結果
| 問題 |
No.1171 Runs in Subsequences
|
| ユーザー |
 ecottea
|
| 提出日時 | 2025-09-01 23:12:05 |
| 言語 | C++17 (gcc 13.3.0 + boost 1.87.0) |
| 結果 |
AC
|
| 実行時間 | 40 ms / 2,000 ms |
| コード長 | 42,777 bytes |
| コンパイル時間 | 9,357 ms |
| コンパイル使用メモリ | 352,684 KB |
| 実行使用メモリ | 7,716 KB |
| 最終ジャッジ日時 | 2025-09-01 23:12:18 |
| 合計ジャッジ時間 | 10,996 ms |
|
ジャッジサーバーID （参考情報） |
judge4 / judge2 |
（要ログイン）
| ファイルパターン | 結果 |
|---|---|
| sample | AC * 4 |
| other | AC * 18 |
ソースコード
#ifndef HIDDEN_IN_VS // 折りたたみ用
// 警告の抑制
#define _CRT_SECURE_NO_WARNINGS
// ライブラリの読み込み
#include <bits/stdc++.h>
using namespace std;
// 型名の短縮
using ll = long long; using ull = unsigned long long; // -2^63 ～ 2^63 = 9e18（int は -2^31 ～ 2^31 = 2e9）
using pii = pair<int, int>; using pll = pair<ll, ll>; using pil = pair<int, ll>; using pli = pair<ll, int>;
using vi = vector<int>; using vvi = vector<vi>; using vvvi = vector<vvi>; using vvvvi = vector<vvvi>;
using vl = vector<ll>; using vvl = vector<vl>; using vvvl = vector<vvl>; using vvvvl = vector<vvvl>;
using vb = vector<bool>; using vvb = vector<vb>; using vvvb = vector<vvb>;
using vc = vector<char>; using vvc = vector<vc>; using vvvc = vector<vvc>;
using vd = vector<double>; using vvd = vector<vd>; using vvvd = vector<vvd>;
template <class T> using priority_queue_rev = priority_queue<T, vector<T>, greater<T>>;
using Graph = vvi;
// 定数の定義
const double PI = acos(-1);
int DX[4] = { 1, 0, -1, 0 }; // 4 近傍（下，右，上，左）
int DY[4] = { 0, 1, 0, -1 };
int INF = 1001001001; ll INFL = 4004004003094073385LL; // (int)INFL = INF, (int)(-INFL) = -INF;
// 入出力高速化
struct fast_io { fast_io() { cin.tie(nullptr); ios::sync_with_stdio(false); cout << fixed << setprecision(18); } } fastIOtmp;
// 汎用マクロの定義
#define all(a) (a).begin(), (a).end()
#define sz(x) ((int)(x).size())
#define lbpos(a, x) (int)distance((a).begin(), std::lower_bound(all(a), (x)))
#define ubpos(a, x) (int)distance((a).begin(), std::upper_bound(all(a), (x)))
#define Yes(b) {cout << ((b) ? "Yes\n" : "No\n");}
#define rep(i, n) for(int i = 0, i##_len = int(n); i < i##_len; ++i) // 0 から n-1 まで昇順
#define repi(i, s, t) for(int i = int(s), i##_end = int(t); i <= i##_end; ++i) // s から t まで昇順
#define repir(i, s, t) for(int i = int(s), i##_end = int(t); i >= i##_end; --i) // s から t まで降順
#define repe(v, a) for(const auto& v : (a)) // a の全要素（変更不可能）
#define repea(v, a) for(auto& v : (a)) // a の全要素（変更可能）
#define repb(set, d) for(int set = 0, set##_ub = 1 << int(d); set < set##_ub; ++set) // d ビット全探索（昇順）
#define repis(i, set) for(int i = lsb(set), bset##i = set; i < 32; bset##i -= 1 << i, i = lsb(bset##i)) // set の全要素（昇順）
#define repp(a) sort(all(a)); for(bool a##_perm = true; a##_perm; a##_perm = next_permutation(all(a))) // a の順列全て（昇順）
#define uniq(a) {sort(all(a)); (a).erase(unique(all(a)), (a).end());} // 重複除去
#define EXIT(a) {cout << (a) << endl; exit(0);} // 強制終了
#define inQ(x, y, u, l, d, r) ((u) <= (x) && (l) <= (y) && (x) < (d) && (y) < (r)) // 半開矩形内判定
// 汎用関数の定義
template <class T> inline ll powi(T n, int k) { ll v = 1; rep(i, k) v *= n; return v; }
template <class T> inline bool chmax(T& M, const T& x) { if (M < x) { M = x; return true; } return false; } // 最大値を更新（更新されたら true を返す）
template <class T> inline bool chmin(T& m, const T& x) { if (m > x) { m = x; return true; } return false; } // 最小値を更新（更新されたら true を返す）
template <class T> inline int getb(T set, int i) { return (set >> i) & T(1); }
template <class T> inline T smod(T n, T m) { n %= m; if (n < 0) n += m; return n; } // 非負mod
// 演算子オーバーロード
template <class T, class U> inline istream& operator>>(istream& is, pair<T, U>& p) { is >> p.first >> p.second; return is; }
template <class T> inline istream& operator>>(istream& is, vector<T>& v) { repea(x, v) is >> x; return is; }
template <class T> inline vector<T>& operator--(vector<T>& v) { repea(x, v) --x; return v; }
template <class T> inline vector<T>& operator++(vector<T>& v) { repea(x, v) ++x; return v; }
#endif // 折りたたみ用
#if __has_include(<atcoder/all>)
#include <atcoder/all>
using namespace atcoder;
#ifdef _MSC_VER
#include "localACL.hpp"
#endif
//using mint = modint998244353;
using mint = static_modint<(int)1e9+7>;
//using mint = modint; // mint::set_mod(m);
using vm = vector<mint>; using vvm = vector<vm>; using vvvm = vector<vvm>; using vvvvm = vector<vvvm>; using pim = pair<int, mint>;
#endif
#ifdef _MSC_VER // 手元環境（Visual Studio）
#include "local.hpp"
#else // 提出用（gcc）
int mute_dump = 0;
int frac_print = 0;
#if __has_include(<atcoder/all>)
namespace atcoder {
inline istream& operator>>(istream& is, mint& x) { ll x_; is >> x_; x = x_; return is; }
inline ostream& operator<<(ostream& os, const mint& x) { os << x.val(); return os; }
}
#endif
inline int popcount(int n) { return __builtin_popcount(n); }
inline int popcount(ll n) { return __builtin_popcountll(n); }
inline int lsb(int n) { return n != 0 ? __builtin_ctz(n) : 32; }
inline int lsb(ll n) { return n != 0 ? __builtin_ctzll(n) : 64; }
inline int msb(int n) { return n != 0 ? (31 - __builtin_clz(n)) : -1; }
inline int msb(ll n) { return n != 0 ? (63 - __builtin_clzll(n)) : -1; }
#define dump(...)
#define dumpel(v)
#define dump_math(v)
#define input_from_file(f)
#define output_to_file(f)
#define Assert(b) { if (!(b)) { vc MLE(1<<30); EXIT(MLE.back()); } } // RE の代わりに MLE を出す
#endif
// 愚直
mint naive(const string& s) {
int n = sz(s);
if (n == 0) return 0;
mint res = 1;
rep(i, n - 1) if (s[i] != s[i + 1]) res++;
return res;
}
//【行列】
/*
* Matrix<T>(int n, int m) : O(n m)
* n×m 零行列で初期化する．
*
* Matrix<T>(int n) : O(n^2)
* n×n 単位行列で初期化する．
*
* Matrix<T>(vvT a) : O(n m)
* 二次元配列 a[0..n)[0..m) の要素で初期化する．
*
* bool empty() : O(1)
* 行列が空かを返す．
*
* A + B : O(n m)
* n×m 行列 A, B の和を返す．+= も使用可．
*
* A - B : O(n m)
* n×m 行列 A, B の差を返す．-= も使用可．
*
* c * A ／ A * c : O(n m)
* n×m 行列 A とスカラー c のスカラー積を返す．*= も使用可．
*
* A * x : O(n m)
* n×m 行列 A と n 次元列ベクトル x の積を返す．
*
* x * A : O(n m)（やや遅い）
* m 次元行ベクトル x と n×m 行列 A の積を返す．
*
* A * B : O(n m l)
* n×m 行列 A と m×l 行列 B の積を返す．
*
* Mat pow(ll d) : O(n^3 log d)
* 自身を d 乗した行列を返す．
*/
template <class T>
struct Matrix {
int n, m; // 行列のサイズ（n 行 m 列）
vector<vector<T>> v; // 行列の成分
// n×m 零行列で初期化する．
Matrix(int n, int m) : n(n), m(m), v(n, vector<T>(m)) {}
// n×n 単位行列で初期化する．
Matrix(int n) : n(n), m(n), v(n, vector<T>(n)) { rep(i, n) v[i][i] = T(1); }
// 二次元配列 a[0..n)[0..m) の要素で初期化する．
Matrix(const vector<vector<T>>& a) : n(sz(a)), m(sz(a[0])), v(a) {}
Matrix() : n(0), m(0) {}
// 代入
Matrix(const Matrix&) = default;
Matrix& operator=(const Matrix&) = default;
// アクセス
inline vector<T> const& operator[](int i) const { return v[i]; }
inline vector<T>& operator[](int i) {
// verify : https://judge.yosupo.jp/problem/matrix_product
// inline を付けて [] でアクセスするとなぜか v[] への直接アクセスより速くなった．
return v[i];
}
// 入力
friend istream& operator>>(istream& is, Matrix& a) {
rep(i, a.n) rep(j, a.m) is >> a.v[i][j];
return is;
}
// 行の追加
void push_back(const vector<T>& a) {
Assert(sz(a) == m);
v.push_back(a);
n++;
}
// 行の削除
void pop_back() {
Assert(n > 0);
v.pop_back();
n--;
}
// サイズ変更
void resize(int n_) {
v.resize(n_);
n = n_;
}
void resize(int n_, int m_) {
n = n_;
m = m_;
v.resize(n);
rep(i, n) v[i].resize(m);
}
// 空か
bool empty() const { return min(n, m) == 0; }
// 比較
bool operator==(const Matrix& b) const { return n == b.n && m == b.m && v == b.v; }
bool operator!=(const Matrix& b) const { return !(*this == b); }
// 加算，減算，スカラー倍
Matrix& operator+=(const Matrix& b) {
rep(i, n) rep(j, m) v[i][j] += b[i][j];
return *this;
}
Matrix& operator-=(const Matrix& b) {
rep(i, n) rep(j, m) v[i][j] -= b[i][j];
return *this;
}
Matrix& operator*=(const T& c) {
rep(i, n) rep(j, m) v[i][j] *= c;
return *this;
}
Matrix operator+(const Matrix& b) const { return Matrix(*this) += b; }
Matrix operator-(const Matrix& b) const { return Matrix(*this) -= b; }
Matrix operator*(const T& c) const { return Matrix(*this) *= c; }
friend Matrix operator*(const T& c, const Matrix<T>& a) { return a * c; }
Matrix operator-() const { return Matrix(*this) *= T(-1); }
// 行列ベクトル積 : O(m n)
vector<T> operator*(const vector<T>& x) const {
vector<T> y(n);
rep(i, n) rep(j, m) y[i] += v[i][j] * x[j];
return y;
}
// ベクトル行列積 : O(m n)
friend vector<T> operator*(const vector<T>& x, const Matrix& a) {
vector<T> y(a.m);
rep(i, a.n) rep(j, a.m) y[j] += x[i] * a[i][j];
return y;
}
// 積：O(n^3)
Matrix operator*(const Matrix& b) const {
// verify : https://judge.yosupo.jp/problem/matrix_product
Matrix res(n, b.m);
rep(i, res.n) rep(k, m) rep(j, res.m) res[i][j] += v[i][k] * b[k][j];
return res;
}
Matrix& operator*=(const Matrix& b) { *this = *this * b; return *this; }
// 累乗：O(n^3 log d)
Matrix pow(ll d) const {
// verify : https://judge.yosupo.jp/problem/pow_of_matrix
Matrix res(n), pow2 = *this;
while (d > 0) {
if (d & 1) res *= pow2;
pow2 *= pow2;
d >>= 1;
}
return res;
}
#ifdef _MSC_VER
friend ostream& operator<<(ostream& os, const Matrix& a) {
rep(i, a.n) {
os << "[";
rep(j, a.m) os << a[i][j] << " ]"[j == a.m - 1];
if (i < a.n - 1) os << "\n";
}
return os;
}
#endif
};
//【行簡約形（行交換なし）】O(n m min(n, m))
/*
* 行基本変形（行交換なし）で n×m 行列 A を行簡約形に変形し，ピボット位置のリストを返す．
*/
template <class T>
vector<pii> row_reduced_form(Matrix<T>& A) {
int n = A.n, m = A.m;
vector<pii> piv;
piv.reserve(min(n, m));
// 未確定の列を記録しておくリスト
list<int> rjs;
rep(j, m) rjs.push_back(j);
rep(i, n) {
// 第 i 行の係数を左から走査し非 0 を見つける．
auto it = rjs.begin();
for (; it != rjs.end(); it++) if (A[i][*it] != 0) break;
// 第 i 行の全てが 0 なら無視する．
if (it == rjs.end()) continue;
// A[i][j] をピボットに選択する．
int j = *it;
rjs.erase(it);
piv.emplace_back(i, j);
// A[i][j] が 1 になるよう行全体を A[i][j] で割る．
T Aij_inv = T(1) / A[i][j];
repi(j2, j, m - 1) A[i][j2] *= Aij_inv;
// 第 i 行以外の第 j 列の成分が全て 0 になるよう第 i 行を定数倍して減じる．
rep(i2, n) if (A[i2][j] != 0 && i2 != i) {
T mul = A[i2][j];
repi(j2, j, m - 1) A[i2][j2] -= A[i][j2] * mul;
}
}
return piv;
}
//【逆行列】O(n^3)
/*
* n 次正方行列 mat の逆行列を返す（存在しなければ空）
*/
template <class T>
Matrix<T> inverse_matrix(const Matrix<T>& mat) {
// verify : https://judge.yosupo.jp/problem/inverse_matrix
int n = mat.n;
// 元の行列 mat と単位行列を繋げた拡大行列 v を作る．
vector<vector<T>> v(n, vector<T>(2 * n));
rep(i, n) rep(j, n) {
v[i][j] = mat[i][j];
if (i == j) v[i][n + j] = 1;
}
int m = 2 * n;
// 注目位置を (i, j)（i 行目かつ j 列目）とする．
int i = 0, j = 0;
// 拡大行列に対して行基本変形を行い，左側を単位行列にすることを目指す．
while (i < n && j < m) {
// 同じ列の下方の行から非 0 成分を見つける．
int i2 = i;
while (i2 < n && v[i2][j] == T(0)) i2++;
// 見つからなかったら全て 0 の列があったので mat は非正則
if (i2 == n) return Matrix<T>();
// 見つかったら i 行目とその行を入れ替える．
if (i != i2) swap(v[i], v[i2]);
// v[i][j] が 1 になるよう行全体を v[i][j] で割る．
T vij_inv = T(1) / v[i][j];
repi(j2, j, m - 1) v[i][j2] *= vij_inv;
// v[i][j] と同じ列の成分が全て 0 になるよう i 行目を定数倍して減じる．
rep(i2, n) {
// i 行目だけは引かない．
if (i2 == i) continue;
T mul = v[i2][j];
repi(j2, j, m - 1) v[i2][j2] -= v[i][j2] * mul;
}
// 注目位置を右下に移す．
i++; j++;
}
// 拡大行列の右半分が mat の逆行列なのでコピーする．
Matrix<T> mat_inv(n, n);
rep(i, n) rep(j, n) mat_inv[i][j] = v[i][n + j];
return mat_inv;
}
// 遷移行列の係数を計算し，埋め込み用のコードを出力する．
void embed_coefs(int COL, int len_max, int L_max, int loop_cnt,
const vector<string>& ssT_ini = { "" }, const vector<string>& ssB_ini = { "" }) {
mt19937_64 mt((int)time(NULL));
uniform_int_distribution<int> rnd_len(1, len_max);
uniform_int_distribution<int> rnd_col(0, COL - 1);
uniform_int_distribution<int> rnd(7, 20);
vector<string> ssT(ssT_ini), ssB(ssB_ini);
rep(loop, loop_cnt) {
if (loop % 100 == 1) {
dump("loop:", loop, "L:", sz(ssT));
// 途中再開用
sort(all(ssT));
sort(all(ssB));
string eb = "vvi ssT = {";
repe(s, ssT) {
eb += "{";
repe(c, s) {
eb += to_string((int)(c - '0'));
eb += ",";
}
if (eb.back() == ',') eb.pop_back();
eb += "},";
}
if (eb.back() == ',') eb.pop_back();
eb += "};\n";
eb += "vvi ssB = {";
repe(s, ssB) {
eb += "{";
repe(c, s) {
eb += to_string((int)(c - '0'));
eb += ",";
}
if (eb.back() == ',') eb.pop_back();
eb += "},";
}
if (eb.back() == ',') eb.pop_back();
eb += "};\n";
cerr << eb;
}
// 候補とする文字列をランダムに L_max 個追加する．
rep(hoge, L_max) {
int len = rnd_len(mt);
string s;
rep(fuga, len) s += '0' + rnd_col(mt);
ssT.push_back(s);
}
rep(hoge, L_max) {
int len = rnd_len(mt);
string s;
rep(fuga, len) s += '0' + rnd_col(mt);
ssB.push_back(s);
}
uniq(ssT);
uniq(ssB);
// (i,j) 成分が naive(ss[i] + ss[j]) であるような行列 mat を得る．
int LT = sz(ssT), LB = sz(ssB);
Matrix<mint> mat(LT, LB);
rep(i, LT) rep(j, LB) mat[i][j] = naive(ssT[i] + ssB[j]);
// mat に対して行基本変形を行いピボット位置のリスト piv を得る．
auto piv = row_reduced_form(mat);
// ランク上昇に寄与した文字列だけ残す．
vector<string> nssT, nssB;
for (auto [i, j] : piv) {
nssT.push_back(ssT[i]);
nssB.push_back(ssB[j]);
}
ssT = move(nssT);
ssB = move(nssB);
}
// (i,j) 成分が naive(ss[i] + ss[j]) であるような行列 mat を得る．
int LT = sz(ssT), LB = sz(ssB);
Matrix<mint> mat(LT, LB);
rep(i, LT) rep(j, LB) mat[i][j] = naive(ssT[i] + ssB[j]);
// mat に対して行基本変形を行いピボット位置のリスト piv を得る．
auto piv = row_reduced_form(mat);
int DIM = sz(piv);
// 選択した行 is と列 js を並べ直して "" 始まりにする．
vi is(DIM), js(DIM);
rep(r, DIM) tie(is[r], js[r]) = piv[r];
repi(i, 1, DIM - 1) if (ssT[is[i]] == "") swap(is[i], is[0]);
repi(j, 1, DIM - 1) if (ssB[js[j]] == "") swap(js[j], js[0]);
if (ssT[is[0]] != "" || ssB[js[0]] != "") {
dump("ERROR! ssT[is[0]]:", ssT[is[0]], "ssB[js[0]]:", ssB[js[0]]);
exit(-1);
}
// 基底の変換行列 P を得る．
Matrix<mint> matP(DIM, DIM);
rep(i, DIM) rep(j, DIM) matP[i][j] = naive(ssT[is[i]] + ssB[js[j]]);
// P の逆行列 P_inv を得る．
auto matP_inv = inverse_matrix(matP);
// 各文字に対応する表現行列を得る．
vector<Matrix<mint>> matAs(COL, Matrix<mint>(DIM, DIM));
rep(k, COL) {
char c = '0' + k;
rep(i, DIM) rep(j, DIM) matAs[k][i][j] = naive(ssT[is[i]] + c + ssB[js[j]]);
matAs[k] = matAs[k] * matP_inv;
}
// 右端を閉じるためのベクトルを得る．
vm vecP(DIM);
rep(i, DIM) vecP[i] = matP[i][0];
// スパース埋め込み用の文字列を出力する．
vector<tuple<int, int, mint>> elems; vi offsets{ 0 };
rep(c, COL) {
rep(i, DIM) rep(j, DIM) {
if (matAs[c][i][j] != 0) elems.emplace_back(i, j, matAs[c][i][j]);
}
offsets.push_back(sz(elems));
}
auto to_signed_string = [](mint x) {
int v = x.val();
int mod = mint::mod();
if (2 * v > mod) v -= mod;
return to_string(v);
};
string eb = "constexpr int DIM = ";
eb += to_string(DIM);
eb += ";\n";
eb += "constexpr int COL = ";
eb += to_string(COL);
eb += ";\n";
eb += "tuple<int, int, VTYPE> matAs[] = {";
for (auto [i, j, v] : elems) {
eb += "{";
eb += to_string(i);
eb += ",";
eb += to_string(j);
eb += ",";
eb += to_signed_string(v);
eb += "},";
}
eb.pop_back();
eb += "};\n";
eb += "int offset[COL + 1] = {";
repi(c, 0, COL) eb += to_string(offsets[c]) + ",";
eb.pop_back();
eb += "};\n";
eb += "VTYPE vecP[DIM] = {";
rep(i, DIM) eb += to_signed_string(vecP[i]) + ",";
eb.pop_back();
eb += "};\n";
cout << eb;
exit(0);
}
template <class VTYPE>
VTYPE solve(const string& s) {
// --------------- embed_coefs() からの出力を貼る ----------------
constexpr int DIM = 28;
constexpr int COL = 26;
tuple<int, int, VTYPE> matAs[] = { {0,1,1},{1,1,1},{2,1,1},{2,6,-500000002},{2,12,500000002},{3,1,1},{3,6,-500000000},{3,12,500000000},{4,1,1},{4,6,-500000000},{4,12,500000000},{5,1,1},{5,6,-500000002},{5,12,500000002},{6,1,1},{6,6,3},{6,12,-3},{7,1,1},{7,6,1},{7,12,-1},{8,1,1},{8,6,-500000001},{8,12,500000001},{9,1,1},{9,6,-500000000},{9,12,500000000},{10,1,1},{10,6,-500000001},{10,12,500000001},{11,1,1},{11,6,1},{11,12,-1},{12,1,1},{12,6,2},{12,12,-2},{13,1,1},{13,6,4},{13,12,-4},{14,1,1},{14,6,2},{14,12,-2},{15,1,1},{15,6,3},{15,12,-3},{16,1,1},{16,6,1},{16,12,-1},{17,1,1},{17,6,-500000001},{17,12,500000001},{18,1,1},{18,6,-500000001},{18,12,500000001},{19,1,1},{19,6,2},{19,12,-2},{20,1,1},{20,6,-500000000},{20,12,500000000},{21,1,1},{21,6,3},{21,12,-3},{22,1,1},{22,6,2},{22,12,-2},{23,1,1},{23,6,3},{23,12,-3},{24,1,1},{24,6,-500000002},{24,12,500000002},{25,1,1},{25,6,-500000001},{25,12,500000001},{26,1,1},{26,6,-500000000},{26,12,500000000},{27,1,1},{27,6,4},{27,12,-4},{0,6,-3},{0,12,3},{0,26,1},{1,6,500000001},{1,12,-500000001},{1,26,1},{2,6,500000002},{2,12,-500000002},{2,26,1},{3,6,-500000003},{3,12,500000003},{3,26,1},{4,6,-500000003},{4,12,500000003},{4,26,1},{5,6,500000002},{5,12,-500000002},{5,26,1},{6,26,1},{7,6,-2},{7,12,2},{7,26,1},{8,6,500000003},{8,12,-500000003},{8,26,1},{9,6,-500000003},{9,12,500000003},{9,26,1},{10,6,500000003},{10,12,-500000003},{10,26,1},{11,6,-2},{11,12,2},{11,26,1},{12,6,-1},{12,12,1},{12,26,1},{13,6,1},{13,12,-1},{13,26,1},{14,6,-1},{14,12,1},{14,26,1},{15,26,1},{16,6,-2},{16,12,2},{16,26,1},{17,6,500000003},{17,12,-500000003},{17,26,1},{18,6,500000003},{18,12,-500000003},{18,26,1},{19,6,-1},{19,12,1},{19,26,1},{20,6,-500000003},{20,12,500000003},{20,26,1},{21,26,1},{22,6,-1},{22,12,1},{22,26,1},{23,26,1},{24,6,500000002},{24,12,-500000002},{24,26,1},{25,6,500000003},{25,12,-500000003},{25,26,1},{26,26,1},{27,6,1},{27,12,-1},{27,26,1},{0,6,500000003},{0,7,1},{0,12,-500000003},{1,7,1},{2,6,1},{2,7,1},{2,12,-1},{3,6,3},{3,7,1},{3,12,-3},{4,6,3},{4,7,1},{4,12,-3},{5,6,1},{5,7,1},{5,12,-1},{6,6,-500000001},{6,7,1},{6,12,500000001},{7,7,1},{8,6,2},{8,7,1},{8,12,-2},{9,6,3},{9,7,1},{9,12,-3},{10,6,2},{10,7,1},{10,12,-2},{11,6,-500000003},{11,7,1},{11,12,500000003},{12,6,-500000002},{12,7,1},{12,12,500000002},{13,6,-500000000},{13,7,1},{13,12,500000000},{14,6,-500000002},{14,7,1},{14,12,500000002},{15,6,-500000001},{15,7,1},{15,12,500000001},{16,6,-500000003},{16,7,1},{16,12,500000003},{17,6,2},{17,7,1},{17,12,-2},{18,6,2},{18,7,1},{18,12,-2},{19,6,-500000002},{19,7,1},{19,12,500000002},{20,6,3},{20,7,1},{20,12,-3},{21,6,-500000001},{21,7,1},{21,12,500000001},{22,6,-500000002},{22,7,1},{22,12,500000002},{23,6,-500000001},{23,7,1},{23,12,500000001},{24,6,1},{24,7,1},{24,12,-1},{25,6,2},{25,7,1},{25,12,-2},{26,6,3},{26,7,1},{26,12,-3},{27,6,-500000000},{27,7,1},{27,12,500000000},{0,6,500000003},{0,11,1},{0,12,-500000003},{1,11,1},{2,6,1},{2,11,1},{2,12,-1},{3,6,3},{3,11,1},{3,12,-3},{4,6,3},{4,11,1},{4,12,-3},{5,6,1},{5,11,1},{5,12,-1},{6,6,-500000001},{6,11,1},{6,12,500000001},{7,6,-500000003},{7,11,1},{7,12,500000003},{8,6,2},{8,11,1},{8,12,-2},{9,6,3},{9,11,1},{9,12,-3},{10,6,2},{10,11,1},{10,12,-2},{11,11,1},{12,6,-500000002},{12,11,1},{12,12,500000002},{13,6,-500000000},{13,11,1},{13,12,500000000},{14,6,-500000002},{14,11,1},{14,12,500000002},{15,6,-500000001},{15,11,1},{15,12,500000001},{16,6,-500000003},{16,11,1},{16,12,500000003},{17,6,2},{17,11,1},{17,12,-2},{18,6,2},{18,11,1},{18,12,-2},{19,6,-500000002},{19,11,1},{19,12,500000002},{20,6,3},{20,11,1},{20,12,-3},{21,6,-500000001},{21,11,1},{21,12,500000001},{22,6,-500000002},{22,11,1},{22,12,500000002},{23,6,-500000001},{23,11,1},{23,12,500000001},{24,6,1},{24,11,1},{24,12,-1},{25,6,2},{25,11,1},{25,12,-2},{26,6,3},{26,11,1},{26,12,-3},{27,6,-500000000},{27,11,1},{27,12,500000000},{0,6,500000002},{0,12,-500000002},{0,22,1},{1,6,-1},{1,12,1},{1,22,1},{2,22,1},{3,6,2},{3,12,-2},{3,22,1},{4,6,2},{4,12,-2},{4,22,1},{5,22,1},{6,6,-500000002},{6,12,500000002},{6,22,1},{7,6,500000003},{7,12,-500000003},{7,22,1},{8,6,1},{8,12,-1},{8,22,1},{9,6,2},{9,12,-2},{9,22,1},{10,6,1},{10,12,-1},{10,22,1},{11,6,500000003},{11,12,-500000003},{11,22,1},{12,6,-500000003},{12,12,500000003},{12,22,1},{13,6,-500000001},{13,12,500000001},{13,22,1},{14,6,-500000003},{14,12,500000003},{14,22,1},{15,6,-500000002},{15,12,500000002},{15,22,1},{16,6,500000003},{16,12,-500000003},{16,22,1},{17,6,1},{17,12,-1},{17,22,1},{18,6,1},{18,12,-1},{18,22,1},{19,6,-500000003},{19,12,500000003},{19,22,1},{20,6,2},{20,12,-2},{20,22,1},{21,6,-500000002},{21,12,500000002},{21,22,1},{22,22,1},{23,6,-500000002},{23,12,500000002},{23,22,1},{24,22,1},{25,6,1},{25,12,-1},{25,22,1},{26,6,2},{26,12,-2},{26,22,1},{27,6,-500000001},{27,12,500000001},{27,22,1},{0,6,-2},{0,8,1},{0,12,2},{1,6,500000002},{1,8,1},{1,12,-500000002},{2,6,500000003},{2,8,1},{2,12,-500000003},{3,6,-500000002},{3,8,1},{3,12,500000002},{4,6,-500000002},{4,8,1},{4,12,500000002},{5,6,500000003},{5,8,1},{5,12,-500000003},{6,6,1},{6,8,1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};
int offset[COL + 1] = { 0,80,154,234,314,390,464,544,616,690,764,838,912,986,1062,1134,1210,1282,1358,1432,1508,1584,1656,1728,1802,1848,1922 };
VTYPE vecP[DIM] = { 0,1,3,7,7,3,6,2,5,7,5,2,4,8,4,6,2,5,5,4,7,6,4,6,3,5,7,8 };
// --------------------------------------------------------------
// 部分列に対するスコアの和を求める DP
array<VTYPE, DIM> dp;
dp.fill(0);
dp[0] = 1;
auto apply = [&](const array<VTYPE, DIM>& x, int col) {
array<VTYPE, DIM> z;
z.fill(0);
repi(pt, offset[col], offset[col + 1] - 1) {
auto [i, j, v] = matAs[pt];
z[j] += x[i] * v;
}
return z;
};
repe(c, s) {
auto ndp = apply(dp, c - '0');
rep(i, DIM) dp[i] += ndp[i];
}
VTYPE res = 0;
rep(i, DIM) res += dp[i] * vecP[i];
return res;
}
int main() {
// input_from_file("input.txt");
// output_to_file("output.txt");
//【方法】
// 愚直を書いて集めたデータをもとに遷移行列を復元する．
//【使い方】
// 1. mint naive(文字列) を実装する．
// 2. embed_coefs(文字の種類数); を実行する．
// 3. 出力を solve() 内に貼る．
// 4. auto dp = solve<答えの型>(文字列) で勝手に DP してくれる．
dump("naive:", naive("3301")); dump("=====");
dump("naive:", naive("3303")); dump("=====");
dump("naive:", naive("3312")); dump("=====");
vector<string> ssT_ini{ "" }, ssB_ini{ "" };
// 途中から再開
//repe(a, ssT) {
// string s;
// repe(x, a) s += '0' + x;
// ssT_ini.push_back(s);
//}
//repe(a, ssB) {
// string s;
// repe(x, a) s += '0' + x;
// ssB_ini.push_back(s);
//}
// (文字の種類数，長さの最大値，1回で追加する文字列の量，反復回数)
// embed_coefs(26, 9, 100, 1002, ssT_ini, ssB_ini);
string s;
cin >> s;
rep(i, sz(s)) s[i] = s[i] - 'a' + '0';
EXIT(solve<mint>(s));
}