#include <algorithm>
#include <cassert>
#include <iostream>
#include <random>
#include <tuple>
#include <vector>
using namespace std;

mt19937 mt(1967176539);

// Berlekamp–Massey algorithm
// https://en.wikipedia.org/wiki/Berlekamp%E2%80%93Massey_algorithm
// Complexity: O(N^2)
// input: S = sequence from field K
// return: L          = degree of minimal polynomial,
//         C_reversed = monic min. polynomial (size = L + 1, reversed order, C_reversed[0] = 1))
// Formula: convolve(S, C_reversed)[i] = 0 for i >= L
// Example:
// - [1, 2, 4, 8, 16]   -> (1, [1, -2])
// - [1, 1, 2, 3, 5, 8] -> (2, [1, -1, -1])
// - [0, 0, 0, 0, 1]    -> (5, [1, 0, 0, 0, 0, 998244352]) (mod 998244353)
// - []                 -> (0, [1])
// - [0, 0, 0]          -> (0, [1])
// - [-2]               -> (1, [1, 2])
template <typename Tfield> std::pair<int, std::vector<Tfield>> find_linear_recurrence(const std::vector<Tfield> &S) {
    int N = S.size();
    using poly = std::vector<Tfield>;
    poly C_reversed{1}, B{1};
    int L = 0, m = 1;
    Tfield b = 1;

    // adjust: C(x) <- C(x) - (d / b) x^m B(x)
    auto adjust = [](poly C, const poly &B, Tfield d, Tfield b, int m) -> poly {
        C.resize(std::max(C.size(), B.size() + m));
        Tfield a = d / b;
        for (unsigned i = 0; i < B.size(); i++) C[i + m] -= a * B[i];
        return C;
    };

    for (int n = 0; n < N; n++) {
        Tfield d = S[n];
        for (int i = 1; i <= L; i++) d += C_reversed[i] * S[n - i];

        if (d == 0)
            m++;
        else if (2 * L <= n) {
            poly T = C_reversed;
            C_reversed = adjust(C_reversed, B, d, b, m);
            L = n + 1 - L;
            B = T;
            b = d;
            m = 1;
        } else
            C_reversed = adjust(C_reversed, B, d, b, m++);
    }
    return std::make_pair(L, C_reversed);
}


template <typename ModInt> std::vector<ModInt> gen_random_vector(int len) {
    static std::uniform_int_distribution<int> rnd(1, ModInt::mod() - 1);
    std::vector<ModInt> ret(len);
    for (auto &x : ret) x = rnd(mt);
    return ret;
};

// Sparse matrix
template <typename Tp> struct sparse_matrix {
    int H, W;
    std::vector<std::vector<std::pair<int, Tp>>> vals;
    sparse_matrix(int H = 0, int W = 0) : H(H), W(W), vals(H) {}
    int height() const { return H; }
    int width() const { return W; }
    void add_element(int i, int j, Tp val) {
        assert(i >= 0 and i < H);
        assert(j >= 0 and i < W);
        vals[i].emplace_back(j, val);
    }
    std::vector<Tp> prod(const std::vector<Tp> &vec) const {
        assert(W == int(vec.size()));
        std::vector<Tp> ret(H);
        for (int i = 0; i < H; i++) {
            for (const auto &p : vals[i]) ret[i] += p.second * vec[p.first];
        }
        return ret;
    }
};


template <class Matrix, class Tp> int blackbox_rank(const Matrix &M) {
    assert(M.height() == M.width());
    const int N = M.height();
    std::vector<Tp> b = gen_random_vector<Tp>(N), u = gen_random_vector<Tp>(N), D = gen_random_vector<Tp>(N);
    std::vector<Tp> uMDib(2 * N);
    for (int i = 0; i < 2 * N; i++) {
        uMDib[i] = std::inner_product(u.begin(), u.end(), b.begin(), Tp(0));
        for (int j = 0; j < N; j++) b[j] *= D[j];
        b = M.prod(b);
    }
    return find_linear_recurrence<Tp>(uMDib).first - 1;
}


#include <atcoder/modint>
using mint = atcoder::static_modint<(1 << 30) + 3>;


uniform_int_distribution<int> rndgen(0, mint::mod());

vector<int> solve(int r, const vector<tuple<int, int, int>> &uvws) {
    const int m = uvws.size();
    vector<int> ret(m);
    ret[0] = 1;
    {
        sparse_matrix<mint> mat(r, r);
        for (int i = 0; i < m; ++i) {
            auto [u, v, w] = uvws[i];
            mint x = rndgen(mt);
            mat.add_element(u, v, x);
            mat.add_element(v, w, x);
            mat.add_element(w, u, x);

            mat.add_element(v, u, -x);
            mat.add_element(w, v, -x);
            mat.add_element(u, w, -x);
            if (ret[i] or ret[i - 1]) continue;
            int rank = blackbox_rank<sparse_matrix<mint>, mint>(mat);
            ret[i] = rank / 2;
        }
    }
    {
        sparse_matrix<mint> mat(r, r);
        for (int i = 0; i < m; ++i) {
            auto [u, v, w] = uvws[i];
            mint x = rndgen(mt);
            mat.add_element(u, v, x);
            mat.add_element(v, w, x);
            mat.add_element(w, u, x);

            mat.add_element(v, u, -x);
            mat.add_element(w, v, -x);
            mat.add_element(u, w, -x);
            if (ret[i]) continue;
            if (ret[i - 1] + 2 == ret[i + 1]) ret[i] = ret[i - 1] + 1;
            if (ret[i - 1] == ret[i + 1]) ret[i] = ret[i - 1];
            if (ret[i]) continue;
            int rank = blackbox_rank<sparse_matrix<mint>, mint>(mat);
            ret[i] = rank / 2;
        }
    }
    return ret;
}

int main() {
    int N, M;
    cin >> N >> M;
    vector<tuple<int, int, int>> uvws;
    while (M--) {
        int u, v, w;
        cin >> u >> v >> w;
        u--, v--, w--;
        uvws.emplace_back(u, v, w);
    }
    auto ret = solve(N, uvws);
    for (auto x : ret) cout << x << '\n';
}