結果

問題 No.2660 Sweep Cards (Easy)
ユーザー chineristACchineristAC
提出日時 2024-01-23 01:44:33
言語 PyPy3
(7.3.15)
結果
RE  
(最新)
AC  
(最初)
実行時間 -
コード長 9,579 bytes
コンパイル時間 437 ms
コンパイル使用メモリ 83,072 KB
実行使用メモリ 84,672 KB
最終ジャッジ日時 2024-09-29 11:03:36
合計ジャッジ時間 5,168 ms
ジャッジサーバーID
(参考情報)
judge4 / judge3
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テストケース

テストケース表示
入力 結果 実行時間
実行使用メモリ
testcase_00 RE -
testcase_01 RE -
testcase_02 RE -
testcase_03 RE -
testcase_04 RE -
testcase_05 RE -
testcase_06 RE -
testcase_07 RE -
testcase_08 RE -
testcase_09 RE -
testcase_10 RE -
testcase_11 RE -
testcase_12 RE -
testcase_13 RE -
testcase_14 RE -
testcase_15 RE -
testcase_16 RE -
testcase_17 RE -
testcase_18 RE -
testcase_19 RE -
testcase_20 RE -
testcase_21 RE -
testcase_22 RE -
testcase_23 RE -
testcase_24 RE -
testcase_25 RE -
testcase_26 RE -
testcase_27 RE -
testcase_28 RE -
testcase_29 RE -
testcase_30 RE -
testcase_31 RE -
testcase_32 RE -
testcase_33 RE -
testcase_34 RE -
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ソースコード

diff #

import sys
from itertools import permutations
from heapq import heappop,heappush
from collections import deque
import random
import bisect
from math import gcd


input = lambda :sys.stdin.readline().rstrip()
mi = lambda :map(int,input().split())
li = lambda :list(mi())

mod = 998244353
omega = pow(3,119,mod)
rev_omega = pow(omega,mod-2,mod)

N = 5*10**5
g1 = [1]*(N+1) # 元テーブル
g2 = [1]*(N+1) #逆元テーブル
inv = [1]*(N+1) #逆元テーブル計算用テーブル

for i in range( 2, N + 1 ):
    g1[i]=( ( g1[i-1] * i ) % mod )
    inv[i]=( ( -inv[mod % i] * (mod//i) ) % mod )
    g2[i]=( (g2[i-1] * inv[i]) % mod )
inv[0]=0

_fft_mod = 998244353
_fft_imag = 911660635
_fft_iimag = 86583718
_fft_rate2 = (911660635, 509520358, 369330050, 332049552, 983190778, 123842337, 238493703, 975955924, 603855026, 856644456, 131300601,
              842657263, 730768835, 942482514, 806263778, 151565301, 510815449, 503497456, 743006876, 741047443, 56250497, 867605899)
_fft_irate2 = (86583718, 372528824, 373294451, 645684063, 112220581, 692852209, 155456985, 797128860, 90816748, 860285882, 927414960,
               354738543, 109331171, 293255632, 535113200, 308540755, 121186627, 608385704, 438932459, 359477183, 824071951, 103369235)
_fft_rate3 = (372528824, 337190230, 454590761, 816400692, 578227951, 180142363, 83780245, 6597683, 70046822, 623238099,
              183021267, 402682409, 631680428, 344509872, 689220186, 365017329, 774342554, 729444058, 102986190, 128751033, 395565204)
_fft_irate3 = (509520358, 929031873, 170256584, 839780419, 282974284, 395914482, 444904435, 72135471, 638914820, 66769500,
               771127074, 985925487, 262319669, 262341272, 625870173, 768022760, 859816005, 914661783, 430819711, 272774365, 530924681)
 
 
def _butterfly(a):
    n = len(a)
    h = (n - 1).bit_length()
    len_ = 0
    while len_ < h:
        if h - len_ == 1:
            p = 1 << (h - len_ - 1)
            rot = 1
            for s in range(1 << len_):
                offset = s << (h - len_)
                for i in range(p):
                    l = a[i + offset]
                    r = a[i + offset + p] * rot % _fft_mod
                    a[i + offset] = (l + r) % _fft_mod
                    a[i + offset + p] = (l - r) % _fft_mod
                if s + 1 != (1 << len_):
                    rot *= _fft_rate2[(~s & -~s).bit_length() - 1]
                    rot %= _fft_mod
            len_ += 1
        else:
            p = 1 << (h - len_ - 2)
            rot = 1
            for s in range(1 << len_):
                rot2 = rot * rot % _fft_mod
                rot3 = rot2 * rot % _fft_mod
                offset = s << (h - len_)
                for i in range(p):
                    a0 = a[i + offset]
                    a1 = a[i + offset + p] * rot
                    a2 = a[i + offset + p * 2] * rot2
                    a3 = a[i + offset + p * 3] * rot3
                    a1na3imag = (a1 - a3) % _fft_mod * _fft_imag
                    a[i + offset] = (a0 + a2 + a1 + a3) % _fft_mod
                    a[i + offset + p] = (a0 + a2 - a1 - a3) % _fft_mod
                    a[i + offset + p * 2] = (a0 - a2 + a1na3imag) % _fft_mod
                    a[i + offset + p * 3] = (a0 - a2 - a1na3imag) % _fft_mod
                if s + 1 != (1 << len_):
                    rot *= _fft_rate3[(~s & -~s).bit_length() - 1]
                    rot %= _fft_mod
            len_ += 2
 
 
def _butterfly_inv(a):
    n = len(a)
    h = (n - 1).bit_length()
    len_ = h
    while len_:
        if len_ == 1:
            p = 1 << (h - len_)
            irot = 1
            for s in range(1 << (len_ - 1)):
                offset = s << (h - len_ + 1)
                for i in range(p):
                    l = a[i + offset]
                    r = a[i + offset + p]
                    a[i + offset] = (l + r) % _fft_mod
                    a[i + offset + p] = (l - r) * irot % _fft_mod
                if s + 1 != (1 << (len_ - 1)):
                    irot *= _fft_irate2[(~s & -~s).bit_length() - 1]
                    irot %= _fft_mod
            len_ -= 1
        else:
            p = 1 << (h - len_)
            irot = 1
            for s in range(1 << (len_ - 2)):
                irot2 = irot * irot % _fft_mod
                irot3 = irot2 * irot % _fft_mod
                offset = s << (h - len_ + 2)
                for i in range(p):
                    a0 = a[i + offset]
                    a1 = a[i + offset + p]
                    a2 = a[i + offset + p * 2]
                    a3 = a[i + offset + p * 3]
                    a2na3iimag = (a2 - a3) * _fft_iimag % _fft_mod
                    a[i + offset] = (a0 + a1 + a2 + a3) % _fft_mod
                    a[i + offset + p] = (a0 - a1 +
                                         a2na3iimag) * irot % _fft_mod
                    a[i + offset + p * 2] = (a0 + a1 -
                                             a2 - a3) * irot2 % _fft_mod
                    a[i + offset + p * 3] = (a0 - a1 -
                                             a2na3iimag) * irot3 % _fft_mod
                if s + 1 != (1 << (len_ - 1)):
                    irot *= _fft_irate3[(~s & -~s).bit_length() - 1]
                    irot %= _fft_mod
            len_ -= 2
 
 
def _convolution_naive(a, b):
    n = len(a)
    m = len(b)
    ans = [0] * (n + m - 1)
    if n < m:
        for j in range(m):
            for i in range(n):
                ans[i + j] = (ans[i + j] + a[i] * b[j]) % _fft_mod
    else:
        for i in range(n):
            for j in range(m):
                ans[i + j] = (ans[i + j] + a[i] * b[j]) % _fft_mod
    return ans
 
 
def _convolution_fft(a, b):
    a = a.copy()
    b = b.copy()
    n = len(a)
    m = len(b)
    z = 1 << (n + m - 2).bit_length()
    a += [0] * (z - n)
    _butterfly(a)
    b += [0] * (z - m)
    _butterfly(b)
    for i in range(z):
        a[i] = a[i] * b[i] % _fft_mod
    _butterfly_inv(a)
    a = a[:n + m - 1]
    iz = pow(z, _fft_mod - 2, _fft_mod)
    for i in range(n + m - 1):
        a[i] = a[i] * iz % _fft_mod
    return a
 
 
def _convolution_square(a):
    a = a.copy()
    n = len(a)
    z = 1 << (2 * n - 2).bit_length()
    a += [0] * (z - n)
    _butterfly(a)
    for i in range(z):
        a[i] = a[i] * a[i] % _fft_mod
    _butterfly_inv(a)
    a = a[:2 * n - 1]
    iz = pow(z, _fft_mod - 2, _fft_mod)
    for i in range(2 * n - 1):
        a[i] = a[i] * iz % _fft_mod
    return a
 
 
def convolution(a, b):
    """It calculates (+, x) convolution in mod 998244353. 
    Given two arrays a[0], a[1], ..., a[n - 1] and b[0], b[1], ..., b[m - 1], 
    it calculates the array c of length n + m - 1, defined by
 
    >   c[i] = sum(a[j] * b[i - j] for j in range(i + 1)) % 998244353.
 
    It returns an empty list if at least one of a and b are empty.
 
    Constraints
    -----------
 
    >   len(a) + len(b) <= 8388609
 
    Complexity
    ----------
 
    >   O(n log n), where n = len(a) + len(b).
    """
    n = len(a)
    m = len(b)
    if n == 0 or m == 0:
        return []
    if min(n, m) <= 0:
        return _convolution_naive(a, b)
    if a is b:
        return _convolution_square(a)
    return _convolution_fft(a, b)

def taylor_shift(f,a):
    g = [f[i]*g1[i]%mod for i in range(len(f))][::-1]
    e = [g2[i] for i in range(len(f))]
    t = 1
    for i in range(1,len(f)):
        t = t * a % mod
        e[i] = e[i] * t % mod
    
    res = convolution(g,e)[:len(f)]
    return [res[len(f)-1-i]*g2[i]%mod for i in range(len(f))]

def cmb(n,r,mod):
    if r < 0 or n < r:
        return 0
    return g1[n] * (g2[r] * g2[n-r] % mod) % mod

"""
n項をまとめる方法のfpsを考える
まとめる過程はマージ過程を表す木+左右どちらにまとめたかで表すことができる
例えば(1,2,...,n)の表し方は複数あるが、左にまとめた山を左でまとめるのを禁止すると一意になり、特にマージ過程を表す木+最後のまとめが左右どちらかになる
結局葉がn個あり、子が存在するならば2つ以上存在する順序木の数え上げができればよく、このfpsをfとすると

f = x + f^2/(1-f)

が成り立つ。このfに対してk=1,2,...,nに対して [x^n](2f-x)^k を求めればいい
h = 2f-x と置くと -h^2+h = (1+h)x が成り立ち、g = x(1-x)/(1+x) とすると g(h) = x
(一般の)ラグランジュの反転公式から

[x^n]h^k = k/n[x^(-k)]1/g^n = k/n[x^(n-k)] (1+x)^n/(1-x)^n

となるので、(1+x/(1-x))^n = (-1+2/(1-x))^n のn項目までの列挙
これはmaspyさんのやつに載っていてpolynomial-taylor-shiftを利用してO(NlogN)でできる
"""

n = int(input())

def calc1(n,k):
    """
    sum_{j=0...n-k} (n+j)!(n-k)!/j!(n-k-j)!(n-k-1)!(k+j+1)!
    sum_{j=0...n-k} cmb(n-k,j) * cmb(n-k-1 + k+1+j ,n-k-1)
    sum_{j=0...n-k} [x^(n-k-j)](1+x)^(n-k) * [x^(k+1+j)] 1/(1-x)^(n-k)
    = [x^(n+1)] (1+x)^(n-k)/(1-x)^(n-k)

           
    """
    res = 0
    for j in range(n-k+1):
        res += cmb(n+1,k+j+1,mod) * cmb(n+j,j,mod) % mod
        res %= mod
    res = res * (k+1) * inv[n+1] % mod
    return res

def calc2(n):
    """
    2^m/(n+1-m)!m! * (n-k-m)!(m+1)!
    """
    f = [(g2[n+1-m]*g2[m] % mod)* (g2[m-1]*pow(2,m,mod) % mod) % mod for m in range(n+1)]
    f[0] = 0
    g = [g2[m] for m in range(n+1)]
    h = convolution(f,g)

    res = []
    for k in range(n):
        tmp = ((k+1) * inv[n+1] % mod) * ((g1[n-k-1] * g1[n+1] % mod) * h[n-k] % mod) % mod
        res.append(tmp % mod)
    res.append(1)
    
    return res

print(*calc2(n-1),sep="\n")
0