

"""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


__UpperCAmelCase			=					False







class   __lowercase      (     unittest.TestCase       ):

  def  __lowercase							(	self		:      Optional[int]       ,A		:      List[Any]=32     ):






   '''simple docstring'''
   set_seed(0     )
   UpperCAmelCase__       :	Optional[int]        =       UNetaDModel(sample_size=a__       ,in_channels=3       ,out_channels=3     )
   UpperCAmelCase__       :	List[Any]        =       torch.optim.SGD(model.parameters()       ,lr=0.0_0_0_1     )
   return model, optimizer




  @slow
  def  __lowercase							(	self		:      int     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Any        =       "cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
   UpperCAmelCase__       :	Union[str, Any]        =       DDPMScheduler(
       num_train_timesteps=1_000       ,beta_start=0.0_0_0_1       ,beta_end=0.0_2       ,beta_schedule="""linear"""       ,clip_sample=a__       ,)
   UpperCAmelCase__       :	Optional[Any]        =       DDIMScheduler(
       num_train_timesteps=1_000       ,beta_start=0.0_0_0_1       ,beta_end=0.0_2       ,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     )
   UpperCAmelCase__       :	List[str]        =       [torch.randn((4, 3, 32, 32)     ).clip(-1       ,1     ).to(a__     ) for _ in range(4     )]
   UpperCAmelCase__       :	Optional[int]        =       [torch.randn((4, 3, 32, 32)     ).to(a__     ) for _ in range(4     )]
   UpperCAmelCase__       :	List[Any]        =       [torch.randint(0       ,1_000       ,(4,)     ).long().to(a__     ) for _ in range(4     )]

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

   # recreate the model and optimizer, and retry with DDIM
   UpperCAmelCase__       :	Tuple        =       self.get_model_optimizer(resolution=32     )
   model.train().to(a__     )
   for i in range(4     ):
    optimizer.zero_grad()
    UpperCAmelCase__       :	str        =       ddim_scheduler.add_noise(clean_images[i]       ,noise[i]       ,timesteps[i]     )
    UpperCAmelCase__       :	Any        =       model(a__       ,timesteps[i]     ).sample
    UpperCAmelCase__       :	Optional[Any]        =       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     )     )







'''simple docstring'''

def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 return number & 1 == 0


if __name__ == "__main__":
     import doctest

     doctest.testmod()





