import numpy as np
import torch
import torch.nn as nn
from .utils import  ctx_noparamgrad_and_eval
from .base import Attack, LabelMixin
from typing import Dict
from .utils import batch_clamp
from .utils import batch_multiply
from .utils import clamp
from .utils import clamp_by_pnorm
from .utils import is_float_or_torch_tensor
from .utils import normalize_by_pnorm
from .utils import rand_init_delta
from .utils import replicate_input
from utils.distributed import DistributedMetric
from tqdm import tqdm
from torchpack import distributed as dist
from utils import accuracy

def perturb_iterative(xvar, yvar, predict, nb_iter, eps, eps_iter, loss_fn, delta_init=None, minimize=False, ord=np.inf, 
                      clip_min=0.0, clip_max=1.0):
    """
    Iteratively maximize the loss over the input. It is a shared method for iterative attacks.
    Arguments:
        xvar (torch.Tensor): input data.
        yvar (torch.Tensor): input labels.
        predict (nn.Module): forward pass function.
        nb_iter (int): number of iterations.
        eps (float): maximum distortion.
        eps_iter (float): attack step size.
        loss_fn (nn.Module): loss function.
        delta_init (torch.Tensor): (optional) tensor contains the random initialization.
        minimize (bool): (optional) whether to minimize or maximize the loss.
        ord (int): (optional) the order of maximum distortion (inf or 2).
        clip_min (float): mininum value per input dimension.
        clip_max (float): maximum value per input dimension.
    Returns: 
        torch.Tensor containing the perturbed input, 
        torch.Tensor containing the perturbation
    """
    if delta_init is not None:
        delta = delta_init
    else:
        delta = torch.zeros_like(xvar)

    delta.requires_grad_()
    for ii in range(nb_iter):
        outputs = predict(xvar + delta)
        loss = loss_fn(outputs, yvar)
        if minimize:
            loss = -loss

        loss.backward()
        if ord == np.inf:
            grad_sign = delta.grad.data.sign()
            delta.data = delta.data + batch_multiply(eps_iter, grad_sign)
            delta.data = batch_clamp(eps, delta.data)
            delta.data = clamp(xvar.data + delta.data, clip_min, clip_max) - xvar.data
        elif ord == 2:
            grad = delta.grad.data
            grad = normalize_by_pnorm(grad)
            delta.data = delta.data + batch_multiply(eps_iter, grad)
            delta.data = clamp(xvar.data + delta.data, clip_min, clip_max) - xvar.data
            if eps is not None:
                delta.data = clamp_by_pnorm(delta.data, ord, eps)
        else:
            error = "Only ord=inf and ord=2 have been implemented"
            raise NotImplementedError(error)
        delta.grad.data.zero_()

    x_adv = clamp(xvar + delta, clip_min, clip_max)
    r_adv = x_adv - xvar
    return x_adv, r_adv


class PGDAttack(Attack, LabelMixin):
    """
    The projected gradient descent attack (Madry et al, 2017).
    The attack performs nb_iter steps of size eps_iter, while always staying within eps from the initial point.
    Arguments:
        predict (nn.Module): forward pass function.
        loss_fn (nn.Module): loss function.
        eps (float): maximum distortion.
        nb_iter (int): number of iterations.
        eps_iter (float): attack step size.
        rand_init (bool): (optional) random initialization.    
        clip_min (float): mininum value per input dimension.
        clip_max (float): maximum value per input dimension.
        ord (int): (optional) the order of maximum distortion (inf or 2).
        targeted (bool): if the attack is targeted.
        rand_init_type (str): (optional) random initialization type.
    """

    def __init__(
            self, predict, loss_fn=None, eps=0.3, nb_iter=40, eps_iter=0.01, rand_init=True, clip_min=0., clip_max=1.,
            ord=np.inf, targeted=False, rand_init_type='uniform'):
        super(PGDAttack, self).__init__(predict, loss_fn, clip_min, clip_max)
        self.eps = eps
        self.nb_iter = nb_iter
        self.eps_iter = eps_iter
        self.rand_init = rand_init
        self.rand_init_type = rand_init_type
        self.ord = ord
        self.targeted = targeted
        if self.loss_fn is None:
            self.loss_fn = nn.CrossEntropyLoss(reduction="sum")
        assert is_float_or_torch_tensor(self.eps_iter)
        assert is_float_or_torch_tensor(self.eps)

    def perturb(self, x, y=None):
        """
        Given examples (x, y), returns their adversarial counterparts with an attack length of eps.
        Arguments:
            x (torch.Tensor): input tensor.
            y (torch.Tensor): label tensor.
                - if None and self.targeted=False, compute y as predicted
                labels.
                - if self.targeted=True, then y must be the targeted labels.
        Returns: 
            torch.Tensor containing perturbed inputs,
            torch.Tensor containing the perturbation    
        """
        x, y = self._verify_and_process_inputs(x, y)

        delta = torch.zeros_like(x)
        delta = nn.Parameter(delta)
        if self.rand_init:
            if self.rand_init_type == 'uniform':
                rand_init_delta(
                    delta, x, self.ord, self.eps, self.clip_min, self.clip_max)
                delta.data = clamp(
                    x + delta.data, min=self.clip_min, max=self.clip_max) - x
            elif self.rand_init_type == 'normal':
                delta.data = 0.001 * torch.randn_like(x) # initialize as in TRADES
            else:
                raise NotImplementedError('Only rand_init_type=normal and rand_init_type=uniform have been implemented.')
        
        x_adv, r_adv = perturb_iterative(
            x, y, self.predict, nb_iter=self.nb_iter, eps=self.eps, eps_iter=self.eps_iter, loss_fn=self.loss_fn, 
            minimize=self.targeted, ord=self.ord, clip_min=self.clip_min, clip_max=self.clip_max, delta_init=delta
        )

        return x_adv.data, r_adv.data

    def eval_pgd(self,data_loader_dict: Dict)-> Dict:

        test_criterion = nn.CrossEntropyLoss().cuda()
        val_loss = DistributedMetric()
        val_top1 = DistributedMetric()
        val_top5 = DistributedMetric()
        val_advloss = DistributedMetric()
        val_advtop1 = DistributedMetric()
        val_advtop5 = DistributedMetric()
        self.predict.eval()
        with tqdm(
                total=len(data_loader_dict["val"]),
                desc="Eval",
                disable=not dist.is_master(),
            ) as t:
                for images, labels in data_loader_dict["val"]:
                    images, labels = images.cuda(), labels.cuda()
                    # compute output
                    output = self.predict(images)
                    loss = test_criterion(output, labels)
                    val_loss.update(loss, images.shape[0])
                    acc1, acc5 = accuracy(output, labels, topk=(1, 5))
                    val_top5.update(acc5[0], images.shape[0])
                    val_top1.update(acc1[0], images.shape[0])
                    with ctx_noparamgrad_and_eval(self.predict):
                        images_adv,_ = self.perturb(images, labels)
                    output_adv = self.predict(images_adv)   
                    loss_adv = test_criterion(output_adv,labels) 
                    val_advloss.update(loss_adv, images.shape[0])   
                    acc1_adv, acc5_adv = accuracy(output_adv, labels, topk=(1, 5))   
                    val_advtop1.update(acc1_adv[0], images.shape[0])
                    val_advtop5.update(acc5_adv[0], images.shape[0])                  
                    t.set_postfix(
                        {
                            "loss": val_loss.avg.item(),
                            "top1": val_top1.avg.item(),
                            "top5": val_top5.avg.item(),
                            "adv_loss": val_advloss.avg.item(),
                            "adv_top1": val_advtop1.avg.item(),
                            "adv_top5": val_advtop5.avg.item(),
                            "#samples": val_top1.count.item(),
                            "batch_size": images.shape[0],
                            "img_size": images.shape[2],
                        }
                    )
                    t.update()

        val_results = {
            "val_top1": val_top1.avg.item(),
            "val_top5": val_top5.avg.item(),
            "val_loss": val_loss.avg.item(),
            "val_advtop1": val_advtop1.avg.item(),
            "val_advtop5": val_advtop5.avg.item(),
            "val_advloss": val_advloss.avg.item(),
        }
        return val_results
class LinfPGDAttack(PGDAttack):
    """
    PGD Attack with order=Linf
    Arguments:
        predict (nn.Module): forward pass function.
        loss_fn (nn.Module): loss function.
        eps (float): maximum distortion.
        nb_iter (int): number of iterations.
        eps_iter (float): attack step size.
        rand_init (bool): (optional) random initialization.    
        clip_min (float): mininum value per input dimension.
        clip_max (float): maximum value per input dimension.
        targeted (bool): if the attack is targeted.
        rand_init_type (str): (optional) random initialization type.
    """

    def __init__(
            self, predict, loss_fn=None, eps=0.3, nb_iter=40, eps_iter=0.01, rand_init=True, clip_min=0., clip_max=1.,
            targeted=False, rand_init_type='uniform'):
        ord = np.inf
        super(LinfPGDAttack, self).__init__(
            predict=predict, loss_fn=loss_fn, eps=eps, nb_iter=nb_iter, eps_iter=eps_iter, rand_init=rand_init, 
            clip_min=clip_min, clip_max=clip_max, targeted=targeted, ord=ord, rand_init_type=rand_init_type)


class L2PGDAttack(PGDAttack):
    """
    PGD Attack with order=L2
    Arguments:
        predict (nn.Module): forward pass function.
        loss_fn (nn.Module): loss function.
        eps (float): maximum distortion.
        nb_iter (int): number of iterations.
        eps_iter (float): attack step size.
        rand_init (bool): (optional) random initialization.    
        clip_min (float): mininum value per input dimension.
        clip_max (float): maximum value per input dimension.
        targeted (bool): if the attack is targeted.
        rand_init_type (str): (optional) random initialization type.
    """

    def __init__(
            self, predict, loss_fn=None, eps=0.3, nb_iter=40, eps_iter=0.01, rand_init=True, clip_min=0., clip_max=1.,
            targeted=False, rand_init_type='uniform'):
        ord = 2
        super(L2PGDAttack, self).__init__(
            predict=predict, loss_fn=loss_fn, eps=eps, nb_iter=nb_iter, eps_iter=eps_iter, rand_init=rand_init, 
            clip_min=clip_min, clip_max=clip_max, targeted=targeted, ord=ord, rand_init_type=rand_init_type)