





# 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 	UpperCamelCase_							:


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



									"""simple docstring"""



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


							def   _snake_case	(				self     :List[Any]				,					*__A     :Dict				,					**__A     :Any		) -> Optional[int]:



									"""simple docstring"""



									if not self.step_with_optimizer:
											# No link between scheduler and optimizer -> just step
											self.scheduler.step(*__A				,					**__A		)
											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(*__A				,					**__A		)
									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__								=   AcceleratorState().num_processes
											for _ in range(__A		):
													# 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(*__A				,					**__A		)
													else:
															self.scheduler.step(*__A				,					**__A		)


							def   _snake_case	(				self     :Tuple		) -> List[str]:



									"""simple docstring"""



									return self.scheduler.get_last_lr()


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



									"""simple docstring"""



									return self.scheduler.state_dict()


							def   _snake_case	(				self     :Tuple				,					__A     :Any		) -> Optional[int]:



									"""simple docstring"""



									self.scheduler.load_state_dict(__A		)


							def   _snake_case	(				self     :Optional[int]		) -> Optional[Any]:



									"""simple docstring"""



									return self.scheduler.get_lr()


							def   _snake_case	(				self     :Any				,					*__A     :Optional[int]				,					**__A     :Optional[int]		) -> List[str]:



									"""simple docstring"""



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







'''simple docstring'''

from abc import ABC, abstractmethod
from argparse import ArgumentParser




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 @staticmethod
 @abstractmethod
 def 	_UpperCAmelCase							(				__lowerCAmelCase			):
       raise NotImplementedError()





 @abstractmethod
 def 	_UpperCAmelCase							(				self			):
       raise NotImplementedError()






