Problem:


Score : 300 points

Problem Statement

We have two desks: A and B. Desk A has a vertical stack of N books on it, and Desk B similarly has M books on it.

It takes us A_i minutes to read the i-th book from the top on Desk A (1 \leq i \leq N), and B_i minutes to read the i-th book from the top on Desk B (1 \leq i \leq M).

Consider the following action:

Choose a desk with a book remaining, read the topmost book on that desk, and remove it from the desk.

How many books can we read at most by repeating this action so that it takes us at most K minutes in total? We ignore the time it takes to do anything other than reading.

Constraints

1 \leq N, M \leq 200000

1 \leq K \leq 10^9

1 \leq A_i, B_i \leq 10^9

All values in input are integers.

Input

Input is given from Standard Input in the following format:

N M K
A_1 A_2 \ldots A_N
B_1 B_2 \ldots B_M

Output

Print an integer representing the maximum number of books that can be read.

Sample Input 1

3 4 240
60 90 120
80 150 80 150

Sample Output 1

3

In this case, it takes us 60, 90, 120 minutes to read the 1-st, 2-nd, 3-rd books from the top on Desk A, and 80, 150, 80, 150 minutes to read the 1-st, 2-nd, 3-rd, 4-th books from the top on Desk B, respectively.

We can read three books in 230 minutes, as shown below, and this is the maximum number of books we can read within 240 minutes.

Read the topmost book on Desk A in 60 minutes, and remove that book from the desk.

Read the topmost book on Desk B in 80 minutes, and remove that book from the desk.

Read the topmost book on Desk A in 90 minutes, and remove that book from the desk.

Sample Input 2

3 4 730
60 90 120
80 150 80 150

Sample Output 2

7

Sample Input 3

5 4 1
1000000000 1000000000 1000000000 1000000000 1000000000
1000000000 1000000000 1000000000 1000000000

Sample Output 3

0

Watch out for integer overflows.



Sample input:
3 4 240
60 90 120
80 150 80 150


Sample output:
3

Submission status: Accepted

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


def main(in_file):
    stdin = open(in_file)
    N, M, K = [int(x) for x in stdin.readline().split()]
    a = np.array([int(x) for x in stdin.readline().split()])
    b = np.array([int(x) for x in stdin.readline().split()])

    sum_ = 0
    i = 0
    while i < N:
        sum_ += a[i]
        if sum_ > K:
            sum_ -= a[i]
            break
        i += 1
    sum_ = K - sum_

    j = 0
    ans = 0

    while True:
        while True:
            if sum_ < 0:
                ans = max(ans, i + j - 1)
                break
            else:
                if j == M:
                    ans = max(ans, i + j)
                    break
                sum_ -= b[j]
                j += 1

        if i == 0 or j == M:
            break
        i -= 1
        sum_ += a[i]

    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)