
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	)


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)
