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:

rank = []
p = []


def makeSet(x):
    p.append(x)
    rank.append(0)


def union(x, y):
    link(findSet(x), findSet(y))


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


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


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

def kruskal(g):
    MST = 0
    edges = g

    for e in edges:
        u = e[0]
        v = e[1]


        if findSet(u) != findSet(v):
            union(u, v)
            MST += e[2]

    return MST


V, E = map(int, input().split())
G = [list(map(int, input().split())) for i in range(E)]
G.sort(key=lambda x: x[2])

for i in range(V):
    makeSet(i)

print(kruskal(G))