






"""simple docstring"""
import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")



class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = True     ,     lowerCAmelCase_ = False							):
		A_		:						Any	    =   scheduler
		A_		:						Tuple	    =   optimizers if isinstance(lowerCAmelCase_     ,     (list, tuple)							) else [optimizers]
		A_		:						Tuple	    =   split_batches
		A_		:						List[str]	    =   step_with_optimizer
		A_		:						Optional[int]	    =   GradientState()





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





	def    lowerCamelCase(self							):
		return self.scheduler.get_last_lr()





	def    lowerCamelCase(self							):
		return self.scheduler.state_dict()





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		self.scheduler.load_state_dict(lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		return self.scheduler.get_lr()





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.scheduler.print_lr(*lowerCAmelCase_     ,     **lowerCAmelCase_							)









"""simple docstring"""
_lowerCAmelCase         =							{"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
_lowerCAmelCase         =							["a", "b", "c", "d", "e"]






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						int	    =   start
	# add current to visited
	visited.append(snake_case__					)
	A_		:						Any	    =   edges[current]
	for neighbor in neighbors:
		# if neighbor not in visited, visit
		if neighbor not in visited:
			A_		:						Optional[Any]	    =   topological_sort(snake_case__   ,   snake_case__   ,   snake_case__					)
    # if all neighbors visited add current to sort
	sort.append(snake_case__					)
	# if all vertices haven't been visited select a new one to visit
	if len(snake_case__					) != len(snake_case__					):
		for vertice in vertices:
			if vertice not in visited:
				A_		:						Optional[Any]	    =   topological_sort(snake_case__   ,   snake_case__   ,   snake_case__					)
    # return sort
	return sort


if __name__ == "__main__":
							_lowerCAmelCase         =							topological_sort("a", [], [])
							print(sort)

