




# 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 :Any     ,			__snake_case :Optional[Any]     ,			__snake_case :Optional[int]     ,			__snake_case :bool = True     ,			__snake_case :bool = False			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=scheduler
										__magic_name__						:						Optional[Any]										=optimizers if isinstance(__snake_case     ,			(list, tuple)			) else [optimizers]
										__magic_name__						:						Any										=split_batches
										__magic_name__						:						List[str]										=step_with_optimizer
										__magic_name__						:						Dict										=GradientState()

						def      A__    (		self :List[str]     ,			*__snake_case :Optional[Any]     ,			**__snake_case :Any			):
										'''simple docstring'''

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

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										return self.scheduler.get_last_lr()

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

										return self.scheduler.state_dict()

						def      A__    (		self :Union[str, Any]     ,			__snake_case :Dict			):
										'''simple docstring'''

										self.scheduler.load_state_dict(__snake_case			)

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

										return self.scheduler.get_lr()


						def      A__    (		self :Any     ,			*__snake_case :int     ,			**__snake_case :Union[str, Any]			):
										'''simple docstring'''

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








from .imports import is_rich_available


if is_rich_available():
		from rich.traceback import install

		install(show_locals=False)

else:
		raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
