





"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__				  =   logging.get_logger(__name__)

lowerCamelCase__				  =   {
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}



class      __SCREAMING_SNAKE_CASE    (						lowercase_   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Tuple	     =					"roc_bert"

  def __init__(		self   :       int					,   __a   :       str=3_0522					,   __a   :       List[str]=768					,   __a   :       Optional[Any]=12					,   __a   :       str=12					,   __a   :       Dict=3072					,   __a   :       Dict="gelu"					,   __a   :       Tuple=0.1					,   __a   :       Optional[int]=0.1					,   __a   :       Union[str, Any]=512					,   __a   :       int=2					,   __a   :       Union[str, Any]=0.02					,   __a   :       Any=1e-1_2					,   __a   :       Any=True					,   __a   :       Dict=0					,   __a   :       Union[str, Any]="absolute"					,   __a   :       List[str]=None					,   __a   :       Union[str, Any]=True					,   __a   :       Optional[Any]=True					,   __a   :       Dict=768					,   __a   :       Optional[Any]=910					,   __a   :       Optional[Any]=512					,   __a   :       Optional[Any]=2_4858					,   __a   :       str=True					,   **__a   :       Tuple					,   )		->				int:
        _UpperCamelCase      :	List[Any]						=       vocab_size
        _UpperCamelCase      :	List[str]						=       max_position_embeddings
        _UpperCamelCase      :	Dict						=       hidden_size
        _UpperCamelCase      :	Optional[Any]						=       num_hidden_layers
        _UpperCamelCase      :	List[str]						=       num_attention_heads
        _UpperCamelCase      :	Any						=       intermediate_size
        _UpperCamelCase      :	Tuple						=       hidden_act
        _UpperCamelCase      :	Optional[int]						=       hidden_dropout_prob
        _UpperCamelCase      :	List[str]						=       attention_probs_dropout_prob
        _UpperCamelCase      :	Optional[Any]						=       initializer_range
        _UpperCamelCase      :	List[str]						=       type_vocab_size
        _UpperCamelCase      :	Any						=       layer_norm_eps
        _UpperCamelCase      :	str						=       use_cache
        _UpperCamelCase      :	Optional[int]						=       enable_pronunciation
        _UpperCamelCase      :	Dict						=       enable_shape
        _UpperCamelCase      :	Optional[int]						=       pronunciation_embed_dim
        _UpperCamelCase      :	List[str]						=       pronunciation_vocab_size
        _UpperCamelCase      :	Optional[Any]						=       shape_embed_dim
        _UpperCamelCase      :	Tuple						=       shape_vocab_size
        _UpperCamelCase      :	List[str]						=       concat_input
        _UpperCamelCase      :	Dict						=       position_embedding_type
        _UpperCamelCase      :	List[str]						=       classifier_dropout
        super().__init__(pad_token_id=lowerCamelCase_					,   **lowerCamelCase_	)







"""simple docstring"""


def 					lowercase__    (					lowercase_			)					->		set:




      """simple docstring"""

      _UpperCamelCase      :	Union[str, Any]						=       set()
      # edges = list of graph's edges
      _UpperCamelCase      :	Union[str, Any]						=       get_edges(lowercase_			)

      # While there are still elements in edges list, take an arbitrary edge
      # (from_node, to_node) and add his extremity to chosen_vertices and then
      # remove all arcs adjacent to the from_node and to_node
      while edges:
            _UpperCamelCase,  _UpperCamelCase      :	str						=       edges.pop()
            chosen_vertices.add(lowercase_			)
            chosen_vertices.add(lowercase_			)
            for edge in edges.copy():
                  if from_node in edge or to_node in edge:
                        edges.discard(lowercase_			)
      return chosen_vertices




def 					lowercase__    (					lowercase_			)					->		set:




      """simple docstring"""

      _UpperCamelCase      :	List[str]						=       set()
      for from_node, to_nodes in graph.items():
            for to_node in to_nodes:
                  edges.add((from_node, to_node)			)
      return edges


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
  # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
