"""Commands do not run evaluations unless `evaluate` is explicitly invoked."""
from __future__ import annotations

import argparse
import json
from pathlib import Path


def main():
    parser = argparse.ArgumentParser(prog="clc-mini-bench", description="CLC NKp46 benchmark preparation and frozen-feature evaluation")
    sub = parser.add_subparsers(dest="command", required=True)
    build = sub.add_parser("build", help="Prepare annotations, deduplicated datasets, and family folds")
    build.add_argument("--raw", type=Path, default=Path("data/raw/measurements.csv"))
    build.add_argument("--output", type=Path, default=Path("data/processed"))
    build.add_argument("--protocol", type=Path)
    check = sub.add_parser("validate", help="Validate both prepared datasets and file checksums")
    check.add_argument("--data-dir", type=Path, default=Path("data/processed"))
    embeddings = sub.add_parser("embed", help="Extract frozen embeddings with ESM-2 or a custom adapter")
    embeddings.add_argument("--dataset", type=Path, required=True)
    embeddings.add_argument("--output", type=Path, required=True)
    embeddings.add_argument("--adapter", help="Python module:function receiving label-free sequence records")
    embeddings.add_argument("--model", default="facebook/esm2_t33_650M_UR50D")
    embeddings.add_argument("--revision", help="Model repository revision or commit")
    embeddings.add_argument("--batch-size", type=int, default=16)
    embeddings.add_argument("--device", default="cpu")
    evaluate = sub.add_parser("evaluate", help="Run grouped outer CV with nested Ridge model selection")
    evaluate.add_argument("--dataset", type=Path, required=True)
    feature_source = evaluate.add_mutually_exclusive_group(required=True)
    feature_source.add_argument("--features", type=Path)
    feature_source.add_argument("--composition-baseline", action="store_true")
    evaluate.add_argument("--output", type=Path, required=True)
    evaluate.add_argument("--protocol", type=Path)
    args = parser.parse_args()
    if args.command == "build":
        from .data import build as run
        result = run(args.raw, args.output, args.protocol)
        print(json.dumps(result["datasets"], indent=2))
    elif args.command == "validate":
        import pandas as pd
        from .data import sha256_file, validate_dataset
        manifest = json.loads((args.data_dir / "manifest.json").read_text())
        for name, expected in manifest["files"].items():
            if sha256_file(args.data_dir / name) != expected:
                raise ValueError(f"File checksum mismatch: {name}")
        for platform in ("omniflic", "omniclic"):
            frame = pd.read_csv(args.data_dir / f"{platform}_nkp46.csv")
            validate_dataset(frame)
            print(f"{platform}: {len(frame)} samples, {frame.family_id.nunique()} families, {frame.fold_id.nunique()} folds; valid")
    elif args.command == "embed":
        from .embeddings import extract_embeddings
        result = extract_embeddings(args.dataset, args.output, adapter=args.adapter,
            model_name=args.model, batch_size=args.batch_size, device=args.device, revision=args.revision)
        print(json.dumps(result, indent=2))
    elif args.command == "evaluate":
        from .evaluation import evaluate as run
        result = run(args.dataset, args.output, features=args.features,
            baseline=args.composition_baseline, protocol_path=args.protocol)
        print(json.dumps(result["pooled_out_of_fold"], indent=2))


if __name__ == "__main__":
    main()