"""
MLX-LM LoRA training wrapper for the Echo Flow transcript-cleanup model.

Wraps `mlx_lm.lora` with sensible defaults for an M-series Mac and the
Qwen2.5-0.5B base model. After training, fuses the adapter into a
deployable model directory.

Usage:
    # Smoke test
    python scripts/train_mlx.py --smoke

    # Full run on the synthetic corpus
    python scripts/train_mlx.py --full
"""

import argparse
import os
import shutil
import subprocess
import sys
from pathlib import Path


SMOKE_CONFIG = {
    "data": "data/mlx_dataset_smoke",
    "adapter_path": "data/models/mlx-smoke/adapters",
    "fused_path": "data/models/mlx-smoke/fused",
    "iters": 30,
    "batch_size": 1,
    "grad_accumulation": 4,
    "learning_rate": 2e-4,
    "max_seq_length": 1024,
    "steps_per_report": 5,
    "steps_per_eval": 10,
    "save_every": 50,
    "lora_layers": 8,
    "lora_rank": 8,
}

FULL_CONFIG = {
    "data": "data/mlx_dataset",
    "adapter_path": "data/models/mumble-cleanup-v2-mlx/adapters",
    "fused_path": "data/models/mumble-cleanup-v2-mlx/fused",
    "iters": 2000,
    "batch_size": 4,
    "grad_accumulation": 4,
    "learning_rate": 2e-4,
    "max_seq_length": 1024,
    "steps_per_report": 20,
    "steps_per_eval": 100,
    "save_every": 200,
    "lora_layers": 16,
    "lora_rank": 16,
}


def run(cmd: list[str], cwd: Path):
    print(f"\n$ {' '.join(cmd)}")
    proc = subprocess.run(cmd, cwd=cwd)
    if proc.returncode != 0:
        sys.exit(proc.returncode)


def main():
    parser = argparse.ArgumentParser(description="MLX-LM training wrapper")
    parser.add_argument("--smoke", action="store_true", help="Use smoke-test config")
    parser.add_argument("--full", action="store_true", help="Use full training config")
    parser.add_argument("--model", type=str, default="Qwen/Qwen2.5-0.5B-Instruct")
    parser.add_argument("--resume-adapter-file", type=str, default=None)
    args = parser.parse_args()

    if not args.smoke and not args.full:
        print("Choose --smoke or --full")
        sys.exit(1)

    cfg = SMOKE_CONFIG if args.smoke else FULL_CONFIG

    here = Path(__file__).resolve().parent.parent
    data_dir = here / cfg["data"]
    adapter_path = here / cfg["adapter_path"]
    fused_path = here / cfg["fused_path"]

    if not data_dir.exists():
        print(f"Data dir not found: {data_dir}")
        print("Run scripts/prepare_mlx_data.py first.")
        sys.exit(1)

    adapter_path.mkdir(parents=True, exist_ok=True)

    lora_cmd = [
        sys.executable, "-m", "mlx_lm", "lora",
        "--model", args.model,
        "--train",
        "--data", str(data_dir),
        "--fine-tune-type", "lora",
        "--batch-size", str(cfg["batch_size"]),
        "--grad-accumulation-steps", str(cfg["grad_accumulation"]),
        "--iters", str(cfg["iters"]),
        "--learning-rate", str(cfg["learning_rate"]),
        "--steps-per-report", str(cfg["steps_per_report"]),
        "--steps-per-eval", str(cfg["steps_per_eval"]),
        "--save-every", str(cfg["save_every"]),
        "--adapter-path", str(adapter_path),
        "--max-seq-length", str(cfg["max_seq_length"]),
        "--num-layers", str(cfg["lora_layers"]),
        "--seed", "42",
        "--mask-prompt",
    ]
    if args.resume_adapter_file:
        lora_cmd.extend(["--resume-adapter-file", args.resume_adapter_file])

    run(lora_cmd, cwd=here)

    fuse_cmd = [
        sys.executable, "-m", "mlx_lm", "fuse",
        "--model", args.model,
        "--adapter-path", str(adapter_path),
        "--save-path", str(fused_path),
        "--dequantize",
    ]
    run(fuse_cmd, cwd=here)

    print("\n=== Training complete ===")
    print(f"Adapter: {adapter_path}")
    print(f"Fused:   {fused_path}")
    print("\nNext steps:")
    print(f"  python scripts/convert_to_gguf.py --model {fused_path} \\")
    print(f"      --outfile data/models/mumble-cleanup-v2-q4km.gguf --quant Q4_K_M")


if __name__ == "__main__":
    main()