




import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 		UpperCAmelCase   (				UpperCAmelCase__			):







						'''simple docstring'''





						lowerCAmelCase_     		=   (EulerDiscreteScheduler,)
						lowerCAmelCase_     		=   10






						def        snake_case__						(						self							:     List[Any]       ,	**__lowercase							:     List[Any]						):






							"""simple docstring"""






							snake_case_		    =     {
							    "num_train_timesteps": 11_00,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							}

							config.update(**__lowercase						)
							return config






						def        snake_case__						(						self							:     Optional[int]						):






							"""simple docstring"""






							for timesteps in [10, 50, 1_00, 10_00]:
								self.check_over_configs(num_train_timesteps=__lowercase						)






						def        snake_case__						(						self							:     int						):






							"""simple docstring"""






							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]       ,	[0.0002, 0.002, 0.02]						):
								self.check_over_configs(beta_start=__lowercase       ,	beta_end=__lowercase						)






						def        snake_case__						(						self							:     Any						):






							"""simple docstring"""






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






						def        snake_case__						(						self							:     List[Any]						):






							"""simple docstring"""






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






						def        snake_case__						(						self							:     Optional[int]						):






							"""simple docstring"""






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

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		    =     torch.manual_seed(0						)

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

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

								snake_case_		    =     model(__lowercase       ,	__lowercase						)

								snake_case_		    =     scheduler.step(__lowercase       ,	__lowercase       ,	__lowercase       ,	generator=__lowercase						)
								snake_case_		    =     output.prev_sample

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

							assert abs(result_sum.item() - 10.0807						) < 1E-2
							assert abs(result_mean.item() - 0.0131						) < 1E-3






						def        snake_case__						(						self							:     int						):






							"""simple docstring"""






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

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		    =     torch.manual_seed(0						)

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

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

								snake_case_		    =     model(__lowercase       ,	__lowercase						)

								snake_case_		    =     scheduler.step(__lowercase       ,	__lowercase       ,	__lowercase       ,	generator=__lowercase						)
								snake_case_		    =     output.prev_sample

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

							assert abs(result_sum.item() - 0.0002						) < 1E-2
							assert abs(result_mean.item() - 2.2676E-06						) < 1E-3






						def        snake_case__						(						self							:     Optional[int]						):






							"""simple docstring"""






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

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

							snake_case_		    =     torch.manual_seed(0						)

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

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

								snake_case_		    =     model(__lowercase       ,	__lowercase						)

								snake_case_		    =     scheduler.step(__lowercase       ,	__lowercase       ,	__lowercase       ,	generator=__lowercase						)
								snake_case_		    =     output.prev_sample

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

							assert abs(result_sum.item() - 10.0807						) < 1E-2
							assert abs(result_mean.item() - 0.0131						) < 1E-3






						def        snake_case__						(						self							:     Union[str, Any]						):






							"""simple docstring"""






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

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

							snake_case_		    =     torch.manual_seed(0						)

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

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

								snake_case_		    =     model(__lowercase       ,	__lowercase						)

								snake_case_		    =     scheduler.step(__lowercase       ,	__lowercase       ,	__lowercase       ,	generator=__lowercase						)
								snake_case_		    =     output.prev_sample

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

							assert abs(result_sum.item() - 124.52_2994_9951_1719						) < 1E-2
							assert abs(result_mean.item() - 0.1_6213_9326_3339_9963						) < 1E-3






from torch import nn


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	if act_fn in ["swish", "silu"]:
		return nn.SiLU()
	elif act_fn == "mish":
		return nn.Mish()
	elif act_fn == "gelu":
		return nn.GELU()
	else:
		raise ValueError(f"Unsupported activation function: {act_fn}"							)
