





'''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					:		Union[str, Any]		=							False





class lowerCamelCase						(unittest.TestCase					):







  def 				UpperCAmelCase_				(		self     ,     lowercase__=32		)		->						Optional[int]:






        """simple docstring"""





        set_seed(0		)
        _snake_case		:	Any					     =     UNetaDModel(sample_size=lowercase__     ,     in_channels=3     ,     out_channels=3		)
        _snake_case		:	List[Any]					     =     torch.optim.SGD(model.parameters()     ,     lr=0.0_001		)
        return model, optimizer







  @slow
  def 				UpperCAmelCase_				(		self		)		->						str:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     '''cpu'''  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
        _snake_case		:	int					     =     DDPMScheduler(
            num_train_timesteps=1_000     ,     beta_start=0.0_001     ,     beta_end=0.02     ,     beta_schedule='''linear'''     ,     clip_sample=lowercase__     ,     )
        _snake_case		:	List[str]					     =     DDIMScheduler(
            num_train_timesteps=1_000     ,     beta_start=0.0_001     ,     beta_end=0.02     ,     beta_schedule='''linear'''     ,     clip_sample=lowercase__     ,     )

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

        # shared batches for DDPM and DDIM
        set_seed(0		)
        _snake_case		:	Dict					     =     [torch.randn((4, 3, 32, 32)		).clip(-1     ,     1		).to(lowercase__		) for _ in range(4		)]
        _snake_case		:	Any					     =     [torch.randn((4, 3, 32, 32)		).to(lowercase__		) for _ in range(4		)]
        _snake_case		:	int					     =     [torch.randint(0     ,     1_000     ,     (4,)		).long().to(lowercase__		) for _ in range(4		)]

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

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

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









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase					:		int		=							{
    'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase					:		Union[str, Any]		=							[
       'PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST',
       'PegasusXForConditionalGeneration',
       'PegasusXModel',
       'PegasusXPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_pegasus_x import (
          PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
          PegasusXForConditionalGeneration,
          PegasusXModel,
          PegasusXPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase					:		Dict		=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


