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 = {}

class Edge: pass

def makeSet(x):
    p[x] = x
    rank[x] = 0
 
def link(x, y):
    if  rank[x] > rank[y]: p[y] = x
    else: p[x] = y
    if  rank[x] == rank[y]:
        rank[y] += 1
 
def union(x, y):
    link(findSet(x), findSet(y))
     
def findSet(x):
    if  x != p[x]: p[x] = findSet(p[x])
    return p[x]
     
def isSameSet(x, y):
    return findSet(x) == findSet(y)
 
V, E = map(int, input().split())
el = []

for i in range(E):
    s,t,w = map(int, input().split())
    e=Edge()
    e.source = s
    e.target = t
    e.weight = w
    el.append(e)

def kruskal(v, e):
    e = sorted(e, key=lambda x: x.weight)
    for i in v:
        makeSet(i)
    K = []
    for i in e:
        if findSet(i.source) != findSet(i.target):
            union(i.source, i.target)
            K.append(i.weight)
    return K

print(sum(kruskal(range(V), el)))