






from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass






@dataclass
class 		lowercase_					:






      '''simple docstring'''




      __snake_case       =  42
      __snake_case       =  None
      __snake_case       =  None
UpperCAmelCase__				   =		namedtuple("CoinsDistribResult", "moves excess")
def 						_a    (      a :TreeNode | None )							->			int:

 if root is None:
  return 0

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

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

 def count_coins(a :TreeNode | None ) -> int:
  if node is None:
   return 0

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

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

 # Main calculation
 def get_distrib(a :TreeNode | None ) -> CoinsDistribResult:

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

  a  ,     a		  =      get_distrib(node.left )
  a  ,     a		  =      get_distrib(node.right )

  a		  =      1 - left_distrib_excess
  a		  =      1 - right_distrib_excess

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

  return CoinsDistribResult(a	,							a )

 return get_distrib(a )[0]


if __name__ == "__main__":
     import doctest

     doctest.testmod()










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__				   =		{
    "configuration_nllb_moe": [
        "NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "NllbMoeConfig",
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__				   =		[
         "NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST",
         "NllbMoeForConditionalGeneration",
         "NllbMoeModel",
         "NllbMoePreTrainedModel",
         "NllbMoeTop2Router",
         "NllbMoeSparseMLP",
     ]


if TYPE_CHECKING:
     from .configuration_nllb_moe import (
         NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
         NllbMoeConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nllb_moe import (
              NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
              NllbMoeForConditionalGeneration,
              NllbMoeModel,
              NllbMoePreTrainedModel,
              NllbMoeSparseMLP,
              NllbMoeTopaRouter,
          )


else:
     import sys

     UpperCAmelCase__				   =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


