import torch
import numpy as np
from transformers import AutoProcessor, AutoModelForZeroShotImageClassification


def get_device():
    device = "cuda" if torch.cuda.is_available() else "cpu"
    #device = "mps" if torch.mps.is_available() else device
    return device


def get_model_and_processor(model_name: str, device: str):
    processor = AutoProcessor.from_pretrained(model_name)
    model = AutoModelForZeroShotImageClassification.from_pretrained(model_name)
    return model.to(device), processor


def to_embedding(images: np.ndarray, processor, model, device: str):
    img_proc = processor(images=images, return_tensors="pt")["pixel_values"]
    with torch.no_grad():
        img_emb = model.get_image_features(img_proc.to(device)).cpu().numpy()
    return img_emb


def embed(batch, processor, model, device: str, remove_background: bool):
    batch["embeddings" + ("_no_bg" if remove_background else "")] = to_embedding(
        np.array(batch["image_no_bg" if remove_background else "image"]),
        processor,
        model,
        device,
    )
    return batch