Problem:


Score : 400 points

Problem Statement

Takahashi will play a game using a piece on an array of squares numbered 1, 2, \cdots, N. Square i has an integer C_i written on it. Also, he is given a permutation of 1, 2, \cdots, N: P_1, P_2, \cdots, P_N.

Now, he will choose one square and place the piece on that square. Then, he will make the following move some number of times between 1 and K (inclusive):

In one move, if the piece is now on Square i (1 \leq i \leq N), move it to Square P_i. Here, his score increases by C_{P_i}.

Help him by finding the maximum possible score at the end of the game. (The score is 0 at the beginning of the game.)

Constraints

2 \leq N \leq 5000

1 \leq K \leq 10^9

1 \leq P_i \leq N

P_i \neq i

P_1, P_2, \cdots, P_N are all different.

-10^9 \leq C_i \leq 10^9

All values in input are integers.

Input

Input is given from Standard Input in the following format:

N K
P_1 P_2 \cdots P_N
C_1 C_2 \cdots C_N

Output

Print the maximum possible score at the end of the game.

Sample Input 1

5 2
2 4 5 1 3
3 4 -10 -8 8

Sample Output 1

8

When we start at some square of our choice and make at most two moves, we have the following options:

If we start at Square 1, making one move sends the piece to Square 2, after which the score is 4. Making another move sends the piece to Square 4, after which the score is 4 + (-8) = -4.

If we start at Square 2, making one move sends the piece to Square 4, after which the score is -8. Making another move sends the piece to Square 1, after which the score is -8 + 3 = -5.

If we start at Square 3, making one move sends the piece to Square 5, after which the score is 8. Making another move sends the piece to Square 3, after which the score is 8 + (-10) = -2.

If we start at Square 4, making one move sends the piece to Square 1, after which the score is 3. Making another move sends the piece to Square 2, after which the score is 3 + 4 = 7.

If we start at Square 5, making one move sends the piece to Square 3, after which the score is -10. Making another move sends the piece to Square 5, after which the score is -10 + 8 = -2.

The maximum score achieved is 8.

Sample Input 2

2 3
2 1
10 -7

Sample Output 2

13

Sample Input 3

3 3
3 1 2
-1000 -2000 -3000

Sample Output 3

-1000

We have to make at least one move.

Sample Input 4

10 58
9 1 6 7 8 4 3 2 10 5
695279662 988782657 -119067776 382975538 -151885171 -177220596 -169777795 37619092 389386780 980092719

Sample Output 4

29507023469

The absolute value of the answer may be enormous.



Sample input:
5 2
2 4 5 1 3
3 4 -10 -8 8


Sample output:
8

Submission status: Wrong Answer

Solution code:
# compiled by pythran
"""
#pythran export main2()
import numpy as np
i4 = np.int32
i8 = np.int64


def solve_core(a, n, u):
    res = -1000000000
    s = 0
    i0 = 0
    for i in range(n + u - 1):
        if i - i0 == u:
            s -= a[i0]
            i0 += 1
        s += a[i]
        if s <= a[i]:
            s = a[i]
            i0 = i
        res = max(res, s)
    return res


def solve(n, k, p, c):
    mark = np.zeros_like(p)
    cwork = np.empty(n * 2, i8)

    ans = -1000000000
    for i in range(n):
        if mark[i] == 1:
            continue

        cwork[0] = c[i]
        mark[i] = 1
        m = 1
        j = p[i]
        while j != i:
            cwork[m] = c[j]
            mark[j] = 1
            m += 1
            j = p[j]

        r = k // m
        s = k % m

        t = np.sum(cwork[:m])
        if t > 0:
            res = t * (r - 1) if r > 1 else 0
            u = m + s if k > m else k
            cwork[m: 2 * m] = cwork[: m]
            cwork[2 * m: 2 * m + s] = cwork[: s]
            res += solve_core(cwork[: m + u - 1], m, u)
        else:
            u = m - 1 if k >= m else k
            cwork[m: m + u - 1] = cwork[: u - 1]
            res = solve_core(cwork[: m + u - 1], m, u)
        ans = max(ans, res)
    return ans


def main(file_in):
    f = open(file_in)
    n, k = [int(x) for x in f.readline().split()]
    p = np.array([int(x) - 1 for x in f.readline().split()])
    c = np.array([int(x) for x in f.readline().split()])
    ans = solve(n, k, p, c)
    print(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)