import os
import sys

sys.path.append(os.path.join(os.path.dirname(__file__), "..", ".."))

import matplotlib.pyplot as plt
import numpy as np
from PIL import Image

from models.base_model import BaseModelGradCAM
from utils import configs

from .backbone_model import CLIPModel, TorchModel


class PrototypicalNetworksGradCAM(BaseModelGradCAM):
    def __init__(
        self,
        name_model: str,
        freeze_model: bool,
        pretrained_model: bool,
        support_set_method: str,
    ):
        super().__init__(name_model, freeze_model, pretrained_model, support_set_method)
        self.init_model()
        self.set_grad_cam()

    def init_model(self):
        if self.name_model == "clip":
            self.model = CLIPModel(
                configs.CLIP_NAME_MODEL, self.freeze_model, self.pretrained_model
            )
        else:
            self.model = TorchModel(
                self.name_model, self.freeze_model, self.pretrained_model
            )
        self.model.to(self.device)
        self.model.eval()


if __name__ == "__main__":
    model = PrototypicalNetworksGradCAM("resnet50", False, True, "5_shot")
    image = np.array(
        Image.open(
            "../../assets/example_images/gon/306e5d35-b301-4299-8022-0c89dc0b7690.png"
        ).convert("RGB")
    )
    gradcam = model.get_grad_cam(image)
    plt.imshow(gradcam)
    plt.show()