from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftConfig
from peft import PeftModel
import torch.cuda
from typing import Any, Dict
device = "cuda" if torch.cuda.is_available() else "cpu"

class EndpointHandler():
    def __init__(self, path=""):
        config = PeftConfig.from_pretrained(path)
        model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)
        self.tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
        # Load the Lora model
        self.model = PeftModel.from_pretrained(model, path)


    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
            """
            Args:
                data (Dict): The payload with the text prompt 
            and generation parameters.
            """
            # Get inputs
            prompt = data.pop("inputs", None)
            parameters = data.pop("parameters", None)
            if prompt is None:
                raise ValueError("Missing prompt.")
            # Preprocess
            input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(device)
            # Forward
            if parameters is not None:
                output = self.model.generate(input_ids=input_ids, **parameters)
            else:
                output = self.model.generate(input_ids=input_ids)
            # Postprocess
            prediction = self.tokenizer.decode(output[0])
            return {"generated_text": prediction}