import os.path
import torch
from foolbox.attacks.base import *
from foolbox.attacks.gradient_descent_base import *
from tqdm import tqdm
import pandas as pd
from attacks.attack_config import SustainableAttack
from utils.plot import save_grad_cam, plot_asr_per_target
import logging
from foolbox import PyTorchModel, accuracy
import numpy as np
from utils import factory
from utils.data_manager import DataManager, get_dataloader
from attacks.UnivIntruder.att import train
from attacks.UnivIntruder.loss import UniversalPerturbation


class UnivIntruder(SustainableAttack):
    def __init__(self, args, device='cuda'):
        super().__init__(args, device)
        self.device = device
        self.args = args
        self.surrogate_model = None  # surrogate__model.to(device).eval()

        self.target_class = args['target_class']
        self.epsilon = 32
        self.image_size = 32 if self.args['dataset'] == 'cifar100' else 224
        self.eval_batch_szie = 128

        self.adv_name = f'adv_eps{self.epsilon}_tc{self.target_class}'
        self.out_path = f'{self.args["logs_eval_name"]}/{self.adv_name}'
        os.makedirs(self.out_path, exist_ok=True)

        self.eval = args['eval']
        self.plot_gradcam = True
        self.ckpt_num = None  # None

    def train_adv(self):
        if self.eval:
            pass
        else:
            train(self.args)

    def run_test(self):
        pth_name = self.get_max_step_filename(f'{self.args["logs_eval_name"]}/{self.adv_name}/ckpts')

        self.ckpt = f'{self.args["logs_eval_name"]}/{self.adv_name}/ckpts/{pth_name}.pth'
        self.prefix = f'{self.adv_name}_{pth_name.split("_")[-1]}'
        a = torch.load(self.ckpt)
        self.adv = UniversalPerturbation((3, self.image_size, self.image_size), self.epsilon / 255,
                                             initialization=a, device=self.device)
        self.adv.eval()

        # Load Batch Data
        self.loader = get_dataloader(self.data_manager, batch_size=self.eval_batch_szie,
                                     start_class=0, end_class=10,
                                     train=False, shuffle=False, num_workers=0)
        for i, (_, imgs, labels) in enumerate(tqdm(self.loader, total=len(self.loader),
                                                   desc=f'Loading Data with Batch Size of {self.batch_size}) :')):
            if i > 0:
                break

            imgs, labels = ep.astensors(*(imgs.to(self.device), labels.to(self.device)))

            imgs_f = imgs[labels != self.target_class]
            labels_f = labels[labels != self.target_class]
            labels_t_f = ep.full_like(labels_f, fill_value=self.target_class)

            self.attacks(i, imgs_f, labels_f, labels_t_f)

    def attacks(self, i_batch, imgs, labels, labels_t):
        if self.args["model_name"] != 'finetune':
            imgs_f, labels_f = self.to_all(imgs, labels)
            labels_t = labels_t[:len(imgs_f)]
        else:
            imgs_f, labels_f = imgs, labels
        clean_acc_matrix = []
        asr_matrix = np.ones((self.data_manager.nb_tasks, 1))
        self.model = factory.get_model(self.args["model_name"], self.args)
        eval_path = os.path.join(self.args["logs_eval_name"], self.adv_name)
        cnn_matrix, nme_matrix = [], []
        for task in range(self.data_manager.nb_tasks):
            logging.info("***** Starting attack on task [{}]. *****".format(task))
            self.model.incremental_train(self.data_manager)
            self.model._network.load_state_dict(
                torch.load(self.ckpt_paths[task], map_location=self.device)['model_state_dict'])
            self.model._network.to(self.device)
            self.model._network.eval()

            # Run attack on ecah target image
            current_model = PyTorchModel(self.model._network, bounds=(0, 1), preprocessing=self.preprocessing)
            verify_input_bounds(imgs_f, current_model)

            # Evaluate the model perfromance with clean data
            acc = accuracy(current_model, imgs, labels)[0]
            logging.info("Clean accuracy on task {}: {}%".format(task, acc * 100))
            clean_acc_matrix.append(acc)

            advs = self.adv(imgs_f.raw)
            asr = accuracy(current_model, ep.astensor(advs), labels_t)[0]
            asr_matrix[task] = asr
            if self.plot_gradcam:
                save_grad_cam(self.args, torch.clip(advs.detach(), 0, 1), labels_t.raw,
                              self.model._network, self.out_path + "/GradCam", prefix=f'task{task}',
                              layer_name='stage_3', save_num=100, save_raw=True)

            del advs, current_model
            torch.cuda.empty_cache()

            self.model.after_task()

        # Save all target images info: everage asr,
        prefix = f'batch{i_batch}_{self.prefix}'
        plot_asr_per_target(asr_matrix, eval_path, prefix, self.args, clean_acc_matrix)
        df = pd.DataFrame(asr_matrix, columns=['ASR'])
        df.to_excel(os.path.join(eval_path, f"{prefix}.xlsx"), index=False)

        del asr_matrix, imgs, labels, labels_t, imgs_f, labels_f
        torch.cuda.empty_cache()

    def get_max_step_filename(self, folder_path):
        files = [f for f in os.listdir(folder_path) if f.endswith('.pth')]
        step_files = [(f, int(f.split('_')[-1].split('.')[0])) for f in files]
        step_files.sort(key=lambda x: x[1], reverse=True)
        max_step_file = step_files[0][0]
        return os.path.splitext(max_step_file)[0]

    def __call__(
            self,
            model: Model,
            inputs: T,
            criterion: Any,
            *,
            epsilons: Union[Sequence[Union[float, None]], float, None],
            **kwargs: Any,
    ) -> Union[Tuple[List[T], List[T], T], Tuple[T, T, T]]:
        ...

    def repeat(self, times: int) -> "UnivIntruder":
        ...