#include <bits/stdc++.h>
using namespace std;

class xrand {
    uint64_t x;

   public:
    using result_type = uint32_t;
    static constexpr result_type min() {
        return std::numeric_limits<result_type>::min();
    }
    static constexpr result_type max() {
        return std::numeric_limits<result_type>::max();
    }
    xrand(uint64_t k) : x(k) {}
    xrand() : xrand(1) {}
    result_type operator()() {
        x ^= x << 9;
        x ^= x >> 7;
        return (x * 0x123456789abcdef) >> 32;
    }
};

xrand rng;
uniform_real_distribution<double> dist_d(0.5, 1.5);

constexpr int T = 52, N = 10;
constexpr int sample_size = 1000;

double calc_p(int s, int p, int r, double d) {
    assert(s <= r);
    const double base = sqrt(r) * pow(1.05, p) * d;
    double low = clamp(s / base, 0.75, 1.25);
    double high = clamp((s + 1) / base, 0.75, 1.25);
    if (s == r) {
        high = 1.25;
    }
    return (high - low) * 2;
}

int convert(int x) {
    const int r = x % 10;
    if (r == 0) return x;
    if (r <= 3) return x + 3 - r;
    if (r <= 6) return x + 6 - r;
    return x + 10 - r;
}

int main() {
    int t, n, money;
    cin >> t >> n >> money;
    money /= 500;
    array<int, N> s, p, r;
    s.fill(0);
    p.fill(0);
    r.fill(0);

    array<array<double, sample_size>, N> d_particle;
    for (int i = 0; i < N; i++) {
        for (auto &&x : d_particle[i]) {
            x = dist_d(rng);
        }
    }

    int score = 0;

    for (int week = 0; week < T; week++) {
        array<double, N> d2_avg;
        d2_avg.fill(0.0);
        for (int i = 0; i < N; i++) {
            for (int j = 0; j < sample_size; j++) {
                d2_avg[i] += d_particle[i][j] * d_particle[i][j];
            }
            d2_avg[i] /= sample_size;
        }
        array<double, N> weight;
        for (int i = 0; i < N; i++) {
            weight[i] = pow(1.05, 2 * p[i]) * d2_avg[i];
        }
        double v = 0.0, v_l = 0.0, v_r = 1e5;
        while (v_r - v_l > 1e-5) {
            double mid = 0.5 * (v_l + v_r);
            int count = 0;
            for (int i = 0; i < N; i++) {
                count += max(0, int(weight[i] * mid) - r[i]);
            }
            if (count <= money) {
                v_l = mid;
            } else {
                v_r = mid;
            }
        }
        v = v_l;
        if (week >= 44) {
            cout << 1;
            for (int i = 0; i < 10; i++) {
                cout << ' ' << max(0, int(weight[i] * v) - r[i]);
            }
            cout << endl;
        } else if (week % 3 != 1 or money < 2000) {
            cout << 1;
            for (int i = 0; i < 10; i++) {
                double d_min =
                    *min_element(d_particle[i].begin(), d_particle[i].end());
                cout << ' '
                     << max(0, min(int(weight[i] * v) - r[i],
                                   convert(7 * d_min * d_min *
                                           pow(1.05, 2 * p[i])) -
                                       r[i]));
            }
            cout << endl;
        } else {
            cout << "2 2" << endl;
        }

        cin >> money;
        money /= 500;

        for (int i = 0; i < N; i++) {
            cin >> s[i];
            score += s[i];
        }
        double p2_sum = 0;
        for (int i = 0; i < N; i++) {
            cin >> p[i];
            double p_expect = p[i];
            for (int w = week + 1; w < T; w++) {
                if (w < 44) {
                    p_expect += 1.2;
                } else {
                    p_expect -= 0.7;
                }
                p2_sum += pow(1.05, 2 * p_expect) * d2_avg[i];
            }
        }
        for (auto &&x : r) {
            cin >> x;
        }
        for (int i = 0; i < N; i++) {
            array<double, sample_size> d_prob;
            for (int j = 0; j < sample_size; j++) {
                d_prob[j] = calc_p(s[i], p[i], r[i] + s[i], d_particle[i][j]);
            }
            discrete_distribution dist_idx(d_prob.begin(), d_prob.end());
            array<double, sample_size> d_new;
            for (int j = 0; j < sample_size; j++) {
                d_new[j] = d_particle[i][dist_idx(rng)];
            }
            d_particle[i] = move(d_new);
        }
        // cerr << score + 7 * p2_sum << endl;
        // if (week == T - 1) {
        //     for (int i = 0; i < N; i++) {
        //         cerr << reduce(d_particle[i].begin(), d_particle[i].end()) /
        //                     sample_size
        //              << endl;
        //     }
        // }
    }

    return 0;
}