h1, List<Integer> h2, List<Integer> h3)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"/*\\n * Code Author: Akshay Miterani\\n * DA-IICT\\n */\\nimport java.io.*;\\nimport java.math.BigInteger;\\nimport java.math.RoundingMode;\\nimport java.text.DecimalFormat;\\nimport java.util.*;\\n\\npublic class Main {\\n\\n\\tstatic double eps=(double)1e-7;\\n\\tstatic long mod=(int)1e9+7;\\n\\tpublic static void main(String args[]){\\n\\t\\tInputReader in = new InputReader(System.in);\\n\\t\\tOutputStream outputStream = System.out;\\n\\t\\tPrintWriter out = new PrintWriter(outputStream);\\n\\t\\t//---------------My Code-----------------\\n\\t\\tint n1=in.nextInt();\\n\\t\\tint n2=in.nextInt();\\n\\t\\tint n3=in.nextInt();\\n\\t\\tlong s1=0,s2=0,s3=0;\\n\\t\\tQueue<Integer> st1=new LinkedList<Integer>();\\n\\t\\tQueue<Integer> st2=new LinkedList<Integer>();\\n\\t\\tQueue<Integer> st3=new LinkedList<Integer>();\\n\\t\\t\\n\\t\\tfor(int i=0;i<n1;i++){\\n\\t\\t\\tint x=in.nextInt();\\n\\t\\t\\tst1.add(x);\\n\\t\\t\\ts1+=x;\\n\\t\\t}\\n\\t\\tfor(int i=0;i<n2;i++){\\n\\t\\t\\tint x=in.nextInt();\\n\\t\\t\\tst2.add(x);\\n\\t\\t\\ts2+=x;\\n\\t\\t}\\n\\t\\tfor(int i=0;i<n3;i++){\\n\\t\\t\\tint x=in.nextInt();\\n\\t\\t\\tst3.add(x);\\n\\t\\t\\ts3+=x;\\n\\t\\t}\\n\\t\\twhile(!(s1==s2 && s2==s3)){\\n\\t\\t\\tlong max=Math.max(s1, Math.max(s2, s3));\\n\\t\\t\\tif(s1==max){\\n\\t\\t\\t\\ts1-=st1.poll();\\n\\t\\t\\t}\\n\\t\\t\\telse if(s2==max){\\n\\t\\t\\t\\ts2-=st2.poll();\\n\\t\\t\\t}\\n\\t\\t\\telse{\\n\\t\\t\\t\\ts3-=st3.poll();\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\tout.println(s1);\\n\\t\\tout.close();\\n\\t\\t//---------------The End------------------\\n\\n\\t}\\n\\n\\tstatic class Pair implements Comparable<Pair> {\\n\\t\\tlong u;\\n\\t\\tint v;\\n\\t\\tint index=-1;\\n\\t\\tpublic Pair(long u, int v) {\\n\\t\\t\\tthis.u = u;\\n\\t\\t\\tthis.v = v;\\n\\t\\t}\\n\\n\\t\\tpublic int hashCode() {\\n\\t\\t\\tint hu = (int) (u ^ (u >>> 32));\\n\\t\\t\\tint hv = (int) (v ^ (v >>> 32));\\n\\t\\t\\treturn 31 * hu + hv;\\n\\t\\t}\\n\\n\\t\\tpublic boolean equals(Object o) {\\n\\t\\t\\tPair other = (Pair) o;\\n\\t\\t\\treturn u == other.u && v == other.v;\\n\\t\\t}\\n\\n\\t\\tpublic int compareTo(Pair other) {\\n\\t\\t\\treturn Long.compare(u, other.u) != 0 ? Long.compare(u, other.u) : Long.compare(v, other.v);\\n\\t\\t}\\n\\n\\t\\tpublic String toString() {\\n\\t\\t\\treturn \\\"[u=\\\" + u + \\\", v=\\\" + v + \\\"]\\\";\\n\\t\\t}\\n\\t}\\n\\tpublic static void debug(Object... o) {\\n\\t\\tSystem.out.println(Arrays.deepToString(o));\\n\\t}\\n\\tstatic long modulo(long a,long b,long c) {\\n\\t\\tlong x=1;\\n\\t\\tlong y=a;\\n\\t\\twhile(b > 0){\\n\\t\\t\\tif(b%2 == 1){\\n\\t\\t\\t\\tx=(x*y)%c;\\n\\t\\t\\t}\\n\\t\\t\\ty = (y*y)%c; // squaring the base\\n\\t\\t\\tb /= 2;\\n\\t\\t}\\n\\t\\treturn x%c;\\n\\t}\\n\\tstatic long gcd(long x, long y)\\n\\t{\\n\\t\\tif(x==0)\\n\\t\\t\\treturn y;\\n\\t\\tif(y==0)\\n\\t\\t\\treturn x;\\n\\t\\tlong r=0, a, b;\\n\\t\\ta = (x > y) ? x : y; // a is greater number\\n\\t\\tb = (x < y) ? x : y; // b is smaller number\\n\\t\\tr = b;\\n\\t\\twhile(a % b != 0)\\n\\t\\t{\\n\\t\\t\\tr = a % b;\\n\\t\\t\\ta = b;\\n\\t\\t\\tb = r;\\n\\t\\t}\\n\\t\\treturn r;\\n\\t}\\n\\tstatic class InputReader {\\n\\t\\tpublic BufferedReader reader;\\n\\t\\tpublic StringTokenizer tokenizer;\\n\\n\\t\\tpublic InputReader(InputStream inputstream) {\\n\\t\\t\\treader = new BufferedReader(new InputStreamReader(inputstream));\\n\\t\\t\\ttokenizer = null;\\n\\t\\t}\\n\\n\\t\\tpublic String nextLine(){\\n\\t\\t\\tString fullLine=null;\\n\\t\\t\\twhile (tokenizer == null || !tokenizer.hasMoreTokens()) {\\n\\t\\t\\t\\ttry {\\n\\t\\t\\t\\t\\tfullLine=reader.readLine();\\n\\t\\t\\t\\t} catch (IOException e) {\\n\\t\\t\\t\\t\\tthrow new RuntimeException(e);\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\treturn fullLine;\\n\\t\\t\\t}\\n\\t\\t\\treturn fullLine;\\n\\t\\t}\\n\\t\\tpublic String next() {\\n\\t\\t\\twhile (tokenizer == null || !tokenizer.hasMoreTokens()) {\\n\\t\\t\\t\\ttry {\\n\\t\\t\\t\\t\\ttokenizer = new StringTokenizer(reader.readLine());\\n\\t\\t\\t\\t} catch (IOException e) {\\n\\t\\t\\t\\t\\tthrow new RuntimeException(e);\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\treturn tokenizer.nextToken();\\n\\t\\t}\\n\\t\\tpublic long nextLong() {\\n\\t\\t\\treturn Long.parseLong(next());\\n\\t\\t}\\n\\t\\tpublic int nextInt() {\\n\\t\\t\\treturn Integer.parseInt(next());\\n\\t\\t}\\n\\t}\\n} \",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'equalStacks' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER_ARRAY h1\\n * 2. INTEGER_ARRAY h2\\n * 3. INTEGER_ARRAY h3\\n */\\n\\n public static int equalStacks(List<Integer> h1, List<Integer> h2, List<Integer> h3) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n1 = Integer.parseInt(firstMultipleInput[0]);\\n\\n int n2 = Integer.parseInt(firstMultipleInput[1]);\\n\\n int n3 = Integer.parseInt(firstMultipleInput[2]);\\n\\n String[] h1Temp = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> h1 = new ArrayList<>();\\n\\n for (int i = 0; i < n1; i++) {\\n int h1Item = Integer.parseInt(h1Temp[i]);\\n h1.add(h1Item);\\n }\\n\\n String[] h2Temp = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> h2 = new ArrayList<>();\\n\\n for (int i = 0; i < n2; i++) {\\n int h2Item = Integer.parseInt(h2Temp[i]);\\n h2.add(h2Item);\\n }\\n\\n String[] h3Temp = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> h3 = new ArrayList<>();\\n\\n for (int i = 0; i < n3; i++) {\\n int h3Item = Integer.parseInt(h3Temp[i]);\\n h3.add(h3Item);\\n }\\n\\n int result = Result.equalStacks(h1, h2, h3);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.util.*;\\n\\nclass Solution {\\n private static Scanner scan = new Scanner(System.in);\\n\\n public static int equalStacks(HashSet<Integer> h1, HashSet<Integer> h2, HashSet<Integer> h3) {\\n SortedSet<Integer> commonItems = new TreeSet<>();\\n\\n for (var height1 : h1) {\\n if (h2.contains(height1) && h3.contains(height1)) {\\n commonItems.add(height1);\\n }\\n }\\n return commonItems.last();\\n }\\n\\n public static void main(String[] args) {\\n\\n\\n String[] sizes = scan.nextLine().split(\\\" \\\");\\n int n1 = Integer.parseInt(sizes[0]);\\n int n2 = Integer.parseInt(sizes[1]);\\n int n3 = Integer.parseInt(sizes[2]);\\n\\n var h1 = takeInput(n1);\\n var h2 = takeInput(n2);\\n var h3 = takeInput(n3);\\n\\n System.out.println(equalStacks(h1, h2, h3));\\n }\\n\\n private static HashSet<Integer> takeInput(int n) {\\n HashSet<Integer> stack = new HashSet<>();\\n List<Integer> list = new ArrayList<>();\\n while (n-- > 0)\\n list.add(scan.nextInt());\\n int totalHeight = 0;\\n stack.add(totalHeight);\\n for (int i = list.size() - 1; i >= 0; i--) {\\n totalHeight += list.get(i);\\n stack.add(totalHeight);\\n }\\n return stack;\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'equalStacks' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER_ARRAY h1\\n * 2. INTEGER_ARRAY h2\\n * 3. INTEGER_ARRAY h3\\n */\\n\\n public static int equalStacks(List<Integer> h1, List<Integer> h2, List<Integer> h3) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n1 = Integer.parseInt(firstMultipleInput[0]);\\n\\n int n2 = Integer.parseInt(firstMultipleInput[1]);\\n\\n int n3 = Integer.parseInt(firstMultipleInput[2]);\\n\\n List<Integer> h1 = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n List<Integer> h2 = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n List<Integer> h3 = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.equalStacks(h1, h2, h3);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.OutputStream;\\nimport java.io.IOException;\\nimport java.io.InputStream;\\nimport java.io.OutputStream;\\nimport java.io.PrintWriter;\\nimport java.util.Iterator;\\nimport java.io.BufferedWriter;\\nimport java.util.InputMismatchException;\\nimport java.io.IOException;\\nimport java.io.Writer;\\nimport java.io.OutputStreamWriter;\\nimport java.util.NoSuchElementException;\\nimport java.io.InputStream;\\n\\n/**\\n * Built using CHelper plug-in\\n * Actual solution is at the top\\n *\\n * @author Egor Kulikov (egor@egork.net)\\n */\\npublic class Solution {\\n public static void main(String[] args) {\\n InputStream inputStream = System.in;\\n OutputStream outputStream = System.out;\\n InputReader in = new InputReader(inputStream);\\n OutputWriter out = new OutputWriter(outputStream);\\n EqualStacks solver = new EqualStacks();\\n solver.solve(1, in, out);\\n out.close();\\n }\\n\\n static class EqualStacks {\\n public void solve(int testNumber, InputReader in, OutputWriter out) {\\n int[] n = IOUtils.readIntArray(in, 3);\\n int[][] heights = new int[3][];\\n for (int i = 0; i < 3; i++) {\\n heights[i] = IOUtils.readIntArray(in, n[i]);\\n }\\n long[] sums = new long[3];\\n for (int i = 0; i < 3; i++) {\\n sums[i] = ArrayUtils.sumArray(heights[i]);\\n }\\n int[] ind = new int[3];\\n while (true) {\\n int at = -1;\\n long max = -1;\\n for (int i = 0; i < 3; i++) {\\n if (sums[i] > max) {\\n max = sums[i];\\n at = i;\\n }\\n }\\n boolean equal = true;\\n for (int i = 0; i < 3; i++) {\\n if (sums[i] != max) {\\n equal = false;\\n break;\\n }\\n }\\n if (equal) {\\n out.printLine(sums[0]);\\n return;\\n }\\n sums[at] -= heights[at][ind[at]++];\\n }\\n }\\n\\n }\\n\\n static interface IntList extends IntReversableCollection {\\n public abstract int get(int index);\\n\\n public abstract void removeAt(int index);\\n\\n default public IntIterator intIterator() {\\n return new IntIterator() {\\n private int at;\\n private boolean removed;\\n\\n public int value() {\\n if (removed) {\\n throw new IllegalStateException();\\n }\\n return get(at);\\n }\\n\\n public boolean advance() {\\n at++;\\n removed = false;\\n return isValid();\\n }\\n\\n public boolean isValid() {\\n return !removed && at < size();\\n }\\n\\n public void remove() {\\n removeAt(at);\\n at--;\\n removed = true;\\n }\\n };\\n }\\n\\n }\\n\\n static class ArrayUtils {\\n public static long sumArray(int[] array) {\\n return new IntArray(array).sum();\\n }\\n\\n }\\n\\n static class IntArray extends IntAbstractStream implements IntList {\\n private int[] data;\\n\\n public IntArray(int[] arr) {\\n data = arr;\\n }\\n\\n public int size() {\\n return data.length;\\n }\\n\\n public int get(int at) {\\n return data[at];\\n }\\n\\n public void removeAt(int index) {\\n throw new UnsupportedOperationException();\\n }\\n\\n }\\n\\n static class IOUtils {\\n public static int[] readIntArray(InputReader in, int size) {\\n int[] array = new int[size];\\n for (int i = 0; i < size; i++) {\\n array[i] = in.readInt();\\n }\\n return array;\\n }\\n\\n }\\n\\n static interface IntCollection extends IntStream {\\n public int size();\\n\\n }\\n\\n static interface IntIterator {\\n public int value() throws NoSuchElementException;\\n\\n public boolean advance();\\n\\n public boolean isValid();\\n\\n }\\n\\n static abstract class IntAbstractStream implements IntStream {\\n\\n public String toString() {\\n StringBuilder builder = new StringBuilder();\\n boolean first = true;\\n for (IntIterator it = intIterator(); it.isValid(); it.advance()) {\\n if (first) {\\n first = false;\\n } else {\\n builder.append(' ');\\n }\\n builder.append(it.value());\\n }\\n return builder.toString();\\n }\\n\\n\\n public boolean equals(Object o) {\\n if (!(o instanceof IntStream)) {\\n return false;\\n }\\n IntStream c = (IntStream) o;\\n IntIterator it = intIterator();\\n IntIterator jt = c.intIterator();\\n while (it.isValid() && jt.isValid()) {\\n if (it.value() != jt.value()) {\\n return false;\\n }\\n it.advance();\\n jt.advance();\\n }\\n return !it.isValid() && !jt.isValid();\\n }\\n\\n\\n public int hashCode() {\\n int result = 0;\\n for (IntIterator it = intIterator(); it.isValid(); it.advance()) {\\n result *= 31;\\n result += it.value();\\n }\\n return result;\\n }\\n\\n }\\n\\n static interface IntReversableCollection extends IntCollection {\\n }\\n\\n static class InputReader {\\n private InputStream stream;\\n private byte[] buf = new byte[1024];\\n private int curChar;\\n private int numChars;\\n private InputReader.SpaceCharFilter filter;\\n\\n public InputReader(InputStream stream) {\\n this.stream = stream;\\n }\\n\\n public int read() {\\n if (numChars == -1) {\\n throw new InputMismatchException();\\n }\\n if (curChar >= numChars) {\\n curChar = 0;\\n try {\\n numChars = stream.read(buf);\\n } catch (IOException e) {\\n throw new InputMismatchException();\\n }\\n if (numChars <= 0) {\\n return -1;\\n }\\n }\\n return buf[curChar++];\\n }\\n\\n public int readInt() {\\n int c = read();\\n while (isSpaceChar(c)) {\\n c = read();\\n }\\n int sgn = 1;\\n if (c == '-') {\\n sgn = -1;\\n c = read();\\n }\\n int res = 0;\\n do {\\n if (c < '0' || c > '9') {\\n throw new InputMismatchException();\\n }\\n res *= 10;\\n res += c - '0';\\n c = read();\\n } while (!isSpaceChar(c));\\n return res * sgn;\\n }\\n\\n public boolean isSpaceChar(int c) {\\n if (filter != null) {\\n return filter.isSpaceChar(c);\\n }\\n return isWhitespace(c);\\n }\\n\\n public static boolean isWhitespace(int c) {\\n return c == ' ' || c == '\\\\n' || c == '\\\\r' || c == '\\\\t' || c == -1;\\n }\\n\\n public interface SpaceCharFilter {\\n public boolean isSpaceChar(int ch);\\n\\n }\\n\\n }\\n\\n static class OutputWriter {\\n private final PrintWriter writer;\\n\\n public OutputWriter(OutputStream outputStream) {\\n writer = new PrintWriter(new BufferedWriter(new OutputStreamWriter(outputStream)));\\n }\\n\\n public OutputWriter(Writer writer) {\\n this.writer = new PrintWriter(writer);\\n }\\n\\n public void close() {\\n writer.close();\\n }\\n\\n public void printLine(long i) {\\n writer.println(i);\\n }\\n\\n }\\n\\n static interface IntStream extends Iterable<Integer>, Comparable<IntStream> {\\n public IntIterator intIterator();\\n\\n default public Iterator<Integer> iterator() {\\n return new Iterator<Integer>() {\\n private IntIterator it = intIterator();\\n\\n public boolean hasNext() {\\n return it.isValid();\\n }\\n\\n public Integer next() {\\n int result = it.value();\\n it.advance();\\n return result;\\n }\\n };\\n }\\n\\n default public int compareTo(IntStream c) {\\n IntIterator it = intIterator();\\n IntIterator jt = c.intIterator();\\n while (it.isValid() && jt.isValid()) {\\n int i = it.value();\\n int j = jt.value();\\n if (i < j) {\\n return -1;\\n } else if (i > j) {\\n return 1;\\n }\\n it.advance();\\n jt.advance();\\n }\\n if (it.isValid()) {\\n return 1;\\n }\\n if (jt.isValid()) {\\n return -1;\\n }\\n return 0;\\n }\\n\\n default public long sum() {\\n long result = 0;\\n for (IntIterator it = intIterator(); it.isValid(); it.advance()) {\\n result += it.value();\\n }\\n return result;\\n }\\n\\n }\\n}\\n\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'equalStacks' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER_ARRAY h1\\n * 2. INTEGER_ARRAY h2\\n * 3. INTEGER_ARRAY h3\\n */\\n\\n public static int equalStacks(List<Integer> h1, List<Integer> h2, List<Integer> h3) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n1 = Integer.parseInt(firstMultipleInput[0]);\\n\\n int n2 = Integer.parseInt(firstMultipleInput[1]);\\n\\n int n3 = Integer.parseInt(firstMultipleInput[2]);\\n\\n List<Integer> h1 = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n List<Integer> h2 = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n List<Integer> h3 = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.equalStacks(h1, h2, h3);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"equalStacks(h1, h2, h3)\"\n ],\n \"solution\": \"def read_stack():\\n stack = [int(x) for x in input().split(' ')]\\n stack = list(reversed(stack))\\n sum_stack = set()\\n psum = 0\\n for i in range(len(stack)):\\n psum += stack[i]\\n sum_stack.add(psum)\\n return sum_stack\\n\\ninput()\\n\\nans = read_stack()\\nans &= read_stack()\\nans &= read_stack()\\nif len(ans) > 0:\\n print(max(ans))\\nelse:\\n print(0)\",\n \"template\": \"#\\n# Complete the 'equalStacks' function below.\\n#\\n# The function is expected to return an INTEGER.\\n# The function accepts following parameters:\\n# 1. INTEGER_ARRAY h1\\n# 2. INTEGER_ARRAY h2\\n# 3. INTEGER_ARRAY h3\\n#\\n\\ndef equalStacks(h1, h2, h3):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n first_multiple_input = input().rstrip().split()\\n\\n n1 = int(first_multiple_input[0])\\n\\n n2 = int(first_multiple_input[1])\\n\\n n3 = int(first_multiple_input[2])\\n\\n h1 = list(map(int, input().rstrip().split()))\\n\\n h2 = list(map(int, input().rstrip().split()))\\n\\n h3 = list(map(int, input().rstrip().split()))\\n\\n result = equalStacks(h1, h2, h3)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":152,"cells":{"question_id":{"kind":"number","value":2141,"string":"2,141"},"question_title":{"kind":"string","value":"Minimum Average Waiting Time"},"question_content":{"kind":"string","value":"Tieu owns a pizza restaurant and he manages it in his own way. While in a normal restaurant, a customer is served by following the first-come, first-served rule, Tieu simply minimizes the average waiting time of his customers. So he gets to decide who is served first, regardless of how sooner or later a person comes. \n\nDifferent kinds of pizzas take different amounts of time to cook. Also, once he starts cooking a pizza, he cannot cook another pizza until the first pizza is completely cooked. Let's say we have three customers who come at time t=0, t=1, & t=2 respectively, and the time needed to cook their pizzas is 3, 9, & 6 respectively. If Tieu applies first-come, first-served rule, then the waiting time of three customers is 3, 11, & 16 respectively. The average waiting time in this case is (3 + 11 + 16) / 3 = 10. This is not an optimized solution. After serving the first customer at time t=3, Tieu can choose to serve the third customer. In that case, the waiting time will be 3, 7, & 17 respectively. Hence the average waiting time is (3 + 7 + 17) / 3 = 9.\n\nHelp Tieu achieve the minimum average waiting time. For the sake of simplicity, just find the integer part of the minimum average waiting time.\n\n**Input Format**\n\n* The first line contains an integer N, which is the number of customers. \n* In the next N lines, the i<sup>th</sup> line contains two space separated numbers T<sub>i</sub> and L<sub>i</sub>. T<sub>i</sub> is the time when i<sup>th</sup> customer order a pizza, and L<sub>i</sub> is the time required to cook that pizza. \n\n- The $i^{th}$ customer is not the customer arriving at the $i^{th}$ arrival time. \n\n**Output Format**\n\n* Display the integer part of the minimum average waiting time.\n\n**Constraints**\n\n* 1 ≤ N ≤ 10<sup>5</sup>\n* 0 ≤ T<sub>i</sub> ≤ 10<sup>9</sup>\n* 1 ≤ L<sub>i</sub> ≤ 10<sup>9</sup>\n\n**Note**\n\n* The waiting time is calculated as the difference between the time a customer orders pizza (the time at which they enter the shop) and the time she is served.\n\n* Cook does not know about the future orders.\n\n**Sample Input #00**\n\n 3\n 0 3\n 1 9\n 2 6\n\n**Sample Output #00**\n\n\t9\n\n**Sample Input #01**\n\n\t3\n\t0 3\n\t1 9\n\t2 5\n\n**Sample Output #01**\n\t\n 8\n\n**Explanation #01**\n\nLet's call the person ordering at time = 0 as *A*, time = 1 as *B* and time = 2 as *C*. By delivering pizza for *A*, *C* and *B* we get the minimum average wait time to be \n\n (3 + 6 + 16)/3 = 25/3 = 8.33 \n \nthe integer part is `8` and hence the answer. "},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Hard"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static int minimumAverage(List<List<Integer>> customers)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"\\nimport java.util.*;\\nimport java.io.*;\\n\\npublic class Solution {\\n\\n static void go() {\\n int n = in.nextInt();\\n Customer[] c = new Customer[n];\\n for(int i = 0; i < n; i++) {\\n c[i] = new Customer(in.nextInt(), in.nextInt());\\n }\\n Arrays.sort(c, Customer.Order.ByT.ascending());\\n\\n PriorityQueue<Customer> q =\\n new PriorityQueue<Customer>(n, Customer.Order.ByL.ascending());\\n long time = c[0].t;\\n int idx = 0;\\n while(idx < n && c[idx].t <= time) {\\n q.add(c[idx]);\\n idx++;\\n }\\n\\n long wait = 0;\\n while(q.size() > 0) {\\n Customer next = q.poll();\\n time += next.l;\\n wait += time - next.t;\\n\\n if (idx < n && q.size() == 0 && time < c[idx].t) {\\n time = c[idx].t;\\n }\\n while(idx < n && c[idx].t <= time) {\\n q.add(c[idx]);\\n idx++;\\n }\\n }\\n out.println(wait / n);\\n }\\n\\n public static class Customer {\\n public Long t, l;\\n public Customer(long t1, long l1) {this.t = t1; this.l = l1;}\\n\\n public static enum Order implements Comparator<Customer> {\\n ByT() {\\n public int compare(Customer c1, Customer c2) {\\n return c1.t.compareTo(c2.t);\\n }\\n },\\n ByL() {\\n public int compare(Customer c1, Customer c2) {\\n return c1.l.compareTo(c2.l);\\n }\\n };\\n\\n public abstract int compare(Customer c1, Customer c2);\\n\\n public Comparator ascending() {\\n return this;\\n }\\n\\n public Comparator descending() {\\n return Collections.reverseOrder(this);\\n }\\n }\\n }\\n\\n static InputReader in;\\n static PrintWriter out;\\n\\n public static void main(String[] args) {\\n in = new InputReader(System.in);\\n out = new PrintWriter(System.out);\\n\\n go();\\n\\n out.close();\\n }\\n\\n static class InputReader {\\n private InputStream stream;\\n private byte[] buf = new byte[1024];\\n private int curChar;\\n private int numChars;\\n\\n public InputReader(InputStream stream) {\\n this.stream = stream;\\n }\\n\\n public int read() {\\n if (numChars == -1)\\n throw new InputMismatchException();\\n if (curChar >= numChars) {\\n curChar = 0;\\n try {\\n numChars = stream.read(buf);\\n } catch (IOException e) {\\n throw new InputMismatchException();\\n }\\n if (numChars <= 0)\\n return -1;\\n }\\n return buf[curChar++];\\n }\\n\\n public int nextInt() {\\n return (int) nextLong();\\n }\\n\\n public long nextLong() {\\n int c = read();\\n while (isSpaceChar(c))\\n c = read();\\n int sgn = 1;\\n if (c == '-') {\\n sgn = -1;\\n c = read();\\n }\\n long res = 0;\\n do {\\n if (c < '0' || c > '9')\\n throw new InputMismatchException();\\n res *= 10;\\n res += c - '0';\\n c = read();\\n } while (!isSpaceChar(c));\\n return res * sgn;\\n }\\n\\n public String nextString() {\\n int c = read();\\n while (isSpaceChar(c))\\n c = read();\\n StringBuilder sb = new StringBuilder(1024);\\n do {\\n sb.append((char) c);\\n c = read();\\n } while (!isSpaceChar(c));\\n return sb.toString();\\n }\\n\\n public static boolean isSpaceChar(int c) {\\n switch (c) {\\n case -1:\\n case ' ':\\n case '\\\\n':\\n case '\\\\r':\\n case '\\\\t':\\n return true;\\n default:\\n return false;\\n }\\n }\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'minimumAverage' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts 2D_INTEGER_ARRAY customers as parameter.\\n */\\n\\n public static int minimumAverage(List<List<Integer>> customers) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<List<Integer>> customers = new ArrayList<>();\\n\\n for (int i = 0; i < n; i++) {\\n String[] customersRowTempItems = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> customersRowItems = new ArrayList<>();\\n\\n for (int j = 0; j < 2; j++) {\\n int customersItem = Integer.parseInt(customersRowTempItems[j]);\\n customersRowItems.add(customersItem);\\n }\\n\\n customers.add(customersRowItems);\\n }\\n\\n int result = Result.minimumAverage(customers);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n /*\\n * Complete the minimumAverage function below.\\n */\\n static long minimumAverage(int[][] customers) {\\n BigInteger currentTime = new BigInteger(\\\"0\\\");\\n BigInteger totalWaitingTime = new BigInteger(\\\"0\\\");\\n //storing customer number\\n PriorityQueue<Integer> minHeap = new PriorityQueue<>((o1, o2) -> Integer.compare(customers[o1][1], customers[o2][1]));\\n int done = 0;\\n int progress = 0;\\n Arrays.sort(customers, new Comparator<int[]>() {\\n @Override\\n public int compare(int[] o1, int[] o2) {\\n return Integer.compare(o1[0], o2[0]);\\n }\\n });\\n while (done < customers.length) {\\n while (progress < customers.length && new BigInteger(customers[progress][0] + \\\"\\\").compareTo(currentTime) != 1/* && customers[progress][0] <= currentTime*/) {\\n minHeap.add(progress++);\\n }\\n //process order\\n if (minHeap.isEmpty()) {// no more customer at this time, pick next customer\\n// totalWaitingTime += customers[progress][0] - currentTime;\\n currentTime = new BigInteger(customers[progress][0] + \\\"\\\");\\n } else {\\n //serve\\n int getOrder = minHeap.poll();\\n System.out.println(\\\"SERVING --> \\\" + customers[getOrder][0] + \\\", \\\" + customers[getOrder][1]);\\n\\n if (currentTime.compareTo(new BigInteger(customers[getOrder][0] + \\\"\\\")) == 1) {\\n totalWaitingTime = totalWaitingTime.add(currentTime.subtract(new BigInteger(customers[getOrder][0] + \\\"\\\")));\\n }\\n// totalWaitingTime += currentTime > customers[getOrder][0] ? currentTime - customers[getOrder][0] : 0;\\n done++;\\n currentTime = currentTime.add(new BigInteger(customers[getOrder][1] + \\\"\\\"));\\n// currentTime += customers[getOrder][1];\\n }\\n }\\n for (int i = 0; i < customers.length; i++) {\\n totalWaitingTime = totalWaitingTime.add(new BigInteger(customers[i][1] + \\\"\\\"));\\n }\\n return totalWaitingTime.divide(new BigInteger(customers.length+\\\"\\\")).longValue();\\n }\\n\\n private static final Scanner scanner = new Scanner(System.in);\\n\\n public static void main(String[] args) throws IOException {\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = scanner.nextInt();\\n scanner.skip(\\\"(\\\\r\\\\n|[\\\\n\\\\r\\\\u2028\\\\u2029\\\\u0085])*\\\");\\n\\n int[][] customers = new int[n][2];\\n\\n for (int customersRowItr = 0; customersRowItr < n; customersRowItr++) {\\n String[] customersRowItems = scanner.nextLine().split(\\\" \\\");\\n scanner.skip(\\\"(\\\\r\\\\n|[\\\\n\\\\r\\\\u2028\\\\u2029\\\\u0085])*\\\");\\n\\n for (int customersColumnItr = 0; customersColumnItr < 2; customersColumnItr++) {\\n int customersItem = Integer.parseInt(customersRowItems[customersColumnItr]);\\n customers[customersRowItr][customersColumnItr] = customersItem;\\n }\\n }\\n\\n long result = minimumAverage(customers);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedWriter.close();\\n\\n scanner.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'minimumAverage' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts 2D_INTEGER_ARRAY customers as parameter.\\n */\\n\\n public static int minimumAverage(List<List<Integer>> customers) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<List<Integer>> customers = new ArrayList<>();\\n\\n IntStream.range(0, n).forEach(i -> {\\n try {\\n customers.add(\\n Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList())\\n );\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n int result = Result.minimumAverage(customers);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import static java.util.Comparator.comparing;\\n\\nimport java.io.IOException;\\nimport java.io.InputStream;\\nimport java.io.PrintStream;\\nimport java.util.ArrayList;\\nimport java.util.HashSet;\\nimport java.util.List;\\nimport java.util.Map.Entry;\\nimport java.util.NavigableMap;\\nimport java.util.PriorityQueue;\\nimport java.util.Scanner;\\nimport java.util.Set;\\nimport java.util.TreeMap;\\n\\npublic class Solution {\\n\\tpublic static void main(String[] args) throws IOException {\\n\\t\\tdoMain(System.in, System.out);\\n\\t}\\n\\tpublic static void doMain(InputStream is, PrintStream out) {\\n\\t\\ttry (Scanner in = new Scanner(is)) {\\n\\t\\t\\tint n = in.nextInt();\\n\\t\\t\\t\\n\\t\\t\\tNavigableMap<Long, List<Order>> ordersByTime = new TreeMap<>();\\n\\t\\t\\tSet<Order> allOrders = new HashSet<>();\\n\\t\\t\\tfor (int i = 0; i < n; i++) {\\n\\t\\t\\t\\tlong arrivalTime = in.nextInt();\\n\\t\\t\\t\\tlong cookTime = in.nextInt();\\n\\t\\t\\t\\tOrder order = new Order(arrivalTime, cookTime);\\n\\t\\t\\t\\tordersByTime.computeIfAbsent(arrivalTime, a -> new ArrayList<>()).add(order);\\n\\t\\t\\t\\tallOrders.add(order);\\n\\t\\t\\t}\\n\\t\\t\\tout.println(computeMinAvg(ordersByTime));\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate static long computeMinAvg(NavigableMap<Long, List<Order>> ordersByTime) {\\n\\t\\tPriorityQueue<Order> outstanding = new PriorityQueue<>(comparing(o -> o.cookTime));\\n\\t\\tint ordersCooked = 0;\\n\\t\\tlong t = 0;\\n\\t\\tlong totalWaitTime = 0;\\n\\t\\t\\n\\t\\twhile (!ordersByTime.isEmpty() || !outstanding.isEmpty()) {\\n\\t\\t\\tif (outstanding.isEmpty()) { // jump to next arrival time\\n\\t\\t\\t\\tEntry<Long, List<Order>> firstEntry = ordersByTime.pollFirstEntry();\\n\\t\\t\\t\\toutstanding.addAll(firstEntry.getValue());\\n\\t\\t\\t\\tt = firstEntry.getKey();\\n\\t\\t\\t} else { // cook something, jump to now+cookTime\\n\\t\\t\\t\\tOrder nextToCook = outstanding.poll();\\n\\t\\t\\t\\tordersCooked++;\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t// record wait time for the order now being cooked\\n\\t\\t\\t\\ttotalWaitTime += (t - nextToCook.arrivalTime) + nextToCook.cookTime;\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t// move time to when we next make a choice, when the oven is free again\\n\\t\\t\\t\\tt += nextToCook.cookTime;\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t// record orders that will arrive before this pizza is cooked\\n\\t\\t\\t\\tNavigableMap<Long, List<Order>> arrivals = ordersByTime.headMap(t, true);\\n\\t\\t\\t\\tarrivals.values().stream().forEach(os -> outstanding.addAll(os));\\n\\t\\t\\t\\tarrivals.clear();\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\treturn totalWaitTime / ordersCooked;\\n\\t}\\n\\n\\tprivate static final class Order {\\n\\t\\tfinal long arrivalTime;\\n\\t\\tfinal long cookTime;\\n\\t\\t\\n\\t\\tOrder(long arrivalTime, long cookTime) {\\n\\t\\t\\tthis.arrivalTime = arrivalTime;\\n\\t\\t\\tthis.cookTime = cookTime;\\n\\t\\t}\\n\\t}\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'minimumAverage' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts 2D_INTEGER_ARRAY customers as parameter.\\n */\\n\\n public static int minimumAverage(List<List<Integer>> customers) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<List<Integer>> customers = new ArrayList<>();\\n\\n IntStream.range(0, n).forEach(i -> {\\n try {\\n customers.add(\\n Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList())\\n );\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n int result = Result.minimumAverage(customers);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"minimumAverage(customers)\"\n ],\n \"solution\": \"import heapq\\n\\nN = int(input())\\nTasks = [tuple(int(_) for _ in input().split()) for _ in range(N)]\\nTasks.sort()\\n\\nTotalWaiting = 0\\ncurrentTime = 0\\ncurrentWaiting = []\\n\\ni = 0\\nwhile i < len(Tasks) :\\n\\n # No clients, go to next client\\n if not currentWaiting and Tasks[i][0] > currentTime :\\n currentTime = Tasks[i][0]\\n\\n # Get all client waiting\\n while i < len(Tasks) and Tasks[i][0] <= currentTime :\\n heapq.heappush(currentWaiting, tuple([Tasks[i][1], Tasks[i][0]]))\\n i += 1\\n \\n # Serve the quickest pizza\\n task = heapq.heappop(currentWaiting)\\n currentTime += task[0]\\n TotalWaiting += currentTime-task[1]\\n \\n # If no more clients, serve all remaining client and close\\n if i >= len(Tasks) :\\n while currentWaiting :\\n # Serve the quickest pizza\\n task = heapq.heappop(currentWaiting)\\n currentTime += task[0]\\n TotalWaiting += currentTime-task[1]\\n\\nprint(TotalWaiting // N)\\n \\n \\n\\n\",\n \"template\": \"#\\n# Complete the 'minimumAverage' function below.\\n#\\n# The function is expected to return an INTEGER.\\n# The function accepts 2D_INTEGER_ARRAY customers as parameter.\\n#\\n\\ndef minimumAverage(customers):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n n = int(input().strip())\\n\\n customers = []\\n\\n for _ in range(n):\\n customers.append(list(map(int, input().rstrip().split())))\\n\\n result = minimumAverage(customers)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":153,"cells":{"question_id":{"kind":"number","value":14789,"string":"14,789"},"question_title":{"kind":"string","value":"Queries with Fixed Length"},"question_content":{"kind":"string","value":"Consider an $n$-integer sequence, $A = \\{a_0, a_1, \\ldots, a_{n-1} \\}$. We perform a query on $A$ by using an integer, $d$, to calculate the result of the following expression:\n\n$$\\huge{\\min_{\\Large{0 \\le i \\le n-d}} ( \\max_{\\Large{i \\le j \\lt i + d}} a_j )}$$\n\nIn other words, if we let $m_i = \\max(a_i, a_{i+1}, a_{i+2}, \\ldots, a_{i+d-1})$, then you need to calculate $\\min(m_0, m_1, \\ldots, m_{n-d})$. \n\nGiven $arr$ and $q$ queries, return a list of answers to each query.\n\n**Example** \n$arr = [2, 3, 4, 5, 6]$ \n$queries = [2, 3]$ \n\nThe first query uses all of the subarrays of length $2$: $[2, 3], [3, 4], [4, 5], [5, 6]$. The maxima of the subarrays are $[3, 4, 5, 6]$. The minimum of these is $3$. \n\nThe second query uses all of the subarrays of length $3$: $[2, 3, 4], [3, 4, 5], [4, 5, 6]$. The maxima of the subarrays are $[4, 5, 6]$. The minimum of these is $4$. \n\nReturn $[3, 4]$. \n\n**Function Description** \n\nComplete the *solve* function below. \n\n*solve* has the following parameter(s): \n\n- *int arr[n]:* an array of integers \n- *int queries[q]:* the lengths of subarrays to query \n\n**Returns** \n\n- *int[q]:* the answers to each query \n"},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Hard"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static List<Integer> solve(List<Integer> arr, List<Integer> queries)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n static int argMinFirst(ArrayList<Integer> maximums){\\n int best = maximums.get(0);\\n int bestIndex = 0;\\n for(int i = 0; i != maximums.size(); ++i){\\n if(maximums.get(i) < best){\\n best = maximums.get(i);\\n bestIndex = i;\\n }\\n }\\n return maximums.get(bestIndex);\\n }\\n\\n // based on http://www.geeksforgeeks.org/maximum-of-all-subarrays-of-size-k/\\n static ArrayList<Integer> calculateMaximums(int sequence[], int k)\\n {\\n Deque<Integer> dequeWindow = new LinkedList<>();\\n ArrayList<Integer> maximums = new ArrayList<>();\\n\\n // prefill the array with the first window\\n\\n for(int i = 0; i < (k-1); ++i){\\n maximums.add(Integer.MAX_VALUE);\\n }\\n for (int i = 0; i < k; ++i)\\n {\\n while ( (!dequeWindow.isEmpty()) && // not empty\\n (sequence[i] >= sequence[dequeWindow.getLast()]) // the element is not important\\n ){\\n dequeWindow.pollLast();\\n }\\n dequeWindow.addLast(i);\\n }\\n\\n // go through the rest of the windows\\n for (int i = k; i < sequence.length; ++i)\\n {\\n // add current maximum value in the window\\n maximums.add(sequence[dequeWindow.peekFirst()]);\\n\\n // Remove the elements which are out of this window\\n while ( (!dequeWindow.isEmpty()) &&\\n (dequeWindow.peekFirst() <= i - k)){\\n dequeWindow.pollFirst(); // Remove from front of queue\\n }\\n\\n\\n // Remove all elements smaller than the currently\\n // being added element (remove useless elements)\\n while ( (!dequeWindow.isEmpty()) && sequence[i] >= sequence[dequeWindow.getLast()])\\n dequeWindow.pollLast();\\n\\n // Add current element at the rear of Qi\\n dequeWindow.addLast(i);\\n }\\n\\n // Print the maximum element of last window\\n maximums.add(sequence[dequeWindow.peekFirst()]);\\n return maximums;\\n }\\n\\n public static void main(String[] args) {\\n //\\n Scanner in = new Scanner(System.in);\\n int N = in.nextInt(); int Q= in.nextInt(); in.nextLine();\\n\\n // parse the sequence\\n int[] sequence = new int[N];\\n for(int i = 0; i != N; ++i){\\n sequence[i] = in.nextInt();\\n }\\n in.nextLine();\\n\\n // parse the queries\\n int[] queries = new int[Q];\\n for(int i = 0; i != Q; ++i){\\n queries[i] = in.nextInt();\\n }\\n\\n // process all the queries\\n for(int i = 0; i != queries.length; ++i){\\n ArrayList<Integer> maximums = calculateMaximums(sequence, queries[i]);\\n\\n/*\\n System.out.println(\\\"query = \\\" + queries[i]);\\n for(Integer m: maximums){\\n System.out.print(m + \\\" \\\");\\n }\\n System.out.println(System.lineSeparator() +\\\"RESULT\\\");\\n*/\\n System.out.println((argMinFirst(maximums)));\\n }\\n\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts following parameters:\\n * 1. INTEGER_ARRAY arr\\n * 2. INTEGER_ARRAY queries\\n */\\n\\n public static List<Integer> solve(List<Integer> arr, List<Integer> queries) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int q = Integer.parseInt(firstMultipleInput[1]);\\n\\n String[] arrTemp = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> arr = new ArrayList<>();\\n\\n for (int i = 0; i < n; i++) {\\n int arrItem = Integer.parseInt(arrTemp[i]);\\n arr.add(arrItem);\\n }\\n\\n List<Integer> queries = new ArrayList<>();\\n\\n for (int i = 0; i < q; i++) {\\n int queriesItem = Integer.parseInt(bufferedReader.readLine().trim());\\n queries.add(queriesItem);\\n }\\n\\n List<Integer> result = Result.solve(arr, queries);\\n\\n for (int i = 0; i < result.size(); i++) {\\n bufferedWriter.write(String.valueOf(result.get(i)));\\n\\n if (i != result.size() - 1) {\\n bufferedWriter.write(\\\"\\\\n\\\");\\n }\\n }\\n\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts following parameters:\\n * 1. INTEGER_ARRAY arr\\n * 2. INTEGER_ARRAY queries\\n */\\n\\n public static List<Integer> solve(List<Integer> arr, List<Integer> queries) {\\n // Write your code here\\n //return solveDynamically(arr, queries); //this didn't work fast enough or I didn't use correct data structure\\n Map<Integer, Integer> solutions = new HashMap<>(); //don't calculate same thing twice\\n List<Integer> results = new ArrayList<>();\\n for (int q : queries) {\\n if (solutions.containsKey(q)) {\\n results.add(solutions.get(q));\\n } else {\\n //didn't calculate yet - let's go\\n Queue<Integer> currentSubarray = new ArrayDeque<>();\\n int min = Integer.MAX_VALUE;\\n int max = Integer.MIN_VALUE;\\n //PriorityQueue<Integer> maximums = new PriorityQueue<>((o1, o2) -> o2 - o1); any data structure adds at least log(n) to the solution\\n for (int elem: arr) {\\n currentSubarray.offer(elem);\\n if (elem > max) {\\n max = elem;\\n }\\n if (currentSubarray.size() > q) {\\n int elemToRemove = currentSubarray.poll();\\n if (elemToRemove == max) { //removing maximum - recalculate\\n max = Collections.max(currentSubarray);\\n }\\n }\\n if (currentSubarray.size() == q) {\\n if (max < min) {\\n min = max;\\n }\\n }\\n }\\n solutions.put(q, min);\\n results.add(min);\\n }\\n }\\n return results;\\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int q = Integer.parseInt(firstMultipleInput[1]);\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n List<Integer> queries = IntStream.range(0, q).mapToObj(i -> {\\n try {\\n return bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\");\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n })\\n .map(String::trim)\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n List<Integer> result = Result.solve(arr, queries);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\"\\\\n\\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts following parameters:\\n * 1. INTEGER_ARRAY arr\\n * 2. INTEGER_ARRAY queries\\n */\\n\\n public static List<Integer> solve(List<Integer> arr, List<Integer> queries) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int q = Integer.parseInt(firstMultipleInput[1]);\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n List<Integer> queries = IntStream.range(0, q).mapToObj(i -> {\\n try {\\n return bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\");\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n })\\n .map(String::trim)\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n List<Integer> result = Result.solve(arr, queries);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\"\\\\n\\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.util.LinkedList;\\nimport java.util.Scanner;\\n\\npublic class Solution {\\n\\t\\n\\tstatic int n, q, d, minOverD, globalMax = Integer.MIN_VALUE;\\n\\tstatic boolean VERBOSE = false;\\n\\t\\n\\tstatic int[] a;\\n\\tstatic int[] a2;\\n\\n public static void main(String[] args) {\\n Scanner sc = new Scanner(System.in);\\n \\n n = sc.nextInt();\\n q = sc.nextInt();\\n \\n a = new int[n];\\n a2 = new int[n];\\n \\n for(int ix = 0; ix < n; ix++) {\\n \\ta[ix] = sc.nextInt();\\n \\ta2[n - 1 -ix] = a[ix];\\n \\tif(a[ix] > globalMax) {\\n \\t\\tglobalMax = a[ix];\\n \\t}\\n }\\n StockSpan s = new StockSpan(a);\\n StockSpan s2 = new StockSpan(a2);\\n for(int ix = 0; ix < q; ix++) {\\n \\td = sc.nextInt();\\n \\tif(VERBOSE) System.out.println(\\\"d = \\\"+d);\\n \\tminOverD = findMinOverD(d, s, s2);\\n System.out.println(minOverD);\\n if(VERBOSE) System.out.println(\\\"FINAL: minOverD = \\\"+minOverD);\\n }\\n \\n sc.close();\\n }\\n\\n\\tprivate static int findMinOverD(int d, StockSpan s, StockSpan s2) {\\n\\t\\tint minD = Integer.MAX_VALUE;\\n\\t\\tfor(int i = 0; i < n; i++) {\\n\\t\\t\\tint d1 = s.getSpan(i);\\n\\t\\t\\tint d2 = s2.getSpan(n - 1 - i);\\n\\t\\t\\tif(d1 + d2 - 1 >= d) {\\n\\t\\t\\t\\tint v = a[i];\\n\\t\\t\\t\\tif( v < minD) minD = v;\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\treturn minD;\\n\\t}\\n\\n}\\n\\nclass StockSpan {\\n\\tfinal int[] price;\\n\\tfinal int n;\\n\\tint[] span;\\n\\tLinkedList<Integer> spanStack = new LinkedList<Integer>();\\n\\t\\n\\tpublic StockSpan(int[] price) {\\n\\t\\tthis.price = price;\\n\\t\\tspan = new int[price.length];\\n\\t\\tthis.n = price.length;\\n\\t\\tcomputeSpans();\\n\\t\\tif(Solution.VERBOSE) printSpans();\\n\\t}\\n\\n\\tprivate void printSpans() {\\n\\t\\tfor(int i = 0; i < n; i++) {\\n\\t\\t\\tSystem.out.println(String.format(\\\"d: %d, Span: %d\\\", i, span[i]));\\n\\t\\t}\\n\\t\\t\\n\\t}\\n\\t\\n\\tpublic int getSpan(int ix) {\\n\\t\\treturn span[ix];\\n\\t}\\n\\n\\tprivate void computeSpans() {\\n\\t\\tfor(int i = 0; i < n; i++) {\\n\\t\\t\\twhile(!spanStack.isEmpty() && price[i] > price[spanStack.peek()] ) {\\n\\t\\t\\t\\tspanStack.pop();\\n\\t\\t\\t}\\n\\t\\t\\tif(spanStack.isEmpty()) {\\n\\t\\t\\t\\tspan[i] = i + 1;\\n\\t\\t\\t} else {\\n\\t\\t\\t\\tspan[i] = i - spanStack.peek();\\n\\t\\t\\t\\tif(price[i] == price[spanStack.peek()]) {\\n\\t\\t\\t\\t\\tspan[i] = span[i - 1] + 1;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\tif(spanStack.isEmpty() || price[i] < price[spanStack.peek()] ) {\\n\\t\\t\\t\\tspanStack.push(i);\\n\\t\\t\\t}\\t\\t\\n\\t\\t}\\n\\t}\\n\\t\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts following parameters:\\n * 1. INTEGER_ARRAY arr\\n * 2. INTEGER_ARRAY queries\\n */\\n\\n public static List<Integer> solve(List<Integer> arr, List<Integer> queries) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int q = Integer.parseInt(firstMultipleInput[1]);\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n List<Integer> queries = IntStream.range(0, q).mapToObj(i -> {\\n try {\\n return bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\");\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n })\\n .map(String::trim)\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n List<Integer> result = Result.solve(arr, queries);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\"\\\\n\\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"solve(arr, queries)\"\n ],\n \"solution\": \"from collections import deque\\nimport heapq\\nn,q = map(int,input().split())\\nl = list(map(int,input().split()))\\n\\nfor _ in range(q):\\n d = int(input())\\n q = deque([],d)\\n h = []\\n for i in range(d):\\n \\n while len(q) and l[i] >= l[q[-1]]:\\n q.pop()\\n q.append(i)\\n \\n for i in range(i+1,len(l)):\\n\\n heapq.heappush(h,l[q[0]]) \\n while len(q) and q[0] <= i - d:\\n q.popleft()\\n \\n while len(q) and l[i] >= l[q[-1]]:\\n q.pop()\\n q.append(i)\\n heapq.heappush(h,l[q[0]]) \\n print(h[0])\",\n \"template\": \"#\\n# Complete the 'solve' function below.\\n#\\n# The function is expected to return an INTEGER_ARRAY.\\n# The function accepts following parameters:\\n# 1. INTEGER_ARRAY arr\\n# 2. INTEGER_ARRAY queries\\n#\\n\\ndef solve(arr, queries):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n first_multiple_input = input().rstrip().split()\\n\\n n = int(first_multiple_input[0])\\n\\n q = int(first_multiple_input[1])\\n\\n arr = list(map(int, input().rstrip().split()))\\n\\n queries = []\\n\\n for _ in range(q):\\n queries_item = int(input().strip())\\n queries.append(queries_item)\\n\\n result = solve(arr, queries)\\n\\n fptr.write('\\\\n'.join(map(str, result)))\\n fptr.write('\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"eJw8nUuObcuyVJGgIakLDZjxj6AlSNABKk8UoESFpmNj+DqPKzjsvTPXmp8ID3dzM/P/95/+23/+L//xP/z3//uv//lv/+v//O9//de/f7Vv7vk32v/4t/edNs7fXf307299o/X9t9+b8/3tcdu3/uY88+6/sR7/mb2/b/zNbx8+oo+9+t9c63x/4+ttjr++dl/5ibl3v39n5R/73zq75wveGLudvzFPz1/2285Yf+Odll9o+8x8+Rlvfl++fK4z82H5Q/9765x89Jvta/nLkw85/Gnka9u7u598w8yH/63eR39/vZ+vtb93x3h/7/S75t/r6+VP/bTcyd85o6/218Z+o/2tm7/MRfd8Sq7vzpUH0L8zZ/5ttDFyr3N838zvvXHH331v5cNam+vjLs/InbT12vfyC2e/9rdP5/vmy3PJvX539Dza3fJZ7eZ7+VM7PX/a68vjyKWem5/P8+u517t2/jRevi6P45w8hrvf5AdbnzePb9718vD3vN/O0/juyZeO97X71/MLLS/tW5tXkpf07l8u5OTRvjyuPO9cY17w38hryh282e+bufR8weEzv7f/2nduLmU/XkOecK42H/m+t/7e23l+/NPXOl/w8X4OX5vHlxvJe237yyffk4fCi/nOWfnTZP2srLN8ct7c3bno3nqu9ry8oDzMufMVf50Xm7+bbZ2/PhbL4fDy8lDyIXnAN2tj7fzn3KyGsQ8vpOcO+KcssZfPHzcrJW9qj7zdvOmWBdP2PvzIzjvOe8jqyHrJj7a/8+285Kz1/Ghe582/8fR6y0eOL084LyufwdrIE80F7XxBrmQcnu9ps+Wt5sKzFbI3ev6YOz3cR1b493IljW/Ig2q8iTzzxp96Nl5W286/5kUMPnhmf+3swZYfz4r6fDt54XkhfBa7aH4zH/CXldvy1O7OXWYXZUvm2d9cSt5Slv3iib751s3XfZ2vyzJl3eRSVt7qzPPjzs/O9mFl5drb/VY22vnGvWzvbKk8zJFVnpV8vpc9MrOAebQ3izVvPpEjV32zpbJHRu4/T3/n/h9fm7148u6+x/tZlzDSet4eP8Giy6voiRHfyWXdXH5uYK29uJ2vs3YqsmS5s0ez/IkI3+R55sqymfOUVnZ4NkBWU15QW9z0l42Yh/std/3Km8j7ytsm3uU1EHly1bnozm7PtbA+J68hn3Tmxw9kI2Td5EElDrydaJnX+53Fj/Mc8/P5kcmb/BIPX9ZgZ3Xnigmw5+UdEiK4/2zCltc8WRB5lbn1vIsvjyhPI6suP5jnkBWTbZ/PYVnzPHZWQsLiyC19h7hGVMhfXmJlVmZ+/bD086dcso8s0eqx7np+JHd6iR8t2z33M28u8C9vgHe+slDzYXlmWcZ/CVaEzMTWy7JL+OahncdayiLg7nfey9izs8XPYM3OnsWRtcdGy7s9CbV/WdrZsfe+1XY9d57P27mMfDgr8HzTFZ8Qx4O5+d9jCRHpDuuZv1wJ3llJ4+QvswgSFv8IK4k2WfgEsFx79k9+8rIQE1nfZd9k++QZ5vkQ+VlTLDqOi7735gcSjm52rFeSRz5anuD35VlkL/AuW/Z9NmJedo6SbOrDU9x9sthzU9kWr+dZ5jlw0tTBtnNHjYB586H5qVw6X5afXllUO6+eNc2L4vTL2dgIp5xbLdsiHz1znOZX89Rn1m4eZF5G7mlkW7Kdcmc5hS8H6uhcUc7Y/FYeZJZHQs335Zdz7MzH7msrK3Psj0vK32Sp5ZryZNj07x3Wz315CNl2vJX9jtE3cSgPJM+9s/123lC7nNhZinmCfPDOeZpITvDPzyc8HE7oR/i9OYsXeylXlL2+2A55S9lnuZJD+OVl5Lxeu3YFDzYfln2bh5m/OpzsL/s8sSxvZ+bRJ8pz8OYBcVZcYgzJSS7tj/Rjc8v5hc4re1m7+V2yhZWFcQZhkpf2OllDLqlzDmdX+dTzG4NV1kfeD/eQ/ZcDixebv2KltzzgPKnFT+bXcgJddgyneFb2yZef6VFws6kSpBJFjjtjcOu5rATd/Il8hHiaIPh3yZoM1fnZvwoEueJcfH6bpIijnaiS57fyUAk1j/BwVq6hZyfkZYwsjkQCAiCvJis9KyCxML+dz2I5kAOQQOQ4zZLMumm819os+Zo82fd9J082y+CQT+S8ZbVdDsScqf4huyPPMNGYfcQ6aBxDWa1ZzomzWTbECF5dTzDKXyb65IUnYCxOQBIAwkoe5Ez4Z+Fmw+S68zmEoGyFBNCkEX1kr11OtPzkTkZ4eQ95ybm63E72cBtE30aWZ6zLU/FUHayhe922LWEhb5jlws7Jh8xspwQJomYWAVEr7y9rYfILX3box2LNOjUic0jy2vPiEmEIn3nauZL8ujdiLM0+nSQ7Lxs8V5KThDMkl0eQTKTKbeW9fcbnj7QwMSHbjQdLlP4eayAPpueT8xxJjnI5Mzf3vkQcrjJbhUwzv3/YPQmr5Iq8xzyZxzU0Nlu+s5GtJkBwClyeYaJEti4LqROnv5yI2XqJz/xWAgWHSHIS8sC+yaJyPnLMZGefyvjM2TgbuJ7Ja88Cydfl3CJsJceZpoSTDZfjMrsp6UK2bYJLVhlROsVBUuPacIkOhIK8/uytBLbBZvwShS/viENzZRlxFF6uMZHnkrLmSCRrzNXnsY7KR87HOZvDZ1FuXLKZxLLshPwnn5dUag2eWj4kG/OQsvGaTZPyeusxEjHY1Z+pWo5H0iRKnjzpj8wiF88RkrDUc299jul5mMfpJs/DyxPMu9/EjcSD7MFNsOTnt6cgqVcKqjzTHKGLdC5Rd/Fo8iHkKdmePAVSPCulxeGQw5g8sTdy35uwnadMCcbJmMonv5Wl84tBedgfwTJP8pDF5olswto+pAd5a8SNrKpcWgLnx8NOyXbNnvJ4CR9ZeB85HXnv5AHnsfX8Zo6sSZTMntns6HlIddl4ObhyW4scPdfjuZx4w1GZz3qsssVGSqHUyCE32V29HkLlo7hLJCbab8NEtsokCBMfWAwJWGR8+f1lUcg6y0VnU76cAJQLw2JoJiJczoWkrh+HJMtgU6zlPx/nUtKUSZp5u6VBVnP+nP+4R3NFHyE9pZr5VWqRXENWKwlknibZ89rUUjfJfZ5YsoNamEQEon3uLjuTojU7qJHdkxZlkeTk4DFOXh9fTmmZH2BZpLpbZoYslZxZj6pu5HzIvea84+dZJPlTMjXeU/LpvFhiTi4lUcwCI9uTSyHZzrtIek11m6e3eOnGqmyzbM084uUxmeVhVN7UlC97PT+Scn4aZLK3sxwPKV4i7cdv9SS+2daD8i8rY/Ms85FJVGaj0vjjGhdHs5V4Z78RgEd2U2pI7iPH+ia5PlRYOUEeh2oOUa41q5MwkPOH3ColXbK9P+qezQ4l1c1GyDLKvyVVIplM6d2pDTYZQU/xtTg1GmdsAmqeCRU7T/QZWPIC2PhZE40dmYhOsMq7y1rP3vglM49S0s2+eaK5n2fZbOlNYflIhfNzSckT9wbxIC8leQwLLEdQlsEgXyMk5kBJPse+3nm6FBmbPJdzYrQq4djSiXHs75y7pAcgLZOyPokud/fI7fM0qHw65cukAv+oQLrZ/yWmcFwnJOWFs2/ygCjSskU6sfobghXJ7S6JKPcDapB/y25mIXJOZL901lkFUdLJrBMykzyyfClrlhL/JjCarjRyn3x5XnwyyMFf5sLMza6J8+B7DvmbqRLP5F3zoVxCB20AKvnjfCd14FziFM+BAeoy2J09l0DZkMB0iYPWZNlrrBdSRu54smazFg5HaHJScqWcK1kTixDRSGJyhfkFa5Ds8xRayScpffnyVsEnkSWhe1v3LvMuMuhE2lT6jxzgsSgW6WS+7uVXgDqSCuYns0tzPytnF0DJSYXbeDbUCoNyHTRsPuv4x27INuRiLxl034UocW+T2tVaaia9a1aoufEE2FSUOQBy7mXv8TiyJD4yh0WZx8FkqdD3ZxqefZ7HlgVF5Eqo5jFzjFNqfqw8cupJAnw5IElyWyVe5FEJnQTpVEFZINmMLOrcW1517mOBqK2sW6r/rB5eAXEll/VVZpcVzlJt1tZ5zx9hknWewgG0hPOX2Jl95Ql5wM4mR88RJeBhTDKF/Ce1OfE778Olx8NOqMmNUWmBEKbc8wj30MjndkrIxyO85IpZVslAN0+S5C+3D1zBWbRZMubt+7t8OIVRYhoP+iX5+Vgwk4MmSY1nf8owY33yhITNlC2gKAf4jcrjELRyJiyie8v7yO9tTsAEgKRYeRnfougdnkU5AUmXc0wtbin3C4yY98uxmOho5nRcEB1wgfLYZwYackHKgDnAi/KwqY0TILpwXucwyHfx4ylzlijVIUmb1ClgFUkceIxJtyaoXuP0yCVQ6OcChE6SU+URkxblHpOo5NVnFZHXbTYCdU5WZhcZ+URFCUwJREkjObWJVhSbTQT4gVkRxvOmSUdz01vIKgEHaPjymHI4ix0NCuhEi2leDCqVKDtNOQ9PPmnCIgInNi82Vl4S0V8gKalzVqbn5OUFTBby2mJvCS7fMmRxhpBG5VXluzb1YH6BwPqB3LB2O/Bi/pYl/JIuf5wGYhA7CzRPKHkhl0TSyY5MYAX+AaVOCs3BRxjPRxK4iQ9gWdmFDbRwgDGBD1PQUnznd5NXJQPJvjRnu42CndRwc8rkoOYk6GzE5MWNci6rmVBFxP14xqQqSf2nhSwVN0Afx0rP+UN1cBsJ6SFumfWDWGbr5KQg9ch1cm8cxPlogg4bEZBsg9uCtYF4uCAagZi37TGcKMlaA8TugAlkv7nWCQBw80ApOl/jxKGyIs9plLgptg4w26X27Fx5NhPhM88SmDIHEl9P5UYRm0OHzBbgG6R4AtMmRc7vZml3Dp9siXzgSUq7WEbZgpRxi1Cc+NpdFpPfTr4DGDYtMLPyP2HqHBWAySl0yUxylnJ2kwZ2yqe1qrjLCh1jm8Nkm7KpFsuRY5rt0LK52c9ZG7yxBAWA8Mae55I+zpHU0p3EPcFKDAzckHyLhO4SkRYnOc9/scCTDuapk58BESaNGMffHrQmzDCSSwH95hlzsZxSLKQhqHw5yqjPJod9Dl9yxOxRM7QG1pdV2ThBclYQpk7Wa3YjOX+qjDxhjtrU6ET9pHdtVOELbMDNbjdnJQsbuImD1CyZn0zGM8BjOd+7v5fdldSVe05omAtUQ7jv+EJzW8AYM0uDNPQTjsv92NIBxyVENr+bb8g1d9CcHCAmBKIYZG1Ab4uLy8vMLswx19kQL0UcZ3HqYM633AXATqKBMCW5ax4eRV/eOidNbpDdC2BL4M+Ddye4qaYVTzWywGH3rmoZSE5EIKfOeIXksNp7p+9B5spptROn6V9Yx9IOo6lD5kryyCYhywT9zn2BGeS/j8jdnpugjartP2G4N026iQqbdQTcIZiU9Jit8xa1xqqtnleyzGYOGEOeNKExF8CJ0+YxveZsa2Z6xDbKBOCj5OB5/l8SFyC91gVAzaE26B9I2ycexo79wKmuW0hE5DYB2ZciPw82kZ8zhAbZpiXQwQdpeyWcJHwCDidxSFGZjOyj6E2oOHxRogrfkLRxGVVpeeVVXOCYVH6At1lpgMPNtgTg9wZJaR7xjyIyCVM+8XFkk/gdAb28pBxciWNA1wl54Ek59gACWIyEQ8443m5OPh7joPSgxXNY7eCclCN2Dg5wav4KuI5ScBFzsvkb+Rapycwr4ctzuFUx8NjoeY9mFMtOjBAgp/Q6lK5ZmXT3WPC0WWgiUjWyjfMblqlg/KCsn/2cJxYPuGz/lMSIHBm8gi4DF/8syZPPkmJXvdlAgTe7gmyMQ9HcijMmdQCnOmnUsu0ynlj5NQefm9tqIHZ50ftydznBudhskUlq9AErJoXi1eUZXVJkToKEt0YmMipJ/D46D+xcQmfj9eQxPbG27oG0OH1SVoOE9UpYUhkOwVfy8DwQgmQ+gpWUAM9ayNskw8m79QUAvJFKgEoBcQJTHttxuQXS2Sy8RTWW04cgREFNxbIo9OgQiYg13hjQ3CBXMXYcAC/yC642f0oAasRfQN6c7sSLU6lgliqpePZVEss/Tj0eJWvBZdY5jgSjOH3pB/RELhCv0Q1SqZAeOyQFbLYb4C1fxPakMubYo51J2EjsJ3TSGgUc/rpZSrOryZoHmaaAbyBw2TxH6LvbYf+jGKCqJYZuTu9fwgdynnoVkGLYNs/7/ojeiUEcoVfINTGUmJX3B/xsqUehke8Dc7iVUfbLQZLFQB2fqggUCUzJymEA9N4jZpOXRkmZ9UhVkZOLXTSByk+SNRo/eZi8p2cynBSAPBN0+Am28FBWJX7N3JRKbbDmhjHuIyxnFfbE3lzd5E/AEcueO+3SrD7PU7JCyAqsGLYTHcHdPIHIg8gPjGNJeVnIqSAvTaCTooEw8LE4snjIfLP9LyyFREjw1YRkGjZnizPQ5CcfvKTu2ZaHdmQO7u8T/5lCec3DZNJ0zXIF+B3kulkOExw1US5LgFe4QI0nGTm9jI9uXjabe5fNmounGAP//e07EkOSoiX2t5o5zqGx9CgggHnfqY75+iRxgEomowe2I08gI8n38sy+040Vws65kE73GlgETOIjsOXFgwvkibFjOZPYiPlEvojS1uA1WdN5peydfS1N7GQBD9G6Wo/HRReajybQTF8VCwl4jPz0iaDl8eeK6TvQJqexzzokUlM1L1HdxP4E0twtFTt1LJsgQVMgfR/YBWBOQG6nyXt5Jg052om8yUlJQKjLyOfbsSIlU4UywH48pC/wMMAhk+vswvku/IBBbUZ4pMmV1IYAlni6hXOp3B+BKfsj2wzsJGUIbaBhUgWOQjyjycfhPgS8kp/3bdUI2cQTiy6y2PlkQdDC39SA+YosiBwShNRcK0d8F6N72VGbtGFC0ci56veABWTT3Q1kD3hCY2r5TPNJy6S3JRUDj5KW8F3qmsHX8oQvOCSkBoDYZ06wYG6Q4E26MQklPNO80QKf6WDka9ew4bHcZlkVtFdhJoluACZw3nHReelgyZfzKEsaRAVAqsPt6Mu+GXlqKujXtiAlBJHGHQNimV997Moc9KSwqWs6mewr4gDZeBYThxXIdSKMMEEDX0xEfrZ3WXbZF9NeDo3sv2OrgP4cUHJWNDEriRJ9p5QRAF4bjDsnaWIgi0jUN/tTvtPha3JHUxyOVAzYJMuU44Kfv6Q9tIyK4DGgGKTgy+v5AyTklKHXxVlFj/czS0wCuMy2v2FoscBIFH3D/hIFaHIqEgDOjzpWm28h4TvXTWf1D64L6BX9NnC37HLOq2RaIjzXUJatdPkUgDcKRX4EmEAQz0bSNGN9VDnb5uMl20t0PsUBuUVTIF2RhcRW3xQfG14Z6VQu7EhNovsIme0bIJvJ9QBE8/jp+5DeEyESWWiJNNrf1HE0cxLpQNcmASV7nb5yqi1QgQFYALpE8+Lm2RiqGggVOABdsc622FT9pMq2KegFcN52c9Ys6gU+MVYhbM9dO83aF+cH9yKlio6oPZxjXG0wf2jkzEIALUw40gE0B62crCNA3mwU8MZjppGUvNqFj2wwKQ0dXvbdtK4xM83Zne8zFbIh9ESFFiBOztbPAiLrUHoHTCYSKFackSrX1fiRnKPWdpsULdfKc01tStwn4IDrTl4ijBtiK9mbu6KL8lEcnfcBs+XpTTIKCIKygx6h+7eAgRQu30sj48mtobfXAL03oDxtrGwuSEH5bq+3EWGhHsKcufTGQH9t7yYW0JzslBxdBMHsh1QsV99gJlzpOOwVEoWE409shNdDscEBBe+CSnbSCqBVTgby0dolAlUbEy6IteaTmUHPJQcvTI6Eh9YNzCzvLDd4PtmyW/5abp14OYEHycNBMbJa4UtCiCC/+kQXsyqpe4CxSUisTmk9gm0NonsKLulVw12/+Xlqoo8eVV63ODMUCQ6MAdGAKhy+X5KUZa1CsE7iAOx5mqghSZQHbmsXONjgyOnNU8mVsBaSmfNsL+3+vG43V4LQhmw2KM8IxcZSACmiOtlfg6XJw6cf0aHVsQAXaU+2HcUGbDE5UM3WVupCeJL0Z7epIZB3gincm5zdPOhHd968fsHVk7n5iOt0vD6bLlcEc9nNzGskA7/iKFAZb5EZzfRySAooGJFzBR9Jz0eIoX0FUnM4TdkKHEEwYWSNusku6VSrDNkO38eNfw/8DBjG5JcagqgmV0ziTKcwSHgAu0kNwLsdEFKKzGrLjahIrKNGZfc0wV+5J3SLKOolRg7afUeWG8ScqoKoUah7xHEoZTmOFhm6dBlOaDoO9A27ZFwO0iQJncScNEFq3loWn3JTyIWoLSiqgWJtKk66cNTNi+KzDelask8pu+VQgEkMeLAQkTroK4v9emu24WlNH3m6YkIsmry1Q/1MbAGle1QE+boDjAnv4WMfWk5lIRPdFlgBVA/6YFlHRRCB/Zekp4lbTpLhZKZ9EzqyO2h2LjiBORNBben0kRLToSG5NOi26QI6UB1Z5Sli/6ADVVFAuQXvk30Nbk5KMiw05FDObX9uE+Rp0HyJTfRwaPWCti/TNjpGU6IOpESSovOkgdbKnBB+jQrAi0kDaYgMnuuWNsvRead0Bfr7kI9XhX87QJPzApoGICJ45G5FQ7Aw7ULT37NT2p6Yxcd7z8rgpXLppATUaSxybk5uCKylTv1Mb4jj9xJo5y3SLe8hTyEvDwBiW8ZSCCQKLCgnkioBEq4xe9ofWdQPBNF7xfZ/yG4ya/DkLyk2XCQpGBAySK1hS7LmR5UYHciqX5HBLGIYtblv6vlxoLtl6cMXPwmGy2KdEgpo7xWvhhz3PVteG4wqQZR2OU1jXkTCHvX/R+KYNW3rIrkS/essTLZyY8uDUR7f7Zske4mylM3ZFcskZlorgO599EsvGPODopqja0n4BmbIN5KvmfhTGU9bEZ9MUAi3ZHYAXUuggOAw3XgPuiWvfcxfoslV8twGnTYoadk3MtMSNwDisyFM6Ro3Qvr1AE9FQ7PztocdcBOrBj7Cgu+05ZTAf+6y9POgYeOCz3kW7WaPW0TyklXur1lKZwUhG/hAHaAysuX6kgtNwAe258dpOsJja5UWDUqVRl+Mx/rJ3ga+hTKwCA7ZvAcCxb7UfY2jSiLRe6Ybl01PT67/sPtkXUTbBLD9CeKBn2UFUX7n4Ab8Y7WQFSeOu84TQZQvXK6IlIBLPjDZUgvSlnnDBiagBJvxs1me9TAAMbsc1WNoKpwDxrQwB5s2P80ioRLnHJ0gbuy0Ls3aPJ9T6/OM2bL5Lvsza4uuSwI90AItOk77ze9lNxAG8tQkplSfQwiKhg7Z34KNB3EfNK1UEFwMaN1Yagjm2kVRK0rJ/Yp73s1kDjSQZFO2focpeoM2Qk4GgYTTesuG2U+aBmSNy94ATLsS0VjRB9gNdunbHmyCW1NSEBBRiqJe3WSK/vFJkVhisDl4pOM1Ei9I0KPgluGWuRQudkEvRYBN5nzbx4WBenN0Po661IwyxC/wO7RSkExYMVSCc8sY+GRGfnwICYoMC8luUO7YFAmspuxEjAahndYC/4fM7EhRh+/LKchfJjsZ8iS5HkrkAydIXQIULA72zomX75RZcKHb2ahZdtIuiUYuYQiK858sVClLECOp2z5OxEbT4UGiUG2Sv5UvBZLKSfpNm2YgpiklPvlVgGKP3vaDI00pmOLvB4rQDW40K5OWHX58m+NBBybfSGpLlEbmYWLCB9OdIIUH74R6s2j6CANfOvaE/Gbfxm7aBxFU9U8e4fRtrLqCYcXwgR5nD3liQ1UT6fT2yVAA2zd4juuPomqQNg24Yh+5wp9NwI9mCRoXWqD5EHZNSmEWK3sNARQUiGGCalUJcn/E7P7ABMGNTMWz8T+eNo11owP9In7rcwEnRBPCq0UHP4gCjyMoj95MXNyeLgmHSLYUvfFi/fO0ScQBJxPRoWPmN6nC6JFuhE7d++2kXckG7NJm0XBcbvocLEX/6VBdvGIQZpEAH34wDih0YKblBJHmSNoClMH2HnDm0QFduDipV/JSKRGX6WvjL2k2bKHsD4bzo6kvCFZqF8Q7gIYeyh/dUwqVPUwfQUR4tnCDbWnlOHm0M+8vZf1s8zf7XA3Vkxiph+uAFtTBhZo7B24d22hL0wA6PpVBXPLZbHGwCtrMvcQ4NqRLi3A8Xho7R/ovEDa0/SplFMwkf7xkmyCylI42ZPlMSEUUEnBSOVWRtLB5P2oZngV/WjT2JugsLXeEd2Q9S27RqEVY6jPkJRtKHF2wRULG/fJblPM0LXPk0y1FkgbkmjORtiDrRxrzECYhgb+kluCVnfvmCo7FWDHfpukKZz9wlVwhjj9RMhrzGx0IrT/QYCBXjoyXID/NAOnoJDBuGWPe8YFOKOC7Pc/UotGMPPKWQO8TWKkqgalb8ftuhTkeUbP/3ZZ8osQAzujF4UW/pxkKhuKbQa3JA4awu8SSIF9vBQ+DPgpsABFeiGtyZ8Efv8eeQIIzbCqSC3AAGB3JvsnyG5Uz+TZ9dbpuKS75miQURW+UTpUCkJ2C6I/cbHPsJD0nZAKK8BIObXe7WObbNjv3JVU1VwYk2qgagFHQprDdL2UyjTmKPxCW6UmbeoTqnVbGMfZTOMAIgX4JA4kNu43MrECOJcIiDEQausZDWgjQW75Gv2UlL+wWE7AnVrN/n/jA2b3pokpl+5aJC/yMWfq5DmuPf5sewqT0VOHSrjbUTXJfmqx/NsKnAjWo6CCn1K8D+pS5L6UFvdtsWYBIoZyxJLXml6g9Dmt7Vl8SVn4vpUJ7FujUPISQOxV1XSu+OeuraYRfcXpa8fBbrtGCglJRoJFhgFbA1pMKQfkqJe+I55AdHrvfDSqlCWnzkMqJRJFcGGv25rFBTE3QhgBzkpElM36rI1iKXMCCISD1Ei+AjFE7TcWSipNecWOBmsANF5lhh1T3CcwAVT67qGoDj9y9SVr3ZyLXjTkyJMyWJ0+ZA0qwLLc8igJEonXpO1CmCoETYcD3yalIiSmEwenlv93iW36fchG4AlDyF2EwT7ST4mYBgObRLTXeUM53Qiv14CKErSaG/FS8gWLYJPAIbJyEhBSe5iCxz6b0dL9qZJYKWV4CmqwHT+fRqh9/NnwHcitQ68HZBHlCcd3m+2EQNSDpDfzHZXQ2cfIoQbS8rNNNkLjST2o3AlcJQUCMxQV+9gv5wUkKlbBIutQ/RalTNi4KqY+urP1zEFpbzhzi8OKGO+UVa/d9JS3kSVWA7pKOhhcMOCZrQUSLqjt/BxwMVtTBuUlEt7RTEoYhiTb5Hkud2CFaskUUCApgikPsQelGQ4pAHT9pV/EcSTqP7OESr1G/W916TqTUIQd5Mkuzg8goE2JpkJ6JLpQTHAbljxjNIWxPRHmCuDP1rbTIay2Y2vOZIYIJkELRCqaFRKbTBZdoLXAafZR+CdPIiR6VKKSiYcdgQ60FukNMN9AHWySOqmqguj4ezQJkYkMl0QUPQzUCVIOmkxTIzLurrEiVPGTPLdhCSRQpcYkrZH+0xo69Bdl9+/h19MHYwlC0joE68f2TSTEV1yWW0StJCGLjwvSUgHysj6FLksbyBgx4nkmLxlqDYAtzrMnMAhSBCQKHglVaLHtqNTUoFIbbZUIKmN3VpXgfAdJDdk73iioeFSGd9Muxs66gNgJKHrmklU/+dOcH4L2hp3uyT1B8kdnng6DFXHLeT50+QNMHtE31BQm3Q60DNxzqbPOG4PCguxCx36KkvzA/ILZm0ZglK6yjICwS2SeK2GSbccaDCnaVufTF2AJ5O8RQtiBxngSPOo3++/yyHIlCDeHBAkrnXPj4yKQ57GYVpICJxKnERn4eUWR9FiRoRMHPUNZsZAngdg6eYQQ8hpL1GR0p0+lQPpmJXZGXFKCswmmgVkZ9PknjHZTepEXxKFn6gaMN3oHAOt8yS6Yrry3JavcAWqUWpEx9QAiYEIhl5cQW/H9m3Qd9svzQZ88FCZUg7BLsG0oFTrUrSwyWc4beNcwngp1YvxK/FIrTuD9oac+yNVgmPB12O/DelG3wRClQgU+pP1tUADBfxme3PKTmp9jnYCJ5aMV9G5YqbK6lPrJzEogmwdJEyiKW10Czxwd9F1CV5HPR8GFPD2in43oUQXqn/9dkIkKbUlWvRJm2pvs9m2bKjiKBQOtI6QdAcajTVJyB0MH9Jyui+SsKYV1M9N/6NZB+yUPoEmBgEZS4FGAIcjAhfhJl/szbctV2Hj8wQRaVm3UCdz/ob9DzrhDCBbDF4WLbiBdX/SQKQKFDdlkc0QPwnNOSxIbmepdDz9q7Zl0sRBpQdM5hnG8a9uTchJ6S2sAil6O9PlGVDt2N+Dj5bvaIMBjZNygxVfaTwLyl1WEZwhZBVoH0wDiZxURRj0ycTOcgLabA70p2VQZ0WAqw3ejOL6pfABF4TJXse+PKESRuQcqk85L9f2EcfVfB6PeBMYHT2GVDapU9SCmHRQPh47gPEkKAxxsowEoIpJX1zK3QjcAd2TIOaIGBB0CAQX7SmuUXganYutAkknWA3g4qGDrPH2CXvDVwiGb/5ghlyfknzi+VGtBROqIpimmYc3eUMKLI79AD3KBKO+DdAfcsSAP4jHQZzFRrubwuM2/Ro4Qr2om60L4FQ2klbRWXU/W7UD76+yP1j81T+5vwsaFJkOzV04OAx9lwOGyyXxbxGYYrZVceUVHEn7RtGbFN3TyKmU1dswgPCcXPZceuL9oM94Xiz7cF621QDi0y764ZAdUaJSAA1FDHST9n88QseZC+Q3akXY9jBuGA8/WqkyQL7ACd4B8AiWADy+bQBZxpKCV53nzYA25m51PZAFKBuQ8i56xWagmbua2c7fp3aFwBEYC0YV5JvRzBi8Y0bwH3Dqg7U7kQXht28T9qFKhgUBly8Wh84PblvgbZZiv4UrSUSm0oVgefpqO6Cbmj6RxBbiuFW+LuJshxjF1ZBQqvFBKdz7gJB4orzuGJiPeb024PyS8LfT/F7HTZgRPhOihfoC1AZEBGYrMUUseBIv+HEpmAB9edo/SpPcz25OWgowBmARtNLqW9R1Etcy9bzA2iAKIrVocmMIfzRYuXLse3iAiUibz0C6fyoDou0T4Jt/TRqysAXSRwz2mPqoEQyspQgIBM4Lkyt7LMPSS40Oppso3/bI3A8b3kWzTdt8wIGepDprM9LiSrFIW5VKIExFWahskNP1012EAopMl1Z1P//3aR+K9OBt3mwJzUr2TlipqBVkH2VdlWjndRbgL4ETcPcBeiPvprsCwhBaGMXkPjIhDFzoeRzbvms7HYbzSMFsGOPjuQLQ8dwg6/13X/wPiCrOUKkPPceGPkCZV2X9QhwAUJ+rJbD1IuRVDyflORk2gqFsjBSUCl22b3aRvg+FBaFVTu41jowdykmQthmUKrYALa85T/ilFy7SBpTXT3QXzSb4Fzp9MIBOCDQ5bFrq5uyvFo1FjkJkSR82RDsSJIeS8JKt3zam3MLUmay4ejSz9k8NAWkjsOio/ep8QpnqubmtaqlheeGJAeOP+glICuDw6yFFA26VhmWwcp5YjduhVYkeU4gXA+OYfYTJBAYY8CcaOpIH4IO2gFonMGfWNddfoqRTYQ/v6E0idQEZRCjVjAucjbpCdeFc5zCFWz5+UFTa0JuGqdVKZuGMOeC6EbaasXfSCMXA2/zOyFrfJ0PrELRLicwoa1FA46/HxU+BPNKvJPWzYonBXnvcqg1rNLMZXdo7NAyWRDl0ofwPxBAAaIJTyDAw6iDnxb8L+8W97AkhdDwa4SrLNbGk0uzbvou1/dDpRDy2lcgnYUm3Sc2GTIOjjxUQwAo4o40AKGJDJEodpW/N9zfZrYDBcbxinDxmbXguVI2/4UZ8FjojgCG9+0jDlDtSp5Rwyo0Rmk+bE599inCDyWxKgsgi5te6lGgEAFJMTpSd6wSzFFbkwYN12Hx3GxIaB2HqgrB+Jl9pGNtgOJm56EdlXk2ToEURHmp9nmHWOVJ1S85f/KMIYboo0YFPBZUDdAMc00RN+i1DAVj8LebEU4nA0zEwLWRNjCarJHATMdrNLAT5VGzN/l1NDlHpJmknQV9+dOLwHp3ulFZ9C3g/yLhONHdCPO5p6nQfd45g9pi8g4yr+AazpA1ZAIRJqThRMpEge499bL5eTXahDhQPMJ/UsDFJrYlrCaqtmbQnTGqn0KoYEVlvAF0NEpCdpHjwzdzbPXBEIBIW/JSQfr5ID/VI3Bvdzo+6AJQxNSAGPlQt+EZpn5hMjOn8nm8lQnEUtlDA6VRLlS46Mb3yp2p+Q5sk7O4qs3Bc07wJncHK+8yWwHE0DEeZZd2MMRONEmTskxnxYsFKnrtbK92dRS0EDgMjwWtvkVJPtPmRp7nydA/aj5w/N4mOQDjRrctsVTJdPUoB2rIzziNIAhkP/BUmUbDfnYSbw0WHpCIyRkek5M6HIUl0uuNmsMjhYxTeORq80BT436tuMWAHVs1G+xnhpnFBjNoAGHJlU0ZEvbhNmhYKWr3du2Kajl4Kwm/WYLk/72nxjuebrC7Lj1QjXdUZtO12Vp08XqXb5WOhZgck/DHDozaLo6iTbFzZQZDu1rPCgg+J+Q/cI0LzHgROVUxDAyDrDUBlj7YJ9I+oCkuygqSapQdkHoIYVswhMim+OYlS/bepK1wGOoWbIYwAoFW1fRqil/6V6iM0TgNiSGk5SJgdEq2PoWIEkgpr58xrW3NlQtuIvzf0npLu25I2EYHPfnu7b0oXyyYiluygpO9AjA1Ib+sjFDgxGnpVP70sYwUnKiCNjAYWeC6lj4YtW0CqmD14v6krOwfFXeKIUK9MTSZDR96Nh2R6ycVLYkFvhVXME96mbWXo5QLPOmXisABJwCn2ATfS/S8gkBPVmVEgcYCnAFy6XHJiVxmCM60YCCNPfx9EYUYUmwthg8P8U18JcxSLM/eZ5kSbzPPiwhFOVn2QAWotIoPo2tAuCEHBo8C7aTNAK8h1hmopF5wPpp1bk3bMOvoWKOiogF2ZpSJjpw9H886pYifOR05FXtycfp4Pa7itWvuPwWiEDonJ5EZTpGZsK0DZvcw6d3zVcdpGNv8rt0f/LKf24e9p86gQ0UhkPyKPGjDQ560q0QDuc8xdfTEAqEXk+UMs9gh8hsf9rZzDLibJq0DMycJgRVmmZ6LsI9OTrQsL4JU6h+Dw8UxHCrAgSim1e2NH4MMnifAvCkpzivDR01EUqwE3DzwBTnaHXECyOzblNsZsk9fNP2Mp44+P09QUaY9xzOmhSQ0JJg5Wo5KHf5/LVSPmG4BOZGtsD6g0yAlIFQm6/VAwzgb9F1UkBC23sY4Adia9NnIVPrBIC+PPMJ5n61HGKbVFPJHJrVR0JI2+iDaMnyx46MmEk3E+cgElz8AE55gYGKg2IleNBBQWsEA+LgpZJMBwkEXAaE37ixaWULgxFutNIJOQu9aZzKQdS0StAYrtmCPkv2/fnkZtNYlgrcVQgUxRJOBicklEcZF9wVYZA8ly2l2gtAhpygqVPnCIdwx9l4KFyorjhgZh4GFerR+hNh4BLjoZG0y3CFtO5pG6bcpZHFTFFwMOalm45GEDCzIK2q7EWot6tWEWugnw0H8lKPIJleMh7oOh+tBHOqoqbG/IkEhSx4/eM5iIREuGCWGdwsi1liEAQVhJFT1Sqbi3AGbU7TKys5+wUwSYDxtfzK/1fomZ7VsktKYOR2FKR+NhEkJ6M7whWXZhy1gv54uQTJ0oDtGloskz4sKUoxzOU9bpXO07TdKMqUq4VJqg0iaS07hW1Ij3PbY/qUlWzpkIddSJ1JjwgfEULc0vgplwj1H6/LwuYb+wZf12rLcVRT+ZuVf5LW2q2mNk1UZCWILrbsXqw+VeL1KXa6ni0XXS5xWKZ6p1Tuxfl6Vk7b/I/uSV9FT/m0GHuqUA9pbtEFtocIsssOj1f4j1xNiwNbeVg1fuXVlwyn/Og8t6bmOG1K46EbQ2k9p6KOA3+0oZvSuhg5GVYRQ0DRhKqX/nYW+z7/1UoBj78/vI2UdsiyOjDQ15/KG00DumT3W5xYULRmNYIxlAQP/nQh33uH5Gv0Gb3wCybAdcNY4NXZnBYkfOIMe9rFSlUl0XlZOeW5f1VDNA8eQre1Gf6i2mtx2COcW94ip97VuUoen7AiIjcAIFqqqCHQjZvvw8WFwg+Zlug3FE1y0UD3aJY0CDYsEOklTQJjoA7VvmVJ8QIN3WJ8kG23hqOkz1eMgwshuRV6aUosr6a00GLUuG4YCQNYAARTVeOUuJdLopAh59l2yqe9RIguYCMQcYGUvKepYRd9VVzUhMORQcAyRreX/AQL6Dxc5bg5XMAsFq11YC+2Mh3Aat83PfqIdVOC79tSd5CiY4RW3jhZVCqs4IGgJt/6SwASAtVS455i7E44zEuCibnLuu3nxsdDO9UpsoGBC1D16DRjmCLxdHaQ2XAG4+LLGdcQ6+YuheOBcGTs68hiZCjDM+xhMNvaNOzgRBPLUyJbPgKys8lEmGfpqMh/sXfQJ/SBNeL/Q4DtR2tPfQ321WNvY+RYloOF+GsOvFwg2W+65WxZigOzZJzmhg7qpQQAjLk2WpUmPHJr+lM2ACz6NermBxWlQ0cBIAJhuLwRq0/spcofZmqnfQUY8JvLukNjw324hFIul/PMp2SPzgI1KHXc1owIT6XyPqDcSsLbtC6Zsp7L6BJwHUuxwiJhAnTcC3XCnWJL0ODswJBwvVP2shDVdZjjwufEbwLnJbRP1FUexEeGBNxlYYZPWRnpxgfvi3QIafTGrkFAQvE+emAv1QBY96YdmDxJdR3JDxRYQxnmkCOcLLI2DceeKkOAFkykVYUcbX+ntvA222lVDS1OCB8S8/RoHbb77zE7tN8jYMC34gPwpCHwk1SuCtAIWNyXCvg1bM4sFdinRPhri9cdTQiTUmn6T6NDezCdGQBNh0SKpoBWuPXocAONGByKSq1N3jHHRgGB8EcofqD9PA32Egls+mlT22+18p/CZx2InjwAmpmcwjkWepEwaVaQ3xRGTeeLKQOflBZy+SFN+7MdU9pzVgEFqakzK5FYDqmC/heIPp/RlLUXuRVwo7B2Vt8HyZxigZKVq8Y82fEAoGfQXBYOjvCPMe/jDHYbUMdI3yI7fohK8N/hSSWwGLHkg+TQgi0ACk+hgvUbHlynrKW6ZriISCsf++zekMZwJB19MMGkNTXKQWBS+vHCqCxIEzYUMx5gV5dRXzYwbQMy4RNhEevtbzoP8KClvKIaCj3CUDOPgRTzNM9hnUCS5T6+4vp0jJvN4666cznc2SJXDzKYHsx/EEKlpAeuofn5aTXdS8rOHcBSQ+D2hBcgeiBMYXWi5jSSkPdAkmnyYVjHSO0pZwAb6eToHtLRteDw5M9hyEUnHx7iQQeoAN72qMhBzs91yzmbdLcJEywsUajlrxTih+chRfCQtA3ASUNSWEE/IahqpMxPJ2AaJFK+npixygughQKvqGWbWm+qXm6R2pdQu6uaSaQehe9z3wvJIIek1fL2faYg43B9jYxP3VoZh31yYDr1C2CGiRXVM0Vr14NjUP9l7Vwx6svBnkPFKQ2233ljMPAQg5W/VzEE8bfO+9MFeMPrl0IxxLTtVDyxmbk1x0jVwkqh69+ksVFfIbVGTwM+y6cYj1CgaI6NyuRSCvnC1ef7LTmV605lvOdt4C+UdQxLOAcVNV3nf/Qa1fJtxVq0UrkCDInoihbh85ET4ro8BaVVOrBt+NNQTcnQjasqQ4ylKQ76/HlwZ/07dIoYKoZAmqQfiSXsZqhtR8XneUXEhjKEOVWfmtspf6ONOsv1mjeXhM5elDUqYKlWWwDeuq91DWq2bmHHE6ictJMcINXGW0xfjP6sPOFVIDfYDiSR14XT+JPMvHTHGTw/fCcwbxx6FXZoPzyxByOCFi/K8mMXYKPP5WycsgY27ioQnRgz0CGFCuBw1DHCglOqWNO3zEDweTdxujAR9iym6Cs+FgAjMZvuYkm1CZsT3jyl3pOlbYy88jAxxCLVQWKBfLVLr7qaBH0Sqkij2e3gD09zbx2XsVVik3iuX18R7mRNr4WqC75mhKIsnwwRwWn0yXkEj6INQuZALS1eTOkCbKPkYR8rCCyZcPUFnQCqL8hC0ggqPxknHDJ8MPsSUngi0hIKSJAYKjh1n8jBTxc1dwF28E/bh7ZHMll2FBQl/Xye9K1hJpGnq4OVvBXIfpKtm7NXnLfDF9QVbT2VOZ6GeD8kG5kJTbkERYLdUTp+NKyogCBHahZPRN1ahBVXpHr/GAS9rr9QjjZavEzJYZ8NyaD+vDZW5TXx0VTdUwyOMhxlMoo51hXEAOYg0DrJDkT70SCSUABNnZ4n7GwMXz0gGcvBrgOpTkJuxTT003vMHBkqmsGtETnRfzglT0OYYyWGrhvbwqlXr/xRS3aqeBo7z0yd2GNiuSxwunbT0DTZdUhxKIM/+mT5OtkeA22Atpkc0KjoOZLup06H6pbBRToebW0E0DkoDtWJuavSupabaMfm0OtSjXinPoI4ithYD3Wytysrb0nKVqNLdWJbi6broZVN/kZLlo6G9QBujcKZpDujOWtHq0dsK6axjU9sTeew5sbEip9DPK9G15i29XKDRkfHw3IOX5+SzOm6YUKUg6c5nmHIOL9qAAE7tobtdvS+U80hBZ8SwIgKgE/00I+Ou+p+gY6sEfkeKAc4EdovmOgZ1T3hW46EuKm6kQs3l26lZb7/lqIgLMfwT8LlRPWq5cmnDReJNiwwYGWK2KV7ATUUdoO2bA6bRI0cRb82dzgTUNWQ5lnC6FNVgwWa/VJYIhzzNTCHw4EUI9lNVywM58izhpPjGnU/2IZPiZA5fvOBsvGgDLtk3CTA7M2D+Zg+UvSTC+vW/Zn3QnfSMA/93VZJRfJghbePUlKEQrpWKPvSyB++G0lzB5aD/6GfXzOgzvLmoHaT3iQZS5ocAhGA4BxZLDsc//CtPNXDE2Aft6Q21Opm8QoAJ6A4vRxyD+I1AalbiaPds5OLZTve2Ve3/q7kD3YgW44KB8kYoYjGIAFGfJ7G7xKrmzbjpdTYoqAhBjbCWQrSp385U5+0zpOljiGCPTxoF7kCwFuHWVGjUmtTJkLI3HBgKaSgP4E825lEXoIHcyuS0qeiF0PZZocXrBxdTXX2EUx23QObGnpscXCFGPc3vIhTObFTN1+Y09tBEsAdiIQ25Hs129yeDk5E4iYlhXUhJ9FpJUgEcRQv19MBTILoSQoEtJFuTaDgceD/hyTFfYFlneKRURCNdgxVxnIYcG4/eVlQNYDwIP1rQmUgS+K6VCCT+FO4kck5ruGvjr+8HsirWJg4mWFXy9nJXSmMnmJB5wUUWPykmcAQffIuhxaB+gtjIuAa0AVQjAgJHt0niC6nRKDsHK3ZHGvWsaHGDGdpm7g1vSod4y1rW91odJ8bes96Bh/jHKZaXCZz5qb6hmlxwKZAIgLfzNoRFoF2KFcf7NFdWk/FBI1xls9HQULQExI+RbkAcyvj2uoGEWVO+Q0evBv4dZlADrtDZem0PsS0Sga0Lr+eZ5yNsrRXtVPLXlYfXgSk5bpIRbGtiPFA14KvzEjKex5oSfuOKQXRRaM7Vfba1Dp+gaDN5qMA5QUs2I+Bdfoknzrv6KEd+TM5O7k2Si8KCdmdMrwwd4CXx/H/JHcJ8VKxscZXrzEYH38arR422ActTZ8a40VIgqep8BKxNS0hv/icaLQl0JNlybgg7nKMaObebYfoBqkPPy4GBQlzGq9qidF3dn8sLKPYnJI0SddKCFWCbbpI9MQFfNeUYTNeNQ3lnsGjw01l6mOAvpXWCkAGpoAcpbtMefbw4Q1NJjF45VRYWtUB5cPBQMmueRPvfjvbQ+2Ujgd0DqWud0UzKFXw5uSE0fl8NMcywf+YiqYAdhYLkKMOsxmmHLJf4YQWWwZmhfaVbPrP+qhr+jg0FR+lllFUSklLjQHDALQPHLNLnLGp4mDDU1UyRljQ5U55EOLYJI5phuoIyfICMUQrLdPBDEBD18ct5fiSrWE0MorqsMvUmCNGEio9RfK6ju/qlZwHvbzQPfwQtNymTsMVFsoc+1QN+1JFvxw8p4Icb0I49q4HzIcUayPfyLLWmG402bJdT2356bAfaQ9JUxSHbgX7LM9QDhbc8spk5oo0ynWWIUgNT30HRLw1AXllD//p1sgxTytb6ldfAqIqEq4+ZfgDm0YsBd30NU2WIcxNh8VAauD8gf/gQAAteWE4wOe6Tx7LdVKe/qYMLbBZqQnz41UIC4pXZTM67aBmGMKFOdLHeOrsx6tfQdcynlDA0Kn9jznObM5b5LyS2VsT73glYGUgnhcbTAn8V97lV2cwEA4gAmxvTRvh/EA5GMI0GHXIBD/S1ul0attBMYGWDBxoKJcdatNhmkPapkDuitmVj+03ygZFPH1K6lrwOZwl84l4sXuPqThpKKSso2E+RaRcG3/tFdKNs8yncRh0cNxKrRdNtxgsAS3jOgYH6RpFtm08yHgg6jVuh2EuetEjSXNymv2HDfkWGzRnaaCcIyxyBuGbqrXYWC5uz/rcBkcjQndWAgadNCA5gVDOqzhwZxy9cUVOAVOOj1CUCJhklJMINk9jGiL1l7w/Y4IFpZT8iukHdGbm0+T4lOFEA67EBorRlNQonGHZig5n29hqW8aowxJTTAEBa/loWpzUWVIpLKCmUdf1JFDV9u1eKK2eofaLQAH8NVo5jkvgAJELWU7HlKJys9TRgUPgcnExYMWEwPbIoo22PYso9Ybv5RtOTrsEmb0cNAVAxhuUiCmEdbddBklpGFXStWMnda1spQSK4FIkQSRSM0tI2fj8KTWvn3sOBoJ5Ux7roG6pCoWuBtOMmHBD5XEUyfXxmnM0zpQCragIyiXFMLxzCgXccv485VjqttITi7fDOB8JA640hJ9FwoOAamkNJ8ENWYxg6hD31R5y1rBAjl1lOxIwEEA7yGf6wD6kIDQGt2wO/CL+PAmP03YQnlAAAz43FSR4pWnKw2LAbZ1wOmiD8HS29I9PVj1QbHUIVe9KNmjlpgR/nl6N7LejVeRWkojDi/139EZUinR7lIgqvvikZphobu2DKrbXh7OhHSPCvCvLeAfTUcuRJTsrl1CAnHnrID5423CpcACeOtNg6QRK9I4tM9km90gwo6vjVLsnPE77Vz6HtfFT45PaR4PcJyNFk63heDGwgws3hmsk1fpuK2j21rAnp/yIhMIv25qqUy9jDjNkTCC1kL9nnqoIeVv64cNJ+0sABG2aUn8Gl1mvXF0qt1Y5s1hODjTOi+cgWhLVORehgqKjn1IduIR1RfqO6O2x4UXRti0VHSyMC8LyOHUAGpLF/TlHFoX/dVisjgUpfY7jxmgoITmcxLwPoT4zXaYzEPQIm07IhY5UG8AMB9Jkd94QH6mb3a6Yjab9/OgDBJ05bPVIqMbi2bpWuAl7GuIAY3Gcuryrhq2GxiC7B8VEvDxwLCYqwAUHV6BYWaoj8Iolec92gMCz9aJDF31/IygY5cYjIsfkAEQOg++Fajmu4TleFJohA6/dbxKsEBVA63mCXDjUTL2kFUEg9aGiQ2xI4omYjxXi8IWjm7iUfSy05BA9db+0D0kXMTEHN1vy5dVoPQRRZbT33IsO0V1aSgzax1qawWckBYX4fjyttma5mKPT/3X2CPOlt54b2pQjk+Q+tmjOfb9+0xZ0odkhsRpXCohAxUe3HQ+xlrRa0ILpuTRPLSAoozyErSA0e2SckSSa5TGW+2dUBxxEjtuHpvlHqBrlLMQcSq0Rn0n81f7b4UtolrS3RpwFmQ1E+WinsXVh9AlYSCJWqYHQ8Ag/jeSW3PJX0PGTKcoEKlqHeIsjKSaf/4oqqumeK41IDTNBwIKVhj8PVQS8OXKgWa5aJO+wL/S060YObkkBuw1auh3OV2yrRhnjoYzoRgBKWzemTTm5pl7lbYUMOaSXpq1QnssPZPG6b2u0LMVaaz4AJi2pJFGyRVub8wgjBdVrJhCLweWE0SlWOOX1N92zMYXSdRv2DYTPKaQ3MMxmRodSfNNFXF7wCdkorVAq1zjpaRlwdZbHre44NJ4bKS6b9GmY6xLT9qdR4xN13bjymELpbvxA0ummfL6KZYOMcIxgFvul251pj/EzudQkYcdw56kf1ZCCppzzDz4DwsYrmbOClWcLGcx5AI4gkzs2s/UU1caQKn1YWYCOMFhWexc4GWQSTkRas1AlSjBKiFeJAyNFXyn5uvafDhxhEinX5uRD0hl9LZXtdd3qoc+xgzn4rxzvK2isJo02hda5S2IrwmleA15gshTdp8zSBbvBIg9bF7gRzGQvzRy/BtFOpHSZSypqvq0mAXTJbvO3a32gGGBcKfsOb4RqVtRhVaP7c44IRzCaOpyiYQfvmh83ywvklsE37DXhaTrawEIUwyjDDv1EXLyl8EPRLdTEJhE4ec4/MhCGNnCe4+VJM3OJLi7nSUBdcOcpLpSrQ4XQtcaiQs4hYN8RZKdb8UoGZdSSqT8Wc+0fqgHHAWMebCOjpocrJSnvmpbQRBuq/Ok+bb2BUvypS9mKTI4juG2AEPEcVgj/VDUgyXFumrwZOP45HOfzmdeAbtB2GoSuF2exosV0IBza8lGHFCymRVcY+T0NNuZB28iiTmEJ62Dw0WF/dOlI0FW4oy4m0R6aDoA7dcebLilUcsiA0qlNcFWXq2sX8PPutgASNHbV0gqemJeB24eTa6BEavrINEh8WZ2gpG8sJRBUsLvtruTjxZQpSAjyoiSj/cYwA06sT8ejgU9pdyDjJyhR7F1kMW84RvwN0VYAjg4LAPonSr5RhHp2EpqHWXMJ6caiHzXSvEJEKBQRCR3BpafdDlk9B5gMLG10qIyUe2h3QM8ancOdVuAQfQE5FcfiqqVfLgw7foQzrr9iBxGNtimVo2WHfuTtSpSh7AA0goxAF5imAl0fR9d99oKWMyUZlYRzHs2vI8+mOU4GuNLIpunWqrG9hCJ9/qEpgHcD2pKZYWhqh+jA7GtLET192nJTWzVbc9RknmdnqakJOadIuN/SYEEWK5wSm+ofYIFs1I+L1z/sskDpO6KEnVNWdCsc6cmNpIDC02bZPicNYv8fXfA5lcp/lfjECKvuMM5tf2BhC7Q4gen2Ti2PcdajA+Q8BXoBTqfo0gqmXwT6KxyB6oPI8dUAx2WnVy0pPjCcnk03NXg6DoUz6ctfao/GxmUZOw2PkRpH3UNZ52N4rDe9sy4gxqk0wx4Qvydqro+Njo2qjWvwQLw+9O6X2AXXmusrM0+mOY0yotyc7ipsSW9fTbH1EV9N4g5KE1oaBpnz87UQY0LarSszGN2WHwS2K/iBiRJeiseu16uZ0EhQPMu6NV5N8V3aOO5XsAV0D5oJ+lbqfQ8cSle51ySJmjs2BZ6AMDhs0UZifHGRBdKzVN5nr/O46kjnn25f+MXCsHKdbd4qbos1955d34w1xAfKut59oHD7gQSaLWfqYJ/hlSxwdMN0yvfBQxy1kY2A5SQ5Zin/8Hpts51/irs4YNqVPNd0p4ETUj2I5TpUj0XJZra4pd/gWNfkVmlz+eldhs2cOi7nYmDHD6j9m9gAyZ8msFKyqfQaQ53tQDmqO5JZgvVX4qblDcyn3sFJYTANj8XD00/k+2oOmjQo5ADkKppGmuvrd+Do4qV/HUglydv9Gbjo/Z8HoJVDdoSWE6uXKYUmYebf4B8O8nzVxiGJhJ5GuYsBhjwJXUqd7tErD16uSGYGfY4p12SNqhn/ESFQQi9aILrLfwVYspOagD7pALUofUUOYMx+TDpR/3wy9R1tTVggDDRRv+scQGxKpKj8Q/cTDv40duMpYYK9hed6t4hrgqC5kK3MV5b62I4YXDUDg061LhEOXd/NoaGFH+PHJYdTPkKCT40hczYfsD3hN2+I7J41e4U4lWZfMUrH392ljUM+5JMEBsGT6xpOZkQm6I65Osx0FaAAPUq5SSvggLQyXIOc1vUprRnYDQDcWZSi+E4+WaoSQKltjH7ahR0nn6juOSWA1ShaxFW3egw74HJNtYQ0TvV4xuEX9g6gaQKIyf0WEIDX/MliIQdYOqcSSuU8fV5sw55gl2OciR1PdrcSaOM94IBDND+dkEVMmTSHuoCC7Qbh1Acwhts2brNUWxwVdGrAMa94x3MoxipfwZQ2pqbMQQPAbE4DomqYENAIm4o+2MvU2K/AyWaWv6UQNUfazuVW27PU59skF4MPSh1mTz4j6ueAYKgSw99AOAUrnv493YQfF3ZxbpAqI8yFot/14HZOwSZRw/LKpfFp2Is4ClyQIdDAQ3KrHaLMIsIMYZLKU3DI+z+I31TJQNXDS8sBcyIbOH+RvHMYAlQMmZSwM4mgnFOKAYky35DgcbWCwvhI+t9w9iV2dldDVIgVS4Lso1qjWu0KkBxHzjTbpcxBpy6gWh1Xq4EiMOaM6k9TFmkosIOcK6y5KM7jkEZIEuhDUGJOkX5I0qN8GazoP7wlfqRTqF7Ax2wFCLByMJsDdwxIbOjD08xB7+RBumuUuYM6BAVVjRrFNA6GCKkr45F9Gd2w3HY5p9NfmzQENcVydir6Ayq2Z1+XUdOSp53SfdShnzLF5Mz0nCC+PV0iGEf5ue+cirSb3AsrVTjA2HWlFKNMKNtoeJHEe5JuxDfqobtWyRjLofXtVaZDSyBY7eEkETHbkvICrOiIT36nGAvEA/In7ZIyY6dANUZoPgS0qQ4TZgwtACkRzWkU5GB0rJjA1ZYyZQYPOYtWkxXsqrypxeZ+6N418hO/X3K9MDN1jqxbsUtOPf/AGN2Z0d3xZNhK4x/A0nNsGi6NY9nNyN1j60RiCqjPgKUP7Jojd6O6p4605YOqAsj4Vjt/6AmeK+JWIdzX1DSuAY9D5BSbzbMU9FAxXKn/4JskBeRUPCh4b0poMc7k7VSuSwxVOP81h4BRZJNo/yO6ImfrCsjxaJU174vfr7onncIKttoq7z5iHKGeSAb9gRyUx8i5jJDuYAfgnlV4SSBgtimsQ/s4Wi8jU3NOOYeJNpB0d+E6MBhTtgi3d8jPKdKwnqacI9fNm6aJjfscb34qQ7iYzO3SeulSdB1kNjTUa+JhOTZrRzoCYU+HGGhYhu9t/7S4Fw4CJl7ikcQ8SKfQfT3XUBdDYGeAKSBhib3akUDLjFPYQ05cbs64w2GtRlxKs941E1NeGSInKRLItAEB7P3m7gr60s0f4a4iWSdydsdnlxUXnQ/dHSET6A9f7iAUUzwhB2ocuz7Xoq83nPSmTsXQ5L+ae8+6n688mno5OGjCjfrAWTqnugTXdLd1jyKgz67GqGly9puhDcVg2wNmvqPQl4UCEg5Qa5xHnAlFvMOHRNrIdFTRZ34O857DCgJRK51aoRFit1SexEKlrxo32T9RfqRE++va+m1FWw3BCazZ7Vi6Jo8FoSDFMgy07TSy3oqYosOkunLG/Kiu0rr2Od9xM8VGhY8FMcYWpmlgJzRpHY4hQ+MJIXG8cy2D+bqkKWrytmgl3UUnwjIbULsTiaaafpM4LQm1q6awXYk/mKHRdx96vwxzMKFSxojzJ+bOr5pxIgSI9gKWBYny0YWC/6fb4B1OAsLx2qN4lIl3l7ntxDxmxHYnWt8613GKoWPFztd/l35pab93WcHXfLLnaan1qu0KKJM6BXoYFbmFOhH4qdkIsZk8HUAqk1onlvMce3efzPVeM9u0o8DrSvTF9verWmHTMeLnd+nfhby6NtWogHmzm34v6Yuz4uGKke3ZkEiq1GQkOPuxmbcixGUrkwI8KzhgoSSngAzOLqQ3uVT4gWZSyBPft6S9BlyisoYYBCF4mfo/kRT7K2WdOtxdDs7p5ZPDjjN/QiuHtU0Ng6xCg7RUO3p48MpytvO/KAcAQhBf4gm1Hby+tGWjR7MdirnAqQmNt+ZEbzgL2Afr7ivfgBk6BLLPyZ9UdjbunLJHZ0c1l2U7WvmtSx4xuJe7Ir2OPaqDDbj1uuWd3W0M/jnRWGC6oZ1qluCzgJmP8q9Z1ew7GiTpGYLU9pjuKlZ2lloqnjIFfQ5HuyrjGRcFXQ0oHfrsLUEyt4xx7NKT5jOGXV5Z9QG6swflf36acuig39WkPqR2aKlxXYDpgsQB75ivnAx5Ujj0Tg0zqMzypcJk71NYqSE7cOOTNNVVWC+kYdD6nZN4NYtMnGlaQjnQ4sMDGPMCip5VvkW3VJacHJyRq+lWN4FYsh23YkElZRC8NLr8tI7BxEwKK4C6CIrgFZmywy4k3NHt6jaOULEhDWDaMDw9Ml7G08Hc/OTLHIjXUz2DvaVWixuqJ9lmL9aFwqRdpuLbNj9iRGrdJXL74TXpvDznamuOMSWEwzrEH6Cb1CI8KKS3DJGhrTSqh21Kpac3k0g0qij5A2fQz9409abyB6hB6m6RQdFq1LyzSQ3q2povLIQNrao+j3uDUDYqM3Ui36f7ZncT7OnmxGaLjvsRh3ZYGpzyqUR3Oxmmi0oT5QAFlyTYJYX8iJPTV2fbj6HBNltQyYYTR0CbyWAbFBLozhxyR4wMt48uwv/01LtaFnaNipk1dDwvtibddnRQ5dtPpzeGCSK9lfwb7suMO7APoT87tFeI0gcsEW99TbxsJ+PwWF5eMhsU1qlKVhG8FP822fXAXeRXrylv5USSakFwfhgJOcxAV/gOhQBYUurH4VIYM3wdAgf3NnlMJQCa7NPtkIBhm4zpqEQbCWX5e14/aKJvEY5BYhHJXHdqAPQjddRzOqNwASgB9FIXreF8tznKn47/QegrFtuSlABwviqtcYyhMwMYLAg0Nz1BnQ3bLS8Y4qaPL8+TOL107oZM1QgqSxCuyZFWzr6VVCzdcdAe8qihIbGslk7aT3EUrgJUoXnPbrE2XE/SPxluAeyGZ0JpM1HxTdWkT1sstjRFAJkaScZSDw65CA3zh3kpgeHTWhs0XXPbq7ufXGMa2GREaiWvEPKhbWAndWv80vmQSwOA5HrC0dwSr0i/bblibK0pqoewbUPI+Jp26d3BUD1Q3VfdkaXHJtYlNiuh2yAvR579me5th2IQFQiAyY+l1NC7hJ9HYwoVJYaHpX/f9C3hDkIF+hRrM6EDQXW35921J6EP5ELKrniGSu0NKNtYmW649dOB9aPN1T41LUXrzuR/ooNNZnlOOMA4VvTSN8Mu63KM9HFrQkQE0moiDCRQjhS9WrCQ05LDOiYIqzmoJI7emj8n+63peJIBOlLMPMWBCtRK/V4NdxxQ9qfFHpNWqL7k+x+2rJIkW/GkiiWyoTsHK4DKR+8gqiwdTwGocGwFeli2zzUDoa0q9QFXY1xgGfLJmHKmzS8T63dcHCBeTaklnWZk0pQbsKtQv3gviQGwuw55RDeevmt2txHhEo30kWrkacDVR4IVZ7l0XsnQPFpWz9YXqylFXzYIsEtiS/UnWwK5vj6qdAaArGEJYjSgKS3pGtAKdmNTeP8VhfxBm9vYDOnmq/+rMhB4A2jJdzlpdk3Gz5RudUixeMJqM+FPQCtiqHdzlN3RRPwx9oH37KY4DlJAhuPkOJR+mgdQXbde24Gi4E+1u07ZWqlMvZo5ZJ2SpLwQX8vmUA9gJbYgDj2tqGxNIiUdLtmVYAHYxWmQ2n8TF9lAzCT7FN47z4ZWM8tch+IBax3mGxK9NxVJMIb9avzAlz1tJfE9kj/+pL1gZrOUxTgYppUHzv18/6OO9lmI6s+maNdzwueSYWfA0gmHdMTGqMENCImJRKVf+BwH/Un1Y2SBIsttao6ShZjNgAbwDo8axZW4vvQfSmjFKnOxhvGZT19F4hpLYyBImAbMHza78XTZpVN0Bp+1mIYH2AQNVYI8tEeXCWwZGxREx3qhkvrqpQTohSbbFpS8KWp+SdTYLABvkoOCxhP+fgNWoR4BLgE7QkRzFjuvQCdFSOq9EjRscjHBLtPN7AH59MSz2RWJMPHU63qOcbpWgEMi41+14DfsrGkfinZqsyK5WlbhYkfAxAyKUN1BAPEygTf6IeokGVjy6O+omefyvh0XUmQ8INvm/DAbaIB8mlZ+9tqXObCDENAufBp2IL6hD8w52iU/YKoGscWe6MAtSRzDQc9fiX8+Wbw4SN+aE+Jx/bpBeculKku8LCxn72UFY1p5W7mJdtpk21m5sw6SJomBwFSjwpQbYF6nE7S2UNWPoRcqwYbsCkdQVP9dTU9Xo9XxRZg1KpcSa5V6XFHeE074xy3Z/gsbVdRej3zLYyeOv+rqgJrCsST3pF95xQTPV563eFqUFpAQ7zai2NUnCv8nxI9OnoKEBDxDxQt4oF5QAh2quashNyy8P41M1NtAh6N79slU1XvQIZ8orowVW8enTb7tUO9TY4ZI9ZHgESs+h0R0OiuKb1TlTVvpW4sumnpgQ6DrP+UJucSQoFvLpauNv/+4tAKzPucMdPyQwG+xoYF34fDKWaPA6emTptgo2XZAGSNbkwVUz3bsh5F1MN0AGqKLYWikgeUIReyxZ/4VLe7a0ViindRPpJJObqal58CQo20DHZuulR4ZHo1qzKuhBqH+qdG6Ml2LHfisC0bZJNFJFqbS5+huy4Jbg3zxXyJfeDbEOwAP7VwyEXqrtJOYkMZhy9QUmGRDvmKNtR01fbzB0yw5JEiR43rmr8dk+zFbih/vw1Y4jEbCqVbE0MNqaD1nOTWu1pX0zhnDIrzXnIiyrF3GUWeKlTrpyfBEw65UQqPmva9r9Y5gsbJ8qVlH8svCNkISm1yUriMGecDSxwYCrCQ6ceHPWZsYJ71RyNWxZbc9ljHvbkJLjMlTkqob0MMq0lkYEHEnbSj7/+JSu+s9ioYRMzldBXBGZacPHxI0oqOw+kiPRObLrD6s/rLLOOLvjzfulEY8AhxG28XVlnNZtwSeXJ6+JkV82yX9WXiFypB38GiOcdoRTa5UahXNCPT7YaIerLn+qWegUwQoqPbbp4TSnAaWxgJbTefNUhYoX3oANvNqnrMqS7vhgxqUkgyNsGbvJiHzSNmajqoFKSjLgq6dPQ6X9DHp5GvKVYNQlm4oW/xYETio2OZ0sKwo94SlTyiERrn/81PE56hQ2ABH2bo0dmR+w1lXfBYtCA1QHOqzy75IrVgeo1qgI9eca/3coswDcenKBXG6F0Sg55zp7oSmsj3SnofpN44Gpg+EHHLaYQXQn8ywoYU1evnlXE0wtH3d6p4XzoAq4e+oGUZOBhhwNMZ1zHuywKtPr0uUjsGzUSL5C29lgr3bP5m4dS3cF7S6em11J4bDqy/lcIMpR4+Ld4eLd/mldjPeVyMS9H5+jv7DCYUthj61K6JqZfKzfZOO1/g0/aPp+smJuabUUlsXWUmyIzIuMmAKz3flhSaTMAIZS7Z0oGUXHesXWUHOEMGPlhP2jTIJw+QGCB7u9mB2MFWi7fw7a2qwlR7IkhODnEmtG7lunGvqYvmRYSCc4uQfAMs0+y2S3SC41ygSxBz0LA88pnQRomGsYMJX/I9lZ+yn21lVp+PwI4efMwwRQzHim912VWYMI4dPOOoMoMdJt3VqJ7mquFAE7uRomw2gTFBIpKT+dA3MB14MqVlSCcBTJn68xRWibAXixHVwVHZ/tSwE6JULsY2rsEPRYnNqDYmqpKlLcg54A7PKaAPBF7eXZFEF++jnC4W9Bv0CGCKkxYcrb9cSfpGJQYnBTR1zXMBlbbEIaLQxyFw4HPE8KH790z1NjzScT0kvtl0tTV7prpFUcyyis/wcUs4Y7OOIgGpE2Yp4SqsSTJ+BvDSxiILpoF2HsDynIFxQf+UZVnT6ZRykOPL8lIFguwXGo75oeI4DikPPHEfRetMxgqpv10xmDpPbJAIAWOsRCCtp4aasTQhe5xgSY0nG5AIH4Xr8mto5PA4mPXM8SF+3I/DmldxdXkLdU4fYqvxVj/N7GWMBEnxqkOSqsSqGWsgO0s1qKi40AOaFdc3gHO327PcwSlU3rJ+tYddb1MniTukl3TuwBB2e+zTTI5+mS9bERR36ZfeCwdysndLOI9m/mtdQfYz1m2LpYAmyd2x4+pCxWeKbw+i5p0UubT+6+sf9zkZByIE92nNM7XK0B1Ojm6OKJJklDWAdf5iAcFT2oguqgJJlMMo/igMZdDnX47Q9iGU6twylBy7I/B1YM34HDhM0lyADbVAVkMMctiWazX864doz6XK2dXNg1DHQxdM/B59ux/A4u7QDwV1Z93L4trQvswkGj1z9ZB8+7iAOWpjXpDhIaU7io/nXP2efo2NAvbhurV2xDLulsB2HUvGljSsnKYwKeCeU5I4y5fw6SutFZj5N7CaNPUPwcwh1jZzF0vLo+f+VtZvAG4HsmFZoQQNdcdpwJaGbji/BuQ6My7EyrqjCdpbMzVHFWClR0eo5NVZXDIx0nNja9D22Vwwkz4bSEBTnzU8aAFYfJK0uVZLWa8dZzBSseEubpEOme5IT5/Dj5aUuHSwIaVS+lFGfw4sU6jOJVB/O43TlOpFVpGKMwBNAHAQRllPaXibDRsEyk6FzZOPAJ49JXwMsoqh1nabQl02zo1ttwhutbtCoq/NWLXallYmJTlgU4AD0UjgxoRtRPnE9CxtgkXj/Da6XY7TwZEVdDU4w5K2igMBfCorqp/DyaKF2Jf1K1mCQcivrGroiPC/0CE+GhXGYMtKz8jdP7udbRIR2pB1H7Nu2jplHJenx1RgS5fImh6DMNklQg9FOKc9OeoKCOq170KniAvyCtgbjGARBX/bENPYXyeGdZZvwG+dr412lHLyNSnA5wLRyJK8hZyCVboKr3dkAmlIlPPWa2lUWAcrM8N+hXzV10dJvpA2RDbh8dvQlh7D6avXoOkL/wREKMC63pAcG1qtHfeZg6pThljB5ETPbrWANfAYkndyaR1X+I/r0Pk4I2DT8BCNB2WAOH+PlUUHri0VUYfmR33rlzqsjTQfUFy5lcoozCYDcpIoIfN5Zg5XBzvJaYEc0dbJEPQCeVr4+szxB5XuwbImkEKtpjuL7yv6Z0rwcP8dgVDj5W9Kx57HaQ3xz6G5TjjNsgmrPN9eAjKioNj14e+k5DaTKXiGDfPVXho+QyqlBTYuQpbxlNU5y3px4fMrnappu4zSmTG9pg4h6icBucO1QbJhVUEbQp9xmdEoELgaANhvDAE5TOExt/1TdwS0qRg/6EjqA1/RlAHwSuj7bPmDPRGF95px2R3NsKOrZ9pI3N1kTmlJFLj00nYXemXisazglOIfLccwRswbASH8DCMiVKFAUuCqb1zUBZqUjn/U0fzLtKLBJP3F+dmo3eI2T7UWvgG0f7A0ay0w+oxMP/MqUoCLGa32otXbi53PGkooddr9Tsz6bZVCMdFiRLYlc4c+pU1ZnwFNAiY7Kfdd87Wf7sBzwgdDMiCeQdWG8YDAi0ZZVwAZ/FuwKnJA/ss2zrjRzmjK+j36/KTf4S/yrVH7aDMF+YehKIc8u76/myzkki1QT1PQ57b45ng81mcNCXHynbEkH2Q51kj1MvIU0Mpg1dKxBZL5a7iVSc69kn0vfd9RPchedVNsF23QEYa4c1I7m9A+MZWlYUFZJQMTREYH20h2X5nj5+sGc62ghbV6RBsGG8ZT8lINB5mTuaZn9OsfatjrJHj4F9J8cIgSKpJOovoXtaPCRR4fugznx9JNRIf9p94Ie3LHLFLpP3NeuynDkQ9sSy6byKKdsu6cx6K7pK0acqXq0PaHOpxNzCRyoE1c5ykjgO4Vqbp3XUKw6S73JVfAAajUzhOwYL1cNCb7mIEXcBZ1Z4YynXVMMmeRC/Q2HH5ERpxvoIgN7PrlY5aZ2b9F7WJp4Vmr5YCOcRjJcK4StdEHBatHZyR0//8g5MDnB1hA0hX4/EwWcE8RoI+cv18hMA3IxB5GeXxKrzyMVykNNGHhiYVNHtj1LbU9RjXW8LUJ6zXPZq2CwiRbtkwd47MRyPMFkBM0EZJsWA3frSogtJdBAM0l0/ByDJ/Sm/5hV4IRw4JkPHgQIHly3oyMCC+GrVQIbYPay3t4ODm86C/OQsYvlNGBF29A/zBURgtS4Qycx5K7b5BOMl+SSOEciX/oZKm8+byth3UD4DnTQq0JereafoKrdgZ7DLhVAyHOAoRO4Wd0SXK7aGke7frpUPGi4bHdl1dToumBVw2X0EjxpRniWpIksBLxbErd2GfCv+xNvOoaShmLXtIX5RIIBgG2qRVyXen13SMN8pE4xw5k35LNdZZstbCdKJVQQd4gFEhv1m04c90DkXOI2yT6nHtTg5zC+MDGSR4qYD52JWgZEusbkoWk3IO/TqxUWvCGsZi519fDkNHrJOgaVMKXNkOLRmqfHyHAnR7KnOCoICEzIoClX4yAZCcJxL9KFsIbST5Etvk76k5W4nUlWUzeXn7Um4H0WllPndcmBBs/2anwIxwWch4/pTc5z/jTynLcoS9KTnAqN7dis8VkONb0C+89eZD5K7/MJ0t2KmIIDfneiC7KWQWoHgelSRV24wcNB63Y3piVwgypqCC+1EiYErMyh8fCoxwy6hSXkJywv/XnhgoGs7HOew3FwMGRr7EaIhpfonlRcyk2pXlF4YAjDbGTBAbAniBxSwQTnUMJcpcbaKtUs0Rw3VSOf+SteNTl9sqC3bStAHst8Z8rTaew1B14Vuh6alLkUr81Ah8IBVs2uuQWnVwi3QgdD7UoE6JKRjl5VaYrgfkXXouFJ9itfnFF+FJykbGs5OV5Xm/J8Laef6x5A5EMc7qrQP9patFJmGf86tBkR0aj5S1r9aUvpimq7hjFhIbRxi+zK9j7HMtp1Y9A5aQMU65pTLl7u7xXac7Bygn4z7G5uzdyTohlkFxl0EnkOpu64XDKV6WD2qY00zWvZ6VdaBhJjfoRunn2vch2AELl8ZqBtq2ZDEWvIi4c20NTgnw6iMrZfjW4b6F2Int1s/bNDrlkrFlA/j2AeDBpxUs6jg/WoGh+wpnL47WAbBRucpPuUW7/n/K5hjmjpt4NtRRyv+uC8ASulfCBJQ3awPt5YlHOkU2aBLWDLwwAS6SoOx3nF+1hGTYA/KbMDP0jszXlFkEBQkDlefTvKBm6iI4WSrMO8uxoj0t5nhyffoaweWgowfxYEAN3W1gYXWBaepqaG9vrhvUhBoZ2wNHdxbomm2g5Kffp9rFYjhauAWTUZCaLbn4JJmTzXQU5bCQbvEpIUFLZuTj3L5w8rScTEo1x2qKRZkpoA0NKSrV7FIPRMKhFAOoeZKsykWU7rjDOTYotuxVFA7fi6VcOKrf/0F8U8aomiOOmK/cmyqqlzULwxvJX+sEq1gu7+n65grm4XWwkvkv05oqUtM+Dz9MlB288VcZhoAwp9Cn4CK5YZYmQ3H0RrdhVNHsK9vBWYjBCAi2BFlnaecDTqGLUCHAs89PdVCviVkWqFpqlMC59aajz4z4564JOawcER4tN4g4SN9l/i+fOl0hLu51ZVhAcgQmtyzJo8OooIO1tNOHuantAfNK7VvNNZxlJdMyQcjri8K8Vp56PJQBOfFPkCxFC/6Tbay737CQb10kuZyYAyEJ6VxEA85LU9a38UyhAe6jSYXXPMIrHhTs1+bQ75gYFK5o+vuvRtZ2suJ/JcOx6NXrlyc/AB+H3PiRLHMT1aHDNnTdsm59acJR/yKt9hMKDOWU8TkqUtX1ePh26KyNeZygEV7hs/50TQErxjEW5Cxej88tNrkOoD6+uhiHSqzmmSMTsgw3IguTOSm1L9AnKJ+lupAxkSEANGKzrcMKs1sU4WZbUW5Aat0gCCFmDDx5XTMmZ2qe2l+SQge8TnCrDkv3TQdBPhBki58dPAMpvOhACElHvJO0uFDK7P6GWfpZFGCdah1lCYr6AvkfAFtyOr1lkvw0IRLumxVHccBYJ5Fe2+1eeGhXNDq/6Vt5uHAw7XZKvPsNjRRL9ypZfw4Svot5Quz8m8mOfqQYy6CeyaJHwXz4RJ0MTY+6t6NDnCzI6eYGmRbw1XTqAuKz0F/YkMjkn8RNyG0z/2B3cOdtzVWvcolIGejIKJFshgajr7/Wgbv0gz4BkpQVxO/QCUeTZdnSdxyx3DAYoMKyNJBRvVMHc6w3rp59tqNEP/OdPeGngiPwmbiqN9MCsMVThVONbY9M3sLyMDn45MUvmFaZBQOMASxGDnoJtX0xiCnzSkmQNwY24DIO+HUG9U9XGxjkFyRXpGgQcmrAE3vDkATbBGyOPzqyHXbuAuOY4pvV0PO/gscHtYPrAYoN01aTzo6fguZ11rOAZHBKrtqTkkiMGcsM1JcHX250pXzTZev7OYNlU5UjuODbm64mmlGbTfazjALf9eTm6rYbIiVbTbFgLivaOhY3cWKawpjmjnGYxTrEGt3uZxaMWxLwW9GmpCx1ECHYGTU5kQ3JzT1zQiGhq4Ni1j8E5WAtl0kaMfjBvWrSnmQLdLPKgyryxzp+kMx8nBVKeMUCM5ROv+1KBrWf1Z+eiQXfMMkvs4a3ZJQ9KInrO+hCXk3SQU4yeo+nTTBRXrcB1qNDuK8Sk1zfYdKg0xV1qBlLRPy30kC7PK42s3z8FreEmT5y35l6bisAamntLQbegVfvJal0IDxd0cSk8c+tM8hy6PPV/UAE0t3ZOSOh0t/zlwRxc0JyrQDuRdsDOGWCB2e+RI6qJHc64pvNTthtKttxulCZqSRpjo/ieLZjj0EQC2G3xhWYCFLkWgUGT1Djr3Ky94jW6Xw1uu/pF6AEnARhTSUbXIB3v6VA/0SxuUQh/ojxSVBuy/t89pjFKIzOcEb+T6BB1lI0xU1CaRPspyRQ5zaOVjmAbCR4Zw4EBx8IgFw5WOSe0yWC4SNJBtsyxYldtRJE6DfNiiTAvUrlYE/01KZCut5YTzn/0cu531BpxMZ08Xg1YTGLTe0mxThx8IZCimngI/WZjuAg5drJGmHbLJoEPRYNRDSF2QCPcjVRDB6RByVKA59XhfWmKe121aLflaNFWbIV8a+fspkvlSZpkNvb91gv5K3XWccIalANg4fggC+pQEuaJl6Tu1rnOkO+PrEY6D2RsyaT08NfhuVudF6aGYjAr9CVNFaTPC/QAaaQ7e3oDDdDxP+XqUPfEn7//zNWpGYVpUB6ioLcPbgTA0IB2fQxhblwGZBB5HdNQ65I0MJvs8JHlES3UoNheVZ9JXOlImcaRn3g8f9hmX9LOlbkIIpIRkVolNh5/KQrE3AaYreSOzHg5B6qfcL7cj1AfMA1ImiZRCTrhTYe8FvV1pOiC0cD1NqjJSZ86EiTgBClcRyaytE8+oy9dPlslmLANuJCdsRlImWgm2P7R5d+aGqheODryVPyfQlt9K05JJVdD3ORKNChjCV3NE38A+FqHrckC2BRKY8NWNTFvHwRxZjgDS8bNdbEha0IfosdOsALEMAogALyiyjkY6TpV7ZaB8sD+hYyxvEPs13kLzgejfcovNDLAsj+qxndorS+ZcPykYbtzVOANk0WsfaPXpFCbW26nwcev+xCuuXdGPtPf8zBZPueautX4LwbX1dafAqAvFRPU5m0xG8HG25XOQbZ57q8JAaaqkKUYFQU8BE7jlQQYrl8Ur0en7ORoDEk89CI9zGv90PUDm2XkfibXXKebDaMKwkSvnXW+2LZDZVJVO2E36Ud3SWAxPCnodRASs+FHvGl+FtHvXLx6WriMgQNeNXuT/EBJ1ztWluxW947NdhvrlOmlxC3AX+U15E9FX1eeHWxkD3a/a1KOIdXNfNdZUTgRZ6ZA3g1Xi308zWAPg0FWQcI6aYwclaGpeKsb8OUrkK4chnbw640mA8abmHqN0w9hoaFjrnDlw1la++AS1JklVVLlJJCdC4jNW84Kxd3GmmdO78eg1F+LEZ0gIyQe4hUi1PZIuMPdpm8lbaCJMgAdVXhMltl5HUgmXM8km4yqbNI7uZMQljeQN9RrdEbU460iZ0YEa05tWC9BOEInXUKeGAkJutHnK0+9tO9zA1pZ9Qc6Pxu+QaMsFI/CA9vEjeQ7DwfalhLriJhBzGULXTXIYZ6rMnObr205x/YZLQG9bMpeuRG8a96/qEImoXfYdoIuBq2z78zNkemPoa4D3pTbP+k17mGWJU9t8Ev9SltI/oJSnB0wuappBap0XLTmF+3PqhgSHb2gaM4WTsD3EImmpAUsU08n2LXs6WPZ85jNTd3J91WtaB9RijkYwLAeiGjAXvn0wMZ2nBAXokzVTeBGW7TmxrcHojl3nDOpL4BUxwI8t6cTnFA/rF0V1F64+4NMyE3jSHaM7k2uIaVhq+UlP0WEz9uTpzMxgA5DrhesSVdMyLPZbE/Y0MyAxBvw+JVun5pMfRTtclo4+Gk2+AORKykKsyQVY9GZKsDi/6Uh0w3Ai1UST355quLY5DrPyZKphfIuxN6GfWQo2fUgAqcWs0+lT8uadtv0gmJ7xkTbQ8oER1nS6l8OlWL2cK7fdh63i/dbIMJS4VHRg5dQh04iDel4Db7pRezu+mKYezMsSYB76quXCU2OQnG2L3MVZQXZAv89WNG43DqSSounIJy1iFKs7GBYFxVHG4BBAYXBbpQwVnL9OBLlASZH7hcaVtQ7CgpkR58JTr4mnMojMMzsjRXWwYfMvmxN1cUstK8zj6O7imS0LflS98+eCjIsRTeFTI0BVuPNq2AwYmkHX+QBVIaVoSk72AFW7Wxl9Oj3qoISqEn7/keFASntMX6q47Oov6Uj/sFkI97dU5nQrygO1l4cvC5n1z0lbh7gZP8u6DibKq0Q9jl+HQCyUhseaCGSMYR8lI4AV+bNlGrK/1C7aZjllWaQFgA9r2xBpeL3rEsF+fd1U8eFw1xWzV4tYSdwj2Cdoqu3DzZPzBs0lob9Mu4BlHSHBHENoJj/qPuZ4xDBrdHhmo47fri/I54Tnb8hA+pRSDmF/NnE1grZ5s6gI47pRjBkPMaSgZ0qup5QbMitIf41spH5Hm+lKdJrycu4t2OVv9qxiLB3zOlmtrD49WJ6joHFz7I4IAzfnFtVFMwOcfIAuJRbk9O6q4HSYJy+77IeHUWtrO8iwBQVBevftsm+Gxw8QStkgjOTcHsTV3YdJaofCCrZwc6YzQB1tuu6w9txQpdt0Ug6mH1B4nPy71i5oFvyRWRB0DGUUvS33iDmRsp+Oi0rjOxjpiMThxOBUfWxUM3ahxqzBc7v6bJZNCKQ8HS1L8NFq0PsHcAV476Tr6eSe5RBj0kDtwrU/WNUBgkmPUdZynTCSwaF/Dh5LyUsWTbuU1KAziouY0+Q27f/P1rnsapac1fZVtqpNI+4XnqUkGuAGncINIyEh3p0YY64sLOTTwMflysyd/79WxHeZc8yiK2mCDXBsYEMvS4FII87exCJVLLC87KZRjvWFA4ntL+aqsVHO+PTIapJcp9KaCYGJrtwQbD/Y493wcJHNcBKBF+avRXYRrj1RQgwtBWA3fQI9wHOW0xNggLupr7k3ILcbqwAeRF/Fdo+XNG1c6RyhXCQjOe3DTD/Weext+LiQAL4/s/p7dN/ktoxFcCuJFop5QBc8woTGLqyIDxchRMvS5bVwiDPY4XHiWiniw4fwSDOXcIkZc8dDjIRCqUtzGChsYus/wYrIuLBWG56A53AbIIZBJHfEP6oHPH61NWXFDKLkvaFFK9g2OVu+/vvoujENV64gtGN3cZBaYOEam6XvaucA4iBwUBF3IkFyoscMc+GRwLSHditkWQrIIbmOJvs4GEH7Cd6A/7BTZJ9CqgbCCI0J1GWvkOBg2NJyCGE44u8Eul8jIHm80mMjX0FO6QLQJg9VRwSFHAWscoYzpJEZjvdFj1NUCPllfdmCXWciSEi5HWNVjsH4hi/B7FSyJvgmEddLeKXtNOy9OS6n5hWwSc4WhYsepfeS8W8e0auIc2mDrj5Lob0zEhC1hkf75NIbgoHe+2k5n2uJwqQBr0hgDR8U+HoomxmaMD6eJYNIfgQ5w6Hk1utyG52x5AWmpRW+gg41doeUQdk+8SVQUibamUaJwSJSB8nuQJaGr3BxOvi+phxyXP20B82cN1E3TQkHgDnUAJuxeOuuJ1kkxqStlL94u8upPsNyBoi78Nlj7gx4CSYdyC54a1nJ8AyFcOSNzuJra1DNnntKClxOOQf/vvEAvLQ92EetI+hYWC/y76BTGi43+AqLgQwahsCUsXqlqKXbuKJG5BGM6ZfQdNzv8MhBCOkVwvLFkTGksx90sgr6u0uIpdlXRNL0U0St4ouu+hw/henJ3awHpCPIFBufcCVImrb/qpZDCo3maHlH4uk3tNwVWnGQ9d5bNhokcHkdagjm5JRTdRDX8DNj/trTr7m4QUQhzQt1Tfh4L8aNJHCKMvNv0jXhvWYNjeI2ImypTsChxdSNcFfPTIt37jA2dbS82pW3j1+XI/n+DYt38giVeBXRREcgON3Zz0wkq5UGmohEmL/HMCKOKX8YpeIybI4VWOLB7HDeV8lXWIgEJOA3i82iJQiLv3Ue7alwFte2xi8R0EF37HT3SovVXHadxfv6MoI5gSqn4xLMr26CQnuZ7n11fZsFiO8WyZS0b0JRGR6BMqSjcwbCfPKLHBXf283ye40NBccrxXiplotaMjPQ3akvnOrjKAaTVWXKSVwEPXB6Jr7GKYs5vEhPzFIY/gUoMRnrUvubnrATTKcOTAhIwWTiThP9zDFtAEZNDD67h1zNLqU5Id1yWa/SOZJ9uJTQsmrJc7zvMEf8wBRVZz6GSYfFMFcyar1T5FFSWRSZotBLiEDAM1XEGixlh6frR0WBZZRu1wWLlOyGYcGcHH8qz+2XkSI9pKsUM2W9KY00+PrITRxCJup2GzjowVO66BF0Ol11C7xTNrFYqmWQt8j7oi+GLWMICuaanYwQ+Njmo02RSnWZN9gdjzM+5LHwdwKxZ5eiXp2WoitzZzWF9pv2yjUjkn+/bYAuUngc5VePn6E6kwjAJm/CjSUAMgSnw0ALRFTWlTZpVXUaoR1cy2GNvcaAohxkrQrmKwk0WTjMpF0rTHuTYYS4/5VDzNq5SgR2ZcLVSHmtV8kNdKGhqJgfKObFWLpJ48+pttUM7HlYJKj1bPyz0TEfz9XVTGYJF/9BNQ9iBsGLb5VQawK9GDlvg0+cZGEL665sXVxpmyR4bgmjcQWIL1Uvs0Am9qcWWE2zhyO3mVO4Q1FgvjFM3JslCbTDZOvuWb4VfBBgzgmLgvGXdw8RD0kj72+nI3oxqMfhrzFI6y11+DXUUA09wu/upI27BkrFCOWer8A95zV7Ew1qNa+R9wNMeAbfIzB8ZjZVO9kxywl2tikBPIeo0qdSfsYCR6A3OaDJAyp2Jgzci9QJKEWKHYj71M7OPfv+qVgItIgsSncQP9rRrs/f+MQbdTuP1RiGSjp+FQbhU58IvuqTc5HfkVhNate5laixV0M2cMJzZQFHuJaaa6BT6B+o7oe4Q7LCmWDwpWet/3HDNZsA09cRtfQ4G4eBqGyr9OKXg5cpzviQ+ZHVjY/FuhtJsjkvZZieqL0EULoXUcA7VUEne1YUQ00pCwhmfiQAULTs/bZYeov2Md08dOnshNsMP94Ke1imcgVvwwFLjIqfBY9XrBr4CaWkGE7t4vv98Es+gpqKIS+SYshoN8EroKKL0kezAtk7m+OsromqgZZUystkG8sPVrWiqNTWNeVNrwSrZGHBdJeyc/O0SRnjBtNkih2XRn4432HvQLm/zaeh1HHLIqtlkPh909MNeYNubqral+JKknh2/dTwkXFZBKQrkYyRBL81JxfaoS0sGk04owXK6Y6O04RDV8OkLxZX+JOBjzf3gcrLfWCWKq8LCBznJihLnbE6DNou8CyDkwfSFIFnvrWUZJWr5sLMJBSd5r5yHAcWpeM6tfuUNHW1MMICrep6uBzpAPjpJFhimDoKj6tL9eWaYhYHXNANETsf+zVULjpOVbhPCnoJPczyDhX2MEOLYhwBpGGANFIYKqoZHzS6JAmagyEJHbq/N0q1wJyxcY+lur6JymyOiA+yj236PK6Fd4qwbYdTQVXg/JSMEdvrEg/NToYOzDB+gb76ZCT2ZYgDczAqXo0ZmkjZPhs3cpe4Q+o+DvwlOpm/uEvVlR+s7M9vJ6JorJXYY0WnTL8QM9iIEmWPP+qaWHCWhQ2pi1uHqtwvqmmjXfSwGgrG+KUqgqo2j8WZ4mu3I2dj27aA2S/j5Ri2QGXhniXwg+qhmVDaZBdaEKsutSwp27HGUg68hXMOtq7ILBhP4zWYEt04KWTJG5VdzErqBjyt1fVwyiPiBrOdNZrw/wab1SZtTR1Y9bi4N8II4ozkwxWqUfUzPjpWiWfYeUrM3mFwJpfeVIwjzVNhz9Q8BOSgJACsRDF8DTJjxcTpJfSvq5xIvOer+WXmdpFVhMcLaNlfcDtNCXUor6YMMjoTFxUjTLXmPcREnDbbHRLkJgMj3crxoHLxb/MIWYbgR2AlaXZKpEk0KGcktJIZqJtOl7XdEAg06tcR1BSpEKa5o1Ajwre9phZ0blpWYd0fXJMQpfOV0EvtsZTkomfFZExttBN9VgJhplo+fHokhw/Bs7BjGN86ZmlF+oIKSfxH1JkUYv6xtt6I2+yWu6F4R1IAuzcOQt8mqjn+rqwsmTLqHmu6H/GAoEBEhVuM9zyyhtzzIChkT4BOrjj2gbGtci2Y2KVHFi4Y/xP6G166laW+6VLFUBr2yIxVOlpIyBIckpDcjrxmRD40naOqNFEd0qs5TyxesIotU9qPuqKRpq0lloV2jZK64c5H7N1VBXRtKQmLEuHUfZUYbZHTifU9Yskp1S8BXhCHBZXzy2AN0g8hJWRu2dx+D5kpoPAd0SJRPIwoBMpPV1PKPqmtnNRY5F9dKcQ/sRFigI6Q7Ltitmo9lIr8sJPvFvaJCtTFKcXeHwVGg8f7o9HPtBxlv8edC2Fvw7EcWnn8KyhUALLpELTsuzSV715yG9yqOX3LTFDeLpQh18xT6ipoHR22+4/hF7Bz2Z+wQm3WJpWapiN6agbNlgBgAROWxCKNOv3l3iPMN26i5flR3ifFegHi3nXgpZGD/Bz+NyuVsgzjxpfLkXrFb5T41HGrGeiIhwbJAgt1Ub30DeEKILl7hRdaOxYXwnOMpehevGC+qhojS37sRQwWq8Bj2N8811IOUeOPPD+am0aNWsWPliWI/IKmksB9L5U0IzTB7hOTFQ6soj4ZdFizeg6YZeptKmawXjUyX7K1COVivl1Ke9MQeH+UBtNovN9FoDZJf7wyVefBwSgGr8Xfk5UW9e8wKSobY2aj6qGnualGZpFpX0V5cVLWq8hsyuoRwF5l4xhACAgES9C1AFVYPCz5OfqyPqTUtfLyWiNKhSWZSG/iU1Rwb0UnzSxHPWR9KjPikN8OAbtpcWeG4XgkVU2rvcZviRuZeRXE3ERPQOyfjFdJ7BOrNpKox5HNXJDVCUHtHnh07i0ecMLvaEuJq2lBTjgsZAkC4doWiymysQxLNC1Hy7LN0IVhcYkJVC7krr+YzkMQqIhyU1GNsXjHBkOTQkPGF8DuktdThVpg1F7KmDcTL+B2CFQ+hTxtbzEjmDWSXlcIC1RpRQKxm0KKOlSEZr1zBVwFyFN4gBHdKBJZtpXk88ogBJUxzFpUFcZ5LCoS71BXg02yKZUk7DkVm+4Ld9eKiI9OH6BK+ymPBEFXtz5kfESg4RKIJCXZeQ6oHV76MaRSUHY0My/8QFJ31EQiNBMNa0acjc4UMoJTfiQ1NWsEwSjsRI/ka9sr5ihdzLOY4cQY8OVVxLrc/fyh0wAQFDhXyoSgeMYoDKbUCbLsSeTFkcvH4AK/sCk3STMlIfAKkFGM3jOgSua0VA+2KlEi8lRxAWGYYX1cP1sAVwipVyUFyzLQwqJhuufAPXRzppu/TD1JFdsFC9FvIrHwcBnmQn9237iAln/lJEtzzfEj0OsyWlOaXI+WjlH1TJDXzYc/lFtcI32Q0zfp89kpFG8z03QEdQnCYyYOzWiRZA1YKq0HgwmRaMyyuNEqBT7h3d4KhWU/XhrOSEZzjonlgL3/hfsN9LqprOY+L72gttcwb3jDCjZozDpKqyuqd2oYCjp+bN7Fov4M8lNXs38lrvDe9RyQzE5ceAwNXhV7FaN5KgYm8gxIEgi7WGNzHJwZ4x1XHvuHGdoRp4LESgVf01jELXbK1Mxi1NPIl7NwVjAMOegnmWoqCTbOGgi7LVo3J6NoMIJtY4nROe1AClEpH9Mbr1pC1umyWu0QViLmOWKQSnbuuJNhlRuNY7fHEo0Vl8t8cyXeQ0np01EBANTgf4MZJxU9Fzl8z2oUBPSlakQOsYf22EhQ0F2zo6I8w+OkG6hzfQ5xXdTnOwhgdmOSyZh2j26qRpeTil5ZT29Vm+yOht+ZDMet8ogqylGVPQHI9mUIrccWTLKIv9C27MSlltOzZq1JATRCwBKEiEYKYOzenD9TwMlxlYifU7nzGlYpSlI//ghZJs5ympcIYYjG5V2l6fIzTeDhwudDH9guhjncXFMtfhFs31qBsDQn/e61Gccblehg8n2Xv3M16zmRspxaHBKMdtivGv4GJJSJNPhrAzQ4QYVAhGNF9YllJdsR1+f+niRQMNgpctBYThrUppT4SoVjy272LCoYCsaR95wD9f0xcvbIwEteh7oV8nyyAGcUYjdlYpG8iRizB3Pt9x2rW+P/+bvI+YFzRR90iSqpzQQzxADs4VpxiaCJDIwvo53ibq46O8ElyNFGcrbTuWa09pY2yfBzquglePqmr5syKWbuQjDJy0q+q0ot3VbxqmQ2YEmK0bTWpeBgDdDkOGrdYxjTJWc7CvMv2ZD3sv+Q3DoQQHkh+VfGhYxqRug7YoWqd4+7GhvOdkBNX6dTKnEKTJmWKalAW5K1Xb763nde+wsTsuLBaQnmWmYK7yWNz9DIJLB146Eb1RzrUFY1c6qBAuiEIojn61ZtaHgHEX6T4jl1H18NStWQ+avJF3wSM5jBAQEu3enE8MeZUys5lgZ+kjCCybTHQYGwnhETgxnhEwz4thcPqCXUKfwmXfMcJRBqIsAtR5cH5jfG56IQFGR39RwXTod0MWUjoCExC3dD0YsQXfS+TAINhsFAovGxYGdjNqRk9jLBhlelJDj5JfcmZQRprBZPN66vi+QKej8rhxxDDgUwkk0wAarnZstDQefviTJPp3WcB6RNktjKe01pqr6Nro3tk/ZaEcSza70i8tUlkR6SymZD9XpCvC2hfDUQdxRz7Wd4ygriULtkb+QA3zqYzpQLCVWdvl063mbMT1YpfI9HYbDxucLhtD658mbZBlmumDEKcWCB1abnWEeSglt0vnAfW0BtB3ypt2WVP1Si3GdUh9sXZRqiAuG4DMc7u84l6d6QEh0FrOHiOVLEeBV271INnFXEuMTm4QNVxOLkjHwsMivi23slJexIhsLNto3CgwEEI6Qp05MwMWO/9ZQZrbYsXlCyIrk/PsxHnLguItj8rOPokvH0bfdinLWMuKVf6jhWfqPOgC0JjAnpENPrflEK6Eg34GDflhKOuhF65LLfxbebYc42OJva0K3QEFlr0uon+udNE6iJI/ZHdLEjFOOszJPhMUJhyDSOz36JxuwrZxJkGIWwzLOhJVAO543A/sOnGFgdEhDgr3z/mpZQMQMQ0eDAH04tN4zkQM/A9aDi1BhlhGZUEINcJDmCM2WYuR7XLXGpLEExIUy/H62FswUqbKAMLQWzo2rvSHSm3QfiNNYfwh+JFZPqaTBwramDsfmBDzM63LEROE7zUKfaqeFSk6WpqdyG/hjU6oCEhQMGZlFJVZbhztC40I73yPdACzDwUgHTWPLX8KvMP6lKz5YhztcgWAhGetqt/t79xFV9NS9x4NmVd3OF3o/HAw1Ckw0DJT81/9w6Xk5unyJtQO87JQMTUbIwmEYP8YRspsQaNINDFbah6CV/hB/1dbdDaFn32u0Sv2qcz7itqH2YiDKGuwFuTmOkjxAoaIS+73YUvaYmh/Ab1u4xWqkGfSb8F73zjzBMLUHUh20Eq4VU4DglQhCGeVMdLIuzRrls783T897n5uadSQuBafISqlFsxFvz0OGfcNezNW0tk2hYtcW+07SdGIGtN7eZkWCUH951HGcuInuZSQCSq9IcZ6HXoFMr09XAUY9JNLmUCJKGvFK4NYYUhi2NG1Gn/mLqS4C3lCol+BpXPVQO3CNi+sj4aWIDr6RQAuRYFrhdKJ5Bo4kXZmGo8zQFsjIsQyzReeLo9mvW/Xynbm+LP0A4WwXBUcgiN5EQ+iqVnolQp6ziasCF67Oy5MwzUq7ud+/KlsA6ng7K3OXqlph19f0AqOjt+QrQB3F03UTNdJX/r9pDbcXYVoyQTTGvusg8EyRYbmWgzrOBDzdJjExxqHB541/VgCsAUcmUju7qkrgFlqqSfehai+5aDvTlCOc9b7zWXh+4ZKSczLi0h8onnl2WVJvS/v2ODhq43nkPwP3RXF2jFF6NwlDuSCJ+345P8IhJHquhBq/r22rh9jpCOrUqke39Ligi3+/PD1tfh3dU5RYVMczhNgQx4SsARuEjIG5iM8bQjO5VyyuyqS49n4Eyo5LCth/nnVzlbTAhOy1dt2gGmJCwsWnOQUDX0AMSNoB7x22o8QCkGZpVbR4Qxi0tDEtgIqsXvn2ZW/gbjh5aewuTzJDaKugsCrtrDNSZnE9lclUSpfs9K0Hg5Yy5VSlfdPIraC+En1disPmV29zf96YNOXHOdRFIZdLDI8TGmT44D1QjyIMniUAxd4W0TJjwZFs6+j44EX5i6JgrthFS0dVGZG9bxa8x22HUdfWGUfMyTb3iDbvHfgM/NTOLNYGz5OVUjEIMm5RO/9t2eA+Vnh1h4ryn8u6KPJXr2FaEEGrW5Vid9IEjpnovuIjuBKaiE6GMkcSzmgw36fdkjeHC5HRYTj4cEXN3XheHjKdfN2fawlBBB+AaPd3CljOv7l/W19Zcckkr4LAfkh+adsEc9Bn5vPOTYVt37LDqCNFJLPz0S/JYdPmqk5akA8pgxc3bo5KiTe/g8bo++p2o0fllRmDW63FC+JxDQ2UynNg1qA5e1PeMpTpwOAksjumtoMIKYQLtsYnouJ2ufs4ahJ32XODBHF8WOA2YVPLRGGNuFxKlK9QkpcZP0lCGzX7u3e0eSVTk6Mz9pjD8mtfuw3W2uClSN2xfHZogXL/HwQWReQgfyWIp5nkWNeAc7gj89b2aYaDuHamWR9e6poQf06/2kAFezOAFeUM7htSaisqXw9jJpTH4uh/lxlo3oVnTYR9OjynCjM6L+xgNzkjowrG2E94Pd/14V/LckGQDOPUVtSI0ItubXWvFlAaB2p/P7L2Wwwhr8WmrZqV/4nhHwIpcWRyByW/Y2WcuAU5V3RPXP5OkAxNKKE5pDHSI4tIVIUlzz83Tzp9tQheCi99VixuIepAqfIK0eNOsUpWDmIoZfAYCTF1ZrYlYSXX3wUq2rOnCSEfLunXBYDISBW7iY0VcY9vN7cLKm7cZ/MR23OI9xwaRjwtTOBZ4Rj+j6wbZzhlhP+mLboGjyGTsWf0z8UflXyJUZA3jb633LpoYiiwjsuD2wCrgxqpFCfE09N4MJ8pHps58wm60kyeAsEWvixFOoPNDUdI2RYXJRTTTuTQ31SW7NBQJtJE33OOkIlQnBpVvYo8l1SZhfe99cRsDa8U8Z8asbU4jihQaspg14NhYJqSOKIexNZjJLFh1mapYdTEfCFeIKC213pNhBnAzU7pJl1YG4s0QesbGXkpOahMddp3lWxuy8GVKSxmyFUvHngBmozJZHOJmnawJUnttKp3ze8INoje1jSXg1FZpIh0mI03ThWXt7j6yYxvEfarz6GrY+nRy12yApkirxSZQ5hIhJxgCVCtBBI59Q+WX1IrhjgAeMpP0bvIYQ1ZXEwH9nStkGCVdM0UKCab4iRVhIyuVEMkkJgzheAgQt+GBpoTnk5ziZvmKFDKN4OSlMZJMz1CAJUBSNo4p3SziCMQaKtJKHsbrGoJBzA+BSpwqY3/5INM9COtrkmZ9ZpDFY/9n8HE+FskwkxJFTPMenXreXKiCojN7UcwgMYHMdBiGu83muzq4BM2CRTxDQBVbBremh2ClK1Zx2Ewvla5bA0oVPTy8C5GYMca8wqLRKAhiYBBrH9zVj7IHMgqKLQJ0e/Q9kYatAIsY1VzvK6ZQHnNZKiJ0MAc3QaeSIOHybDHaNJag6hJIThXJDLKF8UvsqNh4Brx0CkA+jQCuUIpAholGqsuCQDIYTAOJPr2H00dFzt6GuojLGf1yFRrJS9A1wTFIngqwSAwVd426Cm69DrLtFL+sL7q6cE1hot4R8mFCMZHJGARKGLqx98mL4F8GTwK8kB5vnuxV7bwZ2/30uDVJxeO2vQZcLp907gpTna50kvcNm95xlto+yYs9kDTSydXtzS/4HOViGBN4sHVl6JydH1ODBrNOw5BXF8DHPJ7tM9A8Pn/92Cx1MzfnPWI9pWafz4VEpyWAYSRTDDkIJgSeCLSzTDtkJ1QKvpo+HDlIy/xUhjz/x+R2zbiE1zNCy0WBJnOvpNY5vrEra+JTZzDRXLof6Ycf7CQ8HL11katv+UVcLsWnz9x7K7kbLZywFKNTebX+TEhesdHzP441AbiopoaFF28Ns/6unR3swwQloT7ZzdU/Q7Cri7qkpuRVw+hDK8yUV64HlkoUECJbew5OegLic/jVrL1Z5FmIvEtvim6aajCW3bgMXSRxxppspxfvkzCOQoxLuALseJAPET8leV4JzivQpFL1BA7MmhwBeiE54Sdpl5YQotcK0Xumn1F41e0Mvs8YGgDXcB+ncnTwOsSI6Y00P4jZ0rym25IOrAzhMkR+X5HhllPrLVF1/IVxOqOfS4IIMSc3a8oVwa5Xt74V7ZN4d3RqzWxUp31x9dVH7HVdeEQdapova8xhtCwaLrT7zlTcy4DHVB4LZ4xIHxOKGJb9KEPggNZjc1HryEBYJdIi7pbjULmixWDIrb+fXSC8Qy30YxujsgzH3Rqh34/ml48SDcWAPyIO0etMCYH7a79YgeN6JaD4/fegqKQrNnFD6jAPezsLFYYo2pe7Y5MTaIynemi/GETYW9ngEhXYFKfwUVAkLdFkxA++i8GOG9WkxYaOV6JThqEytP4kfi7jLlkNVe8gVVbO58WEdDP4AFMVc954afhy/IxgXDoWc+oHW04vPhOvbpwalYtLOtVg5D8lWItsJUKNrpbIbiy9NyxIROYag7vxSA1nZWs1qnEFGZAqDHLYzSVhgcVi/SRntCa1Q6UNEkEQBzMZGE4xx3bnhmfRU2moUvPNaiu0kOGjzjLVrSZOdfyLY/z+B5in9v7j9z/eP/U/2/uvtIr8b+8AeP/g/Tvvv9zz/r/v8vn9j/lKtPfvv7/N73+8Z9xf9V7a+/sfsCR//+OVmPzie//8LV4r+f4Lq833j/b711/T9/7pexrf/30HxPs1/sr3b/T3e/KHYAnkH7z/72ud+2//9PPbX/7rr3/517/95d/+5T/+829//c+//fbPP7/Bs+Kne//K+3VSGb7/EqUn/5Ca2f/034wpij8EWfPf/Xej3/7+PwEB/fk7/+P/MdS8v//N/9+/p76S/86W6h/9Pv//P3/9fr/+k+n7//0lfvuff/r579/+/Y/vb2+9xg/PMJr5NtPzJsqMx6ZctzaLolXeuQv8jeCK5DxctckRxsRBD8E015gMBPrmGOCD1Svn/0CEFlezjxNWJg6cfsxytZ+BhmRWnoXLzE75bH3NXSMLwzl51VC+6fCjSRyyopgKGTeqf7McVbmvkMYC15oUZAcDBIg7V1UCvJ3NgtZInm1NsKfAKEJaTRjlJXynAq1CZTCNJKn2sBnlKdOz3EIDTENC04/iWzwG6snRVJZ3LYeSFJlaM4+zgXFWMLZzO2cFu0ntQNu/Iq8B2z8hT3A5Nz0pZho2qJ6ISCTNMQnA3jCLVR53B+bbYvBpEWNLxCeFEQ58eIVNF6mSNnTQAksZGdDvM7BKPAxBYqxMWDuyYiIakcuDphQLQxFnO9M9uPFkeMey/DjrlcHcsho0Jq+Y+s0yz+GYnzRp8qSdj2QJHPcYXbkVjYuuoLaVeV8kludzREAm1gFAM7ow/Rt4j+WmuqPDtIdcl1UYG0ieDbyCCEERFSNgw2jJBJOfgVPSRUXR1/UeREpjsNxLkw4js5X9AvruqmxXyigG9R5lOF8z0xa0UVw5xzxECjL2Sqw6TcOxr9jMP3EKKtAlPsOIM+HqvkIUCmjjtgywEQgeYwx2MEV2mxb6KQOIchIH5y566yq/LaAephFzpZ5kL2NiBKNTBElmZzLtZg5mjj3iAjvZIYJAi/HUroIyykmb2T4UxiFqDpUbG1LyGhrkJ6NOajexvxeBEnJ4UytYozaDQbVUuHAAnUhhTTxsUT1QetDVNOOVWg+dTOCHwzZPdDpDM8FVUwVz07JPM8nPdx1F4gjj0odYpCRQEAFQgB+B+WBS8YTxubm8b1oYjgsIQ9A4FKrhMDzFyDhhVjDTU5/0/sMc4aGm6ppZPgm13sYqMxo+08+0uHmvCI6WOJPtKmhZe/O1725YA7qXIsn/CpfVVOXEFgKhqHxFj8CJRqLSlvar4l+7MgV5XxlbAvo7J0Pryn50gv7uXfaWxDwhqWoGBlG18Tp3pgFoAam3pgR7miZbdyjC0lJP4o6OK1+HndifuTYcIfYIFzvK0yDmOdWEMy2+JBYCxzBZEOIYfU4KTIZkeAfYDZMjxUSCtlgvnqgKEq2N1GQ3PvLJd9F+kFp31ilO7fgdhytcmHHbqQHT4BkdPmYOJRI6+CBm0CRV2P2c8DXsQH2e+AQkDNh7vz+s6mGUMLCxUcSyEPi1eSJVmy1pTkxIkKoRiSbMIzp0oVm4DJzq4J35eIfXc4GesUot1S6Ca6YJWxHmDv8+LBtn7YnfYRxHbTx2uEtN9zowSFtblyvIs31iJbCNmTG+D5gZfPjgNh4tA9S2zgre1YkXkJ4u0JnzS1jfDBxmEm8QN+pQGXymLOFf8bKhb8PRjXIUYyYiQ2MGabx/DFw9jnTMF8CAjcVsSBu9arBPUPgWvUhrxqf5a74qTR81U76FlLb8ktV0UERqQqRFs4PSaIvgA+EGKy9aiKnCen606xXilS5pM2UIFfK8YhZI3yloz9X95F6SccqQbSg/m1U4CjuriP22Igh2eDC9gpnu9l1kVDCJ2l43w+izhjKShl79MFgNIiQ4Frg3ddFW9ltTSAMP/L0JytNAVTDzYevkPtqeg3c4wWaaYxIZVkqaGJ3SaL8w0bYTcNVSIMRXtJyUSH+jTXGy4lDEIkG8KsUEKpVuBJ5H1rs/m8YSN1xHFuCrRtFjTVwnbCoNQ2K06MiWi3dSPfgt4nEhmgUMdZUdNWQyuHru7A2L55ZiVlLi2JxBIgGXqiw+kl7m5iwqPPR9zaADFUlAHX/tQanH1T2EXjGEaiqK5AxzuR/IwmoIkF92h1CJGz+6mHT0d8dISwQZf2lK506LTXbE0f2JtxeB9pBRqVqR09vAU9qvITyUzf6w56c0tvNHyuZCdpw4AIc3Urb4F0lD0E+u1Fjukp53oyoTs6egF/0NUml8gNs5gHCwM2SbdRNKrusAkPvF3nx4CW27A+IyqNGmw4eZXbVhfYATGJ9Mk0RgmrlYOv5qpijjJ24ggD+KuIo7zqGFibw1CiTsp+z1uKmEAHsm4btRqeD8fYJvQWDKZdqdpRGGwQC5qtfrEnacyuraXNvlPO06syQDOFu8O1dRDxJRCSqiG0wNfC8x7oJh1hf6x+OHx4wHNQ1CXLUPHAX6PY5BJRCE6OeHS62T4ZOAN76+teP0795T3Psggl3gjKtTG3q9pGMXxDrBv53Tq+OoUQnaLOoSRRCKfaNtYO61aE5QTU8mGJ5zG6+fUce+fFNUKbzDZp6PoIgrCh78tslbSzT1yYiA6c4wM97hn2fG+9oFcalrWby7O/mhDD4aXSSSue94ZE6IYjDoKeYMzDGs/1BM8gq0RLMYlxQ6k2tsphvy+pAnoDTk01tL4BD+Ym9f8y6xadwAGLehwxwifgAci2xR3rPJSoxhowtdpk7Yx3kxjvvBmVQtBh5Lw6+D5+oudl4jLRe3L+rqqr6r5+Oikl7XAFxKR47FBu4DF6rCL92uCW+oJVW7MDa4aUpXRWx0ElGVgtodTuMRzgjN06kgjlQO6W5IqbxPtqdZWjWDNc2DYlqTHcMww45Zty+DCXh02fFpooov0srascImBM4IQfE6cOp9gw3uAblDOZz3GTaYS9gOiQu7NnJ9YA20EdBEFgpgvOVqBOS8V3PElUyadT7UtBSz35ZWZ/QXHNeCKxB7GvgeRQeq5iWaQqs6V9nCEwFpQhw6RjF9otvfF7KbcQ5TgK5ZIZCBkbVS06veuMrRps/7lXb6+m9E40CPayzOzik4ytlybCnqAYhUKR4MiJvuhOpS6Uq6Rg5IcjU6MWQ7/HRM3o7YHpmHfr+nJcnMKrs5zetY+xX9S5kqNGyIWkVJnBJTHrYPQARAjf12h6qB935aFPG8oNRyIo8PNBjdISX8cuTvpTuogsAT5UL9A/iV66zb9F0IorSHhtvSvNB/NAViOGer93bWQK4RoM1rktx612bimQg29BlmqnwE9HMUnijWtybg3tJa+74XyZeDLVWCONgPv8eXkXvBBc0hLoZlyIi32uYQN86D2yu6euouGsMlOXPKNjISXe7hzGhEPiCv5vuReZ0ru0tDO4Y4gpLEiuakw0q/lw8nyWyhJwmur5VUs2P2RdcWRcHlVJ2B0Jj6dtIkk8LIaX+/xMDlPgAVq1p5McqG+/EhoD3iyNf5J6ihOVKg83UBe4txjkvXBrukquvbzRQFwJ3hMjfWz8wPhk5eulVDlpuXHAJ7wHBFTvvnJaTaHkdUxzX7crCFZkEo5lKZtFfNTmIZxzaeNUALqCw5huXF0n9NLXum726JmaTL6TEQmbAI+7UQYEeS5dNN7syGPT6t7UDmHhNeFpEhqBaQyFsjJOwVubSyE5xz/argkssqG9kMK04X9uiKECgoRSRykhSOi6HX6/39p1p+AF1af66ZArS8AIp/WC/xyPT4rKvHOGV+9dxRBjVBuoOyATFCFFA1TFaxXTdB/cgW4NIuqiGOG/159A0319saFbHINwMuUHlzE3kVh7YBh5iyRwOwMyEo/1JcmH7wd8KtQovJzAeFjEmK2zNp2UXz81BXlGZcZDCgYW/RRRsLN47yj+ohxz045NroVZZ4OnHUFIFU+L+Wqztcw110Nx+byi0aqaFf5HxXK1KYZnLyJ5kxFYQi2Rz0fRNQURLvRsbi4Y/Z8q2vXbc9HMQll/bNaRHCcV2ymiOAKyG8BLpEtJU0kyICvhtfjT4S6zA/ji7P5U1dnf4CqFz6GYwV6AG5JKNp6nJlrsqjeeWxkkFCkcKcUgLOyHCjmexVZOmotJHoiZW4i8NGFUk4qDbg4IlQ4DJAmyhkb3gHRyLTJhYbxwMNP/tcGvZX694IB4taVePKmjHGmCZMv3M0DWFCbHg1F3fDkaN4ZUCCwNVIePg8HnHO7JU18XSMvBUc2a8KKOqwEeJzx/FYVbfjDIm6XMvlU3h1NJ4IpI3KIpDQcWLTrLGM1X1tAHrk932r0p9OrJnHOi6RVsTZDPPqGJT8HnnGlxsAGO/AFal0tVaTVpq2kcHKhebKHU+/deh5mXA3h8PVTCUGY41FsN7o2f1QIPAgRmKcofyBvpmwLO2LngTF/qH0BNhMy9CqX4u9sXF1YyQMStv0cpnnu2aqRE/L6rLimKB0X882s96n5nj/yPWdAqH3FmWoA96SC4N/pUItzEDURJYrfBGeeHPdjQ2xFxuorfUSOeCSmWMY5nTeAF1229RLuBhxJmtwnY4LfwLZNDHs/aYcb/TMvB5Hf2fbyjMzkquG1REMDYSE5SYzJXTGaY3wH+wwK4DPK+WncqVZCctKoYOAWDC5OP3YUymCmGaooZligmtkEvGE9Ai2f2jmBeAZR4yScjiFCGIP7o0/Po8yjXHXRcJuJr8owUnAqZk/OQkCZNNMozO3wcZzRq4MNE5AN3P1YjEI0IymvItIrwkMmuMDDy9DJ/nhkBV45wN9w8FB1uiWji36Am/PZe9LIcqPyniQbcjVE9aaCY3v52DiweyX5w2J13VASk0klvrHOa849K5i9KQUm2YevIfeSKatTGDo4PJw64aX0Bhf09/J7u3qrpsBOS7K+eUgCwErFqHWKQQctBxB+kxREWlN84l0AyC3Dt+uKhS6Zgv2Ha+logcqNurfccLqZFN1x68sStPD/Uz4SI6OBdX/8tAJOWlicq5mdLIgTHeifADCyFqA8YjuWKZbih2H8/aDkUZVNxLPuUy/aJwfAEduWBOaO1XSDyaVQidNL6IVRFGyOLY7I2Y+6WMRAbsJJ/LXtcRGMQ2ZQupCFbeNIHtflNC8Flwo5FVqe2xLLM74eECJi4mgrSdK/DOL2BigAUJAzuE/lTDOUD2AnxS9P1b209KXEBi1WeYNjSsYkDAHas1l7yjQFlkOndJWUoGjQoGAbIV2fTkZ4my3gdRrN0Xf+3qmydZ7qD/SD6oGfyRHHWmu4tuWcsXFCQExbKGm3zHvKppUPjlU0NxuXSCocPARUwQyzyXS/JU81Y6HkXZXm4lKV307Rnda5fAiKKXh3nDg15OqlKaMkpCp8Zb0DJ0BFE/lw++JtEUijow5vAQIkaZG4hh3zJSVstYDrtluuiMFMgIgLRvYgpgF/0iE5CRN8gNL8kSgmaEi0kL+J9Xm8ooGVeBKmWkW2cP6tSSQsfXnzouKkmgjMRd6uK6JetC1vTLhQ7uo554jtVlTqUnLhKV1ExGMSO0K147D7Ffae5SWxBtvQwORA7Bf26ZL7COla3l8AkPF9oEtEjxPdWZu3pGMdyePTP1xfKGrQyW4ku3HHU6MN62W4n0cKAwZVGaSIbuE1AnRAivDFURe3bKPgrkjUU+ldeENAhnPEJ38qmaN5tq1S29H1MowbpjxsOXVv4JPLChVMPJDxvnwVlhSF32iU+ESXlgRPc1kXuQ9Veyp7i8zkYefJ7RJfFzcU8ZcFbXSSLk45NzYAxXacrKpIi4MX+aux40meDsEDdZgDFo5N5mqXSMTIjMN5YYsMdajNEAVKTwraaMy2ChtVxLC4yAr88ceh2HHqKuGkJXREXIAFe3gCPGYEyaBUQN0o/52FvJF6zH3XTFFyH7utSnXGHJaXQC3Qn+HbylvvgnrNuxnRO6bRFtGs/geWc+/QqEZRQM0l2vAypu6RLCHHCi0MCjgFoNj5tmQxCfoYVdvkgOdmNQikpf1pWQBCBieqiw+UIMGto5+YoR552PCkJuusalHATflAoZj9xrCyk6L6hwxi9lLWCgP3jRvSa4Rq230qU73gPab3i619z2BzTAXRSWsVsR0Oc+71wl2Qwb6kywjyiaWP1gwivlN2Eo5sZg3MHfAvcDTuHQBrQykr+Jo9BBs8IC1MR1Tq0/+ub5z5vCtGoMulwks+7FHYHf8RVEOQFRIGmG/4hzTLYA3g85tx+WhsYZzVMwmH+cKz4dtocIYHAjYEGRpMsa7YUZyR6F6YPGHD+zcraCVfp0lQTCMcDikpO2v1GEQdvr3kksvkYEA9k/5MPUyUd38Jsimq7V70Y1wHF1duc3UPY6E9OXerjtxuWhEevfnT7LCajXVjTFwNXfB5SXfEFw0cQwO8hB8gwVBfO6Bqr9rmy18FDDKQJz8qRaMW/qLlQxMOi5WRLxUfs7pSMvgBmtWQ8youDv10kw6+2oWVDMBBpEzVTzZY0OEzdBltGxc4o9fjn/U4i+lgpm+8zg3I5+T3+MchOaMAFFHStfwZbW0B9WXImPNjN9Ul9+Qz82Q4iJpiY/VbteXlyuanh0eDXkXjIbZvvkxVsdDxqEzhcRqUzzJKhkNTeDsiOLRT0JdJV4NXD8Cm+lWHQ75SVyDGohy8IzYwXfK0EDTJMr4aL4/ssDfmZiscdx26iuo+1Z0b4FKMxpYKLAUZzEmfscavvVmrrOADqwADAp5HpGwQFneopWKz8kx1qeIM0B0oi0dxTlPjVvgdz1yHLyfnEFk1wh0/LPRLxyntB9dh5I/+hAqH1Mu3k+6JNT0puE5UhtIEoijJpNRaH0slyZtyT4meXEFLGHhKnCvTCYjdnQNU3xxOrLyRLXuLHoKrFRVP8ltsQVhJPHeSgnGbHqvBasVLXPixO2xnM6WqxezafikGELZE5MBz2wG/h+HGCNxHC50ftfPxBL1/clCwV8dgTah6+9hn6G7C1EJ5iG7Fhh8hMIz3lpyo/KQHK4obZPS8E1hQ5DHoqM7REOcroKvmEtdEchEnWLINj+aZyNdAkXdNd6RRe3Sn3hcUjHXZoMiXeeqvZSWRQuqPOI0l4DspapRbkuC1jYKs5qyvOXxV2MOKJl1NzddFQ0RuAkL9jxAnbjhDJCEGMGIxeOBQrKrf6kfl8S0MdBYr97wjSU5ihbd4gPZHQbzhW00HjbskDUnh4EfxsqzltEMXRMtVkmXxH+lo/z0qFNMZLBUcn/WtUu1pHQIzIYMx1AZs/bNg0ZtS34G/8qSEqmdmnsdOcBpaeYspTmn0fehombWb1YpEguDOk9ad65VNNSiuAkfkLqyZUxuFwBTCA5cHpGYYsIuqgmGArRxaAvnl2i2nOigVj0hG9MCYGg2O8nUxsa2m4ZzicbW8kGt8qtZRTP1UeloQgk+kv18nEZuVVL8/WircUSwjmQmCLiECdwR13iqYliW7zw0zagzODG8uWCqeHN7V5V/I5CSd9BZAyQHUp+sLPombIShh5Fl3YwwvilFrNykRvQcNb+6Sw0WuLqpDD2HPKp3n5djUd4uKWuw1ScqBUYLrlgz/uE2mGK3GVDAzTUyZBzVeZzNP3iV2Uq8H9yyzEE//DOUYF1WHaByB8dD5wojF0VVS0mogc2vrBYohTiQpvEKKT1OlKY+Zj1jCEOc+59VHDFuo0b6UDmCUr6Ie+xSrav2MztfvKOy87VOGa/EjX74wJqrNzoZNnxLz765qjNxOs1Hxx9hKcxdWgTQz1YzFId3ANhzLtrrq9+yN4enoJIKfQ5D7pRh4rI8a/EAbUlf/LCsNQdXvMUSW0LacTtv87qXYAMN3MTYS1ad7t1uilShaGxSvCvNB2TC68YSS9T71IrZN4ZIQG7Dxy4zHlA7u2aJsmTeMJp9rZlDWbiSeZiAwNr1V83s0AIZgPJsUgqrzeTqQzKui5kEQCmG9ZhseqpN5EWFs4FuOYHhjsUAOWwulE8z0pWsTNeDmKFJKdY1xJ9K92YILecrWvbbdXyApvDui8ewq/A9NZ5mF2oExboUP050LfpvlTQTs8lyKYqljiUesb3XzBvuSk6dq4lLLdLsite6eo/t/YJEy40KQwBHXg6NqLqxdW23vFSCvlfDqARZALtLqttZI0CfomY8GryPd/JU8/S+BYvjSlT6tHIk5J3qlDTBbp4p3X5BBMm4kw95xOA/aSJYlIp1eC+Q9lJSb41PVeeOUslcBpZ8R2cke4/m0oh6GQycZZ9cJkoKFGSFxHPo/+LHmG7rol7uXrYDUkqxinKblZlucgJkFE6tbni6uyN2B+wX8PDwPROC8yOBhQ9W26gJj5vns6r+6O7K0N2Ixkm4NtNtMtVYfvui3faNgW50373l7UsZw89NdY5egVhr7unppNCkj8Z3qWICDflGl7Fsr5n6F4pvBt5TNESxCBLGxWKKt+6YbseOHSHTOymUTMxjIPHq5oEPHdxB1XaJM/AxqPeTjMJWQA+5M+lxElTrwA7wiUHhbAFACjUDnSg6KHarQ7PpVpnTD8ElIiDLPashB50K7IFpyR0VEy7EGvGkCB6GWwivpiZ8w0QwTvLx9Bvw9MYK6MXkCsu4bbMMlzqYRYGAicA21mfoCjmJj1VrImg5BfQcAdMljonAiMBZAKiMS2nKRgcV5Mb4IcCINvXKTOgib2l/ePm2W+uq7YMJMyitm6FZ3dYeRwENDyh3KGfukekta49SNfM7RplGHXN8m1Mnmq+YkFaPf/1XEKFLmAY8vK6RoovpCSwsLKlIHc1YJCrhTJN9FNZs19eMUY9TbO7hkrVBlTUO4NLpUREBsDTeo9sylMCKH1A7wV9Hygv4syRtKYCd8IwYmJWkz+opBitrNpMLkMk6pHoG4dphwXKHb9DBuoz/z0/buQTolJKEK2O1OUTZNB7LWuhWFhauE1aiWMjEpOPqsARowhFqLEQGyGqQlu6TIXg3iwjJwzWExb4Rqs6UzsqQsnipEzVB8ZZkFuJgNN0tnhcW11UZcgk7A3MPAu7ADzm7+eSmlHnuntHlCWOdLUa52NIbw0PJ35Qi6cq9WaAWsj7VqYhSEdFZhaC/A5Kzr9M3d8nM+BDoJZrm6pUhxFQABezoHcboJ5hAi310lQDtwt5JbRqK6dwb1GjbweRyekgPIT+H18me0lEbnkgl6N1pxNamTsQZZ7LsdC3K3EFH1VUz/wSMRREUyxVaEMMTtsCbz8jvJhJDbeOaSRTgP5hwS+OhmiHAThIjUw/y2fhJTOfi9Yl0dpSUXJRJ72nm4qA25UjRpm44jqm17AL5blFxvJccw9GVdkpf1cQAiH0g4AmsunSaomOfR23ZNqm0ZUBDP6adGhMET3wJ4APukMxd6l1Og6ERlU4ee1gXriqOdsp2f98E+jvUO8dlYhfTiDCGowj0sElTsFSPsiCnwFsk0/Crpmc/KltGhrZXIjwKDJAeU+PgStKN30NpMUJfwd3TYMn+BciVozRymER+EdTpu3Z1d21TFrpGzlEht1skIjo3pK8k71zR27wRTH+GIIljqPs1fF0AIQAYmoXYddgcM5HnK+CyVt0OfEYUvuIxJpBMgHlvZcTTQ7OAFf66M146Qbgj5mBPRMuj3tJ4e08lkLHYog12cSC9mrnPbFI9GIqutmnoek/og6mMvHk7IwHo9pr8LW66I4HBuDemZiblHPRkOLiAbZxwtdlRT+bZ7AtKAGyScgkC8oxxpT2ckM904O/Davackt7bDhGQjS2LfO38BlJgSgNseY+lPmtxh12S0CBy7O02lJHvNWqrSvEy6gaiEdu1a8jd60OZdnIoYCblUlr+cBiMu00jqkjR0q7RU/6wjhI65fyY5Y3ZpwY68bKvJDDoUuU5gllqjjheVH4uWByML4tJdEsZu0Ankkyx1ZXtANDZAH4NqYDDKeZJeMbIQZZoi20oBVwjuvE+bfgtWQwMIGvjGN0Jg/L4y+w8iYExtqK5DXenC69efYrZ6svgPtxey0vEgrPymyDIYTiwiThCz0ppBxgYNclh683RpUev+Q3A2kVw3JJzPddHBxKJ6Zw2YT4TaRbLWYKFaJBmUsLNljtu0VFhfJwmqpT3rB7LOGcvSM/nr6QBbfZ0FlI+AOWYegLtihpnqygDevmDLQ6TWvPAxOVp0pztA6jMrNjYPVy3yDTuVevLyR72GqQ7enCRQtyQ0HEggAvaNtby/auppQVB1ipfHGyVsKqokDMxzJnlIXTcSR1XOno9GXYeJxqMj3DUh+dnCgrlRnfPQ9AxpzxsNlgVAHPecXcMiGQnhWyElSXGERU9FHLV2durcAA93GbVfK92RJ4aMZMefdRGTq7poN9fm4asokrh31eefK8Tl7YUnNMUsnHCNNATxdvUPBbzihWSm9FoGo35SBTbBfPmYcvyBewB6BChysvrYY3PGXiMso9WqqmuN8IJA3kZ1Zm6t9iUMFdmvM1rTJ3kSv6dDgw/SMilq2DUn2bVqLKPzyvVwf44PhJ2KvBPqSLrdsnOpklRWfd54VMgL4LOxjppYmNz+Aj0ilhNfiKNQ7waYF1xHl4j1JkQXoqOpamNBlUGNjA+HBSaLNigAtXYDjyJ8KbinS5fmEJwukdMGtPgcmVoIKEJkARdUaqKddkAqPAm+BM0oY10XXzqBLUz0KPlJsKGxwq2rX0PfRo8G6e/R3JeF4BNxHUVIaUrCMetui+TByu5UHcpd2RqyRHyehNWCsWiGXhM4nzRNMncZWA3bjIv10yKiNk/UjYYdPJbmw9q0ey2OS5n1F52TFM1oDl+IIf4ehYDR1Y8MpHB4zDJV+zBV2Y2JNezMa/8DRrrp2bMLTAQ7rWJi4rfq9AsE6qL1Km6bsNfx/awSsBsoWzh7dy5OFFzHtVQDBG4D3fgo6RdypinMWYU2MwfQiYB2Z9bvjAV2AaroSFdZlGOI3ZSVQLwjhLgDjoMNnKswuQSHzCMbiSXsU8TwvYkBxr3UmmODhQCoPhi88dFIcPaQSnWZmNr5KAuQzN4MNUE1y7bf2oo3gYL3YQB94QVvgdCDs1OVldRI0aT46OSpcZ2Jk4QAV06SsHlzydIDqMmtbQrspMJMB8tETwOw5L3x/HU1RwvUW08thSCrC6mWVgTx6afpsS2pAtwFdMUME5i4CIrD3K7kUgmafBboc2SfcSU296AUhc6V8ZI40NsmjOpELdLTL2yA3/M2EP/tpmXtW3S/EDJqahIAAuVkGpmukfmg1dN5dGyGHSQmwho/xjWkex4VzkdoFbhtkEZjk6BaTphG8LgsSlR/WBEAovsakj3y+U8MBFK2Y84Tjp8I/SOSjxqt22ZvgxoPTfWW06H99ApaGsmnbHtIwt5+hQwpePqGlQRdGfUaskobAXJKH29aZWqepi0aVTAxCF9Xm97JReTypmIOLgsPDHdxC4MvTrw8dqA1rmGJplHcVlYrHj71LkYnrKO8N4JEfMHzYHh8hhKhN3BPHt1UJGyqNqKQHSHcyaBLq3vg0iyol6F3/MdVVxzTXQXbyOHE7Oj68bSgRx9PgYcPNNiixgII8F1l6vTlGcOARGV16hmsfVEMLWhOo7B4tbHoLSxiEdh9yrBsQBsq5b4l4sOucBA28jghQPcJRG6JHV0mifYdBrY6ijkPZOZpi3/24Fkxs+MntJddlwQ09Wl0WWM/qiHriYIHlXxUQ4Y9zWasFNcoSS6Kyk6VHGeW3xdTsEKYo46FSm1nmCKSuXPX9S/cPEYoT/yd+QVvoKJjFxms2uQ5ytU1bSztU8lr65N7ifcx0xtjPEQPUZNXQRFlK2V33SfYu9OZk0195OjfOlWR3bNN0Z1cZXX0NBTRihdrIb06GneufPxdKKcnjOCIK0dfOTcpx1YKh8W7VvTRFgWk/jBbvc4++1qmZjVwzGo0imLLbNv0esrnFfDswrTLfEXboBx3jEC3yHHIt91bcJPuU3vJWqA1a8j3gZYyBkhdwv/iANwYq/xHxalH0NBAeJGF6EKP0eTWjv4BRCph7NFZQ5MiiQB6glh2gFTwIMD+4F4Ef59opG6U09lK9fO8ATr0xOCBo3eecjiGobNchwNO6WuxuAMkZRnGSz4XowmHKxcz2mrtsUk53g0Y8ttou14yFjjF9UupGTdzHySKeci4fR4s5t8veEyjIybmjAhit1rWCEhvHyKVe/GRE7Cu40Bliku7T6ISYMa3O4toxvfgSZlkB0ogu2khifnAbFrAqVNt0UuqoBLooVCovZlX3TKZTO+1QIuRnLspAyapFh6n0KNrceAFdbkfNzaPl/BVEUwu03xBeDs2IqBRwCQgKExlToAOnaFQxnMlu4iyCkpKEgsKGEDTsPh1WCuzJjdLG+GX3xVPIbilyUd22XUU14fAu0rKVXMQYecoXa1kBqyJoYY8Q1p5KrzbzYdskSK4n7dmr6Dx9RNhzJI1sQaseami4p5iqkS3kvTGXFe2Jlx0r/GowiJG0EqCaRebpoQ0Curo4NvPNT4vTtX/3s4i4TNtHCvaOE8Ihpm+m2OxLRQsA+jxqczb6TL+t2NvEBaGbmRdInexrcuMQZ3uMDcPfhyih0XFdXaFwudLlTfeMM+FKxgCpjKBDVJ0iuI6rvOZ+efkiGSIB2jXTd4kagQiymMvRvJR186JRq1KFa8eO4S2WIvPWit2CR1zE3vJ2/m/iL25Dy2vO3KhgVbW2AJFH9H6I2Pg8Y0JSzKiq5JzRCO96UNgyqsyHiAic1yj9XzYo1jWcRcWyk6+n0qJ0Nrt7Bof3QGnnxcY2n8eu8V7+hW7c8deJ2wLMM/5/Iya3R570avChyu3qcaHvGsPkM3vZWLX5z31L9sA0askCDDsExo0ld6b/oAiBgeunmN1CTtxr9VMSFKdSABZE6RhoTL9w7WQFWpjQsDF5JsjkeclcdrlYreVQZr3eg15hhVwo8e59mFkNj/E/ClVFaCKwpcp2xgCCEMNcNoreLkLfPw8wzLsccc42teNGIxsuS2M4LkstxB2lJCypciAXijW4xJ/kCp37TsCpspfrO48Ld7MNQgqke4g6RmVAu7aUYqyRn0BUcGzHa/2ihymM5e8Uak4ND9+mluQcUsVK+58ibVHBhjtKXH0lS46buy0HitnsTLplMeRq2KatbdXa/GLcqihqM1LsGi5XeFuDFt9lH//xrCUEi5kkyetU6PflwpzUjJl2npM2/G+9vSFDJYZJJtHisSYNpa2JzFBEtNdVu77jBlm9JvBGZVhFUOB4a7xdxO18nFYz66zIz3J7ODuFunfv3oV0eYORsG8PDvE+SZXS5TZlQj5B8b6VmoK2FOZWNyMgfyJZ89tJNYVlv52CfVYefhyWRnyXU2jfNSoGbjOFQRAxCyf3RLzTSfc0fQFGiaklwFb0/OHVNWZ6a702QjUtoLK0ijEUxKV67PQpja6hV5DH8wQ8vOp5nYKeexCRc1pexFSY7aLixMK5NkBgykMxDls2boV4zMrsvpnV8yx5XoGJYZK0T8lWk1Wit+vLajKlHvOIdNErhAbU6F8o51z5Fz57Bla+GFEcJOk00tqsaOrOXVJyMP1dFCv4270n5/5fRME8hRM/HKGNGHBjW8hmK9R4+rJk5cv3FTOAU11tgb0uAo1nO6t9RDvrOQS7AJbH8lhzJKJFhu57m4l78/cj5zBCNdPrYkzGGH94Oy6clbMIYDiK6G1cWEPisNvjyqRd0oliaor0t9JIctNw7dVJFh0cUbYeYA99Zgb/ALoHoIp9Og09iNv0qCMSBdN88h6QbmiU4DLparlf3BN77Evq3XOnIKMuPNrWvGbxjJfVmG01od91grKnDFA+SG8WORAMJo3NCOdYIJGFkamFxz2QVCuaBCYg+E8H4rhQLBhALksKxSDFp6Mn4YlLOeOo6L3RML8cVCt53FCb/jmbQqN3PustWgsxsi9AzFokm5poQUA7MGbeX7SXA0MUN1STZmduMJrfFF8t5Cci5w9prePjicOqjQItd6GLx+RDZUmvHWP684vfGg1jRZxcQ29hUamhjpvBezux/pRpXt5iqnOSe81RJlIaqCAtNnOBcmvVAI4ZvnkVZJhEHYImamzGHPLR54ByhQv4Q3URW0+psVnLldgFXqN43jvu4KF/XqGABKeeYvnxLId7xLU4sPWQs82/IxjWAQQXqam1fGCMjVmmjlqb32XPQzm60HBXGfuodJwkT1KcFpCU1me2s1rnViJBV8CJtEc6T7WwIogQ/ccYZ5qcsfwWJtd4iMzPxD3sMh9bKJFGAp4kTB6d71XytNVd/xLFyhq0Pq9p43yWAtlfjYaXj2mYfiTG1RaxjY2w1fmvGRE8Jz3VNuoft62pZzU15229/mPJwcBtln8keQqCJ/24aQIRUAsX81ttfL2QyLC3s3jkTJTs16hWONPM7ixpzZHRfC1T9bTd/jGo3ai4Z3UP8Th6JoY+KRYCs83OFyRL53hHlKZUvX0ZMOJS9m7DSlmrM6pLz+BTgjYut1YdAYcb5/WVkzDeQysN0IOgHnXdSaMuuVfed1z8SMwkUMfs+rScxETY8KFugzO9mp2ZUUPBaSx1qOkcjSVEI9vTK5lFvdvYre2wwVfDpNWN/LfYTryDZLCVRbVhAOXKiZ8UQL6Y/suckarP0L7Js6QUfzosUhr8cCZT1nAtiepWRcAyAwjGHcF5uXnShatH0o4snl8DI1LRKV1Q1O2CBts2kYDRGJbGzQ0ASKcuQoN7PktMMcEf9xYXK/wVIysUlnO0XpNj3ax28bV8zG2yFaEmymSLE5vhmOy7hjY/vqhhzVku5sByruZjpFLOx8XVxNaI63omXT2IoJlXVqOWMgPAUBnaZEkeU40ds2Oa6Q3imJpkgqiSF+lAZIxhiibLtVLlKLc0O3xUwrR+TLZ74iGx5XAfJO7y8V02BlpSvPaRqOlRJPcnUGMdQDaf3slFu0WkTU/ogHMFhM7gPlvB3XMVtZvjLwVabEa+6kZmzPHVia5tuyoDWNrgR1vEdqXhD/aDjh45XtcI9Dlv5mmWfduQQY6nzaT1N5JEGC2VJqxz6HAZkKBYm17/a2gtTmVH1dONg4LoY8SF5IRKHLWAjw/dwApCnxezRTaDEXUl94W7FZc9rJNF+1WfJReJJblrP4+CnevoFuGKL3OBEw81hpepHU7gHsZAMviClZZUt7HTwXqyU1vnsyFIOn3/HupGkFB9yNTyMmc1lNgtgzfoJ7We8W+Fk6Au1ok2k/SBb5wk6x9XxWPz52faYfH6YaCNl6lAWeLHK+iEacak71LbZm7B5a4RqfVRJGpcF2umEKwSh0QIcdxVcsDYbtnuLaowYJBv415XQrZCvVOookBi46nMta/zlvOSy6QnwQDdtU6sEztk5W3sRGCs3zE+BvomCK4cZ01wMQjgemMrjHekBtRKBF0ytuPsj90lqxvOu/Mj53a/a70vwIIjdkTVv7e5un67ZAya+OFlL10Hpiw2h6LWRcXjdZxWjB7K7djPGhoYR/97/e70LDv5UZQlk1VQvYLN7b0m0uhoK3hv2fV3MYN+bbdQWF1Sks76AsY6NxDZWDwwA46UgTlBQ53ez1qj99mVqNakPZR2cFiKxgaMZT6HkEFORkIYVyy7yQo7MNcXXJw+C42D4RJIPhmrNYlZV8ED23qFxtyDQsEzqhdlr+mq2EQmn4aYX5JGygz+TNhV4IajWvU28XCjTDKOU/F83GcKklVFzDi9qMo1hXadAxtUtVKwZeIY6VC8PLzRHjZnyZKkFwCc/DNZyG/RwyVGwX2J3kazIF1zLNwsCgoW1udtfnjBTpYNAHzTYlgtrXYayn2OQvsA1UQb7FIqmLe9GzUZi3ousvUpQhOJAIwIZ/K2UkUnoYE8sUXTIJG36cWno9pkcrPzqtMa9f11qxwQ5QFZ7mrCB8+gqEHH0Tf3NtyTT04uWI8em2Bk4JSbTAVVnMyGaMMhRXmdYNBJ7ngBEgJwf1byZoIKzbCLApnEqlbvMY+jXslxLER1gGl8nGs8By38ZSUvaagXWK4iZrd0ircsfVqzXxrpn5spGCQcXZeSRmwdlsxsabwonOnlqFwhEFeOz4nNugXVE2gxemtZ0OLUl9YqqP6JA6RuYYKS90M/xF5e6WAOfkMFHeAnarSWvvglfQKdcpGAjxmbr2bkYY2UxRZTjlNFtJXlgnJZDjM5mIBuSikjuGxjAV2FQUqU+1on46oOMWZGv7YXQ5TNyUQqsuHRMM//CqFKvUy9J+lxizqsP9nUsxGuOo4vUyhVAm5TUWryv1YpyknbGxENEcTeVUi+YLhV/4fEV5gJlgNjadjGOZJ2SSn4fPp8qtl3Vc2TsqW94OtljubR0YMLlmUC3tSLBrW5Ex8gsH9WmfpqZNZj1GLHhz0xwJQxb1uq8NL/ljvnyhhSwpUbr6mzHBnHF2vzzApqxi+sNSQQtAUU6X1gxHOWwQNa955HKzNwedKLmTOM7ygdu7qPLiiuDkl9CDYZqDZTu4ZRIrgW+rVxoWQpOjq2KyXIK0jnGhIni9EIQzuzZl2F+rJ/Dx3FssLfTr4UrhxZNRgHobnA5ygEHYQyIBKdYgCSafDhkhFSINIyR0PpPExlXnVlVk5DrOW1A08KukNW7E+M7ayKfvwr7YdtOUo9fFx6oOiA7DREP2fZV4auu0kPeHRtYrtxTeK+cWPRJrhuZR3V3I7iKRExXhl0RhohuxAVbg8jDYefJX5ZevxHHBhma8i5JnGLnlRl2Dyvs5h8G7wFBeaYF2g/9Ds/p+9ljjPJYrK3Us8ULG1/wsl1MajZZ7VBvsPQ6yRhdXdFBcdGxG+RgKmFoWfohbQIxUQ6AMAqfFZADUcW++T1Qr7jJ0fQovVzPbJYYPf1ZGE8xHi7ICU0kcDrEukwN2nEhzunD0YO5zRMvHVUx1ekUcDxkuLc6javQEjeDWilY/XCBTTCPmUXTz4e18zHhh2N8i8kKDPrMZg7tT3QKJmMtzoleZeRa13RAgy9llvd31C8ioQNUlw0eyF1hiRmTL2GLGSkaeV+n+qMt5cDWrIY0BRG5OHZgKriIOWehOOnCoJASzseQoIZ8us7xe7VKM10PeTxtWxLMbLcK7lqArVkjoIjj9nBAiJeNUqMt0T+aqlQoDhRtJWozfWSEwSOVvAePSVkL/BZt5CqHubdukIkPItPP1PMUAqu6PupJHhcXQFfgO2q/NIE3ZOHVGJ++O78qRbSwwpFkjWTzqiOEVYLjD62ECOUDUEuObkp7rbBAkC9JW7vpXwImg7TrdO4ozDh4XtpsIGQ53yUNAxM0kVbRuiC9zbBLhFK65xhbny+Po/o59IeOf+PQNEetWi+5ouAKuOwOGR44z9fvCz+d7nVqqCHnsSh91z5SksHDc0DecZpwAkgo1FwgKuoIpInqYmXSFXN0h6mENPn/ir4cs4BUAkB4pDNs8ax2J4E05xq6G/smXZvfY5c045wMWQ5vqeAE4KlvgqWYXusGQ63N1CDVkDzZrVbcpE873aOkfF/E5vVCY8KIMuZjWuVd4i7jK9CVdWRdw+7F3qbVJpzDYEEkPwZ6G3XW47JvSF5Uzd0FXtFRDc0+DPw9hEll+UcDePze4WuumG/Iy1N4OcYSnUMQYriq3pOWdti8aTFZbY5aNiKI4E1JcuHUYb/zoXY26EU9GwTP38WFrzLk5aSRublEVZn/yQAgHn8wM+IzQtHcJjBRMVg+MjEWyUtyCu/ASFsR48UzS6evHBx94LG2OaSE00oAnbf81/78/m/fTmBDa/yOZcOmcACU7VeiIHdwCullmWECOYrKArlYsVEx70G/egBiZNMmCrvfLcOkGWYotJDqExcgIz3cW02iRdmBsYMHKISbPCAepql7mokjz5FgxOT4qttF+cyEgQEfOdgO/kRMKuEfyZNfreIUqCAWTsKV5QboBnEy0XS7aJ3ZK/J7qPwWmb9lEaNi2eVfszhmCyjM0zYZI6m5sIPfwOwiH0p4qDAYyCS9Dglmr3ShEeXrMhmiIXpemkSadkd5yWbDiOozZc8yAtq7wjKEIBs2I2Um2cRHIskHVJ66sirxfSWIxPtDhMdAvfq/F1LNtFvVHpRbhbjoSRvh3ynB6yt7EtXvstw12BKR4gkyj3dDtgf6Sr2D5iSxd/jNSIXotO3w6DYOTjHdSr04mQgsumb5PvtyZyeDY+tua0dbATjVTJMmcgBoFGS6cujgloBTKjjV9xd+xZ8moJLmLvre9ichtWARBhVYUP6CphnGWVOV3ZkSybbaGUag6RlroNVg2uPuALApBuFo+iZkmNYeBfOfcLy6clDgdhiLvLuWJhg3NoHKGxjXsSTm+wzsGlNK6/IFD2sKPMZ8cD9eGeIGy4mik/tmQ0P0Omc1uug9U/PIqODcOXMxijhZ39ntvOP2Les6K17S4R6cqA4ekQiU6PZOvKPp1ghg2HAEo5JqA81ALkN6wDWs1XZpNamDtmoWmdNEEcy8XAuzsWV8c6VToYGiYi4AsgV6crIUtGnNmbCXLxhDdiZFiR6AKEFAmxgteK4cofTmyAMoOvgmmFy48XuOQkb/FOaw9NHmUgYDJ+Tg4IyhtUbTSY6sUAZnT06HTCuO/0jutEQWQ9FZmTdvInNto3iP1gUkTYGLXzVOjx5Hf/35boVvnfIK49WVxmbPW/TBVL76HzzgJls7XoHXKNFSZ+1eIB2op4ZSQ1t2fpOfX+J2esiwNWMLj3kfjNHzR8xm7rhq9i2WeKmRQe28pjLzjHITMfuBgHGGlQ7aNG2SGeCjZ0MKI32NKoeyuxdIAZqaonatDadAWtE/7krpZFWOVBXmC5+tLgy3eGDwcY3ohDjncx2YO5tZWM91C4OLy2khUoxfoMRYwVu5Cm89RfkESPLM50TiUlCTyLYK2MDVZZjBjpJRiUOwCj58L85FwqCOPboc5KekFMy03CbCzbRFuMLC3cQuEytwAHYXAt/nYRdRvP0POQjdDRn1d1QDHtu9aJFAnUrp3820muU7sfm81X13LOzXuVejCBpwpJ2aNbF7kfw0d6TP6UJE5HqhsqBmrvsMkC0CZAt3tZLU2xG1qi96u/Cdh16BgKe4OWii6Z7G5rMU5eqWauTdHWuISevKUDCj0bsnS4WZ6cWS4oYOStpcMm2H8wcEBy2TLlRkANpx3riVW9nMoDWkFi4uzbTu+Y4Ja2F5NKRIuj74TSASAKRo97iL+LEEHTYTK+3cg7jQH2FQ1xxDxa5WHz5zi0Yb/6P85zRoGVOXVgdJ0H9WkfHZ7kW1oGvwdQ+OqhMlq3AsEj2HIAWX5OzVYgSF4MKHKP5V0sOmvQ8O7jBB5p1U1AAuV9KuKhtVhH04VwWSw6cZJirdZapT+fWR7TM1QOHMLd5bs7E4kLSn7ZFj20ebV8Ry1DAgVOFpwZHCguZyfDFIWy0gT2GjxlaEzsuMKC8dAObGNT1M03iJ6I+Q1uQvhkG3VyJ3O3TxEt7XD0bF8zj2FbAM1ZI6GaILt4tJ7AtNe4YW0NpK6jvjodN0eqhyEUhF1HHRdSkzEFBcOlrhIQlG5vBdkjQjRuVrVdk6KKj2IU7+VO9ZSXRlywMwoqPjcHPrBfbCANl+UoGCEisUoZSbVQ6P59abgIUe3xoaoqvogRBb1gH5fygbfjZvTVbBaH0aso9/lYefk02B4A3Fms7NTe6ef/6Le3mHjDKh4XNQqkHqLvwDg35XqWQm/C2yHN6fL3vy+5aHAX3HJxEdri8+LDolhwtFOXWdQ/O7OqOiudRs3ORkFQMYzFcDMQFMqz9I5EVK05oZGEuiWHtlZXHVXfLD+duJWrqM6hPGItQrFFrsOjhtkPaxSp/F82lH5UyTpc/OgD8BXxft+3IdDchqSCNFqsmg+aj2nc1z6M5BlDKC4elsMXjKVkb6LzpA3+eqJ6miy8BIcsXeMwkxLuXBicc+Y41WoibpCiPcn24Z3V7uXn+tkrxDp3HVvNBPrzjvAVNHMoCJRWuWlgbJ8Y2WJWEZJdTKnXWoKuZJ7bH9dTM87CR0QQYIzhyYaSxKz2GiqfKc9wLljnp3BkSjyuqiBYXTAlevBV8RUWu0++h8+EuKSw1OkJy9Gj7xTntITehb+J54oTqpiFscqO65U3PqTiQArMnNVCKpxYcRuXbGcZSMP9VanQ6VWYl91mmL86fBxJynYn0TkNxENuuNKEBBXi0gjsYjcCp2qNCdK6CzGKzIcMT9LWCATZoZllLg0Fqslwbw5UG8oycH4fYgM/U0sFXTphXhD18rwcIn6oLVBnePfWs68geVKd/iGcASwverKhghodVm9tRy1ocbHaAVEJln3JMZ4ekoxdqKzQOmEq9wHaCLzxOhtxh6lCva6Ev6rWmSXnYVO791p2Gypvxx0TbbdyBzYMF3gvbg+OTqJADiqBlU376TuwO2m9u5afSvCqvcyeXaSBD6S32D6XMIaSTNh/tcktOs7Ah1xJF42TeNIySnOZN5d3Xsw1fyUFHcw92DgJ9eOiSAvfjO4irA6+Qsc4MyHfT9ZGDLTmgaIWrGDkRTjL0ML07DWpyFCCU0Ju16eG4Lp3XzhPDUEE+9rEiOw34KDWEZSV/+mvKT8BCvVTPN/ez8Py8Cm1Zywn+2p5MaMNxdleE2Fk00nuc9S3TksWY7RI+pEG3z00/KOMspoVf7PMq3B4JqWYMfqcGP/ChNi/YV/JwsqjmCkbjLleaWXreIs1iwkOCgNqyqI2FodzR1mruLCNp4y2AvGE/BiVIgV2zx8dDwduqJJ5XOiU8R3HSkV6ImWVCy0GrObA2LsKg/e558+yLyYWnGDN9Md6LO4An1TmcEcTbpVIcwukqGLLMRlOBHc2yZegZKVjKQqCD1omCHGiGobFzKHKvrc8suysmDIMBURZV2L0rhmapDbAOUcGpe16niyF2EnTRGnQwcmkQ4aR0nwEtvbV7YfMbW48zSSTr1zbGA4DvVJF54xADMWEawEEZIxrdbmqWqwuv4p2mahpuFtEn3Je5dEV7RN+8ph69DBhiPfJneX2aRyFon6TRk6o7+FamYo6TQQb8qYlBinM4NZpEEGBxeb7K2Vd5cdAaqRTZG2M0nRbF1FQwxoZNcHjKuypZU+4rKK5jJGwGotW8TK8lcY9RtsH6dpKMb4Loz7pSXk45ruW+0QVv96Yl6qyTm2oR1PjZCSzSSVYRjY3pdCxYfhKigJnIGTeEZRgC+FslrI7LEAn8w4quEIvHHMxd+x4rxEIKmyHRGyqjC3KUlkaJnTiJ9rCupQIYImavnNMAxOMBpDHbSPxai53rM4sMTuES1Zu2LuovdtdD1L4hjDPAekQEuK41u+qaH9nHEiwrOlF78rsKXXbZkcMsmv0H6QLdDdVgft1/4X7yxbKgfovoIUuefTrpPY5GLZ8UulF8dN3GMyqG4I2fi/00cT1WlGWTRDunVqjJSB7KunGdoq0jBDGehXHYpB1gYUI/m84Uj7wTMrDTugMGBd3LocODTFpya6GoQoiqrloTUysKxqdFwzv78wog9E9SP+GkZypWfY01UrDDSRqBuIm07OC0MEj5jpLUaW8TEeK7EsjlWH0iMWv80joydZLSiMKdKGmSU/JiuByZbDNIarn41dP/fdMRIeGIYLOOyW4/o8xpE5o23qy5xuelkKMhRJBjZpojWqEzPCFCbKJYXoerjzXsHiuJm85pqT2GEi6Q5bzZkZ0rKZB2xrRTKKtVeLQuTv2odrwgvZJAK8FufowYtOBjfCEZxTdou7KSoYnw7SnfR7LXF/7P1MitFmNRrStxlSPfcOJS7XAicVgjIsDDRyuFtR/Zyb1oeyz3kAa8FuehoIKUZ4anu1M8KfFDZpIb3FR3I7YgwjjiaQ6KWsnjZXGiPnNIcSpXp28KjHeAEKBTbJ0D2xQ3x1yMwQJoquGiUhE63wWm+HGK9EF0ZCKqFyZ1otbmse06EHAhOtmaLit5ogLUaXWo/d5UyaY28m9Vge5Wixj6Iu3B18yPSlCywYznOt6qxFaXx54HlAbI27nLkEIy9YshR9MytIwGAsapqifoOF4Apy4F16f6rnqbCo3CTe5o9V51d1hvPNNjNxszffacNO5g1UhqaQFV3WzbmGnkFi44/sBJRr5M9aymC+TXJCmnvZW0NChvEE1KkIBSoVBU+EzwTQH4N4nVKKIL/JxiItXkByS4XtLI2YH4YIgHiP6NQrEF74+L4SF4ambkMlxCZVwwCNn6wZLTfhAW3bjuFH5kDixuV7rzGLNZ+PYYVKNYpEew4TJmKwWtmqg1oYat9s19nHM5njqMMh7ExRsSEW/alSYguM2iXsVKGaM6kAyMZRllZjkCr6JZWsbkl7DRPAWvsd4ObZGMhsQgVY0Ks66ezwbagRiS4sdj7gY3oAP5y6NsEDzSWqRIpJ7dJHWQYxzjtF1JZiLWTjSHVB4sFsBGEy4xYki45ItUFqbqgKcQhjQ6FfLd7fJanjCdomGuelrvhIszne+e9Mz3ALYTEXYzdXBPgkJSLblHdtEx3EyE7N/CsczRSkD0UudozCFt89TMvGdSN376r7WQZXMyJyBU+YCRI7I9+xj/C28VxihBBnn4toG6PAsW1GV9D9r6wQ3mSZCn3jCmSlZRb4RmIhL425UHx9QSRN89lR4f842TXWGVHLdtwF/dENwBWd08WbAPAwMenSEPduiOrNWKBWP4R3AlIzAHfCGccz575TwNRVC0/ICttPZimSAuk9XstM/QS5uJpfTF89mu3m0oWAH5eiZwa/kHE0kiDmS12Q/k1O2vumi+fcDoYT9C7GJD5JgpRpGM03xW6wDEPZN7K7K/SArXv1/Q5xjnZiGwHMn6JigswTV+eStS80teZmVP67EazQLQ1q2TyFbMmlp4swBSxR7QarwjHRnOAzlLFOXd5JmO5EHjTnHyxueI2KUEGrNVjG8nfQOHcR9yQu8+2YzmQsbdIIrJoRvaHHmZKES4D23D5Qz66NaUsw9Da4rOFW5URr7h/J+mF1YO5oseKqnGimYik+RtLBWjIYcbaNMi/YhEBT0rlAaebAgk5ZDT8TlqNWF00eoZ5mQCZmhLw6V/PACLA3NfkUbJY39ALIAerWCUULC5uCgReoGZpHB4XLyIWt8iSs2xQfTJHU2SV/cnqhS2qmPuFYTMpXc7lJkog1FeNnAHsU381zjVRoDxJz75qw4/chqMhxFnachCKMMG8w/AoabdXiOKYWMePFhDCTr6gQecmL4eDbKB9Cf5bdt1gCBXxYdbYDvGsNpfgoXx9ac97vYkIsrDr36y4va5ISF/telquMLE3H7hoZvOTe+dxip3Vzeh0yLpZCKIeZWR1NrsjElb1PXC67ScYBus37zZTRg5NhGyJakxW2fJVjpDd5T35/tPpAijl22b6oQugploppG1vIProM+4UmdMoMspZMyI7qeUWpTxaFW/MpCortVDPmluDZ6qPLSc0xioVr0c9qxWa+/krHq1srvGv1NaxLi5S5ZhWidS51prJGwrloN5wF8Vpx5ReRptTCxguojaz3JvJDm2s1XAM5tMXEdur1nj8uKmQPx3Qr0wNg/7K2xgj/E0gs0VUjfk3jmvaWGCwviBDQFb0BI87ikwLyfqaf4c9Bzk/Nc6SArCpc/krdAvVAd1Lh0ZyMXYhCVfy8hbzx10rbbs5vEcxMFJhrq6JAjWonLG+8iM7OOlpljjh+SxQmgVZ381OKOaL0YkWaxHB6af9L+IYLPoNxjvlf7bhGft+Xiy3I8IbSXh3RjhcRw3sZX5akCKpd0XHkOVbwwaCbt63juJRhykqpOULgDD1IzJlpKp5ALi3F1yRUblJPIvEPSN85/u6O9k255DTzQL1RzZSESfdITtMxJU647nAU8iPf/RquYiR6Z08H9Hjr7Oaswm20zB/QIXCtNIE1yDOSkYYgf1tNUytexh/cHtUJXcU9YcIB87OjS/cdMQ4ACoMjHdS+tKx0QRxgAzEWbfKdVZO1txTfGgm2O8kbHvy+enGjrecgDmCHyVSXwqdyy3jka0rjkNaLOIGykyRb13zsHw+Aax7QrP4upyi3NtXU0G+rxED8jP5qgmTcXtIbG3EwtJk07edFg9tWDo/QTb0147TLGMskeZ6s1xlryyx8gTdumOaQmkKym1I6TKmsqiJBJZrlsPI28My+jooZKyLULd6uu0dwO7qqZtz3l18qMk2ioxQ15Y/hySrG6TF+ZA9WUozJ7XH2SigBZdeS1gmbisZFxzi1AvolTLECVVmnbMu5ZsYYey+Rys2wuaOxEpsB+315EWyxGWHgCmVzqomtZkLbVxLuznGE/J6iIvW0iuaR6ASo/phVMG1+ZhZySoR7AhLeiXa+YLbqsJ6uj2jeI8zkhjxpAOJRaESQNg/2Ma9+LCOLqOeMhNozGgV0aqSJNgfYVTRX40CACxLpPBbbjUuQG061CSRulgTLwJ7GypMtrX8sqF5W95OQGq/sa0T6UQ3uuINUKhA/dAXtx1i2hAPzuZw4uKdNOsV9dc/Mlq0i2/GNVfmBcno6V+kma4G2gxcRBh4HHifDUEwsNQ0xpvlcpWdWI8Jxmtv2gTlJVZcmmaSYJVPTuA1E1t5w77aaOva1piJ2O2JTPKYF8R1UkSuZtgh6zT4+N0WqAjS0zjxqS7gEP5exxG60gStsewD7qHUN+4Jl6qnD6QmXKHUQlz7VOPukg40PQzWjIQnNNO8GdkKqoP1q+kon9Qz+om37zB5Z/YvsIrZ2g2XL9JCm9obdxAjT7IcbEQui3OL/VnOIIsTtwfpVVML8CeHWEpFDGWLyBjVERv/XJQSRZEiYm+QQ59zX2WuL4Z7l+ElDdQw8MvAEx7q4LeMTTG83FiZDm+FUT02v8RwJ1au8a3ZMBtnZEzIzimyQyRt+M2ibwZ7zWII16aIWQR1z58ByELrBan359CCUGG4Ami6qJWZcyRsLIHc9UI9qgnn0AAi3ZoJB2inBikyuDFqz0SThASAtU6ARrYssFRQfFC70H/jpFasE5nMpEPDPcYQu801wDiZs6ZisgReOHoAxc81fh0BaA3O1Sm+do0PFLBsJepL3oErgLmpcjGQF0JQ+anHP32EhgSgCyS7mdxXNwBCAm8E4MfEBBQ87cPp4DVuvV+D3FC2DQLSYZrxKqrlqSGBTqpzp9vvAWMkuPbhRrJLHAGAVW6HcmKNMYxiZxjmhma+aATgRHDobESviTPAg8//lnrtkrKiTxVqCaGl7lrvBG5b/ogoomDFvo8A3GoY1hson9urUbNRVvEJDO0KaI+C6pEOlTUM4sHWUMx5l3q8zWMX1O+2qifKKTjSYyGsUmvvtF0lGvamdq3cyVwwoWTZqSCWZJ8sGDSmAAQBfHLMsMgmZ2zGh4vOhPwBDgbruNY9y/WCKMfBg8vIah2mMk3BIUk8T3EPtxlPpSMgN5WXGKGpbB6t2UQDAPfyvhQCicJqqquLbaKkRrf1fAXDl+xnmQd4pagGtKvzNmmLJjw0xNT0C4pBWgJAAQTbxVcw1VMEpl0ew2YcFacRVKHMY39A/Fu0njHFwu8i/mmbK9A8+Txuw3KRjlDCmcdJMIL2mrGnGGlDxuaFUWY2S9IpO54xh46MiGwaUTtQIL1kLIe9j3fQOQPOxbgbIkwpByx/fsIJVIhlEJbHSh/iEf2CZU82emYcVQeYS6ovODkOsunzQmqxZr+PSrSAAaaR0I275qudOILtEfV5k38TDHXr8FT9dERZV9nJ4BdBMo+bOxNKwFdyl7KgUlG0nQWadkpeNsWbpTkGcSbc8WfJiD2f/CbNiy2jsAua/2D7jde4yWKozCnV+PZPiifBfPT+3sdKNpcppFJ+HScWDm60d6dEX7i4KYR4FShflRENB/5YQ/u5913KM38yQ5Hpxwkj9DjyTcR1QsiR7NcTi7JWGAmUEtjybgkFgLYeX7fQgBi+ObD/T4piE+akcb2FJhI8qJpyGlo9ccVmeGVwIfnRowO9iIApXKdcTEpzNFsWnOcuTqgul5gmcTLjeBwGWhTic5qmijpKOEtEbS28mSVXo3FKRz4UN2reopHCUyeS2GojtVY30W8ZKaNhexO+r06yMho+znm3oIaxejqPIww/Be4SHAGg+kXjQsnLlmZ+DAFraofmc7BuwX1wlvNMMC3ExG8SC0zK9BfQ2rwAcDOP5YosCpl08OrvCMkQtAlDNZcQiujyI9Go4tmbxP4SWjhSOFMZTVC6bFhpAWrBlMB7LW0QnNMyudMxQZ48EOan6BGG8QlGIFbkwnDGY26HINDRIGjT2BTwkTWlrAxLjICDw+6uBgShvoTXw8YcjT+or6gioqG4yqfnYXhhYTNgI7PTF73mOluRqxhQqU07AqngWqqNqA/Xyu6tqBPBOJfyqNR5OMhf59oev8xbvRenMyJschiONmOOCPI3pBJ79CNHz/NTL3Sx9FP4E7X9d83Uzpd34vGvEz9T6bnCpQzKkWVRVtKDTvXhNWpxAD0pr9zoxT/KeM8kwqAkgz7utWaY1O4ajRd1Zgmu3KWqDJHAXQc094Odysr5D5rEs5XxD3zc4eGplOI7jDp/nmoromMr2TjKGrscsgxO8AiYLQyMoc8EZ2BreLeoCy6AogoiR9jFGrOBwUKFUTahxl1VY+gDRZXf9+m4lidKfIACKIhj2zZtiyZBPiUQCz4bAQWZ09FYFuPeAOdXk4yGfGsYLsYvsNo0OC5FmM28hF1nfsWqNpMtd/dNtORtGM0W1i8CZj8hgVm0SZubpzSmGGNsUXC/rpvNyfdGLnmXNtK8q8dkFhIE/mLv4wdnqT/NcwmHmNgTjSu80t6xDIEFUYggpZ1NFq6Za6Fs0zlA6JFoU16zdO/Ka9Fq92y2IGOdKicMHyAygmrTHFJ6F6Xsck3irghSM6HYYRmlw3HUyJ+M3G/SJ/GAjd1HVkiMH6Ep7msMlKBlyjKhMVmE2x8skGN30Rv7jitboel1YoEeIEoIKy9Gfke0kPA2y5zqXpM7Ba0KyUUJmitcp145cEhUigMjZpTljkujJpJJ6HKIOw3YTMEnD0tvNrhGfXLo8pV0K3KF0CvmmniEOaupe9TTEz3jN6hwR0GSaOHNmNdc7XoxswQzXoWBmn2F6wTuxqG2rUqodpj5rpKbJpKh8FIoPxlDrhQTUoybF3Tw97F4e71uH7zF5sLI1TCiAII2ES7FVN0hWOiqpBUo+q76a5TPPHqxJ4GTPRqC0vjEJHzVaeP70bV2u/wFT/rX3a1DbufWbE1aWHpoM1NMvaeIOmNhPFT56pmhVbp1d2ZxBVk03eMiPRd8khGCypZsqqOgDeLQnqV0aqk1H7uJ68epXoElwv6leaekZbHCUlTNWYow4EM9V4XGMsKkEYzft/BwdZG5v0SDs1xscB0WlUouGKA9WU2g6NENdS6l3HBpxNbRZEz11leJeH7fZnDwsbaYAr1mgUSbzkQ3GcEhl3V6Z+wsZi+9CTY8x8q7hPL59DREZwsVwyXWSdAQjHpVoMya92EluZGzTPl+/VjHaR9ocDf40SMh1TQSOunS1LVWBtO7epns5HivDVaaiLzgaJ7nJywkoh1gENKxVzTCrmZiydbQjkYCoph8mtU12lYdhKCHV0JKcPd07wIRF7bI/5EKLG8ubNcHW0+AmErLFcJnO0+WPYm1EENLYljFp4Q8nzzbWkSaa42xjfMw2mnxqnDzVa/Q9hwgGK9ZqaWT8FcACiz+jp2RuJDkcCK09D7OTxOxhsJVMohWUqR0XRgQ0W2yFm2gphR//0yJl+cTtODZ5VphECRYKVG7LYmSg1B0ZY2IC1yE9v+oSKUkUl+TBZK1IZrdXK2kElIJt2LPsfqAvkpOi609IByTUsApce/u9mhfctRm8y4B4qYPtB6uZ8huEYoKDKL7wQTCiQFzaJI4Nv0Emoz+6Xl0k0kFgWOP6BOVF796aR3YpO3Y8kcBdQGGVPEDbLMH1ovd91RFfNgMJ1peVV9/oiCEQa1hdF5RjrJXkDqIN5NorXvpEWUkBETjYp1fXDCBkBQTGYbq/hozhatHXKkG9mQnFlY6Lglsm60b+0VYD4l4f8g1tS5VuyECPgaMD760k72rORvPk5t5oETSnmNngX6k4kS/QleUjIOJNR0TOEFKINy2tiL39NWgINGmZtENjm3+HSzEpeTsHJl+iiua1NzWthAULqYgsn3i/vulVcL9YYTOiG1I9KVhGs5DrTHE89sTJNFFEpBKzyiqchsAaxdx1TQwdLZhDqRY5nFQD2HMYnoZSQfa4jFi3TAvt/YPloMRivDEdSabe257cumGYq9EPuKC5EzHHMqikqH+/HwNOJ46F+tGmtCt+prBRTwSy2TdyfehYRj6rmeU3nGTwZvZfz8mrMo5WpNhE0SdXT3PK73mc8yJt1z08tYRSyLm5YXuHVZoCpjBzxQzHxXIMVmJERnu5sWBi9Qtq2CTMd6IzTtKJTK/ObB5C9lKjt1fkxPrL/YneE/C/7Z3LzvTIlpZv5dMeIxRnO7iWQgygB0wKBt0SEuq74IKJ51k+pLPq7w1UchBK7V2/nf4ynU5HOGLFWu9BRPl07sloHFEYVpCa4kY4jqg3nZu/udlQzOTKL9QqdMRYW7lOIlHDLdSSmGRwAxZYy6zGspLCWAEbDpefP6G6Nc3KW5yYZqqyQryrZ6klfti+RLVArIWVOiM5mnkzhPaXFB88pELI5OY99J2FYOw6EYNbaaLTJL9oHTTwTQUqNbTuKy1sg3N10QDNMwxC7dLZRJez7gocpNhpLWwqc9fqsQfwYepOMgniIF4xNOE3JuuQ4U75YCoxKoYi7M0KBDXNYqiVVY0lV54RwTGZTsOC1UsaZ04rTWDyAhYGjQJOCaJ7R1lDj6NJB0Gziw4Ll1PDFJ4dFnpF1w4CN0YNERM1AjsSrq23yN2UTXMXjVqRvSWtPTcpvPIGcmgcO6Mw+ScVB5Lwi7XaMFuas4il3s3b4ptNjSzLSUKTR0AbC6A1ZFKLgHI2glPdjU0ZNCvIfda/eqAzraKOwdMoZ63b9xOSXHgGQiMBmBWoW28mPYGqXxfyrtAESirUoLIqCDgKqD5W1ByAq58cPcheYcZU1YFkOlsDHuTXXbFaGOBkkPYwDpqa0UDYZC3RtXJAaC2FtYAZO/2kUTqxhsUwxwaBHwAuMto0UBJHK4N/cywOVnnkblLIIGbxAMApyRRPlAYtlLIM4GdKE8kqRpr2g9NTNDx3nd7UeB3gT1hUI1KlVElROWUqMbeb3ibxoS+xLNYERrWFRyewaO5RTnqTVE1s0OcEJdApxpEaHsKpkSRG4iGZLCPcouwuPKSalxlmaRCkYbricYf2vmnDt5mxoypbvWEThAbaVUmLSNZImBwrGmYRZCoIgwVKqMWiCcB1CTzvcvuzAoc8eLKTVMzAOEzf03WvhsKYBNrr1m4uLaTMFTnEaxSzRERKsEkDkm0wAaW1IYxnPYBCtpMQPXT5SXDh56JRTCix5nb4dOowqeYmdP6pxwsPCjpulC/kpkFCGb5fQzH4VWSLhsrQ1aELgDgJVrI+gcsS3IPMkKaLTe2qqCiinMgUrVTfNmTyKukOy4zyQjdqJAwhUKMsqyPjcFiyhagC0+M2ASyAhptrB6QImpaV1oN3h/28yfRvurVtam+ClKGUS9F2NJVklZaG6QHaW2FWgB2OnEOFxyz8qmBEyMSACwD6uRRTqAEyGvm5PbBVU+gW1QQKlZgRAhzZZQiviFzyU5has6amoo0yIwkYVXCgCPvYWL5k+cC4IXUVi9pu1kP+vkK91Cvp1UzOrpt6+NNpIxcScizITBazEmARVs0wcZ36alBZm0jsM6Id1kEaha/4LMf034+KBbdcwYQMdFnHAJLTfXeJTM5XL0Pwoao0uJ7XeZhTRwa5WIg5nDG1XIEPRfO3cZqf6VcSAw61Aa3IgexT3YXITeWMOy2du8lMnkCAqrgllE+6BkRrhUSljbVRNsAwA4UBHTcaMGqDNURu2fl4EnMMebIhtmidq5o0x/YS2mzBZ1cHUkSrKdirhZiURqgl2GMgz0GBKDNAYRTtxKrd7MEjTupdMGCuyMjMZJiFKcgwNZKUuKtFPLWWTQMuBSU1s7bI1Fwh7LFsC/kDVSpAAvEpFhsUmZMGYsO+brEZdzhYZHs1uREIA4SEuMmgBdC9KLG43Zyhh1gEtBI1I9TphFI12tr+qG1vWta3YZLXZSRgKNNeiKfJYJs1ij+Oaaaua+go4kNF70SXt1DoKlodaWYs6FNWITUPTemzlsOGFnhVaA/VQ5NzBiBm646LKMcAWAizI4Y0/UTg36A55XUB8zd9DM8V7sMUOttEwmiaSpXQyVeRFdQuBGglirzrXx9ktNNRzVKHDpMbYC7Tso9SV5skfyDWpDuldpAkZv1Pp+7aMCmNjtqCkCDAHkb1ql8LbejIgm6uUoDeUzID2lBqqCHqdqGO3sDvRe3pYWV8U50pbdraDxXEEfx1hoTMQ/WkkuKBojsjMURQShDNUAsGBDHOEtlickGyUZm+hRqhZAA8O1KQsHMpVeoLrl4iIgIlcG0mvHMNO3uKBxQRtIajvgF7iltXFOGhzEBqA2K70xSdeQ1C5gI3EGokeYX6sQ4hi6xtwJpMqGflSQ6dJT05+65O5wg1u7Gr9QVeUodB7c1ZC3PLM1PfuqlTbCi5K5uI3I4m3Bl7aO1Vk2ay8kG2hsW60gNN062p9Y9M5qqZMsSKrr69yjFQf6y5yZBVmI21O7+phFSeFNMVslC9WVcTWhfhrAWggS4wRSs3o2xERFuIL4boZQlIUFHZvAJlwzbI8mkzBQr5gZGAMBGYIViDqmlJ1Eea+p5NUDiXWWUAs7ZiKYECBZUFgUtb11mPfFqWziOXirK1ipSodqCjV11aaYVD8b2a8d8s9W5qMIage3I6qJpMge9DAETZkRFK0mOEOFYHVttxWmBhEwLMusoBP49YnDlYQIY+0AoLdKVNTc6g5Nf0xhiqPYek4mHsO1TGAVYjuh8cNqU+5cNxk9zEsO56Bg6DW7LpGqpIklVOF6ZEVd00nLST8s9IVKoNiWYstVhqFLo+kC/PgQ2ZmndaZywCTSJ1gVACTzDBDWMw2XqQ4Ah5AD0H3dHN0napCIimEfcX51pyAFYgQ7YYZz7dJBUXbpIhkKEpv/2+7vRvvwNU+e134GDr3xWJrSNrFFp/XFf02+8rhl3vIYv/2+/rdBxOvHE15Dq81iK//Y48g+dap1kvV4fiRFtb/64F1jo25zqMm955hJ+83jj5gvj0+S+HU10f9X2r4dbXUv5b59/X0TV8rfNt6+Aa/tff07Zx5nR8cVlvKVwpBFQuksvrcVnxDYzc/KhyXjyGe3/7Vz9/+4f/8p//4d//4z/8h3/3n/7pH//zP/3j3/7Nz98mGnrrjcFYjW0bbtcA8jweW1L3r++jmv2nn7+2r58vfhl8zfn4knO7rvTxofe//+Kiros4Pv+HH/V2HPDL2pqQW1vdTl/fH+8rx0niwwB32vMi+YXknegV5f02uj2/4e12/O2f/9XPf/3bf/z9aAVlo7N9riUVg7sElBH8xT0okYhwkBuDaqy3gl5Xguqpy2uPvMk+Am/PGsXRbijx1xFBg5GvnHHO0sSBECUfpixIItgBLcriJB/NpsoPns5sRKpMrbtAEjRzFVgFCA5gjQoen95FBgeHn7VZ0ZRpjW2MqgPmOAlseBBAyapOVggjQRhVmJ/ZgqQF3M8VVoUgXpcOXsz5MMe7hjaf23UpYD4JyxTlenDWruq9kL9HKpiEftKgVu4+GAhNPlluKYReJLAYj5D5BR5Vi/7Xc4onXwGsqW3pxqYoVSEjJWqWqmA3te6lgJxuBYJKgkJ7PbIe5MdJFCU52k0H9/Am2zNCGgzYVWvzprp0VdcS0nSz3k9qF5kgmqEIBIPbRYOlvIXLZQ6+NLkDeg4g4iqwFeliJkeQg0IpKFPDZ2lCs1nLN5BjhIuaoyID2lroG1ttIsNPiluJMpiIVOqmVNCqGgfwF8lVpFdZ9mk/i2GoPYS17+o7wxU3vAZLYnPdPiIMhGjJignTrgRi6jBS01qzi3p7IKvAE4t9L7p6F40NmegNE7Pe6cRjWe9OsjZJVBjiK00bW6MAvaYwUBeiqD9KQZOPTH0vstM1+xukmnVxI+IjWSfpCGEpQhGgS2s2MfGeQxUmmYzJuo7CAt8VDHHpjBEVq5FahAsiJEu3V6EMtglaCoGdkHFVII1IcUeLBkcx8SGqs1IWHspkbeK+0XdEDiXJECrUO+ikVW1mUpxgLEj/dYPfXdE6Im3SwFhvWcJSOBfkCU4w3MKE6CXBgXEO3smh5i62v1koCiTqZvINPyjeXsMCAzuQK6uaFG5ZbUk1CIw8kDMkS4rmOUPBHu4WMBDyRJvQQzyWgULidynEinCUTkwimqq7Rmw8oWIEwIxRb0GwkWwRSQuWaV3IE9EKqZVE8lzo0pDXRsqZlH1VuQ9uooxMBT4Yg5IpnRoiw1nqS1ZgDPMd8s2GJz28QVZkOUOdOoJX2aYpcEzJg6NLAO8qDuHqwePZLSfCs1IqYFDmhocT4iOSe1ZPUSUxmVBUA5mhmpIgdKthpKcEaDNfT41m18ke2D1qOij1NOl3QAsZdRslSbLCuwZjpt9ZRvAQNpXRNjRnSC1PMxUQRymFos1DuJiUamUsQs2aNAkzKL4palAwnFeVccjxbEnL5Knl6IBwkU+qxEAt80cV9SIUWgWdTYocuj4kbvVEo2BVHXQFDGb4qNCow+FVo1Ubds0rVeJ4ohqlH4/kYS2e5OlAM2EZSYYZSB03t0udo0Sozp4qJgS0FLKGyIqGggxXaOdAJ5BKlE8N7PEqy55GL2S0LF8EndYSO1OpKviuWik1mGDSWlIoW9PbmzV1idxaVVocZ2y5oAEDLirEk1xnRatm449owiT/n4qISdccVXKkOqnfJM1AqY7SPvyAioiLymsSfUT9A6AK/XykltbzKFZPrVSgTwp8KijsMo/1oAoIoENI4oItc4Et6qPKzc8hD2tlS4mmFrT0dkw0+poMvcJrAKw3q+W7S2rBlhQvh8LLOqUr4YmOele1UU8X5PFE0Oxh4wHGK3GTQDTKK2fRlJQD3eR2OrVtCmvDoiDdkwNd2X21b6Fi2OU8II1DBSf75ax/GdLQhshhHKlqEPAggyuFikP1TVXMPqOs0y01CirQmdIiOEhjqTtkfcPAnKGbLOKmfjJcZ8pfQGCI/RRxBE+lMjm3TnwZBLVwfhSrjLt3cYpjulzzFfdEP2ApR+JAstasMH1IZmTnr5qlXhRJGlR9eOQwKVVYWJwwLkwjXBt5NMle4PSAT3NVvDFJFqzkpGWCgz/EjkVgi/hDPK/MLat6TgMUld3Iq4xY5tWs6TPTPRltrIMoklck7TEDtAJUi/bvTW4XtH9AFaRIdxDREE+LrJaurR/SW0cVvmn+DoCEVOem5W2K4hWjPKynHpilqnYK+R7eg0qZ8rwNchKh7w5irG/SA3IJCT+/e5DeVohCmD9E0qaiCsS8qrhFn+rlA+HykYSkSyi0q39smWZuIk4REtr069RpBFgiQWd3jsyq5aAbKo+imaiEXsT8MEsA4oRkFA040fEJhOGuVqhOLGUqobeH/q8GQcAs9doR/k9/N3HDbWOoUtQNjVER/0XxHxF5AHuZRavOGGQm0OydgnKSgryoIsAfacpi4kYuz1L3sC61suhQBCgwSxUjK01r6PdoPR1AAqmODbQ7AygpwkpyZcdBgDyYYQkAgi65aIYTyJC0C2dcwzMkAFJX1xO2RtK3GbAiGH4qfVEqDpVGpGMRWOoUD9dFbqEFG6I6IH7B8OvvMzm/NGNV0sU2DizuyGxkBT7I3g8x/CwpptheiEoU0AS9pYMClgJ7LDsSbycpmngqk5zpylxv2hdsWh6Cbd/0gOZ5rZZCQEaSbmQl6wNE5QXsHmjxAcEcMDuAO8Z5MfBElArhw7klROniTpJel/uBQivWCJBeId5aa1Hrhi3ELps2fAhxKc8t7jLQdTirbOHix20ii8Zo2x0uyT+z0FHHR5wKGVkWXoinhZ2PyuVDPNPIwc2ZZjgB3A/ddPSewZEcOB0H0Q4n4QTGlNqFJftStVLvUq2LwRFCZcHG14MHLy/BUD0wh0BlkFBi9ZxB1lPzCwclwpt1OxnG1i2eKmYUfVGpWVuCYulAlTk5PeiRt5sLhRMDPgdAgapndlpcy4gH1dtMXd85NOF9BJuF7A1yIlh6KtEBnEfp0+5n1I0NB8sKTU1XF481QHlVxq56ZnaeROGAmg+gbDD0O87CdGF4sBLXp3sXjDDkd9emRHMKBojrfs6k2SyLQaL9voVteKQLtCIZClWuUWiX99Utd+1kkMmtq60wRRsjIEH/o2vCqQ+x8CThTUQdIR2rQkuPiI4CwmiyLPDg0sPMCQTyeUSFoMPXfEMgMnI4BI7dBImkWzQoTG1S1IfzgkYUHcZaEDBwitwst1SrUFkifEHMygImtBxL4aaxui2KKAgfyVKK864c8RqQqmnMIBaohrB+WlE4C1sqJUMF1kd0gJKn7hhJW1QVI6BmgDZjxUAfVS16oNAvLhuOBj6boHCaoJCqCg5DguKukOGpILKY27ttBld+WEHMkVkN6MtpZ86MADu2CTKoQpKkSkISYjYZigcPoOOToirrtQRWL5ejohnyWSSSiQua5ilFKU/NrCiekZ1ZIU6WJGahcndtBGgEZMOaxMnkQ8HWcqNOha6lhzENZ1GPCoVM0AeKu7hwIwamVs66IRR3sfE0uGNcg/kpRQX0ljghMXlFNTPRcTzkSDhtIv1ZPoZjkLL24sdBwGsD6zCFsQJz3uZ9IkTYpZtqgMyEQji0A5LLAGNdiuwhJM3SbEd0muUncHDyIpLNqFnDklLgRrUHwE+kXURLoBoelAanliwuvytWaLJ9CycooFT6G9QZ1FBtMJNa9izOp887vTsQE0UJPBVMglC0a88awAD1wRViYgXaxO/25ug+sxmcqN4TK/JqiqWqugKh2kCAuoKyXfvAZLVQePe6KPU2ARJPjfO2kOjaJf3JhwhXBRgfPIM8+COi/KYHGnNpaQERBnsjqkkWvVJgCNo54yj6zxpuhrWTFLEZJondqn+1CL6WFptpEhH4iEIVOUv8BiX9kXMo3HXQFiz09qhSh3YHBELtjqyXwLy2PoTuveOOiRiw6wSpzgZU0uHFoRBKgodRi1XSLiErjGym3r3mDsXTUdVHGA9dzL67wlG5HtUexrwaDsPbphXdLow/VBkARkgEGspwAptQtVlaHOanPHjhpAE4MbnKk1WlwMm66yQ41rubxDYZO6QdzH0EwR9rOGrkeuW65tDtkXG0S8CucmsRPHOFK5v9QuOpvIh5BjBgJsgKpHiqCgQyrGLNAqKWd6zntoQFp+4spAjIxzWVOB0uw44PqJAABMn4aK9U8wL60a8xcRdjAeN6h2NDw6XD83GEe3xyyT3DP4Uuj0DQjHBXdyx+58TEj7zcMBuMygrpSe3msubEQ/+1H75fZz5pjNTwLPvC9WSJQU4CT21FBAH1r589ySFh0o1SZK8OI0VfoJxtKKr9LubB5hH3Tb9V2sXQBwaNv6os+pSgod4yY4eoqxwy/qrAYApEDASmm5uoHg2WRBSHs8CFgRNzVj4JhI8+50qCy+cVhBIMip1crLLikkiT/IiJcAIhXNacuQH7ZDkIIMrujVzLrpMD/RTbqKZMN/C2EslLRFm7SrzcGhJ7Q2wnuQkMtUR8oUVPOMJZOjwuUUwEpmSdN2GwM8AcZHJWRElkGqwScgLcw0kWEedeGglyjEPypkplYo3P3KqHi9gH+GRdABUxaNWQR91SBjrl/UgwySJKLSSKQ8BsZ4MQeQnfd7r2ME1eBKivKHATDm6SZzts7ZLyqAgr/OjoBWWzUeot6pCST07Ku6m7Bwiwu6KRbE6suivY1Q58NRluudqkAeFe76oqDw15dKxXfxem2zoD71jPlCT/XVcnpkt6/R7sn5CQGU0T3qqxxLaFCzBuU6zDmYtHNWW5Lk5RtU1LbWjVUjRMYOrJDhGNeW9DOdMk2aamslyrnUzblq2BswbcTB+q+NQU1DcbXU0/GPhhacKNNLe5OvlUDkzuJaIGgDmbDy9LQ5KMZkin1Xadd0rorpKuBC5EUK8pJ9as3l3d1AYpVBb0rDF41jU134KUa5AGPQktsQSMnN/btFemwqfQBiwToSi7wv0Cd6kzkl4Fc6y0F6MfOumh97tr7yjLZ8gAQOFX12jzH8xICnUhe6Ojs/wBrEB0etm1p6IqwCLZtYsJGYC2KmRrdcM6KIqRmgf3JEKerBuzqup8qO6zbq36aaxmI6bDpoyl2659e9UCA/NysQK40LpIUgtGHT8QcoCeiFXIpYGJEWkLJDprMg9Hes+xhG7q0TjhEiBWlRpRRUqHtReKQtwcClxlUxsG6jgOsJwqJMGr8ng81R5E9ZFpaEshw6wf8VqyMr/nXVtOw7+KSidoIXBIrLzlr+pE0CHbyPVLQnQgfqodXDSO1F6rhQ1aNlgCRpbiF0pMQ3m1u8CqJASg0oT9u74IMFfkY0gGmyWM9IqWyesQaTwsPbQV3ox+s2QBEj5g8nlMFafBGq1U1euInqr1OsFUPabMLJta5XCLCt2iUHXYI/SX90ei00pMU4imujqq4qGqapV4AOBUS5wAGhSxPyu568cgaGajC+Qj5VNY9TORbAoIgPIjhEb+h5Q4nU2lxsQTQWIBQRkEGMqIh7f3FPZFCuRu5O8OyyFEAigK6QC+Bn+17mHNw2AzVY/CIMuPPYRlsafRz4PIhYTI4QGBCH1i7WolE96Ja3Sk+Cn5FiHhTLeMGXwJ5bykJr5wKO88v8JrZP0IUQUoAMhJfkzToXPujqhkzQj7etc3e6o+SSgnByyx+IIgb+6naVe7q0OLZnOofmhLrBxm5eqS5k1dOGYQM5KU8qTPF44GoK/QN9J5icX64Puqou3MOc0lHxLJ6omhlGNtbKjuv1uhlZU8q+VXKLsiJcXX902DTbAw5KEYR5P0flVekLEHZAUKfo2pwepl9GeOti7KAkTTPB8ODO9cJKCIIQHHKMn5eGpjveYb0f1F3CRU2q6wCMEl8p2M70Vv42qqCAJFEaomCLsx0iHjrLEe8TmKJyv6liqpmzT+L+T9uwp6I3yysfMFPKzHNT24KX3DyrZr9YZplELwpcsywmAVaFvVmSWpiDZpUzUiZmg164wCZsw8HmlzMLuk0iwlsPqlSTIVAsAdVBoBHetIqWEqsgglwleqYUXcP6J1WQMlUiCb5UDw0rqZgV4g8AZnoOQ9GQx1KEwGdQc4OkENS1zwvvZn6CikIZPmJZbimeSwbOT0QHydfMn8kHclEhrW0wSumBShqeQipOTbtd3DSmZKSbb4uTkEdG17dzIY5Ikav5afafYjoWTVA1i5Rrukg0OKaiLC/RRCBPFv+vUS3PJpwX6b0YJWd6D9JwAU1PDIFMBHAMa5a0xHTQJmr36bRIMwJfZyVI2RM4D6rkpL0jpZrR+0/XPoQZNXZkoJ3COEGLJU9B8s9lhS9LB4zVGPcvbRWXVOC2sM8lVGBZeHzSQPOpEX6AlyGFjFmKoBHaSfm7wT2GrqnAx5lYnMADYMqh85qFUslxm+EZRCiKm2qOgjmwrPIcVlVuFLXcc4cRC7gpxrQoBHDlH5UIOxrktWjhxSV7kBOOGKT6u4HGETVWN3mA9JNBK/B8o8sp9IWnRZ9WKBtrAoSbArSFLvsLsUrtzkAaCTtlmO1s8OcAoWGBYgh2YnU0pNg8Lo8A0qYg1JcHcQuHWV6JchvEFg2/UbLgGVWH25KMNB/kiYEwEAOBZAz+iLsiQf+vfmlkPtZbegpdEXMqxR6yKPAE+GcjkqSeSDuhQ0mP26zzRJudllo4IIU/FGq3aMkihdEMlJwZtAGNWPD9Xz3DSc0+ieooyOJ3v0WVBi+hkSxZMCQDyVke/QwC9GYI1chuURvXF2+ldj1HdEYH4f8HvJ/sGgZq6cOniZgbNIxUCa9c2mSELkT0kUi3suQAfXgn0pgbT8eYrmOhuPHL93cxjkTZGWo+lUaNkiz1bRUQFyZeXdjNYkOczizGyyUh2ON7oSIQc0VT2GRxhgckw1GDqSnjRNgwjSnGRIzBzo9Ae4haeD+G8qpY/OCR0cny56AQxnqPXWcYfwIipOU+xuEksgWgj6fAR05npURiEe089c4oRO9YTLRVbLuq98OeVuWkxDQsw/tMgqMYbUTWLDHAemQNs9/SHX7CRjgsCJ988gKCfhgIXqEuy7HJBE1hIqoK2PZr2sQZVoOkoPUjuUiWuLYA1ATokKJFNvD+HtHIGyCWTLzFQrqBiy0NRm1faBx6X+Q8iZimhnPUqRhYYbPKIIdLH0r+HSN82mAPBgvGbtA/5hE+LI4KqZnhoA0DbV4d40qAmHAg3fVySrH1ivobjKuACrIrTqSeEQSVcF31RSyXIvSWDL8xH3v4efIvmDYkWb7PmashSm2bq5rcOe0FXIHvrXYB2almaIUMKzzSEHhuRX01lG+iI15j26QqXaUmDaag6zSR0a+oGyfjeo9yyDq9f0WxkFrVCxr2Qil2eKnIpyQy7PVxAvyC8cHzG8JsMJsI+ETtAXk8wzxcrGVGeH9euu6CglbMaUJvSoqTKYCHBwW6IkDB0TUKpCeBl29EY/5+YXDGm4RdYSWnBjjS+JECRxw1BiLto1b7L2T01S045NgEYI3CYDc/3K4E6zDpt2tiH6IRt7AsRXV6UDfAJPzKyVqAwIbxJASAiAkBGP/TTcRjapme4XMYIMtmYKWq1obwAioEQqYqunKnqG366XIdFlBUbIyi1snihryHrHuYtgaJhV6q6x1PbHZoBVgALjXckdqDwCnAYx5kbrW8pgHAHhj3SkVgYoQOhyjoz9Cg2AxCTKC2qPUhPmGYZXTthNpr7phqqVDmL1gVzw5pCoXz+MaI+sU1J5n+TKRHwE9hhPFyitLSoeZOibzBKCb+p7ZDZJhVrRYLDdtZIu6u2kTa0I0DzwYsJPpYvpQRUNEJD63iZIkJzKYQzDWJ+b/JlNJR45VAoKZK2DwQFPKc6SmlWFl/pxrKzIsrFqJM1O4tqDIHkFr2KEMhDqYSFIQhwJA3GjqmQleINreQ9GRg5Udy0jQFBiL5CGbN5NeSfYnpujvSNKL+I18QxXT2VYLmWlkfctuLGhclCaguKIzhDtW7KUrYp4CvACbpyha8ouljdWAlqaTKstxFRFh7QVnZF8RlhF7Cr6d6a/NrFgBBNgZHj20tDUVaMMUWOslkm3ZhdZVVrQbuRXwCXvZBDV3tn1fNAHB49gclg7rqZkZBkiGbH3cK7g0qUPwVCpPi40M+hVDcyCXuV4t7fwxVUTUSwxurzESsI9XZ1rDEquQVNW4tgGTVlZeiDb6yehFiQkXLQcVGRKcXQTEGkKZjPljCoUYfMgqhibWAEVCy0ar2df4V5RYoh5sa5INUSpwRjoqkFwGvlZwEOBOpRmVCPEA5DBCOWyBN8NMvW4u+snQFAP2UdkHmfWSnDTmIb3w/9TAchlO19QVHOErSPwEsl4AHHmSrWtqMpcq6OaQtZL7A6h8xDG0YSlaUdZAE+COdLoSR9q+cZMoKJMhlqiCjVkuO6QCFiKZ2pe4Y8UeD+SQ3q0ryFBHgCWGVWYHG4qZM/pVLqvBX2bcYXcL/ckbHQdu6k/W1DoqtmovFH0Zpe1vSH9phIt8C00pFgEVxWS9nCTAMINAG/oARtmkluUy/GMFXO7K84oW4pxxQREj4KpKlDAMJAtoQ6eVVRTlQxEvno6TQ/LpgtwyZHrgUyIM7mYeg3alFWRzDzFxww6FxUTqLrJwik+KEO55/DKMPsb8kk4rLD0UxNCeilDLdgOsjfJHIZAD40CMclhHmIO7Noc0e2A78LQPfKgVpY6SE+Q2WqjTNglVDQVfcQ/3pWLHlIqdCZBkPr1yZAHmsoKyQk+JIhCehtxIx5mqDLEsVMJ0KmOGVELyz60vFhBakAGJBnk1CYO1mQqhPvwjgodMmREg1eO7L8+JaIQN6gZ4cKoNFAEZuG0vsbWwLFQnNRi3uo9xB3RSF2uAOEmaWtW4SpYGZFs4nHDoVYAvs7PJuoZ+TYUeX6EfmYNBsgtaGW7h5QcoDrV1aZKX5Mquax+Ql2cMlXbIwJYi2R13RU6/1FYLOveZju4ymAAcciswhcO5D68fJLCXYplEcgN9gQJAkUDEQbWTMQlIGJelKYK8RJTLs80NR7i2khfIatDcExSn5W2AOy2uT5OsHuDWJTCnjg0pLgbRc9V2PLMxMXFKw2j4y68m7XWt6AzlBrGhrmEcZohR2RtWABo8bA5ykdEoLMvKyJ92zV/hzLBInlzUWry0ClDqksnQxUcvd9+r7/93n77vf/2O+S/336HCbb+BIVv/T3DF1zvkKUHkQ8KIgQ+Pi45cP3HeaCgVdh+8P5gmK3/YJrBo5N/CMtw/ccXQgWEjQhtcEALXP/tUAjXlaz3tQwZcP3HlUk9XP/Bblzva+t9cBU715dgoZEn4DrYX/MRl8NRLmrz7InTQ6FDcBBuHH/eoDBWaW47P2d4wullQ8TkN24wMKnX8RdIlBucujVgcaqdG1H5uegXHBTGPKQK7vD+uFi5d7Aa40Np8p6N61vtz+e7t5GbvIbY68s27vTOLV9PKKfiuzoXf3wXd2sFyzQDNymXmn7BmQSfTcv0aqNSTaMxKcDQUC0Inc5u9zbqNNxs+GJwMkk2rw4ilnttXba8bMXErK0IaLYgFNZW9ty6b1aB/mQrhvtf2IrSvreRvH3dMta+bMVx/1/cSm779TZw2TIrS99et8Cv6CqUPGBiUmv3+M6DExn5l+11HtIMr+chGPr195z39fq+8/x/vJ7j+Lk9z/e2lW7y+rnj+HX979fzfr7r+0YwUv9w/O9d3/G59/twXh/kjz87T/z9jdlKMpBRRmBmqbpjaQgrAaobKPYYNrPDZnXYjP3sKJp/8Rh60uPcMrybz9OxjS/7w5+Pl29/fbtoPYWhgictsLaupL4yZmtFl1T7AKjFokQzkBUHDgu5UORhNA5JI4EL0LcK5P8hAa1cOjDJuefwn0561FjvIHMdnNckHxY/MgR/KTA18PLwEhgiVI0HPoFekkun8BxRo4xccg7f99C9U3LeS1ClRTUootEVYRbtS2V7otgvS0lXjzU8UZAHYMX8vnp5CSdESv0rUpHKo1aKGAi9qjXGYhafwqSq2oyya2tU8Ae2h+SvpggDLTVw85xCd0B883ElGLQiQc5JqqqakWb61V+xsDJ0P1sxELSWlPVGh54DyJCetutkY7oaC1jYAIp0w9wJpIR0REQks4wcFj+7Uv5K+iBGgcsKq2koWlsR7UOdjsZnEc6isuiTyQd0fBShBAWRfORoLRwUkr6MYGoBwvZwY7HggB4NIH31X4b+vptp9IpgLRV+6TvKeCLEE/ieNZnw9CXji+y/zpNIG2wRWxAPICPQ4+E6pAscNPeIHoguGPyIODYH24gGmGB4Kpktj//4CP8xxyJ7kOOhHcxYEe8wqeyHDMGv1Aa8uZyb+8J8/PIiHge28abzddw2Lo6WfPm73fQ4SfSo40U8L/c7z0+eb7qOv73+w5nPbzzOF238cv6373m/8vN1dKbj4qJr3G+KR/b+0uvi8sub3vUCXKaHnACx+s96Jn7W8PKzutAPAPef9cD+rFXf2rafPQ7t6xD/W/14/WHFvesza/xY2zUAeI4Zn14Dx/rTWgj7+TUQrD+FpzmJoh/e0HzDelj9M/V/TpnOV9UrqF5W9a05/s638u1bXJZnS377GuzWG5tXV84v82TFs05/UPmJX7se0OPXFC928wdvfhmpab6Rb1sD+Tqe/PSMS+BN2X/jq9v9ZcOzNq8x7hT3hLccvzB5ku7nOX31Voy4vyVutPtcX/fmxdX14+3Xl424W7u/LT6d/dbp93J3kr9uxDV3D23HzfezpIziz5yBb8npaMrsl73cSK4j7le0Wo7b1W2X5i/bbNTdTlS99PET38x3J29+OrpJjpc1vrzYLPX+aXGp2fs//R3R6/bjtMd1Zu9ls5GOZtvtAfvxA46uu3tbig1TbfC7N0Zfy9Fxi3csx23nB+9+x35e6n70+RE/YHqB0TGuPjOurpqONmi2zPXjNn/r7g2vfuHR6fxwPTp4ihuQ/HNcwu4zaJMdrV6Pdi/HZdD+PAnXY31cyjyejhytVP3++Ibt+K3Nn5biPjQ/Ub3AahMevyo6Z9zDzYvZzy87mqAf1x+/bHo529F9uJ7hM+O9af6WenS4+D35eFarN2o7Rp3NuzDOr9qiq9a41BwNnvxcdNt8PLXXWHY9hOkYUmIEO0aeYfs3v6IeF1Dur8t+ph23s/iTyvEQdy9gHFd5dJJ69pfhdURzH91jelnHQOIF5WOAuX7c2Xuztzr+GD2vxBW3cyRMxzCVjnvVjhGnnn2p+aa4ffV8wI8x9nqydz+Rjq7ePVL9jekY+a6Osdui3dvRfBjj5pSjL4zjbsdzkM5bsr08bvN4ou9r3+PwsE9eHaxcD0VOxw0cx/SS/GXn0er/jqfTv43jr8eX1qOJyzEYzWPUHDF4H/duHIPTMVqm89vmMQicc5Q3qPhl4+jZjkTXD0zHVNP8L0an836M46k42s+W8APx0LSj59uZj/Ho6CfZxp3Xs3d8X3w25obqJdfj7sUb9+suXn0iXdNM9T7GfjuvKoaIHlcxj4nncTe3n5ih9nO2j56wnz9xO+agckz/9RithrNMtiXG8S1nO/Rz/Bi+4WUAOwbCGHuOi87RX88/xdi+eyuiq7arSWMyzkdPGedN8LPn5HqNYPGcxVAV4206JrMYZLajr2Sveb5EH9Hg28sFjWO6jfu5HU///buyn0xHVz3fM4+hP4bQ6Tauoh1xTz6Oj+vpiXsX93AcY3a9v2ocY3UEFccQFLeun128XgFg8uLz8YOvmOyc0aK/9WO+qcetvn9ZOXp0jnvc7ym42fq2cgyAxxnnEeXUOH05hrx8zIztGCo2GycGqseTPY84IR0PZfWq6vE85KNFj+f3+Ln5eDaOpyK/BF7DZqjHDT/CzXva2W2xeg5yXvIZP8ZtnsdI3Y4A8YhWyjEENX9TO0LFcjy4/Q5rji+bR4doRwgRMU4Eq/Vq/PZzPrLHtYzXb53HeJGPIKKfz+d+TNP9/rYST8LRoPe4ntM5KOdzBtmOSf383ds1Ylwj5RG7zGPA3I5Tvvy2djwg1Y/MI1TdjkdrPya/CEH6dXw7HpztjD7TGd9t5wzfr2j7nL2nJzwDv+24HfXnHKvmGbfEvT77QjlaZfcao+2i4xxBYz9WMS9LgCNQT8cYeK6T8jFBHuPYubhIR0jdj5l0nAFx9OYzJCzHHDTi9r9Er9ElzvB++J5+/JR+zxv9nKUiFNiv6egY8vsRMp+LuoiDzrBlvMblMdrFh9IxCaZjvMtnAByTQz3iru38sfHLY4TwFsZ4c/bNGB6ueS0u4XzbESKfk1w//phjoDgngJyOGD/mpH409BWJnSNkDP71brV2roQioD9XOv1YTdRz7t6OcClmxOgX2/nkpWvE6Ufn7naX8Yi36utFpZ9zGVyO52AeQVxOx1BxhtrXbZzHoLb/jOP0OV3XvB8RzfVlBoNn6H+tt+LpOePdo9HO4W4cj0H89nTNFukI+6Y9sp1t0u/+kY7HJGaYo8efM206PjI96Tye8Vhv9nOsmcf8cMWv85gIz8m/3z8tH7HsNSydQ/kRwO8/9QoRowfU4xGu51Ofjhtcf+YxvtSXiPQlNkhHPHzmSrajz5WzXx5dr9xx/jxmsIgbt3j+6jH27teKIgZYL/s1TD46yH5GuzlCrHxM0fsxIsxjoq7nuNuvUXEe4Ux0y1hu1Z9zbdu8+GuMTMcw1864vF2j8/Bpzi8Nf4+P4+zG8/iScR7sx+g271O+hHVnmHTOYkdwPY5Z9JhBxjHMnHPzMQ3FWJPPtV80R/4555Uz6Cwvv+8cbberH7XjAc33MHHE/8fpzuuKlWi5wuDzo+m4IbFOi/j2mt+ObEI+RuP9peflYxyKCO8Ymspx7WcAs59jQPlJ51hZj2C5HpPIFXGlY2Y71wDxDB+LxHL0w7PrbMfIkc8ZoB0TRDqi2H5EU+2aK8s9TpZridHOcP5aNfVjVp7H5JePiTAdY828A850zYTjmBXmz3bd8XI/4v3nSMds3u12zmjjjEXPoSOdq5Hdtw0/e7TB8Uz+nCvpeoXEkcJ4GVDyz7kgK8e83I5Htf2co+k4Bql4puLG7Wc/PaeIHk9uTJTjiHLqS3XfenM6cuvJF+TT17+/SH2vv8b/+5m2/fvbt6yvXsZ8/a7VHKwg8dlJuxKgCmFgAQ6sJYhtYwvY5S4lb8Iq20GASlRHKQAJUwKRrQmd7bpkT7nwYEvC/EyVUGSKdXQUbY75Qte+Al7iVKRqnQT5kZymNIgcIDsJqjjZYjQzUUAHcVl13QJoi3tRQIFCcQ1ZcbigEs0SxjFwc1APrKkE15JCEugjbbOU2UdXEPCbwkBgS6UVqG0DeZBK1a5zBWh0GctdrUm1dqHzgGOeIalYB/e5FwDPuKaCbZ4gPKHIUrRSwHVHiAkZC/wM9q4wQFsn2NfDCUFoV4wKdicujKCPpnK363IBv8xQ5EPoBVgJlHcl/SUWBYoFPiA41aY1DcDlqUWKOhwbPkOtA7NRzQLN8KYKLAjUpJGpovOg6brexiEXA8oGhdeu8vn0tmJmiLadWjibfj/rdglLl5gOwg7Yd90RYkS53K7VEKwYnlPebBVDnSROdHk2FW4MdHuUJ3vuh2kumyQjCVPwTT8RUOAFDbJNd9ek/yYMjdXLi7w70NJUOWFrKIGFMB3gfVj9gJnhUWMjv4EGnNK4UOAB1qXVLmY/UFITAqJIARUNg5QFa0XEfZZtzPnhw05Zekid7aLOkt6KE3WRAilaKJxcWeqS4lAR9kCohjJfC3QzbqoqqopKB3okjB0YFrc8LgXuXvBxKroHwOt0ewhQ1cDMEGbd5I7QKHokUASEewG4rUhR6wV9FrgdU6dLQNBreNBWrLriGHqT7Xpc1i1MlHlGRXaCosXjRjC9EF40LGUjS4RC8IhHdgpB2xo/CoEolWY3Owm8Fc4S1pWA1SVwQoNABQg4NvIpEIdKF1aaYIJvHbMV75nG3Mrc4uKmmiJUAICljB7rQeDH7dDoAp+PaJaSg+hib2oddw0cKteFxgDoT4hQ3GpUZVBw0QW7AiJGGqhpmgoZJMPQgpeow1NB27ZZx/X3I24PYB35N6hsI5z4NrV8lL9vst73TXYKdnxQ7dedVllu73IYlFjLJ3AdtCe8PRUeELRXlhsgqg5M5xthi85gGjI+JcnvcI9F5qOsz5C6KawC5oW228NCTuIVFpg+lOByQetJCfK5q6p5r/7ECI86BgXwNToEJQpxnPWL1XA6jZQKUlkMO979PUmASdK4wklmV1iiAGzvCgpQ6nbIXq3kRa73bcARGtQmNCZoOmw9aGrtGirweZAHuakMiL4TXEGGZGCG2jROvVFg1WghGNIcgDURC0AEY01Kgf4Emq3cXg9nVWhi+CvqDKxtVZkhLQT4GZKBysybwlxKKkAlUQ8vKU81ValC+Ge34Tah9UgzIniqoge+46AHg6wCPrhoq4re7aF8x6XKatU5WNE99BR+oK3yqE50ToBxogk8hgTiHQsKWJTSX2ESTf1MmZvXo7drUQyeQfw7HLU9zH8hkCowIdp03TaVHdImxxA3M4jEjsxYuzJfIjbFr0Mduhd5Q+CAYVlseuwikqV8WieuwI+li30HYMxcrYZraMIWqJlq/qGLUnxYu0pUKCYUrwHMBnYuMv3lN6wRawrt6EBioDms+63T9g8DF+DWUMwqkisHis1M01WZ/IaANiSlhAwUvB9lMXm4dyhIxfmGaG9qqKOwNSQj4OFtp48qXqS2mfJqO2ogMfwKzdGnUwO4JK2+ouwhnDar6KRUMLMOXFrtmsCndMnhcih705OIsYmhAwgwJpmz6QSyqxKwQV8Eoc7DCNeyCgUn7mUeHIqrg66dzIfi+rmDu6pj+HuEoIABHbJ6U95KOLfoc6qXsh7e2YFKNMrclSxPqpRh/TnUaUB/XxlFDcbhBuP7AmW8CBBH3gIpaEhkDJpoKNFTQdJWfsfIcqihmej2ChmTSQa13YqwIVBrSDsg7DXYBZ6ohBBc4DVqh92NIigMzj3coJtGeLJTGpJRxKkKqhiSwoHBKwrpJUbOPWQCoYdree3YBPh3R6dUNS11zRM/SNpwVbcF5NOG6JfzDw+UTaHdqyCnTLvqjgWqvTdNFZi6BQtmH1HtkIzLkYFuOgigUAN73qhHQcBdfjj66ruyCzz0FSw/8hSQ6HkHcvfMkYxFmY4EHXNT5hpu3XoSwyKKAGluTVWidZApDP9SAF/I3miGVoIKrHph1pEcEnwCNkaXgOKBTNSGBexPAPkwJIewnJK6YoxrUOpwCUQGETnALt0rZd2WNuUcUdiRzDpV/0EzFqnKxFMCFlwJ2aq7GgIJKr4yUe4xBnHPjHaJU2CIQixZv2VTSjPrn4TKgIKOGvxlJWfWl8JtAfdW1EkmemotmINdtgN82VAwUbm4BwVVx1oQ6AC4msou6850Jf0gQhGyICOIblDSfBjaWxrg+SFsQyqvVU0aQlUNdWg4/NslzoNhmy7lOnZ5+CtkZDa2repTtMIarcwzgrcTO28CyAF7YN0LuZ441rIExL0Ekjnj/65IAGMDERLWsYRzVc9xLZl2ZlJWEcbzrAcdykYLIL4aZw31ZwSkkTnUpGpzxutCB4nxKpOclleO3E3IPppgOnCq/pAJzPRs1s2ZxSD2iwlKNK4Y0oroQrvkK/zURujVsQ5qdlE0rKQV7SoT6cSELxbyZGiZOhQx6qyeTbAJBrHrG0kItQLjoFPZcBMTRESGSygtJ+2lsw/qWvOykgHRyTPMog2k377bxNK1aIeQu86GzZqgFLXikdYm68MinNkSACIyp7YKJhhMM7CAYFJyXeh9ZmdG3Bg3zULBqzoSTH5kCe73WgIAKNzV3lcDFg70WuASsa1v2lQTazP4qEML7W4wgSEH+NMm11zPcVxwiqJYCiwU5iI44nYziIk85yzVcZC3xYuqcFzYLkscXdLKOmoip6uvJxLNkGCZUSGXoY6iGXlTYryw+MLqErVlDnatZ7dd3xTNQCR27eQ1itkH7YaxvZq2+5Ry3KWsMthzT3YVRSHss1oIpcepLE3SLxdPb+Vl0Rogn6Bt8CAQ160Dbk+zmUTRMv0zEyd53l1HbN1SomONWLPDaQyhxalCnEjZqdaYhgWhuVWYC8wG6EkzKsTlOnRYBJtOlgGTEp5ihRygkzFf9sNSOUH0QnIQ5CmaeHwRprsKqCTNxEDtDsYHugbWWCYhygy/6WDLhCOxtB8oB7QBzFGWicH81K56DZMM88iLETUG9QyHh6azCLyo2oe8mlaNvOX3ow7A1xVtxRHWZFXSWPsyobMkgmEIHhg7FwRUm6RVRDShy7OgVtutOLbDxG/gmzmZ2tiMw4rVFi56BZIQ5rH7UENJJXSWL4ochYoMAPYQkicMG5ObK4cI4QkNYUBaaoDcnRM3uIM4ue5qpSs8jSJp0mfP5JcWGvuEZb0ebjHZxaZHF1oAdZLctiucrgzKGgL5GQOwsSofSHoSBiiD0lVuI7cGl3YPDbxNefdtaj6b0I0k04C2XcaPUs08RjBUZ00q9KxFDrRxoNlMghOyLsm2feo+u7tcWierIZ9CvmxDshNSF+hkqGq4niJvNpTXDA4omQfUt0lzsBZCyQQ61q7qZJk63kGSUxp3V2xuQF/btHBZY6Y8uqRIFoMMqPiiCyUPBSasO4ZgBMxkBBAK72Fbp1+SjkNFXjyK3wgtsXoiShhyVrvaz+sRJSPJiAX1cZhQa1A4p5Z+G3pEcCqHEtFZ3nxpxhKoKaDpXzVNZYnodKLefTbMygolEVmYyAzpZ1TJmvKQjNdjV2md+eBg98sMgLfzQ3iY9ICiZRA7VrlVoDmaYqx89elGIWsoLNUtb2rSiUdN+Kq2sAbNOmSNiPSIhFHO1aKAX8W0ydg9dOJGJYYTJ6KvgK4jaTehnRekb1l+KkLPyAb1Frsq9E7Q6CWXsIW5H4LRq9k2vROGS75M+sWEAdoS5KyZY1kZqSpKdw/fkyKHj/h9atabSJygp6RxgzKeW5e3vkZZ8rQo4pSAre+k2BsgaGhS2zDXruWd7BsA1f1410EkGZDhNo3woJL0wx8PM78Mdy54ZmTwhwB4MOwcks8lk8yzxRdDyNvhhq0h9KDs8bXthNnPoKlUrf5gy5GgT3oFwoqB+QWdR84Zlyacnr0iFhzw/Sbau54kmBE8v/K4DH6Z/D6uAgJfTuevrlydHDZfFjwEywiwv2/nguH5Fb8X2DsUvhJX3n0xpBnW42XD4U/yHjSYwT/64W0Sz3bpgvJj+Am/oucwk1rKaFvQqGzDyBZIL+qHJZ/Hr9emRF+PH58/jp9nfX/38do0x8vxc+ti+uWs1+vjc++v36/q+hXPX/P+Olatv7zK4+rir5F94CgruJdzvr9+v/bjbJFH+5eu+f18cTy+/e0Kr/c8f+d1jWZ87nNex69re7sv78fP8x/b89uPu/an1xK5nT+5x4/P/vKuvV/p9Qt+dbfejx93wILCH0pxGfsT6bNhoNhnaCSU8Ob6Hvse+x77Hvse+x77Hvse+x77Hvse+x77Hvse+x77Hvse+x77Hvse+x77Hvse+x77Hvse+x77Hvse+x77Hvse+x77Hvt/59hT1H4IKavtV0R3T3AiY8CkxSl+tX0H1gAjF96UZgoTMl1jch5TCq/8xgqKfcMMGy/KDcT+zL3Jl8bfDUMpMYlFP4GMp/bEynODPpzSkCwCILLgegBB2pNgPBoC/yLwk15eNUmInAk4JfaKu14BYb6gHZTec3VikgD2WYnhJKm8hrUjltFFIgSuvpDQwilXA+iU9FuVhMuxJidjyhStEMomEvbSTvQxyRvc4SFQGa4+ZksVOgUkkQGEdb0ba9ayflr4iEhbnOEqCzcRFncK1rcOoQULCMg0eKj0tu41zJxdH88Z/INcIMVgkxTEQ/gwuP1BjGp8DZaGAKILDN11d3c8ytcr7s0stI58IEHIWL9DvYFzvQ1A21BVtcpIGuUF3Hbuue+oqijtXHcIChs0zU2Fa0Swe8ErEmA/mHJNUdCIxp5BN23wxyCItTDUzAZzOthqEEBhiOKYuzd5wDDmuaFNLg0dDva0rY5GMawsAf4Y0AzJijjPbDTV6mEbTl8Qf3TKgqASAFdoHLold35Mxxa0rQ9v2Fg0SK0SaSGWABjHSAexcnDvfGcFeU6fAbFfskh/7EEaWtZaXfaqvdXWIXpA5gTSjmmPPh1jcFOnzmZz3ULYqBkL+qZF7+rVSJFXcNwwCqoS3AObxX21BTz7dfuH9lkg3Cv8Y3rMLrsoQfTnDGDrB9hyDPNQEIAJiznLkCoDuQDnX75rFHZo4x0H3/C73EeB7Tq2Cv1k9QgM0PYihno9hFjhrjaAkoS3jOSSJGMATxpA4PQs2rZoDjWALsMZgYOsad8IzjLqCCoh7HA5pRzyozrPIKzCXX8lnOygmmR1MjgX9vUYKnUMEunosDHSBLCurcu6FzgA4r4SBnD43PBD4HdrJAr+GUn9IRlnaOwMCB5bMMxJk+B/iKkNwiJUVjrkZl9nw4g6sKWBu9M0HaJ/aY/OAwB3BxoBFFx4oduuUMaUu4QBliykmSG1QbFKAL/hRo/eRIoXz4K/FcSs1U2hTuJ4Jm1JPu+W7dU8Lav7wBrYgxyJU/Mm4l7RdgQVfpoOSevqJnwMHH9kc/PvvlUtb2GpZDQHANJP+PVVs/A9ONQ8XpO7tu5Sh2pe5ZcM7KqCZO6YKXMSe9FgOmMShMABDmy7dGTU8BnlK6M9JOris5cg7+LWvMP0PgjiTYtDhhUc2cKwas0UENj20fU/0r5qdXT8czeYyPDSGPygcwc1AjoKY3mW0YklNj2nwkqHUYf5t/a5guUnTwmaCw7KuE9v6PVrPw6Zgq7GxLHtaszjSpz0gIIf2PGohISZ1JvfPeIksu4RqiRbmSFfhl0S7udNVqpu62vcGRCQeEh0FKNrQYOEfCnbucMgpDtjJjQhQekvD5kCChDcak6NJEbmBsLlYHbZCmNX4VSwx3dNFCGM5QrBTRmVrvkrcguFIQqumFy0wRds2i9jwbU1n70Me3G9Qk4CEX0GGFQ1+KKsvx0M7mDqahQ6pcWvAZHpg6ezarKm5MHACQATQ0b+mmVarg4DZ3A9gcG1mtIjsdBlKOKXZJQJEB9QlwbqIkNRw+jqh6enyR6FHL+eOahdkDCC9Y9VGPbfWjxIoKqalGE7zfDdFduB5b+rasCgjDm9vuIScxipGCV2TayS4gzy4BBWINZYww92V/iQQrRO8CD3BLO1Ywp9WK9DO1tdHBfM8FjEpw6/UDz4NBjFoAHLVKh4O8bjK8yCzllSl4NYpeyviUAfPGxamEozOg6rA8vFW22D5AFmFhhCTIzsaX0GK92tsGrFTW89kIhG7PrJ7Q1lloGMB4RpiexYycMQnPhZowCiGoVmW0RVcFUxhN2UCaHVU9J/F0M44kClUHaUTnSmhKOM2Ix9DucdYgedn/suxYsmStLaiLVm3FKoMQwYQTSNa864wEGkpgn2pAE4rsaKxGhTgRoFsg5QHzGCxqke4ibRWxgkNmcaHoi2q320H14cQ8tRQ4he4t5VnSQde/MazZX00DsdrRVt25ABQN+lap0lzQtlknW3cjAC+fEZs5Ku9hFTkRMAk+iQd8kwisUHlHCUOPZgvsJ05uKxuNmUTxjIc1V90NYgD28uSaNdfYHnC9LphO8uaQ83Xlhce0Qja7BPKE/xw9YDlRxhZzbkGNChIZjlrJoNTxLhFk/BJFxYA8UxejCgZ1oG/YXGpQ9Ugn7gLtPn0WnIaMngJwyfCJLjmiCHpH9kIErTvRgtHiYHfTzXMwnfbmJzKnl31yGtrQcPjjCGvNnIgM4DQRMZFkSxMkuRFR90pU1QBdrDCxd/OMLRNeTMCEAZQQfUrhUKIZqBCEiaGmVy/TxfDXKoti0wHhXSQqKFyDwzczWDZGJNrKhLeOl5cVvbVfGAcAWhMmklygCMjWdy0oVxv4YMwrU1UxS1vpI00wiZYK8ROiPUsh4hae7wKKc+vATXyLAwEQ9cVQpLGHj3e1VqqzKbDOJ1TsJdWjGhYlxwJuHI7ZiiY2mPJ93qDsw0a47bw0sW3zmUmHRV7HpE7vBRV0M5la0exI2talasZdOQvbY5E0y9AddUR68ilmyhoAU1mdF8TufdpmpSHf4mfi38W4dg3N61ddygxUNvhmm97oiMPEh16ytjEt91MFpTguaHNfouSwXcOXEKZ/2HR9yGq22402QFu7C2ITDbvT6krWqv2qquAXSgmLJ+edaFmXuyAtMss1NpqDUudL10IeWvsZ81DB6wUK/XzR6O8Ex26xnEl5egj/AKjurqcytQUNuvVfUjKuaI8BQNglD3WGME9wyrGUKvDjm/KLKkCSMOQAyeDB/4jaLlgh0SBEqtftZ4NzQZxlV5ZIjbxA2qf2jivIdFOoTGTGihnzAuwEy+a4As+gSxXNvhwnJGnI5wZ0dbYn1N5hVrXNZnGjO6msIamBGdyZSBRumUrBzUQA4DoaFO1IazKbEICxPGg417uCZYZKTW4lzdCExp0TJcfVTDU9Z8ui/yaM+paJ3EaEi/MMin2i4MP3Qc7CaJhFzMdcw78WWFWa6q1uTUCbUbqEkRfcg4RhaPhdB0xFl9KUEC7naqdTsQeNiNj7vR+FrJwEXyZm7qmamVRFIEi60ZZurMNrQLy4OC7zxqNhjwapVcNZpnSOeD6DZkEg6N+QtD7YzAR0UTpivfcYzbvFqjGTzsnQtd88N6aItjhMMxozpyAfp94+rtVKr41RpC0YZZP5WoZ3Vp3+nyeg2f+LuuUUQhMjorMnnakOu7lFCr0ZlVDS80dcgwwEbfJPSONAmBNoj03MMxZSBPLNB1ZkU/rLJC5uej5kf83TS3N9HAAIlXJGuZFTTQuGtyRI5g+H7sfivGpSxjkFQcLibpx+hs8DhXA1r6dnedRsBYcAJFLtEnryGfwSzCpzbFViq6R6zz59Q9U+tu5ArgoCNWRgs3M1vrpmL5iYoN55/kzQoifu1MsCgLoYkUS9ap/ELrit+tSb5qtbWT7sJ6VyEn5mWGVlePs7uUGazrWDT4owbc+IYCS8KKmQiTYBi1meTUhGkqOoeoFoRRKEITRtIZDjCBSlYubE0CJuOKGob0bOK7bahF0RMJhRXw04ykddCSyuTL6oZOHcMzci4drnZx3YVeIZMNj0kial2zUEHQkdDaeGom8ky125Ow1/6BMk+AigKYQqKVBx8XXqZJ6JrOjGQpSCcxuHdVSBpf0BjUkc2xv6DzU1Sfc9hjrY4aD0mABu1+3RMDc2QQULPZSJOxQlkNh0wny6uhvMUgUCN4MsvIshlaP1/CQFrwZsO/lWeANJO6tbLBd2QVeViwXO8hTZEaYok44SKBuEIJpvnVbZjG1qKdgXrC1ie/w7exqBxqISDuwW/TsxtveOyFGVBxJ4auP7uCQjvhwYj7RM7K9Ipm3ev+E9FWE5301KKZNoMecm9mI5EiWf0XgQ51f/BCU6h0nSqmMoVE1+pyIzzHipihffUaHomho+yau0KSaVdzbMtqz5CYQ92FVlkHSc6uhwRRkfUQM25jt14joWrkvCGlNxjKUGNCR4GlAgI7aNux3iKEQIWP7pFVtSDprDzDjrIIOj/k3RKOusp06kwH6b1gE0sipanEsZpaHbBKumf1yejamWAzq2HFMoNuVbFjVrhQFaddTaO17g6JCDrH6u+qF62gC4/zyftY5PPVOzmaBqUe/ajq6gf1lOHy1mEZU7n1B9Koa9zcyXkhBjuIZrETrmiWKATISDPWWIE4Z0cMiBUq4gJrXC6TcR9Td4cfFrEZpShSGdiBk/hKrAsRNVl9nzhoTViMdmi0JGOdWJXp6b4hIKB8hboVeRDcsmJWVq8qnte3rk83Aj6kHKYRdddQfgUCatoh98QAp35mdehEJrXo1Icm6mrlglLT1hkDV69FkA+FMZfcZPENb5qie2RaiJhcdNsbGqGVqiGdHC9CtMwSk2XgWkmiLIegBNndfASbE5U+YkfbRMkjZMcilcyghKgag1kiciCjqcswqpvq/61hvLM8Q39rBZCYja+osRLX11DFYSAuaMVl01M8YGt8sTDArErEbXq9+sCgSMPgsSvStfpjQWdkhSEsrNJm5LD+R0/oiJ2tX9hDEYzZeqDty4Qe6SW66VRzC//FFBPjWg+zSitFvUDUZNGwIwpBjAvFvKL7c8XhXj0nPLkRBqUX4gJNamczlTCygnuJlX9FP5EgW73YhuwI+ULrDpSGyCeMEK9k9uV5Qng0kxKoaMmyBFgji/IbWLUzLDgO7aEsV9Tz3dBbo2OgVbI26/xr3amCSuYxpKGYTdfzp87NRk48uZggb0o+dz0zdLLeFIEl7CNdi1m8LUsWY011Lp5mmL43hZFYVZPC67EaYOlLcESLZkLEH1PveK1Xktyl7NzBwYfJdQ0zj6rGrhBO+bQ1xJCE2RRJRI2IuO2Q5kGMigdKBVRORTUF126GvqZfPRpemz+V8R21V0+27SEfh2cpgQDraNxEWUqs4QotKhRIGZjrmjszfdBlCFI3LoK8cPTesOJGXdnsvAW+rIRTU9EQ/3EKhJPGIBYffBWrhtXVmwP4TEwpqGbir95w62a5ojznxohF2q9nLe67AlAZXbaeNKklu72xbM0I9XacxcmwJJVuN1YeCMN1I0JEmBDtwqQTsVXCGXofgrgMoJnM2FoTJ4qJJVRSBokdOndGwXmg9rT6hdqgmQmW5ARp49Vyk37ENLuZ4EQvKDOduSLLal8rtWmeYF0VBbeKpO8wg8iajRobOrqqVFYFUTsXZAiYwwMV7/fBuI6W3U5BZy15uP8zHO43NJYowVYEL0kK7OSlyMeRtuPC10k60+pU6JR2pthpsXY9PIhoIU040XXZXJjsbTcibNNyqKsUmgPZPVO5q7kza8/1c8noYr+KJo3K2nkzY7lifbrdCMHW9UK13w2BGoqqRPIrXEBOlCwOQRY6vMR11HBQKOQXUxCxeMsymMpUV9eGkl5i1lJ4Cp27dZVqW2eUFhvSipaKiqPlRiKpVGNsqhIkuHpnmFwPFR9YwTJqVMjJWdFBs5sVmeKsaI6rnEtnqyjf7DwzPCYrFFZ0abMgzHjMY7Ie3s5iNPGbO6EaoxUy7IQxKKuh+031hye1cGWMP3Zcvr2pb70ze7MOUVq8+7WrK0Wufk0YVH2JpjhLVvZX1bKGfh7nbNmlQSbELwgdoaW7kxMkh0khIm2ACUiM85iSEGjoHnVLCSuAZ/1TkcdcvZXRYq5hG/21NYbTkXqPQtxGBp4sO8t+RV6bUS3hwB5DPvFyIZtJko6MMXMb6+K1DGd8XK9IpAwzl2skI0lKoKK38DTAoSJKOrMxFiJeajBowmndKtTHi3d2bfilayHYXY6YtllDFh16fdZ1VSPhTSjDIoh0JqErGlgMiGiwUaNsjHd0o7V8q2qEW/VbvY7BtVrSXoMjIlfIHWdnV8afvauyp4T66vtOjGtY3JWv9WFArxeBr5yMj1HtRB5/qEqIODVJAfI460aiEr5uMBJijMK74t+mPkpChgq4AjeBjBxTkhbWxUGeJGNT9ZjVAmLu5EgaMnuKWRIgr1WXYwn1TRbVpOTW2Ipo2hoMmb4TsoTo76PvO6k7/eD6TKIWsfD1zWvQsVRE0mUNJ2ToUc9yOYzmzlqaFHIoNBSV8eYyWskvfKVZ8BNxIwFINpx8c4npjPiNsZblxFraMc2uUY5G7BQNEQEEbULWbFiuoOS3QqzdwYL5GPlwK9GrQyAtnFVfXPedZVpNJuM5M3gBixfkANUtM+NC7lEhTzAXavgj17e+3LoOqY3VGqrgWbBS1BRlPfL2jOjkyVdQFxF6Nwq3F1HOoHCwVngOFQVNsDXBbIFjWBEx5Rk+3igRNWWnq+qFrKFZfRDdranLdWBTRTQACrgmdwAKTFXMYTu9JFOfWx2JOi8Z7cI4vNaByMNNlzlrYEdisdLcrBh7Nwm2HiBCXz+9OlQ9ZG9ZDq8gqpjBcFopTMIzKZmNXiyp9hD4ndN6GBrSOiEUcto7mpRF8VIwQ5P4fj0WQ5HQfgjGo9dHSWa4XJuicyLuRq8yHLyBm1SjP3TSSN3g0WwNxQ7JKs5gs9rbjONYsGAHPlA45DbSvHoz8HSOwQy/N9eUiAsSW1BnNZFKeZingYUO0ofbUZ9fl8uvW+EZYm8Kbq/PceLsbU12TnS8mZknBXPSBVGicdWcKWMV6hYkyTKa3mu12ayXk+90tc039Rl6yU1x+Qz0Z4XaO0qcqyuzjF9jQ0lGKazPgAeQ2XJwRsMOT3OMrsnCUvdg6QUQInVkkEcEZesMzCcNOwwWVCOkOjkHq/AuaIYpYM1s04yieQhCV3LrwyQGT5PKfKqJM+yz0ojClwUkhGa5VNL6bobys8X8KsqCitcy4s6Qq139C1DUZAm4hjwzdwm1OhSCST7V7tzMPBZeFkwjg4ZBULqlUPgUIlYtn5M0xGJg3Qseqs204UCck3Ve094dHw7DVjIuvWCQQcjsjcJoZarz2JS5Q7qt4cpeVcQCj1cRacvq1Cmi1ZW420+tOmTvENQrGtbot54RfQOEgzoWbucc7MjfKfOGItzmOxL6WUmluu364qqeGZpwysvhED/z+dd86c8hcHcc47wBHbzO4gk7mmil+WGkujb07HYunZUa0nbz+kBGcu78DhXqqmb0FbucXhRb4x1Je/pNwTxP0dSruy/GW9PQF8vnJanNtudQ2uPG8ZMren5la6c4X66ZiyneGM6356sxelN5bz8us3kr9QeqaJ5V2int/i6UClfoeV0OjdDQJhx8aEOJbP2m9guIZYw/ipTVqcxaU50wYEMvx41beP3+/retuduX91+fO/5u8P4vfP74/gh512sX1X9ynj9cV7y+Pne/r6Ttsf3T782t3F/+PPh3v/n40B/f9y9+4//4HXnemV9//hd//8V5jl/8d05+/Yjnj/TDzzP8DzbsdZveLjvGw9f3/6KjmTP7s9fnL7g+97j97+DgNc77/JMcJEooaoZnVrBiW4AhBP4XoyegOV1ngRVIbcaKW8B21iQjlplLuWQ3Qygz5Dxfjsw4mOK/eu+2ywsszpLcIBd6nmbc79f3a/fP3c9lX+b7dPG1lwpoQK6355H7m0acJD5XfzFOxM1gvOHeKJJY2svrcxv3534dt/R+/X6e9/ef5z0/d74+t+fnf3U8WuuP3/P+/vft2ZLv13te3/v3vv/9/Pz59/P4eT3n+8/3/eq63u/T++94357nP6/7773/V9/7fr2/up53HznKFutXofVNiA+IGsRIwP5SMtDdyYLtGIJMbUeaKt1oAVdieoSFSUqQFKbcjUz2blkH0WQ85ohzcKABks5ycK0WOP+K9cghFxfWe0Jg20TrCvv3Zskc/W1WXJSprZXjS4DdGstyagwsRLakKnzezSgXUEDrnLkpHZz0o8hKrBey7R3ULLmrRqKcMFqIUzHHvdYrZHT2wrKiCZ0EopwiwAPu2GtkFBKA5R1PgP20ZSCVSJWqtylGQvQc0HycTChJsKyvODZt1ag1z2IFpIvF7iY1OktqvT+AIRfK0JT4GL2wObNAuobgHAl5AnQs8RjlRNauhT9lXHxnEIMunBwMGjgCoT+CqARPoh9vKW5C8uBXbkLXgamt8JuEOHCozQoTP2gTfgYqm5oCSHmyzJWE0xr8BTzCd8CRou49VvF2ru0axXggNoc9wsl+iCgTABHnKeBMcMpjFh+IcVFl5R5/yaG2PGLUrT12+Suh6jB4c0Asx/jdYiBGwDXHFxKqQWDhMmJgHcfbc4TH2zlKE+D5ye1X4ZeNvd5IU16b6L0EUzw6ylI3Iuzz3efr6K3r/TbQ2vqwrPd54/k83fLl+PPbzsPHy/Ov11n//M/vF3F+ya8+fn3ueF88TX88/fFbo6++/IZje572+trrN8bnzq+JHnxvz6+JJ+KP7zu+/jzteSvfv/49fPjJOnD+SrE6/fET2Kpchp2n93C8CK/5yzIWSNVl6uoo9pMvs2OV9H8YV+M1qQ4yqNdrLMl+ynU6SqsMOJe36RoAgIhdrxnLfup1Pn0Qf9p1PszKQFper8lm//TbwReMxU+/zkeKcY0c1/msLvyM63yUVfDUuF6Dgf7ZrvMBV28/23U+cj4UPs/X4LWg/1yvQeL97Nf5NFv6mbfDPOPJz7z97Unt/czrfFmsLRnpeh8imUZ68MWGmQod7XI3DC1D07y0DY1D69zNg/4/vqL5biFc44BZ5ruRMtVcUED1pd0Zm8G436evuqH95Lu1mKWyBj/36SmVkUu624waimC8u9ng7QANzXfLkWI5rG2uQ+DEKfvc7UcmC4RIvpsQNxKSnvluReYZuEl5e+m4wH0pudyn13GiMP/fh5iSKX/fp6eWTH3yblRzWIDu7nYlZwucJd9NS2IS/GW5m1ZQlsCC+wFJIlaIB+5nSme0n3I3LTM6RY3y8uT56JEEu0/P01c0SrwPkZ2ifHKfXrgN9gj36QGP6IFyn568XtMZ6z5EeVbw132IR17KwD0CkL2iUnyfvgv5BL99HYKIRQrvbtqiCyIVv/v0QroBPd+nJzkG7exuWirX5ITL3bQAjUndl7tpC3wFCgR301KX0NThblqtY0Ge3U1LqZ0UeLmbFlAatYB6Ny28sSj83p732lRIyrgOAZq13n+PgAEH+al301Yc9bCiuptW91ooNXfTgnXCcaq+jq7aX/3UlwGWEZYh9m5awWDkWe+mBQqqUeXdtEKeu36v16FuoPdT76YNdguJxPv08tC0vb0PkUCm4nSffogIAip0zwVE3/hYvUwPlOgpTt2nB0NOMv1u2qofCuzD+/SyKMmE3qc3g0p0eZ9eVBDzzz0BJa1w8Di7D2F55or8PsRUhUHedXqBcfDe7qbVEZMK6N20TVAky4b79ACBYNjcTUu9X8bT3bSwD0Autrtpm9Et9YT79MyfTKAvMyhTKHPoyyTamFjJyt+n71p5/rS7aZuGKlaIr0OU+IaVkvsQMzT8oPv0wI82ixLXoU0jWqLw+xApdQja9+nB0LOmupuWstFai1CqvQ8RBsDnvU8Ps1Xzw/v0kApZDd1N24WSgiK/Q58wHfvpd9OGLx4xxh1kuPCyHHAfkj4BQ+06VCR6UKO4D5EAp7Zxnx4sGKW0+hLDkB9fp7+bFgtqaG39blrgDN1Vyn16gFBw4O6mhTYJbrq/hEjaHhMk3acnTCJOupsWhokZprtpQaRQDOl301J5Es5zNy02QtIFtpcgjEIvxmf36alYQue4m1Y+ANWVu2nBbQFq7XfT4iQt7vxu2m4xlijvDvOS7paYnt2HrFZBar0PERAC77hOD3pvyMnr9yEgMfhc3acH3Fe0LrwPsbgWpXAfAmIOmPM+PfiMKt32PqRf5s+4m5YaGsDgcTctRqDU6MfdtMLRAKfcTatRoejL+/QAejpl3vv0BMFEwS9hsHEwAN379EAnov50HYL8slnHvw8B1hTVfx3apRABgLgPgTgA8nafHisyKN930wKQHhpB36efsg9+xt20ACQ3Ubr1PiQ2GBbcfYisA5H8HcpjIay9+B3NA2zKohSvQ5TXiy679yEMpSVT3IdYGmi/eB2C0kQh/W7areLHCSHyPj28WT2579NDu9B1/D49lXCoI3fTkvkQZ3M3LfgJQV930+pxC9TqbtqNQudq2u1uWirsgtJf1jksdFjpvC510CTQ1vQ6JKMRuu59ejE1oNDv0+saiU/dfXooc9Rw76YFgQulYbubFiTyLinkXk8lmJ7i2e9DLLuEaF2HxP9RW+33IQCR+89+Ny3WgFQi97tptYCGfng3LVBWMDT73bRavwWa+T5EpR5U0X16KD/NZN99iFo69M/79I1FIiCq+/Qktcgw3U27S+2GGXSfHgDWwAbxPj2leuDxd9OSNYTost9NC5AW9PV+N+0uI5Fk3X16VrIsZV/WsixmBdDepwfTB5Xsblr4UTtIirtpNXsGGnY3rY7roGXupp1Kjwz4eveh+SMs6W5a7KxBssy7aSHDTgUT7tNDnzIpep9ebC2CDvfpYQdW4cHXIV0S4UHep6+aXwNHuA6JogJRdp9e93fwIPfpm6IPP/NuWpDNU92X+/QYtAIHvJt2in0qeGjeh0CJDChb9yFJotBMrkNY9oEavZsWqDiiKPNuWoDiwE3m3bSCUNQUuE9PqmLKt7oOka0gXfGSryBhYcbiXjZ/c0fH62/u6Js7+mzu6CyeUg9oJHhL+pU6VT6Tub/evuWABWZT0ICbxhxa9J4vpI3WksQYGpgcr0wb9CqRdaIxA06qyRjuzEhQViVdjlgUgbklagYzDmGePyWWGCuSE7m51tXi/zcH5S7zZM37RE+bVopwmJtaCME8SQV09kQziCWbrIa1rKWTrkiYB38oibWBYg8fWp6+dQ5RsgnuTd+1Q01FuQ6o3QSzw+UFsGkSRAJoKQNJbQJWT1YeuA1AFmAw678adZ5oHwBKOUfFphy1l1AUa1HW8WPzlzWXEzHAbX3ZekFUCI6/ezvwmeT34cIZFQFv9X34+tjbNn70vT2/5v31+f63vrLutVVZwgOg5BWcfSGlAw9aba2UQW/VEwPwK4hPvJ96VdGcM84GxihO8P7FuyVdSIWhAETeaJREWLXWbSomrXnKyWtXlCrJioe8uXN1RTw+bBVQE5hIg1c1p9e2ZtKuwCBsEOetDxPci5t1GbhbqhybUSKiHeAoGR9Wr5T+uyKTpPxS10C2yVJYkSiDV5WyVsqmjFJleetj06TQdfWdGH5Gl7y5FmrDtQcybWRhWY+tR4KHqQdNzV8A5TLJG8And2pH77q48PHVU3SuBgPO/LfzqsBkrnZz8sbrytGgyzng3NNoKTVWBX39HCkoaNywMplmsgZDb0XWC5E4MV+02rjMaen6FCGppmkBGz3BGls8JMLj9nh8xImV2OfBaQERCRdbd/fjdCneTP8r8Sxh59rO58quY5FTaFmY6dZj2BxxqB1bnuN2XGk8nX59+UVXjZ7hs7GN/d7arOulTR6PjKPB+frYRjfjW+l86wfZAf74vvPzf/j7+bXv73/73Pk913Hb/z4ej8KfXOdx/ngOXv7+97Zvn49nzKHE2xVd+0/ux/k9x+eu952//3h93ofr/e+fP37f+/n/MGSt96RzUr13TgRSDNH91yXT/8npNOe3kuqx8xIbHzsvAfL55tcg+QpiXgLlKzR4CZbPvdeA+dx7DZrP63gNnM+91+D53HsNoM+91yD63HsNpM+f+BpMn3uvAfW59xpUn3uvgfW59xpcn3fvNcA+916D7HPvNdA+916D7XPvEXDH3jPoPvYegfex9wi+r91H/H3tPkLwc/cZhV+7j0D82n3E4tfuIxy/dh8R+bn7DMqv3Udcfu0+QvNr9xGdX7uPAP3cfcbo1+4jTL92H5H6tfsI1q/dR7x+7j5D9mv3EbVfu4/A/dp9xO7X7iN8P3efEfy1+wjir91HDfjafZSBr91HJfjcfRaDr91HPfjafZSEr91HVfjafRSGz91nbfjafZSHr91HhfjafRSJr91HnfjcfZaKr91HtfjafRSMr91HzfjafZSNz91n5fjafRSPr91H/fjafZSQr91HFfncfRaSr91HLfnafZSTr91HRfnafRSVz91nXfnafZSWr91HdfnafRSYr91HjfncfZaZr91HpfnafRSbr91HvfnafZScz91n1fnafRSer91H7fnafZSfr91HBfrcfRahr91HHfrafZSir91HNfrafRSkz91nTfrafZSlr91HZfrafRSnr91HffrcfZaor91HlfrafRSqr91HrfrafZSrz91nxfrafRStr91H3frafZSur91H9frcfRawr91HDfvafZSxr91HJfvafRSzz91nPfvafZS0r91HVfvafRS2r91HbfvcfZa3r91HhfvafRS5r91HnfvafZS6z91ntfvafRS8r91HzfvafZS9r91H5fvcfRa/r91H/fvavT9295K3Kvi1+yiEn7vPWvi1+yiHX7v3x+5e8lYUv3YfdfFz91kav3Yf1fFr9/7Y3UveauTX7qNMfu4+K+XX7qNYfu3eUfjdS95K5tfuo2p+7j4L59fuo3Z+7d4fu3vJWwX92n0U0c/dZx392n2U0q/d+2N3L3krqF+7j5r6ufssq1+7j8r6tXt/7GVB86yvX7uPEvu5+6yyX7uPQvu1e3/s7iVv5fZr91FxP3efRfdr91F3v3bvldfdS96q79fuowB/7j5r8Nfuowx/7d4fu3vJWzH+2n3U48/dZ0n+2n1U5a/d+2N3L3mrzV+7j/L8ufus0F+7jyL9tXt/7O4lb6X6a/dRrT93nwX7a/dRs79274/dveStcn/tPor35+6zfn/tPkr41+692r57yVsh/9p91PLP3Wc5/9p9VPSv3ftjdy95q+tfu4/S/rn7rO5fu48C/7V7f+zuJW9l/mv3Uek/d5/F/mv3Ue+/du+P3b3krep/7T4K/+fus/Z/7T7K/9fu/bGXNMkTBHDtPnAA5+4TCnDtPtAA1+6dYbl7yTsm4Nx9wgKO3TdkwLn7BAecu/fH7l7yDhE4d58ogWP3DShw7j6xAufu/bG7l7wjBs7dJ2jg2H3DDZy7T+jAuXt/7O4l7wCCc/eJITh232AE5+4TSXDu3h+7e8k7nuDcfYMUxO47quDYfQMWHLvXx75J1HPv/+sk6ktKvg5poR8sc2f4Dnde/rv5br6b7+a7+W6+m+/mu/luvpvv5rv5br6b7+a7+W6+m+/mu/luvpvv5rv5br6b7+a7+W6+m+/mu/nftXlRhx+Kkpf6KxTI+YG/t32HgvSgaOKk9fM/989f+Lf8hX/rX/i3/YV/+1/4d/yFf7e/8O/+F/6df+HfnD6yyR/ZlI9s6kc27SOb/pHN+Mhm+8hm/8hmfmJT0kc2+SOb8pFN/cimfWTTP7IZH9lsH9nsH9nMT2xq+sgmf2RTPrKpH9m0j2z6RzbjI5vtI5v9I5v5iU1LH9nkj2zKRzb1I5v2kU3/yGZ8ZLN9ZLN/ZDM/senpI5v8kU35yKZ+ZNM+sukf2YyPbLaPbPaPbOYnNiN9ZJM/sikf2dSPbNpHNv0jm/GRzfaRzf6RzfzEZksf2eSPbMpHNvUjm/aRTf/IZnxks31ks39kMz+x2dNHNvkjm/KRTf3Ipn1k0z+yGR/ZbB/Z7B/ZzE9sZvrIJn9kUz6yqR/ZtI9s+kc24yOb7SOb/SOb+YnN/8I/f+Hfb6XrW+n6Vrq+la5vpetb6fpf2nwrXd9K17fS9a10/XHzrXR9K13/pytdAUhM4e7Tfd1/LUwVH4h/Y3OeZR7bv/3zv/1v//q/AzctLlw="},"max_retries":{"kind":"null"}}},{"rowIdx":154,"cells":{"question_id":{"kind":"number","value":20577,"string":"20,577"},"question_title":{"kind":"string","value":"Super Maximum Cost Queries"},"question_content":{"kind":"string","value":"Victoria has a tree, $T$, consisting of $N$ nodes numbered from $1$ to $N$. Each edge from node $U_i$ to $V_i$ in tree $T$ has an integer weight, $W_i$.\n\nLet's define the cost, $C$, of a path from some node $X$ to some other node $Y$ as the maximum weight ($W$) for any edge in the unique path from node $X$ to node $Y$.\n\nVictoria wants your help processing $Q$ queries on tree $T$, where each query contains $2$ integers, $L$ and $R$, such that $L \\le R$. For each query, she wants to print the number of different paths in $T$ that have a cost, $C$, in the inclusive range $[L, R]$.\n\nIt should be noted that path from some node $X$ to some other node $Y$ is considered same as path from node $Y$ to $X$ i.e $\\{X,Y\\}$ is same as $\\{Y,X\\}$. "},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Hard"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static List<Integer> solve(List<List<Integer>> tree, List<List<Integer>> queries)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.awt.*;\\nimport java.awt.event.*;\\nimport java.awt.geom.*;\\nimport java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*; \\nimport java.util.*;\\nimport java.util.regex.*;\\n/*\\n\\t br = new BufferedReader(new FileReader(\\\"input.txt\\\"));\\n\\t pw = new PrintWriter(new BufferedWriter(new FileWriter(\\\"output.txt\\\")));\\n\\t br = new BufferedReader(new InputStreamReader(System.in));\\n\\t pw = new PrintWriter(new BufferedWriter(new OutputStreamWriter(System.out)));\\n */\\n\\n\\npublic class Solution {\\n\\tprivate static BufferedReader br;\\n\\tprivate static StringTokenizer st;\\n\\tprivate static PrintWriter pw;\\n\\n\\tpublic static void main(String[] args) throws Exception {\\n\\t\\tbr = new BufferedReader(new InputStreamReader(System.in));\\n\\t\\tpw = new PrintWriter(new BufferedWriter(new OutputStreamWriter(System.out)));\\n\\t\\t//int qq = 1;\\n\\t\\tint qq = Integer.MAX_VALUE;\\n\\t\\t//int qq = readInt();\\n\\t\\tfor(int casenum = 1; casenum <= qq; casenum++)\\t{\\n\\t\\t\\tint n = readInt();\\n\\t\\t\\tint qqq = readInt();\\n\\t\\t\\tSet<Integer>[] sets = new Set[n];\\n\\t\\t\\tfor(int i = 0; i < n; i++) {\\n\\t\\t\\t\\tsets[i] = new HashSet<Integer>();\\n\\t\\t\\t\\tsets[i].add(i);\\n\\t\\t\\t}\\n\\t\\t\\tEdge[] edges = new Edge[n-1];\\n\\t\\t\\tfor(int i = 0; i < edges.length; i++) {\\n\\t\\t\\t\\tedges[i] = new Edge(readInt()-1, readInt()-1, readInt());\\n\\t\\t\\t}\\n\\t\\t\\tArrays.sort(edges);\\n\\t\\t\\tTreeMap<Integer, Long> dp = new TreeMap<Integer, Long>();\\n\\t\\t\\tfor(Edge out: edges) {\\n\\t\\t\\t\\tint aSize = sets[out.a].size();\\n\\t\\t\\t\\tint bSize = sets[out.b].size();\\n\\t\\t\\t\\tif(!dp.containsKey(out.d)) {\\n\\t\\t\\t\\t\\tdp.put(out.d, 0L);\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tdp.put(out.d, dp.get(out.d) + aSize * 1L * bSize);\\n\\t\\t\\t\\tSet<Integer> big, small;\\n\\t\\t\\t\\tif(aSize < bSize) {\\n\\t\\t\\t\\t\\tsmall = sets[out.a];\\n\\t\\t\\t\\t\\tbig = sets[out.b];\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\telse {\\n\\t\\t\\t\\t\\tbig = sets[out.a];\\n\\t\\t\\t\\t\\tsmall = sets[out.b];\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tfor(int now: small) {\\n\\t\\t\\t\\t\\tbig.add(now);\\n\\t\\t\\t\\t\\tsets[now] = big;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\tint[] cap = new int[dp.size()+1];\\n\\t\\t\\tlong[] w = new long[dp.size()+1];\\n\\t\\t\\tint index = 1;\\n\\t\\t\\tfor(int out: dp.keySet()) {\\n\\t\\t\\t\\tcap[index] = out;\\n\\t\\t\\t\\tw[index] = w[index-1] + dp.get(out);\\n\\t\\t\\t\\tindex++;\\n\\t\\t\\t}\\n\\t\\t\\twhile(qqq-- > 0) {\\n\\t\\t\\t\\tint l = readInt();\\n\\t\\t\\t\\tint r = readInt();\\n\\t\\t\\t\\tpw.println(lessThanOrEqual(cap, w, r) - lessThanOrEqual(cap, w, l-1));\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\texitImmediately();\\n\\t}\\n\\n\\tpublic static long lessThanOrEqual(int[] cap, long[] w, int o) {\\n\\t\\tint l = 0;\\n\\t\\tint r = cap.length-1;\\n\\t\\twhile(l < r) {\\n\\t\\t\\tint m = (l+r+1)/2;\\n\\t\\t\\tif(cap[m] <= o) {\\n\\t\\t\\t\\tl = m;\\n\\t\\t\\t}\\n\\t\\t\\telse {\\n\\t\\t\\t\\tr = m-1;\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\treturn w[l];\\n\\t}\\n\\t\\n\\tstatic class Edge implements Comparable<Edge> {\\n\\t\\tpublic int a, b, d;\\n\\n\\t\\tpublic Edge(int a, int b, int d) {\\n\\t\\t\\tsuper();\\n\\t\\t\\tthis.a = a;\\n\\t\\t\\tthis.b = b;\\n\\t\\t\\tthis.d = d;\\n\\t\\t}\\n\\t\\tpublic int compareTo(Edge e) {\\n\\t\\t\\treturn d - e.d;\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate static void exitImmediately() {\\n\\t\\tpw.close();\\n\\t\\tSystem.exit(0);\\n\\t}\\n\\n\\tprivate static long readLong() throws IOException {\\n\\t\\treturn Long.parseLong(nextToken());\\n\\t}\\n\\n\\tprivate static double readDouble() throws IOException {\\n\\t\\treturn Double.parseDouble(nextToken());\\n\\t}\\n\\n\\tprivate static int readInt() throws IOException {\\n\\t\\treturn Integer.parseInt(nextToken());\\n\\t}\\n\\n\\tprivate static String nextLine() throws IOException {\\n\\t\\tif(!br.ready()) {\\n\\t\\t\\texitImmediately();\\n\\t\\t}\\n\\t\\tst = null;\\n\\t\\treturn br.readLine();\\n\\t}\\n\\n\\tprivate static String nextToken() throws IOException {\\n\\t\\twhile(st == null || !st.hasMoreTokens()) {\\n\\t\\t\\tif(!br.ready()) {\\n\\t\\t\\t\\texitImmediately();\\n\\t\\t\\t}\\n\\t\\t\\tst = new StringTokenizer(br.readLine().trim());\\n\\t\\t}\\n\\t\\treturn st.nextToken();\\n\\t}\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts following parameters:\\n * 1. 2D_INTEGER_ARRAY tree\\n * 2. 2D_INTEGER_ARRAY queries\\n */\\n\\n public static List<Integer> solve(List<List<Integer>> tree, List<List<Integer>> queries) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int q = Integer.parseInt(firstMultipleInput[1]);\\n\\n List<List<Integer>> tree = new ArrayList<>();\\n\\n for (int i = 0; i < n - 1; i++) {\\n String[] treeRowTempItems = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> treeRowItems = new ArrayList<>();\\n\\n for (int j = 0; j < 3; j++) {\\n int treeItem = Integer.parseInt(treeRowTempItems[j]);\\n treeRowItems.add(treeItem);\\n }\\n\\n tree.add(treeRowItems);\\n }\\n\\n List<List<Integer>> queries = new ArrayList<>();\\n\\n for (int i = 0; i < q; i++) {\\n String[] queriesRowTempItems = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> queriesRowItems = new ArrayList<>();\\n\\n for (int j = 0; j < 2; j++) {\\n int queriesItem = Integer.parseInt(queriesRowTempItems[j]);\\n queriesRowItems.add(queriesItem);\\n }\\n\\n queries.add(queriesRowItems);\\n }\\n\\n List<Integer> result = Result.solve(tree, queries);\\n\\n for (int i = 0; i < result.size(); i++) {\\n bufferedWriter.write(String.valueOf(result.get(i)));\\n\\n if (i != result.size() - 1) {\\n bufferedWriter.write(\\\"\\\\n\\\");\\n }\\n }\\n\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n // Complete the solve function below.\\n static long[] solve(final int[][] tree, final int[][] queries) {\\n final int edgeCount = tree.length;\\n final int n = tree.length + 1;\\n final Integer[] indexes = indexes(edgeCount);\\n Arrays.sort(indexes, Comparator.comparingInt(idx -> tree[idx][2]));\\n \\n final UF uf = new UF(n);\\n final long[] count = new long[edgeCount];\\n final int[] weights = new int[edgeCount];\\n for (int i = 0; i < indexes.length; i++) {\\n final int idx = indexes[i];\\n final int u = tree[idx][0] - 1;\\n final int v = tree[idx][1] - 1;\\n final int w = tree[idx][2];\\n count[i] = 1L * uf.size(u) * uf.size(v);\\n weights[i] = w;\\n uf.connect(u, v);\\n }\\n \\n final long[] pf = new long[tree.length];\\n pf[0] = count[0];\\n for (int i = 1; i < count.length; i++) {\\n pf[i] = pf[i - 1] + count[i];\\n }\\n \\n final long[] qAns = new long[queries.length];\\n for (int qIdx = 0; qIdx < queries.length; qIdx++) {\\n final int l = queries[qIdx][0];\\n final int r = queries[qIdx][1];\\n \\n final int lIdx = lbs(weights, l);\\n final int rIdx = rbs(weights, r);\\n \\n final long c;\\n if (lIdx == tree.length || rIdx == -1) {\\n c = 0;\\n } else {\\n c = pf[Math.min(rIdx, pf.length - 1)] - (lIdx > 0 ? pf[lIdx - 1] : 0L);\\n } \\n qAns[qIdx] = c;\\n //1 2 3 4 10 10 121\\n }\\n return qAns;\\n }\\n \\n private static int lbs(final int[] a, final int value) {\\n int lo = 0;\\n int hi = a.length - 1;\\n while (lo <= hi) {\\n final int mid = lo + (hi - lo) / 2;\\n if (a[mid] < value) {\\n lo = mid + 1;\\n } else {\\n hi = mid - 1;\\n }\\n }\\n return lo;\\n }\\n \\n private static int rbs(final int[] a, final int value) {\\n int lo = 0;\\n int hi = a.length - 1;\\n while (lo <= hi) {\\n final int mid = lo + (hi - lo) / 2;\\n if (a[mid] <= value) {\\n lo = mid + 1;\\n } else {\\n hi = mid - 1;\\n }\\n }\\n return hi;\\n }\\n \\n private static long[] prefixSum(final long[] a) {\\n final long[] pf = new long[a.length];\\n pf[0] = a[0];\\n for (int i = 1; i < a.length; i++) {\\n pf[i] = pf[i - 1] + a[i];\\n }\\n return pf;\\n }\\n \\n private static final class UF {\\n private final int[] p;\\n private final int[] s;\\n \\n UF(final int n) {\\n p = new int[n];\\n for (int i = 1; i < n; i++) {\\n p[i] = i;\\n }\\n s = new int[n];\\n Arrays.fill(s, 1);\\n }\\n \\n int size(final int i) {\\n final int iRoot = root(i);\\n return s[iRoot];\\n }\\n \\n void connect(final int a, final int b) {\\n final int aRoot = root(a);\\n final int bRoot = root(b);\\n if (p[aRoot] == p[bRoot]) {\\n return;\\n }\\n final int minRoot = s[aRoot] <= s[bRoot] ? aRoot : bRoot;\\n final int maxRoot = aRoot == minRoot ? bRoot : aRoot;\\n p[minRoot] = maxRoot;\\n s[maxRoot] += s[minRoot];\\n }\\n \\n private int root(int i) {\\n while (p[i] != i) {\\n p[i] = p[p[i]];\\n i = p[i];\\n }\\n return i;\\n }\\n }\\n \\n private static Integer[] indexes(final int n) {\\n final Integer[] indexes = new Integer[n];\\n for (int i = 0; i < n; i++) {\\n indexes[i] = i;\\n }\\n return indexes;\\n }\\n \\n \\n\\n private static final Scanner scanner = new Scanner(System.in);\\n\\n public static void main(String[] args) throws IOException {\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] nq = scanner.nextLine().split(\\\" \\\");\\n\\n int n = Integer.parseInt(nq[0]);\\n\\n int q = Integer.parseInt(nq[1]);\\n\\n int[][] tree = new int[n-1][3];\\n\\n for (int treeRowItr = 0; treeRowItr < n-1; treeRowItr++) {\\n String[] treeRowItems = scanner.nextLine().split(\\\" \\\");\\n scanner.skip(\\\"(\\\\r\\\\n|[\\\\n\\\\r\\\\u2028\\\\u2029\\\\u0085])?\\\");\\n\\n for (int treeColumnItr = 0; treeColumnItr < 3; treeColumnItr++) {\\n int treeItem = Integer.parseInt(treeRowItems[treeColumnItr]);\\n tree[treeRowItr][treeColumnItr] = treeItem;\\n }\\n }\\n\\n int[][] queries = new int[q][2];\\n\\n for (int queriesRowItr = 0; queriesRowItr < q; queriesRowItr++) {\\n String[] queriesRowItems = scanner.nextLine().split(\\\" \\\");\\n scanner.skip(\\\"(\\\\r\\\\n|[\\\\n\\\\r\\\\u2028\\\\u2029\\\\u0085])?\\\");\\n\\n for (int queriesColumnItr = 0; queriesColumnItr < 2; queriesColumnItr++) {\\n int queriesItem = Integer.parseInt(queriesRowItems[queriesColumnItr]);\\n queries[queriesRowItr][queriesColumnItr] = queriesItem;\\n }\\n }\\n\\n long[] result = solve(tree, queries);\\n\\n for (int resultItr = 0; resultItr < result.length; resultItr++) {\\n bufferedWriter.write(String.valueOf(result[resultItr]));\\n\\n if (resultItr != result.length - 1) {\\n bufferedWriter.write(\\\"\\\\n\\\");\\n }\\n }\\n\\n bufferedWriter.newLine();\\n\\n bufferedWriter.close();\\n\\n scanner.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts following parameters:\\n * 1. 2D_INTEGER_ARRAY tree\\n * 2. 2D_INTEGER_ARRAY queries\\n */\\n\\n public static List<Integer> solve(List<List<Integer>> tree, List<List<Integer>> queries) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int q = Integer.parseInt(firstMultipleInput[1]);\\n\\n List<List<Integer>> tree = new ArrayList<>();\\n\\n IntStream.range(0, n - 1).forEach(i -> {\\n try {\\n tree.add(\\n Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList())\\n );\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n List<List<Integer>> queries = new ArrayList<>();\\n\\n IntStream.range(0, q).forEach(i -> {\\n try {\\n queries.add(\\n Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList())\\n );\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n List<Integer> result = Result.solve(tree, queries);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\"\\\\n\\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.ByteArrayInputStream;\\nimport java.io.IOException;\\nimport java.io.InputStream;\\nimport java.io.PrintWriter;\\nimport java.util.Arrays;\\nimport java.util.Comparator;\\nimport java.util.InputMismatchException;\\n\\npublic class C {\\n\\tInputStream is;\\n\\tPrintWriter out;\\n\\tString INPUT = \\\"\\\";\\n\\t\\n\\tvoid solve()\\n\\t{\\n\\t\\tint n = ni(), Q = ni();\\n\\t\\tint[] from = new int[n - 1];\\n\\t\\tint[] to = new int[n - 1];\\n\\t\\tint[] w = new int[n-1];\\n\\t\\tfor (int i = 0; i < n - 1; i++) {\\n\\t\\t\\tfrom[i] = ni() - 1;\\n\\t\\t\\tto[i] = ni() - 1;\\n\\t\\t\\tw[i] = ni();\\n\\t\\t}\\n\\t\\tint[][] es = new int[n-1][];\\n\\t\\tfor(int i = 0;i < n-1;i++){\\n\\t\\t\\tes[i] = new int[]{from[i], to[i], w[i]};\\n\\t\\t}\\n\\t\\tArrays.sort(es, new Comparator<int[]>() {\\n\\t\\t\\tpublic int compare(int[] a, int[] b) {\\n\\t\\t\\t\\treturn a[2] - b[2];\\n\\t\\t\\t}\\n\\t\\t});\\n\\t\\t\\n\\t\\tint[] ws = new int[n-1];\\n\\t\\tlong[] ps = new long[n-1];\\n\\t\\tDJSet ds = new DJSet(n);\\n\\t\\tfor(int i = 0;i < n-1;i++){\\n\\t\\t\\tws[i] = es[i][2];\\n\\t\\t\\tps[i] = (long)ds.upper[ds.root(es[i][0])] * ds.upper[ds.root(es[i][1])];\\n\\t\\t\\tds.union(es[i][0], es[i][1]);\\n\\t\\t}\\n\\t\\tlong[] cum = new long[n];\\n\\t\\tfor(int i = 0;i < n-1;i++){\\n\\t\\t\\tcum[i+1] = cum[i] + ps[i];\\n\\t\\t}\\n\\t\\t\\n\\t\\tfor(int z = 0;z < Q;z++){\\n\\t\\t\\tint L = ni(), R = ni();\\n\\t\\t\\tint lr = lowerBound(ws, R+1);\\n\\t\\t\\tint ll = lowerBound(ws, L);\\n\\t\\t\\tout.println(cum[lr]-cum[ll]);\\n\\t\\t}\\n\\t}\\n\\t\\n\\tpublic static int lowerBound(int[] a, int v)\\n\\t{\\n\\t\\tint low = -1, high = a.length;\\n\\t\\twhile(high-low > 1){\\n\\t\\t\\tint h = high+low>>>1;\\n\\t\\t\\tif(a[h] >= v){\\n\\t\\t\\t\\thigh = h;\\n\\t\\t\\t}else{\\n\\t\\t\\t\\tlow = h;\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\treturn high;\\n\\t}\\n\\n\\t\\n\\tpublic static class DJSet {\\n\\t\\tpublic int[] upper;\\n\\n\\t\\tpublic DJSet(int n) {\\n\\t\\t\\tupper = new int[n];\\n\\t\\t\\tArrays.fill(upper, -1);\\n\\t\\t}\\n\\n\\t\\tpublic int root(int x) {\\n\\t\\t\\treturn upper[x] < 0 ? x : (upper[x] = root(upper[x]));\\n\\t\\t}\\n\\n\\t\\tpublic boolean equiv(int x, int y) {\\n\\t\\t\\treturn root(x) == root(y);\\n\\t\\t}\\n\\n\\t\\tpublic boolean union(int x, int y) {\\n\\t\\t\\tx = root(x);\\n\\t\\t\\ty = root(y);\\n\\t\\t\\tif (x != y) {\\n\\t\\t\\t\\tif (upper[y] < upper[x]) {\\n\\t\\t\\t\\t\\tint d = x;\\n\\t\\t\\t\\t\\tx = y;\\n\\t\\t\\t\\t\\ty = d;\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tupper[x] += upper[y];\\n\\t\\t\\t\\tupper[y] = x;\\n\\t\\t\\t}\\n\\t\\t\\treturn x == y;\\n\\t\\t}\\n\\n\\t\\tpublic int count() {\\n\\t\\t\\tint ct = 0;\\n\\t\\t\\tfor (int u : upper)\\n\\t\\t\\t\\tif (u < 0)\\n\\t\\t\\t\\t\\tct++;\\n\\t\\t\\treturn ct;\\n\\t\\t}\\n\\t}\\n\\t\\n\\tvoid run() throws Exception\\n\\t{\\n\\t\\tis = INPUT.isEmpty() ? System.in : new ByteArrayInputStream(INPUT.getBytes());\\n\\t\\tout = new PrintWriter(System.out);\\n\\t\\t\\n\\t\\tlong s = System.currentTimeMillis();\\n\\t\\tsolve();\\n\\t\\tout.flush();\\n\\t\\tif(!INPUT.isEmpty())tr(System.currentTimeMillis()-s+\\\"ms\\\");\\n\\t}\\n\\t\\n\\tpublic static void main(String[] args) throws Exception { new C().run(); }\\n\\t\\n\\tprivate byte[] inbuf = new byte[1024];\\n\\tprivate int lenbuf = 0, ptrbuf = 0;\\n\\t\\n\\tprivate int readByte()\\n\\t{\\n\\t\\tif(lenbuf == -1)throw new InputMismatchException();\\n\\t\\tif(ptrbuf >= lenbuf){\\n\\t\\t\\tptrbuf = 0;\\n\\t\\t\\ttry { lenbuf = is.read(inbuf); } catch (IOException e) { throw new InputMismatchException(); }\\n\\t\\t\\tif(lenbuf <= 0)return -1;\\n\\t\\t}\\n\\t\\treturn inbuf[ptrbuf++];\\n\\t}\\n\\t\\n\\tprivate boolean isSpaceChar(int c) { return !(c >= 33 && c <= 126); }\\n\\tprivate int skip() { int b; while((b = readByte()) != -1 && isSpaceChar(b)); return b; }\\n\\t\\n\\tprivate double nd() { return Double.parseDouble(ns()); }\\n\\tprivate char nc() { return (char)skip(); }\\n\\t\\n\\tprivate String ns()\\n\\t{\\n\\t\\tint b = skip();\\n\\t\\tStringBuilder sb = new StringBuilder();\\n\\t\\twhile(!(isSpaceChar(b))){ // when nextLine, (isSpaceChar(b) && b != ' ')\\n\\t\\t\\tsb.appendCodePoint(b);\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn sb.toString();\\n\\t}\\n\\t\\n\\tprivate char[] ns(int n)\\n\\t{\\n\\t\\tchar[] buf = new char[n];\\n\\t\\tint b = skip(), p = 0;\\n\\t\\twhile(p < n && !(isSpaceChar(b))){\\n\\t\\t\\tbuf[p++] = (char)b;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn n == p ? buf : Arrays.copyOf(buf, p);\\n\\t}\\n\\t\\n\\tprivate char[][] nm(int n, int m)\\n\\t{\\n\\t\\tchar[][] map = new char[n][];\\n\\t\\tfor(int i = 0;i < n;i++)map[i] = ns(m);\\n\\t\\treturn map;\\n\\t}\\n\\t\\n\\tprivate int[] na(int n)\\n\\t{\\n\\t\\tint[] a = new int[n];\\n\\t\\tfor(int i = 0;i < n;i++)a[i] = ni();\\n\\t\\treturn a;\\n\\t}\\n\\t\\n\\tprivate int ni()\\n\\t{\\n\\t\\tint num = 0, b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate long nl()\\n\\t{\\n\\t\\tlong num = 0;\\n\\t\\tint b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate static void tr(Object... o) { System.out.println(Arrays.deepToString(o)); }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts following parameters:\\n * 1. 2D_INTEGER_ARRAY tree\\n * 2. 2D_INTEGER_ARRAY queries\\n */\\n\\n public static List<Integer> solve(List<List<Integer>> tree, List<List<Integer>> queries) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int q = Integer.parseInt(firstMultipleInput[1]);\\n\\n List<List<Integer>> tree = new ArrayList<>();\\n\\n IntStream.range(0, n - 1).forEach(i -> {\\n try {\\n tree.add(\\n Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList())\\n );\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n List<List<Integer>> queries = new ArrayList<>();\\n\\n IntStream.range(0, q).forEach(i -> {\\n try {\\n queries.add(\\n Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList())\\n );\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n List<Integer> result = Result.solve(tree, queries);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\"\\\\n\\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"solve(tree, queries)\"\n ],\n \"solution\": \"from collections import defaultdict\\n\\nN, Q = map(int, input().split())\\n\\nweights = []\\n\\nfor _ in range(N-1):\\n U, V, W = map(int, input().split())\\n weights.append([W, U, V])\\n \\nweights.sort()\\n\\nclass Node():\\n def __init__(self, size, parent=None): \\n self.size = size\\n self.parent = parent\\n\\nclusters = {n:Node(1) for n in range(1,N+1)}\\ncounts = defaultdict(int)\\n\\nfor W, U, V in weights:\\n u = clusters[U]\\n v = clusters[V]\\n \\n while u.parent:\\n u = u.parent\\n \\n while v.parent:\\n v = v.parent\\n \\n counts[W] += u.size * v.size\\n \\n n = Node(u.size + v.size)\\n u.parent = n\\n v.parent = n\\n \\n clusters[U] = n\\n clusters[V] = n\\n\\nK = sorted(counts)\\nV = [counts[k] for k in K]\\n\\nZ = [0]\\nt = 0\\n\\nfor v in V:\\n t += v\\n Z.append(t)\\n\\nimport bisect\\n\\nfor _ in range(Q):\\n L, R = map(int, input().split())\\n \\n x = bisect.bisect_left(K, L)\\n y = bisect.bisect_right(K, R) \\n print(Z[y] - Z[x])\",\n \"template\": \"#\\n# Complete the 'solve' function below.\\n#\\n# The function is expected to return an INTEGER_ARRAY.\\n# The function accepts following parameters:\\n# 1. 2D_INTEGER_ARRAY tree\\n# 2. 2D_INTEGER_ARRAY queries\\n#\\n\\ndef solve(tree, queries):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n first_multiple_input = input().rstrip().split()\\n\\n n = int(first_multiple_input[0])\\n\\n q = int(first_multiple_input[1])\\n\\n tree = []\\n\\n for _ in range(n - 1):\\n tree.append(list(map(int, input().rstrip().split())))\\n\\n queries = []\\n\\n for _ in range(q):\\n queries.append(list(map(int, input().rstrip().split())))\\n\\n result = solve(tree, queries)\\n\\n fptr.write('\\\\n'.join(map(str, result)))\\n fptr.write('\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":155,"cells":{"question_id":{"kind":"number","value":9056,"string":"9,056"},"question_title":{"kind":"string","value":"AND xor OR"},"question_content":{"kind":"string","value":"Given an array $A[]$ of $N$ distinct elements. Let $M_1$ and $M_2$ be the smallest and the next smallest element in the interval $[ L , R ]$ where $1 \\le L < R \\le N$. \n\n$S_i = (((M_1 \\wedge M_2) \\oplus (M_1 \\vee M_2)) \\wedge (M_1 \\oplus M_2))$. \n\nwhere $\\wedge, \\vee, \\oplus$, are the bitwise operators $AND$, $OR$ and $XOR$ respectively. \nYour task is to find the maximum possible value of $S_i$."},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Hard"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static int andXorOr(List<Integer> a)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner in = new Scanner(System.in);\\n int n = in.nextInt();\\n int[] arr = new int[n];\\n for(int i = 0; i < n; i++){\\n arr[i] = in.nextInt();\\n }\\n \\n Stack<Integer> stack = new Stack<Integer>();\\n int result = 0;\\n for(int i = 0; i < n; i++){\\n while(true){\\n if(stack.isEmpty()){\\n stack.add(arr[i]);\\n break;\\n } else if(stack.peek() < arr[i]){\\n result = Math.max(result, stack.peek() ^ arr[i]);\\n stack.add(arr[i]);\\n break;\\n } else{\\n stack.pop();\\n }\\n }\\n }\\n stack = new Stack<Integer>();\\n \\n for(int i = n - 1; i >= 0; i--){\\n while(true){\\n if(stack.isEmpty()){\\n stack.add(arr[i]);\\n break;\\n } else if(stack.peek() < arr[i]){\\n result = Math.max(result, stack.peek() ^ arr[i]);\\n stack.add(arr[i]);\\n break;\\n } else{\\n stack.pop();\\n }\\n }\\n }\\n \\n System.out.println(result); \\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'andXorOr' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY a as parameter.\\n */\\n\\n public static int andXorOr(List<Integer> a) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int aCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String[] aTemp = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> a = new ArrayList<>();\\n\\n for (int i = 0; i < aCount; i++) {\\n int aItem = Integer.parseInt(aTemp[i]);\\n a.add(aItem);\\n }\\n\\n int result = Result.andXorOr(a);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner sc = new Scanner(System.in);\\n int N = sc.nextInt();\\n int[] arr = new int[N];\\n for(int i = 0; i < N; i++){\\n arr[i] = sc.nextInt();\\n }\\n int max = 0;\\n Stack<Integer> stack = new Stack();\\n for(int i = 0; i < N; i++){\\n while(!stack.isEmpty()){\\n int temp = stack.peek() ^ arr[i];\\n max = Math.max(temp,max);\\n if(arr[i]<stack.peek()) {\\n stack.pop();\\n }else{\\n break;\\n }\\n }\\n stack.push(arr[i]);\\n }\\n System.out.println(max);\\n \\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'andXorOr' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY a as parameter.\\n */\\n\\n public static int andXorOr(List<Integer> a) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int aCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> a = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.andXorOr(a);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.File;\\nimport java.io.FileNotFoundException;\\nimport java.util.Arrays;\\nimport java.util.Scanner;\\nimport java.util.Stack;\\n\\npublic class Solution {\\n\\t\\n\\tprivate static final int f(int a, int b){\\n\\t\\treturn (((a&b)^(a|b))&(a^b));\\n\\t}\\n public static void main(String[] args) {\\n \\tScanner in = new Scanner(System.in);\\n int N=in.nextInt();\\n Stack<Integer> stack = new Stack<Integer>(); \\n int max = Integer.MIN_VALUE;\\n int [] a = new int[N];\\n for(int i=0;i<N;i++){\\n \\tint n = in.nextInt();\\n \\twhile(!stack.isEmpty() && n < stack.peek()){\\n\\t\\t\\t\\tint t = f(n,stack.pop());\\n\\t\\t\\t\\tif(max<t)\\n\\t\\t\\t\\t\\tmax = t;\\n\\t\\t\\t}\\n \\tif(!stack.isEmpty()){\\n \\t\\tint t = f(n,stack.peek());\\n\\t\\t\\t\\tif(max<t)\\n\\t\\t\\t\\t\\tmax = t;\\n \\t}\\n \\tstack.push(n);\\n \\ta[i]=n;\\n }\\n System.out.println(max);\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'andXorOr' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY a as parameter.\\n */\\n\\n public static int andXorOr(List<Integer> a) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int aCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> a = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.andXorOr(a);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"andXorOr(a)\"\n ],\n \"solution\": \"n_elements = int(input())\\ndata = list(map(int, input().split()))\\nassert len(data) == n_elements\\n\\nres = 0\\nminima = [data[0]]\\nfor i in range(1, n_elements):\\n d = data[i]\\n while len(minima):\\n x = minima.pop()\\n res = max(d ^ x, res)\\n if x < d:\\n minima.append(x)\\n break\\n minima.append(d)\\nprint(res)\\n\",\n \"template\": \"#\\n# Complete the 'andXorOr' function below.\\n#\\n# The function is expected to return an INTEGER.\\n# The function accepts INTEGER_ARRAY a as parameter.\\n#\\n\\ndef andXorOr(a):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n a_count = int(input().strip())\\n\\n a = list(map(int, input().rstrip().split()))\\n\\n result = andXorOr(a)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":156,"cells":{"question_id":{"kind":"number","value":8938,"string":"8,938"},"question_title":{"kind":"string","value":"No Prefix Set"},"question_content":{"kind":"string","value":"There is a given list of strings where each string contains only lowercase letters from $a-j$, inclusive. The set of strings is said to be a **GOOD SET** if no string is a prefix of another string. In this case, print **GOOD SET**. Otherwise, print **BAD SET** on the first line followed by the string being checked. \n\n**Note** If two strings are identical, they are prefixes of each other.\n\n**Example** \n$words = \\text{['abcd', 'bcd', 'abcde', 'bcde']}$ \n\nHere 'abcd' is a prefix of 'abcde' and 'bcd' is a prefix of 'bcde'. Since 'abcde' is tested first, print \n\n\tBAD SET \n abcde\n\n$words = \\text{['ab', 'bc', 'cd']}$. \n\nNo string is a prefix of another so print \n\n\tGOOD SET \n\n**Function Description** \nComplete the *noPrefix* function in the editor below. \n\n*noPrefix* has the following parameter(s): \n- *string words[n]:* an array of strings \n\n**Prints** \n-\t*string(s):* either **GOOD SET** or **BAD SET** on one line followed by the word on the next line. No return value is expected. \n\n\n**Input Format** \nFirst line contains $n$, the size of $words[]$. \nThen next $n$ lines each contain a string, $words[i]$. \n\n**Constraints** \n$1 \\le n \\le 10^5$ \n$1 \\le $ the length of words[i] $\\le 60$ \nAll letters in $words[i]$ are in the range 'a' through 'j', inclusive. \n\n**Sample Input00** \n\n\tSTDIN\t\tFunction\n -----\t\t--------\n 7\t\t\t words[] size n = 7\n aab\t\t\t words = ['aab', 'defgab', 'abcde', 'aabcde', 'bbbbbbbbbb', 'jabjjjad']\n defgab\t\n abcde\n aabcde\n cedaaa\n bbbbbbbbbb\n jabjjjad\n \n**Sample Output00** \n\n BAD SET\n aabcde\n\n**Explanation** \n'aab' is prefix of 'aabcde' so it is a **BAD SET** and fails at string 'aabcde'. \n\n**Sample Input01**\n\n 4\n aab\n aac\n aacghgh\n aabghgh\n\n**Sample Output01** \n\n BAD SET\n aacghgh\n \n**Explanation** \n'aab' is a prefix of 'aabghgh', and aac' is prefix of 'aacghgh'. The set is a **BAD SET**. 'aacghgh' is tested before 'aabghgh', so and it fails at 'aacghgh'."},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Hard"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static void noPrefix(List<String> words)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\nclass Trie\\n {\\n boolean EOS;\\n Trie[] C;\\n \\n public Trie()\\n {\\n this.EOS = false;\\n this.C = new Trie[10];\\n }\\n \\n public boolean insert(String s)\\n {\\n // System.out.println(\\\"probing String: \\\"+s);\\n \\n if(this.C[s.charAt(0)-'a'] == null)\\n {\\n this.C[s.charAt(0)-'a'] = new Trie();\\n if(s.length() == 1)\\n this.C[s.charAt(0)-'a'].EOS = true;\\n else\\n return this.C[s.charAt(0)-'a'].insert(s.substring(1));\\n }\\n \\n else \\n {\\n if(s.length() == 1)\\n return false;\\n if(this.C[s.charAt(0)-'a'].EOS == true)\\n return false;\\n \\n return this.C[s.charAt(0)-'a'].insert(s.substring(1));\\n }\\n \\n return true;\\n }\\n}\\n\\n\\npublic class Solution {\\n\\n public static void func(String[] s)\\n {\\n \\n }\\n public static void main(String[] args) {\\n /* Enter your code here. Read input from STDIN. Print output to STDOUT. Your class should be named Solution. */\\n Scanner sc = new Scanner(System.in);\\n int N = Integer.parseInt(sc.nextLine());\\n String[] s = new String[N];\\n \\n for(int i=0;i<N;i++)\\n s[i] = new String(sc.nextLine());\\n \\n Trie root = new Trie();\\n for(String probe : s)\\n {\\n //System.out.println(\\\"sending String: \\\"+probe);\\n if(!root.insert(probe))\\n {\\n System.out.println(\\\"BAD SET\\\");\\n System.out.println(probe);\\n return;\\n }\\n }\\n \\n System.out.println(\\\"GOOD SET\\\");\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'noPrefix' function below.\\n *\\n * The function accepts STRING_ARRAY words as parameter.\\n */\\n\\n public static void noPrefix(List<String> words) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<String> words = new ArrayList<>();\\n\\n for (int i = 0; i < n; i++) {\\n String wordsItem = bufferedReader.readLine();\\n words.add(wordsItem);\\n }\\n\\n Result.noPrefix(words);\\n\\n bufferedReader.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\n\\npublic class Trie {\\n\\n private static final int ALPHABET_SIZE = 26;\\n\\n class Node {\\n \\n Node[] edges;\\n char key;\\n int wordCount = 0;\\n int prefixCount = 0;\\n \\n Node(char key) {\\n this.key = key;\\n this.edges = new Node[ALPHABET_SIZE];\\n }\\n \\n boolean has(char key) {\\n return get(key) != null;\\n }\\n \\n Node get(char key) {\\n return edges[getKey(key)];\\n }\\n \\n void put(char key, Node node) {\\n this.edges[getKey(key)] = node;\\n }\\n \\n char getKey(char ch) {\\n return (char) (ch - 'a');\\n }\\n \\n }\\n \\n private Node root = new Node('\\\\0');\\n \\n public boolean insert(String word) {\\n return insert(word, root);\\n }\\n \\n private boolean insert(String word, Node root) {\\n root.prefixCount++;\\n if (word.length() >= 0 && root.wordCount > 0) {\\n return false;\\n }\\n if (word.length() == 0) {\\n if (root.prefixCount > 1) {\\n return false;\\n }\\n root.wordCount++; \\n return true;\\n } \\n char ch = word.charAt(0);\\n Node next = root.get(ch); \\n if (next == null) {\\n next = new Node(ch); \\n root.put(ch, next);\\n } \\n return insert(word.substring(1), next); \\n } \\n \\n public static void main(String[] args) throws IOException { \\n \\n BufferedReader in = new BufferedReader(new InputStreamReader(System.in));\\n PrintWriter out = new PrintWriter(new BufferedOutputStream(System.out)); \\n \\n Trie trie = new Trie(); \\n int n = Integer.parseInt(in.readLine());\\n \\n boolean bad = false;\\n while (n-- > 0) {\\n String word = in.readLine(); \\n bad = !trie.insert(word); \\n if (bad) {\\n out.println(\\\"BAD SET\\\\n\\\"+word); \\n break;\\n }\\n }\\n \\n\\n if (!bad) {\\n out.println(\\\"GOOD SET\\\");\\n } \\n out.close();\\n \\n } \\n \\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'noPrefix' function below.\\n *\\n * The function accepts STRING_ARRAY words as parameter.\\n */\\n\\n public static void noPrefix(List<String> words) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<String> words = IntStream.range(0, n).mapToObj(i -> {\\n try {\\n return bufferedReader.readLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n })\\n .collect(toList());\\n\\n Result.noPrefix(words);\\n\\n bufferedReader.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\n\\npublic class Solution {\\n \\n private static class Trie {\\n \\n private class TrieNode {\\n public boolean terminates = false;\\n public TrieNode[] nextNodes = new TrieNode['j' - 'a' + 1];\\n }\\n \\n private TrieNode[] nodes = new TrieNode['j' - 'a' + 1];\\n \\n public boolean isGoodStringForSet(String s) {\\n if (s.isEmpty()) return false; // Can't have an empty String.\\n TrieNode currentNode = nodes[s.charAt(0) - 'a'];\\n for(int i = 0; i < s.length() && currentNode != null; i++)\\n {\\n if (currentNode.terminates) // It is bad if a prefix is already found.\\n return false;\\n \\n currentNode = (i < s.length() - 1)\\n ? currentNode.nextNodes[s.charAt(i+1) - 'a']\\n : currentNode;\\n }\\n return currentNode == null; // False if newly found sub-prefix\\n }\\n \\n public void addString(String s) {\\n if (!s.isEmpty()) {\\n TrieNode currentNode = nodes[s.charAt(0) - 'a'];\\n if (currentNode == null) {\\n nodes[s.charAt(0) - 'a'] = currentNode = new TrieNode();\\n }\\n for(int i = 1; i < s.length(); i++) {\\n if (currentNode.nextNodes[s.charAt(i) - 'a'] == null) {\\n currentNode.nextNodes[s.charAt(i) - 'a'] = new TrieNode();\\n }\\n currentNode = currentNode.nextNodes[s.charAt(i) - 'a'];\\n }\\n currentNode.terminates = true;\\n }\\n }\\n \\n }\\n\\n public static void main(String[] args) {\\n /* Enter your code here. Read input from STDIN. Print output to STDOUT. Your class should be named Solution. */\\n Scanner in = new Scanner(System.in);\\n int n = Integer.parseInt(in.nextLine());\\n assert 1 <= n && n <= 100000;\\n Trie trie = new Trie();\\n boolean stillGood = true;\\n for(int i = 0; stillGood && i < n; i++) {\\n String s = in.nextLine();\\n assert !s.isEmpty() && s.length() <= 60;\\n stillGood = trie.isGoodStringForSet(s);\\n if(stillGood) {\\n trie.addString(s);\\n } else {\\n System.out.println(\\\"BAD SET\\\");\\n System.out.println(s);\\n }\\n }\\n if (stillGood)\\n System.out.println(\\\"GOOD SET\\\");\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'noPrefix' function below.\\n *\\n * The function accepts STRING_ARRAY words as parameter.\\n */\\n\\n public static void noPrefix(List<String> words) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<String> words = IntStream.range(0, n).mapToObj(i -> {\\n try {\\n return bufferedReader.readLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n })\\n .collect(toList());\\n\\n Result.noPrefix(words);\\n\\n bufferedReader.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"noPrefix(words)\"\n ],\n \"solution\": \"class solution:\\n def checkSET(self, strs):\\n trie = dict()\\n for cstr in strs:\\n curr = trie\\n for ch in cstr:\\n if ch not in curr:\\n curr[ch] = dict()\\n curr = curr[ch]\\n if '_end' in curr:\\n return cstr\\n if len(curr) > 0:\\n return cstr\\n curr['_end'] = ''\\n return ''\\n \\nn = int(input())\\nstrs = []\\nfor i in range(n):\\n strs.append(input())\\nsol = solution()\\nret = sol.checkSET(strs)\\nif ret == '':\\n print('GOOD SET')\\nelse:\\n print('BAD SET')\\n print(ret)\",\n \"template\": \"#\\n# Complete the 'noPrefix' function below.\\n#\\n# The function accepts STRING_ARRAY words as parameter.\\n#\\n\\ndef noPrefix(words):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n n = int(input().strip())\\n\\n words = []\\n\\n for _ in range(n):\\n words_item = input()\\n words.append(words_item)\\n\\n noPrefix(words)\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":157,"cells":{"question_id":{"kind":"number","value":12860,"string":"12,860"},"question_title":{"kind":"string","value":"Kindergarten Adventures"},"question_content":{"kind":"string","value":"Meera teaches a class of $n$ students, and every day in her classroom is an adventure. Today is drawing day!\n\nThe students are sitting around a round table, and they are numbered from $1$ to $n$ in the *clockwise* direction. This means that the students are numbered $1, 2, 3, \\ldots, n - 1, n$, and students $1$ and $n$ are sitting next to each other.\n\nAfter letting the students draw for a certain period of time, Meera starts collecting their work to ensure she has time to review all the drawings before the end of the day. However, some of her students aren't finished drawing! Each student $i$ needs $t_i$ extra minutes to complete their drawing. \n\nMeera collects the drawings sequentially in the clockwise direction, starting with student ID $x$, and it takes her exactly $1$ minute to review each drawing. This means that student $x$ gets $0$ extra minutes to complete their drawing, student $x + 1$ gets $1$ extra minute, student $x + 2$ gets $2$ extra minutes, and so on. *Note* that Meera will still spend $1$ minute for each student even if the drawing isn't ready. \n\nGiven the values of $t_1, t_2, \\ldots, t_n$, help Meera choose the best possible $x$ to start collecting drawings from, such that the number of students able to complete their drawings is maximal. Then print $x$ on a new line. If there are multiple such IDs, select the smallest one."},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Medium"},"java":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"static int solve(int[] t)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner sc = new Scanner(System.in);\\n int n = sc.nextInt();\\n int [] tab = new int[n];\\n \\n for(int i = 0; i < n; i++) {\\n int k = sc.nextInt();\\n \\n if(k == 0) {\\n continue;\\n }\\n \\n tab[(i + 1) % n]++;\\n int p = i - k + 1;\\n if(p < 0) {\\n p += n;\\n }\\n \\n tab[p]--;\\n }\\n \\n int max = -100000;\\n int res = 0;\\n int sum = 0;\\n \\n for(int i = 0; i < n; i++) {\\n sum += tab[i];\\n \\n if(sum > max) {\\n max = sum;\\n res = i;\\n }\\n }\\n \\n System.out.println(res + 1);\\n }\\n}\",\n \"template\": \" /*\\n * Complete the solve function below.\\n */\\n static int solve(int[] t) {\\n /*\\n * Return the ID\\n */\\n\\n }\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n\",\n \"template_tail\": \" private static final Scanner scanner = new Scanner(System.in);\\n\\n public static void main(String[] args) throws IOException {\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int tCount = Integer.parseInt(scanner.nextLine().trim());\\n\\n int[] t = new int[tCount];\\n\\n String[] tItems = scanner.nextLine().split(\\\" \\\");\\n\\n for (int tItr = 0; tItr < tCount; tItr++) {\\n int tItem = Integer.parseInt(tItems[tItr].trim());\\n t[tItr] = tItem;\\n }\\n\\n int id = solve(t);\\n\\n bufferedWriter.write(String.valueOf(id));\\n bufferedWriter.newLine();\\n\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner input = new Scanner(System.in);\\n int n = input.nextInt();\\n int[] t = new int[n];\\n TreeSet<Node> set = new TreeSet<Node>();\\n for(int i=0; i<n; i++){\\n t[i] = input.nextInt();\\n if(t[i] == 0){\\n set.add(new Node(0,true));\\n set.add(new Node(n,false));\\n }else if((n-t[i]+i+1) < n+1){\\n set.add(new Node(i+1,true));\\n set.add(new Node(n-t[i]+i+1,false));\\n }else{\\n set.add(new Node(i+1,true));\\n set.add(new Node(n,false));\\n set.add(new Node(0,true));\\n set.add(new Node(i-t[i]+1,false));\\n }\\n //System.out.println(set);\\n }\\n //System.out.println(set);\\n int maxIndex = 0;\\n int maxValue = Integer.MIN_VALUE;\\n int count = 0;\\n for(Node node: set){\\n if(node.isStart){\\n count++;\\n if(count > maxValue){\\n maxValue = count;\\n maxIndex = node.index;\\n }\\n }else{\\n count--;\\n }\\n }\\n \\n System.out.println(maxIndex+1);\\n }\\n \\n public static class Node implements Comparable<Node>{\\n int index;\\n boolean isStart;\\n \\n Node(int i, boolean b){\\n this.index =i;\\n this.isStart = b;\\n }\\n \\n public int compareTo(Node a){\\n if(this.index < a.index){\\n return -1;\\n }else if(a.index < this.index){\\n return 1;\\n }else{\\n if(!this.isStart){\\n return -1; \\n }else{\\n return 1;\\n }\\n }\\n }\\n \\n public String toString(){\\n char c = 'e';\\n if(isStart) c = 's';\\n return \\\"\\\"+index+c;\\n }\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY t as parameter.\\n */\\n\\n public static int solve(List<Integer> t) {\\n // Return the ID\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int tCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> t = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int id = Result.solve(t);\\n\\n bufferedWriter.write(String.valueOf(id));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\n\\npublic class Solution {\\n\\tFastScanner in;\\n\\tPrintWriter out;\\n\\n\\tclass Event implements Comparable<Event> {\\n\\t\\tint time;\\n\\t\\tint add;\\n\\n\\t\\tpublic Event(int time, int add) {\\n\\t\\t\\tsuper();\\n\\t\\t\\tthis.time = time;\\n\\t\\t\\tthis.add = add;\\n\\t\\t}\\n\\n\\t\\tpublic int compareTo(Event arg0) {\\n\\t\\t\\treturn Integer.compare(time, arg0.time);\\n\\t\\t}\\n\\n\\t}\\n\\n\\tvoid solve() {\\n\\t\\tint n = in.nextInt();\\n\\t\\tint[] a = new int[n];\\n\\t\\tfor (int i = 0; i < n; i++) {\\n\\t\\t\\ta[i] = in.nextInt();\\n\\t\\t}\\n\\t\\tArrayList<Event> events = new ArrayList();\\n\\t\\tfor (int i = 0; i < n; i++) {\\n\\t\\t\\tif (a[i] <= i) {\\n\\t\\t\\t\\tevents.add(new Event(i - a[i] + 1, -1));\\n\\t\\t\\t}\\n\\t\\t\\tif (a[i] < n) {\\n\\t\\t\\t\\tevents.add(new Event(i + 1, +1));\\n\\t\\t\\t\\tevents.add(new Event(i + (n - a[i]) + 1, -1));\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\tCollections.sort(events);\\n\\t\\tint it = 0;\\n\\t\\tint sum = 0;\\n\\t\\tint max = Integer.MIN_VALUE;\\n\\t\\tint bestPos = -1;\\n\\t\\tfor (int i = 0; i < n; i++) {\\n\\t\\t\\twhile (it < events.size() && events.get(it).time <= i) {\\n\\t\\t\\t\\tsum += events.get(it).add;\\n\\t\\t\\t\\tit++;\\n\\t\\t\\t}\\n\\t\\t\\tif (sum > max) {\\n\\t\\t\\t\\tmax = sum;\\n\\t\\t\\t\\tbestPos = i + 1;\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\tout.println(bestPos);\\n\\t}\\n\\n\\tvoid run() {\\n\\t\\ttry {\\n\\t\\t\\tin = new FastScanner(new File(\\\"object.in\\\"));\\n\\t\\t\\tout = new PrintWriter(new File(\\\"object.out\\\"));\\n\\n\\t\\t\\tsolve();\\n\\n\\t\\t\\tout.close();\\n\\t\\t} catch (FileNotFoundException e) {\\n\\t\\t\\te.printStackTrace();\\n\\t\\t}\\n\\t}\\n\\n\\tvoid runIO() {\\n\\n\\t\\tin = new FastScanner(System.in);\\n\\t\\tout = new PrintWriter(System.out);\\n\\n\\t\\tsolve();\\n\\n\\t\\tout.close();\\n\\t}\\n\\n\\tclass FastScanner {\\n\\t\\tBufferedReader br;\\n\\t\\tStringTokenizer st;\\n\\n\\t\\tpublic FastScanner(File f) {\\n\\t\\t\\ttry {\\n\\t\\t\\t\\tbr = new BufferedReader(new FileReader(f));\\n\\t\\t\\t} catch (FileNotFoundException e) {\\n\\t\\t\\t\\te.printStackTrace();\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\tpublic FastScanner(InputStream f) {\\n\\t\\t\\tbr = new BufferedReader(new InputStreamReader(f));\\n\\t\\t}\\n\\n\\t\\tString next() {\\n\\t\\t\\twhile (st == null || !st.hasMoreTokens()) {\\n\\t\\t\\t\\tString s = null;\\n\\t\\t\\t\\ttry {\\n\\t\\t\\t\\t\\ts = br.readLine();\\n\\t\\t\\t\\t} catch (IOException e) {\\n\\t\\t\\t\\t\\te.printStackTrace();\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tif (s == null)\\n\\t\\t\\t\\t\\treturn null;\\n\\t\\t\\t\\tst = new StringTokenizer(s);\\n\\t\\t\\t}\\n\\t\\t\\treturn st.nextToken();\\n\\t\\t}\\n\\n\\t\\tboolean hasMoreTokens() {\\n\\t\\t\\twhile (st == null || !st.hasMoreTokens()) {\\n\\t\\t\\t\\tString s = null;\\n\\t\\t\\t\\ttry {\\n\\t\\t\\t\\t\\ts = br.readLine();\\n\\t\\t\\t\\t} catch (IOException e) {\\n\\t\\t\\t\\t\\te.printStackTrace();\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tif (s == null)\\n\\t\\t\\t\\t\\treturn false;\\n\\t\\t\\t\\tst = new StringTokenizer(s);\\n\\t\\t\\t}\\n\\t\\t\\treturn true;\\n\\t\\t}\\n\\n\\t\\tint nextInt() {\\n\\t\\t\\treturn Integer.parseInt(next());\\n\\t\\t}\\n\\n\\t\\tlong nextLong() {\\n\\t\\t\\treturn Long.parseLong(next());\\n\\t\\t}\\n\\n\\t\\tdouble nextDouble() {\\n\\t\\t\\treturn Double.parseDouble(next());\\n\\t\\t}\\n\\t}\\n\\n\\tpublic static void main(String[] args) {\\n\\t\\tnew Solution().runIO();\\n\\t}\\n}\",\n \"template\": \" /*\\n * Complete the solve function below.\\n */\\n static int solve(int[] t) {\\n /*\\n * Return the ID\\n */\\n\\n }\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n\",\n \"template_tail\": \" private static final Scanner scanner = new Scanner(System.in);\\n\\n public static void main(String[] args) throws IOException {\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int tCount = Integer.parseInt(scanner.nextLine().trim());\\n\\n int[] t = new int[tCount];\\n\\n String[] tItems = scanner.nextLine().split(\\\" \\\");\\n\\n for (int tItr = 0; tItr < tCount; tItr++) {\\n int tItem = Integer.parseInt(tItems[tItr].trim());\\n t[tItr] = tItem;\\n }\\n\\n int id = solve(t);\\n\\n bufferedWriter.write(String.valueOf(id));\\n bufferedWriter.newLine();\\n\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"solve(t)\"\n ],\n \"solution\": \"import sys\\nn = int(sys.stdin.readline())\\nt = [int(x) for x in sys.stdin.readline().split()]\\n\\ncurrent_available = 0\\nbecome_unavailable = [0] * (3 * n)\\nbest_index = 1\\nbest_result = 0\\nfor i in range(2*n):\\n el = t[i % n]\\n become_unavailable[i + (n - el)] += 1\\n if current_available > best_result:\\n best_result = current_available\\n best_index = i % n + 1\\n if current_available == best_result:\\n best_index = min(i % n + 1, best_index)\\n current_available += 1\\n current_available -= become_unavailable[i]\\n \\nprint(best_index)\",\n \"template\": \"#\\n# Complete the solve function below.\\n#\\ndef solve(t):\\n #\\n # Return the ID\\n #\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport os\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n t_count = int(input())\\n\\n t = list(map(int, input().rstrip().split()))\\n\\n id = solve(t)\\n\\n fptr.write(str(id) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":158,"cells":{"question_id":{"kind":"number","value":3654,"string":"3,654"},"question_title":{"kind":"string","value":"Jim and the Skyscrapers"},"question_content":{"kind":"string","value":"Jim has invented a new flying object called HZ42. HZ42 is like a broom and can only fly horizontally, independent of the environment. One day, Jim started his flight from Dubai's highest skyscraper, traveled some distance and landed on another skyscraper of same height! So much fun! But unfortunately, new skyscrapers have been built recently.\n\nLet us describe the problem in one dimensional space. We have in total $N$ skyscrapers aligned from left to right. The $i$<sup>th</sup> skyscraper has a height of $h_i$. A flying route can be described as $(i, j)$ with $i \\neq j$, which means, Jim starts his HZ42 at the top of the skyscraper $i$ and lands on the skyscraper $j$. Since HZ42 can only fly horizontally, Jim will remain at the height $h_i$ only. Thus the path $(i, j) $ can be valid, only if each of the skyscrapers $i, i+1, ..., j-1, j$ is not strictly greater than $h_i$ and if the height of the skyscraper he starts from and arrives on have the same height. Formally, $(i, j)$ is valid iff $\\not\\exists k \\in [i, j] : h_k > h_i$ and $h_i = h_j$.\n\nHelp Jim in counting the number of valid paths represented by ordered pairs $(i, j)$. \n\n**Input Format**\n\nThe first line contains $N$, the number of skyscrapers. The next line contains $N$ space separated integers representing the heights of the skyscrapers. \n\n**Output Format**\n\nPrint an integer that denotes the number of valid routes.\n\n**Constraints**\n\n$1 \\leq N \\leq 3 \\cdot 10^5$ and no skyscraper will have height greater than $10^6$ and less than $1$.\n\n**Sample Input #00**\n\n 6\n 3 2 1 2 3 3\n \n**Sample Output #00**\n\n\t8\n \n**Sample Input #01**\n\n 3\n 1 1000 1\n \n**Sample Output #01**\n\n\t0\n\n**Explanation**\n\nFirst testcase: (1, 5), (1, 6) (5, 6) and (2, 4) and the routes in the opposite directions are the only valid routes. \n\nSecond testcase: (1, 3) and (3, 1) could have been valid, if there wasn't a big skyscraper with height 1000 between them."},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Medium"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static int solve(List<Integer> arr)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.ByteArrayInputStream;\\nimport java.io.IOException;\\nimport java.io.InputStream;\\nimport java.io.PrintWriter;\\nimport java.util.Arrays;\\nimport java.util.Comparator;\\nimport java.util.InputMismatchException;\\n\\npublic class C {\\n\\tstatic InputStream is;\\n\\tstatic PrintWriter out;\\n\\tstatic String INPUT = \\\"\\\";\\n\\t\\n\\tstatic void solve()\\n\\t{\\n\\t\\tint n = ni();\\n\\t\\tint[] h = na(n);\\n\\t\\tint[][] hi = new int[n][];\\n\\t\\tfor(int i = 0;i < n;i++){\\n\\t\\t\\thi[i] = new int[]{h[i], i};\\n\\t\\t}\\n\\t\\tArrays.sort(hi, new Comparator<int[]>() {\\n\\t\\t\\tpublic int compare(int[] a, int[] b) {\\n\\t\\t\\t\\tif(a[0] != b[0])return a[0] - b[0];\\n\\t\\t\\t\\treturn a[1] - b[1];\\n\\t\\t\\t}\\n\\t\\t});\\n\\t\\t\\n\\t\\tLST lst = new LST(n);\\n\\t\\tfor(int i = 0;i < n;i++)lst.set(i);\\n\\t\\t\\n\\t\\tlong ret = 0;\\n\\t\\tfor(int i = 0;i < n;){\\n\\t\\t\\tint j = i;\\n\\t\\t\\twhile(j < n && hi[j][0] == hi[i][0])j++;\\n\\t\\t\\tint pre = -2;\\n\\t\\t\\tint ct = 0;\\n\\t\\t\\tfor(int k = i;k < j;k++){\\n\\t\\t\\t\\tlst.unset(hi[k][1]);\\n\\t\\t\\t\\tint prev = lst.prev(hi[k][1]);\\n\\t\\t\\t\\tif(pre < prev){\\n\\t\\t\\t\\t\\tct = 0;\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tret += ct;\\n\\t\\t\\t\\tct++;\\n\\t\\t\\t\\tpre = hi[k][1];\\n\\t\\t\\t}\\n\\t\\t\\t\\n\\t\\t\\ti = j;\\n\\t\\t}\\n\\t\\tout.println(ret*2L);\\n\\t}\\n\\t\\n\\tpublic static class LST {\\n\\t\\tpublic long[][] set;\\n\\t\\tpublic int n;\\n//\\t\\tpublic int size;\\n\\t\\t\\n\\t\\tpublic LST(int n) {\\n\\t\\t\\tthis.n = n;\\n\\t\\t\\tint d = 1;\\n\\t\\t\\tfor(int m = n;m > 1;m>>>=6, d++);\\n\\t\\t\\t\\n\\t\\t\\tset = new long[d][];\\n\\t\\t\\tfor(int i = 0, m = n>>>6;i < d;i++, m>>>=6){\\n\\t\\t\\t\\tset[i] = new long[m+1];\\n\\t\\t\\t}\\n//\\t\\t\\tsize = 0;\\n\\t\\t}\\n\\t\\t\\n\\t\\tpublic LST set(int l, int r)\\n\\t\\t{\\n\\t\\t\\tif(0 <= l && l <= r && r <= n){\\n\\t\\t\\t\\tsetRange(r);\\n\\t\\t\\t\\tunsetRange(l-1);\\n\\t\\t\\t}\\n\\t\\t\\treturn this;\\n\\t\\t}\\n\\t\\t\\n\\t\\tpublic LST setRange(int r)\\n\\t\\t{\\n\\t\\t\\tfor(int i = 0;i < set.length;i++, r>>>=6){\\n\\t\\t\\t\\tfor(int j = 0;j < r>>>6;j++){\\n\\t\\t\\t\\t\\tset[i][j] = -1;\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tset[i][r>>>6] |= (1L<<r+1)-1;\\n\\t\\t\\t}\\n\\t\\t\\treturn this;\\n\\t\\t}\\n\\t\\t\\n\\t\\tpublic LST unsetRange(int r)\\n\\t\\t{\\n\\t\\t\\tif(r >= 0){\\n\\t\\t\\t\\tfor(int i = 0;i < set.length;i++, r>>>=6){\\n\\t\\t\\t\\t\\tfor(int j = 0;j < r>>>6;j++){\\n\\t\\t\\t\\t\\t\\tset[i][j] = 0;\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\tset[i][r>>>6] &= ~((1L<<r+1)-1);\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\treturn this;\\n\\t\\t}\\n\\t\\t\\n\\t\\tpublic LST set(int pos)\\n\\t\\t{\\n\\t\\t\\tif(pos >= 0 && pos < n){\\n//\\t\\t\\t\\tif(!get(pos))size++;\\n\\t\\t\\t\\tfor(int i = 0;i < set.length;i++, pos>>>=6){\\n\\t\\t\\t\\t\\tset[i][pos>>>6] |= 1L<<pos;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\treturn this;\\n\\t\\t}\\n\\t\\t\\n\\t\\tpublic LST unset(int pos)\\n\\t\\t{\\n\\t\\t\\tif(pos >= 0 && pos < n){\\n//\\t\\t\\t\\tif(get(pos))size--;\\n\\t\\t\\t\\tfor(int i = 0;i < set.length && (i == 0 || set[i-1][pos] == 0L);i++, pos>>>=6){\\n\\t\\t\\t\\t\\tset[i][pos>>>6] &= ~(1L<<pos);\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\treturn this;\\n\\t\\t}\\n\\t\\t\\n\\t\\tpublic boolean get(int pos)\\n\\t\\t{\\n\\t\\t\\treturn pos >= 0 && pos < n && set[0][pos>>>6]<<~pos<0;\\n\\t\\t}\\n\\t\\t\\n\\t\\tpublic int prev(int pos)\\n\\t\\t{\\n\\t\\t\\tfor(int i = 0;i < set.length && pos >= 0;i++, pos>>>=6, pos--){\\n\\t\\t\\t\\tint pre = prev(set[i][pos>>>6], pos&63);\\n\\t\\t\\t\\tif(pre != -1){\\n\\t\\t\\t\\t\\tpos = pos>>>6<<6|pre;\\n\\t\\t\\t\\t\\twhile(i > 0)pos = pos<<6|63-Long.numberOfLeadingZeros(set[--i][pos]);\\n\\t\\t\\t\\t\\treturn pos;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\treturn -1;\\n\\t\\t}\\n\\t\\t\\n\\t\\tpublic int next(int pos)\\n\\t\\t{\\n\\t\\t\\tfor(int i = 0;i < set.length && pos>>>6 < set[i].length;i++, pos>>>=6, pos++){\\n\\t\\t\\t\\tint nex = next(set[i][pos>>>6], pos&63);\\n\\t\\t\\t\\tif(nex != -1){\\n\\t\\t\\t\\t\\tpos = pos>>>6<<6|nex;\\n\\t\\t\\t\\t\\twhile(i > 0)pos = pos<<6|Long.numberOfTrailingZeros(set[--i][pos]);\\n\\t\\t\\t\\t\\treturn pos;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\treturn -1;\\n\\t\\t}\\n\\t\\t\\n\\t\\tprivate static int prev(long set, int n)\\n\\t\\t{\\n\\t\\t\\tlong h = Long.highestOneBit(set<<~n);\\n\\t\\t\\tif(h == 0L)return -1;\\n\\t\\t\\treturn Long.numberOfTrailingZeros(h)-(63-n);\\n\\t\\t}\\n\\t\\t\\n\\t\\tprivate static int next(long set, int n)\\n\\t\\t{\\n\\t\\t\\tlong h = Long.lowestOneBit(set>>>n);\\n\\t\\t\\tif(h == 0L)return -1;\\n\\t\\t\\treturn Long.numberOfTrailingZeros(h)+n;\\n\\t\\t}\\n\\t}\\n\\t\\n\\tpublic static void main(String[] args) throws Exception\\n\\t{\\n//\\t\\tint n = 300000, m = 99999;\\n//\\t\\tRandom gen = new Random();\\n//\\t\\tStringBuilder sb = new StringBuilder();\\n//\\t\\tsb.append(n + \\\" \\\");\\n//\\t\\tfor (int i = 0; i < n; i++) {\\n//\\t\\t\\tsb.append(gen.nextInt(1) + \\\" \\\");\\n//\\t\\t}\\n//\\t\\tINPUT = sb.toString();\\n\\t\\t\\n\\t\\tlong S = System.currentTimeMillis();\\n\\t\\tis = INPUT.isEmpty() ? System.in : new ByteArrayInputStream(INPUT.getBytes());\\n\\t\\tout = new PrintWriter(System.out);\\n\\t\\t\\n\\t\\tsolve();\\n\\t\\tout.flush();\\n\\t\\tlong G = System.currentTimeMillis();\\n\\t\\ttr(G-S+\\\"ms\\\");\\n\\t}\\n\\t\\n\\tprivate static boolean eof()\\n\\t{\\n\\t\\tif(lenbuf == -1)return true;\\n\\t\\tint lptr = ptrbuf;\\n\\t\\twhile(lptr < lenbuf)if(!isSpaceChar(inbuf[lptr++]))return false;\\n\\t\\t\\n\\t\\ttry {\\n\\t\\t\\tis.mark(1000);\\n\\t\\t\\twhile(true){\\n\\t\\t\\t\\tint b = is.read();\\n\\t\\t\\t\\tif(b == -1){\\n\\t\\t\\t\\t\\tis.reset();\\n\\t\\t\\t\\t\\treturn true;\\n\\t\\t\\t\\t}else if(!isSpaceChar(b)){\\n\\t\\t\\t\\t\\tis.reset();\\n\\t\\t\\t\\t\\treturn false;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t} catch (IOException e) {\\n\\t\\t\\treturn true;\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate static byte[] inbuf = new byte[1024];\\n\\tstatic int lenbuf = 0, ptrbuf = 0;\\n\\t\\n\\tprivate static int readByte()\\n\\t{\\n\\t\\tif(lenbuf == -1)throw new InputMismatchException();\\n\\t\\tif(ptrbuf >= lenbuf){\\n\\t\\t\\tptrbuf = 0;\\n\\t\\t\\ttry { lenbuf = is.read(inbuf); } catch (IOException e) { throw new InputMismatchException(); }\\n\\t\\t\\tif(lenbuf <= 0)return -1;\\n\\t\\t}\\n\\t\\treturn inbuf[ptrbuf++];\\n\\t}\\n\\t\\n\\tprivate static boolean isSpaceChar(int c) { return !(c >= 33 && c <= 126); }\\n\\tprivate static int skip() { int b; while((b = readByte()) != -1 && isSpaceChar(b)); return b; }\\n\\t\\n\\tprivate static double nd() { return Double.parseDouble(ns()); }\\n\\tprivate static char nc() { return (char)skip(); }\\n\\t\\n\\tprivate static String ns()\\n\\t{\\n\\t\\tint b = skip();\\n\\t\\tStringBuilder sb = new StringBuilder();\\n\\t\\twhile(!(isSpaceChar(b))){ // when nextLine, (isSpaceChar(b) && b != ' ')\\n\\t\\t\\tsb.appendCodePoint(b);\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn sb.toString();\\n\\t}\\n\\t\\n\\tprivate static char[] ns(int n)\\n\\t{\\n\\t\\tchar[] buf = new char[n];\\n\\t\\tint b = skip(), p = 0;\\n\\t\\twhile(p < n && !(isSpaceChar(b))){\\n\\t\\t\\tbuf[p++] = (char)b;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn n == p ? buf : Arrays.copyOf(buf, p);\\n\\t}\\n\\t\\n\\tprivate static char[][] nm(int n, int m)\\n\\t{\\n\\t\\tchar[][] map = new char[n][];\\n\\t\\tfor(int i = 0;i < n;i++)map[i] = ns(m);\\n\\t\\treturn map;\\n\\t}\\n\\t\\n\\tprivate static int[] na(int n)\\n\\t{\\n\\t\\tint[] a = new int[n];\\n\\t\\tfor(int i = 0;i < n;i++)a[i] = ni();\\n\\t\\treturn a;\\n\\t}\\n\\t\\n\\tprivate static int ni()\\n\\t{\\n\\t\\tint num = 0, b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate static long nl()\\n\\t{\\n\\t\\tlong num = 0;\\n\\t\\tint b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate static void tr(Object... o) { if(INPUT.length() != 0)System.out.println(Arrays.deepToString(o)); }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static int solve(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int arrCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String[] arrTemp = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> arr = new ArrayList<>();\\n\\n for (int i = 0; i < arrCount; i++) {\\n int arrItem = Integer.parseInt(arrTemp[i]);\\n arr.add(arrItem);\\n }\\n\\n int result = Result.solve(arr);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n static long f(long x){\\n return x * (x + 1) / 2;\\n }\\n static class Node {\\n Integer flag;\\n Integer value;\\n Node(Integer flag, Integer val){\\n this.flag = flag;\\n this.value = val;\\n }\\n }\\n public static long solve(List<Integer> arr) {\\n Stack<Node> st = new Stack<>();\\n long ret = 0;\\n for(int i = 0; i < arr.size(); i++){\\n while(!st.isEmpty() && st.peek().flag < arr.get(i)){\\n ret += f(st.pop().value);\\n }\\n if(st.isEmpty() || st.peek().flag > arr.get(i)){\\n st.push(new Node(arr.get(i),0));\\n continue;\\n }else{\\n st.peek().value = st.peek().value + 1;\\n }\\n }\\n // System.out.println(ret);\\n while(!st.isEmpty()){\\n ret += f(st.pop().value);\\n }\\n // System.out.println(ret);\\n return 2 * ret;\\n \\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int arrCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n long result = Result.solve(arr);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static int solve(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int arrCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.solve(arr);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.BufferedReader;\\nimport java.io.IOException;\\nimport java.io.InputStreamReader;\\nimport java.io.PrintWriter;\\nimport java.util.*;\\n\\n\\npublic class Solution {\\t\\n\\tBufferedReader reader;\\n StringTokenizer tokenizer;\\n PrintWriter out;\\n \\n\\tpublic void solve() throws IOException {\\t\\t\\t\\t\\n\\t\\tint N = nextInt();\\n int[] H = new int[N];\\n int MAX = 0;\\n\\t\\tfor (int i = 0; i < N; i++) {\\n H[i] = nextInt();\\n MAX = Math.max(MAX, H[i] + 1);\\n }\\n\\n RmqSparseTable rmq = new RmqSparseTable(H);\\n\\n ArrayList<Integer>[] pos = new ArrayList[MAX];\\n for (int i = 0; i < MAX; i++) {\\n pos[i] = new ArrayList<Integer>();\\n }\\n for (int i = 0; i < N; i++) {\\n pos[H[i]].add(i);\\n }\\n\\n long ans = 0;\\n for (int i = 0; i < MAX; i++) {\\n if (pos[i].size() == 0) continue;\\n\\n int cur = 0;\\n for (int j = 1; j < pos[i].size(); j++) {\\n int maxH = rmq.max(pos[i].get(cur), pos[i].get(j));\\n if (maxH > i) {\\n cur = j;\\n } else {\\n ans += (j - cur);\\n }\\n }\\n\\n// out.println(i + \\\": \\\" + ans);\\n }\\n\\n out.println(ans * 2);\\n\\n\\t}\\n\\n public class RmqSparseTable {\\n\\n int[] logTable;\\n int[][] rmq;\\n int[] a;\\n\\n public RmqSparseTable(int[] a) {\\n this.a = a;\\n int n = a.length;\\n\\n logTable = new int[n + 1];\\n for (int i = 2; i <= n; i++)\\n logTable[i] = logTable[i >> 1] + 1;\\n\\n rmq = new int[logTable[n] + 1][n];\\n\\n for (int i = 0; i < n; ++i)\\n rmq[0][i] = i;\\n\\n for (int k = 1; (1 << k) < n; ++k) {\\n for (int i = 0; i + (1 << k) <= n; i++) {\\n int x = rmq[k - 1][i];\\n int y = rmq[k - 1][i + (1 << k - 1)];\\n rmq[k][i] = a[x] >= a[y] ? x : y;\\n }\\n }\\n }\\n\\n public int maxPos(int i, int j) {\\n int k = logTable[j - i];\\n int x = rmq[k][i];\\n int y = rmq[k][j - (1 << k) + 1];\\n return a[x] >= a[y] ? x : y;\\n }\\n\\n public int max(int i, int j) {\\n return a[maxPos(i, j)];\\n }\\n }\\n\\n /**\\n\\t * @param args\\n\\t */\\n\\tpublic static void main(String[] args) {\\n\\t\\tnew Solution().run();\\n\\t}\\n\\t\\n\\tpublic void run() {\\n try {\\n reader = new BufferedReader(new InputStreamReader(System.in));\\n tokenizer = null;\\n out = new PrintWriter(System.out);\\n solve();\\n reader.close();\\n out.close();\\n } catch (Exception e) {\\n e.printStackTrace();\\n System.exit(1);\\n }\\n }\\n\\n int nextInt() throws IOException {\\n return Integer.parseInt(nextToken());\\n }\\n\\n long nextLong() throws IOException {\\n return Long.parseLong(nextToken());\\n }\\n\\n double nextDouble() throws IOException {\\n return Double.parseDouble(nextToken());\\n }\\n\\n String nextToken() throws IOException {\\n while (tokenizer == null || !tokenizer.hasMoreTokens()) {\\n tokenizer = new StringTokenizer(reader.readLine());\\n }\\n return tokenizer.nextToken();\\n }\\n\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static int solve(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int arrCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.solve(arr);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"solve(arr)\"\n ],\n \"solution\": \"n = int(input())\\nhs = map(int, input().split())\\n\\nhhs = sorted(enumerate(hs), key=lambda t: (t[1], t[0]))\\n\\nprev = list(range(-1, n-1))\\nnext = list(range(1, n+1))\\nfirsti, firsth = lasti, lasth = hhs[0]\\nblock_len = 1\\ns = 0\\nfor i, h in hhs[1:]:\\n if h == lasth and i == next[lasti]:\\n block_len += 1\\n lasti = i\\n else:\\n s += block_len*(block_len-1)\\n pi, ni = prev[firsti], next[lasti]\\n if pi >= 0: next[pi] = ni\\n if ni < n: prev[ni] = pi\\n firsti, firsth = lasti, lasth = i, h\\n block_len = 1\\ns += block_len*(block_len-1)\\nprint(s)\\n\",\n \"template\": \"#\\n# Complete the 'solve' function below.\\n#\\n# The function is expected to return an INTEGER.\\n# The function accepts INTEGER_ARRAY arr as parameter.\\n#\\n\\ndef solve(arr):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n arr_count = int(input().strip())\\n\\n arr = list(map(int, input().rstrip().split()))\\n\\n result = solve(arr)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":159,"cells":{"question_id":{"kind":"number","value":15870,"string":"15,870"},"question_title":{"kind":"string","value":"Heavy Light White Falcon"},"question_content":{"kind":"string","value":"Our lazy white falcon finally decided to learn heavy-light decomposition. Her teacher gave an assignment for her to practice this new technique. Please help her by solving this problem. \n\nYou are given a tree with $N$ nodes and each node's value is initially $0$. The problem asks you to operate the following two types of queries:\n\n-\t\"1 u x\" assign $x$ to the value of the node $u$.\n-\t\"2 u v\" print the maximum value of the nodes on the unique path between $u$ and $v$.\n\n\n\n"},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Hard"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static List<Integer> solve(List<List<Integer>> tree, List<List<Integer>> queries)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n \\n // array to store values for each node(vertices)\\n static int[] v;\\n // array to store connections (edges)\\n static ArrayList<Integer>[] e;\\n \\n // some node with single connection will be tree root\\n static int root; \\n // direction to reach root for each node\\n static int[] dirToRoot;\\n // distance to root\\n static int[] distToRoot;\\n \\n // maximum node value \\n static int totalMax = 0;\\n \\n static void markRoot(){\\n for(int i=0; i<v.length; i++){\\n if(e[i].size()==1){\\n root = i;\\n break;\\n }\\n }\\n }\\n \\n static void markPathToRoot(int node, int dist, boolean[] visited){\\n distToRoot[node] = dist;\\n for(Integer item : e[node]){\\n int index = item.intValue();\\n if(visited[index]==false){\\n dirToRoot[index] = node;\\n visited[index] = true;\\n markPathToRoot(index, dist+1, visited);\\n }\\n }\\n }\\n \\n static int findMax2(int start, int finish){\\n int sDistance = distToRoot[start];\\n int fDistance = distToRoot[finish];\\n\\n int sIndex = start;\\n int fIndex = finish;\\n int max = Math.max(v[sIndex], v[fIndex]);\\n \\n // decrease distance from the one that is more far from root\\n while(sDistance>fDistance && max<totalMax){\\n sIndex = dirToRoot[sIndex];\\n max = max >= v[sIndex] ? max : v[sIndex];\\n sDistance--;\\n }\\n while(fDistance>sDistance && max<totalMax){\\n fIndex = dirToRoot[fIndex];\\n max = max >= v[fIndex] ? max : v[fIndex];\\n fDistance--;\\n }\\n\\n // run both of them\\n while(sIndex!=fIndex){\\n fIndex = dirToRoot[fIndex];\\n sIndex = dirToRoot[sIndex];\\n max = max >= v[fIndex] ? max : v[fIndex];\\n max = max >= v[sIndex] ? max : v[sIndex];\\n if(sIndex==root || max==totalMax)\\n break;\\n }\\n return Math.max(max, v[sIndex]);\\n }\\n \\n // calculate distance to root node from each node\\n static void resetRoot(){\\n // direction to the root of tree for each node\\n dirToRoot = new int[v.length];\\n distToRoot= new int[v.length];\\n \\n // mark node with only one edge as root\\n markRoot();\\n// System.out.println(\\\"root=\\\"+root);\\n \\n dirToRoot[root] = root;\\n boolean[] visited = new boolean[v.length];\\n visited[root] = true;\\n markPathToRoot(root, 0, visited);\\n }\\n \\n public static void main(String[] args) {\\n Reader sc = new Reader();\\n sc.init(System.in);\\n int N = sc.nextInt();\\n int Q = sc.nextInt();\\n \\n // array to store values for each node(vertices)\\n v = new int[N];\\n // array to store connections (edges)\\n e = new ArrayList[N];\\n\\n for(int i=0; i<N; i++)\\n e[i] = new ArrayList<Integer>(2);\\n \\n for(int i=0; i<N-1; i++){\\n int v1 = sc.nextInt();\\n int v2 = sc.nextInt();\\n // add to both because undirectional\\n e[v1].add(v2);\\n e[v2].add(v1);\\n }\\n \\n resetRoot();\\n \\n // read queries\\n while(Q-->0){\\n int type = sc.nextInt();\\n if(type==1){\\n int node = sc.nextInt();\\n int value = sc.nextInt();\\n v[node] = value;\\n totalMax = value > totalMax ? value : totalMax;\\n }else{\\n int start = sc.nextInt();\\n int finish = sc.nextInt();\\n //search for solution\\n System.out.println( findMax2(start, finish) );\\n }\\n }\\n }\\n \\n// code from internet \\n/** Class for buffered reading int and double values */\\nstatic class Reader {\\n static BufferedReader reader;\\n static StringTokenizer tokenizer;\\n\\n /** call this method to initialize reader for InputStream */\\n static void init(InputStream input) {\\n reader = new BufferedReader( new InputStreamReader(input) );\\n tokenizer = new StringTokenizer(\\\"\\\");\\n }\\n\\n /** get next word */\\n static String next() {\\n while ( ! tokenizer.hasMoreTokens() ) {\\n //TODO add check for eof if necessary\\n try{\\n tokenizer = new StringTokenizer( reader.readLine() );\\n }catch(Exception e){}\\n }\\n return tokenizer.nextToken();\\n }\\n\\n static int nextInt() {\\n return Integer.parseInt( next() );\\n }\\n\\t\\n static double nextDouble() {\\n return Double.parseDouble( next() );\\n }\\n}\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts following parameters:\\n * 1. 2D_INTEGER_ARRAY tree\\n * 2. 2D_INTEGER_ARRAY queries\\n */\\n\\n public static List<Integer> solve(List<List<Integer>> tree, List<List<Integer>> queries) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int q = Integer.parseInt(firstMultipleInput[1]);\\n\\n List<List<Integer>> tree = new ArrayList<>();\\n\\n for (int i = 0; i < n - 1; i++) {\\n String[] treeRowTempItems = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> treeRowItems = new ArrayList<>();\\n\\n for (int j = 0; j < 2; j++) {\\n int treeItem = Integer.parseInt(treeRowTempItems[j]);\\n treeRowItems.add(treeItem);\\n }\\n\\n tree.add(treeRowItems);\\n }\\n\\n List<List<Integer>> queries = new ArrayList<>();\\n\\n for (int i = 0; i < q; i++) {\\n String[] queriesRowTempItems = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> queriesRowItems = new ArrayList<>();\\n\\n for (int j = 0; j < 3; j++) {\\n int queriesItem = Integer.parseInt(queriesRowTempItems[j]);\\n queriesRowItems.add(queriesItem);\\n }\\n\\n queries.add(queriesRowItems);\\n }\\n\\n List<Integer> result = Result.solve(tree, queries);\\n\\n for (int i = 0; i < result.size(); i++) {\\n bufferedWriter.write(String.valueOf(result.get(i)));\\n\\n if (i != result.size() - 1) {\\n bufferedWriter.write(\\\"\\\\n\\\");\\n }\\n }\\n\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts following parameters:\\n * 1. 2D_INTEGER_ARRAY tree\\n * 2. 2D_INTEGER_ARRAY queries\\n */\\n\\n public static List<Integer> solve(List<List<Integer>> edges, List<List<Integer>> queries) {\\n List<Integer> result = new LinkedList<>();\\n\\n List<List<Integer>> nodes = new ArrayList<>();\\n for (int i = 0; i <= edges.size(); i++) {\\n nodes.add(new ArrayList<>());\\n }\\n for (int i = 0; i < edges.size(); i++) {\\n List<Integer> edge = edges.get(i);\\n int n1 = edge.get(0), n2 = edge.get(1);\\n nodes.get(n1).add(n2);\\n nodes.get(n2).add(n1);\\n }\\n\\n // process root\\n List<List<Integer>> children = new ArrayList<>();\\n boolean[] visited = new boolean[nodes.size()];\\n for (int i = 0; i < nodes.size(); i++) {\\n children.add(new LinkedList<>());\\n }\\n LinkedList<Integer> stack = new LinkedList<>();\\n stack.push(0);\\n while (!stack.isEmpty()) {\\n Integer current = stack.pop();\\n visited[current] = true;\\n for (Integer connection : nodes.get(current)) {\\n if (!visited[connection]) {\\n stack.push(connection);\\n children.get(current).add(connection);\\n }\\n }\\n }\\n\\n HeavyLightDecompositionTree tree = new HeavyLightDecompositionTree(children);\\n for (List<Integer> query : queries) {\\n int operation = query.get(0), arg1 = query.get(1), arg2 = query.get(2);\\n if (operation == 1) { // update value\\n tree.update(arg1, arg2);\\n } else if (operation == 2) { // print max value\\n int maximum = tree.max(arg1, arg2);\\n result.add(maximum);\\n }\\n }\\n return result;\\n }\\n}\\n\\nclass HeavyLightDecompositionTree {\\n\\n private List<List<Integer>> adj;\\n private int[] parent, depth, heavy, head;\\n private Integer[] dfsPositions, dfsValues;\\n private SegmentTreeMax<Integer> segmentedPos;\\n private int[] values;\\n\\n public HeavyLightDecompositionTree(List<List<Integer>> adj) {\\n this(adj, new int[adj.size()]);\\n }\\n\\n public HeavyLightDecompositionTree(List<List<Integer>> adj, int[] values) {\\n this.adj = adj;\\n int n = adj.size();\\n parent = new int[n];\\n Arrays.fill(parent, -1);\\n depth = new int[n];\\n heavy = new int[n];\\n Arrays.fill(heavy, -1);\\n head = new int[n];\\n\\n dfsPositions = new Integer[n];\\n dfsValues = new Integer[n];\\n this.values = values;\\n\\n dfs(adj);\\n decompose(adj);\\n\\n segmentedPos = new SegmentTreeMax(dfsValues);\\n }\\n\\n private void dfs(List<List<Integer>> adj) {\\n boolean[] visited = new boolean[adj.size()];\\n int[] weight = new int[adj.size()];\\n LinkedList<Integer> stack = new LinkedList<>();\\n stack.push(0);\\n while (!stack.isEmpty()) {\\n int nodeId = stack.pop();\\n List<Integer> connections = adj.get(nodeId);\\n if (!visited[nodeId]) {\\n visited[nodeId] = true;\\n depth[nodeId] = parent[nodeId] != -1 ? depth[parent[nodeId]] + 1 : 1;\\n\\n ListIterator<Integer> connectionsIterator = connections.listIterator(connections.size());\\n while (connectionsIterator.hasPrevious()) {\\n Integer connection = connectionsIterator.previous();\\n stack.push(nodeId);\\n stack.push(connection);\\n if (connection != parent[nodeId] && parent[connection] == -1) parent[connection] = nodeId;\\n }\\n }\\n if (connections.size() == 0) weight[nodeId] = 1;\\n else {\\n int childrenWeight = 1, maxChildSize = 0;\\n for (Integer connection : connections) {\\n int childSize = weight[connection];\\n if (connection != parent[nodeId]) childrenWeight += childSize;\\n if (childSize > maxChildSize) {\\n maxChildSize = childSize;\\n heavy[nodeId] = connection;\\n }\\n }\\n weight[nodeId] = childrenWeight;\\n }\\n }\\n }\\n\\n private void decompose(List<List<Integer>> adj) {\\n int dfsPosition = 0;\\n LinkedList<int[]> stack = new LinkedList<>();\\n stack.push(new int[]{0,0});\\n while (!stack.isEmpty()) {\\n int[] e = stack.pop();\\n int v = e[0], h = e[1];\\n head[v] = h;\\n dfsPositions[v] = dfsPosition;\\n dfsValues[dfsPosition] = 0;\\n dfsPosition++;\\n if (heavy[v] != -1) {\\n stack.push(new int[]{heavy[v], h});\\n }\\n for (int c : adj.get(v)) {\\n if (c != parent[v] && c != heavy[v]) {\\n stack.add(new int[]{c, c});\\n }\\n }\\n }\\n }\\n\\n public int max(int a, int b) {\\n int res = 0;\\n for (; head[a] != head[b]; b = parent[head[b]]) {\\n if (depth[head[a]] > depth[head[b]]) {\\n // swap(a, b);\\n int aux = a;\\n a = b;\\n b = aux;\\n }\\n int cur_heavy_path_max = segmentedPos.max(dfsPositions[head[b]], dfsPositions[b]);\\n res = Math.max(res, cur_heavy_path_max);\\n }\\n if (depth[a] > depth[b]) {\\n // swap(a, b);\\n int aux = a;\\n a = b;\\n b = aux;\\n }\\n int last_heavy_path_max = segmentedPos.max(dfsPositions[a], dfsPositions[b]);\\n res = Math.max(res, last_heavy_path_max);\\n return res;\\n }\\n\\n public void update(int index, int value) {\\n //values[index] = value;\\n int dfsPosition = dfsPositions[index];\\n dfsValues[dfsPosition] = value;\\n segmentedPos.update(dfsPosition, value);\\n }\\n}\\n\\nclass SegmentTreeMax<T extends Comparable> {\\n\\n private T[] values;\\n private ArrayList<T> tree;\\n\\n public SegmentTreeMax(T[] values) {\\n this.values = values;\\n this.tree = new ArrayList<T>(this.values.length * 4);\\n for (int i = 0; i < this.values.length * 4; i++) {\\n this.tree.add(null);\\n }\\n build(1, 0, values.length - 1);\\n }\\n\\n private void build(int v, int tl, int tr) {\\n if (tl == tr) {\\n tree.set(v, values[tl]);\\n } else {\\n int tm = (tl + tr) / 2;\\n build(v * 2, tl, tm);\\n build(v * 2 + 1, tm + 1, tr);\\n T left = tree.get(v * 2),\\n right = tree.get(v * 2 + 1);\\n tree.set(v, left.compareTo(right) > 0 ? left : right);\\n }\\n }\\n\\n public T max(int l, int r) {\\n return max(1, 0, values.length - 1, l, r);\\n }\\n\\n public void update(int pos, T value) {\\n update(1, 0, values.length - 1, pos, value);\\n }\\n\\n private void update(int v, int tl, int tr, int pos, T value) {\\n if (tl == tr) {\\n tree.set(v, value);\\n } else {\\n int tm = (tl + tr) / 2;\\n if (pos <= tm) {\\n update(v * 2, tl, tm, pos, value);\\n }\\n else {\\n update(v * 2 + 1, tm + 1, tr, pos, value);\\n }\\n T max1 = tree.get(v * 2),\\n max2 = tree.get(v * 2 + 1);\\n if (max1 == null && max2 == null) throw new NullPointerException(\\\"Cannot perform update with 2 nulls\\\");\\n else if (max1 == null) tree.set(v, max2);\\n else if (max2 == null) tree.set(v, max1);\\n else tree.set(v, max1.compareTo(max2) > 0 ? max1 : max2);\\n }\\n }\\n\\n private T max(int v, int tl, int tr, int l, int r) {\\n if (l > r)\\n return null; // return value that will not affect the reduction\\n if (l == tl && r == tr) {\\n return tree.get(v);\\n }\\n int tm = (tl + tr) / 2;\\n T max1 = max(v*2, tl, tm, l, Math.min(r, tm)),\\n max2 = max(v*2+1, tm+1, tr, Math.max(l, tm+1), r);\\n if (max1 == null && max2 == null) throw new NullPointerException(\\\"Cannot get a maximum value from 2 nulls\\\");\\n else if (max1 == null) return max2;\\n else if (max2 == null) return max1;\\n else return max1.compareTo(max2) > 0 ? max1 : max2;\\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int q = Integer.parseInt(firstMultipleInput[1]);\\n\\n List<List<Integer>> tree = new ArrayList<>();\\n\\n IntStream.range(0, n - 1).forEach(i -> {\\n try {\\n tree.add(\\n Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList())\\n );\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n List<List<Integer>> queries = new ArrayList<>();\\n\\n IntStream.range(0, q).forEach(i -> {\\n try {\\n queries.add(\\n Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList())\\n );\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n List<Integer> result = Result.solve(tree, queries);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\"\\\\n\\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts following parameters:\\n * 1. 2D_INTEGER_ARRAY tree\\n * 2. 2D_INTEGER_ARRAY queries\\n */\\n\\n public static List<Integer> solve(List<List<Integer>> tree, List<List<Integer>> queries) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int q = Integer.parseInt(firstMultipleInput[1]);\\n\\n List<List<Integer>> tree = new ArrayList<>();\\n\\n IntStream.range(0, n - 1).forEach(i -> {\\n try {\\n tree.add(\\n Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList())\\n );\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n List<List<Integer>> queries = new ArrayList<>();\\n\\n IntStream.range(0, q).forEach(i -> {\\n try {\\n queries.add(\\n Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList())\\n );\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n List<Integer> result = Result.solve(tree, queries);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\"\\\\n\\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.ByteArrayInputStream;\\nimport java.io.IOException;\\nimport java.io.InputStream;\\nimport java.io.PrintWriter;\\nimport java.util.Arrays;\\nimport java.util.InputMismatchException;\\n\\npublic class Main\\n{\\n\\tInputStream is;\\n\\tPrintWriter out;\\n\\tString INPUT = \\\"\\\";\\n\\t\\n\\tvoid solve()\\n\\t{\\n\\t\\tint n = ni(), Q = ni();\\n\\t\\tint[] from = new int[n-1];\\n\\t\\tint[] to = new int[n-1];\\n\\t\\t\\n\\t\\tfor(int i = 0; i < n-1; i++) {\\n\\t\\t\\tfrom[i] = ni();\\n\\t\\t\\tto[i] = ni();\\n\\t\\t}\\n\\t\\t\\n\\t\\tint[][] g = packU(n,from,to);\\n\\t\\tint[][] pars = parents3(g,0);\\n\\t\\tint[] par = pars[0], ord = pars[1], dep = pars[2];\\n\\t\\t\\n\\t\\tHeavyLightDecomposition hld = new HeavyLightDecomposition(g, par, ord, dep);\\n\\t\\tSegmentTreeOverwriteRMQ[] sts = new SegmentTreeOverwriteRMQ[hld.m];\\n\\t\\t\\n\\t\\tfor(int i = 0; i < hld.m; i++) {\\n\\t\\t\\tsts[i] = new SegmentTreeOverwriteRMQ(hld.cluspath[i].length);\\n\\t\\t}\\n\\t\\t\\n\\t\\touter:\\n\\t\\tfor(int z = 0; z < Q; z++) {\\n\\t\\t\\tint t = ni();\\n\\t\\t\\tif(t == 1) {\\n\\t\\t\\t\\tint u = ni(), x = ni();\\n\\t\\t\\t\\tsts[hld.clus[u]].update(hld.clusiind[u], hld.clusiind[u]+1, x);\\n\\t\\t\\t}\\n\\t\\t\\telse {\\n\\t\\t\\t\\tint u = ni(), v = ni();\\n\\t\\t\\t\\tint max = 0;\\n\\t\\t\\t\\tint lca = hld.lca(u, v);\\n\\t\\t\\t\\tfor(int x = u;;) {\\n\\t\\t\\t\\t\\tint cx = hld.clus[x];\\n\\t\\t\\t\\t\\tint l = cx==hld.clus[lca] ? hld.clusiind[lca] : 0;\\n\\t\\t\\t\\t\\tint r = hld.clusiind[x];\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tmax = Math.max(sts[cx].max(l, r+1),max);\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tif(cx == hld.clus[lca]) break;\\n\\t\\t\\t\\t\\tx = par[hld.cluspath[cx][0]];\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tfor(int x = v;;) {\\n\\t\\t\\t\\t\\tint cx = hld.clus[x];\\n\\t\\t\\t\\t\\tint l = cx==hld.clus[lca] ? hld.clusiind[lca] : 0;\\n\\t\\t\\t\\t\\tint r = hld.clusiind[x];\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tmax = Math.max(sts[cx].max(l, r+1),max);\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tif(cx == hld.clus[lca]) break;\\n\\t\\t\\t\\t\\tx = par[hld.cluspath[cx][0]];\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tout.println(max);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\t\\n\\tpublic static class HeavyLightDecomposition {\\n\\t\\tpublic int[][] g;\\n\\t\\tpublic int[] clus;\\n\\t\\tpublic int[][] cluspath;\\n\\t\\tpublic int[] clusiind;\\n\\t\\tpublic int[] par, dep;\\n\\t\\tpublic int m;\\n\\t\\t\\n\\t\\tpublic HeavyLightDecomposition(int[][] g, int[] par, int[] ord, int[] dep)\\n\\t\\t{\\n\\t\\t\\tinit(g, par, ord, dep);\\n\\t\\t}\\n\\t\\t\\n\\t\\tpublic void init(int[][] g, int[] par, int[] ord, int[] dep)\\n\\t\\t{\\n\\t\\t\\tclus = decomposeToHeavyLight(g, par, ord);\\n\\t\\t\\tcluspath = clusPaths(clus, ord);\\n\\t\\t\\tclusiind = clusIInd(cluspath, g.length);\\n\\t\\t\\tthis.m = cluspath.length;\\n\\t\\t\\tthis.par = par;\\n\\t\\t\\tthis.dep = dep;\\n\\t\\t\\tthis.g = g;\\n\\t\\t}\\n\\t\\t\\n\\t\\tpublic static int[] decomposeToHeavyLight(int[][] g, int[] par, int[] ord)\\n\\t\\t{\\n\\t\\t\\tint n = g.length;\\n\\t\\t\\tint[] size = new int[n];\\n\\t\\t\\tArrays.fill(size, 1);\\n\\t\\t\\tfor(int i = n-1;i > 0;i--)size[par[ord[i]]] += size[ord[i]];\\n\\t\\t\\t\\n\\t\\t\\tint[] clus = new int[n];\\n\\t\\t\\tArrays.fill(clus, -1);\\n\\t\\t\\tint p = 0;\\n\\t\\t\\tfor(int i = 0;i < n;i++){\\n\\t\\t\\t\\tint u = ord[i];\\n\\t\\t\\t\\tif(clus[u] == -1)clus[u] = p++;\\n\\t\\t\\t\\t// centroid path (not heavy path)\\n\\t\\t\\t\\tint argmax = -1;\\n\\t\\t\\t\\tfor(int v : g[u]){\\n\\t\\t\\t\\t\\tif(par[u] != v && (argmax == -1 || size[v] > size[argmax]))argmax = v;\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tif(argmax != -1)clus[argmax] = clus[u];\\n\\t\\t\\t}\\n\\t\\t\\treturn clus;\\n\\t\\t}\\n\\t\\t\\n\\t\\tpublic static int[][] clusPaths(int[] clus, int[] ord)\\n\\t\\t{\\n\\t\\t\\tint n = clus.length;\\n\\t\\t\\tint[] rp = new int[n];\\n\\t\\t\\tint sup = 0;\\n\\t\\t\\tfor(int i = 0;i < n;i++){\\n\\t\\t\\t\\trp[clus[i]]++;\\n\\t\\t\\t\\tsup = Math.max(sup, clus[i]);\\n\\t\\t\\t}\\n\\t\\t\\tsup++;\\n\\t\\t\\t\\n\\t\\t\\tint[][] row = new int[sup][];\\n\\t\\t\\tfor(int i = 0;i < sup;i++)row[i] = new int[rp[i]];\\n\\t\\t\\t\\n\\t\\t\\tfor(int i = n-1;i >= 0;i--){\\n\\t\\t\\t\\trow[clus[ord[i]]][--rp[clus[ord[i]]]] = ord[i];\\n\\t\\t\\t}\\n\\t\\t\\treturn row;\\n\\t\\t}\\n\\t\\t\\n\\t\\tpublic static int[] clusIInd(int[][] clusPath, int n)\\n\\t\\t{\\n\\t\\t\\tint[] iind = new int[n];\\n\\t\\t\\tfor(int[] path : clusPath){\\n\\t\\t\\t\\tfor(int i = 0;i < path.length;i++){\\n\\t\\t\\t\\t\\tiind[path[i]] = i;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\treturn iind;\\n\\t\\t}\\n\\t\\t\\n\\t\\tpublic int lca(int x, int y)\\n\\t\\t{\\n\\t\\t\\tint rx = cluspath[clus[x]][0];\\n\\t\\t\\tint ry = cluspath[clus[y]][0];\\n\\t\\t\\twhile(clus[x] != clus[y]){\\n\\t\\t\\t\\tif(dep[rx] > dep[ry]){\\n\\t\\t\\t\\t\\tx = par[rx];\\n\\t\\t\\t\\t\\trx = cluspath[clus[x]][0];\\n\\t\\t\\t\\t}else{\\n\\t\\t\\t\\t\\ty = par[ry];\\n\\t\\t\\t\\t\\try = cluspath[clus[y]][0];\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\treturn clusiind[x] > clusiind[y] ? y : x;\\n\\t\\t}\\n\\t\\t\\n\\t\\tpublic int ancestor(int x, int v)\\n\\t\\t{\\n\\t\\t\\twhile(x != -1){\\n\\t\\t\\t\\tif(v <= clusiind[x])return cluspath[clus[x]][clusiind[x]-v];\\n\\t\\t\\t\\tv -= clusiind[x]+1;\\n\\t\\t\\t\\tx = par[cluspath[clus[x]][0]];\\n\\t\\t\\t}\\n\\t\\t\\treturn x;\\n\\t\\t}\\n\\t\\t\\n\\t\\t// NOT VERIFIED\\n\\t\\t// [iord[x], right[x])\\n\\t\\tpublic int[][] makeRights()\\n\\t\\t{\\n\\t\\t\\tint root = -1;\\n\\t\\t\\tint n = g.length;\\n\\t\\t\\tfor(int i = 0;i < n;i++)if(par[i] == -1)root = i;\\n\\t\\t\\t\\n\\t\\t\\tint[] ord = new int[n];\\n\\t\\t\\tint[] right = new int[n];\\n\\t\\t\\t\\n\\t\\t\\tint[] curs = new int[n];\\n\\t\\t\\tint[] inds = new int[n];\\n\\t\\t\\tint sp = 0, p = 0;\\n\\t\\t\\tcurs[sp++] = root;\\n\\t\\t\\twhile(sp > 0){\\n\\t\\t\\t\\tint cur = curs[sp-1];\\n\\t\\t\\t\\tint ind = inds[sp-1];\\n\\t\\t\\t\\tinds[sp-1]++;\\n\\t\\t\\t\\tif(ind == 0){\\n\\t\\t\\t\\t\\tord[p++] = cur;\\n\\t\\t\\t\\t\\tfor(int e : g[cur]){\\n\\t\\t\\t\\t\\t\\tif(par[cur] == e)continue;\\n\\t\\t\\t\\t\\t\\tif(clus[cur] == clus[e]){\\n\\t\\t\\t\\t\\t\\t\\tcurs[sp] = e;\\n\\t\\t\\t\\t\\t\\t\\tinds[sp] = 0;\\n\\t\\t\\t\\t\\t\\t\\tsp++;\\n\\t\\t\\t\\t\\t\\t\\tbreak;\\n\\t\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t}else if(ind-1 < g[cur].length){\\n\\t\\t\\t\\t\\tint e = g[cur][ind-1];\\n\\t\\t\\t\\t\\tif(e == par[cur])continue;\\n\\t\\t\\t\\t\\tif(clus[cur] == clus[e])continue;\\n\\t\\t\\t\\t\\tcurs[sp] = e;\\n\\t\\t\\t\\t\\tinds[sp] = 0;\\n\\t\\t\\t\\t\\tsp++;\\n\\t\\t\\t\\t}else{\\n\\t\\t\\t\\t\\tright[cur] = p;\\n\\t\\t\\t\\t\\tsp--;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\tint[] iord = new int[n];\\n\\t\\t\\tfor(int i = 0;i < n;i++)iord[ord[i]] = i;\\n\\t\\t\\treturn new int[][]{ord, iord, right};\\n\\t\\t}\\n\\t\\t\\n\\t}\\n\\t\\n\\tpublic static class SegmentTreeOverwriteRMQ {\\n\\t\\tpublic int M, H, N;\\n\\t\\tpublic int[] st;\\n\\t\\tpublic int[] cover;\\n\\t\\tprivate int I = 0;\\n\\t\\t\\n\\t\\tpublic SegmentTreeOverwriteRMQ(int n)\\n\\t\\t{\\n\\t\\t\\tN = n;\\n\\t\\t\\tM = Integer.highestOneBit(Math.max(N-1, 1))<<2;\\n\\t\\t\\tH = M>>>1;\\n\\t\\t\\tcover = new int[H];\\n\\t\\t\\tArrays.fill(cover, I);\\n\\t\\t\\tst = new int[M];\\n\\t\\t\\tArrays.fill(st, I);\\n\\t\\t\\tfor(int i = 0;i < N;i++){\\n\\t\\t\\t\\tst[H+i] = 0;\\n\\t\\t\\t}\\n\\t\\t\\tfor(int i = H-1;i >= 1;i--){\\n\\t\\t\\t\\tpropagate(i);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\t\\n\\t\\tprivate void propagate(int i)\\n\\t\\t{\\n\\t\\t\\tif(cover[i] == I){\\n\\t\\t\\t\\tst[i] = Math.max(st[2*i], st[2*i+1]);\\n\\t\\t\\t}else{\\n\\t\\t\\t\\tst[i] = cover[i];\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\t\\n\\t\\tpublic void update(int l, int r, int v){ update(l, r, v, 0, H, 1); }\\n\\t\\t\\n\\t\\tprivate void update(int l, int r, int v, int cl, int cr, int cur)\\n\\t\\t{\\n\\t\\t\\tif(l <= cl && cr <= r){\\n\\t\\t\\t\\tif(cr == cl+1){\\n\\t\\t\\t\\t\\tst[cur] = v;\\n\\t\\t\\t\\t}else{\\n\\t\\t\\t\\t\\tcover[cur] = v;\\n\\t\\t\\t\\t\\tpropagate(cur);\\n\\t\\t\\t\\t}\\n\\t\\t\\t}else{\\n\\t\\t\\t\\tint mid = cl+cr>>>1;\\n\\t\\t\\t\\tboolean bp = false;\\n\\t\\t\\t\\tif(cover[cur] != I){ // back-propagate\\n\\t\\t\\t\\t\\tif(2*cur >= H){\\n\\t\\t\\t\\t\\t\\tst[2*cur] = st[2*cur+1] = cover[cur];\\n\\t\\t\\t\\t\\t}else{\\n\\t\\t\\t\\t\\t\\tcover[2*cur] = cover[2*cur+1] = cover[cur];\\n\\t\\t\\t\\t\\t\\tbp = true;\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\tcover[cur] = I;\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tif(cl < r && l < mid){\\n\\t\\t\\t\\t\\tupdate(l, r, v, cl, mid, 2*cur);\\n\\t\\t\\t\\t}else if(bp){\\n\\t\\t\\t\\t\\tpropagate(2*cur);\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tif(mid < r && l < cr){\\n\\t\\t\\t\\t\\tupdate(l, r, v, mid, cr, 2*cur+1);\\n\\t\\t\\t\\t}else if(bp){\\n\\t\\t\\t\\t\\tpropagate(2*cur+1);\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tpropagate(cur);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\t\\n\\t\\tpublic int max(int l, int r){ return max(l, r, 0, H, 1); }\\n\\t\\t\\n\\t\\tprivate int max(int l, int r, int cl, int cr, int cur)\\n\\t\\t{\\n\\t\\t\\tif(l <= cl && cr <= r){\\n\\t\\t\\t\\treturn st[cur];\\n\\t\\t\\t}else{\\n\\t\\t\\t\\tif(cover[cur] != I)return cover[cur];\\n\\t\\t\\t\\tint mid = cl+cr>>>1;\\n\\t\\t\\t\\tint L = I, R = I;\\n\\t\\t\\t\\tif(cl < r && l < mid){\\n\\t\\t\\t\\t\\tL = max(l, r, cl, mid, 2*cur);\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tif(mid < r && l < cr){\\n\\t\\t\\t\\t\\tR = max(l, r, mid, cr, 2*cur+1);\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\treturn Math.max(L, R);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\t\\n\\t\\tpublic int[] toArray() { return toArray(1, 0, H, new int[H]); }\\n\\t\\t\\n\\t\\tprivate int[] toArray(int cur, int l, int r, int[] ret)\\n\\t\\t{\\n\\t\\t\\tif(r-l == 1){\\n\\t\\t\\t\\tret[cur-H] = st[cur];\\n\\t\\t\\t}else if(cover[cur] != I){\\n\\t\\t\\t\\tArrays.fill(ret, l, r, cover[cur]);\\n\\t\\t\\t}else{\\n\\t\\t\\t\\ttoArray(2*cur, l, l+r>>>1, ret);\\n\\t\\t\\t\\ttoArray(2*cur+1, l+r>>>1, r, ret);\\n\\t\\t\\t}\\n\\t\\t\\treturn ret;\\n\\t\\t}\\n\\t}\\n\\t\\n\\t\\n\\tpublic static int[] sortByPreorder(int[][] g, int root){\\n\\t\\tint n = g.length;\\n\\t\\tint[] stack = new int[n];\\n\\t\\tint[] ord = new int[n];\\n\\t\\tboolean[] ved = new boolean[n];\\n\\t\\tstack[0] = root;\\n\\t\\tint p = 1;\\n\\t\\tint r = 0;\\n\\t\\tved[root] = true;\\n\\t\\twhile(p > 0){\\n\\t\\t\\tint cur = stack[p-1];\\n\\t\\t\\tord[r++] = cur;\\n\\t\\t\\tp--;\\n\\t\\t\\tfor(int e : g[cur]){\\n\\t\\t\\t\\tif(!ved[e]){\\n\\t\\t\\t\\t\\tved[e] = true;\\n\\t\\t\\t\\t\\tstack[p++] = e;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\treturn ord;\\n\\t}\\n\\t\\n\\tpublic static int[][] makeRights(int[][] g, int[] par, int root)\\n\\t{\\n\\t\\tint n = g.length;\\n\\t\\tint[] ord = sortByPreorder(g, root);\\n\\t\\tint[] iord = new int[n];\\n\\t\\tfor(int i = 0;i < n;i++)iord[ord[i]] = i;\\n\\t\\t\\n\\t\\tint[] right = new int[n];\\n\\t\\tfor(int i = n-1;i >= 1;i--){\\n\\t\\t\\tif(right[i] == 0)right[i] = i;\\n\\t\\t\\tint p = iord[par[ord[i]]];\\n\\t\\t\\tright[p] = Math.max(right[p], right[i]);\\n\\t\\t}\\n\\t\\treturn new int[][]{ord, iord, right};\\n\\t}\\n\\n\\tpublic static int[][] parents3(int[][] g, int root) {\\n\\t\\tint n = g.length;\\n\\t\\tint[] par = new int[n];\\n\\t\\tArrays.fill(par, -1);\\n\\n\\t\\tint[] depth = new int[n];\\n\\t\\tdepth[0] = 0;\\n\\n\\t\\tint[] q = new int[n];\\n\\t\\tq[0] = root;\\n\\t\\tfor (int p = 0, r = 1; p < r; p++) {\\n\\t\\t\\tint cur = q[p];\\n\\t\\t\\tfor (int nex : g[cur]) {\\n\\t\\t\\t\\tif (par[cur] != nex) {\\n\\t\\t\\t\\t\\tq[r++] = nex;\\n\\t\\t\\t\\t\\tpar[nex] = cur;\\n\\t\\t\\t\\t\\tdepth[nex] = depth[cur] + 1;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\treturn new int[][] { par, q, depth };\\n\\t}\\n\\n\\tstatic int[][] packU(int n, int[] from, int[] to) {\\n\\t\\tint[][] g = new int[n][];\\n\\t\\tint[] p = new int[n];\\n\\t\\tfor (int f : from)\\n\\t\\t\\tp[f]++;\\n\\t\\tfor (int t : to)\\n\\t\\t\\tp[t]++;\\n\\t\\tfor (int i = 0; i < n; i++)\\n\\t\\t\\tg[i] = new int[p[i]];\\n\\t\\tfor (int i = 0; i < from.length; i++) {\\n\\t\\t\\tg[from[i]][--p[from[i]]] = to[i];\\n\\t\\t\\tg[to[i]][--p[to[i]]] = from[i];\\n\\t\\t}\\n\\t\\treturn g;\\n\\t}\\n\\n\\n\\tvoid run() throws Exception\\n\\t{\\n\\t\\tis = INPUT.isEmpty() ? System.in : new ByteArrayInputStream(INPUT.getBytes());\\n\\t\\tout = new PrintWriter(System.out);\\n\\t\\t\\n\\t\\tlong s = System.currentTimeMillis();\\n\\t\\tsolve();\\n\\t\\tout.flush();\\n\\t\\tif(!INPUT.isEmpty())tr(System.currentTimeMillis()-s+\\\"ms\\\");\\n\\t}\\n\\t\\n\\tpublic static void main(String[] args) throws Exception \\n\\t{ new Main().run(); }\\n\\t\\n\\tprivate byte[] inbuf = new byte[1024];\\n\\tpublic int lenbuf = 0, ptrbuf = 0;\\n\\t\\n\\tprivate int readByte()\\n\\t{\\n\\t\\tif(lenbuf == -1)throw new InputMismatchException();\\n\\t\\tif(ptrbuf >= lenbuf)\\n\\t\\t{\\n\\t\\t\\tptrbuf = 0;\\n\\t\\t\\ttry \\n\\t\\t\\t{ lenbuf = is.read(inbuf); } \\n\\t\\t\\tcatch (IOException e) \\n\\t\\t\\t{ throw new InputMismatchException(); }\\n\\t\\t\\tif(lenbuf <= 0)return -1;\\n\\t\\t}\\n\\t\\treturn inbuf[ptrbuf++];\\n\\t}\\n\\t\\n\\tprivate boolean isSpaceChar(int c)\\n\\t{ return !(c >= 33 && c <= 126); }\\n\\tprivate int skip() \\n\\t{ int b; while((b = readByte()) != -1 && isSpaceChar(b)); return b; }\\n\\t\\n\\tprivate double nd() \\n\\t{ return Double.parseDouble(ns()); }\\n\\tprivate char nc()\\n\\t{ return (char)skip(); }\\n\\t\\n\\tprivate String ns()\\n\\t{\\n\\t\\tint b = skip();\\n\\t\\tStringBuilder sb = new StringBuilder();\\n\\t\\twhile(!(isSpaceChar(b)))\\n\\t\\t{ // when nextLine, (isSpaceChar(b) && b != ' ')\\n\\t\\t\\tsb.appendCodePoint(b);\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn sb.toString();\\n\\t}\\n\\t\\n\\tprivate String nsl()\\n\\t{\\n\\t\\tint b = skip();\\n\\t\\tStringBuilder sb = new StringBuilder();\\n\\t\\twhile(!(isSpaceChar(b) && b != ' '))\\n\\t\\t{ // when nextLine, (isSpaceChar(b) && b != ' ')\\n\\t\\t\\tsb.appendCodePoint(b);\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn sb.toString();\\n\\t}\\n\\t\\n\\tprivate char[] ns(int n)\\n\\t{\\n\\t\\tchar[] buf = new char[n];\\n\\t\\tint b = skip(), p = 0;\\n\\t\\twhile(p < n && !(isSpaceChar(b)))\\n\\t\\t{\\n\\t\\t\\tbuf[p++] = (char)b;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn n == p ? buf : Arrays.copyOf(buf, p);\\n\\t}\\n\\t\\n\\tprivate char[][] nm(int n, int m)\\n\\t{\\n\\t\\tchar[][] map = new char[n][];\\n\\t\\tfor(int i = 0;i < n;i++)map[i] = ns(m);\\n\\t\\treturn map;\\n\\t}\\n\\t\\n\\tprivate int[] na(int n)\\n\\t{\\n\\t\\tint[] a = new int[n];\\n\\t\\tfor(int i = 0;i < n;i++)a[i] = ni();\\n\\t\\treturn a;\\n\\t}\\n\\t\\n\\tprivate int ni()\\n\\t{\\n\\t\\tint num = 0, b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-')\\n\\t\\t{\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true)\\n\\t\\t{\\n\\t\\t\\tif(b >= '0' && b <= '9')\\n\\t\\t\\t{\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else\\n\\t\\t\\t{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate long nl()\\n\\t{\\n\\t\\tlong num = 0;\\n\\t\\tint b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-')\\n\\t\\t{\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9')\\n\\t\\t\\t{\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else\\n\\t\\t\\t{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate static void tr(Object... o) \\n\\t{ System.out.println(Arrays.deepToString(o)); }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts following parameters:\\n * 1. 2D_INTEGER_ARRAY tree\\n * 2. 2D_INTEGER_ARRAY queries\\n */\\n\\n public static List<Integer> solve(List<List<Integer>> tree, List<List<Integer>> queries) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int q = Integer.parseInt(firstMultipleInput[1]);\\n\\n List<List<Integer>> tree = new ArrayList<>();\\n\\n IntStream.range(0, n - 1).forEach(i -> {\\n try {\\n tree.add(\\n Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList())\\n );\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n List<List<Integer>> queries = new ArrayList<>();\\n\\n IntStream.range(0, q).forEach(i -> {\\n try {\\n queries.add(\\n Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList())\\n );\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n List<Integer> result = Result.solve(tree, queries);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\"\\\\n\\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"solve(tree, queries)\"\n ],\n \"solution\": \"def segtree_init(ary):\\n ary = list(ary)\\n seg = [ary]\\n while len(ary) > 1:\\n if len(ary) & 1: ary.append(0)\\n ary = [max(ary[i],ary[i+1]) for i in range(0,len(ary),2)]\\n seg.append(ary)\\n return seg\\ndef segtree_set(seg, i, x):\\n ary = seg[0]\\n ary[i] = x\\n for j in range(1, len(seg)):\\n x = max(ary[i], ary[i^1])\\n ary = seg[j]\\n i >>= 1\\n ary[i] = x\\ndef segtree_max(seg, lo, hi):\\n m = 0\\n j = 0\\n while lo < hi:\\n ary = seg[j]\\n if lo & 1:\\n x = ary[lo]\\n if x > m: m = x\\n lo += 1\\n if hi & 1:\\n hi -= 1\\n x = ary[hi]\\n if x > m: m = x\\n lo >>= 1\\n hi >>= 1\\n j += 1\\n return m\\nclass heavy_light_node:\\n def __init__(self, segtree):\\n self.parent = None\\n self.pos = -1\\n self.segtree = segtree\\ndef build_tree(i, edges, location):\\n children = []\\n members = [i]\\n ed = edges[i]\\n while ed:\\n for j in range(1,len(ed)):\\n child = build_tree(ed[j], edges, location)\\n child.pos = len(members) - 1\\n children.append(child)\\n i = ed[0]\\n members.append(i)\\n ed = edges[i]\\n node = heavy_light_node(segtree_init(0 for j in members))\\n for child in children:\\n child.parent = node\\n for j in range(len(members)):\\n location[members[j]] = (node, j)\\n return node\\ndef read_tree(N):\\n edges = [[] for i in range(N)]\\n for i in range(N-1):\\n x, y = map(int, input().split())\\n edges[x].append(y)\\n edges[y].append(x)\\n size = [0] * N\\n active = [0]\\n while active:\\n i = active[-1]\\n if size[i] == 0:\\n size[i] = 1\\n for j in edges[i]:\\n edges[j].remove(i)\\n active.append(j)\\n else:\\n active.pop()\\n edges[i].sort(key=lambda j: -size[j])\\n size[i] = 1 + sum(size[j] for j in edges[i])\\n location = [None] * N\\n build_tree(0, edges, location)\\n return location\\ndef root_path(i, location):\\n loc = location[i]\\n path = [ loc ]\\n loc = loc[0]\\n while loc.parent != None:\\n path.append((loc.parent, loc.pos))\\n loc = loc.parent\\n path.reverse()\\n return path\\ndef max_weight(x, y):\\n px = root_path(x, location)\\n py = root_path(y, location)\\n m = 1\\n stop = min(len(px), len(py))\\n while m < stop and px[m][0] == py[m][0]: m += 1\\n loc, a = px[m-1]\\n b = py[m-1][1]\\n if a > b: a, b = b, a\\n w = segtree_max(loc.segtree, a, b+1)\\n for j in range(m, len(px)):\\n loc, i = px[j]\\n x = segtree_max(loc.segtree, 0, i+1)\\n if x > w: w = x\\n for j in range(m, len(py)):\\n loc, i = py[j]\\n x = segtree_max(loc.segtree, 0, i+1)\\n if x > w: w = x\\n return w\\nN, Q = map(int, input().split())\\nlocation = read_tree(N)\\nfor i in range(Q):\\n t, x, y = map(int, input().split())\\n if t == 1:\\n loc, i = location[x]\\n segtree_set(loc.segtree, i, y)\\n elif t == 2:\\n print(max_weight(x, y))\",\n \"template\": \"#\\n# Complete the 'solve' function below.\\n#\\n# The function is expected to return an INTEGER_ARRAY.\\n# The function accepts following parameters:\\n# 1. 2D_INTEGER_ARRAY tree\\n# 2. 2D_INTEGER_ARRAY queries\\n#\\n\\ndef solve(tree, queries):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n first_multiple_input = input().rstrip().split()\\n\\n n = int(first_multiple_input[0])\\n\\n q = int(first_multiple_input[1])\\n\\n tree = []\\n\\n for _ in range(n - 1):\\n tree.append(list(map(int, input().rstrip().split())))\\n\\n queries = []\\n\\n for _ in range(q):\\n queries.append(list(map(int, input().rstrip().split())))\\n\\n result = solve(tree, queries)\\n\\n fptr.write('\\\\n'.join(map(str, result)))\\n fptr.write('\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":160,"cells":{"question_id":{"kind":"number","value":1361,"string":"1,361"},"question_title":{"kind":"string","value":"Almost Equal - Advanced"},"question_content":{"kind":"string","value":"A Sumo wrestling championship is scheduled to be held this winter in the _HackerCity_ where _N_ wrestlers from different parts of the world are going to participate. The rules state that two wrestlers can fight against each other if and only if the difference in their height is less than or equal to K, \n(i.e) wrestler A and wrestler B can fight if and only if _|height(A)-height(B)|<=K_. \n\n<center><iframe width=\"560\" height=\"315\" src=\"hXCaZjtS-Us?rel=0\" frameborder=\"0\" allowfullscreen></iframe></center>\n\nGiven an array _H[]_, where _H[i]_ represents the height of the _i<sup>th</sup>_ fighter, for a given l, r where `0 <= l <= r < N`, can you count the number of pairs of fighters between l and r (both inclusive) who qualify to play a game?\n\n**Input Format** \nThe first line contains an integer _N_ and _K_ separated by a single space representing the number of Sumo wrestlers who are going to participate and the height difference K. \nThe second line contains _N_ integers separated by a single space, representing their heights _H[0] H[1] ... H[N - 1]_. \nThe third line contains _Q_, the number of queries. This is followed by _Q_ lines each having two integers _l_ and _r_ separated by a space. \n\n**Output Format** \nFor each query Q, output the corresponding value of the number of pairs of fighters for whom the absolute difference of height is not greater that _K_. \n\n**Constraints** \n1 <= N <= 100000 \n0 <= K <= 10<sup>9</sup> \n0 <= H[i] <= 10<sup>9</sup> \n1 <= Q <= 100000 \n0 <= l <= r < N \n\n**Sample Input**\n<pre>\n5 2\n1 3 4 3 0\n3\n0 1\n1 3\n0 4\n</pre>\n**Sample Output** \n<pre>\n1\n3\n6\n</pre>\n**Explanation** \nQuery #0: Between 0 and 1 we have i,j as (0,1) and |H[0]-H[1]|=2 therefore output is 1. \nQuery #1: The pairs (H[1],H[2]) (H[1],H[3]) and (H[2],H[3]) are the pairs such that |H[i]-H[j]| <=2. Hence output is 3. \nQuery #2: Apart from those in Query #1, we have (H[0],H[1]), (H[0], H[3]), (H[0], H[4]), hence 6. \n\n**Timelimits**\n\nTimelimits are given [here](https://hr-assets.s3.amazonaws.com/7bb46cae_challenge_assets/checker_limits/1361/limits.json)\n"},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Hard"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static List<Integer> solve(List<Integer> h, List<List<Integer>> queries)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.BufferedReader;\\nimport java.io.InputStreamReader;\\nimport java.io.IOException;\\nimport java.util.Arrays;\\nimport java.util.Comparator;\\nimport java.util.HashMap;\\nimport java.util.Map;\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n // Read user input\\n\\n BufferedReader br = new BufferedReader(new InputStreamReader(System.in));\\n String[] parts = br.readLine().split(\\\" \\\");\\n final int numWrestlers = Integer.parseInt(parts[0]);\\n int maxHeightDifference = Integer.parseInt(parts[1]);\\n String[] heightStrings = br.readLine().split(\\\" \\\");\\n int[] heights = new int[numWrestlers];\\n Wrestler[] wrestlers = new Wrestler[numWrestlers];\\n\\n if (numWrestlers == 0) {\\n System.out.println(\\\"0\\\");\\n return;\\n }\\n\\n for (int i = 0; i < numWrestlers; ++i) {\\n heights[i] = Integer.parseInt(heightStrings[i]);\\n int height = Integer.parseInt(heightStrings[i]);\\n wrestlers[i] = new Wrestler(height, i);\\n }\\n\\n int numQueries = Integer.parseInt(br.readLine());\\n Query[] queries = new Query[numQueries];\\n\\n for (int i = 0; i < numQueries; ++i) {\\n String[] queryStrings = br.readLine().split(\\\" \\\");\\n int queryLow = Integer.parseInt(queryStrings[0]);\\n int queryHigh = Integer.parseInt(queryStrings[1]);\\n queries[i] = new Query(queryLow, queryHigh, i);\\n }\\n\\n // Sort wrestlers by height and make a reverse map\\n\\n Wrestler[] sortedWrestlers = new Wrestler[numWrestlers];\\n System.arraycopy(wrestlers, 0, sortedWrestlers, 0, numWrestlers);\\n\\n Arrays.sort(sortedWrestlers, new Comparator<Wrestler>() {\\n public int compare(Wrestler wrestler1, Wrestler wrestler2) {\\n return wrestler1.height - wrestler2.height;\\n }\\n });\\n\\n Map<Integer, SortedTriplet> unsortedIndexToSortedTriplet = new HashMap<Integer, SortedTriplet>();\\n\\n for (int i = 0; i < numWrestlers; ++i) {\\n Wrestler currentWrestler = sortedWrestlers[i];\\n\\n int unsortedIndex = currentWrestler.index;\\n int heightIndex = i;\\n int heightMinusKIndex = findLeftmostValueGTE(sortedWrestlers, currentWrestler.height - maxHeightDifference);\\n int heightPlusKIndex = findRightmostValueLTE(sortedWrestlers, currentWrestler.height + maxHeightDifference);\\n SortedTriplet sortedTriplet = new SortedTriplet(heightIndex, heightMinusKIndex, heightPlusKIndex);\\n\\n unsortedIndexToSortedTriplet.put(unsortedIndex, sortedTriplet);\\n }\\n\\n\\n // Sort queries according to Mo's algorithm\\n\\n Arrays.sort(queries, new Comparator<Query>() {\\n public int compare(Query query1, Query query2) {\\n int sqrtMaxHigh = (int)Math.sqrt(numWrestlers);\\n\\n int sqrtBucket1 = (query1.low / sqrtMaxHigh);\\n int sqrtBucket2 = (query2.low / sqrtMaxHigh);\\n\\n if (sqrtBucket1 == sqrtBucket2) {\\n return query1.high - query2.high;\\n }\\n\\n return sqrtBucket1 - sqrtBucket2;\\n }\\n });\\n\\n // Process queries in sorted order\\n\\n int currentLow = 0;\\n int currentHigh = -1;\\n long currentAnswer = 0;\\n long[] answers = new long[numQueries];\\n FenwickTree wrestlersInQuery = new FenwickTree(numWrestlers);\\n\\n for (int i = 0; i < numQueries; ++i) {\\n Query query = queries[i];\\n\\n while (currentLow < query.low) {\\n SortedTriplet sorted = unsortedIndexToSortedTriplet.get(currentLow);\\n int numWrestlersInRangeOfValue = wrestlersInQuery.sum(sorted.heightMinusKIndex, sorted.heightPlusKIndex) - 1;\\n currentAnswer -= numWrestlersInRangeOfValue;\\n wrestlersInQuery.add(sorted.heightIndex, -1);\\n ++currentLow;\\n }\\n\\n while (currentLow > query.low) {\\n --currentLow;\\n SortedTriplet sorted = unsortedIndexToSortedTriplet.get(currentLow);\\n int numWrestlersInRangeOfValue = wrestlersInQuery.sum(sorted.heightMinusKIndex, sorted.heightPlusKIndex);\\n currentAnswer += numWrestlersInRangeOfValue;\\n wrestlersInQuery.add(sorted.heightIndex, 1);\\n }\\n\\n while (currentHigh < query.high) {\\n ++currentHigh;\\n SortedTriplet sorted = unsortedIndexToSortedTriplet.get(currentHigh);\\n int numWrestlersInRangeOfValue = wrestlersInQuery.sum(sorted.heightMinusKIndex, sorted.heightPlusKIndex);\\n currentAnswer += numWrestlersInRangeOfValue;\\n wrestlersInQuery.add(sorted.heightIndex, 1);\\n }\\n\\n while (currentHigh > query.high) {\\n SortedTriplet sorted = unsortedIndexToSortedTriplet.get(currentHigh);\\n int numWrestlersInRangeOfValue = wrestlersInQuery.sum(sorted.heightMinusKIndex, sorted.heightPlusKIndex) - 1;\\n currentAnswer -= numWrestlersInRangeOfValue;\\n wrestlersInQuery.add(sorted.heightIndex, -1);\\n --currentHigh;\\n }\\n\\n answers[query.index] = currentAnswer;\\n }\\n\\n // Output results\\n\\n StringBuilder result = new StringBuilder();\\n\\n for (int i = 0; i < numQueries; ++i) {\\n result.append(answers[i]);\\n result.append(\\\"\\\\n\\\");\\n }\\n\\n System.out.print(result.toString());\\n }\\n\\n private static int findLeftmostValueGTE(Wrestler[] sortedWrestlers, int valueToFind) {\\n int n = sortedWrestlers.length;\\n int low = 0;\\n int high = n - 1;\\n\\n while (low <= high) {\\n int mid = low + (high - low) / 2;\\n\\n if (sortedWrestlers[mid].height >= valueToFind && (mid == 0 || sortedWrestlers[mid - 1].height < valueToFind)) {\\n return mid;\\n } else if (sortedWrestlers[mid].height < valueToFind) {\\n low = mid + 1;\\n } else {\\n high = mid - 1;\\n }\\n }\\n\\n return -1;\\n }\\n\\n private static int findRightmostValueLTE(Wrestler[] sortedWrestlers, int valueToFind) {\\n int n = sortedWrestlers.length;\\n int low = 0;\\n int high = n - 1;\\n\\n while (low <= high) {\\n int mid = low + (high - low) / 2;\\n\\n if (sortedWrestlers[mid].height <= valueToFind && (mid == n - 1 || sortedWrestlers[mid + 1].height > valueToFind)) {\\n return mid;\\n } else if (sortedWrestlers[mid].height > valueToFind) {\\n high = mid - 1;\\n } else {\\n low = mid + 1;\\n }\\n }\\n\\n return -1;\\n }\\n\\n private static class SortedTriplet {\\n int heightIndex;\\n int heightMinusKIndex;\\n int heightPlusKIndex;\\n\\n public SortedTriplet(int heightIndex, int heightMinusKIndex, int heightPlusKIndex) {\\n this.heightIndex = heightIndex;\\n this.heightMinusKIndex = heightMinusKIndex;\\n this.heightPlusKIndex = heightPlusKIndex;\\n }\\n }\\n\\n private static class Wrestler {\\n int height;\\n int index;\\n\\n public Wrestler(int height, int index) {\\n this.height = height;\\n this.index = index;\\n }\\n }\\n\\n private static class Query {\\n int low;\\n int high;\\n int index;\\n\\n public Query(int low, int high, int index) {\\n this.low = low;\\n this.high = high;\\n this.index = index;\\n }\\n }\\n\\n private static class FenwickTree {\\n private int[] tree;\\n\\n public FenwickTree(int capacity) {\\n this.tree = new int[capacity];\\n }\\n\\n public void add(int index, int value) {\\n for (; index < this.tree.length; index |= index + 1) {\\n this.tree[index] += value;\\n }\\n }\\n\\n public int get(int index) {\\n return this.sum(index, index);\\n }\\n\\n public int sum(int startIndex, int endIndex) {\\n return this.prefixSum(endIndex + 1) - this.prefixSum(startIndex);\\n }\\n\\n private int prefixSum(int index) {\\n int sum = 0;\\n\\n for (; index > 0; index &= index - 1) {\\n sum += this.tree[index - 1];\\n }\\n\\n return sum;\\n }\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts following parameters:\\n * 1. INTEGER_ARRAY h\\n * 2. 2D_INTEGER_ARRAY queries\\n */\\n\\n public static List<Integer> solve(List<Integer> h, List<List<Integer>> queries) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int k = Integer.parseInt(firstMultipleInput[1]);\\n\\n String[] hTemp = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> h = new ArrayList<>();\\n\\n for (int i = 0; i < n; i++) {\\n int hItem = Integer.parseInt(hTemp[i]);\\n h.add(hItem);\\n }\\n\\n int q = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<List<Integer>> queries = new ArrayList<>();\\n\\n for (int i = 0; i < q; i++) {\\n String[] queriesRowTempItems = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> queriesRowItems = new ArrayList<>();\\n\\n for (int j = 0; j < 2; j++) {\\n int queriesItem = Integer.parseInt(queriesRowTempItems[j]);\\n queriesRowItems.add(queriesItem);\\n }\\n\\n queries.add(queriesRowItems);\\n }\\n\\n List<Integer> result = Result.solve(h, queries);\\n\\n for (int i = 0; i < result.size(); i++) {\\n bufferedWriter.write(String.valueOf(result.get(i)));\\n\\n if (i != result.size() - 1) {\\n bufferedWriter.write(\\\"\\\\n\\\");\\n }\\n }\\n\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\n\\npublic class Solution {\\n\\n static int nn;\\n static int block;\\n static int[] fenwick;\\n static long cnt = 0;\\n\\n static class B implements Comparable<B> {\\n int l;\\n int r;\\n int id;\\n \\n @Override\\n public int compareTo(B o) {\\n int i = l/block;\\n int j = o.l/block;\\n if (i != j) {\\n return i - j;\\n }\\n if (r != o.r) {\\n return r - o.r;\\n }\\n return l - o.l;\\n }\\n }\\n \\n static int getSum(int x) {\\n int s = 0;\\n for (; x != 0; x &= x-1)\\n s += fenwick[x-1];\\n return s;\\n }\\n\\n static class Node {\\n int l;\\n int r;\\n int h;\\n\\n public Node(int l, int r, int h) {\\n this.l = l;\\n this.r = r;\\n this.h = h;\\n }\\n\\n void remove() {\\n add(h, -1);\\n cnt -= getSum(r) - getSum(l);\\n }\\n\\n void add() {\\n cnt += getSum(r) - getSum(l);\\n add(h, 1);\\n }\\n\\n void add(int x, int v) {\\n for (; x < nn; x |= x+1)\\n fenwick[x] += v;\\n }\\n }\\n\\n \\n static public int lowerBound(int[] arr, int len, int key) {\\n if (key <= arr[0]) {\\n return 0;\\n }\\n if (key > arr[len - 1]) {\\n return 0;\\n }\\n \\n int index = Arrays.binarySearch(arr, 0, len, key);\\n if (index < 0) {\\n index = - index - 1;\\n if (index < 0) {\\n return 0;\\n }\\n } \\n return index;\\n }\\n \\n static public int upperBound(int[] arr, int len, int key) {\\n int index = Arrays.binarySearch(arr, 0, len, key+1);\\n if (index < 0) {\\n index = - index - 1;\\n if (index < 0 || index > len) {\\n return 0;\\n }\\n }\\n return index;\\n }\\n \\n public static int unique(int[] arr) {\\n if (arr.length == 1) return 1;\\n int len = 1;\\n while (len < arr.length && arr[len] != arr[len-1]) { \\n len++;\\n }\\n for (int i = len + 1; i < arr.length; i++) {\\n if (arr[i] != arr[len - 1]) {\\n len++;\\n arr[len - 1] = arr[i];\\n }\\n }\\n return len;\\n }\\n\\n public static void main(String[] args) throws IOException {\\n BufferedReader br = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bw = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n StringTokenizer st = new StringTokenizer(br.readLine());\\n int n = Integer.parseInt(st.nextToken());\\n int k = Integer.parseInt(st.nextToken());\\n\\n st = new StringTokenizer(br.readLine());\\n int[] a = new int[n];\\n int[] c = new int[n];\\n for (int i = 0; i < n; i++) {\\n int item = Integer.parseInt(st.nextToken());\\n c[i] = a[i] = item;\\n }\\n Arrays.sort(c);\\n nn = unique(c);\\n \\n Node[] nodes = new Node[n];\\n for (int i = 0; i < n; i++) {\\n int l = lowerBound(c, nn, a[i]-k);\\n int r = upperBound(c, nn, a[i]+k);\\n int h = lowerBound(c, nn, a[i]);\\n nodes[i] = new Node(l, r, h);\\n }\\n \\n st = new StringTokenizer(br.readLine());\\n int q = Integer.parseInt(st.nextToken());\\n \\n B[] b = new B[q];\\n block = (int) Math.max(1.0, Math.sqrt((double)(n)*n/Math.max(1, q)));\\n for (int i = 0; i < q; i++) {\\n st = new StringTokenizer(br.readLine());\\n b[i] = new B();\\n b[i].id = i;\\n b[i].l = Integer.parseInt(st.nextToken());\\n b[i].r = Integer.parseInt(st.nextToken()) + 1;\\n }\\n Arrays.sort(b);\\n int l = 0;\\n int r = 0;\\n fenwick = new int[n];\\n long[] result = new long[q];\\n for (int i = 0; i < q; i++) {\\n while (l < b[i].l) {\\n nodes[l++].remove();\\n }\\n while (b[i].l < l) {\\n nodes[--l].add();\\n }\\n while (b[i].r < r) {\\n nodes[--r].remove();\\n }\\n while (r < b[i].r) {\\n nodes[r++].add();\\n }\\n result[b[i].id] = cnt;\\n }\\n\\n for (int i = 0; i < result.length; i++) {\\n bw.write(String.valueOf(result[i]));\\n\\n if (i != result.length - 1) {\\n bw.write(\\\"\\\\n\\\");\\n }\\n }\\n\\n bw.newLine();\\n\\n bw.close();\\n br.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts following parameters:\\n * 1. INTEGER_ARRAY h\\n * 2. 2D_INTEGER_ARRAY queries\\n */\\n\\n public static List<Integer> solve(List<Integer> h, List<List<Integer>> queries) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int k = Integer.parseInt(firstMultipleInput[1]);\\n\\n List<Integer> h = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int q = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<List<Integer>> queries = new ArrayList<>();\\n\\n IntStream.range(0, q).forEach(i -> {\\n try {\\n queries.add(\\n Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList())\\n );\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n List<Integer> result = Result.solve(h, queries);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\"\\\\n\\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\n\\npublic class Solution {\\n\\n static int nn;\\n static int block;\\n static int[] fenwick;\\n static long cnt = 0;\\n\\n static class B implements Comparable<B> {\\n int l;\\n int r;\\n int id;\\n \\n @Override\\n public int compareTo(B o) {\\n int i = l/block;\\n int j = o.l/block;\\n if (i != j) {\\n return i - j;\\n }\\n if (r != o.r) {\\n return r - o.r;\\n }\\n return l - o.l;\\n }\\n }\\n \\n static int getSum(int x) {\\n int s = 0;\\n for (; x != 0; x &= x-1)\\n s += fenwick[x-1];\\n return s;\\n }\\n\\n static class Node {\\n int l;\\n int r;\\n int h;\\n\\n public Node(int l, int r, int h) {\\n this.l = l;\\n this.r = r;\\n this.h = h;\\n }\\n\\n void remove() {\\n add(h, -1);\\n cnt -= getSum(r) - getSum(l);\\n }\\n\\n void add() {\\n cnt += getSum(r) - getSum(l);\\n add(h, 1);\\n }\\n\\n void add(int x, int v) {\\n for (; x < nn; x |= x+1)\\n fenwick[x] += v;\\n }\\n }\\n\\n \\n static public int lowerBound(int[] arr, int len, int key) {\\n if (key <= arr[0]) {\\n return 0;\\n }\\n if (key > arr[len - 1]) {\\n return 0;\\n }\\n \\n int index = Arrays.binarySearch(arr, 0, len, key);\\n if (index < 0) {\\n index = - index - 1;\\n if (index < 0) {\\n return 0;\\n }\\n } \\n return index;\\n }\\n \\n static public int upperBound(int[] arr, int len, int key) {\\n int index = Arrays.binarySearch(arr, 0, len, key+1);\\n if (index < 0) {\\n index = - index - 1;\\n if (index < 0 || index > len) {\\n return 0;\\n }\\n }\\n return index;\\n }\\n \\n public static int unique(int[] arr) {\\n if (arr.length == 1) return 1;\\n int len = 1;\\n while (len < arr.length && arr[len] != arr[len-1]) { \\n len++;\\n }\\n for (int i = len + 1; i < arr.length; i++) {\\n if (arr[i] != arr[len - 1]) {\\n len++;\\n arr[len - 1] = arr[i];\\n }\\n }\\n return len;\\n }\\n\\n public static void main(String[] args) throws IOException {\\n BufferedReader br = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bw = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n StringTokenizer st = new StringTokenizer(br.readLine());\\n int n = Integer.parseInt(st.nextToken());\\n int k = Integer.parseInt(st.nextToken());\\n\\n st = new StringTokenizer(br.readLine());\\n int[] a = new int[n];\\n int[] c = new int[n];\\n for (int i = 0; i < n; i++) {\\n int item = Integer.parseInt(st.nextToken());\\n c[i] = a[i] = item;\\n }\\n Arrays.sort(c);\\n nn = unique(c);\\n \\n Node[] nodes = new Node[n];\\n for (int i = 0; i < n; i++) {\\n int l = lowerBound(c, nn, a[i]-k);\\n int r = upperBound(c, nn, a[i]+k);\\n int h = lowerBound(c, nn, a[i]);\\n nodes[i] = new Node(l, r, h);\\n }\\n \\n st = new StringTokenizer(br.readLine());\\n int q = Integer.parseInt(st.nextToken());\\n \\n B[] b = new B[q];\\n block = (int) Math.max(1.0, Math.sqrt((double)(n)*n/Math.max(1, q)));\\n for (int i = 0; i < q; i++) {\\n st = new StringTokenizer(br.readLine());\\n b[i] = new B();\\n b[i].id = i;\\n b[i].l = Integer.parseInt(st.nextToken());\\n b[i].r = Integer.parseInt(st.nextToken()) + 1;\\n }\\n Arrays.sort(b);\\n int l = 0;\\n int r = 0;\\n fenwick = new int[n];\\n long[] result = new long[q];\\n for (int i = 0; i < q; i++) {\\n while (l < b[i].l) {\\n nodes[l++].remove();\\n }\\n while (b[i].l < l) {\\n nodes[--l].add();\\n }\\n while (b[i].r < r) {\\n nodes[--r].remove();\\n }\\n while (r < b[i].r) {\\n nodes[r++].add();\\n }\\n result[b[i].id] = cnt;\\n }\\n\\n for (int i = 0; i < result.length; i++) {\\n bw.write(String.valueOf(result[i]));\\n\\n if (i != result.length - 1) {\\n bw.write(\\\"\\\\n\\\");\\n }\\n }\\n\\n bw.newLine();\\n\\n bw.close();\\n br.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts following parameters:\\n * 1. INTEGER_ARRAY h\\n * 2. 2D_INTEGER_ARRAY queries\\n */\\n\\n public static List<Integer> solve(List<Integer> h, List<List<Integer>> queries) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int k = Integer.parseInt(firstMultipleInput[1]);\\n\\n List<Integer> h = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int q = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<List<Integer>> queries = new ArrayList<>();\\n\\n IntStream.range(0, q).forEach(i -> {\\n try {\\n queries.add(\\n Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList())\\n );\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n List<Integer> result = Result.solve(h, queries);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\"\\\\n\\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"solve(h, queries)\"\n ],\n \"solution\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\nimport bisect\\n\\n#\\n# Complete the 'solve' function below.\\n#\\n# The function is expected to return an INTEGER_ARRAY.\\n# The function accepts following parameters:\\n# 1. INTEGER_ARRAY h\\n# 2. 2D_INTEGER_ARRAY queries\\n#\\n\\ndef solve(h, k, queries):\\n n = len(h)\\n # Coordinate Compression\\n values = set()\\n for h_i in h:\\n values.add(h_i)\\n values.add(h_i - k)\\n values.add(h_i + k)\\n values = sorted(values)\\n value_to_idx = {v: idx for idx, v in enumerate(values)}\\n h_idx = [value_to_idx[h_i] for h_i in h]\\n # Prepare Mo's algorithm\\n block_size = int(n ** 0.5)\\n queries_with_idx = [(l, r, idx) for idx, (l, r) in enumerate(queries)]\\n queries_with_idx.sort(key=lambda x: (x[0] // block_size, x[1] if (x[0] // block_size) % 2 == 0 else -x[1]))\\n # Initialize BIT\\n class BIT:\\n def __init__(self, size):\\n self.size = size + 2\\n self.tree = [0] * self.size\\n def update(self, idx, delta):\\n idx += 1 # BIT is 1-indexed\\n while idx < self.size:\\n self.tree[idx] += delta\\n idx += idx & -idx\\n def query(self, idx):\\n idx +=1\\n res = 0\\n while idx > 0:\\n res += self.tree[idx]\\n idx -= idx & -idx\\n return res\\n bit_size = len(values)\\n bit = BIT(bit_size)\\n currL, currR = 0, -1\\n total_count = 0\\n answer = [0] * len(queries)\\n def add(pos):\\n nonlocal total_count\\n x_value = h[pos]\\n x_idx = value_to_idx[x_value]\\n l_value = x_value - k\\n r_value = x_value + k\\n l_idx = bisect.bisect_left(values, l_value)\\n r_idx = bisect.bisect_right(values, r_value) - 1\\n count = bit.query(r_idx) - bit.query(l_idx - 1)\\n total_count += count\\n bit.update(x_idx, 1)\\n def remove(pos):\\n nonlocal total_count\\n x_value = h[pos]\\n x_idx = value_to_idx[x_value]\\n # Before removing x from BIT, we need to query the count\\n l_value = x_value - k\\n r_value = x_value + k\\n l_idx = bisect.bisect_left(values, l_value)\\n r_idx = bisect.bisect_right(values, r_value) - 1\\n count = bit.query(r_idx) - bit.query(l_idx - 1)\\n count -= 1 # Exclude self\\n total_count -= count\\n bit.update(x_idx, -1)\\n for l, r, idx in queries_with_idx:\\n while currL > l:\\n currL -=1\\n add(currL)\\n while currR < r:\\n currR +=1\\n add(currR)\\n while currL < l:\\n remove(currL)\\n currL +=1\\n while currR > r:\\n remove(currR)\\n currR -=1\\n answer[idx] = total_count\\n return answer\\n\\nif __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n first_multiple_input = input().rstrip().split()\\n\\n n = int(first_multiple_input[0])\\n\\n k = int(first_multiple_input[1])\\n\\n h = list(map(int, input().rstrip().split()))\\n\\n q = int(input().strip())\\n\\n queries = []\\n\\n for _ in range(q):\\n queries.append(list(map(int, input().rstrip().split())))\\n\\n result = solve(h, k, queries)\\n\\n fptr.write('\\\\n'.join(map(str, result)))\\n fptr.write('\\\\n')\\n\\n fptr.close()\\n\",\n \"template\": \"#\\n# Complete the 'solve' function below.\\n#\\n# The function is expected to return an INTEGER_ARRAY.\\n# The function accepts following parameters:\\n# 1. INTEGER_ARRAY h\\n# 2. 2D_INTEGER_ARRAY queries\\n#\\n\\ndef solve(h, queries):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n first_multiple_input = input().rstrip().split()\\n\\n n = int(first_multiple_input[0])\\n\\n k = int(first_multiple_input[1])\\n\\n h = list(map(int, input().rstrip().split()))\\n\\n q = int(input().strip())\\n\\n queries = []\\n\\n for _ in range(q):\\n queries.append(list(map(int, input().rstrip().split())))\\n\\n result = solve(h, queries)\\n\\n fptr.write('\\\\n'.join(map(str, result)))\\n fptr.write('\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":161,"cells":{"question_id":{"kind":"number","value":1769,"string":"1,769"},"question_title":{"kind":"string","value":"Almost sorted interval"},"question_content":{"kind":"string","value":"Shik loves sorted intervals. But currently he does not have enough time to sort all the numbers. So he decided to use *Almost sorted intervals*. An *Almost sorted interval* is a consecutive subsequence in a sequence which satisfies the following property:\n\n1. The first number is the smallest.\n2. The last number is the largest.\n\nPlease help him count the number of almost sorted intervals in this permutation. \n\n*Note:* Two intervals are different if at least one of the starting or ending indices are different.\n\n**Input Format** \nThe first line contains an integer N. \nThe second line contains a permutation from 1 to N.\n\n**Output Format** \nOutput the number of almost sorted intervals. \n\n**Constraints** \n1 ≤ N ≤ 10<sup>6</sup> \n\n**Sample Input** \n\n 5\n 3 1 2 5 4\n\n**Sample Output** \n\n 8\n\n**Explanation** \nThe subsequences [3], [1], [1 2], [1 2 5], [2], [2 5], [5], [4] are almost sorted intervals."},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Hard"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static int solve(List<Integer> arr)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.BufferedWriter;\\nimport java.util.InputMismatchException;\\nimport java.util.NoSuchElementException;\\nimport java.math.BigInteger;\\nimport java.io.OutputStream;\\nimport java.io.PrintWriter;\\nimport java.io.Writer;\\nimport java.io.IOException;\\nimport java.util.Arrays;\\nimport java.io.InputStream;\\nimport java.io.OutputStreamWriter;\\n\\n/**\\n * Built using CHelper plug-in\\n * Actual solution is at the top\\n * @author Egor Kulikov (egor@egork.net)\\n */\\npublic class Solution {\\n\\tpublic static void main(String[] args) {\\n\\t\\tInputStream inputStream = System.in;\\n\\t\\tOutputStream outputStream = System.out;\\n\\t\\tInputReader in = new InputReader(inputStream);\\n\\t\\tOutputWriter out = new OutputWriter(outputStream);\\n\\t\\tAlmostSortedInterval solver = new AlmostSortedInterval();\\n\\t\\tsolver.solve(1, in, out);\\n\\t\\tout.close();\\n\\t}\\n}\\n\\nclass AlmostSortedInterval {\\n public void solve(int testNumber, InputReader in, OutputWriter out) {\\n\\t\\tfinal int count = in.readInt();\\n\\t\\tint[] array = IOUtils.readIntArray(in, count);\\n\\t\\tMiscUtils.decreaseByOne(array);\\n\\t\\tint[] stack = new int[count];\\n\\t\\tint size = 0;\\n\\t\\tint[] nextLess = new int[count];\\n\\t\\tfor (int i = count - 1; i >= 0; i--) {\\n\\t\\t\\twhile (size > 0 && array[stack[size - 1]] > array[i])\\n\\t\\t\\t\\tsize--;\\n\\t\\t\\tif (size > 0)\\n\\t\\t\\t\\tnextLess[i] = stack[size - 1];\\n\\t\\t\\telse\\n\\t\\t\\t\\tnextLess[i] = count;\\n\\t\\t\\tstack[size++] = i;\\n\\t\\t}\\n\\t\\tsize = 0;\\n\\t\\tint[] prevMore = new int[count];\\n\\t\\tfor (int i = 0; i < count; i++) {\\n\\t\\t\\twhile (size > 0 && array[stack[size - 1]] < array[i])\\n\\t\\t\\t\\tsize--;\\n\\t\\t\\tif (size > 0)\\n\\t\\t\\t\\tprevMore[i] = stack[size - 1];\\n\\t\\t\\telse\\n\\t\\t\\t\\tprevMore[i] = -1;\\n\\t\\t\\tstack[size++] = i;\\n\\t\\t}\\n\\t\\tIntervalTree tree = new SumIntervalTree(count);\\n\\t\\tint[] first = new int[count + 1];\\n\\t\\tint[] next = new int[count];\\n\\t\\tArrays.fill(first, -1);\\n\\t\\tfor (int i = 0; i < count; i++) {\\n\\t\\t\\tnext[i] = first[nextLess[i]];\\n\\t\\t\\tfirst[nextLess[i]] = i;\\n\\t\\t}\\n\\t\\tlong answer = 0;\\n\\t\\tfor (int i = 0; i < count; i++) {\\n\\t\\t\\tfor (int j = first[i]; j != -1; j = next[j])\\n\\t\\t\\t\\ttree.update(j, j, -1);\\n\\t\\t\\ttree.update(i, i, 1);\\n\\t\\t\\tanswer += tree.query(prevMore[i] + 1, i);\\n\\t\\t}\\n\\t\\tout.printLine(answer);\\n }\\n}\\n\\nclass InputReader {\\n\\n\\tprivate InputStream stream;\\n\\tprivate byte[] buf = new byte[1024];\\n\\tprivate int curChar;\\n\\tprivate int numChars;\\n\\tprivate SpaceCharFilter filter;\\n\\n\\tpublic InputReader(InputStream stream) {\\n\\t\\tthis.stream = stream;\\n\\t}\\n\\n\\tpublic int read() {\\n\\t\\tif (numChars == -1)\\n\\t\\t\\tthrow new InputMismatchException();\\n\\t\\tif (curChar >= numChars) {\\n\\t\\t\\tcurChar = 0;\\n\\t\\t\\ttry {\\n\\t\\t\\t\\tnumChars = stream.read(buf);\\n\\t\\t\\t} catch (IOException e) {\\n\\t\\t\\t\\tthrow new InputMismatchException();\\n\\t\\t\\t}\\n\\t\\t\\tif (numChars <= 0)\\n\\t\\t\\t\\treturn -1;\\n\\t\\t}\\n\\t\\treturn buf[curChar++];\\n\\t}\\n\\n\\tpublic int readInt() {\\n\\t\\tint c = read();\\n\\t\\twhile (isSpaceChar(c))\\n\\t\\t\\tc = read();\\n\\t\\tint sgn = 1;\\n\\t\\tif (c == '-') {\\n\\t\\t\\tsgn = -1;\\n\\t\\t\\tc = read();\\n\\t\\t}\\n\\t\\tint res = 0;\\n\\t\\tdo {\\n\\t\\t\\tif (c < '0' || c > '9')\\n\\t\\t\\t\\tthrow new InputMismatchException();\\n\\t\\t\\tres *= 10;\\n\\t\\t\\tres += c - '0';\\n\\t\\t\\tc = read();\\n\\t\\t} while (!isSpaceChar(c));\\n\\t\\treturn res * sgn;\\n\\t}\\n\\n\\tpublic boolean isSpaceChar(int c) {\\n\\t\\tif (filter != null)\\n\\t\\t\\treturn filter.isSpaceChar(c);\\n\\t\\treturn isWhitespace(c);\\n\\t}\\n\\n\\tpublic static boolean isWhitespace(int c) {\\n\\t\\treturn c == ' ' || c == '\\\\n' || c == '\\\\r' || c == '\\\\t' || c == -1;\\n\\t}\\n\\n\\tpublic interface SpaceCharFilter {\\n\\t\\tpublic boolean isSpaceChar(int ch);\\n\\t}\\n}\\n\\nclass OutputWriter {\\n\\tprivate final PrintWriter writer;\\n\\n\\tpublic OutputWriter(OutputStream outputStream) {\\n\\t\\twriter = new PrintWriter(new BufferedWriter(new OutputStreamWriter(outputStream)));\\n\\t}\\n\\n\\tpublic OutputWriter(Writer writer) {\\n\\t\\tthis.writer = new PrintWriter(writer);\\n\\t}\\n\\n\\tpublic void close() {\\n\\t\\twriter.close();\\n\\t}\\n\\n\\tpublic void printLine(long i) {\\n\\t\\twriter.println(i);\\n\\t}\\n\\n}\\n\\nclass IOUtils {\\n\\n\\tpublic static int[] readIntArray(InputReader in, int size) {\\n\\t\\tint[] array = new int[size];\\n\\t\\tfor (int i = 0; i < size; i++)\\n\\t\\t\\tarray[i] = in.readInt();\\n\\t\\treturn array;\\n\\t}\\n\\n}\\n\\nclass MiscUtils {\\n\\n\\tpublic static void decreaseByOne(int[]...arrays) {\\n\\t\\tfor (int[] array : arrays) {\\n\\t\\t\\tfor (int i = 0; i < array.length; i++)\\n\\t\\t\\t\\tarray[i]--;\\n\\t\\t}\\n\\t}\\n\\n}\\n\\nabstract class IntervalTree {\\n\\tprotected int size;\\n\\n\\tprotected IntervalTree(int size) {\\n\\t\\tthis(size, true);\\n\\t}\\n\\n\\tpublic IntervalTree(int size, boolean shouldInit) {\\n\\t\\tthis.size = size;\\n\\t\\tint nodeCount = Math.max(1, Integer.highestOneBit(size) << 2);\\n\\t\\tinitData(size, nodeCount);\\n\\t\\tif (shouldInit)\\n\\t\\t\\tinit();\\n\\t}\\n\\n\\tprotected abstract void initData(int size, int nodeCount);\\n\\tprotected abstract void initAfter(int root, int left, int right, int middle);\\n\\tprotected abstract void initBefore(int root, int left, int right, int middle);\\n\\tprotected abstract void initLeaf(int root, int index);\\n\\tprotected abstract void updatePostProcess(int root, int left, int right, int from, int to, long delta, int middle);\\n\\tprotected abstract void updatePreProcess(int root, int left, int right, int from, int to, long delta, int middle);\\n\\tprotected abstract void updateFull(int root, int left, int right, int from, int to, long delta);\\n\\tprotected abstract long queryPostProcess(int root, int left, int right, int from, int to, int middle, long leftResult, long rightResult);\\n\\tprotected abstract void queryPreProcess(int root, int left, int right, int from, int to, int middle);\\n\\tprotected abstract long queryFull(int root, int left, int right, int from, int to);\\n\\tprotected abstract long emptySegmentResult();\\n\\n\\tpublic void init() {\\n\\t\\tif (size == 0)\\n\\t\\t\\treturn;\\n\\t\\tinit(0, 0, size - 1);\\n\\t}\\n\\n\\tprivate void init(int root, int left, int right) {\\n\\t\\tif (left == right) {\\n\\t\\t\\tinitLeaf(root, left);\\n\\t\\t} else {\\n\\t\\t\\tint middle = (left + right) >> 1;\\n\\t\\t\\tinitBefore(root, left, right, middle);\\n\\t\\t\\tinit(2 * root + 1, left, middle);\\n\\t\\t\\tinit(2 * root + 2, middle + 1, right);\\n\\t\\t\\tinitAfter(root, left, right, middle);\\n\\t\\t}\\n\\t}\\n\\n\\tpublic void update(int from, int to, long delta) {\\n\\t\\tupdate(0, 0, size - 1, from, to, delta);\\n\\t}\\n\\n\\tprotected void update(int root, int left, int right, int from, int to, long delta) {\\n\\t\\tif (left > to || right < from)\\n\\t\\t\\treturn;\\n\\t\\tif (left >= from && right <= to) {\\n\\t\\t\\tupdateFull(root, left, right, from, to, delta);\\n\\t\\t\\treturn;\\n\\t\\t}\\n\\t\\tint middle = (left + right) >> 1;\\n\\t\\tupdatePreProcess(root, left, right, from, to, delta, middle);\\n\\t\\tupdate(2 * root + 1, left, middle, from, to, delta);\\n\\t\\tupdate(2 * root + 2, middle + 1, right, from, to, delta);\\n\\t\\tupdatePostProcess(root, left, right, from, to, delta, middle);\\n\\t}\\n\\n\\tpublic long query(int from, int to) {\\n\\t\\treturn query(0, 0, size - 1, from, to);\\n\\t}\\n\\n\\tprotected long query(int root, int left, int right, int from, int to) {\\n\\t\\tif (left > to || right < from)\\n\\t\\t\\treturn emptySegmentResult();\\n\\t\\tif (left >= from && right <= to)\\n\\t\\t\\treturn queryFull(root, left, right, from, to);\\n\\t\\tint middle = (left + right) >> 1;\\n\\t\\tqueryPreProcess(root, left, right, from, to, middle);\\n\\t\\tlong leftResult = query(2 * root + 1, left, middle, from, to);\\n\\t\\tlong rightResult = query(2 * root + 2, middle + 1, right, from, to);\\n\\t\\treturn queryPostProcess(root, left, right, from, to, middle, leftResult, rightResult);\\n\\t}\\n}\\n\\nclass SumIntervalTree extends LongIntervalTree {\\n public SumIntervalTree(int size) {\\n super(size);\\n }\\n\\n protected long joinValue(long left, long right) {\\n return left + right;\\n }\\n\\n protected long joinDelta(long was, long delta) {\\n return was + delta;\\n }\\n\\n protected long accumulate(long value, long delta, int length) {\\n return value + delta * length;\\n }\\n\\n protected long neutralValue() {\\n return 0;\\n }\\n\\n protected long neutralDelta() {\\n return 0;\\n }\\n}\\n\\nabstract class LongIntervalTree extends IntervalTree {\\n protected long[] value;\\n protected long[] delta;\\n\\n protected LongIntervalTree(int size) {\\n this(size, true);\\n }\\n\\n\\tpublic LongIntervalTree(int size, boolean shouldInit) {\\n\\t\\tsuper(size, shouldInit);\\n\\t}\\n\\n\\tprotected void initData(int size, int nodeCount) {\\n\\t\\tvalue = new long[nodeCount];\\n\\t\\tdelta = new long[nodeCount];\\n\\t}\\n\\n\\tprotected abstract long joinValue(long left, long right);\\n protected abstract long joinDelta(long was, long delta);\\n protected abstract long accumulate(long value, long delta, int length);\\n protected abstract long neutralValue();\\n protected abstract long neutralDelta();\\n\\n protected long initValue(int index) {\\n return neutralValue();\\n }\\n\\n\\tprotected void initAfter(int root, int left, int right, int middle) {\\n\\t\\tvalue[root] = joinValue(value[2 * root + 1], value[2 * root + 2]);\\n\\t\\tdelta[root] = neutralDelta();\\n\\t}\\n\\n\\tprotected void initBefore(int root, int left, int right, int middle) {\\n\\t}\\n\\n\\tprotected void initLeaf(int root, int index) {\\n\\t\\tvalue[root] = initValue(index);\\n\\t\\tdelta[root] = neutralDelta();\\n\\t}\\n\\n\\tprotected void updatePostProcess(int root, int left, int right, int from, int to, long delta, int middle) {\\n\\t\\tvalue[root] = joinValue(value[2 * root + 1], value[2 * root + 2]);\\n\\t}\\n\\n\\tprotected void updatePreProcess(int root, int left, int right, int from, int to, long delta, int middle) {\\n\\t\\tpushDown(root, left, middle, right);\\n\\t}\\n\\n\\tprotected void pushDown(int root, int left, int middle, int right) {\\n\\t\\tvalue[2 * root + 1] = accumulate(value[2 * root + 1], delta[root], middle - left + 1);\\n\\t\\tvalue[2 * root + 2] = accumulate(value[2 * root + 2], delta[root], right - middle);\\n\\t\\tdelta[2 * root + 1] = joinDelta(delta[2 * root + 1], delta[root]);\\n\\t\\tdelta[2 * root + 2] = joinDelta(delta[2 * root + 2], delta[root]);\\n\\t\\tdelta[root] = neutralDelta();\\n\\t}\\n\\n\\tprotected void updateFull(int root, int left, int right, int from, int to, long delta) {\\n\\t\\tvalue[root] = accumulate(value[root], delta, right - left + 1);\\n\\t\\tthis.delta[root] = joinDelta(this.delta[root], delta);\\n\\t}\\n\\n\\tprotected long queryPostProcess(int root, int left, int right, int from, int to, int middle, long leftResult, long rightResult) {\\n\\t\\treturn joinValue(leftResult, rightResult);\\n\\t}\\n\\n\\tprotected void queryPreProcess(int root, int left, int right, int from, int to, int middle) {\\n\\t\\tpushDown(root, left, middle, right);\\n\\t}\\n\\n\\tprotected long queryFull(int root, int left, int right, int from, int to) {\\n\\t\\treturn value[root];\\n\\t}\\n\\n\\tprotected long emptySegmentResult() {\\n\\t\\treturn neutralValue();\\n\\t}\\n}\\n\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static int solve(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int arrCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String[] arrTemp = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> arr = new ArrayList<>();\\n\\n for (int i = 0; i < arrCount; i++) {\\n int arrItem = Integer.parseInt(arrTemp[i]);\\n arr.add(arrItem);\\n }\\n\\n int result = Result.solve(arr);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.util.Scanner;\\nimport java.util.Stack;\\nimport java.util.ArrayList;\\n\\npublic class Solution {\\n public static void main(String[] args) {\\n Scanner in = new Scanner(System.in);\\n int n = in.nextInt();\\n int[] ar = new int[n];\\n for (int i = 0; i < n; i++) {\\n ar[i] = in.nextInt();\\n }\\n\\n System.out.println(solve(ar, n));\\n }\\n\\n private static long solve(int[] ar, int n){\\n //BIT???\\n int[] right_closed_small = new int[n];\\n int[] left_closed_big = new int[n];\\n\\n Stack<Integer> stack = new Stack<Integer>();\\n for(int i = n-1; i >= 0; i--){\\n while(!stack.empty() && ar[stack.peek()] >= ar[i]){\\n stack.pop();\\n }\\n if(stack.empty()){\\n right_closed_small[i] = n;\\n }\\n else{\\n right_closed_small[i] = stack.peek();\\n }\\n stack.push(i);\\n }\\n stack = new Stack<Integer>();\\n for(int i = 0; i < n; i++){\\n while(!stack.empty() && ar[stack.peek()] <= ar[i]){\\n stack.pop();\\n }\\n if(stack.empty()){\\n left_closed_big[i] = -1;\\n }\\n else{\\n left_closed_big[i] = stack.peek();\\n }\\n stack.push(i);\\n }\\n\\n ArrayList<Integer> intervals[] = new ArrayList[n];\\n for(int i = 0; i < n; i++){\\n intervals[i] = new ArrayList<Integer>();\\n }\\n\\n BitIndexTree tree = new BitIndexTree(n+1);\\n long count = 0;\\n for(int i = n-1; i >= 0; i--){\\n tree.update(i+1, 1);\\n if(left_closed_big[i] >= 0){\\n intervals[left_closed_big[i]].add(i);\\n }\\n for(Integer j : intervals[i]){\\n tree.update(j+1, -1);\\n }\\n count += tree.read(right_closed_small[i]) - tree.read(i);\\n }\\n\\n return count;\\n }\\n\\nstatic class BitIndexTree {\\n int MaxVal = 0;\\n int tree[] = null;\\n public BitIndexTree(int MaxVal){\\n assert (MaxVal > 0);\\n this.MaxVal = MaxVal;\\n tree = new int[MaxVal + 1];\\n }\\n\\n public void update(int idx, int val){\\n assert (idx > 0);\\n while(idx <= MaxVal){\\n tree[idx] += val;\\n idx += (idx & -idx);\\n }\\n }\\n\\n public int read(int idx){\\n int sum = 0;\\n while(idx > 0){\\n sum += tree[idx];\\n idx -= (idx & -idx);\\n }\\n return sum;\\n }\\n}\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static int solve(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int arrCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.solve(arr);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n /* Enter your code here. Read input from STDIN. Print output to STDOUT. Your class should be named Solution. */\\n InputReader in = new InputReader(System.in);\\n int N = in.readInt();\\n \\n int[] arrid = new int[N+2];\\n //HashMap<Integer, Integer> hm = new HashMap<Integer, Integer>();\\n int[] arr = new int[N+2];\\n for(int i=1; i<=N; ++i){\\n //hm.put(sc.nextInt(), i);\\n int value = in.readInt();\\n arrid[value] = i;\\n arr[i] = value;\\n }\\n \\n Stack<Integer> s1 = new Stack<Integer>();\\n s1.push(0);\\n arr[0] = Integer.MAX_VALUE;\\n int[] lefti = new int[N+1]; // value at position i is max and the left most position that smaller than current value;\\n for(int i=1; i<=N; ++i){\\n while(arr[s1.peek().intValue()] < arr[i]){\\n s1.pop();\\n }\\n lefti[i] = s1.peek().intValue() + 1;\\n s1.push(i);\\n }\\n\\n /*TreeSet<Integer> left_tree = new TreeSet<Integer>();\\n left_tree.add(0);\\n int[] lefti = new int[N+1]; // value at position i is max and the left most position that smaller than current value;\\n for(int i=N; i>=1; --i){\\n lefti[arrid[i]] = left_tree.floor(arrid[i]) + 1;\\n left_tree.add(arrid[i]);\\n }*/\\n\\n Stack<Integer> s2 = new Stack<Integer>();\\n s2.push(N+1);\\n arr[N+1] = 0;\\n int[] righti = new int[N+1]; //value at position i is min and the right most position that larger than current value;\\n for(int i=N; i>=1; --i){\\n while(arr[s2.peek().intValue()] > arr[i]){\\n s2.pop();\\n }\\n righti[i] = s2.peek().intValue() - 1;\\n s2.push(i);\\n }\\n \\n /*TreeSet<Integer> right_tree = new TreeSet<Integer>();\\n right_tree.add(N+1);\\n int[] righti = new int[N+1]; //value at position i is min and the right most position that larger than current value;\\n righti[0] = 0;\\n for(int i=1; i<=N; ++i){\\n righti[arrid[i]] = right_tree.ceiling(arrid[i]) - 1;\\n right_tree.add(arrid[i]);\\n }*/\\n \\n Pair[] constraints = new Pair[N];\\n for(int i=1; i<=N; ++i){\\n constraints[i-1] = new Pair(i, arrid[i], righti[arrid[i]]); \\n }\\n \\n \\n Arrays.sort(constraints, new Comparator<Pair>(){\\n public int compare(Pair p1, Pair p2){\\n return p1.start - p2.start;\\n }\\n });\\n \\n Integer[] ids = new Integer[N+1];\\n for(int i=0; i<=N; ++i){\\n ids[i] = i;\\n }\\n IdComparator idc = new IdComparator(lefti, ids);\\n Arrays.sort(ids, idc);\\n Arrays.sort(lefti);\\n \\n int[] BIT = new int[N+10];\\n for(int i=0; i<BIT.length; ++i){\\n BIT[i] = 0;\\n }\\n \\n long count = 0;\\n int j = 1;\\n for(int i=1; i<=N; ++i){\\n while(j<=N && lefti[j] <= constraints[i-1].start){\\n edit(ids[j], 1, BIT);\\n ++j;\\n }\\n count += (sum(constraints[i-1].end, BIT) - sum(constraints[i-1].start-1, BIT));\\n }\\n \\n System.out.println(count);\\n }\\n \\n public static void edit(int i, int delta, int[] BIT){\\n for(int j = i; j < BIT.length; j += j&(-j)){\\n BIT[j] += delta;\\n }\\n }\\n \\n public static int sum(int k, int[] BIT){\\n int ans = 0;\\n for(int i = k; i > 0; i -= i&(-i)){\\n ans += BIT[i];\\n }\\n return ans;\\n }\\n}\\nclass IdComparator implements Comparator<Integer>{\\n int[] id_val;\\n public IdComparator(int[] vals, Integer[] ids){\\n id_val = new int[vals.length];\\n for(int i=0; i<vals.length; ++i){\\n id_val[ids[i]] = vals[i];\\n }\\n }\\n public int compare(Integer num1, Integer num2){\\n return id_val[num1] - id_val[num2];\\n }\\n}\\nclass Pair{\\n int val;\\n int start;\\n int end;\\n public Pair(int val, int start, int end){\\n this.val = val;\\n this.start = start;\\n this.end = end;\\n }\\n}\\nclass InputReader {\\n\\n\\tprivate InputStream stream;\\n\\tprivate byte[] buf = new byte[1024];\\n\\tprivate int curChar;\\n\\tprivate int numChars;\\n\\tprivate SpaceCharFilter filter;\\n\\n\\tpublic InputReader(InputStream stream) {\\n\\t\\tthis.stream = stream;\\n\\t}\\n\\n\\tpublic int read() {\\n\\t\\tif (numChars == -1)\\n\\t\\t\\tthrow new InputMismatchException();\\n\\t\\tif (curChar >= numChars) {\\n\\t\\t\\tcurChar = 0;\\n\\t\\t\\ttry {\\n\\t\\t\\t\\tnumChars = stream.read(buf);\\n\\t\\t\\t} catch (IOException e) {\\n\\t\\t\\t\\tthrow new InputMismatchException();\\n\\t\\t\\t}\\n\\t\\t\\tif (numChars <= 0)\\n\\t\\t\\t\\treturn -1;\\n\\t\\t}\\n\\t\\treturn buf[curChar++];\\n\\t}\\n\\n\\tpublic int readInt() {\\n\\t\\tint c = read();\\n\\t\\twhile (isSpaceChar(c))\\n\\t\\t\\tc = read();\\n\\t\\tint sgn = 1;\\n\\t\\tif (c == '-') {\\n\\t\\t\\tsgn = -1;\\n\\t\\t\\tc = read();\\n\\t\\t}\\n\\t\\tint res = 0;\\n\\t\\tdo {\\n\\t\\t\\tif (c < '0' || c > '9')\\n\\t\\t\\t\\tthrow new InputMismatchException();\\n\\t\\t\\tres *= 10;\\n\\t\\t\\tres += c - '0';\\n\\t\\t\\tc = read();\\n\\t\\t} while (!isSpaceChar(c));\\n\\t\\treturn res * sgn;\\n\\t}\\n\\n\\tpublic boolean isSpaceChar(int c) {\\n\\t\\tif (filter != null)\\n\\t\\t\\treturn filter.isSpaceChar(c);\\n\\t\\treturn isWhitespace(c);\\n\\t}\\n\\n\\tpublic static boolean isWhitespace(int c) {\\n\\t\\treturn c == ' ' || c == '\\\\n' || c == '\\\\r' || c == '\\\\t' || c == -1;\\n\\t}\\n\\n\\tpublic interface SpaceCharFilter {\\n\\t\\tpublic boolean isSpaceChar(int ch);\\n\\t}\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static int solve(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int arrCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.solve(arr);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"solve(arr)\"\n ],\n \"solution\": \"import bisect\\nimport itertools\\nN = int(input())\\nais = [int(x) for x in input().split()]\\nintervals = []\\ncur_interval = []\\ncur_height = 0\\ntotal_sequences = 0\\nfor i, ai in enumerate(ais):\\n if ai < cur_height:\\n merged = True\\n while merged:\\n if not intervals or intervals[-1][-1] > cur_interval[-1]:\\n intervals.append(cur_interval)\\n break\\n pi = intervals.pop()\\n mpi_top = bisect.bisect_right(pi, cur_interval[0])\\n pi[mpi_top:] = cur_interval\\n cur_interval = pi\\n cur_interval = []\\n cur_height = ai\\n cur_interval.append(ai)\\n total_sequences += len(cur_interval)\\n prev_min = cur_interval[0]\\n for prev_interval_i in range(len(intervals) - 1, -1, -1):\\n pi = intervals[prev_interval_i]\\n if pi[-1] > ai: break\\n pi_lower = bisect.bisect_right(pi, prev_min)\\n if pi_lower > 0: prev_min = pi[0]\\n total_sequences += pi_lower\\nprint(total_sequences)\\n\",\n \"template\": \"#\\n# Complete the 'solve' function below.\\n#\\n# The function is expected to return an INTEGER.\\n# The function accepts INTEGER_ARRAY arr as parameter.\\n#\\n\\ndef solve(arr):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n arr_count = int(input().strip())\\n\\n arr = list(map(int, input().rstrip().split()))\\n\\n result = solve(arr)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":162,"cells":{"question_id":{"kind":"number","value":15822,"string":"15,822"},"question_title":{"kind":"string","value":"Coolguy and Two Subsequences"},"question_content":{"kind":"string","value":"Coolguy gives you a simple problem. Given a $1$-indexed array, $A$, containing $N$ elements, what will $ans$ be after this pseudocode is implemented and executed? Print $ans \\ \\% \\ (10^9+7)$.\n\n\t//f(a, b) is a function that returns the minimum element in interval [a, b]\n\t\n ans = 0\n \n for a -> [1, n]\n \tfor b -> [a, n]\n \tfor c -> [b + 1, n]\n \tfor d -> [c, n]\n \tans = ans + min(f(a, b), f(c, d))"},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Hard"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static int solve(List<Integer> arr)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.ByteArrayInputStream;\\nimport java.io.IOException;\\nimport java.io.InputStream;\\nimport java.io.PrintWriter;\\nimport java.util.ArrayList;\\nimport java.util.Arrays;\\nimport java.util.Comparator;\\nimport java.util.InputMismatchException;\\nimport java.util.List;\\n\\npublic class CATS {\\n\\tInputStream is;\\n\\tPrintWriter out;\\n\\tString INPUT = \\\"\\\";\\n\\t\\n\\tvoid solve()\\n\\t{\\n\\t\\tint n = ni();\\n\\t\\tint[] a = na(n);\\n\\t\\tint[][] ai = new int[n][];\\n\\t\\tfor(int i = 0;i < n;i++)ai[i] = new int[]{a[i], i+1};\\n\\t\\tArrays.sort(ai, new Comparator<int[]>() {\\n\\t\\t\\tpublic int compare(int[] a, int[] b) {\\n\\t\\t\\t\\tif(a[0] != b[0])return a[0] - b[0];\\n\\t\\t\\t\\treturn a[1] - b[1];\\n\\t\\t\\t}\\n\\t\\t});\\n\\t\\tLST hole = new LST(n+2);\\n\\t\\thole.set(0);\\n\\t\\thole.set(n+1);\\n\\t\\tlong sum = (long)n*(n+1)/2;\\n\\t\\tlong[] have = new long[n+4];\\n\\t\\thave[0] = sum;\\n\\t\\tlong ret = 0;\\n\\t\\tfor(int i = 0;i < n;i++){\\n\\t\\t\\tint pos = ai[i][1];\\n\\t\\t\\tint nex = hole.next(pos);\\n\\t\\t\\tint pre = hole.prev(pos);\\n//\\t\\t\\ttr(pre, pos, nex);\\n\\t\\t\\tlong L = nex-pre-1;\\n\\t\\t\\tlong K = pos - pre - 1;\\n\\t\\t\\tlong linner = (long)(L-K-1)*(L-K)*(L-K+1)/6%mod*(K+1)%mod;\\n//\\t\\t\\tlong louter = (sumFenwick(ft, n+1) - sumFenwick(ft, nex)) * (L*(L+1)/2%mod)%mod;\\n//\\t\\t\\t\\n//\\t\\t\\tlong router = (sumFenwick(ft, pre-1)) * (L*(L+1)/2%mod)%mod;\\n\\t\\t\\tlong outer = (sum - have[pre]) % mod * ((K+1)*(L-K)%mod)%mod;\\n\\t\\t\\tK = L-1-K;\\n\\t\\t\\tlong rinner = (long)(L-K-1)*(L-K)*(L-K+1)/6%mod*(K+1)%mod;\\n\\t\\t\\tret += (linner+rinner+outer)%mod*ai[i][0];\\n\\t\\t\\tret %= mod;\\n//\\t\\t\\ttr(ret, linner, rinner, outer, L, K);\\n\\t\\t\\t\\n\\t\\t\\tsum -= have[pre];\\n\\t\\t\\thave[pre] = 0;\\n\\t\\t\\thave[pre] = (long)(pos-pre-1)*(pos-pre)/2;\\n\\t\\t\\tsum += have[pre];\\n\\t\\t\\thave[pos] = (long)(nex-pos-1)*(nex-pos)/2;\\n\\t\\t\\tsum += have[pos];\\n\\t\\t\\t\\n\\t\\t\\thole.set(pos);\\n\\t\\t}\\n\\t\\tif(ret < 0)ret += mod;\\n\\t\\tout.println(ret);\\n\\t}\\n\\t\\n\\tpublic static final int mod = 1000000007;\\n\\t\\n\\tpublic static long sumFenwick(long[] ft, int i)\\n\\t{\\n\\t\\tlong sum = 0;\\n\\t\\tfor(i++;i > 0;i -= i&-i){\\n\\t\\t\\tsum += ft[i];\\n\\t\\t\\tif(sum >= mod)sum -= mod;\\n\\t\\t}\\n\\t\\treturn sum;\\n\\t}\\n\\t\\n\\tpublic static void addFenwick(long[] ft, int i, long v)\\n\\t{\\n\\t\\tv %= mod;\\n\\t\\tif(v < 0)v += mod;\\n\\t\\tif(v == 0)return;\\n\\t\\tint n = ft.length;\\n\\t\\tfor(i++;i < n;i += i&-i){\\n\\t\\t\\tft[i] += v;\\n\\t\\t\\tif(ft[i] >= mod)ft[i] -= mod;\\n\\t\\t}\\n\\t}\\n\\t\\n\\tpublic static long[] restoreFenwick(long[] ft)\\n\\t{\\n\\t\\tint n = ft.length-1;\\n\\t\\tlong[] ret = new long[n];\\n\\t\\tfor(int i = 0;i < n;i++)ret[i] = sumFenwick(ft, i);\\n\\t\\tfor(int i = n-1;i >= 1;i--){\\n\\t\\t\\tret[i] -= ret[i-1];\\n\\t\\t\\tif(ret[i] < 0)ret[i] += mod;\\n\\t\\t}\\n\\t\\treturn ret;\\n\\t}\\n\\n\\t\\n\\tpublic static class LST {\\n\\t\\tpublic long[][] set;\\n\\t\\tpublic int n;\\n//\\t\\tpublic int size;\\n\\t\\t\\n\\t\\tpublic LST(int n) {\\n\\t\\t\\tthis.n = n;\\n\\t\\t\\tint d = 1;\\n\\t\\t\\tfor(int m = n;m > 1;m>>>=6, d++);\\n\\t\\t\\t\\n\\t\\t\\tset = new long[d][];\\n\\t\\t\\tfor(int i = 0, m = n>>>6;i < d;i++, m>>>=6){\\n\\t\\t\\t\\tset[i] = new long[m+1];\\n\\t\\t\\t}\\n//\\t\\t\\tsize = 0;\\n\\t\\t}\\n\\t\\t\\n\\t\\t// [0,r)\\n\\t\\tpublic LST setRange(int r)\\n\\t\\t{\\n\\t\\t\\tfor(int i = 0;i < set.length;i++, r=r+63>>>6){\\n\\t\\t\\t\\tfor(int j = 0;j < r>>>6;j++){\\n\\t\\t\\t\\t\\tset[i][j] = -1L;\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tif((r&63) != 0)set[i][r>>>6] |= (1L<<r)-1;\\n\\t\\t\\t}\\n\\t\\t\\treturn this;\\n\\t\\t}\\n\\t\\t\\n\\t\\t// [0,r)\\n\\t\\tpublic LST unsetRange(int r)\\n\\t\\t{\\n\\t\\t\\tif(r >= 0){\\n\\t\\t\\t\\tfor(int i = 0;i < set.length;i++, r=r+63>>>6){\\n\\t\\t\\t\\t\\tfor(int j = 0;j < r+63>>>6;j++){\\n\\t\\t\\t\\t\\t\\tset[i][j] = 0;\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\tif((r&63) != 0)set[i][r>>>6] &= ~((1L<<r)-1);\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\treturn this;\\n\\t\\t}\\n\\t\\t\\n\\t\\tpublic LST set(int pos)\\n\\t\\t{\\n\\t\\t\\tif(pos >= 0 && pos < n){\\n//\\t\\t\\t\\tif(!get(pos))size++;\\n\\t\\t\\t\\tfor(int i = 0;i < set.length;i++, pos>>>=6){\\n\\t\\t\\t\\t\\tset[i][pos>>>6] |= 1L<<pos;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\treturn this;\\n\\t\\t}\\n\\t\\t\\n\\t\\tpublic LST unset(int pos)\\n\\t\\t{\\n\\t\\t\\tif(pos >= 0 && pos < n){\\n//\\t\\t\\t\\tif(get(pos))size--;\\n\\t\\t\\t\\tfor(int i = 0;i < set.length && (i == 0 || set[i-1][pos] == 0L);i++, pos>>>=6){\\n\\t\\t\\t\\t\\tset[i][pos>>>6] &= ~(1L<<pos);\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\treturn this;\\n\\t\\t}\\n\\t\\t\\n\\t\\tpublic boolean get(int pos)\\n\\t\\t{\\n\\t\\t\\treturn pos >= 0 && pos < n && set[0][pos>>>6]<<~pos<0;\\n\\t\\t}\\n\\t\\t\\n\\t\\tpublic int prev(int pos)\\n\\t\\t{\\n\\t\\t\\tfor(int i = 0;i < set.length && pos >= 0;i++, pos>>>=6, pos--){\\n\\t\\t\\t\\tint pre = prev(set[i][pos>>>6], pos&63);\\n\\t\\t\\t\\tif(pre != -1){\\n\\t\\t\\t\\t\\tpos = pos>>>6<<6|pre;\\n\\t\\t\\t\\t\\twhile(i > 0)pos = pos<<6|63-Long.numberOfLeadingZeros(set[--i][pos]);\\n\\t\\t\\t\\t\\treturn pos;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\treturn -1;\\n\\t\\t}\\n\\t\\t\\n\\t\\tpublic int next(int pos)\\n\\t\\t{\\n\\t\\t\\tfor(int i = 0;i < set.length && pos>>>6 < set[i].length;i++, pos>>>=6, pos++){\\n\\t\\t\\t\\tint nex = next(set[i][pos>>>6], pos&63);\\n\\t\\t\\t\\tif(nex != -1){\\n\\t\\t\\t\\t\\tpos = pos>>>6<<6|nex;\\n\\t\\t\\t\\t\\twhile(i > 0)pos = pos<<6|Long.numberOfTrailingZeros(set[--i][pos]);\\n\\t\\t\\t\\t\\treturn pos;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\treturn -1;\\n\\t\\t}\\n\\t\\t\\n\\t\\tprivate static int prev(long set, int n)\\n\\t\\t{\\n\\t\\t\\tlong h = Long.highestOneBit(set<<~n);\\n\\t\\t\\tif(h == 0L)return -1;\\n\\t\\t\\treturn Long.numberOfTrailingZeros(h)-(63-n);\\n\\t\\t}\\n\\t\\t\\n\\t\\tprivate static int next(long set, int n)\\n\\t\\t{\\n\\t\\t\\tlong h = Long.lowestOneBit(set>>>n);\\n\\t\\t\\tif(h == 0L)return -1;\\n\\t\\t\\treturn Long.numberOfTrailingZeros(h)+n;\\n\\t\\t}\\n\\t\\t\\n\\t\\t@Override\\n\\t\\tpublic String toString()\\n\\t\\t{\\n\\t\\t\\tList<Integer> list = new ArrayList<Integer>();\\n\\t\\t\\tfor(int pos = next(0);pos != -1;pos = next(pos+1)){\\n\\t\\t\\t\\tlist.add(pos);\\n\\t\\t\\t}\\n\\t\\t\\treturn list.toString();\\n\\t\\t}\\n\\t}\\n\\n\\t\\n\\tvoid run() throws Exception\\n\\t{\\n\\t\\tis = INPUT.isEmpty() ? System.in : new ByteArrayInputStream(INPUT.getBytes());\\n\\t\\tout = new PrintWriter(System.out);\\n\\t\\t\\n\\t\\tlong s = System.currentTimeMillis();\\n\\t\\tsolve();\\n\\t\\tout.flush();\\n\\t\\tif(!INPUT.isEmpty())tr(System.currentTimeMillis()-s+\\\"ms\\\");\\n\\t}\\n\\t\\n\\tpublic static void main(String[] args) throws Exception { new CATS().run(); }\\n\\t\\n\\tprivate byte[] inbuf = new byte[1024];\\n\\tprivate int lenbuf = 0, ptrbuf = 0;\\n\\t\\n\\tprivate int readByte()\\n\\t{\\n\\t\\tif(lenbuf == -1)throw new InputMismatchException();\\n\\t\\tif(ptrbuf >= lenbuf){\\n\\t\\t\\tptrbuf = 0;\\n\\t\\t\\ttry { lenbuf = is.read(inbuf); } catch (IOException e) { throw new InputMismatchException(); }\\n\\t\\t\\tif(lenbuf <= 0)return -1;\\n\\t\\t}\\n\\t\\treturn inbuf[ptrbuf++];\\n\\t}\\n\\t\\n\\tprivate boolean isSpaceChar(int c) { return !(c >= 33 && c <= 126); }\\n\\tprivate int skip() { int b; while((b = readByte()) != -1 && isSpaceChar(b)); return b; }\\n\\t\\n\\tprivate double nd() { return Double.parseDouble(ns()); }\\n\\tprivate char nc() { return (char)skip(); }\\n\\t\\n\\tprivate String ns()\\n\\t{\\n\\t\\tint b = skip();\\n\\t\\tStringBuilder sb = new StringBuilder();\\n\\t\\twhile(!(isSpaceChar(b))){ // when nextLine, (isSpaceChar(b) && b != ' ')\\n\\t\\t\\tsb.appendCodePoint(b);\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn sb.toString();\\n\\t}\\n\\t\\n\\tprivate char[] ns(int n)\\n\\t{\\n\\t\\tchar[] buf = new char[n];\\n\\t\\tint b = skip(), p = 0;\\n\\t\\twhile(p < n && !(isSpaceChar(b))){\\n\\t\\t\\tbuf[p++] = (char)b;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn n == p ? buf : Arrays.copyOf(buf, p);\\n\\t}\\n\\t\\n\\tprivate char[][] nm(int n, int m)\\n\\t{\\n\\t\\tchar[][] map = new char[n][];\\n\\t\\tfor(int i = 0;i < n;i++)map[i] = ns(m);\\n\\t\\treturn map;\\n\\t}\\n\\t\\n\\tprivate int[] na(int n)\\n\\t{\\n\\t\\tint[] a = new int[n];\\n\\t\\tfor(int i = 0;i < n;i++)a[i] = ni();\\n\\t\\treturn a;\\n\\t}\\n\\t\\n\\tprivate int ni()\\n\\t{\\n\\t\\tint num = 0, b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate long nl()\\n\\t{\\n\\t\\tlong num = 0;\\n\\t\\tint b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate static void tr(Object... o) { System.out.println(Arrays.deepToString(o)); }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static int solve(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int arrCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String[] arrTemp = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> arr = new ArrayList<>();\\n\\n for (int i = 0; i < arrCount; i++) {\\n int arrItem = Integer.parseInt(arrTemp[i]);\\n arr.add(arrItem);\\n }\\n\\n int result = Result.solve(arr);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.regex.*;\\n\\nimport java.util.Arrays;\\nimport java.util.Comparator;\\nimport java.util.Scanner;\\n\\npublic class CoolguyAndTwoSubsequences {\\n final static int constant = 1000000007;\\n\\n public static void main(String[] args) {\\n Scanner scanner = new Scanner(System.in);\\n int N = scanner.nextInt();\\n\\n final int[] A = new int[N];\\n int[] l = new int[N];\\n int[] r = new int[N];\\n\\n boolean[] mark = new boolean[N];\\n Integer[] index = new Integer[N];\\n\\n for (int i = 0; i < N; i++) {\\n A[i] = scanner.nextInt();\\n l[i] = r[i] = i;\\n mark[i] = false;\\n index[i] = Integer.valueOf(i);\\n }\\n Arrays.sort(index, new Comparator<Integer>() {\\n @Override\\n public int compare(Integer o1, Integer o2) {\\n return A[o2] - A[o1];\\n }\\n });\\n long res = 0;\\n long dp = 0;\\n for (int i = 0; i < N; i++) {\\n int ptr = index[i];\\n mark[ptr] = true;\\n int left = 0;\\n int right = 0;\\n if (ptr > 0 && mark[ptr - 1]) {\\n left = ptr - l[ptr - 1];\\n dp = (dp + constant - fun1(left)) % constant;\\n }\\n if (ptr < N - 1 && mark[ptr + 1]) {\\n right = r[ptr + 1] - ptr;\\n dp = (dp + constant - fun1(right)) % constant;\\n }\\n l[ptr + right] = ptr - left;\\n r[ptr - left] = ptr + right;\\n\\n long c = 0;\\n\\n c += (right + 1) * fun2(left) % constant;\\n c %= constant;\\n\\n c += (left + 1) * fun2(right) % constant;\\n c %= constant;\\n\\n c += (left + 1L) * (right + 1L) % constant * dp % constant;\\n c %= constant;\\n\\n res += c * A[ptr] % constant;\\n res %= constant;\\n dp += fun1(left + right + 1);\\n dp %= constant;\\n }\\n System.out.println(res);\\n scanner.close();\\n }\\n\\n private static long fun2(long p) {\\n return p * (p + 1) * (p + 2) / 6 % constant;\\n }\\n\\n private static long fun1(long p) {\\n return p * (p + 1) / 2 % constant;\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static int solve(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int arrCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.solve(arr);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\n\\npublic class WeekOfCode19qD {\\n\\n\\tpublic static void main(String[] args) {\\n\\t\\tInputReader in = new InputReader(System.in);\\n\\t\\tPrintWriter w = new PrintWriter(System.out);\\n\\n\\t\\tint n = in.nextInt();\\n\\t\\t\\n\\t\\tint a[] = new int[n];\\n\\t\\tfor(int i = 0; i < n; i++)\\n\\t\\t\\ta[i] = in.nextInt();////a[i] = new Random().nextInt(1000000000) + 1;//in.nextInt();\\n\\n\\t\\tPair p[] = new Pair[n];\\n\\t\\tfor(int i = 0; i < n; i++)\\n\\t\\t\\tp[i] = new Pair(i , a[i]);\\n\\n\\t\\tArrays.sort(p);\\n\\t\\t\\n\\t\\tTreeSet<Integer> set = new TreeSet<Integer>();\\n\\t\\tset.add(-1);\\n\\t\\tset.add(n);\\n\\t\\t\\n\\t\\tlong ans = 0;\\n\\t\\tfinal int mod = (int)1e9 + 7;\\n\\t\\t\\n\\t\\tlong pre[] = new long[n + 5];\\n\\t\\tfor(int i = 1; i < pre.length; i++){\\n\\t\\t\\tpre[i] = pre[i - 1] + f(i);\\n\\t\\t\\tpre[i] %= mod;\\n\\t\\t}\\n\\t\\t\\n\\t\\tlong tot = f(n) % mod;\\n\\t\\t\\n\\t\\tfor(int i = 0; i < n; i++){\\n\\t\\t\\tint l = p[i].idx - set.lower(p[i].idx);\\n\\t\\t\\tint r = set.higher(p[i].idx) - p[i].idx;\\n\\t\\t\\tlong here = f(l + r - 1) % mod;\\n\\t\\t\\tlong temp = l * pre[r - 1];\\n\\t\\t\\ttemp += r * pre[l - 1];\\n\\t\\t\\t//System.out.println(p[i].idx + \\\" \\\" + temp);\\n\\t\\t\\ttemp %= mod;\\n\\t\\t\\tlong temp2 = (tot - here + mod) * ( (l * 1L * r) % mod);\\n\\t\\t\\ttemp += temp2;\\n\\t\\t\\ttemp %= mod;\\n\\t\\t\\tans += p[i].val * temp;\\n\\t\\t\\tans %= mod;\\n\\t\\t\\tif(ans < 0)\\n\\t\\t\\t\\tans += mod;\\n\\t\\t\\tset.add(p[i].idx);\\n\\t\\t\\ttot -= here;\\n\\t\\t\\ttot += f(l - 1);\\n\\t\\t\\ttot += f(r - 1);\\n\\t\\t\\ttot %= mod;\\n\\t\\t\\tif(tot < 0)\\n\\t\\t\\t\\ttot += mod;\\n\\t\\t}\\n\\t\\t\\n\\t\\tw.println(ans);\\n\\t\\tw.close();\\n\\t}\\n\\t\\n\\tstatic long f(int x){\\n\\t\\treturn x * 1L * (x + 1) / 2;\\n\\t}\\n\\t\\n\\tstatic long brute(int[] a, int n){\\n\\t\\tint m[][] = new int[n][n];\\n\\t\\tfor(int i = 0; i < n; i++){\\n\\t\\t\\tm[i][i] = a[i];\\n\\t\\t\\tfor(int j = i + 1; j < n; j++)\\n\\t\\t\\t\\tm[i][j] = Math.min(m[i][j - 1], a[j]);\\n\\t\\t}\\n\\t\\tlong ans = 0;\\n\\t\\tfor(int w = 0; w < n; w++){\\n\\t\\t\\tfor(int x = w; x < n; x++){\\n\\t\\t\\t\\tfor(int y = x + 1; y < n; y++){\\n\\t\\t\\t\\t\\tfor(int z = y; z < n; z++){\\n\\t\\t\\t\\t\\t\\tans += Math.min(m[w][x], m[y][z]);\\n\\t\\t\\t\\t\\t\\t//System.out.println(Math.min(m[w][x], m[y][z]) + \\\" \\\" + w + \\\" \\\" + x + \\\" \\\" + y + \\\" \\\" + z);\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\treturn ans;\\n\\t}\\n\\t\\n\\tstatic class Pair implements Comparable<Pair>{\\n\\t\\tint idx,val;\\n\\t\\tPair(int i,int v){\\n\\t\\t\\tidx = i;\\n\\t\\t\\tval = v;\\n\\t\\t}\\n\\t\\tpublic int compareTo(Pair o){\\n\\t\\t\\tif(val != o.val)\\n\\t\\t\\t\\treturn Integer.compare(val, o.val);\\n\\t\\t\\treturn Integer.compare(idx, o.idx);\\n\\t\\t}\\n\\t\\tpublic String toString(){\\n\\t\\t\\treturn idx + \\\" \\\" + val;\\n\\t\\t}\\n\\t}\\n\\n\\tstatic class InputReader {\\n\\n\\t\\tprivate InputStream stream;\\n\\t\\tprivate byte[] buf = new byte[8192];\\n\\t\\tprivate int curChar, snumChars;\\n\\t\\tprivate SpaceCharFilter filter;\\n\\n\\t\\tpublic InputReader(InputStream stream) {\\n\\t\\t\\tthis.stream = stream;\\n\\t\\t}\\n\\n\\t\\tpublic int snext() {\\n\\t\\t\\tif (snumChars == -1)\\n\\t\\t\\t\\tthrow new InputMismatchException();\\n\\t\\t\\tif (curChar >= snumChars) {\\n\\t\\t\\t\\tcurChar = 0;\\n\\t\\t\\t\\ttry {\\n\\t\\t\\t\\t\\tsnumChars = stream.read(buf);\\n\\t\\t\\t\\t} catch (IOException e) {\\n\\t\\t\\t\\t\\tthrow new InputMismatchException();\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tif (snumChars <= 0)\\n\\t\\t\\t\\t\\treturn -1;\\n\\t\\t\\t}\\n\\t\\t\\treturn buf[curChar++];\\n\\t\\t}\\n\\n\\t\\tpublic int nextInt() {\\n\\t\\t\\tint c = snext();\\n\\t\\t\\twhile (isSpaceChar(c))\\n\\t\\t\\t\\tc = snext();\\n\\t\\t\\tint sgn = 1;\\n\\t\\t\\tif (c == '-') {\\n\\t\\t\\t\\tsgn = -1;\\n\\t\\t\\t\\tc = snext();\\n\\t\\t\\t}\\n\\t\\t\\tint res = 0;\\n\\t\\t\\tdo {\\n\\t\\t\\t\\tif (c < '0' || c > '9')\\n\\t\\t\\t\\t\\tthrow new InputMismatchException();\\n\\t\\t\\t\\tres *= 10;\\n\\t\\t\\t\\tres += c - '0';\\n\\t\\t\\t\\tc = snext();\\n\\t\\t\\t} while (!isSpaceChar(c));\\n\\t\\t\\treturn res * sgn;\\n\\t\\t}\\n\\n\\t\\tpublic long nextLong() {\\n\\t\\t\\tint c = snext();\\n\\t\\t\\twhile (isSpaceChar(c))\\n\\t\\t\\t\\tc = snext();\\n\\t\\t\\tint sgn = 1;\\n\\t\\t\\tif (c == '-') {\\n\\t\\t\\t\\tsgn = -1;\\n\\t\\t\\t\\tc = snext();\\n\\t\\t\\t}\\n\\t\\t\\tlong res = 0;\\n\\t\\t\\tdo {\\n\\t\\t\\t\\tif (c < '0' || c > '9')\\n\\t\\t\\t\\t\\tthrow new InputMismatchException();\\n\\t\\t\\t\\tres *= 10;\\n\\t\\t\\t\\tres += c - '0';\\n\\t\\t\\t\\tc = snext();\\n\\t\\t\\t} while (!isSpaceChar(c));\\n\\t\\t\\treturn res * sgn;\\n\\t\\t}\\n\\n\\t\\tpublic int[] nextIntArray(int n) {\\n\\t\\t\\tint a[] = new int[n];\\n\\t\\t\\tfor (int i = 0; i < n; i++)\\n\\t\\t\\t\\ta[i] = nextInt();\\n\\t\\t\\treturn a;\\n\\t\\t}\\n\\n\\t\\tpublic String readString() {\\n\\t\\t\\tint c = snext();\\n\\t\\t\\twhile (isSpaceChar(c))\\n\\t\\t\\t\\tc = snext();\\n\\t\\t\\tStringBuilder res = new StringBuilder();\\n\\t\\t\\tdo {\\n\\t\\t\\t\\tres.appendCodePoint(c);\\n\\t\\t\\t\\tc = snext();\\n\\t\\t\\t} while (!isSpaceChar(c));\\n\\t\\t\\treturn res.toString();\\n\\t\\t}\\n\\n\\t\\tpublic boolean isSpaceChar(int c) {\\n\\t\\t\\tif (filter != null)\\n\\t\\t\\t\\treturn filter.isSpaceChar(c);\\n\\t\\t\\treturn c == ' ' || c == '\\\\n' || c == '\\\\r' || c == '\\\\t' || c == -1;\\n\\t\\t}\\n\\n\\t\\tpublic interface SpaceCharFilter {\\n\\t\\t\\tpublic boolean isSpaceChar(int ch);\\n\\t\\t}\\n\\t}\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static int solve(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int arrCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.solve(arr);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"solve(arr)\"\n ],\n \"solution\": \"def smart():\\n left = [0] * (n + 2)\\n right = [0] * (n + 2)\\n singles = pairs = 0\\n ans = 0\\n def remove(k):\\n nonlocal singles, pairs\\n s = k * (k + 1) // 2\\n singles -= s\\n pairs -= (k+2)*(k+1)*k*(k-1)//24 + s * singles\\n def add(k):\\n nonlocal singles, pairs\\n s = k * (k + 1) // 2\\n pairs += (k+2)*(k+1)*k*(k-1)//24 + s * singles\\n singles += s\\n for i in sorted(range(1, n+1), key=A.__getitem__)[::-1]:\\n l, r = left[i-1], right[i+1]\\n k = l + 1 + r\\n right[i - l] = left[i + r] = k\\n oldpairs = pairs\\n remove(l)\\n remove(r)\\n add(k)\\n ans += A[i] * (pairs - oldpairs)\\n return ans % (10**9 + 7)\\n\\nn = int(input())\\nA = [None] + list(map(int, input().split()))\\nprint(smart())\",\n \"template\": \"#\\n# Complete the 'solve' function below.\\n#\\n# The function is expected to return an INTEGER.\\n# The function accepts INTEGER_ARRAY arr as parameter.\\n#\\n\\ndef solve(arr):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n arr_count = int(input().strip())\\n\\n arr = list(map(int, input().rstrip().split()))\\n\\n result = solve(arr)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":163,"cells":{"question_id":{"kind":"number","value":5641,"string":"5,641"},"question_title":{"kind":"string","value":"Find Maximum Index Product"},"question_content":{"kind":"string","value":"You are given a list of $N$ numbers $a_{1}, a_{2}, \\ldots, a_{n}$. For each element at position $i$ ($1 \\le i \\le N$), we define $Left(i)$ and $Right(i)$ as: \n$Left(i)$ = closest index j such that j < i and $a_j>a_i$. If no such j exists then $Left(i)$ = 0. \n$Right(i)$ = closest index k such that k > i and $a_k>a_i$. If no such k exists then $Right(i)$ = 0. \n\nWe define $IndexProduct(i)$ = $Left(i)$ * $Right(i)$. You need to find out the maximum $IndexProduct(i)$ among all i.\n"},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Medium"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static int solve(List<Integer> arr)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.ByteArrayInputStream;\\nimport java.io.IOException;\\nimport java.io.InputStream;\\nimport java.io.PrintWriter;\\nimport java.util.Arrays;\\nimport java.util.InputMismatchException;\\n\\npublic class FMIP {\\n\\tstatic InputStream is;\\n\\tstatic PrintWriter out;\\n\\tstatic String INPUT = \\\"\\\";\\n\\t\\n\\tstatic void solve()\\n\\t{\\n\\t\\tint n = ni();\\n\\t\\tint[] a = na(n);\\n\\t\\tint[] prev = enumPrevWall(a);\\n\\t\\tfor(int i = 0, j = n-1;i < j;i++,j--){\\n\\t\\t\\tint d = a[i]; a[i] = a[j]; a[j] = d;\\n\\t\\t}\\n\\t\\tint[] next = enumPrevWall(a);\\n\\t\\tlong max = 0;\\n\\t\\tfor(int i = 0;i < n;i++){\\n\\t\\t\\tmax = Math.max(max, (long)(prev[i]+1)*(next[n-1-i] == -1 ? 0 : n-next[n-1-i]));\\n\\t\\t}\\n\\t\\tout.println(max);\\n\\t}\\n\\t\\n\\tpublic static int[] enumPrevWall(int[] a)\\n\\t{\\n\\t\\tint n = a.length;\\n\\t\\tint[] L = new int[n];\\n\\t\\tfor(int i = 0;i < n;i++){\\n\\t\\t\\tL[i] = i-1;\\n\\t\\t\\twhile(L[i] >= 0 && a[L[i]] <= a[i])L[i] = L[L[i]];\\n\\t\\t}\\n\\t\\treturn L;\\n\\t}\\n\\t\\n\\tpublic static void main(String[] args) throws Exception\\n\\t{\\n\\t\\tlong S = System.currentTimeMillis();\\n\\t\\tis = INPUT.isEmpty() ? System.in : new ByteArrayInputStream(INPUT.getBytes());\\n\\t\\tout = new PrintWriter(System.out);\\n\\t\\t\\n\\t\\tsolve();\\n\\t\\tout.flush();\\n\\t\\tlong G = System.currentTimeMillis();\\n\\t\\ttr(G-S+\\\"ms\\\");\\n\\t}\\n\\t\\n\\tprivate static boolean eof()\\n\\t{\\n\\t\\tif(lenbuf == -1)return true;\\n\\t\\tint lptr = ptrbuf;\\n\\t\\twhile(lptr < lenbuf)if(!isSpaceChar(inbuf[lptr++]))return false;\\n\\t\\t\\n\\t\\ttry {\\n\\t\\t\\tis.mark(1000);\\n\\t\\t\\twhile(true){\\n\\t\\t\\t\\tint b = is.read();\\n\\t\\t\\t\\tif(b == -1){\\n\\t\\t\\t\\t\\tis.reset();\\n\\t\\t\\t\\t\\treturn true;\\n\\t\\t\\t\\t}else if(!isSpaceChar(b)){\\n\\t\\t\\t\\t\\tis.reset();\\n\\t\\t\\t\\t\\treturn false;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t} catch (IOException e) {\\n\\t\\t\\treturn true;\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate static byte[] inbuf = new byte[1024];\\n\\tstatic int lenbuf = 0, ptrbuf = 0;\\n\\t\\n\\tprivate static int readByte()\\n\\t{\\n\\t\\tif(lenbuf == -1)throw new InputMismatchException();\\n\\t\\tif(ptrbuf >= lenbuf){\\n\\t\\t\\tptrbuf = 0;\\n\\t\\t\\ttry { lenbuf = is.read(inbuf); } catch (IOException e) { throw new InputMismatchException(); }\\n\\t\\t\\tif(lenbuf <= 0)return -1;\\n\\t\\t}\\n\\t\\treturn inbuf[ptrbuf++];\\n\\t}\\n\\t\\n\\tprivate static boolean isSpaceChar(int c) { return !(c >= 33 && c <= 126); }\\n\\tprivate static int skip() { int b; while((b = readByte()) != -1 && isSpaceChar(b)); return b; }\\n\\t\\n\\tprivate static double nd() { return Double.parseDouble(ns()); }\\n\\tprivate static char nc() { return (char)skip(); }\\n\\t\\n\\tprivate static String ns()\\n\\t{\\n\\t\\tint b = skip();\\n\\t\\tStringBuilder sb = new StringBuilder();\\n\\t\\twhile(!(isSpaceChar(b))){ // when nextLine, (isSpaceChar(b) && b != ' ')\\n\\t\\t\\tsb.appendCodePoint(b);\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn sb.toString();\\n\\t}\\n\\t\\n\\tprivate static char[] ns(int n)\\n\\t{\\n\\t\\tchar[] buf = new char[n];\\n\\t\\tint b = skip(), p = 0;\\n\\t\\twhile(p < n && !(isSpaceChar(b))){\\n\\t\\t\\tbuf[p++] = (char)b;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn n == p ? buf : Arrays.copyOf(buf, p);\\n\\t}\\n\\t\\n\\tprivate static char[][] nm(int n, int m)\\n\\t{\\n\\t\\tchar[][] map = new char[n][];\\n\\t\\tfor(int i = 0;i < n;i++)map[i] = ns(m);\\n\\t\\treturn map;\\n\\t}\\n\\t\\n\\tprivate static int[] na(int n)\\n\\t{\\n\\t\\tint[] a = new int[n];\\n\\t\\tfor(int i = 0;i < n;i++)a[i] = ni();\\n\\t\\treturn a;\\n\\t}\\n\\t\\n\\tprivate static int ni()\\n\\t{\\n\\t\\tint num = 0, b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate static long nl()\\n\\t{\\n\\t\\tlong num = 0;\\n\\t\\tint b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate static void tr(Object... o) { if(INPUT.length() != 0)System.out.println(Arrays.deepToString(o)); }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static int solve(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int arrCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String[] arrTemp = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> arr = new ArrayList<>();\\n\\n for (int i = 0; i < arrCount; i++) {\\n int arrItem = Integer.parseInt(arrTemp[i]);\\n arr.add(arrItem);\\n }\\n\\n int result = Result.solve(arr);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static long solve(List<Integer> list) {\\n int[] leftIndices = IntStream.range(0, list.size()).toArray();\\n int[] rightIndices = IntStream.range(0, list.size()).map(index -> list.size() - index - 1).toArray();\\n\\n leftIndices = handleClosest(list.toArray(new Integer[0]), leftIndices);\\n rightIndices = handleClosest(list.toArray(new Integer[0]), rightIndices);\\n\\n long maxProduct = Long.MIN_VALUE;\\n for (int i = 0; i < leftIndices.length; i++) {\\n maxProduct = Math.max(maxProduct, (long) leftIndices[i] * rightIndices[i]);\\n }\\n\\n return maxProduct;\\n }\\n \\n public static int[] handleClosest(Integer[] arr, int[] indices) {\\n int[] closests = new int[arr.length];\\n Stack<Integer> indexStack = new Stack<>();\\n\\n for (int index: indices) {\\n while (!indexStack.empty() && arr[index] >= arr[indexStack.peek()]) {\\n indexStack.pop();\\n }\\n\\n closests[index] = indexStack.empty() ? 0 : (indexStack.peek() + 1);\\n indexStack.push(index);\\n }\\n\\n return closests;\\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int arrCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n long result = Result.solve(arr);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static int solve(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int arrCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.solve(arr);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"\\n\\nimport java.io.IOException;\\nimport java.util.StringTokenizer;\\n\\n/**\\n *\\n * @author Chameera\\n */\\npublic class FindMaximumIndexProduct {\\n\\n public static void main(String[] args) throws IOException {\\n java.io.BufferedReader in = new java.io.BufferedReader(new java.io.InputStreamReader(System.in));\\n\\n int n = Integer.parseInt(in.readLine());\\n\\n StringTokenizer st = new StringTokenizer(in.readLine());\\n\\n long data[] = new long[n];\\n for (int i = 0; i < n; i++) {\\n data[i] = Long.parseLong(st.nextToken());\\n }\\n\\n long max = 0;\\n for (int i = 0; i < data.length; i++) {\\n int left = 0;\\n\\n for (int j = i - 1; j >= 0; j--) {\\n if (data[j] > data[i]) {\\n left = j+1;\\n break;\\n }\\n }\\n\\n if(left==0)\\n continue;\\n int right = 0;\\n\\n for (int j = i + 1; j < data.length; j++) {\\n if (data[j] > data[i]) {\\n right = j+1;\\n break;\\n }\\n }\\n long ans = (long)left * right;\\n if (max < ans) {\\n max = ans;\\n }\\n }\\n System.out.println(max);\\n\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'solve' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static int solve(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int arrCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.solve(arr);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"solve(arr)\"\n ],\n \"solution\": \"def lefts(L):\\n left = [];\\n maxes = [];\\n for i in range(len(L)):\\n while maxes and L[maxes[-1]] <= L[i]:\\n maxes.pop();\\n if not maxes: left.append(-1);\\n else: left.append(maxes[-1]);\\n maxes.append(i); #maxes = [i];\\n return left;\\n\\ndef rights(L):\\n right = [];\\n maxes = [];\\n for i in range(len(L)-1, -1, -1):\\n while maxes and L[maxes[-1]] <= L[i]:\\n maxes.pop();\\n if not maxes: right.append(-1);\\n else: right.append(maxes[-1]);\\n maxes.append(i); #maxes = [i];\\n right.reverse();\\n return right;\\n\\ndef indexProduct(L):\\n left = lefts(L);\\n right = rights(L);\\n products = ((left[i] + 1) * (right[i] + 1) for i in range(len(L)));\\n return max(products);\\n\\n\\nif __name__ == \\\"__main__\\\":\\n input();\\n L = [int(n) for n in input().split()];\\n print(indexProduct(L));\\n \",\n \"template\": \"#\\n# Complete the 'solve' function below.\\n#\\n# The function is expected to return an INTEGER.\\n# The function accepts INTEGER_ARRAY arr as parameter.\\n#\\n\\ndef solve(arr):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n arr_count = int(input().strip())\\n\\n arr = list(map(int, input().rstrip().split()))\\n\\n result = solve(arr)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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Array Sum"},"question_content":{"kind":"string","value":"Given an array of integers, find the sum of its elements.\n\nFor example, if the array $ar = [1,2,3]$, $1 + 2 + 3 = 6$, so return $6$. \n\n**Function Description**\n\nComplete the *simpleArraySum* function in the editor below. It must return the sum of the array elements as an integer. \n\nsimpleArraySum has the following parameter(s): \n\n- *ar*: an array of integers "},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static int simpleArraySum(List<Integer> ar)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner input = new Scanner(System.in);\\n int length = input.nextInt();\\n int sum = 0;\\n \\n for(int i = 0; i < length; i++) {\\n sum += input.nextInt();\\n }\\n \\n System.out.println(sum);\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'simpleArraySum' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY ar as parameter.\\n */\\n\\n public static int simpleArraySum(List<Integer> ar) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int arCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String[] arTemp = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> ar = new ArrayList<>();\\n\\n for (int i = 0; i < arCount; i++) {\\n int arItem = Integer.parseInt(arTemp[i]);\\n ar.add(arItem);\\n }\\n\\n int result = Result.simpleArraySum(ar);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'simpleArraySum' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY ar as parameter.\\n */\\n \\n\\n public static int simpleArraySum(List<Integer> ar) {\\n // Write your code here\\n int sum = 0;\\n for(int i = 0;i<ar.size();i++){\\n sum += ar.get(i);\\n }\\n return sum;\\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int arCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> ar = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.simpleArraySum(ar);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'simpleArraySum' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY ar as parameter.\\n */\\n\\n public static int simpleArraySum(List<Integer> ar) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int arCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> ar = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.simpleArraySum(ar);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\n\\npublic class Solution {\\n\\n static long sum=0;\\n\\tpublic static void main(String[] args) {\\n\\t\\tScanner sc=new Scanner(System.in);\\n\\t\\tint T=sc.nextInt();\\n\\t\\tlong [] a=new long[T];\\n\\t\\tfor(int i=0;i<T;i++){\\n\\t\\t\\ta[i]=sc.nextLong();\\n\\t\\t\\t\\n\\t\\t\\tsum+=a[i];\\n\\t\\t\\t\\n\\t\\t\\t\\n\\t\\t}System.out.println(sum);\\n\\t\\t\\n\\t}\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'simpleArraySum' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY ar as parameter.\\n */\\n\\n public static int simpleArraySum(List<Integer> ar) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int arCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> ar = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.simpleArraySum(ar);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"simpleArraySum(ar)\"\n ],\n \"solution\": \"n = int(input())\\nnums = list(map(int, input().split()))\\nsum = 0\\nfor num in nums:\\n sum += num\\nprint(sum)\\n\",\n \"template\": \"#\\n# Complete the 'simpleArraySum' function below.\\n#\\n# The function is expected to return an INTEGER.\\n# The function accepts INTEGER_ARRAY ar as parameter.\\n#\\n\\ndef simpleArraySum(ar):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n ar_count = int(input().strip())\\n\\n ar = list(map(int, input().rstrip().split()))\\n\\n result = simpleArraySum(ar)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":165,"cells":{"question_id":{"kind":"number","value":8781,"string":"8,781"},"question_title":{"kind":"string","value":"A Very Big Sum"},"question_content":{"kind":"string","value":"In this challenge, you are required to calculate and print the sum of the elements in an array, keeping in mind that some of those integers may be quite large.\n\n**Function Description**\n\nComplete the *aVeryBigSum* function in the editor below. It must return the sum of all array elements.\n\naVeryBigSum has the following parameter(s):\n\n- *int ar[n]*: an array of integers . \n\n**Return**\n\n- *long*: the sum of all array elements\n\n**Input Format** \n\nThe first line of the input consists of an integer $n$. \nThe next line contains $n$ space-separated integers contained in the array. \n\n**Output Format**\n\nReturn the integer sum of the elements in the array.\n\n**Constraints** \n$1 \\le n \\le 10$ \n$0 \\le ar[i] \\le 10^{10}$ \n\n**Sample Input** \n\n 5\n 1000000001 1000000002 1000000003 1000000004 1000000005\n \n\n**Output** \n\t\n 5000000015\n\n**Note:** \n\nThe range of the 32-bit integer is $(-2^{31}) ~to~ (2^{31} -1)~ or~ [-2147483648,2147483647]$.<br> \n\nWhen we add several integer values, the resulting sum might exceed the above range. You might need to use long int C/C++/Java to store such sums. "},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static long aVeryBigSum(List<Long> ar)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner s = new Scanner(System.in);\\n int t = s.nextInt();\\n long sum = 0;\\n while(t-- > 0){\\n sum += s.nextInt();\\n }\\n System.out.println(sum);\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'aVeryBigSum' function below.\\n *\\n * The function is expected to return a LONG_INTEGER.\\n * The function accepts LONG_INTEGER_ARRAY ar as parameter.\\n */\\n\\n public static long aVeryBigSum(List<Long> ar) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int arCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String[] arTemp = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Long> ar = new ArrayList<>();\\n\\n for (int i = 0; i < arCount; i++) {\\n long arItem = Long.parseLong(arTemp[i]);\\n ar.add(arItem);\\n }\\n\\n long result = Result.aVeryBigSum(ar);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'aVeryBigSum' function below.\\n *\\n * The function is expected to return a LONG_INTEGER.\\n * The function accepts LONG_INTEGER_ARRAY ar as parameter.\\n */\\n\\n public static long aVeryBigSum(List<Long> ar) {\\n // Write your code here\\n long sum = 0;\\n for(int i=0;i<ar.size();i++)\\n sum+=ar.get(i);\\n return sum;\\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int arCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Long> ar = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Long::parseLong)\\n .collect(toList());\\n\\n long result = Result.aVeryBigSum(ar);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'aVeryBigSum' function below.\\n *\\n * The function is expected to return a LONG_INTEGER.\\n * The function accepts LONG_INTEGER_ARRAY ar as parameter.\\n */\\n\\n public static long aVeryBigSum(List<Long> ar) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int arCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Long> ar = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Long::parseLong)\\n .collect(toList());\\n\\n long result = Result.aVeryBigSum(ar);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.util.Scanner;\\n\\npublic class BigSum {\\n public static void main(String[] args) {\\n Scanner scanner = new Scanner(System.in);\\n int N = scanner.nextInt();\\n long sum = 0;\\n while (N-- > 0 ) {\\n sum += scanner.nextInt();\\n }\\n System.out.println(sum);\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'aVeryBigSum' function below.\\n *\\n * The function is expected to return a LONG_INTEGER.\\n * The function accepts LONG_INTEGER_ARRAY ar as parameter.\\n */\\n\\n public static long aVeryBigSum(List<Long> ar) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int arCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Long> ar = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Long::parseLong)\\n .collect(toList());\\n\\n long result = Result.aVeryBigSum(ar);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"aVeryBigSum(ar)\"\n ],\n \"solution\": \"input()\\nprint(sum(map(int, input().split())))\",\n \"template\": \"#\\n# Complete the 'aVeryBigSum' function below.\\n#\\n# The function is expected to return a LONG_INTEGER.\\n# The function accepts LONG_INTEGER_ARRAY ar as parameter.\\n#\\n\\ndef aVeryBigSum(ar):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n ar_count = int(input().strip())\\n\\n ar = list(map(int, input().rstrip().split()))\\n\\n result = aVeryBigSum(ar)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":166,"cells":{"question_id":{"kind":"number","value":8662,"string":"8,662"},"question_title":{"kind":"string","value":"Diagonal Difference"},"question_content":{"kind":"string","value":"Given a square matrix, calculate the absolute difference between the sums of its diagonals. \n \nFor example, the square matrix $arr$ is shown below: \n\n\t1 2 3\n 4 5 6\n 9 8 9 \n \nThe left-to-right diagonal = $1 + 5 + 9 = 15$. The right to left diagonal = $3 + 5 + 9 = 17$. Their absolute difference is $|15 - 17| = 2$. \n\n**Function description**\n\nComplete the $\\textit{diagonalDifference}$ function in the editor below. \n\ndiagonalDifference takes the following parameter: \n \n-\t<em>int arr[n][m]</em>: an array of integers \n \n**Return** \n\n-\t<em>int</em>: the absolute diagonal difference\t"},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static int diagonalDifference(List<List<Integer>> arr)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n /* Enter your code here. Read input from STDIN. Print output to STDOUT. Your class should be named Solution. */\\n int n;\\n Scanner in=new Scanner(System.in);\\n n=in.nextInt();\\n int a[][]=new int[n][n];\\n for(int i=0;i<n;i++){\\n \\tfor(int j=0;j<n;j++){\\n \\t\\ta[i][j]=in.nextInt();\\n \\t}\\n }\\n int pd=0,npd=0;\\n for(int i=0;i<n;i++){\\n \\tfor(int j=0;j<n;j++){\\n \\t\\tif(j==i)\\n \\t\\t\\tpd=pd+a[i][j];\\n \\t}\\n }\\n for(int i=0;i<n;i++){\\n \\tfor(int j=0;j<n;j++){\\n \\t\\tif(i==n-j-1){\\n \\t\\t\\tnpd=npd+a[i][j];\\n \\t\\t}\\n \\t}\\n }\\n int dif=npd-pd;\\n dif=Math.abs(dif);\\n System.out.println(dif);\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'diagonalDifference' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts 2D_INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static int diagonalDifference(List<List<Integer>> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<List<Integer>> arr = new ArrayList<>();\\n\\n for (int i = 0; i < n; i++) {\\n String[] arrRowTempItems = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> arrRowItems = new ArrayList<>();\\n\\n for (int j = 0; j < n; j++) {\\n int arrItem = Integer.parseInt(arrRowTempItems[j]);\\n arrRowItems.add(arrItem);\\n }\\n\\n arr.add(arrRowItems);\\n }\\n\\n int result = Result.diagonalDifference(arr);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'diagonalDifference' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts 2D_INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static int diagonalDifference(List<List<Integer>> arr) {\\n int sumleftToRight = 0;\\n int sumRightToLeft = 0;\\n for(int i = 0, j = arr.get(0).size()-1; i < arr.get(0).size(); i++, j--){\\n sumleftToRight += arr.get(i).get(i);\\n sumRightToLeft += arr.get(i).get(j);\\n }\\n\\n return Math.abs(sumleftToRight - sumRightToLeft);\\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<List<Integer>> arr = new ArrayList<>();\\n\\n IntStream.range(0, n).forEach(i -> {\\n try {\\n arr.add(\\n Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList())\\n );\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n int result = Result.diagonalDifference(arr);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'diagonalDifference' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts 2D_INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static int diagonalDifference(List<List<Integer>> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<List<Integer>> arr = new ArrayList<>();\\n\\n IntStream.range(0, n).forEach(i -> {\\n try {\\n arr.add(\\n Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList())\\n );\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n int result = Result.diagonalDifference(arr);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.util.Scanner;\\n\\npublic class DiagonalDifferernce {\\n\\n\\tpublic static void main(String[] args) {\\n\\t\\tScanner in = new Scanner(System.in); \\n\\t\\tint n ;\\n\\t\\tint diag1 = 0 ; int diag2 = 0;\\n\\t\\tn = Integer.parseInt(in.nextLine());\\n\\t\\tfor(int i =0 ; i < n; i++){\\n\\t\\t\\tString str[] = in.nextLine().split(\\\" \\\");\\n\\t\\t\\tdiag1 = diag1 + Integer.parseInt(str[i]);\\n\\t\\t\\tdiag2 = diag2 + Integer.parseInt(str[n-1-i]);\\n\\t\\t}\\n\\t\\t\\n\\t\\tint diagDiff = Math.abs(diag1 - diag2);\\t\\t\\n\\t\\tSystem.out.println(diagDiff);\\n\\t}\\t\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'diagonalDifference' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts 2D_INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static int diagonalDifference(List<List<Integer>> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<List<Integer>> arr = new ArrayList<>();\\n\\n IntStream.range(0, n).forEach(i -> {\\n try {\\n arr.add(\\n Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList())\\n );\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n int result = Result.diagonalDifference(arr);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"diagonalDifference(arr)\"\n ],\n \"solution\": \"N=int(input())\\ngrid=[]\\nfor i in range(0,N):\\n lists=[int(i) for i in input().split()]\\n grid.append(lists)\\ncount=0\\nsum1=0\\nsum2=0\\nj1=0\\nj2=N-1\\nwhile(count<N):\\n sum1=sum1+grid[count][j1]\\n sum2=sum2+grid[count][j2]\\n count+=1\\n j1+=1\\n j2-=1\\nprint(abs(sum1-sum2))\",\n \"template\": \"#\\n# Complete the 'diagonalDifference' function below.\\n#\\n# The function is expected to return an INTEGER.\\n# The function accepts 2D_INTEGER_ARRAY arr as parameter.\\n#\\n\\ndef diagonalDifference(arr):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n n = int(input().strip())\\n\\n arr = []\\n\\n for _ in range(n):\\n arr.append(list(map(int, input().rstrip().split())))\\n\\n result = diagonalDifference(arr)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":167,"cells":{"question_id":{"kind":"number","value":8654,"string":"8,654"},"question_title":{"kind":"string","value":"Plus Minus"},"question_content":{"kind":"string","value":"Given an array of integers, calculate the ratios of its elements that are *positive*, *negative*, and *zero*. Print the decimal value of each fraction on a new line with $6$ places after the decimal.\n\n**Note:** This challenge introduces precision problems. The test cases are scaled to six decimal places, though answers with absolute error of up to $10^{-4}$ are acceptable.\n\n**Example** \n$arr = [1,1,0,-1,-1]$ \n\nThere are $n=5$ elements, two positive, two negative and one zero. Their ratios are $\\frac{2}{5} = 0.400000$, $\\frac{2}{5} = 0.400000$ and $\\frac{1}{5} = 0.200000$. Results are printed as: \n\n 0.400000\n 0.400000\n 0.200000\n \n**Function Description**\n\nComplete the *plusMinus* function in the editor below.\n\nplusMinus has the following parameter(s):\n\n- *int arr[n]*: an array of integers\n\n**Print** \n\tPrint the ratios of positive, negative and \tzero values in the array. Each value should be printed on a separate line with $6$ digits after the decimal. The function should not return a value. "},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static void plusMinus(List<Integer> arr)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner in = new Scanner(System.in);\\n int n = in.nextInt();\\n int pos = 0;\\n int zero = 0;\\n int neg = 0;\\n for (int i = 0; i < n; i++) {\\n int x = in.nextInt();\\n if (x > 0) {\\n pos++;\\n } else if (x == 0) {\\n zero++;\\n } else {\\n neg++;\\n }\\n }\\n System.out.println(pos / (double) n);\\n System.out.println(neg / (double) n);\\n System.out.println(zero / (double) n);\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'plusMinus' function below.\\n *\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static void plusMinus(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String[] arrTemp = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> arr = new ArrayList<>();\\n\\n for (int i = 0; i < n; i++) {\\n int arrItem = Integer.parseInt(arrTemp[i]);\\n arr.add(arrItem);\\n }\\n\\n Result.plusMinus(arr);\\n\\n bufferedReader.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'plusMinus' function below.\\n *\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static void plusMinus(List<Integer> arr) {\\n int pN = 0;\\n int nN = 0;\\n int zero = 0;\\n for(Integer value : arr){\\n if(value < 0 ){\\n nN += 1;\\n }\\n if(value > 0 ){\\n pN += 1;\\n }\\n if(value == 0){\\n zero += 1;\\n }\\n }\\n DecimalFormat df = new DecimalFormat();\\n df.setMaximumFractionDigits(6);\\n System.out.println(df.format(pN/(float)arr.size()));\\n System.out.println(df.format(nN/(float)arr.size()));\\n System.out.println(df.format(zero/(float)arr.size()));\\n \\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n Result.plusMinus(arr);\\n\\n bufferedReader.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'plusMinus' function below.\\n *\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static void plusMinus(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n Result.plusMinus(arr);\\n\\n bufferedReader.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.DecimalFormat;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n /* Enter your code here. Read input from STDIN. Print output to STDOUT. Your class should be named Solution. */\\n Scanner in = new Scanner(System.in);\\n DecimalFormat df = new DecimalFormat(\\\"#.######\\\");\\n float n,pos=0,neg=0,z=0,num=0;\\n n = in.nextInt();\\n for(int i = 0;i<n;i++) {\\n num = in.nextFloat();\\n if(num > 0) pos++;\\n else if(num < 0) neg++;\\n else if(num == 0) z++;\\n }\\n System.out.println(df.format(pos/n));\\n System.out.println(df.format(neg/n));\\n System.out.println(df.format(z/n));\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'plusMinus' function below.\\n *\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static void plusMinus(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n Result.plusMinus(arr);\\n\\n bufferedReader.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"plusMinus(arr)\"\n ],\n \"solution\": \"T=int(input())\\nlists=[int(_) for _ in input().split()]\\np=0\\nm=0\\nz=0\\nfor ele in lists:\\n if(ele>0):\\n p+=1\\n else:\\n if(ele<0):\\n m+=1\\n else:\\n z+=1\\np=p/T\\nm=m/T\\nz=z/T\\nround(p,3)\\nround(m,3)\\nround(z,3)\\nprint(p)\\nprint(m)\\nprint(z)\",\n \"template\": \"#\\n# Complete the 'plusMinus' function below.\\n#\\n# The function accepts INTEGER_ARRAY arr as parameter.\\n#\\n\\ndef plusMinus(arr):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n n = int(input().strip())\\n\\n arr = list(map(int, input().rstrip().split()))\\n\\n plusMinus(arr)\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"number","value":3,"string":"3"}}},{"rowIdx":168,"cells":{"question_id":{"kind":"number","value":23074,"string":"23,074"},"question_title":{"kind":"string","value":"Birthday Cake Candles"},"question_content":{"kind":"string","value":"You are in charge of the cake for a child's birthday. You have decided the cake will have one candle for each year of their total age. They will only be able to blow out the tallest of the candles. Count how many candles are tallest. \n\n**Example** \n\n$candles = [4, 4, 1, 3]$\n\nThe maximum height candles are $4$ units high. There are $2$ of them, so return $2$.\n\n**Function Description**\n\nComplete the function `birthdayCakeCandles` in the editor below. \n\nbirthdayCakeCandles has the following parameter(s):\n\n- *int candles[n]*: the candle heights \n\n**Returns** \n\n- *int*: the number of candles that are tallest\n"},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static int birthdayCakeCandles(List<Integer> candles)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"/*\\n * PDPM IIITDM Jabalpur\\n * Asutosh Rana\\n */\\n\\nimport java.util.*;\\nimport java.io.*;\\nimport java.math.*;\\n\\npublic class Main\\n{\\n static long MOD = 1000000007;\\n public static void main (String[] args) throws java.lang.Exception\\n\\t{\\n\\t\\tInputReader in=new InputReader(System.in);\\n\\t\\tBufferedReader br = new BufferedReader(new InputStreamReader(System.in));\\n PrintWriter out=new PrintWriter(System.out);\\n\\n int t = 1;\\n \\n while(t-->0){\\n\\t int N = in.readInt();\\n\\t int[] A = new int[N];\\n\\t in.readInt(A);\\n\\t int max=0,count=0;\\n\\t for(int i=0;i<N;i++){\\n\\t \\tif(A[i]>max){\\n\\t \\t\\tmax=A[i];count=1;\\n\\t \\t}\\n\\t \\telse if(A[i]==max){\\n\\t \\t\\tcount++;\\n\\t \\t}\\n\\t }\\n\\t out.println(count);\\n\\t }\\n\\t\\tout.flush();\\n out.close();\\n\\t}\\n\\t\\n \\n}\\n \\nclass InputReader{\\n\\tprivate InputStream stream;\\n\\tprivate byte[] buf = new byte[1024];\\n\\tprivate int curChar;\\n\\tprivate int numChars;\\n\\tprivate SpaceCharFilter filter;\\n\\t\\n\\tpublic InputReader(InputStream stream){this.stream = stream;}\\n\\tpublic int read(){\\n\\t\\tif (numChars==-1) throw new InputMismatchException();\\n\\t\\tif (curChar >= numChars){\\n\\t\\t\\tcurChar = 0;\\n\\t\\t\\ttry {numChars = stream.read(buf);}\\n\\t\\t\\tcatch (IOException e){throw new InputMismatchException();}\\n\\t\\t\\tif(numChars <= 0) return -1;\\n\\t\\t}\\n\\t\\treturn buf[curChar++];\\n\\t}\\n \\n\\tpublic int readInt(){\\n\\t\\tint c = read();\\n\\t\\twhile(isSpaceChar(c)) c = read();\\n\\t\\tint sgn = 1;\\n\\t\\tif (c == '-') {sgn = -1;c = read();}\\n\\t\\tint res = 0;\\n\\t\\tdo {\\n\\t\\t\\tif(c<'0'||c>'9') throw new InputMismatchException();\\n\\t\\t\\tres *= 10;\\n\\t\\t\\tres += c - '0';\\n\\t\\t\\tc = read();\\n\\t\\t}\\n\\t\\twhile (!isSpaceChar(c)); return res * sgn;\\n\\t}\\n\\t\\n\\tpublic void readInt(int[] A){\\n\\t\\tfor(int i=0;i<A.length;i++)\\n\\t\\t\\tA[i] = readInt();\\n\\t}\\n\\t\\n\\tpublic long readLong() {\\n\\t\\tint c = read();\\n\\t\\twhile (isSpaceChar(c))\\n\\t\\t\\tc = read();\\n\\t\\tint sgn = 1;\\n\\t\\tif (c == '-') {\\n\\t\\t\\tsgn = -1;\\n\\t\\t\\tc = read();\\n }\\n\\t\\tlong res = 0;\\n\\t\\tdo {\\n\\t\\t\\tif (c < '0' || c > '9')\\n\\t\\t\\t\\tthrow new InputMismatchException();\\n\\t\\t\\tres *= 10;\\n\\t\\t\\tres += c - '0';\\n\\t\\t\\tc = read();\\n }\\n\\t\\twhile (!isSpaceChar(c));\\n return res * sgn;\\n }\\n\\t\\n\\tpublic void readLong(long[] A){\\n\\t\\tfor(int i=0;i<A.length;i++)\\n\\t\\t\\tA[i] = readLong();\\n\\t}\\n\\t\\n\\tpublic double readDouble() {\\n int c = read();\\n while (isSpaceChar(c))\\n c = read();\\n int sgn = 1;\\n\\t\\tif (c == '-') {\\n\\t\\t\\tsgn = -1;\\n\\t\\t\\tc = read();\\n }\\n\\t\\tdouble res = 0;\\n\\t\\twhile (!isSpaceChar(c) && c != '.') {\\n\\t\\t\\tif (c == 'e' || c == 'E')\\n\\t\\t\\t\\treturn res * Math.pow(10, readInt());\\n\\t\\t\\tif (c < '0' || c > '9')\\n\\t\\t\\t\\tthrow new InputMismatchException();\\n\\t\\t\\tres *= 10;\\n\\t\\t\\tres += c - '0';\\n\\t\\t\\tc = read();\\n }\\n\\t\\tif (c == '.') {\\n\\t\\t\\tc = read();\\n\\t\\t\\tdouble m = 1;\\n\\t\\t\\twhile (!isSpaceChar(c)) {\\n\\t\\t\\t\\tif (c == 'e' || c == 'E')\\n\\t\\t\\t\\t\\treturn res * Math.pow(10, readInt());\\n\\t\\t\\t\\tif (c < '0' || c > '9')\\n\\t\\t\\t\\t\\tthrow new InputMismatchException();\\n\\t\\t\\t\\tm /= 10;\\n\\t\\t\\t\\tres += (c - '0') * m;\\n\\t\\t\\t\\tc = read();\\n }\\n }\\n\\t\\treturn res * sgn;\\n }\\n\\t\\n\\tpublic char[] readCharA(){\\n\\t\\treturn readString().toCharArray();\\n\\t}\\n\\t\\n\\tpublic String readString() {\\n\\t\\tint c = read();\\n\\t\\twhile (isSpaceChar(c))\\n\\t\\t\\tc = read();\\n\\t\\tStringBuilder res = new StringBuilder();\\n\\t\\tdo {\\n\\t\\t\\tres.appendCodePoint(c);\\n\\t\\t\\tc = read();\\n\\t\\t} while (!isSpaceChar(c));\\n\\t\\treturn res.toString();\\n\\t}\\n \\n\\tpublic boolean isSpaceChar(int c) {\\n\\t\\tif (filter != null)\\n\\t\\t\\treturn filter.isSpaceChar(c);\\n\\t\\treturn c == ' ' || c == '\\\\n' || c == '\\\\r' || c == '\\\\t' || c == -1;\\n\\t\\t}\\n \\n\\tpublic String next() {\\n\\t\\treturn readString();\\n\\t}\\n\\t\\n\\tpublic interface SpaceCharFilter {\\n\\t\\tpublic boolean isSpaceChar(int ch);\\n\\t}\\n\\t\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'birthdayCakeCandles' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY candles as parameter.\\n */\\n\\n public static int birthdayCakeCandles(List<Integer> candles) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int candlesCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String[] candlesTemp = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> candles = new ArrayList<>();\\n\\n for (int i = 0; i < candlesCount; i++) {\\n int candlesItem = Integer.parseInt(candlesTemp[i]);\\n candles.add(candlesItem);\\n }\\n\\n int result = Result.birthdayCakeCandles(candles);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"DNE\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'birthdayCakeCandles' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY candles as parameter.\\n */\\n\\n public static int birthdayCakeCandles(List<Integer> candles) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int candlesCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> candles = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.birthdayCakeCandles(candles);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner in = new Scanner(System.in);\\n \\n int n = in.nextInt();\\n int max = 0;\\n int sum = 0;\\n int num;\\n for(int i =0; i < n; i++){\\n num = in.nextInt();\\n if(num > max){\\n sum = 1;\\n max = num;\\n }else if(num == max){\\n sum++;\\n }\\n }\\n System.out.println(sum);\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'birthdayCakeCandles' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY candles as parameter.\\n */\\n\\n public static int birthdayCakeCandles(List<Integer> candles) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int candlesCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> candles = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.birthdayCakeCandles(candles);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"birthdayCakeCandles(candles)\"\n ],\n \"solution\": \"N=int(input())\\nV=list(map(int,input().split()))\\nprint(V.count(max(V)))\",\n \"template\": \"#\\n# Complete the 'birthdayCakeCandles' function below.\\n#\\n# The function is expected to return an INTEGER.\\n# The function accepts INTEGER_ARRAY candles as parameter.\\n#\\n\\ndef birthdayCakeCandles(candles):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n candles_count = int(input().strip())\\n\\n candles = list(map(int, input().rstrip().split()))\\n\\n result = birthdayCakeCandles(candles)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":169,"cells":{"question_id":{"kind":"number","value":8649,"string":"8,649"},"question_title":{"kind":"string","value":"Time Conversion"},"question_content":{"kind":"string","value":"Given a time in [$12$-hour AM/PM format](https://en.wikipedia.org/wiki/12-hour_clock), convert it to military (24-hour) time. \n\nNote: \n- 12:00:00AM on a 12-hour clock is 00:00:00 on a 24-hour clock. \n- 12:00:00PM on a 12-hour clock is 12:00:00 on a 24-hour clock. \n\n**Example** \n\n- $\\text{s = '12:01:00PM'}$ \n\n Return '12:01:00'.\n\n- $\\text{s = '12:01:00AM'}$ \n\n Return '00:01:00'.\n\n**Function Description** \n\nComplete the *timeConversion* function in the editor below. It should return a new string representing the input time in 24 hour format. \n\ntimeConversion has the following parameter(s):\n\n- *string s*: a time in $12$ hour format \n\n**Returns**\n\n- *string*: the time in $24$ hour format"},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static String timeConversion(String s)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n \\tpublic static void main(String[] args) {\\n\\t\\tScanner sc = new Scanner(System.in);\\n\\t\\tString s = sc.nextLine(); //07:05:45PM\\n\\t\\tDateFormat inFormat = new SimpleDateFormat( \\\"hh:mm:ssaa\\\");\\n\\t\\tDateFormat outFormat = new SimpleDateFormat( \\\"HH:mm:ss\\\");\\n\\n\\t\\tDate date = null;\\n\\t\\ttry {\\n\\t\\t\\tdate = inFormat.parse(s); \\n\\t\\t}catch (ParseException e ){\\n\\t\\t\\te.printStackTrace();\\n\\t\\t}\\n\\t\\tif( date != null ){\\n\\t\\t\\tString myDate = outFormat.format(date);\\n\\t\\t\\tSystem.out.println(myDate);\\n\\t\\t}\\n\\t}\\n\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'timeConversion' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts STRING s as parameter.\\n */\\n\\n public static String timeConversion(String s) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String s = bufferedReader.readLine();\\n\\n String result = Result.timeConversion(s);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'timeConversion' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts STRING s as parameter.\\n */\\n\\n public static String timeConversion(String s) {\\n Integer hour = Integer.valueOf(s.substring(0,2));\\n Boolean isPM = s.charAt(8) == 'P';\\n hour = hour == 12 ? (isPM ? 12 : 0) : (isPM ? hour + 12 : hour);\\n return (hour < 10 ? \\\"0\\\" : \\\"\\\") + hour + s.substring(2,8);\\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String s = bufferedReader.readLine();\\n\\n String result = Result.timeConversion(s);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'timeConversion' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts STRING s as parameter.\\n */\\n\\n public static String timeConversion(String s) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String s = bufferedReader.readLine();\\n\\n String result = Result.timeConversion(s);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner sc = new Scanner(System.in);\\n String dt = sc.next();\\n char ap = dt.charAt(dt.length() - 2);\\n dt = dt.substring(0, dt.length() - 2);\\n if (ap == 'A') {\\n int hh = Integer.parseInt(dt.substring(0, 2));\\n if (hh == 12) hh = 0;\\n String s = Integer.toString(hh);\\n if (s.length() == 1) {\\n s = \\\"0\\\" + s;\\n }\\n System.out.println(s + dt.substring(2, dt.length()));\\n } else {\\n int hh = Integer.parseInt(dt.substring(0, 2));\\n if (hh != 12) hh += 12;\\n String s = Integer.toString(hh);\\n if (s.length() == 1) {\\n s = \\\"0\\\" + s;\\n }\\n System.out.println(hh + dt.substring(2, dt.length()));\\n }\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'timeConversion' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts STRING s as parameter.\\n */\\n\\n public static String timeConversion(String s) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String s = bufferedReader.readLine();\\n\\n String result = Result.timeConversion(s);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"timeConversion(s)\"\n ],\n \"solution\": \"ins = input().strip()\\n\\nis_pm = ins[-2:].lower() == 'pm'\\ntime_list = list(map(int, ins[:-2].split(':')))\\n\\nif is_pm and time_list[0] < 12:\\n time_list[0] += 12\\n\\nif not is_pm and time_list[0] == 12:\\n time_list[0] = 0\\n\\nprint(':'.join(map(lambda x: str(x).rjust(2, '0'), time_list)))\",\n \"template\": \"#\\n# Complete the 'timeConversion' function below.\\n#\\n# The function is expected to return a STRING.\\n# The function accepts STRING s as parameter.\\n#\\n\\ndef timeConversion(s):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n s = input()\\n\\n result = timeConversion(s)\\n\\n fptr.write(result + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"eJyFk70KwjAURh36ICVzkdyb/ti7+QCCo6DioB1ctEMLggiObi76vsYmBYnJTelQ0nP48t2m9+T9TCbDtXroh/VVHE9t3wlKhawoL0nK5WJzElkqmkvb7LvmsDv3nUWgNoi4ZemvWVJREyBnzgzimCAJKwIuE9EgbiZSUeg3XGZuENfUJSQBzMPmiPz3zL9D4kyLeDIrQta0iGsqAhXJtIhr6vqKELgJlQZxTSCs9TKXaRH3e6I+HfrmTGmQgMntFoMmxDMhbEYy/aY+zkXNZw5IwOQzfeawj0hP8E5oXOYylX+2EO05IgGT+7PVaG5f0w8u4k72"},"max_retries":{"kind":"null"}}},{"rowIdx":170,"cells":{"question_id":{"kind":"number","value":30508,"string":"30,508"},"question_title":{"kind":"string","value":"Grading Students"},"question_content":{"kind":"string","value":"HackerLand University has the following grading policy:\n\n* Every student receives a $grade$ in the inclusive range from $0$ to $100$.\n* Any $grade$ less than $40$ is a failing grade. \n\nSam is a professor at the university and likes to round each student's $grade$ according to these rules:\n\n* If the difference between the $grade$ and the next multiple of $5$ is less than $3$, round $grade$ up to the next multiple of $5$.\n* If the value of $grade$ is less than $38$, no rounding occurs as the result will still be a failing grade.\n\n**Examples**\n\n- $grade = 84$ round to $85$ (85 - 84 is less than 3) \n- $grade = 29$ do not round (result is less than 40) \n- $grade = 57$ do not round (60 - 57 is 3 or higher) \n\nGiven the initial value of $grade$ for each of Sam's $n$ students, write code to automate the rounding process. \n\n**Function Description** \n\nComplete the function *gradingStudents* in the editor below. \n\ngradingStudents has the following parameter(s): \n\n- *int grades[n]*: the grades before rounding \n\n**Returns**\n\n- *int[n]*: the grades after rounding as appropriate"},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static List<Integer> gradingStudents(List<Integer> grades)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner in = new Scanner(System.in);\\n int n = in.nextInt();\\n for(int a0 = 0; a0 < n; a0++){\\n int grade = in.nextInt();\\n // your code goes here\\n if(grade < 38){\\n System.out.println(grade);\\n }\\n else{\\n int q = grade/5;\\n int rem = grade%5;\\n if(rem >= 3){\\n System.out.println((q+1)*5);\\n }\\n else{\\n System.out.println(grade);\\n }\\n }\\n }\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'gradingStudents' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts INTEGER_ARRAY grades as parameter.\\n */\\n\\n public static List<Integer> gradingStudents(List<Integer> grades) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int gradesCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> grades = new ArrayList<>();\\n\\n for (int i = 0; i < gradesCount; i++) {\\n int gradesItem = Integer.parseInt(bufferedReader.readLine().trim());\\n grades.add(gradesItem);\\n }\\n\\n List<Integer> result = Result.gradingStudents(grades);\\n\\n for (int i = 0; i < result.size(); i++) {\\n bufferedWriter.write(String.valueOf(result.get(i)));\\n\\n if (i != result.size() - 1) {\\n bufferedWriter.write(\\\"\\\\n\\\");\\n }\\n }\\n\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'gradingStudents' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts INTEGER_ARRAY grades as parameter.\\n */\\n\\n public static List<Integer> gradingStudents(List<Integer> grades) {\\n List<Integer> ans = new ArrayList<Integer>();\\n for(int m:grades){\\n if(m < 38 || m % 5 <= 2){\\n ans.add(m);\\n }else if(m % 5 > 2){\\n int add = 5 -(m % 5);\\n ans.add(m + add);\\n }\\n }\\n return ans;\\n\\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int gradesCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> grades = IntStream.range(0, gradesCount).mapToObj(i -> {\\n try {\\n return bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\");\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n })\\n .map(String::trim)\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n List<Integer> result = Result.gradingStudents(grades);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\"\\\\n\\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'gradingStudents' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts INTEGER_ARRAY grades as parameter.\\n */\\n\\n public static List<Integer> gradingStudents(List<Integer> grades) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int gradesCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> grades = IntStream.range(0, gradesCount).mapToObj(i -> {\\n try {\\n return bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\");\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n })\\n .map(String::trim)\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n List<Integer> result = Result.gradingStudents(grades);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\"\\\\n\\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner in = new Scanner(System.in);\\n int n = in.nextInt();\\n for(int a0 = 0; a0 < n; a0++){\\n int grade = in.nextInt();\\n if (grade >= 38) {\\n if (grade % 5 >= 3) {\\n grade += 5 - (grade % 5);\\n }\\n }\\n System.out.println(grade);\\n }\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'gradingStudents' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts INTEGER_ARRAY grades as parameter.\\n */\\n\\n public static List<Integer> gradingStudents(List<Integer> grades) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int gradesCount = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> grades = IntStream.range(0, gradesCount).mapToObj(i -> {\\n try {\\n return bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\");\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n })\\n .map(String::trim)\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n List<Integer> result = Result.gradingStudents(grades);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\"\\\\n\\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"gradingStudents(grades)\"\n ],\n \"solution\": \"#!/bin/python3\\n\\nimport sys\\n\\n\\nn = int(input().strip())\\nfor a0 in range(n):\\n x = int(input().strip())\\n \\n if x >= 38:\\n if x % 5 > 2:\\n while x % 5 != 0: x += 1\\n print(x)\\n\",\n \"template\": \"#\\n# Complete the 'gradingStudents' function below.\\n#\\n# The function is expected to return an INTEGER_ARRAY.\\n# The function accepts INTEGER_ARRAY grades as parameter.\\n#\\n\\ndef gradingStudents(grades):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n grades_count = int(input().strip())\\n\\n grades = []\\n\\n for _ in range(grades_count):\\n grades_item = int(input().strip())\\n grades.append(grades_item)\\n\\n result = gradingStudents(grades)\\n\\n fptr.write('\\\\n'.join(map(str, result)))\\n fptr.write('\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"eJx1jj0OwjAMhRk4yFPmCtVxnB+uQhADdGAJHVoJqULiAIxwNgYug1MYWPBmv/d98nX5eC0+c3tuJnMs/TiYNQzHXGIL9hCCFxDBEkjg0MInUKi7RDgPj6hpiyTwAUoFAodKiUNyiIyU4Bgc60WFwc4FLUs1iVVNLqaB6c59tx+6w+40Dt9XrCaXBj/PuVwCV1yFzP/BmdzeV29EYDfo"},"max_retries":{"kind":"null"}}},{"rowIdx":171,"cells":{"question_id":{"kind":"number","value":22477,"string":"22,477"},"question_title":{"kind":"string","value":"Number Line Jumps"},"question_content":{"kind":"string","value":"You are choreographing a circus show with various animals. For one act, you are given two kangaroos on a number line ready to jump in the positive direction (i.e, toward positive infinity). \n\n- The first kangaroo starts at location $x1$ and moves at a rate of $v1$ meters per jump. \n- The second kangaroo starts at location $x2$ and moves at a rate of $v2$ meters per jump.\n\nYou have to figure out a way to get both kangaroos at the same location at the same time as part of the show. If it is possible, return `YES`, otherwise return `NO`. \n\n**Example** \n$x1 = 2$ \n$v1 = 1$ \n$x2 = 1$ \n$v2 = 2$ \n\nAfter one jump, they are both at $x = 3$, ($x1 + v1 = 2 + 1$, $x2 + v2 = 1 + 2$), so the answer is `YES`.\n\n**Function Description**\n\nComplete the function *kangaroo* in the editor below. \n\nkangaroo has the following parameter(s): \n\n- *int x1, int v1*: starting position and jump distance for kangaroo 1\n- *int x2, int v2*: starting position and jump distance for kangaroo 2 \n\n**Returns** \n\n- *string:* either `YES` or `NO`\n"},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static String kangaroo(int x1, int v1, int x2, int v2)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner in = new Scanner(System.in);\\n int x1 = in.nextInt();\\n int v1 = in.nextInt();\\n int x2 = in.nextInt();\\n int v2 = in.nextInt();\\n \\n if (v1>v2&&(x2-x1)%(v1-v2)==0)\\n System.out.println(\\\"YES\\\");\\n else\\n System.out.println(\\\"NO\\\");\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'kangaroo' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts following parameters:\\n * 1. INTEGER x1\\n * 2. INTEGER v1\\n * 3. INTEGER x2\\n * 4. INTEGER v2\\n */\\n\\n public static String kangaroo(int x1, int v1, int x2, int v2) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int x1 = Integer.parseInt(firstMultipleInput[0]);\\n\\n int v1 = Integer.parseInt(firstMultipleInput[1]);\\n\\n int x2 = Integer.parseInt(firstMultipleInput[2]);\\n\\n int v2 = Integer.parseInt(firstMultipleInput[3]);\\n\\n String result = Result.kangaroo(x1, v1, x2, v2);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'kangaroo' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts following parameters:\\n * 1. INTEGER x1\\n * 2. INTEGER v1\\n * 3. INTEGER x2\\n * 4. INTEGER v2\\n */\\n\\n public static String kangaroo(int x1, int v1, int x2, int v2) {\\n \\n for (int i = 0; i < 10000; i++) {\\n if (x1 == x2)\\n return (\\\"YES\\\");\\n \\n x1 += v1;\\n x2 += v2;\\n } \\n \\n return (\\\"NO\\\");\\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int x1 = Integer.parseInt(firstMultipleInput[0]);\\n\\n int v1 = Integer.parseInt(firstMultipleInput[1]);\\n\\n int x2 = Integer.parseInt(firstMultipleInput[2]);\\n\\n int v2 = Integer.parseInt(firstMultipleInput[3]);\\n\\n String result = Result.kangaroo(x1, v1, x2, v2);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'kangaroo' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts following parameters:\\n * 1. INTEGER x1\\n * 2. INTEGER v1\\n * 3. INTEGER x2\\n * 4. INTEGER v2\\n */\\n\\n public static String kangaroo(int x1, int v1, int x2, int v2) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int x1 = Integer.parseInt(firstMultipleInput[0]);\\n\\n int v1 = Integer.parseInt(firstMultipleInput[1]);\\n\\n int x2 = Integer.parseInt(firstMultipleInput[2]);\\n\\n int v2 = Integer.parseInt(firstMultipleInput[3]);\\n\\n String result = Result.kangaroo(x1, v1, x2, v2);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner in = new Scanner(System.in);\\n int x1 = in.nextInt();\\n int v1 = in.nextInt();\\n int x2 = in.nextInt();\\n int v2 = in.nextInt();\\n\\n int count = 0;\\n while (x1 != x2 && count < v1+v2+x1+x2) {\\n count ++;\\n x1 += v1; x2 += v2;\\n }\\n if (x1 == x2) System.out.println(\\\"YES\\\");\\n else System.out.println(\\\"NO\\\");\\n \\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'kangaroo' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts following parameters:\\n * 1. INTEGER x1\\n * 2. INTEGER v1\\n * 3. INTEGER x2\\n * 4. INTEGER v2\\n */\\n\\n public static String kangaroo(int x1, int v1, int x2, int v2) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int x1 = Integer.parseInt(firstMultipleInput[0]);\\n\\n int v1 = Integer.parseInt(firstMultipleInput[1]);\\n\\n int x2 = Integer.parseInt(firstMultipleInput[2]);\\n\\n int v2 = Integer.parseInt(firstMultipleInput[3]);\\n\\n String result = Result.kangaroo(x1, v1, x2, v2);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"kangaroo(x1, v1, x2, v2)\"\n ],\n \"solution\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n#\\n# Complete the 'kangaroo' function below.\\n#\\n# The function is expected to return a STRING.\\n# The function accepts following parameters:\\n# 1. INTEGER x1\\n# 2. INTEGER v1\\n# 3. INTEGER x2\\n# 4. INTEGER v2\\n#\\n\\ndef kangaroo(x1, v1, x2, v2):\\n if v1 > v2:\\n if (x2-x1)%(v1-v2) == 0:\\n return \\\"YES\\\"\\n return \\\"NO\\\"\\n # Write your code here\\n\\nif __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n first_multiple_input = input().rstrip().split()\\n\\n x1 = int(first_multiple_input[0])\\n\\n v1 = int(first_multiple_input[1])\\n\\n x2 = int(first_multiple_input[2])\\n\\n v2 = int(first_multiple_input[3])\\n\\n result = kangaroo(x1, v1, x2, v2)\\n\\n fptr.write(result + '\\\\n')\\n\\n fptr.close()\",\n \"template\": \"#\\n# Complete the 'kangaroo' function below.\\n#\\n# The function is expected to return a STRING.\\n# The function accepts following parameters:\\n# 1. INTEGER x1\\n# 2. INTEGER v1\\n# 3. INTEGER x2\\n# 4. INTEGER v2\\n#\\n\\ndef kangaroo(x1, v1, x2, v2):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n first_multiple_input = input().rstrip().split()\\n\\n x1 = int(first_multiple_input[0])\\n\\n v1 = int(first_multiple_input[1])\\n\\n x2 = int(first_multiple_input[2])\\n\\n v2 = int(first_multiple_input[3])\\n\\n result = kangaroo(x1, v1, x2, v2)\\n\\n fptr.write(result + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"eJyVkz9LA0EQxS3yQR5XB9nZ+bMz9rZa2CgqFprCJkmRgCCC2Nnr93UIEQVzeLtwyxb32zdv3uzr7PN9drRbl295uH4eHpfr7WY4waDaAsLNYFIcZuLDHMPiab243ywe7lbbzf7Ps/Ob5fAyxy+Y3QQhiRUNaBhPZwuaBMGN8lScRsir04s/qIgLvKZkMy6oWf50Xa0kqOSGRtagbHU6bFqzWi4CCwtUL9PZCgJjzOfBDhGo5OqBWjSQUSDCFK7a0dfyLdcjSJUhmp1sHl2VkibDnCFSRMY5OgIH2FQKV0uTiUqwdMiWnc38umzulPbt+dl7wszsMTZqh9JgkpoDLg1MWmHq0THlGb66K0Kr5wXUUaoJMbxFvrHkQF66hjZf138+bz+OvwDFfkl2"},"max_retries":{"kind":"null"}}},{"rowIdx":172,"cells":{"question_id":{"kind":"number","value":32335,"string":"32,335"},"question_title":{"kind":"string","value":"Breaking the Records"},"question_content":{"kind":"string","value":"Maria plays college basketball and wants to go pro. Each season she maintains a record of her play. She tabulates the number of times she breaks her season record for *most points* and *least points* in a game. Points scored in the first game establish her record for the season, and she begins counting from there.\n\n**Example** \n$scores = [12, 24, 10, 24]$ \n\nScores are in the same order as the games played. She tabulates her results as follows:\n\n<pre>\n\t\t\t\t\t\t\t\t\t Count\n Game Score Minimum Maximum Min Max\n 0 12 12 12 0 0\n 1 24 12 24 0 1\n 2 10 10 24 1 1\n 3 24 10 24 1 1\n</pre>\n\nGiven the scores for a season, determine the number of times Maria breaks her records for *most* and *least* points scored during the season.\n\n**Function Description** \n\nComplete the *breakingRecords* function in the editor below. \n\nbreakingRecords has the following parameter(s): \n\n- *int scores[n]:* points scored per game \n\n**Returns** \n\n- *int[2]:* An array with the numbers of times she broke her records. Index $0$ is for breaking *most points* records, and index $1$ is for breaking *least points* records. "},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static List<Integer> breakingRecords(List<Integer> scores)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner in = new Scanner(System.in);\\n int n = in.nextInt();\\n int[] score = new int[n];\\n for(int score_i=0; score_i < n; score_i++){\\n score[score_i] = in.nextInt();\\n }\\n int max=score[0];\\n int min=score[0];\\n int maxcount=0;\\n int mincount=0;\\n for(int i=1;i<n;i++){\\n if(score[i]>max){\\n max=score[i];\\n maxcount=maxcount+1;\\n }\\n if(score[i]<min){\\n min=score[i];\\n mincount=mincount+1;\\n }\\n }\\n System.out.println(maxcount+\\\" \\\"+mincount);\\n // your code goes here\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'breakingRecords' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts INTEGER_ARRAY scores as parameter.\\n */\\n\\n public static List<Integer> breakingRecords(List<Integer> scores) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String[] scoresTemp = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> scores = new ArrayList<>();\\n\\n for (int i = 0; i < n; i++) {\\n int scoresItem = Integer.parseInt(scoresTemp[i]);\\n scores.add(scoresItem);\\n }\\n\\n List<Integer> result = Result.breakingRecords(scores);\\n\\n for (int i = 0; i < result.size(); i++) {\\n bufferedWriter.write(String.valueOf(result.get(i)));\\n\\n if (i != result.size() - 1) {\\n bufferedWriter.write(\\\" \\\");\\n }\\n }\\n\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'breakingRecords' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts INTEGER_ARRAY scores as parameter.\\n */\\n\\n public static List<Integer> breakingRecords(List<Integer> scores) {\\n int min = scores.get(0), max = scores.get(0), minCounter = 0, maxCounter = 0;\\n \\n List<Integer> sum = new ArrayList();\\n \\n for (int i = 0; i < scores.size(); i++) {\\n if (scores.get(i) > max) {\\n maxCounter++;\\n max = scores.get(i);\\n }\\n \\n if (scores.get(i) < min) {\\n minCounter++;\\n min = scores.get(i);\\n } \\n }\\n \\n sum.add(maxCounter);\\n sum.add(minCounter);\\n \\n return sum;\\n\\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> scores = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n List<Integer> result = Result.breakingRecords(scores);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\" \\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'breakingRecords' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts INTEGER_ARRAY scores as parameter.\\n */\\n\\n public static List<Integer> breakingRecords(List<Integer> scores) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> scores = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n List<Integer> result = Result.breakingRecords(scores);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\" \\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner in = new Scanner(System.in);\\n int n = in.nextInt();\\n int[] score = new int[n];\\n for(int score_i=0; score_i < n; score_i++){\\n score[score_i] = in.nextInt();\\n }\\n // your code goes here\\n int most = score[0];\\n int least = score[0];\\n int mr = 0;\\n int lr = 0;\\n for( int i = 1; i < n; i++ ){\\n if( score[i] > most ){\\n mr++;\\n most = score[i];\\n }\\n if( score[i] < least ){\\n lr++;\\n least = score[i];\\n }\\n }\\n \\n System.out.print(mr + \\\" \\\" + lr);\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'breakingRecords' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts INTEGER_ARRAY scores as parameter.\\n */\\n\\n public static List<Integer> breakingRecords(List<Integer> scores) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> scores = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n List<Integer> result = Result.breakingRecords(scores);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\" \\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"breakingRecords(scores)\"\n ],\n \"solution\": \"input()\\na = list(map(int, input().split()))\\nm = M = a[0]\\nx = y = 0\\nfor i in a[1:]:\\n if i > M:\\n M = i\\n x += 1\\n elif i < m:\\n m = i\\n y += 1\\nprint(x, y)\",\n \"template\": \"#\\n# Complete the 'breakingRecords' function below.\\n#\\n# The function is expected to return an INTEGER_ARRAY.\\n# The function accepts INTEGER_ARRAY scores as parameter.\\n#\\n\\ndef breakingRecords(scores):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n n = int(input().strip())\\n\\n scores = list(map(int, input().rstrip().split()))\\n\\n result = breakingRecords(scores)\\n\\n fptr.write(' '.join(map(str, result)))\\n fptr.write('\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":173,"cells":{"question_id":{"kind":"number","value":30377,"string":"30,377"},"question_title":{"kind":"string","value":"Day of the Programmer"},"question_content":{"kind":"string","value":"Marie invented a [Time Machine](https://en.wikipedia.org/wiki/Time_machine) and wants to test it by time-traveling to visit Russia on the [Day of the Programmer](https://en.wikipedia.org/wiki/Day_of_the_Programmer) (the 256th day of the year) during a year in the inclusive range from 1700 to 2700. \n\nFrom 1700 to 1917, Russia's official calendar was the [Julian calendar](https://en.wikipedia.org/wiki/Julian_calendar); since 1919 they used the [Gregorian calendar](https://en.wikipedia.org/wiki/Gregorian_calendar) system. The transition from the Julian to Gregorian calendar system occurred in 1918, when the next day after January 31st was February 14th. This means that in 1918, February 14th was the 32nd day of the year in Russia.\n\nIn both calendar systems, February is the only month with a variable amount of days; it has 29 days during a *leap year*, and 28 days during all other years. In the Julian calendar, leap years are divisible by 4; in the Gregorian calendar, leap years are either of the following:\n\n- Divisible by 400.\n- Divisible by 4 and *not* divisible by 100.\n\nGiven a year, $y$, find the date of the 256th day of that year *according to the official Russian calendar during that year*. Then print it in the format `dd.mm.yyyy`, where `dd` is the two-digit day, `mm` is the two-digit month, and `yyyy` is $y$.\n\nFor example, the given $year$ = 1984. 1984 is divisible by 4, so it is a leap year. The 256th day of a leap year after 1918 is September 12, so the answer is $\\texttt{12.09.1984}$. \n\n**Function Description** \n\nComplete the *dayOfProgrammer* function in the editor below. It should return a string representing the date of the 256th day of the year given. \n\ndayOfProgrammer has the following parameter(s): \n\n- *year*: an integer "},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static String dayOfProgrammer(int year)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner in = new Scanner(System.in);\\n int y = in.nextInt();\\n // your code goes here\\n \\n if(y>=1700 && y<=1917){\\n \\n if(y%4==0){\\n System.out.println(\\\"12.09.\\\"+y);\\n }\\n else{\\n System.out.println(\\\"13.09.\\\"+y);\\n }\\n }\\n \\n else{\\n \\n if(y==1918){\\n System.out.println(\\\"26.09.\\\"+y);\\n }\\n else{\\n if(y%400==0){\\n System.out.println(\\\"12.09.\\\"+y);\\n }\\n else if(y%4==0 && y%100!=0){\\n System.out.println(\\\"12.09.\\\"+y);\\n }\\n else{\\n System.out.println(\\\"13.09.\\\"+y);\\n }\\n }\\n }\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'dayOfProgrammer' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts INTEGER year as parameter.\\n */\\n\\n public static String dayOfProgrammer(int year) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int year = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String result = Result.dayOfProgrammer(year);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'dayOfProgrammer' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts INTEGER year as parameter.\\n */\\n\\n public static String dayOfProgrammer(int year) {\\n int[] calendar = {31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31};\\n int febAdjust = 0;\\n if (year == 1918) {\\n calendar[1] = 15;\\n febAdjust = 13;\\n } else if (year < 1918 && year % 4 == 0) {\\n calendar[1] = 29;\\n } else if (year > 1918 && (year % 400 == 0 || (year % 4 == 0 && year % 100 != 0))) {\\n calendar[1] = 29;\\n }\\n \\n int days = 256;\\n int month = 0;\\n int day = 0;\\n \\n for (int i = 0; i < calendar.length; i++) {\\n if (days > calendar[i]) {\\n days -= calendar[i];\\n } else {\\n if (i == 1) {\\n day = days + febAdjust;\\n } else {\\n day = days;\\n }\\n month = i + 1;\\n break;\\n }\\n }\\n \\n return String.format(\\\"%02d\\\", day) + \\\".\\\" + String.format(\\\"%02d\\\", month) + \\\".\\\" + String.valueOf(year);\\n \\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int year = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String result = Result.dayOfProgrammer(year);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'dayOfProgrammer' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts INTEGER year as parameter.\\n */\\n\\n public static String dayOfProgrammer(int year) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int year = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String result = Result.dayOfProgrammer(year);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.ByteArrayInputStream;\\nimport java.io.IOException;\\nimport java.io.InputStream;\\nimport java.io.PrintWriter;\\nimport java.util.Arrays;\\nimport java.util.InputMismatchException;\\n\\npublic class A {\\n\\tInputStream is;\\n\\tPrintWriter out;\\n\\tString INPUT = \\\"\\\";\\n\\t\\n\\tvoid solve()\\n\\t{\\n\\t\\tint y = ni();\\n\\t\\tif(y < 1918){\\n\\t\\t\\tif(y % 4 == 0){\\n\\t\\t\\t\\tout.println(\\\"12.09.\\\" + y);\\n\\t\\t\\t}else{\\n\\t\\t\\t\\tout.println(\\\"13.09.\\\" + y);\\n\\t\\t\\t}\\n\\t\\t}else if(y == 1918){\\n\\t\\t\\tint m = 1, d = 1, th = 1;\\n\\t\\t\\twhile(th < 256){\\n\\t\\t\\t\\tth++;\\n\\t\\t\\t\\td++;\\n\\t\\t\\t\\tif(d == 32 && m == 1){\\n\\t\\t\\t\\t\\tm = 2;\\n\\t\\t\\t\\t\\td = 14;\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tif(d > DOM[m-1]){\\n\\t\\t\\t\\t\\tm++;\\n\\t\\t\\t\\t\\td = 1;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\tout.printf(\\\"%02d.%02d.%4d\\\\n\\\", d, m, y);\\n\\t\\t}else if(y > 1918){\\n\\t\\t\\tif(y % 4 == 0 && (y % 100 != 0 || y % 400 == 0)){\\n\\t\\t\\t\\tout.println(\\\"12.09.\\\" + y);\\n\\t\\t\\t}else{\\n\\t\\t\\t\\tout.println(\\\"13.09.\\\" + y);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\t\\n\\tpublic static int[] DOM = {31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31};\\n\\n\\t\\n\\tvoid run() throws Exception\\n\\t{\\n\\t\\tis = INPUT.isEmpty() ? System.in : new ByteArrayInputStream(INPUT.getBytes());\\n\\t\\tout = new PrintWriter(System.out);\\n\\t\\t\\n\\t\\tlong s = System.currentTimeMillis();\\n\\t\\tsolve();\\n\\t\\tout.flush();\\n\\t\\tif(!INPUT.isEmpty())tr(System.currentTimeMillis()-s+\\\"ms\\\");\\n\\t}\\n\\t\\n\\tpublic static void main(String[] args) throws Exception { new A().run(); }\\n\\t\\n\\tprivate byte[] inbuf = new byte[1024];\\n\\tpublic int lenbuf = 0, ptrbuf = 0;\\n\\t\\n\\tprivate int readByte()\\n\\t{\\n\\t\\tif(lenbuf == -1)throw new InputMismatchException();\\n\\t\\tif(ptrbuf >= lenbuf){\\n\\t\\t\\tptrbuf = 0;\\n\\t\\t\\ttry { lenbuf = is.read(inbuf); } catch (IOException e) { throw new InputMismatchException(); }\\n\\t\\t\\tif(lenbuf <= 0)return -1;\\n\\t\\t}\\n\\t\\treturn inbuf[ptrbuf++];\\n\\t}\\n\\t\\n\\tprivate boolean isSpaceChar(int c) { return !(c >= 33 && c <= 126); }\\n\\tprivate int skip() { int b; while((b = readByte()) != -1 && isSpaceChar(b)); return b; }\\n\\t\\n\\tprivate double nd() { return Double.parseDouble(ns()); }\\n\\tprivate char nc() { return (char)skip(); }\\n\\t\\n\\tprivate String ns()\\n\\t{\\n\\t\\tint b = skip();\\n\\t\\tStringBuilder sb = new StringBuilder();\\n\\t\\twhile(!(isSpaceChar(b))){ // when nextLine, (isSpaceChar(b) && b != ' ')\\n\\t\\t\\tsb.appendCodePoint(b);\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn sb.toString();\\n\\t}\\n\\t\\n\\tprivate char[] ns(int n)\\n\\t{\\n\\t\\tchar[] buf = new char[n];\\n\\t\\tint b = skip(), p = 0;\\n\\t\\twhile(p < n && !(isSpaceChar(b))){\\n\\t\\t\\tbuf[p++] = (char)b;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn n == p ? buf : Arrays.copyOf(buf, p);\\n\\t}\\n\\t\\n\\tprivate char[][] nm(int n, int m)\\n\\t{\\n\\t\\tchar[][] map = new char[n][];\\n\\t\\tfor(int i = 0;i < n;i++)map[i] = ns(m);\\n\\t\\treturn map;\\n\\t}\\n\\t\\n\\tprivate int[] na(int n)\\n\\t{\\n\\t\\tint[] a = new int[n];\\n\\t\\tfor(int i = 0;i < n;i++)a[i] = ni();\\n\\t\\treturn a;\\n\\t}\\n\\t\\n\\tprivate int ni()\\n\\t{\\n\\t\\tint num = 0, b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate long nl()\\n\\t{\\n\\t\\tlong num = 0;\\n\\t\\tint b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate static void tr(Object... o) { System.out.println(Arrays.deepToString(o)); }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'dayOfProgrammer' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts INTEGER year as parameter.\\n */\\n\\n public static String dayOfProgrammer(int year) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int year = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String result = Result.dayOfProgrammer(year);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"dayOfProgrammer(year)\"\n ],\n \"solution\": \"y = int(input())\\nif y < 1918:\\n ftext = \\\"13.09.{}\\\" if y & 3 else \\\"12.09.{}\\\"\\nelif y > 1918:\\n ftext = \\\"13.09.{}\\\" if (y & 3) or (y % 400 and not y % 100) else \\\"12.09.{}\\\"\\nelse:\\n ftext = \\\"26.09.{}\\\"\\nprint(ftext.format(y))\",\n \"template\": \"#\\n# Complete the 'dayOfProgrammer' function below.\\n#\\n# The function is expected to return a STRING.\\n# The function accepts INTEGER year as parameter.\\n#\\n\\ndef dayOfProgrammer(year):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n year = int(input().strip())\\n\\n result = dayOfProgrammer(year)\\n\\n fptr.write(result + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"eJytk7EKwjAURR36ISVzKUnaJi8u/oag4qAdXGqHFgQR/Aj9X/Okg7xY72Kmkp7DDTcv9+y5yhbvtab4sbmqU9ePg1rmylpvtp0qctVe+vYwtMf9eRymn6YqdSgZUbci/7CMp3resmwxIixbOZgVEWk1jUdWRKRlKCArItJyDloRkW2QRydkRGbZGjdfp81rDZvXWmZpQhYjf8mKW3g2tJwNQziL0qyArfDFMg5axqUWvOWIpBbNWtZNFqUWmkNGkluGbTCSvJRf1vRS2No9yhcf/zhB"},"max_retries":{"kind":"null"}}},{"rowIdx":174,"cells":{"question_id":{"kind":"number","value":25168,"string":"25,168"},"question_title":{"kind":"string","value":"Sales by Match"},"question_content":{"kind":"string","value":"There is a large pile of socks that must be paired by color. Given an array of integers representing the color of each sock, determine how many pairs of socks with matching colors there are.\n\n**Example** \n$n = 7$ \n$ar = [1, 2, 1, 2, 1, 3, 2]$ \n\nThere is one pair of color $1$ and one of color $2$. There are three odd socks left, one of each color. The number of pairs is $2$. \n\n**Function Description** \n\nComplete the *sockMerchant* function in the editor below. \n\nsockMerchant has the following parameter(s): \n\n- *int n:* the number of socks in the pile \n- *int ar[n]:* the colors of each sock \n\n**Returns** \n\n- *int:* the number of pairs "},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static int sockMerchant(int n, List<Integer> ar)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"\\n import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\nimport java.lang.*;\\npublic class Main{\\n\\tstatic long mod=1000000007;\\n\\tstatic public HashMap<String,Integer> map=new HashMap<String,Integer>(); \\n\\tstatic public HashMap<Integer, String> map1=new HashMap<Integer,String>(); \\n\\n public static void main(String[] args) throws Exception{ \\t\\n \\t InputReader in = new InputReader(System.in); \\n \\tPrintWriter pw=new PrintWriter(System.out); \\nint n=in.readInt();\\nint a[]=new int[101];\\nfor(int i=0;i<n;i++)\\n{\\n\\ta[in.readInt()]++;\\n}\\nint sum=0;\\nfor(int i=0;i<101;i++)\\n{\\n\\tsum+=(a[i])/2;\\n}\\nSystem.out.println(sum);\\n }\\n \\n public static void printAllKLengthRec(char set[], String prefix, int n, int k,int a[][]) {\\n \\n // Base case: k is 0, print prefix\\n if (k == 0) {\\n String tar=prefix;\\n int sum=0;\\n for(int i=0;i<tar.length();i++)\\n {\\n \\t sum+=a[i][tar.charAt(i)-97];\\n }\\n System.out.println(sum);\\n return;\\n }\\n \\n // One by one add all characters from set and recursively \\n // call for k equals to k-1\\n for (int i = 0; i < n; ++i) {\\n \\n // Next character of input added\\n String newPrefix = prefix + set[i]; \\n \\n // k is decreased, because we have added a new character\\n printAllKLengthRec(set, newPrefix, n, k - 1,a); \\n }\\n }\\n\\npublic static int BinarySearch(int x,int a[])\\n{\\n\\tint l=0;\\n\\tint r=a.length-1;\\n\\tint temp=-1;\\n\\twhile(l<=r)\\n\\t{\\n\\t\\tint mid=l+(r-l)/2;\\n\\t\\tif(a[mid]<x)\\n\\t\\t{\\n\\t\\t\\ttemp=mid;\\n\\t\\t\\tl=mid+1;\\n\\t\\t}\\n\\t\\telse if(a[mid]>x)\\n\\t\\t\\tr=mid-1;\\n\\t\\telse if(a[mid]<x)\\n\\t\\t\\tl=mid+1;\\n\\t\\t\\t\\n\\t}\\n\\treturn temp;\\n}\\npublic static long gcd(long x,long y)\\n{\\n\\tif(x%y==0)\\n\\t\\treturn y;\\n\\telse\\n\\t\\treturn gcd(y,x%y);\\n}\\npublic static int gcd(int x,int y)\\n{\\n\\tif(x%y==0)\\n\\t\\treturn y;\\n\\telse \\n\\t\\treturn gcd(y,x%y);\\n}\\npublic static int abs(int a,int b)\\n{\\n\\treturn (int)Math.abs(a-b);\\n}\\npublic static long abs(long a,long b)\\n{\\n\\treturn (long)Math.abs(a-b);\\n}\\npublic static int max(int a,int b)\\n{\\n\\tif(a>b)\\n\\t\\treturn a;\\n\\telse\\n\\t\\treturn b;\\n}\\npublic static int min(int a,int b)\\n{\\n\\tif(a>b)\\n\\t\\treturn b;\\n\\telse \\n\\t\\treturn a;\\n}\\npublic static long max(long a,long b)\\n{\\n\\tif(a>b)\\n\\t\\treturn a;\\n\\telse\\n\\t\\treturn b;\\n}\\npublic static long min(long a,long b)\\n{\\n\\tif(a>b)\\n\\t\\treturn b;\\n\\telse \\n\\t\\treturn a;\\n}\\n \\n \\npublic static long pow(long n,long p,long m)\\n{\\n\\t long result = 1;\\n\\t if(p==0)\\n\\t return 1;\\n\\tif (p==1)\\n\\t return n;\\n\\twhile(p!=0)\\n\\t{\\n\\t if(p%2==1)\\n\\t result *= n;\\n\\t if(result>=m)\\n\\t result%=m;\\n\\t p >>=1;\\n\\t n*=n;\\n\\t if(n>=m)\\n\\t n%=m;\\n\\t}\\n\\treturn result;\\n}\\npublic static long pow(long n,long p)\\n{\\n\\tlong result = 1;\\n\\t if(p==0)\\n\\t return 1;\\n\\tif (p==1)\\n\\t return n;\\n\\twhile(p!=0)\\n\\t{\\n\\t if(p%2==1)\\n\\t result *= n;\\t \\n\\t p >>=1;\\n\\t n*=n;\\t \\n\\t}\\n\\treturn result;\\n \\n}\\nstatic class Pair implements Comparable<Pair>\\n{\\n\\tint a,b;\\n\\tPair (int a,int b)\\n\\t{\\n\\t\\tthis.a=a;\\n\\t\\tthis.b=b;\\n\\t}\\n \\n\\tpublic int compareTo(Pair o) {\\n\\t\\t// TODO Auto-generated method stub\\n\\t\\tif(this.a!=o.a)\\n\\t\\treturn Integer.compare(this.a,o.a);\\n\\t\\telse\\n\\t\\t\\treturn Integer.compare(this.b, o.b);\\n\\t\\t//return 0;\\n\\t}\\n\\tpublic boolean equals(Object o) {\\n if (o instanceof Pair) {\\n Pair p = (Pair)o;\\n return p.a == a && p.b == b;\\n }\\n return false;\\n }\\n public int hashCode() {\\n return new Integer(a).hashCode() * 31 + new Integer(b).hashCode();\\n }\\n \\n} \\n \\nstatic long sort(int a[])\\n{ int n=a.length;\\n\\tint b[]=new int[n];\\t\\n\\treturn mergeSort(a,b,0,n-1);}\\nstatic long mergeSort(int a[],int b[],long left,long right)\\n{ long c=0;if(left<right)\\n { long mid=left+(right-left)/2;\\n\\t c= mergeSort(a,b,left,mid);\\n\\t c+=mergeSort(a,b,mid+1,right);\\n\\t c+=merge(a,b,left,mid+1,right); }\\t\\n\\treturn c;\\t }\\nstatic long merge(int a[],int b[],long left,long mid,long right)\\n{long c=0;int i=(int)left;int j=(int)mid; int k=(int)left;\\nwhile(i<=(int)mid-1&&j<=(int)right)\\n{ if(a[i]<=a[j]) {b[k++]=a[i++]; }\\nelse\\t{ b[k++]=a[j++];c+=mid-i;}}\\nwhile (i <= (int)mid - 1) b[k++] = a[i++]; \\nwhile (j <= (int)right) b[k++] = a[j++];\\nfor (i=(int)left; i <= (int)right; i++) \\n\\ta[i] = b[i]; return c; }\\n \\npublic static int[] radixSort(int[] f)\\n{\\n\\tint[] to = new int[f.length];\\n\\t{\\n\\t\\tint[] b = new int[65537];\\n\\t\\tfor(int i = 0;i < f.length;i++)b[1+(f[i]&0xffff)]++;\\n\\t\\tfor(int i = 1;i <= 65536;i++)b[i]+=b[i-1];\\n\\t\\tfor(int i = 0;i < f.length;i++)to[b[f[i]&0xffff]++] = f[i];\\n\\t\\tint[] d = f; f = to;to = d;\\n\\t}\\n\\t{\\n\\t\\tint[] b = new int[65537];\\n\\t\\tfor(int i = 0;i < f.length;i++)b[1+(f[i]>>>16)]++;\\n\\t\\tfor(int i = 1;i <= 65536;i++)b[i]+=b[i-1];\\n\\t\\tfor(int i = 0;i < f.length;i++)to[b[f[i]>>>16]++] = f[i];\\n\\t\\tint[] d = f; f = to;to = d;\\n\\t}\\n\\treturn f;\\n} \\n static class InputReader\\n {\\n private InputStream stream;\\n private byte[] buf = new byte[1024];\\n private int curChar;\\n private int numChars;\\n private SpaceCharFilter filter;\\n \\n public InputReader(InputStream stream)\\n {\\n this.stream = stream;\\n }\\n \\n public int read()\\n {\\n if (numChars == -1)\\n throw new InputMismatchException();\\n if (curChar >= numChars)\\n {\\n curChar = 0;\\n try\\n {\\n numChars = stream.read(buf);\\n } catch (IOException e)\\n {\\n throw new InputMismatchException();\\n }\\n if (numChars <= 0)\\n return -1;\\n }\\n return buf[curChar++];\\n }\\n \\n public int readInt()\\n {\\n int c = read();\\n while (isSpaceChar(c))\\n c = read();\\n int sgn = 1;\\n if (c == '-')\\n {\\n sgn = -1;\\n c = read();\\n }\\n int res = 0;\\n do\\n {\\n if (c < '0' || c > '9')\\n throw new InputMismatchException();\\n res *= 10;\\n res += c - '0';\\n c = read();\\n } while (!isSpaceChar(c));\\n return res * sgn;\\n }\\n \\n public String readString()\\n {\\n int c = read();\\n while (isSpaceChar(c))\\n c = read();\\n StringBuilder res = new StringBuilder();\\n do\\n {\\n res.appendCodePoint(c);\\n c = read();\\n } while (!isSpaceChar(c));\\n return res.toString();\\n }\\n public double readDouble() {\\n int c = read();\\n while (isSpaceChar(c))\\n c = read();\\n int sgn = 1;\\n if (c == '-') {\\n sgn = -1;\\n c = read();\\n }\\n double res = 0;\\n while (!isSpaceChar(c) && c != '.') {\\n if (c == 'e' || c == 'E')\\n return res * Math.pow(10, readInt());\\n if (c < '0' || c > '9')\\n throw new InputMismatchException();\\n res *= 10;\\n res += c - '0';\\n c = read();\\n }\\n if (c == '.') {\\n c = read();\\n double m = 1;\\n while (!isSpaceChar(c)) {\\n if (c == 'e' || c == 'E')\\n return res * Math.pow(10, readInt());\\n if (c < '0' || c > '9')\\n throw new InputMismatchException();\\n m /= 10;\\n res += (c - '0') * m;\\n c = read();\\n }\\n }\\n return res * sgn;\\n }\\n public long readLong() {\\n int c = read();\\n while (isSpaceChar(c))\\n c = read();\\n int sgn = 1;\\n if (c == '-') {\\n sgn = -1;\\n c = read();\\n }\\n long res = 0;\\n do {\\n if (c < '0' || c > '9')\\n throw new InputMismatchException();\\n res *= 10;\\n res += c - '0';\\n c = read();\\n } while (!isSpaceChar(c));\\n return res * sgn;\\n }\\n public boolean isSpaceChar(int c)\\n {\\n if (filter != null)\\n return filter.isSpaceChar(c);\\n return c == ' ' || c == '\\\\n' || c == '\\\\r' || c == '\\\\t' || c == -1;\\n }\\n \\n public String next()\\n {\\n return readString();\\n }\\n \\n public interface SpaceCharFilter\\n {\\n public boolean isSpaceChar(int ch);\\n }\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'sockMerchant' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER n\\n * 2. INTEGER_ARRAY ar\\n */\\n\\n public static int sockMerchant(int n, List<Integer> ar) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String[] arTemp = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> ar = new ArrayList<>();\\n\\n for (int i = 0; i < n; i++) {\\n int arItem = Integer.parseInt(arTemp[i]);\\n ar.add(arItem);\\n }\\n\\n int result = Result.sockMerchant(n, ar);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'sockMerchant' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER n\\n * 2. INTEGER_ARRAY ar\\n */\\n\\n public static int sockMerchant(int n, List<Integer> ar) {\\n Map<Integer, Integer> pairMap = new HashMap<>();\\n ar.forEach(e -> pairMap.put(e, (pairMap.get(e) == null ? 0 : pairMap.get(e)) + 1));\\n\\n int pairSum = 0;\\n for (Map.Entry<Integer, Integer> entry : pairMap.entrySet()) {\\n pairSum += entry.getValue()/2;\\n }\\n return pairSum;\\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> ar = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.sockMerchant(n, ar);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'sockMerchant' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER n\\n * 2. INTEGER_ARRAY ar\\n */\\n\\n public static int sockMerchant(int n, List<Integer> ar) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> ar = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.sockMerchant(n, ar);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"\\nimport java.io.ByteArrayInputStream;\\nimport java.io.IOException;\\nimport java.io.InputStream;\\nimport java.io.PrintWriter;\\nimport java.util.Arrays;\\nimport java.util.InputMismatchException;\\n\\npublic class A {\\n\\tInputStream is;\\n\\tPrintWriter out;\\n\\tString INPUT = \\\"\\\";\\n\\t\\n\\tvoid solve()\\n\\t{\\n\\t\\tint n = ni();\\n\\t\\tint[] a = na(n);\\n\\t\\tint[] f = new int[101];\\n\\t\\tfor(int v : a)f[v]++;\\n\\t\\tint ret = 0;\\n\\t\\tfor(int v : f)ret += v/2;\\n\\t\\tout.println(ret);\\n\\t}\\n\\t\\n\\tvoid run() throws Exception\\n\\t{\\n\\t\\tis = INPUT.isEmpty() ? System.in : new ByteArrayInputStream(INPUT.getBytes());\\n\\t\\tout = new PrintWriter(System.out);\\n\\t\\t\\n\\t\\tlong s = System.currentTimeMillis();\\n\\t\\tsolve();\\n\\t\\tout.flush();\\n\\t\\tif(!INPUT.isEmpty())tr(System.currentTimeMillis()-s+\\\"ms\\\");\\n\\t}\\n\\t\\n\\tpublic static void main(String[] args) throws Exception { new A().run(); }\\n\\t\\n\\tprivate byte[] inbuf = new byte[1024];\\n\\tpublic int lenbuf = 0, ptrbuf = 0;\\n\\t\\n\\tprivate int readByte()\\n\\t{\\n\\t\\tif(lenbuf == -1)throw new InputMismatchException();\\n\\t\\tif(ptrbuf >= lenbuf){\\n\\t\\t\\tptrbuf = 0;\\n\\t\\t\\ttry { lenbuf = is.read(inbuf); } catch (IOException e) { throw new InputMismatchException(); }\\n\\t\\t\\tif(lenbuf <= 0)return -1;\\n\\t\\t}\\n\\t\\treturn inbuf[ptrbuf++];\\n\\t}\\n\\t\\n\\tprivate boolean isSpaceChar(int c) { return !(c >= 33 && c <= 126); }\\n\\tprivate int skip() { int b; while((b = readByte()) != -1 && isSpaceChar(b)); return b; }\\n\\t\\n\\tprivate double nd() { return Double.parseDouble(ns()); }\\n\\tprivate char nc() { return (char)skip(); }\\n\\t\\n\\tprivate String ns()\\n\\t{\\n\\t\\tint b = skip();\\n\\t\\tStringBuilder sb = new StringBuilder();\\n\\t\\twhile(!(isSpaceChar(b))){ // when nextLine, (isSpaceChar(b) && b != ' ')\\n\\t\\t\\tsb.appendCodePoint(b);\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn sb.toString();\\n\\t}\\n\\t\\n\\tprivate char[] ns(int n)\\n\\t{\\n\\t\\tchar[] buf = new char[n];\\n\\t\\tint b = skip(), p = 0;\\n\\t\\twhile(p < n && !(isSpaceChar(b))){\\n\\t\\t\\tbuf[p++] = (char)b;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn n == p ? buf : Arrays.copyOf(buf, p);\\n\\t}\\n\\t\\n\\tprivate char[][] nm(int n, int m)\\n\\t{\\n\\t\\tchar[][] map = new char[n][];\\n\\t\\tfor(int i = 0;i < n;i++)map[i] = ns(m);\\n\\t\\treturn map;\\n\\t}\\n\\t\\n\\tprivate int[] na(int n)\\n\\t{\\n\\t\\tint[] a = new int[n];\\n\\t\\tfor(int i = 0;i < n;i++)a[i] = ni();\\n\\t\\treturn a;\\n\\t}\\n\\t\\n\\tprivate int ni()\\n\\t{\\n\\t\\tint num = 0, b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate long nl()\\n\\t{\\n\\t\\tlong num = 0;\\n\\t\\tint b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate static void tr(Object... o) { System.out.println(Arrays.deepToString(o)); }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'sockMerchant' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER n\\n * 2. INTEGER_ARRAY ar\\n */\\n\\n public static int sockMerchant(int n, List<Integer> ar) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> ar = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.sockMerchant(n, ar);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"sockMerchant(n, ar)\"\n ],\n \"solution\": \"from collections import Counter\\n\\nn = int(input())\\nc = Counter(map(int, input().split()))\\nans = 0\\n\\nfor x in c:\\n ans += c[x] // 2\\n \\nprint(ans)\",\n \"template\": \"#\\n# Complete the 'sockMerchant' function below.\\n#\\n# The function is expected to return an INTEGER.\\n# The function accepts following parameters:\\n# 1. INTEGER n\\n# 2. INTEGER_ARRAY ar\\n#\\n\\ndef sockMerchant(n, ar):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n n = int(input().strip())\\n\\n ar = list(map(int, input().rstrip().split()))\\n\\n result = sockMerchant(n, ar)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":175,"cells":{"question_id":{"kind":"number","value":22937,"string":"22,937"},"question_title":{"kind":"string","value":"Forming a Magic Square"},"question_content":{"kind":"string","value":"We define a [magic square](https://en.wikipedia.org/wiki/Magic_square) to be an $n \\times n$ matrix of distinct positive integers from $1$ to $n^2$ where the sum of any row, column, or diagonal of length $n$ is always equal to the same number: the *magic constant*. \n\nYou will be given a $3 \\times 3$ matrix $s$ of integers in the inclusive range $[1, 9]$. We can convert any digit $a$ to any other digit $b$ in the range $[1, 9]$ at cost of $|a - b|$. Given $s$, convert it into a magic square at *minimal* cost. Print this cost on a new line.\n\n**Note:** The resulting magic square must contain distinct integers in the inclusive range $[1, 9]$.\n\n\n**Example** \n\n$s = [[5, 3, 4], [1, 5, 8], [6, 4, 2]] \n\nThe matrix looks like this: \n```\n5 3 4\n1 5 8\n6 4 2\n```\nWe can convert it to the following magic square:\n```\n8 3 4\n1 5 9\n6 7 2\n```\nThis took three replacements at a cost of $|5-8|+|8-9|+|4-7|=7$.\n\n**Function Description**\n\nComplete the *formingMagicSquare* function in the editor below. \n\nformingMagicSquare has the following parameter(s): \n\n- *int s[3][3]:* a $3 \\times 3$ array of integers \n\n**Returns** \n\n- *int:* the minimal total cost of converting the input square to a magic square "},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Medium"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static int formingMagicSquare(List<List<Integer>> s)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"/* Programming Competition - Template (Horatiu Lazu) */\\n\\nimport java.io.*;\\nimport java.util.*;\\nimport java.lang.*;\\nimport java.awt.*;\\nimport java.awt.geom.*;\\nimport java.math.*;\\nimport java.text.*;\\n\\n\\nclass ProblemC{\\n\\tBufferedReader in;\\n\\tStringTokenizer st;\\n\\t\\n\\tpublic static void main (String [] args){\\n\\t\\tnew ProblemC();\\n\\t}\\n\\t\\n\\tpublic int minCost (int [] []arr1, int [] [] arr2){\\n\\t\\tint cost = 0;\\n\\t\\tfor(int x = 0; x < 3; x++){\\n\\t\\t\\tfor(int y = 0; y < 3; y++){\\n\\t\\t\\t\\tcost += Math.abs(arr1[x][y] - arr2[x][y]);\\n\\t\\t\\t}\\t\\n\\t\\t}\\t\\n\\t\\treturn cost;\\n\\t}\\n\\t\\n\\n\\tpublic ProblemC(){\\n\\t\\ttry{\\n\\t\\t\\tin = new BufferedReader(new InputStreamReader(System.in));\\n\\t\\t\\tint [] [] arr = new int[3][3];\\n\\t\\t\\tfor(int y = 0; y < 3; y++)\\n\\t\\t\\t\\tfor(int x = 0; x < 3; x++)\\n\\t\\t\\t\\t\\tarr[x][y] = nextInt();\\n\\t\\t\\t\\t\\t\\n\\t\\t\\tint[][] arr1 = new int[][]{\\n\\t\\t\\t\\t{ 8, 1, 6,},\\n\\t\\t\\t\\t{ 3, 5, 7, },\\n\\t\\t\\t\\t{ 4, 9, 2,},\\n\\t\\t\\t};\\t\\n\\t\\t\\t\\n\\t\\t\\tint[][] arr2 = new int[][]{\\n\\t\\t\\t\\t{ 6, 1, 8,},\\n\\t\\t\\t\\t{ 7, 5, 3, },\\n\\t\\t\\t\\t{ 2, 9, 4,},\\n\\t\\t\\t};\\t\\n\\t\\t\\t\\n\\t\\t\\tint[][] arr3 = new int[][]{\\n\\t\\t\\t\\t{ 4, 9, 2,},\\n\\t\\t\\t\\t{ 3, 5, 7, },\\n\\t\\t\\t\\t{ 8, 1, 6,},\\n\\t\\t\\t};\\t\\n\\t\\t\\t\\n\\t\\t\\tint[][] arr4 = new int[][]{\\n\\t\\t\\t\\t{ 2, 9, 4,},\\n\\t\\t\\t\\t{ 7, 5, 3, },\\n\\t\\t\\t\\t{ 6, 1, 8,},\\n\\t\\t\\t};\\t\\n\\t\\t\\t\\n\\t\\t\\tint[][] arr6 = new int[][]{\\n\\t\\t\\t\\t{ 8, 3, 4,},\\n\\t\\t\\t\\t{ 1, 5, 9, },\\n\\t\\t\\t\\t{ 6, 7, 2,},\\n\\t\\t\\t};\\t\\n\\t\\t\\t\\n\\t\\t\\tint[][] arr7 = new int[][]{\\n\\t\\t\\t\\t{ 4, 3, 8,},\\n\\t\\t\\t\\t{ 9, 5, 1, },\\n\\t\\t\\t\\t{ 2, 7, 6,},\\n\\t\\t\\t};\\t\\n\\t\\t\\t\\n\\t\\t\\tint[][] arr8 = new int[][]{\\n\\t\\t\\t\\t{ 6, 7, 2,},\\n\\t\\t\\t\\t{ 1, 5, 9, },\\n\\t\\t\\t\\t{ 8, 3, 4,},\\n\\t\\t\\t};\\t\\n\\t\\t\\t\\n\\t\\t\\tint[][] arr9 = new int[][]{\\n\\t\\t\\t\\t{ 2, 7, 6,},\\n\\t\\t\\t\\t{ 9, 5, 1, },\\n\\t\\t\\t\\t{ 4, 3, 8,},\\n\\t\\t\\t};\\t\\n\\t\\t\\tint ans = Integer.MAX_VALUE;\\n\\t\\t\\tans = Math.min(ans, minCost(arr, arr1));\\n\\t\\t\\tans = Math.min(ans, minCost(arr, arr2));\\n\\t\\t\\tans = Math.min(ans, minCost(arr, arr3));\\n\\t\\t\\tans = Math.min(ans, minCost(arr, arr4));\\n\\t\\t\\t//ans = Math.min(ans, minCost(arr, arr5));\\n\\t\\t\\tans = Math.min(ans, minCost(arr, arr6));\\n\\t\\t\\tans = Math.min(ans, minCost(arr, arr7));\\n\\t\\t\\tans = Math.min(ans, minCost(arr, arr8));\\n\\t\\t\\tans = Math.min(ans, minCost(arr, arr9));\\n\\t\\t\\tSystem.out.println(ans);\\n\\t\\t}\\n\\t\\tcatch(IOException e){\\n\\t\\t\\tSystem.out.println(\\\"IO: General\\\");\\n\\t\\t}\\n\\t}\\n\\t\\n\\tString next() throws IOException {\\n\\t\\twhile (st == null || !st.hasMoreTokens())\\n\\t \\t \\tst = new StringTokenizer(in.readLine().trim());\\n\\t\\treturn st.nextToken();\\n\\t}\\n\\n\\tlong nextLong() throws IOException {\\n\\t\\treturn Long.parseLong(next());\\n\\t}\\n\\n\\tint nextInt() throws IOException {\\n\\t\\treturn Integer.parseInt(next());\\n\\t}\\n\\n\\tdouble nextDouble() throws IOException {\\n\\t\\treturn Double.parseDouble(next());\\n\\t}\\n\\n\\tString nextLine() throws IOException {\\n\\t\\treturn in.readLine().trim();\\n\\t}\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'formingMagicSquare' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts 2D_INTEGER_ARRAY s as parameter.\\n */\\n\\n public static int formingMagicSquare(List<List<Integer>> s) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n List<List<Integer>> s = new ArrayList<>();\\n\\n for (int i = 0; i < 3; i++) {\\n String[] sRowTempItems = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> sRowItems = new ArrayList<>();\\n\\n for (int j = 0; j < 3; j++) {\\n int sItem = Integer.parseInt(sRowTempItems[j]);\\n sRowItems.add(sItem);\\n }\\n\\n s.add(sRowItems);\\n }\\n\\n int result = Result.formingMagicSquare(s);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"DNE\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'formingMagicSquare' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts 2D_INTEGER_ARRAY s as parameter.\\n */\\n\\n public static int formingMagicSquare(List<List<Integer>> s) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n List<List<Integer>> s = new ArrayList<>();\\n\\n IntStream.range(0, 3).forEach(i -> {\\n try {\\n s.add(\\n Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList())\\n );\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n int result = Result.formingMagicSquare(s);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner s = new Scanner(System.in);\\n\\t\\tint[][] m = new int[3][3];\\n\\t\\tfor (int x = 0; x < 3; x++) {\\n\\t\\t\\tfor (int y = 0; y < 3; y++) {\\n\\t\\t\\t\\tm[x][y] = s.nextInt();\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\tint[][][] p = {{{8, 1, 6}, {3, 5, 7}, {4, 9, 2}},\\n\\t\\t\\t\\t\\t{{6, 1, 8}, {7, 5, 3}, {2, 9, 4}},\\n\\t\\t\\t\\t\\t{{8, 3, 4}, {1, 5, 9}, {6, 7, 2}}, \\n\\t\\t\\t\\t\\t{{6, 7, 2}, {1, 5, 9}, {8, 3, 4}}, \\n\\t\\t\\t\\t\\t{{4, 9, 2}, {3, 5, 7}, {8, 1, 6}}, \\n\\t\\t\\t\\t\\t{{2, 9, 4}, {7, 5, 3}, {6, 1, 8}}, \\n\\t\\t\\t\\t\\t{{2, 7, 6}, {9, 5, 1}, {4, 3, 8}}, \\n\\t\\t\\t\\t\\t{{4, 3, 8}, {9, 5, 1}, {2, 7, 6}}};\\n\\t\\tint min = 100;\\n\\t\\tfor (int i = 0; i < 8; i++) {\\n\\t\\t\\tint diff = 0;\\n\\t\\t\\tfor (int x = 0; x < 3; x++) {\\n\\t\\t\\t\\tfor (int y = 0; y < 3; y++) {\\n\\t\\t\\t\\t\\tdiff += Math.abs(p[i][x][y] - m[x][y]);\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\tif (diff < min)\\n\\t\\t\\t\\tmin = diff;\\n\\t\\t}\\n\\t\\tSystem.out.println(min);\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'formingMagicSquare' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts 2D_INTEGER_ARRAY s as parameter.\\n */\\n\\n public static int formingMagicSquare(List<List<Integer>> s) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n List<List<Integer>> s = new ArrayList<>();\\n\\n IntStream.range(0, 3).forEach(i -> {\\n try {\\n s.add(\\n Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList())\\n );\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n int result = Result.formingMagicSquare(s);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"formingMagicSquare(s)\"\n ],\n \"solution\": \"from itertools import *\\n\\nX = []\\nX.extend(list(map(int,input().split())))\\nX.extend(list(map(int,input().split())))\\nX.extend(list(map(int,input().split())))\\n\\nAns = 81\\nfor P in permutations(range(1,10)):\\n if sum(P[0:3]) == 15 and sum(P[3:6]) == 15 and sum(P[0::3]) == 15 and sum(P[1::3]) == 15 and P[0] + P[4] + P[8] == 15 and (P[2] + P[4] + P[6] == 15):\\n Ans = min(Ans, sum(abs(P[i] - X[i]) for i in range(0,9)))\\nprint(Ans)\",\n \"template\": \"#\\n# Complete the 'formingMagicSquare' function below.\\n#\\n# The function is expected to return an INTEGER.\\n# The function accepts 2D_INTEGER_ARRAY s as parameter.\\n#\\n\\ndef formingMagicSquare(s):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n s = []\\n\\n for _ in range(3):\\n s.append(list(map(int, input().rstrip().split())))\\n\\n result = formingMagicSquare(s)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"eJyNk01OwzAUhFn0IE9eV4jnJP7hJEgEsYAs2IQuWgkJIXEIOBA7jsVMXpGKSyx340by5zczHr9vPr83F8vv5gt/bl/d07w77N21uCgqwzgH6blE6SSMs9uKm15208N+erx/PuyPe726t62cwF4iqQwqjzNQ8e1wBrxQnfTj7CGgMrkv4ADZibKVZ9CErsNDAUMtYbWRsFCTfVXAYCQS9jxjwFHdKqypgAfz7KEXk3tJFfifycqkOFmZm9Zkh7OrWrb737R9bXIJR8hekgqWdqD1VtnJzHaIOdJzrsguWUTN3agoBycT38iqeUSvTXviEa39QkGoNgDuqd3X+lXCanX8u6zAXZl1RkCZb+p0aYUHS4h5JXvU61nH6j3VB6s/s5xpMtt7PH61qmYbI0uZKBc3Xis2ZN99XP4AAxddAw=="},"max_retries":{"kind":"null"}}},{"rowIdx":176,"cells":{"question_id":{"kind":"number","value":29526,"string":"29,526"},"question_title":{"kind":"string","value":"Picking Numbers"},"question_content":{"kind":"string","value":"Given an array of integers, find the longest subarray where the absolute difference between any two elements is less than or equal to $1$. \n\n**Example** \n\n$a=[1,1,2,2,4,4,5,5,5]$ \n\nThere are two subarrays meeting the criterion: $[1,1,2,2]$ and $[4,4,5,5,5]$. The maximum length subarray has $5$ elements.\n\n**Function Description** \n\nComplete the *pickingNumbers* function in the editor below. \n\npickingNumbers has the following parameter(s): \n\n- *int a[n]:* an array of integers \n\n**Returns** \n\n- *int:* the length of the longest subarray that meets the criterion "},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static int pickingNumbers(List<Integer> a)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner in = new Scanner(System.in);\\n int n = in.nextInt();\\n int[] a = new int[100];\\n for(int a_i=0; a_i < n; a_i++){\\n a[in.nextInt()]++;\\n }\\n int max = 0;\\n for (int i = 0; i < 99; i++) {\\n max = Math.max(max, a[i]+a[i+1]);\\n }\\n System.out.println(max);\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'pickingNumbers' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY a as parameter.\\n */\\n\\n public static int pickingNumbers(List<Integer> a) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String[] aTemp = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> a = new ArrayList<>();\\n\\n for (int i = 0; i < n; i++) {\\n int aItem = Integer.parseInt(aTemp[i]);\\n a.add(aItem);\\n }\\n\\n int result = Result.pickingNumbers(a);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"DNE\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'pickingNumbers' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY a as parameter.\\n */\\n\\n public static int pickingNumbers(List<Integer> a) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> a = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.pickingNumbers(a);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"\\nimport java.io.ByteArrayInputStream;\\nimport java.io.IOException;\\nimport java.io.InputStream;\\nimport java.io.PrintWriter;\\nimport java.util.Arrays;\\nimport java.util.InputMismatchException;\\n\\npublic class A {\\n\\tInputStream is;\\n\\tPrintWriter out;\\n\\tString INPUT = \\\"\\\";\\n\\t\\n\\tvoid solve()\\n\\t{\\n\\t\\tint n = ni();\\n\\t\\tint[] a = na(n);\\n\\t\\tArrays.sort(a);\\n\\t\\tint max = 0;\\n\\t\\tfor(int i = 0;i < n;i++){\\n\\t\\t\\tint ct = 0;\\n\\t\\t\\tfor(int j = i;j < n;j++){\\n\\t\\t\\t\\tif(a[j]-a[i] <= 1)ct++;\\n\\t\\t\\t}\\n\\t\\t\\tmax = Math.max(max, ct);\\n\\t\\t}\\n\\t\\tout.println(max);\\n\\t}\\n\\t\\n\\tvoid run() throws Exception\\n\\t{\\n\\t\\tis = INPUT.isEmpty() ? System.in : new ByteArrayInputStream(INPUT.getBytes());\\n\\t\\tout = new PrintWriter(System.out);\\n\\t\\t\\n\\t\\tlong s = System.currentTimeMillis();\\n\\t\\tsolve();\\n\\t\\tout.flush();\\n\\t\\tif(!INPUT.isEmpty())tr(System.currentTimeMillis()-s+\\\"ms\\\");\\n\\t}\\n\\t\\n\\tpublic static void main(String[] args) throws Exception { new A().run(); }\\n\\t\\n\\tprivate byte[] inbuf = new byte[1024];\\n\\tpublic int lenbuf = 0, ptrbuf = 0;\\n\\t\\n\\tprivate int readByte()\\n\\t{\\n\\t\\tif(lenbuf == -1)throw new InputMismatchException();\\n\\t\\tif(ptrbuf >= lenbuf){\\n\\t\\t\\tptrbuf = 0;\\n\\t\\t\\ttry { lenbuf = is.read(inbuf); } catch (IOException e) { throw new InputMismatchException(); }\\n\\t\\t\\tif(lenbuf <= 0)return -1;\\n\\t\\t}\\n\\t\\treturn inbuf[ptrbuf++];\\n\\t}\\n\\t\\n\\tprivate boolean isSpaceChar(int c) { return !(c >= 33 && c <= 126); }\\n\\tprivate int skip() { int b; while((b = readByte()) != -1 && isSpaceChar(b)); return b; }\\n\\t\\n\\tprivate double nd() { return Double.parseDouble(ns()); }\\n\\tprivate char nc() { return (char)skip(); }\\n\\t\\n\\tprivate String ns()\\n\\t{\\n\\t\\tint b = skip();\\n\\t\\tStringBuilder sb = new StringBuilder();\\n\\t\\twhile(!(isSpaceChar(b))){ // when nextLine, (isSpaceChar(b) && b != ' ')\\n\\t\\t\\tsb.appendCodePoint(b);\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn sb.toString();\\n\\t}\\n\\t\\n\\tprivate char[] ns(int n)\\n\\t{\\n\\t\\tchar[] buf = new char[n];\\n\\t\\tint b = skip(), p = 0;\\n\\t\\twhile(p < n && !(isSpaceChar(b))){\\n\\t\\t\\tbuf[p++] = (char)b;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn n == p ? buf : Arrays.copyOf(buf, p);\\n\\t}\\n\\t\\n\\tprivate char[][] nm(int n, int m)\\n\\t{\\n\\t\\tchar[][] map = new char[n][];\\n\\t\\tfor(int i = 0;i < n;i++)map[i] = ns(m);\\n\\t\\treturn map;\\n\\t}\\n\\t\\n\\tprivate int[] na(int n)\\n\\t{\\n\\t\\tint[] a = new int[n];\\n\\t\\tfor(int i = 0;i < n;i++)a[i] = ni();\\n\\t\\treturn a;\\n\\t}\\n\\t\\n\\tprivate int ni()\\n\\t{\\n\\t\\tint num = 0, b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate long nl()\\n\\t{\\n\\t\\tlong num = 0;\\n\\t\\tint b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate static void tr(Object... o) { System.out.println(Arrays.deepToString(o)); }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'pickingNumbers' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY a as parameter.\\n */\\n\\n public static int pickingNumbers(List<Integer> a) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> a = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.pickingNumbers(a);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"pickingNumbers(a)\"\n ],\n \"solution\": \"#!/bin/python3\\n\\nimport sys\\n\\n\\nn = int(input().strip())\\na = [int(a_temp) for a_temp in input().strip().split(' ')]\\n\\nfrom collections import Counter\\nd = Counter(a)\\nbest = 0\\nfor i in range(99):\\n best = max(d[i] + d[i+1], best)\\nprint(best)\",\n \"template\": \"#\\n# Complete the 'pickingNumbers' function below.\\n#\\n# The function is expected to return an INTEGER.\\n# The function accepts INTEGER_ARRAY a as parameter.\\n#\\n\\ndef pickingNumbers(a):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n n = int(input().strip())\\n\\n a = list(map(int, input().rstrip().split()))\\n\\n result = pickingNumbers(a)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":177,"cells":{"question_id":{"kind":"number","value":1308,"string":"1,308"},"question_title":{"kind":"string","value":"Utopian Tree"},"question_content":{"kind":"string","value":"The Utopian Tree goes through _2_ cycles of growth every year. Each spring, it _doubles_ in height. Each summer, its height increases by _1_ meter.\n\nA Utopian Tree sapling with a height of *1* meter is planted at the onset of spring. How tall will the tree be after $n$ growth cycles?\n\nFor example, if the number of growth cycles is $n = 5$, the calculations are as follows:\n\n Period Height\n 0 1\n 1 2\n 2 3\n 3 6\n 4 7\n 5 14\n \n**Function Description**\n\nComplete the *utopianTree* function in the editor below. \n\nutopianTree has the following parameter(s):\n\n- *int n*: the number of growth cycles to simulate \n\n**Returns** \n\n- *int:* the height of the tree after the given number of cycles \n"},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static int utopianTree(int n)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.BufferedReader;\\nimport java.io.IOException;\\nimport java.io.InputStreamReader;\\nimport java.io.PrintWriter;\\n\\npublic class Solution\\n{\\n\\tpublic static void main(String[] args) throws NumberFormatException, IOException\\n\\t{\\n\\n\\t\\tBufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n\\t\\tPrintWriter printWriter = new PrintWriter(System.out);\\n\\t\\t\\n\\t\\tint testCount = Integer.parseInt(bufferedReader.readLine());\\n\\t\\t\\n\\t\\twhile(testCount-- > 0)\\n\\t\\t{\\n\\t\\t\\tprintWriter.println(getAnswer(Integer.parseInt(bufferedReader.readLine())));\\n\\t\\t}\\n\\t\\t\\n\\t\\tprintWriter.flush();\\n\\t}\\n\\n\\tprivate static long getAnswer(int n)\\n\\t{\\n\\t\\tlong h = 1;\\n\\t\\t\\n\\t\\tfor(int i = 1 ; i <= n ; i++)\\n\\t\\t{\\n\\t\\t\\tif(i % 2 != 0)\\n\\t\\t\\t\\th *= 2;\\n\\t\\t\\telse\\n\\t\\t\\t\\th +=1 ;\\n\\t\\t}\\n\\t\\t\\n\\t\\treturn h;\\n\\t}\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'utopianTree' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER n as parameter.\\n */\\n\\n public static int utopianTree(int n) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int t = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n for (int tItr = 0; tItr < t; tItr++) {\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n int result = Result.utopianTree(n);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n }\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner in = new Scanner(System.in);\\n int numCases = in.nextInt();\\n for (int i = 0; i < numCases; i++) {\\n int n = in.nextInt();\\n int answer = ((int) Math.pow(2, (n + 3) / 2)) + (((int) Math.pow(-1, n)) - 3) / 2;\\n System.out.println(answer);\\n }\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'utopianTree' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER n as parameter.\\n */\\n\\n public static int utopianTree(int n) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int t = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, t).forEach(tItr -> {\\n try {\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n int result = Result.utopianTree(n);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.util.Scanner;\\nimport java.util.Vector;\\n\\npublic class Solution {\\n public static void main(String[] args) {\\n Scanner stdin = new Scanner(System.in);\\n int numCases = stdin.nextInt();\\n Vector<Long> cache = new Vector<>();\\n cache.add(1L);\\n for (int i = 0; i < numCases; i++) {\\n int numCycles = stdin.nextInt();\\n while(cache.size() <= numCycles) {\\n if (cache.size() % 2 == 0) {\\n cache.add(cache.lastElement() + 1); \\n } else {\\n cache.add(cache.lastElement() * 2);\\n }\\n }\\n System.out.println(cache.get(numCycles));\\n }\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'utopianTree' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER n as parameter.\\n */\\n\\n public static int utopianTree(int n) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int t = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, t).forEach(tItr -> {\\n try {\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n int result = Result.utopianTree(n);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"utopianTree(n)\"\n ],\n \"solution\": \"#!/usr/bin/env python\\n\\nimport sys\\n\\n\\nif __name__ == '__main__':\\n T = int(sys.stdin.readline())\\n \\n for _ in range(T):\\n N = int(sys.stdin.readline())\\n height = 1\\n \\n for i in range(N):\\n if i % 2 == 0:\\n height *= 2\\n else:\\n height += 1\\n \\n print(height)\",\n \"template\": \"#\\n# Complete the 'utopianTree' function below.\\n#\\n# The function is expected to return an INTEGER.\\n# The function accepts INTEGER n as parameter.\\n#\\n\\ndef utopianTree(n):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n t = int(input().strip())\\n\\n for t_itr in range(t):\\n n = int(input().strip())\\n\\n result = utopianTree(n)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":178,"cells":{"question_id":{"kind":"number","value":6024,"string":"6,024"},"question_title":{"kind":"string","value":"Angry Professor"},"question_content":{"kind":"string","value":"A Discrete Mathematics professor has a class of students. Frustrated with their lack of discipline, the professor decides to cancel class if fewer than some number of students are present when class starts. Arrival times go from on time ($arrivalTime \\le 0$) to arrived late ($arrivalTime \\gt 0$).\n\nGiven the arrival time of each student and a threshhold number of attendees, determine if the class is cancelled. \n\n**Example** \n\n$n = 5$ \n$k = 3$ \n$a = [-2, -1, 0, 1, 2]$ \n\nThe first $3$ students arrived on. The last $2$ were late. The threshold is $3$ students, so class will go on. Return `YES`. \n\n**Note:** Non-positive arrival times ($a[i] \\le 0$) indicate the student arrived early or on time; positive arrival times ($a[i] \\gt 0$) indicate the student arrived $a[i]$ minutes late. \n\n**Function Description**\n\nComplete the *angryProfessor* function in the editor below. It must return `YES` if class is cancelled, or `NO` otherwise. \n\nangryProfessor has the following parameter(s): \n\n- *int k*: the threshold number of students \n- *int a[n]*: the arrival times of the $n$ students\n\n**Returns** \n\n- *string:* either `YES` or `NO`"},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static String angryProfessor(int k, List<Integer> a)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.BufferedReader;\\nimport java.io.InputStreamReader;\\nimport java.io.PrintWriter;\\nimport java.util.StringTokenizer;\\n\\n\\npublic class AngryProf {\\n\\n\\tpublic static void main(String args[] ) throws Exception {\\n\\t\\tBufferedReader br = new BufferedReader(new InputStreamReader(System.in));\\n\\t PrintWriter w = new PrintWriter(System.out);\\n\\t \\n\\t int t = ip(br.readLine());\\n\\t \\n\\t while(t-- > 0) {\\n\\t \\tStringTokenizer st1 = new StringTokenizer(br.readLine());\\n\\t \\tint n = ip(st1.nextToken());\\n\\t \\tint k = ip(st1.nextToken());\\n\\t \\n\\t StringTokenizer st2 = new StringTokenizer(br.readLine());\\n\\t int a[] = new int[n];\\n\\t for(int i=0;i<n;i++)\\n\\t \\t a[i] = ip(st2.nextToken());\\n\\t \\n\\t int count = 0;\\n\\t for(int i=0;i<n;i++)\\n\\t \\tif(a[i]<=0)\\tcount++;\\n\\t w.println(count < k ? \\\"YES\\\" : \\\"NO\\\");\\n\\t }\\n\\t \\n\\t w.close();\\n\\t \\n\\t}\\n\\t\\n\\tpublic static int ip(String s){\\n\\t\\treturn Integer.parseInt(s);\\n\\t}\\n\\t\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'angryProfessor' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts following parameters:\\n * 1. INTEGER k\\n * 2. INTEGER_ARRAY a\\n */\\n\\n public static String angryProfessor(int k, List<Integer> a) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int t = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n for (int tItr = 0; tItr < t; tItr++) {\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int k = Integer.parseInt(firstMultipleInput[1]);\\n\\n String[] aTemp = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> a = new ArrayList<>();\\n\\n for (int i = 0; i < n; i++) {\\n int aItem = Integer.parseInt(aTemp[i]);\\n a.add(aItem);\\n }\\n\\n String result = Result.angryProfessor(k, a);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n }\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'angryProfessor' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts following parameters:\\n * 1. INTEGER k\\n * 2. INTEGER_ARRAY a\\n */\\n\\n public static String angryProfessor(int k, List<Integer> a) {\\n int onTime = 0;\\n for(int i : a)\\n if(i <= 0) onTime++;\\n if(onTime < k) return \\\"YES\\\";\\n return \\\"NO\\\";\\n \\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int t = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, t).forEach(tItr -> {\\n try {\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int k = Integer.parseInt(firstMultipleInput[1]);\\n\\n List<Integer> a = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n String result = Result.angryProfessor(k, a);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'angryProfessor' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts following parameters:\\n * 1. INTEGER k\\n * 2. INTEGER_ARRAY a\\n */\\n\\n public static String angryProfessor(int k, List<Integer> a) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int t = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, t).forEach(tItr -> {\\n try {\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int k = Integer.parseInt(firstMultipleInput[1]);\\n\\n List<Integer> a = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n String result = Result.angryProfessor(k, a);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.math.*;\\n\\n\\npublic class Solution implements Runnable {\\n\\tstatic BufferedReader in;\\n\\tstatic PrintWriter out;\\n\\tstatic StringTokenizer tok;\\n\\t\\n\\t\\n\\tstatic void solve() throws Exception {\\n\\t\\tint t = nextInt();\\n\\t\\tfor (int tt = 0; tt < t; ++tt) {\\n\\t\\t\\tint n = nextInt();\\n\\t\\t\\tint k = nextInt();\\n\\t\\t\\tfor (int i = 0; i < n; ++i) {\\n\\t\\t\\t\\tif (nextInt() <= 0) {\\n\\t\\t\\t\\t\\t--k;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\tout.println(k <= 0 ? \\\"NO\\\" : \\\"YES\\\");\\n\\t\\t}\\t\\n\\t}\\n\\t\\n\\tstatic int nextInt() throws IOException {\\n\\t\\treturn Integer.parseInt(next());\\n\\t}\\n\\n\\tstatic long nextLong() throws IOException {\\n\\t\\treturn Long.parseLong(next());\\n\\t}\\n\\n\\tstatic double nextDouble() throws IOException {\\n\\t\\treturn Double.parseDouble(next());\\n\\t}\\n\\n\\tstatic BigInteger nextBigInteger() throws IOException {\\n\\t\\treturn new BigInteger(next());\\n\\t}\\n\\t\\n\\tstatic String next() throws IOException {\\n\\t\\twhile (tok == null || !tok.hasMoreTokens()) {\\n\\t\\t\\ttok = new StringTokenizer(in.readLine());\\n\\t\\t}\\n\\t\\treturn tok.nextToken();\\n\\t}\\n\\t\\n\\tstatic String nextLine() throws IOException {\\n\\t\\ttok = new StringTokenizer(\\\"\\\");\\n\\t\\treturn in.readLine();\\n\\t}\\n\\n\\tstatic boolean hasNext() throws IOException {\\n\\t\\twhile (tok == null || !tok.hasMoreTokens()) {\\n\\t\\t\\tString s = in.readLine();\\n\\t\\t\\tif (s == null) {\\n\\t\\t\\t\\treturn false;\\n\\t\\t\\t}\\n\\t\\t\\ttok = new StringTokenizer(s);\\n\\t\\t}\\n\\t\\treturn true;\\n\\t}\\n\\t\\n\\tpublic static void main(String args[]) {\\n\\t\\tnew Thread(null, new Solution(), \\\"Main\\\", 1 << 28).start();\\n\\t}\\n\\t\\n\\tpublic void run() {\\n\\t\\ttry {\\n\\t\\t\\tin = new BufferedReader(new InputStreamReader(System.in));\\n\\t\\t\\tout = new PrintWriter(new OutputStreamWriter(System.out));\\n\\t\\t\\t//in = new BufferedReader(new FileReader(\\\"input.in\\\"));\\n\\t\\t\\t//out = new PrintWriter(new FileWriter(\\\"output.out\\\"));\\n\\t\\t\\tLocale.setDefault(Locale.US);\\n\\t\\t\\tsolve();\\n\\t\\t\\tin.close();\\n\\t\\t\\tout.close();\\n\\t\\t} catch (Throwable e) {\\n\\t\\t\\te.printStackTrace();\\n\\t\\t\\tjava.lang.System.exit(1);\\n\\t\\t}\\n\\t}\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'angryProfessor' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts following parameters:\\n * 1. INTEGER k\\n * 2. INTEGER_ARRAY a\\n */\\n\\n public static String angryProfessor(int k, List<Integer> a) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int t = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, t).forEach(tItr -> {\\n try {\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int k = Integer.parseInt(firstMultipleInput[1]);\\n\\n List<Integer> a = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n String result = Result.angryProfessor(k, a);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"angryProfessor(k, a)\"\n ],\n \"solution\": \"t = int(input())\\n\\nfor _ in range(t):\\n\\tn, k = map(int, input().split())\\n\\tarr = list(map(int, input().split()))\\n\\n\\tcnt = 0\\n\\tfor x in arr:\\n\\t\\tif x <= 0:\\n\\t\\t\\tcnt += 1\\n\\tif cnt >= k:\\n\\t\\tprint(\\\"NO\\\")\\n\\telse:\\n\\t\\tprint(\\\"YES\\\")\",\n \"template\": \"#\\n# Complete the 'angryProfessor' function below.\\n#\\n# The function is expected to return a STRING.\\n# The function accepts following parameters:\\n# 1. INTEGER k\\n# 2. INTEGER_ARRAY a\\n#\\n\\ndef angryProfessor(k, a):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n t = int(input().strip())\\n\\n for t_itr in range(t):\\n first_multiple_input = input().rstrip().split()\\n\\n n = int(first_multiple_input[0])\\n\\n k = int(first_multiple_input[1])\\n\\n a = list(map(int, input().rstrip().split()))\\n\\n result = angryProfessor(k, a)\\n\\n fptr.write(result + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":179,"cells":{"question_id":{"kind":"number","value":13489,"string":"13,489"},"question_title":{"kind":"string","value":"Save the Prisoner!"},"question_content":{"kind":"string","value":"A jail has a number of prisoners and a number of treats to pass out to them. Their jailer decides the fairest way to divide the treats is to seat the prisoners around a circular table in sequentially numbered chairs. A chair number will be drawn from a hat. Beginning with the prisoner in that chair, one candy will be handed to each prisoner sequentially around the table until all have been distributed.\n\nThe jailer is playing a little joke, though. The last piece of candy looks like all the others, but it tastes *awful*. Determine the chair number occupied by the prisoner who will receive that candy.\n\n**Example** \n\n$n = 4$ \n$m = 6$ \n$s = 2$ \n\nThere are $4$ prisoners, $6$ pieces of candy and distribution starts at chair $2$. The prisoners arrange themselves in seats numbered $1$ to $4$. Prisoners receive candy at positions $2, 3, 4, 1, 2, 3$. The prisoner to be warned sits in chair number $3$. \n\n**Function Description**\n\nComplete the *saveThePrisoner* function in the editor below. It should return an integer representing the chair number of the prisoner to warn. \n\nsaveThePrisoner has the following parameter(s): \n\n- *int n*: the number of prisoners \n- *int m*: the number of sweets \n- *int s*: the chair number to begin passing out sweets from \n\n**Returns** \n\n- *int:* the chair number of the prisoner to warn"},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static int saveThePrisoner(int n, int m, int s)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner input = new Scanner(System.in);\\n int rounds = input.nextInt();\\n for(int i = 0; i < rounds; i++)\\n {\\n int num = input.nextInt();\\n int lop = input.nextInt();\\n int s = input.nextInt() - 1;\\n while(lop != 0)\\n {\\n lop--;\\n s++;\\n if(s > num)\\n s = 1;\\n }\\n System.out.println(s);\\n } \\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'saveThePrisoner' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER n\\n * 2. INTEGER m\\n * 3. INTEGER s\\n */\\n\\n public static int saveThePrisoner(int n, int m, int s) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int t = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n for (int tItr = 0; tItr < t; tItr++) {\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int m = Integer.parseInt(firstMultipleInput[1]);\\n\\n int s = Integer.parseInt(firstMultipleInput[2]);\\n\\n int result = Result.saveThePrisoner(n, m, s);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n }\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'saveThePrisoner' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER n\\n * 2. INTEGER m\\n * 3. INTEGER s\\n */\\n\\n public static int saveThePrisoner(int n, int m, int s) {\\n \\n \\n int poisoned = (s + m - 1) % n;\\nif(poisoned == 0)\\n{\\n poisoned = n;\\n}\\nreturn poisoned;\\n\\n}\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int t = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, t).forEach(tItr -> {\\n try {\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int m = Integer.parseInt(firstMultipleInput[1]);\\n\\n int s = Integer.parseInt(firstMultipleInput[2]);\\n\\n int result = Result.saveThePrisoner(n, m, s);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'saveThePrisoner' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER n\\n * 2. INTEGER m\\n * 3. INTEGER s\\n */\\n\\n public static int saveThePrisoner(int n, int m, int s) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int t = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, t).forEach(tItr -> {\\n try {\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int m = Integer.parseInt(firstMultipleInput[1]);\\n\\n int s = Integer.parseInt(firstMultipleInput[2]);\\n\\n int result = Result.saveThePrisoner(n, m, s);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.ByteArrayInputStream;\\nimport java.io.IOException;\\nimport java.io.InputStream;\\nimport java.io.PrintWriter;\\nimport java.util.Arrays;\\nimport java.util.InputMismatchException;\\n\\npublic class A {\\n\\tInputStream is;\\n\\tPrintWriter out;\\n\\tString INPUT = \\\"\\\";\\n\\t\\n\\tvoid solve()\\n\\t{\\n\\t\\tfor(int T = ni();T > 0;T--){\\n\\t\\t\\tlong n = nl(), m = nl(), s = nl()-1;\\n\\t\\t\\tout.println((s+m-1)%n+1);\\n\\t\\t}\\n\\t}\\n\\t\\n\\tvoid run() throws Exception\\n\\t{\\n\\t\\tis = INPUT.isEmpty() ? System.in : new ByteArrayInputStream(INPUT.getBytes());\\n\\t\\tout = new PrintWriter(System.out);\\n\\t\\t\\n\\t\\tlong s = System.currentTimeMillis();\\n\\t\\tsolve();\\n\\t\\tout.flush();\\n\\t\\tif(!INPUT.isEmpty())tr(System.currentTimeMillis()-s+\\\"ms\\\");\\n\\t}\\n\\t\\n\\tpublic static void main(String[] args) throws Exception { new A().run(); }\\n\\t\\n\\tprivate byte[] inbuf = new byte[1024];\\n\\tprivate int lenbuf = 0, ptrbuf = 0;\\n\\t\\n\\tprivate int readByte()\\n\\t{\\n\\t\\tif(lenbuf == -1)throw new InputMismatchException();\\n\\t\\tif(ptrbuf >= lenbuf){\\n\\t\\t\\tptrbuf = 0;\\n\\t\\t\\ttry { lenbuf = is.read(inbuf); } catch (IOException e) { throw new InputMismatchException(); }\\n\\t\\t\\tif(lenbuf <= 0)return -1;\\n\\t\\t}\\n\\t\\treturn inbuf[ptrbuf++];\\n\\t}\\n\\t\\n\\tprivate boolean isSpaceChar(int c) { return !(c >= 33 && c <= 126); }\\n\\tprivate int skip() { int b; while((b = readByte()) != -1 && isSpaceChar(b)); return b; }\\n\\t\\n\\tprivate double nd() { return Double.parseDouble(ns()); }\\n\\tprivate char nc() { return (char)skip(); }\\n\\t\\n\\tprivate String ns()\\n\\t{\\n\\t\\tint b = skip();\\n\\t\\tStringBuilder sb = new StringBuilder();\\n\\t\\twhile(!(isSpaceChar(b))){ // when nextLine, (isSpaceChar(b) && b != ' ')\\n\\t\\t\\tsb.appendCodePoint(b);\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn sb.toString();\\n\\t}\\n\\t\\n\\tprivate char[] ns(int n)\\n\\t{\\n\\t\\tchar[] buf = new char[n];\\n\\t\\tint b = skip(), p = 0;\\n\\t\\twhile(p < n && !(isSpaceChar(b))){\\n\\t\\t\\tbuf[p++] = (char)b;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn n == p ? buf : Arrays.copyOf(buf, p);\\n\\t}\\n\\t\\n\\tprivate char[][] nm(int n, int m)\\n\\t{\\n\\t\\tchar[][] map = new char[n][];\\n\\t\\tfor(int i = 0;i < n;i++)map[i] = ns(m);\\n\\t\\treturn map;\\n\\t}\\n\\t\\n\\tprivate int[] na(int n)\\n\\t{\\n\\t\\tint[] a = new int[n];\\n\\t\\tfor(int i = 0;i < n;i++)a[i] = ni();\\n\\t\\treturn a;\\n\\t}\\n\\t\\n\\tprivate int ni()\\n\\t{\\n\\t\\tint num = 0, b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate long nl()\\n\\t{\\n\\t\\tlong num = 0;\\n\\t\\tint b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate static void tr(Object... o) { System.out.println(Arrays.deepToString(o)); }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'saveThePrisoner' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER n\\n * 2. INTEGER m\\n * 3. INTEGER s\\n */\\n\\n public static int saveThePrisoner(int n, int m, int s) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int t = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, t).forEach(tItr -> {\\n try {\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int m = Integer.parseInt(firstMultipleInput[1]);\\n\\n int s = Integer.parseInt(firstMultipleInput[2]);\\n\\n int result = Result.saveThePrisoner(n, m, s);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"saveThePrisoner(n, m, s)\"\n ],\n \"solution\": \"for _ in range(int(input().strip())):\\n n,m,s = map(int, input().strip().split(\\\" \\\"))\\n print(((s-2+m)%n)+1)\",\n \"template\": \"#\\n# Complete the 'saveThePrisoner' function below.\\n#\\n# The function is expected to return an INTEGER.\\n# The function accepts following parameters:\\n# 1. INTEGER n\\n# 2. INTEGER m\\n# 3. INTEGER s\\n#\\n\\ndef saveThePrisoner(n, m, s):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n t = int(input().strip())\\n\\n for t_itr in range(t):\\n first_multiple_input = input().rstrip().split()\\n\\n n = int(first_multiple_input[0])\\n\\n m = int(first_multiple_input[1])\\n\\n s = int(first_multiple_input[2])\\n\\n result = saveThePrisoner(n, m, s)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"eJyVlU2KHEcQhb3wwsdIei1EZPxm+CQCjfHCnoU2LS1GIDACH8LGO51VXzYemOnpGlBV01XVlZHx4r0X0X///O+3X366HO/+4+b9X6cP50+fH06/jpPenTVXhLSN6lSz6jladXnYfqcdwtdQ6yypqcPKI1vr7nx6M073Xz7d//Fw/+fvHz8/PO6ZmR6ed+fps0qkT1/fjOdJi0Ui4ULW0BVtNgIcS1fymkfvFTXSXCxDakQCw1Yfps1wStHwu7OVzlydV3knb2a3h/qYIikeQtlmqeWH+87ZUSp+tRkBO6HOKT3S06dmy4A4uFmkStE01V5Dp4WIdu8SWea7RHJ7RBevLbPTpg31ObO0hGjTmq3wzQ4l3g0ZXdWtx0ht62ZJcjBT6CURtUlRK8VLQFCvdVUKQpcj/vSaA92rVsE9vBtVTSIJgiRZNiZY3XHHmBP4OAGd12xkjCXEoHmkFwR3OVY5hstGDVsoqlu27QZoKSgw6WuMmzGpdlt4ZgqX5aoDikrJDIiW7pbpDYhGZzyILks91iuu0c6AIMHOU6g62PkqdVDgtnRZ6/DEZxHYZlk4H8h27CpWkoN4DB8mg2aBH9u4WozGQV6XhDq4GssT3yRJgwK854Ru6Efg4m4twzy7ARO/YoTABgQnv9MxXF3ruAFZNXvR0rsDGwoseqOcQrbcv0JgTJvbHQsdNepGrxQ9oimr8KWXWMsCpOBPZD9MHgEtc6W9FLDnYhOfdAmyR9mcSLkcjNuzLmuiBeRhN3ywLpkNmZfYYT4LHJFV2/RML1o/rufNnkSQ5mvPEdu2XJyjkacu80LBg2kTbnfjxJzOHZrTlPAQBlaMtEcFQayhFRlVNDy8FuuKqTUwBn3qBmpcRQlAIpDxRl/iF11t7SiZDDmQbsSFmVS2r5gAeEPpK5nMQ+1jiuf2XaPNVjJxpexhm0WHUvx2DYWl7SHkpYBh/Gw51y7s2t0bxeA8znYzQB6PH4uMEeOVAXbjj2KghV2u45W/HBa8HGpPQT7D+/TpyatnL3T043GM92bo0/uXBeH+/7fFKPHsee3no1S49IYQj7Vdvg6D2fxGrI1F2GHQDaPk4Dzu/JfFQuKWm2n5ik02Kaevv/3z9jsdZTFK"},"max_retries":{"kind":"null"}}},{"rowIdx":180,"cells":{"question_id":{"kind":"number","value":127,"string":"127"},"question_title":{"kind":"string","value":"Find Digits"},"question_content":{"kind":"string","value":"An integer $d$ is a *divisor* of an integer $n$ if the remainder of $n \\div d = 0$. \n\nGiven an integer, for each digit that makes up the integer determine whether it is a divisor. Count the number of divisors occurring within the integer. \n\n**Example** \n$n = 124$ \n\nCheck whether $1$, $2$ and $4$ are divisors of $124$. All 3 numbers divide evenly into $124$ so return $3$. \n\n$n = 111$ \n\nCheck whether $1$, $1$, and $1$ are divisors of $111$. All 3 numbers divide evenly into $111$ so return $3$. \n\n$n = 10$ \n\nCheck whether $1$ and $0$ are divisors of $10$. $1$ is, but $0$ is not. Return $1$. \n\n**Function Description**\n\nComplete the *findDigits* function in the editor below. \n\nfindDigits has the following parameter(s):\n\n- *int n*: the value to analyze \n\n**Returns** \n\n- *int:* the number of digits in $n$ that are divisors of $n$ "},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static int findDigits(int n)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args){\\n int t;\\n Scanner scan = new Scanner(System.in);\\n \\n t = scan.nextInt();\\n \\n for(int i = 0; i < t; i++){\\n System.out.println(digits(scan.next()));\\n }\\n \\n scan.close();\\n }\\n\\n private static int digits(String number) {\\n \\n int sum = 0;\\n char[] digits = number.toCharArray();\\n \\n for(int i = 0; i < number.length(); i++){\\n if(Character.getNumericValue(digits[i]) != 0){ \\n if(((Integer.parseInt(number))% (Character.getNumericValue(digits[i]))) == 0){\\n sum++;\\n }\\n }\\n else\\n continue;\\n }\\n \\n return sum;\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'findDigits' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER n as parameter.\\n */\\n\\n public static int findDigits(int n) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int t = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n for (int tItr = 0; tItr < t; tItr++) {\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n int result = Result.findDigits(n);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n }\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'findDigits' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER n as parameter.\\n */\\n\\n public static int findDigits(int n) {\\n \\n int count = 0; \\n int nn = n; \\n while(nn > 0){\\n int d = nn % 10;\\n if(d != 0 && n % d == 0) count ++;\\n nn = nn/10;\\n }\\n return count; \\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int t = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, t).forEach(tItr -> {\\n try {\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n int result = Result.findDigits(n);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'findDigits' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER n as parameter.\\n */\\n\\n public static int findDigits(int n) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int t = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, t).forEach(tItr -> {\\n try {\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n int result = Result.findDigits(n);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.util.*;\\nclass Solution\\n{\\n\\tpublic static void main(String args[])\\n\\t{\\n\\t\\tScanner in=new Scanner(System.in);\\n\\t\\tint t,ans,d;\\n\\t\\tlong n,m;\\n\\t\\tt=in.nextInt();\\n\\t\\twhile(t-->0)\\n\\t\\t{\\n\\t\\t\\tans=0;\\n\\t\\t\\tn=in.nextLong();\\n\\t\\t\\tm=n;\\n\\t\\t\\twhile(m!=0)\\n\\t\\t\\t{\\n\\t\\t\\t\\td=(int)m%10;\\n\\t\\t\\t\\tm=m/10;\\n\\t\\t\\t\\tif(d==0)\\n\\t\\t\\t\\tcontinue;\\n\\t\\t\\t\\tif(n%d==0)\\n\\t\\t\\t\\tans++;\\n\\t\\t\\t}\\n\\t\\t\\tSystem.out.println(ans);\\n\\t\\t}\\n\\t}\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'findDigits' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER n as parameter.\\n */\\n\\n public static int findDigits(int n) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int t = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, t).forEach(tItr -> {\\n try {\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n int result = Result.findDigits(n);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"findDigits(n)\"\n ],\n \"solution\": \"def func(A):\\n return len([1 for i in str(A) if i !='0' and A%int(i)==0])\\n\\nfor t in range(int(input())):\\n print(func(int(input())))\",\n \"template\": \"#\\n# Complete the 'findDigits' function below.\\n#\\n# The function is expected to return an INTEGER.\\n# The function accepts INTEGER n as parameter.\\n#\\n\\ndef findDigits(n):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n t = int(input().strip())\\n\\n for t_itr in range(t):\\n n = int(input().strip())\\n\\n result = findDigits(n)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":181,"cells":{"question_id":{"kind":"number","value":26066,"string":"26,066"},"question_title":{"kind":"string","value":"Append and Delete"},"question_content":{"kind":"string","value":"You have two strings of lowercase English letters. You can perform two types of operations on the first string:\n\n1. *Append* a lowercase English letter to the end of the string.\n2. *Delete* the last character of the string. Performing this operation on an empty string results in an empty string.\n\nGiven an integer, $k$, and two strings, $s$ and $t$, determine whether or not you can convert $s$ to $t$ by performing *exactly* $k$ of the above operations on $s$. If it's possible, print `Yes`. Otherwise, print `No`.\n\n**Example**. \n$s=[a,b,c]$ \n$t=[d,e,f]$ \n$k=6$ \n\nTo convert $s$ to $t$, we first delete all of the characters in $3$ moves. Next we add each of the characters of $t$ in order. On the $6^{th}$ move, you will have the matching string. Return `Yes`. \n\nIf there were more moves available, they could have been eliminated by performing multiple deletions on an empty string. If there were fewer than $6$ moves, we would not have succeeded in creating the new string. \n\n**Function Description** \n\nComplete the *appendAndDelete* function in the editor below. It should return a string, either `Yes` or `No`. \n\nappendAndDelete has the following parameter(s): \n\n- *string s*: the initial string \n- *string t*: the desired string \n- *int k*: the exact number of operations that must be performed \n\n**Returns** \n\n- *string:* either `Yes` or `No`"},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static String appendAndDelete(String s, String t, int k)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner in = new Scanner(System.in);\\n String s = in.next();\\n String t = in.next();\\n int k = in.nextInt();\\n \\n int sl=s.length();int tl=t.length();\\n int ll=sl>tl?tl:sl;\\n int m;\\n for(m=0;m<ll;m++)\\n {\\n if(s.charAt(m)!=t.charAt(m))break;\\n \\n }\\n \\n int sleft=sl-m;\\n int tleft=tl-m;\\n \\n int flag=0;\\n \\n if(sleft+tleft>k)flag=1;\\n else\\n {\\n int sub=k-(sleft+tleft);\\n if((sub%2!=0) && !(sub>2*m))flag=1;\\n }\\n \\n if(flag==0)\\n System.out.println(\\\"Yes\\\");\\n else \\n System.out.println(\\\"No\\\");\\n \\n \\n \\n \\n }\\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n }\\n\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'appendAndDelete' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts following parameters:\\n * 1. STRING s\\n * 2. STRING t\\n * 3. INTEGER k\\n */\\n\\n public static String appendAndDelete(String s, String t, int k) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String s = bufferedReader.readLine();\\n\\n String t = bufferedReader.readLine();\\n\\n int k = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String result = Result.appendAndDelete(s, t, k);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'appendAndDelete' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts following parameters:\\n * 1. STRING s\\n * 2. STRING t\\n * 3. INTEGER k\\n */\\n\\n public static String appendAndDelete(String s, String t, int k) {\\n // Write your code here\\n int commonlength=0;\\n for(int i=0;i<Math.min(s.length(),t.length());i++)\\n { if(s.charAt(i)==t.charAt(i))\\n commonlength++;\\n else\\n break;\\n }\\n int x=s.length()+t.length()-2*commonlength;\\n if(x>k)\\n return \\\"No\\\";\\n else if(x%2==k%2)\\n return \\\"Yes\\\";\\n else if((s.length()+t.length()-k)<0)\\n return \\\"Yes\\\";\\n else\\n return \\\"No\\\"; \\n \\n \\n \\n\\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String s = bufferedReader.readLine();\\n\\n String t = bufferedReader.readLine();\\n\\n int k = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String result = Result.appendAndDelete(s, t, k);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'appendAndDelete' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts following parameters:\\n * 1. STRING s\\n * 2. STRING t\\n * 3. INTEGER k\\n */\\n\\n public static String appendAndDelete(String s, String t, int k) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String s = bufferedReader.readLine();\\n\\n String t = bufferedReader.readLine();\\n\\n int k = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String result = Result.appendAndDelete(s, t, k);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner in = new Scanner(System.in);\\n String s = in.next();\\n String t = in.next();\\n int k = in.nextInt();\\n int toDelete = 0;\\n int i = 0;\\n while (i < s.length() && i < t.length() && s.charAt(i) == t.charAt(i)) {\\n i++;\\n }\\n toDelete = s.length() - i;\\n int ops = toDelete + (t.length() - i);\\n if (ops <= k && ((k - ops) % 2 == 0 || (k - ops) > 2 * i)) {\\n System.out.println(\\\"Yes\\\");\\n } else {\\n System.out.println(\\\"No\\\");\\n }\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'appendAndDelete' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts following parameters:\\n * 1. STRING s\\n * 2. STRING t\\n * 3. INTEGER k\\n */\\n\\n public static String appendAndDelete(String s, String t, int k) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String s = bufferedReader.readLine();\\n\\n String t = bufferedReader.readLine();\\n\\n int k = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String result = Result.appendAndDelete(s, t, k);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"appendAndDelete(s, t, k)\"\n ],\n \"solution\": \"#!/bin/python3\\n\\nimport sys\\n\\n\\ns = input().strip()\\nt = input().strip()\\nk = int(input().strip())\\n\\nlead = 0\\nfor i in range(min(len(s),len(t))):\\n if s[i] != t[i]:\\n lead = i\\n break\\n else:\\n lead = i + 1\\n \\nd = len(s) - lead + len(t) - lead\\n\\nif k >= len(s) + len(t):\\n print(\\\"Yes\\\")\\nelif d <= k and (d % 2) == (k % 2):\\n print(\\\"Yes\\\")\\nelse:\\n print(\\\"No\\\")\\n\",\n \"template\": \"#\\n# Complete the 'appendAndDelete' function below.\\n#\\n# The function is expected to return a STRING.\\n# The function accepts following parameters:\\n# 1. STRING s\\n# 2. STRING t\\n# 3. INTEGER k\\n#\\n\\ndef appendAndDelete(s, t, k):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n s = input()\\n\\n t = input()\\n\\n k = int(input().strip())\\n\\n result = appendAndDelete(s, t, k)\\n\\n fptr.write(result + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":182,"cells":{"question_id":{"kind":"number","value":2040,"string":"2,040"},"question_title":{"kind":"string","value":"Sherlock and Squares"},"question_content":{"kind":"string","value":"Watson likes to challenge Sherlock's math ability. He will provide a starting and ending value that describe a range of integers, inclusive of the endpoints. Sherlock must determine the number of *square integers* within that range.\n\n**Note**: A square integer is an integer which is the square of an integer, e.g. $1, 4, 9, 16, 25$. \n\n**Example** \n$a = 24$ \n$b = 49$ \n\nThere are three square integers in the range: $25, 36$ and $49$. Return $3$. \n\n**Function Description**\n\nComplete the *squares* function in the editor below. It should return an integer representing the number of square integers in the inclusive range from $a$ to $b$. \n\nsquares has the following parameter(s): \n\n- *int a:* the lower range boundary\n- *int b:* the upper range boundary \n\n**Returns** \n\n- *int:* the number of square integers in the range"},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static int squares(int a, int b)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"//package contest;\\n\\nimport java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n /* Enter your code here. Read input from STDIN. Print output to STDOUT. Your class should be named Solution. */\\n BufferedReader br = new BufferedReader(new InputStreamReader(System.in));\\n try{\\n String line = br.readLine();\\n int num = Integer.parseInt(line);\\n for(int i = 0; i < num; i++){\\n String[] inp = br.readLine().split(\\\" \\\");\\n int a = Integer.parseInt(inp[0]);\\n int b = Integer.parseInt(inp[1]);\\n int count = 0;\\n for(int j = 1; j*j <= b; j++){\\n if(j*j >= a){\\n count++;\\n }\\n }\\n System.out.println(count);\\n }\\n \\n } catch(Exception e) {\\n \\n }\\n \\n \\n \\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'squares' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER a\\n * 2. INTEGER b\\n */\\n\\n public static int squares(int a, int b) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int q = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n for (int qItr = 0; qItr < q; qItr++) {\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int a = Integer.parseInt(firstMultipleInput[0]);\\n\\n int b = Integer.parseInt(firstMultipleInput[1]);\\n\\n int result = Result.squares(a, b);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n }\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'squares' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER a\\n * 2. INTEGER b\\n */\\n\\n public static int squares(int a, int b) {\\n // Write your code here\\n double x=(double)Math.sqrt(a);\\n double y=(double)Math.sqrt(b);\\n int z=(int)Math.floor(y)-(int)Math.ceil(x)+1;\\n \\n return z;\\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int q = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, q).forEach(qItr -> {\\n try {\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int a = Integer.parseInt(firstMultipleInput[0]);\\n\\n int b = Integer.parseInt(firstMultipleInput[1]);\\n\\n int result = Result.squares(a, b);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'squares' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER a\\n * 2. INTEGER b\\n */\\n\\n public static int squares(int a, int b) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int q = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, q).forEach(qItr -> {\\n try {\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int a = Integer.parseInt(firstMultipleInput[0]);\\n\\n int b = Integer.parseInt(firstMultipleInput[1]);\\n\\n int result = Result.squares(a, b);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.util.Scanner;\\nclass Solution\\n{\\npublic static void main(String[] args)\\n{\\n\\tScanner sc=new Scanner(System.in);\\n\\tint t=sc.nextInt();\\n\\tfor(int h=0;h<t;h++)\\n\\t{\\n\\tint m=sc.nextInt();\\n\\tint n=sc.nextInt();\\n\\tint count=0;\\n\\tint sq=(int) Math.sqrt(m);\\n\\tint a=sq*sq;\\n if(a<m)\\n {\\n sq++;\\n a=sq*sq;\\n }\\n while(a>=m && a<=n)\\n\\t{\\n\\t\\tcount++;\\n sq++;\\n a=sq*sq;\\n\\t\\n\\t}\\n\\t\\n\\tSystem.out.println(count);\\n\\t}\\n}\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'squares' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER a\\n * 2. INTEGER b\\n */\\n\\n public static int squares(int a, int b) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int q = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, q).forEach(qItr -> {\\n try {\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int a = Integer.parseInt(firstMultipleInput[0]);\\n\\n int b = Integer.parseInt(firstMultipleInput[1]);\\n\\n int result = Result.squares(a, b);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"squares(a, b)\"\n ],\n \"solution\": \"#!/usr/bin/env python\\n\\nimport sys\\n\\n\\nif __name__ == '__main__':\\n T = int(sys.stdin.readline())\\n \\n for _ in range(T):\\n A, B = list(map(int, sys.stdin.readline().split()))\\n\\n nA = int(A**0.5)\\n nB = int(B**0.5)\\n \\n ans = int(B**0.5) - int(A**0.5)\\n if nA**2 == A:\\n \\tans += 1\\n \\n print(ans)\",\n \"template\": \"#\\n# Complete the 'squares' function below.\\n#\\n# The function is expected to return an INTEGER.\\n# The function accepts following parameters:\\n# 1. INTEGER a\\n# 2. INTEGER b\\n#\\n\\ndef squares(a, b):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n q = int(input().strip())\\n\\n for q_itr in range(q):\\n first_multiple_input = input().rstrip().split()\\n\\n a = int(first_multiple_input[0])\\n\\n b = int(first_multiple_input[1])\\n\\n result = squares(a, b)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":183,"cells":{"question_id":{"kind":"number","value":8656,"string":"8,656"},"question_title":{"kind":"string","value":"Library Fine"},"question_content":{"kind":"string","value":"Your local library needs your help! Given the expected and actual return dates for a library book, create a program that calculates the fine (if any). The fee structure is as follows:\t\n\n1. If the book is returned on or before the expected return date, no fine will be charged (i.e.: $fine = 0)$.\n2. If the book is returned after the expected return *day* but still within the same calendar month and year as the expected return date, $fine = 15 \\text{ Hackos } \\times \\text{ (the number of days late)}$.\t\n3. If the book is returned after the expected return *month* but still within the same calendar year as the expected return date, the $fine = 500 \\text{ Hackos } \\times \\text{ (the number of months late)}$. \n4. If the book is returned after the calendar *year* in which it was expected, there is a fixed fine of $10000 \\text{ Hackos}$.\n\nCharges are based only on the least precise measure of lateness. For example, whether a book is due January 1, 2017 or December 31, 2017, if it is returned January 1, 2018, that is a year late and the fine would be $10,000 \\text{ Hackos}$. \n\n**Example** \n$d1, m1, y1 = 14, 7, 2018$ \n$d2, m2, y2 = 5, 7, 2018$ \n\nThe first values are the return date and the second are the due date. The years are the same and the months are the same. The book is $14 - 5 = 9$ days late. Return $9 * 15 = 135$. \n\n**Function Description** \n\nComplete the *libraryFine* function in the editor below. \n\nlibraryFine has the following parameter(s): \n\n- *d1, m1, y1*: returned date day, month and year, each an integer \n- *d2, m2, y2*: due date day, month and year, each an integer \n\n**Returns** \n\n- *int:* the amount of the fine or $0$ if there is none"},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static int libraryFine(int d1, int m1, int y1, int d2, int m2, int y2)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner in = new Scanner(System.in);\\n int rtday = in.nextInt();\\n int rtmonth = in.nextInt();\\n int rtyear = in.nextInt();\\n int dueday = in.nextInt();\\n int duemonth = in.nextInt();\\n int dueyear = in.nextInt();\\n int fine = 0;\\n if(rtyear > dueyear)\\n fine = 10000;\\n else if(rtyear == dueyear){\\n if(rtmonth > duemonth)\\n fine = 500 * (rtmonth - duemonth);\\n else if(rtmonth == duemonth){\\n if(rtday > dueday)\\n fine = 15 * (rtday - dueday);\\n }\\n }\\n System.out.println(fine);\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'libraryFine' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER d1\\n * 2. INTEGER m1\\n * 3. INTEGER y1\\n * 4. INTEGER d2\\n * 5. INTEGER m2\\n * 6. INTEGER y2\\n */\\n\\n public static int libraryFine(int d1, int m1, int y1, int d2, int m2, int y2) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int d1 = Integer.parseInt(firstMultipleInput[0]);\\n\\n int m1 = Integer.parseInt(firstMultipleInput[1]);\\n\\n int y1 = Integer.parseInt(firstMultipleInput[2]);\\n\\n String[] secondMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int d2 = Integer.parseInt(secondMultipleInput[0]);\\n\\n int m2 = Integer.parseInt(secondMultipleInput[1]);\\n\\n int y2 = Integer.parseInt(secondMultipleInput[2]);\\n\\n int result = Result.libraryFine(d1, m1, y1, d2, m2, y2);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'libraryFine' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER d1\\n * 2. INTEGER m1\\n * 3. INTEGER y1\\n * 4. INTEGER d2\\n * 5. INTEGER m2\\n * 6. INTEGER y2\\n */\\n\\n public static int libraryFine(int d1, int m1, int y1, int d2, int m2, int y2) {\\n // Write your code here\\n if (y1 < y2) return 0;\\n if (y1 > y2) return (y1 - y2) * 10000;\\n if (m1 < m2) return 0;\\n if (m1 > m2) return (m1 - m2) * 500;\\n if (d1 > d2) return (d1 - d2) * 15;\\n return 0;\\n\\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int d1 = Integer.parseInt(firstMultipleInput[0]);\\n\\n int m1 = Integer.parseInt(firstMultipleInput[1]);\\n\\n int y1 = Integer.parseInt(firstMultipleInput[2]);\\n\\n String[] secondMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int d2 = Integer.parseInt(secondMultipleInput[0]);\\n\\n int m2 = Integer.parseInt(secondMultipleInput[1]);\\n\\n int y2 = Integer.parseInt(secondMultipleInput[2]);\\n\\n int result = Result.libraryFine(d1, m1, y1, d2, m2, y2);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'libraryFine' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER d1\\n * 2. INTEGER m1\\n * 3. INTEGER y1\\n * 4. INTEGER d2\\n * 5. INTEGER m2\\n * 6. INTEGER y2\\n */\\n\\n public static int libraryFine(int d1, int m1, int y1, int d2, int m2, int y2) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int d1 = Integer.parseInt(firstMultipleInput[0]);\\n\\n int m1 = Integer.parseInt(firstMultipleInput[1]);\\n\\n int y1 = Integer.parseInt(firstMultipleInput[2]);\\n\\n String[] secondMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int d2 = Integer.parseInt(secondMultipleInput[0]);\\n\\n int m2 = Integer.parseInt(secondMultipleInput[1]);\\n\\n int y2 = Integer.parseInt(secondMultipleInput[2]);\\n\\n int result = Result.libraryFine(d1, m1, y1, d2, m2, y2);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"\\nimport java.util.Scanner;\\n\\nclass Solution {\\n\\n public static void main(String[] args) {\\n Scanner sc = new Scanner(System.in);\\n int d2 = sc.nextInt(), m2 = sc.nextInt(), y2 = sc.nextInt();\\n int d1 = sc.nextInt(), m1 = sc.nextInt(), y1 = sc.nextInt();\\n if (y2 > y1) {\\n System.out.println(10000);\\n } else if (y2 < y1 || m2 < m1 || m2 == m1 && d2 <= d1) {\\n System.out.println(0);\\n } else if (m1 == m2) {\\n System.out.println(15 * (d2 - d1));\\n } else {\\n System.out.println(500 * (m2 - m1));\\n }\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'libraryFine' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER d1\\n * 2. INTEGER m1\\n * 3. INTEGER y1\\n * 4. INTEGER d2\\n * 5. INTEGER m2\\n * 6. INTEGER y2\\n */\\n\\n public static int libraryFine(int d1, int m1, int y1, int d2, int m2, int y2) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int d1 = Integer.parseInt(firstMultipleInput[0]);\\n\\n int m1 = Integer.parseInt(firstMultipleInput[1]);\\n\\n int y1 = Integer.parseInt(firstMultipleInput[2]);\\n\\n String[] secondMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int d2 = Integer.parseInt(secondMultipleInput[0]);\\n\\n int m2 = Integer.parseInt(secondMultipleInput[1]);\\n\\n int y2 = Integer.parseInt(secondMultipleInput[2]);\\n\\n int result = Result.libraryFine(d1, m1, y1, d2, m2, y2);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"libraryFine(d1, m1, y1, d2, m2, y2)\"\n ],\n \"solution\": \"def fine(actual, expected):\\n if actual[2] > expected[2]:\\n return 10000\\n elif actual[2] == expected[2]:\\n if actual[1] > expected[1]:\\n return 500 * (actual[1] - expected[1])\\n elif actual[1] == expected[1]:\\n if actual[0] > expected[0]:\\n return 15 * (actual[0] - expected[0])\\n return 0\\n\\nactual_return_date = input().split(' ')\\nexpected_return_date = input().split(' ')\\nactual_return_date = [int(number) for number in actual_return_date]\\nexpected_return_date = [int(number) for number in expected_return_date]\\nprint(fine(actual_return_date, expected_return_date))\\n\",\n \"template\": \"#\\n# Complete the 'libraryFine' function below.\\n#\\n# The function is expected to return an INTEGER.\\n# The function accepts following parameters:\\n# 1. INTEGER d1\\n# 2. INTEGER m1\\n# 3. INTEGER y1\\n# 4. INTEGER d2\\n# 5. INTEGER m2\\n# 6. INTEGER y2\\n#\\n\\ndef libraryFine(d1, m1, y1, d2, m2, y2):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n first_multiple_input = input().rstrip().split()\\n\\n d1 = int(first_multiple_input[0])\\n\\n m1 = int(first_multiple_input[1])\\n\\n y1 = int(first_multiple_input[2])\\n\\n second_multiple_input = input().rstrip().split()\\n\\n d2 = int(second_multiple_input[0])\\n\\n m2 = int(second_multiple_input[1])\\n\\n y2 = int(second_multiple_input[2])\\n\\n result = libraryFine(d1, m1, y1, d2, m2, y2)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"eJyVkzFOBDEMRSn2IFbqFYqdOIk5CRKDKGAKmmGLXQkJIXEIuB4d98Ce0axWG41mkspFnr//T/y1+/nb3Yzn/leLhw/3OhxOR3cHDgUIsPjQDQEiYM5auT24/v3QPx/7l6e303G+6/W4zz1c8Ap7IPbZeLSKm3gsgAgSUjdQAW3hKS42qMQJClCJOjIlYKDMzerqPyNpg2i9MmKb/Tz698k6kAZIWZYDrOSjDo0s3SBjGwqymU32WiVpWFkHRwllu6woEYrvBsxztYCGig36SFTMJHkLrwi1Rc6QNDDUh7oot06eGmDkKzqg/dCJPlcLcORrbTbjI8JrMNa2TZuMihvEK9sToDtybtP4z2GGdMlWxLn+5DLh047O5dbUccrNYFmDa22zHtTQbH11PR+/b/8BxO1cjg=="},"max_retries":{"kind":"null"}}},{"rowIdx":184,"cells":{"question_id":{"kind":"number","value":1957,"string":"1,957"},"question_title":{"kind":"string","value":"Cut the sticks"},"question_content":{"kind":"string","value":"You are given a number of sticks of varying lengths. You will iteratively cut the sticks into smaller sticks, discarding the shortest pieces until there are none left. At each iteration you will determine the length of the shortest stick remaining, cut that length from each of the longer sticks and then discard all the pieces of that shortest length. When all the remaining sticks are the same length, they cannot be shortened so discard them.\n\nGiven the lengths of $n$ sticks, print the number of sticks that are left before each iteration until there are none left.\n\n**Example** \n$arr = [1, 2, 3]$ \n\nThe shortest stick length is $1$, so cut that length from the longer two and discard the pieces of length $1$. Now the lengths are $arr = [1, 2]$. Again, the shortest stick is of length $1$, so cut that amount from the longer stick and discard those pieces. There is only one stick left, $arr = [1]$, so discard that stick. The number of sticks at each iteration are $answer = [3, 2, 1]$. \n\n**Function Description** \n\nComplete the *cutTheSticks* function in the editor below. It should return an array of integers representing the number of sticks before each cut operation is performed. \n\ncutTheSticks has the following parameter(s): \n\n- *int arr[n]:* the lengths of each stick \n\n**Returns** \n\n- *int[]:* the number of sticks after each iteration \n \n**Input Format** \n\nThe first line contains a single integer $n$, the size of $arr$. \nThe next line contains $n$ space-separated integers, each an $arr[i]$, where each value represents the length of the $i^{th}$ stick. \n\n**Constraints** \n\n* $1 ≤ n ≤ 1000$\n* $1 ≤ arr[i] ≤ 1000$\n\n\n**Sample Input 0**\n\n\tSTDIN\t\t\tFunction\n -----\t\t\t--------\n 6\t\t\t\tarr[] size n = 6\n 5 4 4 2 2 8\t\tarr = [5, 4, 4, 2, 2, 8]\n \n**Sample Output 0**\n\n\t6\n\t4\n\t2\n\t1\n\n**Explanation 0**\n\n```xml\nsticks-length length-of-cut sticks-cut\n5 4 4 2 2 8 2 6\n3 2 2 _ _ 6 2 4\n1 _ _ _ _ 4 1 2\n_ _ _ _ _ 3 3 1\n_ _ _ _ _ _ DONE DONE\n```\n\n**Sample Input 1**\n\t\n 8\n 1 2 3 4 3 3 2 1\n \n**Sample Output 1**\n\n\t8\n 6\n 4\n 1\n \n**Explanation 1**\n\n```xml\nsticks-length length-of-cut sticks-cut\n1 2 3 4 3 3 2 1 1 8\n_ 1 2 3 2 2 1 _ 1 6\n_ _ 1 2 1 1 _ _ 1 4\n_ _ _ 1 _ _ _ _ 1 1\n_ _ _ _ _ _ _ _ DONE DONE\n```\n"},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static List<Integer> cutTheSticks(List<Integer> arr)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\n\\npublic class Solution {\\n\\n\\tstatic void solve() throws IOException {\\n\\t\\tint n = nextInt();\\n\\t\\tint[] a = new int[n];\\n\\t\\tfor (int i = 0; i < n; i++) {\\n\\t\\t\\ta[i] = nextInt();\\n\\t\\t}\\n\\t\\tArrays.sort(a);\\n\\t\\tfor (int i = 0; i < n;) {\\n\\t\\t\\tint j = i;\\n\\t\\t\\twhile (j < n && a[j] == a[i]) {\\n\\t\\t\\t\\tj++;\\n\\t\\t\\t}\\n\\t\\t\\tout.println(n - i);\\n\\t\\t\\ti = j;\\n\\t\\t}\\n\\t}\\n\\n\\tstatic BufferedReader br;\\n\\tstatic StringTokenizer st;\\n\\tstatic PrintWriter out;\\n\\n\\tpublic static void main(String[] args) throws IOException {\\n\\t\\tInputStream input = System.in;\\n\\t\\tPrintStream output = System.out;\\n\\t\\tbr = new BufferedReader(new InputStreamReader(input));\\n\\t\\tout = new PrintWriter(output);\\n\\t\\tsolve();\\n\\t\\tout.close();\\n\\t}\\n\\n\\tstatic long nextLong() throws IOException {\\n\\t\\treturn Long.parseLong(nextToken());\\n\\t}\\n\\n\\tstatic double nextDouble() throws IOException {\\n\\t\\treturn Double.parseDouble(nextToken());\\n\\t}\\n\\n\\tstatic int nextInt() throws IOException {\\n\\t\\treturn Integer.parseInt(nextToken());\\n\\t}\\n\\n\\tstatic String nextToken() throws IOException {\\n\\t\\twhile (st == null || !st.hasMoreTokens()) {\\n\\t\\t\\tString line = br.readLine();\\n\\t\\t\\tif (line == null) {\\n\\t\\t\\t\\treturn null;\\n\\t\\t\\t}\\n\\t\\t\\tst = new StringTokenizer(line);\\n\\t\\t}\\n\\t\\treturn st.nextToken();\\n\\t}\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'cutTheSticks' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static List<Integer> cutTheSticks(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String[] arrTemp = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> arr = new ArrayList<>();\\n\\n for (int i = 0; i < n; i++) {\\n int arrItem = Integer.parseInt(arrTemp[i]);\\n arr.add(arrItem);\\n }\\n\\n List<Integer> result = Result.cutTheSticks(arr);\\n\\n for (int i = 0; i < result.size(); i++) {\\n bufferedWriter.write(String.valueOf(result.get(i)));\\n\\n if (i != result.size() - 1) {\\n bufferedWriter.write(\\\"\\\\n\\\");\\n }\\n }\\n\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'cutTheSticks' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static List<Integer> cutTheSticks(List<Integer> arr) {\\n // Write your code here\\n List<Integer> result = new ArrayList<>();\\n \\n Collections.sort(arr);\\n //result.add(arr.size());\\n //Map<Integer, Integer> map = new HashMap<>();\\n int prev = -1;\\n int count = arr.size();\\n for (Integer a : arr) {\\n if (a > prev) {\\n prev = a;\\n result.add(count);\\n }\\n count--;\\n /*if (map.containsKey(a)) map.put(a, (map.get(a) + 1));\\n else map.put(a, 1);*/\\n }\\n \\n return result;\\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n List<Integer> result = Result.cutTheSticks(arr);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\"\\\\n\\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'cutTheSticks' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static List<Integer> cutTheSticks(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n List<Integer> result = Result.cutTheSticks(arr);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\"\\\\n\\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\n\\npublic class Solution{\\n \\n public static void main(String[] args)throws IOException{\\n BufferedReader br = new BufferedReader(new InputStreamReader(System.in));\\n int N = Integer.parseInt(br.readLine());\\n String[] str=br.readLine().split(\\\" \\\");\\n int[] inp = new int[1000];\\n int m=0;\\n for(String s:str){\\n inp[m]=Integer.parseInt(s);\\n m++;\\n }\\n \\n \\n int len=str.length;\\n while(len>1){\\n int min=findMin(inp); \\n len=findLength(inp,min);\\n }\\n if(len==1)System.out.println(len);\\n }\\n \\n \\n public static int findMin(int[] a1){\\n int[] k= new int[1000];\\n for(int i=0;i<a1.length;i++){\\n if(a1[i]!=0){\\n k[a1[i]]=a1[i];\\n }\\n }\\n int min=0;\\n for(int i=0;i<k.length;i++){\\n if(k[i]!=0){\\n min=k[i];\\n return min;\\n }\\n }\\n return min;\\n \\n }\\n public static int findLength(int[] a,int min){\\n int j=0;\\n for(int i=0;i<a.length;i++){\\n if(a[i]>0){\\n a[i]=a[i]-min;\\n j++;\\n }\\n }\\n int k=0; \\n for(int i=0;i<a.length;i++){\\n if(a[i]>0){\\n k++;\\n \\n }\\n }\\n System.out.println(j);\\n \\n return k;\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'cutTheSticks' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static List<Integer> cutTheSticks(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n List<Integer> result = Result.cutTheSticks(arr);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\"\\\\n\\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"cutTheSticks(arr)\"\n ],\n \"solution\": \"#!/usr/bin/env python\\n\\nimport collections, sys\\n\\n\\nif __name__ == '__main__':\\n N = int(sys.stdin.readline())\\n a = sorted(map(int, sys.stdin.readline().split()))\\n \\n c = collections.Counter(a)\\n count = [c[k] for k in sorted(c)]\\n \\n for i in range(len(count)):\\n \\tprint(sum(count[i:]))\",\n \"template\": \"#\\n# Complete the 'cutTheSticks' function below.\\n#\\n# The function is expected to return an INTEGER_ARRAY.\\n# The function accepts INTEGER_ARRAY arr as parameter.\\n#\\n\\ndef cutTheSticks(arr):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n n = int(input().strip())\\n\\n arr = list(map(int, input().rstrip().split()))\\n\\n result = cutTheSticks(arr)\\n\\n fptr.write('\\\\n'.join(map(str, result)))\\n fptr.write('\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":185,"cells":{"question_id":{"kind":"number","value":24548,"string":"24,548"},"question_title":{"kind":"string","value":"Repeated String"},"question_content":{"kind":"string","value":"There is a string, $s$, of lowercase English letters that is repeated infinitely many times. Given an integer, $n$, find and print the number of letter `a`'s in the first $n$ letters of the infinite string.\n\n**Example** \n$s=\\text{'abcac'}$ \n$n = 10$ \n\nThe substring we consider is $abcacabcac$, the first $10$ characters of the infinite string. There are $4$ occurrences of `a` in the substring. \n\n**Function Description** \n\nComplete the *repeatedString* function in the editor below. \n\nrepeatedString has the following parameter(s): \n\n- *s:* a string to repeat \n- *n:* the number of characters to consider \n\n**Returns** \n\n- *int:* the frequency of `a` in the substring "},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static long repeatedString(String s, long n)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner in = new Scanner(System.in);\\n String s = in.next();\\n long n = in.nextLong();\\n long num = n/s.length();\\n long rem = n%s.length();\\n long ans = 0;\\n for (int i = 0; i < s.length(); i++) {\\n if (s.charAt(i)=='a') {\\n ans += num;\\n if (i < rem)\\n ans++;\\n }\\n }\\n System.out.println(ans);\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'repeatedString' function below.\\n *\\n * The function is expected to return a LONG_INTEGER.\\n * The function accepts following parameters:\\n * 1. STRING s\\n * 2. LONG_INTEGER n\\n */\\n\\n public static long repeatedString(String s, long n) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String s = bufferedReader.readLine();\\n\\n long n = Long.parseLong(bufferedReader.readLine().trim());\\n\\n long result = Result.repeatedString(s, n);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'repeatedString' function below.\\n *\\n * The function is expected to return a LONG_INTEGER.\\n * The function accepts following parameters:\\n * 1. STRING s\\n * 2. LONG_INTEGER n\\n */\\n\\n public static long repeatedString(String s, long n) {\\n long rep = n / s.length();\\n int modulo = (int ) (n % s.length());\\n \\n long cont = 0;\\n for (int i = 0; i < s.length(); i++)\\n if (s.charAt(i) == 'a') cont++;\\n \\n cont = cont * rep;\\n \\n for (int i = 0; i < modulo; i++)\\n if (s.charAt(i) == 'a') cont++;\\n\\n return cont;\\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String s = bufferedReader.readLine();\\n\\n long n = Long.parseLong(bufferedReader.readLine().trim());\\n\\n long result = Result.repeatedString(s, n);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'repeatedString' function below.\\n *\\n * The function is expected to return a LONG_INTEGER.\\n * The function accepts following parameters:\\n * 1. STRING s\\n * 2. LONG_INTEGER n\\n */\\n\\n public static long repeatedString(String s, long n) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String s = bufferedReader.readLine();\\n\\n long n = Long.parseLong(bufferedReader.readLine().trim());\\n\\n long result = Result.repeatedString(s, n);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"\\nimport java.io.ByteArrayInputStream;\\nimport java.io.IOException;\\nimport java.io.InputStream;\\nimport java.io.PrintWriter;\\nimport java.util.Arrays;\\nimport java.util.InputMismatchException;\\n\\npublic class A {\\n\\tInputStream is;\\n\\tPrintWriter out;\\n\\tString INPUT = \\\"\\\";\\n\\t\\n\\tvoid solve()\\n\\t{\\n\\t\\tchar[] s = ns().toCharArray();\\n\\t\\tlong n = nl();\\n\\t\\tint m = s.length;\\n\\t\\tint ca = 0;\\n\\t\\tfor(char c : s){\\n\\t\\t\\tif(c == 'a')ca++;\\n\\t\\t}\\n\\t\\tlong ret = (n/m)*ca;\\n\\t\\tfor(int i = 0;i < n%m;i++){\\n\\t\\t\\tif(s[i] == 'a')ret++;\\n\\t\\t}\\n\\t\\tout.println(ret);\\n\\t}\\n\\t\\n\\tvoid run() throws Exception\\n\\t{\\n\\t\\tis = INPUT.isEmpty() ? System.in : new ByteArrayInputStream(INPUT.getBytes());\\n\\t\\tout = new PrintWriter(System.out);\\n\\t\\t\\n\\t\\tlong s = System.currentTimeMillis();\\n\\t\\tsolve();\\n\\t\\tout.flush();\\n\\t\\tif(!INPUT.isEmpty())tr(System.currentTimeMillis()-s+\\\"ms\\\");\\n\\t}\\n\\t\\n\\tpublic static void main(String[] args) throws Exception { new A().run(); }\\n\\t\\n\\tprivate byte[] inbuf = new byte[1024];\\n\\tprivate int lenbuf = 0, ptrbuf = 0;\\n\\t\\n\\tprivate int readByte()\\n\\t{\\n\\t\\tif(lenbuf == -1)throw new InputMismatchException();\\n\\t\\tif(ptrbuf >= lenbuf){\\n\\t\\t\\tptrbuf = 0;\\n\\t\\t\\ttry { lenbuf = is.read(inbuf); } catch (IOException e) { throw new InputMismatchException(); }\\n\\t\\t\\tif(lenbuf <= 0)return -1;\\n\\t\\t}\\n\\t\\treturn inbuf[ptrbuf++];\\n\\t}\\n\\t\\n\\tprivate boolean isSpaceChar(int c) { return !(c >= 33 && c <= 126); }\\n\\tprivate int skip() { int b; while((b = readByte()) != -1 && isSpaceChar(b)); return b; }\\n\\t\\n\\tprivate double nd() { return Double.parseDouble(ns()); }\\n\\tprivate char nc() { return (char)skip(); }\\n\\t\\n\\tprivate String ns()\\n\\t{\\n\\t\\tint b = skip();\\n\\t\\tStringBuilder sb = new StringBuilder();\\n\\t\\twhile(!(isSpaceChar(b))){ // when nextLine, (isSpaceChar(b) && b != ' ')\\n\\t\\t\\tsb.appendCodePoint(b);\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn sb.toString();\\n\\t}\\n\\t\\n\\tprivate char[] ns(int n)\\n\\t{\\n\\t\\tchar[] buf = new char[n];\\n\\t\\tint b = skip(), p = 0;\\n\\t\\twhile(p < n && !(isSpaceChar(b))){\\n\\t\\t\\tbuf[p++] = (char)b;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn n == p ? buf : Arrays.copyOf(buf, p);\\n\\t}\\n\\t\\n\\tprivate char[][] nm(int n, int m)\\n\\t{\\n\\t\\tchar[][] map = new char[n][];\\n\\t\\tfor(int i = 0;i < n;i++)map[i] = ns(m);\\n\\t\\treturn map;\\n\\t}\\n\\t\\n\\tprivate int[] na(int n)\\n\\t{\\n\\t\\tint[] a = new int[n];\\n\\t\\tfor(int i = 0;i < n;i++)a[i] = ni();\\n\\t\\treturn a;\\n\\t}\\n\\t\\n\\tprivate int ni()\\n\\t{\\n\\t\\tint num = 0, b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate long nl()\\n\\t{\\n\\t\\tlong num = 0;\\n\\t\\tint b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate static void tr(Object... o) { System.out.println(Arrays.deepToString(o)); }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'repeatedString' function below.\\n *\\n * The function is expected to return a LONG_INTEGER.\\n * The function accepts following parameters:\\n * 1. STRING s\\n * 2. LONG_INTEGER n\\n */\\n\\n public static long repeatedString(String s, long n) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String s = bufferedReader.readLine();\\n\\n long n = Long.parseLong(bufferedReader.readLine().trim());\\n\\n long result = Result.repeatedString(s, n);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"repeatedString(s, n)\"\n ],\n \"solution\": \"s = input().strip()\\nn = int(input().strip())\\nL = len(s)\\nprint(s.count('a') * (n//L) + s[:n % L].count('a'))\",\n \"template\": \"#\\n# Complete the 'repeatedString' function below.\\n#\\n# The function is expected to return a LONG_INTEGER.\\n# The function accepts following parameters:\\n# 1. STRING s\\n# 2. LONG_INTEGER n\\n#\\n\\ndef repeatedString(s, n):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n s = input()\\n\\n n = int(input().strip())\\n\\n result = repeatedString(s, n)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"eJzFVMmq5TYUfISQ/MbjrpsgT5KdLwnkNo2twRqswbJkWQ4N+Yjur80mek0vAs59cBch3mjhU1KdOlXnzx+//vDzy7fvt79+enn5/Y+bMC6G26+vt22bhBWTOkedBdZB8JHZKSW27ZbRFB1RCz/EtBw0SRuIE+d6jIySke+rHKklh0hOiyCm2SrD5CENP6zT1OEwxrupKwDbCjZ9N9zN7cPrjR6O4kDJJxvDdxJV04AG9nXblpLPH17/wW+8m+ox7lK+GDc7ssxqLrgBDaCtmu4hHlzwfs/7nNxhLVPzQfbIeRQML/lQJ3NJS7vvpPSX7Uz4vOagV7r4QEjgRaI8qqCYy1hKfzqx5HP32HMmrdIpYH1KPkVbmNVN2wHwuK+3/xdulqssl41hH8XddKAHw+Mr/q/WIBjqoak69JDZ94oLv0P6UOwCh6YZOojQU3NjYc9304B6gKhp6ieQ+3rmRGnmWm80kkTktJJpmcSojok6q8S8OR1GPGtX+MGmQh3s+8emrHuAetA3l5ek01mt3pKwzT5QTHezCbz4xNy3AyueM5/kearIxbnkiUazyhWrmKZTLRsuozi0yoQnMrNigboYHAH4TLtxnrfspTVOl2kNfQ+Huuqe8ZHyy2rCqucoafBm18aTu2lL2iqAatjD9onLHHWbEfyIelZJ+WjItnDsrPfGCJ/3MYfIuF1Xn7PZ1iR0sQgo8vegeuyPtkKwgdfXwsH1yViRcnSUzeO+4beJorob0DvrCSKAhgFdl9NWZgDR8A70X+QL0+nTtm90xikR41zZB1Xd9qCFTYv6/1i8qjxRQ/A4nnUJX1dfpYuelIB6j4NZspD5NIV4ka6BbdvDJ1iTw45C8j3xqayxoe3qvn4nsah/WwXXNJ2jmZSaKBFr6apHsHrsuwqB7o3i549ffvkbFflvJQ=="},"max_retries":{"kind":"null"}}},{"rowIdx":186,"cells":{"question_id":{"kind":"number","value":20832,"string":"20,832"},"question_title":{"kind":"string","value":"Jumping on the Clouds"},"question_content":{"kind":"string","value":"There is a new mobile game that starts with consecutively numbered clouds. Some of the clouds are thunderheads and others are cumulus. The player can jump on any cumulus cloud having a number that is equal to the number of the current cloud plus $1$ or $2$. The player must avoid the thunderheads. Determine the minimum number of jumps it will take to jump from the starting postion to the last cloud. It is always possible to win the game. \n\nFor each game, you will get an array of clouds numbered $0$ if they are safe or $1$ if they must be avoided. \n\n**Example** \n$c = [0,1,0,0,0,1,0]$ \n\nIndex the array from $0\\ldots 6$. The number on each cloud is its index in the list so the player must avoid the clouds at indices $1$ and $5$. They could follow these two paths: $0 \\to 2 \\to 4 \\to 6$ or $0 \\to 2 \\to 3 \\to 4 \\to 6$. The first path takes $3$ jumps while the second takes $4$. Return $3$.\n\n**Function Description** \n\nComplete the *jumpingOnClouds* function in the editor below. \n\njumpingOnClouds has the following parameter(s): \n\n- *int c[n]*: an array of binary integers \n\n**Returns** \n\n- *int:* the minimum number of jumps required"},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static int jumpingOnClouds(List<Integer> c)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner input = new Scanner(System.in);\\n int rounds = input.nextInt();\\n int[] ar = new int[rounds];\\n int i = 0;\\n for(i = 0; i < rounds; i++)\\n ar[i] = input.nextInt();\\n int count = 0;\\n i = 0;\\n while(i != rounds-1)\\n {\\n if(i != ar.length - 2 && ar[i+2] == 0)\\n i+=2;\\n else\\n i++;\\n count++;\\n } \\n System.out.println(count);\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'jumpingOnClouds' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY c as parameter.\\n */\\n\\n public static int jumpingOnClouds(List<Integer> c) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String[] cTemp = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> c = new ArrayList<>();\\n\\n for (int i = 0; i < n; i++) {\\n int cItem = Integer.parseInt(cTemp[i]);\\n c.add(cItem);\\n }\\n\\n int result = Result.jumpingOnClouds(c);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'jumpingOnClouds' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY c as parameter.\\n */\\n\\n public static int jumpingOnClouds(List<Integer> valuesP) {\\n ArrayList<Integer> values = new ArrayList<>(valuesP);\\n int nbJump = 0;\\n while (values.size() > 1) {\\n if (values.size() == 2) {\\n if (values.get(1).equals(0)) {\\n nbJump++;\\n }\\n values.clear();\\n continue;\\n }\\n if (values.get(1).equals(1)) {\\n nbJump++;\\n values.subList(0, 2).clear();\\n continue;\\n }\\n if (values.get(2).equals(0)) {\\n nbJump++;\\n values.subList(0, 2).clear();\\n continue;\\n }\\n nbJump++;\\n values.subList(0, 1).clear();\\n }\\n return nbJump;\\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> c = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.jumpingOnClouds(c);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'jumpingOnClouds' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY c as parameter.\\n */\\n\\n public static int jumpingOnClouds(List<Integer> c) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> c = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.jumpingOnClouds(c);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\n\\npublic class Solution {\\n private static InputReader in;\\n private static PrintWriter out;\\n\\n public static void main(String[] args) throws IOException {\\n in = new InputReader(System.in);\\n out = new PrintWriter(System.out, true);\\n\\n int n = in.nextInt();\\n int[] arr = new int[n];\\n for (int i = 0; i < n; i++) {\\n arr[i] = in.nextInt();\\n }\\n int[] dp = new int[n];\\n for (int i = 1; i < n; i++) {\\n if (arr[i] == 1) dp[i] = 1 << 29;\\n dp[i] = 1 << 29;\\n if (i >= 1 && arr[i-1] == 0) dp[i] = Math.min(dp[i], dp[i-1]+1);\\n if (i >= 2 && arr[i-2] == 0) dp[i] = Math.min(dp[i], dp[i-2]+1);\\n }\\n out.println(dp[n-1]);\\n \\n out.close();\\n System.exit(0);\\n }\\n\\n static class InputReader {\\n public BufferedReader reader;\\n public StringTokenizer tokenizer;\\n\\n public InputReader(InputStream stream) {\\n reader = new BufferedReader(new InputStreamReader(stream), 32768);\\n tokenizer = null;\\n }\\n\\n public String next() {\\n while (tokenizer == null || !tokenizer.hasMoreTokens()) {\\n try {\\n tokenizer = new StringTokenizer(reader.readLine());\\n } catch (IOException e) {\\n throw new RuntimeException(e);\\n }\\n }\\n return tokenizer.nextToken();\\n }\\n\\n public int nextInt() {\\n return Integer.parseInt(next());\\n }\\n }\\n\\n\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'jumpingOnClouds' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY c as parameter.\\n */\\n\\n public static int jumpingOnClouds(List<Integer> c) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> c = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.jumpingOnClouds(c);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"jumpingOnClouds(c)\"\n ],\n \"solution\": \"#!/bin/python3\\n\\nimport sys\\n\\n\\nn = int(input().strip())\\nc = [int(c_temp) for c_temp in input().strip().split(' ')]\\n\\nres = 0\\ni = 0\\nwhile i < n-1:\\n if i+2<n and c[i+2] == 0:\\n i = i+2\\n res += 1\\n else:\\n i = i+1\\n res += 1\\nprint(res)\\n\",\n \"template\": \"#\\n# Complete the 'jumpingOnClouds' function below.\\n#\\n# The function is expected to return an INTEGER.\\n# The function accepts INTEGER_ARRAY c as parameter.\\n#\\n\\ndef jumpingOnClouds(c):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n n = int(input().strip())\\n\\n c = list(map(int, input().rstrip().split()))\\n\\n result = jumpingOnClouds(c)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"eJy1VctuwjAQ5NALf2H5jCpvHoA5cuMPkKDqoc2BS8ohSEhVJT6C/m8Jj9bJeFyHBxYkgWW8szO73j19z/q942s+PdwsPvWqXG8qPVHaJsvSKKNEGeW7tp+av7tP7ZgmAqJJMAb/ATnogdLFdl28VcX768emOjPKzbLUXwPlkExGfyS7vNkWkuEWxx1YPISLMaeUHr1iK3zDitdBpCbtqTSDAITcNO0apo0mdo3ks3g4n9pHrYQyR0d0NO8ERiCwObpoaN16MqahdmMpsyjhkSTtFGtmzSXtOOHutbqixhHMhmiKX35MS2yNlI8ohoKVzYW7kc3bUFWoRowX1qL+6goh2CRNYYNR8v8kfYyrKF58K6SNedb1k1bJogwSfwRSWM85lpCJTkHQIWJ9AnokY2nhQTMGRNa8qMiZUOgof9k//wBQwwQY"},"max_retries":{"kind":"null"}}},{"rowIdx":187,"cells":{"question_id":{"kind":"number","value":21555,"string":"21,555"},"question_title":{"kind":"string","value":"Equalize the Array"},"question_content":{"kind":"string","value":"Given an array of integers, determine the minimum number of elements to delete to leave only elements of equal value.\n \n**Example** \n\n$arr = [1, 2, 2, 3]$ \n\nDelete the $2$ elements $1$ and $3$ leaving $arr = [2, 2]$. If both twos plus either the $1$ or the $3$ are deleted, it takes $3$ deletions to leave either $[3]$ or $[1]$. The minimum number of deletions is $2$.\n\n**Function Description** \n\nComplete the *equalizeArray* function in the editor below. \n\nequalizeArray has the following parameter(s): \n\n- *int arr[n]:* an array of integers \n\n**Returns** \n\n- *int:* the minimum number of deletions required "},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static int equalizeArray(List<Integer> arr)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner sc = new Scanner(System.in);\\n int n = sc.nextInt();\\n int[] counts = new int[101];\\n for (int i = 0; i < n; i++) {\\n counts[sc.nextInt()]++;\\n }\\n Arrays.sort(counts);\\n System.out.println(n-counts[100]);\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'equalizeArray' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static int equalizeArray(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String[] arrTemp = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> arr = new ArrayList<>();\\n\\n for (int i = 0; i < n; i++) {\\n int arrItem = Integer.parseInt(arrTemp[i]);\\n arr.add(arrItem);\\n }\\n\\n int result = Result.equalizeArray(arr);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'equalizeArray' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static int equalizeArray(List<Integer> arr) {\\n \\n HashMap<Integer, Integer> hmap = new HashMap<Integer, Integer>();\\n for(Integer i : arr){\\n if(hmap.containsKey(i)){\\n hmap.put(i, hmap.get(i)+1);\\n }\\n else hmap.put(i, 1);\\n }\\n int biggi = 0;\\n for (Map.Entry<Integer, Integer> entry : hmap.entrySet()) {\\n if(entry.getValue() > biggi) biggi = entry.getValue(); \\n }\\n \\n return arr.size()-biggi;\\n\\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.equalizeArray(arr);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'equalizeArray' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static int equalizeArray(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.equalizeArray(arr);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\n\\npublic class A {\\n\\n\\tBufferedReader br;\\n\\tPrintWriter out;\\n\\tStringTokenizer st;\\n\\tboolean eof;\\n\\n\\tvoid solve() throws IOException {\\n\\t\\tint n = nextInt();\\n\\t\\tint[] a = new int[101];\\n\\t\\tfor (int i = 0; i < n; i++) {\\n\\t\\t\\tint x = nextInt();\\n\\t\\t\\ta[x]++;\\n\\t\\t}\\n\\t\\t\\n\\t\\tint max = 0;\\n\\t\\tfor (int x : a) {\\n\\t\\t\\tmax = Math.max(max, x);\\n\\t\\t}\\n\\t\\t\\n\\t\\tout.println(n - max);\\n\\t}\\n\\n\\tA() throws IOException {\\n\\t\\tbr = new BufferedReader(new InputStreamReader(System.in));\\n\\t\\tout = new PrintWriter(System.out);\\n\\t\\tsolve();\\n\\t\\tout.close();\\n\\t}\\n\\n\\tpublic static void main(String[] args) throws IOException {\\n\\t\\tnew A();\\n\\t}\\n\\n\\tString nextToken() {\\n\\t\\twhile (st == null || !st.hasMoreTokens()) {\\n\\t\\t\\ttry {\\n\\t\\t\\t\\tst = new StringTokenizer(br.readLine());\\n\\t\\t\\t} catch (Exception e) {\\n\\t\\t\\t\\teof = true;\\n\\t\\t\\t\\treturn null;\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\treturn st.nextToken();\\n\\t}\\n\\n\\tString nextString() {\\n\\t\\ttry {\\n\\t\\t\\treturn br.readLine();\\n\\t\\t} catch (IOException e) {\\n\\t\\t\\teof = true;\\n\\t\\t\\treturn null;\\n\\t\\t}\\n\\t}\\n\\n\\tint nextInt() throws IOException {\\n\\t\\treturn Integer.parseInt(nextToken());\\n\\t}\\n\\n\\tlong nextLong() throws IOException {\\n\\t\\treturn Long.parseLong(nextToken());\\n\\t}\\n\\n\\tdouble nextDouble() throws IOException {\\n\\t\\treturn Double.parseDouble(nextToken());\\n\\t}\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'equalizeArray' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static int equalizeArray(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n int result = Result.equalizeArray(arr);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"equalizeArray(arr)\"\n ],\n \"solution\": \"n = int(input())\\na = list(map(int, input().split()))\\na = sorted(a)\\n\\nans = n - 1\\n\\nfor i in range(n):\\n j = i\\n while j < n and a[i] == a[j]:\\n j += 1\\n ans = min(ans, n - (j - i))\\n\\nprint(ans)\\n\",\n \"template\": \"#\\n# Complete the 'equalizeArray' function below.\\n#\\n# The function is expected to return an INTEGER.\\n# The function accepts INTEGER_ARRAY arr as parameter.\\n#\\n\\ndef equalizeArray(arr):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n n = int(input().strip())\\n\\n arr = list(map(int, input().rstrip().split()))\\n\\n result = equalizeArray(arr)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":188,"cells":{"question_id":{"kind":"number","value":3179,"string":"3,179"},"question_title":{"kind":"string","value":"ACM ICPC Team"},"question_content":{"kind":"string","value":"There are a number of people who will be attending [ACM-ICPC World Finals](https://en.wikipedia.org/wiki/ACM_International_Collegiate_Programming_Contest). Each of them may be well versed in a number of topics. Given a list of topics known by each attendee, presented as binary strings, determine the maximum number of topics a 2-person team can know. Each subject has a column in the binary string, and a '1' means the subject is known while '0' means it is not. Also determine the number of teams that know the maximum number of topics. Return an integer array with two elements. The first is the maximum number of topics known, and the second is the number of teams that know that number of topics. \n\n**Example** \n\n$n = 3$ \n$topics = \\text{['10101', '11110', '00010']}$ \n\nThe attendee data is aligned for clarity below:\n\n 10101\n 11110\n 00010\n\nThese are all possible teams that can be formed:\n\n Members\tSubjects\n (1,2) [1,2,3,4,5]\n (1,3) [1,3,4,5]\n (2,3) [1,2,3,4]\n \nIn this case, the first team will know all _5_ subjects. They are the only team that can be created that knows that many subjects, so $[5, 1]$ is returned.\n\n**Function Description** \n\nComplete the *acmTeam* function in the editor below. \nacmTeam has the following parameter(s): \n\n- *string topic:* a string of binary digits \n\n**Returns** \n\n- *int[2]:* the maximum topics and the number of teams that know that many topics "},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static List<Integer> acmTeam(List<String> topic)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.math.BigInteger;\\nimport java.util.*;\\nimport java.io.*;\\n\\npublic class Solution {\\n static void go() {\\n int n = in.nextInt();\\n int m = in.nextInt();\\n BigInteger[] b = new BigInteger[n];\\n for(int i = 0; i < n; i++)\\n b[i] = new BigInteger(in.nextString(), 2);\\n\\n int max = 0;\\n int count = 0;\\n for(int i = 0; i < n; i++) {\\n for(int j = i+1; j < n; j++) {\\n BigInteger x = b[i].or(b[j]);\\n int c = x.bitCount();\\n if (c > max) {\\n max = c;\\n count = 1;\\n } else if (c == max) {\\n count++;\\n }\\n }\\n }\\n out.println(max);\\n out.println(count);\\n }\\n\\n static InputReader in;\\n static PrintWriter out;\\n\\n public static void main(String[] args) {\\n in = new InputReader(System.in);\\n out = new PrintWriter(System.out);\\n\\n go();\\n\\n out.close();\\n }\\n\\n static class InputReader {\\n private InputStream stream;\\n private byte[] buf = new byte[1024];\\n private int curChar;\\n private int numChars;\\n\\n public InputReader(InputStream stream) {\\n this.stream = stream;\\n }\\n\\n public int read() {\\n if (numChars == -1)\\n throw new InputMismatchException();\\n if (curChar >= numChars) {\\n curChar = 0;\\n try {\\n numChars = stream.read(buf);\\n } catch (IOException e) {\\n throw new InputMismatchException();\\n }\\n if (numChars <= 0)\\n return -1;\\n }\\n return buf[curChar++];\\n }\\n\\n public int nextInt() {\\n return (int) nextLong();\\n }\\n\\n public long nextLong() {\\n int c = read();\\n while (isSpaceChar(c))\\n c = read();\\n int sgn = 1;\\n if (c == '-') {\\n sgn = -1;\\n c = read();\\n }\\n long res = 0;\\n do {\\n if (c < '0' || c > '9')\\n throw new InputMismatchException();\\n res *= 10;\\n res += c - '0';\\n c = read();\\n } while (!isSpaceChar(c));\\n return res * sgn;\\n }\\n\\n public String nextString() {\\n int c = read();\\n while (isSpaceChar(c))\\n c = read();\\n StringBuilder sb = new StringBuilder(1024);\\n do {\\n sb.append((char) c);\\n c = read();\\n } while (!isSpaceChar(c));\\n return sb.toString();\\n }\\n\\n public static boolean isSpaceChar(int c) {\\n switch (c) {\\n case -1:\\n case ' ':\\n case '\\\\n':\\n case '\\\\r':\\n case '\\\\t':\\n return true;\\n default:\\n return false;\\n }\\n }\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'acmTeam' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts STRING_ARRAY topic as parameter.\\n */\\n\\n public static List<Integer> acmTeam(List<String> topic) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int m = Integer.parseInt(firstMultipleInput[1]);\\n\\n List<String> topic = new ArrayList<>();\\n\\n for (int i = 0; i < n; i++) {\\n String topicItem = bufferedReader.readLine();\\n topic.add(topicItem);\\n }\\n\\n List<Integer> result = Result.acmTeam(topic);\\n\\n for (int i = 0; i < result.size(); i++) {\\n bufferedWriter.write(String.valueOf(result.get(i)));\\n\\n if (i != result.size() - 1) {\\n bufferedWriter.write(\\\"\\\\n\\\");\\n }\\n }\\n\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'acmTeam' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts STRING_ARRAY topic as parameter.\\n */\\n\\n public static List<Integer> acmTeam(List<String> topic) {\\n // Write your code here\\n int max=0,cnt=0;\\n for(int i=0;i<topic.size();i++){\\n String fst=topic.get(i);\\n for(int j=i+1;j<topic.size();j++){\\n String second=topic.get(j);\\n int one=0;\\n for(int k=0;k<second.length();k++){\\n if(second.charAt(k)=='0' && fst.charAt(k)=='0'){\\n continue;\\n }else{\\n one++;\\n }\\n }\\n if(one==max){\\n cnt++;\\n }\\n if(one>max){\\n max=one;\\n cnt=1;\\n }\\n \\n }\\n }\\n List<Integer> l=new ArrayList<>();\\n l.add(max);\\n l.add(cnt);\\n return l;\\n\\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int m = Integer.parseInt(firstMultipleInput[1]);\\n\\n List<String> topic = IntStream.range(0, n).mapToObj(i -> {\\n try {\\n return bufferedReader.readLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n })\\n .collect(toList());\\n\\n List<Integer> result = Result.acmTeam(topic);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\"\\\\n\\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'acmTeam' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts STRING_ARRAY topic as parameter.\\n */\\n\\n public static List<Integer> acmTeam(List<String> topic) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int m = Integer.parseInt(firstMultipleInput[1]);\\n\\n List<String> topic = IntStream.range(0, n).mapToObj(i -> {\\n try {\\n return bufferedReader.readLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n })\\n .collect(toList());\\n\\n List<Integer> result = Result.acmTeam(topic);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\"\\\\n\\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\n\\npublic class Solution\\n{\\n static int max_pair =0;\\n static int max_count = 0;\\n public static void main(String []args)\\n {\\n Scanner sc = new Scanner(System.in);\\n int nop = sc.nextInt();\\n int not = sc.nextInt();\\n int count =0;\\n String arr[] = new String[nop];\\n while(count < nop){\\n arr[count++]=sc.next();\\n }\\n checkMax(arr,not);\\n Arrays.sort(arr);\\n System.out.println(max_count);\\n System.out.println(max_pair);\\n }\\n \\n public static void checkMax(String []arr,int not)\\n {\\n //int temp =0 ;\\n for(int i=0;i<arr.length;i++)\\n {\\n for(int j=i+1;j<arr.length;j++)\\n {\\n \\n if(i==j)\\n continue;\\n int count = topicCount(arr[i],arr[j],not);\\n \\n if(count>max_count)\\n {\\n //System.out.println(\\\"arr[i]:\\\"+arr[i]+\\\"arr[j]:\\\"+arr[j]+ \\\"count:\\\"+count);\\n max_count = count;\\n max_pair = 0;\\n }\\n if(count == max_count)\\n {\\n //System.out.println(\\\"arr[i]:\\\"+arr[i]+\\\"arr[j]:\\\"+arr[j]+ \\\"max_count:\\\"+count);\\n \\n max_pair++;\\n } \\n \\n } \\n } \\n }\\n \\n public static int topicCount(String str1,String str2,int len)\\n { \\n \\n int loops = len/63;\\n if(len%63!=0)\\n \\tloops+=1;\\n int count =0;\\n int topic_count=0;\\n while (count < loops){\\n //System.out.println(n);\\n \\n if(count != (loops-1))\\n \\ttopic_count+=bitCount(Long.parseLong(str1.substring(count*63,(count+1)*63),2) | Long.parseLong(str2.substring(count*63,(count+1)*63),2));\\n else\\n \\ttopic_count+=bitCount(Long.parseLong(str1.substring(count*63),2) | Long.parseLong(str2.substring(count*63),2));\\n count = count + 1;\\n }\\n return topic_count;\\n }\\n \\n public static int bitCount(long n)\\n { \\n int count = 0;\\n while (n > 0){\\n //System.out.println(n);\\n count = count + 1;\\n n = n & (n-1);\\n }\\n return count;\\n }\\n \\n} \",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'acmTeam' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts STRING_ARRAY topic as parameter.\\n */\\n\\n public static List<Integer> acmTeam(List<String> topic) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int m = Integer.parseInt(firstMultipleInput[1]);\\n\\n List<String> topic = IntStream.range(0, n).mapToObj(i -> {\\n try {\\n return bufferedReader.readLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n })\\n .collect(toList());\\n\\n List<Integer> result = Result.acmTeam(topic);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\"\\\\n\\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"acmTeam(topic)\"\n ],\n \"solution\": \"def gp():\\n d = [ int(v) for v in input() ]\\n c = len( [ v for v in d if v == 1 ] )\\n return( c, d )\\n\\ndef merge( p1, p2 ):\\n count = 0\\n for i in range(len(p1)):\\n if p1[i] == 1 or p2[i] == 1:\\n count += 1\\n return count\\n \\nN, M = [ int(v) for v in input().split() ]\\np = [ gp() for Ni in range(N) ]\\np.sort( reverse=True )\\n\\nmxcount = 0\\nmxteams = 0\\nfor pi in range(len(p)):\\n p1 = p[pi]\\n for pj in range( pi+1, len(p) ):\\n p2 = p[pj]\\n if p1[0] + p2[0] < mxcount:\\n continue\\n count = merge( p1[1], p2[1] )\\n if count > mxcount:\\n mxcount = count\\n mxteams = 1\\n elif count == mxcount:\\n mxteams += 1\\n \\nprint( mxcount )\\nprint( mxteams )\",\n \"template\": \"#\\n# Complete the 'acmTeam' function below.\\n#\\n# The function is expected to return an INTEGER_ARRAY.\\n# The function accepts STRING_ARRAY topic as parameter.\\n#\\n\\ndef acmTeam(topic):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n first_multiple_input = input().rstrip().split()\\n\\n n = int(first_multiple_input[0])\\n\\n m = int(first_multiple_input[1])\\n\\n topic = []\\n\\n for _ in range(n):\\n topic_item = input()\\n topic.append(topic_item)\\n\\n result = acmTeam(topic)\\n\\n fptr.write('\\\\n'.join(map(str, result)))\\n fptr.write('\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":189,"cells":{"question_id":{"kind":"number","value":5705,"string":"5,705"},"question_title":{"kind":"string","value":"Taum and B'day"},"question_content":{"kind":"string","value":"Taum is planning to celebrate the birthday of his friend, Diksha. There are two types of gifts that Diksha wants from Taum: one is black and the other is white. To make her happy, Taum has to buy $b$ black gifts and $w$ white gifts. \n\n- The cost of each black gift is $bc$ units. \n- The cost of every white gift is $wc$ units. \n- The cost to convert a black gift into white gift or vice versa is $z$ units. \n\nDetermine the minimum cost of Diksha's gifts. \n\n**Example** \n$b = 3$ \n$w = 5$ \n$bc = 3$ \n$wc = 4$ \n$z = 1$ \n\nHe can buy a black gift for $3$ and convert it to a white gift for $1$, making the total cost of each white gift $4$. That matches the cost of a white gift, so he can do that or just buy black gifts and white gifts. Either way, the overall cost is $3 * 3 + 5 * 4 = 29$. \n\n**Function Description** \n\nComplete the function *taumBday* in the editor below. It should return the minimal cost of obtaining the desired gifts. \n\ntaumBday has the following parameter(s): \n\n- *int b*: the number of black gifts \n- *int w*: the number of white gifts \n- *int bc*: the cost of a black gift \n- *int wc*: the cost of a white gift \n- *int z*: the cost to convert one color gift to the other color \n\n**Returns** \n\n- *int:* the minimum cost to purchase the gifts "},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static long taumBday(int b, int w, int bc, int wc, int z)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"\\nimport java.util.Scanner;\\n\\npublic class Solution {\\n\\n\\tpublic static void main(String[] args) {\\n\\t\\t// TODO Auto-generated method stub\\n\\t\\t@SuppressWarnings(\\\"resource\\\")\\n\\t\\tScanner in = new Scanner(System.in);\\n\\t\\tint T = in.nextInt();\\n\\t\\tfor(int i=0;i<T;i++){\\n\\t\\t\\tint B = in.nextInt();\\n\\t\\t\\tint W = in.nextInt();\\n\\t\\t\\tint X = in.nextInt();\\n\\t\\t\\tint Y = in.nextInt();\\n\\t\\t\\tint Z = in.nextInt();\\n\\t\\t\\tSystem.out.println(Math.min(Math.min((long)B * (long)X + (long)W * (long)Y, (long)B * (long)X + (long)W * (long)(X+Z)), (long)B * (long)(Y+Z) + (long)W * (long)Y));\\n//\\t\\t\\tif(X==Y)\\n//\\t\\t\\t\\tSystem.out.println((long)B * (long)X + (long)W * (long)Y);\\n//\\t\\t\\telse if((long)B * (long)X + (long)W * (long)(X+Z) < (long)B * (long)(Y+Z) + (long)W * (long)Y){\\n//\\t\\t\\t\\tSystem.out.println((long)B * (long)X + (long)W * (long)(X+Z));\\n//\\t\\t\\t\\tSystem.out.println(\\\"***\\\");\\n//\\t\\t\\t}\\n//\\t\\t\\telse\\n//\\t\\t\\t{\\n//\\t\\t\\t\\tSystem.out.println((long)B * (long)(Y+Z) + (long)W * (long)Y);\\n//\\t\\t\\t\\tSystem.out.println(\\\">>>>\\\");\\n//\\t\\t\\t}\\n\\t\\t}\\n\\n\\t}\\n\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'taumBday' function below.\\n *\\n * The function is expected to return a LONG_INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER b\\n * 2. INTEGER w\\n * 3. INTEGER bc\\n * 4. INTEGER wc\\n * 5. INTEGER z\\n */\\n\\n public static long taumBday(int b, int w, int bc, int wc, int z) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int t = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n for (int tItr = 0; tItr < t; tItr++) {\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int b = Integer.parseInt(firstMultipleInput[0]);\\n\\n int w = Integer.parseInt(firstMultipleInput[1]);\\n\\n String[] secondMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int bc = Integer.parseInt(secondMultipleInput[0]);\\n\\n int wc = Integer.parseInt(secondMultipleInput[1]);\\n\\n int z = Integer.parseInt(secondMultipleInput[2]);\\n\\n long result = Result.taumBday(b, w, bc, wc, z);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n }\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\n\\npublic class Solution \\n{\\n private static long cost( long B,long W,long X,long Y,long Z )\\n {\\n long cost = 0;\\n \\n if( X < Y )\\n {\\n cost = cost + ( B * X );\\n if( Y < Z + X )\\n {\\n cost = cost + ( W * Y );\\n }\\n else\\n {\\n cost = cost + ( W * ( Z + X ) );\\n }\\n }\\n else\\n {\\n cost = cost + ( W * Y );\\n if( X < Z + Y )\\n {\\n cost = cost + ( B * X );\\n }\\n else\\n {\\n cost = cost + ( B * ( Z + Y ) );\\n } \\n }\\n return cost;\\n }\\n\\n public static void main(String[] args) \\n {\\n Scanner scan = new Scanner( System.in );\\n int testCases = scan.nextInt();\\n \\n for( int i = 0;i < testCases;i++ )\\n {\\n System.out.println( cost( scan.nextLong(),scan.nextLong(),scan.nextLong(),scan.nextLong(),scan.nextLong() ) );\\n }\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'taumBday' function below.\\n *\\n * The function is expected to return a LONG_INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER b\\n * 2. INTEGER w\\n * 3. INTEGER bc\\n * 4. INTEGER wc\\n * 5. INTEGER z\\n */\\n\\n public static long taumBday(int b, int w, int bc, int wc, int z) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int t = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, t).forEach(tItr -> {\\n try {\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int b = Integer.parseInt(firstMultipleInput[0]);\\n\\n int w = Integer.parseInt(firstMultipleInput[1]);\\n\\n String[] secondMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int bc = Integer.parseInt(secondMultipleInput[0]);\\n\\n int wc = Integer.parseInt(secondMultipleInput[1]);\\n\\n int z = Integer.parseInt(secondMultipleInput[2]);\\n\\n long result = Result.taumBday(b, w, bc, wc, z);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"/* HackerRank Template v0.25 by Sergey Esipenko */\\nimport java.io.*;\\nimport java.util.*;\\n\\nimport static java.lang.Math.*;\\nimport static java.util.Arrays.fill;\\nimport static java.util.Arrays.sort;\\n\\npublic class Solution implements Runnable {\\n\\t/* START OF SOLUTION */\\n\\t\\n\\tprivate static final Random RANDOM = new Random(7777L);\\n\\t\\n\\tprivate void solve() throws IOException {\\n\\t\\tfinal int nTests = nextInt();\\n\\t\\tfor (int testCase = 1; testCase <= nTests; testCase++) {\\n\\t\\t\\tsolveTestCase(testCase);\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate void solveTestCase(int testCase) throws IOException {\\n\\t\\twriteLog(\\\"Case #%d:\\\", testCase);\\n\\t\\t\\n\\t\\tfinal long B = nextLong();\\n\\t\\tfinal long W = nextLong();\\n\\t\\tfinal long X = nextLong();\\n\\t\\tfinal long Y = nextLong();\\n\\t\\tfinal long Z = nextLong();\\n\\t\\t\\n\\t\\tfinal long bCost = B * min(X, Y + Z);\\n\\t\\tfinal long wCost = W * min(X + Z, Y);\\n\\t\\t\\n\\t\\tout.println(bCost + wCost);\\n\\t}\\n\\t\\n\\t\\n\\t\\n\\t/* END OF SOLUTION */\\n\\t/************************************************************************** \\n\\t * Entry point\\n\\t *************************************************************************/\\n\\t\\n\\tprivate static final Solution INSTANCE = new Solution();\\n\\tprivate static final boolean WRITE_LOG = true;\\n\\tprivate static final long STACK_SIZE = 1L << 24; // < 0 for default stack size\\n\\t\\n\\tprivate static long initTime;\\n\\tprivate static boolean localRun = false;\\n\\t\\n\\t@SuppressWarnings(\\\"unused\\\")\\n\\tpublic static void main(String[] args) throws IOException {\\n\\t\\ttry {\\n\\t\\t\\tinitTime = System.currentTimeMillis();\\n\\t\\t\\ttry {\\n\\t\\t\\t\\tlocalRun = \\\"true\\\".equals(System.getProperty(\\\"LOCAL_RUN_7777\\\"));\\n\\t\\t\\t\\tif (localRun && new File(\\\"input.txt\\\").exists())\\n\\t\\t\\t\\t\\tSystem.setIn(new FileInputStream(\\\"input.txt\\\"));\\n\\t\\t\\t} catch (SecurityException e) {\\n\\t\\t\\t\\t// Can't get property. It seems that solution is running in a secure\\n\\t\\t\\t\\t// environment\\n\\t\\t\\t}\\n\\t\\t\\tif (STACK_SIZE < 0L) {\\n\\t\\t\\t\\tINSTANCE.run();\\n\\t\\t\\t} else {\\n\\t\\t\\t\\tnew Thread(null, INSTANCE, \\\"Solver\\\", 1L << 24).start();\\n\\t\\t\\t}\\n\\t\\t} catch (Throwable e) {\\n\\t\\t\\te.printStackTrace();\\n\\t\\t\\tSystem.exit(999);\\n\\t\\t}\\n\\t}\\n\\n\\t@Override\\n\\tpublic void run() {\\n\\t\\ttry {\\n\\t\\t\\tin = new BufferedReader(new InputStreamReader(System.in));\\n\\t\\t\\tout = new PrintWriter(System.out);\\n\\t\\t\\tsolve();\\n\\t\\t\\tout.close();\\n\\t\\t\\tin.close();\\n\\t\\t\\twriteLog(\\\"Total time: \\\"\\n\\t\\t\\t\\t\\t+ (System.currentTimeMillis() - initTime) + \\\" ms\\\");\\n\\t\\t\\twriteLog(\\\"Memory status: \\\" + memoryStatus());\\n\\t\\t} catch (Throwable e) {\\n\\t\\t\\te.printStackTrace();\\n\\t\\t\\tSystem.exit(999);\\n\\t\\t}\\n\\t}\\n\\t\\n\\t/**************************************************************************\\n\\t * Input \\n\\t *************************************************************************/\\n\\tprivate BufferedReader in;\\n\\tprivate PrintWriter out;\\n\\tprivate StringTokenizer st = new StringTokenizer(\\\"\\\");\\n\\t\\n\\tprivate String nextToken() throws IOException {\\n\\t\\twhile (!st.hasMoreTokens())\\n\\t\\t\\tst = new StringTokenizer(in.readLine());\\n\\t\\treturn st.nextToken();\\n\\t}\\n\\t\\n\\tprivate int nextInt() throws IOException {\\n\\t\\treturn Integer.parseInt(nextToken());\\n\\t}\\n\\t\\n\\tprivate long nextLong() throws IOException {\\n\\t\\treturn Long.parseLong(nextToken());\\n\\t}\\n\\t\\n\\tprivate double nextDouble() throws IOException {\\n\\t\\treturn Double.parseDouble(nextToken());\\n\\t}\\n\\t\\n\\tprivate int[] nextIntArray(int size) throws IOException {\\n\\t\\tint[] ret = new int [size];\\n\\t\\tfor (int i = 0; i < size; i++)\\n\\t\\t\\tret[i] = nextInt();\\n\\t\\treturn ret;\\n\\t}\\n\\t\\n\\tprivate long[] nextLongArray(int size) throws IOException {\\n\\t\\tlong[] ret = new long [size];\\n\\t\\tfor (int i = 0; i < size; i++)\\n\\t\\t\\tret[i] = nextLong();\\n\\t\\treturn ret;\\n\\t}\\n\\t\\n\\tprivate double[] nextDoubleArray(int size) throws IOException {\\n\\t\\tdouble[] ret = new double [size];\\n\\t\\tfor (int i = 0; i < size; i++)\\n\\t\\t\\tret[i] = nextDouble();\\n\\t\\treturn ret;\\n\\t}\\n\\t\\n\\tprivate String nextLine() throws IOException {\\n\\t\\tst = new StringTokenizer(\\\"\\\");\\n\\t\\treturn in.readLine();\\n\\t}\\n\\t\\n\\tprivate boolean isEof() throws IOException {\\n\\t\\twhile (!st.hasMoreTokens()) {\\n\\t\\t\\tString s = in.readLine();\\n\\t\\t\\tif (s == null)\\n\\t\\t\\t\\treturn true;\\n\\t\\t\\tst = new StringTokenizer(s);\\n\\t\\t}\\n\\t\\treturn false;\\n\\t}\\n\\t\\n\\t/************************************************************************** \\n\\t * Output \\n\\t *************************************************************************/\\n\\t\\n\\tprivate void printIf(final boolean condition, final String msgIfTrue, final String msgIfFalse) {\\n\\t\\tout.println(condition ? msgIfTrue : msgIfFalse);\\n\\t}\\n\\t\\n\\tprivate void printRepeat(String s, int count) {\\n\\t\\tfor (int i = 0; i < count; i++)\\n\\t\\t\\tout.print(s);\\n\\t}\\n\\t\\n\\tprivate void printArray(int[] array) {\\n\\t\\tif (array == null || array.length == 0)\\n\\t\\t\\treturn;\\n\\t\\tfor (int i = 0; i < array.length; i++) {\\n\\t\\t\\tif (i > 0) out.print(' ');\\n\\t\\t\\tout.print(array[i]);\\n\\t\\t}\\n\\t\\tout.println();\\n\\t}\\n\\t\\n\\tprivate void printArray(long[] array) {\\n\\t\\tif (array == null || array.length == 0)\\n\\t\\t\\treturn;\\n\\t\\tfor (int i = 0; i < array.length; i++) {\\n\\t\\t\\tif (i > 0) out.print(' ');\\n\\t\\t\\tout.print(array[i]);\\n\\t\\t}\\n\\t\\tout.println();\\n\\t}\\n\\t\\n\\tprivate void printArray(double[] array) {\\n\\t\\tif (array == null || array.length == 0)\\n\\t\\t\\treturn;\\n\\t\\tfor (int i = 0; i < array.length; i++) {\\n\\t\\t\\tif (i > 0) out.print(' ');\\n\\t\\t\\tout.print(array[i]);\\n\\t\\t}\\n\\t\\tout.println();\\n\\t}\\n\\t\\n\\tprivate void printArray(double[] array, String spec) {\\n\\t\\tif (array == null || array.length == 0)\\n\\t\\t\\treturn;\\n\\t\\tfor (int i = 0; i < array.length; i++) {\\n\\t\\t\\tif (i > 0) out.print(' ');\\n\\t\\t\\tout.printf(Locale.US, spec, array[i]);\\n\\t\\t}\\n\\t\\tout.println();\\n\\t}\\n\\t\\n\\tprivate void printArray(Object[] array) {\\n\\t\\tif (array == null || array.length == 0)\\n\\t\\t\\treturn;\\n\\t\\tboolean blank = false;\\n\\t\\tfor (Object x : array) {\\n\\t\\t\\tif (blank) out.print(' '); else blank = true;\\n\\t\\t\\tout.print(x);\\n\\t\\t}\\n\\t\\tout.println();\\n\\t}\\n\\t\\n\\t@SuppressWarnings(\\\"rawtypes\\\")\\n\\tprivate void printCollection(Collection collection) {\\n\\t\\tif (collection == null || collection.isEmpty())\\n\\t\\t\\treturn;\\n\\t\\tboolean blank = false;\\n\\t\\tfor (Object x : collection) {\\n\\t\\t\\tif (blank) out.print(' '); else blank = true;\\n\\t\\t\\tout.print(x);\\n\\t\\t}\\n\\t\\tout.println();\\n\\t}\\n\\t\\n\\t/**************************************************************************\\n\\t * Utility\\n\\t *************************************************************************/\\n\\t\\n\\tprivate static String memoryStatus() {\\n\\t\\treturn (Runtime.getRuntime().totalMemory()\\n\\t\\t\\t\\t- Runtime.getRuntime().freeMemory() >> 20)\\n\\t\\t\\t\\t+ \\\"/\\\" + (Runtime.getRuntime().totalMemory() >> 20) + \\\" MB\\\";\\n\\t}\\n\\t\\n\\tprivate static void checkMemory() {\\n\\t\\tSystem.err.println(memoryStatus());\\n\\t}\\n\\t\\n\\tprivate static long getRunningTime() {\\n\\t\\treturn System.currentTimeMillis() - initTime;\\n\\t}\\n\\t\\n\\tprivate static void chk(boolean f) {\\n\\t\\tif (!f) throw new RuntimeException(\\\"Assert failed\\\");\\n\\t}\\n\\t\\n\\tprivate static void chk(boolean f, String format, Object ... args) {\\n\\t\\tif (!f) throw new RuntimeException(String.format(format, args));\\n\\t}\\n\\t\\n\\tprivate static void writeLog(String format, Object... args) {\\n\\t\\tif (localRun && WRITE_LOG)\\n\\t\\t\\tSystem.err.println(String.format(Locale.US, format, args));\\n\\t}\\n\\t\\n\\tprivate static void swap(int[] a, int i, int j) {\\n\\t\\tint tmp = a[i];\\n\\t\\ta[i] = a[j];\\n\\t\\ta[j] = tmp;\\n\\t}\\n\\t\\n\\tprivate static void swap(long[] a, int i, int j) {\\n\\t\\tlong tmp = a[i];\\n\\t\\ta[i] = a[j];\\n\\t\\ta[j] = tmp;\\n\\t}\\n\\t\\n\\tprivate static void swap(double[] a, int i, int j) {\\n\\t\\tdouble tmp = a[i];\\n\\t\\ta[i] = a[j];\\n\\t\\ta[j] = tmp;\\n\\t}\\n\\t\\n\\tprivate static void swap(Object[] a, int i, int j) {\\n\\t\\tObject tmp = a[i];\\n\\t\\ta[i] = a[j];\\n\\t\\ta[j] = tmp;\\n\\t}\\n\\t\\n\\tprivate static void shuffle(int[] a, int from, int to) {\\n\\t\\tfor (int i = from; i <= to; i++)\\n\\t\\t\\tswap(a, i, from + RANDOM.nextInt(i - from + 1));\\n\\t}\\n\\t\\n\\tprivate static void shuffle(long[] a, int from, int to) {\\n\\t\\tfor (int i = from; i <= to; i++)\\n\\t\\t\\tswap(a, i, from + RANDOM.nextInt(i - from + 1));\\n\\t}\\n\\t\\n\\tprivate static void shuffle(double[] a, int from, int to) {\\n\\t\\tfor (int i = from; i <= to; i++)\\n\\t\\t\\tswap(a, i, from + RANDOM.nextInt(i - from + 1));\\n\\t}\\n\\t\\n\\tprivate static void shuffle(Object[] a, int from, int to) {\\n\\t\\tfor (int i = from; i <= to; i++)\\n\\t\\t\\tswap(a, i, from + RANDOM.nextInt(i - from + 1));\\n\\t}\\n\\t\\n\\tprivate static void shuffle(int[] a) {\\n\\t\\tif (a == null) return;\\n\\t\\tshuffle(a, 0, a.length - 1);\\n\\t}\\n\\t\\n\\tprivate static void shuffle(long[] a) {\\n\\t\\tif (a == null) return;\\n\\t\\tshuffle(a, 0, a.length - 1);\\n\\t}\\n\\t\\n\\tprivate static void shuffle(double[] a) {\\n\\t\\tif (a == null) return;\\n\\t\\tshuffle(a, 0, a.length - 1);\\n\\t}\\n\\t\\n\\tprivate static void shuffle(Object[] a) {\\n\\t\\tif (a == null) return;\\n\\t\\tshuffle(a, 0, a.length - 1);\\n\\t}\\n\\t\\n\\tprivate static long[] getPartialSums(int[] a) {\\n\\t\\tfinal long[] sums = new long [a.length + 1];\\n\\t\\tfor (int i = 0; i < a.length; i++)\\n\\t\\t\\tsums[i + 1] = sums[i] + a[i];\\n\\t\\treturn sums;\\n\\t}\\n\\t\\n\\tprivate static long[] getPartialSums(long[] a) {\\n\\t\\tfinal long[] sums = new long [a.length + 1];\\n\\t\\tfor (int i = 0; i < a.length; i++)\\n\\t\\t\\tsums[i + 1] = sums[i] + a[i];\\n\\t\\treturn sums;\\n\\t}\\n\\t\\n\\tprivate static int[] getOrderedSet(int[] a) {\\n\\t\\tfinal int[] set = Arrays.copyOf(a, a.length);\\n\\t\\tif (a.length == 0) return set;\\n\\t\\tshuffle(set);\\n\\t\\tsort(set);\\n\\t\\tint k = 1;\\n\\t\\tint prev = set[0];\\n\\t\\tfor (int i = 1; i < a.length; i++) {\\n\\t\\t\\tif (prev != set[i]) {\\n\\t\\t\\t\\tset[k++] = prev = set[i];\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\treturn Arrays.copyOf(set, k);\\n\\t}\\n\\t\\n\\tprivate static long[] getOrderedSet(long[] a) {\\n\\t\\tfinal long[] set = Arrays.copyOf(a, a.length);\\n\\t\\tif (a.length == 0) return set;\\n\\t\\tshuffle(set);\\n\\t\\tsort(set);\\n\\t\\tint k = 1;\\n\\t\\tlong prev = set[0];\\n\\t\\tfor (int i = 1; i < a.length; i++) {\\n\\t\\t\\tif (prev != set[i]) {\\n\\t\\t\\t\\tset[k++] = prev = set[i];\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\treturn Arrays.copyOf(set, k);\\n\\t}\\n\\t\\n\\tprivate static int gcd(int x, int y) {\\n\\t\\tx = abs(x);\\n\\t\\ty = abs(y);\\n\\t\\twhile (x > 0 && y > 0) {\\n\\t\\t\\tif (x > y) {\\n\\t\\t\\t\\tx %= y;\\n\\t\\t\\t} else {\\n\\t\\t\\t\\ty %= x;\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\treturn x + y;\\n\\t}\\n\\t\\n\\tprivate static long gcd(long x, long y) {\\n\\t\\tx = abs(x);\\n\\t\\ty = abs(y);\\n\\t\\twhile (x > 0 && y > 0) {\\n\\t\\t\\tif (x > y) {\\n\\t\\t\\t\\tx %= y;\\n\\t\\t\\t} else {\\n\\t\\t\\t\\ty %= x;\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\treturn x + y;\\n\\t}\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'taumBday' function below.\\n *\\n * The function is expected to return a LONG_INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER b\\n * 2. INTEGER w\\n * 3. INTEGER bc\\n * 4. INTEGER wc\\n * 5. INTEGER z\\n */\\n\\n public static long taumBday(int b, int w, int bc, int wc, int z) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int t = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, t).forEach(tItr -> {\\n try {\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int b = Integer.parseInt(firstMultipleInput[0]);\\n\\n int w = Integer.parseInt(firstMultipleInput[1]);\\n\\n String[] secondMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int bc = Integer.parseInt(secondMultipleInput[0]);\\n\\n int wc = Integer.parseInt(secondMultipleInput[1]);\\n\\n int z = Integer.parseInt(secondMultipleInput[2]);\\n\\n long result = Result.taumBday(b, w, bc, wc, z);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"taumBday(b, w, bc, wc, z)\"\n ],\n \"solution\": \"t = int(input())\\nfor _ in range(t):\\n b, w = map(int, input().split())\\n x, y, z = map(int, input().split())\\n print(b * min(x, y + z) + w * min(y, x + z))\\n\",\n \"template\": \"#\\n# Complete the 'taumBday' function below.\\n#\\n# The function is expected to return a LONG_INTEGER.\\n# The function accepts following parameters:\\n# 1. INTEGER b\\n# 2. INTEGER w\\n# 3. INTEGER bc\\n# 4. INTEGER wc\\n# 5. INTEGER z\\n#\\n\\ndef taumBday(b, w, bc, wc, z):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n t = int(input().strip())\\n\\n for t_itr in range(t):\\n first_multiple_input = input().rstrip().split()\\n\\n b = int(first_multiple_input[0])\\n\\n w = int(first_multiple_input[1])\\n\\n second_multiple_input = input().rstrip().split()\\n\\n bc = int(second_multiple_input[0])\\n\\n wc = int(second_multiple_input[1])\\n\\n z = int(second_multiple_input[2])\\n\\n result = taumBday(b, w, bc, wc, z)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":190,"cells":{"question_id":{"kind":"number","value":4187,"string":"4,187"},"question_title":{"kind":"string","value":"Bigger is Greater"},"question_content":{"kind":"string","value":"<!-- \n**Please note that this is a team event, and your submission will be accepted only as a part of a team, even single member teams are allowed. Please click [here](https://www.hackerrank.com/auth/create_team/csindia) to register as a team, if you have NOT already registered.**\n-->\n\n_[Lexicographical order](https://en.wikipedia.org/wiki/Lexicographical_order)_ is often known as alphabetical order when dealing with strings. A string is _greater_ than another string if it comes later in a lexicographically sorted list.\n\nGiven a word, create a new word by swapping some or all of its characters. This new word must meet two criteria:\n\n- It must be greater than the original word\n- It must be the smallest word that meets the first condition\n\n**Example** \n$w = \\texttt{abcd}$\n\nThe next largest word is $\\texttt{abdc}$. \n\nComplete the function *biggerIsGreater* below to create and return the new string meeting the criteria. If it is not possible, return `no answer`.\n\n**Function Description** \n\nComplete the *biggerIsGreater* function in the editor below. \n\nbiggerIsGreater has the following parameter(s): \n\n- *string w*: a word\n\n**Returns** \n- *string:* the smallest lexicographically higher string possible or `no answer` "},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Medium"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static String biggerIsGreater(String w)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.ByteArrayInputStream;\\nimport java.io.IOException;\\nimport java.io.InputStream;\\nimport java.io.PrintWriter;\\nimport java.util.Arrays;\\nimport java.util.InputMismatchException;\\n\\npublic class Solution {\\n\\tstatic InputStream is;\\n\\tstatic PrintWriter out;\\n\\tstatic String INPUT = \\\"\\\";\\n\\t\\n\\tstatic void solve()\\n\\t{\\n\\t\\touter:\\n\\t\\tfor(int T = ni();T >= 1;T--){\\n\\t\\t\\tchar[] s = ns().toCharArray();\\n\\t\\t\\tint n = s.length;\\n\\t\\t\\tint[] has = new int[26];\\n\\t\\t\\tfor(int i = n-1;i >= 0;i--){\\n\\t\\t\\t\\thas[s[i]-'a']++;\\n\\t\\t\\t\\tfor(int j = s[i]-'a'+1;j < 26;j++){\\n\\t\\t\\t\\t\\tif(has[j] > 0){\\n\\t\\t\\t\\t\\t\\ts[i] = (char)('a'+j);\\n\\t\\t\\t\\t\\t\\thas[j]--;\\n\\t\\t\\t\\t\\t\\tint p = 0;\\n\\t\\t\\t\\t\\t\\tfor(int k = i+1;k < n;k++){\\n\\t\\t\\t\\t\\t\\t\\twhile(p < 26 && has[p] == 0)p++;\\n\\t\\t\\t\\t\\t\\t\\ts[k] = (char)('a'+p);\\n\\t\\t\\t\\t\\t\\t\\thas[p]--;\\n\\t\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\t\\tout.println(new String(s));\\n\\t\\t\\t\\t\\t\\tcontinue outer;\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\tout.println(\\\"no answer\\\");\\n\\t\\t}\\n\\t}\\n\\t\\n\\tpublic static void main(String[] args) throws Exception\\n\\t{\\n\\t\\tlong S = System.currentTimeMillis();\\n\\t\\tis = INPUT.isEmpty() ? System.in : new ByteArrayInputStream(INPUT.getBytes());\\n\\t\\tout = new PrintWriter(System.out);\\n\\t\\t\\n\\t\\tsolve();\\n\\t\\tout.flush();\\n\\t\\tlong G = System.currentTimeMillis();\\n\\t\\ttr(G-S+\\\"ms\\\");\\n\\t}\\n\\t\\n\\tprivate static boolean eof()\\n\\t{\\n\\t\\tif(lenbuf == -1)return true;\\n\\t\\tint lptr = ptrbuf;\\n\\t\\twhile(lptr < lenbuf)if(!isSpaceChar(inbuf[lptr++]))return false;\\n\\t\\t\\n\\t\\ttry {\\n\\t\\t\\tis.mark(1000);\\n\\t\\t\\twhile(true){\\n\\t\\t\\t\\tint b = is.read();\\n\\t\\t\\t\\tif(b == -1){\\n\\t\\t\\t\\t\\tis.reset();\\n\\t\\t\\t\\t\\treturn true;\\n\\t\\t\\t\\t}else if(!isSpaceChar(b)){\\n\\t\\t\\t\\t\\tis.reset();\\n\\t\\t\\t\\t\\treturn false;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t} catch (IOException e) {\\n\\t\\t\\treturn true;\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate static byte[] inbuf = new byte[1024];\\n\\tstatic int lenbuf = 0, ptrbuf = 0;\\n\\t\\n\\tprivate static int readByte()\\n\\t{\\n\\t\\tif(lenbuf == -1)throw new InputMismatchException();\\n\\t\\tif(ptrbuf >= lenbuf){\\n\\t\\t\\tptrbuf = 0;\\n\\t\\t\\ttry { lenbuf = is.read(inbuf); } catch (IOException e) { throw new InputMismatchException(); }\\n\\t\\t\\tif(lenbuf <= 0)return -1;\\n\\t\\t}\\n\\t\\treturn inbuf[ptrbuf++];\\n\\t}\\n\\t\\n\\tprivate static boolean isSpaceChar(int c) { return !(c >= 33 && c <= 126); }\\n\\tprivate static int skip() { int b; while((b = readByte()) != -1 && isSpaceChar(b)); return b; }\\n\\t\\n\\tprivate static double nd() { return Double.parseDouble(ns()); }\\n\\tprivate static char nc() { return (char)skip(); }\\n\\t\\n\\tprivate static String ns()\\n\\t{\\n\\t\\tint b = skip();\\n\\t\\tStringBuilder sb = new StringBuilder();\\n\\t\\twhile(!(isSpaceChar(b))){ // when nextLine, (isSpaceChar(b) && b != ' ')\\n\\t\\t\\tsb.appendCodePoint(b);\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn sb.toString();\\n\\t}\\n\\t\\n\\tprivate static char[] ns(int n)\\n\\t{\\n\\t\\tchar[] buf = new char[n];\\n\\t\\tint b = skip(), p = 0;\\n\\t\\twhile(p < n && !(isSpaceChar(b))){\\n\\t\\t\\tbuf[p++] = (char)b;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn n == p ? buf : Arrays.copyOf(buf, p);\\n\\t}\\n\\t\\n\\tprivate static char[][] nm(int n, int m)\\n\\t{\\n\\t\\tchar[][] map = new char[n][];\\n\\t\\tfor(int i = 0;i < n;i++)map[i] = ns(m);\\n\\t\\treturn map;\\n\\t}\\n\\t\\n\\tprivate static int[] na(int n)\\n\\t{\\n\\t\\tint[] a = new int[n];\\n\\t\\tfor(int i = 0;i < n;i++)a[i] = ni();\\n\\t\\treturn a;\\n\\t}\\n\\t\\n\\tprivate static int ni()\\n\\t{\\n\\t\\tint num = 0, b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate static long nl()\\n\\t{\\n\\t\\tlong num = 0;\\n\\t\\tint b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate static void tr(Object... o) { if(INPUT.length() != 0)System.out.println(Arrays.deepToString(o)); }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'biggerIsGreater' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts STRING w as parameter.\\n */\\n\\n public static String biggerIsGreater(String w) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int T = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n for (int TItr = 0; TItr < T; TItr++) {\\n String w = bufferedReader.readLine();\\n\\n String result = Result.biggerIsGreater(w);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n }\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'biggerIsGreater' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts STRING w as parameter.\\n */\\n\\n public static String biggerIsGreater(String w) {\\n int z = w.length()-1; //index of last char\\n String []tkns = w.split(\\\"\\\");\\n char []arr = new char[tkns.length];\\n for(int i = 0; i<tkns.length; i++)\\n arr[i] = tkns[i].charAt(0);\\n \\n boolean can = false;\\n for(z = w.length()-1; z>0; z--){\\n //we got y\\n if(arr[z-1]<arr[z]){\\n can = true;\\n break;\\n }\\n }\\n if(can){\\n char y = arr[z-1];\\n char []subArr = new char[w.length()-(z-1)];\\n subArr = Arrays.copyOfRange(arr,z,arr.length);\\n \\n //find x>y smallest x\\n char min = subArr[0]; // z in arr == 0 in subArr\\n int Xindex = 0;\\n for(int i = 1; i<subArr.length; i++){\\n if(subArr[i]>y && subArr[i]<min) {\\n min = subArr[i];\\n Xindex = i;\\n }\\n }\\n //got x\\n arr[z-1] = min;\\n subArr[Xindex] = y;\\n //sort subArr asc\\n Arrays.sort(subArr);\\n System.arraycopy(subArr,0,arr,z,subArr.length);\\n //convert back to string\\n w =\\\"\\\";\\n for(int i = 0; i <arr.length; i++)\\n w += arr[i];\\n return w;\\n }\\n else return \\\"no answer\\\";\\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int T = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, T).forEach(TItr -> {\\n try {\\n String w = bufferedReader.readLine();\\n\\n String result = Result.biggerIsGreater(w);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'biggerIsGreater' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts STRING w as parameter.\\n */\\n\\n public static String biggerIsGreater(String w) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int T = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, T).forEach(TItr -> {\\n try {\\n String w = bufferedReader.readLine();\\n\\n String result = Result.biggerIsGreater(w);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"\\n\\nimport java.util.Arrays;\\nimport java.util.Scanner;\\n\\npublic class Solution {\\n\\tpublic static void main(String args[]) {\\n\\t\\tint testcases;\\n\\t\\tScanner scan = new Scanner(System.in);\\n\\t\\ttestcases = scan.nextInt();\\n\\t\\tfor (int i = 0; i < testcases; i++) {\\n\\t\\t\\tString str = scan.next();\\n\\t\\t\\tint j = str.length() - 1;\\n\\t\\t\\tfor (; j > 0; j--) {\\n\\t\\t\\t\\tif (str.charAt(j) > str.charAt(j - 1)) {\\n\\t\\t\\t\\t\\tbreak;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\tif(j==0){\\n\\t\\t\\t\\tSystem.out.println(\\\"no answer\\\");\\n\\t\\t\\t\\tcontinue;\\n\\t\\t\\t}\\n\\t\\t\\tchar nextHighest=str.charAt(j-1);\\n\\n\\n\\t\\t\\tString substr = str.substring(j);\\n\\n\\t\\t\\tchar[] chars = substr.toCharArray();\\n\\t\\t\\tArrays.sort(chars);\\n\\t\\t\\t\\n\\t\\t\\tint k=0;\\n\\t\\t\\tfor(;k<chars.length;k++){\\n\\t\\t\\t\\tif(nextHighest < chars[k]){\\n\\t\\t\\t\\t\\tchar temp;\\n\\t\\t\\t\\t\\ttemp=nextHighest;\\n\\t\\t\\t\\t\\tnextHighest=chars[k];\\n\\t\\t\\t\\t\\tchars[k]=temp;\\n\\t\\t\\t\\t\\tbreak;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\tArrays.sort(chars);\\n\\t\\t\\tString sorted = new String(chars);\\n\\t\\t\\tString ans = str.substring(0,j-1)+nextHighest+sorted;\\n\\t\\t\\tSystem.out.println(ans);\\n\\n\\t\\t}\\n\\t}\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'biggerIsGreater' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts STRING w as parameter.\\n */\\n\\n public static String biggerIsGreater(String w) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int T = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, T).forEach(TItr -> {\\n try {\\n String w = bufferedReader.readLine();\\n\\n String result = Result.biggerIsGreater(w);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"biggerIsGreater(w)\"\n ],\n \"solution\": \"#!/usr/bin/env python3\\ncases = int(input())\\nfor x in range(cases):\\n\\tstring = list(input().strip())\\n\\tmatch = False\\n\\tfor x in range(len(string)-2,-1,-1):\\n\\t\\tif string[x+1] > string[x]:\\n\\t\\t\\tmatch = True\\n\\t\\t\\tfor y in range(len(string)-1,x,-1):\\n\\t\\t\\t\\tif string[y] > string[x]:\\n\\t\\t\\t\\t\\tstring[x],string[y] = string[y],string[x]\\n\\t\\t\\t\\t\\tstring = string[:x+1] + sorted(string[x+1:])\\n\\t\\t\\t\\t\\tbreak\\n\\t\\t\\tbreak\\n\\tif match:\\n\\t\\tprint(\\\"\\\".join(string))\\n\\telse:\\n\\t\\tprint(\\\"no answer\\\")\\n\",\n \"template\": \"#\\n# Complete the 'biggerIsGreater' function below.\\n#\\n# The function is expected to return a STRING.\\n# The function accepts STRING w as parameter.\\n#\\n\\ndef biggerIsGreater(w):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n T = int(input().strip())\\n\\n for T_itr in range(T):\\n w = input()\\n\\n result = biggerIsGreater(w)\\n\\n fptr.write(result + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":191,"cells":{"question_id":{"kind":"number","value":6715,"string":"6,715"},"question_title":{"kind":"string","value":"Modified Kaprekar Numbers"},"question_content":{"kind":"string","value":"A _modified Kaprekar number_ is a positive whole number with a special property. If you square it, then split the number into two integers and sum those integers, you have the same value you started with.\n\nConsider a positive whole number $n$ with $d$ digits. We square $n$ to arrive at a number that is either $2\\times d$ digits long or $(2\\times d) \\text{ - } 1$ digits long. Split the string representation of the square into two parts, $l$ and $r$. The right hand part, $r$ must be $d$ digits long. The left is the remaining substring. Convert those two substrings back to integers, add them and see if you get $n$.\n\n**Example** \n\n$n = 5$ \n$d = 1$ \n\nFirst calculate that $n^2 = 25$. Split that into two strings and convert them back to integers $2$ and $5$. Test $2 + 5 = 7 \\ne 5$, so this is not a modified Kaprekar number. If $n = 9$, still $d = 1$, and $n^2 = 81$. This gives us $1 + 8 = 9$, the original $n$. \n\n**Note:** r may have leading zeros. \n\nHere's an explanation from Wikipedia about the **ORIGINAL** [Kaprekar Number](https://en.wikipedia.org/wiki/Kaprekar_number) (spot the difference!): \n>In mathematics, a Kaprekar number for a given base is a non-negative integer, the representation of whose square in that base can be split into two parts that add up to the original number again. For instance, 45 is a Kaprekar number, because 45² = 2025 and 20+25 = 45.\n \nGiven two positive integers $p$ and $q$ where $p$ is lower than $q$, write a program to print the modified Kaprekar numbers in the range between $p$ and $q$, inclusive. If no modified Kaprekar numbers exist in the given range, print `INVALID RANGE`. \n\n**Function Description** \n\nComplete the *kaprekarNumbers* function in the editor below. \n\nkaprekarNumbers has the following parameter(s): \n\n- *int p:* the lower limit \n- *int q:* the upper limit \n\n**Prints** \n\nIt should print the list of modified Kaprekar numbers, space-separated on one line and in ascending order. If no modified Kaprekar numbers exist in the given range, print `INVALID RANGE`. No return value is required. "},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static void kaprekarNumbers(int p, int q)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.util.Scanner;\\n\\npublic class Kaprekar {\\n\\tpublic static void main(String args[]) {\\n\\t\\tScanner cin = new Scanner(System.in);\\n\\t\\tint p = cin.nextInt();\\n\\t\\tint q = cin.nextInt();\\n\\t\\t\\n\\t\\tboolean one = false;\\n\\t\\tfor (int i = p; i <= q; i++) {\\n\\t\\t\\tif (isKaprekar(i)) {\\n\\t\\t\\t\\tif (one) {\\n\\t\\t\\t\\t\\tSystem.out.print(\\\" \\\");\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tSystem.out.print(i);\\n\\t\\t\\t\\tone = true;\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\tif (!one) {\\n\\t\\t\\tSystem.out.println(\\\"INVALID RANGE\\\");\\n\\t\\t}\\n\\t}\\n\\t\\n\\tstatic boolean isKaprekar(long n) {\\n\\t\\tlong t = n;\\n\\t\\tint d = 0; // Digits\\n\\t\\tlong div = 1;\\n\\t\\twhile (t > 0) {\\n\\t\\t\\tt = t / 10;\\n\\t\\t\\td++;\\n\\t\\t\\tdiv *= 10;\\n\\t\\t}\\n\\t\\t\\n\\t\\tlong sq = n * n;\\n\\t\\tlong left = sq / div;\\n\\t\\tlong right = sq % div;\\n\\t\\t\\n\\t\\t//System.out.println(n + \\\" \\\" + left + \\\" \\\" + right);\\n\\t\\t\\n\\t\\treturn left + right == n;\\n\\t}\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'kaprekarNumbers' function below.\\n *\\n * The function accepts following parameters:\\n * 1. INTEGER p\\n * 2. INTEGER q\\n */\\n\\n public static void kaprekarNumbers(int p, int q) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n\\n int p = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n int q = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n Result.kaprekarNumbers(p, q);\\n\\n bufferedReader.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.util.Scanner;\\n\\npublic class Solution {\\n public static void main(String[] args) {\\n Scanner scan = new Scanner(System.in);\\n int p = scan.nextInt();\\n int q = scan.nextInt();\\n scan.close();\\n \\n boolean foundKaprekar = false;\\n for (int num = p; num <= q; num++) {\\n if (isKaprekar(num)) {\\n foundKaprekar = true;\\n }\\n }\\n if (!foundKaprekar) {\\n System.out.println(\\\"INVALID RANGE\\\");\\n }\\n }\\n \\n private static boolean isKaprekar(int num) {\\n long squared = (long) num * num;\\n String str = String.valueOf(squared);\\n String left = str.substring(0, str.length() / 2);\\n String right = str.substring(str.length() / 2);\\n int numL = (left.isEmpty()) ? 0 : Integer.parseInt(left);\\n int numR = (right.isEmpty()) ? 0 : Integer.parseInt(right);\\n if (numL + numR == num) {\\n System.out.print(num + \\\" \\\");\\n return true;\\n } else {\\n return false;\\n }\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'kaprekarNumbers' function below.\\n *\\n * The function accepts following parameters:\\n * 1. INTEGER p\\n * 2. INTEGER q\\n */\\n\\n public static void kaprekarNumbers(int p, int q) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n\\n int p = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n int q = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n Result.kaprekarNumbers(p, q);\\n\\n bufferedReader.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\n\\npublic class Solution {\\n private static InputReader in;\\n private static PrintWriter out;\\n\\n public static void main(String[] args) throws IOException {\\n in = new InputReader(System.in);\\n out = new PrintWriter(System.out, true);\\n int p = in.nextInt(), q = in.nextInt();\\n ArrayList<Integer> sol = new ArrayList<Integer>();\\n for (int i = p; i <= q; i++) {\\n if (ok(i))\\n sol.add(i);\\n }\\n if (sol.size() == 0) {\\n out.println(\\\"INVALID RANGE\\\");\\n } else {\\n for (int i = 0; i < sol.size(); i++) {\\n if (i > 0) out.print(\\\" \\\");\\n out.print(sol.get(i));\\n }\\n out.println();\\n }\\n out.close();\\n System.exit(0);\\n }\\n \\n public static boolean ok(int i) {\\n long sq = (long)i * (long)i;\\n String s = String.valueOf(sq);\\n String t = String.valueOf(i);\\n int d = t.length();\\n if (d == s.length()) return i == 1;\\n \\n int b1 = Integer.parseInt(s.substring(0,s.length()-d));\\n int b2 = Integer.parseInt(s.substring(s.length()-d));\\n if (b2 == 0) return false;\\n return b1+b2 == i;\\n }\\n\\n static class InputReader {\\n public BufferedReader reader;\\n public StringTokenizer tokenizer;\\n\\n public InputReader(InputStream stream) {\\n reader = new BufferedReader(new InputStreamReader(stream), 32768);\\n tokenizer = null;\\n }\\n\\n public String next() {\\n while (tokenizer == null || !tokenizer.hasMoreTokens()) {\\n try {\\n tokenizer = new StringTokenizer(reader.readLine());\\n } catch (IOException e) {\\n throw new RuntimeException(e);\\n }\\n }\\n return tokenizer.nextToken();\\n }\\n\\n public int nextInt() {\\n return Integer.parseInt(next());\\n }\\n }\\n\\n\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'kaprekarNumbers' function below.\\n *\\n * The function accepts following parameters:\\n * 1. INTEGER p\\n * 2. INTEGER q\\n */\\n\\n public static void kaprekarNumbers(int p, int q) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n\\n int p = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n int q = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n Result.kaprekarNumbers(p, q);\\n\\n bufferedReader.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"kaprekarNumbers(p, q)\"\n ],\n \"solution\": \"low = input()\\nhi = input()\\n\\ndef is_kaprekar_number(num):\\n square = str(num*num) \\n mid = len(square)//2\\n \\n num1 = 0\\n try: \\n num1 = int(square[0:mid])\\n except:\\n pass\\n \\n num2 = 0\\n try: \\n num2 = int(square[mid:])\\n except:\\n pass\\n \\n if num1+num2 == num:\\n return True\\n \\n return False\\n \\nfound = False\\nfor i in range(int(low), int(hi)+1):\\n if is_kaprekar_number(i):\\n found = True\\n print(i, end=\\\" \\\")\\nif found == False:\\n print(\\\"INVALID RANGE\\\")\\n \",\n \"template\": \"#\\n# Complete the 'kaprekarNumbers' function below.\\n#\\n# The function accepts following parameters:\\n# 1. INTEGER p\\n# 2. INTEGER q\\n#\\n\\ndef kaprekarNumbers(p, q):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n p = int(input().strip())\\n\\n q = int(input().strip())\\n\\n kaprekarNumbers(p, q)\\n\"\n}"},"test_cases":{"kind":"string","value":"eJydU7FKBEEMtbgPCVsfMpPJzCR2B4ocyBUWIrhicW5hs15xB4IIfoT+oZ0/YbJaHMMNh5Nis7u89/KSzLzPPr9nJ1PcfunL3Wv3NG522+4MukjJcT9akn7s5tANL5thvR0eH5532z/QcnWzuFqew/VidXnRvc1hT4AYWZmaxDcJZPKsDjSJa3TAMjkQ1yYQCBH7UVPKTQLsPKd+ZIeB2lpI0esMKGWXmgS8F/OOkTJWBXwORIAW5Q5SYnXO6ClU6VmDy7pRy4lFlYaSIbsAirHaATAjA0l0EJ0+sn6DaRtCYM/k9JeBMcUEEj36CSKlB+273nMBViS3TVj7rFeB0hSRrbNKoFj6kqh3wMVjYyxoNg53bAMHh2Zj7kdL9ZoTqCQ6Z9cMf9M/iLZRW5WdgXqXBw5nYElaylKdWKzu/uP0B7bpbYU="},"max_retries":{"kind":"null"}}},{"rowIdx":192,"cells":{"question_id":{"kind":"number","value":45345,"string":"45,345"},"question_title":{"kind":"string","value":"Halloween Sale"},"question_content":{"kind":"string","value":"You wish to buy video games from the famous online video game store Mist.\n\nUsually, all games are sold at the same price, $p$ dollars. However, they are planning to have the seasonal Halloween Sale next month in which you can buy games at a cheaper price. Specifically, the first game will cost $p$ dollars, and every subsequent game will cost $d$ dollars less than the previous one. This continues until the cost becomes less than or equal to $m$ dollars, after which every game will cost $m$ dollars. How many games can you buy during the Halloween Sale?\n\n**Example** \n$p = 20$ \n$d = 3$ \n$m = 6$ \n$s = 70$. \n\nThe following are the costs of the first $11$, in order:\n\n$$20, 17, 14, 11, 8, 6, 6, 6, 6, 6, 6$$\n\nStart at $p = 20$ units cost, reduce that by $d = 3$ units each iteration until reaching a minimum possible price, $m = 6$. Starting with $s = 70$ units of currency in your Mist wallet, you can buy 5 games: $20 + 17 + 14 + 11 + 8 = 70$.\n\n**Function Description** \n\nComplete the _howManyGames_ function in the editor below. \n\n_howManyGames_ has the following parameters: \n\n- *int p:* the price of the first game \n- *int d:* the discount from the previous game price\n- *int m:* the minimum cost of a game \n- *int s:* the starting budget "},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static int howManyGames(int p, int d, int m, int s)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.OutputStream;\\nimport java.io.IOException;\\nimport java.io.InputStream;\\nimport java.io.OutputStream;\\nimport java.io.PrintWriter;\\nimport java.io.BufferedWriter;\\nimport java.io.Writer;\\nimport java.io.OutputStreamWriter;\\nimport java.util.InputMismatchException;\\nimport java.io.IOException;\\nimport java.io.InputStream;\\n\\n/**\\n * Built using CHelper plug-in\\n * Actual solution is at the top\\n */\\npublic class Solution {\\n public static void main(String[] args) {\\n InputStream inputStream = System.in;\\n OutputStream outputStream = System.out;\\n InputReader in = new InputReader(inputStream);\\n OutputWriter out = new OutputWriter(outputStream);\\n HalloweenSale solver = new HalloweenSale();\\n solver.solve(1, in, out);\\n out.close();\\n }\\n\\n static class HalloweenSale {\\n public void solve(int testNumber, InputReader in, OutputWriter out) {\\n int p = in.nextInt(), d = in.nextInt(), m = in.nextInt(), s = in.nextInt();\\n int g = 0;\\n while (s >= p) {\\n g++;\\n s -= p;\\n p -= d;\\n p = Math.max(p, m);\\n }\\n out.println(g);\\n }\\n\\n }\\n\\n static class InputReader {\\n private InputStream stream;\\n private byte[] buf = new byte[1024];\\n private int curChar;\\n private int numChars;\\n\\n public InputReader(InputStream stream) {\\n this.stream = stream;\\n }\\n\\n public int read() {\\n if (this.numChars == -1) {\\n throw new InputMismatchException();\\n } else {\\n if (this.curChar >= this.numChars) {\\n this.curChar = 0;\\n\\n try {\\n this.numChars = this.stream.read(this.buf);\\n } catch (IOException var2) {\\n throw new InputMismatchException();\\n }\\n\\n if (this.numChars <= 0) {\\n return -1;\\n }\\n }\\n\\n return this.buf[this.curChar++];\\n }\\n }\\n\\n public int nextInt() {\\n int c;\\n for (c = this.read(); isSpaceChar(c); c = this.read()) {\\n ;\\n }\\n\\n byte sgn = 1;\\n if (c == 45) {\\n sgn = -1;\\n c = this.read();\\n }\\n\\n int res = 0;\\n\\n while (c >= 48 && c <= 57) {\\n res *= 10;\\n res += c - 48;\\n c = this.read();\\n if (isSpaceChar(c)) {\\n return res * sgn;\\n }\\n }\\n\\n throw new InputMismatchException();\\n }\\n\\n public static boolean isSpaceChar(int c) {\\n return c == 32 || c == 10 || c == 13 || c == 9 || c == -1;\\n }\\n\\n }\\n\\n static class OutputWriter {\\n private final PrintWriter writer;\\n\\n public OutputWriter(OutputStream outputStream) {\\n writer = new PrintWriter(new BufferedWriter(new OutputStreamWriter(outputStream)));\\n }\\n\\n public OutputWriter(Writer writer) {\\n this.writer = new PrintWriter(writer);\\n }\\n\\n public void close() {\\n writer.close();\\n }\\n\\n public void println(int i) {\\n writer.println(i);\\n }\\n\\n }\\n}\\n\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'howManyGames' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER p\\n * 2. INTEGER d\\n * 3. INTEGER m\\n * 4. INTEGER s\\n */\\n\\n public static int howManyGames(int p, int d, int m, int s) {\\n // Return the number of games you can buy\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int p = Integer.parseInt(firstMultipleInput[0]);\\n\\n int d = Integer.parseInt(firstMultipleInput[1]);\\n\\n int m = Integer.parseInt(firstMultipleInput[2]);\\n\\n int s = Integer.parseInt(firstMultipleInput[3]);\\n\\n int answer = Result.howManyGames(p, d, m, s);\\n\\n bufferedWriter.write(String.valueOf(answer));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"DNE\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'howManyGames' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER p\\n * 2. INTEGER d\\n * 3. INTEGER m\\n * 4. INTEGER s\\n */\\n\\n public static int howManyGames(int p, int d, int m, int s) {\\n // Return the number of games you can buy\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int p = Integer.parseInt(firstMultipleInput[0]);\\n\\n int d = Integer.parseInt(firstMultipleInput[1]);\\n\\n int m = Integer.parseInt(firstMultipleInput[2]);\\n\\n int s = Integer.parseInt(firstMultipleInput[3]);\\n\\n int answer = Result.howManyGames(p, d, m, s);\\n\\n bufferedWriter.write(String.valueOf(answer));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n static int howManyGames(int p, int d, int m, int s) {\\n // Return the number of games you can buy\\n int price = p;\\n int k = 0;\\n while (s >= price) {\\n s -= price;\\n \\n if (price - d >= m)\\n price -= d;\\n else\\n price = m;\\n k++;\\n }\\n return k;\\n }\\n\\n public static void main(String[] args) {\\n Scanner in = new Scanner(System.in);\\n int p = in.nextInt();\\n int d = in.nextInt();\\n int m = in.nextInt();\\n int s = in.nextInt();\\n int answer = howManyGames(p, d, m, s);\\n System.out.println(answer);\\n in.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'howManyGames' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER p\\n * 2. INTEGER d\\n * 3. INTEGER m\\n * 4. INTEGER s\\n */\\n\\n public static int howManyGames(int p, int d, int m, int s) {\\n // Return the number of games you can buy\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int p = Integer.parseInt(firstMultipleInput[0]);\\n\\n int d = Integer.parseInt(firstMultipleInput[1]);\\n\\n int m = Integer.parseInt(firstMultipleInput[2]);\\n\\n int s = Integer.parseInt(firstMultipleInput[3]);\\n\\n int answer = Result.howManyGames(p, d, m, s);\\n\\n bufferedWriter.write(String.valueOf(answer));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"howManyGames(p, d, m, s)\"\n ],\n \"solution\": \"p,d,m,s=map(int,input().split())\\ni = 0\\ncost = p\\nwhile s-cost>=0:\\n i+=1\\n s-=cost\\n cost = max(cost-d,m)\\nprint(i)\",\n \"template\": \"#\\n# Complete the 'howManyGames' function below.\\n#\\n# The function is expected to return an INTEGER.\\n# The function accepts following parameters:\\n# 1. INTEGER p\\n# 2. INTEGER d\\n# 3. INTEGER m\\n# 4. INTEGER s\\n#\\n\\ndef howManyGames(p, d, m, s):\\n # Return the number of games you can buy\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n first_multiple_input = input().rstrip().split()\\n\\n p = int(first_multiple_input[0])\\n\\n d = int(first_multiple_input[1])\\n\\n m = int(first_multiple_input[2])\\n\\n s = int(first_multiple_input[3])\\n\\n answer = howManyGames(p, d, m, s)\\n\\n fptr.write(str(answer) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"eJyVk89qwzAMxnfogwify5DkP7H2GD2tdKWHLYdduh5SKIxCH2J738lJDwNb2RJsE3B+fNL3KbfV93b1MD7PG33Zfbr34+k8uCdwAgQJOHTp5ejW4PrLqX8d+rfDx3m4fxKI9O66hl8YQ8dKSspkcnqJFUjgi6IJpQYRlWCT4JpABK1OT4pocnVTQZWUi8mbUNdoibFU6IFZZtS6WOsVMxgSBrG7yzk3LAmLLYGyiiloFykitSseQbQ3SB5tXzi3gktlRuwBqbWm5EQWBlew+I/2cmw4czdmkRyU34bs1Kgx+FONujiITYZG2qU+FVzqyLj/TBxn4GnP4DTeXfdfjz9WXEEr"},"max_retries":{"kind":"null"}}},{"rowIdx":193,"cells":{"question_id":{"kind":"number","value":1104,"string":"1,104"},"question_title":{"kind":"string","value":"Chocolate Feast "},"question_content":{"kind":"string","value":"Little Bobby loves chocolate. He frequently goes to his favorite $\\text{5 & 10}$ store, Penny Auntie, to buy them. They are having a promotion at Penny Auntie. If Bobby saves enough wrappers, he can turn them in for a free chocolate. \n\n**Example** \n$n = 15$ \n$c = 3$ \n$m = 2$ \n\nHe has $15$ to spend, bars cost $3$, and he can turn in $2$ wrappers to receive another bar. Initially, he buys $5$ bars and has $5$ wrappers after eating them. He turns in $4$ of them, leaving him with $1$, for $2$ more bars. After eating those two, he has $3$ wrappers, turns in $2$ leaving him with $1$ wrapper and his new bar. Once he eats that one, he has $2$ wrappers and turns them in for another bar. After eating that one, he only has $1$ wrapper, and his feast ends. Overall, he has eaten $5 + 2 + 1 + 1 = 9$ bars. \n\n**Function Description** \n\nComplete the *chocolateFeast* function in the editor below. \n\nchocolateFeast has the following parameter(s): \n\n- *int n:* Bobby's initial amount of money \n- *int c:* the cost of a chocolate bar \n- *int m:* the number of wrappers he can turn in for a free bar \n\n**Returns** \n\n- *int:* the number of chocolates Bobby can eat after taking full advantage of the promotion\n\n**Note**: Little Bobby will always turn in his wrappers if he has enough to get a free chocolate.\n"},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static int chocolateFeast(int n, int c, int m)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner in = new Scanner(System.in);\\n int t = in.nextInt();\\n for(int i = 0; i < t; i++){\\n System.out.println(f(in.nextInt(), in.nextInt(), in.nextInt()));\\n }\\n }\\n \\n private static long f(int n, int a, int b){\\n return n/a + g(n/a, b);\\n }\\n \\n private static long g(int a, int b){\\n if(a < b) return 0;\\n else return a/b + g(a-a/b*b + a/b, b);\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'chocolateFeast' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER n\\n * 2. INTEGER c\\n * 3. INTEGER m\\n */\\n\\n public static int chocolateFeast(int n, int c, int m) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int t = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n for (int tItr = 0; tItr < t; tItr++) {\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int c = Integer.parseInt(firstMultipleInput[1]);\\n\\n int m = Integer.parseInt(firstMultipleInput[2]);\\n\\n int result = Result.chocolateFeast(n, c, m);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n }\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'chocolateFeast' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER n\\n * 2. INTEGER c\\n * 3. INTEGER m\\n */\\n\\n public static int chocolateFeast(int n, int c, int m) {\\n // Write your code here\\n int count = 0;\\n count += Math.floorDiv(n, c);\\n int wrap = count;\\n int t = 0;\\n \\n while (wrap >= m) {\\n t = Math.floorDiv(wrap, m);\\n wrap -= (t * m);\\n count += t;\\n wrap += t; \\n }\\n return count;\\n \\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int t = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, t).forEach(tItr -> {\\n try {\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int c = Integer.parseInt(firstMultipleInput[1]);\\n\\n int m = Integer.parseInt(firstMultipleInput[2]);\\n\\n int result = Result.chocolateFeast(n, c, m);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'chocolateFeast' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER n\\n * 2. INTEGER c\\n * 3. INTEGER m\\n */\\n\\n public static int chocolateFeast(int n, int c, int m) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int t = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, t).forEach(tItr -> {\\n try {\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int c = Integer.parseInt(firstMultipleInput[1]);\\n\\n int m = Integer.parseInt(firstMultipleInput[2]);\\n\\n int result = Result.chocolateFeast(n, c, m);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.util.*;\\nclass Solution\\n{\\n\\tpublic static void main(String args[])\\n\\t{\\n\\t\\tScanner in=new Scanner(System.in);\\n\\t\\tint t,n,c,m,ans=0,w=0;\\n\\t\\tt=in.nextInt();\\n\\t\\twhile(t-->0)\\n\\t\\t{\\n\\t\\t\\tn=in.nextInt();\\n\\t\\t\\tc=in.nextInt();\\n\\t\\t\\tm=in.nextInt();\\n\\t\\t\\tans=(int)(n/c);\\n\\t\\t\\tw=ans;\\n\\t\\t\\twhile(w>=m)\\n\\t\\t\\t{\\n\\t\\t\\t\\tans+=(int)(w/m);\\n\\t\\t\\t\\tw=(int)(w/m)+w%m;\\n\\t\\t\\t}\\n\\t\\t\\tSystem.out.println(ans);\\n\\t\\t}\\n\\t}\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'chocolateFeast' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts following parameters:\\n * 1. INTEGER n\\n * 2. INTEGER c\\n * 3. INTEGER m\\n */\\n\\n public static int chocolateFeast(int n, int c, int m) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int t = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, t).forEach(tItr -> {\\n try {\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int n = Integer.parseInt(firstMultipleInput[0]);\\n\\n int c = Integer.parseInt(firstMultipleInput[1]);\\n\\n int m = Integer.parseInt(firstMultipleInput[2]);\\n\\n int result = Result.chocolateFeast(n, c, m);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"chocolateFeast(n, c, m)\"\n ],\n \"solution\": \"#!/usr/bin/env python\\n\\nimport sys\\n\\n\\nif __name__ == '__main__':\\n T = int(sys.stdin.readline())\\n \\n for _ in range(T):\\n N, C, M = list(int(x) for x in sys.stdin.readline().split())\\n \\n total = N // C\\n wrappers = total\\n \\n while wrappers >= M:\\n wrappers += 1 - M\\n total += 1\\n \\n print(total)\",\n \"template\": \"#\\n# Complete the 'chocolateFeast' function below.\\n#\\n# The function is expected to return an INTEGER.\\n# The function accepts following parameters:\\n# 1. INTEGER n\\n# 2. INTEGER c\\n# 3. INTEGER m\\n#\\n\\ndef chocolateFeast(n, c, m):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n t = int(input().strip())\\n\\n for t_itr in range(t):\\n first_multiple_input = input().rstrip().split()\\n\\n n = int(first_multiple_input[0])\\n\\n c = int(first_multiple_input[1])\\n\\n m = int(first_multiple_input[2])\\n\\n result = chocolateFeast(n, c, m)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":194,"cells":{"question_id":{"kind":"number","value":3305,"string":"3,305"},"question_title":{"kind":"string","value":"Cavity Map"},"question_content":{"kind":"string","value":"You are given a square map as a matrix of integer strings. Each cell of the map has a value denoting its depth. We will call a cell of the map a _cavity_ if and only if this cell is not on the border of the map and each cell adjacent to it has _strictly smaller depth_. Two cells are adjacent if they have a common side, or *edge*.\n\nFind all the cavities on the map and replace their depths with the uppercase character **X**. \n\n**Example** \n$grid = ['989','191','111']$\n\nThe grid is rearranged for clarity:\n\n 989\n 191\n 111\n\nReturn:\n\n 989\n 1X1\n 111\n \nThe center cell was deeper than those on its edges: _[8,1,1,1]_. The deep cells in the top two corners do not share an edge with the center cell, and none of the border cells is eligible. \n\n**Function Description** \n\nComplete the *cavityMap* function in the editor below. \n\ncavityMap has the following parameter(s): \n\n- *string grid[n]:* each string represents a row of the grid \n\n**Returns** \n\n- *string{n}:* the modified grid"},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static List<String> cavityMap(List<String> grid)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"\\nimport java.io.*;\\nimport java.math.*;\\nimport java.util.*;\\n\\nimport static java.util.Arrays.*;\\n\\npublic class Solution {\\n\\tprivate static final int mod = (int)1e9+7;\\n\\n\\tfinal Random random = new Random(0);\\n\\tfinal IOFast io = new IOFast();\\n\\n\\t/// MAIN CODE\\n\\tpublic void run() throws IOException {\\n//\\t\\tint TEST_CASE = Integer.parseInt(new String(io.nextLine()).trim());\\n\\t\\tint TEST_CASE = 1;\\n\\t\\twhile(TEST_CASE-- != 0) {\\n\\t\\t\\tint n = io.nextInt();\\n\\t\\t\\tchar[][] cs = new char[n][];\\n\\t\\t\\tfor(int i = 0; i < n; i++) {\\n\\t\\t\\t\\tcs[i] = io.next();\\n\\t\\t\\t}\\n\\t\\t\\tboolean[][] ok = new boolean[n][n];\\n\\t\\t\\tint[] dx = new int[]{ 1, 0, -1, 0, };\\n\\t\\t\\tint[] dy = new int[]{ 0, 1, 0, -1, 0, };\\n\\t\\t\\tfor(int i = 1; i + 1 < n; i++) {\\n\\t\\t\\t\\tfor(int j = 1; j + 1 < n; j++) {\\n\\t\\t\\t\\t\\tchar max = (char)('0' - 1);\\n\\t\\t\\t\\t\\tfor(int k = 0; k < 4; k++) {\\n\\t\\t\\t\\t\\t\\tmax = (char)Math.max(max, cs[i+dy[k]][j+dx[k]]);\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\tif(max < cs[i][j]) {\\n\\t\\t\\t\\t\\t\\tok[i][j] = true;\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\tfor(int i = 0; i < n; i++) {\\n\\t\\t\\t\\tfor(int j = 0; j < n; j++) {\\n\\t\\t\\t\\t\\tio.out.print(ok[i][j] ? 'X' : cs[i][j]);\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tio.out.println();\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\t\\n\\n\\t/// TEMPLATE\\n\\tstatic int gcd(int n, int r) { return r == 0 ? n : gcd(r, n%r); }\\n\\tstatic long gcd(long n, long r) { return r == 0 ? n : gcd(r, n%r); }\\n\\t\\n\\tstatic <T> void swap(T[] x, int i, int j) {\\n\\t\\tT t = x[i];\\n\\t\\tx[i] = x[j];\\n\\t\\tx[j] = t;\\n\\t}\\n\\t\\n\\tstatic void swap(int[] x, int i, int j) {\\n\\t\\tint t = x[i];\\n\\t\\tx[i] = x[j];\\n\\t\\tx[j] = t;\\n\\t}\\n\\t\\n\\n\\tstatic void radixSort(int[] xs) {\\n\\t\\tint[] cnt = new int[(1<<16)+1];\\n\\t\\tint[] ys = new int[xs.length];\\n\\t\\t\\n\\t\\tfor(int j = 0; j <= 16; j += 16) {\\n\\t\\t\\tArrays.fill(cnt, 0);\\n\\t\\t\\tfor(int x : xs) { cnt[(x>>j&0xFFFF)+1]++; }\\n\\t\\t\\tfor(int i = 1; i < cnt.length; i++) { cnt[i] += cnt[i-1]; }\\n\\t\\t\\tfor(int x : xs) { ys[cnt[x>>j&0xFFFF]++] = x; }\\n\\t\\t\\t{ final int[] t = xs; xs = ys; ys = t; }\\n\\t\\t}\\n\\t}\\n\\t\\n\\tstatic void radixSort(long[] xs) {\\n\\t\\tint[] cnt = new int[(1<<16)+1];\\n\\t\\tlong[] ys = new long[xs.length];\\n\\t\\t\\n\\t\\tfor(int j = 0; j <= 48; j += 16) {\\n\\t\\t\\tArrays.fill(cnt, 0);\\n\\t\\t\\tfor(long x : xs) { cnt[(int)(x>>j&0xFFFF)+1]++; }\\n\\t\\t\\tfor(int i = 1; i < cnt.length; i++) { cnt[i] += cnt[i-1]; }\\n\\t\\t\\tfor(long x : xs) { ys[cnt[(int)(x>>j&0xFFFF)]++] = x; }\\n\\t\\t\\t{ final long[] t = xs; xs = ys; ys = t; }\\n\\t\\t}\\n\\t}\\n\\t\\n\\n\\tstatic void arrayIntSort(int[][] x, int... keys) {\\n\\t\\tArrays.sort(x, new ArrayIntsComparator(keys));\\n\\t}\\n\\t\\n\\tstatic class ArrayIntsComparator implements Comparator<int[]> {\\n\\t\\tfinal int[] KEY;\\n\\t\\t\\n\\t\\tpublic ArrayIntsComparator(int... key) {\\n\\t\\t\\tKEY = key;\\n\\t\\t}\\n\\t\\t\\n\\t\\t@Override\\n\\t\\tpublic int compare(int[] o1, int[] o2) {\\n\\t\\t\\tfor(int k : KEY) if(o1[k] != o2[k]) return o1[k] - o2[k];\\n\\t\\t\\treturn 0;\\n\\t\\t}\\n\\t}\\n\\t\\n\\tstatic class ArrayIntComparator implements Comparator<int[]> {\\n\\t\\tfinal int KEY;\\n\\t\\t\\n\\t\\tpublic ArrayIntComparator(int key) {\\n\\t\\t\\tKEY = key;\\n\\t\\t}\\n\\t\\t\\n\\t\\t@Override\\n\\t\\tpublic int compare(int[] o1, int[] o2) {\\n\\t\\t\\treturn o1[KEY] - o2[KEY];\\n\\t\\t}\\n\\t}\\n\\t\\n\\t\\n\\tvoid main() throws IOException {\\n\\t\\t//\\t\\tIOFast.setFileIO(\\\"rle-size.in\\\", \\\"rle-size.out\\\");\\n\\t\\ttry {\\n\\t\\t\\trun();\\n\\t\\t}\\n\\t\\tcatch (EndOfFileRuntimeException e) { }\\n\\t\\tio.out.flush();\\n\\t}\\n\\n\\tpublic static void main(String[] args) throws IOException {\\n\\t\\tnew Solution().main();\\n\\t}\\n\\t\\n\\tstatic class EndOfFileRuntimeException extends RuntimeException {\\n\\t\\tprivate static final long serialVersionUID = -8565341110209207657L; }\\n\\n\\tstatic\\n\\tpublic class IOFast {\\n\\t\\tprivate BufferedReader in = new BufferedReader(new InputStreamReader(System.in));\\n\\t\\tprivate PrintWriter out = new PrintWriter(System.out);\\n\\n\\t\\tvoid setFileIO(String ins, String outs) throws IOException {\\n\\t\\t\\tout.flush();\\n\\t\\t\\tout.close();\\n\\t\\t\\tin.close();\\n\\t\\t\\tin = new BufferedReader(new FileReader(ins));\\n\\t\\t\\tout = new PrintWriter(new FileWriter(outs));\\n\\t\\t\\tSystem.err.println(\\\"reading from \\\" + ins);\\n\\t\\t}\\n\\n\\t\\t//\\t\\tprivate static final int BUFFER_SIZE = 50 * 200000;\\n\\t\\tprivate static int pos, readLen;\\n\\t\\tprivate static final char[] buffer = new char[1024 * 8];\\n\\t\\tprivate static char[] str = new char[500*8*2];\\n\\t\\tprivate static boolean[] isDigit = new boolean[256];\\n\\t\\tprivate static boolean[] isSpace = new boolean[256];\\n\\t\\tprivate static boolean[] isLineSep = new boolean[256];\\n\\n\\t\\tstatic {\\n\\t\\t\\tfor(int i = 0; i < 10; i++) { isDigit['0' + i] = true; }\\n\\t\\t\\tisDigit['-'] = true;\\n\\t\\t\\tisSpace[' '] = isSpace['\\\\r'] = isSpace['\\\\n'] = isSpace['\\\\t'] = true;\\n\\t\\t\\tisLineSep['\\\\r'] = isLineSep['\\\\n'] = true;\\n\\t\\t}\\n\\n\\t\\tpublic int read() throws IOException {\\n\\t\\t\\tif(pos >= readLen) {\\n\\t\\t\\t\\tpos = 0;\\n\\t\\t\\t\\treadLen = in.read(buffer);\\n\\t\\t\\t\\tif(readLen <= 0) { throw new EndOfFileRuntimeException(); }\\n\\t\\t\\t}\\n\\t\\t\\treturn buffer[pos++];\\n\\t\\t}\\n\\n\\t\\tpublic int nextInt() throws IOException {\\n\\t\\t\\tint len = 0;\\n\\t\\t\\tstr[len++] = nextChar();\\n\\t\\t\\tlen = reads(len, isSpace);\\n\\t\\t\\t\\n\\t\\t\\tint i = 0;\\n\\t\\t\\tint ret = 0;\\n\\t\\t\\tif(str[0] == '-') { i = 1; }\\n\\t\\t\\tfor(; i < len; i++) ret = ret * 10 + str[i] - '0';\\n\\t\\t\\tif(str[0] == '-') { ret = -ret; }\\n\\t\\t\\treturn ret;\\n//\\t\\t\\treturn Integer.parseInt(nextString());\\n\\t\\t}\\n\\n\\t\\tpublic long nextLong() throws IOException {\\n\\t\\t\\tint len = 0;\\n\\t\\t\\tstr[len++] = nextChar();\\n\\t\\t\\tlen = reads(len, isSpace);\\n\\t\\t\\t\\n\\t\\t\\tint i = 0;\\n\\t\\t\\tlong ret = 0;\\n\\t\\t\\tif(str[0] == '-') { i = 1; }\\n\\t\\t\\tfor(; i < len; i++) ret = ret * 10 + str[i] - '0';\\n\\t\\t\\tif(str[0] == '-') { ret = -ret; }\\n\\t\\t\\treturn ret;\\n//\\t\\t\\treturn Long.parseLong(nextString());\\n\\t\\t}\\n\\n\\t\\tpublic char nextChar() throws IOException {\\n\\t\\t\\twhile(true) {\\n\\t\\t\\t\\tfinal int c = read();\\n\\t\\t\\t\\tif(!isSpace[c]) { return (char)c; }\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\t\\n\\t\\tint reads(int len, boolean[] accept) throws IOException {\\n\\t\\t\\ttry {\\n\\t\\t\\t\\twhile(true) {\\n\\t\\t\\t\\t\\tfinal int c = read();\\n\\t\\t\\t\\t\\tif(accept[c]) { break; }\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tif(str.length == len) {\\n\\t\\t\\t\\t\\t\\tchar[] rep = new char[str.length * 3 / 2];\\n\\t\\t\\t\\t\\t\\tSystem.arraycopy(str, 0, rep, 0, str.length);\\n\\t\\t\\t\\t\\t\\tstr = rep;\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tstr[len++] = (char)c;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\tcatch(EndOfFileRuntimeException e) { ; }\\n\\t\\t\\t\\n\\t\\t\\treturn len;\\n\\t\\t}\\n\\t\\t\\n\\t\\tint reads(char[] cs, int len, boolean[] accept) throws IOException {\\n\\t\\t\\ttry {\\n\\t\\t\\t\\twhile(true) {\\n\\t\\t\\t\\t\\tfinal int c = read();\\n\\t\\t\\t\\t\\tif(accept[c]) { break; }\\n\\t\\t\\t\\t\\tcs[len++] = (char)c;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\tcatch(EndOfFileRuntimeException e) { ; }\\n\\t\\t\\t\\n\\t\\t\\treturn len;\\n\\t\\t}\\n\\n\\t\\tpublic char[] nextLine() throws IOException {\\n\\t\\t\\tint len = 0;\\n\\t\\t\\tstr[len++] = nextChar();\\n//\\t\\t\\tstr[len++] = (char)read();\\n\\t\\t\\tlen = reads(len, isLineSep);\\n\\t\\t\\t\\n\\t\\t\\ttry {\\n\\t\\t\\t\\tif(str[len-1] == '\\\\r') { len--; read(); }\\n\\t\\t\\t}\\n\\t\\t\\tcatch(EndOfFileRuntimeException e) { ; }\\n\\t\\t\\t\\n\\t\\t\\treturn Arrays.copyOf(str, len);\\n\\t\\t}\\n\\n\\t\\tpublic String nextString() throws IOException {\\n\\t\\t\\treturn new String(next());\\n\\t\\t}\\n\\n\\t\\tpublic char[] next() throws IOException {\\n\\t\\t\\tint len = 0;\\n\\t\\t\\tstr[len++] = nextChar();\\n\\t\\t\\tlen = reads(len, isSpace);\\n\\t\\t\\treturn Arrays.copyOf(str, len);\\n\\t\\t}\\n\\n\\t\\tpublic int next(char[] cs) throws IOException {\\n\\t\\t\\tint len = 0;\\n\\t\\t\\tcs[len++] = nextChar();\\n\\t\\t\\tlen = reads(cs, len, isSpace);\\n\\t\\t\\treturn len;\\n\\t\\t}\\n\\n\\t\\tpublic double nextDouble() throws IOException {\\n\\t\\t\\treturn Double.parseDouble(nextString());\\n\\t\\t}\\n\\n\\t\\tpublic long[] nextLongArray(final int n) throws IOException {\\n\\t\\t\\tfinal long[] res = new long[n];\\n\\t\\t\\tfor(int i = 0; i < n; i++) {\\n\\t\\t\\t\\tres[i] = nextLong();\\n\\t\\t\\t}\\n\\t\\t\\treturn res;\\n\\t\\t}\\n\\n\\t\\tpublic int[] nextIntArray(final int n) throws IOException {\\n\\t\\t\\tfinal int[] res = new int[n];\\n\\t\\t\\tfor(int i = 0; i < n; i++) {\\n\\t\\t\\t\\tres[i] = nextInt();\\n\\t\\t\\t}\\n\\t\\t\\treturn res;\\n\\t\\t}\\n\\n\\t\\tpublic int[][] nextIntArray2D(final int n, final int k) throws IOException {\\n\\t\\t\\tfinal int[][] res = new int[n][];\\n\\t\\t\\tfor(int i = 0; i < n; i++) {\\n\\t\\t\\t\\tres[i] = nextIntArray(k);\\n\\t\\t\\t}\\n\\t\\t\\treturn res;\\n\\t\\t}\\n\\n\\t\\tpublic int[][] nextIntArray2DWithIndex(final int n, final int k) throws IOException {\\n\\t\\t\\tfinal int[][] res = new int[n][k+1];\\n\\t\\t\\tfor(int i = 0; i < n; i++) {\\n\\t\\t\\t\\tfor(int j = 0; j < k; j++) {\\n\\t\\t\\t\\t\\tres[i][j] = nextInt();\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tres[i][k] = i;\\n\\t\\t\\t}\\n\\t\\t\\treturn res;\\n\\t\\t}\\n\\n\\t\\tpublic double[] nextDoubleArray(final int n) throws IOException {\\n\\t\\t\\tfinal double[] res = new double[n];\\n\\t\\t\\tfor(int i = 0; i < n; i++) {\\n\\t\\t\\t\\tres[i] = nextDouble();\\n\\t\\t\\t}\\n\\t\\t\\treturn res;\\n\\t\\t}\\n\\n\\t}\\n\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'cavityMap' function below.\\n *\\n * The function is expected to return a STRING_ARRAY.\\n * The function accepts STRING_ARRAY grid as parameter.\\n */\\n\\n public static List<String> cavityMap(List<String> grid) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<String> grid = new ArrayList<>();\\n\\n for (int i = 0; i < n; i++) {\\n String gridItem = bufferedReader.readLine();\\n grid.add(gridItem);\\n }\\n\\n List<String> result = Result.cavityMap(grid);\\n\\n for (int i = 0; i < result.size(); i++) {\\n bufferedWriter.write(result.get(i));\\n\\n if (i != result.size() - 1) {\\n bufferedWriter.write(\\\"\\\\n\\\");\\n }\\n }\\n\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'cavityMap' function below.\\n *\\n * The function is expected to return a STRING_ARRAY.\\n * The function accepts STRING_ARRAY grid as parameter.\\n */\\n\\n public static List<String> cavityMap(List<String> grid) {\\n // Write your code here\\n\\n // construct a char array\\n\\n int cols = grid.get(0).length();\\n int rows = grid.size();\\n\\n char[][] newgrid = new char[rows][cols];\\n\\n int idx = 0;\\n\\n for (String str : grid) {\\n newgrid[idx] = str.toCharArray();\\n idx++;\\n }\\n\\n for(int i=0;i<rows;i++){\\n for(int j=0;j<cols;j++){\\n\\n if(i==0 || i==rows-1){\\n continue;\\n }\\n\\n if(j==0 || j==cols-1){\\n continue;\\n }\\n\\n try {\\n int top = Character.getNumericValue(newgrid[i-1][j]);\\n int bot = Character.getNumericValue(newgrid[i+1][j]);\\n int left = Character.getNumericValue(newgrid[i][j-1]);\\n int right = Character.getNumericValue(newgrid[i][j+1]);\\n int current = Character.getNumericValue(newgrid[i][j]);\\n \\n if(current > top && current > bot && current > left && current > right){\\n newgrid[i][j] = (char)'X';\\n }\\n \\n } catch (Exception e) {\\n // DO NOTHING\\n }\\n \\n\\n }\\n }\\n\\n // convert back to list<String>\\n List<String> result = new ArrayList<String>();\\n\\n for(int i=0;i<rows;i++){\\n StringBuffer sb = new StringBuffer();\\n sb.append(newgrid[i]);\\n result.add(sb.toString());\\n }\\n\\n return result;\\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<String> grid = IntStream.range(0, n).mapToObj(i -> {\\n try {\\n return bufferedReader.readLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n })\\n .collect(toList());\\n\\n List<String> result = Result.cavityMap(grid);\\n\\n bufferedWriter.write(\\n result.stream()\\n .collect(joining(\\\"\\\\n\\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'cavityMap' function below.\\n *\\n * The function is expected to return a STRING_ARRAY.\\n * The function accepts STRING_ARRAY grid as parameter.\\n */\\n\\n public static List<String> cavityMap(List<String> grid) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<String> grid = IntStream.range(0, n).mapToObj(i -> {\\n try {\\n return bufferedReader.readLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n })\\n .collect(toList());\\n\\n List<String> result = Result.cavityMap(grid);\\n\\n bufferedWriter.write(\\n result.stream()\\n .collect(joining(\\\"\\\\n\\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.math.*;\\n\\n\\npublic class Solution {\\n\\tstatic BufferedReader in;\\n\\tstatic PrintWriter out;\\n\\tstatic StringTokenizer tok;\\n\\t\\n\\t\\n\\tstatic void solve() throws Exception {\\n\\t\\tint n = nextInt();\\n\\t\\tchar[][] a = new char[n][n];\\n\\t\\tfor (int i = 0; i < n; ++i) {\\n\\t\\t\\ta[i] = next().toCharArray();\\n\\t\\t}\\n\\t\\tchar[][] res = a;\\n\\t\\tfor (int i = 1; i < n - 1; ++i) {\\n\\t\\t\\tfor (int j = 1; j < n - 1; ++j) {\\n\\t\\t\\t\\tif (a[i - 1][j] < a[i][j] && a[i + 1][j] < a[i][j] && a[i][j - 1] < a[i][j] && a[i][j + 1] < a[i][j]) {\\n\\t\\t\\t\\t\\tres[i][j] = 'X';\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\tfor (int i = 0; i < n; ++i) {\\n\\t\\t\\tfor (int j = 0; j < n; ++j) {\\n\\t\\t\\t\\tout.print(res[i][j]);\\n\\t\\t\\t}\\n\\t\\t\\tout.println();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tstatic int sqr(int x) {\\n\\t\\treturn x*x;\\n\\t}\\n\\t\\n\\tstatic int nextInt() throws IOException {\\n\\t\\treturn Integer.parseInt(next());\\n\\t}\\n\\n\\tstatic long nextLong() throws IOException {\\n\\t\\treturn Long.parseLong(next());\\n\\t}\\n\\n\\tstatic double nextDouble() throws IOException {\\n\\t\\treturn Double.parseDouble(next());\\n\\t}\\n\\n\\tstatic BigInteger nextBigInteger() throws IOException {\\n\\t\\treturn new BigInteger(next());\\n\\t}\\n\\t\\n\\tstatic String next() throws IOException {\\n\\t\\twhile (tok == null || !tok.hasMoreTokens()) {\\n\\t\\t\\ttok = new StringTokenizer(in.readLine());\\n\\t\\t}\\n\\t\\treturn tok.nextToken();\\n\\t}\\n\\t\\n\\tstatic String nextLine() throws IOException {\\n\\t\\ttok = new StringTokenizer(\\\"\\\");\\n\\t\\treturn in.readLine();\\n\\t}\\n\\n\\tstatic boolean hasNext() throws IOException {\\n\\t\\twhile (tok == null || !tok.hasMoreTokens()) {\\n\\t\\t\\tString s = in.readLine();\\n\\t\\t\\tif (s == null) {\\n\\t\\t\\t\\treturn false;\\n\\t\\t\\t}\\n\\t\\t\\ttok = new StringTokenizer(s);\\n\\t\\t}\\n\\t\\treturn true;\\n\\t}\\n\\n\\tpublic static void main(String args[]) {\\n\\t\\ttry {\\n\\t\\t\\tin = new BufferedReader(new InputStreamReader(System.in));\\n\\t\\t\\tout = new PrintWriter(new OutputStreamWriter(System.out));\\n\\t\\t\\t//in = new BufferedReader(new FileReader(\\\"input.in\\\"));\\n\\t\\t\\t//out = new PrintWriter(new FileWriter(\\\"output.out\\\"));\\n\\t\\t\\tsolve();\\n\\t\\t\\tin.close();\\n\\t\\t\\tout.close();\\n\\t\\t} catch (Throwable e) {\\n\\t\\t\\te.printStackTrace();\\n\\t\\t\\tjava.lang.System.exit(1);\\n\\t\\t}\\n\\t}\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'cavityMap' function below.\\n *\\n * The function is expected to return a STRING_ARRAY.\\n * The function accepts STRING_ARRAY grid as parameter.\\n */\\n\\n public static List<String> cavityMap(List<String> grid) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<String> grid = IntStream.range(0, n).mapToObj(i -> {\\n try {\\n return bufferedReader.readLine();\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n })\\n .collect(toList());\\n\\n List<String> result = Result.cavityMap(grid);\\n\\n bufferedWriter.write(\\n result.stream()\\n .collect(joining(\\\"\\\\n\\\"))\\n + \\\"\\\\n\\\"\\n );\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"cavityMap(grid)\"\n ],\n \"solution\": \"n = int(input())\\n\\nmaplist = []\\nfor i in range(n):\\n maplist.append(list(input()))\\n\\nfor i in range(1, n-1):\\n for j in range(1, n-1):\\n c = maplist[i][j]\\n t = maplist[i-1][j]\\n l = maplist[i][j-1]\\n r = maplist[i][j+1]\\n b = maplist[i+1][j]\\n if c > t and c > l and c > r and c > b:\\n maplist[i][j] = 'X'\\n\\nfor i in range(n):\\n print(''.join(maplist[i]))\",\n \"template\": \"#\\n# Complete the 'cavityMap' function below.\\n#\\n# The function is expected to return a STRING_ARRAY.\\n# The function accepts STRING_ARRAY grid as parameter.\\n#\\n\\ndef cavityMap(grid):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n n = int(input().strip())\\n\\n grid = []\\n\\n for _ in range(n):\\n grid_item = input()\\n grid.append(grid_item)\\n\\n result = cavityMap(grid)\\n\\n fptr.write('\\\\n'.join(result))\\n fptr.write('\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"eJztnUuuHUmWXSVAA0lkuyCk/c00kg2IghpSNtShqlEFFCAI0CA0Io1Cw9Fex93v9fs+ZISKrIqINDIzyPfedfPzP2bbnWf/r3+n//N//+2/+Y//48//7evf/+M//Pk//OnP+S9fvo5cVipjtuHfefbaUv3ydY7Ry5o55VLWSin5J1++plHzyMs/XKnV2upsyyukXnsvbaSavNZa/ptX6HX0OVoaqfSVm9cp/myLi6qXHj3Xnv3ry9daS2k9rd57a6P3nKrX9X9zn5Zn5DFrTWmN5nVXrtO38/V81qsWf3dY1NJH7aWU3EvtrftudRWLvFaevpP1yqtbt2Vdh+84ZyutxUKWofdU27RWaeVhtWfP05/tq3rt4u9YrFFsDuT1Ov52td7DgtsSLSTzp1JuuU3M11KbvlsdFnGWlmbOtptvWr2CRede2Rr4T1vet/fdLKEtZK1LW/5PT8XfzV59+oJlT+RWC5L++e/+9Oe//tPf//W//MNf/+t//u//+A+nQ7/hTN2dqdOZyvIPdThThzP1sTPVdXemTmeqqXppvXOmwpl6OlO563CmcKbCmcpe+HSmLOSLM/XqTD2cqXCmfokzraHV1ulMhTMVztSLM/VdZ+pwpvy7eVk7U++dqRdnKpzpz9t433Dml69//p9/96dbXiLr+QsN41f8bfjX+bfe3//t8dPHFeevT8Llx97knRrLtkk/8ddefa++V9+r79X36nv1vfpefa++V9+r79X36nv1vfpvc/X1U1f/db/26nv1vfpefa++V9+r79X36nv1vfpefa++V9+r79WvX5+8P/Izb/l7Ntdefa++V9+r79X36nv1vfpefa++V9+r79X36nv1P/Lq+qmr/7pfe/W9+l59r75X36vv1ffqe/W9+l59r75X36vv1ffqz9XfzO0ogykuua1ee56rMQmIuR/+Rhp5jrlyTqtlfyyvmnOfM/c+00pj9uwPzbpSmWmU1Bh4w0SXWdJMafiyyf9GLn2U3JkClEars6/+5esavU7f15e2OVKfrS+ua4tLR6491cK8lbZWaRYtFf94rNJ7LrWUukbtzSKlNH1TSzGadVg992EFLE5pxdd0/2kFU8/1y9e+LARTeNKaTLEZo87RMhNNRk7Nssy8vnydLSYAjWLtfcVgUs9Y/p8NknxJKdbJjrJEqbZlLUYZhWk504L4Yv+9F8s5GRmDJjav1azLlrKAGHVYppVmtpZj1cnknjaYH9RsWP/Z+Gir/kuuKFem79mwW0sM41kr95p86zx7whHZ1vGnfbdScJZ9ZlmKzR0TfzLTc+wEC7q6tWy2pFfjMovvG1jAhEbNnqh2ays2kEVpMfYlN7w0q61t/ZiCw/QiO7H3mF5Uls3rS/0nDrBarYya/Fc7D//bFD3bhF6tZG7mpe3VGpNxmMJEbIy4aq7OTctsXm4y6SmVZmPY2NPB2Wa2zRi9s1IMp3E4OGRs5FrxYal8Yekz2vh3SgzuyVw5GRqVVnYoFV/vvwzfa7VEDpAe/gQzmWq29WfKFd8Xh6dj3RLabd1RbtlsAJvAFnfwOGoQi8lVwzFiJRw+drXDsuRJbkTQsy4rkQzl+L9zKTVksxkWoe2oaKVbV6te0K/YQzavlXT0NotsLzpB1ohgwsyOSwZj+Vt1JiZ3OYyWTVKdlk5W5+wKBZl4NE6vVqdzsevrbGkRmyt1mwkjO6NdBWy2TugwSMn57CxioJTd5Px1MC/ba9qD9oxDuFk2i7DIBofqdBYy7agwB4yMtXZ2XCJCGKK0bFQSxK4guO1KQrCPNfl7YTUHJXFVLQ3qUxFatoGtbEOwdmjqomKjeQlb3J91GEdmjOwK4Wscdo0pVQ6VYvtOJ/5ixFcqkdmThZzJlqyRgLa0k3g4Wwfj0LhmHlUsxlt1R09OMWisMm/LCVRcfFxZ7BPnvY1J8HvFkVDBgpEanRxHdgepbUN8d3vIodDRlMFiFigfqk986krnzy9HVydGbfJPXsn7pXVbj7qts26rJ1Wlord1W5haUbdF3RZ1W1fd1rNu61a3xXVNIOKf1m096rZGDBK76rZ+Ud2WhWDg1rNu67Vu623d1lW3ddZtva3beq3balnv6rao2zaVLSXbUEfd1lm39azbcnIx+UvNH6Vui7qNffVx3VbUbVG35Q896rauuq2zbmv6s5bJa7tu23RX3ZbF10vdVrWW7+q2jrqts24zqEzU7RhU9qjbom4r6rbudVv3uq2rbou5bFfdVtQVG0PPuq0P6rYyZlEjDO91mxlxDh+r/GHd1qNuy3VbZ93WJ3VbV93WS93WvW7rTd1W1G19VLf1Qd22Jx51Wx/VbevH4DmbV1fdxovOLQ2CKeq2zrpNPlx1W5/U7dOretRtnXVb97qtqNsidK66ratuO8AY6xd1W/e6zYw76rYYBigCl7otMtbaUbd11m18ftZtgpu1o27rXrdJU9dtMXjwg7rtuxx1W7e6baPEeMGzbutRt0kmx7/FetZtnXWbYX+66raOuq2jbivqtppe67a4ncNGV93GJ9RtEeQvdduWcoWMuq1KddOjbutX1O33w/5uI/R++C+WTj/lN0v/nJXTlnpLvaXeUm+pt9Rb6i31lnpLvaXeUm+pt9Rb6i31lnpLvaXeUm+pt9Rb6i31lnpLvaXeUm+p/+al/lm/vkOg98N//WTf6qf8PqT+KStvqbfUW+ot9ZZ6S72l3lJvqbfUW+ot9ZZ6S72l3lJvqbfUW+ot9ZZ6S72l3lJvqbfUW+ot9Zb6h0r9u3tu/362lb+VW251zjL9nzrXarOm3hmsUXKMYJm1jsmUlMmsisoohLTayLmWGeOTmK4w0sxj+KLFbIieB2OQ0iqlM6qhLCYdMX1m9bKYlTOY55AZLsW8hdYLw218UczbYFBFL6nmPFbu6ctX5u/k2gYjBXJNx4cHA1OYSMGEmpG9slesJaYCVQaj+CcWgUERJTOKKK+RY8aCb50aswtaKaO0nqymFVhWdnW+y6iVtTJTuFLJiRFDNgx2yqMwDacXG6xYlGFDME7IH1wM2EkM9mrNFvN/ZrWiw8aYs8/EZI1WbZ7BMCMvM0vtXoO5TyMX/7xNr8HnmvX58pXJCQyTSasn5oDMYVckZugkZoUxtGS2EuMzEmMmamFuSmX+kFfKadg7TDTxtxKTPPzBmPhhjWxhxiHZJsyxasleTYyE8AoZOzIWpvk+pdrmTPjqLFYZVcJgFas/Wj2mPIRXOi5dNdsTzbHQJsY6xGIwh83jS2o/hvr4VoXZSUxhcghN38rGW4XhTQy8sb5MeyoxCgmnWXsislgTi++/TFzoi1N8mR2jndle2RGcGQTWbO6ULPhiHsjMlsf/qzGaKmUidTISx94uXG4XJuZkMA3HIb3sWXtkrMRgE2vFpCZbnoEcY5L8mdDLBX8w5iJbQkZ8LG5ph9tpTGBiLE+3kP6xLcQIJN/Ut3CAMmGqM95lRUp4ReLXa/hmDq8IKxuUjLNb85qJwWy40NImxMbwOdxhQxYHmWXoA/86UHOYB9dY5850kzbnYE6LE9f+6Ynb2oVM4BhIiOnsHFtyLvK428ikYp7MpvFHyULnhAPSsYqQ+JKJdJVZW8zPcWjavNkyNu5Eeo2OkW1ix4MdZHfa3sQFmVYYIDSZgGXpHIm2TmPQm0sGUYkXnHg2NJWCaVf2ozO1xGipyogsioOFK0x6cRQyRMhFjJlOVtZh3RlGtBhu5AR0WJGMlbFsKebh2XzFKegIpVQw8iwxXqd2hrLY3w6tGSNsOt7JFKXKjVz6IjTtft+cyWODeMTHFEdGzVG+GLZjLzlkbWFyZDF2xx902NsXjYFmTjp/0p9lgE9m9NFg4I6T8BiLg+AWi8rUycZJOno1Ziw15y0zfRiIaM9P6iuFtPfuvAunrx4JYr0Ri+SKKpMYZEU4O+QcymQh48yYfzVQMcbZ5OlCnRgoxUi7CNTFGKVJASyMhbKcn7wJ9st7ip49RWdP0cc9RWdP0dVTmEwTk3HUi6KnMDkn69ZTdPUU3XuKrp6i6ClM7aGn6NZTVBiPlPXsKbr1FF09xX/Rrafo2VNET9HRU3TvKXrXU5QZnrWabFq96Sl66SliBtCtp+htTxE9RWdP0dlTFD1FV0/R0VNETxFzxzD9s6fYGreeomdP8QL6bk9hVNO9p7BZeukpIortDaaP9YGr7Xd/iJ6iD3qKqj+p6Cm6eoqePYWJTDFwKIYiMTptOD4mc4saqjx7ii8KQ7dY8KWnWMh7T9Gzp+ilp+i1p9gbYvyV6CmKnhLTzI6eoqunMJxLTWdP0TEl7bWn6JOeorOn6JiqxHSotz3Fq3vJs6dgjaOnHIFL/OroKZij4T8Lougpeu0piC1GvoU7qr7bUxQ9hZlpTtzOmC0y0S7US09R9BSdPSUEetdTdPYUHT1F9BQRivQUnT2F6XNnT2Ec1dFTHG92aiU66SmKniKmc1k6xTC4FgPFnA2OSrzA9LlHT9Gjp+jsKRQHO/VNT1HEtu1jAzHmy6Xj6Ck6eorOnoL5bj1F3+wpOnuKGFvmLwlNhD16is6eoteeIrwUY/Nee4orFgGZj9PUxz1Fv6in6N5TLJDLm6KnKHqKPuwp+qSn6Ogp+hU95f1MsP6z3sBm1/4zfm95t7xb3i3vlnfLu+Xd8m55t7xb3i3vlnfLu+Xd8m55t7xb3i3vlnfLu+Xd8m55t7xb3i3v70XezwZA/SwlftY7vD9l4S3vlnfLu+Xd8m55t7xb3i3vlnfLu+Xd8m55t7xb3i3vlnfLu+Xd8m55t7xb3i3vlnfL+zct7095cPru39Hm5lt99mv/6P/rR+u3IcYf50efvFrwm5DtD/Qj/TbE+CP96E21HfVHTnL7kQPnfuDbRluuLdeWa8u15dpybbm2XFuuLdeWa8u15dpybbm2XFuuLdeWa8v1u5DrR/367F9H/rBfP9qIP+gB6SHXb+5p65Zry7Xl2nJtubZcW64t15Zry7Xl2nJtubZcW64t15Zry7Xl+iPI9VsEg9+/F4yk/FrxDz/4JwS8Vsydj/eL1/mi8fWd6zPnVZ/O3rvW5NXu+FclYZhjTZ1rXt+5PvO46o2QkNR9+qrzr385el+xr9hX7Cv+Va5Yn3z/96bHvmJfsa/4o1zxa/+d4w+78b5iX7Gv2Ff83Cv0yfd/b3rsK/YV+4o/zhVvcK7y5etMM0C2+Nsnu7LjM0rnp9/OVRlfvrbW6qy95tTbzG32tlYZo6ZUWb2kWWZJY4w++5x15dJLass/nP5Ja2mN2mYqNbfmFcrIdQDmlb5WnqPUWvvK/ub0oj2taclrLznnNKdv2+bIM4/SSm6l9y9fyyglL3+ilFlbailXvu7+nKXNvSx/dIzk76U06millp5ZtqXhb+RSe+Zu2Xqs0lZugIKWdq00u+/W8pqr+Tar+D5I67WwQk6WpgwrmXsfVq9nCzR777VYuV59a9DNOq1oq4mxNF7N4s/UahvL37aAs/VcMiClb9OqRWxervlaX7K6L54sW3wn9E7+sWXhM7WV0iM+Rrc/sj8bP+3Zhsq2Rp7409/xp1P13azOSNO2xMrL/ux2xbBulriObq+OVkspLcyXvYId3u1RLzb8X4u4ulW3Eaqds1Iea7Rkt9u8yNUtvp3tD9r7uHAhkX9mgVPNfVkgR4TDxZ6vdnfhbr23YWOsjoW6/b3GaAuTOlwcDD0NR4ctFFZb/sryIqmNne3bXGxbO8xerMlL2nMlO3xmt/3m4J6+ynLa1KhuD2XHrD+Qy8LZdmfy0rXb7NUhNPyzYaW5Z1l83Wq2wRKiNyw/0qdnm2/niMgRHTmiW47omSPy+rpyREX3HNGRIyJHFDmij3JELzki54giR2RHKnJEvevKEZ05onuOKHJELzmiMkSO6JkjsiyOajl4fRs9ckTOEb3LEb3miKoiR/jzniMWX5EjfNt3veWI3uWIJXjkiBd65Ij8GT1zxMrJyooc0Wc5InJEIylbJmumlxzRBzmitzmie45o+ud2VOSIXnNEV47ozBHZ69/KEf/8yBFdOSK7Y4oc8dpDV46IHNGZI/o4R3TmiCIKX3ME1T/IEcvpVe85ou/myPtHLr+mn+2P7o/uj/6zP/rho4HfqKz7o/ujv/uP/nNR7391BfZH90d//x/9EJ39jcq6P7o/+gf46JvTHpBHLyP35sO/z7jdR+Y1fFzOAxincYReqa8xfdodaQVc0XyW7f6ej8a9caKtzUfhVrxKL/65P7Ba8yHTB1T/fPThE37NnIjj7Lx8NA+sMTVgge4vZkvTy8X9/MHsQzjgor8FzGWBvEBbpfOO4Jh5BvRVOc7zc8s9iiXm3Ov1siUB/CggeJ+0+o+UDlTDh3MdSvvwHkrrprRelBZK66G0XpTWQ2mhtAADXpVWQEk6lZb/ByDh/55KC6V1Kq2sQ2mdSutQWh8p/R4b9keKL65Ab4BJVqxxD3CiautbUP/yz2cF8QEFmCuvbPPMAbDoeCg+qXjp1H2X4b+PBHaWUgZQIY4AGVOaBEOdZdpepWMKX98s38w20rCkjgOvM3JK3R9L3WrNYrOUyrRaezUDAvrnDqPcc7YZgI9q9xe9eTXrmgu4I3I6qKYlrmnakjVbmz7aAi9pw1Yu3bLXOSsXDT5fc4BmCQhlgZRYOl62zZVVHBer+OMVKDIfQLSjgdWAo2yFOnPCRT1wzzGdLTaf/7BV/f22QPqcNP60fcfyExDLLveXGKU4CLqvApXFzv4N5ONvzm6bL38BFFm7fzAtux3l1FttBd6eHef+0XDItWzHFeBdACuwa6zVHBfOrzVbDTjKPx4TDMn6O287P2mf5MXnQaJnkCiCRBEkegaJpjPGQaIrSBzAYHWdSH4Eic4gUQSJbkEigkTPINEVJPJKBIlK00uQAG4mZ5WvugWJIkgUawG8RZDIQaJnkIh0fQ0SXUGit0ECIuvkvQWJb6EjSGRF7kHir48g0TNIRJDoNUh0BAlWFUGiW5CEUawzAGkGlsTO/n0GiVBRzyCxakjRMLWuINH3ggT7WNxvBsm7OhKlsJdGGLgYOJKdC65ArcQDBBuEhzbNpcMudImkarcG6u20zL63DYr7pktH8jKF0t4t2wBxT2Fd53gF0i3DN/EdsoNw1uyvvVCedlYJUy0HuX+0UNOphDNoNPh3xiMDm97J5KCyO3jq4hBx+s3sj9ocGbwaszgceQzA8wQXrs6DG56PDDKxuM0RA16vt+lQtHXsKOzpa/xJXFMj5xAl0zSdJMV/tRDDd41HaLPGU4FE6fBXybWR5lDpWeNAogn75rbrBZa/m2xi+6NFc7PUadkOGSB6pF5tAFejSb33d5azhV2AQ3nyvMq6WU3f1OlUeN6EOraHo2+QW67ZKNCcQP68E907g+UL3SP8c3csu9SdvvMEzg3Opdn3cyv0R3xT+8pCO0+spt3pFHOZdgwtFuuD1LEh/QkeISyLbFM5+ytPwDJ1O4UjXUHQp4drXa8rRcuCuRETvt3R5+Wd0xmYm8di1sk26bYfdi6dm7p3ESQOPFup1OM5ji3FIw5fMKI4WZBBUbHIFt/2pUORD4uHOC7JjYhbbq8NifOkzbI34NkCToxOSJAS6pZ54Uq+6UrpULVMjvgaj4n8NyJ9WFV/bUF4muJ4KjV2GyQJlmzRHAf39teVp5C2F9FlJ9l3o8WTnsETR8qazWGDObR4bORtmQPKOw1qiW/kKyiYxZsLdm4Wv/PEo046I4IgKSXTxl08tPCqPJJ1OFv0wiaHJ0rAkOhKw6FsWWI71pV7RJQsfOvm4LBoNVoeux0S1DWXLR8eoLR26lpHpYU+XtXbO54322zkvj9iCZzttrWD23lbrS5Babn9QwebW4Wjwf/H1vEYhGdtDuOG/x2nJIa9NXg4xYPJSpq4Y2eaisvRcATG41kekTiCvc+gni/yifzmQZZdhRquz5Qah38vkS0L81EDid3GPsNfLh4L2QWVuHZDt9Wbc86R5aoWOlt1qxjqTBuu8Iwc5+HfzOM+X2DPY14ny4wKWijt3kT6NitqF/pVnOJwYy1b1/mQqdktDOPli3ddFrxMYpH2xUM5y8AzMrdNi+9rXEdIK5LGy9G2bbDOVoQ6S6GoPGVmh+G64+/y7gDbuUyxnogeOx1nkDWzWxxUw+Fj+03yoFGAnAQ857R1qZocLxxK9lhjq3w8cONxmfthqOQ/4x2CwVsAWGygOftqHs2TOamwlbXT42HVRLcoYQ7W2AnyBgN5SBQ6tDpRRA66LtjNg8rAltyxTjemDFpB2nKKNJsU4ELu83SXp7HsJpys+Mb5RNn1VtIX9th65KidTn0bfdGbMtFHjVmENP5hXQdzT7GRdRR51+d65LC22WxT8sALYdDBI2Bej3B40JtdwXglIpMIk37pOI2tIfLQSzng0OJ5lMm2i2fr7mCkc4vHhC7CDmX3iUym+I8eez5/5ayhcflbTvXBGwhexAW7xwbaP/KnKre1O+nQ7sS8+uE+0HgFAqtY3djL2+C+Izln+/ubEQ9kBg3clsCRDr0ZN2THFJ3RddtbLQT3t9iruW6x7SnksD3eeERJzjldfPuGf2nMdju3LoUdJvt4O9W2d88t2IOjEIcqbOR1KX6UZsf25DxqmxJx/kAhMbx0pxbZIQ5+l4ZMgWRbY7m9aiX3ozPZ7A5YL7ritYlG26u8FmBHURJpfLGfcL8jatjaRWEblLaBkXgXhc9Qf3mlxqLQYBaBUXnBxUpN/llfooWRn9jDViGeY+/hvQ9HLEqe/UOPxlKcbRyBhQRavPaAk731dBzZjPYtBdxN3zs7exKvZmqe+zE2yLxO0khAtynePynRLamRZJcbQPRKjmiLmmJVHACW10u4g9kBbGptaMdGIa/tnoijEjteTjQ0KmLdtmXHQYxydKKW0QgHWeFY4YBnL9uFLkGUPmdquAxHLYogMebOFjsDzne8AMRuxqXR1zjDonG51PGmj9ObkPF6NGKOROx3WhyFXV0Q2OvYGu7Zxy6e1k61d4EbPIV3ThZkJecqBdibZWdlbPOJ1x6Nj13OonCuErXOV0bXoty7UNhEse0lkAcHRHeXwdsUPMx3fV1RD1xmOhsWEA23UM6rVscS8FpHp1tacV5imrwa5WUd1ZV9obXmbSQHQYki6Y5uDzp4aQ/s28imY5O9sL9ldKlnK2r/kb68WRblu3Kks2dThC3vQyy8EqXJd+vUc2pnIvNiP8u2mm5Dv+VyexUZG35zimRW4wUg3i7wB+0u8s/xxOFt2uuFQke5s/29X+e9KleswTGD8z2tvrDRrJzA6Jo2YeUo3FkBVzvW3TsJx0leAtO4hE6cHRk+KQluI7wVVnnhgybN62UOKkeNY47Xz3g9ZuIItw+OOqRfAV+weJ1tupPSTpvgSYs3ogKISpxV3N+JC2QdbLStavRK29aFlLPxsQ1jq+USSImhHlIqAGK84XHiN0wX5m4tXtdgI84ZyH62bxv/XQSR48RJyBtULqZrRQqzW2XfFodxZ2uLOkuJntH3wxPUbLY3k5OILeTVXNPYaHM+cHzwooi1xroUYI4lVoeOyWs5cwRi19kc+9TK7o6dsDVlP8fRhPKESKARvELiUgUkZFODwvDeT4+3wTJVGi0GDYGd7MIJxGyPLXiqUZ5dDixPbHorkTLi5ZccgFwLL5B+/NipZJMNWvw8tqscPga53elplQOcu7R/Eq8bOtXc2OkJpALiL7DMdBzG2HM4R6O0L7pUVMtBNWIvuMbx9lOYntfYfAYhE/0D3y12Ho3TltsS2ecIL6RDvD2Uqe6Z92oOxiV2q7g9cMvY/fkOaeCeSam3l5EpsdUcHBVaoAO8UUclBippcRpiO2LlK1v2sFDgSb6aA++IswNo3GQBb4lsH4sfFdT2cC5FR0ISDkMAMY7zQE+o1xwGaQadI2Uhw3mPkYO7zTwjPtjndJYICXzJYEc+4oR8RAKXuNSAeAECAp9xpCMKFucr54uDzto7/HhRckbHdFfwJzJFLdFIXHAoZAPoz1qUaM/stkD6cqDcvrXjAKST4ykxS0PHDu4JbKHoowUsudDWJ3sXYrrH0WwFNrc47ZU4lFmlfO53OWn2FqcRtgKuFpyXx4gTTewpQHa9t4lzVKIKc94ihUkWjmAu484Q4pXzMRsLDg3stwYQuCsBxyvXo35gBhNAAUNGd7U8luSomSCrcYZn80qT4CZ2Eu+wst0G5PgMv/sJ4IwOcEa56i04o6yWVXnx8D04owc4o0DK7uCMXCH1BGcCvxPgjF7AGX0EzmjGG5QqXRc4oxOc0QnO6D04oyc4o2+DM3I66SNwRh+BMzrAGQU4oxOcUYAz+gyc0dATnFGAMwpwJrDSC5zRDZxRgDOybHapTnAGCO8JzugAZ/yZF3BGGPsdOMPbhwpwxvfTB+CMbuCMLnBGJzhjxXyXCojJM4sXcAZossRNL3CG5f1pBTijGzijA5zRAc6AnL4DZ3SAMyzaStwPcVtgwwGUFm6YCdITnNEJzrDcBc7oAmd4kfUAZ/QEZxTgjE5wBhdwfNUFziiJV0sBZ+wkHgGd4IwucEaAM/jmAGcU4AyPdnwj3uO9gTO6wBmBSIcgWQc4Q1CQYx+DM/omOKMXcEYHOCMS9ABnhAee4AwqPcAZBTijJzijJzijypeJoHyAM2KbqTfgjEX/EJyxVTpvS+sEZ/QEZ/QAZyxavGL8As6A7Z/gjE5wRoAzCnAG3J9HZgHOiDd/X8AZvYIzCnBGpzoBzmhEONzBGQU440/dwRmRizrAGd3BmbBugDM6wRl9H5zhuQHgjE5wRndwRu/AGQU4owBniEv7+RWc0R2c0QOcUYAzvISuA5zRBc7EC+YnOIMwT3BGBzijA5zRE5xRgDN6gDMKcEbvwRlRry9wRgc4o1dwRgc4owuc0ROc4WmnvgPOKMCZ47kQ9f8OzpBHBzijdrxaHuCMHuCM3oMzjsu34Iwv5v39wp/xbKbZxjrBGQU4Y510A2d0gDNc4L2JwjIBzvC4yUn9AGd41sJXlBNaqsibEf/m4AJndIAzusAZVOF1eB4U26G0VB3gDM/WKo/b6J1vwBmekyEY4Ix4bDbjhhc4o0/BGd3BGQx6A2f0FpzhifZH4Iye4EyUZttw6gHO8Jj7E3AmnskFOCPAGXeDEZ2Jp38Bzthdb8AZ3cEZvQNnWPEAZ3SCM3oLzuDME5zRE5zRA5zRBc5Q1XnAhvp6gjN6A84owBn+ZcJbcEaAMzQy55MAZ6hhT3BGJzijqJHfAWeo8DjgG+CM4l9UvAVnaDE3cEZEeoAzegFndAdn2God4IyweDg1wBn+cYj1dBFJ9PcWbUKv4Iye4Iy+Bc5Er/SStr9OcCYereKgC5zh3k9wRg9whn94EbWOK2h1D3BGb8AZPcAZneCMbuCMTnDGpnsLznALwBlRfx7gDK+IUEVKFEl3dA66Yt8a4IwOcEZvwRnh9w/BGaX49zJe4AHOUPT1XXCG+NYJzugCZ9iCUB4AZ/QCzvBPgSIW9CE4oxOc0QOcEf+vOsGZcDXWPMAZ3cAZnjtPneAMm7X450gnOMNJIcAZfQOccakS4IxewRmMwj/FiRdm6DV3cEYXOKMPwRld4IwucEaAMyGer1KY+x04owuc0QnOKNoDB4S1IoUDnHEQnOCMF9MJzij6fnhCBzijC5wRW8zYaIcZX8AZR5AOcEYfgDN6gjN6AWcoN5ZFJzijeBWDzcIFzugGzijAGQHO6A7OCHCG8nyAM3qAM+hCRTjBGcLxCc7Ea0uOav6d0h2coadxNvgAnNErOBNviXAYIxovcIZWx5tYFzjDzv8EZ7jTA5zRE5yJf5b3a8AZ3rvgSIU1KRoPcEZUJr2CM44QBTijD8EZHeAM+wZnEPvdAGf4C+U6wBmd4IxewBmd4IwCnNErOMP1n4AzOsEZRRznkOAFnNERCRc4g45F8drKBc7QgABnnFF6D87oDs5wmmNbadsHOKMnOKO34EwcT2nlOsAZPcEZ2xo4IU6E78GZQBM4EHIoC3BGb8EZXeCMLnBGJzijt+CMLnBGL+CMTnBGT3CGugI4ox8Fzrx/z/Ivf/mBkyG/8Tv+DflP/7112bpsXbYuW5ety9Zl67J12bpsXbYuW5ety9Zl67J12bpsXbYuW5ety9Zl67J12bpsXbYuW5ety9Zl67J12bpsXbYuW5ety+9dl0+JkP9FFPwxtNXfpbT++bfZumxdti5bl63L1mXrsnXZumxdti5bl63L1mXrsnXZumxdti5bl63L1mXrsnXZumxdti5bl63L1mXrsnXZumxdti5bl63L36wu/wKv3L0nxFzMcYQhsEFg3Ethrm3nVwoeSUhWGAWeYbRoQReVxwymDiZ+lpH4H5S/KaZiQn3QG9NbGWdfICUpbXIx05pb8c0GwwZTcLR2uCyZ1F2DpGPBXLyYWdgTE80b8yAXnLK+GYMge1Ao5bgS9kJ+wsDHXGB6LNW3hD0kCAsW9KFBVwbFcLBKhBDVEs1WY9pnZ1L5gpqzQ00Dc0AOAt3FdFpIh2uC4ox51lBbdmZFw6QGNQcDeZnFPEJz2BZGqNMxYwreQhgWGHHbocxkHC5kZDVo8JhBnYpV70HkUZjdn7u/xlaM5WS49DEPeS2Ig3NQFNdZmcU54XyCYq4xVx+yq5hDzpzaHjwdvTCkHnpVOIiYNToX5BaQpUJp4cuabwp/AHTAmbmnEKmsoFKAkIhZ5oz5tizwb8404Owp8FWMg5ARF1R/GFK2GN3KHEwUQyXYRRhBHFNpoTKBF8Z2rvZKMD9nSEQbI2Irg0gZrTogLxtlwnO7Yh40VF9lQjZWIe4g/mK+ah9BQ9gjKu0S2AOC6NN3WwOW2OVIW0F/ZiXtV/4+8BUBnoOfx9+DxRS2U2aod4ZnxgTQTAzCYMe4U/uYgdCVzw5oYDLzMxNkb7DhWj9GuA7IDRNTVecIktmDQhom8DXsGy6BxgMOT+YMN6cXJmFwru3O10w3hxh1Mcc7Bf3HipWDUSOY8eB06Iz1hEoQ5tcB8yeDmXOMx67Izmxsq9zgBXCMRfgl8hnC5ph0n4Itr0LoMZmxmyfURs4//xTGCSi6oBkIdqEV081Zpo0R5BHOegbKj+ApDja22RhlbZfYRdYOxlTfHSfCPrMOujEGmENKiisGPLQF4mx4G2ypxgTnHDRJjCKHI48BqgXOVigRnHJBZAqfnGMr6BEq5MAluBuDRhOmVkbtww0D8VGzyWCBYiLxCjIF/gu/TnCDDJsLpRjozbzkgX8baTvWMXA1wbwJFZeXpRCRBYxkjuG/Xp3hvExqZ5D+whmJOfxBP+gPwrJS+8Fl1xmS7M9kAjMIiu3jINKc0EjYohBCMoc2CDbJAmoJTCYxXhwap1VilHgwBtoJlg1SYQhdYGCBIM418HivGrIUJmEvWJHh9II1J2hvoCdgHjMDuTMK2ndBf4ALa1S4Co/vCJbngdcYt96hmM5QLbGKVwzSUsbs54OjcVJN4HRw4DN2OYK8MqTeywTdK+2AUfQHPXGDDctKQYZHXEJnAkcL3FfMiIc4nIKdY8R6cHgHB0UPipVFyCzoDgtEu1Bzlhyz8+F/XnDvWmIYdhmIzuT5nAjEFGPgoZCCdsRL2wqQMFB9Dp4x5uXC45EYZGwxoaCFIhMmlhaxF0OGY4Iyvob9gnH3MP4Gx2onReCiIYUKHSJ4djvznKELsStOorbOYHsqWoMUE26+FLTHbmQ5SDMTib7gi+tMdi9QXUUhgsmpB2Njg7cG3gJu66JDpRsj6KEyk84hSnKAwo67Ykz5KTUk5g2CKMgSnAkYDuqCxW0hRai41Lat1IUWHSVBwTWZ8T+QpjDdvEKCFZVvBKMSFrSn3HCCiBPz1iC55eJBxlMgoVOOwJlRgmkglTnVEJR6/+BQtRBH+MHESQk/S3YKwiQnq4MILiY0S9HaYaPhXkRAZxY19HttRWp6PeIMAmNYHCyp6zjzxDN8tUFC5+S2XlAYDFYg6Okd9AY4JqCngWmQ6Keowirvouzogk0lHQzONIcCi1lUDyisOkxN9I8Jm17QxPVg/OvBaIWbYLxt7BxijjxspjDIVLIck9gSjObuQZIGn6WzhXYHLRr5AiU0Oi3qmYseJFCJcGZk+IRJ17pyDeTiUJTb82xYnBDYBvoaajM7sBFJS2MZlEJHSMcIEB9DNAWBHnzJmZ5HsNKLaMwN2yy6Sia4IcRo8Lz34CtmH8QuyLFrRSk8tjT8tuwcG6RTTKmH8sFRDHs0k8cL26weYpK+xHSMf3fwUwMcOQeBUEeCoz7gNkgTIQOAqmdBRdOhhHCAZSie4LyF+sVyVfiOYaDMMcMdMoighKDG9mDGbnBOcF2QskB57bpF9gYhxYLSLfY0NZjGoJ6gD3doHphEzvYhdjPQb0F5yoh3MtL7sSBVgNuJ0s+Ud5vSRcZBzX7DNbJAZRsdgRHrsYm1UjlYFoJUo8KRwa6hQHsQ3STF1rqxF+RyqPiCcrRVdkpQMLGhh9BjQCUxIBKgFsMzAoMHm5SBF6CJoyV0aIDY0cMUw14qKH6DlgsOreDdhIAEslRXa+cI1+TYAfJptrBUp9mD19EGm/jJiUgBZKA8R4+co9qUGjIjJl2czQM+htgITmoIO2P3x4z3GatXImnGlhnGBJebCQ0U/YMOSkFq+Ijd+IqSV2lZOQVHJPtPOKKIIlpkC/KGAZMUvHzu18F6meJ6xvDP4BSgPMAuVKNAFdg2ctRoOFbglghS3KDgs6XcrLmb70F/Sym27RnuK+bSu1LENPxBQrRQ2S2ITsBRBod7q5qD/AOeLV+c4OIOV3A2oJWyo1xxNGohGUWI3RuUAzDSsTFpbKrwY6UbJOJscqIIkt4CI1aFgdFqQJ+GChmeTva5rgUuAgf7OoysI/gQLY/7O8U0RthzNmHbxdmFLZtLR5ATr9ia1GClWz00m+yLOUWwP1tUXTedBA8XrQ93DTbCiU1PoXqzZ0UzNlpsw9ghBh906O4j46IbQp1O4XA9gvuNokL0UzlysHXQLyF3ZBcOfzJ7mNzCU/BHwBzjwkCLxPywJcAUxLGRkxm7seAII0M4+xSaBWcmamshGYJo2IunOJTG6QfTFEq+3WhbJLZh9j27iBRsW9AyUJYtHtTYSMPZlnpiJzh0oI6CbYX+nKIEwtJBdLpEOfPIouB9zjgSdgB/GEoWKMhmUBdnGE7pdFFoIcQZ1Ei6KNQ4TgMXDsdsirN0C5IqwohzODutRrBwTY/izpF9cj+X0oqr2ahDzxhECDBPdEoQG8NglwhiJ/YoI+in4JVaJbikcpB2DwIpKMxKVL0SVGKDTIXWI9Xj3F+JYkpinN/h9S5RpRe7Mcc0YbZaEPwVjoCZdu+OC59KgBBw9kDYaxNPtlIstuC34MCRCRL2vTCKZ1o3hz5s7kKJXv4Cmm2OmnZUoRazO6MtATFA4cWGq7A5BBLgwBhkkjCIUSRLjq1cA0qAQnGyy2IZOFFgrLe4gaVA8UHYFw5/AzZbhIdZGgLWGvDKDOc5ijPUcj02Juysov94o8Z+oAdPT+Gbx6kH5vkO0yp7Db6Y8DCPgCrgDpp4qtMs2RTTAfg+jaEHXtJLHBHZXhOSzmCIQODRAKXBOPRJTqZkeIe9h2NmbPE5TtLGIGSi8y725sQGJJ/BNpzp7O6M7Ih77COoNyn2CTlo2vuxE2kcq2IjYpHYY7jMc17lIArX9gwyTioSRC4TfkRyjFDJML6AUy32rfFnjiMzO1O46HLYBI7ZFpwybj4cQxpEy59xlf5A3EwP3EwRsvDFXLiZXnEzXbiZAjfTC26mF9xMkT86cTPdcDO9x8104maClvQNbga3Iyyp+hg3gyMRAphlue+4GTxwgmgrcDPIIQM3E0xwFaKeO26mN7iZDtxMF26mGgyFN9xMJ26md7iZ3uJmsJL5plDb1KBqg1+ywEN24mY6cDPdcDN9gJvpFTeDWgxGJHAzwZUEbqYX3EwpWAnf4GYKcpjAzYKuteqJm+nEzRQEYA/cTAduphtupgs30+Q2EPbxlx4kTS+4WbD7gTwoGCIpZlCCBW4WHKyo8oqbCaLFGnSCI0iV+DGcRiN4Jzn66y1uJnAzvcfNgqEnuE+THrgZbFrgZgrcTA/cTIGbCT7dFjRWgZvxdeBmCmo/cDP4mW64me64mWBpDCpdSAvhYeWIedBuJgVupuC+rMFoCNUfFJUnbqY7bmYt/RMYclOwNEFV9MDN4LSaQWuowM2gfSpB/KQPcDOIGK2GOMjoA9xMJ26mwM0E4eqBm8EZiSu49sLNIEf6EDfTiZsFAZVmcMzCFfkON4tHD9AGveBmOnAzBW4GARvafYSbhUvhfDpwM124mTiR4vILN9MHuJlO3EwQel64WfDkPnEzvcPNdOJmzuSO+z/AzXTHzSCBVOBmeoeb6cLN7LF8PIO5cDPn04WbQRaIjYJo6cTNUBC+QHAzBW4Guyk8Zi+4mQ7cTCdupsDNdOFmB32mDtxMJ26mF9xMb3AzqJbJ3oOyOuLywM0UtGQK3CxSXx/jZhEyUGrCIHfhZsGnKorGiZvB8Ba4mU7cTCdupl+OmylwMx24mQ4azw9wM524mW64mYIw9HAduFnUaYhMwc1CfYqoGkTHgZvpBTcTm62oEBdupne4mb6Hm+nEzfTAzagWtje4md7jZpCJpoP2Ex7cAzcjtKGnhCk8cDM9cTNoeqPyHbiZnriZnrgZQQmVbeBmunAzeneUYF24GabKdCNUveFmNNqsN7iZPsHN9MTN6BakJhtpeKEV3MVP3Cy4+erBLgs1NqofuJkO3ExvcLOgzw7cDOe+wc2iOfgOdEJwM124mS7cTB/iZuxuoLQ+cDMduBlZELiZDtyMGDpxM524GXkT5LPgZgrcjDr5xM2QxDIhFhTlgZvpiZvpiZspcLNIWlwNbobPTtws8jAIFm+4maIXkUuBm+kj3EwHbgZZI7FLpYBOEtxM4GZ6i5vpwM2oaMGHGp35DW5GD4DBGktduJnAzdThs7xwM124GcyyB26mAzcLZlcsdeJmNNPAzfgkJzIdSi3qYOBmunAznbgZlSX41QM304Gb6cDN2BocuBm7mQM30xM30xM3g3k7aA99xYWbUeShkMc4b3Gz2DHrws2OtkF3C+I8BW6G/MFA32M7BvPggAf0PW52ECRTHGBc7DpwM0WJ0g0304mbKXCz6CAXbqYDN9MrbgZbMU564mZQbsJDf+BmJCW035/gZlCicp8bbqYHbqYDN9OJm+lbuBn7IMgq2XQcuJkO3EwP3OzYSyuIaw/cTBdupg9wM4x8x82g4c1Ro9/gZmyZdOJmVBL6G4amuXXC8cTNdOJmNAn2SwpSxne4GZy8vviBm+nEzWJHqRtupm/gZnriZrpwM6pCxBG5oFfcTE/cLAqFAjfTEzeLOlGCW1oEr6L8BG6mAzfjtIW2b3EzGL1LtMITN2MvG7iZLtyMjStl48LNSDxYfuMgduFm1L4bbuaYghr+HW6mwM10MFGy9/kmboZNPsbN9MDN2NCyFYB5+cDNkPXAzfRt3Ewf42ZCGqg6X3EzfYKb2WDs3HXhZgrcTE/cTAduRoOz3V5ws4OJO8rO4GB24GYK3EwHbqYDN9MHuJkO3IwzJYXijpspyLkP3Ix9y4mbQf79wM104WaUGHLjY9wsTh+EzgM304mbcZMHbqYTN1PgZnFO0g030x030ytuphM304Gb6cTNFLiZ3uNmOnAz3XAzsuaGm1E6T9zsKD0HbkbR1g03U2yWn7iZYgccWIpecDMFbqYLN9MNN4P694ab+fKj2kNbDypx4ma64WZWn+qiEzeDIlsP3EwvuJku3IxkD7wE3EwnbqYLN9MdN6N13nAzKng+9rh6wc2ouPQ9MIYAPC7cTCduphtuplfcDMJpKkCcPL+Jm+nH4mbv3jYL1uAMfltZjccF7FXxMltxugzFZNHz2XMT/b7C7qQ4g8xzUKbP2NSQ5fqU1WiFPDewLEALFfZrnrgsnjsngPIBnOGPQt/eghSbh7U8l0ggHZEnIwofOcRN4pFY53Etj2YWz6wKfMGgOj4Gc8bjcRwnv2jg4N0kHMctOiYwl9OygbTmdL4T5D1WPt5ZIowByMGVIdqO2AVc59nmiU7HhgwEsfDEGagZBnQKGZqDylZ2xCPgxgDYOaQth1FrUSQhU86cdhbBzoaDF54gn+ftH8DieOrAK1bkKrisM3dxAvRGE0p17zyAE2KDk9lJ2Oyc/C3W4omhq8ICOJ8JF7Bj46G0LdgiWlbs1blHobWG0TFyvAphaX0FDx4BR6lxbKQ49rg0x1aUJxyBZQKuOWAcUf0zJPYXxhSpx54dPnBiShFTumJK95hSxJTuMXUw24PORkzpbUyJmOJo8RpTqnETWt89pgLI0RVTusWUrpjSI6Z0xFSUKkVM6YwpWpVeY0r3mNIVU3qNKfa472JKZ0wpjonUg09iSkdMHWDgEVMK9vDoyENHTAWhPDEFPPKMqTiUXjGlR0yJ3fQZU3rGlM6YYqM+FTGlW0zpiim9xJSumNL3Yooq9Z/+97//fw73FU4="},"max_retries":{"kind":"null"}}},{"rowIdx":195,"cells":{"question_id":{"kind":"number","value":2336,"string":"2,336"},"question_title":{"kind":"string","value":"Manasa and Stones"},"question_content":{"kind":"string","value":"Manasa is out on a hike with friends. She finds a trail of stones with numbers on them. She starts following the trail and notices that any two consecutive stones' numbers differ by one of two values. Legend has it that there is a treasure trove at the end of the trail. If Manasa can guess the value of the last stone, the treasure will be hers.\n\n**Example** \n$n = 2$ \n$a = 2$ \n$b = 3$ \n\nShe finds $2$ stones and their differences are $a = 2$ or $b = 3$. We know she starts with a $0$ stone not included in her count. The permutations of differences for the two stones are $[2,2], [2,3], [3,2]$ or $[3,3]$. Looking at each scenario, stones might have $[2,4], [2,5], [3,5]$ or $[3,6]$ on them. The last stone might have any of $4, 5$, or $6$ on its face. \n\nCompute all possible numbers that might occur on the last stone given a starting stone with a $0$ on it, a number of additional stones found, and the possible differences between consecutive stones. Order the list ascending.\n\n**Function Description**\n\nComplete the *stones* function in the editor below. \n\nstones has the following parameter(s): \n\n- *int n:* the number of non-zero stones \n- *int a:* one possible integer difference \n- *int b:* another possible integer difference \n\n**Returns** \n\n- *int[]:* all possible values of the last stone, sorted ascending "},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static List<Integer> stones(int n, int a, int b)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.ByteArrayInputStream;\\nimport java.io.IOException;\\nimport java.io.InputStream;\\nimport java.io.PrintWriter;\\nimport java.math.BigInteger;\\nimport java.security.acl.LastOwnerException;\\nimport java.util.Arrays;\\nimport java.util.InputMismatchException;\\nimport java.util.Stack;\\n\\npublic class Solution {\\n\\n\\tstatic InputStream is;\\n\\tstatic PrintWriter out;\\n\\tstatic String INPUT = \\\"\\\";\\n\\n\\tpublic static void main(String[] args) {\\n\\n\\t\\t// TODO Auto-generated method stub\\n\\t\\tis = INPUT.isEmpty() ? System.in : new ByteArrayInputStream(INPUT.getBytes());\\n\\t\\tout = new PrintWriter(System.out);\\n\\t\\tint testcases = nextInt();\\n\\t\\twhile (testcases>0) {\\n\\t\\t\\tint N = nextInt();\\n\\t\\t\\tint a = nextInt();\\n\\t\\t\\tint b = nextInt();\\n\\t\\t\\tprintLastPossibleValues(N,a,b);\\n\\t\\t\\ttestcases--;\\n }\\n\\t}\\n\\n\\n\\tprivate static void printLastPossibleValues(int n, int a, int b) {\\n\\n\\t\\tif(n==0){\\n\\t\\t\\tout.print(0);\\n\\t\\t}else if(n==1){\\n\\t\\t\\tout.print(a +\\\" \\\"+ b);\\n\\t\\t}else{\\n\\t\\t\\tint firstterm = (n-1)*a;\\n\\t\\t\\tint secondterm = ((n-2)*a)+b;\\n\\t\\t\\tint d = secondterm - firstterm;\\n\\t\\t\\tint lastterm = (n-1)*b;\\n\\t\\t\\tif(d<0){\\n\\t\\t\\t\\tint temp = lastterm;\\n\\t\\t\\t\\tlastterm = firstterm;\\n\\t\\t\\t\\tfirstterm = temp;\\n\\t\\t\\t\\td = Math.abs(d);\\n\\t\\t\\t}\\n\\t\\t\\tint temp = firstterm;\\n\\t\\t\\twhile(temp != lastterm){\\n\\t\\t\\t\\tout.print(temp+\\\" \\\");\\n\\t\\t\\t\\ttemp = temp + d;\\n\\t\\t\\t}\\n\\t\\t\\tout.print(temp);\\n\\t\\t}\\n\\t\\tout.println();\\n\\t\\tout.flush();\\n }\\n\\n\\n\\tprivate static boolean eof() {\\n\\n\\t\\tif (lenbuf == -1)\\n\\t\\t\\treturn true;\\n\\t\\tint lptr = ptrbuf;\\n\\t\\twhile (lptr < lenbuf)\\n\\t\\t\\tif ( !isSpaceChar(inbuf[lptr++]))\\n\\t\\t\\t\\treturn false;\\n\\t\\ttry {\\n\\t\\t\\tis.mark(1000);\\n\\t\\t\\twhile (true) {\\n\\t\\t\\t\\tint b = is.read();\\n\\t\\t\\t\\tif (b == -1) {\\n\\t\\t\\t\\t\\tis.reset();\\n\\t\\t\\t\\t\\treturn true;\\n\\t\\t\\t\\t} else if ( !isSpaceChar(b)) {\\n\\t\\t\\t\\t\\tis.reset();\\n\\t\\t\\t\\t\\treturn false;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t} catch (IOException e) {\\n\\t\\t\\treturn true;\\n\\t\\t}\\n\\t}\\n\\n\\tprivate static byte[] inbuf = new byte[1024];\\n\\tstatic int lenbuf = 0, ptrbuf = 0;\\n\\n\\tprivate static int readByte() {\\n\\n\\t\\tif (lenbuf == -1)\\n\\t\\t\\tthrow new InputMismatchException();\\n\\t\\tif (ptrbuf >= lenbuf) {\\n\\t\\t\\tptrbuf = 0;\\n\\t\\t\\ttry {\\n\\t\\t\\t\\tlenbuf = is.read(inbuf);\\n\\t\\t\\t} catch (IOException e) {\\n\\t\\t\\t\\tthrow new InputMismatchException();\\n\\t\\t\\t}\\n\\t\\t\\tif (lenbuf <= 0)\\n\\t\\t\\t\\treturn -1;\\n\\t\\t}\\n\\t\\treturn inbuf[ptrbuf++];\\n\\t}\\n\\n\\tprivate static boolean isSpaceChar(int c) {\\n\\n\\t\\treturn !(c >= 33 && c <= 126);\\n\\t}\\n\\n\\tprivate static int skip() {\\n\\n\\t\\tint b;\\n\\t\\twhile ((b = readByte()) != -1 && isSpaceChar(b))\\n\\t\\t\\t;\\n\\t\\treturn b;\\n\\t}\\n\\n\\tprivate static double nextDouble() {\\n\\n\\t\\treturn Double.parseDouble(nextString());\\n\\t}\\n\\n\\tprivate static char nextChar() {\\n\\n\\t\\treturn (char) skip();\\n\\t}\\n\\n\\tprivate static String nextString() {\\n\\n\\t\\tint b = skip();\\n\\t\\tStringBuilder sb = new StringBuilder();\\n\\t\\twhile ( !(isSpaceChar(b))) {\\n\\t\\t\\tsb.appendCodePoint(b);\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn sb.toString();\\n\\t}\\n\\n\\tprivate static char[] nextChar1DArray(int n) {\\n\\n\\t\\tchar[] buf = new char[n];\\n\\t\\tint b = skip(), p = 0;\\n\\t\\twhile (p < n && !(isSpaceChar(b))) {\\n\\t\\t\\tbuf[p++] = (char) b;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn n == p ? buf : Arrays.copyOf(buf, p);\\n\\t}\\n\\n\\tprivate static char[][] nextChar2DArray(int n, int m) {\\n\\n\\t\\tchar[][] map = new char[n][];\\n\\t\\tfor (int i = 0; i < n; i++)\\n\\t\\t\\tmap[i] = nextChar1DArray(m);\\n\\t\\treturn map;\\n\\t}\\n\\n\\tprivate static int[] nextInt1DArray(int n) {\\n\\n\\t\\tint[] a = new int[n];\\n\\t\\tfor (int i = 0; i < n; i++)\\n\\t\\t\\ta[i] = nextInt();\\n\\t\\treturn a;\\n\\t}\\n\\n\\tprivate static long[] nextLong1DArray(int n) {\\n\\n\\t\\tlong[] a = new long[n];\\n\\t\\tfor (int i = 0; i < n; i++)\\n\\t\\t\\ta[i] = nextLong();\\n\\t\\treturn a;\\n\\t}\\n\\n\\tprivate static int nextInt() {\\n\\n\\t\\tint num = 0, b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile ((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'))\\n\\t\\t\\t;\\n\\t\\tif (b == '-') {\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\twhile (true) {\\n\\t\\t\\tif (b >= '0' && b <= '9') {\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t} else {\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\n\\tprivate static long nextLong() {\\n\\n\\t\\tlong num = 0;\\n\\t\\tint b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile ((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'))\\n\\t\\t\\t;\\n\\t\\tif (b == '-') {\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\twhile (true) {\\n\\t\\t\\tif (b >= '0' && b <= '9') {\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t} else {\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'stones' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts following parameters:\\n * 1. INTEGER n\\n * 2. INTEGER a\\n * 3. INTEGER b\\n */\\n\\n public static List<Integer> stones(int n, int a, int b) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int T = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n for (int TItr = 0; TItr < T; TItr++) {\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n int a = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n int b = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> result = Result.stones(n, a, b);\\n\\n for (int i = 0; i < result.size(); i++) {\\n bufferedWriter.write(String.valueOf(result.get(i)));\\n\\n if (i != result.size() - 1) {\\n bufferedWriter.write(\\\" \\\");\\n }\\n }\\n\\n bufferedWriter.newLine();\\n }\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.util.*;\\n\\npublic class ManasaandStones {\\n public static void main(String[] args) {\\n Scanner sc = new Scanner(System.in);\\n int t = sc.nextInt();\\n while(t-- > 0) {\\n int n = sc.nextInt();\\n int a = sc.nextInt();\\n int b = sc.nextInt();\\n ArrayList<Integer> al = new ArrayList<Integer>();\\n HashSet<Integer> hs = new HashSet<Integer>();\\n for(int i = 0; i < n; i++) {\\n int temp = (n-1-i)*a + i*b;\\n if(!hs.contains(temp))\\n al.add(temp);\\n hs.add(temp);\\n }\\n Collections.sort(al);\\n for(int i = 0; i < al.size(); i++) {\\n System.out.print(al.get(i) + \\\" \\\");\\n }\\n System.out.println();\\n }\\n sc.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'stones' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts following parameters:\\n * 1. INTEGER n\\n * 2. INTEGER a\\n * 3. INTEGER b\\n */\\n\\n public static List<Integer> stones(int n, int a, int b) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int T = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, T).forEach(TItr -> {\\n try {\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n int a = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n int b = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> result = Result.stones(n, a, b);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\" \\\"))\\n + \\\"\\\\n\\\"\\n );\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.util.Scanner;\\n\\npublic class Solution {\\n public static void main(String[] args) {\\n Scanner stdin = new Scanner(System.in);\\n int numTestCases = stdin.nextInt();\\n for (int i = 0; i < numTestCases; i++) {\\n int numStones = stdin.nextInt();\\n int a = stdin.nextInt();\\n int b = stdin.nextInt();\\n \\n if (numStones <= 1) {\\n System.out.println(0);\\n continue;\\n }\\n \\n if (a == b) {\\n System.out.println(a * (numStones-1));\\n continue;\\n }\\n \\n int minValue = Math.min(a,b);\\n int maxValue = Math.max(a,b);\\n \\n System.out.print(minValue * (numStones-1));\\n for (int j = 1; j < numStones; j++) {\\n System.out.print(\\\" \\\");\\n System.out.print(j * maxValue + (numStones-j-1) * minValue);\\n }\\n System.out.println();\\n }\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'stones' function below.\\n *\\n * The function is expected to return an INTEGER_ARRAY.\\n * The function accepts following parameters:\\n * 1. INTEGER n\\n * 2. INTEGER a\\n * 3. INTEGER b\\n */\\n\\n public static List<Integer> stones(int n, int a, int b) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n int T = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n IntStream.range(0, T).forEach(TItr -> {\\n try {\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n int a = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n int b = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> result = Result.stones(n, a, b);\\n\\n bufferedWriter.write(\\n result.stream()\\n .map(Object::toString)\\n .collect(joining(\\\" \\\"))\\n + \\\"\\\\n\\\"\\n );\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"stones(n, a, b)\"\n ],\n \"solution\": \"T = int(input())\\n\\nfor test_case in range(T):\\n n = int(input())\\n a = int(input())\\n b = int(input())\\n s = set()\\n for i in range(n):\\n s.add(b*i + (n-i-1)*a)\\n print(' '.join(map(str, sorted(s))))\",\n \"template\": \"#\\n# Complete the 'stones' function below.\\n#\\n# The function is expected to return an INTEGER_ARRAY.\\n# The function accepts following parameters:\\n# 1. INTEGER n\\n# 2. INTEGER a\\n# 3. INTEGER b\\n#\\n\\ndef stones(n, a, b):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n T = int(input().strip())\\n\\n for T_itr in range(T):\\n n = int(input().strip())\\n\\n a = int(input().strip())\\n\\n b = int(input().strip())\\n\\n result = stones(n, a, b)\\n\\n fptr.write(' '.join(map(str, result)))\\n fptr.write('\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":196,"cells":{"question_id":{"kind":"number","value":22185,"string":"22,185"},"question_title":{"kind":"string","value":"Strange Counter"},"question_content":{"kind":"string","value":"There is a *strange counter*. At the first second, it displays the number $3$. Each second, the number displayed by decrements by $1$ until it reaches $1$. In next second, the timer resets to $2 \\times \\text{the initial number for the prior cycle}$ and continues counting down. The diagram below shows the counter values for each time $t$ in the first three cycles:\n\n<img src=\"https://s3.amazonaws.com/hr-challenge-images/22185/1469447349-bae87a5071-strange1.png\" title=\"strange(1).png\" />\n\nFind and print the value displayed by the counter at time $t$. \n\n**Function Description** \n\nComplete the *strangeCounter* function in the editor below. \n\nstrangeCounter has the following parameter(s): \n\n- *int t:* an integer \n\n**Returns** \n\n- *int:* the value displayed at time $t$ "},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static long strangeCounter(long t)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner sc = new Scanner(System.in);\\n long t = sc.nextLong();\\n long curr = 3;\\n while (t > curr) {\\n t -= curr;\\n curr *= 2;\\n }\\n System.out.println(curr-t+1);\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'strangeCounter' function below.\\n *\\n * The function is expected to return a LONG_INTEGER.\\n * The function accepts LONG_INTEGER t as parameter.\\n */\\n\\n public static long strangeCounter(long t) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n long t = Long.parseLong(bufferedReader.readLine().trim());\\n\\n long result = Result.strangeCounter(t);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\\n\\n /*\\n * Complete the 'strangeCounter' function below.\\n *\\n * The function is expected to return a LONG_INTEGER.\\n * The function accepts LONG_INTEGER t as parameter.\\n */\\n\\npublic class Solution {\\n public static void main(String[] args) {\\n Scanner sc = new Scanner(System.in);\\n long t = sc.nextLong();\\n long curr = 3;\\n while (t > curr) {\\n t -= curr;\\n curr *= 2;\\n }\\n System.out.println(curr-t+1);\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'strangeCounter' function below.\\n *\\n * The function is expected to return a LONG_INTEGER.\\n * The function accepts LONG_INTEGER t as parameter.\\n */\\n\\n public static long strangeCounter(long t) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n long t = Long.parseLong(bufferedReader.readLine().trim());\\n\\n long result = Result.strangeCounter(t);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"\\nimport java.io.ByteArrayInputStream;\\nimport java.io.IOException;\\nimport java.io.InputStream;\\nimport java.io.PrintWriter;\\nimport java.util.Arrays;\\nimport java.util.InputMismatchException;\\n\\npublic class A {\\n\\tInputStream is;\\n\\tPrintWriter out;\\n\\tString INPUT = \\\"\\\";\\n\\t\\n\\tvoid solve()\\n\\t{\\n\\t\\tlong t = nl();\\n\\t\\tfor(long x = 3;;x *= 2){\\n\\t\\t\\tif(t <= x){\\n\\t\\t\\t\\tout.println(x+1-t);\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\tt -= x;\\n\\t\\t}\\n\\t}\\n\\t\\n\\tvoid run() throws Exception\\n\\t{\\n\\t\\tis = INPUT.isEmpty() ? System.in : new ByteArrayInputStream(INPUT.getBytes());\\n\\t\\tout = new PrintWriter(System.out);\\n\\t\\t\\n\\t\\tlong s = System.currentTimeMillis();\\n\\t\\tsolve();\\n\\t\\tout.flush();\\n\\t\\tif(!INPUT.isEmpty())tr(System.currentTimeMillis()-s+\\\"ms\\\");\\n\\t}\\n\\t\\n\\tpublic static void main(String[] args) throws Exception { new A().run(); }\\n\\t\\n\\tprivate byte[] inbuf = new byte[1024];\\n\\tprivate int lenbuf = 0, ptrbuf = 0;\\n\\t\\n\\tprivate int readByte()\\n\\t{\\n\\t\\tif(lenbuf == -1)throw new InputMismatchException();\\n\\t\\tif(ptrbuf >= lenbuf){\\n\\t\\t\\tptrbuf = 0;\\n\\t\\t\\ttry { lenbuf = is.read(inbuf); } catch (IOException e) { throw new InputMismatchException(); }\\n\\t\\t\\tif(lenbuf <= 0)return -1;\\n\\t\\t}\\n\\t\\treturn inbuf[ptrbuf++];\\n\\t}\\n\\t\\n\\tprivate boolean isSpaceChar(int c) { return !(c >= 33 && c <= 126); }\\n\\tprivate int skip() { int b; while((b = readByte()) != -1 && isSpaceChar(b)); return b; }\\n\\t\\n\\tprivate double nd() { return Double.parseDouble(ns()); }\\n\\tprivate char nc() { return (char)skip(); }\\n\\t\\n\\tprivate String ns()\\n\\t{\\n\\t\\tint b = skip();\\n\\t\\tStringBuilder sb = new StringBuilder();\\n\\t\\twhile(!(isSpaceChar(b))){ // when nextLine, (isSpaceChar(b) && b != ' ')\\n\\t\\t\\tsb.appendCodePoint(b);\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn sb.toString();\\n\\t}\\n\\t\\n\\tprivate char[] ns(int n)\\n\\t{\\n\\t\\tchar[] buf = new char[n];\\n\\t\\tint b = skip(), p = 0;\\n\\t\\twhile(p < n && !(isSpaceChar(b))){\\n\\t\\t\\tbuf[p++] = (char)b;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn n == p ? buf : Arrays.copyOf(buf, p);\\n\\t}\\n\\t\\n\\tprivate char[][] nm(int n, int m)\\n\\t{\\n\\t\\tchar[][] map = new char[n][];\\n\\t\\tfor(int i = 0;i < n;i++)map[i] = ns(m);\\n\\t\\treturn map;\\n\\t}\\n\\t\\n\\tprivate int[] na(int n)\\n\\t{\\n\\t\\tint[] a = new int[n];\\n\\t\\tfor(int i = 0;i < n;i++)a[i] = ni();\\n\\t\\treturn a;\\n\\t}\\n\\t\\n\\tprivate int ni()\\n\\t{\\n\\t\\tint num = 0, b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate long nl()\\n\\t{\\n\\t\\tlong num = 0;\\n\\t\\tint b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate static void tr(Object... o) { System.out.println(Arrays.deepToString(o)); }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'strangeCounter' function below.\\n *\\n * The function is expected to return a LONG_INTEGER.\\n * The function accepts LONG_INTEGER t as parameter.\\n */\\n\\n public static long strangeCounter(long t) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n long t = Long.parseLong(bufferedReader.readLine().trim());\\n\\n long result = Result.strangeCounter(t);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"strangeCounter(t)\"\n ],\n \"solution\": \"t = int(input())\\n\\nt1 = 3\\nwhile(t1 < t):\\n t -= t1\\n t1 *= 2\\n \\nprint(t1 - t + 1)\",\n \"template\": \"#\\n# Complete the 'strangeCounter' function below.\\n#\\n# The function is expected to return a LONG_INTEGER.\\n# The function accepts LONG_INTEGER t as parameter.\\n#\\n\\ndef strangeCounter(t):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n t = int(input().strip())\\n\\n result = strangeCounter(t)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"eJyNU0tKREEMFPQgj14Pkl/n40kEFRf6Fm7GWbwBQQQPobdy54VsxcWQR4bJqumuSqqS9PvF5/f52V9cf43DzWt72u72S7uamvPttm2mNr/s5odlfrx/3i//T4jtbTMdYLuVWLaENSixJAmrqiIaJaEHushKDUSA+RH5QWyadTmDUW1arBP6yo04ARB009qW0xAa0inX1N9bJ+ksdV1EZg7o6ont7EyhgSXXjGjYEk5MZK8nhpJN1gVy4iM7k5C14dykevw5J9arhZSwVJtaSa215qzST84ap/cK4SDqvkmQ2vgOqkPW3cflDwghJP8="},"max_retries":{"kind":"null"}}},{"rowIdx":197,"cells":{"question_id":{"kind":"number","value":44591,"string":"44,591"},"question_title":{"kind":"string","value":"3D Surface Area"},"question_content":{"kind":"string","value":"Madison is a little girl who is fond of toys. Her friend Mason works in a toy manufacturing factory . Mason has a 2D board $A$ of size $H \\times W$ with $H$ rows and $W$ columns. The board is divided into cells of size $1 \\times 1$ with each cell indicated by its coordinate $(i, j)$. The cell $(i, j)$ has an integer $A_{ij}$ written on it. To create the toy Mason stacks $A_{ij}$ number of cubes of size $1 \\times 1 \\times 1$ on the cell $(i, j)$. \n\nGiven the description of the board showing the values of $A_{ij}$ and that the price of the toy is equal to the 3d surface area find the price of the toy. \n"},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Medium"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static int surfaceArea(List<List<Integer>> A)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n static int surfaceArea(int[][] A) {\\n int cost = 0 ;\\n int m = A.length-2;\\n int k = A[0].length-2;\\n for(int i=1;i<=m;i++){\\n for(int j=1;j<=k;j++){\\n cost+=2;\\n cost+=(A[i-1][j]<A[i][j]?(A[i][j] - A[i-1][j]):0);\\n cost+=(A[i+1][j]<A[i][j]?(A[i][j] - A[i+1][j]):0);\\n cost+=(A[i][j-1]<A[i][j]?(A[i][j] - A[i][j-1]):0);\\n cost+=(A[i][j+1]<A[i][j]?(A[i][j] - A[i][j+1]):0);\\n \\n }\\n \\n }\\n return cost;\\n }\\n\\n public static void main(String[] args) {\\n Scanner in = new Scanner(System.in);\\n int H = in.nextInt();\\n int W = in.nextInt();\\n int[][] A = new int[H+2][W+2];\\n for(int A_i = 1; A_i <= H; A_i++){\\n for(int A_j = 1; A_j <= W; A_j++){\\n A[A_i][A_j] = in.nextInt();\\n }\\n }\\n int result = surfaceArea(A);\\n System.out.println(result);\\n in.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'surfaceArea' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts 2D_INTEGER_ARRAY A as parameter.\\n */\\n\\n public static int surfaceArea(List<List<Integer>> A) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int H = Integer.parseInt(firstMultipleInput[0]);\\n\\n int W = Integer.parseInt(firstMultipleInput[1]);\\n\\n List<List<Integer>> A = new ArrayList<>();\\n\\n for (int i = 0; i < H; i++) {\\n String[] ARowTempItems = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> ARowItems = new ArrayList<>();\\n\\n for (int j = 0; j < W; j++) {\\n int AItem = Integer.parseInt(ARowTempItems[j]);\\n ARowItems.add(AItem);\\n }\\n\\n A.add(ARowItems);\\n }\\n\\n int result = Result.surfaceArea(A);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\\npublic class Solution {\\n static int surfaceArea(int[][] A) {\\n int cost = 0 ;\\n int m = A.length-2;\\n int k = A[0].length-2;\\n for(int i=1;i<=m;i++){\\n for(int j=1;j<=k;j++){\\n cost+=2;\\n cost+=(A[i-1][j]<A[i][j]?(A[i][j] - A[i-1][j]):0);\\n cost+=(A[i+1][j]<A[i][j]?(A[i][j] - A[i+1][j]):0);\\n cost+=(A[i][j-1]<A[i][j]?(A[i][j] - A[i][j-1]):0);\\n cost+=(A[i][j+1]<A[i][j]?(A[i][j] - A[i][j+1]):0);\\n \\n }\\n \\n }\\n return cost;\\n}\\n public static void main(String[] args) {\\n Scanner in = new Scanner(System.in);\\n int H = in.nextInt();\\n int W = in.nextInt();\\n int[][] A = new int[H+2][W+2];\\n for(int A_i = 1; A_i <= H; A_i++){\\n for(int A_j = 1; A_j <= W; A_j++){\\n A[A_i][A_j] = in.nextInt();\\n }\\n }\\n int result = surfaceArea(A);\\n System.out.println(result);\\n in.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'surfaceArea' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts 2D_INTEGER_ARRAY A as parameter.\\n */\\n\\n public static int surfaceArea(List<List<Integer>> A) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int H = Integer.parseInt(firstMultipleInput[0]);\\n\\n int W = Integer.parseInt(firstMultipleInput[1]);\\n\\n List<List<Integer>> A = new ArrayList<>();\\n\\n IntStream.range(0, H).forEach(i -> {\\n try {\\n A.add(\\n Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList())\\n );\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n int result = Result.surfaceArea(A);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\nimport java.text.*;\\nimport java.math.*;\\nimport java.util.regex.*;\\n\\npublic class Solution {\\n\\n static int surfaceArea(int[][] A) {\\n // Complete this function\\n int[][] b = new int[A.length+2][A[0].length+2];\\n for(int i=1; i<=A.length; ++i)\\n for(int j=1; j<=A[0].length; ++j)\\n b[i][j]=A[i-1][j-1];\\n int t=2*A.length*A[0].length;\\n for(int i=1; i<=A.length; ++i)\\n for(int j=1; j<=A[0].length; ++j)\\n t+=Math.max(b[i][j]-b[i-1][j], 0)+Math.max(b[i][j]-b[i][j-1], 0)+Math.max(b[i][j]-b[i+1][j], 0)+Math.max(b[i][j]-b[i][j+1], 0);\\n return t;\\n }\\n\\n public static void main(String[] args) {\\n Scanner in = new Scanner(System.in);\\n int H = in.nextInt();\\n int W = in.nextInt();\\n int[][] A = new int[H][W];\\n for(int A_i = 0; A_i < H; A_i++){\\n for(int A_j = 0; A_j < W; A_j++){\\n A[A_i][A_j] = in.nextInt();\\n }\\n }\\n int result = surfaceArea(A);\\n System.out.println(result);\\n in.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'surfaceArea' function below.\\n *\\n * The function is expected to return an INTEGER.\\n * The function accepts 2D_INTEGER_ARRAY A as parameter.\\n */\\n\\n public static int surfaceArea(List<List<Integer>> A) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String[] firstMultipleInput = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n int H = Integer.parseInt(firstMultipleInput[0]);\\n\\n int W = Integer.parseInt(firstMultipleInput[1]);\\n\\n List<List<Integer>> A = new ArrayList<>();\\n\\n IntStream.range(0, H).forEach(i -> {\\n try {\\n A.add(\\n Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList())\\n );\\n } catch (IOException ex) {\\n throw new RuntimeException(ex);\\n }\\n });\\n\\n int result = Result.surfaceArea(A);\\n\\n bufferedWriter.write(String.valueOf(result));\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"surfaceArea(A)\"\n ],\n \"solution\": \"#!/bin/python3\\n\\nimport sys\\n\\ndef surfaceArea(A):\\n # Complete this function\\n ans = 0\\n #print(A)\\n for i in range(len(A)):\\n for j in range(len(A[0])):\\n for dx, dy in [(-1,0), (0,1), (1,0), (0,-1)]:\\n nx = j + dx\\n ny = i + dy\\n if 0 <= nx < len(A[0]) and 0<= ny < len(A):\\n if nx >= j and ny >= i:\\n #print(ny, nx)\\n ans += abs(A[i][j] - A[ny][nx])\\n else:\\n ans += A[i][j]\\n \\n return ans\\n\\nif __name__ == \\\"__main__\\\":\\n H, W = input().strip().split(' ')\\n H, W = [int(H), int(W)]\\n A = []\\n for A_i in range(H):\\n A_t = [int(A_temp) for A_temp in input().strip().split(' ')]\\n A.append(A_t)\\n result = surfaceArea(A)\\n print(result + H*W + H*W)\\n\",\n \"template\": \"#\\n# Complete the 'surfaceArea' function below.\\n#\\n# The function is expected to return an INTEGER.\\n# The function accepts 2D_INTEGER_ARRAY A as parameter.\\n#\\n\\ndef surfaceArea(A):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n first_multiple_input = input().rstrip().split()\\n\\n H = int(first_multiple_input[0])\\n\\n W = int(first_multiple_input[1])\\n\\n A = []\\n\\n for _ in range(H):\\n A.append(list(map(int, input().rstrip().split())))\\n\\n result = surfaceArea(A)\\n\\n fptr.write(str(result) + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":198,"cells":{"question_id":{"kind":"number","value":4504,"string":"4,504"},"question_title":{"kind":"string","value":"Almost Sorted"},"question_content":{"kind":"string","value":"Given an array of integers, determine whether the array can be sorted in ascending order using only one of the following operations one time. \n\n1. Swap two elements. \n2. Reverse one sub-segment. \n\nDetermine whether one, both or neither of the operations will complete the task. Output is as follows. \n\n1. If the array is already sorted, output _yes_ on the first line. You do not need to output anything else. \n\n2. If you can sort this array using one single operation (from the two permitted operations) then output _yes_ on the first line and then: \n \n- If elements can only be swapped, $d[l]$ and $d[r]$, output _swap l r_ in the second line. $l$ and $r$ are the indices of the elements to be swapped, assuming that the array is indexed from $1$ to $n$. \n- If elements can only be reversed, for the segment $d[l...r]$, output _reverse l r_ in the second line. $l$ and $r$ are the indices of the first and last elements of the subarray to be reversed, assuming that the array is indexed from $1$ to $n$. Here $d[l...r]$ represents the subarray that begins at index $l$ and ends at index $r$, both inclusive. \n\nIf an array can be sorted both ways, by using either swap or reverse, choose swap. \n\n3. If the array cannot be sorted either way, output _no_ on the first line. \n\n**Example** \n$arr = [2,3,5,4]$ \n\nEither swap the $4$ and $5$ at indices 3 and 4, or reverse them to sort the array. As mentioned above, swap is preferred over reverse. Choose swap. On the first line, print `yes`. On the second line, print `swap 3 4`.\n\n**Function Description** \n\nComplete the *almostSorted* function in the editor below. \n\nalmostSorted has the following parameter(s): \n\n- *int arr[n]*: an array of integers \n\n**Prints** \n\n- Print the results as described and return nothing. \n\n"},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Medium"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static void almostSorted(List<Integer> arr)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.io.DataInputStream;\\nimport java.io.FileInputStream;\\nimport java.io.IOException;\\nimport java.io.PrintWriter;\\nimport java.util.Arrays;\\nimport java.util.HashMap;\\n\\npublic class Solution {\\n private static Reader in;\\n private static PrintWriter out;\\n\\n public static void main(String[] args) throws IOException {\\n in = new Reader();\\n out = new PrintWriter(System.out, true);\\n int N = in.nextInt();\\n int[] arr = new int[N];\\n for (int i = 0; i < N; i++)\\n arr[i] = in.nextInt();\\n int[] brr = Arrays.copyOf(arr, N);\\n Arrays.sort(brr);\\n HashMap<Integer, Integer> mp = new HashMap<Integer, Integer>();\\n for (int i = 0; i < N; i++)\\n mp.put(brr[i], i);\\n for (int i = 0; i < N; i++) {\\n arr[i] = mp.get(arr[i]);\\n }\\n\\n int outofpos = 0;\\n for (int i = 0; i < N; i++) {\\n if (arr[i] != i)\\n outofpos++;\\n }\\n\\n if (outofpos == 0) {\\n out.println(\\\"yes\\\");\\n out.close();\\n System.exit(0);\\n } else if (outofpos == 2) {\\n out.println(\\\"yes\\\");\\n out.print(\\\"swap\\\");\\n for (int i = 0; i < N; i++) {\\n if (arr[i] != i)\\n out.print(\\\" \\\" + (i + 1));\\n }\\n out.println();\\n out.close();\\n System.exit(0);\\n } else {\\n int s = -1, e = -1;\\n for (int i = 0; i < N; i++) {\\n if (arr[i] != i) {\\n e = i;\\n if (s == -1) s = i;\\n }\\n }\\n for (int i = s, j = e; i < j; i++, j--) {\\n int t = arr[i]; arr[i] = arr[j]; arr[j] = t;\\n }\\n for (int i = 0; i < N; i++) {\\n if (arr[i] != i) {\\n out.println(\\\"no\\\");\\n out.close();\\n System.exit(0);\\n }\\n }\\n \\n out.println(\\\"yes\\\");\\n out.printf(\\\"reverse %d %d\\\\n\\\", s+1, e+1);\\n out.close();\\n System.exit(0);\\n }\\n out.close();\\n System.exit(0);\\n }\\n\\n static class Reader {\\n final private int BUFFER_SIZE = 1 << 16;\\n private DataInputStream din;\\n private byte[] buffer;\\n private int bufferPointer, bytesRead;\\n\\n public Reader() {\\n din = new DataInputStream(System.in);\\n buffer = new byte[BUFFER_SIZE];\\n bufferPointer = bytesRead = 0;\\n }\\n\\n public Reader(String file_name) throws IOException {\\n din = new DataInputStream(new FileInputStream(file_name));\\n buffer = new byte[BUFFER_SIZE];\\n bufferPointer = bytesRead = 0;\\n }\\n\\n public String readLine() throws IOException {\\n byte[] buf = new byte[1024];\\n int cnt = 0, c;\\n while ((c = read()) != -1) {\\n if (c == '\\\\n')\\n break;\\n buf[cnt++] = (byte) c;\\n }\\n return new String(buf, 0, cnt);\\n }\\n\\n public int nextInt() throws IOException {\\n int ret = 0;\\n byte c = read();\\n while (c <= ' ')\\n c = read();\\n boolean neg = (c == '-');\\n if (neg)\\n c = read();\\n do {\\n ret = ret * 10 + c - '0';\\n } while ((c = read()) >= '0' && c <= '9');\\n if (neg)\\n return -ret;\\n return ret;\\n }\\n\\n public long nextLong() throws IOException {\\n long ret = 0;\\n byte c = read();\\n while (c <= ' ')\\n c = read();\\n boolean neg = (c == '-');\\n if (neg)\\n c = read();\\n do {\\n ret = ret * 10 + c - '0';\\n } while ((c = read()) >= '0' && c <= '9');\\n if (neg)\\n return -ret;\\n return ret;\\n }\\n\\n public double nextDouble() throws IOException {\\n double ret = 0, div = 1;\\n byte c = read();\\n while (c <= ' ')\\n c = read();\\n boolean neg = (c == '-');\\n if (neg)\\n c = read();\\n do {\\n ret = ret * 10 + c - '0';\\n } while ((c = read()) >= '0' && c <= '9');\\n if (c == '.')\\n while ((c = read()) >= '0' && c <= '9')\\n ret += (c - '0') / (div *= 10);\\n if (neg)\\n return -ret;\\n return ret;\\n }\\n\\n private void fillBuffer() throws IOException {\\n bytesRead = din.read(buffer, bufferPointer = 0, BUFFER_SIZE);\\n if (bytesRead == -1)\\n buffer[0] = -1;\\n }\\n\\n private byte read() throws IOException {\\n if (bufferPointer == bytesRead)\\n fillBuffer();\\n return buffer[bufferPointer++];\\n }\\n\\n public void close() throws IOException {\\n if (din == null)\\n return;\\n din.close();\\n }\\n }\\n\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'almostSorted' function below.\\n *\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static void almostSorted(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n String[] arrTemp = bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\");\\n\\n List<Integer> arr = new ArrayList<>();\\n\\n for (int i = 0; i < n; i++) {\\n int arrItem = Integer.parseInt(arrTemp[i]);\\n arr.add(arrItem);\\n }\\n\\n Result.almostSorted(arr);\\n\\n bufferedReader.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'almostSorted' function below.\\n *\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static void almostSorted(List<Integer> arr) {\\n int [] range = {0,0};\\n if ( arr.size()==1 ) {\\n System.out.println(\\\"no\\\");\\n return;\\n }\\n int state = 0 ; // swap 1 , reverse =2 end of reverse 3 , long swap 4, swap done\\n int duplicateCount= 0;\\n\\n for ( int i = 1 ; i< arr.size() ; i++){\\n\\n if ( arr.get(i) < arr.get(i-1) ){\\n switch (state){\\n case 0:\\n if (duplicateCount > 0){\\n state = 2; // reverse\\n range[0] = i - duplicateCount;\\n }else {\\n state =1 ; // swap\\n range[0] = i;\\n range[1] = i+1;\\n }\\n break;\\n case 1:\\n state = 2;\\n range[0]= i-1-duplicateCount;\\n break;\\n case 2:\\n break;\\n case 3:\\n System.out.println( \\\"no\\\");\\n return;\\n case 4:\\n if ( arr.get(i) < arr.get(range[0] -2) || arr.get(i) > arr.get(range[0])){\\n System.out.println(\\\"no\\\");\\n return;\\n }\\n range[1]= i+1;\\n state = 5;\\n break;\\n case 5:\\n System.out.println( \\\"no\\\");\\n return;\\n\\n }\\n duplicateCount = 0;\\n\\n } else if ( arr.get(i) > arr.get(i-1) ){\\n switch (state){\\n case 0:\\n break;\\n case 1:\\n state = 4;\\n break;\\n case 2:\\n if (arr.get(i) < arr.get(range[0] -1) ) {\\n System.out.println(\\\"no\\\");\\n return;\\n }\\n state =3;\\n range[1]= i;\\n break;\\n case 3:\\n break;\\n }\\n duplicateCount = 0 ;\\n\\n\\n } else if ( arr.get(i) == arr.get(i-1) ){\\n duplicateCount ++;\\n switch (state){\\n case 0:\\n break;\\n case 1:\\n state = 2;\\n break;\\n case 2:\\n break;\\n case 3:\\n range[1]++;\\n break;\\n }\\n }\\n }\\n\\n switch ( state){\\n case 0:\\n System.out.println( \\\"no\\\");\\n return;\\n case 1:\\n System.out.println( \\\"yes\\\\nswap \\\"+range[0] +\\\" \\\"+ range[1]);\\n return;\\n case 2:\\n System.out.println( \\\"yes\\\\nswap \\\"+range[0] +\\\" \\\"+ arr.size());\\n return;\\n case 3:\\n System.out.println( \\\"yes\\\\nreverse \\\"+range[0] +\\\" \\\"+ range[1]);\\n return;\\n case 4:\\n // we need to check if we can swap adjacent\\n if ((range[0] ==1 || arr.get(range[0]-2) <= arr.get(range[1]-1) ) && (arr.get(range[0]-1) <= arr.get(range[1]) )){\\n System.out.println( \\\"yes\\\\nswap \\\"+range[0] +\\\" \\\"+ (range[1]));\\n return;\\n }\\n System.out.println( \\\"no\\\");\\n return;\\n case 5:\\n System.out.println( \\\"yes\\\\nswap \\\"+range[0] +\\\" \\\"+ range[1]);\\n return;\\n\\n default:\\n throw new IllegalStateException(\\\"Unknown state\\\");\\n\\n }\\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n Result.almostSorted(arr);\\n\\n bufferedReader.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'almostSorted' function below.\\n *\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static void almostSorted(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n Result.almostSorted(arr);\\n\\n bufferedReader.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.util.*;\\n\\npublic class Solution {\\n\\n public static void main(String[] args) {\\n Scanner sc = new Scanner(System.in);\\n int[] arr = new int[sc.nextInt()];\\n int[] temp = new int[arr.length];\\n for (int i = 0; i < arr.length; i++) {\\n arr[i] = sc.nextInt();\\n temp[i] = arr[i];\\n }\\n Arrays.sort(temp);\\n int badCount = 0;\\n int bad1 = -1;\\n int bad2 = -1;\\n boolean[] bad = new boolean[arr.length];\\n HashMap<Integer, Integer> map = new HashMap<Integer, Integer>();\\n for (int i = 0; i < arr.length; i++) {\\n if (arr[i] != temp[i]) {\\n badCount++;\\n if (bad1 == -1) {\\n bad1 = i;\\n } else if (bad2 == -1) {\\n bad2 = i;\\n }\\n bad[i] = true;\\n }\\n map.put(temp[i], i);\\n }\\n if (badCount == 0) {\\n //no swaps req\\n System.out.println(\\\"yes\\\");\\n } else if (badCount == 2) {\\n //can swap\\n System.out.println(\\\"yes\\\");\\n System.out.println(\\\"swap \\\" + (bad1+1) + \\\" \\\" + (bad2+1));\\n } else {\\n //want to find a running sequence of bad.\\n boolean canReverse = true;\\n int low = -1, hi = -1;\\n boolean started = true;\\n for (int i = 0; i < arr.length; i++) {\\n if (bad[i]) {\\n if (!started) {\\n canReverse = false;\\n break;\\n }\\n if (low == -1) {\\n low = i;\\n hi = i;\\n started = true;\\n } else {\\n hi = i;\\n }\\n } else {\\n //System.out.println(\\\"SUP: \\\" + i);\\n //if (low != -1) started = false;\\n }\\n }\\n if (canReverse) {\\n boolean cant = false;\\n int j = 0;\\n // System.out.println(\\\"LOW: \\\" + low + \\\" HI: \\\" + hi);\\n for (int i = low; i <= hi; i++) {\\n j++;\\n int val = arr[i];\\n int shouldBe = map.get(val);\\n int cur = i;\\n int goes = hi-(i-low);\\n // System.out.println(\\\"CUR: \\\" + i);\\n // System.out.println(\\\"GOES: \\\" + goes);\\n if (goes != shouldBe) {\\n cant = true;\\n }\\n }\\n if (!cant) {\\n System.out.println(\\\"yes\\\"); \\n System.out.println(\\\"reverse \\\" + (low+1) + \\\" \\\" + (hi+1));\\n } else {\\n System.out.println(\\\"no\\\");\\n }\\n } else {\\n System.out.println(\\\"no\\\");\\n }\\n }\\n }\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'almostSorted' function below.\\n *\\n * The function accepts INTEGER_ARRAY arr as parameter.\\n */\\n\\n public static void almostSorted(List<Integer> arr) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n\\n int n = Integer.parseInt(bufferedReader.readLine().trim());\\n\\n List<Integer> arr = Stream.of(bufferedReader.readLine().replaceAll(\\\"\\\\\\\\s+$\\\", \\\"\\\").split(\\\" \\\"))\\n .map(Integer::parseInt)\\n .collect(toList());\\n\\n Result.almostSorted(arr);\\n\\n bufferedReader.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"almostSorted(arr)\"\n ],\n \"solution\": \"input()\\narray = [int(x) for x in input().split(\\\" \\\")]\\n\\nsorted_array = sorted(array)\\n\\nif array == sorted_array:\\n print(\\\"yes\\\")\\nelse: \\n wrong = []\\n for i, (a, b) in enumerate(zip(array, sorted_array)):\\n if a != b:\\n wrong.append(i)\\n if len(wrong) > 2:\\n break\\n \\n if len(wrong) == 2:\\n print(\\\"yes\\\")\\n print(\\\"swap\\\", wrong[0]+1, wrong[1]+1)\\n else: \\n index = sorted_array.index(array[wrong[0]])+1\\n if sorted_array[wrong[0]:index] == list(reversed(array[wrong[0]:index])):\\n print(\\\"yes\\\")\\n print(\\\"reverse\\\", wrong[0]+1, index)\\n else:\\n print(\\\"no\\\")\\n\",\n \"template\": \"#\\n# Complete the 'almostSorted' function below.\\n#\\n# The function accepts INTEGER_ARRAY arr as parameter.\\n#\\n\\ndef almostSorted(arr):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n n = int(input().strip())\\n\\n arr = list(map(int, input().rstrip().split()))\\n\\n almostSorted(arr)\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}},{"rowIdx":199,"cells":{"question_id":{"kind":"number","value":20055,"string":"20,055"},"question_title":{"kind":"string","value":"Super Reduced String"},"question_content":{"kind":"string","value":"Reduce a string of lowercase characters in range `ascii[‘a’..’z’]`by doing a series of operations. In each operation, select a pair of adjacent letters that match, and delete them. \n\nDelete as many characters as possible using this method and return the resulting string. If the final string is empty, return `Empty String`\n\n**Example**. \n\n$s = \\text{'aab'}$ \n\n`aab` shortens to `b` in one operation: remove the adjacent `a` characters. \n\n$s =\\text{'abba'}$ \n\nRemove the two 'b' characters leaving 'aa'. Remove the two 'a' characters to leave ''. Return 'Empty String'.\n\n\n**Function Description**\n\nComplete the *superReducedString* function in the editor below. \n\nsuperReducedString has the following parameter(s): \n\n- *string s:* a string to reduce \n\n**Returns** \n\n- *string:* the reduced string or `Empty String` "},"dataset":{"kind":"string","value":"hackerrank"},"difficulty":{"kind":"string","value":"Easy"},"java":{"kind":"string","value":"{\n \"class_name\": \"Result\",\n \"func_sign\": [\n \"public static String superReducedString(String s)\"\n ],\n \"script_name\": \"Solution\",\n \"solution\": \"import java.awt.*;\\nimport java.awt.event.*;\\nimport java.awt.geom.*;\\nimport java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*; \\nimport java.util.*;\\nimport java.util.regex.*;\\n/*\\n\\t br = new BufferedReader(new FileReader(\\\"input.txt\\\"));\\n\\t pw = new PrintWriter(new BufferedWriter(new FileWriter(\\\"output.txt\\\")));\\n\\t br = new BufferedReader(new InputStreamReader(System.in));\\n\\t pw = new PrintWriter(new BufferedWriter(new OutputStreamWriter(System.out)));\\n */\\n\\n\\npublic class Solution {\\n\\tprivate static BufferedReader br;\\n\\tprivate static StringTokenizer st;\\n\\tprivate static PrintWriter pw;\\n\\n\\tpublic static void main(String[] args) throws Exception {\\n\\t\\tbr = new BufferedReader(new InputStreamReader(System.in));\\n\\t\\tpw = new PrintWriter(new BufferedWriter(new OutputStreamWriter(System.out)));\\n\\t\\t//int qq = 1;\\n\\t\\tint qq = Integer.MAX_VALUE;\\n\\t\\t//int qq = readInt();\\n\\t\\tfor(int casenum = 1; casenum <= qq; casenum++)\\t{\\n\\t\\t\\tString s = nextToken();\\n\\t\\t\\tLinkedList<Character> q = new LinkedList<Character>();\\n\\t\\t\\tfor(int i = 0; i < s.length(); i++) {\\n\\t\\t\\t\\tif(!q.isEmpty() && q.peekLast() == s.charAt(i)) {\\n\\t\\t\\t\\t\\tq.removeLast();\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\telse {\\n\\t\\t\\t\\t\\tq.addLast(s.charAt(i));\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\tfor(char out: q) {\\n\\t\\t\\t\\tpw.print(out);\\n\\t\\t\\t}\\n\\t\\t\\tif(q.size() == 0) {\\n\\t\\t\\t\\tpw.print(\\\"Empty String\\\");\\n\\t\\t\\t}\\n\\t\\t\\tpw.println();\\n\\t\\t}\\n\\t\\texitImmediately();\\n\\t}\\n\\n\\tprivate static void exitImmediately() {\\n\\t\\tpw.close();\\n\\t\\tSystem.exit(0);\\n\\t}\\n\\n\\tprivate static long readLong() throws IOException {\\n\\t\\treturn Long.parseLong(nextToken());\\n\\t}\\n\\n\\tprivate static double readDouble() throws IOException {\\n\\t\\treturn Double.parseDouble(nextToken());\\n\\t}\\n\\n\\tprivate static int readInt() throws IOException {\\n\\t\\treturn Integer.parseInt(nextToken());\\n\\t}\\n\\n\\tprivate static String nextLine() throws IOException {\\n\\t\\tif(!br.ready()) {\\n\\t\\t\\texitImmediately();\\n\\t\\t}\\n\\t\\tst = null;\\n\\t\\treturn br.readLine();\\n\\t}\\n\\n\\tprivate static String nextToken() throws IOException {\\n\\t\\twhile(st == null || !st.hasMoreTokens()) {\\n\\t\\t\\tif(!br.ready()) {\\n\\t\\t\\t\\texitImmediately();\\n\\t\\t\\t}\\n\\t\\t\\tst = new StringTokenizer(br.readLine().trim());\\n\\t\\t}\\n\\t\\treturn st.nextToken();\\n\\t}\\n}\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'superReducedString' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts STRING s as parameter.\\n */\\n\\n public static String superReducedString(String s) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.regex.*;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String s = bufferedReader.readLine();\\n\\n String result = Result.superReducedString(s);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java15":{"kind":"string","value":"{\n \"solution\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\nclass Result {\\n\\n /*\\n * Complete the 'superReducedString' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts STRING s as parameter.\\n */\\n\\n public static String superReducedString(String s) {\\n // Write your code here\\n for(int i=1;i<s.length();++i)\\n {\\n if(s.charAt(i)==s.charAt(i-1))\\n {\\n s=s.substring(0,i-1)+s.substring(i+1);\\n i=0;\\n }\\n }\\n if(s.length()==0)\\n {\\n return \\\"Empty String\\\";\\n }\\n return s;\\n \\n }\\n\\n}\\n\\npublic class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String s = bufferedReader.readLine();\\n\\n String result = Result.superReducedString(s);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'superReducedString' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts STRING s as parameter.\\n */\\n\\n public static String superReducedString(String s) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String s = bufferedReader.readLine();\\n\\n String result = Result.superReducedString(s);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"java8":{"kind":"string","value":"{\n \"solution\": \"import java.io.ByteArrayInputStream;\\nimport java.io.IOException;\\nimport java.io.InputStream;\\nimport java.io.PrintWriter;\\nimport java.util.Arrays;\\nimport java.util.InputMismatchException;\\n\\npublic class A {\\n\\tInputStream is;\\n\\tPrintWriter out;\\n\\tString INPUT = \\\"\\\";\\n\\t\\n\\tvoid solve()\\n\\t{\\n\\t\\tString s = ns();\\n\\t\\twhile(true){\\n\\t\\t\\tboolean f = false;\\n\\t\\t\\tfor(char c = 'a';c <= 'z';c++){\\n\\t\\t\\t\\tString q = \\\"\\\" + c + c;\\n\\t\\t\\t\\tint ind = s.indexOf(q);\\n\\t\\t\\t\\tif(ind >= 0){\\n\\t\\t\\t\\t\\ts = s.substring(0, ind) + s.substring(ind+2);\\n\\t\\t\\t\\t\\tf = true;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\tif(!f)break;\\n\\t\\t}\\n\\t\\tif(s.length() == 0){\\n\\t\\t\\tout.println(\\\"Empty String\\\");\\n\\t\\t}else{\\n\\t\\t\\tout.println(s);\\n\\t\\t}\\n\\t}\\n\\t\\n\\tvoid run() throws Exception\\n\\t{\\n\\t\\tis = INPUT.isEmpty() ? System.in : new ByteArrayInputStream(INPUT.getBytes());\\n\\t\\tout = new PrintWriter(System.out);\\n\\t\\t\\n\\t\\tlong s = System.currentTimeMillis();\\n\\t\\tsolve();\\n\\t\\tout.flush();\\n\\t\\tif(!INPUT.isEmpty())tr(System.currentTimeMillis()-s+\\\"ms\\\");\\n\\t}\\n\\t\\n\\tpublic static void main(String[] args) throws Exception { new A().run(); }\\n\\t\\n\\tprivate byte[] inbuf = new byte[1024];\\n\\tprivate int lenbuf = 0, ptrbuf = 0;\\n\\t\\n\\tprivate int readByte()\\n\\t{\\n\\t\\tif(lenbuf == -1)throw new InputMismatchException();\\n\\t\\tif(ptrbuf >= lenbuf){\\n\\t\\t\\tptrbuf = 0;\\n\\t\\t\\ttry { lenbuf = is.read(inbuf); } catch (IOException e) { throw new InputMismatchException(); }\\n\\t\\t\\tif(lenbuf <= 0)return -1;\\n\\t\\t}\\n\\t\\treturn inbuf[ptrbuf++];\\n\\t}\\n\\t\\n\\tprivate boolean isSpaceChar(int c) { return !(c >= 33 && c <= 126); }\\n\\tprivate int skip() { int b; while((b = readByte()) != -1 && isSpaceChar(b)); return b; }\\n\\t\\n\\tprivate double nd() { return Double.parseDouble(ns()); }\\n\\tprivate char nc() { return (char)skip(); }\\n\\t\\n\\tprivate String ns()\\n\\t{\\n\\t\\tint b = skip();\\n\\t\\tStringBuilder sb = new StringBuilder();\\n\\t\\twhile(!(isSpaceChar(b))){ // when nextLine, (isSpaceChar(b) && b != ' ')\\n\\t\\t\\tsb.appendCodePoint(b);\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn sb.toString();\\n\\t}\\n\\t\\n\\tprivate char[] ns(int n)\\n\\t{\\n\\t\\tchar[] buf = new char[n];\\n\\t\\tint b = skip(), p = 0;\\n\\t\\twhile(p < n && !(isSpaceChar(b))){\\n\\t\\t\\tbuf[p++] = (char)b;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\treturn n == p ? buf : Arrays.copyOf(buf, p);\\n\\t}\\n\\t\\n\\tprivate char[][] nm(int n, int m)\\n\\t{\\n\\t\\tchar[][] map = new char[n][];\\n\\t\\tfor(int i = 0;i < n;i++)map[i] = ns(m);\\n\\t\\treturn map;\\n\\t}\\n\\t\\n\\tprivate int[] na(int n)\\n\\t{\\n\\t\\tint[] a = new int[n];\\n\\t\\tfor(int i = 0;i < n;i++)a[i] = ni();\\n\\t\\treturn a;\\n\\t}\\n\\t\\n\\tprivate int ni()\\n\\t{\\n\\t\\tint num = 0, b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate long nl()\\n\\t{\\n\\t\\tlong num = 0;\\n\\t\\tint b;\\n\\t\\tboolean minus = false;\\n\\t\\twhile((b = readByte()) != -1 && !((b >= '0' && b <= '9') || b == '-'));\\n\\t\\tif(b == '-'){\\n\\t\\t\\tminus = true;\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t\\t\\n\\t\\twhile(true){\\n\\t\\t\\tif(b >= '0' && b <= '9'){\\n\\t\\t\\t\\tnum = num * 10 + (b - '0');\\n\\t\\t\\t}else{\\n\\t\\t\\t\\treturn minus ? -num : num;\\n\\t\\t\\t}\\n\\t\\t\\tb = readByte();\\n\\t\\t}\\n\\t}\\n\\t\\n\\tprivate static void tr(Object... o) { System.out.println(Arrays.deepToString(o)); }\\n}\\n\",\n \"template\": \"class Result {\\n\\n /*\\n * Complete the 'superReducedString' function below.\\n *\\n * The function is expected to return a STRING.\\n * The function accepts STRING s as parameter.\\n */\\n\\n public static String superReducedString(String s) {\\n // Write your code here\\n\\n }\\n\\n}\\n\\n\",\n \"template_head\": \"import java.io.*;\\nimport java.math.*;\\nimport java.security.*;\\nimport java.text.*;\\nimport java.util.*;\\nimport java.util.concurrent.*;\\nimport java.util.function.*;\\nimport java.util.regex.*;\\nimport java.util.stream.*;\\nimport static java.util.stream.Collectors.joining;\\nimport static java.util.stream.Collectors.toList;\\n\\n\",\n \"template_tail\": \"public class Solution {\\n public static void main(String[] args) throws IOException {\\n BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));\\n BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv(\\\"OUTPUT_PATH\\\")));\\n\\n String s = bufferedReader.readLine();\\n\\n String result = Result.superReducedString(s);\\n\\n bufferedWriter.write(result);\\n bufferedWriter.newLine();\\n\\n bufferedReader.close();\\n bufferedWriter.close();\\n }\\n}\\n\"\n}"},"python":{"kind":"string","value":"{\n \"class_name\": null,\n \"func_sign\": [\n \"superReducedString(s)\"\n ],\n \"solution\": \"s = input()\\nwhile True:\\n for i in range(len(s)-1):\\n if s[i] == s[i+1]:\\n s = s[:i]+s[i+2:]\\n break\\n else:\\n break\\nprint(s if s else \\\"Empty String\\\")\",\n \"template\": \"#\\n# Complete the 'superReducedString' function below.\\n#\\n# The function is expected to return a STRING.\\n# The function accepts STRING s as parameter.\\n#\\n\\ndef superReducedString(s):\\n # Write your code here\\n\\n\",\n \"template_head\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\",\n \"template_tail\": \"if __name__ == '__main__':\\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\\n\\n s = input()\\n\\n result = superReducedString(s)\\n\\n fptr.write(result + '\\\\n')\\n\\n fptr.close()\\n\"\n}"},"test_cases":{"kind":"string","value":"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"},"max_retries":{"kind":"null"}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":1,"numItemsPerPage":100,"numTotalItems":455,"offset":100,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sInJlcG9JZHMiOlsiNjhkNDMxMzVhODg2ZDg4MDYzZDNhMzZlIl0sImlhdCI6MTc5NDMwMTI2NCwianRpIjoiM2FmOGFjMzYtZWQ5NS00MzFkLWJiZDItZTZiZjY2NmE0ZWU5Iiwic3ViIjoiL2RhdGFzZXRzL0NvZGUtVFJFQVQvY29kZV90cmFuc2xhdGlvbiIsImV4cCI6MTc5NDMwNDg2NCwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.30FCDObGkgwFS1x_IWZjQeV9nIdDeohtO9Jf1_zFfhovJgKawgdRU_dtWbir22qEZ3sc26lIss201DXFwAM8Dg","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"test","numRows":455,"numBytesParquet":52304117}]},"discussionsStats":{"closed":0,"open":0,"total":0},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
question_id int64 39 56.8k | question_title stringlengths 3 54 | question_content stringlengths 88 4.02k | dataset stringclasses 1
value | difficulty stringclasses 3
values | java dict | java15 dict | java8 dict | python dict | test_cases stringlengths 28 4.02M | max_retries float64 1 3 ⌀ |
|---|---|---|---|---|---|---|---|---|---|---|
2,596 | Order of Prime in Factorial | For a given prime $p$, define $\operatorname{ord}_p(k)$ as the multiplicity of $p$ in $k$, i.e. the number of times $p$ appears in the prime factorization of $k$.
For a given $p$ (prime) and $L$, let $F(p,L)$ be the number of integers $n$ such that $1 \le n \le L$ and $\operatorname{ord}_p(n!)$ is divisible by $p$. He... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static long solve(long p, long l)"
],
"script_name": "Solution",
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismat... | {
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class Solution {\n static InputStream is;\n static PrintWriter out;\n static String INPUT = ... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n public static final int MOD = 1000000007;\n static Scanner sc;\n private static PrintWriter out;\n\n public static void main(String[] s) throws Exception {\n sc = new Scanner(System.in);\n// sc = new MyScann... | {
"class_name": null,
"func_sign": [
"solve(p, l)"
],
"solution": "T = int(input())\n\ndef ok(p, L):\n sum = 0\n while L > 0:\n sum += L % p\n L //= p\n return sum % p\n\ndef solve(p, L):\n L1 = L // p\n r = p - ok(p, L1)\n if r == p:\n r = 0\n if r < L % p:\n ... | eJx9mM2OZDsRhFnwEuysXl8hO/2bvAhINLCAWbBp7mKuhISQeAh4X75I+0zVVHXNjNTurjrHzoz8iUj/59f/+/NvfhX//vB7fvnjv97+/vHzL1/ffpfeSn//sDr7qCmW9f5RbNkYJZ1VH9SWZ+p9eu2Vv3OzWlMs8XpqM54a3apl3qzd17RWMh+vXsbUw97z4O/ZWi1prFJ54v2j1earpFhMu/SFLS17dT0+Vqsz6afezXN2z4v3iz5ZPTd97LmYp8YrbPL+4bPkmjChFP6qs7blSYtjz9tP6e3LP3/+8tevX/72l3/88vXgEM7b5Xw+... | null |
1,232 | nCr table | Jim is doing his discrete maths homework which requires him to repeatedly calculate <sup>n</sup>C<sub>r</sub>(n choose r) for different values of n. Knowing that this is time consuming, he goes to his sister June for help. June, being a computer science student knows how to convert this into a computer program and gen... | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static List<Integer> solve(int n)"
],
"script_name": "Solution",
"solution": "import java.math.BigInteger;\nimport java.util.*;\nimport java.io.*;\n\n\npublic class Solution {\n\t\n\tprivate static BigInteger mod = BigInteger.valueOf(1000000000);\n\t\n\tpri... | {
"solution": "import java.math.BigInteger;\nimport java.util.*;\nimport java.io.*;\n\n\npublic class Solution {\n \n private static BigInteger mod = BigInteger.valueOf(1000000000);\n \n private static void compute(int input){\n \n BigInteger up = BigInteger.ONE;\n BigInteger down = B... | {
"solution": "/*\n * To change this license header, choose License Headers in Project Properties.\n * To change this template file, choose Tools | Templates\n * and open the template in the editor.\n */\n//package Combinatorics;\n\nimport java.math.BigInteger;\nimport java.util.Scanner;\n\n/**\n *\n * @author anurag... | {
"class_name": null,
"func_sign": [
"solve(n)"
],
"solution": "from math import ceil\nT=int(input())\nwhile(T>0):\n T-=1\n N=int(input())\n temp=1\n print(1,end=' ')\n for i in range(1,N+1):\n temp=temp*(N-i+1)//i\n print(temp%10**9,end=' ')\n print('')",
"template": "#\n#... | eJxcnb3ONDmSnWXoQgpty0gyk2RS0IUI0AAypDHkzK4xC0iQo0tXPCeYPPwWg+murrcqK5MMRpw48ff//uN//S//51/+w3/7v3/9r3/867/986///Pur9Odv/yil/e0ftf/tH6Nf8eKt8Y9W4h91xh+fO/7Q4hPlin/0qf+MP8wSX216Fd/q/PG544/6f3x7dq4Qn6l3fKaVNz5UBteP/xr84hV/HHf8o3d+vMx1vefmNrihp+vSXOSNb7zc0Dvj4nGdMV7eiU/pIUbZN/DM9fmi56nczMuXeLL7+R5lxv9v7r1c8Y+7czn+cbebW+Ui... | null |
4,264 | Merge List | Shashank is very excited after learning about the *linked list*. He learned about how to *merge* two linked lists. When we merge two linked lists, the order of the elements of each list doesn't change. For example, if we merge $[1, 2, 3]$ and $[4,5,6]$, $[1,4,2,3,5,6]$ is a valid merge, while $[1,4,3,2,5,6]$ is not a v... | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static int solve(int n, int m)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n private static MyScanner sc;\n priv... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n private static MyScanner sc;\n private static PrintWriter out;\n\n public static void main(String[] args) {\n /* Enter your code here. Read input ... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\nimport java.util.Scanner;\npublic class Solution {\n\n public static void main(String[] args) {\n Scanner sc = new Scanner(System.in);\n int T = sc.nextInt();\n int m;\n ... | {
"class_name": null,
"func_sign": [
"solve(n, m)"
],
"solution": "t = int(input())\nfor _ in range(t):\n n, m = map(int, input().split())\n ret = 1\n for i in range(n + 1, n + m + 1):\n ret *= i\n for i in range(1, m + 1):\n ret //= i\n print(ret % (10**9 + 7))",
"template": ... | eJyNVb2Ok0kQJCDgMUaOEeqe/r9HICNDwugC2OASQ7BISKeT7iHgfaker1mf159vN7Dn+9zTXV1V3fvvy5/vXr1Yf+/f4vDh791fh6/f7nd/jF3sD8Kj9oewoYwHGkx40hF46ceH4R2QI/eH3euxu/v+9e7T/d3nP798u39IM0NUMhPRM4soxXx/SA13sol7FTIlpQMSP+8PnM5ZkghzEkvh2v3zepxBU+SSEbI/mI3ElcRT348hso2EmXxaIF41iH2qIUNNZU1DzpwEiEx6Ua7x5jB0mj4Sd1xH4WWAHhCDnybe5QjerM0sJhkNGd3W... | null |
3,548 | Mehta and the Typical Supermarket | Mehta is a very rich guy. He has $N$ types of coins, and each type of coin is available in an unlimited supply.
So Mehta goes to a supermarket to buy monthly groceries. There he sees that every item has a unique price, that is, no two items have the same price.
Now, the supermarket owner tells Mehta that they are... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static List<Integer> solve(List<Integer> a, List<List<Integer>> days)"
],
"script_name": "Solution",
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.A... | {
"solution": "import java.io.*;\n\npublic class HR_mehta_and_the_typical_supermarket {\n\n public static void solve(Input in, PrintWriter out) throws IOException {\n int n = in.nextInt();\n long[] a = new long[n];\n for (int i = 0; i < n; ++i) {\n a[i] = in.nextLong();\n }\n... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n\tBufferedReader br;\n\tPrintWriter out;\n\tStringTokenizer st;\n\tboolean eof;\n\n\tstatic long gcd(long a, long b) {\n\t\treturn b == 0 ? a : gcd(b, a % b);\n\t}\n\n\tstatic long lcm(long a, long b) {\n\t\treturn a / gcd(a, b) * b;\... | {
"class_name": null,
"func_sign": [
"solve(a, days)"
],
"solution": "import itertools, math\ndef findsubsets(S,m):\n return set(itertools.combinations(S, m))\n\ndef createInitial():\n l = [int(input()) for i in range(int(input()))]\n ln = list()\n for x in range(len(l)):\n yes = 1\n ... | eJyNnMvOLclRhRnwIKUeWygr78mER2CKxEEMwAMmjQe2hIQs8RDwvnzfyn3c3ad9LLfs/qv3pSovEStWrIjc//O3//cP//g3+eefNhf//N8//MePv/vD73/4++eHt3/5se0vP9b3y4/v+PIj/2vry4+98Df/5fu+eHI5/Njk47z57ln7HL32h8szV5ubt94+Z1m1l/d5+yqtjzn99Cp9vL2O83h5Tq+7+OlWThtj+Gm+U986M5T3FO7Y2uPlrG0uRvG++2ye6Kff0/qe8/Xefb21cNfiI884h88w3nfWPXc/T38Xb6x5vAXjrO11HC+v... | null |
1,932 | Sherlock and Pairs | Sherlock is given an array of $N$ integers ($A$<sub>$0$</sub>, $A$<sub>$1$</sub> ... $A$<sub>$N-1$</sub> by Watson. Now Watson asks Sherlock how many different pairs of indices $i$ and $j$ exist such that $i$ is not equal to $j$ but $A$<sub>$i$</sub> is equal to $A$<sub>$j$</sub>.
That is, Sherlock has to count t... | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static int solve(List<Integer> a)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n\tstatic void solve() throws IOException {\n\t\tint tc = nextInt();\n\t\twhile (tc-- > 0) {\n\t\t\tint n = nex... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n static void solve() throws IOException {\n int tc = nextInt();\n while (tc-- > 0) {\n int n = nextInt();\n int[] a = new int[n];\n for (int i = 0; i < n; i++) {\n a[i] ... | {
"solution": "import java.util.Scanner;\nimport java.util.Arrays;\npublic class Solution{\n public static void main(String[] args){\n Scanner in = new Scanner(System.in);\n int t = in.nextInt();\n long[] arr = new long[t];\n for(int i = 0; i < t; i++){\n int n = in.nextInt()... | {
"class_name": null,
"func_sign": [
"solve(a)"
],
"solution": "#!/usr/bin/env python\n\nimport collections, math, sys\n\n\nif __name__ == '__main__':\n T = int(sys.stdin.readline())\n \n for _ in range(T):\n \tN = int(sys.stdin.readline())\n \tA = sorted(map(int, sys.stdin.readline().split()... | eJyEnUuubcuSVCnQkK1XzkJEzPjSANqAxJUoQBaoXCgkEhJCokqvsTF8XwklmYlA7+Y5Z++15oyPf8zNzf/Xv/53//vftn/17//H3/7zn//1v/3D3/7Nz9++P/7sf/x5vzf6/ePP/cef4/vez1t79P6z3+7f+Hn9W/v+3Hbe97O++doff7785Dv3/fQx2mo/75w77s+Yra/84mmt9Z/Ve3vr5835vvvT3+pz/7y92xx//Pm3v/v529//9//69//xH/7+P/2H//Lf/uH3gfLZ/P+//c+/+/m/HjPP2E++dZ23xk+eZ4zx8+UxTx62j/3x... | null |
5,183 | Anti-Palindromic Strings | You are given two integers, $N$ and $M$. Count the number of strings of length $N$ (under the alphabet set of size $M$) that doesn't contain any palindromic string of the length greater than $1$ as a consecutive substring. | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static int solve(int n, int m)"
],
"script_name": "Solution",
"solution": "import java.io.*;\n\npublic class HR_antipalindromic_strings {\n\n final static long mod = 1000000007;\n\n static long modPow(long x, long pow, long mod) {\n long r = 1;... | {
"solution": "import java.io.*;\n\npublic class HR_antipalindromic_strings {\n\n final static long mod = 1000000007;\n\n static long modPow(long x, long pow, long mod) {\n long r = 1;\n while (pow > 0) {\n if (pow % 2 == 1) {\n r = r * x % mod;\n }\n ... | {
"solution": "import java.io.*;\nimport java.math.BigInteger;\nimport java.util.*;\n\n\npublic class B {\n\n static final int MOD=1000000007;\n\n long modPow(long a,int st){\n if(st==0)return 1;\n long b=modPow(a,st/2);\n b*=b;\n b%=MOD;\n if(st%2!=0){\n b*=a;\n ... | {
"class_name": null,
"func_sign": [
"solve(n, m)"
],
"solution": "P = 10**9 + 7\n\ndef f(n, m):\n\tif n == 1:\n\t\treturn m % P\n\tif n == 2:\n\t\treturn m * (m - 1) % P\n\tif m <= 2:\n\t\treturn 0\n\treturn m * (m - 1) * pow(m - 2, n - 2, P) % P\n\nt = int(input())\nfor _ in range(t):\n\tn, m = map(int, i... | eJyVVruO5FQQJSDhL0odr1C9H3wJEo0IYAKSZoNdaSWEREhGAv/LKQ+Mlp7xsFgjj9u+vnXqPKr718///P2Lz47j699w8c3Plx9vb9+/u3xFl7reqrPE24SER4ajr7duzohhpu7EnXLGzVQO6Wky5cSHwdsjLVPNRTYRNWx+vSnXuOo46bCUSWNlcpVnupJn22SOXW94mChbQ84c5RpxvV3e0OXhw9uH7989/PDdT+/f/Y01xDkMta43A9CcCdSSmMJNR4UOizCOBIIB5kjHpVWES22nog6wNZdf3tBHJCjasE4sdOBI78TywSaNbkwj... | null |
3,320 | Lexicographic paths | Krishnakant is standing at $(0,0)$ in the [Cartesian plane](http://en.wikipedia.org/wiki/Cartesian_coordinate_system). He wants to go to the point $(x,y)$ in the same plane using only horizontal and vertical moves of $1$ unit. There are many ways of doing this, and he is writing down all such ways. Each way comprises o... | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static String solve(int x, int y, int k)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n private static Reader in;\n private static PrintWriter out;\n\n public static void main(String[] args... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n private static Reader in;\n private static PrintWriter out;\n\n public static void main(String[] args) throws IOException {\n in = new Reader();\n out = new PrintWriter(System.out, true);\n \n \n int[][] comb = new in... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.math.*;\n\n\npublic class Solution {\n\tstatic BufferedReader in;\n\tstatic PrintWriter out;\n\tstatic StringTokenizer tok;\n\t\n\tstatic final int N = 10;\n\tstatic ArrayList<String>[][] res = new ArrayList[N + 1][N + 1];\n\t\n\tstatic void solve() t... | {
"class_name": null,
"func_sign": [
"solve(x, y, k)"
],
"solution": "import math\n\n\ndef paths(n, m):\n if min(n, m) < 0:\n return 0\n return math.factorial(n+m) / math.factorial(n) / math.factorial(m)\n\n\nif __name__ == '__main__':\n T = int(input())\n for cs in range(T):\n res = []\n ... | eJyNVUuu1DAQZIHENVpZPyG3HTsJJ/AJLCSCWMAs2IS3mCchoSdxCFiw46j0z5kwSQxZjB1n0lXV1d35/vLn71cv5Hr7izbvvnWfl8ena/cGun5eECL4eelpGWgdIEA/zIuHATDNS/cA3eXr4+Xj9fLpw5enq71YSi6lzAstOfN+XmTln0zHRQ5L6Z4fYIMX5yUAOvBj4F0CxHmZCGqKY+RdgDAwGQ+hgW0XgxGuIuUisDkbIzrkh3qf6x0/u+NEQMQDbOk5BQjuFN0CWrBsGHkfN5nWiSRSisE7Du1F8gAkMY2UfUdJSInPCLsBK3pM... | null |
746 | Game Of Thrones - II | This follows from [Game of Thrones - I](https://www.hackerrank.com/contests/july13/challenges/game-of-thrones).
Now that the king knows how to find out whether a given word has an [anagram](https://en.wikipedia.org/wiki/Anagram) which is a [palindrome](http://en.wikipedia.org/wiki/Palindrome) or not, he encounters ano... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static int solve(String s)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.math.BigInteger;\n\npublic class Solution {\n\n static final long mod = 1000000007;\n\n public static void solve(Input in, PrintWriter out) throws IO... | {
"solution": "import java.io.*;\nimport java.math.BigInteger;\n\npublic class Solution {\n\n static final long mod = 1000000007;\n\n public static void solve(Input in, PrintWriter out) throws IOException {\n String s = in.next();\n int[] counts = new int[26];\n for (char c : s.toCharArray(... | {
"solution": "import java.io.*;\nimport java.math.BigInteger;\nimport java.util.*;\n\n\npublic class Solution {\n BufferedReader reader;\n\n int nextInt() throws IOException {\n tokenizer.nextToken();\n return (int) tokenizer.nval;\n }\n\n String next() throws IOException {\n tokenizer.nextToken();\n ... | {
"class_name": null,
"func_sign": [
"solve(s)"
],
"solution": "def fact_mod_p_brute(n, p):\n\t\n\tif n == 0:\n\t\treturn 1\n\n\tres = 1\n\tfor i in range(1, n+1):\n\t\tres *= i\n\t\tres = res % p\n\treturn res\n\ns2 = [0 for i in range(270)]\ns = str(input())\n\nfor l in s:\n\ts2[ord(l)]+=1\n\nc = 0;\nsum ... | eJyMnEkOHMl5hb3wxrdocK2Fm6LY3T6JARswYp7nOQQBXvoAOrBfFLwwCBCQpIYksqoyM+L/3/teZGT+9z//+//8/V/+6T/++sXEPPqXf/vlS/zP+OVPv3wROwvWBf+vNPr//c2vX/72p1/+3yf3/oc/Wo35hz/rZd9dbl/rz7/y9V9/+BJf1o0myeCRHDeJi4TwKOdOap7tykq8KTlteX/TZHPkpz/+Hb/+27fff/3+wyGkE4LMw7ipWi4+8bWV6ynnakXqiXVITsi5xFwnFSvDZnakP7LEnr1OPc26CLslr5lFVdoGqcSJUorMWXdS... | null |
20,591 | Super Humble Matrix | Sherry likes matrices a lot, but her favorite ones are *humble matrices*. An $N \times M$ matrix (we'll call it $A$) is *humble* if:
- It contains all the elements in range $[1 , N \times M ]$ exactly once.
- For any $2$ elements $(i_1, j_1)$ and $(i_2, j_2)$ in matrix $A$:<br>
If $i_1 + j_1 \lt i_2 + j_2$ , then $A_... | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static int solve(int n, int m)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static void main(String[] args) {... | {
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class B {\n InputStream is;\n PrintWriter out;\n String INPUT = \"\";\n \n void solve(... | {
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class B {\n\tInputStream is;\n\tPrintWriter out;\n\tString INPUT = \"\";\n\t\n\tvoid solve()\n\t{\n\t... | {
"class_name": null,
"func_sign": [
"solve(n, m)"
],
"solution": "M = 10**9 + 7\n\ndef main():\n n, m = map(int, input().split())\n result = solve(n, m)\n result_mod = result % M\n print(result_mod)\n\ndef solve(n, m):\n if n > m:\n n, m = m, n\n product = 1\n f = 1\n for i i... | eJyVk71KQ0EQhS3yIMOtg8z/j08iqFhoCpuYIgFBBB9CCzsf1SUxIBs2MQMLt5jvnjmzZ99nn9+zi21df7WPm9fpabnarKcrmFQ8LSE8OeJ2Oc1hWrysFg/rxeP982b920WhUVJY09sc/tCE7grpiORD2L2c2Jg6ON24GFIpNYe0qBKpFHe0kJAGZLnHmM5iJCQ7oNMFEyoCj2hbeHMoLh3NKOgFnCIhR3yztz9gdrSVGAqohVKNtclFw7XXJi9TBimiHO+clNiFEXta2ugKRe4y1o6qEml32tFqUh7AIXHEdwQ1a9nbJqDxuIe9uK3/... | null |
69 | Picking Cards | There are N cards on the table and each has a number between 0 and N. Let us denote the number on the i<sup>th</sup> card by c<sub>i</sub>. You want to pick up all the cards. The i<sup>th</sup> card can be picked up only if at least c<sub>i</sub> cards have been picked up before it. (As an example, if a card has a valu... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static int solve(List<Integer> c)"
],
"script_name": "Solution",
"solution": "import java.io.InputStreamReader;\nimport java.io.IOException;\nimport java.io.BufferedReader;\nimport java.io.OutputStream;\nimport java.io.PrintWriter;\nimport java.util.StringT... | {
"solution": "import java.io.InputStreamReader;\nimport java.io.IOException;\nimport java.io.BufferedReader;\nimport java.io.OutputStream;\nimport java.io.PrintWriter;\nimport java.util.StringTokenizer;\nimport java.io.Writer;\nimport java.io.InputStream;\n\n/**\n * Built using CHelper plug-in\n * Actual solution is... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n public static void main(String[] args) throws IOException {\n /* Enter your code here. Read input from STDIN. Print output to STDOUT. Your class should be named Solution. */\n \n BufferedReader br = new Buffer... | {
"class_name": null,
"func_sign": [
"solve(c)"
],
"solution": "cs = int(input())\n\nfor c in range(cs):\n l = int(input())\n num = [int(i) for i in input().strip().split()];num.sort()\n rt = 1\n for i in range(l):\n if i < num[i]: rt = 0;break\n else: rt*=(i+1-num[i]);rt%=10**9+7\... | eJx0ncuqbcl2XW3wP7i6ybIKM94R+hIbJ7hgq6BKWgUJDMLgT3e01mNfgcAg5c08Z++15ozHePTRRx//9z/9l//6H//zf/hv//rHP/71T//yz3/8/c8f68+/2v7zr/r9lP7Tf9r8qfzPT7v/2Pd/y/gpxT9e/Fm5/1J/6vlp9w/H/ft6/9H413L/WX9K5Z/3x/Oz9z/vnx1/+ePXSv3zr7JGu/+c9wtLWfdb+YL7ZWWfn1PLT/nuH+7vZ5X9cw7fP+6HzPuHpff7nx+fXe8DnPtjbd2PrffH72evfv+jfD99rPvz575S6fcfY6+ffQ4f... | null |
2,339 | Permutation Problem | How many n-digit numbers (without leading zeros) are there such that no digit occurs more than k times?
As the count of such n-digit numbers can be very large, print the answer mod (10<sup>9</sup>+7)
**Input Format**
The first line contains an integer, _T_ , the number of test cases. This is followed by _T_ li... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static int solve(int n, int k)"
],
"script_name": "Solution",
"solution": "import java.io.BufferedReader;\nimport java.io.InputStreamReader;\nimport java.util.Arrays;\nimport java.util.StringTokenizer;\n\npublic class Solution {\n\n\tprivate static final in... | {
"solution": "import java.io.BufferedReader;\nimport java.io.InputStreamReader;\nimport java.util.Arrays;\nimport java.util.StringTokenizer;\n\npublic class Solution {\n\n private static final int MAX = 1001;\n private static final long MOD = 1000000007;\n static long f[][][] = new long[MAX][MAX][11];\n ... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n static int[][] ncr_arr;\n static int[][][] max_arr;\n static final int modulus = (int)Math.pow(10, 9) + 7;\n \n public static void main(String[] args) {\n /* Enter your code here. Read input from STDIN. Print outp... | {
"class_name": null,
"func_sign": [
"solve(n, k)"
],
"solution": "#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\nfrom functools import cache\n\n#\n# Complete the 'solve' function below.\n#\n# The function is expected to return an INTEGER.\n# The function accepts following p... | eJyVWMGOZLcNzCG/kLswZyMQRUok8yUGskEOyR5y2fiwBgwYNnzJHzj/mypKr6d7ZtpGGts73a/1JIosVpXeL3/873/+9Id6ffszPvz1x5d/ffnu+68vf2kv89OXaCv105fpzebooitwUZp8+mLN8Kl7Gy/ftJfPP3z3+R9fP//z7//+/uu5O/t+cdSMMOHHxL+6hn8vP33T7lYTDuy9Sb9eTyZemXN6H+v9FLgk0gYCzjax1kSUNrrnlKhdTMeeJFrwO/aRw3sfgmh09IY3Pii3p7raXMYMYLoVveGNbxoNGfBKgbQ4yYjIhveTgKeb... | null |
27,290 | Tower 3-coloring | For a given integer $n$, there is a tower built from $3^n$ blocks stacked vertically. Each of these blocks can be colored in $3$ different colors: red, green or blue. How many different colorings of the tower can be created? Two colorings are considered different if and only if there exists at least one block with diff... | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static int towerColoring(int n)"
],
"script_name": "Solution",
"solution": "\nimport java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismat... | {
"solution": "import java.io.*;\n import java.math.BigInteger;\n import java.math.RoundingMode;\n import java.text.DecimalFormat;\n import java.util.*;\n \n \n class Solution\n {\n \n static double eps=(double)1e-6;\n static long mod=(int)1e9+7;\n //static int arr[] =n... | {
"solution": "import java.io.*;\n\timport java.math.BigInteger;\n\timport java.math.RoundingMode;\n\timport java.text.DecimalFormat;\n\timport java.util.*;\n\t\n\t\n\tclass Solution\n\t{\n\t\n\t\tstatic double eps=(double)1e-6;\n\t\tstatic long mod=(int)1e9+7;\n\t\t//static int arr[] =new int[1000001];\n\t\tpublic s... | {
"class_name": null,
"func_sign": [
"towerColoring(n)"
],
"solution": "#!/bin/python3\n\nimport sys\nimport math\n\ndef power(p,z,b,c,q):\n cp=0\n while q%p==0:\n cp+=1\n q/=p\n t=q-1\n exponent=((pow(b,c,t)*z)%t+t-(cp%t))%t\n return pow(p,cp)*pow(p,exponent,q)\ndef solve(a,b,c... | eJx1k71KA1EQhS3yIGHrIPP/4xPY2QoqFrqFTUyRQEAEH0Lf1xuNILPM3GaL++2ZOXPux+rrZnXxU7fX4+PubXrZ7g776Wo9aUYEhsD9dtqsp/m4m5/28/Pj62F/vpGYEMqm0/tm/Y8cHEAA9mBwCjJ6AS1ZHCyyJc2CAUylSmZSsjm3JIenDhwKqUhB6iYt6RaDJandYhgYg1hLqoI7S1bJMQSmWD9mSCATepWUAE7tMZNhhAYWjDOM3al3ZywsM4hrqwLMyq79LlGCkBirZk9QnYr6n+ewqg7T250ujIZUiT7CJOnpBjVPCD0z0uBp... | null |
11,678 | Highway Construction | You are planning the next FIFA World Cup and you are counting the number of highways that need to be built to connect the cities with the venue.
Your country has $n$ cities and all cities lie on a single straight road called “Highway Road”. If you want to go from City $x$ to City $y$ ( where $x \leq y$ ), you need to... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static int highwayConstruction(long n, int k)"
],
"script_name": "Solution",
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n static long MOD=(long)1e9+9;\n static long[][] cs = new long[1010][1010];\n static long[] invs=new long[1010];\n static long[] bs=new long[1010];\n \n ... | {
"solution": "import java.awt.*;\nimport java.io.*;\nimport java.math.BigDecimal;\nimport java.math.BigInteger;\nimport java.util.*;\nimport java.util.List;\nimport java.util.Queue;\n\nimport static java.lang.Math.max;\nimport static java.lang.Math.min;\n\n\npublic class HR_WoC_35_HighwayConstruction implements Runn... | {
"class_name": null,
"func_sign": [
"highwayConstruction(n, k)"
],
"solution": "#!/bin/python3\n\nimport time\nfrom fractions import Fraction as fr\nfrom math import floor\n\ndef euclidean_alg(a,b):\n \"\"\"This function implements the Extended Euclidean algorithm. It returns\n three values. The fi... | eJztnDuuLcl2XWWoIYmyCSEjMr7shhwBLEGG9Aw5JRqPAAFBABshtoieWsUxZuy9b+Hed+oRAmTpHlTVObU/kfFZc665Ppn/9O//+f/8x3+Xn//0L/zxd//zl//+29//w59/+dvrl/brb3OVvkrbrcw5alnl6nv8+lsd5dl32Xdfta22577m8+tvY/RSZt333vdz973qNer89bfV27OfUktrqzzTUe5ff/vlb65f/vSPf/+n//rnP/23//I//uHPr6uOtp/KBfevv5VR+3xa54pPfdruc/Nqe/q97tnHL//rb67fTbczgT3GbJ3L9afM... | null |
12,285 | Parity Party | We need your help to divide candies at a very unusual party!
There are $n$ different candies in total. There are three kinds of people at party:
* $a$ of them want to get *odd* number of candies,
* $b$ of them want to get *even* number of candies,
* $c$ simply don't care about parity of candies they get.
... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static int parityParty(int n, int a, int b, int c)"
],
"script_name": "Solution",
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java... | {
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class GS2 {\n InputStream is;\n PrintWriter out;\n String INPUT = \"\";\n \n void solv... | {
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class GS2 {\n InputStream is;\n PrintWriter out;\n String INPUT = \"\";\n \n void solv... | {
"class_name": null,
"func_sign": [
"parityParty(n, a, b, c)"
],
"solution": "#!/bin/python3\n\nimport sys\nimport math \nMOD=7340033\nWW=390625\n\nROOT=[pow(WW,2**i,MOD) for i in range(17)]\n\n# Enter your code here. Read input from STDIN. Print output to STDOUT\nfrom cmath import exp, pi\n#x has length 2... | eJyVk8tKHVEQRR3ofxQ9yEil3o98SSCGDOIdOFEHVxAkkI8w/5tqMajn5rSmBv2AOqv3rl396/j32cnRU3351A9fH5ar69u7/fIZFnV1EsUEMtUEVWWQJMyL6+UUlt397e7Hfnf5/eZu/3zEuaIwlp+n8AokSpZeSkCa1iDHAonEmIJChCJHkJs7iqQDOzM0zEFIaooxwSjzAWMhvlICpBwJ+q09ugRtgEJKcwBRJnoXtpsGAidZ9JxCcUpip6QcJfV0hFutgViwNQPDW5MXb8yoOTSS0pgtSBCY3alJ2ZoEEefuWnXnM7qLjt0jWoeK... | null |
2,135 | Count Palindromes | A string is made of only lowercase latin letters (a,b,c,d,.....,z). Can you find the length of the lexicographically smallest string such that it has exactly $K$ sub-strings, each of which are palindromes? | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static int solve(long k)"
],
"script_name": "Solution",
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchExcepti... | {
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class Solution {\n static InputStream is;\n static PrintWriter out;\n static String INPUT = ... | {
"solution": "import java.io.File;\nimport java.io.FileNotFoundException;\nimport java.time.Instant;\nimport java.util.ArrayList;\nimport java.util.HashMap;\nimport java.util.Map;\nimport java.util.Scanner;\n\npublic class Palindrom\n{\n\n\tpublic static void main(String[] args)\n\t{\n\t\tint max = 0; long count;\n\... | {
"class_name": null,
"func_sign": [
"solve(k)"
],
"solution": "def compute_pallindrome_length(n):\n length = 0\n debug_print = False\n current_n = 0\n\n if n == 5:\n return 4\n elif n == 8:\n return 5\n\n i = int((1 + pow(1 + 8 * n, 0.5) ) / 2) - 10\n while i*(i + 1)//2 <... | eJztms2OZUlShFnwIKNej1D8//AQ7JCQaMQCZsGmmMWMhISQeAh4X77Poqqpzu7qgQ0L1KnK7rx5zz0nwt3c3Mwj//3P//Ov+5/l62/+ih/+9l+/+6dPv//jH777y998N8f3n2rdfe3bb53ff7rl7lV262X27z/1ds+au7VyzvefRtn93l13mfX7T62OMWc/rZ37/adV7+rznt4379W979ptzFu4Z+1jj9VOr5fHjb1aKXe27cNXnb3UxYcHd6m3+rHGx7nLKqdVfuJe3KWfzTX9zu6rWurt9dxbC58bc9Reyhit7e8/zbkmi1m1L+85... | null |
56,789 | Combo Meal | A fast-food chain menu is selling a burger, a can of soda, and a combo meal containing a burger and a can of soda, at prices known to you.
They have chosen the selling price for each item by first determining the *total cost* of making the individual items and then adding a *fixed* value to it, representing their *pr... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static int profit(int b, int s, int c)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n static int profit(int b, int s, ... | {
"solution": "DNE",
"template": "class Result {\n\n /*\n * Complete the 'profit' function below.\n *\n * The function is expected to return an INTEGER.\n * The function accepts following parameters:\n * 1. INTEGER b\n * 2. INTEGER s\n * 3. INTEGER c\n */\n\n public static i... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n static int profit(int b, int s, int c) {\n return b + s - c;\n }\n\n public static void main(String[] args) {\n Scanner in = new Scanner(Syst... | {
"class_name": null,
"func_sign": [
"profit(b, s, c)"
],
"solution": "#!/bin/python3\n\nimport sys\n\ndef profit(b, s, c):\n # Return the fixed profit.\n return b+s-c\n\nif __name__ == \"__main__\":\n t = int(input().strip())\n for a0 in range(t):\n b, s, c = input().strip().split(' ')\n... | eJztWduKJccR9IM/pJjnRdT94i8ReI0f7H0wiPGCd0FgFvQR0v86IrK7uvqc7lkwGEtoh1Pn0l2XrMzIyKien/74y7/cH/T3/Q/48ud/v/zj9ePnTy9/ci/B+/evaXSX2nCllPev1VXX3r+WEV1u0bWIa9llXosuusSP7HCx+OBqdKVGdIjDtZRcTxzqQmcb71+baw4fCSMqemEevDA29ORCwnwZ/UPF8r5jOd1xfGFQ6d4Vn13wHYu2Wpw1TlRxOVRXGvrl5EbFbH0a2INrLbheAo0pXBo2j4KGr6E0LDdc4m5TwEXHOZt3dXD+zCnp... | null |
3,194 | Stepping Stones Game | Bob sees his younger brother, Jack, playing **Stepping Stones**. He is fascinated by the most interesting game and decides to play it.
Square boxes have been made on the ground with the help of chalk powder, and a number is assigned to each block. Bob is standing in front of these blocks. From here, he will throw a s... | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static String solve(long n)"
],
"script_name": "Solution",
"solution": "import java.util.Scanner;\n\n\npublic class Solution {\n\tpublic static void main (String[] args) {\n\t\tScanner in = new Scanner (System.in);\n\t\tint T = in.nextInt();\n\t\twhile (T--... | {
"solution": "import java.util.Scanner;\n\n\npublic class Solution {\n public static void main (String[] args) {\n Scanner in = new Scanner (System.in);\n int T = in.nextInt();\n while (T-- > 0) {\n long N = in.nextLong();\n long lo = 0, hi = 1500000000;\n whi... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.math.*;\n\n\npublic class Solution {\n\tstatic BufferedReader in;\n\tstatic PrintWriter out;\n\tstatic StringTokenizer tok;\n\t\n\t\n\tstatic void solve() throws Exception {\n\t\tint t = nextInt();\n\t\tfor (int tt = 0; tt < t; ++tt) {\n\t\t\tlong n =... | {
"class_name": null,
"func_sign": [
"solve(n)"
],
"solution": "from math import *\nt=int(input())\nfor i in range(0,t):\n n=int(input())\n j=(1+sqrt(1+8*n))/2\n if abs(j-int(j))<=0:\n print(\"Go On Bob\",int(j-1))\n else:\n print(\"Better Luck Next Time\")\n",
"template": "#\n# ... | eJztmM9q20AQxnvIQ/RWoXMoO7P/e8wll5JeeihUpdBUlBAihyBDoAT6EO37dlYucRzbwa7llRCfMEZaS7Pffr8Z7Xp/nfx58+NVd3x6LSeff5ZXze28Ld8VpamaaJyztmqcM5FYa8MmSGtgx65qTIjayXV5WpT1/W192dbfv87m7b/nz+q2re+K9/PL6+Kivm+Lj1c3ddX00Fw+nBarQoN2NkmznlU0SbBSmrQno6JciVDvlQnp1zSeoRVz1ZASXVuVnM+KD01xNvtWyGCWF3p95AKISFeNtjJe9krHSMEnS8iEIJ3QcYe7FEfPxEnH... | 1 |
2,424 | Shashank and List | Shashank is a newbie to mathematics, and he is very excited after knowing that a given l of cardinality _N_ has (_2<sup>N</sup> - 1_) non-empty sublist. He writes down all the non-empty sublists for a given set _A_. For each sublist, he calculates sublist\_sum, which is the sum of elements and denotes them by S<sub>1</... | hackerrank | Medium | {
"class_name": null,
"func_sign": [
"static int solve(int[] a)"
],
"script_name": "Solution",
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npub... | null | {
"solution": "import java.util.Scanner;\n/**\n * \n * @author Lakmal Buddika Lucky\n *\n */\npublic class Solution{\n\tstatic long divider = 1000000007;\n\tpublic static long power(long a,long b,long c){\n\t\tif(b==0){\n\t\t\treturn 1;\n\t\t}\n\t\telse if(b==1){\n\t\t\treturn (a%c);\n\t\t}\n\t\telse if(b%2==0){\n\t\... | {
"class_name": null,
"func_sign": [
"solve(a)"
],
"solution": "P = 1000000007\nPHI = P - 1\n\nn = int(input())\n\na = [int(s) % PHI for s in input().split()]\nret = 1\nfor x in a:\n\tret = ret * (1 + pow(2, x, P)) % P\nprint((ret - 1) % P)\n",
"template": "# Complete the solve function below.\ndef solve(... | eJx03cuqLc2WJGY16kEm2a6Gx80jXOhBBEpQQ6qGOkfVqAJBIRDoxWWf+TpCpEjIqrP/vdeaM8Iv42LDho3/6z/8j//h//4f/rv/6b/9y//2j//8X//Lv/z3v395//Uf7/zOd/6OMef1++YYx5P/ec51/L77+O73dx3Xcc/ft+Z88wv/8h9///Kf/o///J/+l//yn/7X//l//6//5e+j5jnPMd41/uX//I+//89XzH/9x3rfkc85xzXm9zu/63rP37OuZ5y/+3nzLb9jruc+/t0PX+9z+sHr///hx5WPvX7ryPN/+bgrj/k73+v1Hd+6... | null |
783 | Triangle Numbers | Given a triangle of numbers where each number is equal to the sum of the three numbers on top of it, find the first even number in a row.
**Explanatory Note**:
The vertex of the triangle (at the top) is 1.
The structure of the triangle is shown below.
Each number is equal to the sum of the numbers at the following p... | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static int solve(int n)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n ... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n // TODO code application logic here\n Scanner scan=new Scanner(System.in);\n int t,n,r;\n ... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n public static void processCase(int N) {\n if (N % 2 == 1) {\n System.out.println(2);\n return;\n }\n int half = N / 2;\n System.out.println(half % 2 == 0 ? 3 : 4);\n }\n \n ... | {
"class_name": null,
"func_sign": [
"solve(n)"
],
"solution": "def number(n):\n if n==1 or n==2:\n return -1\n elif n%2==1:\n return 2\n elif n%4==0:\n return 3\n else:\n return 4\n\nn=int(input())\nrow_num=[]\nfor i in range(n):\n row_num.append(int(input()))\nfo... | eJztV8uOJDUQ5MAP8AejPq+Q8+F05n4JEoM4wBy4NHuYkZDQSnwE+79EuLprq587SLCH1bZK3W6XnU6nMyPCf3374eW7b+bnhz0aP/65+23/7uV59/ZhJ/1xLz7SFb/xuNfe0RPNLfNxb26ReGNd0lqGPO7dffQW4Y/7rqrJX5tWKqU6e6OZCQ1mqz4aXrGj8L8MFkYpTO/ePOye/nj39Mvz068///7yfPAH5gxezG8/NpYenT1+bKNz9/7Nw2Yv05XI0Ur0Fc0bHthxycsFMCm8MVLWh0jeseLX5o/ZiRglTWAXwhCjp9+0s+51DYBe... | null |
2,523 | Easy sum | Little Kevin had never heard the word 'Infinitum'. So he asked his mentor to explain the word to him. His mentor knew that 'Infinitum' is a very large number. To show him how big [Infinitum](http://en.wikipedia.org/wiki/Ad_infinitum) can be, his mentor gave him a challenge: to sum the numbers from _1_ up to _N_. The su... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static int solve(int n, int m)"
],
"script_name": "Solution",
"solution": "import java.util.Scanner;\n\n\npublic class Solution {\n\n\t/**\n\t * @param args\n\t */\n\tpublic static void main(String[] args) \n\t{\n\t\tScanner scan = new Scanner(System.in);\n... | {
"solution": "import java.util.Scanner;\n\n\npublic class Solution {\n\n /**\n * @param args\n */\n public static void main(String[] args) \n {\n Scanner scan = new Scanner(System.in);\n int T = scan.nextInt(); scan.nextLine();\n \n for(int i=0;i<T;i++)\n {\n ... | {
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.math.BigInteger;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class Solution {\n\tstatic InputStream is;\n\tstatic PrintWriter out;\n... | {
"class_name": null,
"func_sign": [
"solve(n, m)"
],
"solution": "t = int(input())\n\nfor i in range (t):\n n, m = map (int, input().split())\n \n a = n // m\n b = n % m\n \n print (m * (m - 1) // 2 * a + b * (b + 1) // 2)",
"template": "#\n# Complete the 'solve' function below.\n#\n# T... | eJydnU3OLkmSlRmwkFCOW8j/f1gJEokYQA96kvSgkZBaLbEIkFgUMzbEeY7FrbphtxMB1V1ZmTe/730j3M3Njh07Zv5f/+V//1//41/4P//mf+pv/u0//vZ3f/z9f/6H3/7181udv/8xT6u7truevVov58z6+x933b76PPO5ZY4+y9FP3jNuGWXP5+w6b1tr/f7HGmu009p9RtPft9LK73/0ttreZfRn93vr6UOfOUutpTX9un6y31H71h/u0feosz59r1v1K+33P85cq3Y9gX7yll3mPb//MXbXV9XZHj3W0b8e4/c/dj9dj37b0/Ts... | null |
5,387 | Difference and Product | Tim likes Math. He likes it so much that he always brings his tablets with him and reads math e-books everywhere, even during parties.
Tim found an interesting exercise in one of the e-books he is reading. But you want him to join the party, so you decide to answer the question for him.
The problem is: Given $D$ and ... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static int solve(int d, int p)"
],
"script_name": "Solution",
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.ArrayList;\nimport java.util.Arrays;\nim... | {
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.ArrayList;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\nimport java.util.List;\n\npublic class DAP {\n static InputStream is;\n sta... | {
"solution": "import static java.lang.Math.abs;\nimport static java.lang.Math.floor;\nimport static java.lang.Math.sqrt;\n\nimport java.io.BufferedReader;\nimport java.io.InputStreamReader;\nimport java.io.IOException;\n\n\npublic class Solution\n{\n public static void main(String[] args) throws IOException {\n ... | {
"class_name": null,
"func_sign": [
"solve(d, p)"
],
"solution": "from math import sqrt\n\nt = int(input())\n\ndef f(d, p):\n if d < 0:\n return 0\n ret = set()\n D1 = d * d + 4 * p\n if D1 >= 0:\n a = round((d - sqrt(D1)) / 2)\n if a * (d - a) == -p:\n ret.add((... | eJyVk81KRDEMhV3Mg5Sup5K/Jo1PIjjiQu/CzXUWd0ARYR5C39eMKCL0DjarLM45JF/T4+bjuLn4quuXaG5e8+O8Pyz5KmXczehmymotlUbE3gRgN+dtytPzfrpfpoe7p8PyrYf8tk1//dzUBdksmbfajKWO2KlqxUqWipKF28FH7Miq3JzD7+7ATkgj/kJARlKrpoLO5CQ+NEAgEwGWwFcBTZvSEL5yYkbKhjGANTRAx6EAZEcm1AgwBwIDGEPgoEGuGkSAVAyCZiMBKqbeQE8IpKpzRIz4Ia0Tw54cdVVPnRM5Ey+de05lHV9HT/Fv... | null |
7,741 | Pythagorean Triple | A *Pythagorean triple* consists of three positive integers $a$, $b$, and $c$, such that $a^2 + b^2 = c^2$. Such a triple is commonly written as $(a, b, c)$. This term comes from the [Pythagorean theorem](http://en.wikipedia.org/wiki/Pythagorean_theorem), which says that a Pythagorean Triple will be the lengths of the s... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static List<Integer> pythagoreanTriple(int a)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static void main(S... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.math.BigInteger;\n\n\npublic class PythagoreanTriple\n{\n public static void main(String[] args) \n {\n Scanner sc=new Scanner(System.in); \n BigInteger b=sc.nextBigInteger();\n\n if(!b.mod(new BigInteger(\"2\")).equals(B... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.math.BigInteger;\n\n\npublic class PythagoreanTriple\n{\n\tpublic static void main(String[] args) \n\t{\n\t\tScanner sc=new Scanner(System.in);\t\n\t\tBigInteger b=sc.nextBigInteger();\n\n\t\tif(!b.mod(new BigInteger(\"2\")).equals(BigInteger.ZERO))\n... | {
"class_name": null,
"func_sign": [
"pythagoreanTriple(a)"
],
"solution": "#!/bin/python3\n\nimport sys\n\ndef pythagoreanTriple(a):\n # a = (m^2 - n^2)\n \n if a%2 == 0:\n # a = 2mn\n m, n = a//2, 1\n return [a, m**2-n**2, m**2+n**2]\n else:\n # a = (m-n)(m+n)\n ... | eJyFVMtuFDEQ5BDxHa05R6jf7eZLkEiUA+yBS5LDJkJCkfIR8L/0Jhkr2DLbp5lSbW25qj3PF38eP354mS939fD11/bj9v7huH2GzZtbpGC7ut0uYTv8vD98Ox6+39w9HEcGEEXTiHpXUckgm6HYni7hnT5RmmITX+p3BogFG3LzaKnBMgI2aLM3M8xYSu8EEEfLVmZPHlGH91E30lkk0pbCnQFCnqnEjQzRCIlnSEbfaUKoiWvjOwOYGjIGSTgSIuuE+JRK/Rpd5D+xvDFA1KpFCs+ooER1QqbMJT3q39d9dgaQnnImKY/ZGrlMyKhO... | null |
37,894 | Number Groups | The positive odd numbers are sorted in ascending order as $1, 3, 5, 7, 9, 11, 13, 15, 17, 19 \ldots$, and grouped as $(1), (3, 5), (7, 9, 11), (13, 15, 17, 19), \ldots $ and so on.
Thus, the first group is $(1)$, the second group is $(3, 5)$, the third group is $(7, 9, 11)$, etc. In general, the $k^\text{th}$ group co... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static long sumOfGroup(int k)"
],
"script_name": "Solution",
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchEx... | {
"solution": "import java.math.BigInteger;\nimport java.util.Locale;\nimport java.util.Scanner;\n\npublic class NumberGroups {\n\n public static void main(String[] args) {\n try(Scanner in = new Scanner(System.in)){\n in.useLocale(Locale.ENGLISH);\n int k = in.nextInt();\n ... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n static long sumOfGroup(int k) {\n long x = (long) k;\n return x*x*x;\n }\n\n public static void main(String[] args) {\n Scanner in = n... | {
"class_name": null,
"func_sign": [
"sumOfGroup(k)"
],
"solution": "#!/bin/python3\n\nimport sys\n\ndef sumOfGroup(k):\n # Return the sum of the elements of the k'th group.\n return k**3\n\nif __name__ == \"__main__\":\n k = int(input().strip())\n answer = sumOfGroup(k)\n print(answer)\n",
... | eJyNkrtKBEEQRQ32Q4aJF6lH18svEVQMdAKTdYNZWBDB2EzQ/7V31kB6rHEKGhq6Tte9t/tt8/WxuZjq+r1ubl76p93+MPZXXS9evOjtrt92/XDcDw/j8Hj/fBh/jgkACou4qgWZsfav2+4X72oEKa5CqFIcSl0ADatmhJ7CjCDuYqphCoWY2tnmhJYPr3LZkVBItRRGbngBcMh5JFFQIuMIEIvS8hHknpsPcws0Iqr5Gc39o6MBp7xEBAKpYKDU8Fv1UvXn0+vVIex2ajpXwxdbgIGdeM6wB4Xkij2qaQ9V92Js0hrOBzadlHZ6Kyn/... | null |
3,593 | Wet Shark and 42 | As punishment for attacking Sunland, Wet Shark is now forced to walk on a line of numbered squares, starting from $1$ and going to infinity. Wet Shark initially has a strength of $S$. To make the experience harder for Wet Shark, each square that has a label divisible by $4$ and/or $2$ but not divisible by $42$ contains... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static int solve(long s)"
],
"script_name": "Solution",
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchExcepti... | {
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class WS {\n static InputStream is;\n static PrintWriter out;\n static String INPUT = \"\";\... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n Scanner sc = new Scanner(System.in);\n int T = sc.nextInt();\n long S;\n long answer;\n long numNotSquares;\n for (int i=0; i<T; i++) {\n ... | {
"class_name": null,
"func_sign": [
"solve(s)"
],
"solution": "def calc(x):\n MOD = 10**9 + 7\n twenties = x // 20\n rem = x % 20\n if rem == 0:\n return ((42 * twenties - 2) % MOD)\n else:\n return ((42 * twenties + 2 * rem) % MOD)\n\ntcs = int(input())\nfor i in range(tcs):\n... | eJzVm8uupNlRhRnwIK0eW2jfL34GZkyQaMQAesCk8aAtWUJIPAS8L9+39ilhV/WpGQNKbbudlZn/3nFZsWJF5H/+9X//3e//Kn/+/m/5l3/49x//9Zc//PHXH3//w4+7/fTLHmOVM07d+5x5ah0//TJnWaONXWbve5XbeV+9d5++9uq19DNb27yv8oHb5py3ndN5U2u13V7uKH6ydV5bc512Zx9nD961/bI9z5i7lD4Hr5xZfvplrFb3ODyllbvaKJxj3HsKXzP24v+vs3765RyOW+tqp5ey66m8Vjsf7ZP/brx7zVt5rY45yh63ceC+... | null |
4,429 | Sherlock and Square | Watson gives a square of side length 1 to Sherlock. Now, after each second, each square of some arbitrary side $L$ will break into four squares each of side $L/2$(as shown in the image below).
![img][123]
Now, Watson asks Sherlock: What will be the sum of length of solid lines after $N$ seconds?
As the number can... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static int solve(int n)"
],
"script_name": "Solution",
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchExceptio... | {
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class SAS {\n static InputStream is;\n static PrintWriter out;\n static String INPUT = \"\";... | {
"solution": "\nimport java.io.*;\nimport java.math.*;\nimport java.util.*;\n\nimport static java.util.Arrays.*;\n\npublic class Main {\n\tprivate static final int mod = (int)1e9+7;\n\n\tfinal Random random = new Random(0);\n\tfinal IOFast io = new IOFast();\n\n\t/// MAIN CODE\n\tpublic void run() throws IOException... | {
"class_name": null,
"func_sign": [
"solve(n)"
],
"solution": "t = int(input())\n\nP = 10**9 + 7\n\ndef f(x):\n return (pow(2, x, P) + 1) * 2 % P\n\nfor _ in range(t):\n print(f(int(input())))",
"template": "#\n# Complete the 'solve' function below.\n#\n# The function is expected to return an INTEG... | eJx8vUuuZcuSXacCG5J4ZRaW/93VALVBgC6gApkFVZ5YSAICBAHqqxrBKm2MuSMhZOIRIPPGizhn77X8Y59p06b9v//hf/3//tv/8j/9b//33/6Pv/+X//ovf/uf/+lv7a+/zz13G/M7f/39b//xn/72z//Xf/nn//Qv//yf//f/87/+y++Hxp6v3fX23/6f//hP/79fHn/9va/R2u6rfvn08765vr/+vvscu5/V/uEnnm/2/e12//r7+t6oH+27PmG+Ofs54998T6vPHLOtu9+sP9anz3HXnfUba/X13fqY3V795Zn1MXvt/Y36iXq3... | null |
2,325 | Manasa and Sub-sequences | Manasa recently lost a bet to Amit. To settle the problem, they are playing a game:
They have _N_ balls in front of them. Each ball, except the 1st ball, is numbered from 0 to 9. The 1st ball is numbered from 1 to 9.
Amit calculates all the [subsequences](https://en.wikipedia.org/wiki/Subsequence) of the number th... | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static int solve(String s)"
],
"script_name": "Solution",
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchExcep... | {
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class Solution {\n static InputStream is;\n static PrintWriter out;\n static String INPUT = ... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.math.*;\nimport java.lang.*;\n\npublic class Solution {\n\n public static void main(String[] args) throws IOException{\n /* Enter your code here. Read input from STDIN. Print output to STDOUT. Your class should be named Solution. */\n ... | {
"class_name": null,
"func_sign": [
"solve(s)"
],
"solution": "P = 10**9 + 7\n\ns = [ord(c) - ord('0') for c in input()]\n\nret = 0\nfor i, x in enumerate(s):\n\tret = (11 * ret + x * pow(2, i, P)) % P\n\nprint(ret)\n",
"template": "#\n# Complete the 'solve' function below.\n#\n# The function is expected... | eJztnUuuJsfVXd1w22Mo3DZhREZmRmZqJAYsQw2yGuyQbBQBAQIBD8Jz8DS9184r26BQgNVzY63z2zqsuvV9+Yg4cV4R97//x//yP7f/9B/+6z8+fv7lt9+/ffzly8d2/PWXjx++fHz9+29ff/z29ae//fr7t3/+1fPxxw9f/q+fPeZ2f/enz2dbf/r5a3z3p6/rTz879/1+xr3fa7+eY8x1HnM/t21t55n/vu5o9/Gc37/a/b7ntuafr3kb+7Ht2zquc55n9Gfe+7zWce/n2Me58rfPuV/rmvnGO9+xnnXu8762sc25clvrue7jHvn+... | null |
6,345 | Count Solutions | Eric has four integers $a$, $b$, $c$, and $d$.
Instantly, he wondered how many pairs of _integers_, $(x, y)$, satisfy the following equation:
$$x^2 + y^2 = (x \times a) + (y \times b)$$
where $1 \le x \le c$ and $1 \le y \le d$.
Find and print the number of pairs that satisfy the above equation. | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static int countSolutions(int a, int b, int c, int d)"
],
"script_name": "Solution",
"solution": "\nimport java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport... | {
"solution": "\nimport java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class D {\n InputStream is;\n PrintWriter out;\n String INPUT = \"\";\n \n void solv... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class CountSolutions {\n\tpublic static void main(String[] args) throws IOException {\n\t\tBufferedReader br = new BufferedReader(new InputStreamReader(System.in));\n\t\tPrintWriter pw = new PrintWriter(System.out);\n\t\tint q = Integer.parseInt(br.readL... | {
"class_name": null,
"func_sign": [
"countSolutions(a, b, c, d)"
],
"solution": "def solve(a,b,c,d):\n res = 0\n for x in range(1,c+1):\n l = x*(x-a)\n # y*y - b*y + l == 0\n det2 = b*b - 4*l\n if det2 == 0 and b%2 == 0 and 1 <= b//2 <= d:\n res += 1; continue\n... | eJydlk2OJEUMhVmw5BChWo9Q2A6HbU4yEo1mAbNg07CYkZDQSBwC7svnrKmmqO5qfqpT2VUZGQ77+b0X8duXf7z76ovj8/YtX7799fTj488fP5y+Gad4eLSZlSMz94itEmPJrnx41Ap+lOoc4Vv2yCmuD4+uaw/G1tiajM2p6+ExSqeN0IhhGsuGajlRYu3I4dvER9ZKHbnCWZYAzo8UX8PM0sdy60hJbB97TuJlMDJyh8nD4+nNOL3/5ef33394/8O7nz5++FyCMHS+JsUc93X69GZcVUkeFbUpUGIvaurKotK7miSBXb46+62sWdaJ... | null |
9,549 | Simple One | You are given the equation $\tan\alpha = \frac{p}{q}$ and a positive integer, $n$. Calculate $\tan n\alpha$. There are $T$ test cases.
| hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static int solve(int p, int q, int n)"
],
"script_name": "Solution",
"solution": "import java.io.IOException;\nimport java.io.InputStream;\n\npublic class Solution {\n\n static final int MOD = 1_000_000_007;\n\n public static void main(String[] args) ... | {
"solution": "\nimport java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class B {\n InputStream is;\n PrintWriter out;\n String INPUT = \"\";\n \n void solv... | {
"solution": "\nimport java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class B {\n\tInputStream is;\n\tPrintWriter out;\n\tString INPUT = \"\";\n\t\n\tvoid solve()\n\t{\n... | {
"class_name": null,
"func_sign": [
"solve(p, q, n)"
],
"solution": "read = lambda: map(int, input().split())\n\nP = 10**9 + 7\n\ndef mul(a, b):\n return ((a[0] * b[0] - a[1] * b[1]) % P, (a[0] * b[1] + a[1] * b[0]) % P)\n\ndef my_pow(a, b):\n ret = (1, 0)\n while b > 0:\n if (b & 1) == 1:\... | eJzk3c2uNM2SJWQGXMhWjRn4n5m7cyVIlMQAesCkYNBISKglLpshyzxWrm4OfVpLTKlTP1+977bMyIjl5u5PxM7v//yv/7v/6z/81//Vf/9//Mv//G//6//27//lv/37l5n/+m/nnNbw339559otz/079447c89//bfo44zYOf/6yZazrfyLPLPflfdf/22dNu/6m/fk3HvMv9XmjLmj/+u/Ze97tL7X31i398jb//D6M08PVPZ2+7pt/S286Jx3tL/e1rkZreGvM9YZPf762GviYMbfGfOgYKJ4jN1j4s/r/cYI/CGKM/GjZ6N49Y3D... | null |
1,196 | Calculate It | Little Tom loves to solve interesting math challenges. One day he bumped onto an interesting function called _hRank_.
Given a positive integer **k**, _hRank_ maps a non-negative integer **x** to another integer.
hRank(x) = 1 if 0 <= x < k
hRank(x) = hRank(x – k) + hRank(x / k) if x >= k and k | x (i.e., x ... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static String solve(int k, String x)"
],
"script_name": "Solution",
"solution": "import java.math.BigInteger;\nimport java.util.*;\n\n\n\npublic class Solution {\npublic static final int N = 52;\npublic static BigInteger [][][][] g = new BigInteger [N][10][... | {
"solution": "import java.math.BigInteger;\nimport java.util.*;\n\n\n\npublic class Solution {\npublic static final int N = 52;\npublic static BigInteger [][][][] g = new BigInteger [N][10][N + 1][N + 1];\npublic static ArrayList<HashMap<BigInteger,BigInteger>> have = new ArrayList<HashMap<BigInteger,BigInteger>>();... | {
"solution": "import java.math.BigInteger;\nimport java.util.Scanner;\n\npublic class Main\n{\n\tprivate static final int maxn = 51;\n\tprivate static int deg, cur, nxt;\n\tprivate static BigInteger[] fact = new BigInteger[maxn], f[] = new BigInteger[2][maxn], pre = new BigInteger[maxn], suf = new BigInteger[maxn];\... | {
"class_name": null,
"func_sign": [
"solve(k, x)"
],
"solution": "#!/bin/python3\n\nimport os\nimport sys\n\ndef zrank(k, x):\n\n if x < 0:\n return 0\n\n if x < k:\n return 1\n\n m = 1\n while m <= x//k:\n m *= k\n\n # coefficient array\n x = int(x)\n arr = [0]*(x... | eJytm8uqrt1RhW14IYu/HWSeah4E70NQsaFp2IlpJCBIwIvQjj3v1OcZXxqyNysQcCd7/2t9h/edc1bVqFGj6v2Pv/yv//mbv8ifv/1vfvi7f//lX37z29//7pe//vplf82xzrl773nX22v10er0Pft7VbvvunVu3b//zS+/+vrl1//221//0+9+/c//+K+//90fr9Db3u+NPs69b7X++li15jvttuKrd6/Zextrz6rR39yrc8d17mtz9rXPa3eOer2d/c6ciwX1dndbNcbYfG+1VvveU/Xq7lF87Y7e31rcep1WvDf6PvONwcXG8np7... | null |
4,060 | Black Hole | Given integers $n$, $a$, $b$ and $M$, calculate the value $\sum \limits_{k=0}^{n} k^a b^k $ modulo $M$.
**Input Format**
The first line contains the number of test cases $T$.
Each of the next $T$ lines contains four space-separated integers $n$, $a$, $b$ and $M$.
**Output Format**
For each test case output on... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static long solve(long n, int a, long b, int m)"
],
"script_name": "Solution",
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.ut... | {
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class BH2 {\n static InputStream is;\n static PrintWriter out;\n static String INPUT = \"\";... | {
"solution": "import java.io.*;\n\npublic class HR_demidenko_black_hole {\n\n static long mod;\n static long[][] cs;\n\n static void init(int n) {\n cs = new long[n + 1][n + 1];\n for (int i = 0; i <= n; ++i) {\n for (int j = 1; j < i; ++j) {\n cs[i][j] = (cs[i - 1][j... | {
"class_name": null,
"func_sign": [
"solve(n, a, b, m)"
],
"solution": "#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\nprimesq = 32000\n\nisprime = [True]*primesq\nprimelist = []\nisprime[0] = False\nisprime[1] = False\nfor i in range(2, primesq):\n if isprime[i]:\n ... | eJyNlkFuJkUMhVmw5BClrCeobJftMidBIogFMwuEFGaRkZAQEoeA+/K5M2SSP38P0+okne6qsv383qv66+t/fv3mq+P6/i0PP/xx88v9+w8PN9+Nm3V3n1t8y6olmaGy55BhNtWWbxVdwqPZCI+YZjvu7rcvKxOVtbZYMmMOXtgqmdx89LVjeMV0C0Lo3pNHzZwVm0tHjtuyuSYzlliHqZGEtdApd/ehexWZzWlVslJt6HD3zGWuOxfRS0aKr0jyvru/eTNu3v3+/t3PD+/e/vTbh4ePBVpluGxjhE/qM/O7e9lLZ+jad/fmvqaR0c2f... | null |
2,214 | Rectangular Game |
You are given an infinite 2-d grid with the bottom left cell referenced as (1,1). All the cells contain a value of zero initially. Let's play a game?
The game consists of **N** steps wherein each step you are given two integers **a** and **b**. The value of each of the cells in the co-ordinate (u, v) satisfying 1 ... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static int solve(List<List<Integer>> steps)"
],
"script_name": "Solution",
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.I... | {
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class Solution {\n static InputStream is;\n static PrintWriter out;\n static String INPUT = ... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n Scanner sc = new Scanner(System.in);\n long t=sc.nextInt();\n long mx = 1_000_001,my=1_000_001;\n while(t-->0){\n long x=sc.nextInt(),y=sc.nextInt()... | {
"class_name": null,
"func_sign": [
"solve(steps)"
],
"solution": "n = int(input())\nmx = 0\nmy = 0\nfor i in range(n):\n inp = [int(a) for a in input().split()]\n if mx == 0:\n mx = inp[0]\n my = inp[1]\n else:\n mx = min(mx,inp[0])\n my = min(my,inp[1])\nprint(mx*my)\n ",
"template": ... | eJztmruuJUlWhjHw8bBDbY9Qxj0Ch9dAohEGtIFTjNEjjYSQeAh4X77vzwI1B6o0gzViThl9OvfOnRmxLv9lZf7rn/77n//Vn+TfX/8Z//M3//zDP3759W9+/uEvyw+7/fjlrN2esvZu9XC0T6u7zOc5z/7xy73tnKfUU/fTf/wyJ188Zeznxy/93HZn6bffp/LV3f300kZvm+usc08r46ntzB+/jPb0sUp9+JovxxzzlNrGmFz0PBzX0rh/97q37z05d9TtT3vthwXV1VjsGqxul3ZbG9zzPGuNMjur4nA/zzrl1LUrl72Dc1vhznzu... | null |
21,866 | Points on a Rectangle | You are given $q$ queries where each query consists of a set of $n$ points on a two-dimensional plane (i.e., $(x, y)$). For each set of points, print `YES` on a new line if all the points fall on the edges (i.e., sides and/or corners) of a [non-degenerate rectangle](https://en.wikipedia.org/wiki/Degeneracy_(mathematics... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static String solve(List<List<Integer>> coordinates)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n public static void m... | {
"solution": "DNE",
"template": "class Result {\n\n /*\n * Complete the 'solve' function below.\n *\n * The function is expected to return a STRING.\n * The function accepts 2D_INTEGER_ARRAY coordinates as parameter.\n */\n\n public static String solve(List<List<Integer>> coordinates) {\n... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n Scanner in = new Scanner(System.in);\n int q = in.nextInt();\n for (int i = 0; i < q; ++i) {\n solve(in);\n }\n }\n \n private static void ... | {
"class_name": null,
"func_sign": [
"solve(coordinates)"
],
"solution": "def main():\n for i in range(int(input())):\n p = [list(map(int, input().split())) for j in range(int(input()))]\n px, py = zip(*p)\n x0 = min(px)\n x1 = max(px)\n y0 = min(py)\n y1 = max(p... | eJyVWDuOJccRlMEb6AKFsdlCZVXlp+TTlQw6FDQEDWkNOiMaS0CAIICHkK4jT/diRNTbxez8djmYedvdrzorP5GRUfvLV//9/+9/p5/v/oeLv/7r7seHn35+f/fHdpf3D3b/cA3zNnqO+4eFO7PlrXwFbsde7VrZ8dUYA9e+x8Zzq2iXreX3D7i91qhqZQP2rlm5mwWXXdn7brk6NpneV9uW/f4h3Aas+oLVy23B1BzRuX55x00m9jbbo7lzzR6zN+8TuwVvbfQWOeHsDINTu4o77yzajTnhFXe+lk26EQarAT/oRk1c+4RLxi1XX9UG... | null |
2,501 | Jim Beam | Jim likes to play with laser beams.
Jim stays at point $(0,0)$.
There is a mirror at point $(X_m, Y_m)$ and a wall between points $(X_1, Y_1)$ and $(X_2, Y_2)$.
Jim wants to find out if he can point the laser beam on the mirror.
**Input Format**
First line contains the number of test cases, $T$.
Each subseque... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static String solve(int x1, int y1, int x2, int y2, int xm, int ym)"
],
"script_name": "Solution",
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arr... | {
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class Solution {\n static InputStream is;\n static PrintWriter out;\n static String INPUT = ... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.math.*;\n\n\npublic class Solution {\n\tstatic BufferedReader in;\n\tstatic PrintWriter out;\n\tstatic StringTokenizer tok;\n\t\n\t\n\tstatic void solve() throws Exception {\n\t\tint t = nextInt();\n\t\tfor (int tt = 0; tt < t; ++tt) {\n\t\t\tout.prin... | {
"class_name": null,
"func_sign": [
"solve(x1, y1, x2, y2, xm, ym)"
],
"solution": "def det(a,b,c,d,e,f):\n return (a-e)*(d-f)-(b-f)*(c-e)\n\nT = int(input())\nfor tt in range(T):\n a,b,c,d,e,f = [int(x) for x in input().split()]\n if det(a,b,c,d,e,f) == 0 and det(a,b,c,d,0,0)==0:\n print(\... | eJydUzuO1UAQJIB7tBzvoOl/z+akEJCAABHAC0iWDd5KSCskDgHXI+Me1JjdAHhPaPDYY8/IXV2uKn95+O3Howf78eI7Hl7dbh+urm+O2yVt/PqqDXEXat49VKmVZ6mQ8DCvuRQbTNJHGd7eLmg7fLo+vDse3r/9eHO8w3n6bPt8Qb8DS4kJEAZHTwaSATBxH8MFDSLFZr9SLzuL/PLJ87+hw8JYaEiUJKVqj6BmGQzgMbpUJ/AvPY97inGkogqMa0SRKSczqQd3bFqAplKEeSzSbZLg0oHoOKkpFCjSkTjJIw36s5WwruHODwxQqxSZ... | null |
24,167 | Hyperspace Travel | A group of $n$ friends living in an $m$-dimensional hyperspace want to meet up at some central location. The hyperspace is in the form of an $m$-dimensional grid, and each person can only move along grid lines. For example, to go from $(0, 0) \rightarrow (1, 1)$ in a $2$-dimensional space, one possible route is $(0, 0)... | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static List<Integer> solve(List<List<Integer>> friends)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static v... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.math.*;\n\npublic class Solution {\nstatic class Reader\n {\n final private int BUFFER_SIZE = 1 << 16;\n private DataInputStream din;\n private byte[] buffer;\n private int bufferPointer, bytesRead;\n \n public Re... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.math.*;\n\npublic class Solution {\nstatic class Reader\n {\n final private int BUFFER_SIZE = 1 << 16;\n private DataInputStream din;\n private byte[] buffer;\n private int bufferPointer, bytesRead;\n \n public Re... | {
"class_name": null,
"func_sign": [
"solve(friends)"
],
"solution": "import operator\n\ndef main():\n lines, dim = map(int, input().split())\n points = [list(map(int, input().split())) for i in range(lines)]\n coords = map(sorted, zip(*points))\n getter = operator.itemgetter((lines - 1) // 2)\n... | eJztXc3O5Dhy9MEPIvR5ZYj/pJ/EgNvwwZ6DL+0FPAssYCzsh7Df1xmRoSqJkmp6PQdjvD2D1ldflYpKZUYGkxQZ33/+9X//x7/9Ff/7uz/ai7//9y//8u33f/j5y98uX0Jawvb1W9j037Li5RJfL/3dzN8LjodTG479fOrXb4ev2auwrBHHw9cyfi/86P1u4+/9dOpsV4RdcbIr0q442RVhV/xgV6JdabIrwa402ZVoV3q2K8OuPNmVaVee7MqwK3+wq9CuMtlVYFeZ7Cq0qzzbVWFXneyqtKtOdlXYVT/Y1WhXm+xqsKtNdjXa1Z7t... | null |
3,419 | Solve Equations | You are given a straight line, $a \cdot x + b \cdot y = c$. Find the point closest to the origin that also satisfies the following properties:
1. $x$ and $y$ are *integers*.
2. $x$ is *greater than* zero.
If more than one solution exists satisfying $1$ and $2$, then choose the point in which $x$ is minimal.
... | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static List<Integer> solve(int a, int b, int c)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n \n private static clas... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n \n private static class ExtGcd{\n public long k, l, g;\n public ExtGcd(long a, long b){\n long[] v1 = {1,0,a}, v2 = {0,1,b}, merged = {... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.math.*;\n\npublic class Solution {\nstatic class Reader\n {\n final private int BUFFER_SIZE = 1 << 16;\n private DataInputStream din;\n private byte[] buffer;\n private int bufferPointer, bytesRead;\n \n public Re... | {
"class_name": null,
"func_sign": [
"solve(a, b, c)"
],
"solution": "from math import floor\ndef GCD(a, b):\n\tif a < b:\n\t\tc = a\n\t\ta = b\n\t\tb = c\n\twhile(1):\n\t\tif a % b == 0:\n\t\t\treturn b\n\t\tc = a % b\n\t\ta = b\n\t\tb = c\n\ndef GCDEx(a, b):\n\tif b == 0:\n\t\tsx = 1\n\t\tsy = 0\n\n\t\tre... | eJx0vUuubF2ypYUEDXFFOV2a77UmLUHilyhAFrISZCGRkFIp0QE6SisY32d+gqsI7q3Evvs/232t+bDHsGHD/q//4X/6f/7v//6/+5//69/+09//8//xX/72P37+ts5ff5+3P8/Z7+dd8x1nvZ/Z+jwn/2nM9dw92+f3Q//0v/6+1x3n5t/vZ8w+Pu/46+93PqffZ332fc/TnvnZp+XvT5v3tvfzLz/89fe1dx/v0z+/Hwafffqz5mn5nn57f8b43PxvGyMfdt783X33p+fbT8uX5Q/yj8c9Y3/uPM/T8sMz2r5j5z+tmU/IP/v9sPn3... | null |
4,396 | Polar Angles | A point **(x,y)**, on the cartesian plane, makes an angle **theta** with the positive direction of the x-axis. **Theta** varies in the interval [0 ,**2PI**) radians, i.e, greater than or equal to zero; but less than 2\*PI radians.
For example, the polar angle of the point (1,2) as marked in this plane below, is (ap... | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static List<List<Integer>> solve(List<List<Integer>> coordinates)"
],
"script_name": "Solution",
"solution": "import java.io.DataInputStream;\nimport java.io.FileInputStream;\nimport java.io.IOException;\nimport java.io.PrintWriter;\nimport java.util.Arrays... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n Scanner sc = new Scanner(System.in);\n int N = sc.nextInt();\n Point[] pts = new Point[N];\n for (int i = 0; i < pts.length; i++) {\n pts[i] = new P... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n Scanner sc = new Scanner(System.in);\n int N = sc.nextInt();\n Point[] pts = new Point[N];\n for (int i = 0; i < pts.length; i++) {\n pts[i] = new P... | {
"class_name": null,
"func_sign": [
"solve(coordinates)"
],
"solution": "count = int(input())\npoints = []\n\nfor _ in range(count):\n x, y = [int(x) for x in input().split(\" \")]\n points.append((x,y))\n \nimport math\ndef key(x):\n atan = math.atan2(x[1], x[0])\n return (atan, x[1]**2+x[0... | eJzNvU3OLFuzpAUSA0nddiWKvxU/jASJjWhASdC5VKNKQkIlMQgGxpAIN3vMV55z71cgVA06591n73wzIyPWcjc3M/f1f/xX/+3/9T//l//Ff/e//9P/8s//7j/8+3/6bz7/9NzXn39el8/3WP788/feP8f9/lyf9y+2+ovtc7wvGOfn+yz+/+/9vmI/P+Oo/z8/61L/sF+f7zj//PMxPs+ff76OzzneVz2fq95lH5/v+v7hGp/1fbdr+Zzvi7bz86zvbyyfe623Gp/9/ZXxOVa9wfcY+o3zfdftft991c/D71ufpfc93nf6Puvnux36... | null |
27,019 | Hard Homework | Aaron is struggling with trigonometric functions, so his teacher gave him extra homework. Given an integer, $n$, he must answer the following question:
> What is the maximum value of $sin(x) + sin(y) + sin(z)$, where $x$, $y$, and $z$ are positive integers and $x + y + z = n$?
Help Aaron by finding this maximal value... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static double solve(int n)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n ... | {
"solution": "\nimport java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class F {\n InputStream is;\n PrintWriter out;\n String INPUT = \"\";\n \n // sin a+... | {
"solution": "\nimport java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class F {\n\tInputStream is;\n\tPrintWriter out;\n\tString INPUT = \"\";\n\t\n\t// sin a+sin b=sin(... | {
"class_name": null,
"func_sign": [
"solve(n)"
],
"solution": "from math import sin,pi\nfrom itertools import product\nfrom random import shuffle\n\nn = int(input())\n\na = list(range(1,n-1))\na.sort(key=lambda v: v % (pi*2))\nlena = len(a)\n\noffsets = list(product(range(-500,501),range(-500,501)))\nshuff... | eJyFlE1KA0EQhV3kIGHWIdT/jycRVFxoBBfGLBIISMBD6H3tDC6kImWtGoav36tX1fOx+HpdXM118zwOt+/Ty3Z32E/XywldXJjvttNqOW2Ou83jfvP08HbY/3yndSgpYzBPp9XyFylkqg2XmeGUSYVDGhdGtqQbBRpWUkWp9SqZHJJWvQYSubdNsjOxQ5VEZknqzFJAqIoUclwmgG2wouIsllUzKK11C5rgwRea5oEODYhpyuCiF8naWANrSA5np9FUIbsGlUSQUqtN6ZgcIslphek2zQzANDhqHA3j5DGk4mLgXXzmxACE1VzO1W40... | null |
40 | Meeting Point | There is an infinite integer grid where **N** people live in **N** different houses. They decide to create a meeting point at one person's house.
From any given cell, all 8 adjacent cells can be reached in 1 unit of time, e.g. (x,y) can be reached from (x-1,y+1) in one unit of time. Find a common meeting place whi... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static int solve(List<List<Integer>> coordinates)"
],
"script_name": "Solution",
"solution": "import java.io.BufferedReader;\nimport java.io.IOException;\nimport java.io.InputStreamReader;\nimport java.util.Arrays;\nimport java.util.Comparator;\n\npublic cl... | {
"solution": "/* Enter your code here. Read input from STDIN. Print output to STDOUT */\n\nimport java.util.Arrays;\nimport java.util.Comparator;\nimport java.util.StringTokenizer;\nimport java.io.*;\n\n\nclass Tuple{\n long x;\n long y;\n long sum_x;\n long sum_y;\n public Tuple(long x, long y){\n ... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n Scanner in = new Scanner(System.in);\n int N = in.nextInt();\n int[] xs = new int[N];\n int[] ys = new int[N];\n for (int i = 0; i < N; i++) {\n ... | {
"class_name": null,
"func_sign": [
"solve(coordinates)"
],
"solution": "# Enter your code here. Read input from STDIN. Print output to STDOUT\n\nx = []\ny = []\n\nn = int(input())\n\nrt = 0\n\nfor i in range(n):\n xi,yi = [int(i) for i in input().strip().split()]\n x.append(xi);y.append(yi)\n\ndef d... | eJzs/cvKZc+Sp+upoQuZZE+wA4YdfBx0JQItUEOqhjqp3agNG4RAN6R7lP3MXotVyqoFUldkJqx/xBffHHMczN3N/XH38f/83/4f/nf/r+t/83/8v//b//Xf/+f/5b/+2//+9292/ePf/+R7p5/fnxP2flk/eK4n7vd3MvNz/f0914lf2mdp//j3c8I//72PXXH/498t47H79+e1536ef/y7xxvHf3/Mjt3173++iMjnV/99jtXn/7xXnKt+cNLyjfrBMb+e8zvfe9UH/PbjWb+fGad+/9/+p9+//Zf/9X/+L//n//pf/i//p//b//Jf... | 2 |
2,534 | Sherlock and Probability | Watson gave a string $S$ to Sherlock. It is $N$ characters long and consists of only `1`s and `0`s. Now he asks: Given an integer $K$, I'll pick two indices $i$ and $j$ at random between $1$ and $N$, both inclusive. What's the probability that both $S[i]$ and $S[j]$ are `1` and $|i - j| \le K$?
**Input Format**
F... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static String solve(int n, int k, String s)"
],
"script_name": "Solution",
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.I... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.math.*;\n\n\npublic class Solution {\n\tstatic BufferedReader in;\n\tstatic PrintWriter out;\n\tstatic StringTokenizer tok;\n\t\n\t\n\tstatic void solve() throws Exception {\n\t\tint t = nextInt();\n\t\tfor (int tt = 0; tt < t; ++tt) {\n\t\t\tint n = ... | {
"class_name": null,
"func_sign": [
"solve(n, k, s)"
],
"solution": "from itertools import accumulate\nfrom math import gcd\n\ndef f(s, k):\n n = len(s)\n a = [0] + list(accumulate(ord(c) - ord('0') for c in s))\n ret, tot = 0, n * n\n for i, c in enumerate(s):\n if c == '1':\n ... | eJx0vU2upcmSXaeGBpJ4ULOA5x75GxyJACXABlkNdlLVqAIIEAQ0Wg1BbWW+F7bWMj/ByIh7z/k+d3Ozvdf2DllV/8///n/+v//f//G//V//42//7Y9/+49//9t/+uFvX37/47cv54dffzm//3HPX3/++fOv3/fPj/cfP/757Z777ek/V91/PLr/3HG/ffi24dvXO/u/FfxnyfOt1Pz99m/KzsGeNB2dKX5cMKu+bfu29Nvx/1x1p3CGY9G9JyvS37dxzry/ntKlaYF9I9y3M0aTkWydc5cq3x44q16ox6j0rbiKbm/WGeMJo9HlvR6J... | null |
1,236 | B'day Gift | Isaac has to buy a new _HackerPhone_ for his girlfriend Amy. He is exploring the shops in the town to compare the prices whereupon he finds a shop located on the first floor of a building, that has a unique pricing policy. There are N steps leading to the shop. A numbered ball is placed on each of the steps.
The shop... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static double solve(List<Integer> balls)"
],
"script_name": "Solution",
"solution": "import java.io.BufferedReader;\nimport java.io.IOException;\nimport java.io.InputStreamReader;\npublic class Solution {\n\tstatic BufferedReader in = new BufferedReader(new... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n Scanner sc=new Scanner(System.in);\n int inputs=sc.nextInt();\n int[] array=new int[inputs];\n double sum=0;\n for(int i=0;i<inputs;i++)\n ... | {
"class_name": null,
"func_sign": [
"solve(balls)"
],
"solution": "#!/usr/bin/env python\n\nimport sys\n\n\nif __name__ == '__main__':\n N = int(sys.stdin.readline())\n balls = [int(sys.stdin.readline()) for _ in range(N)]\n \n print('%.1f' % (sum(balls) / 2))",
"template": "#\n# Complete the... | eJztmM+KXkUQxV34CD5AmHUI3dXVVdU+ieCIC83CzZhFAoIIPoS+r7/Tk0kHv9woCC5CJvmg53731p9Tp07Vnd+//PPNV1/sn28eOHz7691PD6/evL77+tldX/cPo881I+b9Q8015jLr9w+9co5ZMe4fbCwfnWsrF7dWcsxuNVbrjatj+hrRg+ctR2/h9w/TM0cUJ6sa5tX4erTZRjdMdvOVNQpL5nP1sSzvH6KiVWtBTNPGCCwV9iOjz1i46muVVXQsrCJOTtxg3NjDsXX3/Nndy19evfzh9csfv//5zeu3SY4Voyoj24t599vzZ+/l... | null |
53 | Palindromes | Given a string, you keep swapping any two characters in the string randomly till the string becomes a palindrome. What is the [expected number](http://en.wikipedia.org/wiki/Expected_value) of swaps you will make? There will always be at least one palindrome which can be formed with the letters of the given string.
**I... | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static double solve(String s)"
],
"script_name": "Solution",
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\n\npublic class Solution {\n\tsta... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.util.HashMap;\nimport java.util.Map;\nimport java.util.Scanner;\n\npublic class Solution {\n static final Map<String,Double> m = new HashMap<String,Double>();\nstatic {\nrun:\nm.put(\"abbaaca\",59.3380);\nm.put(\"aabaabcc\",47.3846);\nm.put(\"abaccbaa\",47.3846);\nm.put(\"abcddcb\",138.0... | {
"class_name": null,
"func_sign": [
"solve(s)"
],
"solution": "from itertools import permutations\nd = {}\nnext = {}\nr = {}\nrets = []\ntpers = {}\nfor _ in range(int(input())):\n s = input()\n if s == s[::-1]:\n print(\"%.4f\" % 0)\n continue\n for o,n in zip(sorted(list(set(s)),ke... | eJydVdGO2jAQ7EPV70B5PlkxwVD6JSeVqrJ3N5Qj5K4nrnCpTupHtJ/Ut35UZw2kSIlTjiiKh4029s7ODj/e/vrz7k28bn8DfPyereqHp232YZS5RR0CsyfyQBQ8sWdF5DkoQogZDyDygXEv6uxmlMn+QWgr/Pn+aXv8li0mZmrdZFHbojCFm+eK2tjYmgKXxqyZTYopPvRyMzo7jF3UQtJQk95iZlye572ZlSTTcpOnsppvuK/Zb737utytlwNsvDe5c71V0mZDrz0sOsVgh1hXLKRkEbNE0jj+ZoD0h9tixqctBiI9Ba+v4JeqffXq... | null |
21,063 | Lazy Sorting | Logan is cleaning his apartment. In particular, he must sort his old favorite sequence, $P$, of $N$ positive integers in nondecreasing order. He's tired from a long day, so he invented an easy way (in his opinion) to do this job. His algorithm can be described by the following pseudocode:
while isNotSorted(P) do {... | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static double solve(List<Integer> P)"
],
"script_name": "Solution",
"solution": "import java.io.BufferedInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.util.Arrays;\n\npublic class Solution {\n\n public static void mai... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n private static InputReader in;\n private static PrintWriter out;\n\n public static void main(String[] args) throws IOException {\n in = new InputReader(System.in);\n out = new PrintWriter(System.out, true);\n\n int n = in.n... | {
"class_name": null,
"func_sign": [
"solve(P)"
],
"solution": "from math import factorial\nN=int(input())\na=list(map(int,input().split()))\nif a==sorted(a):\n print(0)\nelse:\n m={}\n for i in a:\n if i in m:\n m[i]+=1\n else:\n m[i]=1\n num=1\n for i in ... | eJydVE1rFUEQ9CD+jmLPIUx/z/hLBJ940Hfw8szhBQIi5EfEizf/qd1jBAmZ9ZEdFoZhtqq7qnrvX//49ebVfN79zM37b9uX083teXuLjexwGgqGDGgHG8QQAnMw1Ql57UURjE6H03aF7Xh3c/x0Pn7++PX2/Igj7IO6DW6tXbf5bN+v8A9THE4xEARWqGMYkpd4iWhNF0jUs2aDCcLgBmbQAAeGgFr1oEnBUNiYW7ijjyUTeWs2RNlV1vWXUgRCt1JmjCJngQRI0JMru1H0jkg5dUnmJl2Cuq6J8mNp8ETKltIEQRrQyglP6D6li+mH... | null |
13,581 | 2D Array - DS | Given a $6 \times 6$ *2D Array*, $arr$:
1 1 1 0 0 0
0 1 0 0 0 0
1 1 1 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
An hourglass in $A$ is a subset of values with indices falling in this pattern in $arr$'s graphical representation:
a b c
d
e f g
There are $16$ hourglasses in $arr$. A... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static int hourglassSum(List<List<Integer>> arr)"
],
"script_name": "Solution",
"solution": "import java.util.Scanner;\n\npublic class Main {\n\n public static void main(String[] args) {\n\t// write your code here\n\n\n int numbers[][] = new int[6... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n /* Enter your code here. Read input from STDIN. Print output to STDOUT. Your class should be named Solution. */\n Scanner sc = new Scanner(System.in);\n int[][] array... | {
"class_name": null,
"func_sign": [
"hourglassSum(arr)"
],
"solution": "board = []\nfor i in range(6):\n board.append([int(x) for x in input().split()])\ni,j = 0,0\nm = sum([board[i][j],board[i][j+1],board[i][j+2],board[i+1][j+1],board[i+2][j],board[i+2][j+1],board[i+2][j+2]])\nfor i in range(4):\n f... | eJy1VsuOEzEQ5MCJr2jlvEZ+jD1jvgSJIA6QA5ewh6y0EkLiI+DOjd+kq9rJJg7ORuyyM5p12x53dXV1T76//Pn71Qv+vf2lg3dfV5+3t3e71RtZeXFV3MTnLG5Zb12gUcRF0cekM1V0KUuSaDt0+yJZJ7Pk9XbG+7MsmHRFl2e8PfM8vLferm5ktbm/3XzcbT59+HK3a75jWH27kSMwQap4HhzWW/UVgAFIdBD2B7skgEIgFQiLTJxXpIkvAO5EKDPsWfheGuIIscPhAESjie0YHfIkBJjoaIGJC/YMR0G4Sd8kDCBIGgw2zzoiZeBw... | null |
13,724 | Sparse Arrays | There is a collection of input strings and a collection of query strings. For each query string, determine how many times it occurs in the list of input strings. Return an array of the results.
**Example**
$stringList = ['ab','ab',abc']$
$queries = ['ab','abc','bc']$
There are $2$ instances of '$ab$', $1$ ... | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static List<Integer> matchingStrings(List<String> stringList, List<String> queries)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solut... | {
"solution": "import java.util.Scanner;\nimport java.util.HashMap;\n\npublic class Solution {\n public static void main(String[] args) {\n Scanner scan = new Scanner(System.in);\n int N = scan.nextInt();\n scan.nextLine(); // gets rid of pesky newline\n \n /* Create and fill Ha... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n public static void main(String[] args) throws IOException {\n BufferedReader c = new BufferedReader(new InputStreamReader(System.in));\n Integer numstr = Integer.parseInt(c.readLine());\n HashMap<String,Intege... | {
"class_name": null,
"func_sign": [
"matchingStrings(stringList, queries)"
],
"solution": "N = int(input())\nnumbers = list()\ncounts = list()\nfor i in range(0,N):\n numbers.append(input())\nQ = int(input())\nfor i in range(0,Q):\n checkstring = input()\n count = 0\n for num in numbers:\n ... | eJyUvemO7DzSJmYYvpAP/Xt+iBSpxVdiwC9gUBIpiZRELRS3wQAGfFdzdRORZ6vKUp1uA2836mQqJS6xPBHxBPX//h//1//3P//3/+3//u//mrf9dv/6P//rX/yfral419X/bFXtJKfd2F8HV3c7/LMNF/wfvXey/LPpqq2rnk+57Mp/NgJf2kb43fOLplyYf7Z//bf/+peMu+ydHP4fe7ufDyj+9T/+2399eCAp/tn27sp5jpPb0vTPltx1t3UurTVj3DqV/tlqZpZFFF6N9xJhDOXVacaKvvtn2/Zp4CX1tppSrv7ZlBzGCOMrt4aV... | null |
1,147 | Print in Reverse | <sub>This challenge is part of a tutorial track by [MyCodeSchool](http://www.youtube.com/mycodeschool) and is accompanied by a video lesson.</sub>
Given a pointer to the head of a singly-linked list, print each $data$ value from the reversed list. If the given list is empty, do not print anything.
**Example**
$he... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static void reversePrint(SinglyLinkedListNode llist)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.func... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | null | {
"class_name": null,
"func_sign": [
"reversePrint(llist)"
],
"solution": "import math\nimport os\nimport random\nimport re\nimport sys\n\nclass SinglyLinkedListNode:\n def __init__(self, node_data):\n self.data = node_data\n self.next = None\n\nclass SinglyLinkedList:\n def __init__(sel... | eJztvcuuLc22nQUSD2KdsoUyIjIyMnkSJCYCCVygsnHBliwhSzwEj0uBFd/Xeqztfc5vJCqmsKS1/70uc4yRIy69t9b67f/8b/77//t//q//q//h//in/+1v//bf/7t/+u/+1T99P3+77/Hzt/e7f/3uW79+N3/9Zv36z7Pen7/N/us/+zfvuvd/nl9/P3/98W6/XtT3j73z1999V//1u1//z9vsv7nbr7/Z//L9+usx9huPX38z90vm/PWf8fx6hzXH/thf/zD6/s/89Tz93m802q9X9P3D+4G+mye7fr3l/euPvf965/X8+t3az7H6... | null |
1,156 | Get Node Value | <sub>This challenge is part of a tutorial track by [MyCodeSchool](http://www.youtube.com/mycodeschool)</sub>
Given a pointer to the head of a linked list and a specific position, determine the data value at that position. Count backwards from the tail node. The tail is at postion 0, its parent is at 1 and so on.
**E... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static int getNode(SinglyLinkedListNode llist, int positionFromTail)"
],
"script_name": "Result",
"solution": "/*\n Insert Node at the end of a linked list \n head pointer input could be NULL as well for empty list\n Node is defined as \n class Node {\n... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": " // Complete the getNode function below.\n\n /*\n * For your reference:\n *\n * SinglyLinkedListNode {\n * int data;\n * SinglyLinkedListNode next;\n * }\n *\n */\n static int getNode(SinglyLinkedListNode head, int positionFromTail) {\n SinglyLink... | {
"class_name": null,
"func_sign": [
"getNode(llist, positionFromTail)"
],
"solution": "#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\nclass SinglyLinkedListNode:\n def __init__(self, node_data):\n self.data = node_data\n self.next = None\n\nclass SinglyLi... | eJztnU2ubElynDXQDjTTqFFjgjjxH6GVCNAROKB6oEmJgyYgQCDARZAb04qU9pl73Ndd9RrSSJBwiarLrvcybmYei3A3N/+Jf/63//rv/+e/4f/+47/7/I//9D9++a+//sM//umX//CHX8r76zn7/fXzT/n879I//6731/p8/m2ff+fn3/P+2urn3/H59/O6/nld/7yuf143Pq8bn9eNz+vG53Xz87r5ed38vG59Xrc+r1uf1+3P6/bndfvzuv153fm87nxep7cuj9740Ts/Sx/j0Y+mH1M/Pi8vterH0A+taFrRtKJpRdeKrhVdK7pW... | null |
1,157 | Delete duplicate-value nodes from a sorted linked list | <sub>This challenge is part of a tutorial track by [MyCodeSchool](http://www.youtube.com/mycodeschool)</sub>
You are given the pointer to the head node of a sorted linked list, where the data in the nodes is in ascending order. Delete nodes and return a sorted list with each distinct value in the original list. The g... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static SinglyLinkedListNode removeDuplicates(SinglyLinkedListNode llist)"
],
"script_name": "SinglyLinkedListPrintHelper",
"solution": "Node RemoveDuplicates(Node head) {\n // This is a \"method-only\" submission. \n // You only need to complete this meth... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": " // Complete the removeDuplicates function below.\n\n /*\n * For your reference:\n *\n * SinglyLinkedListNode {\n * int data;\n * SinglyLinkedListNode next;\n * }\n *\n */\n static SinglyLinkedListNode removeDuplicates(SinglyLinkedListNode head) {\n ... | {
"class_name": null,
"func_sign": [
"removeDuplicates(llist)"
],
"solution": "#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\nclass SinglyLinkedListNode:\n def __init__(self, node_data):\n self.data = node_data\n self.next = None\n\nclass SinglyLinkedList:... | eJztnc1uJUlyZjWAHiRQ64ZwI/xfTzKAOJjFqBfatHrRAgQNBMwDaDkPrHMs4l7+Z14yySSZ6QDNqirrcyZ5T/y4mbuZ/7+//5//9R//4+/+6f/+8S9/+eu//e2Pf1z+2G7+krBt6z/qxktdOr3ArfnmL+vadYxdt5Nu1W26pFOyFV3VNZ0j/NtWv8uaHOHvuyZHJEckRyRHJEckRyRHZEdkR2RHZEdkR2RHZEdkR2RHZEcURxRHFEcURxRHFEcURxRHFEcUR1RHVEdUR1RHVEdUR1RHVEdUR1RHNEc0RzRHNEc0RzRHNEc0RzRHNEd0... | null |
22,258 | Balanced Forest | Greg has a tree of nodes containing integer data. He wants to insert a node with some non-zero integer value somewhere into the tree. His goal is to be able to cut two edges and have the values of each of the three new trees sum to the same amount. This is called a *balanced forest*. Being frugal, the data value he... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static int balancedForest(List<Integer> c, List<List<Integer>> edges)"
],
"script_name": "Solution",
"solution": "import java.io.File;\nimport java.util.ArrayList;\nimport java.util.Arrays;\nimport java.util.HashMap;\nimport java.util.HashSet;\nimport java.... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n /* Enter your code here. Read input from STDIN. Print output to STDOUT. Your class should be named Solution. */\n Scanner scanner = new Scanner(System.in) ;\n int q =... | {
"solution": "\nimport java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.BitSet;\nimport java.util.Comparator;\nimport java.util.InputMismatchException;\n\npublic class E {\n\tInputStream is;\n\tPrintWriter... | {
"class_name": null,
"func_sign": [
"balancedForest(c, edges)"
],
"solution": "\nfrom random import randrange\nimport sys\nsys.setrecursionlimit(10**5)\n\ndef r():\n return list(map(int, input().split()))\n\ndef bin_search(arr, pred, lo=0):\n hi = len(arr)\n while lo < hi:\n mid = (lo+hi)//... | eJzsvcuuLMu2ZUUB/mPqFlEiubnby/kFfgCJJVGALFA5ZOGmhISQ+HRGa9187X1uppBAFCisvHl22IoZYeFubo/x6H30//O//O//6//uv/ov/of//V/+l3/8h//4r//y3/78S/v1j3Fdv/7Rru///fxp/mn+af5p/mn+af5p/mn+af5p/mn+af5p/mn+af5p/mn+af5p/mn+af5/0/z1j/Zz85/n1z/un85/xq9/PD+T/6xf/+g/m/+8v/4x6lv+t/36x/xpt/+tb62f1v1vfW//tOl/65vvT9v+9yXA+3Nfealvt/rJOy/1/Xb/3D0v... | null |
14,717 | Maximum Element | You have an empty sequence, and you will be given $N$ queries. Each query is one of these three types:
1 x -Push the element x into the stack.
2 -Delete the element present at the top of the stack.
3 -Print the maximum element in the stack.
**Function Description**
Complete the *getMax* function in... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static List<Integer> getMax(List<String> operations)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class maximumElement3 {\n\n public stat... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n private static void getMaxElementFromStack()\n {\n Stack<Integer> stack = new Stack<Integer>();\n Stack<Integer> onlyMaxs = new Stack<Integer>();\n \n Scanner sc = new Scanner(System.in);\n \n ... | {
"class_name": null,
"func_sign": [
"getMax(operations)"
],
"solution": "stack = []\nmax_stack = []\n\nfor _ in range(int(input())):\n try:\n cmd = input()\n except:\n cmd = '3'\n if cmd[0] == '1':\n n = int(cmd.split()[1])\n stack.append(n)\n if len(max_stack) =... | eJzs/cnKrc2ypQdmQt6EeofdjobXha5EkAFqSKehzlE0ToBACHRbeXc5HjNzf32uHYHIhkBKFns35vevOd/C3dzKYcP+9//Xf/ff/H/+n/+P//f/+o//6d/+03/+93/8t//yj9z/47/lfymp1NVLGv/x3+p//LfCf+p7pDJHbvaf8r+MWdtMqS77/q6zpJwSf+Se+PJY58f2/TZ71heGfaXWNGvRF/ljt75rr5vv/+M//Ms//vV/+U//+j/8+7/+j//9//yf/z2e6nme5zmeR3g+Ppf7x//2H/7lebee/AFrWWXMVur3dmvpt2ske/vd... | null |
15,095 | Balanced Brackets | A bracket is considered to be any one of the following characters: `(`, `)`, `{`, `}`, `[`, or `]`.
Two brackets are considered to be a *matched pair* if the an opening bracket (i.e., `(`, `[`, or `{`) occurs to the left of a closing bracket (i.e., `)`, `]`, or `}`) *of the exact same type*. There are three types of ... | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static String isBalanced(String s)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n\tpublic static void main(String args[])... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n /* Enter your code here. Read input from STDIN. Print output to STDOUT. Your class should be named Solution. */\n Scanner sc = new Scanner(System.in);\n try{\n ... | {
"class_name": null,
"func_sign": [
"isBalanced(s)"
],
"solution": "#! /usr/bin/python3\n\ndef check():\n stack = []\n s = input()\n for c in s:\n #print(c)\n if c == '(':\n stack.append(0);\n elif c == ')':\n if len(stack) > 0 and stack[-1] == 0:\n ... | eJztfcuObTlynQ34Qxo1IgHBQJckt1pzT+2BJzZYhAd2DzRwSYMWYIAg4I/wB3tHrBWMFTx5M/OWurvKpZt5M2+ec/bmMxix4rn/z7/7r//rb/7tvxnru3/48Z/++Y/f/f1vvvv9Dz+2H37sP/w4fvhx/fDj/OHH7e/Yv7aef+P5Wsv+WvbC/rcXDX+2FX/6t12/1vOyDXt7NLvV3hjNm7FP7KPlly676PllHz1XNP/UWjg9N3yKpuxS//I/0YO30XC7j8suYae8Dh/YEHCn9+tzsvYbfrdoekW33rW16+9h0LjOZ2L32SQG5uJ/eX+Y... | null |
8,175 | Find the Running Median | The median of a set of integers is the midpoint value of the data set for which an equal number of integers are less than and greater than the value. To find the median, you must first sort your set of integers in non-decreasing order, then:
- If your set contains an odd number of elements, the median is the middle el... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static List<Double> runningMedian(List<Integer> a)"
],
"script_name": "Solution",
"solution": "import java.io.BufferedReader;\nimport java.io.IOException;\nimport java.io.InputStreamReader;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\n\n\n\n\npub... | {
"solution": "import java.util.Comparator;\nimport java.util.PriorityQueue;\nimport java.util.Scanner;\n\n/**\n * @author Kanahaiya Gupta\n *\n */\npublic class FindTheRunningMedian {\n public static void main(String[] args) {\n Scanner sc = new Scanner(System.in);\n PriorityQueue<Integer> highers = new Prior... | {
"solution": "import java.util.*;\nimport java.math.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n Scanner in = new Scanner(System.in);\n int n = in.nextInt();\n PriorityQueue<Integer> min = new PriorityQueue<Integer>(Collections.reverseOrder());\n Priorit... | {
"class_name": null,
"func_sign": [
"runningMedian(a)"
],
"solution": "import heapq\n\nL,H=[],[]\n\nfor _ in range(int(input())) :\n a = int(input())\n if not H :\n heapq.heappush(H,a)\n else :\n if len(H) > len(L) :\n if H[0] < a :\n b = heapq.heappushpop(H... | eJzs3T2OBLnWHugxtBDhs2Qmef6YWokA9QbkNGRIlpzZ9zhTfFhLkFlAIJwm7+2ul5kZ8QTjnP/3P/23/++//Of/57//n//4H//+z//9v/7jv/7n/1j5+Xz++ffv+Dv+jr/j7/g7/o6/4+/4O/6Ov+Pv+Dv+jr/j7/g7/o6/4+/4O/6Ov+Pv+Dv+jr/j7/g7/o6/4+/4O/6Ov+Pv+Dv+jr/j7/g7/o6/4+/4O/6Ov+Pv+Dv+jr/j7/g7/q8c659/9z//xj//5j//1j//9j//zj//nn/+/f78o/uPf/75+hmwfkasnyHrZ8z6GbR+Rq2f... | null |
21,404 | Equal Stacks | You have three stacks of cylinders where each cylinder has the same diameter, but they may vary in height. You can change the height of a stack by removing and discarding its topmost cylinder any number of times.
Find the maximum possible height of the stacks such that all of the stacks are exactly the same height. T... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static int equalStacks(List<Integer> h1, List<Integer> h2, List<Integer> h3)"
],
"script_name": "Solution",
"solution": "/*\n * Code Author: Akshay Miterani\n * DA-IICT\n */\nimport java.io.*;\nimport java.math.BigInteger;\nimport java.math.RoundingMode;\ni... | {
"solution": "import java.util.*;\n\nclass Solution {\n private static Scanner scan = new Scanner(System.in);\n\n public static int equalStacks(HashSet<Integer> h1, HashSet<Integer> h2, HashSet<Integer> h3) {\n SortedSet<Integer> commonItems = new TreeSet<>();\n\n for (var height1 : h1) {\n ... | {
"solution": "import java.io.OutputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.OutputStream;\nimport java.io.PrintWriter;\nimport java.util.Iterator;\nimport java.io.BufferedWriter;\nimport java.util.InputMismatchException;\nimport java.io.IOException;\nimport java.io.Writer;\ni... | {
"class_name": null,
"func_sign": [
"equalStacks(h1, h2, h3)"
],
"solution": "def read_stack():\n stack = [int(x) for x in input().split(' ')]\n stack = list(reversed(stack))\n sum_stack = set()\n psum = 0\n for i in range(len(stack)):\n psum += stack[i]\n sum_stack.add(psum)\n... | eJztnb2OLclShTF4kNKxr1DlfyZPgkQjDBgDp7nGXAkJXQkXH14Aj7ck1reiziCGET0SBmJqunO0z+69q7Ii4z9Ta/3Tn/7rv//zn/DfX/xbvPjLf/z2d5+//8OP3/78+lZWu0pv19gfn71c8Tv61a5zrvjD3teeV+vXLno9xnXmNeOf99Xrta+2r1muFh85V+XLZ1zlvla87te5r32uuHaP71Vdr8xrj2vVqw5dps+rxvvzGk0frlfcNO5SdJe44og3it7p+6pbE1j6y+nXuq9Srpjq1G/ZXLheren/8XddgjfjMXhv3LpHbZrZGprK... | null |
2,141 | Minimum Average Waiting Time | Tieu owns a pizza restaurant and he manages it in his own way. While in a normal restaurant, a customer is served by following the first-come, first-served rule, Tieu simply minimizes the average waiting time of his customers. So he gets to decide who is served first, regardless of how sooner or later a person comes. ... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static int minimumAverage(List<List<Integer>> customers)"
],
"script_name": "Solution",
"solution": "\nimport java.util.*;\nimport java.io.*;\n\npublic class Solution {\n\n static void go() {\n int n = in.nextInt();\n Customer[] c = new Cus... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n /*\n * Complete the minimumAverage function below.\n */\n static long minimumAverage(int[][] customers) {\n BigInteger currentTime = new BigInteg... | {
"solution": "import static java.util.Comparator.comparing;\n\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintStream;\nimport java.util.ArrayList;\nimport java.util.HashSet;\nimport java.util.List;\nimport java.util.Map.Entry;\nimport java.util.NavigableMap;\nimport java.util.PriorityQ... | {
"class_name": null,
"func_sign": [
"minimumAverage(customers)"
],
"solution": "import heapq\n\nN = int(input())\nTasks = [tuple(int(_) for _ in input().split()) for _ in range(N)]\nTasks.sort()\n\nTotalWaiting = 0\ncurrentTime = 0\ncurrentWaiting = []\n\ni = 0\nwhile i < len(Tasks) :\n\n # No clients, ... | eJx03c3KLUuSJmaBdB3ikOMeLP8zN+srEShBA6kGPUnVoBoEjUAXrQuQhz++UWUJVUFlnTwr9vft9UZ4mD/m4fF//Q//0//9P/73/93//N/+9l/+8a//9d/+9p//+lv+fr+//+P3V/v7P9pf/e//6H+Nv/9j/DX//o/51/r7P9Zf8fd/xF/77//Y30fy+0h9H2m/7zOtfR9q/ftUG9/H2rx/1Po+2OJ+ct9P5v1kfZ/sv++TvX2f7P3+1PF9ss/vk319n+xxP7nvJ/N+sr5Pjt/9Bdv3ydG/T47xfXLM75NjfZ8ccT+57yfzfrK+T87f... | null |
14,789 | Queries with Fixed Length | Consider an $n$-integer sequence, $A = \{a_0, a_1, \ldots, a_{n-1} \}$. We perform a query on $A$ by using an integer, $d$, to calculate the result of the following expression:
$$\huge{\min_{\Large{0 \le i \le n-d}} ( \max_{\Large{i \le j \lt i + d}} a_j )}$$
In other words, if we let $m_i = \max(a_i, a_{i+1}, a_{i+2... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static List<Integer> solve(List<Integer> arr, List<Integer> queries)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n stat... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.util.LinkedList;\nimport java.util.Scanner;\n\npublic class Solution {\n\t\n\tstatic int n, q, d, minOverD, globalMax = Integer.MIN_VALUE;\n\tstatic boolean VERBOSE = false;\n\t\n\tstatic int[] a;\n\tstatic int[] a2;\n\n public static void main(String[] args) {\n Scanner sc = new ... | {
"class_name": null,
"func_sign": [
"solve(arr, queries)"
],
"solution": "from collections import deque\nimport heapq\nn,q = map(int,input().split())\nl = list(map(int,input().split()))\n\nfor _ in range(q):\n d = int(input())\n q = deque([],d)\n h = []\n for i in range(d):\n \n while l... | eJw8nUuObcuyVJGgIakLDZjxj6AlSNABKk8UoESFpmNj+DqPKzjsvTPXmp8ID3dzM/P/95/+23/+L//xP/z3//uv//lv/+v//O9//de/f7Vv7vk32v/4t/edNs7fXf307299o/X9t9+b8/3tcdu3/uY88+6/sR7/mb2/b/zNbx8+oo+9+t9c63x/4+ttjr++dl/5ibl3v39n5R/73zq75wveGLudvzFPz1/2285Yf+Odll9o+8x8+Rlvfl++fK4z82H5Q/9765x89Jvta/nLkw85/Gnka9u7u598w8yH/63eR39/vZ+vtb93x3h/7/S7... | null |
20,577 | Super Maximum Cost Queries | Victoria has a tree, $T$, consisting of $N$ nodes numbered from $1$ to $N$. Each edge from node $U_i$ to $V_i$ in tree $T$ has an integer weight, $W_i$.
Let's define the cost, $C$, of a path from some node $X$ to some other node $Y$ as the maximum weight ($W$) for any edge in the unique path from node $X$ to node $Y$.... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static List<Integer> solve(List<List<Integer>> tree, List<List<Integer>> queries)"
],
"script_name": "Solution",
"solution": "import java.awt.*;\nimport java.awt.event.*;\nimport java.awt.geom.*;\nimport java.io.*;\nimport java.math.*;\nimport java.security... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n // Complete the solve function below.\n static long[] solve(final int[][] tree, final int[][] queries) {\n final int edgeCount = tree.length;\n fina... | {
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.Comparator;\nimport java.util.InputMismatchException;\n\npublic class C {\n\tInputStream is;\n\tPrintWriter out;\n\tString INPUT = \"\"... | {
"class_name": null,
"func_sign": [
"solve(tree, queries)"
],
"solution": "from collections import defaultdict\n\nN, Q = map(int, input().split())\n\nweights = []\n\nfor _ in range(N-1):\n U, V, W = map(int, input().split())\n weights.append([W, U, V])\n \nweights.sort()\n\nclass Node():\n def ... | eJytmtuOHdlSRXngQ1J+bqF1v/AlSBjxAP2AkJoj1EdCQkh8BPwvY8TamXZV+dg+gNtqZ9XOnblWXOacEbH+8y//+19/+Yv48zf/zMXf/vunf/rtD3/8/dNfX5/WNT//lq9y7Vl2qZ9/K1e9apm7JH/frlrrrOXzb+3qV0+pb+6v17jKHn0u75lXL6vuz7/1a12rtly4zjv3PC7/6dw1Zx3jmnOnxiu4ffKEsussflZa2nxYVjy8tDI2a2hrsZ6Vc8314h/WxKez77wuvt92//zbp1+uT7/+2x9+/Yfff/3Hv/+XP/7+2hVr//j303/8... | null |
9,056 | AND xor OR | Given an array $A[]$ of $N$ distinct elements. Let $M_1$ and $M_2$ be the smallest and the next smallest element in the interval $[ L , R ]$ where $1 \le L < R \le N$.
$S_i = (((M_1 \wedge M_2) \oplus (M_1 \vee M_2)) \wedge (M_1 \oplus M_2))$.
where $\wedge, \vee, \oplus$, are the bitwise operators $AND$, $OR$ an... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static int andXorOr(List<Integer> a)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static void main(String[] a... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n Scanner sc = new Scanner(System.in);\n int N = sc.nextInt();\n int[] arr = new int[N];\n for(int i = 0; i < N; i++){\n arr[i] = sc.nextInt();\n ... | {
"solution": "import java.io.File;\nimport java.io.FileNotFoundException;\nimport java.util.Arrays;\nimport java.util.Scanner;\nimport java.util.Stack;\n\npublic class Solution {\n\t\n\tprivate static final int f(int a, int b){\n\t\treturn (((a&b)^(a|b))&(a^b));\n\t}\n public static void main(String[] args) {\n ... | {
"class_name": null,
"func_sign": [
"andXorOr(a)"
],
"solution": "n_elements = int(input())\ndata = list(map(int, input().split()))\nassert len(data) == n_elements\n\nres = 0\nminima = [data[0]]\nfor i in range(1, n_elements):\n d = data[i]\n while len(minima):\n x = minima.pop()\n res ... | eJx83c2qbU2XHGY3BL4Ls/jaasycf5nTV2JwgRtyGdz5rEYJBELgS3c8kbvAYHBRUp33nL3Xmj+ZI8eIERHj//4P/8v/8T/9j//D//rf/vF//vM//5d/+8f//PvHeY7vX/75HfN4j28+v+9+5rjW+/ze55vzG8f1m9973edc/nTdz/uev2u+13zO4/md98hH3M/ze9Z3z3zS+K1n3Mca8/yNlV+5vus3nnW841i/+c7n+PIDv+9913Gv9RvHzMc+X/60rnNe4z5/3/k+z3V/+dhrXfdx5k/ncd/rvM/rd1/P+53X6efG91zXmr/7zFW/... | null |
8,938 | No Prefix Set | There is a given list of strings where each string contains only lowercase letters from $a-j$, inclusive. The set of strings is said to be a **GOOD SET** if no string is a prefix of another string. In this case, print **GOOD SET**. Otherwise, print **BAD SET** on the first line followed by the string being checked.
... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static void noPrefix(List<String> words)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\nclass Trie\n {\n boolean EOS;\n Trie[] C;\n ... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Trie {\n\n private static final int ALPHABET_SIZE = 26;\n\n class Node {\n \n Node[] edges;\n char key;\n int wordCount = 0;\n int prefixCount = 0;\n \n Node(char key) {\n this.key =... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n \n private static class Trie {\n \n private class TrieNode {\n public boolean terminates = false;\n public TrieNode[] nextNodes = new TrieNode['j' - 'a' + 1];\n }\n \n priv... | {
"class_name": null,
"func_sign": [
"noPrefix(words)"
],
"solution": "class solution:\n def checkSET(self, strs):\n trie = dict()\n for cstr in strs:\n curr = trie\n for ch in cstr:\n if ch not in curr:\n curr[ch] = dict()\n ... | eJwknEmuLcuVXNXIhoZBcA6CMjWPBATodryuw+syOzl02T4kCfzP9+49J8J9b7Nl7h7x3//2f//xn//zf/y///qn+8oc//w///jn//73//hff1/9mJj5fe9Nyxwz377t/n06Od1qcbfH4lsw3H6jJmb0l/O4YXh17+J/H5eG3eNGyEnttLK3V38SPy9u6CeYr7queO/HXcP9vj0t6Ux8XxL47Z7C/UJQqY0tdC+1sL/PiPzifUEWx94Wo9c+5XUj28ZO8nmr2P6+p/VQXJSkpZxZiWmjZbko3cw32RaOR6a8Ysxc+zIu9JzdePMv6DaF... | null |
12,860 | Kindergarten Adventures | Meera teaches a class of $n$ students, and every day in her classroom is an adventure. Today is drawing day!
The students are sitting around a round table, and they are numbered from $1$ to $n$ in the *clockwise* direction. This means that the students are numbered $1, 2, 3, \ldots, n - 1, n$, and students $1$ and $n$... | hackerrank | Medium | {
"class_name": null,
"func_sign": [
"static int solve(int[] t)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n Scanne... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n Scanner input = new Scanner(System.in);\n int n = input.nextInt();\n int[] t = new int[n];\n TreeSet<Node> set = new TreeSet<Node>();\n for(int i=0; i<n... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\tFastScanner in;\n\tPrintWriter out;\n\n\tclass Event implements Comparable<Event> {\n\t\tint time;\n\t\tint add;\n\n\t\tpublic Event(int time, int add) {\n\t\t\tsuper();\n\t\t\tthis.time = time;\n\t\t\tthis.add = add;\n\t\t}\n\n\t\tpu... | {
"class_name": null,
"func_sign": [
"solve(t)"
],
"solution": "import sys\nn = int(sys.stdin.readline())\nt = [int(x) for x in sys.stdin.readline().split()]\n\ncurrent_available = 0\nbecome_unavailable = [0] * (3 * n)\nbest_index = 1\nbest_result = 0\nfor i in range(2*n):\n el = t[i % n]\n become_una... | eJykvUuSK82yNUQDo80Qyv4RRHi8GQlm0MaMDkYDWnQYOr5WSOkuz1Bqn0vj6n6ndpWkzIzw8Md6/L///f/8P/5v/8N/97/8P//tf/8//s//+//6b//T338rU2b5X/8PSbn2P/6Pz9da6/yTNlL++Lm0tuqfjF6Of5VWrh8/ybVK+8ultfk3u758fkhfp3fZnzRzE/1L/SLvn5eZ+p+k9DeLvP8wS17XL4xRxl/Puf/ljL+TnMf4k5qz/mJZNXxam3rteo29Hj4/z5Tb5zcaPX9eQO41fb2A/dr0c98/6K2931FWr/paerrefNX8/b1q... | null |
3,654 | Jim and the Skyscrapers | Jim has invented a new flying object called HZ42. HZ42 is like a broom and can only fly horizontally, independent of the environment. One day, Jim started his flight from Dubai's highest skyscraper, traveled some distance and landed on another skyscraper of same height! So much fun! But unfortunately, new skyscrapers h... | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static int solve(List<Integer> arr)"
],
"script_name": "Solution",
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.Comparato... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.BufferedReader;\nimport java.io.IOException;\nimport java.io.InputStreamReader;\nimport java.io.PrintWriter;\nimport java.util.*;\n\n\npublic class Solution {\t\n\tBufferedReader reader;\n StringTokenizer tokenizer;\n PrintWriter out;\n \n\tpublic void solve() throws IOException... | {
"class_name": null,
"func_sign": [
"solve(arr)"
],
"solution": "n = int(input())\nhs = map(int, input().split())\n\nhhs = sorted(enumerate(hs), key=lambda t: (t[1], t[0]))\n\nprev = list(range(-1, n-1))\nnext = list(range(1, n+1))\nfirsti, firsth = lasti, lasth = hhs[0]\nblock_len = 1\ns = 0\nfor i, h in ... | eJztnc2ubEt6Vd1A4jWWqm2hmPEfPAkSF9GAatC5uFGWkBASDwHPySswR0zbZXTvkV2ipCqXomKfG7XOiZw7M8aXmesba//8z3/1v//1//mr+79/91f+P//+v//mv/z8N3/7u9/82+83p9Sfflb53sf7eB9/0g/+o/fxPt7Hn/Sj8J/6Pt7H+/gTfvz082/++vvNb//b3/z2P/3ut//5P/7Xv/3d352y1nlUiv/5f/z194/OZDX3Tz/XMeppn/+0Ob7Zz1nnW73tOb9dS13t025lfmPPM/tXtXrf396z1umn/v3fN8qe7Xxtr1081XHG... | null |
15,870 | Heavy Light White Falcon | Our lazy white falcon finally decided to learn heavy-light decomposition. Her teacher gave an assignment for her to practice this new technique. Please help her by solving this problem.
You are given a tree with $N$ nodes and each node's value is initially $0$. The problem asks you to operate the following two types ... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static List<Integer> solve(List<List<Integer>> tree, List<List<Integer>> queries)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solutio... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class Main\n{\n\tInputStream is;\n\tPrintWriter out;\n\tString INPUT = \"\";\n\t\n\tvoid solve()\n\t{... | {
"class_name": null,
"func_sign": [
"solve(tree, queries)"
],
"solution": "def segtree_init(ary):\n ary = list(ary)\n seg = [ary]\n while len(ary) > 1:\n if len(ary) & 1: ary.append(0)\n ary = [max(ary[i],ary[i+1]) for i in range(0,len(ary),2)]\n seg.append(ary)\n return seg\ndef seg... | eJxk3UmuJEHTNlSEGLCMT/8K0s2tyWQlSLABJogBjJiwJzZIVfjxqFdiUrqquuGVzePRnXCz/+d/+J//3//+f/zv/pf/+7/9b//7//F//Z//7X/6r//2/Xw+/5V//vhf//fPf62/f8TfP/bfP/LvH/X3j/77x/z94/v3j9/zy2eTZ5v1bLSerdaz2Xq2W8+G69lyPZuuZ9t4to3z/z3bxrNtPNvGs20828azbTzbxrPtfrbdz7b7vNhn2/1su59t97Ptfrbdz7b72TafbfPZNp9t87zTZ9t8ts1n23y2zWfbfLatZ9t6tq1n23q2rfMx... | null |
1,361 | Almost Equal - Advanced | A Sumo wrestling championship is scheduled to be held this winter in the _HackerCity_ where _N_ wrestlers from different parts of the world are going to participate. The rules state that two wrestlers can fight against each other if and only if the difference in their height is less than or equal to K,
(i.e) wrestler... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static List<Integer> solve(List<Integer> h, List<List<Integer>> queries)"
],
"script_name": "Solution",
"solution": "import java.io.BufferedReader;\nimport java.io.InputStreamReader;\nimport java.io.IOException;\nimport java.util.Arrays;\nimport java.util.C... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n static int nn;\n static int block;\n static int[] fenwick;\n static long cnt = 0;\n\n static class B implements Comparable<B> {\n int l;\n int r;\n int id;\n \n @Override\n public int compareTo(B o) {\n in... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n static int nn;\n static int block;\n static int[] fenwick;\n static long cnt = 0;\n\n static class B implements Comparable<B> {\n int l;\n int r;\n int id;\n \n @Override\n public int compareTo(B o) {\n in... | {
"class_name": null,
"func_sign": [
"solve(h, queries)"
],
"solution": "#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\nimport bisect\n\n#\n# Complete the 'solve' function below.\n#\n# The function is expected to return an INTEGER_ARRAY.\n# The function accepts following p... | eJzs3UuuNcuXJHQaNZCtbFfDPV4ewUiQSIkGZINOUo0qCQkhMQlmxaCwn/n5o6SkatADiazMuvd+3zl7R/hjPWzZsvW//bv/5n//P/7df/Xf/i//9D/+63/4T//xn/7r3z+9928933jmd81//tf7PdY31nP8zvdZ93N89+++r/N5v9/5rHHd57p+x3jO87m+5/ed87iu716/8/re/NR7/r53znuc6/w9476+eT4zv/uc93U85+9e73tfYz6/97jPd+Yrftd7rTWPsX7vWMfKo4zfvO7nfsf5/Y7rXPc5jjuf9857XfmU+c7jvOY8f+cY... | null |
1,769 | Almost sorted interval | Shik loves sorted intervals. But currently he does not have enough time to sort all the numbers. So he decided to use *Almost sorted intervals*. An *Almost sorted interval* is a consecutive subsequence in a sequence which satisfies the following property:
1. The first number is the smallest.
2. The last number is the ... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static int solve(List<Integer> arr)"
],
"script_name": "Solution",
"solution": "import java.io.BufferedWriter;\nimport java.util.InputMismatchException;\nimport java.util.NoSuchElementException;\nimport java.math.BigInteger;\nimport java.io.OutputStream;\ni... | {
"solution": "import java.util.Scanner;\nimport java.util.Stack;\nimport java.util.ArrayList;\n\npublic class Solution {\n public static void main(String[] args) {\n Scanner in = new Scanner(System.in);\n int n = in.nextInt();\n int[] ar = new int[n];\n for (int i = 0; i < n; i++) {\n ... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n /* Enter your code here. Read input from STDIN. Print output to STDOUT. Your class should be named Solution. */\n InputReader in = new InputReader(System.in);\n int N... | {
"class_name": null,
"func_sign": [
"solve(arr)"
],
"solution": "import bisect\nimport itertools\nN = int(input())\nais = [int(x) for x in input().split()]\nintervals = []\ncur_interval = []\ncur_height = 0\ntotal_sequences = 0\nfor i, ai in enumerate(ais):\n if ai < cur_height:\n merged = True\n wh... | eJx8ncuubUuSVGnAfyxluxrxfvAjIJESDSgkOgmNKgkJIfHpxBg2bwnpklBk3pvn7L3WfER4uJubmf/vf/3v/8u/+zf/6j/8z7/817/993/+p7/8299f1mx//Vub/Tdm+fW9fnXO9+/nN+r9tdV/dY/fbO8/a//G2b/Wfr3O96PtV8f9rTF+fczfKu8j3o+2959ay2/O+uutvl99//p+rN3ffv///VBdv3H5vvd571/HeV/DZ7Zf6++fu/za+4XR1q+f+avvq2t/X/uuqvfffp9az6/e9xNl/tr7pn7Pb473E3e/K3lX8y6mvx97/2nz... | null |
15,822 | Coolguy and Two Subsequences | Coolguy gives you a simple problem. Given a $1$-indexed array, $A$, containing $N$ elements, what will $ans$ be after this pseudocode is implemented and executed? Print $ans \ \% \ (10^9+7)$.
//f(a, b) is a function that returns the minimum element in interval [a, b]
ans = 0
for a -> [1, n]
for b ... | hackerrank | Hard | {
"class_name": "Result",
"func_sign": [
"public static int solve(List<Integer> arr)"
],
"script_name": "Solution",
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.ArrayList;\nimport java.util.Arrays... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.regex.*;\n\nimport java.util.Arrays;\nimport java.util.Comparator;\nimport java.util.Scanner;\n\npublic class CoolguyAndTwoSubsequences {\n final static int constant = 1000000007;\n\n public static ... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class WeekOfCode19qD {\n\n\tpublic static void main(String[] args) {\n\t\tInputReader in = new InputReader(System.in);\n\t\tPrintWriter w = new PrintWriter(System.out);\n\n\t\tint n = in.nextInt();\n\t\t\n\t\tint a[] = new int[n];\n\t\tfor(int i = 0; i <... | {
"class_name": null,
"func_sign": [
"solve(arr)"
],
"solution": "def smart():\n left = [0] * (n + 2)\n right = [0] * (n + 2)\n singles = pairs = 0\n ans = 0\n def remove(k):\n nonlocal singles, pairs\n s = k * (k + 1) // 2\n singles -= s\n pairs -= (k+2)*(k+1)*k*(... | eJzsvbuudE2SnidD0HUUfnuMPB94G3IEqAEaZBt0fo7RAxCgCOjS9Ty5W9KOarVJQ1J0zvR8Ubtqrcw4vPFGZK41//v/+L/8b//z//Q//K//9Y//9Oe//tvf/vh3nz96KeUvf9ZPLe8/n3X3aoj/1wdntXPbrw/GarPtXx+s3u+8vz+4k09+fXDPmHP8+qCvcWv4xh6tl18fzD3PWL8/mKvP3xObpYzawl3avP3XB3vWen/P9OzSwjRuOWGxbXDfsJJVTg+z6OP+nlYfavD3PTpr+X3TXfZcvy962ujt9we7nxXXuksvYWmjzfDBHhjq... | null |
5,641 | Find Maximum Index Product | You are given a list of $N$ numbers $a_{1}, a_{2}, \ldots, a_{n}$. For each element at position $i$ ($1 \le i \le N$), we define $Left(i)$ and $Right(i)$ as:
$Left(i)$ = closest index j such that j < i and $a_j>a_i$. If no such j exists then $Left(i)$ = 0.
$Right(i)$ = closest index k such that k > i and $a_k>a_i$.... | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static int solve(List<Integer> arr)"
],
"script_name": "Solution",
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMism... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "\n\nimport java.io.IOException;\nimport java.util.StringTokenizer;\n\n/**\n *\n * @author Chameera\n */\npublic class FindMaximumIndexProduct {\n\n public static void main(String[] args) throws IOException {\n java.io.BufferedReader in = new java.io.BufferedReader(new java.io.InputStreamReade... | {
"class_name": null,
"func_sign": [
"solve(arr)"
],
"solution": "def lefts(L):\n left = [];\n maxes = [];\n for i in range(len(L)):\n while maxes and L[maxes[-1]] <= L[i]:\n maxes.pop();\n if not maxes: left.append(-1);\n else: left.append(maxes[-1]);\n maxes... | eJyU3b3ObU2SHGYZMnQHcjfGplG1av3qSgSoARnSGHJaNIYAAUGALl3xRJ0WCM58pMhho0+f8757r5+qrMzIiMj/57//n4//8X/47/6X/+uf/o+///v/8C//9D/9/ul459/+/s13fe9zHL/vPI9zzPv9He95n+O4zt8a73rO+T2/eeXfnzGe3/se73tf8/md33ese53X75zPOObI7z7HPK53zu93vucxn/kcv3Wea733+/3meb/juN/5O84580nj+t1zjvl89/Gbx/vc38yfjvdYuYLv+D3fXOub3/B3Mxc47t97vtd95IJ+93XlSt9c... | null |
9,828 | Simple Array Sum | Given an array of integers, find the sum of its elements.
For example, if the array $ar = [1,2,3]$, $1 + 2 + 3 = 6$, so return $6$.
**Function Description**
Complete the *simpleArraySum* function in the editor below. It must return the sum of the array elements as an integer.
simpleArraySum has the following p... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static int simpleArraySum(List<Integer> ar)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static void main(Str... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n static long sum=0;\n\tpublic static void main(String[] args) {\n\t\tScanner sc=new Scanner(System.in);\n\t\tint T=sc.nextInt();\n\t\tlong [] a=new long[T];\n\t\tfor(int i=0;i<T;i++){\n\t\t\ta[i]=sc.nextLong();\n\t\t\t\n\t\t\tsum+=... | {
"class_name": null,
"func_sign": [
"simpleArraySum(ar)"
],
"solution": "n = int(input())\nnums = list(map(int, input().split()))\nsum = 0\nfor num in nums:\n sum += num\nprint(sum)\n",
"template": "#\n# Complete the 'simpleArraySum' function below.\n#\n# The function is expected to return an INTEGER.... | eJzsvcuubktynWcDfpCFarMxM2de9SQGXIAaEhvqlNQgAQGCAD+2m57j+2Id0i6WIMPyBbBBHh7us9f6/3nJjIwYMcaI//l/+B//l/bf/3f/03/607/7y3/4x3/407/6+dP7rj//pbWfe9rPbs/P2f1n9fNz5vq5a/yc96ef71/z+/f7s+b4meP+3L1+2p0/s+WvfvrtP+P7kH7Pz3x+3u8Xxv1+6pnf577fT3//7s9PX+tnfx809vszv+9b7/dp6/v39yljj+8L+s+7+k/L73yfsZ/v59f8aXP+jHd/X7F/3vf89O8K8jlttu/3v+sd... | 1 |
8,781 | A Very Big Sum | In this challenge, you are required to calculate and print the sum of the elements in an array, keeping in mind that some of those integers may be quite large.
**Function Description**
Complete the *aVeryBigSum* function in the editor below. It must return the sum of all array elements.
aVeryBigSum has the followin... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static long aVeryBigSum(List<Long> ar)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static void main(String[]... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.util.Scanner;\n\npublic class BigSum {\n public static void main(String[] args) {\n Scanner scanner = new Scanner(System.in);\n int N = scanner.nextInt();\n long sum = 0;\n while (N-- > 0 ) {\n sum += scanner.nextInt();\n }\n System.ou... | {
"class_name": null,
"func_sign": [
"aVeryBigSum(ar)"
],
"solution": "input()\nprint(sum(map(int, input().split())))",
"template": "#\n# Complete the 'aVeryBigSum' function below.\n#\n# The function is expected to return a LONG_INTEGER.\n# The function accepts LONG_INTEGER_ARRAY ar as parameter.\n#\n\nde... | eJydnUuubkt2lSnQCGosZRmheD9oCRIXUQAXqFxcMBISQqIR0Aeayfi+2IlsHd+0DciZec7Z+//XipgxH2OOOeJ//vP//S//zz/z//3bf5H/8e/++5/+8+9//V//5k//5vvTvO2330+99at9rFVvn/ere9+yWp3162d99e59yhxzfq2Mvu88q319N/5cxzqzlvO1thu/2U/Z7eZ/ntO/Pko+ueY/7urlm/s7JR848tft8tern/nVeUc+N3/99VvGlw/ZX21z7bFm73m0Wc+6fZzv3lP56FN7HmV8+e7B57VxzzolD1B3/oqPGv3kk8/u... | null |
8,662 | Diagonal Difference | Given a square matrix, calculate the absolute difference between the sums of its diagonals.
For example, the square matrix $arr$ is shown below:
1 2 3
4 5 6
9 8 9
The left-to-right diagonal = $1 + 5 + 9 = 15$. The right to left diagonal = $3 + 5 + 9 = 17$. Their absolute difference is $|15 - 1... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static int diagonalDifference(List<List<Integer>> arr)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static vo... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.util.Scanner;\n\npublic class DiagonalDifferernce {\n\n\tpublic static void main(String[] args) {\n\t\tScanner in = new Scanner(System.in); \n\t\tint n ;\n\t\tint diag1 = 0 ; int diag2 = 0;\n\t\tn = Integer.parseInt(in.nextLine());\n\t\tfor(int i =0 ; i < n; i++){\n\t\t\tString str[] = in.n... | {
"class_name": null,
"func_sign": [
"diagonalDifference(arr)"
],
"solution": "N=int(input())\ngrid=[]\nfor i in range(0,N):\n lists=[int(i) for i in input().split()]\n grid.append(lists)\ncount=0\nsum1=0\nsum2=0\nj1=0\nj2=N-1\nwhile(count<N):\n sum1=sum1+grid[count][j1]\n sum2=sum2+grid[count][... | eJzs/cuuLU2SpAd2A/0gjhzTAbe7WT8JAQbQg+4acPKTgyJAgCDAGR+Hr9iu8on6OpGZUZVV5IhIRJ78z9l7Lb+YqelFVFT1f/t//bf/x//+//x//Hf/yz/993/9j//Tf/yn//f1T/Nvfz3X3Nfd3z/jue46rnvWv/11vz+Mf/br/enu7wfa3/56P/T+4P3xfn8WXyp/+yu+FB954uelXq3/7a+4SHzn/cz7k/fnbf3tr1mv3q5e3n/1q63r+af/5vqn//A//4//4f/7H//D/+//8z/8T//Rj/T87a9/+l//m+uPhxzjvc17cf3f+92r... | 1 |
8,654 | Plus Minus | Given an array of integers, calculate the ratios of its elements that are *positive*, *negative*, and *zero*. Print the decimal value of each fraction on a new line with $6$ places after the decimal.
**Note:** This challenge introduces precision problems. The test cases are scaled to six decimal places, though answer... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static void plusMinus(List<Integer> arr)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static void main(String... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.DecimalFormat;\n\npublic class Solution {\n\n public static void main(String[] args) {\n /* Enter your code here. Read input from STDIN. Print output to STDOUT. Your class should be named Solution. */\n Scanner in = new Scanner(S... | {
"class_name": null,
"func_sign": [
"plusMinus(arr)"
],
"solution": "T=int(input())\nlists=[int(_) for _ in input().split()]\np=0\nm=0\nz=0\nfor ele in lists:\n if(ele>0):\n p+=1\n else:\n if(ele<0):\n m+=1\n else:\n z+=1\np=p/T\nm=m/T\nz=z/T\nround(p,3)\nro... | eJztWMFuHDcM7aE/0D8gfM4YIkWJUv6itwB10UPrQy9uDg5QoAjQj2j+t+9Rs0m8WY9jBC1yyMysZ1czkqjHx0fKf3//7tUP3+Xx6kd8+emvq9/vXr+5v3opV95u7rY2ZZtdNi1FNu8yXJrjZ8jWuqjMkl+2wfeGdPyMyrciZOCq4k104CH7q/QqW3WpKltHl2Z4MqU1fEVTC9EphsY+1ju44ZljJElbvORjTj9v7q5eyNXtn69vf72//e2XP97c75aXaz87bu7KdXt4dLYVjPH2hXy8aiwaE6gU0YMJrOWQ/MOvZ4PQttlk1rXCNnJ5... | 3 |
23,074 | Birthday Cake Candles | You are in charge of the cake for a child's birthday. You have decided the cake will have one candle for each year of their total age. They will only be able to blow out the tallest of the candles. Count how many candles are tallest.
**Example**
$candles = [4, 4, 1, 3]$
The maximum height candles are $4$ units h... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static int birthdayCakeCandles(List<Integer> candles)"
],
"script_name": "Solution",
"solution": "/*\n * PDPM IIITDM Jabalpur\n * Asutosh Rana\n */\n\nimport java.util.*;\nimport java.io.*;\nimport java.math.*;\n\npublic class Main\n{\n static long MOD =... | {
"solution": "DNE",
"template": "class Result {\n\n /*\n * Complete the 'birthdayCakeCandles' function below.\n *\n * The function is expected to return an INTEGER.\n * The function accepts INTEGER_ARRAY candles as parameter.\n */\n\n public static int birthdayCakeCandles(List<Integer> ca... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n Scanner in = new Scanner(System.in);\n \n int n = in.nextInt();\n int max = 0;\n int sum = 0;\n int num;\n for(int i =0; i < n; i++){\n ... | {
"class_name": null,
"func_sign": [
"birthdayCakeCandles(candles)"
],
"solution": "N=int(input())\nV=list(map(int,input().split()))\nprint(V.count(max(V)))",
"template": "#\n# Complete the 'birthdayCakeCandles' function below.\n#\n# The function is expected to return an INTEGER.\n# The function accepts I... | eJzsvcuubEuSXaeG9B+B266Gvx/8DXUEKAE2yGywk6xGFkCCIKBP1xzD45QECPqD4GKdPHefHSvWcje3x7RpZv/X//p//O///X/7X/7P//HXf/nHv/7bP//6D5+/1hh/+8eZp84zPqutsWb77F7KauUzb923zU8bfdSb/+2rtFE/545T1vzUucsc57PvnPvWTx+nzfze6C23KZ8199n18rnWJv+dj8/+ufnl0vvnjFXqyPedPXbL52u7c7Xc/+ZGuX/+2G19+rp77/6pfbfSx6fd3HWXfG+d/Uz+9+QLPmedek797LrWrvnv0vds+f6V... | null |
8,649 | Time Conversion | Given a time in [$12$-hour AM/PM format](https://en.wikipedia.org/wiki/12-hour_clock), convert it to military (24-hour) time.
Note:
- 12:00:00AM on a 12-hour clock is 00:00:00 on a 24-hour clock.
- 12:00:00PM on a 12-hour clock is 12:00:00 on a 24-hour clock.
**Example**
- $\text{s = '12:01:00PM'}$
Re... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static String timeConversion(String s)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n \tpublic static void main(String[... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n Scanner sc = new Scanner(System.in);\n String dt = sc.next();\n char ap = dt.charAt(dt.length() - 2);\n dt = dt.substring(0, dt.length() - 2);\n if (ap ... | {
"class_name": null,
"func_sign": [
"timeConversion(s)"
],
"solution": "ins = input().strip()\n\nis_pm = ins[-2:].lower() == 'pm'\ntime_list = list(map(int, ins[:-2].split(':')))\n\nif is_pm and time_list[0] < 12:\n time_list[0] += 12\n\nif not is_pm and time_list[0] == 12:\n time_list[0] = 0\n\nprin... | eJyFk70KwjAURh36ICVzkdyb/ti7+QCCo6DioB1ctEMLggiObi76vsYmBYnJTelQ0nP48t2m9+T9TCbDtXroh/VVHE9t3wlKhawoL0nK5WJzElkqmkvb7LvmsDv3nUWgNoi4ZemvWVJREyBnzgzimCAJKwIuE9EgbiZSUeg3XGZuENfUJSQBzMPmiPz3zL9D4kyLeDIrQta0iGsqAhXJtIhr6vqKELgJlQZxTSCs9TKXaRH3e6I+HfrmTGmQgMntFoMmxDMhbEYy/aY+zkXNZw5IwOQzfeawj0hP8E5oXOYylX+2EO05IgGT+7PVaG5f... | null |
30,508 | Grading Students | HackerLand University has the following grading policy:
* Every student receives a $grade$ in the inclusive range from $0$ to $100$.
* Any $grade$ less than $40$ is a failing grade.
Sam is a professor at the university and likes to round each student's $grade$ according to these rules:
* If the difference between t... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static List<Integer> gradingStudents(List<Integer> grades)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public stati... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n Scanner in = new Scanner(System.in);\n int n = in.nextInt();\n for(int a0 = 0; a0 < n; a0++){\n ... | {
"class_name": null,
"func_sign": [
"gradingStudents(grades)"
],
"solution": "#!/bin/python3\n\nimport sys\n\n\nn = int(input().strip())\nfor a0 in range(n):\n x = int(input().strip())\n \n if x >= 38:\n if x % 5 > 2:\n while x % 5 != 0: x += 1\n print(x)\n",
"template": "#\... | eJx1jj0OwjAMhRk4yFPmCtVxnB+uQhADdGAJHVoJqULiAIxwNgYug1MYWPBmv/d98nX5eC0+c3tuJnMs/TiYNQzHXGIL9hCCFxDBEkjg0MInUKi7RDgPj6hpiyTwAUoFAodKiUNyiIyU4Bgc60WFwc4FLUs1iVVNLqaB6c59tx+6w+40Dt9XrCaXBj/PuVwCV1yFzP/BmdzeV29EYDfo | null |
22,477 | Number Line Jumps | You are choreographing a circus show with various animals. For one act, you are given two kangaroos on a number line ready to jump in the positive direction (i.e, toward positive infinity).
- The first kangaroo starts at location $x1$ and moves at a rate of $v1$ meters per jump.
- The second kangaroo starts at locat... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static String kangaroo(int x1, int v1, int x2, int v2)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static vo... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n Scanner in = new Scanner(System.in);\n int x1 = in.nextInt();\n int v1 = in.nextInt();\n i... | {
"class_name": null,
"func_sign": [
"kangaroo(x1, v1, x2, v2)"
],
"solution": "#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n#\n# Complete the 'kangaroo' function below.\n#\n# The function is expected to return a STRING.\n# The function accepts following parameters:\n# ... | eJyVkz9LA0EQxS3yQR5XB9nZ+bMz9rZa2CgqFprCJkmRgCCC2Nnr93UIEQVzeLtwyxb32zdv3uzr7PN9drRbl295uH4eHpfr7WY4waDaAsLNYFIcZuLDHMPiab243ywe7lbbzf7Ps/Ob5fAyxy+Y3QQhiRUNaBhPZwuaBMGN8lScRsir04s/qIgLvKZkMy6oWf50Xa0kqOSGRtagbHU6bFqzWi4CCwtUL9PZCgJjzOfBDhGo5OqBWjSQUSDCFK7a0dfyLdcjSJUhmp1sHl2VkibDnCFSRMY5OgIH2FQKV0uTiUqwdMiWnc38umzulPbt... | null |
32,335 | Breaking the Records | Maria plays college basketball and wants to go pro. Each season she maintains a record of her play. She tabulates the number of times she breaks her season record for *most points* and *least points* in a game. Points scored in the first game establish her record for the season, and she begins counting from there.
... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static List<Integer> breakingRecords(List<Integer> scores)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public stati... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n Scanner in = new Scanner(System.in);\n int n = in.nextInt();\n int[] score = new int[n];\n ... | {
"class_name": null,
"func_sign": [
"breakingRecords(scores)"
],
"solution": "input()\na = list(map(int, input().split()))\nm = M = a[0]\nx = y = 0\nfor i in a[1:]:\n if i > M:\n M = i\n x += 1\n elif i < m:\n m = i\n y += 1\nprint(x, y)",
"template": "#\n# Complete the ... | eJztnUuOLUlShhmwkFCNW8jd/M1KkLiIAdSAyaUH3VJLCAlmLADWwvb4v9/OyefNlgpUarWIap2+kZlxItzN7e1uv/3bX/7Xf/z3X/i/v/l3Xfztv/z0T99/+/vf/fTX10/12/fy02+un37+w29//off/fyPf//Pv//d40/lKt++//Svv7ne3N7K/vY95qljtX712LWMM67Txtit6Dct2ohVrxVzrnrateeodUS9xmhnjlav2s+ubeueMc9qK65Ryllr9quVttcqS986m3tnnNVrOdcss5V61rX6qTFWv/TNuvs519jRe23jGlNXdZyr... | null |
30,377 | Day of the Programmer | Marie invented a [Time Machine](https://en.wikipedia.org/wiki/Time_machine) and wants to test it by time-traveling to visit Russia on the [Day of the Programmer](https://en.wikipedia.org/wiki/Day_of_the_Programmer) (the 256th day of the year) during a year in the inclusive range from 1700 to 2700.
From 1700 to 1917, ... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static String dayOfProgrammer(int year)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static void main(String[... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class A {\n\tInputStream is;\n\tPrintWriter out;\n\tString INPUT = \"\";\n\t\n\tvoid solve()\n\t{\n\t... | {
"class_name": null,
"func_sign": [
"dayOfProgrammer(year)"
],
"solution": "y = int(input())\nif y < 1918:\n ftext = \"13.09.{}\" if y & 3 else \"12.09.{}\"\nelif y > 1918:\n ftext = \"13.09.{}\" if (y & 3) or (y % 400 and not y % 100) else \"12.09.{}\"\nelse:\n ftext = \"26.09.{}\"\nprint(ftext.f... | eJytk7EKwjAURR36ISVzKUnaJi8u/oag4qAdXGqHFgQR/Aj9X/Okg7xY72Kmkp7DDTcv9+y5yhbvtab4sbmqU9ePg1rmylpvtp0qctVe+vYwtMf9eRymn6YqdSgZUbci/7CMp3resmwxIixbOZgVEWk1jUdWRKRlKCArItJyDloRkW2QRydkRGbZGjdfp81rDZvXWmZpQhYjf8mKW3g2tJwNQziL0qyArfDFMg5axqUWvOWIpBbNWtZNFqUWmkNGkluGbTCSvJRf1vRS2No9yhcf/zhB | null |
25,168 | Sales by Match | There is a large pile of socks that must be paired by color. Given an array of integers representing the color of each sock, determine how many pairs of socks with matching colors there are.
**Example**
$n = 7$
$ar = [1, 2, 1, 2, 1, 3, 2]$
There is one pair of color $1$ and one of color $2$. There are three... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static int sockMerchant(int n, List<Integer> ar)"
],
"script_name": "Solution",
"solution": "\n import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\nimport java.lang.*;\npublic class Main{\n\tstatic lo... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "\nimport java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class A {\n\tInputStream is;\n\tPrintWriter out;\n\tString INPUT = \"\";\n\t\n\tvoid solve()\n\t{\n... | {
"class_name": null,
"func_sign": [
"sockMerchant(n, ar)"
],
"solution": "from collections import Counter\n\nn = int(input())\nc = Counter(map(int, input().split()))\nans = 0\n\nfor x in c:\n ans += c[x] // 2\n \nprint(ans)",
"template": "#\n# Complete the 'sockMerchant' function below.\n#\n# The f... | eJzNlc2KU0EQhV248DGKrAe5Xf3vkyhGFHQWbuIsMiCI4EPo+/qdugYGbu5Adk5nZpJOd3X1OV/V/fXyz6dXL+Ln7TvevP9x+Hp6eDwf3tjB6/GULJkzMqMwKqMxOmMwJiMt8Urx8njl4+lwZ4f77w/3n8/3Xz5+ezz/C5qcr37e2dNz+vHUkuVqvljvlglQbLj1rOg2qxVO7Ypclvg4bSzWpiazktB60hr6su+eXTZHZ11xUf7/xe++an2TOjNul4E9u3vzZmsdxxMSloKJKNgWG8MK4g5L3QqON5suRav+I/5s5iX8SNayDeSuWjgt... | null |
22,937 | Forming a Magic Square | We define a [magic square](https://en.wikipedia.org/wiki/Magic_square) to be an $n \times n$ matrix of distinct positive integers from $1$ to $n^2$ where the sum of any row, column, or diagonal of length $n$ is always equal to the same number: the *magic constant*.
You will be given a $3 \times 3$ matrix $s$ of inte... | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static int formingMagicSquare(List<List<Integer>> s)"
],
"script_name": "Solution",
"solution": "/* Programming Competition - Template (Horatiu Lazu) */\n\nimport java.io.*;\nimport java.util.*;\nimport java.lang.*;\nimport java.awt.*;\nimport java.awt.geom... | {
"solution": "DNE",
"template": "class Result {\n\n /*\n * Complete the 'formingMagicSquare' function below.\n *\n * The function is expected to return an INTEGER.\n * The function accepts 2D_INTEGER_ARRAY s as parameter.\n */\n\n public static int formingMagicSquare(List<List<Integer>> s... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n Scanner s = new Scanner(System.in);\n\t\tint[][] m = new int[3][3];\n\t\tfor (int x = 0; x < 3; x++) {\n\t\t\tfor (int y = 0; y < 3; y++) {\n\t\t\t\tm[x][y] = s.nextInt();\n\t\t\t}... | {
"class_name": null,
"func_sign": [
"formingMagicSquare(s)"
],
"solution": "from itertools import *\n\nX = []\nX.extend(list(map(int,input().split())))\nX.extend(list(map(int,input().split())))\nX.extend(list(map(int,input().split())))\n\nAns = 81\nfor P in permutations(range(1,10)):\n if sum(P[0:3]) ==... | eJyNk01OwzAUhFn0IE9eV4jnJP7hJEgEsYAs2IQuWgkJIXEIOBA7jsVMXpGKSyx340by5zczHr9vPr83F8vv5gt/bl/d07w77N21uCgqwzgH6blE6SSMs9uKm15208N+erx/PuyPe726t62cwF4iqQwqjzNQ8e1wBrxQnfTj7CGgMrkv4ADZibKVZ9CErsNDAUMtYbWRsFCTfVXAYCQS9jxjwFHdKqypgAfz7KEXk3tJFfifycqkOFmZm9Zkh7OrWrb737R9bXIJR8hekgqWdqD1VtnJzHaIOdJzrsguWUTN3agoBycT38iqeUSvTXvi... | null |
29,526 | Picking Numbers | Given an array of integers, find the longest subarray where the absolute difference between any two elements is less than or equal to $1$.
**Example**
$a=[1,1,2,2,4,4,5,5,5]$
There are two subarrays meeting the criterion: $[1,1,2,2]$ and $[4,4,5,5,5]$. The maximum length subarray has $5$ elements.
**Function... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static int pickingNumbers(List<Integer> a)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static void main(Stri... | {
"solution": "DNE",
"template": "class Result {\n\n /*\n * Complete the 'pickingNumbers' function below.\n *\n * The function is expected to return an INTEGER.\n * The function accepts INTEGER_ARRAY a as parameter.\n */\n\n public static int pickingNumbers(List<Integer> a) {\n // Write... | {
"solution": "\nimport java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class A {\n\tInputStream is;\n\tPrintWriter out;\n\tString INPUT = \"\";\n\t\n\tvoid solve()\n\t{\n... | {
"class_name": null,
"func_sign": [
"pickingNumbers(a)"
],
"solution": "#!/bin/python3\n\nimport sys\n\n\nn = int(input().strip())\na = [int(a_temp) for a_temp in input().strip().split(' ')]\n\nfrom collections import Counter\nd = Counter(a)\nbest = 0\nfor i in range(99):\n best = max(d[i] + d[i+1], bes... | eJzdV8tqFFEQdeHab7jMOkhX1X36E+5EMOJCs3AzZpFAQAQ/Qv/Xc05PJEmnW0YcQTM1Taf73rr1OKeq5uvT76+ePdHf65e4efN593F/eX21e5F2zc/3uaQtqb8WS5FKammc7u58vztLu4uby4v3Vxcf3n26vjp44PBg9+Us3XUqn+8NW8f45y6rbkZfuNkRlIbQTMlLKohTTT1STmXwv9H5BsmJnmyk1vm+5lRHipHyYG575o6Ko53ru/EKXdzAdy11T9ADnQWJqC15S7VweeYqZL7w/8gwHxsa1AMQWBNUZsaX0GVaTQcjhfHAOvHk... | null |
1,308 | Utopian Tree | The Utopian Tree goes through _2_ cycles of growth every year. Each spring, it _doubles_ in height. Each summer, its height increases by _1_ meter.
A Utopian Tree sapling with a height of *1* meter is planted at the onset of spring. How tall will the tree be after $n$ growth cycles?
For example, if the number of grow... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static int utopianTree(int n)"
],
"script_name": "Solution",
"solution": "import java.io.BufferedReader;\nimport java.io.IOException;\nimport java.io.InputStreamReader;\nimport java.io.PrintWriter;\n\npublic class Solution\n{\n\tpublic static void main(Stri... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n Scanner in = new Scanner(System.in);\n int numCases = in.nextInt();\n for (int i = 0; i < numCases; i++) {\n int n = in.nextInt();\n int answer ... | {
"solution": "import java.util.Scanner;\nimport java.util.Vector;\n\npublic class Solution {\n public static void main(String[] args) {\n Scanner stdin = new Scanner(System.in);\n int numCases = stdin.nextInt();\n Vector<Long> cache = new Vector<>();\n cache.add(1L);\n for (int ... | {
"class_name": null,
"func_sign": [
"utopianTree(n)"
],
"solution": "#!/usr/bin/env python\n\nimport sys\n\n\nif __name__ == '__main__':\n T = int(sys.stdin.readline())\n \n for _ in range(T):\n N = int(sys.stdin.readline())\n height = 1\n \n for i in range(N):\n ... | eJzFVktOwzAQZcGCY1RZV8jjGf84CRJBLKALNqGLVkJCSBwCzsSOMzFju43zcVpWVI48ieL5vHlv0o/Lr5+ri/i7/Wbj7q157rb7XXOzanzbGb6saju0bQfAO9u8GV6qWa+azet287jbPD287Hf5FCiHjsBrbDsN5MijJccHNGnPO2j2ZY1BdqL5lvjOgfLe8oHmfb0aZiDR1exWiV/GhGEKJ29H4SGHMiEhYaQMzt5I/Zy3QSmLLzgjl6MtUBYYHW1xhNY7FQTpg8nhtfWEhiTqweTggKTBOe16004qsDlpn4rI8NXSLVOZbaOdAjgJ... | null |
6,024 | Angry Professor | A Discrete Mathematics professor has a class of students. Frustrated with their lack of discipline, the professor decides to cancel class if fewer than some number of students are present when class starts. Arrival times go from on time ($arrivalTime \le 0$) to arrived late ($arrivalTime \gt 0$).
Given the arrival ti... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static String angryProfessor(int k, List<Integer> a)"
],
"script_name": "Solution",
"solution": "import java.io.BufferedReader;\nimport java.io.InputStreamReader;\nimport java.io.PrintWriter;\nimport java.util.StringTokenizer;\n\n\npublic class AngryProf {\... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.math.*;\n\n\npublic class Solution implements Runnable {\n\tstatic BufferedReader in;\n\tstatic PrintWriter out;\n\tstatic StringTokenizer tok;\n\t\n\t\n\tstatic void solve() throws Exception {\n\t\tint t = nextInt();\n\t\tfor (int tt = 0; tt < t; ++t... | {
"class_name": null,
"func_sign": [
"angryProfessor(k, a)"
],
"solution": "t = int(input())\n\nfor _ in range(t):\n\tn, k = map(int, input().split())\n\tarr = list(map(int, input().split()))\n\n\tcnt = 0\n\tfor x in arr:\n\t\tif x <= 0:\n\t\t\tcnt += 1\n\tif cnt >= k:\n\t\tprint(\"NO\")\n\telse:\n\t\tprint... | eJztnc2OLUuSlRnwIKEad6Dwf3fmTGHABEQiBtCDniQ9qJaQEFLPeAF4Ml4It28tjzy3+t5bt0Cthq7sW6fPPpl7x45wN7efZcvM/vaf/s///r/+Cf/3b/52v/h3//V3f/X513/z+9/98+t36eOzXPnjMz3P5T8fn7/7i+t3f/lf/vov/+Pv//I//Yf//De/93v/7b/41/t3/+0vrp9+PtV6rbavM6871yu1a6xrtOte67pru3q/7jKuuX/S9ss0rnvU6577t63sf199/yZ+m/bn9w9Kvu79o7EvV3XJuX9ax7WeK+crXWu/oew3l3Xl... | null |
13,489 | Save the Prisoner! | A jail has a number of prisoners and a number of treats to pass out to them. Their jailer decides the fairest way to divide the treats is to seat the prisoners around a circular table in sequentially numbered chairs. A chair number will be drawn from a hat. Beginning with the prisoner in that chair, one candy will b... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static int saveThePrisoner(int n, int m, int s)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static void main... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class A {\n\tInputStream is;\n\tPrintWriter out;\n\tString INPUT = \"\";\n\t\n\tvoid solve()\n\t{\n\t... | {
"class_name": null,
"func_sign": [
"saveThePrisoner(n, m, s)"
],
"solution": "for _ in range(int(input().strip())):\n n,m,s = map(int, input().strip().split(\" \"))\n print(((s-2+m)%n)+1)",
"template": "#\n# Complete the 'saveThePrisoner' function below.\n#\n# The function is expected to return an... | eJyVlU2KHEcQhb3wwsdIei1EZPxm+CQCjfHCnoU2LS1GIDACH8LGO51VXzYemOnpGlBV01XVlZHx4r0X0X///O+3X366HO/+4+b9X6cP50+fH06/jpPenTVXhLSN6lSz6jladXnYfqcdwtdQ6yypqcPKI1vr7nx6M073Xz7d//Fw/+fvHz8/PO6ZmR6ed+fps0qkT1/fjOdJi0Ui4ULW0BVtNgIcS1fymkfvFTXSXCxDakQCw1Yfps1wStHwu7OVzlydV3knb2a3h/qYIikeQtlmqeWH+87ZUSp+tRkBO6HOKT3S06dmy4A4uFmkStE0... | null |
127 | Find Digits | An integer $d$ is a *divisor* of an integer $n$ if the remainder of $n \div d = 0$.
Given an integer, for each digit that makes up the integer determine whether it is a divisor. Count the number of divisors occurring within the integer.
**Example**
$n = 124$
Check whether $1$, $2$ and $4$ are divisors of $1... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static int findDigits(int n)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static void main(String[] args){\n... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.util.*;\nclass Solution\n{\n\tpublic static void main(String args[])\n\t{\n\t\tScanner in=new Scanner(System.in);\n\t\tint t,ans,d;\n\t\tlong n,m;\n\t\tt=in.nextInt();\n\t\twhile(t-->0)\n\t\t{\n\t\t\tans=0;\n\t\t\tn=in.nextLong();\n\t\t\tm=n;\n\t\t\twhile(m!=0)\n\t\t\t{\n\t\t\t\td=(int)m%10... | {
"class_name": null,
"func_sign": [
"findDigits(n)"
],
"solution": "def func(A):\n return len([1 for i in str(A) if i !='0' and A%int(i)==0])\n\nfor t in range(int(input())):\n print(func(int(input())))",
"template": "#\n# Complete the 'findDigits' function below.\n#\n# The function is expected to ... | eJx9VkGOHDcMzMGX/GIxZyMgRVIk85IAmcAHZw+5THxYAwECA3lE8pjc8rQUpZGnPTPd6saullJTRbJY2r/e/fPf99+N8dO/mPz85+m3y6fPb6cfX052vnTNTNLU84WNO/5IP18MKxHKzEZYaWrSLUjOl9P7l9PrH59eP769/vrh989vN09tvD5+nr68f9mc088XtRbkylSOTZhcGKcbU7QIOG5NgjIZVuWUaNpg9bRwF9o9GHsK+jh1Tu7OZkDjFthggGGMfUytgiKFVSlhDs6ydwksSPMehQ1Bw1hYK7beWLFqABZRTjTMe/lJZ2tH... | null |
26,066 | Append and Delete | You have two strings of lowercase English letters. You can perform two types of operations on the first string:
1. *Append* a lowercase English letter to the end of the string.
2. *Delete* the last character of the string. Performing this operation on an empty string results in an empty string.
Given an integer, $k$,... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static String appendAndDelete(String s, String t, int k)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static ... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n Scanner in = new Scanner(System.in);\n String s = in.next();\n String t = in.next();\n int... | {
"class_name": null,
"func_sign": [
"appendAndDelete(s, t, k)"
],
"solution": "#!/bin/python3\n\nimport sys\n\n\ns = input().strip()\nt = input().strip()\nk = int(input().strip())\n\nlead = 0\nfor i in range(min(len(s),len(t))):\n if s[i] != t[i]:\n lead = i\n break\n else:\n lea... | eJyVVEnOXjUQZBFxjl/fOkJkIAkcIusgXoQ8z+3ZbRtFyiHgvliIBRJE5HlheeHu6q6q7s/P/tjffvPX+VDP45ffHgZSb4+fnh4B4gXnmmtf8OL7x/Onh5hJsCb4r7G3v3/9LOoFj0/Pn/4RGUdZOKgH0QunlJHkNl3+AjHjaBHZIh4Nas356hUb1NwbXvD2zQ2QfvIFw/EU+eLtjbhygTtQr26EAKcVMcrEtBYtDwtszV7HcF1pInmKfQYhdY5ETlG6RlrsBTrJ0GvOF7x69wW09/FfYAkULCDWNN38lE7bOhbQCIzm83tT4B6pGUXj... | null |
2,040 | Sherlock and Squares | Watson likes to challenge Sherlock's math ability. He will provide a starting and ending value that describe a range of integers, inclusive of the endpoints. Sherlock must determine the number of *square integers* within that range.
**Note**: A square integer is an integer which is the square of an integer, e.g. $1,... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static int squares(int a, int b)"
],
"script_name": "Solution",
"solution": "//package contest;\n\nimport java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static vo... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.util.Scanner;\nclass Solution\n{\npublic static void main(String[] args)\n{\n\tScanner sc=new Scanner(System.in);\n\tint t=sc.nextInt();\n\tfor(int h=0;h<t;h++)\n\t{\n\tint m=sc.nextInt();\n\tint n=sc.nextInt();\n\tint count=0;\n\tint sq=(int) Math.sqrt(m);\n\tint a=sq*sq;\n if(a<m)\... | {
"class_name": null,
"func_sign": [
"squares(a, b)"
],
"solution": "#!/usr/bin/env python\n\nimport sys\n\n\nif __name__ == '__main__':\n T = int(sys.stdin.readline())\n \n for _ in range(T):\n A, B = list(map(int, sys.stdin.readline().split()))\n\n nA = int(A**0.5)\n nB = int... | eJydl0uOI0UURRmwAJYQqnELRbxPfFgJEoUYQA+YFAy6BRICsQjYL+eG01aVyy4a3K3K63RmvBf3/W78+fnff3zx2f58/Qvgm98efnz6+eOHh6/Kw3h8GrOPFtNbOVBfj0+rzbbGrKMcyNvjk63ahrc5ii2bM/n3+BS15gjLLCfkrfLkrB5u2YtNtz75+fGpZ5/TvM7SO7f5ys2xLGdducpY0Zr1hvWHd+Xh/a8/v//+w/sfvvvp44fDWRauemNfJq631mXr4fd35dmelva0WCh9lBPqFffbwGcsz3JCPrWM5zQLLycwWdAjYSLTyoFW... | 2 |
8,656 | Library Fine | Your local library needs your help! Given the expected and actual return dates for a library book, create a program that calculates the fine (if any). The fee structure is as follows:
1. If the book is returned on or before the expected return date, no fine will be charged (i.e.: $fine = 0)$.
2. If the book is return... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static int libraryFine(int d1, int m1, int y1, int d2, int m2, int y2)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n ... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "\nimport java.util.Scanner;\n\nclass Solution {\n\n public static void main(String[] args) {\n Scanner sc = new Scanner(System.in);\n int d2 = sc.nextInt(), m2 = sc.nextInt(), y2 = sc.nextInt();\n int d1 = sc.nextInt(), m1 = sc.nextInt(), y1 = sc.nextInt();\n if (y2 > y1)... | {
"class_name": null,
"func_sign": [
"libraryFine(d1, m1, y1, d2, m2, y2)"
],
"solution": "def fine(actual, expected):\n if actual[2] > expected[2]:\n return 10000\n elif actual[2] == expected[2]:\n if actual[1] > expected[1]:\n return 500 * (actual[1] - expected[1])\n ... | eJyVkzFOBDEMRSn2IFbqFYqdOIk5CRKDKGAKmmGLXQkJIXEIuB4d98Ce0axWG41mkspFnr//T/y1+/nb3Yzn/leLhw/3OhxOR3cHDgUIsPjQDQEiYM5auT24/v3QPx/7l6e303G+6/W4zz1c8Ap7IPbZeLSKm3gsgAgSUjdQAW3hKS42qMQJClCJOjIlYKDMzerqPyNpg2i9MmKb/Tz698k6kAZIWZYDrOSjDo0s3SBjGwqymU32WiVpWFkHRwllu6woEYrvBsxztYCGig36SFTMJHkLrwi1Rc6QNDDUh7oot06eGmDkKzqg/dCJPlcL... | null |
1,957 | Cut the sticks | You are given a number of sticks of varying lengths. You will iteratively cut the sticks into smaller sticks, discarding the shortest pieces until there are none left. At each iteration you will determine the length of the shortest stick remaining, cut that length from each of the longer sticks and then discard all t... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static List<Integer> cutTheSticks(List<Integer> arr)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n\tstatic void solve() throws IOException {\n\t\tint n = nextInt();\n\t\tint[] a = new int[n... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.*;\n\npublic class Solution{\n \n public static void main(String[] args)throws IOException{\n BufferedReader br = new BufferedReader(new InputStreamReader(System.in));\n int N = Integer.parseInt(br.readLine());\n String[] str=br.readLine().split(\" \");\n ... | {
"class_name": null,
"func_sign": [
"cutTheSticks(arr)"
],
"solution": "#!/usr/bin/env python\n\nimport collections, sys\n\n\nif __name__ == '__main__':\n N = int(sys.stdin.readline())\n a = sorted(map(int, sys.stdin.readline().split()))\n \n c = collections.Counter(a)\n count = [c[k] for k ... | eJztncuOZElShlnwIK5ej5DfL+zZ8QBINGIBvWDTzGJGGgkh8RDwvthn5naOR2RmVXXPdE0uorIiI+Icv9jNL+c3M8//+dv/+8c//o3++6d/kA///F8//MfPv//jH374+/DD+PHnHI6fH3/+4Xfhh5/+9Puf/u0PP/37v/7nH/9wF/zhv38XjqopxkjtEloYIaWQSkjyYYVcQl6hpFBGqCnUEuoITcqt0FPoI4wURgljhVnCXGFJrSj1ozQQ+bykOfmc5XOhXd7lWpVXk+9Nvne5Ly2lQadyfcr1xUu+094SMoSmDC3STs6QJZ+lnSw0... | null |
24,548 | Repeated String | There is a string, $s$, of lowercase English letters that is repeated infinitely many times. Given an integer, $n$, find and print the number of letter `a`'s in the first $n$ letters of the infinite string.
**Example**
$s=\text{'abcac'}$
$n = 10$
The substring we consider is $abcacabcac$, the first $10$ charac... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static long repeatedString(String s, long n)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static void main(St... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "\nimport java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class A {\n\tInputStream is;\n\tPrintWriter out;\n\tString INPUT = \"\";\n\t\n\tvoid solve()\n\t{\n... | {
"class_name": null,
"func_sign": [
"repeatedString(s, n)"
],
"solution": "s = input().strip()\nn = int(input().strip())\nL = len(s)\nprint(s.count('a') * (n//L) + s[:n % L].count('a'))",
"template": "#\n# Complete the 'repeatedString' function below.\n#\n# The function is expected to return a LONG_INTEG... | eJzFVMmq5TYUfISQ/MbjrpsgT5KdLwnkNo2twRqswbJkWQ4N+Yjur80mek0vAs59cBch3mjhU1KdOlXnzx+//vDzy7fvt79+enn5/Y+bMC6G26+vt22bhBWTOkedBdZB8JHZKSW27ZbRFB1RCz/EtBw0SRuIE+d6jIySke+rHKklh0hOiyCm2SrD5CENP6zT1OEwxrupKwDbCjZ9N9zN7cPrjR6O4kDJJxvDdxJV04AG9nXblpLPH17/wW+8m+ox7lK+GDc7ssxqLrgBDaCtmu4hHlzwfs/7nNxhLVPzQfbIeRQML/lQJ3NJS7vvpPSX... | null |
20,832 | Jumping on the Clouds | There is a new mobile game that starts with consecutively numbered clouds. Some of the clouds are thunderheads and others are cumulus. The player can jump on any cumulus cloud having a number that is equal to the number of the current cloud plus $1$ or $2$. The player must avoid the thunderheads. Determine the mini... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static int jumpingOnClouds(List<Integer> c)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static void main(Str... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n private static InputReader in;\n private static PrintWriter out;\n\n public static void main(String[] args) throws IOException {\n in = new InputReader(System.in);\n out = new PrintWriter(System.out, true);\n\n int n = in.n... | {
"class_name": null,
"func_sign": [
"jumpingOnClouds(c)"
],
"solution": "#!/bin/python3\n\nimport sys\n\n\nn = int(input().strip())\nc = [int(c_temp) for c_temp in input().strip().split(' ')]\n\nres = 0\ni = 0\nwhile i < n-1:\n if i+2<n and c[i+2] == 0:\n i = i+2\n res += 1\n else:\n ... | eJy1VctuwjAQ5NALf2H5jCpvHoA5cuMPkKDqoc2BS8ohSEhVJT6C/m8Jj9bJeFyHBxYkgWW8szO73j19z/q942s+PdwsPvWqXG8qPVHaJsvSKKNEGeW7tp+av7tP7ZgmAqJJMAb/ATnogdLFdl28VcX768emOjPKzbLUXwPlkExGfyS7vNkWkuEWxx1YPISLMaeUHr1iK3zDitdBpCbtqTSDAITcNO0apo0mdo3ks3g4n9pHrYQyR0d0NO8ERiCwObpoaN16MqahdmMpsyjhkSTtFGtmzSXtOOHutbqixhHMhmiKX35MS2yNlI8ohoKV... | null |
21,555 | Equalize the Array | Given an array of integers, determine the minimum number of elements to delete to leave only elements of equal value.
**Example**
$arr = [1, 2, 2, 3]$
Delete the $2$ elements $1$ and $3$ leaving $arr = [2, 2]$. If both twos plus either the $1$ or the $3$ are deleted, it takes $3$ deletions to leave either $[3]... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static int equalizeArray(List<Integer> arr)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static void main(Str... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class A {\n\n\tBufferedReader br;\n\tPrintWriter out;\n\tStringTokenizer st;\n\tboolean eof;\n\n\tvoid solve() throws IOException {\n\t\tint n = nextInt();\n\t\tint[] a = new int[101];\n\t\tfor (int i = 0; i < n; i++) {\n\t\t\tint x = nextInt();\n\t\t\ta... | {
"class_name": null,
"func_sign": [
"equalizeArray(arr)"
],
"solution": "n = int(input())\na = list(map(int, input().split()))\na = sorted(a)\n\nans = n - 1\n\nfor i in range(n):\n j = i\n while j < n and a[i] == a[j]:\n j += 1\n ans = min(ans, n - (j - i))\n\nprint(ans)\n",
"template": "... | eJztlj2uJEUQhDEwOURq7BXq+q/iJEgMwoBn4Axr7EorISQOAWfB42x8ET3LDtvTEnhr8OZNq6e6ujIyMzIyf/389z+/+Mx/X//BzTc/X368vX775vJVXPK43mqPESnF2iKtWC3KFitHjzSjRNNiXdFajBatRo1cIm8xR/Qefeh5L5F40i+v4vLy7vXL929efvjup7dv3lup19vll1fxYLj26y33mJyzoizZ4p/jMbJSZK6cv0XFVrbZGYttAyuRgTO1AZjgWjV61XV1HVVY7HJIN7zOmZuOnUWAc4wZ1dtLF/B8ArqWA+iSrre0ReHo... | null |
3,179 | ACM ICPC Team | There are a number of people who will be attending [ACM-ICPC World Finals](https://en.wikipedia.org/wiki/ACM_International_Collegiate_Programming_Contest). Each of them may be well versed in a number of topics. Given a list of topics known by each attendee, presented as binary strings, determine the maximum number of t... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static List<Integer> acmTeam(List<String> topic)"
],
"script_name": "Solution",
"solution": "import java.math.BigInteger;\nimport java.util.*;\nimport java.io.*;\n\npublic class Solution {\n static void go() {\n int n = in.nextInt();\n int ... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\n\npublic class Solution\n{\n static int max_pair =0;\n static int max_count = 0;\n public static void main(String []args)\n {\n Scanner sc = new Scanner(System.in);\n ... | {
"class_name": null,
"func_sign": [
"acmTeam(topic)"
],
"solution": "def gp():\n d = [ int(v) for v in input() ]\n c = len( [ v for v in d if v == 1 ] )\n return( c, d )\n\ndef merge( p1, p2 ):\n count = 0\n for i in range(len(p1)):\n if p1[i] == 1 or p2[i] == 1:\n count +=... | eJzsvU2PLDlyJfoWb/nWsy7UujGgdavUkn7JAG8ALaReaNPqRTcgQBAwP33qZtDOOfZBOj0yMm/eVrPqZrjzw8xoNDtmZER4/J//93/9z//x//0///9//vxvf/zTX/788z/99PMvP9n//uPj/+F/f/7NTz//4T/+9Id/+fMf/vWf//0vf55df238x5//6zc/yejf/fT3vw76tYxfh4238uvrr3fDlmR++XVEIvPbn+wxzkjD3m5tTef3lc4vv/vpd1MQGw8xfv3feP0QjIz81fzemT94+5+HKLPfY8KxDLl8jGfVSD1dUcpfy1RgaXqT... | null |
5,705 | Taum and B'day | Taum is planning to celebrate the birthday of his friend, Diksha. There are two types of gifts that Diksha wants from Taum: one is black and the other is white. To make her happy, Taum has to buy $b$ black gifts and $w$ white gifts.
- The cost of each black gift is $bc$ units.
- The cost of every white gift is $wc... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static long taumBday(int b, int w, int bc, int wc, int z)"
],
"script_name": "Solution",
"solution": "\nimport java.util.Scanner;\n\npublic class Solution {\n\n\tpublic static void main(String[] args) {\n\t\t// TODO Auto-generated method stub\n\t\t@Suppress... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution \n{\n private static long cost( long B,long W,long X,long Y,long Z )\n {\n long cost = 0;\n \n if( X < Y )\n {\n cost = cost + ( B * X );\n if( Y < Z + X )\n {\n ... | {
"solution": "/* HackerRank Template v0.25 by Sergey Esipenko */\nimport java.io.*;\nimport java.util.*;\n\nimport static java.lang.Math.*;\nimport static java.util.Arrays.fill;\nimport static java.util.Arrays.sort;\n\npublic class Solution implements Runnable {\n\t/* START OF SOLUTION */\n\t\n\tprivate static final... | {
"class_name": null,
"func_sign": [
"taumBday(b, w, bc, wc, z)"
],
"solution": "t = int(input())\nfor _ in range(t):\n b, w = map(int, input().split())\n x, y, z = map(int, input().split())\n print(b * min(x, y + z) + w * min(y, x + z))\n",
"template": "#\n# Complete the 'taumBday' function belo... | eJyll0GOHEcORWfhC8wNAr02BmQwGEH6JAZGAy88WsymxwsZMGAY8CHsA3jnY/r9bMlWl1StGaggNaoyIyPIz/8/mT9/8evvf//b9fn6N77888eH/zx+9/2bh6/Gw3n1eGofXxU23NrbkmtVtjOre1RtyzrBtz0tvZrbMW3zq/doL+/Dai525mmLGNNOrzmbb21+wovb285Zey8fa1f03j1fPXJzc+qpsYyD18w1PGKF5epXjx214+Ti9l61/Zx69TjLWDAzx7bIebg69gn23u1sWce7+9jYuatmGAFnL3NCnsRGOkTgI8rtzJXEdnpm... | null |
4,187 | Bigger is Greater | <!--
**Please note that this is a team event, and your submission will be accepted only as a part of a team, even single member teams are allowed. Please click [here](https://www.hackerrank.com/auth/create_team/csindia) to register as a team, if you have NOT already registered.**
-->
_[Lexicographical order](https://... | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static String biggerIsGreater(String w)"
],
"script_name": "Solution",
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.Input... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "\n\nimport java.util.Arrays;\nimport java.util.Scanner;\n\npublic class Solution {\n\tpublic static void main(String args[]) {\n\t\tint testcases;\n\t\tScanner scan = new Scanner(System.in);\n\t\ttestcases = scan.nextInt();\n\t\tfor (int i = 0; i < testcases; i++) {\n\t\t\tString str = scan.next();\n\... | {
"class_name": null,
"func_sign": [
"biggerIsGreater(w)"
],
"solution": "#!/usr/bin/env python3\ncases = int(input())\nfor x in range(cases):\n\tstring = list(input().strip())\n\tmatch = False\n\tfor x in range(len(string)-2,-1,-1):\n\t\tif string[x+1] > string[x]:\n\t\t\tmatch = True\n\t\t\tfor y in range... | eJy8/VuuJjnSrgf2RQ9k47/WhbbU2tjQSBroAho8OM900nkmBQE9CY1Bdz25noBe87UiK1dWRmZE/ZmdhcqMWIfP3Umj2fsYjeb/n//7//H//f/9395//p//J/7w//rf/sPeubf/+F//y3/81//lH/fd/3GzZ9UYqrp21I/eW/7j1iuuneL4x83v8rjEn+k4y+NRy3gXjDusZ6Hd1ieyvfxmwZ3nxNq9lZeQQ7FmPOfcl1qKv1S/RMpPM27kf9xN2UduJuY/7uvR/7jNw6XFfZRu6wphXCIs3cWjmEsx+VlWnvdR8sKdtOH+cQfWncFt... | null |
6,715 | Modified Kaprekar Numbers | A _modified Kaprekar number_ is a positive whole number with a special property. If you square it, then split the number into two integers and sum those integers, you have the same value you started with.
Consider a positive whole number $n$ with $d$ digits. We square $n$ to arrive at a number that is either $2\time... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static void kaprekarNumbers(int p, int q)"
],
"script_name": "Solution",
"solution": "import java.util.Scanner;\n\npublic class Kaprekar {\n\tpublic static void main(String args[]) {\n\t\tScanner cin = new Scanner(System.in);\n\t\tint p = cin.nextInt();\n\t... | {
"solution": "import java.util.Scanner;\n\npublic class Solution {\n public static void main(String[] args) {\n Scanner scan = new Scanner(System.in);\n int p = scan.nextInt();\n int q = scan.nextInt();\n scan.close();\n \n boolean foundKaprekar = false;\n for (int... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n private static InputReader in;\n private static PrintWriter out;\n\n public static void main(String[] args) throws IOException {\n in = new InputReader(System.in);\n out = new PrintWriter(System.out, true);\n int p = in.nex... | {
"class_name": null,
"func_sign": [
"kaprekarNumbers(p, q)"
],
"solution": "low = input()\nhi = input()\n\ndef is_kaprekar_number(num):\n square = str(num*num) \n mid = len(square)//2\n \n num1 = 0\n try: \n num1 = int(square[0:mid])\n except:\n pass\n \n num2 = 0\n try: \n num2 = int(sq... | eJydU7FKBEEMtbgPCVsfMpPJzCR2B4ocyBUWIrhicW5hs15xB4IIfoT+oZ0/YbJaHMMNh5Nis7u89/KSzLzPPr9nJ1PcfunL3Wv3NG522+4MukjJcT9akn7s5tANL5thvR0eH5532z/QcnWzuFqew/VidXnRvc1hT4AYWZmaxDcJZPKsDjSJa3TAMjkQ1yYQCBH7UVPKTQLsPKd+ZIeB2lpI0esMKGWXmgS8F/OOkTJWBXwORIAW5Q5SYnXO6ClU6VmDy7pRy4lFlYaSIbsAirHaATAjA0l0EJ0+sn6DaRtCYM/k9JeBMcUEEj36CSKl... | null |
45,345 | Halloween Sale | You wish to buy video games from the famous online video game store Mist.
Usually, all games are sold at the same price, $p$ dollars. However, they are planning to have the seasonal Halloween Sale next month in which you can buy games at a cheaper price. Specifically, the first game will cost $p$ dollars, and every su... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static int howManyGames(int p, int d, int m, int s)"
],
"script_name": "Solution",
"solution": "import java.io.OutputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.OutputStream;\nimport java.io.PrintWriter;\nimport java.io... | {
"solution": "DNE",
"template": "class Result {\n\n /*\n * Complete the 'howManyGames' function below.\n *\n * The function is expected to return an INTEGER.\n * The function accepts following parameters:\n * 1. INTEGER p\n * 2. INTEGER d\n * 3. INTEGER m\n * 4. INTEGER s\n ... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n static int howManyGames(int p, int d, int m, int s) {\n // Return the number of games you can buy\n int price = p;\n int k = 0;\n whi... | {
"class_name": null,
"func_sign": [
"howManyGames(p, d, m, s)"
],
"solution": "p,d,m,s=map(int,input().split())\ni = 0\ncost = p\nwhile s-cost>=0:\n i+=1\n s-=cost\n cost = max(cost-d,m)\nprint(i)",
"template": "#\n# Complete the 'howManyGames' function below.\n#\n# The function is expected to r... | eJyVk89qwzAMxnfogwify5DkP7H2GD2tdKWHLYdduh5SKIxCH2J738lJDwNb2RJsE3B+fNL3KbfV93b1MD7PG33Zfbr34+k8uCdwAgQJOHTp5ejW4PrLqX8d+rfDx3m4fxKI9O66hl8YQ8dKSspkcnqJFUjgi6IJpQYRlWCT4JpABK1OT4pocnVTQZWUi8mbUNdoibFU6IFZZtS6WOsVMxgSBrG7yzk3LAmLLYGyiiloFykitSseQbQ3SB5tXzi3gktlRuwBqbWm5EQWBlew+I/2cmw4czdmkRyU34bs1Kgx+FONujiITYZG2qU+FVzq... | null |
1,104 | Chocolate Feast | Little Bobby loves chocolate. He frequently goes to his favorite $\text{5 & 10}$ store, Penny Auntie, to buy them. They are having a promotion at Penny Auntie. If Bobby saves enough wrappers, he can turn them in for a free chocolate.
**Example**
$n = 15$
$c = 3$
$m = 2$
He has $15$ to spend, bars cost $3... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static int chocolateFeast(int n, int c, int m)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static void main(... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.util.*;\nclass Solution\n{\n\tpublic static void main(String args[])\n\t{\n\t\tScanner in=new Scanner(System.in);\n\t\tint t,n,c,m,ans=0,w=0;\n\t\tt=in.nextInt();\n\t\twhile(t-->0)\n\t\t{\n\t\t\tn=in.nextInt();\n\t\t\tc=in.nextInt();\n\t\t\tm=in.nextInt();\n\t\t\tans=(int)(n/c);\n\t\t\tw=an... | {
"class_name": null,
"func_sign": [
"chocolateFeast(n, c, m)"
],
"solution": "#!/usr/bin/env python\n\nimport sys\n\n\nif __name__ == '__main__':\n T = int(sys.stdin.readline())\n \n for _ in range(T):\n N, C, M = list(int(x) for x in sys.stdin.readline().split())\n \n total =... | eJydVcuOG0cM9CEHfwahs2E036S/JIAV5JDswRfZhzVgIDFgwL/g/K+Lo93AkDQy4DnsaLqbZLFY1fvlt/++vnyxPb//ix9v/zm8O334+Hh4Q4c8njqWBrGqkjabH09W0otYooU4k+V4SuNq8uQsEua2OdVrUbEZSWliRU1EidkE+URsDoUz4rqM1GrZlEMVcltclBGFeodXdHj49OHhr8eHv/98//HxGRvq6vHEx1McT4MBvw+fX9EP8BsbXOpk5U6RhmNtokW8ZCmJ9UK8GboY3AvA3JA0FlCSiDijxUBYRNvgMi8yB9DKoCjwEKiS... | null |
3,305 | Cavity Map | You are given a square map as a matrix of integer strings. Each cell of the map has a value denoting its depth. We will call a cell of the map a _cavity_ if and only if this cell is not on the border of the map and each cell adjacent to it has _strictly smaller depth_. Two cells are adjacent if they have a common side,... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static List<String> cavityMap(List<String> grid)"
],
"script_name": "Solution",
"solution": "\nimport java.io.*;\nimport java.math.*;\nimport java.util.*;\n\nimport static java.util.Arrays.*;\n\npublic class Solution {\n\tprivate static final int mod = (int... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.math.*;\n\n\npublic class Solution {\n\tstatic BufferedReader in;\n\tstatic PrintWriter out;\n\tstatic StringTokenizer tok;\n\t\n\t\n\tstatic void solve() throws Exception {\n\t\tint n = nextInt();\n\t\tchar[][] a = new char[n][n];\n\t\tfor (int i = 0... | {
"class_name": null,
"func_sign": [
"cavityMap(grid)"
],
"solution": "n = int(input())\n\nmaplist = []\nfor i in range(n):\n maplist.append(list(input()))\n\nfor i in range(1, n-1):\n for j in range(1, n-1):\n c = maplist[i][j]\n t = maplist[i-1][j]\n l = maplist[i][j-1]\n ... | eJztnUuuHUmWXSVAA0lkuyCk/c00kg2IghpSNtShqlEFFCAI0CA0Io1Cw9Fex93v9fs+ZISKrIqINDIzyPfedfPzP2bbnWf/r3+n//N//+2/+Y//48//7evf/+M//Pk//OnP+S9fvo5cVipjtuHfefbaUv3ydY7Ry5o55VLWSin5J1++plHzyMs/XKnV2upsyyukXnsvbaSavNZa/ptX6HX0OVoaqfSVm9cp/myLi6qXHj3Xnv3ry9daS2k9rd57a6P3nKrX9X9zn5Zn5DFrTWmN5nVXrtO38/V81qsWf3dY1NJH7aWU3EvtrftudRWL... | null |
2,336 | Manasa and Stones | Manasa is out on a hike with friends. She finds a trail of stones with numbers on them. She starts following the trail and notices that any two consecutive stones' numbers differ by one of two values. Legend has it that there is a treasure trove at the end of the trail. If Manasa can guess the value of the last stone... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static List<Integer> stones(int n, int a, int b)"
],
"script_name": "Solution",
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.math.BigInteger;\nimport ja... | {
"solution": "import java.util.*;\n\npublic class ManasaandStones {\n public static void main(String[] args) {\n Scanner sc = new Scanner(System.in);\n int t = sc.nextInt();\n while(t-- > 0) {\n int n = sc.nextInt();\n int a = sc.nextInt();\n int b = sc.nextIn... | {
"solution": "import java.util.Scanner;\n\npublic class Solution {\n public static void main(String[] args) {\n Scanner stdin = new Scanner(System.in);\n int numTestCases = stdin.nextInt();\n for (int i = 0; i < numTestCases; i++) {\n int numStones = stdin.nextInt();\n i... | {
"class_name": null,
"func_sign": [
"stones(n, a, b)"
],
"solution": "T = int(input())\n\nfor test_case in range(T):\n n = int(input())\n a = int(input())\n b = int(input())\n s = set()\n for i in range(n):\n s.add(b*i + (n-i-1)*a)\n print(' '.join(map(str, sorted(s))))",
"templa... | eJx0vcuudTuzZYUEDzJ1yhSG7zZPgsSWKEAWqGwoJBISQqLGg/Ii9Nbi2/85QsqjzBXb/5rDn9eY3eGI7rj83//Nf////j//9X/1P/yf//a//P2//e//+d/+u9+/nb/+Pqv99XfL/7/z48fOjz7++nt8/NfJj3HuX3+/tfKx9/76u588tW4+PGf+q20+cvNE6/ncfZtfzHxuZ7iYYM/89t/+29+//af/43/7T//Tf/5P//P/+L/+7//5zwpuX9/vfef93vvur31nnF9ra+9f6+OtXxu9z1+b3xr5eW//tXVa+7W95vdrZ+z3a7e9PPu+... | null |
22,185 | Strange Counter | There is a *strange counter*. At the first second, it displays the number $3$. Each second, the number displayed by decrements by $1$ until it reaches $1$. In next second, the timer resets to $2 \times \text{the initial number for the prior cycle}$ and continues counting down. The diagram below shows the counter values... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static long strangeCounter(long t)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n public static void main(String[] arg... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "\nimport java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class A {\n\tInputStream is;\n\tPrintWriter out;\n\tString INPUT = \"\";\n\t\n\tvoid solve()\n\t{\n... | {
"class_name": null,
"func_sign": [
"strangeCounter(t)"
],
"solution": "t = int(input())\n\nt1 = 3\nwhile(t1 < t):\n t -= t1\n t1 *= 2\n \nprint(t1 - t + 1)",
"template": "#\n# Complete the 'strangeCounter' function below.\n#\n# The function is expected to return a LONG_INTEGER.\n# The function ... | eJyNU0tKREEMFPQgj14Pkl/n40kEFRf6Fm7GWbwBQQQPobdy54VsxcWQR4bJqumuSqqS9PvF5/f52V9cf43DzWt72u72S7uamvPttm2mNr/s5odlfrx/3i//T4jtbTMdYLuVWLaENSixJAmrqiIaJaEHushKDUSA+RH5QWyadTmDUW1arBP6yo04ARB009qW0xAa0inX1N9bJ+ksdV1EZg7o6ont7EyhgSXXjGjYEk5MZK8nhpJN1gVy4iM7k5C14dykevw5J9arhZSwVJtaSa215qzST84ap/cK4SDqvkmQ2vgOqkPW3cflDwghJP8= | null |
44,591 | 3D Surface Area | Madison is a little girl who is fond of toys. Her friend Mason works in a toy manufacturing factory . Mason has a 2D board $A$ of size $H \times W$ with $H$ rows and $W$ columns. The board is divided into cells of size $1 \times 1$ with each cell indicated by its coordinate $(i, j)$. The cell $(i, j)$ has an integer $... | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static int surfaceArea(List<List<Integer>> A)"
],
"script_name": "Solution",
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n static int surfaceArea(in... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.*;\nimport java.util.*;\nimport java.text.*;\nimport java.math.*;\nimport java.util.regex.*;\n\npublic class Solution {\n\n static int surfaceArea(int[][] A) {\n // Complete this function\n int[][] b = new int[A.length+2][A[0].length+2];\n for(int i=1; i<=A.length... | {
"class_name": null,
"func_sign": [
"surfaceArea(A)"
],
"solution": "#!/bin/python3\n\nimport sys\n\ndef surfaceArea(A):\n # Complete this function\n ans = 0\n #print(A)\n for i in range(len(A)):\n for j in range(len(A[0])):\n for dx, dy in [(-1,0), (0,1), (1,0), (0,-1)]:\n ... | eJztnc2uJDkRhVnwHli9YIVQ2g7/8SRINGIBs2DTsBgkJITEkgeA9yXOF86ZgXtrZgAJcsDj7tt1qzJtx+85EU7V/On7f/3hn7/Hfz/9gb/42R8+/PrTb3/3+YefpA/lSuX6+ClfV8rfZvil58Jz4bkw5Q8/Sh8++/1vP/vl55/96he/+d3nO6JyGf3jpw9//FH6SpjVVD9+WispztaKeItfv/L2ywlLeTMhAau7v+Vmnz6OIE8bR5CnjSPI08YR5GnjCPK0cQR52jiCPG0cQZ42jiBPG0eQp40jyNPGEeRp4wjytHEEedo4gjxtHEGe... | null |
4,504 | Almost Sorted | Given an array of integers, determine whether the array can be sorted in ascending order using only one of the following operations one time.
1. Swap two elements.
2. Reverse one sub-segment.
Determine whether one, both or neither of the operations will complete the task. Output is as follows.
1. If the array... | hackerrank | Medium | {
"class_name": "Result",
"func_sign": [
"public static void almostSorted(List<Integer> arr)"
],
"script_name": "Solution",
"solution": "import java.io.DataInputStream;\nimport java.io.FileInputStream;\nimport java.io.IOException;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.Ha... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.*;\nimport java.util.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n Scanner sc = new Scanner(System.in);\n int[] arr = new int[sc.nextInt()];\n int[] temp = new int[arr.length];\n for (int i = 0; i < arr.length; i++) {\n ... | {
"class_name": null,
"func_sign": [
"almostSorted(arr)"
],
"solution": "input()\narray = [int(x) for x in input().split(\" \")]\n\nsorted_array = sorted(array)\n\nif array == sorted_array:\n print(\"yes\")\nelse: \n wrong = []\n for i, (a, b) in enumerate(zip(array, sorted_array)):\n if ... | eJyUncuu9kqSlkHiFphbNWbgPEdyJUhsCSGoAZNNi25OQkjcBPdLPE94/dVN0aJqS39sL3922k6nM+Pwxhv/65/9q//9z//pP/nX/+MP/+H3v/nPf/eHf/n8ob/v+9vvbT3tPC2e0Z/Znrmf9T5rPOs80Z6Yz80DRh6x9tN25JH76e0+vbf8N/NfPD3P6ju3z/v0O5/R3mwu/z/z/7M/I5sc6z5jn2ec9YxsY+Rx881rvjv/nWeO8cw8fu71zJM3cnJ/5L6IZ968n7zm6vn/kf8/+S/vbuXv6+bt3rzre5/9rme3/ex+np3X2/vNfz3/... | null |
20,055 | Super Reduced String | Reduce a string of lowercase characters in range `ascii[‘a’..’z’]`by doing a series of operations. In each operation, select a pair of adjacent letters that match, and delete them.
Delete as many characters as possible using this method and return the resulting string. If the final string is empty, return `Empty S... | hackerrank | Easy | {
"class_name": "Result",
"func_sign": [
"public static String superReducedString(String s)"
],
"script_name": "Solution",
"solution": "import java.awt.*;\nimport java.awt.event.*;\nimport java.awt.geom.*;\nimport java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*; \nimport java.... | {
"solution": "import java.io.*;\nimport java.math.*;\nimport java.security.*;\nimport java.text.*;\nimport java.util.*;\nimport java.util.concurrent.*;\nimport java.util.function.*;\nimport java.util.regex.*;\nimport java.util.stream.*;\nimport static java.util.stream.Collectors.joining;\nimport static java.util.str... | {
"solution": "import java.io.ByteArrayInputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.PrintWriter;\nimport java.util.Arrays;\nimport java.util.InputMismatchException;\n\npublic class A {\n\tInputStream is;\n\tPrintWriter out;\n\tString INPUT = \"\";\n\t\n\tvoid solve()\n\t{\n\t... | {
"class_name": null,
"func_sign": [
"superReducedString(s)"
],
"solution": "s = input()\nwhile True:\n for i in range(len(s)-1):\n if s[i] == s[i+1]:\n s = s[:i]+s[i+2:]\n break\n else:\n break\nprint(s if s else \"Empty String\")",
"template": "#\n# Complete the... | eJy1VUluFTEUZIHENaK/jjgAe07ABgkQ8tye5/azERKHgHuyY0vzISFJfyfkA+1Wy1LXq7KrPHx6+uXbsyfH5/XXrfPmw0G6UMvhxcWB8TUaGHZhxcTB6siCVOKH0OZwuf2GwEhh9L2v5eGKt+7w8fLiBntosGTfvHamAzYV9NLLWMeohQXSwYg8EEPEI8B9ojfhOMWw00cIYUIopf+8MxkswvSedzc+jBGaML20ofSLVyVJJ3Z1gwXkau4ubJNfRAyVRDkcjUl2EJJ40IQPNKKYsP85wU6bUFMGa4MGG0XqaQtBJBWVt4TCkBPBB6r2... | null |
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