



'''simple docstring'''

# 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 	a__     :


	def __init__(self : List[Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : bool = True,					__UpperCAmelCase : bool = False      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= scheduler
					SCREAMING_SNAKE_CASE  :						List[str]      					= optimizers if isinstance(__UpperCAmelCase,					(list, tuple)      ) else [optimizers]
					SCREAMING_SNAKE_CASE  :						int      					= split_batches
					SCREAMING_SNAKE_CASE  :						List[Any]      					= step_with_optimizer
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= GradientState()


	def 				lowercase__  (self : Dict,					*__UpperCAmelCase : str,					**__UpperCAmelCase : Optional[Any]      )     ->			int:

					"""simple docstring"""

					if not self.step_with_optimizer:
									# No link between scheduler and optimizer -> just step
									self.scheduler.step(*__UpperCAmelCase,					**__UpperCAmelCase      )
									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(*__UpperCAmelCase,					**__UpperCAmelCase      )
					else:
									# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
									# num_processes steps per training step
									SCREAMING_SNAKE_CASE  :						List[Any]      					= AcceleratorState().num_processes
									for _ in range(__UpperCAmelCase      ):
													# 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(*__UpperCAmelCase,					**__UpperCAmelCase      )
													else:
																	self.scheduler.step(*__UpperCAmelCase,					**__UpperCAmelCase      )


	def 				lowercase__  (self : int      )     ->			Any:

					"""simple docstring"""

					return self.scheduler.get_last_lr()


	def 				lowercase__  (self : Any      )     ->			int:

					"""simple docstring"""

					return self.scheduler.state_dict()


	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : Optional[Any]      )     ->			Optional[Any]:

					"""simple docstring"""

					self.scheduler.load_state_dict(__UpperCAmelCase      )


	def 				lowercase__  (self : Tuple      )     ->			Union[str, Any]:

					"""simple docstring"""

					return self.scheduler.get_lr()






	def 				lowercase__  (self : List[str],					*__UpperCAmelCase : Any,					**__UpperCAmelCase : str      )     ->			str:

					"""simple docstring"""

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









'''simple docstring'''

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
	from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




