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
# AOJ GRL_1_A Single Sourse Shortest Path
import heapq as hp

V, E, s = map(int, input().split())
graph = [[] for _ in range(V)]
INF = 0
for _ in range(E):
    a, b, d = map(int, input().split())
    graph[a].append((d, b)) 
    #graph[b].append((d, a)) #無向グラフならこれ使う
    INF = max(INF, d)

INF = INF*(E+1) + 1
D = [INF for _ in range(V)] # 頂点iへの最短距離がD[i]
D[s] = 0 
q = [] # しまっていく優先度付きキュー
hp.heappush(q, (0, s))

while q:
    nd, np = hp.heappop(q) # 一番距離が近いものを取ってくる
    if D[np] < nd: # 追加した後の残骸は見ない
        continue
    for d, p in graph[np]:
        if D[p] > D[np] + d: # 隣接するやつの中で今よりも近くなれるなら更新
            D[p] = D[np] + d
            hp.heappush(q, (D[p], p))

for d in D:
    if d == INF:
        print('INF')
    else:
        print(d)
