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#include "library/convolution/semi_relaxed_convolution_ntt.hpp"
#ifndef SUISEN_SEMI_RELAXED_CONVOLUTION_NTT #define SUISEN_SEMI_RELAXED_CONVOLUTION_NTT #include <atcoder/convolution> namespace suisen { // reference: https://qiita.com/Kiri8128/items/1738d5403764a0e26b4c template <typename mint> struct SemiRelaxedConvolutionNTT { SemiRelaxedConvolutionNTT() = default; SemiRelaxedConvolutionNTT(const std::vector<mint> &f) : _n(0), _f(f), _g{}, _h{} {} mint append(const mint& gi) { static constexpr int threshold_log = 6; static constexpr int threshold = 1 << threshold_log; static constexpr int threshold_mask = threshold - 1; ++_n; _g.push_back(gi); int q = _n >> threshold_log, r = _n & threshold_mask; if (r == 0) { if (q == (-q & q)) { std::vector<mint> f_fft(_f.begin(), _f.begin() + std::min(int(_f.size()), 2 * _n)); f_fft.resize(2 * _n); atcoder::internal::butterfly(f_fft); _f_fft.push_back(std::move(f_fft)); } const int log_q = __builtin_ctz(q); const int k = (-q & q) << threshold_log; std::vector<mint> g_fft(_g.end() - k, _g.end()); g_fft.resize(2 * k); atcoder::internal::butterfly(g_fft); const mint z = mint(2 * k).inv(); std::vector<mint> h(2 * k); for (int i = 0; i < 2 * k; ++i) h[i] = _f_fft[log_q][i] * g_fft[i] * z; atcoder::internal::butterfly_inv(h); ensure(_n - 1 + k); for (int i = 0; i < k; ++i) _h[_n - 1 + i] += h[k - 1 + i]; } else { // naive convolve r = std::min(r, int(_f.size())); ensure(_n); for (int i = 0; i < r; ++i) { _h[_n - 1] += _f[i] * _g[_n - 1 - i]; } } return _h[_n - 1]; } const mint& operator[](int i) const { return _h[i]; } std::vector<mint> get() const { return _h; } private: int _n; std::vector<mint> _f, _g, _h; std::vector<std::vector<mint>> _f_fft; void ensure(std::size_t n) { if (_h.size() < n) _h.resize(n); } }; } // namespace suisen #endif // SUISEN_SEMI_RELAXED_CONVOLUTION_NTT
#line 1 "library/convolution/semi_relaxed_convolution_ntt.hpp" #include <atcoder/convolution> namespace suisen { // reference: https://qiita.com/Kiri8128/items/1738d5403764a0e26b4c template <typename mint> struct SemiRelaxedConvolutionNTT { SemiRelaxedConvolutionNTT() = default; SemiRelaxedConvolutionNTT(const std::vector<mint> &f) : _n(0), _f(f), _g{}, _h{} {} mint append(const mint& gi) { static constexpr int threshold_log = 6; static constexpr int threshold = 1 << threshold_log; static constexpr int threshold_mask = threshold - 1; ++_n; _g.push_back(gi); int q = _n >> threshold_log, r = _n & threshold_mask; if (r == 0) { if (q == (-q & q)) { std::vector<mint> f_fft(_f.begin(), _f.begin() + std::min(int(_f.size()), 2 * _n)); f_fft.resize(2 * _n); atcoder::internal::butterfly(f_fft); _f_fft.push_back(std::move(f_fft)); } const int log_q = __builtin_ctz(q); const int k = (-q & q) << threshold_log; std::vector<mint> g_fft(_g.end() - k, _g.end()); g_fft.resize(2 * k); atcoder::internal::butterfly(g_fft); const mint z = mint(2 * k).inv(); std::vector<mint> h(2 * k); for (int i = 0; i < 2 * k; ++i) h[i] = _f_fft[log_q][i] * g_fft[i] * z; atcoder::internal::butterfly_inv(h); ensure(_n - 1 + k); for (int i = 0; i < k; ++i) _h[_n - 1 + i] += h[k - 1 + i]; } else { // naive convolve r = std::min(r, int(_f.size())); ensure(_n); for (int i = 0; i < r; ++i) { _h[_n - 1] += _f[i] * _g[_n - 1 - i]; } } return _h[_n - 1]; } const mint& operator[](int i) const { return _h[i]; } std::vector<mint> get() const { return _h; } private: int _n; std::vector<mint> _f, _g, _h; std::vector<std::vector<mint>> _f_fft; void ensure(std::size_t n) { if (_h.size() < n) _h.resize(n); } }; } // namespace suisen