# 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     lowercase     :




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



												'''simple docstring'''

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




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



												'''simple docstring'''

												if not self.step_with_optimizer:
																	# No link between scheduler and optimizer -> just step
																	self.scheduler.step(*__snake_case						,				**__snake_case   )
																	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(*__snake_case						,				**__snake_case   )
												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(__snake_case   ):
																						# 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(*__snake_case						,				**__snake_case   )
																						else:
																											self.scheduler.step(*__snake_case						,				**__snake_case   )




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



												'''simple docstring'''

												return self.scheduler.get_last_lr()




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



												'''simple docstring'''

												return self.scheduler.state_dict()




							def 						__snake_case(    self :       Union[str, Any]						,				_UpperCamelCase :       Dict   )       ->					Tuple:



												'''simple docstring'''

												self.scheduler.load_state_dict(__snake_case   )




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



												'''simple docstring'''

												return self.scheduler.get_lr()





							def 						__snake_case(    self :       Any						,				*_UpperCamelCase :       int						,				**_UpperCamelCase :       Union[str, Any]   )       ->					int:



												'''simple docstring'''

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










import heapq




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						list[list]										=[]

				# for each node and his adjacency list add them and the rank of the node to queue
				# using heapq module the queue will be filled like a Priority Queue
				# heapq works with a min priority queue, so I used -1*len(v) to build it
				for key, value in graph.items():
								# O(log(n))
								heapq.heappush(lowerCamelCase						,  [-1 * len(lowerCamelCase ), (key, value)] )

				# chosen_vertices = set of chosen vertices
				__magic_name__						:						Tuple										=set()

				# while queue isn't empty and there are still edges
				#   (queue[0][0] is the rank of the node with max rank)
				while queue and queue[0][0] != 0:
								# extract vertex with max rank from queue and add it to chosen_vertices
								__magic_name__						:						Tuple										=heapq.heappop(lowerCamelCase )[1][0]
								chosen_vertices.add(lowerCamelCase )

								# Remove all arcs adjacent to argmax
								for elem in queue:
												# if v haven't adjacent node, skip
												if elem[0] == 0:
																continue
												# if argmax is reachable from elem
												# remove argmax from elem's adjacent list and update his rank
												if argmax in elem[1][1]:
																__magic_name__						:						Tuple										=elem[1][1].index(lowerCamelCase )
																del elem[1][1][index]
																elem[0] += 1
        # re-order the queue
								heapq.heapify(lowerCamelCase )
				return chosen_vertices


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							UpperCAmelCase_		:					Optional[int]          =  {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
							print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")




