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 + 1):
        if p[i] == 0:
            j = i
            while j <= N:
                p[j] = i
                j += 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(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)
    stack = np.empty(20, i8)
    p_stack = 0
    for d in a:
        while d > 1:
            x = p[d]
            for i in range(p_stack):
                if stack[i] == x:
                    break
            else:
                stack[p_stack] = x
                p_stack += 1
            d //= x

        while p_stack > 0:
            p_stack -= 1
            check[stack[p_stack]] += 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)