import sys
read = sys.stdin.buffer.read
readline = sys.stdin.buffer.readline
readlines = sys.stdin.buffer.readlines

import numpy as np

MOD = 10 ** 9 + 7

class ModArray(np.ndarray):
    def __new__(cls, input_array, info=None):
        obj = np.asarray(input_array, dtype=np.int64).view(cls)
        return obj
    
    @classmethod
    def zeros(cls, *args):
        obj = np.zeros(*args, dtype=np.int64)
        return obj.view(cls)
    
    @classmethod
    def ones(cls, *args):
        obj = np.ones(*args, dtype=np.int64)
        return obj.view(cls)
    
    @classmethod
    def arange(cls, *args):
        obj = np.arange(*args, dtype=np.int64) % MOD
        return obj.view(cls)
    
    @classmethod
    def powers(cls, a, N):
        B = N.bit_length()
        x = cls.ones(1<<B)
        for n in range(B):
            x[1<<n:1<<(n+1)] = x[:1<<n] * a
            a *= a; a %= MOD
        return x[:N]
    
    @classmethod
    def _set_constant_array(cls, N):
        x = cls.arange(N); x[0] = 1
        fact = x.cumprod()
        x = cls.arange(N,0,-1); x[0] = pow(int(fact[-1]), MOD-2, MOD)
        fact_inv = x.cumprod()[::-1]
        inv = cls.zeros(N)
        inv[1:N] = fact_inv[1:N] * fact[0:N-1]
        fact.flags.writeable = False
        fact_inv.flags.writeable = False
        inv.flags.writeable = False
        cls._fact = fact; cls._fact_inv = fact_inv; cls._inverse = inv
        
    @classmethod
    def fact(cls, N):
        if (getattr(cls, '_fact', None) is None) or len(cls._fact) < N:
            cls._set_constant_array(max(N, 10 ** 6))
        return cls._fact[:N]

    @classmethod
    def fact_inv(cls, N):
        if (getattr(cls, '_fact', None) is None) or len(cls._fact) < N:
            cls._set_constant_array(max(N, 10 ** 6))
        return cls._fact_inv[:N]

    @classmethod
    def inverse(cls, N):
        if (getattr(cls, '_fact', None) is None) or len(cls._fact) < N:
            cls._set_constant_array(max(N, 10 ** 6))
        return cls._inverse[:N]
    
    @classmethod
    def _convolve_small(cls, f, g):
        Lf = len(f); Lg = len(g); L = Lf + Lg - 1
        if min(Lf, Lg) < 100 or Lf + Lg < 300:
            return (np.convolve(f,g) % MOD).view(cls)
        else:
            fft = np.fft.rfft; ifft = np.fft.irfft; n = 1 << L.bit_length()
            return (np.rint(ifft(fft(f, n) * fft(g, n))[:L]).astype(np.int64) % MOD).view(cls)
    
    @classmethod
    def convolve(cls, f, g):
        if f.max() < (1<<15) and g.max() < (1<<15):
            return cls._convolve_small(f, g)
        Lf = len(f); Lg = len(g); L = Lf + Lg - 1
        fl = f & (1 << 15) - 1; fh = f >> 15
        gl = g & (1 << 15) - 1; gh = g >> 15
        x = cls._convolve_small(fl, gl)
        z = cls._convolve_small(fh, gh)
        y = cls._convolve_small(fl+fh, gl+gh) - x - z
        return x + (y<<15) + (z<<30)
    
    def resize(self, N):
        L = len(self)
        if L >= N:
            return self[:N]
        A = np.resize(self, N).view(self.__class__)
        A[L:] = 0
        return A
    
    def diff(self):
        return np.diff(self) % MOD
               
    def cumprod(self):
        L = len(self); Lsq = int(L**.5+1)
        A = self.resize(Lsq**2).reshape(Lsq, Lsq)
        for n in range(1,Lsq):
            A[:,n] *= A[:,n-1]
        for n in range(1,Lsq):
            A[n] *= A[n-1,-1]
        return A.ravel()[:L]

    def __array_wrap__(self, out_arr, context=None):
        if context and context[0] == np.mod:
             return out_arr
        if out_arr.dtype == np.int64:
            np.mod(out_arr, MOD, out=out_arr)
            return out_arr
        else:
            return np.asarray(out_arr)

N = int(readline())
X = ModArray(read().split())

U = 10 ** 5 + 100
inv = ModArray.inverse(U)
fact = ModArray.fact(U)

coef = inv[1:N].cumsum()

answer = (X.diff() * coef).sum()
answer *= fact[N-1]; answer %= MOD

print(answer)

Measured memory_bytes: 73872