



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     List[Any]								=     (EulerDiscreteScheduler,)
				A_   :     str								=     1_0


				def __UpperCamelCase						(							self      :  Union[str, Any]						, **__UpperCamelCase      :  Optional[Any]       )		->     Tuple:
											A      						=    {
											    'num_train_timesteps': 1_100,
											    'beta_start': 0.0_0_0_1,
											    'beta_end': 0.0_2,
											    'beta_schedule': 'linear',
											}

											config.update(**__UpperCamelCase       )
											return config


				def __UpperCamelCase						(							self      :  int       )		->     List[str]:
											for timesteps in [10, 50, 100, 1_000]:
																		self.check_over_configs(num_train_timesteps=__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[str]:
											for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]						, [0.0_0_0_2, 0.0_0_2, 0.0_2]       ):
																		self.check_over_configs(beta_start=__UpperCamelCase						, beta_end=__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Any:
											for schedule in ["linear", "scaled_linear"]:
																		self.check_over_configs(beta_schedule=__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  int       )		->     Any:
											for prediction_type in ["epsilon", "v_prediction"]:
																		self.check_over_configs(prediction_type=__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  int       )		->     Dict:
											A      						=    self.scheduler_classes[0]
											A      						=    self.get_scheduler_config()
											A      						=    scheduler_class(**__UpperCamelCase       )

											scheduler.set_timesteps(self.num_inference_steps       )

											A      						=    torch.manual_seed(0       )

											A      						=    self.dummy_model()
											A      						=    self.dummy_sample_deter * scheduler.init_noise_sigma
											A      						=    sample.to(__UpperCamelCase       )

											for i, t in enumerate(scheduler.timesteps       ):
																		A      						=    scheduler.scale_model_input(__UpperCamelCase						, __UpperCamelCase       )

																		A      						=    model(__UpperCamelCase						, __UpperCamelCase       )

																		A      						=    scheduler.step(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, generator=__UpperCamelCase       )
																		A      						=    output.prev_sample

											A      						=    torch.sum(torch.abs(__UpperCamelCase       )       )
											A      						=    torch.mean(torch.abs(__UpperCamelCase       )       )

											assert abs(result_sum.item() - 1_0.0_8_0_7       ) < 1e-2
											assert abs(result_mean.item() - 0.0_1_3_1       ) < 1e-3


				def __UpperCamelCase						(							self      :  List[Any]       )		->     List[Any]:
											A      						=    self.scheduler_classes[0]
											A      						=    self.get_scheduler_config(prediction_type='v_prediction'       )
											A      						=    scheduler_class(**__UpperCamelCase       )

											scheduler.set_timesteps(self.num_inference_steps       )

											A      						=    torch.manual_seed(0       )

											A      						=    self.dummy_model()
											A      						=    self.dummy_sample_deter * scheduler.init_noise_sigma
											A      						=    sample.to(__UpperCamelCase       )

											for i, t in enumerate(scheduler.timesteps       ):
																		A      						=    scheduler.scale_model_input(__UpperCamelCase						, __UpperCamelCase       )

																		A      						=    model(__UpperCamelCase						, __UpperCamelCase       )

																		A      						=    scheduler.step(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, generator=__UpperCamelCase       )
																		A      						=    output.prev_sample

											A      						=    torch.sum(torch.abs(__UpperCamelCase       )       )
											A      						=    torch.mean(torch.abs(__UpperCamelCase       )       )

											assert abs(result_sum.item() - 0.0_0_0_2       ) < 1e-2
											assert abs(result_mean.item() - 2.2676e-06       ) < 1e-3


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[Any]:
											A      						=    self.scheduler_classes[0]
											A      						=    self.get_scheduler_config()
											A      						=    scheduler_class(**__UpperCamelCase       )

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

											A      						=    torch.manual_seed(0       )

											A      						=    self.dummy_model()
											A      						=    self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											A      						=    sample.to(__UpperCamelCase       )

											for t in scheduler.timesteps:
																		A      						=    scheduler.scale_model_input(__UpperCamelCase						, __UpperCamelCase       )

																		A      						=    model(__UpperCamelCase						, __UpperCamelCase       )

																		A      						=    scheduler.step(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, generator=__UpperCamelCase       )
																		A      						=    output.prev_sample

											A      						=    torch.sum(torch.abs(__UpperCamelCase       )       )
											A      						=    torch.mean(torch.abs(__UpperCamelCase       )       )

											assert abs(result_sum.item() - 1_0.0_8_0_7       ) < 1e-2
											assert abs(result_mean.item() - 0.0_1_3_1       ) < 1e-3


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     int:
											A      						=    self.scheduler_classes[0]
											A      						=    self.get_scheduler_config()
											A      						=    scheduler_class(**__UpperCamelCase						, use_karras_sigmas=__UpperCamelCase       )

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

											A      						=    torch.manual_seed(0       )

											A      						=    self.dummy_model()
											A      						=    self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											A      						=    sample.to(__UpperCamelCase       )

											for t in scheduler.timesteps:
																		A      						=    scheduler.scale_model_input(__UpperCamelCase						, __UpperCamelCase       )

																		A      						=    model(__UpperCamelCase						, __UpperCamelCase       )

																		A      						=    scheduler.step(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, generator=__UpperCamelCase       )
																		A      						=    output.prev_sample

											A      						=    torch.sum(torch.abs(__UpperCamelCase       )       )
											A      						=    torch.mean(torch.abs(__UpperCamelCase       )       )

											assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9       ) < 1e-2
											assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3       ) < 1e-3




'''simple docstring'''

from diffusers.utils.testing_utils import require_onnxruntime






@require_onnxruntime
class 						a_		:
							pass




