code: 
  
  
  
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__	):
  		lowerCamelCase_  :    Optional[int]   	=			sorted(lowerCAmelCase__       ,key=lambda lowerCAmelCase__	: edge[2]	)
  
  		lowerCamelCase_  :    Union[str, Any]   	=			list(range(lowerCAmelCase__	)	)
  
  		def find_parent(lowerCAmelCase__	):
  				if i != parent[i]:
  						lowerCamelCase_  :    List[str]   	=			find_parent(parent[i]	)
  				return parent[i]
  
  		lowerCamelCase_  :    Any   	=			0
  		lowerCamelCase_  :    Union[str, Any]   	=			[]
  
  		for edge in edges:
  				lowerCamelCase_  :    str   	=			find_parent(edge[0]	)
  				lowerCamelCase_  :    Dict   	=			find_parent(edge[1]	)
  				if parent_a != parent_b:
  						minimum_spanning_tree_cost += edge[2]
  						minimum_spanning_tree.append(lowerCAmelCase__	)
  						lowerCamelCase_  :    Dict   	=			parent_b
  
  		return minimum_spanning_tree
  
  
  if __name__ == "__main__":  # pragma: no cover
  		_lowercase		:     Any 					=list(map(int, input().strip().split()))
  		_lowercase		:     int 					=[]
  
  		for _ in range(num_edges):
  				_lowercase		:     Any 					=(int(x) for x in input().strip().split())
  				edges.append((nodea, nodea, cost))
  
  		kruskal(num_nodes, edges)
  
  
  
style_context: 
  
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  _lowercase		:     Dict 					=logging.get_logger(__name__)
  
  _lowercase		:     Any 					={
      """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
  }
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
  					_a			:	List[str]							=				'vilt'
  
  
  
  					def __init__(		self    :    int					,			lowerCamelCase    :    Any=3_05_22					,			lowerCamelCase    :    Union[str, Any]=2					,			lowerCamelCase    :    Optional[Any]=2					,			lowerCamelCase    :    str=40					,			lowerCamelCase    :    List[str]=7_68					,			lowerCamelCase    :    Optional[int]=12					,			lowerCamelCase    :    List[Any]=12					,			lowerCamelCase    :    List[str]=30_72					,			lowerCamelCase    :    Union[str, Any]="gelu"					,			lowerCamelCase    :    int=0.0					,			lowerCamelCase    :    str=0.0					,			lowerCamelCase    :    int=0.02					,			lowerCamelCase    :    Union[str, Any]=1E-12					,			lowerCamelCase    :    Dict=3_84					,			lowerCamelCase    :    List[str]=32					,			lowerCamelCase    :    Dict=3					,			lowerCamelCase    :    List[str]=True					,			lowerCamelCase    :    int=-1					,			lowerCamelCase    :    List[Any]=False					,			lowerCamelCase    :    Any=-1					,			**lowerCamelCase    :    str					,			):
  							super().__init__(tie_word_embeddings=lowerCamelCase					,			**lowerCamelCase	)
  
  							lowerCamelCase_  :    str   	=			vocab_size
  							lowerCamelCase_  :    Dict   	=			type_vocab_size
  							lowerCamelCase_  :    str   	=			modality_type_vocab_size
  							lowerCamelCase_  :    Optional[Any]   	=			max_position_embeddings
  
  							lowerCamelCase_  :    List[Any]   	=			hidden_size
  							lowerCamelCase_  :    Union[str, Any]   	=			num_hidden_layers
  							lowerCamelCase_  :    Any   	=			num_attention_heads
  							lowerCamelCase_  :    List[Any]   	=			intermediate_size
  							lowerCamelCase_  :    Union[str, Any]   	=			hidden_act
  							lowerCamelCase_  :    Any   	=			hidden_dropout_prob
  							lowerCamelCase_  :    Optional[int]   	=			attention_probs_dropout_prob
  							lowerCamelCase_  :    Any   	=			initializer_range
  							lowerCamelCase_  :    str   	=			layer_norm_eps
  
  							lowerCamelCase_  :    Optional[Any]   	=			image_size
  							lowerCamelCase_  :    List[Any]   	=			patch_size
  							lowerCamelCase_  :    Tuple   	=			num_channels
  							lowerCamelCase_  :    Optional[Any]   	=			qkv_bias
  							lowerCamelCase_  :    Optional[int]   	=			max_image_length
  							lowerCamelCase_  :    Optional[Any]   	=			num_images
  