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 sys
readline = sys.stdin.buffer.readline
def even(n): return 1 if n%2==0 else 0

#ネットワークフロー
#最大流,最小カット
import queue
class Dinic():
    #Dinic法,O(|E||V|**2) ※但し、実際にはもっと高速な動作

    def __init__(self, v, inf = 10**10):
        # v:頂点数
        # G:辺情報.各頂点に対し、[行き先,重み,(この辺を含めず)既に存在する行き先の辺数]
        # level:startからの距離(これはcapacityを考慮しない) bfsで毎回リセットされる
        # iter:各頂点について、どこまで調べ終わったかを記録する
        self.V = v
        self.inf = inf
        self.G = [[] for _ in range(v)]
        self.level = [0 for _ in range(v)]
        self.iter = [0 for _ in range(v)]

    def add_edge(self, from_, to, cap):
        # to: 行き先, cap: 容量, rev: 反対側の辺

        # 無向グラフの場合、G[to]の方のcapを0→capにする必要があるので注意.('rev'はそのままで良い)

        self.G[from_].append({'to':to, 'cap':cap, 'rev':len(self.G[to])})
        self.G[to].append({'to':from_, 'cap':0, 'rev':len(self.G[from_])-1})

        

    # sからの最短距離をbfsで計算
    def bfs(self, s):
        self.level = [-1 for _ in range(self.V)]
        self.level[s] = 0
        que = queue.Queue()
        que.put(s)
        while not que.empty():
            v = que.get()
            for i in range(len(self.G[v])):
                e = self.G[v][i]
                if e['cap'] > 0 and self.level[e['to']] < 0:
                    self.level[e['to']] = self.level[v] + 1
                    que.put(e['to'])

    # 増加パスをdfsで探す
    def dfs(self, v, t, f):
        if v == t: return f
        for i in range(self.iter[v], len(self.G[v])):
            self.iter[v] = i
            e = self.G[v][i]
            if e['cap'] > 0 and self.level[v] < self.level[e['to']]:
                d = self.dfs(e['to'], t, min(f, e['cap'])) # d:流量
                if d > 0:
                    e['cap'] -= d #使用済みの分だけcapから引く
                    self.G[e['to']][e['rev']]['cap'] += d
                    return d

        return 0

    def max_flow(self, s, t):
        flow = 0
        while True:
            self.bfs(s) #levelの更新
            # bfsでtに到達不可なら終了
            if self.level[t] < 0 : return flow
            #イテレータの初期化(イテレータ：各頂点に対し、どこまで調べ終わったか)
            self.iter = [0 for _ in range(self.V)]
            f = self.dfs(s, t, self.inf) #f:そのパスの流量
            while f > 0:
                flow += f
                f = self.dfs(s,t, self.inf)

v,e = map(int,readline().split())
V = v
network = Dinic(V)
for i in range(e):
    u,v,c = map(int,readline().split())
    network.add_edge(u,v,c)

print(network.max_flow(0,V-1))
