


import datasets

from .evaluate import evaluate


_lowercase       :      Tuple	=	"\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n"

_lowercase       :      Optional[int]	=	"\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n"

_lowercase       :      Any	=	"\nComputes SQuAD scores (F1 and EM).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair as given in the references (see below)\n        - \'prediction_text\': the text of the answer\n    references: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair (see above),\n        - \'answers\': a Dict in the SQuAD dataset format\n            {\n                \'text\': list of possible texts for the answer, as a list of strings\n                \'answer_start\': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_metric = datasets.load_metric(\"squad\")\n    >>> results = squad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 100.0, \'f1\': 100.0}\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,		_KWARGS_DESCRIPTION				)
class       _UpperCamelCase    (					datasets.Metric				):

				"""simple docstring"""







				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					return datasets.MetricInfo(
					    description=_DESCRIPTION				,   citation=_CITATION				,   inputs_description=_KWARGS_DESCRIPTION				,   features=datasets.Features(
					        {
					            """predictions""": {"""id""": datasets.Value("""string"""   ), """prediction_text""": datasets.Value("""string"""   )},
					            """references""": {
					                """id""": datasets.Value("""string"""   ),
					                """answers""": datasets.features.Sequence(
					                    {
					                        """text""": datasets.Value("""string"""   ),
					                        """answer_start""": datasets.Value("""int32"""   ),
					                    }   ),
					            },
					        }   )				,   codebase_urls=["""https://rajpurkar.github.io/SQuAD-explorer/"""]				,   reference_urls=["""https://rajpurkar.github.io/SQuAD-explorer/"""]				,   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__   )	->				Union[str, Any]:
					A								=				{prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
					A								=				[
					    {
					        """paragraphs""": [
					            {
					                """qas""": [
					                    {
					                        """answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
					                        """id""": ref["""id"""],
					                    }
					                    for ref in references
					                ]
					            }
					        ]
					    }
					]
					A								=				evaluate(dataset=_a				,   predictions=_a   )
					return score







import sys
from collections import defaultdict






class       _UpperCamelCase    :

				"""simple docstring"""







				def __init__(						self   )	->				Any:
					A								=				[]

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

				def      _UpperCAmelCase					(						self				,   a__				,   a__   )	->				Optional[int]:
					A								=				pos

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

							A								=				self.get_position(positions[smallest_child]   )
							self.set_position(
							    positions[smallest_child]				,   self.get_position(positions[start]   )   )
							self.set_position(positions[start]				,   a__   )

							self.top_to_bottom(a__				,   a__				,   a__				,   a__   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__				,   a__   )	->				Dict:
					A								=				position[index]

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

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

				def      _UpperCAmelCase					(						self				,   a__				,   a__   )	->				Optional[int]:
					A								=				len(a__   ) // 2 - 1
					for i in range(a__				,   -1				,   -1   ):
						self.top_to_bottom(a__				,   a__				,   len(a__   )				,   a__   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__   )	->				Any:
					A								=				positions[0]
					A								=				sys.maxsize
					self.top_to_bottom(a__				,   0				,   len(a__   )				,   a__   )
					return temp





def   _lowerCAmelCase				(   UpperCamelCase__:     List[str]						) ->   List[str]:





	"""simple docstring"""







	A								=				Heap()

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

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

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

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


if __name__ == "__main__":  # pragma: no cover
				# < --------- Prims Algorithm --------- >
				_lowercase       :      Tuple	=	int(input("Enter number of edges: ").strip())
				_lowercase       :      int	=	defaultdict(list)
				for _ in range(edges_number):
								_lowercase       :      Optional[Any]	=	[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))



