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 * and other copyright owners as documented in the project's IP log.
 *
 * This program and the accompanying materials are made available
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 * accompanies this distribution, is reproduced below, and is
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package org.eclipse.jgit.diff;

/**
 * An extended form of Bram Cohen's patience diff algorithm.
 *
 * This implementation was derived by using the 4 rules that are outlined in
 * Bram Cohen's <a href="http://bramcohen.livejournal.com/73318.html">blog</a>,
 * and then was further extended to support low-occurrence common elements.
 *
 * The basic idea of the algorithm is to create a histogram of occurrences for
 * each element of sequence A. Each element of sequence B is then considered in
 * turn. If the element also exists in sequence A, and has a lower occurrence
 * count, the positions are considered as a candidate for the longest common
 * subsequence (LCS). After scanning of B is complete the LCS that has the
 * lowest number of occurrences is chosen as a split point. The region is split
 * around the LCS, and the algorithm is recursively applied to the sections
 * before and after the LCS.
 *
 * By always selecting a LCS position with the lowest occurrence count, this
 * algorithm behaves exactly like Bram Cohen's patience diff whenever there is a
 * unique common element available between the two sequences. When no unique
 * elements exist, the lowest occurrence element is chosen instead. This offers
 * more readable diffs than simply falling back on the standard Myers' O(ND)
 * algorithm would produce.
 *
 * To prevent the algorithm from having an O(N^2) running time, an upper limit
 * on the number of unique elements in a histogram bucket is configured by
 * {@link #setMaxChainLength(int)}. If sequence A has more than this many
 * elements that hash into the same hash bucket, the algorithm passes the region
 * to {@link #setFallbackAlgorithm(DiffAlgorithm)}. If no fallback algorithm is
 * configured, the region is emitted as a replace edit.
 *
 * During scanning of sequence B, any element of A that occurs more than
 * {@link #setMaxChainLength(int)} times is never considered for an LCS match
 * position, even if it is common between the two sequences. This limits the
 * number of locations in sequence A that must be considered to find the LCS,
 * and helps maintain a lower running time bound.
 *
 * So long as {@link #setMaxChainLength(int)} is a small constant (such as 64),
 * the algorithm runs in O(N * D) time, where N is the sum of the input lengths
 * and D is the number of edits in the resulting EditList. If the supplied
 * {@link SequenceComparator} has a good hash function, this implementation
 * typically out-performs {@link MyersDiff}, even though its theoretical running
 * time is the same.
 *
 * This implementation has an internal limitation that prevents it from handling
 * sequences with more than 268,435,456 (2^28) elements.
 */
public class HistogramDiff extends LowLevelDiffAlgorithm {
	/** Algorithm to use when there are too many element occurrences. */
	private DiffAlgorithm fallback = MyersDiff.INSTANCE;

	/**
	 * Maximum number of positions to consider for a given element hash.
	 *
	 * All elements with the same hash are stored into a single chain. The chain
	 * size is capped to ensure search is linear time at O(len_A + len_B) rather
	 * than quadratic at O(len_A * len_B).
	 */
	private int maxChainLength = 64;

	/**
	 * Set the algorithm used when there are too many element occurrences.
	 *
	 * @param alg
	 *            the secondary algorithm. If null the region will be denoted as
	 *            a single REPLACE block.
	 */
	public void setFallbackAlgorithm(DiffAlgorithm alg) {
		fallback = alg;
	}

	/**
	 * Maximum number of positions to consider for a given element hash.
	 *
	 * All elements with the same hash are stored into a single chain. The chain
	 * size is capped to ensure search is linear time at O(len_A + len_B) rather
	 * than quadratic at O(len_A * len_B).
	 *
	 * @param maxLen
	 *            new maximum length.
	 */
	public void setMaxChainLength(int maxLen) {
		maxChainLength = maxLen;
	}

	public <S extends Sequence> void diffNonCommon(EditList edits,
			HashedSequenceComparator<S> cmp, HashedSequence<S> a,
			HashedSequence<S> b, Edit region) {
		new State<S>(edits, cmp, a, b).diffReplace(region);
	}

	private class State<S extends Sequence> {
		private final HashedSequenceComparator<S> cmp;

		private final HashedSequence<S> a;

		private final HashedSequence<S> b;

		/** Result edits we have determined that must be made to convert a to b. */
		final EditList edits;

		State(EditList edits, HashedSequenceComparator<S> cmp,
				HashedSequence<S> a, HashedSequence<S> b) {
			this.cmp = cmp;
			this.a = a;
			this.b = b;
			this.edits = edits;
		}

		void diffReplace(Edit r) {
			Edit lcs = new HistogramDiffIndex<S>(maxChainLength, cmp, a, b, r)
					.findLongestCommonSequence();
			if (lcs != null) {
				// If we were given an edit, we can prove a result here.
				//
				if (lcs.isEmpty()) {
					// An empty edit indicates there is nothing in common.
					// Replace the entire region.
					//
					edits.add(r);
				} else {
					diff(r.before(lcs));
					diff(r.after(lcs));
				}

			} else if (fallback instanceof LowLevelDiffAlgorithm) {
				LowLevelDiffAlgorithm fb = (LowLevelDiffAlgorithm) fallback;
				fb.diffNonCommon(edits, cmp, a, b, r);

			} else if (fallback != null) {
				SubsequenceComparator<HashedSequence<S>> cs = subcmp();
				Subsequence<HashedSequence<S>> as = Subsequence.a(a, r);
				Subsequence<HashedSequence<S>> bs = Subsequence.b(b, r);

				EditList res = fallback.diffNonCommon(cs, as, bs);
				edits.addAll(Subsequence.toBase(res, as, bs));

			} else {
				edits.add(r);
			}
		}

		private void diff(Edit r) {
			switch (r.getType()) {
			case INSERT:
			case DELETE:
				edits.add(r);
				break;

			case REPLACE:
				diffReplace(r);
				break;

			case EMPTY:
			default:
				throw new IllegalStateException();
			}
		}

		private SubsequenceComparator<HashedSequence<S>> subcmp() {
			return new SubsequenceComparator<HashedSequence<S>>(cmp);
		}
	}
}