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:
import heapq

class Adjlist_weighted():

    def __init__(self, nodes, edges = None):
        self.nodes = nodes
        lis = [{} for i in range(nodes)]
        if edges is not None:
            for edge in edges:
                lis[edge[0]][edge[1]] = edge[2]
        self.edges = lis

    def add_edge(self, edge):
        self.edges[edge[0]][edge[1]] = edge[2]
        self.edges[edge[1]][edge[0]] = edge[2]
        return self

    def next_edges(self,x):
        return self.edges[x]

class Min_span_tree():

    def __init__(self, graph):
        min_span_tree = Adjlist_weighted(graph.nodes)
        searched_nodes = [False]*graph.nodes
        start = 0
        heap = [(0, start, start)]
        total_length = 0
        while heap:
            (cost, now, prev) = heapq.heappop(heap)
            if not searched_nodes[now]:
                searched_nodes[now] = True
                if (now, prev) != (start, start):
                    min_span_tree.add_edge((prev, now, cost))
                cand = graph.next_edges(now)
                total_length += cost
                for next in cand:
                    if not searched_nodes[next]:
                        heapq.heappush(heap, (cand[next], next, now))
        self.min_span_tree = min_span_tree
        self.total_length = total_length
    

(V, E) = tuple(map(int, input().split(" ")))
graph = Adjlist_weighted(V)
for _ in range(E):
    graph.add_edge(tuple(map(int, input().split(" "))))
mst = Min_span_tree(graph)
print(mst.total_length)