Problem: Find the sum of weights of edges of the Minimum Spanning Tree for a given weighted undirected graph G = (V, E).

Constraints

* 1 ≤ |V| ≤ 10,000
* 0 ≤ |E| ≤ 100,000
* 0 ≤ wi ≤ 10,000
* The graph is connected
* There are no parallel edges
* There are no self-loops

Input


|V| |E|
s0 t0 w0
s1 t1 w1
:
s|E|-1 t|E|-1 w|E|-1


, where |V| is the number of vertices and |E| is the number of edges in the graph. The graph vertices are named with the numbers 0, 1,..., |V|-1 respectively.

si and ti represent source and target verticess of i-th edge (undirected) and wi represents the weight of the i-th edge.

Output

Print the sum of the weights of the Minimum Spanning Tree.

Examples

Input

4 6
0 1 2
1 2 1
2 3 1
3 0 1
0 2 3
1 3 5


Output

3


Input

6 9
0 1 1
0 2 3
1 2 1
1 3 7
2 4 1
1 4 3
3 4 1
3 5 1
4 5 6


Output

5

Solution (in PYTHON3):

Solution:
class DisjointSet:
    def __init__(self, size):
        self.rank = [0] * size
        self.p = list(range(size))

    def findSet(self, x):
        if x != self.p[x]:
            self.p[x] = self.findSet(self.p[x])
        return self.p[x]

    def same(self, x, y):
        return self.findSet(x) == self.findSet(y)

    def link(self, x, y):
        if self.rank[x] > self.rank[y]:
            self.p[y] = x
        else:
            self.p[x] = y
            if self.rank[x] == self.rank[y]:
                self.rank[y] += 1

    def unite(self, x, y):
        self.link(self.findSet(x), self.findSet(y))


class Edge:
    def __init__(self, source=0, target=0, cost=0):
        self.source = source
        self.target = target
        self.cost = cost

def kruskal(n, edges):
    totalCost = 0
    edges.sort(key=lambda e: e.cost)
    dset = DisjointSet(n)

    for e in edges:
        if not dset.same(e.source, e.target):
            totalCost += e.cost
            dset.unite(e.source, e.target)

    return totalCost


if __name__ == '__main__':
    n, m = [int(v) for v in input().split()]
    edges = []
    for i in range(m):
        s, t, w = [int(v) for v in input().split()]
        edges.append(Edge(s, t, w))

    print(kruskal(n, edges))