from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaConfig
from config.config import settings
from sentence_transformers import SentenceTransformer
import torch
import logging

logger = logging.getLogger(__name__)

class ModelService:
    _instance = None

    def __new__(cls):
        if cls._instance is None:
            cls._instance = super().__new__(cls)
            cls._instance._initialized = False
        return cls._instance

    def __init__(self):
        if not self._initialized:
            self._initialized = True
            self._load_models()

    def _load_models(self):
        try:
            # Load tokenizer
            #self.tokenizer = AutoTokenizer.from_pretrained(settings.MODEL_NAME)

            ## Load model configuration
            #config = LlamaConfig.from_pretrained(settings.MODEL_NAME)

            ## Check quantization type and adjust accordingly
            #if config.get('quantization_config', {}).get('type', '') == 'compressed-tensors':
            #    logger.warning("Quantization type 'compressed-tensors' is not supported. Switching to 'bitsandbytes_8bit'.")
            #    config.quantization_config['type'] = 'bitsandbytes_8bit'

            ## Load model with the updated configuration
            #self.model = AutoModelForCausalLM.from_pretrained(
            #    settings.MODEL_NAME, 
            #    config=config, 
            #    torch_dtype=torch.float16 if settings.DEVICE == "cuda" else torch.float32,
            #    device_map="auto" if settings.DEVICE == "cuda" else None
            #)

#-----
            # Load Llama 3.2 model
            model_name = settings.MODEL_NAME #"meta-llama/Llama-3.2-3B-Instruct"  # Replace with the exact model path
            tokenizer = AutoTokenizer.from_pretrained(model_name)
            #model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.float16)
            self.model = AutoModelForCausalLM.from_pretrained(model_name, device_map=None, torch_dtype=torch.float32)

            
            # Load sentence embedder
            self.embedder = SentenceTransformer(settings.EMBEDDER_MODEL)

        except Exception as e:
            logger.error(f"Error loading models: {e}")
            raise

    def get_models(self):
        return self.tokenizer, self.model, self.embedder