Problem:
 Score : 500 points    Problem Statement There are K items placed on a grid of squares with R rows and C columns. Let (i, j) denote the square at the i -th row ( 1 \leq i \leq R ) and the j -th column ( 1 \leq j \leq C ). The i -th item is at (r_i, c_i) and has the value v_i .  Takahashi will begin at (1, 1) , the start, and get to (R, C) , the goal. When he is at (i, j) , he can move to (i + 1, j) or (i, j + 1) (but cannot move to a non-existent square).  He can pick up items on the squares he visits, including the start and the goal, but at most three for each row. It is allowed to ignore the item on a square he visits.  Find the maximum possible sum of the values of items he picks up.      Constraints  1 \leq R, C \leq 3000  1 \leq K \leq \min(2 \times 10^5, R \times C)  1 \leq r_i \leq R  1 \leq c_i \leq C  (r_i, c_i) \neq (r_j, c_j) (i \neq j)  1 \leq v_i \leq 10^9  All values in input are integers.         Input Input is given from Standard Input in the following format:  R  C  K  r_1  c_1  v_1  r_2  c_2  v_2  :  r_K  c_K  v_K       Output Print the maximum possible sum of the values of items Takahashi picks up.        Sample Input 1 2 2 3
1 1 3
2 1 4
1 2 5      Sample Output 1 8  He has two ways to get to the goal:   Visit (1, 1) , (1, 2) , and (2, 2) , in this order. In this case, the total value of the items he can pick up is 3 + 5 = 8 .  Visit (1, 1) , (2, 1) , and (2, 2) , in this order. In this case, the total value of the items he can pick up is 3 + 4 = 7 .   Thus, the maximum possible sum of the values of items he picks up is 8 .       Sample Input 2 2 5 5
1 1 3
2 4 20
1 2 1
1 3 4
1 4 2      Sample Output 2 29  We have four items in the 1 -st row. The optimal choices are as follows:   Visit (1, 1)  (1, 2) , (1, 3) , (1, 4) , (2, 4) , and (2, 5) , in this order, and pick up all items except the one on (1, 2) . Then, the total value of the items he picks up will be 3 + 4 + 2 + 20 = 29 .        Sample Input 3 4 5 10
2 5 12
1 5 12
2 3 15
1 2 20
1 1 28
2 4 26
3 2 27
4 5 21
3 5 10
1 3 10      Sample Output 3 142

Solution (Python):

"""



def main2():
    stdin = open('/dev/stdin')
    R, C, K = [int(x) for x in stdin.readline().split()]
    V = [[] for i in range(R)]
    for i in range(K):
        r, c, v = [int(x) for x in stdin.readline().split()]
        V[r - 1].append([c, v])
    cost = [0] * (C + 1)
    for r in range(R):
        V[r].sort()
        hoge = [0, 0, 0, 0]
        for c, v in V[r]:
            for i in range(3, 0, -1):
                hoge[i] = max(cost[c] + v, hoge[i - 1] + v, hoge[i])
            hoge[0] = cost[c]
            cost[c] = max(hoge)
        for i in range(1, C + 1):
            cost[i] = max(cost[i], cost[i - 1])

    print(cost[-1])
"""
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)