import torch
import numpy as np
from torchvision.transforms import ToTensor

GPU_EFFICIENT_SAM_CHECKPOINT = "efficient_sam_s_gpu.jit"
CPU_EFFICIENT_SAM_CHECKPOINT = "efficient_sam_s_cpu.jit"


def load(device: torch.device) -> torch.jit.ScriptModule:
    if device.type == "cuda":
        model = torch.jit.load(GPU_EFFICIENT_SAM_CHECKPOINT)
    else:
        model = torch.jit.load(CPU_EFFICIENT_SAM_CHECKPOINT)
    model.eval()
    return model


def inference_with_box(
    image: np.ndarray,
    box: np.ndarray,
    model: torch.jit.ScriptModule,
    device: torch.device
) -> np.ndarray:
    bbox = torch.reshape(torch.tensor(box), [1, 1, 2, 2])
    bbox_labels = torch.reshape(torch.tensor([2, 3]), [1, 1, 2])
    img_tensor = ToTensor()(image)

    predicted_logits, predicted_iou = model(
        img_tensor[None, ...].to(device),
        bbox.to(device),
        bbox_labels.to(device),
    )
    predicted_logits = predicted_logits.cpu()
    all_masks = torch.ge(torch.sigmoid(predicted_logits[0, 0, :, :, :]), 0.5).numpy()
    predicted_iou = predicted_iou[0, 0, ...].cpu().detach().numpy()

    max_predicted_iou = -1
    selected_mask_using_predicted_iou = None
    for m in range(all_masks.shape[0]):
        curr_predicted_iou = predicted_iou[m]
        if (
                curr_predicted_iou > max_predicted_iou
                or selected_mask_using_predicted_iou is None
        ):
            max_predicted_iou = curr_predicted_iou
            selected_mask_using_predicted_iou = all_masks[m]
    return selected_mask_using_predicted_iou


def inference_with_boxes(
    image: np.ndarray,
    xyxy: np.ndarray,
    model: torch.jit.ScriptModule,
    device: torch.device
) -> np.ndarray:
    masks = []
    for [x_min, y_min, x_max, y_max] in xyxy:
        box = np.array([[x_min, y_min], [x_max, y_max]])
        mask = inference_with_box(image, box, model, device)
        masks.append(mask)
    return np.array(masks)