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.



Sample input:
3
3 4 5


Sample output:
pairwise coprime

Submission status: Time Limit Exceeded

Solution code:
# compiled by pythran
"""
#pythran export main2()
import numpy as np
i4 = np.int32
i8 = np.int64


def get_primes(p):
    N = p.shape[0]
    n_max = int(np.sqrt(N - 1)) + 1
    p[0] = 1
    p[1] = 1
    for i in range(2, n_max):
        if p[i] == 0:
            j = i * 2
            while j < N:
                p[j] = 1
                j += i


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 = np.zeros(p_max + 1, i8)
    get_primes(p)
    primes = np.where(p == 0)[0]
    a_start = 0
    while a[a_start] == 1:
        a_start += 1
        if a_start == a.shape[0]:
            return 0
    a = a[a_start: ]
    b = a.copy()

    num = a.shape[0]
    for i in range(primes.shape[0] - 1, -1, -1):
        count = 0
        for j in range(a.shape[0] - 1, -1, -1):
            if a[j] < primes[i]:
                break
            if a[j] % primes[i] == 0:
                count += 1
                b[j] = a[j] // primes[i]
                while b[j] % primes[i] == 0:
                    b[j] //= primes[i]
                if b[j] == 1:
                    num -= 1
        if count > 1:
            return 1
        if i % 1000 == 999:
            if num > i:
                return 1
        b = np.sort(b)[b.shape[0] - num:]
        a = b.copy()
    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)

Submission status: Time Limit Exceeded

Solution code:
#!usr/bin/env python3
from collections import defaultdict, deque, Counter, OrderedDict
from bisect import bisect_left, bisect_right
from functools import reduce, lru_cache
from heapq import heappush, heappop, heapify

import itertools
import math, fractions
import sys, copy

def L(): return sys.stdin.readline().split()
def I(): return int(sys.stdin.readline().rstrip())
def SL(): return list(sys.stdin.readline().rstrip())
def LI(): return [int(x) for x in sys.stdin.readline().split()]
def LI1(): return [int(x) - 1 for x in sys.stdin.readline().split()]
def LS(): return [list(x) for x in sys.stdin.readline().split()]
def R(n): return [sys.stdin.readline().strip() for _ in range(n)]
def LR(n): return [L() for _ in range(n)]
def IR(n): return [I() for _ in range(n)]
def LIR(n): return [LI() for _ in range(n)]
def LIR1(n): return [LI1() for _ in range(n)]
def SR(n): return [SL() for _ in range(n)]
def LSR(n): return [LS() for _ in range(n)]

def perm(n, r): return math.factorial(n) // math.factorial(r)
def comb(n, r): return math.factorial(n) // (math.factorial(r) * math.factorial(n-r))

def make_list(n, *args, default=0): return [make_list(*args, default=default) for _ in range(n)] if len(args) > 0 else [default for _ in range(n)]

dire = [[1, 0], [0, 1], [-1, 0], [0, -1]]
dire8 = [[1, 0], [1, 1], [0, 1], [-1, 1], [-1, 0], [-1, -1], [0, -1], [1, -1]]
alphabets = "abcdefghijklmnopqrstuvwxyz"
ALPHABETS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
MOD = 1000000007
INF = float("inf")

def lcm(a, b): return a * b // math.gcd(a, b)

sys.setrecursionlimit(1000000)

def main():
    N = I()
    A = LI()

    cummul = reduce(lambda x, y: x*y, A)
    cumlcm = reduce(lambda x, y: lcm(x, y), A)
    cumgcd = reduce(lambda x, y: math.gcd(x, y), A)
    if cummul == cumlcm:
        print("pairwise coprime")
    elif cumgcd == 1:
        print("setwise coprime")
    else:
        print("not coprime")

if __name__ == '__main__':
    main()