






import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest

@require_torchsde
class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					_A						:			Any									=      (DPMSolverSDEScheduler,)
					_A						:			str									=      10



					def       lowerCAmelCase				(  self				:	List[Any]		,     **__a				:	str							)			->  int:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     {
												    """num_train_timesteps""": 1100,
												    """beta_start""": 0.0001,
												    """beta_end""": 0.02,
												    """beta_schedule""": """linear""",
												    """noise_sampler_seed""": 0,
												}

												config.update(**__a							)
												return config



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


												"""simple docstring"""

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



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


												"""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=__a		,     beta_end=__a							)



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


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	Dict							)			->  List[str]:


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	Tuple							)			->  List[str]:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     self.scheduler_classes[0]
												__lowercase      :							int				=     self.get_scheduler_config()
												__lowercase      :							Union[str, Any]				=     scheduler_class(**__a							)

												scheduler.set_timesteps(self.num_inference_steps							)

												__lowercase      :							Tuple				=     self.dummy_model()
												__lowercase      :							int				=     self.dummy_sample_deter * scheduler.init_noise_sigma
												__lowercase      :							str				=     sample.to(__a							)

												for i, t in enumerate(scheduler.timesteps							):
																			__lowercase      :							int				=     scheduler.scale_model_input(__a		,     __a							)

																			__lowercase      :							Optional[int]				=     model(__a		,     __a							)

																			__lowercase      :							Optional[Any]				=     scheduler.step(__a		,     __a		,     __a							)
																			__lowercase      :							Optional[Any]				=     output.prev_sample

												__lowercase      :							List[str]				=     torch.sum(torch.abs(__a							)							)
												__lowercase      :							str				=     torch.mean(torch.abs(__a							)							)

												if torch_device in ["mps"]:
																			assert abs(result_sum.item() - 167.47821044921875							) < 1E-2
																			assert abs(result_mean.item() - 0.2178705964565277							) < 1E-3
												elif torch_device in ["cuda"]:
																			assert abs(result_sum.item() - 171.59352111816406							) < 1E-2
																			assert abs(result_mean.item() - 0.22342906892299652							) < 1E-3
												else:
																			assert abs(result_sum.item() - 162.52383422851562							) < 1E-2
																			assert abs(result_mean.item() - 0.211619570851326							) < 1E-3



					def       lowerCAmelCase				(  self				:	Dict							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							Any				=     self.scheduler_classes[0]
												__lowercase      :							List[Any]				=     self.get_scheduler_config(prediction_type="""v_prediction"""							)
												__lowercase      :							Dict				=     scheduler_class(**__a							)

												scheduler.set_timesteps(self.num_inference_steps							)

												__lowercase      :							Dict				=     self.dummy_model()
												__lowercase      :							int				=     self.dummy_sample_deter * scheduler.init_noise_sigma
												__lowercase      :							List[str]				=     sample.to(__a							)

												for i, t in enumerate(scheduler.timesteps							):
																			__lowercase      :							Optional[int]				=     scheduler.scale_model_input(__a		,     __a							)

																			__lowercase      :							int				=     model(__a		,     __a							)

																			__lowercase      :							Union[str, Any]				=     scheduler.step(__a		,     __a		,     __a							)
																			__lowercase      :							List[str]				=     output.prev_sample

												__lowercase      :							Tuple				=     torch.sum(torch.abs(__a							)							)
												__lowercase      :							Tuple				=     torch.mean(torch.abs(__a							)							)

												if torch_device in ["mps"]:
																			assert abs(result_sum.item() - 124.77149200439453							) < 1E-2
																			assert abs(result_mean.item() - 0.16226289014816284							) < 1E-3
												elif torch_device in ["cuda"]:
																			assert abs(result_sum.item() - 128.1663360595703							) < 1E-2
																			assert abs(result_mean.item() - 0.16688326001167297							) < 1E-3
												else:
																			assert abs(result_sum.item() - 119.8487548828125							) < 1E-2
																			assert abs(result_mean.item() - 0.1560530662536621							) < 1E-3



					def       lowerCAmelCase				(  self				:	str							)			->  Dict:


												"""simple docstring"""

												__lowercase      :							Dict				=     self.scheduler_classes[0]
												__lowercase      :							Any				=     self.get_scheduler_config()
												__lowercase      :							str				=     scheduler_class(**__a							)

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

												__lowercase      :							Tuple				=     self.dummy_model()
												__lowercase      :							Dict				=     self.dummy_sample_deter.to(__a							) * scheduler.init_noise_sigma

												for t in scheduler.timesteps:
																			__lowercase      :							Tuple				=     scheduler.scale_model_input(__a		,     __a							)

																			__lowercase      :							List[str]				=     model(__a		,     __a							)

																			__lowercase      :							Dict				=     scheduler.step(__a		,     __a		,     __a							)
																			__lowercase      :							Tuple				=     output.prev_sample

												__lowercase      :							Any				=     torch.sum(torch.abs(__a							)							)
												__lowercase      :							Any				=     torch.mean(torch.abs(__a							)							)

												if torch_device in ["mps"]:
																			assert abs(result_sum.item() - 167.46957397460938							) < 1E-2
																			assert abs(result_mean.item() - 0.21805934607982635							) < 1E-3
												elif torch_device in ["cuda"]:
																			assert abs(result_sum.item() - 171.59353637695312							) < 1E-2
																			assert abs(result_mean.item() - 0.22342908382415771							) < 1E-3
												else:
																			assert abs(result_sum.item() - 162.52383422851562							) < 1E-2
																			assert abs(result_mean.item() - 0.211619570851326							) < 1E-3



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


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     self.scheduler_classes[0]
												__lowercase      :							Optional[int]				=     self.get_scheduler_config()
												__lowercase      :							int				=     scheduler_class(**__a		,     use_karras_sigmas=__a							)

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

												__lowercase      :							Tuple				=     self.dummy_model()
												__lowercase      :							Any				=     self.dummy_sample_deter.to(__a							) * scheduler.init_noise_sigma
												__lowercase      :							Union[str, Any]				=     sample.to(__a							)

												for t in scheduler.timesteps:
																			__lowercase      :							Dict				=     scheduler.scale_model_input(__a		,     __a							)

																			__lowercase      :							Optional[Any]				=     model(__a		,     __a							)

																			__lowercase      :							Dict				=     scheduler.step(__a		,     __a		,     __a							)
																			__lowercase      :							Union[str, Any]				=     output.prev_sample

												__lowercase      :							Union[str, Any]				=     torch.sum(torch.abs(__a							)							)
												__lowercase      :							Any				=     torch.mean(torch.abs(__a							)							)

												if torch_device in ["mps"]:
																			assert abs(result_sum.item() - 176.66974135742188							) < 1E-2
																			assert abs(result_mean.item() - 0.23003872730981811							) < 1E-2
												elif torch_device in ["cuda"]:
																			assert abs(result_sum.item() - 177.63653564453125							) < 1E-2
																			assert abs(result_mean.item() - 0.23003872730981811							) < 1E-2
												else:
																			assert abs(result_sum.item() - 170.3135223388672							) < 1E-2
																			assert abs(result_mean.item() - 0.23003872730981811							) < 1E-2







from __future__ import annotations
def       snake_case_		(     lowerCAmelCase_       :						list[int]      ):
							if not nums:
														return 0
							__lowercase      :							Tuple				=     nums[0]
							__lowercase      :							Tuple				=     0
							for num in nums[1:]:
														__lowercase			,			__lowercase      :							List[str]				=     (
														    max_excluding + num,
														    max(lowerCAmelCase_       ,       lowerCAmelCase_      ),
														)
							return max(lowerCAmelCase_       ,       lowerCAmelCase_      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()