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

def cls_pooling(model_output):
    return model_output.last_hidden_state[:,0]
class PreTrainedPipeline():
    def __init__(self, path=""):
        # load the optimized model
        self.model = ORTModelForFeatureExtraction.from_pretrained(path)
        self.tokenizer = AutoTokenizer.from_pretrained(path, model_max_length=128)


    def __call__(self, inputs: Any) -> Dict[str, Any]:
        """
        Args:
            data (:obj:`str`):
                a string containing some text
        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.
        """
        # tokenize the input
        encoded_input = self.tokenizer(inputs, padding="longest", truncation=True, return_tensors='pt')
        # run the model
        model_output = self.model(**encoded_input, return_dict=True)
        embeddings = cls_pooling(model_output)

        return {"vectors": [float(vec) for vec in embeddings[0].tolist()]}