
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			:     int           =   False




class        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  def 							_lowercase							( self			: Union[str, Any]			,							UpperCamelCase__			: List[Any]=32    )			->	List[str]:







    """simple docstring"""






    set_seed(0    )
    __magic_name__				     =       UNetaDModel(sample_size=UpperCamelCase__			,							in_channels=3			,							out_channels=3    )
    __magic_name__				     =       torch.optim.SGD(model.parameters()			,							lr=0.0001    )
    return model, optimizer





  @slow
  def 							_lowercase							( self			: Any    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       """cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
    __magic_name__				     =       DDPMScheduler(
        num_train_timesteps=1000			,							beta_start=0.0001			,							beta_end=0.02			,							beta_schedule="""linear"""			,							clip_sample=UpperCamelCase__			,							)
    __magic_name__				     =       DDIMScheduler(
        num_train_timesteps=1000			,							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    )
    __magic_name__				     =       [torch.randn((4, 3, 32, 32)    ).clip(-1			,							1    ).to(UpperCamelCase__    ) for _ in range(4    )]
    __magic_name__				     =       [torch.randn((4, 3, 32, 32)    ).to(UpperCamelCase__    ) for _ in range(4    )]
    __magic_name__				     =       [torch.randint(0			,							1000			,							(4,)    ).long().to(UpperCamelCase__    ) for _ in range(4    )]

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

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





"""simple docstring"""







lowerCAmelCase__      				=   [
    '''Audio''',
    '''Array2D''',
    '''Array3D''',
    '''Array4D''',
    '''Array5D''',
    '''ClassLabel''',
    '''Features''',
    '''Sequence''',
    '''Value''',
    '''Image''',
    '''Translation''',
    '''TranslationVariableLanguages''',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages





