from typing import Dict, List, Any
from transformers import AutoTokenizer, AutoModel
from optimum.pipelines import pipeline
from optimum.onnxruntime import ORTModelForFeatureExtraction

import torch

#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
    
class EndpointHandler():
    def __init__(self, path=""):
        # self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        # self.tokenizer = AutoTokenizer.from_pretrained("optimum/sbert-all-MiniLM-L6-with-pooler")
        model_regular = ORTModelForFeatureExtraction.from_pretrained("", file_name="model.onnx", from_transformers=False)
        self.onnx_extractor = pipeline(task, model=model_regular, tokenizer=tokenizer)
        # self.model.to(self.device)
        # print("model will run on ", self.device)

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
       data args:
            inputs (:obj: `str` | `PIL.Image` | `np.array`)
            kwargs
      Return:
            A :obj:`list` | `dict`: will be serialized and returned
        """
        sentences = data.pop("inputs",data)
        # inputs = tokenizer("I love burritos!", return_tensors="pt")
        pred = self.onnx_extractor(sentences)
        return pred
        # Perform pooling. In this case, max pooling.
        # sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
        # return sentence_embeddings.tolist()