import java.util.*;

/**
 * CWE-407 regression test for synfig-0001.
 *
 * Defect: remove_layers_inside_included_pastelayers() in
 * synfig-studio/src/synfigapp/actions/layerduplicate.cpp
 * used std::vector<Layer::Handle> + std::find for paste-canvas ancestor
 * membership checks.  For each of L layers, walking D nesting levels,
 * each level calling std::find on a P-element vector: O(L * D * P) total.
 *
 * Fix: replace std::vector with std::unordered_set<Layer*> so each
 * ancestor membership check is O(1), total becomes O(L * D).
 *
 * Run: javac SynfigLayerDuplicateTest.java && java -ea SynfigLayerDuplicateTest
 */
public class SynfigLayerDuplicateTest {

    static class Layer {
        final String name;
        final boolean isPasteCanvas;
        Layer parentPasteCanvas;

        Layer(String name, boolean isPasteCanvas) {
            this.name = name;
            this.isPasteCanvas = isPasteCanvas;
        }
    }

    // Defective: O(L * D * P)
    static List<Layer> removeDuplicatesLinear(List<Layer> layerList) {
        List<Layer> pasteCanvasList = new ArrayList<>();
        for (Layer layer : layerList) {
            if (layer.isPasteCanvas) pasteCanvasList.add(layer);
        }
        List<Layer> result = new ArrayList<>();
        for (Layer layer : layerList) {
            boolean insideSelected = false;
            Layer parent = layer.parentPasteCanvas;
            while (parent != null) {
                if (pasteCanvasList.contains(parent)) { // O(P) per step
                    insideSelected = true;
                    break;
                }
                parent = parent.parentPasteCanvas;
            }
            if (!insideSelected) result.add(layer);
        }
        return result;
    }

    // Fixed: O(L * D)
    static List<Layer> removeDuplicatesHashed(List<Layer> layerList) {
        Set<Layer> pasteCanvasSet = new HashSet<>();
        for (Layer layer : layerList) {
            if (layer.isPasteCanvas) pasteCanvasSet.add(layer);
        }
        List<Layer> result = new ArrayList<>();
        for (Layer layer : layerList) {
            boolean insideSelected = false;
            Layer parent = layer.parentPasteCanvas;
            while (parent != null) {
                if (pasteCanvasSet.contains(parent)) { // O(1) per step
                    insideSelected = true;
                    break;
                }
                parent = parent.parentPasteCanvas;
            }
            if (!insideSelected) result.add(layer);
        }
        return result;
    }

    static void testCorrectnessSimple() {
        Layer group = new Layer("group", true);
        Layer child1 = new Layer("child1", false);
        Layer child2 = new Layer("child2", false);
        child1.parentPasteCanvas = group;
        child2.parentPasteCanvas = group;
        Layer standalone = new Layer("standalone", false);

        List<Layer> all = Arrays.asList(group, child1, child2, standalone);

        List<Layer> linear = removeDuplicatesLinear(all);
        List<Layer> hashed = removeDuplicatesHashed(all);

        assert linear.size() == 2 : "linear: expected 2, got " + linear.size();
        assert hashed.size() == 2 : "hashed: expected 2, got " + hashed.size();
        assert linear.contains(group) && linear.contains(standalone);
        assert hashed.contains(group) && hashed.contains(standalone);
        assert !hashed.contains(child1) && !hashed.contains(child2);
        assert new HashSet<>(linear).equals(new HashSet<>(hashed));
        System.out.println("testCorrectnessSimple: PASS");
    }

    static void testCorrectnessNoGroups() {
        List<Layer> layers = new ArrayList<>();
        for (int i = 0; i < 50; i++) {
            layers.add(new Layer("layer_" + i, false));
        }
        List<Layer> linear = removeDuplicatesLinear(layers);
        List<Layer> hashed = removeDuplicatesHashed(layers);
        assert linear.size() == 50;
        assert hashed.size() == 50;
        System.out.println("testCorrectnessNoGroups: PASS");
    }

    static void testCorrectnessDeepNesting() {
        // 5-level nesting, 30 extra groups, 100 children all inside innermost
        Layer[] chain = new Layer[5];
        List<Layer> all = new ArrayList<>();
        for (int d = 0; d < 5; d++) {
            chain[d] = new Layer("group_" + d, true);
            if (d > 0) chain[d].parentPasteCanvas = chain[d - 1];
            all.add(chain[d]);
        }
        for (int g = 0; g < 30; g++) {
            all.add(new Layer("extra_" + g, true));
        }
        for (int c = 0; c < 100; c++) {
            Layer child = new Layer("child_" + c, false);
            child.parentPasteCanvas = chain[4];
            all.add(child);
        }

        List<Layer> linear = removeDuplicatesLinear(all);
        List<Layer> hashed = removeDuplicatesHashed(all);
        assert linear.size() == hashed.size()
            : "size mismatch: linear=" + linear.size() + " hashed=" + hashed.size();
        assert new HashSet<>(linear).equals(new HashSet<>(hashed));
        System.out.println("testCorrectnessDeepNesting: PASS");
    }

    static void testPerformance() {
        // Scenario: P_selected=2000 paste-canvas layers in selection,
        // D=5-deep unselected chain, C=500 children.
        // Children walk the entire D-chain finding no selected group each time.
        // linear: C * D * P_selected = 500 * 5 * 2000 = 5,000,000 containment checks
        // hashed: C * D = 500 * 5 = 2,500 containment checks
        int P_selected = 2000;
        int D = 5;
        int C = 500;

        List<Layer> all = new ArrayList<>();

        // Selected paste-canvas layers
        for (int i = 0; i < P_selected; i++) {
            all.add(new Layer("sel_" + i, true));
        }

        // Unselected chain - not added to `all` so they are NOT in the paste list
        Layer[] chain = new Layer[D];
        for (int d = 0; d < D; d++) {
            chain[d] = new Layer("unsel_" + d, true);
            if (d > 0) chain[d].parentPasteCanvas = chain[d - 1];
        }

        // Children with unselected chain as parents (while loop walks full D)
        for (int c = 0; c < C; c++) {
            Layer child = new Layer("child_" + c, false);
            child.parentPasteCanvas = chain[D - 1];
            all.add(child);
        }

        // Warmup
        for (int i = 0; i < 5; i++) {
            removeDuplicatesLinear(all);
            removeDuplicatesHashed(all);
        }

        int reps = 30;
        long linearNs = 0;
        for (int i = 0; i < reps; i++) {
            long t = System.nanoTime();
            removeDuplicatesLinear(all);
            linearNs += System.nanoTime() - t;
        }

        long hashedNs = 0;
        for (int i = 0; i < reps; i++) {
            long t = System.nanoTime();
            removeDuplicatesHashed(all);
            hashedNs += System.nanoTime() - t;
        }

        double ratio = (double) linearNs / Math.max(hashedNs, 1);
        System.out.printf("synfig-0001: layers=%d selected_paste_canvases=%d depth=%d children=%d%n",
                all.size(), P_selected, D, C);
        System.out.printf("  linear (defect): %,d ns/iter%n", linearNs / reps);
        System.out.printf("  hashed (fix):    %,d ns/iter%n", hashedNs / reps);
        System.out.printf("  speedup: %.1fx%n%n", ratio);

        assert ratio >= 2.0
            : "Expected >= 2x speedup at P=" + P_selected + ", got " + ratio + "x";
        System.out.println("testPerformance: PASS");
    }

    public static void main(String[] args) {
        System.out.println("=== synfig-0001 CWE-407 unit test ===");
        System.out.println("Defect: remove_layers_inside_included_pastelayers()");
        System.out.println("  std::vector + std::find in ancestor walk O(L*D*P)");
        System.out.println("Fix:    std::unordered_set O(1) lookup O(L*D)");
        System.out.println();

        testCorrectnessSimple();
        testCorrectnessNoGroups();
        testCorrectnessDeepNesting();
        testPerformance();

        System.out.println("=== synfig-0001 PASS ===");
    }
}