"""Tokenize .chord files into .pt tensors for model training.

Usage:
    python scripts/prepare_data.py --input-dir data/raw_user \\
        --output-dir data/processed [--split-ratios 0.9/0.1] [--seed 42]

Arguments:
    --input-dir     Root directory to search recursively for .chord files.
    --output-dir    Output directory. Subdirs train/, val/, holdout/ are created.
    --split-ratios  Train/val ratio as "TRAIN/VAL", e.g. "0.8/0.2". Default: 0.9/0.1.
    --seed          Random seed for reproducible shuffling. Default: 42.
    --log-level     Logging verbosity. Default: INFO.

Files found under any "holdout" directory within --input-dir are written to
<output-dir>/holdout/ and never participate in the train/val split.
"""

from __future__ import annotations

import argparse
import logging
import random
import sys
from collections import Counter
from pathlib import Path

import torch

# Allow running as a script from the project root without installing the package.
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))

from src.tokenizer import parse_chord_file, tokenize_period  # noqa: E402

log = logging.getLogger(__name__)


# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------


def _is_holdout(path: Path, input_dir: Path) -> bool:
    """True when the path lives under a 'holdout' sub-directory of input_dir."""
    try:
        rel = path.relative_to(input_dir)
    except ValueError:
        return False
    return "holdout" in rel.parts


def _parse_ratios(s: str) -> tuple[float, float]:
    parts = s.split("/")
    if len(parts) != 2:
        raise argparse.ArgumentTypeError(
            f"split-ratios must be TRAIN/VAL format, got {s!r}"
        )
    try:
        train_r, val_r = float(parts[0]), float(parts[1])
    except ValueError:
        raise argparse.ArgumentTypeError(
            f"split-ratios values must be floats, got {s!r}"
        )
    total = train_r + val_r
    if abs(total - 1.0) > 1e-6:
        raise argparse.ArgumentTypeError(
            f"split-ratios must sum to 1.0, got {train_r}+{val_r}={total:.6f}"
        )
    return train_r, val_r


def _process_file(path: Path) -> dict | None:
    """Parse and tokenize one .chord file. Returns None on any error."""
    try:
        period = parse_chord_file(path)
        ids = tokenize_period(period)
        tokens = torch.tensor(ids, dtype=torch.long)
        meta = {
            "title": period.title,
            "key": period.key,
            "style": period.style,
            "function": period.function,
            "time": period.time,
            "source_file": str(path),
            "n_tokens": len(ids),
        }
        return {"tokens": tokens, "meta": meta}
    except Exception as exc:
        log.warning("Skipping %s: %s", path, exc)
        return None


def _save(data: dict, out_dir: Path, stem: str) -> None:
    out_path = out_dir / f"{stem}.pt"
    if out_path.exists():
        log.warning("Overwriting existing output file: %s", out_path)
    torch.save(data, out_path)


# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------


def main(argv: list[str] | None = None) -> None:
    parser = argparse.ArgumentParser(
        description="Tokenize .chord files into .pt tensors for model training.",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog=__doc__,
    )
    parser.add_argument(
        "--input-dir", required=True, type=Path,
        help="Root directory containing .chord files (searched recursively).",
    )
    parser.add_argument(
        "--output-dir", required=True, type=Path,
        help="Output directory; train/, val/, holdout/ subdirs are created.",
    )
    parser.add_argument(
        "--split-ratios", default="0.9/0.1",
        help="Train/val split, e.g. '0.8/0.2'. Must sum to 1.0. Default: 0.9/0.1.",
    )
    parser.add_argument(
        "--seed", type=int, default=42,
        help="Random seed for reproducible shuffling. Default: 42.",
    )
    parser.add_argument(
        "--log-level", default="INFO",
        choices=["DEBUG", "INFO", "WARNING", "ERROR"],
        help="Logging verbosity. Default: INFO.",
    )
    args = parser.parse_args(argv)

    logging.basicConfig(level=getattr(logging, args.log_level), format="%(levelname)s %(message)s")

    train_ratio, _val_ratio = _parse_ratios(args.split_ratios)

    input_dir: Path = args.input_dir.resolve()
    output_dir: Path = args.output_dir.resolve()

    if not input_dir.exists():
        log.error("Input directory does not exist: %s", input_dir)
        sys.exit(1)

    for subdir in ("train", "val", "holdout"):
        (output_dir / subdir).mkdir(parents=True, exist_ok=True)

    all_files = sorted(input_dir.rglob("*.chord"))
    if not all_files:
        log.warning("No .chord files found in %s", input_dir)
        return

    holdout_files = [f for f in all_files if _is_holdout(f, input_dir)]
    regular_files = [f for f in all_files if not _is_holdout(f, input_dir)]

    log.info(
        "Found %d .chord files total (%d holdout, %d regular)",
        len(all_files), len(holdout_files), len(regular_files),
    )

    # --- Holdout ---
    holdout_records: list[dict] = []
    for path in holdout_files:
        data = _process_file(path)
        if data is not None:
            holdout_records.append(data)
            _save(data, output_dir / "holdout", path.stem)

    # --- Train / val split ---
    random.seed(args.seed)
    shuffled = list(regular_files)
    random.shuffle(shuffled)

    n_train = round(len(shuffled) * train_ratio)
    train_paths = shuffled[:n_train]
    val_paths = shuffled[n_train:]

    train_records: list[dict] = []
    for path in train_paths:
        data = _process_file(path)
        if data is not None:
            train_records.append(data)
            _save(data, output_dir / "train", path.stem)

    val_records: list[dict] = []
    for path in val_paths:
        data = _process_file(path)
        if data is not None:
            val_records.append(data)
            _save(data, output_dir / "val", path.stem)

    # --- Stats ---
    all_records = train_records + val_records + holdout_records
    if not all_records:
        log.warning("No files were successfully processed.")
        return

    token_lengths = [r["meta"]["n_tokens"] for r in all_records]
    style_counts: Counter[str] = Counter(r["meta"]["style"] for r in all_records)
    function_counts: Counter[str] = Counter(r["meta"]["function"] for r in all_records)

    log.info("--- Processing summary ---")
    log.info("Total processed: %d  (train=%d, val=%d, holdout=%d)",
             len(all_records), len(train_records), len(val_records), len(holdout_records))

    skipped = len(all_files) - len(all_records)
    if skipped:
        log.warning("Skipped due to errors: %d", skipped)

    log.info("Token lengths: mean=%.1f, max=%d",
             sum(token_lengths) / len(token_lengths), max(token_lengths))

    log.info("Style distribution:")
    for style, count in sorted(style_counts.items()):
        log.info("  %-16s %d", style, count)

    log.info("Function distribution:")
    for func, count in sorted(function_counts.items()):
        log.info("  %-16s %d", func, count)


if __name__ == "__main__":
    main()