"""
This file implements the defense method called finetuning (ft), which is a standard fine-tuning that uses clean data to finetune the model.

basic sturcture for defense method:
    1. basic setting: args
    2. attack result(model, train data, test data)
    3. ft defense:
        a. get some clean data
        b. retrain the backdoor model
    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.choose_index import choose_index
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
from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2


class ft(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", 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/ft/config.yaml",
            help="the path of yaml",
        )

        # set the parameter for the ft defense
        parser.add_argument("--ratio", type=float, help="the ratio of clean data loader")
        parser.add_argument("--index", type=str, help="index of clean data")

    def set_result(self, result_file):
        attack_file = "record/" + result_file
        save_path = "record/" + result_file + "/defense/ft/"
        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)

        # Prepare model, optimizer, scheduler
        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)

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

        train_tran = get_transform(
            self.args.dataset,
            *([self.args.input_height, self.args.input_width]),
            train=True,
        )
        clean_dataset = prepro_cls_DatasetBD_v2(self.result["clean_train"].wrapped_dataset)
        data_all_length = len(clean_dataset)
        ran_idx = choose_index(self.args, data_all_length)
        log_index = self.args.log + "index.txt"
        np.savetxt(log_index, ran_idx, fmt="%d")
        clean_dataset.subset(ran_idx)
        data_set_without_tran = clean_dataset
        data_set_o = self.result["clean_train"]
        data_set_o.wrapped_dataset = data_set_without_tran
        data_set_o.wrap_img_transform = train_tran
        # data_set_o = prepro_cls_DatasetBD_v2(
        #     full_dataset_without_transform=data_set,
        #     poison_idx=np.zeros(len(data_set)),  # one-hot to determine which image may take bd_transform
        #     bd_image_pre_transform=None,
        #     bd_label_pre_transform=None,
        #     ori_image_transform_in_loading=train_tran,
        #     ori_label_transform_in_loading=None,
        #     add_details_in_preprocess=False,
        # )
        data_loader = torch.utils.data.DataLoader(
            data_set_o,
            batch_size=self.args.batch_size,
            num_workers=self.args.num_workers,
            shuffle=True,
            pin_memory=args.pin_memory,
        )
        trainloader = data_loader

        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(
        #     train_data = trainloader,
        #     test_data = data_clean_loader,
        #     adv_test_data = data_bd_loader,
        #     end_epoch_num = self.args.epochs,
        #     criterion = criterion,
        #     optimizer = optimizer,
        #     scheduler = scheduler,
        #     device = self.args.device,
        #     frequency_save = self.args.frequency_save,
        #     save_folder_path = self.args.checkpoint_save,
        #     save_prefix = 'defense',
        #     continue_training_path = None,
        # )

        self.trainer.train_with_test_each_epoch_on_mix(
            trainloader,
            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="ft",
            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["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])
    ft.add_arguments(parser)
    args = parser.parse_args()
    ft_method = ft(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 = ft_method.defense(args.result_file)
