


from __future__ import annotations

from dataclasses import dataclass






@dataclass
class _lowerCamelCase:
       lowercase_					:      float
       lowercase_					:      TreeNode | None       					=     None
       lowercase_					:      TreeNode | None       					=     None



def 							UpperCamelCase_( lowerCamelCase_					)       ->		bool:

    # Validation
    def is_valid_tree(lowerCamelCase_					) -> bool:
        if node is None:
            return True

        if not isinstance(lowerCamelCase_				,					lowerCamelCase_					):
            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(lowerCamelCase_					):
        raise ValueError(
            'Each node should be type of TreeNode and data should be float.'					)

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

        if node is None:
            return True

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

    return is_binary_search_tree_recursive_check(lowerCamelCase_				,					-float('inf'					)				,					float('inf'					)					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE			: str				   =					{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: int				   =					[
     "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
     "GPTBigCodeForSequenceClassification",
     "GPTBigCodeForTokenClassification",
     "GPTBigCodeForCausalLM",
     "GPTBigCodeModel",
     "GPTBigCodePreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_bigcode import (
      GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTBigCodeForCausalLM,
      GPTBigCodeForSequenceClassification,
      GPTBigCodeForTokenClassification,
      GPTBigCodeModel,
      GPTBigCodePreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE			: List[Any]				   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


