


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        UpperCamelCase__   (	snake_case__   ):
					__SCREAMING_SNAKE_CASE       :  int						=   (DPMSolverSDEScheduler,)
					__SCREAMING_SNAKE_CASE       :  Optional[Any]						=   10

					def   UpperCAmelCase__       (     self       :   Any							,					**UpperCamelCase__       :   List[Any]       ):






										'''simple docstring'''






										lowercase_  							=			{
										    """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   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






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

					def   UpperCAmelCase__       (     self       :   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   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






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

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






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

					def   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






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

										scheduler.set_timesteps(self.num_inference_steps       )

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

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

															lowercase_  							=			model(UpperCAmelCase_							,					UpperCAmelCase_       )

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

										lowercase_  							=			torch.sum(torch.abs(UpperCAmelCase_       )       )
										lowercase_  							=			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   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






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

										scheduler.set_timesteps(self.num_inference_steps       )

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

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

															lowercase_  							=			model(UpperCAmelCase_							,					UpperCAmelCase_       )

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

										lowercase_  							=			torch.sum(torch.abs(UpperCAmelCase_       )       )
										lowercase_  							=			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   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






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

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

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

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

															lowercase_  							=			model(UpperCAmelCase_							,					UpperCAmelCase_       )

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

										lowercase_  							=			torch.sum(torch.abs(UpperCAmelCase_       )       )
										lowercase_  							=			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   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






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

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

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

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

															lowercase_  							=			model(UpperCAmelCase_							,					UpperCAmelCase_       )

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

										lowercase_  							=			torch.sum(torch.abs(UpperCAmelCase_       )       )
										lowercase_  							=			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



def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					if not isinstance(UpperCAmelCase__						,							UpperCAmelCase__  ):
										raise TypeError("""Input value must be an 'int' type"""  )

					lowercase_  							=			0
					while number:
										position += 1
										number >>= 1

					return position


if __name__ == "__main__":
							import doctest

							doctest.testmod()


