


"""simple docstring"""
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     (snake_case__):





							'''simple docstring'''

							__lowercase:		int							      =    (DPMSolverSDEScheduler,)
							__lowercase:		Optional[Any]							      =    10



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



														"""simple docstring"""



														snake_case_           =		{
														    """num_train_timesteps""": 1_100,
														    """beta_start""": 0.0_001,
														    """beta_end""": 0.02,
														    """beta_schedule""": """linear""",
														    """noise_sampler_seed""": 0,
														}

														config.update(**UpperCAmelCase_       )
														return config



							def 		lowerCAmelCase       (				self							:      Tuple       )					->str:



														"""simple docstring"""



														for timesteps in [10, 50, 100, 1_000]:
																					self.check_over_configs(num_train_timesteps=UpperCAmelCase_       )



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



														"""simple docstring"""



														for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]		,       [0.0_002, 0.002, 0.02]       ):
																					self.check_over_configs(beta_start=UpperCAmelCase_		,       beta_end=UpperCAmelCase_       )



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



														"""simple docstring"""



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



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



														"""simple docstring"""



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



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



														"""simple docstring"""



														snake_case_           =		self.scheduler_classes[0]
														snake_case_           =		self.get_scheduler_config()
														snake_case_           =		scheduler_class(**UpperCAmelCase_       )

														scheduler.set_timesteps(self.num_inference_steps       )

														snake_case_           =		self.dummy_model()
														snake_case_           =		self.dummy_sample_deter * scheduler.init_noise_sigma
														snake_case_           =		sample.to(UpperCAmelCase_       )

														for i, t in enumerate(scheduler.timesteps       ):
																					snake_case_           =		scheduler.scale_model_input(UpperCAmelCase_		,       UpperCAmelCase_       )

																					snake_case_           =		model(UpperCAmelCase_		,       UpperCAmelCase_       )

																					snake_case_           =		scheduler.step(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )
																					snake_case_           =		output.prev_sample

														snake_case_           =		torch.sum(torch.abs(UpperCAmelCase_       )       )
														snake_case_           =		torch.mean(torch.abs(UpperCAmelCase_       )       )

														if torch_device in ["mps"]:
																					assert abs(result_sum.item() - 167.47_821_044_921_875       ) < 1E-2
																					assert abs(result_mean.item() - 0.2_178_705_964_565_277       ) < 1E-3
														elif torch_device in ["cuda"]:
																					assert abs(result_sum.item() - 171.59_352_111_816_406       ) < 1E-2
																					assert abs(result_mean.item() - 0.22_342_906_892_299_652       ) < 1E-3
														else:
																					assert abs(result_sum.item() - 162.52_383_422_851_562       ) < 1E-2
																					assert abs(result_mean.item() - 0.211_619_570_851_326       ) < 1E-3



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



														"""simple docstring"""



														snake_case_           =		self.scheduler_classes[0]
														snake_case_           =		self.get_scheduler_config(prediction_type="""v_prediction"""       )
														snake_case_           =		scheduler_class(**UpperCAmelCase_       )

														scheduler.set_timesteps(self.num_inference_steps       )

														snake_case_           =		self.dummy_model()
														snake_case_           =		self.dummy_sample_deter * scheduler.init_noise_sigma
														snake_case_           =		sample.to(UpperCAmelCase_       )

														for i, t in enumerate(scheduler.timesteps       ):
																					snake_case_           =		scheduler.scale_model_input(UpperCAmelCase_		,       UpperCAmelCase_       )

																					snake_case_           =		model(UpperCAmelCase_		,       UpperCAmelCase_       )

																					snake_case_           =		scheduler.step(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )
																					snake_case_           =		output.prev_sample

														snake_case_           =		torch.sum(torch.abs(UpperCAmelCase_       )       )
														snake_case_           =		torch.mean(torch.abs(UpperCAmelCase_       )       )

														if torch_device in ["mps"]:
																					assert abs(result_sum.item() - 124.77_149_200_439_453       ) < 1E-2
																					assert abs(result_mean.item() - 0.16_226_289_014_816_284       ) < 1E-3
														elif torch_device in ["cuda"]:
																					assert abs(result_sum.item() - 128.1_663_360_595_703       ) < 1E-2
																					assert abs(result_mean.item() - 0.16_688_326_001_167_297       ) < 1E-3
														else:
																					assert abs(result_sum.item() - 119.8_487_548_828_125       ) < 1E-2
																					assert abs(result_mean.item() - 0.1_560_530_662_536_621       ) < 1E-3



							def 		lowerCAmelCase       (				self							:      str       )					->str:



														"""simple docstring"""



														snake_case_           =		self.scheduler_classes[0]
														snake_case_           =		self.get_scheduler_config()
														snake_case_           =		scheduler_class(**UpperCAmelCase_       )

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

														snake_case_           =		self.dummy_model()
														snake_case_           =		self.dummy_sample_deter.to(UpperCAmelCase_       ) * scheduler.init_noise_sigma

														for t in scheduler.timesteps:
																					snake_case_           =		scheduler.scale_model_input(UpperCAmelCase_		,       UpperCAmelCase_       )

																					snake_case_           =		model(UpperCAmelCase_		,       UpperCAmelCase_       )

																					snake_case_           =		scheduler.step(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )
																					snake_case_           =		output.prev_sample

														snake_case_           =		torch.sum(torch.abs(UpperCAmelCase_       )       )
														snake_case_           =		torch.mean(torch.abs(UpperCAmelCase_       )       )

														if torch_device in ["mps"]:
																					assert abs(result_sum.item() - 167.46_957_397_460_938       ) < 1E-2
																					assert abs(result_mean.item() - 0.21_805_934_607_982_635       ) < 1E-3
														elif torch_device in ["cuda"]:
																					assert abs(result_sum.item() - 171.59_353_637_695_312       ) < 1E-2
																					assert abs(result_mean.item() - 0.22_342_908_382_415_771       ) < 1E-3
														else:
																					assert abs(result_sum.item() - 162.52_383_422_851_562       ) < 1E-2
																					assert abs(result_mean.item() - 0.211_619_570_851_326       ) < 1E-3



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



														"""simple docstring"""



														snake_case_           =		self.scheduler_classes[0]
														snake_case_           =		self.get_scheduler_config()
														snake_case_           =		scheduler_class(**UpperCAmelCase_		,       use_karras_sigmas=UpperCAmelCase_       )

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

														snake_case_           =		self.dummy_model()
														snake_case_           =		self.dummy_sample_deter.to(UpperCAmelCase_       ) * scheduler.init_noise_sigma
														snake_case_           =		sample.to(UpperCAmelCase_       )

														for t in scheduler.timesteps:
																					snake_case_           =		scheduler.scale_model_input(UpperCAmelCase_		,       UpperCAmelCase_       )

																					snake_case_           =		model(UpperCAmelCase_		,       UpperCAmelCase_       )

																					snake_case_           =		scheduler.step(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )
																					snake_case_           =		output.prev_sample

														snake_case_           =		torch.sum(torch.abs(UpperCAmelCase_       )       )
														snake_case_           =		torch.mean(torch.abs(UpperCAmelCase_       )       )

														if torch_device in ["mps"]:
																					assert abs(result_sum.item() - 176.66_974_135_742_188       ) < 1E-2
																					assert abs(result_mean.item() - 0.23_003_872_730_981_811       ) < 1E-2
														elif torch_device in ["cuda"]:
																					assert abs(result_sum.item() - 177.63_653_564_453_125       ) < 1E-2
																					assert abs(result_mean.item() - 0.23_003_872_730_981_811       ) < 1E-2
														else:
																					assert abs(result_sum.item() - 170.3_135_223_388_672       ) < 1E-2
																					assert abs(result_mean.item() - 0.23_003_872_730_981_811       ) < 1E-2







"""simple docstring"""
def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				int:
							if index == number_of_items:
														return 0
							snake_case_           =		0
							snake_case_           =		0
							snake_case_           =		knapsack(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, index + 1				)
							if weights[index] <= max_weight:
														snake_case_           =		values[index] + knapsack(
														    _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, max_weight - weights[index]				, index + 1				)
							return max(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
				import doctest

				doctest.testmod()



