






"""simple docstring"""





import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


A__					: Optional[Any]=	logging.get_logger(__name__)
@dataclass
class    __lowerCamelCase    :
   a      :				Optional[Any] 						=field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()  )}  )
   a      :				int 						=field(
       metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}  )
   a      :				Any 						=field(
       default=1_2_8							,	metadata={
           """help""": (
               """The maximum total input sequence length after tokenization. Sequences longer """
               """than this will be truncated, sequences shorter will be padded."""
           )
       }							,	)
   a      :				Optional[Any] 						=field(
       default=UpperCamelCase__							,	metadata={"""help""": """Overwrite the cached training and evaluation sets"""}  )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       UpperCamelCase__         =    self.task_name.lower()







class    __lowerCamelCase    (  UpperCamelCase__  ):
   a      :				Optional[int] 						="""train"""
   a      :				List[str] 						="""dev"""
   a      :				List[Any] 						="""test"""







class    __lowerCamelCase    (  UpperCamelCase__  ):
   a      :				Optional[Any] 						=4_2
   a      :				Optional[Any] 						=4_2
   a      :				List[str] 						=4_2

   def __init__(					self ,				snake_case_ ,				snake_case_ ,				snake_case_ = None ,				snake_case_ = Split.train ,				snake_case_ = None ,				)		->       List[Any]:
       warnings.warn(
           'This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '
           'library. You can have a look at this example script for pointers: '
           'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py' ,				__A ,				)
       UpperCamelCase__         =    args
       UpperCamelCase__         =    glue_processors[args.task_name]()
       UpperCamelCase__         =    glue_output_modes[args.task_name]
       if isinstance(__A ,				__A      ):
           try:
               UpperCamelCase__         =    Split[mode]
           except KeyError:
               raise KeyError('mode is not a valid split name'      )
        # Load data features from cache or dataset file
       UpperCamelCase__         =    os.path.join(
           cache_dir if cache_dir is not None else args.data_dir ,				F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}' ,				)
       UpperCamelCase__         =    self.processor.get_labels()
       if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
           "RobertaTokenizer",
           "RobertaTokenizerFast",
           "XLMRobertaTokenizer",
           "BartTokenizer",
           "BartTokenizerFast",
       ):
           # HACK(label indices are swapped in RoBERTa pretrained model)
           UpperCamelCase__       ,						UpperCamelCase__         =    label_list[2], label_list[1]
       UpperCamelCase__         =    label_list

       # Make sure only the first process in distributed training processes the dataset,
       # and the others will use the cache.
       UpperCamelCase__         =    cached_features_file + '.lock'
       with FileLock(__A      ):
           if os.path.exists(__A      ) and not args.overwrite_cache:
               UpperCamelCase__         =    time.time()
               UpperCamelCase__         =    torch.load(__A      )
               logger.info(
                   F'Loading features from cached file {cached_features_file} [took %.3f s]' ,				time.time() - start      )
           else:
               logger.info(F'Creating features from dataset file at {args.data_dir}'      )

               if mode == Split.dev:
                   UpperCamelCase__         =    self.processor.get_dev_examples(args.data_dir      )
               elif mode == Split.test:
                   UpperCamelCase__         =    self.processor.get_test_examples(args.data_dir      )
               else:
                   UpperCamelCase__         =    self.processor.get_train_examples(args.data_dir      )
               if limit_length is not None:
                   UpperCamelCase__         =    examples[:limit_length]
               UpperCamelCase__         =    glue_convert_examples_to_features(
                   __A ,				__A ,				max_length=args.max_seq_length ,				label_list=__A ,				output_mode=self.output_mode ,				)
               UpperCamelCase__         =    time.time()
               torch.save(self.features ,				__A      )
               # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
               logger.info(
                   F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'      )

   def __len__(					self      )		->       str:
       return len(self.features      )

   def __getitem__(					self ,				snake_case_      )		->       InputFeatures:
       return self.features[i]







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       return self.label_list












"""simple docstring"""





import sys
from collections import defaultdict
class    __lowerCamelCase    :

   def __init__(					self      )		->       Tuple:
       UpperCamelCase__         =    []

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[str]:
       return self.node_position[vertex]

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       Optional[int]:
       UpperCamelCase__         =    pos

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       List[Any]:
       if start > size // 2 - 1:
           return
       else:
           if 2 * start + 2 >= size:
               UpperCamelCase__         =    2 * start + 1
           else:
               if heap[2 * start + 1] < heap[2 * start + 2]:
                   UpperCamelCase__         =    2 * start + 1
               else:
                   UpperCamelCase__         =    2 * start + 2
           if heap[smallest_child] < heap[start]:
               UpperCamelCase__       ,						UpperCamelCase__         =    heap[smallest_child], positions[smallest_child]
               UpperCamelCase__       ,						UpperCamelCase__         =    (
                   heap[start],
                   positions[start],
               )
               UpperCamelCase__       ,						UpperCamelCase__         =    temp, tempa

               UpperCamelCase__         =    self.get_position(positions[smallest_child]      )
               self.set_position(
                   positions[smallest_child] ,				self.get_position(positions[start]      )      )
               self.set_position(positions[start] ,				snake_case_      )

               self.top_to_bottom(snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       Any:
       UpperCamelCase__         =    position[index]

       while index != 0:
           UpperCamelCase__         =    int((index - 2) / 2      ) if index % 2 == 0 else int((index - 1) / 2      )

           if val < heap[parent]:
               UpperCamelCase__         =    heap[parent]
               UpperCamelCase__         =    position[parent]
               self.set_position(position[parent] ,				snake_case_      )
           else:
               UpperCamelCase__         =    val
               UpperCamelCase__         =    temp
               self.set_position(snake_case_ ,				snake_case_      )
               break
           UpperCamelCase__         =    parent
       else:
           UpperCamelCase__         =    val
           UpperCamelCase__         =    temp
           self.set_position(snake_case_ ,				0      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       Any:
       UpperCamelCase__         =    len(snake_case_      ) // 2 - 1
       for i in range(snake_case_ ,				-1 ,				-1      ):
           self.top_to_bottom(snake_case_ ,				snake_case_ ,				len(snake_case_      ) ,				snake_case_      )







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       List[Any]:
       UpperCamelCase__         =    positions[0]
       UpperCamelCase__         =    sys.maxsize
       self.top_to_bottom(snake_case_ ,				0 ,				len(snake_case_      ) ,				snake_case_      )
       return temp







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Tuple:
    """simple docstring"""

    UpperCamelCase__         =    Heap()

    UpperCamelCase__         =    [0] * len(SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    [-1] * len(SCREAMING_SNAKE_CASE					)  # Neighboring Tree Vertex of selected vertex
    # Minimum Distance of explored vertex with neighboring vertex of partial tree
    # formed in graph
    UpperCamelCase__         =    []  # Heap of Distance of vertices from their neighboring vertex
    UpperCamelCase__         =    []

    for vertex in range(len(SCREAMING_SNAKE_CASE					)					):
        distance_tv.append(sys.maxsize					)
        positions.append(SCREAMING_SNAKE_CASE					)
        heap.node_position.append(SCREAMING_SNAKE_CASE					)

    UpperCamelCase__         =    []
    UpperCamelCase__         =    1
    UpperCamelCase__         =    sys.maxsize
    for neighbor, distance in adjacency_list[0]:
        UpperCamelCase__         =    0
        UpperCamelCase__         =    distance
    heap.heapify(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)

    for _ in range(1 ,	len(SCREAMING_SNAKE_CASE					)					):
        UpperCamelCase__         =    heap.delete_minimum(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
        if visited[vertex] == 0:
            tree_edges.append((nbr_tv[vertex], vertex)					)
            UpperCamelCase__         =    1
            for neighbor, distance in adjacency_list[vertex]:
                if (
                    visited[neighbor] == 0
                    and distance < distance_tv[heap.get_position(SCREAMING_SNAKE_CASE					)]
                ):
                    UpperCamelCase__         =    distance
                    heap.bottom_to_top(
                        SCREAMING_SNAKE_CASE ,	heap.get_position(SCREAMING_SNAKE_CASE					) ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
                    UpperCamelCase__         =    vertex
    return tree_edges


if __name__ == "__main__":  # pragma: no cover
  # < --------- Prims Algorithm --------- >
  A__					: Dict=	int(input("""Enter number of edges: """).strip())
  A__					: Dict=	defaultdict(list)
  for _ in range(edges_number):
    A__					: Dict=	[int(x) for x in input().strip().split()]
    adjacency_list[edge[0]].append([edge[1], edge[2]])
    adjacency_list[edge[1]].append([edge[0], edge[2]])
  print(prisms_algorithm(adjacency_list))




