import torch
from typing import  Dict, List, Any
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# multi-model list
# multi_model_list = [
#    {"model_id": "gemma-2B-2nd_filtered_3_full", "model_path": "omarabb315/gemma-2B-2nd_filtered_3_full", "task": "text-generation"},
#    {"model_path": "omarabb315/gemma-2B-2nd_filtered_3_16bit", "task": "text-generation"},
#    {"model_path": "omarabb315/Gemma-2-9B-filtered_3_4bits", "task": "text-generation"},
#]

class EndpointHandler():
    def __init__(self, path=""):
        # self.multi_model={}
        # load all the models onto device
        # for model in multi_model_list:
        #     self.multi_model[model["model_id"]] = pipeline(model["task"], model=model["model_path"], trust_remote_code=True)
        
        model_id = "omarabb315/gemma-2B-2nd_filtered_3_full"
        task_id = "text-generation"
        self.pipeline = pipeline(task_id, model=model_id, trust_remote_code=True)

    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
        # deserialize incomin request
        inputs = data.pop("inputs", data)
        parameters = data.pop("parameters", None)
        
        #model_id = data.pop("model_id", None)
        
        # check if model_id is in the list of models
        # if model_id is None or model_id not in self.multi_model:
        #     raise ValueError(f"model_id: {model_id} is not valid. Available models are: {list(self.multi_model.keys())}")

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

        return prediction