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, n):
        self.par = [i for i in range(n)]
        self.rank = [0] * (n)

    def make_set(self, x):
        self.par[x] = x
        self.rank[x] = 0

    def find(self, x):
        if self.par[x] == x:
            return x
        else:
            self.par[x] = self.find(self.par[x])
            return self.find(self.par[x])

    def unite(self, x, y):
        x = self.find(x)
        y = self.find(y)
        if self.rank[x] < self.rank[y]:
            self.par[x] = y
        else:
            self.par[y] = x
            if self.rank[x] == self.rank[y]:
                self.rank[x] += 1

    def same_check(self, x, y):
        return self.find(x) == self.find(y)

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

    def __lt__(self, e):
        return self.cost < e.cost

def kruskal(N, edges):
    # 今回は総コストのみ
    total_cost = 0
    edges = sorted(edges)

    dset = DisjointSet(N)

    source, target = 0, 0
    for i in range(E):
        e = edges[i]
        if not dset.same_check(e.source, e.target):
            total_cost += e.cost
            dset.unite(e.source, e.target)
    return total_cost

V, E = list(map(int, input().split(' ')))
edges = []
for i in range(E):
    s, t, d = list(map(int, input().split(' ')))
    edges.append(Edge(s, t, d))
print(kruskal(V, edges))