



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase       	=						{"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase       	=						["""XLNetTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase       	=						["""XLNetTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase       	=						[
      """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """XLNetForMultipleChoice""",
      """XLNetForQuestionAnswering""",
      """XLNetForQuestionAnsweringSimple""",
      """XLNetForSequenceClassification""",
      """XLNetForTokenClassification""",
      """XLNetLMHeadModel""",
      """XLNetModel""",
      """XLNetPreTrainedModel""",
      """load_tf_weights_in_xlnet""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase       	=						[
      """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFXLNetForMultipleChoice""",
      """TFXLNetForQuestionAnsweringSimple""",
      """TFXLNetForSequenceClassification""",
      """TFXLNetForTokenClassification""",
      """TFXLNetLMHeadModel""",
      """TFXLNetMainLayer""",
      """TFXLNetModel""",
      """TFXLNetPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

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









"""simple docstring"""





from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass
@dataclass
class 				lowercase__       :







       '''simple docstring'''


       UpperCamelCase						=					42
       UpperCamelCase						=					None
       UpperCamelCase						=					None



lowerCamelCase       	=						namedtuple("""CoinsDistribResult""", """moves excess""")
def        a__     (				lowerCAmelCase__				):

      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_			,   UpperCAmelCase_      			=							get_distrib(node.left				)
            UpperCAmelCase_			,   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()




