






# 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       __SCREAMING_SNAKE_CASE   :






						def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = True       ,  _UpperCamelCase = False				):






												"""simple docstring"""
												lowerCAmelCase__        =  scheduler
												lowerCAmelCase__        =  optimizers if isinstance(_UpperCamelCase       ,  (list, tuple)				) else [optimizers]
												lowerCAmelCase__        =  split_batches
												lowerCAmelCase__        =  step_with_optimizer
												lowerCAmelCase__        =  GradientState()






						def  UpperCamelCase__ (		self       ,  *_UpperCamelCase       ,  **_UpperCamelCase				):






												"""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
																		lowerCAmelCase__        =  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  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self.scheduler.get_last_lr()






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self.scheduler.state_dict()






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												self.scheduler.load_state_dict(_UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self.scheduler.get_lr()







						def  UpperCamelCase__ (		self       ,  *_UpperCamelCase       ,  **_UpperCamelCase				):






												"""simple docstring"""
												return self.scheduler.print_lr(*_UpperCamelCase       ,  **_UpperCamelCase				)













def 		_UpperCamelCase				(      UpperCamelCase_    :   str						)					->				str:

						"""simple docstring"""



						return " ".join(input_str.split()[::-1]						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()





