
import unittest

import torch

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


lowercase   =	False





class    __A(					unittest.TestCase							):

					def  lowercase__   (						self       :  int ,       __UpperCamelCase       :  Tuple=3_2 ):
												set_seed(0 )
												lowerCamelCase_					=       UNetaDModel(sample_size=__UpperCamelCase ,       in_channels=3 ,       out_channels=3 )
												lowerCamelCase_					=       torch.optim.SGD(model.parameters() ,       lr=0.0001 )
												return model, optimizer



					@slow
					def  lowercase__   (						self       :  str ):
												lowerCamelCase_					=       """cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
												lowerCamelCase_					=       DDPMScheduler(
												    num_train_timesteps=1_0_0_0 ,       beta_start=0.0001 ,       beta_end=0.02 ,       beta_schedule="""linear""" ,       clip_sample=__UpperCamelCase ,       )
												lowerCamelCase_					=       DDIMScheduler(
												    num_train_timesteps=1_0_0_0 ,       beta_start=0.0001 ,       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_					=       [torch.randn((4, 3, 3_2, 3_2) ).clip(-1 ,       1 ).to(__UpperCamelCase ) for _ in range(4 )]
												lowerCamelCase_					=       [torch.randn((4, 3, 3_2, 3_2) ).to(__UpperCamelCase ) for _ in range(4 )]
												lowerCamelCase_					=       [torch.randint(0 ,       1_0_0_0 ,       (4,) ).long().to(__UpperCamelCase ) for _ in range(4 )]

												# train with a DDPM scheduler
												lowerCamelCase_	, lowerCamelCase_					=       self.get_model_optimizer(resolution=3_2 )
												model.train().to(__UpperCamelCase )
												for i in range(4 ):
																			optimizer.zero_grad()
																			lowerCamelCase_					=       ddpm_scheduler.add_noise(clean_images[i] ,       noise[i] ,       timesteps[i] )
																			lowerCamelCase_					=       model(__UpperCamelCase ,       timesteps[i] ).sample
																			lowerCamelCase_					=       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_					=       self.get_model_optimizer(resolution=3_2 )
												model.train().to(__UpperCamelCase )
												for i in range(4 ):
																			optimizer.zero_grad()
																			lowerCamelCase_					=       ddim_scheduler.add_noise(clean_images[i] ,       noise[i] ,       timesteps[i] )
																			lowerCamelCase_					=       model(__UpperCamelCase ,       timesteps[i] ).sample
																			lowerCamelCase_					=       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 ) )







import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     (KDPMaDiscreteScheduler,)
					SCREAMING_SNAKE_CASE           =     1_0

					def  lowercase__   (						self       :  str ,       **__UpperCamelCase       :  Optional[int] ):
												lowerCamelCase_					=       {
												    """num_train_timesteps""": 1_1_0_0,
												    """beta_start""": 0.0001,
												    """beta_end""": 0.02,
												    """beta_schedule""": """linear""",
												}

												config.update(**__UpperCamelCase )
												return config

					def  lowercase__   (						self       :  Optional[Any] ):
												for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
																			self.check_over_configs(num_train_timesteps=__UpperCamelCase )

					def  lowercase__   (						self       :  Dict ):
												for beta_start, beta_end in zip([0.00001, 0.0001, 0.001] ,       [0.0002, 0.002, 0.02] ):
																			self.check_over_configs(beta_start=__UpperCamelCase ,       beta_end=__UpperCamelCase )

					def  lowercase__   (						self       :  Union[str, Any] ):
												for schedule in ["linear", "scaled_linear"]:
																			self.check_over_configs(beta_schedule=__UpperCamelCase )

					def  lowercase__   (						self       :  List[str] ):
												for prediction_type in ["epsilon", "v_prediction"]:
																			self.check_over_configs(prediction_type=__UpperCamelCase )

					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       self.scheduler_classes[0]
												lowerCamelCase_					=       self.get_scheduler_config(prediction_type="""v_prediction""" )
												lowerCamelCase_					=       scheduler_class(**__UpperCamelCase )

												scheduler.set_timesteps(self.num_inference_steps )

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

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

																			lowerCamelCase_					=       model(__UpperCamelCase ,       __UpperCamelCase )

																			lowerCamelCase_					=       scheduler.step(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
																			lowerCamelCase_					=       output.prev_sample

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

												if torch_device in ["cpu", "mps"]:
																			assert abs(result_sum.item() - 4.6934E-07 ) < 1E-2
																			assert abs(result_mean.item() - 6.1112E-10 ) < 1E-3
												else:
																			# CUDA
																			assert abs(result_sum.item() - 4.693_4286_5017_0972E-07 ) < 1E-2
																			assert abs(result_mean.item() - 0.0002 ) < 1E-3

					def  lowercase__   (						self       :  List[Any] ):
												if torch_device == "mps":
																			return
												lowerCamelCase_					=       self.scheduler_classes[0]
												lowerCamelCase_					=       self.get_scheduler_config()
												lowerCamelCase_					=       scheduler_class(**__UpperCamelCase )

												scheduler.set_timesteps(self.num_inference_steps )

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

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

																			lowerCamelCase_					=       model(__UpperCamelCase ,       __UpperCamelCase )

																			lowerCamelCase_					=       scheduler.step(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
																			lowerCamelCase_					=       output.prev_sample

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

												if torch_device in ["cpu", "mps"]:
																			assert abs(result_sum.item() - 20.4125 ) < 1E-2
																			assert abs(result_mean.item() - 0.0266 ) < 1E-3
												else:
																			# CUDA
																			assert abs(result_sum.item() - 20.4125 ) < 1E-2
																			assert abs(result_mean.item() - 0.0266 ) < 1E-3



					def  lowercase__   (						self       :  Optional[int] ):
												if torch_device == "mps":
																			return
												lowerCamelCase_					=       self.scheduler_classes[0]
												lowerCamelCase_					=       self.get_scheduler_config()
												lowerCamelCase_					=       scheduler_class(**__UpperCamelCase )

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

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

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

																			lowerCamelCase_					=       model(__UpperCamelCase ,       __UpperCamelCase )

																			lowerCamelCase_					=       scheduler.step(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
																			lowerCamelCase_					=       output.prev_sample

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

												if str(__UpperCamelCase ).startswith("""cpu""" ):
																			# The following sum varies between 148 and 156 on mps. Why?
																			assert abs(result_sum.item() - 20.4125 ) < 1E-2
																			assert abs(result_mean.item() - 0.0266 ) < 1E-3
												else:
																			# CUDA
																			assert abs(result_sum.item() - 20.4125 ) < 1E-2
																			assert abs(result_mean.item() - 0.0266 ) < 1E-3





