from transformers import AutoTokenizer, TextGenerationPipeline, pipeline
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
from typing import Dict, List, Any


class EndpointHandler:
    def __init__(self, path=""):
        # load the model
        tokenizer = AutoTokenizer.from_pretrained(path)
        model = AutoGPTQForCausalLM.from_quantized(path, device="cuda:0", use_safetensors=True)

        # create inference pipeline
        self.pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)

    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
        inputs = data.pop("inputs", data)
        parameters = data.pop("parameters", None)

        # pass inputs with all kwargs in data
        if parameters is not None:
            prediction = self.pipeline(inputs, **parameters)
        else:
            prediction = self.pipeline(inputs)
        # postprocess the prediction
        return prediction