




"""simple docstring"""







import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


__A   :  str					=      '\\n@inproceedings{lin-2004-rouge,\n    title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",\n    author = "Lin, Chin-Yew",\n    booktitle = "Text Summarization Branches Out",\n    month = jul,\n    year = "2004",\n    address = "Barcelona, Spain",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W04-1013",\n    pages = "74--81",\n}\n'

__A   :  Optional[int]					=      '\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n'

__A   :  List[Any]					=      '\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,\n        `"rougeL"`: Longest common subsequence based scoring.\n        `"rougeLSum"`: rougeLsum splits text using `"\n"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric(\'rouge\')\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']\n    >>> print(results["rouge1"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results["rouge1"].mid.fmeasure)\n    1.0\n'




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


				"""simple docstring"""





				def       snake_case    (     self					:	Tuple						):
						return datasets.MetricInfo(
						    description=_DESCRIPTION    ,   citation=_CITATION    ,   inputs_description=_KWARGS_DESCRIPTION    ,   features=datasets.Features(
						        {
						            "predictions": datasets.Value("string"    ,   id="sequence"						),
						            "references": datasets.Value("string"    ,   id="sequence"						),
						        }						)    ,   codebase_urls=["https://github.com/google-research/google-research/tree/master/rouge"]    ,   reference_urls=[
						        "https://en.wikipedia.org/wiki/ROUGE_(metric)",
						        "https://github.com/google-research/google-research/tree/master/rouge",
						    ]    ,   )



				def       snake_case    (     self					:	List[str]    ,   lowercase__					:	Union[str, Any]    ,   lowercase__					:	Dict    ,   lowercase__					:	Any=None    ,   lowercase__					:	Union[str, Any]=True    ,   lowercase__					:	List[Any]=False						):
						if rouge_types is None:
								__lowercase				:		str												=				["rouge1", "rouge2", "rougeL", "rougeLsum"]

						__lowercase				:		List[Any]												=				rouge_scorer.RougeScorer(rouge_types=lowercase__    ,   use_stemmer=lowercase__						)
						if use_aggregator:
								__lowercase				:		List[str]												=				scoring.BootstrapAggregator()
						else:
								__lowercase				:		str												=				[]

						for ref, pred in zip(lowercase__    ,   lowercase__						):
								__lowercase				:		Tuple												=				scorer.score(lowercase__    ,   lowercase__						)
								if use_aggregator:
										aggregator.add_scores(lowercase__						)
								else:
										scores.append(lowercase__						)

						if use_aggregator:
								__lowercase				:		str												=				aggregator.aggregate()
						else:
								__lowercase				:		Union[str, Any]												=				{}
								for key in scores[0]:
										__lowercase				:		Optional[Any]												=				[score[key] for score in scores]

						return result






"""simple docstring"""







from __future__ import annotations

import os
from collections.abc import Mapping

__A   :  str					=      tuple[int, int]




class       lowerCAmelCase__       :


				"""simple docstring"""





				def __init__(     self					:	int    ,   lowercase__					:	set[int]    ,   lowercase__					:	Mapping[EdgeT, int]						):
						__lowercase				:		set[int]												=				vertices
						__lowercase				:		dict[EdgeT, int]												=				{
						    (min(lowercase__						), max(lowercase__						)): weight for edge, weight in edges.items()
						}



				def       snake_case    (     self					:	Optional[Any]    ,   lowercase__					:	EdgeT    ,   lowercase__					:	int						):
						self.vertices.add(edge[0]						)
						self.vertices.add(edge[1]						)
						__lowercase				:		Union[str, Any]												=				weight



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		Graph												=				Graph({min(self.vertices						)}    ,   {}						)
						__lowercase				:		EdgeT
						__lowercase				:		int
						__lowercase				:		EdgeT
						__lowercase				:		int

						while len(subgraph.vertices						) < len(self.vertices						):
								__lowercase				:		int												=				max(self.edges.values()						) + 1
								for edge, weight in self.edges.items():
										if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
												if weight < min_weight:
														__lowercase				:		Optional[Any]												=				edge
														__lowercase				:		List[str]												=				weight

								subgraph.add_edge(lowercase__    ,   lowercase__						)

						return subgraph





def 						snake_case__       (					_lowerCamelCase = "p107_network.txt"				)			->int:






		"""simple docstring"""


		__lowercase				:		str												=				os.path.abspath(os.path.dirname(_lowerCamelCase				)				)
		__lowercase				:		str												=				os.path.join(_lowerCamelCase,  _lowerCamelCase				)
		__lowercase				:		dict[EdgeT, int]												=				{}
		__lowercase				:		list[str]
		__lowercase				:		int
		__lowercase				:		int

		with open(_lowerCamelCase				) as f:
				__lowercase				:		List[str]												=				f.read().strip().split("\n"				)

		__lowercase				:		Any												=				[line.split(","				) for line in data]

		for edgea in range(1,  len(_lowerCamelCase				)				):
				for edgea in range(_lowerCamelCase				):
						if adjaceny_matrix[edgea][edgea] != "-":
								__lowercase				:		Dict												=				int(adjaceny_matrix[edgea][edgea]				)

		__lowercase				:		Graph												=				Graph(set(range(len(_lowerCamelCase				)				)				),  _lowerCamelCase				)

		__lowercase				:		Graph												=				graph.prims_algorithm()

		__lowercase				:		int												=				sum(graph.edges.values()				)
		__lowercase				:		int												=				sum(subgraph.edges.values()				)

		return initial_total - optimal_total


if __name__ == "__main__":
						print(F"""{solution() = }""")
