



'''simple docstring'''

import gc
import threading
import time

import psutil
import torch

class      __magic_name__						:







       def __init__(							self :	int		):
         _a					:				Optional[int]				=							psutil.Process()
         _a					:				str				=							False







       def 							__lowercase      (							self :	Optional[Any]		):
         _a					:				str				=							-1

         while True:
           _a					:				Dict				=							max(self.process.memory_info().rss     ,self.cpu_memory_peak		)

           # can't sleep or will not catch the peak right (this comment is here on purpose)
           if not self.peak_monitoring:
             break







       def 							__lowercase      (							self :	List[str]		):
         _a					:				List[str]				=							True
         _a					:				Tuple				=							threading.Thread(target=self.peak_monitor		)
         _a					:				Optional[Any]				=							True
         self.thread.start()







       def 							__lowercase      (							self :	Union[str, Any]		):
         _a					:				Any				=							False
         self.thread.join()
         return self.cpu_memory_peak
__lowerCAmelCase						      =       PeakCPUMemory()



def 					__lowerCamelCase					(				)      ->				Tuple:
  # Time
  _a					:				Optional[int]				=							{'time': time.time()}

  gc.collect()
  torch.cuda.empty_cache()

  # CPU mem
  _a					:				Optional[int]				=							psutil.Process().memory_info().rss
  cpu_peak_tracker.start()

  # GPU mem
  for i in range(torch.cuda.device_count()  ):
    _a					:				Union[str, Any]				=							torch.cuda.memory_allocated(lowerCAmelCase_  )
  torch.cuda.reset_peak_memory_stats()

  return measures



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				List[Any]:
  # Time
  _a					:				Any				=							{'time': time.time() - start_measures['time']}

  gc.collect()
  torch.cuda.empty_cache()

  # CPU mem
  _a					:				List[str]				=							(psutil.Process().memory_info().rss - start_measures['cpu']) / 2**20
  _a					:				Union[str, Any]				=							(cpu_peak_tracker.stop() - start_measures['cpu']) / 2**20

  # GPU mem
  for i in range(torch.cuda.device_count()  ):
    _a					:				int				=							(torch.cuda.memory_allocated(lowerCAmelCase_  ) - start_measures[str(lowerCAmelCase_  )]) / 2**20
    _a					:				List[str]				=							(torch.cuda.max_memory_allocated(lowerCAmelCase_  ) - start_measures[str(lowerCAmelCase_  )]) / 2**20

  return measures



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Any:
  print(f"""{description}:"""  )
  print(f"""- Time: {measures['time']:.2f}s"""  )
  for i in range(torch.cuda.device_count()  ):
    print(f"""- GPU {i} allocated: {measures[str(lowerCAmelCase_  )]:.2f}MiB"""  )
    _a					:				Union[str, Any]				=							measures[f"""{i}-peak"""]
    print(f"""- GPU {i} peak: {peak:.2f}MiB"""  )
  print(f"""- CPU RAM allocated: {measures['cpu']:.2f}MiB"""  )
  print(f"""- CPU RAM peak: {measures['cpu-peak']:.2f}MiB"""  )









'''simple docstring'''

import heapq



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				set[int]:
		_a					:				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
		_a					:				str				=							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
				_a					:				Optional[int]				=							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]:
								_a					:				Dict				=							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()

							__lowerCAmelCase						      =       {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)}""")




