
"""simple docstring"""



from __future__ import annotations

from dataclasses import dataclass

@dataclass
class  lowerCAmelCase__							:
     __a         =   42
     __a         =   None
     __a         =   None






def    _UpperCAmelCase (					__lowerCamelCase  :			Tuple						)	->   bool:




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

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

  def is_binary_search_tree_recursive_check(
      __lowerCamelCase  :			Dict					,   __lowerCamelCase  :			List[Any]					,   __lowerCamelCase  :			Dict						) -> bool:

    if node is None:
      return True

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

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


if __name__ == "__main__":
 import doctest

 doctest.testmod()










from ... import PretrainedConfig


__A							:			Optional[Any]         =			{
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
     __magic_name__   =			'nezha'




     def __init__(    self				,     snake_case_=2_1128				,     snake_case_=768				,     snake_case_=12				,     snake_case_=12				,     snake_case_=3072				,     snake_case_="gelu"				,     snake_case_=0.1				,     snake_case_=0.1				,     snake_case_=512				,     snake_case_=64				,     snake_case_=2				,     snake_case_=0.02				,     snake_case_=1E-12				,     snake_case_=0.1				,     snake_case_=0				,     snake_case_=2				,     snake_case_=3				,     snake_case_=True				,     **snake_case_				,     ):
         super().__init__(pad_token_id=snake_case_				,     bos_token_id=snake_case_				,     eos_token_id=snake_case_				,     **snake_case_							)

         _A     =							vocab_size
         _A     =							hidden_size
         _A     =							num_hidden_layers
         _A     =							num_attention_heads
         _A     =							hidden_act
         _A     =							intermediate_size
         _A     =							hidden_dropout_prob
         _A     =							attention_probs_dropout_prob
         _A     =							max_position_embeddings
         _A     =							max_relative_position
         _A     =							type_vocab_size
         _A     =							initializer_range
         _A     =							layer_norm_eps
         _A     =							classifier_dropout
         _A     =							use_cache





