import torch
from timm.models import ConvNeXt
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD

from torch.nn import functional as F


class ConvNeXtDiscriminator():
    def __init__(self, model: ConvNeXt, input_size: int) -> None:
        self.net = model

        self.input_size = input_size

        self.img_mean = torch.tensor(IMAGENET_DEFAULT_MEAN).view(1, -1, 1, 1)
        self.img_std = torch.tensor(IMAGENET_DEFAULT_STD).view(1, -1, 1, 1)

    def get_score(self, img):
        pred = self.get_all(img)
        return torch.softmax(pred, dim=-1)[:, 1]

    def get_all(self, img):
        img_mean = self.img_mean.to(device=img.device, dtype=img.dtype)
        img_std = self.img_std.to(device=img.device, dtype=img.dtype)

        img = ((img + 1.) / 2.).sub(img_mean).div(img_std)

        img = F.interpolate(img, size=(self.input_size, self.input_size), mode='bicubic', align_corners=True)
        pred = self.net(img)
        return pred