"""Tests Planungs-Retrieval Phase A (Voll-Library-Ranking)."""
from planning_exercise_profiles import ExerciseMatchProfile, PlanningTargetProfile
from planning_exercise_retrieval import (
    fetch_all_visible_exercise_rows,
    rank_visible_library_hits,
)


def test_fetch_all_visible_has_no_profile_or_pool_filter():
    captured = {}

    class _Cur:
        def execute(self, sql, params):
            captured["sql"] = sql
            captured["params"] = list(params)

        def fetchall(self):
            return []

    fetch_all_visible_exercise_rows(
        _Cur(),
        vis_sql="(e.visibility = 'official' OR (e.visibility = 'private' AND e.created_by = %s))",
        vis_params=[42],
        query="Kime Partner",
        exercise_kind_any=["simple"],
    )

    sql = captured["sql"].lower()
    assert "exercise_skills" not in sql
    assert "@@ plainto_tsquery" not in sql
    assert " exists " not in sql.replace("primary_focus_name", "")
    params = captured["params"]
    assert params[0] == "Kime Partner"
    assert params[1] == 42
    assert params[2] == "archived"
    assert params[3] == "simple"
    assert params[-1] == 8000


def test_rank_visible_library_prefers_profile_over_untagged_pool_miss():
    """Übung ohne Pool-Tags, aber hoher Profil-Match, muss vor schwachem Treffer ranken."""

    target = PlanningTargetProfile(
        focus_area_ids={10: 1.0},
        skill_weights={5: 1.0},
        sources=["framework_catalog"],
    )
    rows = [
        {"id": 1, "title": "Schwach", "summary": "", "primary_focus_name": "X", "ft_rank": 0.9},
        {"id": 2, "title": "Stark", "summary": "", "primary_focus_name": "Karate", "ft_rank": 0.0},
    ]

    profiles = {
        1: ExerciseMatchProfile(exercise_id=1, focus_area_ids={99: 1.0}),
        2: ExerciseMatchProfile(exercise_id=2, focus_area_ids={10: 1.0}, skill_weights={5: 1.0}),
    }

    class _Cur:
        def execute(self, sql, params=None):
            return None

        def fetchall(self):
            return []

    def _fake_load(cur, exercise_ids, *, batch=400):
        _ = (cur, batch)
        return {eid: profiles[eid] for eid in exercise_ids if eid in profiles}

    def _fake_skills(cur, exercise_ids, *, batch=400):
        _ = (cur, batch)
        return {1: {99}, 2: {5, 10}}

    import planning_exercise_retrieval as mod

    orig_profiles = mod._load_match_profiles_chunked
    orig_skills = mod._load_skill_sets_chunked
    try:
        mod._load_match_profiles_chunked = _fake_load
        mod._load_skill_sets_chunked = _fake_skills
        hits, _ = rank_visible_library_hits(
            _Cur(),
            rows,
            query="",
            intent="suggest_next",
            intent_weights={
                "fulltext": 0.08,
                "progression": 0.28,
                "skill": 0.12,
                "plan": 0.10,
                "profile": 0.25,
                "repeat_unit": -0.30,
                "repeat_group": -0.15,
            },
            target=target,
            pack={
                "planned_exercise_ids": [],
                "group_recent_exercise_ids": [],
                "progression_successor_ids": [],
                "anchor_skill_ids": [],
                "anchor_exercise_id": None,
                "progression_edge_notes": {},
            },
        )
    finally:
        mod._load_match_profiles_chunked = orig_profiles
        mod._load_skill_sets_chunked = orig_skills

    assert hits[0]["id"] == 2
    assert hits[0]["score"] > hits[1]["score"]