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

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