class FordFulkerson:
    """Ford-Fulkerson Algorithm: find max-flow
       complexity: O(FE) (F: max flow)
    """
    def __init__(self, V, E, source, sink):
        """ V: the number of vertexes
            E: adjacency list
            source: start point
            sink: goal point
        """
        self.V = V
        self.E = [[] for _ in range(V)]
        for fr in range(V):
            for to, cap in E[fr]:
                self.E[fr].append([to, cap, len(self.E[to])])
                self.E[to].append([fr, 0, len(self.E[fr])-1])
        self.maxflow = self.ford_fulkerson(source, sink)

    def ford_fulkerson(self, source, sink, INF=10**9):
        """find max-flow"""
        def dfs(vertex, sink, flow):
            """find augmenting path"""
            if vertex == sink:
                return flow
            used[vertex] = True
            for i, (to, cap, rev) in enumerate(self.E[vertex]):
                if not used[to] and cap > 0:
                    d = dfs(to, sink, min(flow, cap))
                    if d > 0:
                        self.E[vertex][i][1] -= d
                        self.E[to][rev][1] += d
                        return d
            return 0

        maxflow = 0
        while True:
            used = [False] * self.V
            flow = dfs(source, sink, INF)
            if flow == 0:
                return maxflow
            else:
                maxflow += flow

V, E = map(int, input().split())
edge = [[] for _ in range(E)]
for _ in range(E):
    u, v, cap = map(int, input().split())
    edge[u].append((v, cap))
print(FordFulkerson(V, edge, 0, V-1).maxflow)

### FIXED CODE ###

import collections


class MaxFlow:
    """Dinic Algorithm: find max-flow
       complexity: O(EV^2)
       used in GRL6A(AOJ)
    """
    class Edge:
        def __init__(self, to, cap, rev):
            self.to, self.cap, self.rev = to, cap, rev

    def __init__(self, V):
        """ V: the number of vertexes
            E: adjacency list
            source: start point
            sink: goal point
        """
        self.V = V
        self.E = [[] for _ in range(V)]

    def add_edge(self, fr, to, cap):
        self.E[fr].append(self.Edge(to, cap, len(self.E[to])))
        self.E[to].append(self.Edge(fr, 0, len(self.E[fr])-1))

    def dinic(self, source, sink):
        """find max-flow"""
        INF = float('inf')
        maxflow = 0
        while True:
            self.bfs(source)
            if self.level[sink] < 0:
                return maxflow
            self.itr = [0] * self.V
            while True:
                flow = self.dfs(source, sink, INF)
                if flow > 0:
                    maxflow += flow
                else:
                    break

    def dfs(self, vertex, sink, flow):
        """find augmenting path"""
        if vertex == sink:
            return flow
        for i in range(self.itr[vertex], len(self.E[vertex])):
            self.itr[vertex] = i
            e = self.E[vertex][i]
            if e.cap > 0 and self.level[vertex] < self.level[e.to]:
                d = self.dfs(e.to, sink, min(flow, e.cap))
                if d > 0:
                    e.cap -= d
                    self.E[e.to][e.rev].cap += d
                    return d
        return 0

    def bfs(self, start):
        """find shortest path from start"""
        que = collections.deque()
        self.level = [-1] * self.V
        que.append(start)
        self.level[start] = 0

        while que:
            fr = que.popleft()
            for e in self.E[fr]:
                if e.cap > 0 and self.level[e.to] < 0:
                    self.level[e.to] = self.level[fr] + 1
                    que.append(e.to)

V, E = map(int, input().split())
mf = MaxFlow(V)
source, sink = 0, V-1

for _ in range(E):
    s, t, c = map(int, input().split())
    mf.add_edge(s, t, c)
print(mf.dinic(source, sink))