"""Model loading and status management.

Supports two models:
- MiniCPM5-1B (text-only, fast)
- MiniCPM-V-4.6 (vision + text, image understanding)

Only one model is loaded at a time to conserve memory.
The model is loaded in a background thread on startup.
"""

from __future__ import annotations

import gc
import logging
import threading
from typing import Any

from code.config.constants import DEFAULT_MODEL_KEY, MODEL_CONFIGS

logger = logging.getLogger(__name__)

# ─── Module-level state ─────────────────────────────────────────────────

_current_model_key: str = DEFAULT_MODEL_KEY
_model = None
_tokenizer_or_processor = None
_model_loaded = False
_model_loading = False
_load_error: str | None = None


def _unload_model() -> None:
    """Unload current model and free memory."""
    global _model, _tokenizer_or_processor, _model_loaded

    if _model is not None:
        del _model
        _model = None
    if _tokenizer_or_processor is not None:
        del _tokenizer_or_processor
        _tokenizer_or_processor = None

    _model_loaded = False
    gc.collect()

    try:
        import torch
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
    except ImportError:
        pass


def load_model(model_key: str | None = None) -> None:
    """Load a model by key. Unloads the previous model first."""
    global _model, _tokenizer_or_processor, _model_loaded, _model_loading
    global _load_error, _current_model_key

    if model_key is None:
        model_key = _current_model_key

    if model_key not in MODEL_CONFIGS:
        _load_error = f"Unknown model: {model_key}"
        logger.error(_load_error)
        return

    # Skip if already loading or already loaded with same key
    if _model_loading:
        return
    if _model_loaded and _current_model_key == model_key:
        return

    _model_loading = True
    _load_error = None

    # Unload previous model if switching
    if _model_loaded and _current_model_key != model_key:
        logger.info("Switching model from %s to %s", _current_model_key, model_key)
        _unload_model()

    _current_model_key = model_key
    config = MODEL_CONFIGS[model_key]
    model_id = config["id"]

    try:
        import torch

        dtype = torch.float16 if torch.cuda.is_available() else torch.float32
        device_map = "auto" if torch.cuda.is_available() else None

        if config["type"] == "vlm":
            _load_vlm_model(model_id, dtype, device_map)
        else:
            _load_text_model(model_id, dtype, device_map)

        _model_loaded = True
        logger.info("%s model loaded successfully.", config["name"])

    except Exception as exc:
        _load_error = str(exc)
        logger.exception("Failed to load model %s: %s", model_id, exc)
    finally:
        _model_loading = False


def _load_text_model(model_id: str, dtype, device_map) -> None:
    """Load a text-only model (AutoModelForCausalLM + AutoTokenizer)."""
    global _model, _tokenizer_or_processor

    from transformers import AutoModelForCausalLM, AutoTokenizer

    logger.info("Loading %s (text model)...", model_id)

    _tokenizer_or_processor = AutoTokenizer.from_pretrained(
        model_id,
        trust_remote_code=True,
    )
    _model = AutoModelForCausalLM.from_pretrained(
        model_id,
        torch_dtype=dtype,
        device_map=device_map,
        trust_remote_code=True,
        low_cpu_mem_usage=True,
    )

    if device_map is None:
        _model = _model.to("cpu")

    _model.eval()


def _load_vlm_model(model_id: str, dtype, device_map) -> None:
    """Load a vision-language model (AutoModelForImageTextToText + AutoProcessor)."""
    global _model, _tokenizer_or_processor

    try:
        from transformers import AutoModelForImageTextToText, AutoProcessor
    except ImportError:
        # Fallback for older transformers
        logger.warning("AutoModelForImageTextToText not found, trying AutoModel...")
        from transformers import AutoModel as AutoModelForImageTextToText
        from transformers import AutoProcessor

    logger.info("Loading %s (VLM)...", model_id)

    _tokenizer_or_processor = AutoProcessor.from_pretrained(
        model_id,
        trust_remote_code=True,
    )
    _model = AutoModelForImageTextToText.from_pretrained(
        model_id,
        torch_dtype=dtype,
        device_map=device_map,
        trust_remote_code=True,
        low_cpu_mem_usage=True,
    )

    if device_map is None:
        _model = _model.to("cpu")

    _model.eval()


def start_background_load(model_key: str | None = None) -> threading.Thread:
    """Start loading the model in a background daemon thread."""
    thread = threading.Thread(target=load_model, args=(model_key,), daemon=True)
    thread.start()
    return thread


def switch_model(model_key: str) -> dict[str, Any]:
    """Switch to a different model. Returns status immediately, loads in background."""
    global _current_model_key

    if model_key not in MODEL_CONFIGS:
        return {"success": False, "message": f"Unknown model: {model_key}"}

    if _current_model_key == model_key and _model_loaded:
        return {"success": True, "message": f"Already using {MODEL_CONFIGS[model_key]['name']}"}

    _current_model_key = model_key
    _model_loaded = False

    # Start loading in background
    start_background_load(model_key)

    config = MODEL_CONFIGS[model_key]
    return {
        "success": True,
        "message": f"Switching to {config['name']}...",
        "model_key": model_key,
        "model_name": config["name"],
    }


def get_model_status() -> dict[str, Any]:
    """Return current model loading status."""
    config = MODEL_CONFIGS.get(_current_model_key, {})
    if _model_loaded:
        return {
            "status": "ready",
            "message": f"{config.get('name', 'Model')} loaded and ready",
            "model_key": _current_model_key,
            "model_name": config.get("name", ""),
            "model_type": config.get("type", "text"),
        }
    if _model_loading:
        return {
            "status": "loading",
            "message": f"Loading {config.get('name', 'model')}... (this may take a few minutes)",
            "model_key": _current_model_key,
            "model_name": config.get("name", ""),
            "model_type": config.get("type", "text"),
        }
    if _load_error:
        return {
            "status": "error",
            "message": f"Model load error: {_load_error}",
            "model_key": _current_model_key,
            "model_name": config.get("name", ""),
            "model_type": config.get("type", "text"),
        }
    return {
        "status": "unknown",
        "message": "Model not initialized",
        "model_key": _current_model_key,
        "model_name": config.get("name", ""),
        "model_type": config.get("type", "text"),
    }


def get_model():
    """Return the loaded model instance (or None)."""
    return _model


def get_tokenizer_or_processor():
    """Return the loaded tokenizer or processor (or None)."""
    return _tokenizer_or_processor


def is_model_loaded() -> bool:
    """Return True if the model has been loaded successfully."""
    return _model_loaded


def get_current_model_key() -> str:
    """Return the key of the currently selected model."""
    return _current_model_key


def get_current_model_type() -> str:
    """Return 'text' or 'vlm' for the current model."""
    return MODEL_CONFIGS.get(_current_model_key, {}).get("type", "text")