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)

Solution (Python):
n=int(input())
A=[int(x) for x in input().split()]

u=10**6+1
C=[0]*u
D=[0]*2+[1]*(u-2)

def gcd(a,b):
   while a%b:
     a,b=b,a%b
   return b

n=4
while n<u:
  D[n]=0
  n+=2
i=3
while i*i<u:
  if D[i]:
    n=i*2
    while n<u:
      D[n]=0
      n+=i
  i+=2

for a in A:
  C[a]+=1
for i in [x for x in range(2,u) if D[x]==1]:
  if sum(C[i::i])>1:
    break
else:
  print('pairwise coprime')
  exit()

c=A[0]
for a in A:
  c=gcd(c,a)
  if c==1:
    break
else:
  print('not coprime')
  exit()

print('setwise coprime')

Solution (Python):
n=int(input())
A=[int(x) for x in input().split()]

u=10**6+1
C=[0]*u
D=[0]*2+[1]*(u-2)

def e():
  n=4
  while n<u:
    D[n]=0
    n+=2
  i=3
  while i*i<u:
    if D[i]:
      n=i*2
      while n<u:
        D[n]=0
        n+=i
    i+=2

def gcd(a,b):
   while a%b:
     a,b=b,a%b
   return b

for a in A:
  C[a]+=1
e()
for i in [x for x in range(2,u) if D[x]==1]:
  if sum(C[i::i])>1:
    break
else:
  print('pairwise coprime')
  exit()

c=A[0]
for a in A:
  c=gcd(c,a)
  if c==1:
    break
else:
  print('not coprime')
  exit()

print('setwise coprime')