from typing import Dict, List, Any
from transformers import AutoModelForMaskedLM, AutoTokenizer
import torch


class EndpointHandler():
    def __init__(self, path=""):
        tokenizer = AutoTokenizer.from_pretrained(path)
        model = AutoModelForMaskedLM.from_pretrained(path)
        self.tokenizer = tokenizer
        self.model = model
        if torch.cuda.is_available():
            self.device = torch.device("cuda")
            self.model.to(self.device)
        else:
            self.device = torch.device("cpu")
    def __call__(self, data: Dict[str, Any]) -> List[Dict[Any, Any]]:
        """
       data args:
            inputs (:obj: `str`)
            date (:obj: `str`)
      Return:
            A :obj:`list` | `dict`: will be serialized and returned
        """
        # get inputs
        text = data.pop("text", data)
        tokens = self.tokenizer(text, return_tensors='pt', padding=True, truncation=True).to(self.device)
        outputs = self.model(**tokens)
        results = []
        for idx, x in enumerate(outputs.logits):
            mask = tokens.attention_mask[idx]
            mask = mask[None,:]
            vec = torch.max(
                torch.log(
                    1 + torch.relu(x)
                ) * mask.unsqueeze(-1),
                dim=1)[0].squeeze()
            cols = vec.nonzero().squeeze().cpu().tolist()
            # extract the non-zero values
            weights = vec[cols].cpu().tolist()
            # use to create a dictionary of token ID to weight
            sparse_dict = dict(zip(map(str, cols), weights))
            results.append(sparse_dict)
        return results