<reponame>HanxunH/RobustWRN
# Copyright (c) 2019-present, <NAME>
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import torch
import time

import tensorflow as tf

from .fab_base import FABAttack

#from advertorch.utils import replicate_input

#from .base import Attack
#from .base import LabelMixin


class FABAttack_TF(FABAttack):
    """
    Fast Adaptive Boundary Attack (Linf, L2, L1)
    https://arxiv.org/abs/1907.02044

    :param model:         TF_model
    :param norm:          Lp-norm to minimize ('Linf', 'L2', 'L1' supported)
    :param n_restarts:    number of random restarts
    :param n_iter:        number of iterations
    :param eps:           epsilon for the random restarts
    :param alpha_max:     alpha_max
    :param eta:           overshooting
    :param beta:          backward step
    """

    def __init__(
            self,
            model,
            norm='Linf',
            n_restarts=1,
            n_iter=100,
            eps=None,
            alpha_max=0.1,
            eta=1.05,
            beta=0.9,
            loss_fn=None,
            verbose=False,
            seed=0,
            targeted=False,
            device=None,
            n_target_classes=9):
        """ FAB-attack implementation in TF2 """

        self.model = model
        super().__init__(norm,
                         n_restarts,
                         n_iter,
                         eps,
                         alpha_max,
                         eta,
                         beta,
                         loss_fn,
                         verbose,
                         seed,
                         targeted,
                         device,
                         n_target_classes)

    def _predict_fn(self, x):
        return self.model.predict(x)

    def _get_predicted_label(self, x):
        with torch.no_grad():
            outputs = self._predict_fn(x)
        _, y = torch.max(outputs, dim=1)
        return y

    def get_diff_logits_grads_batch(self, imgs, la):
        y2, g2 = self.model.grad_logits(imgs)
        df = y2 - y2[torch.arange(imgs.shape[0]), la].unsqueeze(1)
        dg = g2 - g2[torch.arange(imgs.shape[0]), la].unsqueeze(1)
        df[torch.arange(imgs.shape[0]), la] = 1e10

        return df, dg

    def get_diff_logits_grads_batch_targeted(self, imgs, la, la_target):
        df, dg = self.model.get_grad_diff_logits_target(imgs, la, la_target)
        df.unsqueeze_(1)
        dg.unsqueeze_(1)

        return df, dg
