from collections import deque from heapq import heappop, heappush from typing import List, Tuple INF = int(1e18) def minCostCycle(n: int, edges: List[Tuple[int, int, int]], directed: bool) -> int: adjList = [[] for _ in range(n)] maxWeight = 0 for u, v, w in edges: if w > maxWeight: maxWeight = w adjList[u].append((v, w)) if not directed: adjList[v].append((u, w)) res = INF for i in range(len(edges)): # remove edge i from_, to, weight = edges[i] dist = _bfs01(adjList, to, from_) if maxWeight <= 1 else _dijkstra(adjList, to, from_) cand = weight + dist if cand < res: res = cand return res def _bfs01(adjList: List[List[Tuple[int, int]]], start: int, target: int) -> int: n = len(adjList) dist = [INF] * n dist[start] = 0 queue = deque([start]) while queue: cur = queue.popleft() if cur == target: return dist[cur] for next, weight in adjList[cur]: if (cur, next) == (start, target) or (cur, next) == (target, start): continue cand = dist[cur] + weight if cand < dist[next]: dist[next] = cand if weight == 0: queue.appendleft(next) else: queue.append(next) return INF def _dijkstra(adjList: List[List[Tuple[int, int]]], start: int, target: int) -> int: n = len(adjList) dist = [INF] * n dist[start] = 0 pq = [(0, start)] while pq: curDist, cur = heappop(pq) if cur == target: return curDist if dist[cur] < curDist: continue for next, weight in adjList[cur]: if (cur, next) == (start, target) or (cur, next) == (target, start): continue cand = curDist + weight if cand < dist[next]: dist[next] = cand heappush(pq, (cand, next)) return INF if __name__ == "__main__": directed = int(input()) n, m = map(int, input().split()) edges = [] for _ in range(m): u, v, w = map(int, input().split()) u -= 1 v -= 1 edges.append((u, v, w)) res = minCostCycle(n, edges, directed == 1) if res == INF: res = -1 print(res)