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:
def find(x):
    if(par[x]==x):
        return x
    else:
        par[x]=find(par[x])
        return par[x]
def union(a,b):
    a=find(a)
    b=find(b)
    if(a==b):
        return 0
    else:
        if rank[a]>rank[b]:
            par[b]=a
        else:
            par[a]=b
            if rank[a]==rank[b]:
                rank[b]+=1
def same(a,b):
    return find(a)==find(b)
def Kruskal():
    cost=0
    Edge.sort(key=lambda x:x[2])
    for i in range(E):
        f,t,c=Edge[i]
        if(not same(f,t)):
            union(f,t)
            cost+=c
    return cost

N,E=map(int,input().split())
par=[i for i in range(N)]
rank=[0 for _ in range(N)]
Edge=[]
for i in range(E):
    s,t,w=map(int,input().split())
    Edge.append([s,t,w])
res=Kruskal()
print(res)