


def        UpperCamelCase_(				_A   :int  )->		Tuple:  # noqa: E741
			UpperCamelCase__					=       len(_A  )
			UpperCamelCase__					=       0
			UpperCamelCase__					=       [0] * n
			UpperCamelCase__					=       [False] * n
			UpperCamelCase__					=       [False] * n

			def dfs(_A   :str				,     _A   :Any				,     _A   :int				,     _A   :Union[str, Any]  ):
						if parent == root:
									out_edge_count += 1
						UpperCamelCase__					=       True
						UpperCamelCase__					=       at

						for to in l[at]:
									if to == parent:
												pass
									elif not visited[to]:
												UpperCamelCase__					=       dfs(_A				,     _A				,     _A				,     _A  )
												UpperCamelCase__					=       min(low[at]				,     low[to]  )

												# AP found via bridge
												if at < low[to]:
															UpperCamelCase__					=       True
												# AP found via cycle
												if at == low[to]:
															UpperCamelCase__					=       True
									else:
												UpperCamelCase__					=       min(low[at]				,     _A  )
						return out_edge_count

			for i in range(_A  ):
						if not visited[i]:
									UpperCamelCase__					=       0
									UpperCamelCase__					=       dfs(_A				,     _A				,     -1				,     _A  )
									UpperCamelCase__					=       out_edge_count > 1

			for x in range(len(_A  )  ):
						if is_art[x] is True:
									print(_A  )


# Adjacency list of graph
__UpperCamelCase    		=       {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}







compute_ap(data)






from collections import OrderedDict
from typing import Mapping

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


__UpperCamelCase    		=       logging.get_logger(__name__)

__UpperCamelCase    		=       {
    '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        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Any    =		'ibert'




			def __init__(							self      ,						snake_case=30522      ,						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=2      ,						snake_case=0.02      ,						snake_case=1E-1_2      ,						snake_case=1      ,						snake_case=0      ,						snake_case=2      ,						snake_case="absolute"      ,						snake_case=False      ,						snake_case="none"      ,						**snake_case      ,						):

						'''simple docstring'''


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

						UpperCamelCase__					=       vocab_size
						UpperCamelCase__					=       hidden_size
						UpperCamelCase__					=       num_hidden_layers
						UpperCamelCase__					=       num_attention_heads
						UpperCamelCase__					=       hidden_act
						UpperCamelCase__					=       intermediate_size
						UpperCamelCase__					=       hidden_dropout_prob
						UpperCamelCase__					=       attention_probs_dropout_prob
						UpperCamelCase__					=       max_position_embeddings
						UpperCamelCase__					=       type_vocab_size
						UpperCamelCase__					=       initializer_range
						UpperCamelCase__					=       layer_norm_eps
						UpperCamelCase__					=       position_embedding_type
						UpperCamelCase__					=       quant_mode
						UpperCamelCase__					=       force_dequant







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


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


