from __future__ import annotations

import logging
import os
import threading
from typing import Any

logger = logging.getLogger(__name__)

_embedders: dict[str, Any] = {}
_inference_lock = threading.Lock()


def local_embedding_model() -> str:
    return (
        os.environ.get("LOCAL_EMBEDDING_MODEL", "").strip()
        or os.environ.get("TASK_B_LOCAL_EMBEDDING_MODEL", "").strip()
        or os.environ.get("TASK_A_EMBEDDING_MODEL", "").strip()
        or "all-MiniLM-L6-v2"
    )


def embedding_model_name_task_a() -> str:
    override = os.environ.get("TASK_A_EMBEDDING_MODEL", "").strip()
    return override or local_embedding_model()


def embedding_model_name_task_b() -> str:
    override = os.environ.get("TASK_B_LOCAL_EMBEDDING_MODEL", "").strip()
    return override or local_embedding_model()


def unique_embedding_model_names() -> list[str]:
    names = {embedding_model_name_task_a(), embedding_model_name_task_b()}
    return sorted(names)


def get_embedder(model_name: str) -> Any:
    key = model_name.strip()
    if key not in _embedders:
        try:
            from sentence_transformers import SentenceTransformer  # type: ignore[import-untyped]
        except ImportError as e:
            raise RuntimeError("sentence-transformers required") from e
        logger.info("Loading shared embedding model %s", key)
        _embedders[key] = SentenceTransformer(key)
    return _embedders[key]


def inference_lock() -> threading.Lock:
    return _inference_lock


def warm_shared_weights() -> None:
    for name in unique_embedding_model_names():
        get_embedder(name)
    logger.info(
        "Shared weights ready (%d embedder(s); generation via Gemini API)",
        len(_embedders),
    )