Buggy python submission (verdict: Time Limit Exceeded):
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(A, B, N, T):
    ret = 0
    d = np.empty(T, i8)
    for k in range(N):
        for i in range(T):
            d[i] = -1
        d[0] = 0
        for i in range(N):
            if i == k:
                continue
            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])
            ret = max(ret, np.max(d) + B[k])
    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)
'''


Fixed submission:
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(A, B, N, T, can_add):
    ret = 0
    d = np.empty(T, i8)
    for i in range(T):
        d[i] = -1
    d[0] = 0
    for i in range(0, N - 1):
        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])
        ret = max(ret, np.max(d) + can_add[i + 1])
    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())
    ind = np.argsort(A)
    A = A[ind]
    B = B[ind]
    can_add = np.empty(N, i8)
    can_add[N - 1] = B[N - 1]
    for i in range(N - 2, -1, -1):
        can_add[i] = max(can_add[i + 1], B[i])
    ans = solve(A, B, N, T, can_add)
    print(ans)
'''