"""CPU tests of the variant sub-tiers: labels, ``--tiers`` parsing, quotas, small-anchor and clique specs
(no models).
"""
import csv, json, pathlib, subprocess, sys

import numpy as np
import pytest

from strangebench.common import variants as v
from strangebench.scene_specs import never_cliques as nc
from strangebench.generation import gen_sd35l_e2e as gen
from strangebench.audit import filter_small_anchor as fsa
from strangebench.audit import make_complete_annotations as mca
from strangebench.scene_specs.layout_solver import rel_area_of_box, shrink_anchor_box

ROOT = pathlib.Path(__file__).resolve().parents[1]
PY = sys.executable


def test_tag_strip_and_variant_of_label():
    assert v.tag_label("tier2_1typical", "small_anchor") == "tier2_1typical__small_anchor"
    assert v.strip_variant("tier2_1typical__small_anchor") == "tier2_1typical"
    assert v.strip_variant("tier_never_k3") == "tier_never_k3" and v.strip_variant("tier_never") == "tier_never"
    assert v.variant_of_label("tier2_1atypical__small_anchor") == "small_anchor"
    assert v.variant_of_label("tier6_5typical") == "k5" and v.variant_of_label("tier6_5atypical") == "k5"
    assert v.variant_of_label("tier_never_k3") == "never_clique" and v.variant_of_label("tier_never_k4") == "never_clique"
    for lbl in v.V1_LABELS:
        assert v.variant_of_label(lbl) is None
    assert v.variant_of_scene({"variant": "k5", "sub_tier": "tier2_1typical"}) == "k5"
    assert v.variant_of_scene({"sub_tier": "tier_never_k4"}) == "never_clique"
    assert v.variant_of_scene({"sub_tier": "tier3_2typical"}) is None
    assert v.prompt_base_label("tier_never_k3") == "tier_never"
    assert v.prompt_base_label("tier2_1atypical__small_anchor") == "tier2_1atypical"
    assert v.prompt_base_label("tier6_5typical") == "tier6_5typical"


def test_tier_tables_are_consistent():
    labels = [t[0] for t in v.VARIANT_TIERS]
    assert labels == ["tier6_5typical", "tier6_5atypical", "tier_never_k3", "tier_never_k4",
                      "tier2_1typical__small_anchor", "tier2_1atypical__small_anchor"]
    assert not any("natural" in l or "tier7" in l for l in labels)
    assert set(v.SOLVER_ONLY_LABELS) == {"tier6_5typical", "tier6_5atypical", "tier_never_k3", "tier_never_k4"}
    assert set(v.NEVER_CLIQUE_LABELS) == {"tier_never_k3", "tier_never_k4"}
    expanded = {l for grp in v.TIER_GROUPS.values() for l in grp}
    assert expanded == set(v.V1_LABELS) | set(labels)
    for label, mode, n, bg, variant, base in v.VARIANT_TIERS:
        assert variant in v.VARIANTS and base in v.V1_LABELS or base == label
        assert (mode == "never_clique") == (variant == "never_clique")
        assert n == {"k5": 5, "small_anchor": 1}.get(variant, n)


def test_parse_tiers_expands_pairs_and_singles_in_order():
    counts = v.parse_tiers_arg("tier6=4, never_k3=3,never_k4=2,tier2__small_anchor=5,tier1=7,never=1")
    assert list(counts.items()) == [
        ("tier6_5typical", 4), ("tier6_5atypical", 4), ("tier_never_k3", 3), ("tier_never_k4", 2),
        ("tier2_1typical__small_anchor", 5), ("tier2_1atypical__small_anchor", 5),
        ("tier1_natural_bg", 7), ("tier1_atypical_bg", 7), ("tier_never", 1)]
    assert v.parse_tiers_arg("tier3=0") == {"tier3_2typical": 0, "tier3_2atypical": 0}


@pytest.mark.parametrize("bad", ["tier9=4", "tier6=4,tier6=5", "tier6=x", "tier6", "", "never_k3=-1"])
def test_parse_tiers_rejects(bad):
    with pytest.raises(ValueError):
        v.parse_tiers_arg(bad)


def test_pair_tiers_and_order():
    labels = ["tier_never_k3", "tier2_1atypical__small_anchor", "tier6_5atypical", "tier_never",
              "tier2_1typical", "tier6_5typical", "tier2_1atypical", "tier2_1typical__small_anchor",
              "tier1_atypical_bg", "tier1_natural_bg"]
    assert v.order_tiers(labels)[:5] == ["tier1_natural_bg", "tier1_atypical_bg", "tier2_1typical",
                                         "tier2_1atypical", "tier_never"]
    pairs, singles = v.pair_tiers(labels)
    assert ("tier1_natural_bg", "tier1_atypical_bg") in pairs
    assert ("tier2_1typical", "tier2_1atypical") in pairs
    assert ("tier6_5typical", "tier6_5atypical") in pairs
    assert ("tier2_1typical__small_anchor", "tier2_1atypical__small_anchor") in pairs
    assert singles == ["tier_never", "tier_never_k3"]
    assert v.pair_label_of("tier_never_k4") is None
    pairs2, singles2 = v.pair_tiers(["tier6_5typical", "tier_never_k4"])
    assert pairs2 == [] and singles2 == ["tier6_5typical", "tier_never_k4"]
    pv1, sv1 = v.pair_tiers(list(v.V1_LABELS))
    assert pv1 == [("tier1_natural_bg", "tier1_atypical_bg"), ("tier2_1typical", "tier2_1atypical"),
                   ("tier3_2typical", "tier3_2atypical"), ("tier4_3typical", "tier4_3atypical"),
                   ("tier5_4typical", "tier5_4atypical")] and sv1 == ["tier_never"]


def test_allocate_per_anchor_exact_capped_and_deterministic():
    anchors = list(range(80))
    a = v.allocate_per_anchor(50, anchors, seed=5000, tier_label="tier6_5typical")
    assert sum(a.values()) == 50 and max(a.values()) == 1 and len(a) == 50
    assert a == v.allocate_per_anchor(50, anchors, 5000, "tier6_5typical")
    assert a != v.allocate_per_anchor(50, anchors, 5001, "tier6_5typical")
    big = v.allocate_per_anchor(200, anchors, 1, "x")
    assert sum(big.values()) == 200 and set(big.values()) == {2, 3}
    caps = {i: (3 if i < 10 else 0) for i in anchors}
    c = v.allocate_per_anchor(100, [i for i in anchors if caps[i] > 0], 1, "k3", caps=caps)
    assert sum(c.values()) == 30 and all(c[i] == 3 for i in range(10))
    q = v.allocate_per_anchor(100, anchors, 1, "k3", quota_cap=1)
    assert sum(q.values()) == 80 and set(q.values()) == {1}
    assert v.allocate_per_anchor(0, anchors, 1, "k3") == {} and v.allocate_per_anchor(5, [], 1, "k3") == {}
    assert v.stable_hash("a") == v.stable_hash("a") and v.stable_hash("a") != v.stable_hash("b")


def _toy():
    """6 classes. Anchor 0 never co-occurs with 1..5. Among partners: {1,2,3} pairwise zero, {1,2,4}
    pairwise zero, 5 co-occurs with everyone but 0. Class 4 is rare, so E(3,4) < 10 and E(1,4) < 10
    make those zero pairs uninformative."""
    n = 6
    counts = np.full((n, n), 50, dtype=np.int64)
    np.fill_diagonal(counts, 0)
    for a, b in [(0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (1, 2), (1, 3), (2, 3), (1, 4), (2, 4), (3, 4)]:
        counts[a, b] = counts[b, a] = 0
    n_images = 1000
    n_i = np.array([400, 300, 300, 200, 20, 300], dtype=np.float64)
    return counts, n_i / n_images, n_images


def test_expected_counts_and_informative_mask():
    counts, marginal, n = _toy()
    e = nc.expected_pair_counts(marginal, n)
    assert abs(e[0, 1] - 400 * 300 / 1000) < 1e-9
    mask = nc.informative_zero_mask(counts, marginal, n, min_expected=10)
    assert mask[0, 1] and mask[1, 2] and mask[2, 3]
    assert not mask[3, 4] and not mask[1, 4]
    assert not mask[0, 0] and not mask[1, 5]


def test_cliques_respect_min_expected():
    counts, marginal, n = _toy()
    mask = nc.informative_zero_mask(counts, marginal, n, min_expected=10)
    assert nc.candidate_partners(0, mask) == [1, 2, 3, 5]
    assert set(nc.enumerate_cliques(0, mask, 2)) == {(1, 2), (1, 3), (2, 3)}
    assert nc.enumerate_cliques(0, mask, 3) == [(1, 2, 3)]
    assert nc.enumerate_cliques(0, mask, 4) == []
    assert nc.enumerate_cliques(5, mask, 2) == []
    relaxed = nc.informative_zero_mask(counts, marginal, n, min_expected=1)
    assert relaxed[3, 4] and relaxed[1, 4]
    assert nc.enumerate_cliques(0, relaxed, 4) == [(1, 2, 3, 4)]
    assert nc.min_expected_over_members([0, 1, 2, 3], nc.expected_pair_counts(marginal, n)) == pytest.approx(60.0)


def test_clique_sampling_seeded_and_report():
    counts, marginal, n = _toy()
    mask = nc.informative_zero_mask(counts, marginal, n, min_expected=10)
    a = nc.sample_clique(0, mask, 2, np.random.default_rng(1))
    assert a == nc.sample_clique(0, mask, 2, np.random.default_rng(1)) and a in {(1, 2), (1, 3), (2, 3)}
    assert nc.sample_clique(0, mask, 4, np.random.default_rng(1)) is None
    assert sorted(nc.cliques_for_anchor(0, mask, 2, seed=5)) == [(1, 2), (1, 3), (2, 3)]
    assert nc.cliques_for_anchor(0, mask, 2, seed=5) == nc.cliques_for_anchor(0, mask, 2, seed=5)
    rep = nc.clique_report(mask, (2, 3), {i: f"c{i}" for i in range(6)})
    assert rep["per_anchor"][0]["n_cliques_k3"] == 1
    assert rep["summary"]["anchors_with_k3"] == 4 and rep["summary"]["total_cliques_k3"] == 4
    assert rep["per_anchor"][5]["n_cliques_k2"] == 0
    assert rep["summary"]["n_informative_zero_pairs"] == int(mask.sum()) // 2


def test_shrink_big_box_to_two_percent_keeps_center_and_aspect():
    box = [100, 200, 700, 500]
    nb, rel = shrink_anchor_box(box, 1024, max_rel_area=0.02, min_short_side=72)
    assert rel <= 0.02 + 1e-6 and abs(rel - rel_area_of_box(nb, 1024)) < 1e-9
    cx, cy = (box[0] + box[2]) / 2, (box[1] + box[3]) / 2
    assert abs(cx - (nb[0] + nb[2]) / 2) <= 1 and abs(cy - (nb[1] + nb[3]) / 2) <= 1
    w, h = nb[2] - nb[0], nb[3] - nb[1]
    assert abs(w / h - 2.0) < 0.05 and min(w, h) >= 72


def test_shrink_small_box_unchanged_and_floors():
    box = [34, 34, 134, 134]
    nb, rel = shrink_anchor_box(box, 1024)
    assert nb == box and abs(rel - 100 * 100 / 1024 ** 2) < 1e-9
    nb, rel = shrink_anchor_box([0, 400, 1000, 600], 1024, max_rel_area=0.02, min_short_side=72)
    assert nb[3] - nb[1] == 72 and rel <= 0.02 + 1e-6
    assert nb[2] - nb[0] <= int(round(0.02 * 1024 * 1024 / 72)) + 1
    nb, _ = shrink_anchor_box([900, 900, 1024, 1024], 1024, margin=24)
    assert nb[0] >= 24 and nb[1] >= 24 and nb[2] <= 1000 and nb[3] <= 1000 and nb[2] > nb[0]


def test_anchor_rel_area():
    assert gen.anchor_rel_area([10, 20, 128, 64], 1024, 1024) == pytest.approx(128 * 64 / 1024 ** 2, abs=1e-5)
    assert gen.anchor_rel_area([0, 0, 512, 256], 1024, 512) == pytest.approx(0.25)
    assert gen.anchor_rel_area(None, 1024, 1024) is None


def test_attempt_ok_and_small_anchor_choice():
    assert gen.attempt_ok(2, 2) and not gen.attempt_ok(1, 2)
    assert gen.attempt_ok(2, 2, 0.03, 0.04) and not gen.attempt_ok(2, 2, 0.10, 0.04)
    assert not gen.attempt_ok(2, 2, None, 0.04)
    assert gen.attempt_ok(2, 2, 0.10, None)
    assert gen.choose_attempt([2, 2], 2, [0.10, 0.02], 0.04) == 1
    assert gen.choose_attempt([2, 2], 2, [0.10, 0.02], None) == 0
    assert gen.choose_attempt([2, 2], 2) == 0
    assert gen.choose_attempt([2, 1, 2, 2], 2, [0.09, 0.01, 0.06, 0.06], 0.04) == 2
    assert gen.choose_attempt([1, 2, 2], 2, [None, None, None], 0.04) == 1
    with pytest.raises(ValueError):
        gen.choose_attempt([2, 2], 2, [0.1], 0.04)


SCENE = dict(scene_id="tier2_1typical__small_anchor__a064_s0000", sub_tier="tier2_1typical__small_anchor",
             variant="small_anchor", n_partners=1, anchor={"idx": 64, "name": "mouse"},
             labels=["mouse", "keyboard"], layout_boxes=[[450, 450, 550, 550], [100, 700, 900, 1000]],
             img_size=1024, prompt="A photorealistic photo of a mouse and a keyboard, in a natural everyday scene")


def test_small_anchor_prompt_bits_and_v1_wording_unchanged():
    block = gen.make_objects_block(SCENE, small_anchor=True).split("\n")
    assert block[0].startswith("  - computer mouse → ") and block[0].endswith(", " + gen.SMALL_ANCHOR_OBJECT_SUFFIX)
    assert "small and far from the camera" in block[0] and "small and far" not in block[1]
    assert gen.make_objects_block(SCENE) == gen.make_objects_block(SCENE, small_anchor=False)
    assert gen.positive_prompt_suffix(SCENE) == "the computer mouse is small in the distance"
    assert gen.positive_prompt_suffix(SCENE, display_names=False) == "the mouse is small in the distance"
    assert gen.apply_positive_suffix("A wide shot of a mouse.", "the mouse is small in the distance") \
        == "A wide shot of a mouse, the mouse is small in the distance."
    assert gen.apply_positive_suffix("abc", "") == "abc"
    up = gen.build_user_prompt(SCENE, small_anchor=True)
    assert "MINIMALIST" in up and "small and far from the camera" in up
    assert gen.build_user_prompt(SCENE) == gen.build_user_prompt(SCENE, small_anchor=False)
    assert gen.tier_descriptor(SCENE) == gen.tier_descriptor(dict(SCENE, sub_tier="tier2_1typical"))
    assert gen.tier_descriptor(dict(SCENE, sub_tier="tier_never_k3", n_partners=3)) \
        == gen.tier_descriptor(dict(SCENE, sub_tier="tier_never", n_partners=3)) \
        == ("in a highly unusual scene", "would normally never appear together")
    per = {"keyboard": 0.05}
    assert gen.sam3_min_score_for("zebra", True, True, per, 0.15, 0.10) == 0.10
    assert gen.sam3_min_score_for("zebra", False, True, per, 0.15, 0.10) == 0.15
    assert gen.sam3_min_score_for("zebra", True, False, per, 0.15, 0.10) == 0.15
    assert gen.sam3_min_score_for("keyboard", True, True, per, 0.15, 0.10) == 0.05


def _toy_coco():
    cats = [{"id": 74, "name": "mouse"}, {"id": 76, "name": "keyboard"}]
    imgs = [dict(id=i, file_name=f"s{i}.png", width=1024, height=1024, scene_id=f"s{i}",
                 labels_intended=["mouse", "keyboard"]) for i in range(1, 6)]
    anns = [dict(id=1, image_id=1, category_id=74, bbox=[0, 0, 100, 100], iscrowd=0),
            dict(id=2, image_id=1, category_id=76, bbox=[0, 0, 500, 500], iscrowd=0),
            dict(id=3, image_id=2, category_id=74, bbox=[0, 0, 400, 400], iscrowd=0),
            dict(id=4, image_id=3, category_id=74, bbox=[0, 0, 100, 100], iscrowd=0),
            dict(id=5, image_id=4, category_id=76, bbox=[0, 0, 100, 100], iscrowd=0),
            dict(id=6, image_id=5, category_id=74, bbox=[0, 0, 400, 400], iscrowd=0)]
    imgs[4]["anchor_rel_area"] = 0.03
    return dict(info={}, categories=cats, images=imgs, annotations=anns)


def test_filter_small_anchor_toy():
    out, st = fsa.filter_coco(_toy_coco(), {"s1": 0.01, "s2": 0.10}, max_rel=0.04)
    kept = {im["scene_id"]: im["anchor_rel_area"] for im in out["images"]}
    assert kept == {"s1": 0.01, "s3": pytest.approx(100 * 100 / 1024 ** 2, abs=1e-5), "s5": 0.03}
    assert st["imgs_in"] == 5 and st["imgs_out"] == 3
    assert st["dropped_anchor_too_big"] == 1 and st["dropped_anchor_missing"] == 1
    assert {a["image_id"] for a in out["annotations"]} == {1, 3, 5}
    assert out["info"]["small_anchor_filter"]["max_anchor_rel_area"] == 0.04
    assert fsa.anchor_rel_area_from_coco(_toy_coco(), _toy_coco()["images"][3], "mouse") is None


def test_filter_small_anchor_tier_dir_discovery_and_idempotent(tmp_path):
    for name in ("tier2_1typical__small_anchor", "tier2_1typical", "tier6_5typical"):
        d = tmp_path / name
        d.mkdir()
        json.dump(_toy_coco(), open(d / "annotations_clean.json", "w"))
    sa = tmp_path / "tier2_1typical__small_anchor"
    (sa / "s1_anns.json").write_text(json.dumps(dict(scene_id="s1", anchor_rel_area=0.01)))
    (sa / "s2_anns.json").write_text(json.dumps(dict(scene_id="s2", anchor_rel_area=0.10)))
    (sa / "_audit_anns.json").write_text("{}")
    assert [d.name for d in fsa.small_anchor_tier_dirs(tmp_path, "annotations_clean.json")] == [sa.name]
    assert [d.name for d in fsa.small_anchor_tier_dirs(tmp_path, "annotations_clean.json", ["tier6_5typical"])] \
        == ["tier6_5typical"]
    st = fsa.filter_tier_dir(sa, "annotations_clean.json", 0.04)
    assert st["imgs_out"] == 3 and st["fragments_with_area"] == 2
    assert (sa / "annotations_clean_pre_small_anchor.json").exists()
    st2 = fsa.filter_tier_dir(sa, "annotations_clean.json", 0.02)
    assert st2["imgs_in"] == 5 and st2["imgs_out"] == 2
    assert len(json.load(open(sa / "annotations_clean_pre_small_anchor.json"))["images"]) == 5
    assert len(json.load(open(tmp_path / "tier2_1typical" / "annotations_clean.json"))["images"]) == 5


def _run(args, cwd):
    r = subprocess.run([PY] + args, capture_output=True, text=True, cwd=str(cwd))
    assert r.returncode == 0, r.stdout[-2000:] + r.stderr[-2000:]
    return r


CATS = [{"id": 1, "name": "person"}, {"id": 2, "name": "dog"}, {"id": 3, "name": "zebra"}]


def _tier(root, name, n_complete, n_incomplete, labels=("person", "dog"), with_images=False):
    d = root / name
    (d / "images").mkdir(parents=True, exist_ok=True)
    images, anns, aid = [], [], 1
    for i in range(n_complete + n_incomplete):
        sid = f"{name}_s{i}"
        images.append(dict(id=i + 1, file_name=f"{sid}.png", width=8, height=8, scene_id=sid,
                           labels_intended=list(labels)))
        present = labels if i < n_complete else labels[:1]
        for lbl in present:
            anns.append(dict(id=aid, image_id=i + 1, category_id={c["name"]: c["id"] for c in CATS}[lbl],
                             bbox=[0, 0, 2, 2], area=4, iscrowd=0))
            aid += 1
        if with_images:
            from PIL import Image
            Image.new("RGB", (8, 8)).save(d / "images" / f"{sid}.png")
    json.dump(dict(info={}, categories=CATS, images=images, annotations=anns), open(d / "annotations.json", "w"))
    return [im["scene_id"] for im in images]


def test_make_complete_annotations_discovers_suffixed_dirs(tmp_path):
    root = tmp_path / "clean"
    _tier(root, "tier2_1typical__small_anchor", 2, 1)
    _tier(root, "tier_never_k3", 1, 1, labels=("person", "dog", "zebra"))
    _tier(root, "tier2_1typical", 3, 0)
    _tier(root, "tier6_5atypical", 0, 2)
    assert mca.discover_tiers(root) == ["tier2_1typical", "tier2_1typical__small_anchor", "tier6_5atypical",
                                        "tier_never_k3"]
    d_k3 = json.load(open(root / "tier_never_k3" / "annotations.json"))
    res = mca.complete_subset(d_k3, {"tier_never_k3_s0": "person", "tier_never_k3_s1": "person"})
    assert (res["n_images"], res["n_complete"], res["n_anchor_unannotated"]) == (2, 1, 0)
    res = mca.complete_subset(d_k3, {"tier_never_k3_s0": "zebra", "tier_never_k3_s1": "zebra"})
    assert res["n_anchor_unannotated"] == 1
    out_csv = tmp_path / "completeness.csv"
    _run(["strangebench/audit/make_complete_annotations.py", "--root", str(root), "--out-csv", str(out_csv),
          "--scene-specs", str(tmp_path / "missing_specs.json")], ROOT)
    rows = {r["tier"]: r for r in csv.DictReader(l for l in open(out_csv) if not l.startswith("#"))}
    assert list(rows) == ["tier2_1typical", "tier2_1typical__small_anchor", "tier6_5atypical", "tier_never_k3"]
    assert rows["tier2_1typical__small_anchor"]["n_complete"] == "2" and rows["tier6_5atypical"]["n_complete"] == "0"
    for t in rows:
        comp = json.load(open(root / t / "annotations_complete.json"))
        assert len(comp["images"]) == int(rows[t]["n_complete"])
        assert comp["info"]["completeness_filter"] == "every intended class annotated"


def test_build_v11_dataset_pairs_variant_tiers(tmp_path):
    clean = tmp_path / "clean"
    sids = []
    sids += _tier(clean, "tier6_5typical", 3, 1, with_images=True)
    sids += _tier(clean, "tier6_5atypical", 2, 0, with_images=True)
    sids += _tier(clean, "tier_never_k3", 2, 1, labels=("person", "dog", "zebra"), with_images=True)
    sids += _tier(clean, "tier2_1typical__small_anchor", 2, 0, with_images=True)
    sids += _tier(clean, "tier2_1atypical__small_anchor", 1, 1, with_images=True)
    sids += _tier(clean, "tier2_1typical", 2, 0, with_images=True)
    specs = tmp_path / "scene_specs.json"
    json.dump([dict(scene_id=s, anchor={"idx": 0, "name": "person"}, partners=[{"idx": 1, "name": "dog"}],
                    sub_tier=s.rsplit("_s", 1)[0], variant=v.variant_of_label(s.rsplit("_s", 1)[0]),
                    layout_source="solver") for s in sids], open(specs, "w"))
    dst = tmp_path / "out"
    r = _run(["strangebench/strict/build_dataset.py", "--source", "v2var", str(clean), str(specs),
              "--dst", str(dst)], ROOT)
    assert "variant tiers discovered" in r.stdout
    n_bal = {t: len(json.load(open(dst / "balanced" / t / "annotations.json"))["images"])
             for t in ("tier6_5typical", "tier6_5atypical", "tier_never_k3", "tier2_1typical__small_anchor",
                       "tier2_1atypical__small_anchor", "tier2_1typical")}
    assert n_bal == {"tier6_5typical": 2, "tier6_5atypical": 2,
                     "tier_never_k3": 2,
                     "tier2_1typical__small_anchor": 1, "tier2_1atypical__small_anchor": 1,
                     "tier2_1typical": 0}
    assert len(json.load(open(dst / "clean" / "tier6_5typical" / "annotations.json"))["images"]) == 4
    rows = {r["scene_id"]: r for r in csv.DictReader(open(dst / "tier_manifest.csv"))}
    assert rows["tier6_5typical_s0"]["variant"] == "k5" and rows["tier_never_k3_s0"]["variant"] == "never_clique"
    assert rows["tier2_1typical_s0"]["variant"] == ""
    stats = open(dst / "stats.csv").read()
    assert "# variant tiers: tier2_1atypical__small_anchor, tier2_1typical__small_anchor" in stats
    assert "tier_never_k3" in stats.split("singles keep all complete scenes")[1]
    tiers_in_stats = [r["tier"] for r in csv.DictReader(l for l in open(dst / "stats.csv") if not l.startswith("#"))]
    assert tiers_in_stats[:11] == list(v.V1_LABELS)
    assert tiers_in_stats[11:] == ["tier2_1atypical__small_anchor", "tier2_1typical__small_anchor",
                                   "tier6_5atypical", "tier6_5typical", "tier_never_k3"]
    assert (dst / "clean" / "tier1_natural_bg" / "annotations.json").exists()


COOCCUR = ROOT / "data" / "cooccurrence" / "coco_train"


@pytest.mark.skipif(not (COOCCUR / "marginal.npy").exists(), reason="co-occurrence dir not built (code/README.md, step 0)")
def test_stage1_tiers_end_to_end(tmp_path):
    out = tmp_path / "stage1"
    _run(["strangebench/scene_specs/select_scenes_and_layouts.py", "--cooccur-dir", str(COOCCUR), "--metric", "npmi",
          "--tiers", "tier6=2,never_k3=2,never_k4=2,tier2__small_anchor=2", "--layout-source", "qwen", "--no-llm",
          "--out-root", str(out), "--seed", "7", "--write-mode", "json"], ROOT)
    scenes = json.load(open(out / "scene_specs.json"))
    by_tier = {}
    for s in scenes:
        by_tier.setdefault(s["sub_tier"], []).append(s)
    assert {t: len(v_) for t, v_ in by_tier.items()} == {
        "tier6_5typical": 2, "tier6_5atypical": 2, "tier_never_k3": 2, "tier_never_k4": 2,
        "tier2_1typical__small_anchor": 2, "tier2_1atypical__small_anchor": 2}
    for s in by_tier["tier6_5typical"] + by_tier["tier6_5atypical"]:
        assert s["variant"] == "k5" and s["n_partners"] == 5 and len(s["labels"]) == 6
        assert s["layout_source"] == "solver"
    for k, t in ((3, "tier_never_k3"), (4, "tier_never_k4")):
        for s in by_tier[t]:
            assert s["variant"] == "never_clique" and s["mode"] == "never_clique" and s["n_partners"] == k
            assert s["is_strict_clique"] is True and s["clique_size"] == k and s["min_expected"] == 10.0
            assert s["clique_min_expected"] >= 10.0 and s["layout_source"] == "solver"
            assert s["base_sub_tier"] == "tier_never"
    for s in by_tier["tier2_1typical__small_anchor"] + by_tier["tier2_1atypical__small_anchor"]:
        assert s["variant"] == "small_anchor" and s["base_sub_tier"] == s["sub_tier"].split("__")[0]
        assert s["anchor_target_rel_area"] <= 0.02 + 1e-6 and len(s["layout_boxes_pre_shrink"]) == 2
        assert s["layout_source"] == "grid_fallback"
        b = s["layout_boxes"][0]
        assert (b[2] - b[0]) * (b[3] - b[1]) / 1024 ** 2 == pytest.approx(s["anchor_target_rel_area"], abs=1e-5)
    assert (out / "_stage1_partial" / "never_clique_report.json").exists()
    rep = json.load(open(out / "_stage1_partial" / "never_clique_report.json"))
    assert rep["summary"]["anchors_with_k3"] == 48 and rep["summary"]["anchors_with_k4"] == 28
    out2 = tmp_path / "stage1b"
    _run(["strangebench/scene_specs/select_scenes_and_layouts.py", "--cooccur-dir", str(COOCCUR), "--metric", "npmi",
          "--tiers", "tier6=2,never_k3=2,never_k4=2,tier2__small_anchor=2", "--layout-source", "qwen", "--no-llm",
          "--out-root", str(out2), "--seed", "7", "--write-mode", "json"], ROOT)
    assert (out2 / "scene_specs.json").read_bytes() == (out / "scene_specs.json").read_bytes()