#!/usr/bin/env python3
"""Validate generated proxy videos and write mechanics/visual evaluator manifests."""
from __future__ import annotations

import argparse
import csv
import hashlib
import json
import os
from pathlib import Path


ROOT = Path(__file__).resolve().parents[1]


def sha256_file(path: Path) -> str:
    digest = hashlib.sha256()
    with path.open("rb") as handle:
        for chunk in iter(lambda: handle.read(16 * 1024 * 1024), b""):
            digest.update(chunk)
    return digest.hexdigest()


def main() -> None:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--selection", type=Path, default=ROOT / "evaluation/proxy100/selection.json")
    parser.add_argument("--run-name", required=True)
    parser.add_argument("--checkpoint", type=Path, required=True)
    parser.add_argument("--generated-root", type=Path, default=ROOT / "evaluation/proxy100/generated")
    args = parser.parse_args()

    selection = json.loads(args.selection.resolve().read_text(encoding="utf-8"))
    output_dir = (args.generated_root / args.run_name).resolve()
    mechanics_rows = []
    visual_rows = []
    missing = []
    for sample in selection["samples"]:
        prediction = output_dir / sample["output_name"]
        if not prediction.is_file() or prediction.stat().st_size == 0:
            missing.append(str(prediction))
            continue
        relative_prediction = prediction.relative_to(args.selection.resolve().parent).as_posix()
        mechanics_rows.append({"id": sample["id"], "prediction": relative_prediction, "label": sample["label"]})
        if sample.get("reference_video"):
            reference = ROOT / sample["reference_video"]
            visual_rows.append(
                {
                    "id": sample["id"],
                    "prediction": relative_prediction,
                    "reference": os.path.relpath(reference, args.selection.resolve().parent),
                    "label": sample["label"],
                }
            )
    if missing:
        raise FileNotFoundError(f"Missing {len(missing)} generated videos; first: {missing[0]}")

    mechanics_path = args.selection.resolve().parent / f"manifest_{args.run_name}_mechanics.csv"
    with mechanics_path.open("w", newline="", encoding="utf-8") as handle:
        writer = csv.DictWriter(handle, fieldnames=("id", "prediction", "label"))
        writer.writeheader(); writer.writerows(mechanics_rows)
    visual_path = args.selection.resolve().parent / f"manifest_{args.run_name}_visual.csv"
    with visual_path.open("w", newline="", encoding="utf-8") as handle:
        writer = csv.DictWriter(handle, fieldnames=("id", "prediction", "reference", "label"))
        writer.writeheader(); writer.writerows(visual_rows)

    provenance = {
        "run_name": args.run_name,
        "selection": str(args.selection.resolve().relative_to(ROOT)),
        "checkpoint": str(args.checkpoint.resolve().relative_to(ROOT)),
        "checkpoint_sha256": sha256_file(args.checkpoint.resolve()),
        "mechanics_samples": len(mechanics_rows),
        "visual_samples": len(visual_rows),
        "mechanics_manifest": str(mechanics_path.relative_to(ROOT)),
        "visual_manifest": str(visual_path.relative_to(ROOT)),
    }
    (args.selection.resolve().parent / f"provenance_{args.run_name}.json").write_text(
        json.dumps(provenance, indent=2) + "\n", encoding="utf-8"
    )
    print(json.dumps(provenance, indent=2))


if __name__ == "__main__":
    main()