/*
 * To change this template, choose Tools | Templates
 * and open the template in the editor.
 */

package recycled;

import java.io.BufferedReader;
import java.io.DataInputStream;
import java.io.FileInputStream;
import java.io.FileWriter;
import java.io.InputStreamReader;
import java.io.PrintWriter;
import java.util.TreeSet;

/**
 *
 * @author Alfred
 */
public class Main {

    /**
     * @param args the command line arguments
     */
    public static void main(String[] args) {
    try {
            FileInputStream fstream = new FileInputStream("C-small-attempt0.in");
            DataInputStream in = new DataInputStream(fstream);
            BufferedReader br = new BufferedReader(new InputStreamReader(in));
            FileWriter outFile = new FileWriter("out.txt");
            PrintWriter out = new PrintWriter(outFile);
            String line;
            line = br.readLine();
            int cases = Integer.parseInt(line);
            for (int i=1; i<=cases;i++){
                line = br.readLine();
                String[] values = line.split(" ");
                int A = Integer.parseInt(values[0]);
                int B = Integer.parseInt(values[1]);
                int total=0;
                for (int n=A; n<=B;n++){
                    TreeSet<String> solutions = new TreeSet<String>();
                    for (int k=1;k<values[1].length();k++){
                        String m = circshift(n,k);
                        int mVal = Integer.parseInt(m);
                        if(mVal<=B && mVal!=n  && mVal> n && !m.startsWith("0") && m.length()==Integer.toString(n).length() )
                        {
                            solutions.add(m);
                        }                   
                    }
                    total+=solutions.size();
                }
                out.println("Case #"+i+": "+total);
            }
            in.close();
            out.close();
        } catch (Exception e) {//Catch exception if any
            System.err.println("Error: " + e.getMessage());
        }

}

    private static String circshift(int n, int k) {
        String nString=Integer.toString(n);
        int len=nString.length();
        String m =  nString.substring(len-k)+nString.subSequence(0, len-k);
        //System.out.println(n+" "+m);
        return m;
    }

}

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;
	}
}
