from typing import Dict, List, Any
import logging

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftConfig, PeftModel


LOGGER = logging.getLogger(__name__)


class EndpointHandler():
    def __init__(self, path=""):
        config = PeftConfig.from_pretrained(path)
        model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, load_in_8bit=True, device_map='auto')
        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]) -> List[Dict[str, Any]]:
        """
        Args:
            data (Dict): The payload with the text prompt and generation parameters.
        """
        LOGGER.info(f"Received data: {data}")
        # Get inputs
        inputs = data.pop("inputs", data)
        parameters = data.pop("parameters", None)
        LOGGER.info("Data extracted.")
        # Preprocess
        LOGGER.info(f"Start tokenizer: {inputs}")
        inputs_ids = self.tokenizer(inputs, return_tensors="pt").inputs_ids
        # Forward
        LOGGER.info(f"Start generation.")
        if parameters is not None:
            outputs = self.model.generate(inputs_ids, **parameters)
        else:
            outputs = self.model.generate(inputs_ids)
        # Postprocess
        prediction = self.tokenizer.decode(outputs[0])
        LOGGER.info(f"Generated text: {prediction}")
        return {"generated_text": prediction}