from typing import List
import torch
from transformers import SamModel, SamProcessor
from PIL import Image
import numpy as np

class PreTrainedPipeline():
    def __init__(self, path=""):

        self.device = torch.device(
            "cuda" if torch.cuda.is_available() else "cpu")
        self.processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
        self.model = SamModel.from_pretrained(
            "facebook/sam-vit-base").to(self.device)
        self.model.eval()
        self.model = self.model.to(self.device)

    def __call__(self, inputs: "Image.Image") -> List[float]:
        raw_image = inputs.convert("RGB")
        inputs = self.processor(raw_image, return_tensors="pt").to(self.device)
        feature_vector = self.model.get_image_embeddings(
            inputs["pixel_values"])

        return feature_vector.tolist()