import pytest
import networkx as nx
from networkx.algorithms.approximation.steinertree import metric_closure
from networkx.algorithms.approximation.steinertree import steiner_tree
from networkx.utils import edges_equal


class TestSteinerTree:
    @classmethod
    def setup_class(cls):
        G = nx.Graph()
        G.add_edge(1, 2, weight=10)
        G.add_edge(2, 3, weight=10)
        G.add_edge(3, 4, weight=10)
        G.add_edge(4, 5, weight=10)
        G.add_edge(5, 6, weight=10)
        G.add_edge(2, 7, weight=1)
        G.add_edge(7, 5, weight=1)
        cls.G = G
        cls.term_nodes = [1, 2, 3, 4, 5]

    def test_connected_metric_closure(self):
        G = self.G.copy()
        G.add_node(100)
        pytest.raises(nx.NetworkXError, metric_closure, G)

    def test_metric_closure(self):
        M = metric_closure(self.G)
        mc = [
            (1, 2, {"distance": 10, "path": [1, 2]}),
            (1, 3, {"distance": 20, "path": [1, 2, 3]}),
            (1, 4, {"distance": 22, "path": [1, 2, 7, 5, 4]}),
            (1, 5, {"distance": 12, "path": [1, 2, 7, 5]}),
            (1, 6, {"distance": 22, "path": [1, 2, 7, 5, 6]}),
            (1, 7, {"distance": 11, "path": [1, 2, 7]}),
            (2, 3, {"distance": 10, "path": [2, 3]}),
            (2, 4, {"distance": 12, "path": [2, 7, 5, 4]}),
            (2, 5, {"distance": 2, "path": [2, 7, 5]}),
            (2, 6, {"distance": 12, "path": [2, 7, 5, 6]}),
            (2, 7, {"distance": 1, "path": [2, 7]}),
            (3, 4, {"distance": 10, "path": [3, 4]}),
            (3, 5, {"distance": 12, "path": [3, 2, 7, 5]}),
            (3, 6, {"distance": 22, "path": [3, 2, 7, 5, 6]}),
            (3, 7, {"distance": 11, "path": [3, 2, 7]}),
            (4, 5, {"distance": 10, "path": [4, 5]}),
            (4, 6, {"distance": 20, "path": [4, 5, 6]}),
            (4, 7, {"distance": 11, "path": [4, 5, 7]}),
            (5, 6, {"distance": 10, "path": [5, 6]}),
            (5, 7, {"distance": 1, "path": [5, 7]}),
            (6, 7, {"distance": 11, "path": [6, 5, 7]}),
        ]
        assert edges_equal(list(M.edges(data=True)), mc)

    def test_steiner_tree(self):
        S = steiner_tree(self.G, self.term_nodes)
        expected_steiner_tree = [
            (1, 2, {"weight": 10}),
            (2, 3, {"weight": 10}),
            (2, 7, {"weight": 1}),
            (3, 4, {"weight": 10}),
            (5, 7, {"weight": 1}),
        ]
        assert edges_equal(list(S.edges(data=True)), expected_steiner_tree)

    def test_multigraph_steiner_tree(self):
        G = nx.MultiGraph()
        G.add_edges_from(
            [
                (1, 2, 0, {"weight": 1}),
                (2, 3, 0, {"weight": 999}),
                (2, 3, 1, {"weight": 1}),
                (3, 4, 0, {"weight": 1}),
                (3, 5, 0, {"weight": 1}),
            ]
        )
        terminal_nodes = [2, 4, 5]
        expected_edges = [
            (2, 3, 1, {"weight": 1}),  # edge with key 1 has lower weight
            (3, 4, 0, {"weight": 1}),
            (3, 5, 0, {"weight": 1}),
        ]
        T = steiner_tree(G, terminal_nodes)
        assert edges_equal(T.edges(data=True, keys=True), expected_edges)