






"""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_       :							Dict             =				False


class 	__UpperCAmelCase		(				unittest.TestCase	):


	'''simple docstring'''





	def 				UpperCamelCase_  (     self      ,      _A=3_2					):



		'''simple docstring'''

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







	@slow
	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						='''cpu'''  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
		_SCREAMING_SNAKE_CASE						=DDPMScheduler(
		    num_train_timesteps=1_0_0_0      ,      beta_start=0.0001      ,      beta_end=0.02      ,      beta_schedule='''linear'''      ,      clip_sample=_A      ,      )
		_SCREAMING_SNAKE_CASE						=DDIMScheduler(
		    num_train_timesteps=1_0_0_0      ,      beta_start=0.0001      ,      beta_end=0.02      ,      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					)
		_SCREAMING_SNAKE_CASE						=[torch.randn((4, 3, 3_2, 3_2)					).clip(-1      ,      1					).to(_A					) for _ in range(4					)]
		_SCREAMING_SNAKE_CASE						=[torch.randn((4, 3, 3_2, 3_2)					).to(_A					) for _ in range(4					)]
		_SCREAMING_SNAKE_CASE						=[torch.randint(0      ,      1_0_0_0      ,      (4,)					).long().to(_A					) for _ in range(4					)]

		# train with a DDPM scheduler
		_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE						=self.get_model_optimizer(resolution=3_2					)
		model.train().to(_A					)
		for i in range(4					):
			optimizer.zero_grad()
			_SCREAMING_SNAKE_CASE						=ddpm_scheduler.add_noise(clean_images[i]      ,      noise[i]      ,      timesteps[i]					)
			_SCREAMING_SNAKE_CASE						=model(_A      ,      timesteps[i]					).sample
			_SCREAMING_SNAKE_CASE						=torch.nn.functional.mse_loss(_A      ,      noise[i]					)
			loss.backward()
			optimizer.step()
		del model, optimizer

		# recreate the model and optimizer, and retry with DDIM
		_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE						=self.get_model_optimizer(resolution=3_2					)
		model.train().to(_A					)
		for i in range(4					):
			optimizer.zero_grad()
			_SCREAMING_SNAKE_CASE						=ddim_scheduler.add_noise(clean_images[i]      ,      noise[i]      ,      timesteps[i]					)
			_SCREAMING_SNAKE_CASE						=model(_A      ,      timesteps[i]					).sample
			_SCREAMING_SNAKE_CASE						=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					)					)







# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')





class 					__lowerCAmelCase	:




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase = True				,  lowerCAmelCase = False   )    ->					str:
        '''simple docstring'''






        _lowercase      			=scheduler
        _lowercase      			=optimizers if isinstance(lowerCAmelCase				,  (list, tuple)   ) else [optimizers]
        _lowercase      			=split_batches
        _lowercase      			=step_with_optimizer
        _lowercase      			=GradientState()




  def     A__	(							self				,  *lowerCAmelCase				,  **lowerCAmelCase   )    ->					List[Any]:
        '''simple docstring'''






        if not self.step_with_optimizer:
              # No link between scheduler and optimizer -> just step
              self.scheduler.step(*lowerCAmelCase				,  **lowerCAmelCase   )
              return

        # Otherwise, first make sure the optimizer was stepped.
        if not self.gradient_state.sync_gradients:
              if self.gradient_state.adjust_scheduler:
                    self.scheduler._step_count += 1
              return

        for opt in self.optimizers:
              if opt.step_was_skipped:
                    return
        if self.split_batches:
              # Split batches -> the training dataloader batch size is not changed so one step per training step
              self.scheduler.step(*lowerCAmelCase				,  **lowerCAmelCase   )
        else:
              # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
              # num_processes steps per training step
              _lowercase      			=AcceleratorState().num_processes
              for _ in range(lowerCAmelCase   ):
                    # Special case when using OneCycle and `drop_last` was not used
                    if hasattr(self.scheduler				,  'total_steps'   ):
                          if self.scheduler._step_count <= self.scheduler.total_steps:
                                self.scheduler.step(*lowerCAmelCase				,  **lowerCAmelCase   )
                    else:
                          self.scheduler.step(*lowerCAmelCase				,  **lowerCAmelCase   )




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        return self.scheduler.get_last_lr()




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        return self.scheduler.state_dict()




  def     A__	(							self				,  lowerCAmelCase   )    ->					str:
        '''simple docstring'''






        self.scheduler.load_state_dict(lowerCAmelCase   )




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        return self.scheduler.get_lr()




  def     A__	(							self				,  *lowerCAmelCase				,  **lowerCAmelCase   )    ->					Optional[int]:
        '''simple docstring'''






        return self.scheduler.print_lr(*lowerCAmelCase				,  **lowerCAmelCase   )


