/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *      https://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.commons.text.similarity;

import java.util.Arrays;

/**
 * An algorithm for measuring the difference between two character sequences.
 *
 * <p>
 * This is the number of changes needed to change one sequence into another, where each change is a single character modification (deletion, insertion or
 * substitution).
 * </p>
 *
 * @since 1.0
 */
public class LevenshteinDetailedDistance implements EditDistance<LevenshteinResults> {

    /**
     * The singleton instance.
     */
    private static final LevenshteinDetailedDistance INSTANCE = new LevenshteinDetailedDistance();

    /**
     * Finds count for each of the three [insert, delete, substitute] operations needed. This is based on the matrix formed based on the two character sequence.
     *
     * @param <E>     The type of similarity score unit.
     * @param left    character sequence which need to be converted from.
     * @param right   character sequence which need to be converted to.
     * @param matrix  two dimensional array containing.
     * @param swapped tells whether the value for left character sequence and right character sequence were swapped to save memory.
     * @return result object containing the count of insert, delete and substitute and total count needed.
     */
    private static <E> LevenshteinResults findDetailedResults(final SimilarityInput<E> left, final SimilarityInput<E> right, final int[][] matrix,
            final boolean swapped) {
        int delCount = 0;
        int addCount = 0;
        int subCount = 0;
        int rowIndex = right.length();
        int columnIndex = left.length();
        int dataAtLeft = 0;
        int dataAtTop = 0;
        int dataAtDiagonal = 0;
        int data = 0;
        boolean deleted = false;
        boolean added = false;
        while (rowIndex >= 0 && columnIndex >= 0) {
            if (columnIndex == 0) {
                dataAtLeft = -1;
            } else {
                dataAtLeft = matrix[rowIndex][columnIndex - 1];
            }
            if (rowIndex == 0) {
                dataAtTop = -1;
            } else {
                dataAtTop = matrix[rowIndex - 1][columnIndex];
            }
            if (rowIndex > 0 && columnIndex > 0) {
                dataAtDiagonal = matrix[rowIndex - 1][columnIndex - 1];
            } else {
                dataAtDiagonal = -1;
            }
            if (dataAtLeft == -1 && dataAtTop == -1 && dataAtDiagonal == -1) {
                break;
            }
            data = matrix[rowIndex][columnIndex];
            // case in which the character at left and right are the same,
            // in this case none of the counters will be incremented.
            if (columnIndex > 0 && rowIndex > 0 && left.at(columnIndex - 1).equals(right.at(rowIndex - 1))) {
                columnIndex--;
                rowIndex--;
                continue;
            }
            // handling insert and delete cases.
            deleted = false;
            added = false;
            if (data - 1 == dataAtLeft && data <= dataAtDiagonal && data <= dataAtTop || dataAtDiagonal == -1 && dataAtTop == -1) { // NOPMD
                columnIndex--;
                if (swapped) {
                    addCount++;
                    added = true;
                } else {
                    delCount++;
                    deleted = true;
                }
            } else if (data - 1 == dataAtTop && data <= dataAtDiagonal && data <= dataAtLeft || dataAtDiagonal == -1 && dataAtLeft == -1) { // NOPMD
                rowIndex--;
                if (swapped) {
                    delCount++;
                    deleted = true;
                } else {
                    addCount++;
                    added = true;
                }
            }
            // substituted case
            if (!added && !deleted) {
                subCount++;
                columnIndex--;
                rowIndex--;
            }
        }
        return new LevenshteinResults(addCount + delCount + subCount, addCount, delCount, subCount);
    }

    /**
     * Gets the default instance.
     *
     * @return The default instace
     */
    public static LevenshteinDetailedDistance getDefaultInstance() {
        return INSTANCE;
    }

    /**
     * Finds the Levenshtein distance between two CharSequences if it's less than or equal to a given threshold.
     *
     * <p>
     * This implementation follows from Algorithms on Strings, Trees and Sequences by Dan Gusfield and Chas Emerick's implementation of the Levenshtein distance
     * algorithm from <a href="http://www.merriampark.com/ld.htm" >http://www.merriampark.com/ld.htm</a>
     * </p>
     *
     * <pre>
     * limitedCompare(null, *, *)             = IllegalArgumentException
     * limitedCompare(*, null, *)             = IllegalArgumentException
     * limitedCompare(*, *, -1)               = IllegalArgumentException
     * limitedCompare("","", 0)               = 0
     * limitedCompare("aaapppp", "", 8)       = 7
     * limitedCompare("aaapppp", "", 7)       = 7
     * limitedCompare("aaapppp", "", 6))      = -1
     * limitedCompare("elephant", "hippo", 7) = 7
     * limitedCompare("elephant", "hippo", 6) = -1
     * limitedCompare("hippo", "elephant", 7) = 7
     * limitedCompare("hippo", "elephant", 6) = -1
     * </pre>
     *
     * @param <E>       The type of similarity score unit.
     * @param left      the first CharSequence, must not be null.
     * @param right     the second CharSequence, must not be null.
     * @param threshold the target threshold, must not be negative.
     * @return result distance, or -1.
     */
    private static <E> LevenshteinResults limitedCompare(SimilarityInput<E> left, SimilarityInput<E> right, final int threshold) { // NOPMD
        if (left == null || right == null) {
            throw new IllegalArgumentException("CharSequences must not be null");
        }
        if (threshold < 0) {
            throw new IllegalArgumentException("Threshold must not be negative");
        }
        /*
         * This implementation only computes the distance if it's less than or equal to the threshold value, returning -1 if it's greater. The advantage is
         * performance: unbounded distance is O(nm), but a bound of k allows us to reduce it to O(km) time by only computing a diagonal stripe of width 2k + 1
         * of the cost table. It is also possible to use this to compute the unbounded Levenshtein distance by starting the threshold at 1 and doubling each
         * time until the distance is found; this is O(dm), where d is the distance.
         *
         * One subtlety comes from needing to ignore entries on the border of our stripe eg. p[] = |#|#|#|* d[] = *|#|#|#| We must ignore the entry to the left
         * of the leftmost member We must ignore the entry above the rightmost member
         *
         * Another subtlety comes from our stripe running off the matrix if the strings aren't of the same size. Since string s is always swapped to be the
         * shorter of the two, the stripe will always run off to the upper right instead of the lower left of the matrix.
         *
         * As a concrete example, suppose s is of length 5, t is of length 7, and our threshold is 1. In this case we're going to walk a stripe of length 3. The
         * matrix would look like so:
         *
         * <pre> 1 2 3 4 5 1 |#|#| | | | 2 |#|#|#| | | 3 | |#|#|#| | 4 | | |#|#|#| 5 | | | |#|#| 6 | | | | |#| 7 | | | | | | </pre>
         *
         * Note how the stripe leads off the table as there is no possible way to turn a string of length 5 into one of length 7 in edit distance of 1.
         *
         * Additionally, this implementation decreases memory usage by using two single-dimensional arrays and swapping them back and forth instead of
         * allocating an entire n by m matrix. This requires a few minor changes, such as immediately returning when it's detected that the stripe has run off
         * the matrix and initially filling the arrays with large values so that entries we don't compute are ignored.
         *
         * See Algorithms on Strings, Trees and Sequences by Dan Gusfield for some discussion.
         */
        int n = left.length(); // length of left
        int m = right.length(); // length of right
        // if one string is empty, the edit distance is necessarily the length of the other
        if (n == 0) {
            return m <= threshold ? new LevenshteinResults(m, m, 0, 0) : new LevenshteinResults(-1, 0, 0, 0);
        }
        if (m == 0) {
            return n <= threshold ? new LevenshteinResults(n, 0, n, 0) : new LevenshteinResults(-1, 0, 0, 0);
        }
        boolean swapped = false;
        if (n > m) {
            // swap the two strings to consume less memory
            final SimilarityInput<E> tmp = left;
            left = right;
            right = tmp;
            n = m;
            m = right.length();
            swapped = true;
        }
        int[] p = new int[n + 1]; // 'previous' cost array, horizontally
        int[] d = new int[n + 1]; // cost array, horizontally
        int[] tempD; // placeholder to assist in swapping p and d
        final int[][] matrix = new int[m + 1][n + 1];
        // filling the first row and first column values in the matrix
        for (int index = 0; index <= n; index++) {
            matrix[0][index] = index;
        }
        for (int index = 0; index <= m; index++) {
            matrix[index][0] = index;
        }
        // fill in starting table values
        final int boundary = Math.min(n, threshold) + 1;
        for (int i = 0; i < boundary; i++) {
            p[i] = i;
        }
        // these fills ensure that the value above the rightmost entry of our
        // stripe will be ignored in following loop iterations
        Arrays.fill(p, boundary, p.length, Integer.MAX_VALUE);
        Arrays.fill(d, Integer.MAX_VALUE);
        // iterates through t
        for (int j = 1; j <= m; j++) {
            final E rightJ = right.at(j - 1); // jth character of right
            d[0] = j;
            // compute stripe indices, constrain to array size
            final int min = Math.max(1, j - threshold);
            final int max = j > Integer.MAX_VALUE - threshold ? n : Math.min(n, j + threshold);
            // the stripe may lead off of the table if s and t are of different sizes
            if (min > max) {
                return new LevenshteinResults(-1, 0, 0, 0);
            }
            // ignore entry left of leftmost
            if (min > 1) {
                d[min - 1] = Integer.MAX_VALUE;
            }
            // iterates through [min, max] in s
            for (int i = min; i <= max; i++) {
                if (left.at(i - 1).equals(rightJ)) {
                    // diagonally left and up
                    d[i] = p[i - 1];
                } else {
                    // 1 + minimum of cell to the left, to the top, diagonally left and up
                    d[i] = 1 + Math.min(Math.min(d[i - 1], p[i]), p[i - 1]);
                }
                matrix[j][i] = d[i];
            }
            // copy current distance counts to 'previous row' distance counts
            tempD = p;
            p = d;
            d = tempD;
        }
        // if p[n] is greater than the threshold, there's no guarantee on it being the correct distance
        if (p[n] <= threshold) {
            return findDetailedResults(left, right, matrix, swapped);
        }
        return new LevenshteinResults(-1, 0, 0, 0);
    }

    /**
     * Finds the Levenshtein distance between two Strings.
     *
     * <p>
     * A higher score indicates a greater distance.
     * </p>
     *
     * <p>
     * The previous implementation of the Levenshtein distance algorithm was from
     * <a href="http://www.merriampark.com/ld.htm">http://www.merriampark.com/ld.htm</a>
     * </p>
     *
     * <p>
     * Chas Emerick has written an implementation in Java, which avoids an OutOfMemoryError which can occur when my Java implementation is used with very large
     * strings.<br>
     * This implementation of the Levenshtein distance algorithm is from
     * <a href="http://www.merriampark.com/ldjava.htm">http://www.merriampark.com/ldjava.htm</a>
     * </p>
     *
     * <pre>
     * unlimitedCompare(null, *)             = IllegalArgumentException
     * unlimitedCompare(*, null)             = IllegalArgumentException
     * unlimitedCompare("","")               = 0
     * unlimitedCompare("","a")              = 1
     * unlimitedCompare("aaapppp", "")       = 7
     * unlimitedCompare("frog", "fog")       = 1
     * unlimitedCompare("fly", "ant")        = 3
     * unlimitedCompare("elephant", "hippo") = 7
     * unlimitedCompare("hippo", "elephant") = 7
     * unlimitedCompare("hippo", "zzzzzzzz") = 8
     * unlimitedCompare("hello", "hallo")    = 1
     * </pre>
     *
     * @param <E>   The type of similarity score unit.
     * @param left  the first CharSequence, must not be null.
     * @param right the second CharSequence, must not be null.
     * @return result distance, or -1.
     * @throws IllegalArgumentException if either CharSequence input is {@code null}.
     */
    private static <E> LevenshteinResults unlimitedCompare(SimilarityInput<E> left, SimilarityInput<E> right) {
        if (left == null || right == null) {
            throw new IllegalArgumentException("CharSequences must not be null");
        }
        /*
         * The difference between this impl. and the previous is that, rather than creating and retaining a matrix of size s.length() + 1 by t.length() + 1, we
         * maintain two single-dimensional arrays of length s.length() + 1. The first, d, is the 'current working' distance array that maintains the newest
         * distance cost counts as we iterate through the characters of String s. Each time we increment the index of String t we are comparing, d is copied to
         * p, the second int[]. Doing so allows us to retain the previous cost counts as required by the algorithm (taking the minimum of the cost count to the
         * left, up one, and diagonally up and to the left of the current cost count being calculated). (Note that the arrays aren't really copied anymore, just
         * switched...this is clearly much better than cloning an array or doing a System.arraycopy() each time through the outer loop.)
         *
         * Effectively, the difference between the two implementations is this one does not cause an out of memory condition when calculating the LD over two
         * very large strings.
         */
        int n = left.length(); // length of left
        int m = right.length(); // length of right
        if (n == 0) {
            return new LevenshteinResults(m, m, 0, 0);
        }
        if (m == 0) {
            return new LevenshteinResults(n, 0, n, 0);
        }
        boolean swapped = false;
        if (n > m) {
            // swap the input strings to consume less memory
            final SimilarityInput<E> tmp = left;
            left = right;
            right = tmp;
            n = m;
            m = right.length();
            swapped = true;
        }
        int[] p = new int[n + 1]; // 'previous' cost array, horizontally
        int[] d = new int[n + 1]; // cost array, horizontally
        int[] tempD; // placeholder to assist in swapping p and d
        final int[][] matrix = new int[m + 1][n + 1];
        // filling the first row and first column values in the matrix
        for (int index = 0; index <= n; index++) {
            matrix[0][index] = index;
        }
        for (int index = 0; index <= m; index++) {
            matrix[index][0] = index;
        }
        // indexes into strings left and right
        int i; // iterates through left
        int j; // iterates through right
        E rightJ; // jth character of right
        int cost; // cost
        for (i = 0; i <= n; i++) {
            p[i] = i;
        }
        for (j = 1; j <= m; j++) {
            rightJ = right.at(j - 1);
            d[0] = j;
            for (i = 1; i <= n; i++) {
                cost = left.at(i - 1).equals(rightJ) ? 0 : 1;
                // minimum of cell to the left+1, to the top+1, diagonally left and up +cost
                d[i] = Math.min(Math.min(d[i - 1] + 1, p[i] + 1), p[i - 1] + cost);
                // filling the matrix
                matrix[j][i] = d[i];
            }
            // copy current distance counts to 'previous row' distance counts
            tempD = p;
            p = d;
            d = tempD;
        }
        return findDetailedResults(left, right, matrix, swapped);
    }

    /**
     * Threshold.
     */
    private final Integer threshold;

    /**
     * <p>
     * This returns the default instance that uses a version of the algorithm that does not use a threshold parameter.
     * </p>
     *
     * @see LevenshteinDetailedDistance#getDefaultInstance()
     * @deprecated Use {@link #getDefaultInstance()}.
     */
    @Deprecated
    public LevenshteinDetailedDistance() {
        this(null);
    }

    /**
     * If the threshold is not null, distance calculations will be limited to a maximum length.
     *
     * <p>
     * If the threshold is null, the unlimited version of the algorithm will be used.
     * </p>
     *
     * @param threshold If this is null then distances calculations will not be limited. This may not be negative.
     */
    public LevenshteinDetailedDistance(final Integer threshold) {
        if (threshold != null && threshold < 0) {
            throw new IllegalArgumentException("Threshold must not be negative");
        }
        this.threshold = threshold;
    }

    /**
     * Computes the Levenshtein distance between two Strings.
     *
     * <p>
     * A higher score indicates a greater distance.
     * </p>
     *
     * <p>
     * The previous implementation of the Levenshtein distance algorithm was from
     * <a href="http://www.merriampark.com/ld.htm">http://www.merriampark.com/ld.htm</a>
     * </p>
     *
     * <p>
     * Chas Emerick has written an implementation in Java, which avoids an OutOfMemoryError which can occur when my Java implementation is used with very large
     * strings.<br>
     * This implementation of the Levenshtein distance algorithm is from
     * <a href="http://www.merriampark.com/ldjava.htm">http://www.merriampark.com/ldjava.htm</a>
     * </p>
     *
     * <pre>
     * distance.apply(null, *)             = IllegalArgumentException
     * distance.apply(*, null)             = IllegalArgumentException
     * distance.apply("","")               = 0
     * distance.apply("","a")              = 1
     * distance.apply("aaapppp", "")       = 7
     * distance.apply("frog", "fog")       = 1
     * distance.apply("fly", "ant")        = 3
     * distance.apply("elephant", "hippo") = 7
     * distance.apply("hippo", "elephant") = 7
     * distance.apply("hippo", "zzzzzzzz") = 8
     * distance.apply("hello", "hallo")    = 1
     * </pre>
     *
     * @param left  the first input, must not be null.
     * @param right the second input, must not be null.
     * @return result distance, or -1.
     * @throws IllegalArgumentException if either String input {@code null}.
     */
    @Override
    public LevenshteinResults apply(final CharSequence left, final CharSequence right) {
        return apply(SimilarityInput.input(left), SimilarityInput.input(right));
    }

    /**
     * Computes the Levenshtein distance between two Strings.
     *
     * <p>
     * A higher score indicates a greater distance.
     * </p>
     *
     * <p>
     * The previous implementation of the Levenshtein distance algorithm was from
     * <a href="http://www.merriampark.com/ld.htm">http://www.merriampark.com/ld.htm</a>
     * </p>
     *
     * <p>
     * Chas Emerick has written an implementation in Java, which avoids an OutOfMemoryError which can occur when my Java implementation is used with very large
     * strings.<br>
     * This implementation of the Levenshtein distance algorithm is from
     * <a href="http://www.merriampark.com/ldjava.htm">http://www.merriampark.com/ldjava.htm</a>
     * </p>
     *
     * <pre>
     * distance.apply(null, *)             = IllegalArgumentException
     * distance.apply(*, null)             = IllegalArgumentException
     * distance.apply("","")               = 0
     * distance.apply("","a")              = 1
     * distance.apply("aaapppp", "")       = 7
     * distance.apply("frog", "fog")       = 1
     * distance.apply("fly", "ant")        = 3
     * distance.apply("elephant", "hippo") = 7
     * distance.apply("hippo", "elephant") = 7
     * distance.apply("hippo", "zzzzzzzz") = 8
     * distance.apply("hello", "hallo")    = 1
     * </pre>
     *
     * @param <E>   The type of similarity score unit.
     * @param left  the first input, must not be null.
     * @param right the second input, must not be null.
     * @return result distance, or -1.
     * @throws IllegalArgumentException if either String input {@code null}.
     * @since 1.13.0
     */
    public <E> LevenshteinResults apply(final SimilarityInput<E> left, final SimilarityInput<E> right) {
        if (threshold != null) {
            return limitedCompare(left, right, threshold);
        }
        return unlimitedCompare(left, right);
    }

    /**
     * Gets the distance threshold.
     *
     * @return The distance threshold.
     */
    public Integer getThreshold() {
        return threshold;
    }
}