





# 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        snake_case_		:







		def __init__(		self			,     __lowercase			,     __lowercase			,     __lowercase = True			,     __lowercase = False   )				->   List[Any]:
				lowerCamelCase   : str								=scheduler
				lowerCamelCase   : List[str]								=optimizers if isinstance(__lowercase			,     (list, tuple)   ) else [optimizers]
				lowerCamelCase   : Tuple								=split_batches
				lowerCamelCase   : List[Any]								=step_with_optimizer
				lowerCamelCase   : Tuple								=GradientState()







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







		def 			__lowercase   (		self   )				->   int:
				return self.scheduler.get_last_lr()







		def 			__lowercase   (		self   )				->   str:
				return self.scheduler.state_dict()







		def 			__lowercase   (		self			,     __lowercase   )				->   List[str]:
				self.scheduler.load_state_dict(__lowercase   )







		def 			__lowercase   (		self   )				->   Optional[Any]:
				return self.scheduler.get_lr()







		def 			__lowercase   (		self			,     *__lowercase			,     **__lowercase   )				->   Any:
				return self.scheduler.print_lr(*__lowercase			,     **__lowercase   )








def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     bool:
		lowerCamelCase   : Optional[Any]								=[int(SCREAMING_SNAKE_CASE_		) for i in ip_va_address.split('''.'''		) if i.isdigit()]
		return len(SCREAMING_SNAKE_CASE_		) == 4 and all(0 <= int(SCREAMING_SNAKE_CASE_		) <= 2_5_4 for octet in octets		)


if __name__ == "__main__":
				snake_case_							=			input().strip()
				snake_case_							=			'''valid''' if is_ip_va_address_valid(ip) else '''invalid'''
				print(F"""{ip} is a {valid_or_invalid} IP v4 address.""")

