


'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowercase     :						Optional[int]	=     logging.get_logger(__name__)

__lowercase     :						Any	=     {
    'kssteven/ibert-roberta-base': 'https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json',
    'kssteven/ibert-roberta-large': 'https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json',
    'kssteven/ibert-roberta-large-mnli': (
        'https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'
    ),
}





class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   "ibert"




	def __init__(       self						,    __a=3_0522						,    __a=768						,    __a=12						,    __a=12						,    __a=3072						,    __a="gelu"						,    __a=0.1						,    __a=0.1						,    __a=512						,    __a=2						,    __a=0.02						,    __a=1E-1_2						,    __a=1						,    __a=0						,    __a=2						,    __a="absolute"						,    __a=False						,    __a="none"						,    **__a						,    ):

						'''simple docstring'''





						super().__init__(pad_token_id=__a						,    bos_token_id=__a						,    eos_token_id=__a						,    **__a							)

						__a      :	Tuple    =  vocab_size
						__a      :	Any    =  hidden_size
						__a      :	Tuple    =  num_hidden_layers
						__a      :	Dict    =  num_attention_heads
						__a      :	Tuple    =  hidden_act
						__a      :	Any    =  intermediate_size
						__a      :	List[Any]    =  hidden_dropout_prob
						__a      :	str    =  attention_probs_dropout_prob
						__a      :	Optional[int]    =  max_position_embeddings
						__a      :	Any    =  type_vocab_size
						__a      :	Optional[Any]    =  initializer_range
						__a      :	Optional[int]    =  layer_norm_eps
						__a      :	Optional[int]    =  position_embedding_type
						__a      :	int    =  quant_mode
						__a      :	Optional[int]    =  force_dequant





class 							__UpperCamelCase    (		lowerCAmelCase_ ):




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						if self.task == "multiple-choice":
											__a      :	Optional[Any]    =  {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
											__a      :	str    =  {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]							)







'''simple docstring'''



from __future__ import annotations

from dataclasses import dataclass





@dataclass
class 							__UpperCamelCase    :
	A_           =   42
	A_           =   None
	A_           =   None






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       TreeNode | None       ):

					# Validation
					def is_valid_tree(_SCREAMING_SNAKE_CASE				:       TreeNode | None       ) -> 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(
					    _SCREAMING_SNAKE_CASE				:       TreeNode | None						,				_SCREAMING_SNAKE_CASE				:       float						,				_SCREAMING_SNAKE_CASE				:       float       ) -> 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()



