
'''simple docstring'''

import warnings

from .state import AcceleratorState, GradientState


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


class 	a__     :


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= scheduler
					SCREAMING_SNAKE_CASE  :						List[str]      					= optimizers if isinstance(__UpperCAmelCase,					(list, tuple)      ) else [optimizers]
					SCREAMING_SNAKE_CASE  :						int      					= split_batches
					SCREAMING_SNAKE_CASE  :						List[Any]      					= step_with_optimizer
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= GradientState()


	def 				lowercase__  (self : Dict,					*__UpperCAmelCase : str,					**__UpperCAmelCase : Optional[Any]      )     ->			int:

					"""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
									SCREAMING_SNAKE_CASE  :						List[Any]      					= 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 				lowercase__  (self : int      )     ->			Any:

					"""simple docstring"""

					return self.scheduler.get_last_lr()


	def 				lowercase__  (self : Any      )     ->			int:

					"""simple docstring"""

					return self.scheduler.state_dict()


	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : Optional[Any]      )     ->			Optional[Any]:

					"""simple docstring"""

					self.scheduler.load_state_dict(__UpperCAmelCase      )


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

					"""simple docstring"""

					return self.scheduler.get_lr()






	def 				lowercase__  (self : List[str],					*__UpperCAmelCase : Any,					**__UpperCAmelCase : str      )     ->			str:

					"""simple docstring"""

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









'''simple docstring'''

from collections import deque






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]       ):
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= len(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						List[str]      					= deque()
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= [False for _ in range(_SCREAMING_SNAKE_CASE       )]
				SCREAMING_SNAKE_CASE  :						Any      					= [-1 for _ in range(_SCREAMING_SNAKE_CASE       )]
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= index_of[:]

				def strong_connect(_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :List[str]       ):
								SCREAMING_SNAKE_CASE  :						Any      					= index  # the number when this node is seen
								SCREAMING_SNAKE_CASE  :						str      					= index  # lowest rank node reachable from here
								index += 1
								stack.append(_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= True

								for w in g[v]:
												if index_of[w] == -1:
																SCREAMING_SNAKE_CASE  :						List[str]      					= strong_connect(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
																SCREAMING_SNAKE_CASE  :						Optional[int]      					= (
																    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																)
												elif on_stack[w]:
																SCREAMING_SNAKE_CASE  :						List[str]      					= (
																    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																)

								if lowlink_of[v] == index_of[v]:
												SCREAMING_SNAKE_CASE  :						str      					= []
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= stack.pop()
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= False
												component.append(_SCREAMING_SNAKE_CASE       )
												while w != v:
																SCREAMING_SNAKE_CASE  :						int      					= stack.pop()
																SCREAMING_SNAKE_CASE  :						Tuple      					= False
																component.append(_SCREAMING_SNAKE_CASE       )
												components.append(_SCREAMING_SNAKE_CASE       )
								return index

				SCREAMING_SNAKE_CASE  :						int      					= []
				for v in range(_SCREAMING_SNAKE_CASE       ):
								if index_of[v] == -1:
												strong_connect(_SCREAMING_SNAKE_CASE    ,	0    ,	_SCREAMING_SNAKE_CASE       )

				return components






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				SCREAMING_SNAKE_CASE  :						Tuple      					= [[] for _ in range(_SCREAMING_SNAKE_CASE       )]
				for u, v in edges:
								g[u].append(_SCREAMING_SNAKE_CASE       )
				return g


if __name__ == "__main__":
	# Test
	snake_case_			=     7
	snake_case_			=     [0, 0, 1, 2, 3, 3, 4, 4, 6]
	snake_case_			=     [1, 3, 2, 0, 1, 4, 5, 6, 5]
	snake_case_			=     [(u, v) for u, v in zip(source, target)]
	snake_case_			=     create_graph(n_vertices, edges)

	assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)




