import torch
from transformers import AutoModelForCausalLM, AutoProcessor
import logging


logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
)
logger = logging.getLogger(__name__)


class Model:
    """
    Handles loading and managing the Kimi-VL-A3B-Thinking model and processor.
    Loads model and processor once and provides accessors.
    """

    # ALL MODEL CONFIGURATIONS
    _model = None

    _processor = None
    MODEL_PATH = "moonshotai/Kimi-VL-A3B-Thinking-2506"
    model_class = AutoModelForCausalLM
    processor_class = AutoProcessor

    model_kwargs = {
        "device_map": "auto",
        "torch_dtype": "auto",
        "trust_remote_code": True
    }
    processor_kwargs = {
        "device_map": "auto",
        "trust_remote_code": True
    }

    @classmethod
    def load(cls):
        if cls._model is None:
            try:
                cls._model = cls.model_class.from_pretrained(
                    cls.MODEL_PATH, **cls.model_kwargs
                )
                cls._processor = cls.processor_class.from_pretrained(
                    cls.MODEL_PATH, **cls.processor_kwargs
                )
            except Exception as e:
                logger.error(f"Failed to load model or processor: {str(e)}")
                raise

        return cls._model, cls._processor

    @classmethod
    def load_model(cls):
        model, _ = cls.load()
        return model

    @classmethod
    def load_processor(cls):
        _, processor = cls.load()
        return processor

    @classmethod
    def load_raw_model(cls):
        """Get the raw transformers model (not wrapped by outlines)"""
        cls.load()  # Ensure models are loaded
        return cls._raw_model