package Main;

import com.sun.deploy.util.ArrayUtil;
import org.apache.commons.lang3.ArrayUtils;

import java.io.*;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import java.util.HashSet;

/**
 * Created with IntelliJ IDEA.
 * User: arran
 * Date: 14/04/12
 * Time: 3:12 PM
 * To change this template use File | Settings | File Templates.
 */
public class Round {
    public StringBuilder parse(BufferedReader in) throws IOException {
        StringBuilder out = new StringBuilder();
        String lineCount = in.readLine();

        for (int i = 1; i <= Integer.parseInt(lineCount); i++)
        {
            out.append("Case #"+i+": ");
            System.err.println("Case #"+i+": ");
            String line = in.readLine();
            String[] splits = line.split(" ");
            int p1 = Integer.valueOf(splits[0]);
            int p2 = Integer.valueOf(splits[1]);
            int r = pairRecyclable(p1, p2);
            out.append(r);

//            if (i < Integer.parseInt(lineCount))
                out.append("\n");
        }
        return out;
    }

    public static int pairRecyclable(int i1, int i2) {
        HashSet<String> hash = new HashSet<String>();
        for (int i = i1; i < i2; i++)
        {
            String istr = String.valueOf(i);
            if (istr.length() < 2) continue;
            for (int p = 0; p < istr.length() ; p++)
            {
                String nistr = istr.substring(p,istr.length()).concat(istr.substring(0,p));
                if (Integer.valueOf(nistr) < i1) continue;
                if (Integer.valueOf(nistr) > i2) continue;
                if (nistr.equals(istr)) continue;
                String cnistr = (Integer.valueOf(nistr) > Integer.valueOf(istr)) ?  istr + "," + nistr : nistr + "," + istr;
                hash.add(cnistr);
            }
        }
        return hash.size();
    }

    public static void main(String[] args) {
        InputStreamReader converter = null;
        try {
            int attempt = 0;
            String quest = "C";
//            String size = "small-attempt";
            String size = "large";
            converter = new InputStreamReader(new FileInputStream("src/resource/"+quest+"-"+size+".in"));
            BufferedReader in = new BufferedReader(converter);
            Round r = new Round();
            String str = r.parse(in).toString();
            System.out.print(str);
            FileOutputStream fos = new FileOutputStream(quest+"-"+size+".out");
            OutputStreamWriter osw = new OutputStreamWriter(fos);
            BufferedWriter bw = new BufferedWriter(osw);
            bw.write(str);
            bw.flush();
            bw.close();
        } catch (IOException e) {
            e.printStackTrace();
        }

    }
}

package fixjava;

import java.util.concurrent.Callable;
import java.util.concurrent.Future;

import fixjava.ParallelWorkQueue.CallableFactory;

public class ArrayStats {
	public final double[][] data;
	public final int n, k;

	private double[][] centered;
	private double[] mean;
	private double[][] cov;
	private double[] stddev = null;
	private double[][] corr = null;

	/** Calculate covariance matrix */
	public ArrayStats(double[][] data) {
		if (data == null || data.length < 2 || data[0].length < 2)
			throw new IllegalArgumentException("Invalid data");
		this.data = data;
		this.n = data.length;
		this.k = data[0].length;
	}

	/** Compute mean for each dimension */
	public double[] mean() {
		if (mean == null) {
			mean = new double[k];
			for (int dim = 0; dim < k; dim++) {
				double tot = 0;
				for (int pt = 0; pt < n; pt++)
					tot += data[pt][dim];
				mean[dim] = tot / n;
			}
		}
		return mean;
	}

	/** Compute stddev for each dimension */
	public double[] stddev() {
		if (stddev == null) {
			// Make sure means are computed
			mean();

			stddev = new double[k];
			for (int dim = 0; dim < k; dim++) {
				for (int pt = 0; pt < n; pt++) {
					double diff = data[pt][dim] - mean[dim];
					stddev[dim] += diff * diff;
				}
				stddev[dim] = Math.sqrt(stddev[dim] / n);
			}
		}
		return stddev;
	}

	/** Re-center data by subtracting mean from each dimension */
	public double[][] centered() {
		if (centered == null) {
			// Make sure means are computed
			mean();

			centered = new double[n][k];
			for (int pt = 0; pt < n; pt++)
				for (int dim = 0; dim < k; dim++)
					centered[pt][dim] = data[pt][dim] - mean[dim];
		}
		return centered;
	}

	/** Compute covariance matrix */
	public double[][] cov() {
		if (cov == null) {
			// Make sure centered version of data is computed
			centered();

			// Work on transposed array for cache coherence
			final double[][] centeredT = ArrayUtils.transpose(centered);

			cov = new double[k][k];

			try {
				//final DecimalFormat formatPercent1dp = new DecimalFormat("0.0%");
				for (Future<Void> res : new ParallelWorkQueue<Integer, Void>(/* numThreads = */25,
						ParallelWorkQueue.makeIntRangeIterable(k), new CallableFactory<Integer, Void>() {
							//IntegerMutable counter = new IntegerMutable();

							@Override
							public Callable<Void> newInstance(final Integer ii) {
								return new Callable<Void>() {
									int dim1 = ii.intValue();
									double[][] centeredTLocal = centeredT;
									double[][] covLocal = cov;

									@Override
									public Void call() {
										for (int dim2 = dim1; dim2 < k; dim2++) {
											double numer = 0.0;
											for (int pt = 0; pt < n; pt++)
												numer += centeredTLocal[dim1][pt] * centeredTLocal[dim2][pt];
											int denom = n - 1;
											covLocal[dim1][dim2] = covLocal[dim2][dim1] = denom == 0 ? 0 : numer / denom;
										}
										//										int count = counter.increment();
										//										if (count % 10 == 0)
										//											System.out.println(formatPercent1dp.format((count / (float) (k - 1))));
										return null;
									}
								};
							}
						}))
					// Barricade
					res.get();
			} catch (Exception e) {
				e.printStackTrace();
				System.exit(1);
			}
		}
		return cov;
	}

	/** Compute correlation matrix */
	public double[][] corr() {
		if (corr == null) {
			// Make sure per-dim stddevs and covariance matrix are computed
			stddev();
			cov();

			corr = new double[k][k];
			for (int dim1 = 0; dim1 < k; dim1++) {
				for (int dim2 = dim1; dim2 < k; dim2++) {
					double prod = stddev[dim1] * stddev[dim2];
					corr[dim1][dim2] = corr[dim2][dim1] = prod == 0.0 ? 0.0 : cov[dim1][dim2] / prod;
				}
			}
		}
		return corr;
	}
}
