from __future__ import annotations

import json
import subprocess
from pathlib import Path


ROOT = Path(__file__).resolve().parents[2]


def write_hard_negative_summary(
    path: Path,
    *,
    category: str,
    detection_counts: list[int],
) -> None:
    items = [
        {
            "sample_slug": f"{category}_{index}",
            "background_category": category,
            "model_asset_id": "candidate-context-sensitive",
            "tile_size": 512,
            "tile_overlap": 64,
            "threshold": 0.35,
            "tile_count": 4,
            "detection_count": detection_count,
            "false_positive_pressure": detection_count / 4,
        }
        for index, detection_count in enumerate(detection_counts, start=1)
    ]
    path.write_text(
        json.dumps(
            {
                "generated_at": "2026-07-10T00:00:00+00:00",
                "background_category_counts": {category: len(items)},
                "items": items,
            }
        ),
        encoding="utf-8",
    )


def test_promotion_report_uses_split_pure_empty_as_gate_and_sparse_context_as_review(
    tmp_path: Path,
) -> None:
    script_path = ROOT / "scripts" / "build_detection_model_promotion_report.py"
    positive_path = tmp_path / "positive_portfolio.json"
    pure_summary_path = tmp_path / "pure_empty_summary.json"
    sparse_summary_path = tmp_path / "sparse_context_summary.json"
    split_summary_path = tmp_path / "background_corpus_split_summary.json"
    output_dir = tmp_path / "promotion-report"

    positive_path.write_text(
        json.dumps(
            {
                "items": [
                    {
                        "sample_slug": "geel",
                        "model_asset_id": "candidate-context-sensitive",
                        "tile_size": 512,
                        "tile_overlap": 64,
                        "threshold": 0.35,
                        "precision": 0.72,
                        "recall": 0.5,
                        "f1_score": 0.59,
                    },
                    {
                        "sample_slug": "mol",
                        "model_asset_id": "candidate-context-sensitive",
                        "tile_size": 512,
                        "tile_overlap": 64,
                        "threshold": 0.35,
                        "precision": 0.68,
                        "recall": 0.48,
                        "f1_score": 0.56,
                    },
                ]
            }
        ),
        encoding="utf-8",
    )
    write_hard_negative_summary(
        pure_summary_path,
        category="pure_empty_negative",
        detection_counts=[0, 0],
    )
    write_hard_negative_summary(
        sparse_summary_path,
        category="sparse_building_context",
        detection_counts=[4, 7],
    )
    split_summary_path.write_text(
        json.dumps(
            {
                "schema_version": 1,
                "source_summaries": {
                    "pure_empty_negative": str(pure_summary_path),
                    "sparse_building_context": str(sparse_summary_path),
                },
                "strict_default_gate": {
                    "category": "pure_empty_negative",
                    "review_only": False,
                    "sample_count": 2,
                    "run_count": 2,
                    "total_detection_count": 0,
                    "max_detection_count": 0,
                    "passes_zero_detection_gate": True,
                },
                "context_review": {
                    "category": "sparse_building_context",
                    "review_only": True,
                    "sample_count": 2,
                    "run_count": 2,
                    "total_detection_count": 11,
                    "max_detection_count": 7,
                },
            }
        ),
        encoding="utf-8",
    )

    result = subprocess.run(
        [
            "python",
            str(script_path),
            "--positive-portfolio",
            str(positive_path),
            "--background-split-summary",
            str(split_summary_path),
            "--output-dir",
            str(output_dir),
            "--min-positive-samples",
            "2",
            "--min-background-samples",
            "2",
            "--min-mean-f1",
            "0.5",
            "--max-background-detections-per-sample",
            "0",
        ],
        cwd=ROOT,
        check=True,
        text=True,
        capture_output=True,
    )

    assert "Detection model promotion report passed" in result.stdout
    report = json.loads((output_dir / "detection_model_promotion_report.json").read_text(encoding="utf-8"))
    decision = report["candidate_decisions"][0]

    assert report["hard_negative_summary_paths"] == [str(pure_summary_path)]
    assert report["background_split_summary_paths"] == [str(split_summary_path)]
    assert report["background_context_reviews"] == [
        {
            "source_split_summary_path": str(split_summary_path),
            "source_summary_path": str(sparse_summary_path),
            "category": "sparse_building_context",
            "review_only": True,
            "sample_count": 2,
            "run_count": 2,
            "total_detection_count": 11,
            "max_detection_count": 7,
        }
    ]
    assert decision["candidate_key"] == "candidate-context-sensitive|512|64|0.35"
    assert decision["background_sample_count"] == 2
    assert decision["max_background_detections"] == 0
    assert decision["promotion_status"] == "promote_candidate"
    assert report["recommended_candidate"]["candidate_key"] == decision["candidate_key"]

    markdown = (output_dir / "detection_model_promotion_report.md").read_text(encoding="utf-8")
    assert "Background split summaries: 1" in markdown
    assert "Sparse-context review evidence" in markdown
    assert "not used as a default-promotion gate" in markdown