"""simple docstring"""






from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass





@dataclass
class 				_A		:
      snake_case__    :     int
      snake_case__    :     TreeNode | None													=			None
      snake_case__    :     TreeNode | None													=			None






__lowerCAmelCase					: Tuple        =namedtuple("""CoinsDistribResult""", """moves excess""")


def     UpperCAmelCase__ (       lowerCAmelCase__  :Union[str, Any]	)				->					str:
      '''simple docstring'''


      if root is None:
            return 0

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

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

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

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

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

      # Main calculation
      def get_distrib(lowerCAmelCase__  :int	) -> CoinsDistribResult:

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

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

            lowercase														=			1 - left_distrib_excess
            lowercase														=			1 - right_distrib_excess

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

            return CoinsDistribResult(SCREAMING_SNAKE_CASE_      ,	SCREAMING_SNAKE_CASE_	)

      return get_distrib(SCREAMING_SNAKE_CASE_	)[0]


if __name__ == "__main__":
       import doctest

       doctest.testmod()







"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__lowerCAmelCase					: Dict        ={
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase					: int        =[
           """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """XLMForMultipleChoice""",
           """XLMForQuestionAnswering""",
           """XLMForQuestionAnsweringSimple""",
           """XLMForSequenceClassification""",
           """XLMForTokenClassification""",
           """XLMModel""",
           """XLMPreTrainedModel""",
           """XLMWithLMHeadModel""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase					: Union[str, Any]        =[
           """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFXLMForMultipleChoice""",
           """TFXLMForQuestionAnsweringSimple""",
           """TFXLMForSequenceClassification""",
           """TFXLMForTokenClassification""",
           """TFXLMMainLayer""",
           """TFXLMModel""",
           """TFXLMPreTrainedModel""",
           """TFXLMWithLMHeadModel""",
       ]


if TYPE_CHECKING:
       from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
       from .tokenization_xlm import XLMTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xlm import (
                  XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XLMForMultipleChoice,
                  XLMForQuestionAnswering,
                  XLMForQuestionAnsweringSimple,
                  XLMForSequenceClassification,
                  XLMForTokenClassification,
                  XLMModel,
                  XLMPreTrainedModel,
                  XLMWithLMHeadModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xlm import (
                  TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXLMForMultipleChoice,
                  TFXLMForQuestionAnsweringSimple,
                  TFXLMForSequenceClassification,
                  TFXLMForTokenClassification,
                  TFXLMMainLayer,
                  TFXLMModel,
                  TFXLMPreTrainedModel,
                  TFXLMWithLMHeadModel,
              )

else:
       import sys

       __lowerCAmelCase					: Optional[int]        =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



