Problem:


Score : 500 points

Problem Statement

Takahashi is at an all-you-can-eat restaurant.

The restaurant offers N kinds of dishes. It takes A_i minutes to eat the i-th dish, whose deliciousness is B_i.

The restaurant has the following rules:

You can only order one dish at a time. The dish ordered will be immediately served and ready to eat.

You cannot order the same kind of dish more than once.

Until you finish eating the dish already served, you cannot order a new dish.

After T-0.5 minutes from the first order, you can no longer place a new order, but you can continue eating the dish already served.

Let Takahashi's happiness be the sum of the deliciousness of the dishes he eats in this restaurant.

What is the maximum possible happiness achieved by making optimal choices?

Constraints

2 \leq N \leq 3000

1 \leq T \leq 3000

1 \leq A_i \leq 3000

1 \leq B_i \leq 3000

All values in input are integers.

Input

Input is given from Standard Input in the following format:

N T
A_1 B_1
:
A_N B_N

Output

Print the maximum possible happiness Takahashi can achieve.

Sample Input 1

2 60
10 10
100 100

Sample Output 1

110

By ordering the first and second dishes in this order, Takahashi's happiness will be 110.

Note that, if we manage to order a dish in time, we can spend any amount of time to eat it.

Sample Input 2

3 60
10 10
10 20
10 30

Sample Output 2

60

Takahashi can eat all the dishes within 60 minutes.

Sample Input 3

3 60
30 10
30 20
30 30

Sample Output 3

50

By ordering the second and third dishes in this order, Takahashi's happiness will be 50.

We cannot order three dishes, in whatever order we place them.

Sample Input 4

10 100
15 23
20 18
13 17
24 12
18 29
19 27
23 21
18 20
27 15
22 25

Sample Output 4

145



Sample input:
2 60
10 10
100 100


Sample output:
110

Submission status: Wrong Answer

Solution code:
import subprocess
import gzip, base64, os
import stat

gz = b'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'
bin = gzip.decompress(base64.b64decode(gz))
p = os.path.dirname(__file__)
pymain = os.path.join(p, "a.out")
with open(pymain, "wb") as f:
    f.write(bin)
os.chmod(pymain, stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR)
if p == "":
    pymain = "./" + pymain
subprocess.call(pymain)


# compiled by pythran
'''
#pythran export main2()
import numpy as np
i8 = np.int64
i4 = np.int32


def solve_core(a, b, t):
    d = np.empty(t, i8)
    for i in range(t):
        d[i] = -1
    d[0] = 0
    for i in range(a.shape[0]):
        for j in range(t - a[i] - 1, -1, -1):
            if d[j] >= 0:
                d[j + a[i]] = max(d[j + a[i]], d[j] + b[i])
    return np.max(d)


def solve(A, B, N, T):
    dp1 = np.empty((N, T), i8)
    dp2 = np.empty((N + 1, T), i8)
    dp1[0] = 0
    dp1[:, 0] = 0
    dp2[N] = 0
    dp2[:, 0] = 0
    for i in range(1, N):
        for t in range(1, T):
            dp1[i, t] = solve_core(A[:i], B[:i], t)
    for i in range(1, N):
        for t in range(1, T):
            dp2[i, t] = solve_core(A[i:], B[i:], t)
    ret = 0
    for i in range(N):
        m = 0
        for t in range(1, T):
            m = max(m, dp1[i, t] + dp2[i + 1, T - t - 1])
        ret = max(ret, m + B[i])
    return ret

def main2():
    stdin = open('/dev/stdin')
    N, T = map(int, stdin.readline().split())
    A = np.empty(N, i8)
    B = np.empty(N, i8)
    for i in range(N):
        A[i], B[i] = map(int, stdin.readline().split())
    ans = solve(A, B, N, T)
    print(ans)

'''