/*
 * Copyright (C) 2009 eXo Platform SAS.
 *
 * This is free software; you can redistribute it and/or modify it
 * under the terms of the GNU Lesser General Public License as
 * published by the Free Software Foundation; either version 2.1 of
 * the License, or (at your option) any later version.
 *
 * This software is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
 * Lesser General Public License for more details.
 *
 * You should have received a copy of the GNU Lesser General Public
 * License along with this software; if not, write to the Free
 * Software Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
 * 02110-1301 USA, or see the FSF site: http://www.fsf.org.
 */
package org.exoplatform.services.document.impl.diff;

import org.exoplatform.services.document.diff.DiffAlgorithm;
import org.exoplatform.services.document.diff.Revision;

import java.util.Arrays;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Map;
import java.util.Set;

/**
 * Implements a simple differencing algortithm.
 * <p>
 * 
 * @version $Revision: 1.7 $
 * @author <a href="mailto:juanco@suigeneris.org">Juanco Anez</a>
 *         <p>
 *         <b>Overview of Algorithm</b>
 *         </p>
 *         <p>
 *         <i>by <a href='http://www.topmeadow.net/bwm'> bwm</a></i>
 *         </p>
 *         <p>
 *         The algorithm is optimised for situations where the input sequences
 *         have few repeated objects. If it is given input with many repeated
 *         objects it will report sub-optimal changes. However, given
 *         appropriate input, it is fast, and linear in memory usage.
 *         </p>
 *         <p>
 *         The algorithm consists of the following steps:
 *         </p>
 *         <ul>
 *         <li>compute an equivalence set for the input data</li>
 *         <li>translate each element of the orginal and revised input sequences
 *         to a member of the equivalence set</li>
 *         <li>match the the input sequences to determine the deltas, i.e. the
 *         differences between the original and revised sequences.</li>
 *         </ul>
 *         <p>
 *         The first step is to compute a an equivalence set for the input data.
 *         The equivalence set is computed from objects that are in the original
 *         input sequence
 *         </p>
 * 
 *         <pre>
 *    eq(x) = the index of the first occurence of x in the original sequence.
 * </pre>
 *         <p>
 *         With this equivalence function, the algorithm can compare integers
 *         rather than strings, which is considerably more efficient.
 *         </p>
 *         <p>
 *         The second step is to compute the datastructure on which the
 *         algorithm will operate. Having computed the equivalence function in
 *         the previous step, we can compute two arrays where indx[i] =
 *         eqs(orig[i]) and jndx[i] = eqs(rev[i]). The algorithm can now operate
 *         on indx and jndx instead of orig and rev. Thus, comparisons are then
 *         on O(int == int) instead of O(Object.equals(Object)).
 *         </p>
 *         <p>
 *         The algorithm now matches indx and jndx. Whilst indx[i] == jndx[i] it
 *         skips matching objects in the sequence. In seeking to match objects
 *         in the input sequence it assumes that each object is likely to be
 *         unique. It uses the known characteristics of the unique equivalence
 *         function. It can tell from the eq value if this object appeared in
 *         the other sequence at all. If it did not, there is no point in
 *         searching for a match.
 *         </p>
 *         <p>
 *         Recall that the eq function value is the index earliest occurrence in
 *         the orig sequence. This information is used to search efficiently for
 *         the next match. The algorithm is perfect when all input objects are
 *         unique, but degrades when input objects are not unique. When input
 *         objects are not unique an optimal match may not be found, but a
 *         correct match will be.
 *         </p>
 *         <p>
 *         Having identified common matching objects in the orig and revised
 *         sequences, the differences between them are easily computed.
 *         </p>
 * @see DeltaImpl
 * @see RevisionImpl Modifications: 27/Apr/2003 bwm Added some comments whilst
 *      trying to figure out the algorithm 03 May 2003 bwm Created this
 *      implementation class by refactoring it out of the Diff class to enable
 *      plug in difference algorithms
 */
public class SimpleDiff implements DiffAlgorithm
{

   static final int NOT_FOUND_i = -2;

   static final int NOT_FOUND_j = -1;

   static final int EOS = Integer.MAX_VALUE;

   public SimpleDiff()
   {
   }

   protected int scan(int[] ndx, int i, int target)
   {
      while (ndx[i] < target)
      {
         i++;
      }
      return i;
   }

   /**
    * Compute the difference between original and revised sequences.
    * 
    * @param orig The original sequence.
    * @param rev The revised sequence to be compared with the original.
    * @return A Revision object describing the differences.
    * @throws Exception if the diff could not be computed.
    */
   public Revision diff(Object[] orig, Object[] rev) throws Exception
   {
      // create map eqs, such that for each item in both orig and rev
      // eqs(item) = firstOccurrence(item, orig);
      Map eqs = buildEqSet(orig, rev);

      // create an array such that
      // indx[i] = NOT_FOUND_i if orig[i] is not in rev
      // indx[i] = firstOccurrence(orig[i], orig)
      int[] indx = buildIndex(eqs, orig, NOT_FOUND_i);

      // create an array such that
      // jndx[j] = NOT_FOUND_j if orig[j] is not in rev
      // jndx[j] = firstOccurrence(rev[j], orig)
      int[] jndx = buildIndex(eqs, rev, NOT_FOUND_j);

      // what in effect has been done is to build a unique hash
      // for each item that is in both orig and rev
      // and to label each item in orig and new with that hash value
      // or a marker that the item is not common to both.

      eqs = null; // let gc know we're done with this

      Revision deltas = new RevisionImpl(); // !!! new Revision()
      int i = 0;
      int j = 0;

      // skip matching
      // skip leading items that are equal
      // could be written
      // for (i=0; indx[i] != EOS && indx[i] == jndx[i]; i++);
      // j = i;
      for (; indx[i] != EOS && indx[i] == jndx[j]; i++, j++)
      {
         /* void */
      }

      while (indx[i] != jndx[j])
      { // only equal if both == EOS
         // they are different
         int ia = i;
         int ja = j;

         // size of this delta
         do
         {
            // look down rev for a match
            // stop at a match
            // or if the FO(rev[j]) > FO(orig[i])
            // or at the end
            while (jndx[j] < 0 || jndx[j] < indx[i])
            {
               j++;
            }
            // look down orig for a match
            // stop at a match
            // or if the FO(orig[i]) > FO(rev[j])
            // or at the end
            while (indx[i] < 0 || indx[i] < jndx[j])
            {
               i++;
            }

            // this doesn't do a compare each line with each other line
            // so it won't find all matching lines
         }
         while (indx[i] != jndx[j]);

         // on exit we have a match

         // they are equal, reverse any exedent matches
         // it is possible to overshoot, so count back matching items
         while (i > ia && j > ja && indx[i - 1] == jndx[j - 1])
         {
            --i;
            --j;
         }

         deltas.addDelta(DeltaImpl.newDelta(new ChunkImpl(orig, ia, i - ia), new ChunkImpl(rev, ja, j - ja)));
         // skip matching
         for (; indx[i] != EOS && indx[i] == jndx[j]; i++, j++)
         {
            /* void */
         }
      }
      return deltas;
   }

   /**
    * create a <code>Map</code> from each common item in orig and rev to the
    * index of its first occurrence in orig
    * 
    * @param orig the original sequence of items
    * @param rev the revised sequence of items
    */
   protected Map buildEqSet(Object[] orig, Object[] rev)
   {
      // construct a set of the objects that orig and rev have in common

      // first construct a set containing all the elements in orig
      Set items = new HashSet(Arrays.asList(orig));

      // then remove all those not in rev
      items.retainAll(Arrays.asList(rev));

      Map eqs = new HashMap();
      for (int i = 0; i < orig.length; i++)
      {
         // if its a common item and hasn't been found before
         if (items.contains(orig[i]))
         {
            // add it to the map
            eqs.put(orig[i], new Integer(i));
            // and make sure its not considered again
            items.remove(orig[i]);
         }
      }
      return eqs;
   }

   /**
    * build a an array such each a[i] = eqs([i]) or NF if eqs([i]) undefined
    * 
    * @param eqs a mapping from Object to Integer
    * @param seq a sequence of objects
    * @param NF the not found marker
    */
   protected int[] buildIndex(Map eqs, Object[] seq, int NF)
   {
      int[] result = new int[seq.length + 1];
      for (int i = 0; i < seq.length; i++)
      {
         Integer value = (Integer)eqs.get(seq[i]);
         if (value == null || value.intValue() < 0)
         {
            result[i] = NF;
         }
         else
         {
            result[i] = value.intValue();
         }
      }
      result[seq.length] = EOS;
      return result;
   }

}
