


from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass






@dataclass
class   __lowercase				:
      UpperCamelCase								=       42
      UpperCamelCase								=       None
      UpperCamelCase								=       None

__a     =			namedtuple("""CoinsDistribResult""", """moves excess""")
def   _UpperCamelCase				(						lowerCAmelCase_   )						->int:

   if root is None:
      return 0

   # Validation
   def count_nodes(lowerCAmelCase_   ) -> int:
      if node is None:
         return 0

      return count_nodes(node.left   ) + count_nodes(node.right   ) + 1

   def count_coins(lowerCAmelCase_   ) -> int:
      if node is None:
         return 0

      return count_coins(node.left   ) + count_coins(node.right   ) + node.data

   if count_nodes(lowerCAmelCase_   ) != count_coins(lowerCAmelCase_   ):
      raise ValueError("""The nodes number should be same as the number of coins"""   )

   # Main calculation
   def get_distrib(lowerCAmelCase_   ) -> CoinsDistribResult:

      if node is None:
         return CoinsDistribResult(0  ,						1   )

      UpperCAmelCase							=     get_distrib(node.left   )
      UpperCAmelCase							=     get_distrib(node.right   )

      UpperCAmelCase							=     1 - left_distrib_excess
      UpperCAmelCase							=     1 - right_distrib_excess

      UpperCAmelCase							=     (
          left_distrib_moves
          + right_distrib_moves
          + abs(lowerCAmelCase_   )
          + abs(lowerCAmelCase_   )
      )
      UpperCAmelCase							=     node.data - coins_to_left - coins_to_right

      return CoinsDistribResult(lowerCAmelCase_  ,						lowerCAmelCase_   )

   return get_distrib(lowerCAmelCase_   )[0]


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   __lowercase				(  unittest.TestCase  ):





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






         """simple docstring"""







         UpperCAmelCase							=     torch.nn.Linear(1_0					,       1_0       )
         UpperCAmelCase							=     torch.optim.SGD(model.parameters()					,       0.1       )
         UpperCAmelCase							=     Accelerator()
         UpperCAmelCase							=     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()



