






import heapq




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    list[list]           =     []

   # for each node and his adjacency list add them and the rank of the node to queue
   # using heapq module the queue will be filled like a Priority Queue
   # heapq works with a min priority queue, so I used -1*len(v) to build it
   for key, value in graph.items():
      # O(log(n))
      heapq.heappush(_UpperCamelCase						,		[-1 * len(_UpperCamelCase							), (key, value)]							)

   # chosen_vertices = set of chosen vertices
   lowercase_	:    Optional[Any]           =     set()

   # while queue isn't empty and there are still edges
   #   (queue[0][0] is the rank of the node with max rank)
   while queue and queue[0][0] != 0:
      # extract vertex with max rank from queue and add it to chosen_vertices
      lowercase_	:    Any           =     heapq.heappop(_UpperCamelCase							)[1][0]
      chosen_vertices.add(_UpperCamelCase							)

      # Remove all arcs adjacent to argmax
      for elem in queue:
         # if v haven't adjacent node, skip
         if elem[0] == 0:
            continue
         # if argmax is reachable from elem
         # remove argmax from elem's adjacent list and update his rank
         if argmax in elem[1][1]:
            lowercase_	:    str           =     elem[1][1].index(_UpperCamelCase							)
            del elem[1][1][index]
            elem[0] += 1
        # re-order the queue
      heapq.heapify(_UpperCamelCase							)
   return chosen_vertices


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 UpperCamelCase__ =			{0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
 print(f"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")












'''simple docstring'''
import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


UpperCamelCase__ =			namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   return (abs(source - target							) / target) < 0.01





@pytest.mark.integration
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     _TestCommandArgs(dataset=_UpperCamelCase						,		all_configs=_UpperCamelCase						,		save_infos=_UpperCamelCase							)
   lowercase_	:    int           =     TestCommand(*_UpperCamelCase							)
   test_command.run()
   lowercase_	:    List[str]           =     os.path.join(_UpperCamelCase						,		"README.md"							)
   assert os.path.exists(_UpperCamelCase							)
   lowercase_	:    Any           =     DatasetInfosDict.from_directory(_UpperCamelCase							)
   lowercase_	:    Optional[int]           =     DatasetInfosDict(
       {
           "default": DatasetInfo(
               features=Features(
                   {
                       "tokens": Sequence(Value("string"							)							),
                       "ner_tags": Sequence(
                           ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]							)							),
                       "langs": Sequence(Value("string"							)							),
                       "spans": Sequence(Value("string"							)							),
                   }							)						,		splits=[
                   {
                       "name": "train",
                       "num_bytes": 235_1563,
                       "num_examples": 1_0000,
                   },
                   {
                       "name": "validation",
                       "num_bytes": 23_8418,
                       "num_examples": 1000,
                   },
               ]						,		download_size=394_0680						,		dataset_size=258_9981						,		)
       }							)
   assert dataset_infos.keys() == expected_dataset_infos.keys()
   for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
      lowercase_    ,		lowercase_	:    Optional[int]           =     getattr(dataset_infos["default"]						,		_UpperCamelCase							), getattr(expected_dataset_infos["default"]						,		_UpperCamelCase							)
      if key == "num_bytes":
         assert is_apercent_close(_UpperCamelCase						,		_UpperCamelCase							)
      elif key == "splits":
         assert list(_UpperCamelCase							) == list(_UpperCamelCase							)
         for split in result:
            assert result[split].name == expected[split].name
            assert result[split].num_examples == expected[split].num_examples
            assert is_apercent_close(result[split].num_bytes						,		expected[split].num_bytes							)
      else:
         result == expected




