



from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass
@dataclass
class 				_snake_case			:
    UpperCamelCase__				:					int
    UpperCamelCase__				:					TreeNode | None     		=None
    UpperCamelCase__				:					TreeNode | None     		=None
lowercase_								=     namedtuple("""CoinsDistribResult""", """moves excess""")



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):

   if root is None:
      return 0

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

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

   def count_coins(SCREAMING_SNAKE_CASE_  ) -> 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(SCREAMING_SNAKE_CASE_  ) -> 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()









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_								=     {
    """configuration_xmod""": [
        """XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XmodConfig""",
        """XmodOnnxConfig""",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """XMOD_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """XmodForCausalLM""",
							    """XmodForMaskedLM""",
							    """XmodForMultipleChoice""",
							    """XmodForQuestionAnswering""",
							    """XmodForSequenceClassification""",
							    """XmodForTokenClassification""",
							    """XmodModel""",
							    """XmodPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xmod import (
														    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XmodForCausalLM,
														    XmodForMaskedLM,
														    XmodForMultipleChoice,
														    XmodForQuestionAnswering,
														    XmodForSequenceClassification,
														    XmodForTokenClassification,
														    XmodModel,
														    XmodPreTrainedModel,
														)

else:
							import sys

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




