You have N items that you want to put them into a knapsack. Item i has value vi and weight wi.

You want to find a subset of items to put such that:

* The total value of the items is as large as possible.
* The items have combined weight at most W, that is capacity of the knapsack.



Find the maximum total value of items in the knapsack.

Constraints

* 1 ≤ N ≤ 40
* 1 ≤ vi ≤ 1015
* 1 ≤ wi ≤ 1015
* 1 ≤ W ≤ 1015

Input


N W
v1 w1
v2 w2
:
vN wN


The first line consists of the integers N and W. In the following N lines, the value and weight of the i-th item are given.

Output

Print the maximum total values of the items in a line.

Examples

Input

4 5
4 2
5 2
2 1
8 3


Output

13


Input

2 20
5 9
4 10


Output

9
#!/usr/bin/env python3
# DPL_1_H: Huge Knapsack Problem
# 0/1 Knapsack using Branch and Bound
# https://www.youtube.com/watch?v=yV1d-b_NeK8

from heapq import heappush, heappop


def max_value(t, xs):
    def _cost(weight, value, i):
        for j in range(i, n):
            v, w = xs[j]
            if weight + w > t:
                return (-value, -value - (v/w)*(t-weight))
            weight += w
            value += v
        return (-value, -value*1.0)

    n = len(xs)
    xs.sort(key=lambda x: x[0]/x[1], reverse=True)
    u, c = _cost(0, 0, 0)
    heap = [(c, u, 0, 0, 0)]
    maxcost = u
    while heap:
        cost, upper, weight, value, i = heappop(heap)
        if cost > maxcost:
            break
        if i < n:
            v, w = xs[i]
            if weight+w <= t:
                heappush(heap, (cost, upper, weight+w, value+v, i+1))
            u, c = _cost(weight, value, i+1)
            if c < maxcost:
                if u < maxcost:
                    maxcost = u
                heappush(heap, (c, u, weight, value, i+1))

    return -maxcost


def run():
    n, t = [int(i) for i in input().split()]
    xs = []

    for _ in range(n):
        v, w = [int(i) for i in input().split()]
        xs.append((v, w))

    print(max_value(t, xs))


if __name__ == '__main__':
    run()

