from __future__ import annotations

import importlib.util
from pathlib import Path


SCRIPT = Path(__file__).parents[2] / "scripts" / "build_failure_driven_yolo_sampling.py"
SPEC = importlib.util.spec_from_file_location("failure_sampling", SCRIPT)
assert SPEC and SPEC.loader
MODULE = importlib.util.module_from_spec(SPEC)
SPEC.loader.exec_module(MODULE)


def test_dataset_validation_source_preserves_manifest_path(tmp_path: Path):
    source = tmp_path / "dataset.yaml"
    source.write_text(
        "path: /data/source\ntrain: /data/source/train.txt\n"
        "val: /data/source/internal-val.txt\nnames:\n  0: building\n",
        encoding="utf-8",
    )

    assert MODULE.dataset_validation_source(source) == "/data/source/internal-val.txt"


def test_sampling_repeats_only_failed_region_train_tiles() -> None:
    manifest = {
        "samples": [
            {"sample_slug": "train-fl", "split": "train", "region": "flanders"},
            {"sample_slug": "train-wa", "split": "train", "region": "wallonia"},
            {"sample_slug": "test-fl", "split": "test", "region": "flanders"},
        ]
    }
    summary = {
        "tiles": [
            {"sample_slug": "train-fl", "split": "train", "label_count": 2, "image_path": "/tmp/fl-pos.png"},
            {"sample_slug": "train-fl", "split": "train", "label_count": 0, "image_path": "/tmp/fl-neg.png"},
            {"sample_slug": "train-wa", "split": "train", "label_count": 1, "image_path": "/tmp/wa-pos.png"},
            {"sample_slug": "test-fl", "split": "val", "label_count": 1, "image_path": "/tmp/protected.png"},
        ]
    }
    assessment = {
        "status": "continue_training_loop",
        "gates": {
            "min_region_f1": 0.45,
            "min_region_precision": 0.5,
            "min_region_recall": 0.4,
            "max_pure_empty_false_positives": 0,
        },
        "test": {
            "regions": {
                "flanders": {"f1": 0.2, "precision": 0.3, "recall": 0.2},
                "wallonia": {"f1": 0.6, "precision": 0.6, "recall": 0.6},
            }
        },
        "background": {"pure_empty_false_positives": 2},
    }
    paths, metadata = MODULE.build_sampling(
        summary=summary, manifest=manifest, assessment=assessment, max_region_share=1.0
    )
    assert paths.count(str(Path("/tmp/fl-pos.png").resolve())) == 3
    assert paths.count(str(Path("/tmp/fl-neg.png").resolve())) == 4
    assert paths.count(str(Path("/tmp/wa-pos.png").resolve())) == 1
    assert not any("protected" in path for path in paths)
    assert metadata["protected_samples_in_training"] == []
    assert metadata["weak_recall_regions"] == ["flanders"]


def test_sampling_can_use_calibration_before_test_is_opened() -> None:
    manifest = {"samples": [{"sample_slug": "train-fl", "split": "train", "region": "flanders"}]}
    summary = {
        "tiles": [
            {"sample_slug": "train-fl", "split": "train", "label_count": 1, "image_path": "/tmp/fl.png"}
        ]
    }
    assessment = {
        "status": "continue_training_loop",
        "gates": {
            "min_region_f1": 0.45,
            "min_region_precision": 0.5,
            "min_region_recall": 0.4,
            "max_pure_empty_false_positives": 0,
        },
        "calibration": {
            "regions": {"flanders": {"f1": 0.4, "precision": 0.6, "recall": 0.35}}
        },
        "test": None,
        "background": None,
    }
    paths, metadata = MODULE.build_sampling(
        summary=summary, manifest=manifest, assessment=assessment, max_region_share=1.0
    )
    assert len(paths) == 3
    assert metadata["failure_evidence_source"] == "calibration"


def test_precision_correction_can_balance_positive_and_negative_tiles() -> None:
    manifest = {"samples": [{"sample_slug": "train-fl", "split": "train", "region": "flanders"}]}
    summary = {
        "tiles": [
            {"sample_slug": "train-fl", "split": "train", "label_count": 2, "image_path": "/tmp/fl-pos.png"},
            {"sample_slug": "train-fl", "split": "train", "label_count": 0, "image_path": "/tmp/fl-neg.png"},
        ]
    }
    assessment = {
        "status": "continue_training_loop",
        "gates": {
            "min_region_f1": 0.45,
            "min_region_precision": 0.5,
            "min_region_recall": 0.4,
            "max_pure_empty_false_positives": 0,
        },
        "calibration": {
            "regions": {"flanders": {"f1": 0.46, "precision": 0.45, "recall": 0.46}}
        },
    }

    paths, metadata = MODULE.build_sampling(
        summary=summary,
        manifest=manifest,
        assessment=assessment,
        precision_positive_repeat=2,
        negative_repeat=3,
        max_region_share=1.0,
    )

    assert paths.count(str(Path("/tmp/fl-pos.png").resolve())) == 2
    assert paths.count(str(Path("/tmp/fl-neg.png").resolve())) == 3
    assert metadata["precision_positive_repeat"] == 2


def test_sampling_targets_failed_calibration_contexts_without_using_protected_tiles() -> None:
    manifest = {
        "samples": [
            {"sample_slug": "train-industry", "split": "train", "region": "flanders", "context": "industrial"},
            {"sample_slug": "train-suburban", "split": "train", "region": "flanders", "context": "suburban"},
            {"sample_slug": "cal-industry", "split": "calibration", "region": "flanders", "context": "industrial"},
        ]
    }
    summary = {
        "tiles": [
            {"sample_slug": "train-industry", "split": "train", "label_count": 2, "image_path": "/tmp/industry-pos.png"},
            {"sample_slug": "train-industry", "split": "train", "label_count": 0, "image_path": "/tmp/industry-neg.png"},
            {"sample_slug": "train-suburban", "split": "train", "label_count": 2, "image_path": "/tmp/suburban-pos.png"},
            {"sample_slug": "cal-industry", "split": "val", "label_count": 2, "image_path": "/tmp/protected.png"},
        ]
    }
    assessment = {
        "status": "continue_training_loop",
        "gates": {
            "min_region_f1": 0.45,
            "min_region_precision": 0.5,
            "min_region_recall": 0.4,
            "max_pure_empty_false_positives": 0,
        },
        "calibration": {
            "regions": {"flanders": {"f1": 0.3, "precision": 0.35, "recall": 0.27}},
            "samples": {"cal-industry": {"f1": 0.2, "precision": 0.3, "recall": 0.15}},
        },
    }

    paths, metadata = MODULE.build_sampling(
        summary=summary, manifest=manifest, assessment=assessment, max_region_share=1.0
    )

    assert paths.count(str(Path("/tmp/industry-pos.png").resolve())) == 5
    assert paths.count(str(Path("/tmp/industry-neg.png").resolve())) == 1
    assert paths.count(str(Path("/tmp/suburban-pos.png").resolve())) == 3
    assert not any("protected" in path for path in paths)
    assert metadata["weak_recall_contexts"] == ["flanders:industrial"]
    assert metadata["weak_precision_contexts"] == ["flanders:industrial"]
    assert metadata["recall_dominant_regions"] == ["flanders"]


def test_recall_dominance_does_not_suppress_negatives_when_background_gate_failed() -> None:
    manifest = {"samples": [
        {"sample_slug": "positive", "split": "train", "region": "flanders", "context": "industrial"},
        {"sample_slug": "negative", "split": "train", "region": "flanders", "context": "industrial-hard-negative"},
    ]}
    summary = {"tiles": [
        {"sample_slug": "positive", "split": "train", "label_count": 1, "image_path": "/tmp/positive.png"},
        {"sample_slug": "negative", "split": "train", "label_count": 0, "image_path": "/tmp/negative.png"},
    ]}
    assessment = {
        "status": "continue_training_loop",
        "gates": {"min_region_f1": .45, "min_region_precision": .5, "min_region_recall": .4,
                  "max_pure_empty_false_positives": 0},
        "calibration": {"regions": {
            "flanders": {"f1": .25, "precision": .4, "recall": .2},
        }},
        "background": {"pure_empty_false_positives": 1},
    }

    paths, metadata = MODULE.build_sampling(
        summary=summary, manifest=manifest, assessment=assessment, max_region_share=1.0,
    )

    assert paths.count(str(Path("/tmp/negative.png").resolve())) == 4
    assert metadata["recall_dominant_regions"] == []


def test_region_cap_drops_only_repeats_and_preserves_every_unique_tile() -> None:
    manifest = {"samples": [
        {"sample_slug": "fl", "split": "train", "region": "flanders", "context": "industrial"},
        {"sample_slug": "wa", "split": "train", "region": "wallonia", "context": "rural-town"},
        {"sample_slug": "br", "split": "train", "region": "brussels", "context": "dense-urban"},
    ]}
    summary = {"tiles": [
        {"sample_slug": "fl", "split": "train", "label_count": 2, "image_path": f"/tmp/fl-{index}.png"}
        for index in range(4)
    ] + [
        {"sample_slug": "wa", "split": "train", "label_count": 2, "image_path": f"/tmp/wa-{index}.png"}
        for index in range(2)
    ] + [
        {"sample_slug": "br", "split": "train", "label_count": 2, "image_path": f"/tmp/br-{index}.png"}
        for index in range(2)
    ]}
    assessment = {
        "status": "continue_training_loop",
        "gates": {"min_region_f1": .45, "min_region_precision": .5, "min_region_recall": .4,
                  "max_pure_empty_false_positives": 0},
        "calibration": {"regions": {
            "flanders": {"f1": .2, "precision": .3, "recall": .2},
            "wallonia": {"f1": .6, "precision": .6, "recall": .6},
            "brussels": {"f1": .6, "precision": .6, "recall": .6},
        }},
    }

    paths, metadata = MODULE.build_sampling(
        summary=summary, manifest=manifest, assessment=assessment,
        positive_repeat=5, max_region_share=.65,
    )

    assert all(str(Path(f"/tmp/fl-{index}.png").resolve()) in paths for index in range(4))
    assert metadata["pre_cap_entries_by_region"]["flanders"] == 20
    assert metadata["sampled_entries_by_region"]["flanders"] == 7
    assert metadata["dropped_region_repeat_count"] == 13
    assert metadata["sampled_entries_by_region"]["flanders"] / len(paths) <= .65


def test_region_cap_rotates_repeats_between_sampling_rounds() -> None:
    manifest = {"samples": [
        {"sample_slug": "fl", "split": "train", "region": "flanders", "context": "industrial"},
        {"sample_slug": "wa", "split": "train", "region": "wallonia", "context": "rural-town"},
        {"sample_slug": "br", "split": "train", "region": "brussels", "context": "dense-urban"},
    ]}
    summary = {"tiles": [
        {"sample_slug": "fl", "split": "train", "label_count": 2, "image_path": f"/tmp/fl-{index}.png"}
        for index in range(4)
    ] + [
        {"sample_slug": "wa", "split": "train", "label_count": 2, "image_path": f"/tmp/wa-{index}.png"}
        for index in range(2)
    ] + [
        {"sample_slug": "br", "split": "train", "label_count": 2, "image_path": f"/tmp/br-{index}.png"}
        for index in range(2)
    ]}
    assessment = {
        "status": "continue_training_loop",
        "gates": {"min_region_f1": .45, "min_region_precision": .5, "min_region_recall": .4,
                  "max_pure_empty_false_positives": 0},
        "calibration": {"regions": {
            "flanders": {"f1": .2, "precision": .3, "recall": .2},
            "wallonia": {"f1": .6, "precision": .6, "recall": .6},
            "brussels": {"f1": .6, "precision": .6, "recall": .6},
        }},
    }

    first, first_metadata = MODULE.build_sampling(
        summary=summary, manifest=manifest, assessment=assessment,
        positive_repeat=5, max_region_share=.65, sampling_round=1,
    )
    second, second_metadata = MODULE.build_sampling(
        summary=summary, manifest=manifest, assessment=assessment,
        positive_repeat=5, max_region_share=.65, sampling_round=2,
    )

    assert first != second
    assert set(first) == set(second)
    assert first_metadata["sampling_round"] == 1
    assert second_metadata["sampling_round"] == 2


def test_region_cap_preserves_failed_context_positive_before_hard_negative() -> None:
    manifest = {"samples": [
        {"sample_slug": "target", "split": "train", "region": "flanders", "context": "industrial"},
        {"sample_slug": "negative", "split": "train", "region": "flanders", "context": "industrial-hard-negative"},
        {"sample_slug": "wa", "split": "train", "region": "wallonia", "context": "rural-town"},
        {"sample_slug": "br", "split": "train", "region": "brussels", "context": "dense-urban"},
    ]}
    summary = {"tiles": [
        {"sample_slug": "target", "split": "train", "label_count": 2, "image_path": "/tmp/target.png"},
        {"sample_slug": "negative", "split": "train", "label_count": 0, "image_path": "/tmp/negative.png"},
        {"sample_slug": "wa", "split": "train", "label_count": 1, "image_path": "/tmp/wa.png"},
        {"sample_slug": "br", "split": "train", "label_count": 1, "image_path": "/tmp/br.png"},
    ]}
    assessment = {
        "status": "continue_training_loop",
        "gates": {"min_region_f1": .45, "min_region_precision": .5, "min_region_recall": .4,
                  "max_pure_empty_false_positives": 0},
        "calibration": {
            "regions": {
                "flanders": {"f1": .2, "precision": .2, "recall": .3},
                "wallonia": {"f1": .6, "precision": .6, "recall": .6},
                "brussels": {"f1": .6, "precision": .6, "recall": .6},
            },
            "samples": {"target": {"f1": .2, "precision": .2, "recall": .3}},
        },
    }

    paths, metadata = MODULE.build_sampling(
        summary=summary, manifest=manifest, assessment=assessment, max_region_share=.65,
    )

    assert paths.count(str(Path("/tmp/target.png").resolve())) == 2
    assert paths.count(str(Path("/tmp/negative.png").resolve())) == 1
    assert metadata["priority_positive_repeat_count"] == 4


def test_precision_guard_band_keeps_near_gate_region_stabilized() -> None:
    manifest = {"samples": [
        {"sample_slug": "wa-positive", "split": "train", "region": "wallonia", "context": "rural-town"},
        {"sample_slug": "wa-negative", "split": "train", "region": "wallonia", "context": "farmland-hard-negative"},
    ]}
    summary = {"tiles": [
        {"sample_slug": "wa-positive", "split": "train", "label_count": 1, "image_path": "/tmp/wa-positive.png"},
        {"sample_slug": "wa-negative", "split": "train", "label_count": 0, "image_path": "/tmp/wa-negative.png"},
    ]}
    assessment = {
        "status": "continue_training_loop",
        "gates": {"min_region_f1": .45, "min_region_precision": .5, "min_region_recall": .4,
                  "max_pure_empty_false_positives": 0},
        "calibration": {"regions": {
            "wallonia": {"f1": .6, "precision": .52, "recall": .7},
        }},
    }

    paths, metadata = MODULE.build_sampling(
        summary=summary, manifest=manifest, assessment=assessment, max_region_share=1.0,
    )

    assert "wallonia" in metadata["weak_precision_regions"]
    assert paths.count(str(Path("/tmp/wa-negative.png").resolve())) == 4
    assert metadata["precision_guard_band"] == .03


def test_coastal_precision_failure_targets_port_and_dunes_negatives() -> None:
    manifest = {"samples": [
        {"sample_slug": "coastal-train", "split": "train", "region": "flanders", "context": "coastal-urban"},
        {"sample_slug": "port-negative", "split": "train", "region": "flanders", "context": "port-hard-negative"},
        {"sample_slug": "dunes-negative", "split": "train", "region": "flanders", "context": "dunes-negative"},
        {"sample_slug": "coastal-cal", "split": "calibration", "region": "flanders", "context": "coastal-urban"},
    ]}
    summary = {"tiles": [
        {"sample_slug": "coastal-train", "split": "train", "label_count": 2, "image_path": "/tmp/coastal.png"},
        {"sample_slug": "port-negative", "split": "train", "label_count": 0, "image_path": "/tmp/port.png"},
        {"sample_slug": "dunes-negative", "split": "train", "label_count": 0, "image_path": "/tmp/dunes.png"},
    ]}
    assessment = {
        "status": "continue_training_loop",
        "gates": {"min_region_f1": .45, "min_region_precision": .5, "min_region_recall": .4,
                  "max_pure_empty_false_positives": 0},
        "calibration": {
            "regions": {"flanders": {"f1": .3, "precision": .2, "recall": .4}},
            "samples": {"coastal-cal": {"f1": .1, "precision": .05, "recall": .2}},
        },
    }

    paths, metadata = MODULE.build_sampling(
        summary=summary, manifest=manifest, assessment=assessment, max_region_share=1.0
    )

    assert paths.count(str(Path("/tmp/port.png").resolve())) == 6
    assert paths.count(str(Path("/tmp/dunes.png").resolve())) == 6
    assert "flanders:port-hard-negative" in metadata["targeted_negative_contexts"]
    assert "flanders:dunes-negative" in metadata["targeted_negative_contexts"]