"""LLM-based translation backend."""
  
  from __future__ import annotations
  
  import logging
  import os
  import time
  from typing import List, Optional, Sequence, Union
  
  from openai import OpenAI
  
  from config.env_config import DASHSCOPE_API_KEY

  from translation.languages import LANGUAGE_LABELS
  from translation.prompts import TRANSLATION_PROMPTS
  from translation.scenes import normalize_scene_name

  
  logger = logging.getLogger(__name__)
  

  
  def _build_prompt(
      text: str,
      *,
      source_lang: Optional[str],
      target_lang: str,
      scene: Optional[str],
  ) -> str:

      tgt = str(target_lang or "").strip().lower()
      src = str(source_lang or "auto").strip().lower() or "auto"
      normalized_scene = normalize_scene_name(scene)
      group = TRANSLATION_PROMPTS[normalized_scene]

      template = group.get(tgt) or group.get("en")

      if template is None:
          raise ValueError(f"Missing llm translation prompt for scene='{normalized_scene}' target_lang='{tgt}'")

      source_lang_label = LANGUAGE_LABELS.get(src, src)
      target_lang_label = LANGUAGE_LABELS.get(tgt, tgt)

  
      return template.format(
          source_lang=source_lang_label,
          src_lang_code=src,
          target_lang=target_lang_label,
          tgt_lang_code=tgt,
          text=text,
      )
  
  
  class LLMTranslationBackend:
      def __init__(
          self,
          *,

          capability_name: str,
          model: str,
          timeout_sec: float,
          base_url: str,

      ) -> None:

          self.capability_name = capability_name
          self.model = model
          self.timeout_sec = float(timeout_sec)
          self.base_url = base_url

          self.client = self._create_client()
  
      @property
      def supports_batch(self) -> bool:
          return True
  
      def _create_client(self) -> Optional[OpenAI]:
          api_key = DASHSCOPE_API_KEY or os.getenv("DASHSCOPE_API_KEY")
          if not api_key:
              logger.warning("DASHSCOPE_API_KEY not set; llm translation unavailable")
              return None
          try:
              return OpenAI(api_key=api_key, base_url=self.base_url)
          except Exception as exc:
              logger.error("Failed to initialize llm translation client: %s", exc, exc_info=True)
              return None
  
      def _translate_single(
          self,
          text: str,
          target_lang: str,
          source_lang: Optional[str] = None,

          scene: Optional[str] = None,

      ) -> Optional[str]:
          if not text or not str(text).strip():
              return text
          if not self.client:
              return None
  

          tgt = str(target_lang or "").strip().lower()
          src = str(source_lang or "auto").strip().lower() or "auto"
          if scene is None:
              raise ValueError("llm translation scene is required")
          normalized_scene = normalize_scene_name(scene)
          user_prompt = _build_prompt(

              text=text,
              source_lang=src,
              target_lang=tgt,

              scene=normalized_scene,

          )
          start = time.time()
          try:
              logger.info(
                  "[llm] Request | src=%s tgt=%s model=%s prompt=%s",
                  src,
                  tgt,
                  self.model,
                  user_prompt,
              )
              completion = self.client.chat.completions.create(
                  model=self.model,
                  messages=[{"role": "user", "content": user_prompt}],
                  timeout=self.timeout_sec,
              )
              content = (completion.choices[0].message.content or "").strip()
              latency_ms = (time.time() - start) * 1000
              if not content:
                  logger.warning("[llm] Empty result | src=%s tgt=%s latency=%.1fms", src, tgt, latency_ms)
                  return None
              logger.info(
                  "[llm] Success | src=%s tgt=%s src_text=%s response=%s latency=%.1fms",
                  src,
                  tgt,
                  text,
                  content,
                  latency_ms,
              )
              return content
          except Exception as exc:
              latency_ms = (time.time() - start) * 1000
              logger.warning(
                  "[llm] Failed | src=%s tgt=%s latency=%.1fms error=%s",
                  src,
                  tgt,
                  latency_ms,
                  exc,
                  exc_info=True,
              )
              return None
  
      def translate(
          self,
          text: Union[str, Sequence[str]],
          target_lang: str,
          source_lang: Optional[str] = None,

          scene: Optional[str] = None,

      ) -> Union[Optional[str], List[Optional[str]]]:
          if isinstance(text, (list, tuple)):
              results: List[Optional[str]] = []
              for item in text:
                  if item is None:
                      results.append(None)
                      continue
                  results.append(
                      self._translate_single(
                          text=str(item),
                          target_lang=target_lang,
                          source_lang=source_lang,

                          scene=scene,

                      )
                  )
              return results
  
          return self._translate_single(
              text=str(text),
              target_lang=target_lang,
              source_lang=source_lang,

              scene=scene,

          )