Kera likes line segments. He has nn line segments, and each line segment can be described by l_i and r_i on the number axis. Moreover, each line segment Kera has its preferred value val_i.

Then there are m operations:

• (op = 1, l_i, r_i, val_i) — Add a line segment (l_i,r_i,val_i) on the number axis, according to the above.
• (op = 2,L,R) — Query the maximum difference of preferred values of all line segments that are fully contained by the interval represented by [L, R].

In addition, a line segment (l_i, r_i, val_i) fully contained by the interval (L,R) means: L \leq l_i \leq r_i \leq R.

The first line contains two integer n and m.

Each of the next nn lines contains 3 integers l_i , r_i and val_i.

Each of the next m lines contains 4 integers (op = 1, a_i, b_i, val_i) or 3 integers (op = 2,A,B).

Specifically, in order to get the real input, you need to create a variable named \text{LastAns} with an initial value of 0.

For each operation (op = 1), l_i = a_i xor \text{LastAns}, r_i = b_i xor \text{LastAns}.

For each operation (op = 2), L = A xor \text{LastAns}, R = B xor \text{LastAns}, then \text{LastAns} updated to result of this query.

In addition, xor means binary exclusive OR.

1 \leq n, m \leq 100000, 1\leq l_i \leq r_i \leq 3000, 0 \leq val_i \leq 1e9, 1\leq L \leq R \leq 3000.

For each operation (op = 2), print an integer to represent the maximum difference of preferred values.

It is guaranteed that each query fully contains at least one line segment.

The real sample input:

3 5
2 3 1
5 6 4
4 10 5
2 2 5
2 2 6
2 1 10
1 6 7 20
2 1 10