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 PYTHON):

Solution:
class UnionFind:
    def __init__(self,size):
        self.parent=range(size)
        self.rank=[0]*size

    def findset(self,x):
        if self.parent[x]==x:
            return x
        else:
            self.parent[x]=self.findset(self.parent[x])
            return self.parent[x]

    def unite(self,x,y):
        x=self.findset(x)
        y=self.findset(y)
        if x==y:
            return
        if self.rank[x]<self.rank[y]:
            self.parent[x]=y
        else:
            self.parent[y]=x
            if self.rank[x]==self.rank[y]:
                self.rank[x]+=1

    def same(self,x,y):
        if self.findset(x)==self.findset(y):
            return True
        else:
            return False

def kruskal(n,m,edges):
    edges.sort()
    node=UnionFind(n)
    ret=0
    for i in xrange(m):
        cost,a,b=edges[i][0],edges[i][1],edges[i][2]
        if node.same(a,b):
            continue
        else:
            ret+=cost
            node.unite(a,b)
    return ret

n,m=map(int,raw_input().split())
edges=[]
for i in xrange(m):
    s,t,w=map(int,raw_input().split())
    edges.append([w,s,t])
print(kruskal(n,m,edges))