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.

Solution (Python):

"""

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)
    for d in a:
        cl = []
        while d > 1:
            x = p[d]
            if x not in cl:
                cl.append(x)
            d //= x

        for cd in cl:
            check[cd] += 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)