"""Tests wiederverwendbare Fähigkeiten-Erwartungen (Progressionsgraph + später Planung)."""
from unittest.mock import MagicMock

from planning_skill_expectations import (
    SCOPE_PROGRESSION_PATH,
    SCOPE_PROGRESSION_STAGE,
    PlanningSkillExpectationInput,
    apply_expectations_to_target,
    build_planning_skill_expectations,
    expectation_input_from_progression_path,
    expectation_input_from_progression_stage,
)


class _FakeCursor:
    def __init__(self, skills):
        self._skills = skills

    def execute(self, query, params=None):
        del query
        if params and len(params) >= 2:
            needle = str(params[0]).strip("%").lower()
            exact = str(params[1]).lower()
            matches = [
                s
                for s in self._skills
                if needle in s["name"].lower() or s["name"].lower() == exact
            ]
            self._row = matches[0] if matches else None
        else:
            self._row = None

    def fetchone(self):
        return self._row


def _fake_skill_rows():
    return [
        (1, "Koordination", 0),
        (2, "Timing", 0),
        (3, "Distanz", 0),
        (4, "Kime", 0),
    ]


def test_expectation_input_from_progression_stage_merges_sources(monkeypatch):
    monkeypatch.setattr(
        "planning_skill_expectations._load_skills_for_text_match",
        lambda cur: _fake_skill_rows(),
    )
    inp = expectation_input_from_progression_stage(
        goal_query="Kumite Beinarbeit",
        goal_analysis={
            "primary_topic": "Kumite",
            "start_assumption": "gleichförmige Steppbewegung",
            "target_state": "explosiver Angriff",
        },
        resolved_structured={"roadmap_notes": "Kindergruppe"},
        stage_spec={
            "learning_goal": "variable Rhythmen",
            "load_profile": ["timing", "distanz"],
            "phase": "vertiefung",
        },
        semantic_brief_summary={"must_phrases": ["Beinarbeit"], "primary_topic": "Kumite"},
        major_step={"phase": "vertiefung", "learning_goal": "Major-Ziel"},
    )
    assert inp.scope == SCOPE_PROGRESSION_STAGE
    assert inp.primary_topic == "Kumite"
    assert inp.start_situation == "gleichförmige Steppbewegung"
    assert inp.load_profile == ["timing", "distanz"]
    assert "Beinarbeit" in inp.skill_hints


def test_build_planning_skill_expectations_load_profile_and_text(monkeypatch):
    monkeypatch.setattr(
        "planning_skill_expectations._load_skills_for_text_match",
        lambda cur: _fake_skill_rows(),
    )
    cur = _FakeCursor(
        [
            {"id": 2, "name": "Timing"},
            {"id": 3, "name": "Distanz"},
        ]
    )
    inp = PlanningSkillExpectationInput(
        scope=SCOPE_PROGRESSION_STAGE,
        primary_topic="Kumite",
        goal_query="Kumite Beinarbeit mit Timing",
        load_profile=["timing", "distanz"],
    )
    exp = build_planning_skill_expectations(cur, inp)
    assert exp.scope == SCOPE_PROGRESSION_STAGE
    assert "load_profile" in exp.sources or "text_match" in exp.sources
    names = {it.skill_name for it in exp.items}
    assert "Timing" in names or "Distanz" in names
    assert exp.skill_weights


def test_expectation_input_from_progression_path_scope():
    inp = expectation_input_from_progression_path(
        goal_query="Mae Geri Perfektion",
        goal_analysis={"primary_topic": "Mae Geri"},
        resolved_structured={"start_situation": "Grundstellung", "target_state": "freier Kick"},
    )
    assert inp.scope == SCOPE_PROGRESSION_PATH
    assert inp.start_situation == "Grundstellung"
    assert inp.target_state == "freier Kick"


def test_apply_expectations_to_target_noop_without_weights():
    class _Target:
        skill_weights = {}

        def to_summary_dict(self, cur):
            return {}

    target = _Target()
    from planning_skill_expectations import PlanningSkillExpectations

    empty = PlanningSkillExpectations(scope=SCOPE_PROGRESSION_STAGE, skill_weights={}, items=[], sources=[])
    assert apply_expectations_to_target(target, empty) is target