code: 
  from typing import List, Optional, Tuple
  
  from tokenizers import pre_tokenizers
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_clip import CLIPTokenizer
  
  
  snake_case				:	List[str]							   =						logging.get_logger(__name__)
  
  snake_case				:	List[str]							   =						{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
  
  snake_case				:	Dict							   =						{
      "vocab_file": {
          "openai/clip-vit-base-patch32": "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json",
      },
      "merges_file": {
          "openai/clip-vit-base-patch32": "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt",
      },
      "tokenizer_file": {
          "openai/clip-vit-base-patch32": (
              "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"
          ),
      },
  }
  
  snake_case				:	Dict							   =						{
      "openai/clip-vit-base-patch32": 77,
  }
  
  
  
  
  
  class    _snake_case      (  snake_case       ):
  			UpperCamelCase__					   =					VOCAB_FILES_NAMES
  			UpperCamelCase__					   =					PRETRAINED_VOCAB_FILES_MAP
  			UpperCamelCase__					   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  			UpperCamelCase__					   =					['input_ids', 'attention_mask']
  			UpperCamelCase__					   =					CLIPTokenizer
  
  
  
  
  
  
  
  			def __init__(       self		,     _a=None		,     _a=None		,     _a=None		,     _a="<|endoftext|>"		,     _a="<|startoftext|>"		,     _a="<|endoftext|>"		,     _a="<|endoftext|>"		,     **_a		,     ):
  				super().__init__(
  				    _a		,     _a		,     tokenizer_file=_a		,     unk_token=_a		,     bos_token=_a		,     eos_token=_a		,     pad_token=_a		,     **_a		,     )
  
  				if not isinstance(self.backend_tokenizer.pre_tokenizer		,     pre_tokenizers.Sequence	):
  					raise ValueError(
  					    "The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been"
  					    " heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using"
  					    " to be compatible with this version.The easiest way to do so is"
  					    " `CLIPTokenizerFast.from_pretrained(\"path_to_local_folder_or_hub_repo, from_slow=True)`. If you want"
  					    " to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of"
  					    " transformers."	)
  
  				self._wrap_decode_method_backend_tokenizer()
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Optional[int]			     =  self.backend_tokenizer.decode
  
  				def new_decode_method(*_a		,     **_a	):
  					__magic_name__	:	List[Any]			     =  orig_decode_method(*_a		,     **_a	)
  					__magic_name__	:	Any			     =  text.replace(self.backend_tokenizer.model.end_of_word_suffix		,     " "	).strip()
  					return text
  
  				__magic_name__	:	Union[str, Any]			     =  new_decode_method
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  				__magic_name__	:	Any			     =  [self.bos_token_id]
  				__magic_name__	:	List[Any]			     =  [self.eos_token_id]
  
  				if token_ids_a is None:
  					return bos_token + token_ids_a + eos_token
  				return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  				__magic_name__	:	List[str]			     =  [self.bos_token_id]
  				__magic_name__	:	List[str]			     =  [self.eos_token_id]
  
  				if token_ids_a is None:
  					return len(bos_token + token_ids_a + eos_token	) * [0]
  				return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token	) * [0]
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  				__magic_name__	:	Any			     =  self._tokenizer.model.save(_a		,     name=_a	)
  				return tuple(_a	)
  
style_context: 
  def 						lowerCAmelCase_		(      _snake_case   :						int   ,     _snake_case   :						list[tuple[int, int, int]]     )       ->	list[tuple[int, int, int]]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Union[str, Any]			     =  sorted(_snake_case   ,     key=lambda _snake_case     : edge[2]     )
  
  	__magic_name__	:	Union[str, Any]			     =  list(range(_snake_case     )     )
  
  	def find_parent(_snake_case   :						Dict     ):
  		if i != parent[i]:
  			__magic_name__	:	Any			     =  find_parent(parent[i]     )
  		return parent[i]
  
  	__magic_name__	:	List[str]			     =  0
  	__magic_name__	:	int			     =  []
  
  	for edge in edges:
  		__magic_name__	:	Any			     =  find_parent(edge[0]     )
  		__magic_name__	:	int			     =  find_parent(edge[1]     )
  		if parent_a != parent_b:
  			minimum_spanning_tree_cost += edge[2]
  			minimum_spanning_tree.append(_snake_case     )
  			__magic_name__	:	Optional[int]			     =  parent_b
  
  	return minimum_spanning_tree
  
  
  if __name__ == "__main__":  # pragma: no cover
  				snake_case   ,snake_case				:	int							   =						list(map(int, input().strip().split()))
  				snake_case				:	int							   =						[]
  
  				for _ in range(num_edges):
  								snake_case   ,snake_case   ,snake_case				:	Dict							   =						(int(x) for x in input().strip().split())
  								edges.append((nodea, nodea, cost))
  
  				kruskal(num_nodes, edges)
  