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

# check for GPU
device = 0 if torch.cuda.is_available() else -1

# multi-model list
multi_model_list = [
    {"model_id": "distilbert-base-uncased-finetuned-sst-2-english", "task": "text-classification"},
    {"model_id": "Helsinki-NLP/opus-mt-en-de", "task": "translation"},
    {"model_id": "facebook/bart-large-cnn", "task": "summarization"},
    {"model_id": "dslim/bert-base-NER", "task": "token-classification"},
    {"model_id": "textattack/bert-base-uncased-ag-news", "task": "text-classification"},
]


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_id"], device=device)

    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.multi_model[model_id](inputs, **parameters)
        else:
            prediction = self.multi_model[model_id](inputs)
        # postprocess the prediction
        return prediction