package unit;

import java.util.ArrayList;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;

/**
 * Models InferenceContext.notifyChange() diff-hoist defect — defective vs fixed.
 *
 * DEFECT (javac-0007): inferencevars.diff(inferredVars) is recomputed on
 * every iteration of the freeTypeListeners loop.
 *
 *   void notifyChange(List<Type> inferredVars) {
 *       for (entry : freeTypeListeners.entrySet()) {
 *           if (!Type.containsAny(entry.getValue(),
 *                   inferencevars.diff(inferredVars))) {  // ← rebuilt L times
 *               ...
 *           }
 *       }
 *   }
 *
 * List.diff() iterates inferredVars (M elements) and for each scans
 * inferencevars (N elements) → O(N×M) per call.  Called L times: O(L×N×M).
 *
 * FIX: hoist the diff() before the loop.
 *
 *   List<Type> remainingVars = inferencevars.diff(inferredVars);  // once
 *   for (entry : freeTypeListeners.entrySet()) {
 *       if (!Type.containsAny(entry.getValue(), remainingVars)) {
 *           ...
 *       }
 *   }
 *
 * Complexity:
 *   Defective: O(L × N × M)
 *   Fixed:     O(N×M + L×V)
 *
 * Test topology:
 *   N = inferencevars (type-variable list length)
 *   M = inferredVars  (resolved subset — set to N/2 for worst-case diff work)
 *   L = listener count
 *
 * The op-counter measures diff() recomputation cost:
 *   SLOW: diff() called L times → L × N × M ops
 *   FAST: diff() called once   → N × M ops (then L cheap list-scan checks)
 */
public class NotifyChangeAlgorithm {

    // ── Result ───────────────────────────────────────────────────────────────

    public static class Result {
        public final int notifiedCount;  // how many listeners fired
        public final long ops;
        public Result(int notifiedCount, long ops) {
            this.notifiedCount = notifiedCount;
            this.ops = ops;
        }
    }

    // ── helpers ──────────────────────────────────────────────────────────────

    /**
     * Simulates List.diff(that): iterates every element of 'from' (N items),
     * for each does a linear scan of 'that' (M items).  Returns elements of
     * 'from' not present in 'that'.
     */
    static List<String> diff(List<String> from, List<String> that, long[] ops) {
        List<String> result = new ArrayList<>();
        for (String f : from) {
            boolean found = false;
            for (String t : that) {
                ops[0]++;
                if (f.equals(t)) {
                    found = true;
                    break;
                }
            }
            if (!found) result.add(f);
        }
        return result;
    }

    /**
     * Simulates Type.containsAny(ts1, ts2): returns true if any element of
     * ts1 is present in ts2.
     */
    static boolean containsAny(List<String> ts1, List<String> ts2) {
        for (String t : ts1) {
            for (String s : ts2) {
                if (t.equals(s)) return true;
            }
        }
        return false;
    }

    // ── Defective ─────────────────────────────────────────────────────────

    /**
     * diff() recomputed inside every listener iteration.
     */
    public static Result slow(List<String> inferencevars,
                              List<String> inferredVars,
                              Map<String, List<String>> listeners) {
        long[] ops = {0};
        int notified = 0;
        for (Map.Entry<String, List<String>> entry : listeners.entrySet()) {
            // diff recomputed every iteration
            List<String> remaining = diff(inferencevars, inferredVars, ops);
            if (!containsAny(entry.getValue(), remaining)) {
                notified++;
            }
        }
        return new Result(notified, ops[0]);
    }

    // ── Fixed ─────────────────────────────────────────────────────────────

    /**
     * diff() hoisted before the loop.
     */
    public static Result fast(List<String> inferencevars,
                              List<String> inferredVars,
                              Map<String, List<String>> listeners) {
        long[] ops = {0};
        int notified = 0;
        List<String> remaining = diff(inferencevars, inferredVars, ops); // once
        for (Map.Entry<String, List<String>> entry : listeners.entrySet()) {
            if (!containsAny(entry.getValue(), remaining)) {
                notified++;
            }
        }
        return new Result(notified, ops[0]);
    }

    // ── Test harness ─────────────────────────────────────────────────────

    static List<String> makeVars(String prefix, int n) {
        List<String> vars = new ArrayList<>(n);
        for (int i = 0; i < n; i++) vars.add(prefix + i);
        return vars;
    }

    /**
     * Build listener map: L listeners each watching V variables from
     * inferencevars.  We pick variables that are NOT in inferredVars so
     * that containsAny returns false (listener fires).
     */
    static Map<String, List<String>> makeListeners(List<String> inferencevars,
                                                    List<String> inferredVars,
                                                    int l, int v) {
        // build set of "remaining" vars (not inferred) to register listeners on
        List<String> remaining = new ArrayList<>();
        for (String iv : inferencevars) {
            if (!inferredVars.contains(iv)) remaining.add(iv);
        }
        Map<String, List<String>> map = new LinkedHashMap<>();
        for (int i = 0; i < l; i++) {
            List<String> watched = new ArrayList<>();
            // each listener watches v vars from remaining (cycling)
            for (int j = 0; j < v; j++) {
                watched.add(remaining.get((i + j) % remaining.size()));
            }
            map.put("listener_" + i, watched);
        }
        return map;
    }

    public static void main(String[] args) {
        // N=inferencevars, M=inferredVars (=N/2), L=listeners
        int[][] configs = {
            {40,  20,  40},   // N=40,  M=20,  L=40
            {80,  40,  80},   // N=80,  M=40,  L=80
            {160, 80,  160},  // N=160, M=80,  L=160
        };
        int passes = 0;
        int fails  = 0;

        for (int[] c : configs) {
            int n = c[0], m = c[1], l = c[2];
            List<String> inferencevars = makeVars("T", n);
            List<String> inferredVars  = makeVars("T", m); // T0..T(m-1) inferred
            Map<String, List<String>> listeners = makeListeners(inferencevars, inferredVars, l, 3);

            Result slow = slow(inferencevars, inferredVars, listeners);
            Result fast = fast(inferencevars, inferredVars, listeners);

            // correctness: same notification count
            boolean correct = slow.notifiedCount == fast.notifiedCount;
            // ratio: slow should be >> fast
            double ratio = (double) slow.ops / Math.max(fast.ops, 1);
            boolean ratioOk = ratio >= 5.0;

            if (correct && ratioOk) {
                System.out.printf("PASS  N=%4d  M=%4d  L=%4d  slow=%10d  fast=%8d  ratio=%6.1fx%n",
                        n, m, l, slow.ops, fast.ops, ratio);
                passes++;
            } else {
                System.out.printf("FAIL  N=%4d  M=%4d  L=%4d  correct=%b  ratio=%.1fx (need>=5)%n",
                        n, m, l, correct, ratio);
                fails++;
            }
        }

        System.out.printf("%n%d/%d PASS%n", passes, passes + fails);
        if (fails > 0) System.exit(1);
    }
}