import sys,time

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

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

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


mod = 998244353
N = 2*10**5
g1 = [1]*(N+1)
g2 = [1]*(N+1)
inverse = [1]*(N+1)

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

def mul(f,g):
    res = [0 for i in range(len(f)+len(g)-1)]
    for i in range(len(f)):
        for j in range(len(g)):
            res[i+j] += f[i] * g[j] % mod
            res[i+j] %= mod
    return res

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

N = 2*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 BM(A,L):
    """
    L+1項間漸化式を復元する
    """
    assert len(A) >= 2 * L


    # 初期化
    C = [1] # 求める数列
    B = [1] # 1つ前のCの状態を保存
    L = 0   # Cの長さ-1
    m = 1   # ポインタ？っぽいもの
    b = 1   # 前回のdの値


    for n in range(len(A)):
    
        #d = C[0]*A[n] + C[1]*A[n-1] + ... + C[L]*A[n-L]
        d = sum(C[i]*A[n-i] % mod for i in range(min(n,len(C)-1)+1))

        if d == 0:
            m += 1

        elif 2 * L <= n:
            T = C[:]
            for i in range(len(B)):
                if i+m < len(C):
                    C[i+m] -= d * pow(b,mod-2,mod) * B[i] % mod
                    C[i+m] %= mod
                else:
                    C.append(-d * pow(b,mod-2,mod) * B[i] % mod)

            L = n + 1 - L
            B = T[:]
            b = d
            m = 1

        # ③拡張しない場合
        else:
            for i in range(len(B)):
                if i+m < len(C):
                    C[i+m] -= d * pow(b,mod-2,mod) * B[i] % mod
                    C[i+m] %= mod
                else:
                    C.append(-d * pow(b,mod-2,mod) * B[i] % mod)
            m += 1   
    
    return C


def bostan_mori(P,Q,N):
    """
    [x^N]P(x)/Q(x)を求める
    """
    d = len(Q) - 1
    z = 1 << (2*d).bit_length()
    
    iz = pow(z, _fft_mod - 2, _fft_mod)
    while N:
        """
        P(x)/Q(x) = P(x)Q(-x)/Q(x)Q(-x)
        """
        P += [0] * (z-len(P))
        Q += [0] * (z-len(Q))
        _butterfly(P)
        _butterfly(Q)
        dft_t = Q.copy()
        for i in range(0,z,2):
            dft_t[i],dft_t[i^1] = dft_t[i^1],dft_t[i]
        
        P = [a*b % mod for a,b in zip(P,dft_t)]
        _butterfly_inv(P)
        Q = [a*b % mod for a,b in zip(Q,dft_t)]
        _butterfly_inv(Q)

        P = [a * iz % mod for a in P][N&1::2]
        Q = [a * iz % mod for a in Q][0::2]

        N >>= 1
    
    res = P[0] * pow(Q[0],mod-2,mod) % mod
    return res

def taylor_shift(f,a):
    """
    f(x+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 poly_in_exp(f,M):
    from collections import deque
    """
    f(e^x)をM次まで求める
    a_ne^nx->a_n 1/(1-nx)
    """
    deq = deque([])
    for i in range(len(f)):
        deq.append(([[f[i]],[1,-i % mod]]))
    
    while len(deq) > 1:
        fq,fp = deq.popleft()
        gq,gp = deq.popleft()

        hp = convolution(fp,gp)

        hq0 = convolution(fq,gp)
        hq1 = convolution(gq,fp)
        hq = [0] * max(len(hq0),len(hq1))
        for i in range(len(hq0)):
            hq[i] += hq0[i]
            hq[i] %= mod
        for i in range(len(hq1)):
            hq[i] += hq1[i]
            hq[i] %= mod   
        deq.append([hq,hp])     
    
    fq,fp = deq.popleft()

    res = convolution(fq,inverse(fp,M+1))[:M+1]
    for i in range(M+1):
        res[i] *= g2[i]
        res[i] %= mod
    return res

def inverse(f,limit):
    assert(f[0]!=0)
    f += [0] * (limit-len(f))
    l = len(f)
    L = 1<<((l-1).bit_length())
    n = L.bit_length()-1
    f = f[:L]
    f+=[0]*(L-len(f))

    res = [pow(f[0],mod-2,mod)]
    for i in range(1,n+1):
        h = convolution(res,f[:2**i])[:2**i]
        h = [(-h[i]) % mod for i in range(2**i)]
        h[0] = (h[0]+2) % mod
        res = convolution(res,h)[:2**i]
    return res[:limit]

def integral(f,limit):
    res = [0]+[(f[i] * inv[i+1]) % mod for i in range(len(f)-1)]
    return res[:limit]

def diff(f,limit):
    res = [(f[i+1] * (i+1)) % mod for i in range(len(f)-1)]+[0]
    return res[:limit]

def log(f,limit):
    res = convolution(diff(f,limit),inverse(f,limit))[:limit]
    return integral(res,limit)

def exp(f,limit):
    l = len(f)
    L = 1<<((l-1).bit_length())
    n = L.bit_length()-1
    f = f[:L]
    f+=[0]*(L-len(f))

    res = [1]
    for i in range(1,n+1):
        res += [0]*2**(i-1)
        g = log(res,2**i)
        h = [(f[j]-g[j])%mod for j in range(2**i)]
        h[0] = (h[0]+1) % mod
        #res =convolve(res,h,2**i)
        res = convolution(res,h)[:2**i]
    return res[:limit]

def poly_pow_exp(f,k,limit):
    l = len(f)
    L = 1<<((l-1).bit_length())
    n = L.bit_length()-1
    f = f[:L]
    f+=[0]*(L-len(f))

    g = log(f,limit)
    g = [(k * g[i]) % mod for i in range(len(g))]
    h = exp(g,limit)
    return h[:limit]

def poly_pow_rec(_f,k,limit):
    f = _f[:] + [0] * (limit+1-len(_f))
    g = [0] * limit
    g[0] = 1
    for n in range(limit-1):
        if n+1 > (len(_f)-1) * k:
            break
        for i in range(n+1):
            g[n+1] += g[i] * f[n-i+1] * (n-i+1) % mod
            g[n+1] %= mod
        g[n+1] = k * g[n+1] % mod
        for i in range(1,n+1):
            g[n+1] -= (n+1-i) * g[n+1-i] * f[i] % mod
            g[n+1] %= mod
        g[n+1] = g[n+1] * inv[n+1] % mod
    #print(_f,g,k)
    return g

def solve_brute(N,K,A):

    memo = {}
    def sub_solve(g):
        if g in memo:
            return memo[g]
        
        block_size = K//g
        f = [0] * (g+1)
        f[0] = 1
        for a in A:
            a //= block_size
            a = min(a,g)
            ff = [g2[i] for i in range(a+1)]
            f = mul(f,ff)[:g+1]
        ans = f[g] * g1[g] % mod
        memo[g] = ans
        return ans
    
    res = 0
    for i in range(K):
        res += sub_solve(gcd(i,K))
        res %= mod
    #print(memo)
    res *= inv[K]
    return res % mod

def solve_fast(N,K,A):

    a_freq = [0] * (K+1)
    for a in A:
        a_freq[min(a,K)] += 1

    memo = {}
    def sub_solve(g):
        if g in memo:
            return memo[g]
        
        block_size = K//g
        tmp_freq = [0] * (g+1)
        for a in range(1,K+1):
            tmp_freq[a//block_size] += a_freq[a]

        f = [0] * (g+1)
        f[0] = 1
        for a in range(1,g+1):
            ff = [g2[i] for i in range(a+1)]
            ff = poly_pow_exp(ff,tmp_freq[a],g+1)
            f = convolution(f,ff)[:g+1]
        ans = f[g] * g1[g] % mod
        memo[g] = ans
        return ans
    
    res = 0
    for i in range(K):
        res += sub_solve(gcd(i,K))
        res %= mod
    #print(memo)
    res *= inv[K]
    return res % mod


def calc(K):
    res = 0
    for g in range(1,K+1):
        if K % g == 0:
            res += g**2
    return res

N,K = mi()
A = li()
print(solve_fast(N,K,A))