


'''simple docstring'''
import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 					__lowerCAmelCase   (   __SCREAMING_SNAKE_CASE						):

						"""simple docstring"""






						_UpperCAmelCase		:       List[Any]   						=(CMStochasticIterativeScheduler,)
						_UpperCAmelCase		:       Dict   						=10






						def 				_UpperCAmelCase       (  self							:      Optional[int] ,  **lowerCAmelCase							:      Union[str, Any]	):
													A_			    =		{
													    """num_train_timesteps""": 2_01,
													    """sigma_min""": 0.0_0_2,
													    """sigma_max""": 8_0.0,
													}

													config.update(**_A	)
													return config






						def 				_UpperCAmelCase       (  self							:      int	):
													A_			    =		10

													A_			    =		self.get_scheduler_config()
													A_			    =		self.scheduler_classes[0](**_A	)

													scheduler.set_timesteps(_A	)

													A_			    =		scheduler.timesteps[0]
													A_			    =		scheduler.timesteps[1]

													A_			    =		self.dummy_sample
													A_			    =		0.1 * sample

													A_			    =		scheduler.step(_A ,  _A ,  _A	).prev_sample
													A_			    =		scheduler.step(_A ,  _A ,  _A	).prev_sample

													self.assertEqual(output_a.shape ,  sample.shape	)
													self.assertEqual(output_a.shape ,  output_a.shape	)






						def 				_UpperCAmelCase       (  self							:      int	):







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






						def 				_UpperCAmelCase       (  self							:      Any	):







													for clip_denoised in [True, False]:
																				self.check_over_configs(clip_denoised=_A	)






						def 				_UpperCAmelCase       (  self							:      Dict	):
													A_			    =		self.scheduler_classes[0]
													A_			    =		self.get_scheduler_config()
													A_			    =		scheduler_class(**_A	)

													A_			    =		1
													scheduler.set_timesteps(_A	)
													A_			    =		scheduler.timesteps

													A_			    =		torch.manual_seed(0	)

													A_			    =		self.dummy_model()
													A_			    =		self.dummy_sample_deter * scheduler.init_noise_sigma

													for i, t in enumerate(_A	):
																				# 1. scale model input
																				A_			    =		scheduler.scale_model_input(_A ,  _A	)

																				# 2. predict noise residual
																				A_			    =		model(_A ,  _A	)

																				# 3. predict previous sample x_t-1
																				A_			    =		scheduler.step(_A ,  _A ,  _A ,  generator=_A	).prev_sample

																				A_			    =		pred_prev_sample

													A_			    =		torch.sum(torch.abs(_A	)	)
													A_			    =		torch.mean(torch.abs(_A	)	)

													assert abs(result_sum.item() - 1_92.76_14	) < 1e-2
													assert abs(result_mean.item() - 0.2_5_1_0	) < 1e-3






						def 				_UpperCAmelCase       (  self							:      List[str]	):
													A_			    =		self.scheduler_classes[0]
													A_			    =		self.get_scheduler_config()
													A_			    =		scheduler_class(**_A	)

													A_			    =		[1_06, 0]
													scheduler.set_timesteps(timesteps=_A	)
													A_			    =		scheduler.timesteps

													A_			    =		torch.manual_seed(0	)

													A_			    =		self.dummy_model()
													A_			    =		self.dummy_sample_deter * scheduler.init_noise_sigma

													for t in timesteps:
																				# 1. scale model input
																				A_			    =		scheduler.scale_model_input(_A ,  _A	)

																				# 2. predict noise residual
																				A_			    =		model(_A ,  _A	)

																				# 3. predict previous sample x_t-1
																				A_			    =		scheduler.step(_A ,  _A ,  _A ,  generator=_A	).prev_sample

																				A_			    =		pred_prev_sample

													A_			    =		torch.sum(torch.abs(_A	)	)
													A_			    =		torch.mean(torch.abs(_A	)	)

													assert abs(result_sum.item() - 3_47.63_57	) < 1e-2
													assert abs(result_mean.item() - 0.4_5_2_7	) < 1e-3






						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													A_			    =		self.scheduler_classes[0]
													A_			    =		self.get_scheduler_config()
													A_			    =		scheduler_class(**_A	)

													A_			    =		[39, 30, 12, 15, 0]

													with self.assertRaises(_A ,  msg="`timesteps` must be in descending order."	):
																				scheduler.set_timesteps(timesteps=_A	)






						def 				_UpperCAmelCase       (  self							:      Optional[int]	):
													A_			    =		self.scheduler_classes[0]
													A_			    =		self.get_scheduler_config()
													A_			    =		scheduler_class(**_A	)

													A_			    =		[39, 30, 12, 1, 0]
													A_			    =		len(_A	)

													with self.assertRaises(_A ,  msg="Can only pass one of `num_inference_steps` or `timesteps`."	):
																				scheduler.set_timesteps(num_inference_steps=_A ,  timesteps=_A	)






						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													A_			    =		self.scheduler_classes[0]
													A_			    =		self.get_scheduler_config()
													A_			    =		scheduler_class(**_A	)

													A_			    =		[scheduler.config.num_train_timesteps]

													with self.assertRaises(
													    _A ,  msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}" ,  ):
																				scheduler.set_timesteps(timesteps=_A	)












"""simple docstring"""


from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
		from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline



