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 UF:
    def __init__(self, n):
        self.N = n;
        self.d = [-1]*n
    
    def size(self, a):
        x = self.root(a)
        return -self.d[x]

    def root(self, a):
        if self.d[a] < 0:
            return a
        else:
            self.d[a] = self.root(self.d[a])
            return self.d[a]

    def unite(self, a, b):
        x = self.root(a)
        y = self.root(b)
        if self.size(x) < self.size(y):
            x, y = y, x
        self.d[y] = x
    
    def same(self, a, b):
        return self.root(a) == self.root(b)

class Edge:
    def __init__(self, f, t, w):
        self.f = f
        self.t = t
        self.w = w
    def __lt__(self, other):
        return self.w < other.w


def kruskal(edges, n):
    uf = UF(n)
    sum = 0
    for e in sorted(edges):
        if not uf.same(e.f, e.t):
            uf.unite(e.f, e.t)
            sum += e.w
    return sum


n, m = map(int, input().split())
es = []
for i in range(m):
    s, t, w = map(int, input().split())
    es.append(Edge(s, t, w))
print(kruskal(es, n))