






'''simple docstring'''







import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class _lowercase			(       _lowercase	):
			a          = (CMStochasticIterativeScheduler,)
			a          = 10




			def 	lowerCamelCase_       (							self:       List[Any]    ,							**UpperCamelCase__:       int  ):
										lowerCamelCase__      :			Optional[int] 						= {
										    """num_train_timesteps""": 201,
										    """sigma_min""": 0.002,
										    """sigma_max""": 80.0,
										}

										config.update(**UpperCamelCase__  )
										return config




			def 	lowerCamelCase_       (							self:       Optional[int]  ):
										lowerCamelCase__      :			List[str] 						= 10

										lowerCamelCase__      :			int 						= self.get_scheduler_config()
										lowerCamelCase__      :			Optional[int] 						= self.scheduler_classes[0](**UpperCamelCase__  )

										scheduler.set_timesteps(UpperCamelCase__  )

										lowerCamelCase__      :			Tuple 						= scheduler.timesteps[0]
										lowerCamelCase__      :			Optional[Any] 						= scheduler.timesteps[1]

										lowerCamelCase__      :			str 						= self.dummy_sample
										lowerCamelCase__      :			Dict 						= 0.1 * sample

										lowerCamelCase__      :			Tuple 						= scheduler.step(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  ).prev_sample
										lowerCamelCase__      :			Union[str, Any] 						= scheduler.step(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  ).prev_sample

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




			def 	lowerCamelCase_       (							self:       Optional[int]  ):
										for timesteps in [10, 50, 100, 1_000]:
																	self.check_over_configs(num_train_timesteps=UpperCamelCase__  )




			def 	lowerCamelCase_       (							self:       str  ):
										for clip_denoised in [True, False]:
																	self.check_over_configs(clip_denoised=UpperCamelCase__  )




			def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
										lowerCamelCase__      :			Any 						= self.scheduler_classes[0]
										lowerCamelCase__      :			Optional[Any] 						= self.get_scheduler_config()
										lowerCamelCase__      :			int 						= scheduler_class(**UpperCamelCase__  )

										lowerCamelCase__      :			int 						= 1
										scheduler.set_timesteps(UpperCamelCase__  )
										lowerCamelCase__      :			Dict 						= scheduler.timesteps

										lowerCamelCase__      :			Any 						= torch.manual_seed(0  )

										lowerCamelCase__      :			Union[str, Any] 						= self.dummy_model()
										lowerCamelCase__      :			int 						= self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(UpperCamelCase__  ):
																	# 1. scale model input
																	lowerCamelCase__      :			int 						= scheduler.scale_model_input(UpperCamelCase__    ,							UpperCamelCase__  )

																	# 2. predict noise residual
																	lowerCamelCase__      :			str 						= model(UpperCamelCase__    ,							UpperCamelCase__  )

																	# 3. predict previous sample x_t-1
																	lowerCamelCase__      :			Union[str, Any] 						= scheduler.step(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							generator=UpperCamelCase__  ).prev_sample

																	lowerCamelCase__      :			Optional[Any] 						= pred_prev_sample

										lowerCamelCase__      :			Optional[int] 						= torch.sum(torch.abs(UpperCamelCase__  )  )
										lowerCamelCase__      :			Any 						= torch.mean(torch.abs(UpperCamelCase__  )  )

										assert abs(result_sum.item() - 192.7_614  ) < 1e-2
										assert abs(result_mean.item() - 0.2_510  ) < 1e-3




			def 	lowerCamelCase_       (							self:       Optional[int]  ):
										lowerCamelCase__      :			str 						= self.scheduler_classes[0]
										lowerCamelCase__      :			Optional[Any] 						= self.get_scheduler_config()
										lowerCamelCase__      :			Optional[Any] 						= scheduler_class(**UpperCamelCase__  )

										lowerCamelCase__      :			Union[str, Any] 						= [106, 0]
										scheduler.set_timesteps(timesteps=UpperCamelCase__  )
										lowerCamelCase__      :			List[Any] 						= scheduler.timesteps

										lowerCamelCase__      :			Any 						= torch.manual_seed(0  )

										lowerCamelCase__      :			int 						= self.dummy_model()
										lowerCamelCase__      :			List[Any] 						= self.dummy_sample_deter * scheduler.init_noise_sigma

										for t in timesteps:
																	# 1. scale model input
																	lowerCamelCase__      :			str 						= scheduler.scale_model_input(UpperCamelCase__    ,							UpperCamelCase__  )

																	# 2. predict noise residual
																	lowerCamelCase__      :			Tuple 						= model(UpperCamelCase__    ,							UpperCamelCase__  )

																	# 3. predict previous sample x_t-1
																	lowerCamelCase__      :			Tuple 						= scheduler.step(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							generator=UpperCamelCase__  ).prev_sample

																	lowerCamelCase__      :			Dict 						= pred_prev_sample

										lowerCamelCase__      :			int 						= torch.sum(torch.abs(UpperCamelCase__  )  )
										lowerCamelCase__      :			Union[str, Any] 						= torch.mean(torch.abs(UpperCamelCase__  )  )

										assert abs(result_sum.item() - 347.6_357  ) < 1e-2
										assert abs(result_mean.item() - 0.4_527  ) < 1e-3




			def 	lowerCamelCase_       (							self:       int  ):
										lowerCamelCase__      :			Dict 						= self.scheduler_classes[0]
										lowerCamelCase__      :			int 						= self.get_scheduler_config()
										lowerCamelCase__      :			Any 						= scheduler_class(**UpperCamelCase__  )

										lowerCamelCase__      :			List[Any] 						= [39, 30, 12, 15, 0]

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




			def 	lowerCamelCase_       (							self:       str  ):
										lowerCamelCase__      :			Optional[Any] 						= self.scheduler_classes[0]
										lowerCamelCase__      :			str 						= self.get_scheduler_config()
										lowerCamelCase__      :			str 						= scheduler_class(**UpperCamelCase__  )

										lowerCamelCase__      :			str 						= [39, 30, 12, 1, 0]
										lowerCamelCase__      :			Optional[Any] 						= len(UpperCamelCase__  )

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




			def 	lowerCamelCase_       (							self:       int  ):
										lowerCamelCase__      :			Dict 						= self.scheduler_classes[0]
										lowerCamelCase__      :			Optional[int] 						= self.get_scheduler_config()
										lowerCamelCase__      :			int 						= scheduler_class(**UpperCamelCase__  )

										lowerCamelCase__      :			int 						= [scheduler.config.num_train_timesteps]

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









'''simple docstring'''







import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_A			:    Union[str, Any]					  =False

class _lowercase			(       unittest.TestCase	):




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       str=32  ):
														set_seed(0  )
														lowerCamelCase__      :			Optional[int] 						= UNetaDModel(sample_size=UpperCamelCase__    ,							in_channels=3    ,							out_channels=3  )
														lowerCamelCase__      :			List[Any] 						= torch.optim.SGD(model.parameters()    ,							lr=0.0_001  )
														return model, optimizer




							@slow
							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Optional[Any] 						= """cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
														lowerCamelCase__      :			List[Any] 						= DDPMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)
														lowerCamelCase__      :			Any 						= DDIMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)

														assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

														# shared batches for DDPM and DDIM
														set_seed(0  )
														lowerCamelCase__      :			str 						= [torch.randn((4, 3, 32, 32)  ).clip(-1    ,							1  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randn((4, 3, 32, 32)  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randint(0    ,							1_000    ,							(4,)  ).long().to(UpperCamelCase__  ) for _ in range(4  )]

														# train with a DDPM scheduler
														lowerCamelCase__						,	lowerCamelCase__      :			Any 						= self.get_model_optimizer(resolution=32  )
														model.train().to(UpperCamelCase__  )
														for i in range(4  ):
																					optimizer.zero_grad()
																					lowerCamelCase__      :			str 						= ddpm_scheduler.add_noise(clean_images[i]    ,							noise[i]    ,							timesteps[i]  )
																					lowerCamelCase__      :			str 						= model(UpperCamelCase__    ,							timesteps[i]  ).sample
																					lowerCamelCase__      :			Tuple 						= torch.nn.functional.mse_loss(UpperCamelCase__    ,							noise[i]  )
																					loss.backward()
																					optimizer.step()
														del model, optimizer

														# recreate the model and optimizer, and retry with DDIM
														lowerCamelCase__						,	lowerCamelCase__      :			Optional[Any] 						= self.get_model_optimizer(resolution=32  )
														model.train().to(UpperCamelCase__  )
														for i in range(4  ):
																					optimizer.zero_grad()
																					lowerCamelCase__      :			Optional[Any] 						= ddim_scheduler.add_noise(clean_images[i]    ,							noise[i]    ,							timesteps[i]  )
																					lowerCamelCase__      :			Dict 						= model(UpperCamelCase__    ,							timesteps[i]  ).sample
																					lowerCamelCase__      :			Union[str, Any] 						= torch.nn.functional.mse_loss(UpperCamelCase__    ,							noise[i]  )
																					loss.backward()
																					optimizer.step()
														del model, optimizer

														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )
														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )

