



from __future__ import annotations

from dataclasses import dataclass





@dataclass
class  lowerCamelCase__	:
     """simple docstring"""



     _A      =      42
     _A      =      None
     _A      =      None



def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      def is_valid_tree(UpperCAmelCase__			) -> bool:
            if node is None:
                  return True

            if not isinstance(_a , _a			):
                  return False

            try:
                  float(node.data			)
            except (TypeError, ValueError):
                  return False

            return is_valid_tree(node.left			) and is_valid_tree(node.right			)

      if not is_valid_tree(_a			):
            raise ValueError(
                "Each node should be type of TreeNode and data should be float."			)

      def is_binary_search_tree_recursive_check(
          UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			) -> bool:

            if node is None:
                  return True

            return (
                left_bound < node.data < right_bound
                and is_binary_search_tree_recursive_check(node.left , _a , node.data			)
                and is_binary_search_tree_recursive_check(
                    node.right , node.data , _a			)
            )

      return is_binary_search_tree_recursive_check(_a , -float("inf"			) , float("inf"			)			)


if __name__ == "__main__":
     import doctest

     doctest.testmod()


"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase													={
    "configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase													=[
      "MEGA_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MegaForCausalLM",
      "MegaForMaskedLM",
      "MegaForMultipleChoice",
      "MegaForQuestionAnswering",
      "MegaForSequenceClassification",
      "MegaForTokenClassification",
      "MegaModel",
      "MegaPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mega import (
        MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
        MegaForCausalLM,
        MegaForMaskedLM,
        MegaForMultipleChoice,
        MegaForQuestionAnswering,
        MegaForSequenceClassification,
        MegaForTokenClassification,
        MegaModel,
        MegaPreTrainedModel,
    )

else:
  import sys

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




