Problem:
Score : 100 points

Problem Statement

There are N dishes, numbered 1, 2, \ldots, N.
Initially, for each i (1 \leq i \leq N), Dish i has a_i (1 \leq a_i \leq 3) pieces of sushi on it.

Taro will perform the following operation repeatedly until all the pieces of sushi are eaten:

Roll a die that shows the numbers 1, 2, \ldots, N with equal probabilities, and let i be the outcome. If there are some pieces of sushi on Dish i, eat one of them; if there is none, do nothing.

Find the expected number of times the operation is performed before all the pieces of sushi are eaten.

Constraints

All values in input are integers.

1 \leq N \leq 300

1 \leq a_i \leq 3

Input

Input is given from Standard Input in the following format:

N
a_1 a_2 \ldots a_N

Output

Print the expected number of times the operation is performed before all the pieces of sushi are eaten.
The output is considered correct when the relative difference is not greater than 10^{-9}.

Sample Input 1

3
1 1 1

Sample Output 1

5.5

The expected number of operations before the first piece of sushi is eaten, is 1.
After that, the expected number of operations before the second sushi is eaten, is 1.5.
After that, the expected number of operations before the third sushi is eaten, is 3.
Thus, the expected total number of operations is 1 + 1.5 + 3 = 5.5.

Sample Input 2

1
3

Sample Output 2

3

Outputs such as 3.00, 3.000000003 and 2.999999997 will also be accepted.

Sample Input 3

2
1 2

Sample Output 3

4.5

Sample Input 4

10
1 3 2 3 3 2 3 2 1 3

Sample Output 4

54.48064457488221

Solution in PyPy3 (7.3.0) (judge verdict: Accepted)

N = int(input())
A = list(map(int, input().split()))
cnt1 = A.count(1)
cnt2 = A.count(2)
cnt3 = A.count(3)

# PyPyでは事前の型推論によりチェックを一部省略することで高速化を図っている部分があるが、
# 処理の中でintもfloatも取り得る変数にしてしまうとその推論が上手く働かない。
# 今回のように小数が入るとわかっている場合は、初期化する値も小数にしておけば、よりPyPyの恩恵を享受できる
# ↑ほんと？

dp = [[[-1.0] * (N + 1) for _ in range(N + 1)] for _ in range(N + 1)]
dp[0][0][0] = 0


def func(c1, c2, c3):
    if dp[c1][c2][c3] != -1:
        return dp[c1][c2][c3]

    prob0 = (N - c1 - c2 - c3) / N
    temp = 1 / (1 - prob0)
    if c1 > 0:
        temp += func(c1 - 1, c2, c3) * (c1 / N) / (1 - prob0)
    if c2 > 0:
        temp += func(c1 + 1, c2 - 1, c3) * (c2 / N) / (1 - prob0)
    if c3 > 0:
        temp += func(c1, c2 + 1, c3 - 1) * (c3 / N) / (1 - prob0)

    dp[c1][c2][c3] = temp
    return temp


print(func(cnt1, cnt2, cnt3))