"""
Zentrale Persistenz für activity_log + EAV-Nebenwirkungen (Eval, Spalten→EAV).

Alle Schreibpfade (REST, Universal-CSV, Legacy-Upload) laufen hier zusammen.

Feld-Katalog für CSV-Mappings: get_mappable_activity_field_catalog()
"""
from __future__ import annotations

import datetime as dt
import logging
import uuid
from typing import Any, Dict, List, Mapping, Optional

from models import ActivityEntry

from csv_parser.module_registry import get_module_definition
from data_layer.activity_data_canon import get_activity_module_registry_field_keys

logger = logging.getLogger(__name__)

try:
    from evaluation_helper import evaluate_and_save_activity as _evaluate_and_save_activity

    _EVALUATION_AVAILABLE = True
except Exception:  # pragma: no cover
    _evaluate_and_save_activity = None
    _EVALUATION_AVAILABLE = False


def find_activity_duplicate_id(
    cur,
    profile_id: str,
    date_iso: str,
    start_time: Optional[Any],
) -> Optional[str]:
    cur.execute(
        """
        SELECT id FROM activity_log
        WHERE profile_id = %s AND date = %s::date
          AND start_time IS NOT DISTINCT FROM %s::time
        """,
        (profile_id, date_iso, start_time),
    )
    row = cur.fetchone()
    return str(row["id"]) if row else None


# Datum/Start/Ende/Typ setzt der CSV-Executor explizit (Normalisierung); nicht aus diesem Patch überschreiben.
_ACTIVITY_CSV_REGISTRY_EXCLUDE = frozenset({"date", "start_time", "end_time", "activity_type"})


def activity_registry_field_keys() -> frozenset[str]:
    """Gleiche Menge wie ``ACTIVITY_MODULE_REGISTRY_FIELD_KEYS`` (Phase A: eine Quelle)."""
    return get_activity_module_registry_field_keys()


def activity_csv_registry_updates_from_mapped(mapped: Mapping[str, Any]) -> Dict[str, Any]:
    """
    activity_log-Updates nur aus Modul-Registry-Feldern (Kernspalten).
    Trainingsparameter-Keys (nur in training_parameters) laufen über EAV, nicht hier.
    """
    mod = get_module_definition("activity")
    if not mod:
        return {}
    fields = mod.get("fields") or {}
    out: Dict[str, Any] = {}

    def _sf(v: Any) -> float | None:
        try:
            if v is None or (isinstance(v, str) and not str(v).strip()):
                return None
            return round(float(v), 1)
        except (TypeError, ValueError):
            return None

    def _si(v: Any) -> int | None:
        try:
            if v is None or (isinstance(v, str) and not str(v).strip()):
                return None
            return int(round(float(v)))
        except (TypeError, ValueError):
            return None

    def _within_num_bounds(v: float | int, spec: dict, *, as_float: bool) -> bool:
        mn = spec.get("min")
        mx = spec.get("max")
        if mn is not None:
            if as_float and v < float(mn):
                return False
            if not as_float and v < int(mn):
                return False
        if mx is not None:
            if as_float and v > float(mx):
                return False
            if not as_float and v > int(mx):
                return False
        return True

    for key, spec in fields.items():
        if key in _ACTIVITY_CSV_REGISTRY_EXCLUDE:
            continue
        if key not in mapped:
            continue
        raw = mapped[key]
        if raw is None or raw == "":
            continue
        if isinstance(raw, str) and not raw.strip():
            continue
        typ = spec.get("type", "string")
        if typ == "float":
            v = _sf(raw)
            if v is not None and not _within_num_bounds(v, spec, as_float=True):
                v = None
            if v is not None:
                out[key] = v
        elif typ == "int":
            v = _si(raw)
            if v is not None and not _within_num_bounds(v, spec, as_float=False):
                v = None
            if v is not None:
                out[key] = v
        elif typ == "datetime":
            if isinstance(raw, dt.datetime):
                out[key] = raw.strftime("%Y-%m-%d %H:%M:%S")
            elif isinstance(raw, dt.date):
                out[key] = f"{raw.isoformat()} 00:00:00"
            elif isinstance(raw, str) and raw.strip():
                out[key] = raw.strip()
        elif typ == "date":
            if isinstance(raw, dt.date):
                out[key] = raw.isoformat()
            elif isinstance(raw, dt.datetime):
                out[key] = raw.date().isoformat()
            elif isinstance(raw, str) and raw.strip():
                out[key] = raw.strip()
        else:
            out[key] = str(raw).strip()

    return out


def insert_activity_from_entry(cur, profile_id: str, eid: str, e: ActivityEntry) -> None:
    """INSERT activity_log aus ActivityEntry (manueller API-Pfad)."""
    d = e.model_dump()
    cur.execute(
        """INSERT INTO activity_log (id,profile_id,date,start_time,end_time,activity_type,duration_min,kcal_active,kcal_resting,
             hr_avg,hr_max,hr_min,distance_km,pace_min_per_km,cadence,avg_power,elevation_gain,
             temperature_celsius,humidity_percent,avg_hr_percent,kcal_per_km,rpe,source,notes,
             training_type_id,training_category,training_subcategory,created)
            VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,CURRENT_TIMESTAMP)""",
        (
            eid,
            profile_id,
            d["date"],
            d["start_time"],
            d["end_time"],
            d["activity_type"],
            d["duration_min"],
            d["kcal_active"],
            d["kcal_resting"],
            d["hr_avg"],
            d["hr_max"],
            d.get("hr_min"),
            d["distance_km"],
            d.get("pace_min_per_km"),
            d.get("cadence"),
            d.get("avg_power"),
            d.get("elevation_gain"),
            d.get("temperature_celsius"),
            d.get("humidity_percent"),
            d.get("avg_hr_percent"),
            d.get("kcal_per_km"),
            d["rpe"],
            d["source"],
            d["notes"],
            d.get("training_type_id"),
            d.get("training_category"),
            d.get("training_subcategory"),
        ),
    )


def update_activity_from_entry(cur, profile_id: str, eid: str, e: ActivityEntry) -> None:
    """Volles UPDATE aus ActivityEntry (REST PUT)."""
    d = e.model_dump()
    cur.execute(
        f"UPDATE activity_log SET {', '.join(f'{k}=%s' for k in d)} WHERE id=%s AND profile_id=%s",
        list(d.values()) + [eid, profile_id],
    )


def update_activity_columns(
    cur,
    profile_id: str,
    eid: str,
    updates: Dict[str, Any],
) -> None:
    """Teil-UPDATE nur für übergebene Spalten (Importe)."""
    if not updates:
        return
    cols = [f"{k} = %s" for k in updates]
    vals = list(updates.values()) + [eid, profile_id]
    cur.execute(
        f"UPDATE activity_log SET {', '.join(cols)} WHERE id = %s AND profile_id = %s",
        vals,
    )


def insert_activity_csv_minimal(
    cur,
    profile_id: str,
    eid: str,
    *,
    date_iso: str,
    start_time: Any,
    end_time: Any,
    activity_type: str,
    training_type_id: Any,
    training_category: Any,
    training_subcategory: Any,
    source: str,
) -> None:
    """
    INSERT Kopfzeile für Universal-CSV / Legacy-Import.

    Metriken aus ``activity_csv_registry_updates_from_mapped`` (oder manuelles Dict) —
    ausschließlich via ``update_activity_columns``; keine fest verdrahteten hr_avg-Parameter.
    """
    cur.execute(
        """
        INSERT INTO activity_log (
            id, profile_id, date, start_time, end_time, activity_type, duration_min,
            kcal_active, kcal_resting, hr_avg, hr_max, distance_km,
            source, training_type_id, training_category, training_subcategory, created
        )
        VALUES (%s,%s,%s,%s,%s,%s,NULL,NULL,NULL,NULL,NULL,NULL,%s,%s,%s,%s,CURRENT_TIMESTAMP)
        """,
        (
            eid,
            profile_id,
            date_iso,
            start_time,
            end_time,
            activity_type,
            source,
            training_type_id,
            training_category,
            training_subcategory,
        ),
    )


def run_activity_post_write_hooks(cur, profile_id: str, eid: str) -> None:
    """Auto-Eval (falls aktiv). Kein Spalte→EAV-Sync: Lesepfad merge_column_backed_and_eav_metrics."""
    if _EVALUATION_AVAILABLE and _evaluate_and_save_activity:
        cur.execute(
            """
            SELECT id, profile_id, date, training_type_id, duration_min,
                   hr_avg, hr_max, distance_km, kcal_active, kcal_resting,
                   rpe, pace_min_per_km, cadence, elevation_gain
            FROM activity_log
            WHERE id = %s
            """,
            (eid,),
        )
        activity_row = cur.fetchone()
        if activity_row:
            activity_dict = dict(activity_row)
            training_type_id = activity_dict.get("training_type_id")
            if training_type_id:
                try:
                    _evaluate_and_save_activity(cur, eid, activity_dict, training_type_id, profile_id)
                except Exception as eval_error:
                    logger.error("[AUTO-EVAL] activity %s: %s", eid, eval_error)


def run_activity_post_write_hooks_import(
    cur,
    profile_id: str,
    eid: str,
    *,
    training_type_id: Optional[int] = None,
) -> None:
    """Auto-Eval nach Import — liest die Session aus der DB (gleiche Felder wie REST-Hook)."""
    if not _EVALUATION_AVAILABLE or not _evaluate_and_save_activity:
        return
    cur.execute(
        """
        SELECT id, profile_id, date, training_type_id, duration_min,
               hr_avg, hr_max, distance_km, kcal_active, kcal_resting,
               rpe, pace_min_per_km, cadence, elevation_gain
        FROM activity_log
        WHERE id = %s AND profile_id = %s
        """,
        (eid, profile_id),
    )
    row = cur.fetchone()
    if not row:
        return
    activity_dict = dict(row)
    tid = training_type_id if training_type_id is not None else activity_dict.get("training_type_id")
    if not tid:
        return
    try:
        _evaluate_and_save_activity(cur, eid, activity_dict, int(tid), profile_id)
    except Exception as eval_err:
        logger.warning("[activity import] Auto-Eval fehlgeschlagen: %s", eval_err)


def merge_activity_csv_module_fields(
    cur,
    static_fields: Dict[str, Any],
) -> Dict[str, Any]:
    """
    activity-Modul für CSV: statische Registry-Felder + alle aktiven training_parameters.

    Gleiche Quelle wie get_mappable_activity_field_catalog.training_parameters — erscheint
    in Admin-CSV-Ziel-Liste, Validierung und Import-Zeilenaggregation.
    """
    out = dict(static_fields)
    cur.execute(
        """
        SELECT key, data_type, unit, name_de
        FROM training_parameters
        WHERE is_active = true
        ORDER BY key
        """
    )
    for row in cur.fetchall():
        k = row["key"]
        if k in out:
            continue
        dt = row["data_type"] or "float"
        if dt == "integer":
            mtype = "int"
        elif dt == "float":
            mtype = "float"
        elif dt == "boolean":
            mtype = "string"
        else:
            mtype = "string"
        spec: Dict[str, Any] = {
            "type": mtype,
            "required": False,
            "from_training_parameter": True,
        }
        if row.get("unit"):
            spec["unit"] = row["unit"]
        if row.get("name_de"):
            spec["label_de"] = row["name_de"]
        out[k] = spec
    return out


def get_mappable_activity_field_catalog(cur, profile_id: str) -> Dict[str, Any]:
    """
    Felder für konfigurierbare Import-Mappings.

    core_fields: module_registry „activity“ → activity_log.
    training_parameters: alle aktiven Parameter (global); bei Anwendung auf eine Session
    werden Keys verworfen, die nicht in resolve_activity_attribute_schema(Kategorie/Typ) liegen.

    profile_id: reserviert für künftige Profil-Filter.
    """
    _ = profile_id
    mod = get_module_definition("activity") or {}
    fields = mod.get("fields") or {}
    core_fields: List[Dict[str, Any]] = []
    for key, spec in fields.items():
        s = spec or {}
        core_fields.append(
            {
                "key": key,
                "target": "activity_log",
                "column": key,
                "data_type": s.get("type", "string"),
                "required": bool(s.get("required")),
                "unit": s.get("unit"),
                "label_de": s.get("label_de") or key,
            }
        )
    core_fields.sort(key=lambda x: x["key"])

    cur.execute(
        """
        SELECT id, key, name_de, name_en, category AS param_category,
               data_type, unit, source_field
        FROM training_parameters
        WHERE is_active = true
        ORDER BY key
        """
    )
    parameters = [dict(r) for r in cur.fetchall()]

    return {
        "core_fields": core_fields,
        "training_parameters": parameters,
        "notes": (
            "training_parameters listet alle aktiven Keys. Pro Session werden Werte ignoriert, "
            "die für deren training_category/training_type_id nicht im Attribut-Schema vorkommen."
        ),
    }


def new_activity_id() -> str:
    return str(uuid.uuid4())