For a given weighted graph G(V, E) and a source r, find the source shortest path to each vertex from the source (SSSP: Single Source Shortest Path).

Constraints

* 1 ≤ |V| ≤ 100000
* 0 ≤ di ≤ 10000
* 0 ≤ |E| ≤ 500000
* There are no parallel edges
* There are no self-loops

Input

An edge-weighted graph G (V, E) and the source r.


|V| |E| r
s0 t0 d0
s1 t1 d1
:
s|E|-1 t|E|-1 d|E|-1


|V| is the number of vertices and |E| is the number of edges in G. The graph vertices are named with the numbers 0, 1,..., |V|-1 respectively. r is the source of the graph.

si and ti represent source and target vertices of i-th edge (directed) and di represents the cost of the i-th edge.

Output

Print the costs of SSSP in the following format.


c0
c1
:
c|V|-1


The output consists of |V| lines. Print the cost of the shortest path from the source r to each vertex 0, 1, ... |V|-1 in order. If there is no path from the source to a vertex, print INF.

Examples

Input

4 5 0
0 1 1
0 2 4
1 2 2
2 3 1
1 3 5


Output

0
1
3
4


Input

4 6 1
0 1 1
0 2 4
2 0 1
1 2 2
3 1 1
3 2 5


Output

3
0
2
INF
import heapq
def dijkstra(s):
    # 始点sから各頂点への最短距離
    d = [float('inf')]*n
    d[s] = 0
    # 各頂点が訪問済みかどうか
    used = [False]*n
    used[s] = True
    # 仮の距離を記録するヒープ
    que = []
    for e in g[s]:
        heapq.heappush(que, e)
    while que:
        minedge = heapq.heappop(que)
        if used[minedge[1]]:
            continue
        v = minedge[1]
        d[v] = minedge[0]
        used[v] = True
        for e in g[v]:
            if not used[e[1]]:
                heapq.heappush(que, [e[0] + d[v], e[1]])
    return d

##############################
n, w, r = [int(x) for x in input().split()] # n:頂点数 w:辺の数
g = [[] for _ in range(n)]
# g[i]:iから出る辺の[コスト,行先]
for _ in range(w):
    x, y, z = [int(x) for x in input().split()]
    g[x].append([z, y])
    # g[y].append([z, x])
for i in dijkstra(r):
    if i == float("inf"):
        print("INF")
    else:
        print(i)
