#!/usr/bin/env python3
"""Cold-start top-1 fixture reproduction using only NumPy and packaged files."""

from __future__ import annotations

import argparse
import csv
import json
from pathlib import Path

import numpy as np


def read_tsv(path: Path) -> list[dict[str, str]]:
    with path.open("r", encoding="utf-8", newline="") as handle:
        return list(csv.DictReader(handle, delimiter="\t"))


def deterministic_top1(scores: np.ndarray, labels: list[str]) -> int:
    maximum = float(scores.max())
    tied = np.flatnonzero(scores == maximum).tolist()
    return min(tied, key=lambda index: labels[index])


def main() -> int:
    parser = argparse.ArgumentParser()
    parser.add_argument("kernel", type=Path)
    parser.add_argument("--out", type=Path)
    args = parser.parse_args()
    root = args.kernel.resolve()
    queries = np.load(root / "e0-query-features-fp16.npy", mmap_mode="r", allow_pickle=False)
    prototypes = np.load(root / "final-species-centroids-f64.npy", mmap_mode="r", allow_pickle=False)
    species_rows = read_tsv(root / "species-prototype-map.tsv")
    fixtures = read_tsv(root / "minimal-reproduction-fixtures.tsv")
    labels = [row["canonical_taxon_key"] for row in species_rows]
    if queries.shape != (6719, 1024) or prototypes.shape != (19144, 1024):
        raise ValueError("kernel array shape mismatch")
    results = []
    for fixture in fixtures:
        query = np.asarray(queries[int(fixture["query_tensor_row"])], dtype=np.float64)
        scores = query @ prototypes.T
        top1_index = deterministic_top1(scores, labels)
        prediction = labels[top1_index]
        observed_hit = int(prediction == fixture["evaluation_label_key"])
        expected_hit = int(fixture["expected_top1_hit"])
        results.append(
            {
                "fixture_order": int(fixture["fixture_order"]),
                "query_id": fixture["query_id"],
                "evaluation_label_key": fixture["evaluation_label_key"],
                "predicted_top1_label_key": prediction,
                "expected_top1_hit": expected_hit,
                "observed_top1_hit": observed_hit,
                "status": "PASS" if observed_hit == expected_hit else "FAIL",
            }
        )
    passed = sum(row["status"] == "PASS" for row in results)
    summary = {
        "status": "PASS" if passed == len(results) else "FAIL",
        "implementation": "NumPy only; no network, pixels, PyTorch, or absolute source paths",
        "fixtures": len(results),
        "passed": passed,
        "failed": len(results) - passed,
        "results": results,
    }
    text = json.dumps(summary, indent=2, sort_keys=True) + "\n"
    if args.out:
        args.out.write_text(text, encoding="utf-8")
    print(text, end="")
    return 0 if summary["status"] == "PASS" else 1


if __name__ == "__main__":
    raise SystemExit(main())