小 A 和小 B 决定利用假期外出旅行，他们将想去的城市从 1 到 N 编号，且编号较小的城市在编号较大的城市的西边，已知各个城市的海拔高度互不相同，记城市 i 的海拔高度为Hi，城市 i 和城市 j 之间的距离 d[i,j]恰好是这两个城市海拔高度之差的绝对值，即d[i,j]=︱Hi-Hj︱。

旅行过程中，小 A 和小 B 轮流开车，第一天小 A 开车，之后每天轮换一次。他们计划选择一个城市 S 作为起点，一直向东行驶，并且最多行驶 X 公里就结束旅行。小 A 和小 B 的驾驶风格不同，小 B 总是沿着前进方向选择一个最近的城市作为目的地，而小 A 总是沿着前进方向选择第二近的城市作为目的地 （注意：本题中如果当前城市到两个城市的距离相同，则认为离海拔低的那个城市更近）。如果其中任何一人无法按照自己的原则选择目的城市，或者到达目的地会使行驶的总距离超出 X 公里，他们就会结束旅行。

在启程之前，小 A 想知道两个问题：

1．对于一个给定的 X=X0，从哪一个城市出发，小 A 开车行驶的路程总数与小 B 行驶的路程总数的比值最小（如果小 B 的行驶路程为 0，此时的比值可视为无穷大，且两个无穷大视为相等）。如果从多个城市出发，小A 开车行驶的路程总数与小B 行驶的路程总数的比值都最小，则输出海拔最高的那个城市。

2. 对任意给定的 X=Xi 和出发城市 Si ，小 A 开车行驶的路程总数以及小 B 行驶的路程总数。

第一行包含一个整数 N，表示城市的数目。
第二行有 N 个整数，每两个整数之间用一个空格隔开，依次表示城市 1 到城市 N 的海拔高度，即 H1，H2，……，Hn，且每个 Hi 都是不同的。
第三行包含一个整数 X0。
第四行为一个整数 M，表示给定 M 组 Si 和 Xi。
接下来的 M 行，每行包含 2 个整数 Si 和 Xi，表示从城市 Si 出发，最多行驶 Xi 公里。

输出共 M+1 行。
第一行包含一个整数 S0，表示对于给定的 X0，从编号为 S0 的城市出发，小 A 开车行驶的路程总数与小 B 行驶的路程总数的比值最小。
接下来的 M 行，每行包含 2 个整数，之间用一个空格隔开，依次表示在给定的 Si 和 Xi 下小 A 行驶的里程总数和小 B 行驶的里程总数。

输入　 [复制]

4 
2 3 1 4 
3 
4 
1 3 
2 3 
3 3 
4 3

输出

1 
1 1 
2 0 
0 0 
0 0

输入　 [复制]

10 
4 5 6 1 2 3 7 8 9 10 
7 
10 
1 7 
2 7 
3 7 
4 7 
5 7 
6 7 
7 7 
8 7 
9 7 
10 7

输出

2 
3 2 
2 4 
2 1 
2 4 
5 1 
5 1 
2 1 
2 0 
0 0 
0 0

【样例1说明】

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" alt="" />
各个城市的海拔高度以及两个城市间的距离如上图所示。

如果从城市 1 出发，可以到达的城市为 2,3,4，这几个城市与城市 1 的距离分别为 1,1,2，但是由于城市 3 的海拔高度低于城市 2，所以我们认为城市 3 离城市 1 最近，城市 2 离城市1 第二近，所以小 A 会走到城市 2。到达城市 2 后，前面可以到达的城市为 3,4，这两个城市与城市 2 的距离分别为 2,1，所以城市 4 离城市 2 最近，因此小 B 会走到城市 4。到达城市 4 后，前面已没有可到达的城市，所以旅行结束。

如果从城市 2 出发，可以到达的城市为 3,4，这两个城市与城市 2 的距离分别为 2,1，由于城市 3 离城市 2 第二近，所以小 A 会走到城市 3。到达城市 3 后，前面尚未旅行的城市为4，所以城市 4 离城市 3 最近，但是如果要到达城市 4，则总路程为 2+3=5>3，所以小 B 会直接在城市 3 结束旅行。

如果从城市 3 出发，可以到达的城市为 4，由于没有离城市 3 第二近的城市，因此旅行还未开始就结束了。

如果从城市 4 出发，没有可以到达的城市，因此旅行还未开始就结束了。

【样例2说明】
当 X=7 时，如果从城市 1 出发，则路线为 1 -> 2 -> 3 -> 8 -> 9，小 A 走的距离为 1+2=3，小 B 走的距离为 1+1=2。（在城市 1 时，距离小 A 最近的城市是 2 和 6，但是城市 2 的海拔更高，视为与城市 1 第二近的城市，所以小 A 最终选择城市 2；走到 9 后，小 A 只有城市 10 可以走，没有第 2 选择可以选，所以没法做出选择，结束旅行）
如果从城市 2 出发，则路线为 2 -> 6 -> 7 ，小 A 和小 B 走的距离分别为 2，4。
如果从城市 3 出发，则路线为 3 -> 8 -> 9，小 A 和小 B 走的距离分别为 2，1。
如果从城市 4 出发，则路线为 4 -> 6 -> 7，小 A 和小 B 走的距离分别为 2，4。
如果从城市 5 出发，则路线为 5 -> 7 -> 8 ，小 A 和小 B 走的距离分别为 5，1。
如果从城市 6 出发，则路线为 6 -> 8 -> 9，小 A 和小 B 走的距离分别为 5，1。
如果从城市 7 出发，则路线为 7 -> 9 -> 10，小 A 和小 B 走的距离分别为 2，1。
如果从城市 8 出发，则路线为 8 -> 10，小 A 和小 B 走的距离分别为 2，0。
如果从城市 9 出发，则路线为 9，小 A 和小 B 走的距离分别为 0，0（旅行一开始就结束了）。
如果从城市 10 出发，则路线为 10，小 A 和小 B 走的距离分别为 0，0。
从城市 2 或者城市 4 出发小 A 行驶的路程总数与小 B 行驶的路程总数的比值都最小，但是城市 2 的海拔更高，所以输出第一行为 2。

【数据范围】
对于 30% 的数据，有 1≤N≤20，1≤M≤20；
对于 40% 的数据，有 1≤N≤100，1≤M≤100；
对于 50% 的数据，有 1≤N≤100，1≤M≤1,000；
对于 70% 的数据，有 1≤N≤1,000，1≤M≤10,000；
对于 100% 的数据，有 1≤N≤100,000，1≤M≤10,000，-1,000,000,000≤Hi≤1,000,000,000，
0≤X0≤1,000,000,000，1≤Si≤N，0≤Xi≤1,000,000,000，数据保证 Hi 互不相同。

#include<iostream>

#include<cstdio>

#include<cmath>

#include<ctime>

#include<cctype>

#include<cstring>

#include<string>

#include<algorithm>

#include<cstdlib>

#include<set>

using namespace std;

const int N=1e5+;

const int M=1e5+;

const long long inf=1e+;

const long long eps=1e-;

struct node

{

  int pos;long long h;

  inline friend bool operator < (node a,node b)

  {

    return a.h<b.h;

  }

}p[N];

struct node1

{

  int pos;long long val;

  inline friend bool operator < (node1 a,node1 b)

  {

    if(a.val==b.val)  return p[a.pos].h<p[b.pos].h;

    return a.val<b.val;

  }

}temp[N];

int S[M],n,m,g[N][],next[N][];

long long height[N],X[M],disA[N][],disB[N][],anspos;

double ans1=inf;

set<node>::iterator t;

set<node>st;

inline int R()

{

  char c;int f=,i=;

  for(c=getchar();(c<''||c>'')&&c!='-';c=getchar());

  if(c=='-')  c=getchar(),i=-;

  for(;c<=''&&c>='';c=getchar())  f=(f<<)+(f<<)+c-'';

  return f*i;

}

inline long long Abs(long long a)

{

  return a<?-a:a;

}

inline void find(int i)

{

  p[i].pos=i,p[i].h=height[i];

  int cnt=;st.insert(p[i]);

  t=st.find(p[i]);

  if(t!=st.begin())

  {

    --t;

    temp[++cnt]=(node1){t->pos,Abs(t->h-height[i])};

    if(t!=st.begin())

    {

      --t;

      temp[++cnt]=(node1){t->pos,Abs(t->h-height[i])};

      t++;

    }

    t++;

  }

  if((++t)!=st.end())

  {

    temp[++cnt]=(node1){t->pos,Abs(t->h-height[i])};

    if((++t)!=st.end())

    {

      temp[++cnt]=(node1){t->pos,Abs(t->h-height[i])};

    }

  }

  sort(temp+,temp+cnt+);

  if(cnt>=)  next[i][]=temp[].pos;

  if(cnt>=)  next[i][]=temp[].pos;

}

inline void work1()

{

  for(int i=;i<=n;i++)

  {

    double tota=,totb=;

    long long lft=X[];

    int now=i;

    for(int j=;j>=;j--)

      if(g[now][j]!=&&lft>=(disA[now][j]+disB[now][j]))

        tota+=disA[now][j],totb+=disB[now][j],lft-=(disA[now][j]+disB[now][j]),now=g[now][j];

    int tim=;

    while(next[now][tim%]&&lft>=Abs(height[now]-height[next[now][tim%]]))

    {

      if(tim%==)  tota+=Abs(height[now]-height[next[now][tim%]]);

      else totb+=Abs(height[now]-height[next[now][tim%]]);

      lft-=Abs(height[now]-height[next[now][tim%]]);

      now=next[now][tim%];tim++;

    }

    double tmp;

    if(totb==)  tmp=inf;else tmp=tota/totb;

    if(tmp<ans1||(Abs(tmp-ans1)<eps&&height[i]>height[anspos]))  ans1=tmp,anspos=i;

  }

}

inline void work2(int u)

{

    long long tota=,totb=;

    long long lft=X[u];

    int now=S[u];

    for(int j=;j>=;j--)

      if(g[now][j]!=&&lft>=(disA[now][j]+disB[now][j]))

        tota+=disA[now][j],totb+=disB[now][j],lft-=(disA[now][j]+disB[now][j]),now=g[now][j];

    int tim=;

    while(next[now][tim%]&&lft>=Abs(height[now]-height[next[now][tim%]]))

    {

      if(tim%==)  tota+=Abs(height[now]-height[next[now][tim%]]);

      else totb+=Abs(height[now]-height[next[now][tim%]]);

      lft-=Abs(height[now]-height[next[now][tim%]]);

      now=next[now][tim%];tim++;

    }

    cout<<tota<<" "<<totb<<endl;

}

int main()

{

  //freopen("a.in","r",stdin);

  n=R();

  for(int i=;i<=n;i++)  height[i]=R();

  for(int i=n;i>=;i--)  find(i);

  for(int i=n-;i>=;i--)  g[i][]=next[next[i][]][],disA[i][]=Abs(height[i]-height[next[i][]]),disB[i][]=Abs(height[next[next[i][]][]]-height[next[i][]]);

  for(int i=;i<=;i++)

    for(int j=;j<=n-;j++)  g[j][i]=g[g[j][i-]][i-],disA[j][i]=disA[j][i-]+disA[g[j][i-]][i-],disB[j][i]=disB[j][i-]+disB[g[j][i-]][i-];

  X[]=R();work1();cout<<anspos<<endl;

  m=R();

  for(int i=;i<=m;i++)

  {

    S[i]=R(),X[i]=R();work2(i);

  }

  return ;

}