






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 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				Dict				    =						(DPMSolverSDEScheduler,)
		UpperCAmelCase__      :				Optional[int]				    =						10


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   {
						    'num_train_timesteps': 1100,
						    'beta_start': 0.00_01,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						    'noise_sampler_seed': 0,
						}

						config.update(**UpperCamelCase__       )
						return config


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


		def   __snake_case			(						self       ):
						for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01] ,    [0.00_02, 0.0_02, 0.02]       ):
										self.check_over_configs(beta_start=UpperCamelCase__ ,    beta_end=UpperCamelCase__       )


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


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


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.get_scheduler_config()
						SCREAMING_SNAKE_CASE_   :	List[str]													=   scheduler_class(**UpperCamelCase__       )

						scheduler.set_timesteps(self.num_inference_steps       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.dummy_model()
						SCREAMING_SNAKE_CASE_   :	Any													=   self.dummy_sample_deter * scheduler.init_noise_sigma
						SCREAMING_SNAKE_CASE_   :	Any													=   sample.to(UpperCamelCase__       )

						for i, t in enumerate(scheduler.timesteps       ):
										SCREAMING_SNAKE_CASE_   :	Any													=   scheduler.scale_model_input(UpperCamelCase__ ,    UpperCamelCase__       )

										SCREAMING_SNAKE_CASE_   :	List[str]													=   model(UpperCamelCase__ ,    UpperCamelCase__       )

										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   scheduler.step(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   output.prev_sample

						SCREAMING_SNAKE_CASE_   :	Tuple													=   torch.sum(torch.abs(UpperCamelCase__       )       )
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.mean(torch.abs(UpperCamelCase__       )       )

						if torch_device in ["mps"]:
										assert abs(result_sum.item() - 167.47821044921875       ) < 1E-2
										assert abs(result_mean.item() - 0.21_78_70_59_64_56_52_77       ) < 1E-3
						elif torch_device in ["cuda"]:
										assert abs(result_sum.item() - 171.59352111816406       ) < 1E-2
										assert abs(result_mean.item() - 0.2_23_42_90_68_92_29_96_52       ) < 1E-3
						else:
										assert abs(result_sum.item() - 162.52383422851562       ) < 1E-2
										assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26       ) < 1E-3


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	int													=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.get_scheduler_config(prediction_type='v_prediction'       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   scheduler_class(**UpperCamelCase__       )

						scheduler.set_timesteps(self.num_inference_steps       )

						SCREAMING_SNAKE_CASE_   :	Dict													=   self.dummy_model()
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.dummy_sample_deter * scheduler.init_noise_sigma
						SCREAMING_SNAKE_CASE_   :	Tuple													=   sample.to(UpperCamelCase__       )

						for i, t in enumerate(scheduler.timesteps       ):
										SCREAMING_SNAKE_CASE_   :	Tuple													=   scheduler.scale_model_input(UpperCamelCase__ ,    UpperCamelCase__       )

										SCREAMING_SNAKE_CASE_   :	str													=   model(UpperCamelCase__ ,    UpperCamelCase__       )

										SCREAMING_SNAKE_CASE_   :	List[Any]													=   scheduler.step(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   output.prev_sample

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   torch.sum(torch.abs(UpperCamelCase__       )       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   torch.mean(torch.abs(UpperCamelCase__       )       )

						if torch_device in ["mps"]:
										assert abs(result_sum.item() - 124.77149200439453       ) < 1E-2
										assert abs(result_mean.item() - 0.1_62_26_28_90_14_81_62_84       ) < 1E-3
						elif torch_device in ["cuda"]:
										assert abs(result_sum.item() - 128.1663360595703       ) < 1E-2
										assert abs(result_mean.item() - 0.1_66_88_32_60_01_16_72_97       ) < 1E-3
						else:
										assert abs(result_sum.item() - 119.8487548828125       ) < 1E-2
										assert abs(result_mean.item() - 0.15_60_53_06_62_53_66_21       ) < 1E-3


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.get_scheduler_config()
						SCREAMING_SNAKE_CASE_   :	int													=   scheduler_class(**UpperCamelCase__       )

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

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.dummy_model()
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.dummy_sample_deter.to(UpperCamelCase__       ) * scheduler.init_noise_sigma

						for t in scheduler.timesteps:
										SCREAMING_SNAKE_CASE_   :	Tuple													=   scheduler.scale_model_input(UpperCamelCase__ ,    UpperCamelCase__       )

										SCREAMING_SNAKE_CASE_   :	Any													=   model(UpperCamelCase__ ,    UpperCamelCase__       )

										SCREAMING_SNAKE_CASE_   :	List[str]													=   scheduler.step(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   output.prev_sample

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   torch.sum(torch.abs(UpperCamelCase__       )       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   torch.mean(torch.abs(UpperCamelCase__       )       )

						if torch_device in ["mps"]:
										assert abs(result_sum.item() - 167.46957397460938       ) < 1E-2
										assert abs(result_mean.item() - 0.2_18_05_93_46_07_98_26_35       ) < 1E-3
						elif torch_device in ["cuda"]:
										assert abs(result_sum.item() - 171.59353637695312       ) < 1E-2
										assert abs(result_mean.item() - 0.2_23_42_90_83_82_41_57_71       ) < 1E-3
						else:
										assert abs(result_sum.item() - 162.52383422851562       ) < 1E-2
										assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26       ) < 1E-3






		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.get_scheduler_config()
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   scheduler_class(**UpperCamelCase__ ,    use_karras_sigmas=UpperCamelCase__       )

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

						SCREAMING_SNAKE_CASE_   :	Dict													=   self.dummy_model()
						SCREAMING_SNAKE_CASE_   :	str													=   self.dummy_sample_deter.to(UpperCamelCase__       ) * scheduler.init_noise_sigma
						SCREAMING_SNAKE_CASE_   :	int													=   sample.to(UpperCamelCase__       )

						for t in scheduler.timesteps:
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   scheduler.scale_model_input(UpperCamelCase__ ,    UpperCamelCase__       )

										SCREAMING_SNAKE_CASE_   :	List[str]													=   model(UpperCamelCase__ ,    UpperCamelCase__       )

										SCREAMING_SNAKE_CASE_   :	Dict													=   scheduler.step(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   output.prev_sample

						SCREAMING_SNAKE_CASE_   :	int													=   torch.sum(torch.abs(UpperCamelCase__       )       )
						SCREAMING_SNAKE_CASE_   :	int													=   torch.mean(torch.abs(UpperCamelCase__       )       )

						if torch_device in ["mps"]:
										assert abs(result_sum.item() - 176.66974135742188       ) < 1E-2
										assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11       ) < 1E-2
						elif torch_device in ["cuda"]:
										assert abs(result_sum.item() - 177.63653564453125       ) < 1E-2
										assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11       ) < 1E-2
						else:
										assert abs(result_sum.item() - 170.3135223388672       ) < 1E-2
										assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11       ) < 1E-2










import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union


@dataclass
class 							__a							:





		'''simple docstring'''



		UpperCAmelCase__      :				Optional[Union[str, Path]]				    =						None
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				Optional[Dict]				    =						None
		UpperCAmelCase__      :				Optional[str]				    =						None
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				bool				    =						True
		UpperCAmelCase__      :				Optional[int]				    =						None
		UpperCAmelCase__      :				int				    =						1
		UpperCAmelCase__      :				Optional[Union[str, bool]]				    =						None
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				Optional[Dict]				    =						None
		UpperCAmelCase__      :				Optional[str]				    =						None


		def   __snake_case			(						self       ):
						return self.__class__(**{k: copy.deepcopy(UpperCamelCase__       ) for k, v in self.__dict__.items()}       )