"""
  Embedding service (FastAPI).
  

  API (simple list-in, list-out; aligned by index):
  - POST /embed/text   body: ["text1", "text2", ...] -> [[...], ...]
  - POST /embed/image  body: ["url_or_path1", ...]  -> [[...], ...]

  """
  

  import logging

  import os

  import threading
  from typing import Any, Dict, List, Optional
  
  import numpy as np

  from fastapi import FastAPI, HTTPException

  
  from embeddings.config import CONFIG

  from embeddings.protocols import ImageEncoderProtocol

  from config.services_config import get_embedding_backend_config

  logger = logging.getLogger(__name__)

  app = FastAPI(title="saas-search Embedding Service", version="1.0.0")

  # Models are loaded at startup, not lazily

  _text_model: Optional[Any] = None

  _image_model: Optional[ImageEncoderProtocol] = None

  _text_backend_name: str = ""

  open_text_model = True

  open_image_model = True  # Enable image embedding when using clip-as-service

  
  _text_encode_lock = threading.Lock()
  _image_encode_lock = threading.Lock()
  
  

  @app.on_event("startup")
  def load_models():
      """Load models at service startup to avoid first-request latency."""

      global _text_model, _image_model, _text_backend_name

      logger.info("Loading embedding models at startup...")

      # Load text model

      if open_text_model:
          try:

              backend_name, backend_cfg = get_embedding_backend_config()
              _text_backend_name = backend_name
              if backend_name == "tei":
                  from embeddings.tei_model import TEITextModel
  
                  base_url = (
                      os.getenv("TEI_BASE_URL")
                      or backend_cfg.get("base_url")
                      or CONFIG.TEI_BASE_URL
                  )
                  timeout_sec = int(
                      os.getenv("TEI_TIMEOUT_SEC")
                      or backend_cfg.get("timeout_sec")
                      or CONFIG.TEI_TIMEOUT_SEC
                  )
                  logger.info("Loading text backend: tei (base_url=%s)", base_url)
                  _text_model = TEITextModel(
                      base_url=str(base_url),
                      timeout_sec=timeout_sec,
                  )
              elif backend_name == "local_st":
                  from embeddings.qwen3_model import Qwen3TextModel

                  model_id = (
                      os.getenv("TEXT_MODEL_ID")
                      or backend_cfg.get("model_id")
                      or CONFIG.TEXT_MODEL_ID
                  )
                  logger.info("Loading text backend: local_st (model=%s)", model_id)
                  _text_model = Qwen3TextModel(model_id=str(model_id))
              else:
                  raise ValueError(
                      f"Unsupported embedding backend: {backend_name}. "
                      "Supported: tei, local_st"
                  )
              logger.info("Text backend loaded successfully: %s", _text_backend_name)

          except Exception as e:
              logger.error(f"Failed to load text model: {e}", exc_info=True)
              raise
      

      # Load image model: clip-as-service (recommended) or local CN-CLIP

      if open_image_model:
          try:

              if CONFIG.USE_CLIP_AS_SERVICE:

                  from embeddings.clip_as_service_encoder import ClipAsServiceImageEncoder
  

                  logger.info(f"Loading image encoder via clip-as-service: {CONFIG.CLIP_AS_SERVICE_SERVER}")
                  _image_model = ClipAsServiceImageEncoder(
                      server=CONFIG.CLIP_AS_SERVICE_SERVER,
                      batch_size=CONFIG.IMAGE_BATCH_SIZE,
                  )
                  logger.info("Image model (clip-as-service) loaded successfully")
              else:

                  from embeddings.clip_model import ClipImageModel
  

                  logger.info(f"Loading local image model: {CONFIG.IMAGE_MODEL_NAME} (device: {CONFIG.IMAGE_DEVICE})")
                  _image_model = ClipImageModel(
                      model_name=CONFIG.IMAGE_MODEL_NAME,
                      device=CONFIG.IMAGE_DEVICE,
                  )
                  logger.info("Image model (local CN-CLIP) loaded successfully")

          except Exception as e:

              logger.error("Failed to load image model: %s", e, exc_info=True)
              raise

  
      logger.info("All embedding models loaded successfully, service ready")

  def _normalize_vector(vec: np.ndarray) -> np.ndarray:
      norm = float(np.linalg.norm(vec))
      if not np.isfinite(norm) or norm <= 0.0:
          raise RuntimeError("Embedding vector has invalid norm (must be > 0)")
      return vec / norm
  
  
  def _as_list(embedding: Optional[np.ndarray], normalize: bool = False) -> Optional[List[float]]:

      if embedding is None:
          return None
      if not isinstance(embedding, np.ndarray):
          embedding = np.array(embedding, dtype=np.float32)
      if embedding.ndim != 1:
          embedding = embedding.reshape(-1)

      embedding = embedding.astype(np.float32, copy=False)
      if normalize:
          embedding = _normalize_vector(embedding).astype(np.float32, copy=False)
      return embedding.tolist()

  
  
  @app.get("/health")
  def health() -> Dict[str, Any]:

      """Health check endpoint. Returns status and model loading state."""
      return {
          "status": "ok",
          "text_model_loaded": _text_model is not None,

          "text_backend": _text_backend_name,

          "image_model_loaded": _image_model is not None,
      }

  
  
  @app.post("/embed/text")

  def embed_text(texts: List[str], normalize: Optional[bool] = None) -> List[Optional[List[float]]]:

      if _text_model is None:
          raise RuntimeError("Text model not loaded")

      effective_normalize = bool(CONFIG.TEXT_NORMALIZE_EMBEDDINGS) if normalize is None else bool(normalize)

      normalized: List[str] = []

      for i, t in enumerate(texts):

          if not isinstance(t, str):

              raise HTTPException(status_code=400, detail=f"Invalid text at index {i}: must be string")
          s = t.strip()
          if not s:
              raise HTTPException(status_code=400, detail=f"Invalid text at index {i}: empty string")
          normalized.append(s)
  

      try:
          with _text_encode_lock:
              embs = _text_model.encode_batch(
                  normalized,
                  batch_size=int(CONFIG.TEXT_BATCH_SIZE),
                  device=CONFIG.TEXT_DEVICE,

                  normalize_embeddings=effective_normalize,

              )
      except Exception as e:
          logger.error("Text embedding backend failure: %s", e, exc_info=True)
          raise HTTPException(
              status_code=502,
              detail=f"Text embedding backend failure: {e}",
          ) from e

      if embs is None or len(embs) != len(normalized):
          raise RuntimeError(
              f"Text model response length mismatch: expected {len(normalized)}, "
              f"got {0 if embs is None else len(embs)}"
          )
      out: List[Optional[List[float]]] = []
      for i, emb in enumerate(embs):

          vec = _as_list(emb, normalize=effective_normalize)

          if vec is None:
              raise RuntimeError(f"Text model returned empty embedding for index {i}")
          out.append(vec)

      return out
  
  
  @app.post("/embed/image")

  def embed_image(images: List[str], normalize: Optional[bool] = None) -> List[Optional[List[float]]]:

      if _image_model is None:

          raise RuntimeError("Image model not loaded")

      effective_normalize = bool(CONFIG.IMAGE_NORMALIZE_EMBEDDINGS) if normalize is None else bool(normalize)

      urls: List[str] = []

      for i, url_or_path in enumerate(images):

          if not isinstance(url_or_path, str):

              raise HTTPException(status_code=400, detail=f"Invalid image at index {i}: must be string URL/path")
          s = url_or_path.strip()
          if not s:
              raise HTTPException(status_code=400, detail=f"Invalid image at index {i}: empty URL/path")
          urls.append(s)

      with _image_encode_lock:

          vectors = _image_model.encode_image_urls(
              urls,
              batch_size=CONFIG.IMAGE_BATCH_SIZE,
              normalize_embeddings=effective_normalize,
          )

      if vectors is None or len(vectors) != len(urls):
          raise RuntimeError(
              f"Image model response length mismatch: expected {len(urls)}, "
              f"got {0 if vectors is None else len(vectors)}"
          )
      out: List[Optional[List[float]]] = []
      for i, vec in enumerate(vectors):

          out_vec = _as_list(vec, normalize=effective_normalize)

          if out_vec is None:
              raise RuntimeError(f"Image model returned empty embedding for index {i}")
          out.append(out_vec)

      return out