"""
Heuristische Vorschläge für CSV field_mappings / type_conversions (Admin-Editor, Issue #21).
"""

from __future__ import annotations

from copy import deepcopy
from typing import Any, Mapping

from csv_parser.core import normalize_header_for_signature
from csv_parser.module_registry import get_module_definition

# Normalisierte Header-Fragmente → DB-Feld (Substring- oder exakter Norm-Vergleich)
_MODULE_HEADER_ALIASES: dict[str, dict[str, frozenset[str]]] = {
    "nutrition": {
        "date": frozenset(
            {"datum", "date", "tag", "day", "zeit", "timestamp", "uhrzeit", "monat", "jahr"}
        ),
        "kcal": frozenset({"kcal", "kalorie", "calorie", "energie", "energy", "kj", "joule"}),
        "protein_g": frozenset({"protein", "eiwei", "eiweiss"}),
        "fat_g": frozenset({"fett", "fat", "lipid"}),
        "carbs_g": frozenset({"kh", "carb", "kohlenhydr", "carbs", "sugar", "zucker"}),
    },
    "weight": {
        "date": frozenset({"datum", "date", "tag", "day", "zeit"}),
        "weight": frozenset({"gewicht", "weight", "masse", "kg", "kilo"}),
        "note": frozenset({"notiz", "note", "comment", "kommentar"}),
    },
    "blood_pressure": {
        "measured_date": frozenset({"datum", "date", "tag", "day", "messdatum"}),
        "measured_time": frozenset({"zeit", "time", "uhr", "uhrzeit"}),
        "systolic": frozenset({"systol", "sys", "sbp", "oberdruck"}),
        "diastolic": frozenset({"diastol", "dia", "dbp", "unterdruck"}),
        "pulse": frozenset({"puls", "pulse", "hr", "herz", "bpm"}),
    },
    "activity": {
        "date": frozenset({"datum", "date", "tag", "day"}),
        "start_time": frozenset({"start", "beginn", "von"}),
        "end_time": frozenset({"end", "ende", "bis", "stop"}),
        "activity_type": frozenset({"workout", "training", "typ", "type", "art", "aktiv"}),
        "duration_min": frozenset({"dauer", "duration", "min"}),
        "distance_km": frozenset({"strecke", "distance", "km", "distanz"}),
        "kcal_active": frozenset({"kcal", "kalorie", "energie", "active"}),
        "kcal_resting": frozenset({"ruhe", "resting"}),
        "hr_avg": frozenset({"puls", "heart", "hr", "bpm", "herzfrequenz", "durchschn"}),
        "hr_max": frozenset({"max", "peak"}),
    },
    "vitals_baseline": {
        "date": frozenset({"datum", "date", "tag", "start", "zeit"}),
        "resting_hr": frozenset({"ruhepuls", "resting", "rhr"}),
        "hrv": frozenset({"hrv", "variabilit", "vfc"}),
        "vo2_max": frozenset({"vo2"}),
        "spo2": frozenset({"sauerstoff", "spo2", "oxygen"}),
        "respiratory_rate": frozenset({"atem", "respiratory"}),
    },
}

_DEFAULT_TYPE_CONVERSIONS: dict[str, dict[str, dict[str, Any]]] = {
    "nutrition": {
        "date": {"type": "date", "format": "dd.mm.yyyy HH:MM", "extract": "date_only", "flexible": True},
        "kcal": {"type": "float", "decimal_separator": "auto", "flexible": True},
        "protein_g": {"type": "float", "decimal_separator": "auto", "flexible": True},
        "fat_g": {"type": "float", "decimal_separator": "auto", "flexible": True},
        "carbs_g": {"type": "float", "decimal_separator": "auto", "flexible": True},
    },
    "weight": {
        "date": {"type": "date", "format": "dd.mm.yyyy", "flexible": True},
        "weight": {"type": "float", "decimal_separator": "auto", "flexible": True},
        "note": {"type": "string"},
    },
    "blood_pressure": {
        "measured_date": {"type": "date", "format": "dd.mm.yyyy", "flexible": True},
        "measured_time": {"type": "time", "format": "HH:MM", "flexible": True},
        "systolic": {"type": "int", "flexible": True},
        "diastolic": {"type": "int", "flexible": True},
        "pulse": {"type": "int", "flexible": True},
    },
    "activity": {
        "date": {"type": "date", "format": "yyyy-mm-dd", "flexible": True},
        "start_time": {"type": "datetime", "format": "yyyy-mm-dd HH:MM:SS", "flexible": True},
        "end_time": {"type": "datetime", "format": "yyyy-mm-dd HH:MM:SS", "flexible": True},
        "activity_type": {"type": "string"},
        "duration_min": {"type": "duration", "format": "HH:MM:SS", "target_unit": "minutes", "flexible": True},
        "distance_km": {"type": "float", "decimal_separator": "auto", "flexible": True},
        "kcal_active": {"type": "float", "decimal_separator": "auto", "flexible": True},
        "kcal_resting": {"type": "float", "decimal_separator": "auto", "flexible": True},
        "hr_avg": {"type": "int", "flexible": True},
        "hr_max": {"type": "int", "flexible": True},
    },
    "vitals_baseline": {
        "date": {
            "type": "datetime",
            "format": "yyyy-mm-dd HH:MM:SS",
            "extract": "date_only",
            "flexible": True,
        },
        "resting_hr": {"type": "int", "flexible": True},
        "hrv": {"type": "int", "flexible": True},
        "vo2_max": {"type": "float", "decimal_separator": "auto", "flexible": True},
        "spo2": {"type": "int", "flexible": True},
        "respiratory_rate": {"type": "float", "decimal_separator": "auto", "flexible": True},
    },
}


def _norm_key(header: str) -> str:
    return normalize_header_for_signature(header)


def _match_seed_to_db_field(header: str, seed_fm: Mapping[str, str]) -> str | None:
    """Findet Ziel-Feld, wenn Seed-Key zu diesem Header passt (exakt oder normalisiert)."""
    if header in seed_fm:
        v = seed_fm[header]
        if v and v not in ("-", "_skip"):
            return v
    nh = _norm_key(header)
    if nh in seed_fm:
        v = seed_fm[nh]
        if v and v not in ("-", "_skip"):
            return v
    for sk, sv in seed_fm.items():
        if not sv or sv in ("-", "_skip"):
            continue
        if _norm_key(str(sk)) == nh:
            return sv
    return None


def _alias_suggest(norm: str, module: str, used: set[str]) -> str | None:
    aliases = _MODULE_HEADER_ALIASES.get(module, {})
    mod = get_module_definition(module)
    if not mod:
        return None
    field_order = list(mod["fields"].keys())
    for db_field in field_order:
        if db_field in used:
            continue
        tokens = aliases.get(db_field, frozenset())
        nlow = norm.lower()
        if nlow == db_field or nlow.replace("_", "") == db_field.replace("_", ""):
            return db_field
        for tok in tokens:
            if len(tok) >= 2 and tok in nlow:
                return db_field
            if len(tok) >= 4 and tok in norm:
                return db_field
    return None


def suggest_field_mappings(
    headers: list[str],
    module: str,
    seed_fm: Mapping[str, str] | None = None,
) -> dict[str, str]:
    """
    Mappt jede CSV-Spalte (Roh-Header als Key) auf DB-Feld oder '-'.
    Nutzt zuerst eine passende Seed-Vorlage, dann Alias-Heuristik.
    """
    if module == "sleep":
        return {h: "-" for h in headers}

    mod = get_module_definition(module)
    if not mod:
        return {h: "-" for h in headers}

    fm: dict[str, str] = {h: "-" for h in headers}
    used: set[str] = set()

    if seed_fm:
        for h in headers:
            db = _match_seed_to_db_field(h, seed_fm)
            if db and db not in used:
                fm[h] = db
                used.add(db)

    for h in headers:
        if fm[h] != "-":
            continue
        norm = _norm_key(h)
        db = _alias_suggest(norm, module, used)
        if db:
            fm[h] = db
            used.add(db)

    return fm


def build_type_conversions_for_mapping(
    module: str,
    field_mappings: Mapping[str, str],
    seed_tc: Mapping[str, Any] | None = None,
) -> dict[str, Any]:
    """type_conversions nur für zugewiesene Zielfelder; Seed überschreibt Defaults."""
    if module == "sleep":
        return {}

    defaults = _DEFAULT_TYPE_CONVERSIONS.get(module, {})
    out: dict[str, Any] = {}
    targets = {v for v in field_mappings.values() if v and v not in ("-", "_skip")}

    if seed_tc:
        for k, v in seed_tc.items():
            if k in targets and isinstance(v, dict):
                out[k] = deepcopy(v)

    for t in targets:
        if t not in out and t in defaults:
            out[t] = deepcopy(defaults[t])

    _apply_energy_kj_hint_from_headers(module, field_mappings, out)
    return out


_ENERGY_FIELDS = frozenset({"kcal", "kcal_active", "kcal_resting"})


def _apply_energy_kj_hint_from_headers(
    module: str,
    field_mappings: Mapping[str, str],
    out: dict[str, Any],
) -> None:
    """Wenn Überschrift kJ/Kilojoule nahelegt (nicht kcal), source_unit kj setzen (FDDB & Co.)."""
    if module not in ("nutrition", "activity"):
        return
    for csv_col, db_field in field_mappings.items():
        if db_field not in _ENERGY_FIELDS:
            continue
        spec = out.get(db_field)
        if not isinstance(spec, dict):
            continue
        if spec.get("source_unit"):
            continue
        norm = normalize_header_for_signature(str(csv_col)).lower()
        if "kcal" in norm:
            continue
        if "kj" in norm or "kilojoule" in norm:
            spec2 = deepcopy(spec)
            spec2["source_unit"] = "kj"
            out[db_field] = spec2