A flow network is a directed graph which has a $source$ and a $sink$. In a flow network, each edge $(u, v)$ has a capacity $c(u, v)$. Each edge receives a flow, but the amount of flow on the edge can not exceed the corresponding capacity. Find the maximum flow from the $source$ to the $sink$.

Constraints

* $2 \leq |V| \leq 100$
* $1 \leq |E| \leq 1000$
* $0 \leq c_i \leq 10000$

Input

A flow network is given in the following format.

$|V|\;|E|$
$u_0\;v_0\;c_0$
$u_1\;v_1\;c_1$
:
$u_{|E|-1}\;v_{|E|-1}\;c_{|E|-1}$



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

$u_i$, $v_i$, $c_i$ represent $i$-th edge of the flow network. A pair of $u_i$ and $v_i$ denotes that there is an edge from $u_i$ to $v_i$ and $c_i$ is the capacity of $i$-th edge.

Output

Print the maximum flow.

Example

Input

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


Output

3
import collections
class Dinic:
    def __init__(self, n):
        self.n = n
        self.g = [[] for i in xrange(n)]
    def add_edge(self, fr, to, cap):
        self.g[fr].append([to, cap, len(self.g[to])])
        self.g[to].append([fr, 0, len(self.g[fr])-1])
    def add_multi_edge(self, v1, v2, cap1, cap2):
        self.g[v1].append([v2, cap1, len(self.g[v2])])
        self.g[v2].append([v1, cap2, len(self.g[v1])-1])
    def bfs(self, s):
        level = [-1]*self.n
        deq = collections.deque()
        level[s] = 0
        deq.append(s)
        while deq:
            v = deq.popleft()
            for e in self.g[v]:
                if e[1]>0 and level[e[0]]<0:
                    level[e[0]] = level[v] + 1
                    deq.append(e[0])
        self.level = level
    def dfs(self, v, t, f):
        if v==t: return f
        es = self.g[v]
        level = self.level
        for i in xrange(self.it[v], len(self.g[v])):
            e = es[i]
            if e[1]>0 and level[v]<level[e[0]]:
                d = self.dfs(e[0], t, min(f, e[1]))
                if d>0:
                    e[1] -= d
                    self.g[e[0]][e[2]][1] += d
                    self.it[v] = i
                    return d
        self.it[v] = len(self.g[v])
        return 0
    def max_flow(self, s, t):
        flow = 0
        while True:
            self.bfs(s)
            if self.level[t]<0: break
            self.it = [0]*self.n
            while True:
                f = self.dfs(s, t, 10**9+7)
                if f>0:
                    flow += f
                else:
                    break
        return flow
V, E = map(int, raw_input().split())
dinic = Dinic(V)
for i in xrange(E):
    u, v, c = map(int, raw_input().split())
    dinic.add_edge(u, v, c)
print dinic.max_flow(0, V-1)