
import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors





@require_tokenizers
class    _snake_case      (  A__       ,		unittest.TestCase       ):
   UpperCamelCase__					   =					BartTokenizer
   UpperCamelCase__					   =					BartTokenizerFast
   UpperCamelCase__					   =					True
   UpperCamelCase__					   =					filter_roberta_detectors







   def     SCREAMING_SNAKE_CASE					(       self	):
    super().setUp()
    __magic_name__	:	Tuple			     =  [
        "l",
        "o",
        "w",
        "e",
        "r",
        "s",
        "t",
        "i",
        "d",
        "n",
        "\u0120",
        "\u0120l",
        "\u0120n",
        "\u0120lo",
        "\u0120low",
        "er",
        "\u0120lowest",
        "\u0120newer",
        "\u0120wider",
        "<unk>",
    ]
    __magic_name__	:	List[str]			     =  dict(zip(_lowerCAmelCase		,     range(len(_lowerCAmelCase	)	)	)	)
    __magic_name__	:	int			     =  ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
    __magic_name__	:	List[Any]			     =  {"unk_token": "<unk>"}

    __magic_name__	:	Optional[int]			     =  os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["vocab_file"]	)
    __magic_name__	:	List[Any]			     =  os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["merges_file"]	)
    with open(self.vocab_file		,     "w"		,     encoding="utf-8"	) as fp:
     fp.write(json.dumps(_lowerCAmelCase	) + "\n"	)
    with open(self.merges_file		,     "w"		,     encoding="utf-8"	) as fp:
     fp.write("\n".join(_lowerCAmelCase	)	)







   def     SCREAMING_SNAKE_CASE					(       self		,     **_a	):
    kwargs.update(self.special_tokens_map	)
    return self.tokenizer_class.from_pretrained(self.tmpdirname		,     **_lowerCAmelCase	)







   def     SCREAMING_SNAKE_CASE					(       self		,     **_a	):
    kwargs.update(self.special_tokens_map	)
    return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,     **_lowerCAmelCase	)







   def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
    return "lower newer", "lower newer"







   @cached_property
   def     SCREAMING_SNAKE_CASE					(       self	):
    return BartTokenizer.from_pretrained("facebook/bart-large"	)







   @cached_property
   def     SCREAMING_SNAKE_CASE					(       self	):
    return BartTokenizerFast.from_pretrained("facebook/bart-large"	)







   @require_torch
   def     SCREAMING_SNAKE_CASE					(       self	):
    __magic_name__	:	Dict			     =  ["A long paragraph for summarization.", "Another paragraph for summarization."]
    __magic_name__	:	List[str]			     =  [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __magic_name__	:	Optional[int]			     =  tokenizer(_lowerCAmelCase		,     max_length=len(_lowerCAmelCase	)		,     padding=_lowerCAmelCase		,     return_tensors="pt"	)
     self.assertIsInstance(_lowerCAmelCase		,     _lowerCAmelCase	)

     self.assertEqual((2, 9)		,     batch.input_ids.shape	)
     self.assertEqual((2, 9)		,     batch.attention_mask.shape	)
     __magic_name__	:	int			     =  batch.input_ids.tolist()[0]
     self.assertListEqual(_lowerCAmelCase		,     _lowerCAmelCase	)
     # Test that special tokens are reset







   @require_torch
   def     SCREAMING_SNAKE_CASE					(       self	):
    __magic_name__	:	Tuple			     =  ["A long paragraph for summarization.", "Another paragraph for summarization."]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __magic_name__	:	Tuple			     =  tokenizer(_lowerCAmelCase		,     padding=_lowerCAmelCase		,     return_tensors="pt"	)
     # check if input_ids are returned and no labels
     self.assertIn("input_ids"		,     _lowerCAmelCase	)
     self.assertIn("attention_mask"		,     _lowerCAmelCase	)
     self.assertNotIn("labels"		,     _lowerCAmelCase	)
     self.assertNotIn("decoder_attention_mask"		,     _lowerCAmelCase	)







   @require_torch
   def     SCREAMING_SNAKE_CASE					(       self	):
    __magic_name__	:	Dict			     =  [
        "Summary of the text.",
        "Another summary.",
    ]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __magic_name__	:	List[str]			     =  tokenizer(text_target=_lowerCAmelCase		,     max_length=32		,     padding="max_length"		,     return_tensors="pt"	)
     self.assertEqual(32		,     targets["input_ids"].shape[1]	)







   @require_torch
   def     SCREAMING_SNAKE_CASE					(       self	):
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __magic_name__	:	List[str]			     =  tokenizer(
         ["I am a small frog" * 1_024, "I am a small frog"]		,     padding=_lowerCAmelCase		,     truncation=_lowerCAmelCase		,     return_tensors="pt"	)
     self.assertIsInstance(_lowerCAmelCase		,     _lowerCAmelCase	)
     self.assertEqual(batch.input_ids.shape		,     (2, 1_024)	)







   @require_torch
   def     SCREAMING_SNAKE_CASE					(       self	):
    __magic_name__	:	Optional[Any]			     =  ["A long paragraph for summarization."]
    __magic_name__	:	int			     =  [
        "Summary of the text.",
    ]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __magic_name__	:	Any			     =  tokenizer(_lowerCAmelCase		,     return_tensors="pt"	)
     __magic_name__	:	Dict			     =  tokenizer(text_target=_lowerCAmelCase		,     return_tensors="pt"	)
     __magic_name__	:	Dict			     =  inputs["input_ids"]
     __magic_name__	:	int			     =  targets["input_ids"]
     self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()	)
     self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()	)
     self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()	)
     self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()	)







   def     SCREAMING_SNAKE_CASE					(       self	):
    pass







   def     SCREAMING_SNAKE_CASE					(       self	):
    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
      __magic_name__	:	Union[str, Any]			     =  self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase		,     **_lowerCAmelCase	)
      __magic_name__	:	List[str]			     =  self.tokenizer_class.from_pretrained(_lowerCAmelCase		,     **_lowerCAmelCase	)
      __magic_name__	:	Union[str, Any]			     =  "A, <mask> AllenNLP sentence."
      __magic_name__	:	Any			     =  tokenizer_r.encode_plus(_lowerCAmelCase		,     add_special_tokens=_lowerCAmelCase		,     return_token_type_ids=_lowerCAmelCase	)
      __magic_name__	:	Optional[int]			     =  tokenizer_p.encode_plus(_lowerCAmelCase		,     add_special_tokens=_lowerCAmelCase		,     return_token_type_ids=_lowerCAmelCase	)

      # token_type_ids should put 0 everywhere
      self.assertEqual(sum(tokens_r["token_type_ids"]	)		,     sum(tokens_p["token_type_ids"]	)	)

      # attention_mask should put 1 everywhere, so sum over length should be 1
      self.assertEqual(
          sum(tokens_r["attention_mask"]	) / len(tokens_r["attention_mask"]	)		,     sum(tokens_p["attention_mask"]	) / len(tokens_p["attention_mask"]	)		,     )

      __magic_name__	:	Optional[int]			     =  tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]	)
      __magic_name__	:	Optional[int]			     =  tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]	)

      # Rust correctly handles the space before the mask while python doesnt
      self.assertSequenceEqual(tokens_p["input_ids"]		,     [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)
      self.assertSequenceEqual(tokens_r["input_ids"]		,     [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)

      self.assertSequenceEqual(
          _lowerCAmelCase		,     ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)
      self.assertSequenceEqual(
          _lowerCAmelCase		,     ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)


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)
