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:
# 64 Kruskal


class UnionFind():
    def __init__(self, v):
        self.rank = [1] * v
        self.par = [x for x in range(v)]
    
    def find(self, x):
        if self.par[x] == x:
            return x
        else:
            self.par[x] = self.find(self.par[x])
            return self.par[x]

    def unite(self, x, y):
        if self.find(x) == self.find(y):
            return False
        else:
            p_x = self.find(x)
            p_y = self.find(y)
            if self.rank[p_x] >= self.rank[p_y]:
                self.par[p_y] = p_x
                self.rank[p_y] += 1
            else:
                self.par[p_x] = p_y
                self.rank[p_x] += 1
    
    def same(self, x, y):
        p_x = self.find(x)
        p_y = self.find(y)
        return p_x == p_y
    
v, e = map(int, input().split())
e_l = [[int(x) for x in input().split()] for y in range(e)]
e_l.sort(key= lambda x: x[2])

def Kruskal(s):
    ans = 0
    uf = UnionFind(v)

    for _s, _t, _w in e_l:
        if not uf.same(_s, _t):
            uf.unite(_s, _t)
            ans += _w
    return ans
            
print(Kruskal(0))