/**
# Copyright (c) 2025-2026 CMS Manhattan
# All rights reserved.
# Author: Konstantin Vladimirovich Grabko
# Email: grabko@cmsmanhattan.com
# Phone: +1(516)777-0945
*/

package com.cbsinc.cms.llm.ml;

import ai.djl.Device;
import ai.djl.Model;
import ai.djl.engine.Engine;
import ai.djl.ndarray.NDArray;
import ai.djl.ndarray.NDList;
import ai.djl.ndarray.NDManager;
import ai.djl.ndarray.index.NDIndex;
import ai.djl.ndarray.types.DataType;
import ai.djl.ndarray.types.Shape;
import ai.djl.nn.Block;
import ai.djl.nn.Parameter;
import ai.djl.training.DefaultTrainingConfig;
import ai.djl.training.GradientCollector;
import ai.djl.training.Trainer;
import ai.djl.training.TrainingConfig;
import ai.djl.training.initializer.NormalInitializer;
import ai.djl.training.listener.TrainingListener;
import ai.djl.training.loss.Loss;
import ai.djl.training.optimizer.Optimizer;
import ai.djl.training.tracker.Tracker;
import ai.djl.util.Pair;

import java.io.DataInputStream;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.Comparator;
import java.util.List;
import java.util.Random;
import java.util.regex.Matcher;
import java.util.regex.Pattern;
import java.util.stream.Collectors;
import java.util.stream.Stream;

/**
 * =============================================================================
 * STAGE 2: Ternary QAT on SlimOrca — Java on the PYTORCH ENGINE (libtorch).
 *
 * This is the "PyTorch on Java" build: DJL's Java API executing every tensor
 * op on native libtorch (the same C++ kernels CPython PyTorch uses). Ops this
 * model needs that only the PyTorch engine provides reliably: stopGradient
 * (the STE in BitLinear), stepped slicing (interleaved RoPE 0::2 / 1::2),
 * gather (masked cross-entropy), stack, and CUDA execution.
 *
 * --------------------------- DEPENDENCIES (Gradle) ---------------------------
 *   implementation platform("ai.djl:bom:0.36.0")
 *   implementation "ai.djl:api"
 *   implementation "ai.djl.pytorch:pytorch-engine"
 *   // Pick ONE native runtime:
 *   runtimeOnly "ai.djl.pytorch:pytorch-native-cu124::linux-x86_64" // NVIDIA GPU
 *   // runtimeOnly "ai.djl.pytorch:pytorch-native-cpu::linux-x86_64" // CPU only
 *   runtimeOnly "ai.djl.pytorch:pytorch-jni"
 *
 * Maven uses the same artifact IDs. If no native runtime is bundled, the
 * first run downloads libtorch automatically.
 *
 * ------------------------------- JVM FLAGS -------------------------------
 *   -Dai.djl.default_engine=PyTorch
 *   -Xmx8g   (JVM heap holds only Java objects; tensors live in NATIVE
 *             memory, so a huge -Xmx is neither needed nor helpful)
 *
 * ------------------------------ SHARD FORMAT ------------------------------
 * DJL cannot unpickle .pt files. Convert each PyTorch shard once with this
 * small script (run in the Python env that created the shards):
 *
 *   # pt_to_ndlist.py <shard.pt>
 *   import torch, numpy as np, sys
 *   d = torch.load(sys.argv[1], map_location="cpu", weights_only=False)
 *   ids = torch.nn.utils.rnn.pad_sequence(d["input_ids"], batch_first=True,
 *                                          padding_value=0).to(torch.int64)
 *   lbl = torch.nn.utils.rnn.pad_sequence(d["labels"], batch_first=True,
 *                                          padding_value=-100).to(torch.int64)
 *   np.save(sys.argv[1] + ".ids.npy", ids.numpy())
 *   np.save(sys.argv[1] + ".lbl.npy", lbl.numpy())
 *
 * The trainer reads either slimorca_data_<N>.ndlist (named NDList with
 * "input_ids"/"labels") or the .npy pair next to slimorca_data_<N>.pt.
 * =============================================================================
 */
public class JiRackJDLStage2TrainerPt {

    // ========================= SETTINGS =========================
    private static Path DATA_DIR = Paths.get("data/SlimOrca/shards");
    private static Path OUTPUT_DIR = Paths.get("build/JRock_Ternary_SlimOrca_pt");
    private static Path STAGE1_DIR = Paths.get("checkpoints/ternary_stage1");
    private static String STAGE1_NAME = "jirack_data_22";

    private static int BATCH_SIZE = 2;
    private static int GRAD_ACCUM = 5;
    private static float LR = 2e-5f;               // [S-6]
    private static final float WEIGHT_DECAY = 1e-4f;
    private static final float CLIP_GRAD = 1.0f;
    private static final double VAL_RATIO = 0.05;
    private static final int AUTOSAVE_EVERY = 1000;
    private static final long VAL_SEED = 42L;
    private static final long IGNORE_INDEX = -100L;
    private static boolean USE_SMOKE_CONFIG = false;
    // =============================================================

    private static final Pattern SHARD_NUM = Pattern.compile("data_(\\d+)");
    private static final Pattern CKPT_NUM = Pattern.compile("shard_(\\d+)");

    private long globalStep = 0;
    private Device device;

    /* --------------------- Engine & device selection --------------------- */
    private Device selectDevice() {
        Engine engine = Engine.getInstance();
        if (!"PyTorch".equals(engine.getEngineName())) {
            throw new IllegalStateException(
                    "This trainer requires the PyTorch engine (found: "
                    + engine.getEngineName() + "). Add ai.djl.pytorch:pytorch-engine "
                    + "to the classpath and set -Dai.djl.default_engine=PyTorch.");
        }
        System.out.println("Engine: PyTorch " + engine.getVersion());
        int gpus = engine.getGpuCount();
        if (gpus > 0) {
            System.out.println("CUDA devices: " + gpus + " -> using gpu(0)");
            return Device.gpu(0);
        }
        System.out.println("No CUDA device -> CPU (use --smoke for the tiny config)");
        return Device.cpu();
    }

    /* ------------------- Masked, shifted cross-entropy ------------------- */
    /** CE(logits[:, :-1, :], labels[:, 1:]) ignoring label == -100. [S-3] */
    static class MaskedShiftedCELoss extends Loss {
        MaskedShiftedCELoss() {
            super("MaskedShiftedCE");
        }

        @Override
        public NDArray evaluate(NDList labels, NDList predictions) {
            NDArray logits = predictions.singletonOrThrow();      // (B, T, V)
            NDArray target = labels.singletonOrThrow();           // (B, T) INT64

            long T = logits.getShape().get(1);
            long V = logits.getShape().get(2);

            NDArray shiftLogits = logits.get(new NDIndex(":, 0:" + (T - 1) + ", :"))
                                        .reshape(-1, V);
            NDArray shiftLabels = target.get(new NDIndex(":, 1:" + T))
                                        .reshape(-1);

            NDArray mask = shiftLabels.neq(IGNORE_INDEX);
            NDArray maskF = mask.toType(DataType.FLOAT32, false);
            // Replace -100 with 0 so gather() has a valid index; masked later.
            NDArray safeLabels = shiftLabels.mul(mask.toType(DataType.INT64, false))
                                            .reshape(-1, 1);

            // Loss math in FP32 for stability regardless of model dtype.
            NDArray logProb = shiftLogits.toType(DataType.FLOAT32, false).logSoftmax(1);
            NDArray picked = logProb.gather(safeLabels, 1).reshape(-1);
            NDArray nll = picked.neg().mul(maskF);

            return nll.sum().div(maskF.sum().maximum(1.0f));
        }
    }

    /* ----------------------------- Shard IO ----------------------------- */
    private static NDArray[] loadShard(NDManager manager, Path shardFile) throws IOException {
        String fn = shardFile.getFileName().toString();
        if (fn.endsWith(".ndlist")) {
            try (DataInputStream dis = new DataInputStream(Files.newInputStream(shardFile))) {
                NDList list = NDList.decode(manager, dis);
                NDArray ids = null;
                NDArray lbl = null;
                for (NDArray a : list) {
                    if ("input_ids".equals(a.getName())) ids = a;
                    else if ("labels".equals(a.getName())) lbl = a;
                }
                if (ids == null || lbl == null) {
                    ids = list.get(0);
                    lbl = list.get(1);
                }
                return new NDArray[]{ids.toType(DataType.INT64, false),
                                     lbl.toType(DataType.INT64, false)};
            }
        }
        // .npy pair produced by the converter in the header
        Path idsNpy = shardFile.resolveSibling(fn + ".ids.npy");
        Path lblNpy = shardFile.resolveSibling(fn + ".lbl.npy");
        if (Files.exists(idsNpy) && Files.exists(lblNpy)) {
            NDArray ids = manager.decode(Files.readAllBytes(idsNpy));
            NDArray lbl = manager.decode(Files.readAllBytes(lblNpy));
            return new NDArray[]{ids.toType(DataType.INT64, false),
                                 lbl.toType(DataType.INT64, false)};
        }
        throw new IOException("Shard not readable: " + shardFile
                + " (need .ndlist or the .npy pair — see the converter in the header)");
    }

    private static int shardNum(Path p) {
        Matcher m = SHARD_NUM.matcher(p.getFileName().toString());
        if (!m.find()) throw new IllegalArgumentException("Bad shard name: " + p);
        return Integer.parseInt(m.group(1));
    }

    /* --------------------------- Checkpoint IO --------------------------- */
    private void saveCheckpoint(Model model, Path dir, String name) throws IOException {
        Files.createDirectories(dir);
        model.save(dir, name);
        Files.writeString(dir.resolve(name + ".step"), Long.toString(globalStep));
    }

    private long readStep(Path dir, String name) {
        try {
            return Long.parseLong(Files.readString(dir.resolve(name + ".step")).trim());
        } catch (Exception e) {
            return 0L;
        }
    }

    /* ------------------------------ Training ------------------------------ */
    public void train() throws Exception {
        device = selectDevice();
        Files.createDirectories(OUTPUT_DIR);
        System.out.println("Loading JiRack Ternary — Stage 2 (PyTorch engine)...");

        try (Model model = Model.newInstance("jirack_stage2", device);
             NDManager rootManager = NDManager.newBaseManager(device)) {

            Block block = JiRackJDLTernary_10b.buildModel();
            block.setInitializer(new NormalInitializer(JiRackJDLTernary_10b.INIT_STD), Parameter.Type.WEIGHT);
            model.setBlock(block);

            TrainingConfig config = new DefaultTrainingConfig(new MaskedShiftedCELoss())
                    .optOptimizer(Optimizer.adam()
                            .optLearningRateTracker(Tracker.fixed(LR))
                            .optWeightDecays(WEIGHT_DECAY)
                            .optClipGrad(CLIP_GRAD)
                            .build())
                    .optDevices(new Device[]{device})
                    .addTrainingListeners(TrainingListener.Defaults.logging());

            try (Trainer trainer = model.newTrainer(config)) {
                trainer.initialize(new Shape(BATCH_SIZE, 64));
                JiRackJDLTernary_10b.applyDepthScaledInit(block);

                // ---------- Resume: stage-2 checkpoint > stage-1 best ----------
                List<Path> stage2 = listCheckpoints(OUTPUT_DIR);
                int lastDone = -1;
                if (!stage2.isEmpty()) {
                    Path latest = stage2.get(stage2.size() - 1);
                    String name = stripParams(latest);
                    System.out.println("Resuming stage 2 from: " + name);
                    model.load(OUTPUT_DIR, name);
                    globalStep = readStep(OUTPUT_DIR, name);
                    Matcher m = CKPT_NUM.matcher(name);
                    if (m.find()) lastDone = Integer.parseInt(m.group(1));
                } else {
                    Path s1 = STAGE1_DIR.resolve(STAGE1_NAME + "-0000.params");
                    if (Files.exists(s1)) {
                        System.out.println("Starting from stage-1 best: " + s1);
                        model.load(STAGE1_DIR, STAGE1_NAME);  // weights only [S-1]
                        globalStep = 0;   // stage-2 has its own step counter
                    } else {
                        System.out.println("WARNING: no stage-1 checkpoint at "
                                + s1.toAbsolutePath()
                                + " — random init (only sensible with --smoke).");
                    }
                }

                // [S-2] lambda constant 1.0 — warmup was done in stage 1.
                JiRackJDLTernary_10b.setLambda(block, 1.0f);
                System.out.printf(
                        "lambda=1.0 (constant) | global_step=%d | LR=%.1e | device=%s%n",
                        globalStep, LR, device);

                // -------------------- Shards & fixed val set --------------------
                List<Path> allShards = listShards(DATA_DIR);
                if (allShards.isEmpty()) {
                    throw new IOException("No shards in " + DATA_DIR.toAbsolutePath());
                }

                // [S-5] Fixed val set from shard 0, seed 42.
                System.out.println("Building fixed val set from "
                        + allShards.get(0).getFileName());
                long[] trainIdx0;
                NDArray valIds;
                NDArray valLbl;
                {
                    NDArray[] s0 = loadShard(rootManager, allShards.get(0));
                    long n0 = s0[0].getShape().get(0);
                    int valN = (int) (n0 * VAL_RATIO);
                    long[] perm = seededPermutation(n0, VAL_SEED);
                    long[] valIdx = java.util.Arrays.copyOfRange(perm, 0, valN);
                    trainIdx0 = java.util.Arrays.copyOfRange(perm, valN, (int) n0);
                    NDArray vi = rootManager.create(valIdx);
                    valIds = s0[0].get(vi).duplicate();
                    valLbl = s0[1].get(vi).duplicate();
                    s0[0].close();
                    s0[1].close();
                    vi.close();
                    System.out.println("Fixed val set: " + valN
                            + " examples (same for all shards)");
                }

                // -------------------------- Shard loop --------------------------
                for (Path shardPath : allShards) {
                    int shardIdx = shardNum(shardPath);
                    if (shardIdx <= lastDone) {
                        System.out.println("Skipping already processed: "
                                + shardPath.getFileName());
                        continue;
                    }
                    System.out.println("\nStarting shard: " + shardPath.getFileName());

                    // Shard-scoped native memory: freed when this manager closes
                    // (the GPU equivalent of del payload + empty_cache + gc).
                    try (NDManager shardManager = rootManager.newSubManager()) {
                        NDArray[] shard = loadShard(shardManager, shardPath);
                        NDArray ids = shard[0];
                        NDArray lbl = shard[1];

                        if (shardIdx == 0) {
                            NDArray tIdx = shardManager.create(trainIdx0);
                            ids = ids.get(tIdx);
                            lbl = lbl.get(tIdx);
                        }

                        trainOneShard(trainer, config, block, model,
                                ids, lbl, shardIdx, shardManager);

                        System.out.println("Validating...");
                        float valLoss = validate(trainer, config,
                                valIds, valLbl, shardManager);
                        System.out.printf(
                                "Shard %d — Fixed Val Loss: %.4f @ lambda=1.0000 (gstep=%d)%n",
                                shardIdx, valLoss, globalStep);

                        saveCheckpoint(model, OUTPUT_DIR,
                                "slimorca_ternary_shard_" + shardIdx);
                        System.out.println("Saved: slimorca_ternary_shard_" + shardIdx);
                    }
                }
            }
            System.out.println("Stage 2 (Ternary + SlimOrca, PyTorch engine) finished!");
        }
    }

    private void trainOneShard(Trainer trainer, TrainingConfig config, Block block,
                               Model model, NDArray ids, NDArray lbl,
                               int shardIdx, NDManager shardManager) throws IOException {
        long n = ids.getShape().get(0);
        long nBatches = n / BATCH_SIZE;
        long[] order = seededPermutation(n, VAL_SEED + shardIdx + 1);   // shuffle

        int micro = 0;
        GradientCollector gc = trainer.newGradientCollector();
        boolean windowHasGrads = false;
        try {
            for (long b = 0; b < nBatches; b++) {
                float lossVal;
                boolean dropped = false;

                // Per-batch scope: every intermediate tensor of this step is
                // freed when the sub-manager closes. On GPU this is what
                // prevents OOM creep that a shard-level manager can't stop.
                try (NDManager batchManager = shardManager.newSubManager()) {
                    long[] rows = java.util.Arrays.copyOfRange(
                            order, (int) (b * BATCH_SIZE), (int) ((b + 1) * BATCH_SIZE));
                    NDArray rowIdx = batchManager.create(rows);
                    NDArray batchIds = ids.get(rowIdx);
                    NDArray batchLbl = lbl.get(rowIdx);
                    batchIds.attach(batchManager);
                    batchLbl.attach(batchManager);

                    NDList preds = trainer.forward(new NDList(batchIds));
                    NDArray loss = config.getLossFunction()
                            .evaluate(new NDList(batchLbl), preds)
                            .div(GRAD_ACCUM);
                    lossVal = loss.getFloat();

                    if (Float.isNaN(lossVal) || Float.isInfinite(lossVal)) {
                        dropped = true;
                    } else {
                        gc.backward(loss);
                        windowHasGrads = true;
                        micro++;
                    }
                }

                if (dropped) {
                    // NaN/Inf: drop the whole accumulation window.
                    System.out.printf("%nNaN/Inf @ gstep=%d — window dropped%n", globalStep);
                    gc.close();
                    zeroGradients(block);
                    gc = trainer.newGradientCollector();
                    micro = 0;
                    windowHasGrads = false;
                    globalStep++;
                    continue;
                }

                if (micro == GRAD_ACCUM) {
                    trainer.step();   // clipped Adam update + zeroed grads
                    gc.close();
                    gc = trainer.newGradientCollector();
                    micro = 0;
                    windowHasGrads = false;

                    if (AUTOSAVE_EVERY > 0 && globalStep > 0
                            && globalStep % AUTOSAVE_EVERY < GRAD_ACCUM) {
                        saveCheckpoint(model, OUTPUT_DIR, "autosave_latest");
                        System.out.printf("autosave @ gstep=%d%n", globalStep);
                    }
                }

                if (b % 10 == 0) {
                    System.out.printf(
                            "Shard %d [%d/%d] loss=%.4f lambda=1.0000 gstep=%d%n",
                            shardIdx, b, nBatches, lossVal * GRAD_ACCUM, globalStep);
                }
                globalStep++;
            }
        } finally {
            if (windowHasGrads) {
                trainer.step();   // apply the leftover partial window
            }
            gc.close();
        }
    }

    private float validate(Trainer trainer, TrainingConfig config,
                           NDArray valIds, NDArray valLbl, NDManager shardManager) {
        long n = valIds.getShape().get(0);
        long nBatches = Math.max(1, n / BATCH_SIZE);
        double total = 0.0;
        int steps = 0;
        for (long b = 0; b < nBatches; b++) {
            try (NDManager batchManager = shardManager.newSubManager()) {
                long lo = b * BATCH_SIZE;
                long hi = Math.min(n, (b + 1) * BATCH_SIZE);
                NDArray batchIds = valIds.get(new NDIndex(lo + ":" + hi + ", :"));
                NDArray batchLbl = valLbl.get(new NDIndex(lo + ":" + hi + ", :"));
                batchIds.attach(batchManager);
                batchLbl.attach(batchManager);

                NDList preds = trainer.evaluate(new NDList(batchIds));
                float v = config.getLossFunction()
                        .evaluate(new NDList(batchLbl), preds).getFloat();
                if (Float.isFinite(v)) {
                    total += v;
                    steps++;
                }
            }
        }
        return steps > 0 ? (float) (total / steps) : Float.POSITIVE_INFINITY;
    }

    /* ------------------------------ Helpers ------------------------------ */
    private static void zeroGradients(Block block) {
        for (Pair<String, Parameter> p : block.getParameters()) {
            NDArray arr = p.getValue().getArray();
            if (arr.hasGradient()) {
                arr.getGradient().muli(0);
            }
        }
    }

    private static long[] seededPermutation(long n, long seed) {
        long[] idx = new long[(int) n];
        for (int i = 0; i < n; i++) idx[i] = i;
        Random rnd = new Random(seed);
        for (int i = (int) n - 1; i > 0; i--) {
            int j = rnd.nextInt(i + 1);
            long t = idx[i];
            idx[i] = idx[j];
            idx[j] = t;
        }
        return idx;
    }

    private static List<Path> listShards(Path dir) throws IOException {
        if (!Files.isDirectory(dir)) return List.of();
        try (Stream<Path> s = Files.list(dir)) {
            return s.filter(p -> {
                        String f = p.getFileName().toString();
                        return f.matches("slimorca_data_\\d+\\.ndlist")
                                || f.matches("slimorca_data_\\d+\\.pt");
                    })
                    .sorted(Comparator.comparingInt(GPTStage2TrainerPt::shardNum))
                    .collect(Collectors.toList());
        }
    }

    private static List<Path> listCheckpoints(Path dir) throws IOException {
        if (!Files.isDirectory(dir)) return List.of();
        try (Stream<Path> s = Files.list(dir)) {
            return s.filter(p -> p.getFileName().toString()
                            .matches("slimorca_ternary_shard_\\d+-\\d+\\.params"))
                    .sorted(Comparator.comparingInt(p -> {
                        Matcher m = CKPT_NUM.matcher(p.getFileName().toString());
                        m.find();
                        return Integer.parseInt(m.group(1));
                    }))
                    .collect(Collectors.toList());
        }
    }

    private static String stripParams(Path p) {
        String f = p.getFileName().toString();
        return f.substring(0, f.lastIndexOf('-'));   // drop "-0000.params"
    }

    /* -------------------------------- Main -------------------------------- */
    public static void main(String[] args) {
        System.out.println("java -Dai.djl.default_engine=PyTorch -cp Jirackkit.jar "
                + "com.cbsinc.cms.llm.ml.GPTStage2TrainerPt "
                + "[batch] [gradAccum] [lr] [dataDir] [outDir] [stage1Dir] [stage1Name] [--smoke]");

        if (args.length > 0) BATCH_SIZE = Integer.parseInt(args[0]);
        if (args.length > 1) GRAD_ACCUM = Integer.parseInt(args[1]);
        if (args.length > 2) LR = Float.parseFloat(args[2]);
        if (args.length > 3) DATA_DIR = Paths.get(args[3]);
        if (args.length > 4) OUTPUT_DIR = Paths.get(args[4]);
        if (args.length > 5) STAGE1_DIR = Paths.get(args[5]);
        if (args.length > 6) STAGE1_NAME = args[6];
        for (String a : args) {
            if ("--smoke".equals(a)) USE_SMOKE_CONFIG = true;
        }

        if (USE_SMOKE_CONFIG) {
            System.out.println("SMOKE MODE: tiny model config");
            JiRackJDLTernary_10b.smokeConfig();
        }

        try {
            new GPTStage2TrainerPt().train();
        } catch (Exception e) {
            System.err.println("Stage-2 training failed: " + e.getMessage());
            e.printStackTrace();
        }
    }
}