import numpy as np
from numpy.core.numeric import concatenate, isscalar, binary_repr, identity, asanyarray, dot
from numpy.core.numerictypes import issubdtype  
def matrix_power(M, n, mod_val):
    # Implementation shadows numpy's matrix_power, but with modulo included
    M = asanyarray(M)
    if len(M.shape) != 2 or M.shape[0] != M.shape[1]:
        raise ValueError("input  must be a square array")
    if not issubdtype(type(n), int):
        raise TypeError("exponent must be an integer")

    from numpy.linalg import inv

    if n==0:
        M = M.copy()
        M[:] = identity(M.shape[0])
        return M
    elif n<0:
        M = inv(M)
        n *= -1

    result = M % mod_val
    if n <= 3:
        for _ in range(n-1):
            result = dot(result, M) % mod_val
        return result

    # binary decompositon to reduce the number of matrix
    # multiplications for n > 3
    beta = binary_repr(n)
    Z, q, t = M, 0, len(beta)
    while beta[t-q-1] == '0':
        Z = dot(Z, Z) % mod_val
        q += 1
    result = Z
    for k in range(q+1, t):
        Z = dot(Z, Z) % mod_val
        if beta[t-k-1] == '1':
            result = dot(result, Z) % mod_val
    return result % mod_val


a,b,n=map(int,input().split())
a%=mod
b%=mod
mod=10**9+7
p=np.array([[0,1],[b,a]])
if(n==0 or n==1):
    print(p[0][n])
elif(b==0):
    print(pow(a,n-1,mod))
else:
    p_n=matrix_power(p,n-1,mod)
    print(p_n[1][1]%mod)