelCase     				=	UniPCMultistepScheduler.from_config(scheduler.config      )
										UpperCAmelCase     				=	DEISMultistepScheduler.from_config(scheduler.config      )
										UpperCAmelCase     				=	DPMSolverMultistepScheduler.from_config(scheduler.config      )

										UpperCAmelCase     				=	self.full_loop(scheduler=__a      )
										UpperCAmelCase     				=	torch.mean(torch.abs(__a      )      )

										assert abs(result_mean.item() - 0.3301      ) < 1E-3




				def      a__(		self			: str      )->     List[str]:

										"""simple docstring"""




										UpperCAmelCase     				=	self.scheduler_classes[0]
										UpperCAmelCase     				=	self.get_scheduler_config(thresholding=__a      ,    dynamic_thresholding_ratio=0      )
										UpperCAmelCase     				=	scheduler_class(**__a      )

										UpperCAmelCase     				=	10
										UpperCAmelCase     				=	self.dummy_model()
										UpperCAmelCase     				=	self.dummy_sample_deter.half()
										scheduler.set_timesteps(__a      )

										for i, t in enumerate(scheduler.timesteps      ):
																UpperCAmelCase     				=	model(__a      ,    __a      )
																UpperCAmelCase     				=	scheduler.step(__a      ,    __a      ,    __a      ).prev_sample

										assert sample.dtype == torch.floataa





				def      a__(		self			: int      ,    **lowerCAmelCase			: List[Any]      )->     Optional[Any]:

										"""simple docstring"""




										for scheduler_class in self.scheduler_classes:
																UpperCAmelCase     				=	self.get_scheduler_config(**__a      )
																UpperCAmelCase     				=	scheduler_class(**__a      )

																scheduler.set_timesteps(scheduler.config.num_train_timesteps      )
																assert len(scheduler.timesteps.unique()      ) == scheduler.num_inference_steps












"""simple docstring"""


def     lowercase__			(    lowercase_	)    ->						str:




      """simple docstring"""

      return " ".join(sorted(set(sentence.split()	)	)	)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
