from typing import Dict, List, Any
from transformers import pipeline
import torch, PIL, transformers, triton, sentencepiece, protobuf
import torchvision, einops
import xformers, accelerate
from transformers import AutoModelForCausalLM, LlamaTokenizer


class EndpointHandler():
    def __init__(self, path=""):
        self.model = AutoModelForCausalLM.from_pretrained(
            'THUDM/cogvlm-chat-hf',
            torch_dtype=torch.bfloat16,
            low_cpu_mem_usage=True,
            trust_remote_code=True,
            #   cache_dir='/tmp'
        )
        self.tokenizer = LlamaTokenizer.from_pretrained('lmsys/vicuna-7b-v1.5')
        # create inference pipeline
        # self.pipeline = pipeline(model=model, tokenizer=tokenizer)

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
         Args:
             data (:obj:):
                 includes the input data and the parameters for the inference.
         Return:
             A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing :
                 - "label": A string representing what the label/class is. There can be multiple labels.
                 - "score": A score between 0 and 1 describing how confident the model is for this label/class.
         """
        inputs = data.pop("inputs", data)
        gen_kwargs = {"max_length": 2048, "do_sample": False}

        # pass inputs with all kwargs in data
        # prediction = self.pipeline(inputs)

        outputs = self.model.generate(**inputs, **gen_kwargs)
        outputs = outputs[:, inputs['input_ids'].shape[1]:]
        prediction = self.tokenizer.decode(outputs[0])

        # post process the prediction
        return prediction