


def  a   (lowerCAmelCase__   ):  # noqa: E741
   __a					=  len(lowerCAmelCase__   )
   __a					=  0
   __a					=  [0] * n
   __a					=  [False] * n
   __a					=  [False] * n

   def dfs(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
      if parent == root:
         out_edge_count += 1
      __a					=  True
      __a					=  at

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

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

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

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


# Adjacency list of graph
SCREAMING_SNAKE_CASE							    =							{
    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


__a         =   logging.get_logger(__name__)

__a         =   {
    'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json',
    'xlm-mlm-ende-1024': 'https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json',
    'xlm-mlm-enfr-1024': 'https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json',
    'xlm-mlm-enro-1024': 'https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json',
    'xlm-mlm-tlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json',
    'xlm-mlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json',
    'xlm-clm-enfr-1024': 'https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json',
    'xlm-clm-ende-1024': 'https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json',
    'xlm-mlm-17-1280': 'https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json',
    'xlm-mlm-100-1280': 'https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json',
}
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :List[Any]       	=      'xlm'
	a     :Dict       	=      {
	    'hidden_size': 'emb_dim',
	    'num_attention_heads': 'n_heads',
	    'num_hidden_layers': 'n_layers',
	    'n_words': 'vocab_size',  # For backward compatibility
	}
	def __init__(			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Dict=3_0_1_4_5			,				SCREAMING_SNAKE_CASE_    :     Dict=2_0_4_8			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=1_2			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=1_6			,				SCREAMING_SNAKE_CASE_    :     Dict=0.1			,				SCREAMING_SNAKE_CASE_    :     str=0.1			,				SCREAMING_SNAKE_CASE_    :     Any=True			,				SCREAMING_SNAKE_CASE_    :     Any=False			,				SCREAMING_SNAKE_CASE_    :     str=False			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=False			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=1			,				SCREAMING_SNAKE_CASE_    :     Dict=True			,				SCREAMING_SNAKE_CASE_    :     Dict=5_1_2			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=2_0_4_8**-0.5			,				SCREAMING_SNAKE_CASE_    :     Dict=1e-12			,				SCREAMING_SNAKE_CASE_    :     Any=0.02			,				SCREAMING_SNAKE_CASE_    :     str=0			,				SCREAMING_SNAKE_CASE_    :     List[Any]=1			,				SCREAMING_SNAKE_CASE_    :     List[Any]=2			,				SCREAMING_SNAKE_CASE_    :     int=3			,				SCREAMING_SNAKE_CASE_    :     Dict=5			,				SCREAMING_SNAKE_CASE_    :     int=True			,				SCREAMING_SNAKE_CASE_    :     List[Any]="first"			,				SCREAMING_SNAKE_CASE_    :     Any=True			,				SCREAMING_SNAKE_CASE_    :     List[Any]=None			,				SCREAMING_SNAKE_CASE_    :     str=True			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=0.1			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=5			,				SCREAMING_SNAKE_CASE_    :     List[str]=5			,				SCREAMING_SNAKE_CASE_    :     Any=0			,				SCREAMING_SNAKE_CASE_    :     str=0			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=2			,				SCREAMING_SNAKE_CASE_    :     Tuple=0			,				**SCREAMING_SNAKE_CASE_    :     Dict			,				)			->	List[Any]:
					lowercase_	    =   vocab_size
					lowercase_	    =   emb_dim
					lowercase_	    =   n_layers
					lowercase_	    =   n_heads
					lowercase_	    =   dropout
					lowercase_	    =   attention_dropout
					lowercase_	    =   gelu_activation
					lowercase_	    =   sinusoidal_embeddings
					lowercase_	    =   causal
					lowercase_	    =   asm
					lowercase_	    =   n_langs
					lowercase_	    =   use_lang_emb
					lowercase_	    =   layer_norm_eps
					lowercase_	    =   bos_index
					lowercase_	    =   eos_index
					lowercase_	    =   pad_index
					lowercase_	    =   unk_index
					lowercase_	    =   mask_index
					lowercase_	    =   is_encoder
					lowercase_	    =   max_position_embeddings
					lowercase_	    =   embed_init_std
					lowercase_	    =   init_std
					lowercase_	    =   summary_type
					lowercase_	    =   summary_use_proj
					lowercase_	    =   summary_activation
					lowercase_	    =   summary_proj_to_labels
					lowercase_	    =   summary_first_dropout
					lowercase_	    =   start_n_top
					lowercase_	    =   end_n_top
					lowercase_	    =   mask_token_id
					lowercase_	    =   lang_id

					if "n_words" in kwargs:
									lowercase_	    =   kwargs['''n_words''']

					super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_			,				bos_token_id=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)

class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	@property
	def 		_lowercase    (			self    :     int			)			->	Mapping[str, Mapping[int, str]]:
					if self.task == "multiple-choice":
									lowercase_	    =   {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
					else:
									lowercase_	    =   {0: '''batch''', 1: '''sequence'''}
					return OrderedDict(
					    [
					        ('''input_ids''', dynamic_axis),
					        ('''attention_mask''', dynamic_axis),
					        ('''token_type_ids''', dynamic_axis),
					    ]			)




