"""Cálculo de coste por modelo (Fase 2).

Prioridad de fuentes de precio (para que el coste registrado en
`consumo_acaicode` coincida con lo que muestra el Forge Admin Panel):
  1. Catálogo OpenRouter cacheado por el panel en Redis db 0
     (`acai:config:ai:models_cache:openrouter` → price_in_1m / price_out_1m).
  2. Price map de LiteLLM (conoce muchos modelos deepseek/, anthropic/, etc.).
  3. Coste fijo de `settings` (comportamiento previo).
"""

from __future__ import annotations

import asyncio
import json
import logging
import time
import urllib.request

import redis.asyncio as redis

from ..config import settings

logger = logging.getLogger(__name__)

# Caches de catálogo que publica el Forge Admin Panel en Redis db 0, por proveedor.
# El id se guarda SIN el prefijo de proveedor de litellm (p.ej.
# "moonshotai/kimi-k2.7-code", "deepseek-v4-pro").
_CACHE_KEYS = {
    "openrouter": "acai:config:ai:models_cache:openrouter",
    "deepseek": "acai:config:ai:models_cache:deepseek",
}
_CONFIG_DB = 0
_cfg_redis: "redis.Redis | None" = None


def _get_cfg_redis() -> "redis.Redis":
    global _cfg_redis
    if _cfg_redis is None:
        _cfg_redis = redis.Redis(
            host=settings.redis_host,
            port=settings.redis_port,
            db=_CONFIG_DB,
            password=settings.redis_password or None,
            decode_responses=True,
        )
    return _cfg_redis


# --- Catálogo con self-heal -------------------------------------------------
# El catálogo OpenRouter lo publica el Forge Admin Panel con TTL de 1h y solo se
# repuebla al abrir su ventana de IA. En runtime (coste y ventana de contexto)
# eso es frágil: si caduca, perdemos precio Y context_length del modelo activo.
# Aquí lo repoblamos nosotros (fetch público a OpenRouter, mismo shape que el
# admin) cuando falta, con un cooldown para no martillear la API. DeepSeek es
# persistente (lo escribe el admin en el arranque) y no necesita self-heal.
_OPENROUTER_URL = "https://openrouter.ai/api/v1/models"
_OPENROUTER_TIMEOUT = 15
_OR_SELFHEAL_TTL = 86_400      # 24h: persiste bastante; el admin lo refresca aparte
_OR_REFRESH_COOLDOWN = 300     # como mucho un fetch / 5 min
_or_last_refresh = [0.0]


def _fetch_openrouter_catalog_sync() -> list[dict]:
    """GET público al catálogo OpenRouter, normalizado al MISMO shape que el
    admin panel (id, context_length, price_*, supports_reasoning, supports_images).
    Filtra a modelos con soporte `tools` (igual que el admin)."""
    req = urllib.request.Request(_OPENROUTER_URL, method="GET")
    req.add_header("Accept", "application/json")
    with urllib.request.urlopen(req, timeout=_OPENROUTER_TIMEOUT) as resp:
        payload = json.loads(resp.read().decode("utf-8"))
    items = payload.get("data") if isinstance(payload, dict) else None
    if not isinstance(items, list):
        return []
    out: list[dict] = []
    for it in items:
        if not isinstance(it, dict) or not it.get("id"):
            continue
        supported = it.get("supported_parameters") or []
        if not isinstance(supported, list) or "tools" not in supported:
            continue
        pricing = it.get("pricing") or {}
        try:
            pin = float(pricing.get("prompt", 0) or 0) * 1_000_000
            pout = float(pricing.get("completion", 0) or 0) * 1_000_000
        except (TypeError, ValueError):
            pin = pout = 0.0
        try:
            ctx = int(it.get("context_length") or 0)
        except (TypeError, ValueError):
            ctx = 0
        mods = (it.get("architecture") or {}).get("input_modalities") or []
        out.append({
            "id": it.get("id"),
            "name": it.get("name") or it.get("id"),
            "context_length": ctx,
            "price_in_1m": pin,
            "price_out_1m": pout,
            "supports_reasoning": "reasoning" in supported or "include_reasoning" in supported,
            "supports_images": isinstance(mods, list) and "image" in mods,
        })
    return out


async def _get_catalog(provider: str | None) -> list[dict] | None:
    """Catálogo del proveedor desde Redis. Para OpenRouter, si falta (TTL
    caducado) lo repuebla en runtime (self-heal con cooldown)."""
    cache_key = _CACHE_KEYS.get(provider or "")
    if not cache_key:
        return None
    try:
        cached = await _get_cfg_redis().get(cache_key)
        if cached:
            data = json.loads(cached)
            if isinstance(data, list):
                return data
    except Exception as e:  # pragma: no cover - defensivo
        logger.warning("catálogo %s no disponible: %s", provider, e)
    if provider != "openrouter":
        return None
    # Self-heal solo para OpenRouter, con cooldown para no martillear la API.
    now = time.time()
    if now - _or_last_refresh[0] < _OR_REFRESH_COOLDOWN:
        return None
    _or_last_refresh[0] = now
    try:
        models = await asyncio.to_thread(_fetch_openrouter_catalog_sync)
    except Exception as e:
        logger.warning("self-heal catálogo openrouter falló: %s", e)
        return None
    if models:
        try:
            await _get_cfg_redis().set(cache_key, json.dumps(models), ex=_OR_SELFHEAL_TTL)
            logger.info("catálogo openrouter repoblado en runtime: %d modelos", len(models))
        except Exception:
            pass
        return models
    return None


async def _catalog_price_per_1m(model_id: str | None):
    """(price_in_1m, price_out_1m) del catálogo, o None. model_id en formato
    litellm ("<provider>/<id>")."""
    if not model_id or "/" not in model_id:
        return None
    provider, _, raw_id = model_id.partition("/")
    models = await _get_catalog(provider)
    if not models:
        return None
    for m in models:
        if m.get("id") == raw_id:
            pin = m.get("price_in_1m")
            pout = m.get("price_out_1m")
            if pin is not None and pout is not None:
                return (float(pin), float(pout))
    return None


# --- Ventana de contexto por modelo -----------------------------------------
# Cache en proceso con TTL corto: build_context resuelve la ventana en cada step
# del loop, y el catálogo cambia rara vez. Evita pegar a Redis 25x/turno.
_window_cache: dict[str, tuple[float, int | None]] = {}
_WINDOW_TTL = 60.0


async def resolve_context_window(model_id: str | None) -> int | None:
    """Ventana de contexto (tokens) del modelo activo.

    Fuentes en orden: catálogo del Forge Admin Panel en Redis (`context_length`)
    → price/info map de LiteLLM (`max_input_tokens`/`max_tokens`) → None.
    `model_id` viene en formato litellm ("<provider>/<id>").
    """
    if not model_id or "/" not in model_id:
        return None

    now = time.time()
    cached = _window_cache.get(model_id)
    if cached and (now - cached[0]) < _WINDOW_TTL:
        return cached[1]

    window: int | None = None

    # 1. Catálogo del panel (con self-heal para OpenRouter si caducó).
    provider, _, raw_id = model_id.partition("/")
    models = await _get_catalog(provider)
    if models:
        for m in models:
            if m.get("id") == raw_id:
                cl = m.get("context_length")
                if isinstance(cl, int) and cl > 0:
                    window = cl
                break

    # 2. Fallback: LiteLLM conoce muchos modelos (deepseek/, anthropic/, ...).
    if window is None:
        try:
            import litellm

            info = litellm.get_model_info(model_id) or {}
            for key in ("max_input_tokens", "max_tokens"):
                v = info.get(key)
                if isinstance(v, int) and v > 0:
                    window = v
                    break
        except Exception:
            pass

    _window_cache[model_id] = (now, window)
    return window


async def compute_cost(model_id: str | None, input_tokens: int, output_tokens: int) -> dict:
    """Coste de una ejecución para `model_id` y los tokens dados.

    Devuelve {"cost_usd", "input_cost_1m", "output_cost_1m"} — el coste total y
    las tarifas por 1M tokens REALMENTE aplicadas (se almacenan en
    `consumo_acaicode.input_cost_1M` / `output_cost_1M`).
    """
    input_tokens = int(input_tokens or 0)
    output_tokens = int(output_tokens or 0)

    def _result(in_1m: float, out_1m: float) -> dict:
        return {
            "cost_usd": (input_tokens / 1_000_000) * in_1m + (output_tokens / 1_000_000) * out_1m,
            "input_cost_1m": round(in_1m, 6),
            "output_cost_1m": round(out_1m, 6),
        }

    # 1. Precio del catálogo OpenRouter (fuente que muestra el admin).
    prices = await _catalog_price_per_1m(model_id)
    if prices:
        return _result(prices[0], prices[1])

    # 2. Price map de LiteLLM (deepseek/, anthropic/, etc.).
    if model_id and "/" in model_id:
        try:
            import litellm

            prompt_cost, completion_cost = litellm.cost_per_token(
                model=model_id,
                prompt_tokens=input_tokens,
                completion_tokens=output_tokens,
            )
            total = (prompt_cost or 0.0) + (completion_cost or 0.0)
            if total > 0:
                # Derivar tarifa por 1M a partir del coste por-token de litellm.
                in_1m = (prompt_cost / input_tokens) * 1_000_000 if input_tokens else 0.0
                out_1m = (completion_cost / output_tokens) * 1_000_000 if output_tokens else 0.0
                return {
                    "cost_usd": total,
                    "input_cost_1m": round(in_1m, 6),
                    "output_cost_1m": round(out_1m, 6),
                }
        except Exception as e:
            logger.warning("cost_per_token(%s) falló, uso coste fijo: %s", model_id, e)

    # 3. Coste fijo configurado.
    return _result(settings.cost_per_1m_input, settings.cost_per_1m_output)