mport subprocess
import gzip, base64, os
import stat

gz = b'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'
bin = gzip.decompress(base64.b64decode(gz))
p = os.path.dirname(__file__)
pymain = os.path.join(p, "a.out")
with open(pymain, "wb") as f:
    f.write(bin)
os.chmod(pymain, stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR)
if p == "":
    pymain = "./" + pymain
subprocess.call(pymain)


# compiled by pythran
'''
#pythran export main2()
import numpy as np
i8 = np.int64
MOD = 1000000007


def init(fac, inv, finv):
    fac[0] = fac[1] = 1
    finv[0] = finv[1] = 1
    inv[1] = 1
    for i in range(2, fac.shape[0]):
        fac[i] = fac[i - 1] * i % MOD
        inv[i] = MOD - inv[MOD%i] * (MOD // i) % MOD
        finv[i] = finv[i - 1] * inv[i] % MOD

def com(n, k, fac, finv):
    if n < k:
        return 0
    return fac[n] * (finv[k] * finv[n - k] % MOD) % MOD

def main2():
    stdin = open('/dev/stdin')
    X, Y = map(int, stdin.readline().split())
    a = X + Y
    if a % 3:
        print(0)
    else:
        d = a // 3
        if X < d or Y < d:
            print(0)
        else:
            fac = np.empty(d + 1, i8)
            inv = np.empty(d + 1, i8)
            finv = np.empty(d + 1, i8)
            init(fac, inv, finv)
            ans = com(d, X - d, fac, finv)
            print(ans)
'''


### FIXED CODE ###

import subprocess
import gzip, base64, os
import stat

gz = b'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'
bin = gzip.decompress(base64.b64decode(gz))
p = os.path.dirname(__file__)
pymain = os.path.join(p, "a.out")
with open(pymain, "wb") as f:
    f.write(bin)
os.chmod(pymain, stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR)
if p == "":
    pymain = "./" + pymain
subprocess.call(pymain)


# compiled by pythran
'''
#pythran export main2()
import numpy as np
i8 = np.int64
MOD = 1000000007


def init(fac, inv, finv):
    fac[0] = fac[1] = 1
    finv[0] = finv[1] = 1
    inv[1] = 1
    for i in range(2, fac.shape[0]):
        fac[i] = fac[i - 1] * i % MOD
        inv[i] = MOD - inv[MOD%i] * (MOD // i) % MOD
        finv[i] = finv[i - 1] * inv[i] % MOD

def com(n, k, fac, finv):
    if n < k:
        return 0
    return fac[n] * (finv[k] * finv[n - k] % MOD) % MOD

def main2():
    stdin = open('/dev/stdin')
    X, Y = map(int, stdin.readline().split())
    a = X + Y
    if a % 3:
        print(0)
    else:
        d = a // 3
        if X < d or Y < d:
            print(0)
        else:
            fac = np.empty(d + 1, i8)
            inv = np.empty(d + 1, i8)
            finv = np.empty(d + 1, i8)
            init(fac, inv, finv)
            ans = com(d, X - d, fac, finv)
            print(ans)
'''