Problem:


Score : 500 points

Problem Statement

We have N integers. The i-th number is A_i.

\{A_i\} is said to be pairwise coprime when GCD(A_i,A_j)=1 holds for every pair (i, j) such that 1\leq i < j \leq N.

\{A_i\} is said to be setwise coprime when \{A_i\} is not pairwise coprime but GCD(A_1,\ldots,A_N)=1.

Determine if \{A_i\} is pairwise coprime, setwise coprime, or neither.

Here, GCD(\ldots) denotes greatest common divisor.

Constraints

2 \leq N \leq 10^6

1 \leq A_i\leq 10^6

Input

Input is given from Standard Input in the following format:

N
A_1 \ldots A_N

Output

If \{A_i\} is pairwise coprime, print pairwise coprime; if \{A_i\} is setwise coprime, print setwise coprime; if neither, print not coprime.

Sample Input 1

3
3 4 5

Sample Output 1

pairwise coprime

GCD(3,4)=GCD(3,5)=GCD(4,5)=1, so they are pairwise coprime.

Sample Input 2

3
6 10 15

Sample Output 2

setwise coprime

Since GCD(6,10)=2, they are not pairwise coprime. However, since GCD(6,10,15)=1, they are setwise coprime.

Sample Input 3

3
6 10 16

Sample Output 3

not coprime

GCD(6,10,16)=2, so they are neither pairwise coprime nor setwise coprime.



Sample input:
3
3 4 5


Sample output:
pairwise coprime

Submission status: Accepted

Solution code:
# compiled by pythran
"""
#pythran export main2()
import numpy as np
i4 = np.int32
i8 = np.int64


def factorization(N):
    p = np.zeros(N + 1, i8)
    n_max = int(np.sqrt(N)) + 1
    p[0] = 1
    p[1] = 1
    for i in range(2, n_max):
        if p[i] == 0:
            j = i
            while j <= N:
                p[j] = i
                j += i
    for i in range(n_max, N + 1):
        if p[i] == 0:
            p[i] = i
    return p


def gcd(a, b):
    '''Computes the greatest common divisor of integers a and b using
    Euclid's Algorithm.
    '''
    while b != 0:
        a, b = b, a % b
    return a


def solve(A):
    a = np.sort(A)
    g = a[0]
    for i in range(1, a.shape[0]):
        g = gcd(a[i], g)
    if g > 1:
        return 2
    p_max = a[-1]
    p = factorization(p_max)
    primes_num = 0
    for i in range(2, p.shape[0]):
        if i == p[i]:
            primes_num += 1
    a_start = 0
    while a[a_start] == 1:
        a_start += 1
        if a_start == a.shape[0]:
            return 0
    a = a[a_start:]
    if len(a) > primes_num:
        return 1
    check = np.zeros(p_max + 1, i8)
    s = set()
    for d in a:
        while d > 1:
            x = p[d]
            s.add(x)
            d //= x

        while s:
            check[s.pop()] += 1

    if check.max() > 1:
        return 1
    else:
        return 0


def main(in_file):
    stdin = open(in_file)
    stdin.readline()
    A = np.array([int(x) for x in stdin.readline().split()])
    ans = solve(A)
    p = ['pairwise coprime', 'setwise coprime', 'not coprime']
    print(p[ans])


def main2():
    main('/dev/stdin')
"""
import os, sys
p = os.path.dirname(__file__)
pymain = os.path.join(p, "a.out")
if len(sys.argv) == 1:
    import subprocess
    if p == "":
        pymain = "./" + pymain
    subprocess.call(pymain)
else:
    import gzip, base64, stat
    gz = b'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'
    bin = gzip.decompress(base64.b64decode(gz))
    with open(pymain, "wb") as f:
        f.write(bin)
    os.chmod(pymain, 0o775)