from queue import PriorityQueue
from typing import Any

import numpy as np

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					)		->						float | int:




       '''simple docstring'''






       for nxt, d in graph[v]:
              if nxt in visited_forward:
                     continue
              _A=	cst_fwd.get(lowerCAmelCase_       ,					np.inf   )
              _A=	cst_fwd[v] + d
              if new_cost_f < old_cost_f:
                     queue.put((new_cost_f, nxt)   )
                     _A=	new_cost_f
                     _A=	v
              if nxt in visited_backward:
                     if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
                            _A=	cst_fwd[v] + d + cst_bwd[nxt]
       return shortest_distance

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       _A=	-1

       _A=	set()
       _A=	set()
       _A=	{source: 0}
       _A=	{destination: 0}
       _A=	{source: None}
       _A=	{destination: None}
       _A=	PriorityQueue()
       _A=	PriorityQueue()

       _A=	np.inf

       queue_forward.put((0, source)   )
       queue_backward.put((0, destination)   )

       if source == destination:
              return 0

       while not queue_forward.empty() and not queue_backward.empty():
              _A,			_A=	queue_forward.get()
              visited_forward.add(lowerCAmelCase_   )

              _A,			_A=	queue_backward.get()
              visited_backward.add(lowerCAmelCase_   )

              _A=	pass_and_relaxation(
                  lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					)

              _A=	pass_and_relaxation(
                  lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					)

              if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
                     break

       if shortest_distance != np.inf:
              _A=	shortest_distance
       return shortest_path_distance


UpperCAmelCase_								=							{
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}
UpperCAmelCase_								=							{
    '''B''': [['''E''', 1]],
    '''C''': [['''B''', 1]],
    '''D''': [['''C''', 1]],
    '''F''': [['''D''', 1], ['''G''', 1]],
    '''E''': [[None, np.inf]],
    '''G''': [['''E''', 2]],
}

if __name__ == "__main__":
      import doctest

      doctest.testmod()
import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu





@require_cpu
class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	torch.nn.Linear(10				,      10       )
            _A=	torch.optim.SGD(model.parameters()				,      0.1       )
            _A=	Accelerator()
            _A=	accelerator.prepare(lowerCAmelCase__       )
            try:
                   pickle.loads(pickle.dumps(lowerCAmelCase__       )       )
            except Exception as e:
                   self.fail(f"Accelerated optimizer pickling failed with {e}"       )
            AcceleratorState._reset_state()