# MIT License

# Copyright (c) 2017 Brandon Tran and Jerry Li

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

"""
This file is modified based on the following source:
link : https://github.com/MadryLab/backdoor_data_poisoning.
The defense method is called spectral.

The update include:
    1. data preprocess and dataset setting
    2. model setting
    3. args and config
    4. save process
    5. new standard: robust accuracy
    6. use the PyTorch environment instead of TensorFlow
    7. add some addtional backbone such as resnet18 and vgg19
    8. the poison ratio can also be preset when the data for each category is small
basic sturcture for defense method:
    1. basic setting: args
    2. attack result(model, train data, test data)
    3. spectral defense:
        a. prepare the model and dataset
        b. get the activation as representation for each data
        c. detect the backdoor data by the SVD decomposition
        d. retrain the model with remaining data
    4. test the result and get ASR, ACC, RC 
"""

import argparse
import os, sys
import numpy as np
import torch
import torch.nn as nn

sys.path.append("../")
sys.path.append(os.getcwd())


from pprint import pformat
import yaml
import logging
import time
from defense.base import defense

from utils.aggregate_block.train_settings_generate import (
    argparser_criterion,
    argparser_opt_scheduler,
)
from utils.trainer_cls import PureCleanModelTrainer
from utils.aggregate_block.fix_random import fix_random
from utils.aggregate_block.model_trainer_generate import generate_cls_model
from utils.log_assist import get_git_info
from utils.aggregate_block.dataset_and_transform_generate import (
    get_input_shape,
    get_num_classes,
    get_transform,
)
from utils.save_load_attack import load_attack_result, save_defense_result


class spectral(defense):
    def __init__(self, args):
        with open(args.yaml_path, "r") as f:
            defaults = yaml.safe_load(f)

        defaults.update({k: v for k, v in args.__dict__.items() if v is not None})

        args.__dict__ = defaults

        args.terminal_info = sys.argv

        args.num_classes = get_num_classes(args.dataset)
        args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset)
        args.img_size = (args.input_height, args.input_width, args.input_channel)
        args.dataset_path = f"{args.dataset_path}/{args.dataset}"

        self.args = args

        if "result_file" in args.__dict__:
            if args.result_file is not None:
                self.set_result(args.result_file)

    def add_arguments(parser):
        parser.add_argument("--device", type=str, help="cuda, cpu")
        parser.add_argument(
            "-pm",
            "--pin_memory",
            type=lambda x: str(x) in ["True", "true", "1"],
            help="dataloader pin_memory",
        )
        parser.add_argument(
            "-nb",
            "--non_blocking",
            type=lambda x: str(x) in ["True", "true", "1"],
            help=".to(), set the non_blocking = ?",
        )
        parser.add_argument(
            "-pf",
            "--prefetch",
            type=lambda x: str(x) in ["True", "true", "1"],
            help="use prefetch",
        )
        parser.add_argument("--amp", default=False, type=lambda x: str(x) in ["True", "true", "1"])

        parser.add_argument("--checkpoint_load", type=str, help="the location of load model")
        parser.add_argument(
            "--checkpoint_save",
            type=str,
            help="the location of checkpoint where model is saved",
        )
        parser.add_argument("--log", type=str, help="the location of log")
        parser.add_argument("--dataset_path", type=str, help="the location of data")
        parser.add_argument("--dataset", type=str, help="mnist, cifar10, cifar100, gtrsb, tiny")
        parser.add_argument("--result_file", type=str, help="the location of result")

        parser.add_argument("--epochs", type=int)
        parser.add_argument("--batch_size", type=int)
        parser.add_argument("--num_workers", type=float)
        parser.add_argument("--lr", type=float)
        parser.add_argument("--lr_scheduler", type=str, help="the scheduler of lr")
        parser.add_argument("--steplr_stepsize", type=int)
        parser.add_argument("--steplr_gamma", type=float)
        parser.add_argument("--steplr_milestones", type=list)
        parser.add_argument("--model", type=str, help="resnet18")

        parser.add_argument("--client_optimizer", type=int)
        parser.add_argument("--sgd_momentum", type=float)
        parser.add_argument("--wd", type=float, help="weight decay of sgd")
        parser.add_argument("--frequency_save", type=int, help=" frequency_save, 0 is never")

        parser.add_argument("--random_seed", type=int, help="random seed")
        parser.add_argument(
            "--yaml_path",
            type=str,
            default="./config/defense/spectral/config.yaml",
            help="the path of yaml",
        )

        # set the parameter for the spectral defense
        parser.add_argument("--percentile", type=float)
        parser.add_argument("--target_label", type=int)

    def set_result(self, result_file):
        attack_file = "record/" + result_file
        save_path = "record/" + result_file + "/defense/spectral/"
        if not (os.path.exists(save_path)):
            os.makedirs(save_path)
        # assert(os.path.exists(save_path))
        self.args.save_path = save_path
        if self.args.checkpoint_save is None:
            self.args.checkpoint_save = save_path + "checkpoint/"
            if not (os.path.exists(self.args.checkpoint_save)):
                os.makedirs(self.args.checkpoint_save)
        if self.args.log is None:
            self.args.log = save_path + "log/"
            if not (os.path.exists(self.args.log)):
                os.makedirs(self.args.log)
        self.result = load_attack_result(attack_file + "/attack_result.pt")

    def set_trainer(self, model):
        self.trainer = PureCleanModelTrainer(
            model,
        )

    def set_logger(self):
        args = self.args
        logFormatter = logging.Formatter(
            fmt="%(asctime)s [%(levelname)-8s] [%(filename)s:%(lineno)d] %(message)s",
            datefmt="%Y-%m-%d:%H:%M:%S",
        )
        logger = logging.getLogger()

        fileHandler = logging.FileHandler(
            args.log + "/" + time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) + ".log"
        )
        fileHandler.setFormatter(logFormatter)
        logger.addHandler(fileHandler)

        consoleHandler = logging.StreamHandler()
        consoleHandler.setFormatter(logFormatter)
        logger.addHandler(consoleHandler)

        logger.setLevel(logging.INFO)
        logging.info(pformat(args.__dict__))

        try:
            logging.info(pformat(get_git_info()))
        except:
            logging.info("Getting git info fails.")

    def set_devices(self):
        # self.device = torch.device(
        #     (
        #         f"cuda:{[int(i) for i in self.args.device[5:].split(',')][0]}" if "," in self.args.device else self.args.device
        #         # since DataParallel only allow .to("cuda")
        #     ) if torch.cuda.is_available() else "cpu"
        # )
        self.device = self.args.device

    def mitigation(self):
        self.set_devices()
        fix_random(self.args.random_seed)

        ### a. prepare the model and dataset
        model = generate_cls_model(self.args.model, self.args.num_classes)
        model.load_state_dict(self.result["model"])
        if "," in self.device:
            model = torch.nn.DataParallel(
                model,
                device_ids=[int(i) for i in self.args.device[5:].split(",")],  # eg. "cuda:2,3,7" -> [2,3,7]
            )
            self.args.device = f"cuda:{model.device_ids[0]}"
            model.to(self.args.device)
        else:
            model.to(self.args.device)
        # Setting up the data and the model
        train_tran = get_transform(
            self.args.dataset,
            *([self.args.input_height, self.args.input_width]),
            train=True,
        )
        train_dataset = self.result["bd_train"].wrapped_dataset
        data_set_without_tran = train_dataset
        data_set_o = self.result["bd_train"]
        data_set_o.wrapped_dataset = data_set_without_tran
        data_set_o.wrap_img_transform = train_tran
        data_set_o.wrapped_dataset.getitem_all = False
        dataset = data_set_o

        # initialize data augmentation
        logging.info(f"Dataset Size: {len(dataset)}")

        if "target_label" in args.__dict__:
            if isinstance(self.args.target_label, (int)):
                poison_labels = [self.args.target_label]
            else:
                poison_labels = self.args.target_label
        else:
            poison_labels = range(self.args.num_classes)

        re_all = []
        for target_label in poison_labels:
            lbl = target_label
            dataset_y = []
            for i in range(len(dataset)):
                dataset_y.append(dataset[i][1])
            cur_indices = [i for i, v in enumerate(dataset_y) if v == lbl]
            cur_examples = len(cur_indices)
            logging.info(f"Label, num ex: {lbl},{cur_examples}")

            model.eval()
            ### b. get the activation as representation for each data
            for iex in range(cur_examples):
                cur_im = cur_indices[iex]
                x_batch = dataset[cur_im][0].unsqueeze(0).to(self.args.device)
                y_batch = dataset[cur_im][1]
                with torch.no_grad():
                    assert self.args.model in [
                        "preactresnet18",
                        "vgg19",
                        "vgg19_bn",
                        "resnet18",
                        "mobilenet_v3_large",
                        "densenet161",
                        "efficientnet_b3",
                        "convnext_tiny",
                        "vit_b_16",
                    ]
                    if self.args.model == "preactresnet18":
                        inps, outs = [], []

                        def layer_hook(module, inp, out):
                            outs.append(out.data)

                        hook = model.layer4.register_forward_hook(layer_hook)
                        _ = model(x_batch)
                        batch_grads = outs[0].view(outs[0].size(0), -1).squeeze(0)
                        hook.remove()
                    elif self.args.model == "vgg19":
                        inps, outs = [], []

                        def layer_hook(module, inp, out):
                            outs.append(out.data)

                        hook = model.features.register_forward_hook(layer_hook)
                        _ = model(x_batch)
                        batch_grads = outs[0].view(outs[0].size(0), -1).squeeze(0)
                        hook.remove()
                    elif self.args.model == "vgg19_bn":
                        inps, outs = [], []

                        def layer_hook(module, inp, out):
                            outs.append(out.data)

                        hook = model.features.register_forward_hook(layer_hook)
                        _ = model(x_batch)
                        batch_grads = outs[0].view(outs[0].size(0), -1).squeeze(0)
                        hook.remove()
                    elif self.args.model == "resnet18":
                        inps, outs = [], []

                        def layer_hook(module, inp, out):
                            outs.append(out.data)

                        hook = model.layer4.register_forward_hook(layer_hook)
                        _ = model(x_batch)
                        batch_grads = outs[0].view(outs[0].size(0), -1).squeeze(0)
                        hook.remove()
                    elif self.args.model == "mobilenet_v3_large":
                        inps, outs = [], []

                        def layer_hook(module, inp, out):
                            outs.append(out.data)

                        hook = model.avgpool.register_forward_hook(layer_hook)
                        _ = model(x_batch)
                        batch_grads = outs[0].view(outs[0].size(0), -1).squeeze(0)
                        hook.remove()
                    elif self.args.model == "densenet161":
                        inps, outs = [], []

                        def layer_hook(module, inp, out):
                            outs.append(out.data)

                        hook = model.features.register_forward_hook(layer_hook)
                        _ = model(x_batch)
                        outs[0] = torch.nn.functional.relu(outs[0])
                        batch_grads = outs[0].view(outs[0].size(0), -1).squeeze(0)
                        hook.remove()
                    elif self.args.model == "efficientnet_b3":
                        inps, outs = [], []

                        def layer_hook(module, inp, out):
                            outs.append(out.data)

                        hook = model.avgpool.register_forward_hook(layer_hook)
                        _ = model(x_batch)
                        batch_grads = outs[0].view(outs[0].size(0), -1).squeeze(0)
                        hook.remove()
                    elif self.args.model == "convnext_tiny":
                        inps, outs = [], []

                        def layer_hook(module, inp, out):
                            outs.append(out.data)

                        hook = model.avgpool.register_forward_hook(layer_hook)
                        _ = model(x_batch)
                        batch_grads = outs[0].view(outs[0].size(0), -1).squeeze(0)
                        hook.remove()
                    elif self.args.model == "vit_b_16":
                        inps, outs = [], []

                        def layer_hook(module, inp, out):
                            inps.append(inp[0].data)

                        hook = model[1].heads.register_forward_hook(layer_hook)
                        _ = model(x_batch)
                        batch_grads = inps[0].view(inps[0].size(0), -1).squeeze(0)
                        hook.remove()

                if iex == 0:
                    full_cov = np.zeros(shape=(cur_examples, len(batch_grads)))
                full_cov[iex] = batch_grads.detach().cpu().numpy()

            ### c. detect the backdoor data by the SVD decomposition
            total_p = self.args.percentile
            full_mean = np.mean(full_cov, axis=0, keepdims=True)

            centered_cov = full_cov - full_mean
            u, s, v = np.linalg.svd(centered_cov, full_matrices=False)
            logging.info(f"Top 7 Singular Values: {s[0:7]}")
            eigs = v[0:1]
            p = total_p
            corrs = np.matmul(eigs, np.transpose(full_cov))  # shape num_top, num_active_indices
            scores = np.linalg.norm(corrs, axis=0)  # shape num_active_indices
            logging.info(f"Length Scores: {len(scores)}")
            p_score = np.percentile(scores, p)
            top_scores = np.where(scores > p_score)[0]
            logging.info(f"{top_scores}")

            removed_inds = np.copy(top_scores)
            re = [cur_indices[v] for i, v in enumerate(removed_inds)]
            re_all.extend(re)

        left_inds = np.delete(range(len(dataset)), re_all)
        ### d. retrain the model with remaining data
        model = generate_cls_model(self.args.model, self.args.num_classes)
        if "," in self.device:
            model = torch.nn.DataParallel(
                model,
                device_ids=[int(i) for i in args.device[5:].split(",")],  # eg. "cuda:2,3,7" -> [2,3,7]
            )
            self.args.device = f"cuda:{model.device_ids[0]}"
            model.to(self.args.device)
        else:
            model.to(self.args.device)
        dataset.subset(left_inds)
        dataset.wrapped_dataset.getitem_all = True
        # dataset.subset(left_inds)
        dataset_left = dataset
        data_loader_sie = torch.utils.data.DataLoader(
            dataset_left,
            batch_size=self.args.batch_size,
            num_workers=self.args.num_workers,
            shuffle=True,
        )

        optimizer, scheduler = argparser_opt_scheduler(model, self.args)
        # criterion = nn.CrossEntropyLoss()
        self.set_trainer(model)
        criterion = argparser_criterion(args)

        test_tran = get_transform(
            self.args.dataset,
            *([self.args.input_height, self.args.input_width]),
            train=False,
        )
        data_bd_testset = self.result["bd_test"]
        data_bd_testset.wrap_img_transform = test_tran
        data_bd_loader = torch.utils.data.DataLoader(
            data_bd_testset,
            batch_size=self.args.batch_size,
            num_workers=self.args.num_workers,
            drop_last=False,
            shuffle=True,
            pin_memory=args.pin_memory,
        )

        data_clean_testset = self.result["clean_test"]
        data_clean_testset.wrap_img_transform = test_tran
        data_clean_loader = torch.utils.data.DataLoader(
            data_clean_testset,
            batch_size=self.args.batch_size,
            num_workers=self.args.num_workers,
            drop_last=False,
            shuffle=True,
            pin_memory=args.pin_memory,
        )

        self.trainer.train_with_test_each_epoch_on_mix(
            data_loader_sie,
            data_clean_loader,
            data_bd_loader,
            args.epochs,
            criterion=criterion,
            optimizer=optimizer,
            scheduler=scheduler,
            device=self.args.device,
            frequency_save=args.frequency_save,
            save_folder_path=args.save_path,
            save_prefix="spectral",
            amp=args.amp,
            prefetch=args.prefetch,
            prefetch_transform_attr_name="ori_image_transform_in_loading",  # since we use the preprocess_bd_dataset
            non_blocking=args.non_blocking,
        )

        result = {}
        result["dataset"] = dataset_left
        result["model"] = model
        save_defense_result(
            model_name=args.model,
            num_classes=args.num_classes,
            model=model.cpu().state_dict(),
            save_path=args.save_path,
        )
        return result

    def defense(self, result_file):
        self.set_result(result_file)
        self.set_logger()
        result = self.mitigation()
        return result


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description=sys.argv[0])
    spectral.add_arguments(parser)
    args = parser.parse_args()
    spectral_method = spectral(args)
    if "result_file" not in args.__dict__:
        args.result_file = "defense_test_badnet"
    elif args.result_file is None:
        args.result_file = "defense_test_badnet"
    result = spectral_method.defense(args.result_file)
