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: Wrong Answer

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:
        if sum_ > K:
            sum_ -= a[i]
            break
        sum_ += a[i]
        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]

    ans = max(ans, i + j)

    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)

Submission status: Wrong Answer

Solution code:
N,M,K = map(int,input().split())
A = list(map(int,input().split()))
B = list(map(int,input().split()))

A_dp = [0] * (N + 1)
B_dp = [0] * (M + 1)

for i in range(1,N+1):
  if i == 1:
    A_dp[i] += A[i-1]
    continue
  A_dp[i] += A_dp[i-1] + A[i-1]
  
for j in range(1,M+1):
  if j == 1:
    B_dp[j] += B[j-1]
    continue
  B_dp[j] += B_dp[j-1] + B[j-1]
  
a = N
b = 0

minute = A_dp[a] + B_dp[b]
if K > minute:
  while K > A_dp[a] + B_dp[b]:
    b += 1
else:
  while K < A_dp[a] + B_dp[b]:
    a -= 1
  
cnt = a + b
for k in range(a,-1,-1):
  while A_dp[k] + B_dp[b] < K:
    if b == M - 1:
      break
    b += 1
  b -= 1
  if cnt < k + b:
    cnt = k + b
    
print(cnt)

Submission status: Wrong Answer

Solution code:
N, M, K = map(int,input().split())
A = list(map(int,input().split()))
B = list(map(int,input().split()))
Asum = [0]
Bsum = [0]
a = 0
b = 0
for i in range(N):
    a += A[i]
    Asum.append(a)
for i in range(M):
    b += B[i]
    Bsum.append(b)
Asum.append(0)
Bsum.append(0)
res, j = 0, 0
for i in range(N+1):
    if Asum[i] > K:
        break
    while Asum[i] + Bsum[j] > K:
            j -= 1
    res = max(res,i+j)
print(res)

Submission status: Wrong Answer

Solution code:
N, M, K = map(int,input().split())
A = list(map(int,input().split()))
B = list(map(int,input().split()))
Asum = [0]
Bsum = [0]
a = 0
b = 0
for i in range(N):
    a += A[i]
    Asum.append(a)
for i in range(M):
    b += B[i]
    Bsum.append(b)
Asum.append(0)
Bsum.append(0)
res, j = 0, 0
for i in range(N+1):
    if Asum[i] > K:
        break
    while Asum[i] + Bsum[j] > K:
            j += 1
    res = max(res,i+j)
print(res)

Submission status: Wrong Answer

Solution code:
#!/usr/bin/env python3

def main():
    n = int(input())
    string = input()

    result = ''

    for char in string:
        if int(char)%2 == 1:
            result += char

    if len(result) < 2:
        print('-1')
    else:
        print(result[:2])

n, m, k = [int(i) for i in input().split(' ')]

astk = [int(i) for i in input().split(' ')]
bstk = [int(i) for i in input().split(' ')]

bega, enda, begb, endb = 0, 0, 0, 0
suma, sumb = 0, 0
count = 0

while True:
    while enda < n and suma + astk[enda] <= k:
        suma += astk[enda]
        enda += 1
    while endb < m and sumb + bstk[endb] <= k:
        sumb += bstk[endb]
        endb += 1
    if bega < n and k >= astk[bega] and enda - bega >= endb - begb:
        suma -= astk[bega]
        k -= astk[bega]
        bega += 1
        count += 1
    elif begb < m and k >= bstk[begb]:
        sumb -= bstk[begb]
        k -= bstk[begb]
        begb += 1
        count += 1
    else:
        break

print(count)

Submission status: Wrong Answer

Solution code:
import sys
input = sys.stdin.readline

#n = int(input())
#l = list(map(int, input().split()))

'''
a=[]
b=[]
for i in range():
    A, B = map(int, input().split())
    a.append(A)   
    b.append(B)'''

n,m,k=map(int, input().split())
a=list(map(int, input().split()))
b=list(map(int, input().split()))

ina=0
cnt=0
def pl(li,k):
    c=0
    for i in range(len(li)):
        if k>=li[i]:
            k-=li[i]
            c+=1
        else:
            break
    return c

cli=[]
ci=0
if a[0]>k:
    if b[0]>k:
        print(0)
        sys.exit()
    else:
        print(1)
        sys.exit()

import copy
kk=copy.copy(k)
for i in range(n):
    if kk-a[i]>=0:
        ci+=1
        kk-=a[i]
        cli.append(ci+pl(b,kk))
    else: break
#print(cli)
print(max(cli))
#for i in range(min(n,m)):

#for i in range(abs(n-m)):

Submission status: Wrong Answer

Solution code:
import sys
input = sys.stdin.readline

#n = int(input())
#l = list(map(int, input().split()))

'''
a=[]
b=[]
for i in range():
    A, B = map(int, input().split())
    a.append(A)   
    b.append(B)'''

n,m,k=map(int, input().split())
a=list(map(int, input().split()))
b=list(map(int, input().split()))

ina=0
inb=0
now=0
cnt=0
def pl(li,k,index):
    c=0
    for i in range(len(li)-index):
        if k>=li[index]:
            k-=li[index]
            c+=1
            index+=1
        else:
            break
    return c

while True:
    if now>=k:
        break
    
    if ina>n-1:
        cnt+=pl(b,k,inb)
        break
    if inb>m-1:
        cnt+=pl(a,k,ina)
        break

    if now+min(a[ina],b[inb])<k:
        if a[ina]<b[inb]:
            cnt+=1
            k-=a[ina]
            ina+=1
            #print(a[ina-1])
        else:
            cnt+=1
            k-=b[inb]
            inb+=1
            #print(b[inb-1])
    else:
        break
print(cnt)


#for i in range(min(n,m)):

#for i in range(abs(n-m)):

Submission status: Wrong Answer

Solution code:
N, M, K = map(int,input().split())
A = list(map(int,input().split()))
B = list(map(int,input().split()))
Asum = []
Bsum = []
for i in range(N):
    a = 0
    a += A[i]
    Asum.append(a)
for i in range(M):
    b = 0
    b += B[i]
    Bsum.append(b)
Asum.append(0)
Bsum.append(0)
res = 0
for i in range(-1,N-1):
    for j in range(-1,M-1):
        if Asum[i] + Bsum[j] <= K:
           res = max(res,(i+j+2))
        else:
           break
print(res)