from __future__ import annotations

from typing import Any, Dict, List, Optional, Tuple
import time
import logging

from openai import OpenAI, AsyncOpenAI
from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type

from .config import extractor_llm_config, settings


class LLMClient:
  """Cliente unificado para llamadas Chat Completions (SDK OpenAI).

  - Centraliza api_key, base_url, modelo y timeout desde configuracion.
  - Expone helpers para respuestas de texto y JSON.
  - Registra métricas mínimas en `domain.api_calls` (start/end/error + usage).
  - Aplica reintentos ante timeouts.
  """

  def __init__(
    self,
    api_key: Optional[str] = None,
    base_url: Optional[str] = None,
    default_model: Optional[str] = None,
    request_timeout_s: Optional[float] = None,
  ) -> None:
    default_config = extractor_llm_config()
    self.api_key = api_key if api_key is not None else str(default_config["api_key"])
    self.base_url = base_url if base_url is not None else str(default_config["base_url"])
    self.default_model = default_model if default_model is not None else str(default_config["model"])
    self.request_timeout_s = request_timeout_s or float(default_config["timeout_s"])
    self._client = self._build_sync_client()
    self._async_client = self._build_async_client()
    self._api_logger = logging.getLogger("domain.api_calls")

  def _build_sync_client(self) -> OpenAI:
    kwargs: Dict[str, Any] = {"api_key": self.api_key}
    if self.base_url:
      kwargs["base_url"] = self.base_url
    return OpenAI(**kwargs)

  def _build_async_client(self) -> AsyncOpenAI:
    kwargs: Dict[str, Any] = {"api_key": self.api_key}
    if self.base_url:
      kwargs["base_url"] = self.base_url
    return AsyncOpenAI(**kwargs)

  def update_config(
    self,
    *,
    api_key: Optional[str] = None,
    base_url: Optional[str] = None,
    default_model: Optional[str] = None,
    request_timeout_s: Optional[float] = None,
  ) -> None:
    if api_key is not None:
      self.api_key = api_key
    if base_url is not None:
      self.base_url = base_url
    if default_model is not None:
      self.default_model = default_model
    if request_timeout_s is not None:
      self.request_timeout_s = request_timeout_s
    self._client = self._build_sync_client()
    self._async_client = self._build_async_client()

  def list_models(self) -> List[str]:
    """Lista modelos publicados por el backend compatible con OpenAI."""
    models = self._client.models.list()
    return sorted(
      str(getattr(model, "id", ""))
      for model in getattr(models, "data", [])
      if getattr(model, "id", None)
    )

  @retry(
    stop=stop_after_attempt(settings.retry.max_attempts),
    wait=wait_exponential(multiplier=settings.retry.wait_min_s, max=settings.retry.wait_max_s),
    retry=retry_if_exception_type((TimeoutError,)),
    reraise=True,
  )
  def _create_completion(self, *, messages: List[Dict[str, Any]], model: str, timeout_s: float, **extra) -> Any:
    return self._client.chat.completions.create(
      messages=messages,
      model=model,
      timeout=timeout_s,
      **extra,
    )

  @retry(
    stop=stop_after_attempt(settings.retry.max_attempts),
    wait=wait_exponential(multiplier=settings.retry.wait_min_s, max=settings.retry.wait_max_s),
    retry=retry_if_exception_type((TimeoutError,)),
    reraise=True,
  )
  async def _create_completion_async(self, *, messages: List[Dict[str, Any]], model: str, timeout_s: float, **extra) -> Any:
    return await self._async_client.chat.completions.create(
      messages=messages,
      model=model,
      timeout=timeout_s,
      **extra,
    )

  def _log_start(self, *, component: str, model: str, timeout_s: float, messages_count: int) -> None:
    try:
      self._api_logger.info(
        "api_call_start",
        extra={
          "component": component,
          "base_url": self.base_url,
          "model": model,
          "timeout_s": timeout_s,
          "messages": messages_count,
        },
      )
    except Exception:
      pass

  def _log_end(self, *, component: str, model: str, timeout_s: float, elapsed_ms: int, usage: Optional[Dict[str, Any]]) -> None:
    try:
      self._api_logger.info(
        "api_call_end",
        extra={
          "component": component,
          "base_url": self.base_url,
          "model": model,
          "timeout_s": timeout_s,
          "elapsed_ms": elapsed_ms,
          "prompt_tokens": (usage or {}).get("prompt_tokens"),
          "completion_tokens": (usage or {}).get("completion_tokens"),
          "total_tokens": (usage or {}).get("total_tokens"),
        },
      )
    except Exception:
      pass

  def _log_error(self, *, component: str, model: str, timeout_s: float, elapsed_ms: Optional[int], error: Exception) -> None:
    try:
      self._api_logger.info(
        "api_call_error",
        extra={
          "component": component,
          "base_url": self.base_url,
          "model": model,
          "timeout_s": timeout_s,
          "elapsed_ms": (elapsed_ms if elapsed_ms is not None else None),
          "error": str(error),
        },
      )
    except Exception:
      pass

  @staticmethod
  def _extract_usage(obj: Any) -> Optional[Dict[str, Any]]:
    try:
      usage = getattr(obj, "usage", None)
      if usage is None:
        return None
      usage_dict: Dict[str, Any] = {
        "prompt_tokens": getattr(usage, "prompt_tokens", None),
        "completion_tokens": getattr(usage, "completion_tokens", None),
        "total_tokens": getattr(usage, "total_tokens", None),
      }
      details = getattr(usage, "prompt_tokens_details", None)
      if details is not None:
        try:
          usage_dict["prompt_tokens_details"] = dict(details)  # type: ignore[arg-type]
        except Exception:
          try:
            usage_dict["prompt_tokens_details"] = {
              "cached_tokens": getattr(details, "cached_tokens", None),
              "audio_tokens": getattr(details, "audio_tokens", None),
            }
          except Exception:
            pass
      return usage_dict
    except Exception:
      return None

  def chat_text(
    self,
    messages: List[Dict[str, Any]],
    *,
    model: Optional[str] = None,
    timeout_s: Optional[float] = None,
    component: str = "llm_client",
  ) -> Tuple[str, Optional[Dict[str, Any]]]:
    """Devuelve (texto, usage)."""
    eff_model = model or self.default_model
    eff_timeout = float(timeout_s if timeout_s is not None else self.request_timeout_s)
    self._log_start(component=component, model=eff_model, timeout_s=eff_timeout, messages_count=len(messages or []))
    start = time.time()
    try:
      chat_completion = self._create_completion(
        messages=messages,
        model=eff_model,
        timeout_s=eff_timeout,
      )
      elapsed_ms = int((time.time() - start) * 1000)
      usage = self._extract_usage(chat_completion)
      self._log_end(component=component, model=eff_model, timeout_s=eff_timeout, elapsed_ms=elapsed_ms, usage=usage)
      text: str = chat_completion.choices[0].message.content  # type: ignore[assignment]
      return text, usage
    except Exception as ex:
      elapsed_ms = int((time.time() - start) * 1000)
      self._log_error(component=component, model=eff_model, timeout_s=eff_timeout, elapsed_ms=elapsed_ms, error=ex)
      raise

  async def chat_text_async(
    self,
    messages: List[Dict[str, Any]],
    *,
    model: Optional[str] = None,
    timeout_s: Optional[float] = None,
    component: str = "llm_client",
  ) -> Tuple[str, Optional[Dict[str, Any]]]:
    """Devuelve (texto, usage) de forma asíncrona."""
    eff_model = model or self.default_model
    eff_timeout = float(timeout_s if timeout_s is not None else self.request_timeout_s)
    self._log_start(component=component, model=eff_model, timeout_s=eff_timeout, messages_count=len(messages or []))
    start = time.time()
    try:
      chat_completion = await self._create_completion_async(
        messages=messages,
        model=eff_model,
        timeout_s=eff_timeout,
      )
      elapsed_ms = int((time.time() - start) * 1000)
      usage = self._extract_usage(chat_completion)
      self._log_end(component=component, model=eff_model, timeout_s=eff_timeout, elapsed_ms=elapsed_ms, usage=usage)
      text: str = chat_completion.choices[0].message.content  # type: ignore[assignment]
      return text, usage
    except Exception as ex:
      elapsed_ms = int((time.time() - start) * 1000)
      self._log_error(component=component, model=eff_model, timeout_s=eff_timeout, elapsed_ms=elapsed_ms, error=ex)
      raise

  def chat_json(
    self,
    messages: List[Dict[str, Any]],
    *,
    model: Optional[str] = None,
    response_format: str = "json_object",
    timeout_s: Optional[float] = None,
    component: str = "llm_client",
    max_completion_tokens: Optional[int] = None,
  ) -> Tuple[Dict[str, Any], Optional[Dict[str, Any]]]:
    """Devuelve (objeto JSON parseado, usage)."""
    import json
    eff_model = model or self.default_model
    eff_timeout = float(timeout_s if timeout_s is not None else self.request_timeout_s)
    self._log_start(component=component, model=eff_model, timeout_s=eff_timeout, messages_count=len(messages or []))
    start = time.time()
    try:
      completion_kwargs: Dict[str, Any] = {
        "response_format": {"type": response_format},
      }
      if max_completion_tokens is not None:
        completion_kwargs["max_completion_tokens"] = max_completion_tokens
      chat_completion = self._create_completion(
        messages=messages,
        model=eff_model,
        timeout_s=eff_timeout,
        **completion_kwargs,
      )
      elapsed_ms = int((time.time() - start) * 1000)
      usage = self._extract_usage(chat_completion)
      self._log_end(component=component, model=eff_model, timeout_s=eff_timeout, elapsed_ms=elapsed_ms, usage=usage)
      content = chat_completion.choices[0].message.content  # type: ignore[assignment]
      obj = json.loads(content)
      return obj, usage
    except Exception as ex:
      elapsed_ms = int((time.time() - start) * 1000)
      self._log_error(component=component, model=eff_model, timeout_s=eff_timeout, elapsed_ms=elapsed_ms, error=ex)
      raise

  async def chat_json_async(
    self,
    messages: List[Dict[str, Any]],
    *,
    model: Optional[str] = None,
    response_format: str = "json_object",
    timeout_s: Optional[float] = None,
    component: str = "llm_client",
    max_completion_tokens: Optional[int] = None,
  ) -> Tuple[Dict[str, Any], Optional[Dict[str, Any]]]:
    """Devuelve (objeto JSON parseado, usage) de forma asíncrona."""
    import json
    eff_model = model or self.default_model
    eff_timeout = float(timeout_s if timeout_s is not None else self.request_timeout_s)
    self._log_start(component=component, model=eff_model, timeout_s=eff_timeout, messages_count=len(messages or []))
    start = time.time()
    try:
      completion_kwargs: Dict[str, Any] = {
        "response_format": {"type": response_format},
      }
      if max_completion_tokens is not None:
        completion_kwargs["max_completion_tokens"] = max_completion_tokens
      chat_completion = await self._create_completion_async(
        messages=messages,
        model=eff_model,
        timeout_s=eff_timeout,
        **completion_kwargs,
      )
      elapsed_ms = int((time.time() - start) * 1000)
      usage = self._extract_usage(chat_completion)
      self._log_end(component=component, model=eff_model, timeout_s=eff_timeout, elapsed_ms=elapsed_ms, usage=usage)
      content = chat_completion.choices[0].message.content  # type: ignore[assignment]
      obj = json.loads(content)
      return obj, usage
    except Exception as ex:
      elapsed_ms = int((time.time() - start) * 1000)
      self._log_error(component=component, model=eff_model, timeout_s=eff_timeout, elapsed_ms=elapsed_ms, error=ex)
      raise