



import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_lowercase						  =		False



class     _UpperCAmelCase			(       unittest.TestCase     ):




     def 		snake_case_		(				self ,						a__=3_2):
         set_seed(0)
         A__		 =					UNetaDModel(sample_size=a__ ,						in_channels=3 ,						out_channels=3)
         A__		 =					torch.optim.SGD(model.parameters() ,						lr=0.0_0_0_1)
         return model, optimizer




     @slow
     def 		snake_case_		(				self):
         A__		 =					'''cpu'''  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
         A__		 =					DDPMScheduler(
             num_train_timesteps=1_0_0_0 ,						beta_start=0.0_0_0_1 ,						beta_end=0.0_2 ,						beta_schedule='''linear''' ,						clip_sample=a__ ,						)
         A__		 =					DDIMScheduler(
             num_train_timesteps=1_0_0_0 ,						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)
         A__		 =					[torch.randn((4, 3, 3_2, 3_2)).clip(-1 ,						1).to(a__) for _ in range(4)]
         A__		 =					[torch.randn((4, 3, 3_2, 3_2)).to(a__) for _ in range(4)]
         A__		 =					[torch.randint(0 ,						1_0_0_0 ,						(4,)).long().to(a__) for _ in range(4)]

         # train with a DDPM scheduler
         A__ ,	A__		 =					self.get_model_optimizer(resolution=3_2)
         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=3_2)
         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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase						  =		logging.get_logger(__name__)



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''timm_backbone'''




     def __init__(				self ,						a__=None ,						a__=3 ,						a__=True ,						a__=True ,						a__=None ,						**a__ ,						):
         super().__init__(**a__)
         A__		 =					backbone
         A__		 =					num_channels
         A__		 =					features_only
         A__		 =					use_pretrained_backbone
         A__		 =					True
         A__		 =					out_indices if out_indices is not None else (-1,)
