






"""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_	:    int            =      False





class   UpperCamelCase_ (       unittest.TestCase  ):



  def 		UpperCamelCase_				(      self ,		snake_case__=32				)			->			List[str]:



         """simple docstring"""





         set_seed(0				)
         UpperCAmelCase	    =			UNetaDModel(sample_size=snake_case__ ,		in_channels=3 ,		out_channels=3				)
         UpperCAmelCase	    =			torch.optim.SGD(model.parameters() ,		lr=0.0_001				)
         return model, optimizer



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



         """simple docstring"""





         UpperCAmelCase	    =			"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
         UpperCAmelCase	    =			DDPMScheduler(
             num_train_timesteps=10_00 ,		beta_start=0.0_001 ,		beta_end=0.02 ,		beta_schedule="""linear""" ,		clip_sample=snake_case__ ,		)
         UpperCAmelCase	    =			DDIMScheduler(
             num_train_timesteps=10_00 ,		beta_start=0.0_001 ,		beta_end=0.02 ,		beta_schedule="""linear""" ,		clip_sample=snake_case__ ,		)

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

         # shared batches for DDPM and DDIM
         set_seed(0				)
         UpperCAmelCase	    =			[torch.randn((4, 3, 32, 32)				).clip(-1 ,		1				).to(snake_case__				) for _ in range(4				)]
         UpperCAmelCase	    =			[torch.randn((4, 3, 32, 32)				).to(snake_case__				) for _ in range(4				)]
         UpperCAmelCase	    =			[torch.randint(0 ,		10_00 ,		(4,)				).long().to(snake_case__				) for _ in range(4				)]

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

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

         self.assertTrue(torch.allclose(snake_case__ ,		snake_case__ ,		atol=1e-5				)				)
         self.assertTrue(torch.allclose(snake_case__ ,		snake_case__ ,		atol=1e-5				)				)








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase_	:    Optional[int]            =      {
    '''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_	:    str            =      [
     '''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''GraphormerForGraphClassification''',
     '''GraphormerModel''',
     '''GraphormerPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_graphormer import (
      GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      GraphormerForGraphClassification,
      GraphormerModel,
      GraphormerPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase_	:    Optional[Any]            =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
