






'''simple docstring'''
from __future__ import annotations

a_ 		=       {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}






class    __SCREAMING_SNAKE_CASE					:




 def __init__(					self		:							Any    ,					__lowercase		:							dict[str, list[str]]    ,					__lowercase		:							str					)		->  None:
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=graph
       # mapping node to its parent in resulting breadth first tree
       SCREAMING_SNAKE_CASE__   : dict[str, str | None]													={}
       SCREAMING_SNAKE_CASE__   : List[str]													=source_vertex




 def    __magic_name__		(					self		:							Optional[Any]					)		->  None:
       SCREAMING_SNAKE_CASE__   : Any													={self.source_vertex}
       SCREAMING_SNAKE_CASE__   : Any													=None
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=[self.source_vertex]  # first in first out queue

       while queue:
             SCREAMING_SNAKE_CASE__   : Optional[Any]													=queue.pop(0					)
             for adjacent_vertex in self.graph[vertex]:
                   if adjacent_vertex not in visited:
                         visited.add(_SCREAMING_SNAKE_CASE					)
                         SCREAMING_SNAKE_CASE__   : Optional[int]													=vertex
                         queue.append(_SCREAMING_SNAKE_CASE					)




 def    __magic_name__		(					self		:							str    ,					__lowercase		:							str					)		->  str:

       if target_vertex == self.source_vertex:
             return self.source_vertex

       SCREAMING_SNAKE_CASE__   : Optional[int]													=self.parent.get(_SCREAMING_SNAKE_CASE					)
       if target_vertex_parent is None:
             SCREAMING_SNAKE_CASE__   : Optional[int]													=(
                 F"No path from vertex: {self.source_vertex} to vertex: {target_vertex}"
             )
             raise ValueError(_SCREAMING_SNAKE_CASE					)

       return self.shortest_path(_SCREAMING_SNAKE_CASE					) + F"->{target_vertex}"


if __name__ == "__main__":
  a_ 		=       Graph(graph, 'G')
  g.breath_first_search()
  print(g.shortest_path('D'))
  print(g.shortest_path('G'))
  print(g.shortest_path('Foo'))




"""simple docstring"""






import gc
import threading
import time

import psutil
import torch



class 			A__   :




	'''simple docstring'''





	def __init__(     self:		str)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       psutil.Process()
				__lowerCAmelCase      :  str												=       False

	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       -1

				while True:
							__lowerCAmelCase      :  str												=       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 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       int:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       True
				__lowerCAmelCase      :  str												=       threading.Thread(target=self.peak_monitor)
				__lowerCAmelCase      :  Tuple												=       True
				self.thread.start()







	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       False
				self.thread.join()
				return self.cpu_memory_peak






__snake_case							:   Tuple      =    PeakCPUMemory()


def 			_lowercase				(  )						->   str:
			# Time
			__lowerCAmelCase      :  str												=       {"time": time.time()}

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

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

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

			return measures


def 			_lowercase				(  __snake_case	)						->   Optional[Any]:
			# Time
			__lowerCAmelCase      :  str												=       {"time": time.time() - start_measures["time"]}

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

			# CPU mem
			__lowerCAmelCase      :  str												=       (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
			__lowerCAmelCase      :  List[str]												=       (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

			# GPU mem
			for i in range(torch.cuda.device_count()	):
						__lowerCAmelCase      :  Union[str, Any]												=       (torch.cuda.memory_allocated(__snake_case	) - start_measures[str(__snake_case	)]) / 2**20
						__lowerCAmelCase      :  Any												=       (torch.cuda.max_memory_allocated(__snake_case	) - start_measures[str(__snake_case	)]) / 2**20

			return measures





def 			_lowercase				(  __snake_case					,__snake_case	)						->   Dict:
			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(__snake_case	)]:.2f}MiB"""	)
						__lowerCAmelCase      :  Optional[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"""	)