


"""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


lowerCamelCase_   :   Tuple									=  False






class       __A				(       unittest.TestCase			):






 """simple docstring"""




 def       SCREAMING_SNAKE_CASE							(		self ,    __A=32						)					->     List[Any]:
     set_seed(0						)
     a 		=UNetaDModel(sample_size=__A ,    in_channels=3 ,    out_channels=3						)
     a 		=torch.optim.SGD(model.parameters() ,    lr=0.0_001						)
     return model, optimizer





 @slow
 def       SCREAMING_SNAKE_CASE							(		self						)					->     Any:
     a 		='''cpu'''  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
     a 		=DDPMScheduler(
         num_train_timesteps=1000 ,    beta_start=0.0_001 ,    beta_end=0.02 ,    beta_schedule='''linear''' ,    clip_sample=__A ,    )
     a 		=DDIMScheduler(
         num_train_timesteps=1000 ,    beta_start=0.0_001 ,    beta_end=0.02 ,    beta_schedule='''linear''' ,    clip_sample=__A ,    )

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

     # shared batches for DDPM and DDIM
     set_seed(0						)
     a 		=[torch.randn((4, 3, 32, 32)						).clip(-1 ,    1						).to(__A						) for _ in range(4						)]
     a 		=[torch.randn((4, 3, 32, 32)						).to(__A						) for _ in range(4						)]
     a 		=[torch.randint(0 ,    1000 ,    (4,)						).long().to(__A						) for _ in range(4						)]

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

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

     self.assertTrue(torch.allclose(__A ,    __A ,    atol=1E-5						)						)
     self.assertTrue(torch.allclose(__A ,    __A ,    atol=1E-5						)						)



from ..utils import DummyObject, requires_backends


class        _a							(			metaclass=UpperCamelCase__			):
							_lowercase	:			Any        = ['''torch''', '''scipy''']

							def __init__(							self:	int    ,						*UpperCamelCase_:	Any    ,						**UpperCamelCase_:	Optional[Any]							)			->   List[str]:





									"""simple docstring"""







									requires_backends(self    ,						['''torch''', '''scipy''']							)

							@classmethod
							def       lowerCamelCase_ (							cls:	Optional[int]    ,						*UpperCamelCase_:	Any    ,						**UpperCamelCase_:	List[Any]							)			->   int:





									"""simple docstring"""







									requires_backends(cls    ,						['''torch''', '''scipy''']							)


							@classmethod
							def       lowerCamelCase_ (							cls:	Any    ,						*UpperCamelCase_:	Any    ,						**UpperCamelCase_:	Dict							)			->   Optional[int]:





									"""simple docstring"""







									requires_backends(cls    ,						['''torch''', '''scipy''']							)


