"""Tests für {{key|d}}, ai_caption und Unbekannt-Erkennung."""
from placeholder_registry import (
    PlaceholderMetadata,
    PlaceholderType,
    OutputType,
    build_ai_placeholder_caption,
)
import placeholder_resolver as pr
from placeholder_resolver import format_value_with_d_modifier


def test_build_ai_caption_prefers_business_meaning():
    m = PlaceholderMetadata(
        key="test_x",
        category="Test",
        description="Kurzbeschreibung",
        resolver_module="m",
        resolver_function="f",
        semantic_contract="Lang Vertrag " * 50,
        business_meaning="Kernbedeutung für die KI.",
        unit="g/day",
        placeholder_type=PlaceholderType.INTERPRETED,
        output_type=OutputType.NUMERIC,
    )
    cap = build_ai_placeholder_caption(m)
    assert cap.startswith("Kurzbeschreibung")
    assert "Kernbedeutung" in cap


def test_build_ai_caption_description_then_meaning_like_protein_avg():
    m = PlaceholderMetadata(
        key="protein_avg",
        category="Ernährung",
        description="Durchschn. Protein in g (30d)",
        resolver_module="m",
        resolver_function="f",
        business_meaning="Zentraler Placeholder für Muskelerhalt.",
        unit="g/day",
        placeholder_type=PlaceholderType.INTERPRETED,
        output_type=OutputType.NUMERIC,
    )
    cap = build_ai_placeholder_caption(m)
    assert cap.startswith("Durchschn. Protein in g (30d)")
    assert "Muskelerhalt" in cap
    assert "Technischer Bezug" not in cap


def test_build_ai_caption_score_adds_scale():
    m = PlaceholderMetadata(
        key="test_score",
        category="Test",
        description="Score",
        resolver_module="m",
        resolver_function="f",
        business_meaning="Gewichteter Gesamtscore.",
        unit="Score (0-100)",
        placeholder_type=PlaceholderType.SCORE,
        output_type=OutputType.NUMERIC,
    )
    cap = build_ai_placeholder_caption(m)
    assert "0–100" in cap or "0-100" in cap
    assert "Gewichteter" in cap


def test_placeholder_token_regex_optional_modifier():
    m0 = pr._PLACEHOLDER_TOKEN_RE.search("{{fat_avg}}")
    assert m0 and m0.group(1) == "fat_avg" and m0.group(2) is None
    m1 = pr._PLACEHOLDER_TOKEN_RE.search("{{fat_avg|d}}")
    assert m1 and m1.group(1) == "fat_avg" and m1.group(2).strip() == "d"
    m2 = pr._PLACEHOLDER_TOKEN_RE.search("{{ protein_avg  |  d  }}")
    assert m2 and m2.group(1) == "protein_avg" and m2.group(2).strip() == "d"


def test_get_unknown_placeholders_strips_modifier():
    unk = pr.get_unknown_placeholders("{{not_a_real_key|d}}")
    assert set(unk) == {"not_a_real_key"}


def test_format_value_with_d_modifier_matches_prompt_executor():
    row = {
        "key": "protein_avg",
        "description": "Durchschn. Protein in g (30d)",
        "example": "119g/Tag",
        "ai_caption": "Durchschn. Protein in g (30d). Zentral für Muskelerhalt.",
    }
    out = format_value_with_d_modifier("119g/Tag", row)
    assert out == "119g/Tag — Durchschn. Protein in g (30d). Zentral für Muskelerhalt."


def test_format_value_with_d_modifier_falls_back_to_description():
    row = {"description": "Nur Beschreibung", "key": "x"}
    assert format_value_with_d_modifier("42", row) == "42 — Nur Beschreibung"