from datetime import date

from csv_parser.executor import guess_nutrition_item_fields
from data_layer.food_mapping import (
    fetch_unmapped_rows,
    filter_unmapped_since,
    list_quantity_units,
    list_unmapped_payload,
    merge_unmapped_rows,
    names_match_list,
    normalize_food_name,
    parse_quantity,
    parse_quantity_g,
    primary_search_query,
    resolve_quantity,
)
from data_layer.nutrition_items import macros_differ


def test_primary_query_keeps_decimal_comma():
    assert primary_search_query("Joghurt 9,5%") == "Joghurt 9,5%"
    assert primary_search_query("Joghurt, aus Kuhmilch") == "Joghurt"


def test_normalize_strips_leading_quantity():
    assert normalize_food_name("50 g Hähnchen") == "hähnchen"
    assert normalize_food_name("  Vollmilch  3,5% ") == "vollmilch 3.5%"
    assert normalize_food_name("1 ml Olivenöl") == normalize_food_name("2 ml Olivenöl") == "olivenöl"


def test_merge_unmapped_collapses_quantity_variants():
    rows = merge_unmapped_rows([
        {"source_name_raw": "1 ml Olivenöl", "source_name_normalized": "1 ml olivenöl", "count": 3, "kind": "diary"},
        {"source_name_raw": "2 ml Olivenöl", "source_name_normalized": "2 ml olivenöl", "count": 1, "kind": "diary"},
    ])
    assert len(rows) == 1
    assert rows[0]["source_name_normalized"] == "olivenöl"
    assert rows[0]["count"] == 4
    assert rows[0]["source_name_raw"].lower() == "olivenöl"


def test_parse_quantity_g():
    assert parse_quantity_g("150 g") == 150.0
    assert parse_quantity_g("150") == 150.0
    assert parse_quantity_g("1 kg") == 1000.0
    assert parse_quantity_g("5 ml") == 5.0
    assert parse_quantity_g("1 Stück") is None
    assert parse_quantity_g("1 Stück", 60) == 60.0
    assert parse_quantity_g("2 EL") == 30.0
    assert parse_quantity("1 Scheibe")["needs_unit_map"] is True
    assert parse_quantity("2 EL")["unit"] == "el"


def test_resolve_quantity_household_units():
    two_tl = resolve_quantity(quantity_amount=2, source_unit="tl")
    assert two_tl["quantity_g"] == 10.0
    assert two_tl["quantity_raw"] == "2 TL"
    pinch = resolve_quantity(quantity_amount=1, source_unit="prise")
    assert pinch["quantity_g"] == 0.3
    slice_ = resolve_quantity(quantity_amount=1, source_unit="stück")
    assert slice_["quantity_g"] is None
    assert slice_["needs_unit_map"] is True
    assert resolve_quantity(quantity_amount=1, source_unit="stück", grams_per_unit=60)["quantity_g"] == 60.0
    ids = [u["id"] for u in list_quantity_units()]
    assert "tl" in ids and "prise" in ids


def test_guess_fddb_bezeichnung_without_template_mapping():
    name, qty = guess_nutrition_item_fields({
        "bezeichnung": "50 g Hähnchen",
        "menge": "50 g",
        "kj": "800",
    })
    assert name == "50 g Hähnchen"
    assert qty == "50 g"


def test_recent_window_drops_old_and_dateless_foods():
    today = date(2026, 9, 12)
    rows = [
        {"source_name_raw": "Haferflocken", "last_date": "2026-09-10", "count": 2},
        {"source_name_raw": "Weißbrot", "last_date": "2026-01-02", "count": 40},
        {"source_name_raw": "Altes Rezept-Salz", "last_date": None, "count": 1},
    ]
    recent = filter_unmapped_since(rows, 28, today=today)
    assert [r["source_name_raw"] for r in recent] == ["Haferflocken"]
    assert len(filter_unmapped_since(rows, 0, today=today)) == 3


class _FakeCur:
    def __init__(self, batches):
        self.batches = list(batches)
        self._i = 0
        self._rows = []

    def execute(self, sql, params=None):
        self._rows = self.batches[self._i]
        self._i += 1

    def fetchall(self):
        return self._rows


def test_fetch_unmapped_keeps_ingredient_dates_and_skips_mapped():
    cur = _FakeCur([
        [{"source_name_normalized": "haferflocken"}],
        [],
        [],
        [
            {
                "source_name_raw": "Salz",
                "source_name_normalized": "salz",
                "count": 2,
                "first_date": date(2026, 9, 10),
                "last_date": date(2026, 9, 11),
                "sample_quantity_raw": "1 g",
            },
            {
                "source_name_raw": "Haferflocken",
                "source_name_normalized": "haferflocken",
                "count": 1,
                "first_date": date(2026, 9, 1),
                "last_date": date(2026, 9, 8),
                "sample_quantity_raw": "40 g",
            },
        ],
    ])
    rows = fetch_unmapped_rows(cur, "p")
    assert [r["source_name_normalized"] for r in rows] == ["salz"]
    assert rows[0]["last_date"] == "2026-09-11"
    assert rows[0]["kind"] == "recipe_ingredient"


def test_list_name_matches_despite_quantity_prefix():
    assert names_match_list("50 g Müsli Mix", "50 g müsli mix", "müsli mix")
    assert names_match_list("Müsli Mix", "müsli mix", "müsli mix")
    assert not names_match_list("Haferflocken", "haferflocken", "müsli mix")


def test_unmapped_meta_includes_recent_ingredients():
    batches = [
        [],
        [],
        [],
        [{
            "source_name_raw": "Salz",
            "source_name_normalized": "salz",
            "count": 1,
            "first_date": date(2026, 9, 10),
            "last_date": date(2026, 9, 11),
            "sample_quantity_raw": "1 g",
        }],
    ]
    payload = list_unmapped_payload(_FakeCur(batches), "p", since_days=0, meta=True)
    assert payload["total"] == 1
    assert payload["count"] == 1
    assert payload["items"][0]["source_name_normalized"] == "salz"


def test_macros_differ_rounds():
    assert not macros_differ({"kcal": 1.04, "protein_g": 0, "fat_g": 0, "carbs_g": 0}, {"kcal": 1.0, "protein_g": 0, "fat_g": 0, "carbs_g": 0})
    assert macros_differ({"kcal": 10, "protein_g": 0, "fat_g": 0, "carbs_g": 0}, {"kcal": 11, "protein_g": 0, "fat_g": 0, "carbs_g": 0})