Buggy python submission (verdict: Time Limit Exceeded):
import numpy as np
import sys
i4 = np.int32
i8 = np.int64
u4 = np.uint32

if sys.argv[-1] == 'ONLINE_JUDGE':
    from numba.pycc import CC
    from numba import njit
    from numba.types import int64, Array, uint32
    cc = CC('my_module')

    @njit
    def factorization(N):
        p = np.zeros(N + 1, u4)
        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

    @cc.export('solve', (Array(uint32, 1, 'C'),))
    def solve(A):
        a = np.sort(A)
        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, u4)
        s = set()
        for d in a:
            while d > 1:
                x = p[d]
                s.add(x)
                d //= x

            while s:
                check[s.pop()] += 1

        if check.max() > 1:
            return 1
        else:
            return 0
    cc.compile()
    exit()


def main(in_file):
    stdin = open(in_file)
    stdin.readline()
    A = np.fromstring(stdin.readline(), u4, sep=' ')
    ans = solve(A)
    if ans:
        g = np.gcd.reduce(A)
        if g > 1:
            ans = 2
        else:
            ans = 1
    p = ['pairwise coprime', 'setwise coprime', 'not coprime']
    print(p[ans])


if __name__ == '__main__':
    from my_module import solve
    main('/dev/stdin')

Fixed submission:
# 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] = i
                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: ]
    if len(a) > len(primes):
        return 1
    check = np.zeros(p_max + 1, i8)
    stack = np.empty(20, i8)
    p_stack = 0
    for d in a:
        x = p[d]
        while x > 0:
            for i in range(p_stack):
                if stack[i] == x:
                    break
            else:
                stack[p_stack] = x
                p_stack += 1
            d //= x
            x = p[d]

        for i in range(p_stack):
            if stack[i] == d:
                break
        else:
            stack[p_stack] = d
            p_stack += 1

        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)

Buggy python submission (verdict: Time Limit Exceeded):
# 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)


Fixed submission:
# 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] = i
                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: ]
    if len(a) > len(primes):
        return 1
    check = np.zeros(p_max + 1, i8)
    stack = np.empty(20, i8)
    p_stack = 0
    for d in a:
        x = p[d]
        while x > 0:
            for i in range(p_stack):
                if stack[i] == x:
                    break
            else:
                stack[p_stack] = x
                p_stack += 1
            d //= x
            x = p[d]

        for i in range(p_stack):
            if stack[i] == d:
                break
        else:
            stack[p_stack] = d
            p_stack += 1

        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)

Buggy python submission (verdict: Time Limit Exceeded):
# 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)


Fixed submission:
# 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] = i
                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: ]
    if len(a) > len(primes):
        return 1
    check = np.zeros(p_max + 1, i8)
    stack = np.empty(20, i8)
    p_stack = 0
    for d in a:
        x = p[d]
        while x > 0:
            for i in range(p_stack):
                if stack[i] == x:
                    break
            else:
                stack[p_stack] = x
                p_stack += 1
            d //= x
            x = p[d]

        for i in range(p_stack):
            if stack[i] == d:
                break
        else:
            stack[p_stack] = d
            p_stack += 1

        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'H4sIACEYU18C/+29C3xTVbYwftIX4dUTELE81KgRW4XS0AINVmxpA+dACh1akfEVSprSjGlTmxMeCoKT1uEYgvgYh5lP5+LcGceZ6+8OM+Mw6MxAy6sgyktAHgqoCAkBqTCWAkK+tfbeJz1JE14z/3u//+9nlO6z19l77fXea++zT7LQbBmfpNFwyieJG8thbdGgQlIvZPBlf4k0AVg+1x3+3s7dxqVBPUXVrpArjCo7GGql1LJ2yaxffhKt5ycVRpWDWTul1KjKVE79KYwq6xdxUSXH6SP9kNbMJRSauWRGVLmCId2RFt0vifUL+Ck04J8RVXJJXFSp8JfC/lUweAXjSylLWLsSVXv8lH0lVeH1vnm0vm9eYVT5Lmv3bky/H0C/NO7aPzpWTmXjJZLLvmQuqlT0MNzpmDkqb7izapjTUeeZO2xu/qhho/Ky3a7sEYQmHWs7YfJDEX0nqWjux2B43/Hr/pduvVi+Y9Aj3w089OY421+GlhzAe7msTzf415ium9Erq3sS9lzUi9PgvdHw73749/uXCta9bvvHxh7PLbP++dPcPUO/sT6xu3yFd3GTrf+TP3132C8H16/ut7xX8VzxyVVZ5j2WK8nlFAixbxw4qiMevCElPvwPSfHhA7vFh+do4sP7JoALCeh8PQH8bAL4TQnoWZqgfUZqfPhXCeB/S4AnO8G4byfg944E7XsmaD8yQfu/JtDXpATtxQT4/zuBPZgT6P14AjnMTDDuTcnx4QUJ2msSwH+WgP4lCeg8n6D9gwnoyUogT20Cej5OMG7vRPQnsKt/JBi3OAGe5xLQvz2BHnslGHdbAj1eSNC+NsG4ZQno1EVmnujPfQnw7AT4HdzN3GYWr5W+TRyFBxh8OoOvS6LwQjZ/KfPAwwxPGYNnMPhk4KsPtmd4lPg/kvE7Y0BhFD19mF70g6Lhj6ZS/DUMvzI9j2Z0Ckui4WPTcNyB3JGXGJ1s4Cqmr5UDo/HfzuCFMeNyME+5pSrbfffh3DSKs1pn1brqrG6pskGyWjnrI5PLpXyHy22dWem254l1DqnEaJ4N8Ipp5ZJxJEAdNmjdYK+snempFm0ANNpqKhusUkOlQ3KLNrMZcc6da623N7hddZVOhzTPOjuHYHbl1nsks41gm2zMsVptc+dWznTMNo6Aa7fDanNWut1WaV693eqoq3aZsdOkcmmkDUGA2phrLbXOcVTZ6+C+QyJ0Ab0mShVQHZeekTanvbLBDHdGWEXgDP5JlZId+lY5yxBFuYTjSzUNrjlWp71ullRjtTc0uBrKJgGpQGOltdLpdNmgj9U+12avlxyuOtLNOMpqdVFhAEFue4MUl4CpAMulNLLWYoW1IsdqLh9lLZtUnmt1Uj5G4SBOOxM4AEYTCRmNRlNnb0fdLIYjzlDllThesRGZzSXMTqm319W6quxMlBGMIyKaBISJURlzqx11VdZqR4NbsrqqzSCS2toY4q4V1QhQns1VB+08Nslca6OSz1Mk7/LgCNaGyrpZdmt1rQRDPR1jjyUjqCmKoHO1Jm6AmtENdtDXbLu5NqJJSsbMyiqrrdIt4UB1c2qngtBMdS56T4rnH8XGCFHG3AZPneSotVP7YdYZoZSRf0N6GK3SA5Cj1kVFxY2biuJinUTmzamplLoY4LWSmQM6brDXOyttINlapPDGEJkAT2U92G6VmSEBXdyo7EYR2UFsiRUd8W2iWmoEpkgUcM0CxJ1BAFmIUS7zUiCHWQeNairyRqrJSxAsVVroMgIY1tRJNyA7M2OsusEeHbBuUA82MCHJbp5aS10lzwae+i9o1e12zKoDzsrz6HTjGlnt9LhriDQxIMd4HMxXtXSqcj/VQKqzbDASgoyc9aG6OajbqXa3p9aON220bbULbIerluxOiK04JsRma3Wlw8kBjZLLyVXbXdVc9Sy75OZsJLrjDQj+SsCX7HMdEldrr7XVz+OqbU6X287V0oYArHXNtnMzbbX10BwHZfNnbaUDBgWxcxOKi6252TncBIs4rtg6IntE9kh6PX06wPOyR+REVY3GqKqJg6uicaLVmJ0b3S2qXQS7MY+sDJNhJZkMZWrkCmHJUE+DNWMKqdF/Wq47qSdFWvUgPRBG/9G/2Lcnw8WR+6kEI/0vhWFPJnmQhmtO68zXyobM6wmrEW5WN2WdrOH+qLrvGejojjsJrsj9NJJL4f16/NfxMxiyB/dMNwqrJPXu3AusLi/MSsLdhZ+z+sd/eDIVd0J+y+pPkfYp3J9YXcp6WYsctETGS+I+S+vM91596bU0XFl/xNpT+pIi9AVepHkX7inkqNKpNhXcqN6FYflbKsvpIutUFfx+FXyzCp6rggdU8Dw1fn8nfKQ6j1bBR6ngNSr4GBVcyScRXqBe1y6j8G6qHJfk0yp4kgqeqYInq9fxKniKCp6vgqv3rwpVcPX+jaCCd1Pzq4JrVfDpKnh3FXyGCt5DLR8VvKcKXq+C91LB56rgvVXwRSp4ugq+WAXnVfBlKrhOBV+ugvdRwVeo4Or1z9sq+E0q+EoVvJ8KvloFv1kFb1bB89X2qYL3V8F3qOC3qOD7VPAMtb2p4APUdq6CD1T7lwo+SAXvUMEHq9c3L3XCb1Wvv1Xw29TrSxX8drX9q+Dq9adeBb9Dbf8q+J1q+1fB71LbvwpuUNu/Cn632v5V8CFq+1fB71HbvwqeqbZ/FTxLbf8q+ANq+1fB71Xbvwp+n9r+VfChavtXwYep7V8Fz1bbvwo+XG3/KrhJbf8q+Ai1/avgY9Xy9J7UCr7US3P0nNDULCWFdwjeDdr1XHhk0lw9F747Bf7ytxfCFdZrsEvwSBg+d1+ALjUYEoM7SP0M1jEUBptJ/QTWMQQGV5L6F1jH0BdcQeoHsI4hL7iM1HdhHUNdcBGpf4B1JDdYT+rrsI6hLTiD1N/HOoa0YBmp/wnrGMqChaT+e6xjCAvmkPqvsI6hK6gn9V9gHUNWUEfqL2MdQ1WQI/UXsI4hKth2Ges/xrqO8E/qT2O9D+Gf1Buw3pfwT+o/wvpNhH9Sn4n1foR/Un8E6zcT/kl9Ktb7E/5JfSLWbyH8k/o4rGcQ/kl9DNYHEP5JfQTWBxL+Sf1erA8i/JP6nVgfTPgn9YFYv5XwT+p9sX4b4Z/Ue2D9dsL/JawnYV1P+Cf1C7Ohfgfhn9TPYP1Owj+pn8D6XYR/Uv8C6wbCP6kfwPrdhH9S34X1IYR/Uv8A6/cQ/kl9HdYzCf+k/j7Wswj/pP4nrN9L+Cf132P9PsI/qf8K60MJ/6T+C6wPI/yT+stYzyb8k/oLWB9O+P+O6B/rOYR/Un8a60bCP6k3YH0E4Z/Uf4T1XMI/1MvWfvPyDC6wB0S7Fvei1mD1/WbwysAfYbjp66uz+dsbiUvyt5dADnVKlHc+IchfCN6jbWUVYmsz5yjkhNaWZTVYtNYPBp+8eayk5/5J2p+NQlDIrd4MqIUH8K9002ri6eDe3dciIHyketl60quzzxrS/rmvSdFyOVmQ24SWwIOCZpOw87LUJ4KhG8MQ23/RA+8Bv5znlocE+avAsyCATam/BYjmcdoytn1oaTS9RdPKywT5yzXNyKV8OHASNI0lthB8gw1C00EpU/AW68OeOwTf/YK/WCeYWqXUgBeEiw2fwbI1hcwhxmYMYo8WPbFelDddsbeZ9X5A1dsinwucAp2oCeQeKnq4aFrRQ0UV5cJzJ/HhpChfFvzzDcKas65CLvAAkttaYiBBdlOJoYZbi6ayBtPfAPoLyi5TkFsEb4tOkLVCayHJ0jYV6qBfoR7jKJRkhgolAf3vhelnoSCXGOoF2WKYK7Q2GpRtUIvPaRAs8juGZVApbTolpYpes14j+tIEudGwnCkrVfACahUl0xglYwV5veBtBkqS15Ch5cPvY+gM/OYiyqGY0VNM6BHk1w0r6DCejODTEHKMB1UwvnEWgERfLVfywgJubUZksOB3ZDC+8XWCZL3g92iKFoUfEnjzt4L/7wa0qUDKPdg3jbS7fQDq+h1yQ0DE6QSQL/g8XGAQUiYnC8Bm2DNE8C3gBL+ZaXHhBbx3OjAHS5DSamVyay3WMk5ITonIFzGOFtFBPANCBcBO1xvS3TCUNuy5jQzVFIZhMtkwOix9SsuDfOM/kE7v/Rzf+CFcbWo0LMbVguD7O1EPdOYbi0EuazrAuN/DKdK4K/A3ROLtgFv4BFmloz9dpDrKEuSNVEdmYMBMzcWM5mJm6jEr5nIKbDrwSEc4jObuBytEo5lBzFDwPYbm44Q/4AN66gMDonyg6Dz1AROUFlkyzBWJjO8SgXExIuNB5O7pwE3YGixdIAIFNa4kpWSYEcgcgiJ+nUJgtKF0tLujRvu4g462tYPqaqWiK3Cm6RTPkbtpNXPNPvStzw1o71WG6aVNR/nGCzBFit75Bi1I++9Ep48ZMokWZhFryRV8k7QW+TiIuOO8IuK38YmdvKnUV2DobCG0rkMPC7xJ+N+oUsHwC8xyV5B+G0o1ly2aS6gMUU4xWLK+FbwbNYLMLNV7Sddwl+B/l9ToyGvOoy5SYLAlBCqC/+rQqJzQWuOZXvRC+mJtyCp6Swx6jcKBJz/4i2hT+O15agqDgQZldBFkT2yhCPSr40LDzcajVM4zQgWC3GqR2wX5RKDgHNpoIxsduoi+XoZNKRoO+xP7wQvq36T3nVFaGnaOaunuc1TbpOHazAhlvRllw0Cq1fzDzdV8H4xf1MtaCzNYPCMrBlCbp09wHKQ6ZmM70eEocg3mlGewyFWGjNDgZaL8OKh1o7ZUrtNafPNBWt5LoLiBkDCuOQhW8F4KFa5FvhBYRIjbSOCRQPK3O6nxrVaccvotanNAmQRIvxLEDRxb/O8YNuMOR7R95rZTzu9rj44los9i0AryZktWB//j/v0wVjgNWkvWJQuzAxHnGVNA4McHUNPdCRFf3kQbZlrkADGULIVgNPVMMJVXCFF0HAHGgEliCaELkdxj0VwkFqN7oScYTEbnlASaFuRDyNXUb1HTJdix0dBMsFCjQ22rjOmRc1RlIztjv9LSYgoKMKFoJZMg90SrIePLxbrQiMh4vr+zpqdp09RAdxjYeDA0gc7sRbzOzBEz8DEy5GKtIhsUZDMT5CaKnoudZkqb2vnGnZBAl/rqtKX31+n4puMwXZX6RoHsLFltGA5bLiUDC4IceI/05EtaLP6CzMhkebto2m7hJ5wXNAELP+58KUTtvhP9BZfIwvrpf6JPbgglg8OI8uCTFjloyfrGIs836KUfwrTcIW9uCSR7mzWC90g3qcq4y9jeKe5loq9grygX7BZbU/ZSp9nUIXjXaQTTDs+XFkjTss4I/h91WCBtAlK95/PdI4KtyJUfhEUkRqwamWzqD8QHB2jQnr/ADtCa//EvCNJ1HYK8B/MhwQfGVgp5ApO+gFWdkjZAHfRXmhFxOZ+QAXU9uh6r66Geqbgg1ME8SnOUFAPqOVDPh3oOq+dDvRDq+ZS7jYVgI4UWU4BvegokWyqfCxk6xVG9DF1EexYk2kp2oURvq7bUdEnKFH23Y+g/TYNKQOVaHwX2nQmHQxAu0F5gBAhnp6IkI2kx9foTtLL48gyl8hAMBmjaetGfskGUx+twGsggkaHpGEyTJH6GPbeDcgyiHx2buHA9YpCPB2rOoPnUaUXv+AxIjbJV8av0nyzCv9KXRPirxTAx65RFDgktF5OF1YqxCab1JHrxP5nVl2VCLqLU04H0M2QuRrpo6vDZNyhDZEqCUOxPOYQxWYd9lo6DPmLLqWSIfhoLZpShOyDftMi5gne9VpQfA/OBuAh3gd1LGn5pqgb1ZeYI+Sd5rIDuD1vkyzSFGwFjC6bHDAIkXjyZ9kSYYLTi/Y8ZdJ63MRjpRB9MTUCAXsiCDPlTseU7cKuDFvlIxK0m+ofqO91KMH0k8BOCFs0RgR8XxMDUV/AXnCJuddc3yGorulV4kj/lOOLVTpHzjiNGnwTOlW6Rw2RWOhAYj7T5CEvrtaBxok+q9r+1oYRAj8gxSOg0kRDah45IJUoIL168jKrNM4R6wJwCQSjZIp9dq4+ot+4MU++gPkS9lqyzaLDdKK04jeLsqSVzoE6ZDKnngPGqCP7pNzS4gtEVZoQ9t0SstFcb3phvyAgkkauhMNeboVUzBHMokQlpgFKVJiDT03GOrwu+TPztcUhpPFqL6Szf5NfQYbVI4runGTaworfJdSdWz3tR/X6q7jeftC0wBNynqYhxNMgh48ztPzhNZzjxNJ3bpyuZHEbqNVshRw3UDeK4f2gYGAP3mk8R/PAgmm9p/5HE7mEkX3MS790/KCpBONnGNIAH1UR5Y6yDkXVC4NjXqBCwZbIwyhAxxPlepVOGDAnTLlzCjMMU6VU2p5Rpo6F0pipTtQUkWrHVnEHVaibuG3yRUy8+wb9QvsLXKIBkGvDA10yH+MZfJyOL96GNh+ha46h6rbE5cBN0CrUsw/7aOP3nXa3/x6ewv9l4SmWua08zYTnTicOidh4lokkr2jQezXQ8s9LxipHyjR29kPONYKUkLGHmdzlgBuzvo88K8r5ofnd24GzV1OyBtVfeZwSL5mvggrh8YNg9SDa4BJKx9A1EDYsuSJ61fBNZTcrnitYSpXvD4H5bumGce8fwNknKwIfBkT/riITGhaJpA9/4aG+gGFxXs0ZLMMBEG3joJHK1UcX7+q8Z7/W9EVfmJpjh0EiQrDMif+8GZU4DC9pSzWe3kAyuYdIZ/gfNoYmE+tDNOB+1ggVBUn5ZMO3nG6eEid9+BKJql74k+j4n/Fw4d1mQ9wotlx8U7oCrDkGz13Ms1BoIh8LhMwI/VL3sNm3mG39ORDzUgEnr4mPdiT2Rp0B84z5ao8tLb0cS779IZAHhHQIhBCXM5cCYUwx0vpvoLWjTeNJF7yYNOK+nLehIUyTuBWYBhpkAX7oeMh3jLhTIf5QYygR+yg5BftWQk4SjFOk0GA/1kdg7mkizVSVNzykmzZt7ITX5amlGkoMmmMsgt65NQWM7LPgHE2wWTchsbLbIJwT5Uqg3pCJawXQA1raEwTGc55Qg77RkBQV5G9kfatoi8gIkZWglG7Wh7aK/Vv8+Wsga9G91shCZ7yFnApzrBP9UDWR6l3H+nr8HEvAvAbPQEhzLN/XsgWGloK/FdHzhR4IcVOe7fQTNJ4LmG7j17JnQSpQsTnzgCU0bYLkWbCWL8k7gcQSeIEDIlbIu0dxxC8RCmJB7Qg4c2H+CuAnJpNcH7mc13yRw1HeIxKkKvC0AF3R842MpNEPXWeSLqAxIQrwEZEKUukXztJynR8CLeIju5e2BY0GlomgaOj1BOpG0kO4s9Fy0ALqmBoQTdH9hpdKraC05cj/fUMbxTfm4FCKe5ENP6hmM1f28ENP9kB5ddS8jK8wAVH3MSp8z3Uk0WYsr3sBhJEBuAaPEzYdNZsRjZjZkJij4zNQLkClCQIcrfADB9ynUwWUqvdTDZQ96mQOXPL0shMt+9LIMLgfQyxlweRu9rIfLu8jlokXE4zNTDSYs+yzCjRQLP/msBZdhoq8IjKkZQn2RDqZ29JeDAi9+LMohfulKkh+lGCDxb7P4Uz4xQhpSYdCHBkbHQ0hYLaZWvnEBaT4a9zub2uf167S3NotmB7+4LRXXPEf4F4+n4hLPuZHsTC310yj87C3GZpbPmoa2SMVIFKSFnm/g3tzBaE7ei/3m3xFcnxo9/UB+770ItP5XKtp7cT8IMjABrtcwQiBQXYJFjnBun5AF4eoChKt1EKs6b8EM0Q3VJldCrCLPP5CYgYQYRr8pr0UqTERPbgJ6BkXoyaP0gCctRU/FfvyL36Vg40V0xVCyvwNImNediGlLChNTMwYVb0orrKLWhwEVlB2ahTuDSxCJklw3gRfg+id4HyYHVJlhVKbwt0j8xUDHN26D6VQOeI9AdvxdMu89m0ymWo1xC65kbxf9vbYbd1H5+/J2CmCvvOWQsiw7CYEY6NYU8f/RKnqLdZAHiZAH4aKwaNEz4HE9LfLBwMfH0I8g+T0cyD9Gk70y4qgePW49Es+eSzz7d5Hbcmug1zHFs2kk+A2nRAKS01p8bHKEDFgbiQ2zjqliwwdfYQAcj53fwIgJLOdjZIERbyEj9sHgDJHJeIxEJrwHA7/yVdTAjQsuUqpYdGn8FdTVcaNxMzagbR8Nh+OQR4jwDBZ9Y3HsXkWLniXR6P2vcBV1LnAnlGRx82viAWZuoi+lg8QMVzJdfECiORBTloyj4fBaPEhBdibWBfKhZwikmPri43poWBgQ/Kvxtm8l9/IMzrtNE6gmAkl1Kfd9y3fAHZLeTIHRFj2Tz0mpLxQXh/oj/o6j2BpSncNrcKUTLsvh17SRAQO1X6KMWgOpR1GoZkjVh4iQK5cqufIWTpUrlx2lW5nCUbqPu/lK+5V3HaXZ8qCjdD9os7JfCR2bWceepCPf+DwXtQcVgf+OrJAhbQFdbyNbRXRd2LtoEW5kgrBLj9Il61+Aj+B/kfgD+f+XXfNLcu9K+aX7S8wvo1iY8SVlYfqXlIVmFQvKdlnTDzCSPEzSJlXXu1nXW7+M2Q2j3DSOwA6Q480IPINbYs9twEcjjxY9VvR40RNF1sfXoxII+kbw/+BPcNUmbyuVPxPppBkxttIvqbH95QtsEep0+R/3xqcIWRup14dujsQ3b0sKLEv5xrO4D+DL2y76KrZrlQhg+nj+bZAYiz5pOzByZsEgWAJvzxBMh+a3hgaTeKHcmv8VZISRu78T5LOhX6/Ne6qQWzMN/rz3d08h9/5l8KaA73Mw7s4bv5it3GjAG9ucsARajlcz4GrNEVchzMAXjM2BDcDQ2ouwQFoTxs3/5XXQDR8SBe7/HCXHFl0YFgM/V0PeqYWG+HQzcOZzsgrE9b5ngArV3B8pqNqOYAv0Q0+msudL3a0gRXiuFau+d1P2QYE+dwYoCg1CpdYc6RyQuWwTDAYr48x7orYp3ydrBAeyRyh98AjZgLXAvXxMeTNxGZnDHn3lQ1RpNATIjl6zpx9ujGOWTDb0A3dDz1AdErmP7VQeQWsyNc82i1mbSRtYz+8K/APGCo1fO1ECbgeADt57BhWBT7cDSUeiFNH8lKKI4GG0lE4kotwc8ACeP2sIE+hRb32OW5Zr8HDY+/iGAsJQviqx9noS8P2Q6OOwwu+Uw2TrUPCzWCG/nnBXU3mWVY7zna8eovKmfpE96m6oysgiSbiPbZR6O/ryP3kBE7PVMHjgm0NRBHU4FT1rkaDXwTAC27DJQDc0+S38ea87iOl9PEoQ+Cq677cuhZkP4Ubg3GEM5WHcwQgHDhyOarq3VhnmzUMqO6xCK8NTDIGBh9DR0mLN8KOIvBzYz5cj+GCNrnD81WcwSOce9N4jJN303KJsJwumwxBl1kJPWDKFanDtS55zpClb07Ca8/So5nXJuHkQ4kU5Xdmfxv1AZa8ZrrVX1ErrePbMY3wmracYyuZ1Pj2gDphxCMXTzje+9h2yets1OtSbhxWHuuWzLg6VR4T5mCEft4Su3U8ufQo4C+L5yU1RfvJTQB/qF2v1Cw9FSX3GYSr1m3DlkElFbgFSjadC09UPGcnThyh59xbkUrJKBfLNZBGTAxfaOLzEPpWEWf25MpjVI082er9QxiZ5eUmKWk9zwRnRHCjzjQbcuKGTz9ufds5X04ERFG0HKsrK5JyJbv/yQWC2c+PJfYgym2I8GLJA+tRl4wmXUIHaT8kKhzxQbtbiow5BzsJJNRUnVQ0+uxBai9kWcDHdAiZbsdDkDjrvDox+Bn3xIF28nT2Im+IphkwwrzUc/Hl/xxScDI4H5M/Iis14MBgi6FoFmo2C/UMfQj0aUV9ElHCYV9kwSw7ShKHqMwxwwZ8rGJmCewSehAahZAJLPTc5ShPclOvVxG0HE2hiz0GVJrQH2JBI2NZPkTDcL30IEky6Sz9W9I9nfHx4gE74G6BsPCilhYY3HvTchxnJCiUjeQpXCL/XMMbU8eLFA5T50QdoAhfpMha7zNF0ysJiOifdGrAiYf3VsNTAJDpwbxTm6f2dstpaGiWr3aXXK6sz+xPI6ncHVLL6dB/kQ41JePoolZBL9I9M9cX+JG9seh2XY+p07LX9NB1bul/1WDbooRgsckiMnFVw7leZ1Foc7FeQSAb5pM7B8M5v9kVJUBpLU+DRUaPq2agZjLMVLLqGdGpUjzBUy6+UTe/ZR1F9tI+iWq5KRZddqePrrOOrrOMyVUd2DoOc8NGyEz5ECHNYJyvrtCjS6TF6HiLu/riR9craR4VczykHXUSyuDWjasbiZdQBiQuf0FXFmU9UByRwon1zP33qQJZZt1nks2yl1ZMsQlMDP2DjvfcJXWg1vYhWfJZacfA1qrHgMlBfiFy9l0ztu+WrZOB+6BVPW9g/obgfJ0Q1Do2ctvBu7PC0BE1JzFUiTzgG02u68oX8/xPlMccgctXLIPiK2AOJogxPLdtxs0CmA+ssqZAtTTJp7LojOnbt20tj1469sesaT2vn1h1do72DbfCJOlyvINfFdFgkAYbmmxYm0X2eax7+h2z4H1zD8MP20scpwX5UQnRNOdMiB1TLSumBK44X3EPH+3xP1/HYuogO1ryHWs7qPdewHn1lD9Wob0/i9Wjcjo+wjlP3JFwFxu94B+s4YE/cNWBTlibeivaZPTRGf7kbYk8aC8kwRDYd4p6oIdbspkOsgjLUi5i54C0wcNJTRDHEMquvS9nO3VT4VbuvruzC3ezZ2ejdnU/79GQDiPkCZkgE7c27FX/ouftqz9mCH1OmPv846jkbLpbx7WkMDc+DkOj+YefxQu9JCKvnRPmyKoP75R56duMJSGhEeaPo3aizyJmleLDMx6LM/9lF1ygy2Qrq4Gffhqse5A/SxOw95LELOfKB9yHfKW06Kt0BS2yNp3fRC92KFpu5UF9ImbLBEC3yZYv8ReD8LmVnCbMhX088TxJ7aEOUbxO8G3SRFf96uhJeSojZFJkbxu6is/1ruxV+o85TZkAwXfRsJsc3jiSx7ZwgXxR8z+aQk3Pdk2NOzj33sXKs667kqONSc3dTKd0r0qQWnyyK7ORcER6dE1mSKipH55LNxlPkWa1Fvk/0TdCTpRt+NYvFX2XQRZ138IHEj6/Bd3cCZR+zTfOD0ce1OhT4A0lkMx0fvXs3Qb8cdIQLO9Ac1gt4ggwPfVzUNQwACxXkY8CTn9gK5uAY3kp1aFT34ualpz+/ymLQLdaGBsU8v4T5/hg+op66kz34wMTcV8pRNUVn42T2+q2GPdXzPaTFcCMiQcR3+qPv0LQobyf1m6E7cQnEJDKO9MwPjAY1Qr1pkoahrNXQLUHRVwpW8gHdqv5A0HzAr3pMp16CfLaLqiZftd7g13zMDp+eDgzDYWMPNvZWnb8xnlqzH497gSB3knOlMPpesoOZZyBrNrLDB5yJ90/TQjBo2iWNEH2jkLHAih2E7FfX00S0d+jO6mXGg6Qi3UIkIOVDKjg1NPvPGtrkxLKoZPX2HdR8b9nReUqYSm5zdNRp306l9/V2TC5xD5JKsOkflxJsMuRhj+c2oAeots3irq+7b4/Codr/2aZsVLy1jdlYa6Anus1yWH+EBtJ7L3TeezCNU61N0EFPAWc07ELlyDZkNxwI7CCJC/Pi04G520iC4qkILVSDKwEcKiQhG0UyqtOYjm+j4jiMLW4jE8ApqT9txqsk59kT2kcPnQ6Jzqne2kZnxl9uU0IRPQ+F1E3coT6ijefH8Ui2z1qIvtONBGQpV/Q9kY/1ChqgS6MCtLCNBujCbbhkG09OPoXujd7/ZE1upU0K6RYPwel5By5yVIMNh7oe62U0DxUjeejpjyiWTz4iWPRxBvo7a/IubZLDBkJ8npVwoVMNpFVOt0dheJphaKAY6JMW1rB6WTmG1NZuZNPD26oNWOip74CBWOzhR4P3f6QWZ5f2g1n7f25j7XtGt4+Ec4vvQdxiekb0ToBJWwKdiL7bS30umLNDnjvwtOAiqsHjgc0fxszNn0l3W3x34cx0mh3tOh51tOuDwE+hS2g9O2RGJm/vh9RYn/kQ2SavSnbORir7QPrIZLNcQw/vM+ZgPtbT7Z8BH9K9bP2an+GWG3Ic8HxIt28gYEEuqhy9JSdvLfgoqBRiGkkTCnH3RMANGj1u0MjHyfZMO5J7B07E9Ri57tkK1b5C5D7u1Xz8YdReDeofA6VBdeSXTAqm/dJdooz5NwxOzkaFBmDSQlfEC7biDtrRUB6vSyZnMPHEL0kFutAd5wCwL3XPWD1HT4T5en8+VlnyWlL0ZJVJF7vzgQk/XURRi9NuZatQuszFxGbjVrrGxeVZoGNL1M7M/g8pa9MgCvEPN3fZk9n/QeyejEW+A40hjZ2Tjrsp84QWzxf3DmVDRKcVkBM1nkEx5wKnf0BT0rIPiKk0o/qf24D2gBaD9qLelLn3A9UKesEWDH1H2Ylwgp5vepU9ylEN8e0WOsQpKENzCDqqoh6BA1s6NxmqH+gUd8MD1yjun2/pIu5pH6jELW+ObL+cC1i2UrcwQInPd7v4/3fMj+8iDbrcH8pejEnZytoN2tLV36/sT1s2K/70LvoTvqoWuHvLv+RPb22J8qdPWrv6k3dLlD9VfXBD/jRs87/mT033dyr4pfuvUcGrW7so2LNZpeC3N0X5k7zlyv4kt/5/7E/aVmrsXCvxp8wr+9OBTSp/Grbp2vzpN5voEG9s6uJPL2zq9Kf+YzrFfeeYaxS3uKmLuNNaVeI2bVT503et1J92t1J/eki9YMuQKwz9RLldlL8T5W/FrEvic5dwec37/5NMdV+2t2j4JikNz0ic5Rtf0uI68EmYEb/lGxu12OKMkHVA8K5ra1+n5ZveTYu8uSCYvlkwSJAD5HybnCP46mHl0NwB4PmwcJnSAfMmuNJ+wXtBy3t/A/2CeVqyPgDv6bXocw2/agt5+WGTDygMTAiHw8YtpfIlPPT7XJiQuATfpsW3E1oCKUC2BhLR+XeKvn4bRMis8dmpWZT7gffkAWBaBz4xLQxNrIb8GIC+vA0A4P0uEgKgi5xnYK34JdW4i4ULBHo+A5+ykjsLetGmszuwP+IRTHt5/1ZifdAfb/oqNsBdXL4ipvWc0r8N+6eLMt6WZ7dBE2xXge0ABX3XBU80yLT/Bi3e1OHNJf9NkJwRfbMDUEckeFueFsDBoJ0T253g/V8SJBUYE5wIJyMUAJ/n+CWfUhzkkOG3/JIvSPVbwbSHXxIg1yeCQXaeJpoF/wNhTKUrkDSCkHDCL8HH58H74c+yaGr9U0l7Cakg1BGi+SUTlOM+y2LkaWDyNKDmkD+eaAxugOlJNSEPFpWhus7zQfKeKN78BjLifBQsaFW+gBz6byPAoRuIsJBPfwaBOLEZNRHFOmAAD48Hy0VTiaGD998Ha2AZjJC8s9MesKBNy5ssWcfwJGZLOFnwT9eWyufErMMW+bzQcj5ZbDmVZjGd5pe8RN9I4ZfgW3Tt6zR843BYPZEjpRmA9B8Y3MhJxc9DwyL8QE2QTaicLFyGJouaHXguUZC/Eb0d3XnvSZzP/E8BE5v4xp1kPdtH8M2D+icL9gpyENjoJXjPa6VVoIRebfg+CNyaH7BoPg+9Bf4EIapfmG/8Mx55ee7ye8Rzfk72cx5Ohvgm8OInMGySxZ+yh6buQ0EVBbtFcqJ96G5crme1WnyFHYJvuCDXdFjkboCHeNtDxExE3/zdYB3ziYnsRsPDc/rzd+sQpkOYTmjdhG9m45mwc7sE39w2wTd0ByBrE+SCHYKvLADBEa7nQujr1wb+Tak8g+d//D/Aw49A5U6kkrxO4pN2gHjq4Z8FLvy99lAxtgtZ20TfZKBzrCg/2yHI6YAJ6eSXvH2RnaaErhB16nXYVUfePQFIBkAyEJIhtLZQOncCnWC7bfTlmWfbwE7QasHl8raBwQZA6NuCUwErrMfbo8+jgUOO6iAv2ZgC88+I/sGj8CUb7+UO+jrpMsHXoKVnVNsWnCUP1EhoHZWMezeCruhvGmIlu8BQQllkv2Q/PuHL2o62J2o6RO96WOu3aEXvujC1kA8uhMMT/QX76MnAc3zjCTC2ib68fRN9zn0wzrcLtlrkXRZ5C4tnRtxjeFvwPUWNhRw6+3b+8dByNHP5C9H0qfthPP6sZefTRoOpILnz4c9hQf6k4SS+7hPKZecH5XsFOVd13tX3JAZ3wv5ZpF/Nr2A60AACxLcEksmbFMSPouO6H99WDXrxrU0UzK1J5GQfcNbGN+qSusw5Gzn6qInMOechDGrYaVPlvJ9ckAvi1/KN64nzzM9to9o5P/+ooDkUJKcu5aAgf4ZzkfvW4Dsa9RHDZfjes8X0Dd/4quJ7Hg6D163GMOM/zwRW9AAw3Upmma/BiE2C3wGGahR8P9DieUho/9cIPdHz4RmcDx/uCP2MZDz7yIT4Y3zSFhxBntBV4LFdEom+6AmL7z4QqyEV3I0uzy/lyDbW+A58R1KUJ4NxbAIH/caS9bly2tl7vs/8xwT/tLDQcuJBwV/QH8gMCzvDxl2hWyn9/qF9IeAkib7BJ0V5MDj8CcF0acG9CBd9j53Uifc/dlK7YB9rpwntjbZ346n2Fq1nGAxxmQyhOQOquSzshDi8x9MfsWlOQGTCENHvpKA5Rd5AxZOouWGSvwv+hyhtmhPQEUi7AE62G6Odvw8J2XlgbpeSIOwv3B1MIi8IHALfFvyTdERhvODdkKHagRL85drQrerz19A8TfAXaZkmpW1EMZmC6esF+UQH8na1vuXhgumClEHUCa2Ikr6eHwh1V+wdfGA0npj0AUqQsqljwZtR/Z/F01zSTwTfsywOdMw/gSfIgxLOf1H7xx1kzj5n8XUzbomcT63gkVQw+HXzz1j8g3kS3e9UBig1Tego9S/sWNAeugnlr9Tnp5aaLjZ8A3/4V0oML7pBeaPBNmE6vQWRQNjQ4psMOzCV/APZuDjSbPE7DcNF71fNuHGedQiiDgvtxBZLDG/zS/CguOgv1eCL/EcEv0Au9sEKw2zcYjwIPSyy0xAITcI0FXgfiwuOt/Gsc3ORJohHNjcXaU6Kfh9+11RWyMKX7Bf9L+L3dBRlnYDaeToyDIsb1PzSwfAXn4fvJ0Fwi3GX8aDxVPT+se3s4n45xnCpKeXjQv6NVs1lwdbuf1pTuLiAQEs+1qrASZ1gnQqc3AnOUIFTOsF6FTi1E5ypAqd1gnMU8CF/Q7fCxXkZxnYE5wN4k+YszueLLuSDL/A/xnzK3LSLbzqEqvc/3AGiLPwL/N3F9O/3dBdtHxQtlnpzxnaLKamQf32T5hu/p4dg2xKBjutQwD2jwG0KuFcUOKCAe0eBjyjg9CjwPgXMR4F3KGCdxbazeLF0MwVvRnCR5kzhoo0Y55sLF13K5388CtgsMh2QPyviJ58QbVtEcBR/Ly2+crO51HZ0kr/f0BLTx55xGAg+hiRJhqgpGZZ5j2m8lzQWMLFmfN9vtQCZ5wq0uOV4e6XnGWM4eCLqjahliOEl/HYIeTu/qrsg7+BX9RVavkgGw4J8A7LKrYgEpt3+EIC2Ktj5xkmkz3zEXIJjBPylYe/nmiLyYg6+F9O0g2PvKW0OjiWvR7YJySWGfZbkKvCELKAWZwAAny/hV83PD5v5VVJBWGw5Biuvj0qzvhXPHZri63URv19ikjz0oihvnyz3MvKrhuYX8avyCgRoiE8ess4I5w5B/A4Jvr5AYQiW9qMh9iQJJnAoz5uCd75hJfnWjECna+EZ89UQKIBuqC7nG5dBksiv6mXiV/UbE8M8ZkMFIIP+wSfJzshGSOiQA0uyZNgHaQVlGJhk9lfMr7Kkh6nc+kOlole4E6Nw7oDgGwUxeSvc6ZUOkXorVG6BCiSh6lbQ/xaUN2QqSRbTds/v8O1nDBF84yfs3PfycYBCywZCwAoA9NOq0FCVBd+6iGcfIRqAq5TKx4SWr5ONW+QDJd138T9tafk8LWvb+7i9XST/ExxLmtjUXsi/2tp00DO6qV3KNTcdfXYqRpJ240ESv+X98mHw0HzjQYggZwX/bA3/xnrN1kJ+1bYi04cLjxjD8l7Ec19IwtPcx0E6FGPR33EUvKUL/RBbUZqadnkmFTaGPeOb2p8toA7dXvhXDTg0nS9K5J0l8p6ixfOHYVYHDl3Ev76uSPMBDLjdP0+DqZRp28L9oTSS87XjqdTN6DmHwXMuEFsVbUHwncxS27ZJ/pQscBs022UeY1OYX3o3eWBnQQMGiZaAe60ukr8rkT8HU/aGSbBe6XEaTwUP4FdoQ4d/algHcBZ+lQ6lLeJTU0h6P1GcBUyLKK/IexnWMH/R0LdwloOlF0Ei4f1CAzeSsOFKvukkx164BA8PPqShr4a9LWgC+AZHqSYIBqARsz40noqE8FWWzDB4QEUWsyscGSymFGxJ8D2IxloKJgW2kQmt+mVdqRHJW0wnPf9FvaQEppvVdCJC+0LfWME3JuFKflWvLECW2YmM+sZwyEWB5VuC+6gBFbWcvL1I88+ind/JZ0qyvlBMiW/ErzoKfgl/zPI5xpWaUTHrE0UIhD8qyVvUbJ6jHDDqkT1YXID3FPRXsckaQXdi+pS/0wp/kC1L9xJPod3JGITVaN6Y+vDOanzfhkjEBvng59Dggpi13SzvLcq6bOHNF+RDou1D0d9vK1ZLDvCNP6WvyBiaNR0W22aY5wrbN2mkIYTZiRDBgT18OWUTsJiyVfQ93EG8XJ4AqWdrh38KRNcLpq3SVNIeFj/kbZqo+RtIz0XSRd/CDpSECMkvIM1FpJfZjTZ2ow2QtgHGpnbPZjM+K/1v9l0TmwlPmvOCrUW0tRchiUOBuswIdZTXMC6bfLzgGyjIt7MMpQcuRrytmiLTBYylO6SZ2BFflqP+yq8qMAotx6H3JXz57txhAQI4YIDc+EFY60CoJyNcjtPmES1t9COcCHVA9J9DqWQMwTcReO1ukcd1CP5xHS9UGF4UntvQwUU9OlXyv6zLsFzRLdCsJ99k1ZEMSRcMFnzQAtIHu7ss7jwPoDCCBE0Lectv53nQEqyFPof+U/wp3UX0PXlrSEfWj7Co63dO9IY0De9M9vU7bJH3WUz7n51gkQ+FBlYvE/wpo8CEYWndDxYm/c7hO1qmzQsyCdxX0aoT7q9oJfk/adcl/7eYNnt6TvSltE6UU7Zb5GaLad+zPnU+7C9ja488HdHMzssw1DmLaRfvn0HSeRy3zWI6BPk97h7Fvn+J9KcA/UEN/+MAJI6TfTjONotpy7PHoAwNiXkf7F/n59roXxdD/7b49GO6L8qfYMZv8d/9/F167iHBP2wplOVFuO5tt2R9Rb7LsOVScuCeP9AjtHqcrJT9IZgF2kvk00Xhfp+SV9tMn3lOkOejj0ceFy7rHPTsevbdiOTbEOn35Ko/O/JL7lrxEMfNmDeelM2nfnjPzoUc1zFrZ37bd8+8tDI4xoj1sq8pfMPzv/xbr6af91352n13Yf0Ig3vuLnpg+6aBz7cxeP1pCv/rn9wH8n+z5bmcn1F4IfcIgf/5nOfMn3vNtOXc/gSp72Dw8CPNVS73Ym3zm9NIfXiVffZwt1TlqOPqKx0Ncxxuu97mqm9w1No5t12Kqte5pMh1w334vc7c8BpXrX14neNHjuG1jjqHzVVXVZk73F432z28UrK5quwN+EM/w+vnSTWuutzs/OFuh2QfVl9pe7Jylt1N4A2Vdey+wzbcIzmc7uHumsoGe5W1wV6dXVNfzxW7PM6qunskPX5Jtb7a4bTruQr9vZmVda66ebUuj1tfV1lrdwNWe9aYMQquMWMIsjFjOrEVAKNjxrAv4R4zRv0l3AX4/dtj9WPHjIFBGiolV8OwsZlZevL9+/pHK/QP6K/W93GgacgN0HTVLvhjDtDFioyr6Ls3mrzrQvM4p6Z/zBj61fE3yMD1CPXe65Xp3e4xequ13uXWZ86pcdhq9A63/u6nPVn6sXqpxuEeNtbteNoOSKPvcuLwKXo6psNVp4f/yTeSVxHj4cajBaExU3tyNegb7JXgAbNipKL+/QV9ncfpJJ1mVzodVf+K/c2220ASBU5X3ayhVALs5zIYEAR2VTu8BhyPc7TRmDHspxs4Rx2hXe8EH2yodBLOsTHgn+OQavT4Ew16Y86Ye26Qu2s1wYbKOdbKhobKeQqpiZm9QZSP/xv0c61Dg310UcF19L0Obf/PUfQ4p+8B//fuHuMRbs9MuPxX4sT/qnDv/X9Dtjn6ggf0Dv2QIfCngCKbZZcKcsZmWkFc9fasf/u06qizOT1V9uGEkOGADF2FzK0FVXYM/fY6SY83x16zxzEkLARday/Sh/wWDOtovPa+kqfeabfOtje4IaZH6fXRxzMRm6LcIVS9dID68usII/8+8h7nMuEzUz/2AX1Olv5BwIJKJ7qu89TaG8CpnI5ah+QGBdiciCJzZhawVFs5F4x0mH5mln6MPvMa2jvqaHtEX5kFH7SrO11AR7XTNUdvq7Hbnrzz325QiuStwyurqoghobyuLqtIP5B1VVUmFTLRHYfSegCkpZ8/H6V1r54KD9mh/jbTnVmZdT1SHN7ZcSbIpRNvQRy8Y68JL5F2NN7/cYnXepw3JnHoGCXx/2HfryfBreuc/2/zXvUAj/8vcUcsw1En2WdBhkXTx8o6ieEakRMzIf27Wb/66I8rv8tnHGGdYJkyrshitU62GvMVjCMJwtHiFDP+pJL5Ku2Mo8x1sx0NrrpaEPO19cgtB5JAwlXX1NxkVn6P6eqIx0HYVjVnv3vBfg/mybmFUb8Do8CLGHwFKxV4X1ZfHQM/MofWd8TAVzN4YG7M7wey393FA/SknETL2N83VMZX2nGW6HZXYT5nWqXTY2ciVdNVMy+a3sjvRcTQ1TYlejzNoOT732W/dZL5y3AYv9Vp7pvh8IYkjnsbSnxasw/K7xDXr6AEeP5/hsNDk6G+NRxeBOXiD8PhzVDu2xEO50P/jp3hsBPKzF3h8GIop0P5a4RDiU8iO/aEw0egnrEX8OHeNpQz8PHo/nAYT/Rs/iIcroIycDIcXgnl6q/D4bfhfubpcLiN/ViO8pstmqencpq5Os2gXt20yzTddDr2uyc7VoTDyu9vxG9Lf1cG2+6GtspujoH9jpHujXC4DI+ip+vGp2dM5HvO0S7iHhx4/725BvIzJjnstzKXbw+Hb8M96nHpuqL0jKJ0vTd5aVLSKbzUjUvXAsCuXOM3ZeHPJzeCnMhvmADYm2RN17LfgsFv4Fke5x6+iLAB/r0D917j6Fjj0jPGpevHpWcWped405ak+lOWJr+YlNSYnknAGaSJlv3GDPK6Oxy+NYL34ciYg+Fev91dx8S3VVGXmZ+Ewwu703uRMcel5xSl55ekl3m7LUnzpy5NeTH5+aTkVm16PlAzTkVCEQqgDHE1Aq7HjoTDA3rExTUhfWVSFLJL3eMhozrbAbhyToTDLZpO/R6F63fAfu9gfCAXkd8Ef7rMQjTfLRl/DjRZ0f9Qpu9X/yMcfgV/h+nIs4Uxepyi0iNXhr/Lje3AXh7JhLIN2sfjpS0lipdBA+ILBm25hOldtzkcxkewKfsWFsbqtyS9UKXiP+IgUVouSi8ktLVhfAI841OQtoXxaduhjaItKTk+bTu09JfSS0BOu7dRv0/JWaSiDYiYkN6c7E1dQuhK1iUhsWqqmpMpfxgDjuxkOMquH4fy++cYY7SgdzvzgReToOHSZHO63p8CDCxJFdJznk8T0vO93UrTCxsIYzGiIvajS6H2o97BDYc1vdCQ+mkf5gbPIfUvNdx+oHy/pu9r47ntSb23mrnX0vr+ROQ+T+ObRK4tjb8kcCu0fQ+VcH/Sfg7IXunZ9+vJ3JZe/U5O5j7rdRQgLXzqusnc7/tpN5m55f1TD4jcG/3XAxdHBvT9VuDCA4a8KHI7BvZYYeYODETp7L2191qBezvbq/luEvfLvNv2l3B78vpC5+N5KzRfFnM/GTUGkI56+g/F3D9G37G+mDs0+gmO+3t+7nfjuMWmHtBwgykVup42WQCy9/6+vx/H/aLgzr8WccEC4T+LuC0P9thZyP2lsG9rIdda+IqO8NmvnN+l4WZWg4A1PeBqlyaDwMc8n9T77W7cK0lOqCZpd3bjvCm6td24P6X0PtuNW5vySyD4szTtT7txX6WB5M6naV/pxj3XbaOGyXNQBh9M5e7ptziVqxv+bQr3RnKPt1K43ybvSbqG+99/vv98//n+8/3n+8/3n+8/33++/3z/+X//M2Mw3VcqY/tLOYMLvxfK/48+mgTlH9Oi680x9c9YXfkt6MAtVO/Kb0CnOGhd+W3jTLZvqfx2sfIbz8pvHOez+8oejvIb1t9eDrvIvmYSxaf8RvYRLa0rv43dwe4rv2W9j/04tvIb1spvL98cw3/kN7rZ7ygra9EjMfuOym9tK7/trL2pMAqe0bcwiu42VnaPGf9ymPKT36eQ7UPQuvKb4G2svlr3v+tHAbafHvvpn0HpGsLKUawcz8pprKxm5WxWPs/K11j5FitXsXITK/ey8hgr21mZNoCW/Vk5hJWjWDmeldNYWc3K2ax8npWvsfItVq5i5SZW7mXlMVa2szJtIBuflUNYOYqV41k5jZXVrJzNyudZ+Ror32LlKlZuYuVeVh5jZTsr01h87c/KIawcxcrxg+Lby4Ti4jH6zIdmeuokjz4/Oy87Z5jRQ2rGZ4352Tl5WRTK2ZyVdbP07DG33piTDf8Ny4tqmj1Cf0WbSYbokJ9UGAeexFXEhSdzUlx4SsTfo+GpET+PhqdF4kE0vFtcP0oGL1X8LxrePeLH0fAeEX+PhveMxL1oOETFjHjw3pF5MhqeHomX0XCe08WF67h98+LB+3DLno4H78vhVndX+E2RuBwN7xeJx9Hwm7nCuPD+ceNFMkTLwIvx4BmReBsNH8CtfikefCB3JC58UBfYAhJLvwnHn+e6yuEmBm+Lgd/N4Lh9rf7kMzhu26s/ExU8MfDHGDwnBk8dmWs67eEOBvcy+A72nPA7NiGtY3Dled7drP1ZBleeKyrwezQMD2t/K4M/yuD1Zgo/weDPM/jKiRR+G5voVzK4viyanpCCf1L0uPg8D+E5jJ7XGNyZhDoYyJ15eUZUHvEaa6+7lbZ/JJPC/8ngM8az+MYmyJeSKZ5vYvCsIHA+8vxTmW93MLjyHFaBJ6dQ+I4Y+CgGD8TAnQzOxeBfzuCrY9pvYnDlOe1QBm9ncOW59XAGz0ql8L4x8McYXHk+PYLBX2Zw5fk0Exu3hsGV59/3MvhJBl85L7r94DQKL4yBT2HwjBj4M2nx/ev5NKqv5pj4tpjB22Lg/4fBY9cLf0yj+m2L0W8zgXeNS8cZnhkxeM6S9l3jWyL6+3bD9n24T+PEyXjt9d1oFhsbh3MJnq7xsKAbjTax9JcyeCydMwiernFydjeMJl0/cgL4rwB+J8hnxWMsH2cJcns3pH4Atzkmrl4CeB9VnFfkf4uW4tGXUzwLWWL7sDb+uGEISfo48fydBO0/1OK4nfOdkg8fTND+NIFDHGN6f4XB/5mg/YDu8eElCeC/AHifpAFccwz9v03Qfkt3jOE3cwKT2yIlzvSI3/62BPARCeClCeBzEsBT+iK8a16xHNsnQZxn/qisd1YmwLMuAXxPAvionvHhZQngyelUj4sYPcoZjqoE7ecmgPsYfMaAmHiSoP36nlRfm5m+lMz2KR7bd83rbupF7b+5msKVswgze1E7ifWjul7xx92aAP5dAnh57/jw6gTwuQngb/RG/+rMo3SM/nd7UzkEmByms/bBBHgu9aZxI9avBRJPBnIdL0fDe6QnsP8E8IJ0iidWnkKC9s8kgH+SAP5dAvhDfHz4LJ7a57Iaqne74ncJ2rcmgB9IAM/XxYf/LAH8HQZfOTDaPj9K0H5Yn/jw2QngSxPA30oAv9CHyodzRMeTHn3jt78vAXxiX2qHyjk+ZX+opm+nb0bFPYYn9rzd6wxPGcOj5GNrGbzGHx2fe94Un56hDF4Yu16DBbXVTk4jWqXKmU57TheIsSukK2hEF0huF0g+Fzka6PbMtIpWe7atvp6zPnLthwaLR4gTi0aYrDaz2VxSP3VShfW6upfkmGdfvUP0Cc0b6jPiGvrkuiXgx1w2yXbVpnn4Ptg1IVVOxFJBjboeQSldr8YweWPGmNP5xoxYLo2ir6eIk8tzrJ3cmcsrxfKRViThyrTnz/Q4nJKjzp032VVnN2PLSUaj1cpOl1vtebWV+G6IeHWJwmBIRB4CxAqr2WhssLs9TjBBAJinlI+2UuxXIwQlPrraU4ds5eN7d05HnR0omDi1k0fSyIzsVdic7oYEWMyR7u56bAZUVplHV7sa5lQ2VCGJ1fVERPVTyk1IXLlkybdaHS5HnUOCaoV4PWdqr6F97AHka+kScxj6GrrEOXF99V6d57mv3tYUzUL5dUqp/PqlVH79Uiq/ISmVX4eUyq9LStOuU0rTrl9K066VeFuD5JY81dXZNq7K3mCf5XBL9garVGu1OSEIuDmrtcplneV0zax0WqvAC93WSs9czuaqrXfaJXtV9miTaXT8RtZq8B36nqUVBNswj6tuqKy1W6s8tbXzoIuqZkUvi2pqtY6fWlRqtponl1jBHa0TJj9kNQsMKJRM5awlP5xcVCoWw72ozlXRALdU2SBFZrwp48eXmyusFUXjLGYrVy3Znc7CwgkWcVyxdUT2iOyRGJTKXbn1HslsYzemT7fmZueRqJlXX/WU29UgWavsUqXDOWJUPeB2oMitDY5ZNZJ1JkRJW43T7naLZc5yKY9cOSGqwDW+Lw9hZqYZQjH8X5EDXNmrKqVKEmtG17kgxrrmXHVQ4wil7nS56qOGscw0wlCzK6zleYjeMZNwI41mb0wbR6jfzhRt5ZLRiK9NW6WGSofkFm04T8BEZbKWWukb3jgp1kZRlD3CSJHmO1xu8uYbTikxRItTrOQrAqwet72Kg+Gc9rpoKeM0wiFZlVb2XqSdpSggy8JCwFQ0TrQas3OJbEbkWK1EOFZANEuqsdrRcoG2LsK6QWbdbsesOvPUSSC3rtxaq9wuaw1EFKcdhqhyls2GhpKJ6csqxRJRMQ3GGhkZ3V5ZO9NTHY8Ac0xPpMMOeq0GLUeLy0aEFMdQR1Y7Pe6aLgoAmRlNEZm5ZgElCURGVFDdYE8o/ionpBZMQ+BKEqCqd9g591MNUoxK7bW2+nmdMGOsQkxUJC6qESqZxHopNoLPGHOtIpjZFLDFWleVPY5uiAVJ9rkOKVY6qKhoZqvJS/2x7erm1MYxI2Nug6dOckB4IoIrMcbKeKattj4WVUXFjfNrjuNm2NZqra180m6tsVeir08Grc6q8+AII61WV73bONIqYrjGcGzF9/RFddQhsWC0dSqJNdaK0mIlmiLqUVZCG1AF4RINL66NTkVZdHKCPFRgcDGXj7KWTSrPtTqjCTeRtK5cGmkjmZ/NDDostc5xVNnrSFSOkWO20XhjfguTm+rrFsy1ti7yq8ava4hWERp6rNKoJKirzKysstoq3VIcf8qzuTxxXH2yMYcQXjnTMduIccrtgHkTHJnkucBxtcsc5VC18VzZitMAm6mAIckVZ1KSRqEXwvKjiylarTNhPDbPYbjMU/gBkq2uaitMSrPs1upaCfz/6Rg55TB9Xbf8c2Fyr4IZvsGNY5CZ4mpTRZ4IBlDclf4b0n8O6L/BXu+stNnNtbVxJ6pqu6s6VtJOx0yb1eb2WFlafwNDj2ZfkGGOM+Isu+SOo7uYcAIymDrpBkaOFyRuSHmjiPLATiUrzGOJ9QdBvdY1O9ZnrLPmzrXW2xvcrrpKp0OaZ52dEzNn3xBRo1UWdUWqiEhZeAXbihu3Rtqc9soGnENGkDkE/sHsZY83/bG8S83AQ3VzkJapsGStxT7FxdA+58YzDBsETslunhqPmU6TxHSZMWdXb2HEEk06EH+3YiLVZRoS1QhiO8+qhWyVdrYyTXU2zptTUyl19U/EGGPCcUKhSjQj1aJJkP4kNmmYkq+cYnUJKl2DIpftnlcrVc6EEgIqKWuUK3zjuaGeywb89mzQ+TCpcharweSajbsGVcMcVRyp1VS6a7jsqnl1gI+WUgO9ww61RFWscA/ypUpsyK7qnRIOCdEmGxIV+EvUnN3gIrl/tr2GrYNqqho6a4DUZovataM46KKG4lCuEQ8OBSgJgZW1Dhv0d0nkDx2fjgXzBJcNqQIuc/8957kGsWd5yv6sck4nnwEGJzgXqHzuYmfdlP7KeZ4KBtB3eWYa/THG9FfO/UgMYLhKf3wfsD0cdin9lfNBy5PU5yw6zwdqY/pPZmcBk2LOC3YwQIB1TGO8a2PO7U1jZwWTYs4fHtFGnzdMJL8n2Fk/pb9yLmk12+h+Nob+pJjySXZ2MKI/dn4pvw+jh+ukPyUO/08zeFLMecWMvtHnFWPlp/DfyPqPizn/qL0p+rxkKusT238pk0lazHnRwC3R5yoT6V+O6R85X8UGKEuObq+LKV+N6a+cuyljR4j+VBq/f+R5cEx/5blqiiP6nGci+lfE+J/ynFvH+m/WXXn8t2P6K+cO9s1jz/GuMv6qmP7KObFlTzN5xHSItZ/mmP7KOaojzHCXaa48/i74l646b6ucd8hcEp9fbUyJ3/PNq/or5yjyl8SnN7b/CUa/0l8531DI+r8dQ39sPDvHdJ8ccz424I921LSYcXWqgKAeXzkXF3jx2vhPjekfORfCfgh7tebK/dNj+ivPb1e/dOXxlU8/DYUp/ZXnyJtZ/4yryG8AGz8nBq70L7uGc+nx3hPOeJld8FzknFtZnPjTXS07df83afmDm64cv/sk6O9gjnen5sr9/y/TgJcbUL0AAA=='
    bin = gzip.decompress(base64.b64decode(gz))
    with open(pymain, "wb") as f:
        f.write(bin)
    os.chmod(pymain, 0o775)

Buggy python submission (verdict: Runtime Error):
# compiled by pythran
"""
#pythran export main2()
import numpy as np
i4 = np.int32
i8 = np.int64


def get_primes(N):
    n_max = int(np.sqrt(N)) + 1
    p = np.zeros(N + 1, i8)
    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
    return np.where(p == 0)[0]


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):
    g = A[0]
    for i in range(1, A.shape[0]):
        g = gcd(A[i], g)
    if g > 1:
        print('not coprime')
        return
    primes = get_primes(1000000)
    if len(A) > len(primes):
        print('setwise coprime')
        return
    for p in primes:
        count = 0
        for a in A:
            if a % p == 0:
                count += 1
        if count > 1:
            print('setwise coprime')
            return
    print('pairwise coprime')


def main(in_file):
    stdin = open(in_file)
    stdin.readline()
    A = np.array([int(x) for x in stdin.readline().split()])
    solve(A)

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)


Fixed submission:
# 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] = i
                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: ]
    if len(a) > len(primes):
        return 1
    check = np.zeros(p_max + 1, i8)
    stack = np.empty(20, i8)
    p_stack = 0
    for d in a:
        x = p[d]
        while x > 0:
            for i in range(p_stack):
                if stack[i] == x:
                    break
            else:
                stack[p_stack] = x
                p_stack += 1
            d //= x
            x = p[d]

        for i in range(p_stack):
            if stack[i] == d:
                break
        else:
            stack[p_stack] = d
            p_stack += 1

        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)