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)
    s = set()
    for d in a:
        while d > 1:
            x = p[d]
            s.add(x)
            d //= x

        for ds in s:
            check[ds] += 1
        s.clear()

    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)