"""
SID-GPT v2 training script.

nanoGPT-style training loop with frame-aligned batch sampling,
cosine LR schedule, gradient accumulation, and AMP support.
Supports single-GPU and multi-GPU (DDP via torchrun).
"""

import argparse
import math
import os
import struct
import time
from contextlib import nullcontext

import numpy as np
import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP

from model import ModelConfig, Transformer

TOKEN_SEP = 256
TOKEN_FRAME = 257
TOKENS_PER_FRAME = 26
BYTES_PER_FRAME = 25


def setup_ddp():
    """
    Auto-detect DDP: torchrun sets RANK/LOCAL_RANK env vars.
    Returns (rank, local_rank, world_size, is_ddp).
    Without torchrun, returns (0, 0, 1, False).
    """
    if "RANK" not in os.environ:
        return 0, 0, 1, False
    rank = int(os.environ["RANK"])
    local_rank = int(os.environ["LOCAL_RANK"])
    world_size = int(os.environ["WORLD_SIZE"])
    torch.cuda.set_device(local_rank)
    dist.init_process_group(backend="nccl")
    return rank, local_rank, world_size, True


def get_device(requested: str) -> str:
    if requested != "auto":
        return requested
    if torch.cuda.is_available():
        return "cuda"
    if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
        return "mps"
    return "cpu"


def get_dtype(requested: str, device: str) -> torch.dtype:
    if requested == "bfloat16":
        if device == "cuda" and torch.cuda.is_bf16_supported():
            return torch.bfloat16
        print("[WARN] bfloat16 not supported, falling back to float16")
        return torch.float16
    if requested == "float16":
        return torch.float16
    return torch.float32


def load_data(path: str, device: str) -> torch.Tensor:
    raw = np.fromfile(path, dtype=np.uint16)
    print(f"[DATA] Loaded {len(raw)} tokens from {path}")
    return torch.from_numpy(raw.astype(np.int64)).to(device)


def generate_synth_data(device: str) -> torch.Tensor:
    """
    Generate synthetic training data: ~20 short songs with
    deterministic patterns (ascending frequencies, simple ADSR)
    for end-to-end pipeline testing without HVSC data.
    """
    tokens = []
    rng = np.random.RandomState(42)

    for song_idx in range(20):
        # SEP frame
        tokens.extend([TOKEN_SEP] * TOKENS_PER_FRAME)

        num_frames = 80 + song_idx * 5
        base_freq = 1000 + song_idx * 200

        for f in range(num_frames):
            tokens.append(TOKEN_FRAME)
            regs = [0] * BYTES_PER_FRAME

            # Voice 1: ascending frequency
            freq = (base_freq + f * 50) & 0xFFFF
            regs[0] = freq & 0xFF
            regs[1] = (freq >> 8) & 0xFF
            # Pulse width
            regs[2] = 0x00
            regs[3] = 0x08
            # Control: gate on, triangle
            regs[4] = 0x11 if f < num_frames - 5 else 0x10
            # ADSR
            regs[5] = 0x09
            regs[6] = 0x00

            # Voice 2: harmony (offset frequency)
            freq2 = (base_freq + f * 37 + 500) & 0xFFFF
            regs[7] = freq2 & 0xFF
            regs[8] = (freq2 >> 8) & 0xFF
            regs[9] = 0x00
            regs[10] = 0x08
            regs[11] = 0x21 if f % 16 < 12 else 0x20
            regs[12] = 0x0A
            regs[13] = 0x00

            # Voice 3: bass (slow frequency)
            freq3 = (base_freq // 2 + f * 10) & 0xFFFF
            regs[14] = freq3 & 0xFF
            regs[15] = (freq3 >> 8) & 0xFF
            regs[16] = 0x00
            regs[17] = 0x04
            regs[18] = 0x41 if f % 32 < 24 else 0x40
            regs[19] = 0x0C
            regs[20] = 0x00

            # Filter + volume
            regs[21] = 0x00
            regs[22] = rng.randint(0, 8)
            regs[23] = 0x00
            regs[24] = 0x0F

            tokens.extend(regs)

    data = np.array(tokens, dtype=np.uint16)
    print(f"[SYNTH] Generated {len(data)} tokens ({20} songs)")
    return torch.from_numpy(data.astype(np.int64)).to(device)


def split_data(data, block_size):
    """Split at frame-aligned boundary (multiple of 26)."""
    n = len(data)
    split_tok = int(n * 0.95)
    # Align to frame boundary
    split_tok = (split_tok // TOKENS_PER_FRAME) * TOKENS_PER_FRAME
    return data[:split_tok], data[split_tok:]


def get_batch(data, block_size, batch_size, device):
    """
    Frame-aligned batch sampling. Offsets are multiples of 26
    so sequences always start on frame boundaries.
    """
    max_start = (len(data) - block_size - 1) // TOKENS_PER_FRAME
    if max_start < 1:
        max_start = 1
    offsets = torch.randint(max_start, (batch_size,)) * TOKENS_PER_FRAME
    x = torch.stack([data[o : o + block_size] for o in offsets])
    y = torch.stack(
        [data[o + 1 : o + 1 + block_size] for o in offsets]
    )
    return x.to(device), y.to(device)


@torch.no_grad()
def estimate_loss(
    model, train_data, val_data, config, args, device,
):
    model.eval()
    out = {}
    for name, data in [("train", train_data), ("val", val_data)]:
        losses = []
        for _ in range(args.eval_iters):
            x, y = get_batch(
                data, config.block_size,
                args.batch_size, device,
            )
            with torch.amp.autocast(
                device_type=device.split(":")[0],
                dtype=args.amp_dtype,
            ):
                _, loss = model(x, y)
            losses.append(loss.item())
        out[name] = sum(losses) / len(losses)
    model.train()
    return out


def get_lr(step, args):
    """
    Cosine LR schedule with linear warmup.
    Decays from lr to min_lr over max_steps.
    """
    if step < args.warmup:
        return args.lr * (step + 1) / args.warmup
    if step >= args.max_steps:
        return args.min_lr
    progress = (step - args.warmup) / (args.max_steps - args.warmup)
    coeff = 0.5 * (1.0 + math.cos(math.pi * progress))
    return args.min_lr + coeff * (args.lr - args.min_lr)


def configure_optimizer(model, args, device):
    # Separate params: decay 2D+ params, no decay for 1D (norms, biases)
    decay_params = []
    no_decay_params = []
    for name, p in model.named_parameters():
        if not p.requires_grad:
            continue
        if p.dim() >= 2:
            decay_params.append(p)
        else:
            no_decay_params.append(p)

    groups = [
        {"params": decay_params, "weight_decay": args.weight_decay},
        {"params": no_decay_params, "weight_decay": 0.0},
    ]

    use_fused = device.startswith("cuda")
    optimizer = torch.optim.AdamW(
        groups,
        lr=args.lr,
        betas=(args.beta1, args.beta2),
        fused=use_fused,
    )
    return optimizer


def save_checkpoint(model, optimizer, config, step, path):
    torch.save(
        {
            "model": model.state_dict(),
            "optimizer": optimizer.state_dict(),
            "config": config,
            "step": step,
        },
        path,
    )
    print(f"[CKPT] Saved {path}")


def main():
    parser = argparse.ArgumentParser(
        description="SID-GPT v2 training"
    )
    parser.add_argument("--data", type=str, default=None)
    parser.add_argument(
        "--config", type=str, default="small",
        choices=["small", "large"],
    )
    parser.add_argument("--batch-size", type=int, default=8)
    parser.add_argument("--grad-accum", type=int, default=4)
    parser.add_argument("--max-steps", type=int, default=5000)
    parser.add_argument("--lr", type=float, default=3e-4)
    parser.add_argument("--min-lr", type=float, default=3e-5)
    parser.add_argument("--warmup", type=int, default=200)
    parser.add_argument("--weight-decay", type=float, default=0.1)
    parser.add_argument("--beta1", type=float, default=0.9)
    parser.add_argument("--beta2", type=float, default=0.95)
    parser.add_argument("--eval-interval", type=int, default=250)
    parser.add_argument("--eval-iters", type=int, default=50)
    parser.add_argument("--log-interval", type=int, default=10)
    parser.add_argument(
        "--out-dir", type=str, default="training/checkpoints"
    )
    parser.add_argument("--device", type=str, default="auto")
    parser.add_argument(
        "--dtype", type=str, default="bfloat16",
        choices=["bfloat16", "float16", "float32"],
    )
    parser.add_argument("--compile", action="store_true")
    parser.add_argument("--seed", type=int, default=1337)
    parser.add_argument("--synth", action="store_true")
    parser.add_argument("--resume", type=str, default=None)
    args = parser.parse_args()

    if not args.synth and args.data is None and args.resume is None:
        parser.error("--data or --synth or --resume required")

    # Enable experimental Flash Attention on ROCm
    os.environ["TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL"] = "1"

    # DDP setup (auto-detect torchrun)
    rank, local_rank, world_size, is_ddp = setup_ddp()
    is_master = rank == 0

    if is_ddp:
        device = f"cuda:{local_rank}"
        device_type = "cuda"
    else:
        device = get_device(args.device)
        device_type = device.split(":")[0]

    torch.manual_seed(args.seed + rank)
    args.amp_dtype = get_dtype(args.dtype, device)

    if is_master:
        if is_ddp:
            print(
                f"[INIT] DDP: {world_size} GPUs, "
                f"dtype: {args.amp_dtype}"
            )
        else:
            print(
                f"[INIT] Device: {device}, "
                f"dtype: {args.amp_dtype}"
            )

    # Model config
    if args.config == "large":
        config = ModelConfig.large()
    else:
        config = ModelConfig.small()

    start_step = 0

    if args.resume:
        if is_master:
            print(f"[RESUME] Loading checkpoint {args.resume}")
        ckpt = torch.load(
            args.resume, map_location=device,
            weights_only=False,
        )
        config = ckpt["config"]
        model = Transformer(config).to(device)
        model.load_state_dict(ckpt["model"])
        start_step = ckpt["step"]
        if is_master:
            print(f"[RESUME] Resuming from step {start_step}")
    else:
        model = Transformer(config).to(device)

    if is_master:
        print(
            f"[MODEL] {args.config}: "
            f"{model.count_params():,} params, "
            f"{config.n_layer}L/{config.n_head}H/"
            f"{config.n_embd}D"
        )

    if args.compile and device_type == "cuda":
        if is_master:
            print("[COMPILE] torch.compile enabled")
        model = torch.compile(model)

    # Wrap in DDP after compile
    if is_ddp:
        model = DDP(model, device_ids=[local_rank])
    raw_model = model.module if is_ddp else model

    # Data
    if args.synth:
        data = generate_synth_data(device)
    else:
        data = load_data(args.data, device)

    train_data, val_data = split_data(data, config.block_size)
    if is_master:
        print(
            f"[DATA] Train: {len(train_data):,} tokens, "
            f"Val: {len(val_data):,} tokens"
        )

    # Optimizer (on raw model params)
    optimizer = configure_optimizer(raw_model, args, device)

    if args.resume and "optimizer" in ckpt:
        optimizer.load_state_dict(ckpt["optimizer"])

    # GradScaler only for float16
    use_scaler = args.amp_dtype == torch.float16
    scaler = torch.amp.GradScaler(enabled=use_scaler)

    if is_master:
        os.makedirs(args.out_dir, exist_ok=True)

    # Training loop
    model.train()
    t0 = time.time()

    for step in range(start_step, args.max_steps):
        lr = get_lr(step, args)
        for pg in optimizer.param_groups:
            pg["lr"] = lr

        # Eval (rank 0 only)
        if (
            step % args.eval_interval == 0
            and step > 0
            and is_master
        ):
            losses = estimate_loss(
                model, train_data, val_data,
                config, args, device,
            )
            print(
                f"[EVAL] step {step}: "
                f"train={losses['train']:.4f}, "
                f"val={losses['val']:.4f}"
            )
            save_checkpoint(
                raw_model, optimizer, config, step,
                os.path.join(
                    args.out_dir, f"ckpt_{step}.pt"
                ),
            )

        # Gradient accumulation
        optimizer.zero_grad(set_to_none=True)
        accum_loss = 0.0

        for micro in range(args.grad_accum):
            x, y = get_batch(
                train_data, config.block_size,
                args.batch_size, device,
            )
            with torch.amp.autocast(
                device_type=device_type, dtype=args.amp_dtype
            ):
                _, loss = model(x, y)
                loss = loss / args.grad_accum

            accum_loss += loss.item()
            scaler.scale(loss).backward()

        scaler.unscale_(optimizer)
        torch.nn.utils.clip_grad_norm_(
            model.parameters(), 1.0
        )
        scaler.step(optimizer)
        scaler.update()

        # Logging (rank 0 only)
        if step % args.log_interval == 0 and is_master:
            dt = time.time() - t0
            t0 = time.time()
            if dt > 0 and step > start_step:
                ms_per_step = (
                    dt / args.log_interval * 1000
                )
                tps = (
                    args.batch_size * args.grad_accum
                    * config.block_size
                    * args.log_interval
                    * world_size / dt
                )
            else:
                ms_per_step = 0
                tps = 0
            print(
                f"[TRAIN] step {step:5d} | "
                f"loss {accum_loss:.4f} | "
                f"lr {lr:.2e} | "
                f"{ms_per_step:.0f}ms/step | "
                f"{dt:.2f}s/{args.log_interval}steps | "
                f"{tps/1e6:.2f}M tok/s"
            )

    # Final save (rank 0 only)
    if is_master:
        save_checkpoint(
            raw_model, optimizer, config, args.max_steps,
            os.path.join(
                args.out_dir, f"ckpt_{args.max_steps}.pt"
            ),
        )

        losses = estimate_loss(
            model, train_data, val_data,
            config, args, device,
        )
        print(
            f"[DONE] Final: train={losses['train']:.4f}, "
            f"val={losses['val']:.4f}"
        )

    if is_ddp:
        dist.destroy_process_group()


if __name__ == "__main__":
    main()