






"""simple docstring"""






from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass




@dataclass
class 							lowerCAmelCase__				:






      '''simple docstring'''

      __UpperCamelCase	 =  42
      __UpperCamelCase	 =  None
      __UpperCamelCase	 =  None



UpperCAmelCase_   : Dict   			= namedtuple("""CoinsDistribResult""", """moves excess""")



def 					_A			(__a							) ->       int:
    """simple docstring"""






    if root is None:
        return 0

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

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

    def count_coins(__a							) -> 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							) -> CoinsDistribResult:

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

        SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		get_distrib(node.left							)
        SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Tuple    						=		get_distrib(node.right							)

        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		1 - left_distrib_excess
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		1 - right_distrib_excess

        SCREAMING_SNAKE_CASE_      :  Dict    						=		(
            left_distrib_moves
            + right_distrib_moves
            + abs(__a							)
            + abs(__a							)
        )
        SCREAMING_SNAKE_CASE_      :  int    						=		node.data - coins_to_left - coins_to_right

        return CoinsDistribResult(__a    ,				__a							)

    return get_distrib(__a							)[0]


if __name__ == "__main__":
  import doctest

  doctest.testmod()










"""simple docstring"""






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


UpperCAmelCase_   : str   			= {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : Dict   			= ["""GPTNeoXTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : List[str]   			= [
      """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPTNeoXForCausalLM""",
      """GPTNeoXForQuestionAnswering""",
      """GPTNeoXForSequenceClassification""",
      """GPTNeoXForTokenClassification""",
      """GPTNeoXLayer""",
      """GPTNeoXModel""",
      """GPTNeoXPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neox import (
        GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoXForCausalLM,
        GPTNeoXForQuestionAnswering,
        GPTNeoXForSequenceClassification,
        GPTNeoXForTokenClassification,
        GPTNeoXLayer,
        GPTNeoXModel,
        GPTNeoXPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_   : Tuple   			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


