import torch
import torch.nn as nn

from ..attack import Attack


class PGD(Attack):
    r"""
    PGD in the paper 'Towards Deep Learning Models Resistant to Adversarial Attacks'
    [https://arxiv.org/abs/1706.06083]
    
    Distance Measure : Linf
    Arguments:
        model (nn.Module): model to attack.
        eps (float): maximum perturbation. (DEFALUT : 0.3)
        alpha (float): step size. (DEFALUT : 2/255)
        steps (int): number of steps. (DEFALUT : 40)
        random_start (bool): using random initialization of delta. (DEFAULT : False)
        
    Shape:
        - images: :math:`(N, C, H, W)` where `N = number of batches`, `C = number of channels`,        `H = height` and `W = width`. It must have a range [0, 1].
        - labels: :math:`(N)` where each value :math:`y_i` is :math:`0 \leq y_i \leq` `number of labels`.
        - output: :math:`(N, C, H, W)`.
          
    Examples::
        >>> attack = torchattacks.PGD(model, eps = 8/255, alpha = 1/255, steps=40, random_start=False)
        >>> adv_images = attack(images, labels)
        
    """

    def __init__(self, model, eps=0.3, alpha=2 / 255, steps=40, random_start=False):
        super(PGD, self).__init__("PGD", model)
        self.eps = eps
        self.alpha = alpha
        self.steps = steps
        self.random_start = random_start

    def forward(self, images, labels):
        r"""
        Overridden.
        """
        images = images.to(self.device)
        labels = labels.to(self.device)
        labels = self._transform_label(images, labels)
        loss = nn.CrossEntropyLoss()

        adv_images = images.clone().detach()

        if self.random_start:
            # Starting at a uniformly random point
            adv_images = adv_images + torch.empty_like(adv_images).uniform_(
                -self.eps, self.eps
            )
            adv_images = torch.clamp(adv_images, min=0, max=1)

        for i in range(self.steps):
            adv_images.requires_grad = True
            outputs = self.model(adv_images)

            cost = self._targeted * loss(outputs, labels).to(self.device)

            grad = torch.autograd.grad(
                cost, adv_images, retain_graph=False, create_graph=False
            )[0]

            adv_images = adv_images.detach() + self.alpha * grad.sign()
            delta = torch.clamp(adv_images - images, min=-self.eps, max=self.eps)
            adv_images = torch.clamp(images + delta, min=0, max=1).detach()

        return adv_images
