import argparse
import datetime
import time
import json
import gc
import os
import math
import glob
import shutil
from pathlib import Path

import torch
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import numpy as np
from einops import rearrange, repeat
from tensorboardX import SummaryWriter

from timm.data import Mixup
from timm.models import create_model
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from timm.scheduler import create_scheduler
from timm.optim import create_optimizer
from timm.utils import NativeScaler, get_state_dict, ModelEma
from ptflops import get_model_complexity_info

from data.datasets import build_dataset
from engine import train_one_epoch, evaluate
from util.samplers import RASampler
import model.models
import util.utils as utils
from util.visualize import vis_attention

import warnings
warnings.filterwarnings("ignore")

def get_args_parser():
    parser = argparse.ArgumentParser('PerViT training and evaluation script', add_help=False)
    parser.add_argument('--batch-size', default=256, type=int)
    parser.add_argument('--epochs', default=300, type=int)

    # Model parameters
    parser.add_argument('--model', default='pervit_tiny', type=str, metavar='MODEL',
                        help='Name of model to train')
    parser.add_argument('--load', type=str, default='')
    parser.add_argument('--pretrained', action='store_true')

    parser.add_argument('--input-size', default=224, type=int, help='images input size')

    parser.add_argument('--drop', type=float, default=0.0, metavar='PCT',
                        help='Dropout rate (default: 0.)')
    parser.add_argument('--drop-path', type=float, default=0.1, metavar='PCT',
                        help='Drop path rate (default: 0.1)')
    parser.add_argument('--drop-block', type=float, default=None, metavar='PCT',
                        help='Drop block rate (default: None)')

    parser.add_argument('--model-ema', action='store_true')
    parser.add_argument('--no-model-ema', action='store_false', dest='model_ema')
    parser.set_defaults(model_ema=False)
    parser.add_argument('--model-ema-decay', type=float, default=0.99996, help='')
    parser.add_argument('--model-ema-force-cpu', action='store_true', default=False, help='')
    parser.add_argument('--use_pos_embed', action='store_true', help='Absolute positional embedding')

    # Optimizer parameters
    parser.add_argument('--opt', default='adamw', type=str, metavar='OPTIMIZER',
                        help='Optimizer (default: "adamw"')
    parser.add_argument('--opt-eps', default=1e-8, type=float, metavar='EPSILON',
                        help='Optimizer Epsilon (default: 1e-8)')
    parser.add_argument('--opt-betas', default=None, type=float, nargs='+', metavar='BETA',
                        help='Optimizer Betas (default: None, use opt default)')
    parser.add_argument('--clip-grad', type=float, default=None, metavar='NORM',
                        help='Clip gradient norm (default: None, no clipping)')
    parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
                        help='SGD momentum (default: 0.9)')
    parser.add_argument('--weight-decay', type=float, default=0.05,
                        help='weight decay (default: 0.05)')
    # Learning rate schedule parameters
    parser.add_argument('--sched', default='cosine', type=str, metavar='SCHEDULER',
                        help='LR scheduler (default: "cosine"')
    parser.add_argument('--lr', type=float, default=5e-4, metavar='LR',
                        help='learning rate (default: 5e-4)')
    parser.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct',
                        help='learning rate noise on/off epoch percentages')
    parser.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT',
                        help='learning rate noise limit percent (default: 0.67)')
    parser.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV',
                        help='learning rate noise std-dev (default: 1.0)')
    parser.add_argument('--warmup-lr', type=float, default=1e-6, metavar='LR',
                        help='warmup learning rate (default: 1e-6)')
    parser.add_argument('--min-lr', type=float, default=1e-5, metavar='LR',
                        help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')

    parser.add_argument('--decay-epochs', type=float, default=30, metavar='N',
                        help='epoch interval to decay LR')
    parser.add_argument('--warmup-epochs', type=int, default=5, metavar='N',
                        help='epochs to warmup LR, if scheduler supports')
    parser.add_argument('--cooldown-epochs', type=int, default=10, metavar='N',
                        help='epochs to cooldown LR at min_lr, after cyclic schedule ends')
    parser.add_argument('--patience-epochs', type=int, default=10, metavar='N',
                        help='patience epochs for Plateau LR scheduler (default: 10')
    parser.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE',
                        help='LR decay rate (default: 0.1)')

    # Augmentation parameters
    parser.add_argument('--color-jitter', type=float, default=0.4, metavar='PCT',
                        help='Color jitter factor (default: 0.4)')
    parser.add_argument('--aa', type=str, default='rand-m9-mstd0.5-inc1', metavar='NAME',
                        help='Use AutoAugment policy. "v0" or "original". " + \
                             "(default: rand-m9-mstd0.5-inc1)'),
    parser.add_argument('--smoothing', type=float, default=0.1, help='Label smoothing (default: 0.1)')
    parser.add_argument('--train-interpolation', type=str, default='bicubic',
                        help='Training interpolation (random, bilinear, bicubic default: "bicubic")')

    parser.add_argument('--repeated-aug', action='store_true')
    parser.add_argument('--no-repeated-aug', action='store_false', dest='repeated_aug')
    parser.set_defaults(repeated_aug=True)

    # * Random Erase params
    parser.add_argument('--reprob', type=float, default=0.25, metavar='PCT',
                        help='Random erase prob (default: 0.25)')
    parser.add_argument('--remode', type=str, default='pixel',
                        help='Random erase mode (default: "pixel")')
    parser.add_argument('--recount', type=int, default=1,
                        help='Random erase count (default: 1)')
    parser.add_argument('--resplit', action='store_true', default=False,
                        help='Do not random erase first (clean) augmentation split')

    # * Mixup params
    parser.add_argument('--mixup', type=float, default=0.8,
                        help='mixup alpha, mixup enabled if > 0. (default: 0.8)')
    parser.add_argument('--cutmix', type=float, default=1.0,
                        help='cutmix alpha, cutmix enabled if > 0. (default: 1.0)')
    parser.add_argument('--cutmix-minmax', type=float, nargs='+', default=None,
                        help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)')
    parser.add_argument('--mixup-prob', type=float, default=1.0,
                        help='Probability of performing mixup or cutmix when either/both is enabled')
    parser.add_argument('--mixup-switch-prob', type=float, default=0.5,
                        help='Probability of switching to cutmix when both mixup and cutmix enabled')
    parser.add_argument('--mixup-mode', type=str, default='batch',
                        help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"')

    # Dataset parameters
    parser.add_argument('--data-path', default='../Datasets_CLS/ILSVRC2012/', type=str, help='dataset path')
    parser.add_argument('--data-set', default='IMNET', choices=['CIFAR10', 'CIFAR100', 'IMNET', 'INAT', 'INAT19'],
                        type=str, help='Image Net dataset path')
    parser.add_argument('--sampling_ratio', default=1.,
                        type=float, help='fraction of samples to keep in the training set of imagenet')
    parser.add_argument('--nb_classes', default=1000,
                        type=int, help='number of classes in imagenet')
    parser.add_argument('--inat-category', default='name',
                        choices=['kingdom', 'phylum', 'class', 'order', 'supercategory', 'family', 'genus', 'name'],
                        type=str, help='semantic granularity')

    parser.add_argument('--output_dir', default='logs/test/',
                        help='path where to save, empty for no saving')
    parser.add_argument('--device', default='cuda',
                        help='device to use for training / testing')
    parser.add_argument('--seed', default=0, type=int)
    parser.add_argument('--resume', default='yes', help='resume from checkpoint')
    parser.add_argument('--save_every', default=1, type=int, help='save model every epochs')
    parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
                        help='start epoch')
    parser.add_argument('--eval', action='store_true', help='Perform evaluation only')
    parser.add_argument('--num_workers', default=10, type=int)
    parser.add_argument('--pin-mem', action='store_true',
                        help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
    parser.add_argument('--no-pin-mem', action='store_false', dest='pin_mem',
                        help='')
    parser.set_defaults(pin_mem=True)

    # distributed training parameters
    parser.add_argument('--world_size', default=1, type=int,
                        help='number of distributed processes')
    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')

    parser.add_argument('--visualize', action='store_true', help='Visualize learned attentions')

    return parser

def main(args):
    utils.init_distributed_mode(args)

    if utils.is_main_process():
        tbd_writer = SummaryWriter(os.path.join(args.output_dir, 'tbd/runs'))

    print(args)

    device = torch.device(args.device)

    seed = args.seed + utils.get_rank()
    print('seed: ', seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)

    cudnn.benchmark = True

    if not args.eval:
        dataset_train, args.nb_classes = build_dataset(is_train=True, args=args)

        num_tasks = utils.get_world_size()
        global_rank = utils.get_rank()
        if args.repeated_aug:
            sampler_train = RASampler(
                dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
            )
        else:
            sampler_train = torch.utils.data.DistributedSampler(
                dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
            )

        data_loader_train = torch.utils.data.DataLoader(
            dataset_train, sampler=sampler_train,
            batch_size=args.batch_size,
            num_workers=args.num_workers,
            pin_memory=args.pin_mem,
            drop_last=True,
        )

    dataset_val, _ = build_dataset(is_train=False, args=args)
    data_loader_val = torch.utils.data.DataLoader(
        dataset_val, batch_size=int(1.5 * args.batch_size),
        shuffle=False, num_workers=args.num_workers,
        pin_memory=args.pin_mem, drop_last=False
    )

    mixup_fn = None
    mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
    if mixup_active:
        mixup_fn = Mixup(
            mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax,
            prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode,
            label_smoothing=args.smoothing, num_classes=args.nb_classes)

    print(f"Creating model: {args.model}")
    model = create_model(
        args.model,
        pretrained=args.pretrained,
        num_classes=args.nb_classes,
        drop_rate=args.drop,
        drop_path_rate=args.drop_path,
        drop_block_rate=args.drop_block,
        use_pos_embed = args.use_pos_embed
    )

    print(model)
    model.to(device)

    if args.pretrained:
        checkpoint = torch.load(args.load)
        model.load_state_dict(checkpoint['model'])
        model.init_rpe()

        with torch.cuda.device(0):
            macs, params = get_model_complexity_info(model, (3, 224, 224), as_strings=True, print_per_layer_stat=True, verbose=True)
            print('{:<30}  {:<8}'.format('Computational complexity: ', macs))
            print('{:<30}  {:<8}'.format('Number of parameters: ', params))

        if args.visualize:
            basepath = 'vis/%s' % args.load.split('/')[-2]
            os.makedirs('%s/weight' % basepath, exist_ok=True)
            vis_attention(model, basepath, side=14, q=(7, 7))
            print('Attention visualized at %s' % basepath)

    model_ema = None
    if args.model_ema:
        model_ema = ModelEma(
            model,
            decay=args.model_ema_decay,
            device='cpu' if args.model_ema_force_cpu else '',
            resume='')

    model_without_ddp = model
    if args.distributed:
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
        model_without_ddp = model.module
    n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
    print('number of params:', n_parameters)

    linear_scaled_lr = args.lr * args.batch_size * utils.get_world_size() / 512.0
    args.lr = linear_scaled_lr
    optimizer = create_optimizer(args, model)
    loss_scaler = NativeScaler()

    lr_scheduler, _ = create_scheduler(args, optimizer)

    criterion = LabelSmoothingCrossEntropy()

    if args.mixup > 0.:
        # smoothing is handled with mixup label transform
        criterion = SoftTargetCrossEntropy()
    elif args.smoothing:
        criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
    else:
        criterion = torch.nn.CrossEntropyLoss()

    output_dir = Path(args.output_dir)
    if utils.is_main_process():
        torch.save(args, output_dir / "args.pyT")

    if args.resume and utils.is_main_process():
        if str(args.resume).startswith('https'):
            shutil.rmtree('%s/tbd/' % args.output_dir)
            os.remove('%s/args.pyT' % args.output_dir)
            file_id = args.resume[args.resume.index('/')+2:]
            utils.download_from_google_drive(file_id, args.output_dir)
        resume_path = os.path.join(args.output_dir, 'checkpoint_latest.pth')
        latest_exist = os.path.exists(resume_path)
        if latest_exist:
            checkpoint = torch.load(resume_path, map_location='cpu')

        if latest_exist and not args.eval:
            model_without_ddp.load_state_dict(checkpoint['model'])
            if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:
                optimizer.load_state_dict(checkpoint['optimizer'])
                lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
                args.start_epoch = checkpoint['epoch'] + 1
                if args.model_ema:
                    utils._load_checkpoint_for_ema(model_ema, checkpoint['model_ema'])

    if args.eval:
        throughput = utils.compute_throughput(model, resolution=args.input_size)
        print(f"Throughput : {throughput:.2f}")
        model.initialize()
        test_stats = evaluate(data_loader_val, model, device)
        print(f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%")
        return


    print("Start training")
    start_time = time.time()
    max_accuracy = 0.0

    for epoch in range(args.start_epoch, args.epochs):
        gc.collect()

        if args.distributed:
            data_loader_train.sampler.set_epoch(epoch)

        train_stats = train_one_epoch(
            model, criterion, data_loader_train,
            optimizer, device, epoch, loss_scaler,
            args.clip_grad, model_ema, mixup_fn
        )

        lr_scheduler.step(epoch)
        if args.output_dir:
            checkpoint_paths = [output_dir / 'checkpoint.pth']
            if args.save_every is not None:
                # if epoch % args.save_every == 0: checkpoint_paths.append(output_dir / 'checkpoint_{}.pth'.format(epoch))
                if epoch % args.save_every == 0: checkpoint_paths.append(output_dir / 'checkpoint_latest.pth')
            for checkpoint_path in checkpoint_paths:
                utils.save_on_master({
                    'model': model_without_ddp.state_dict(),
                    'optimizer': optimizer.state_dict(),
                    'lr_scheduler': lr_scheduler.state_dict(),
                    'epoch': epoch,
                    'model_ema': get_state_dict(model_ema) if model_ema else None,
                    'args': args,
                }, checkpoint_path)

        test_stats = evaluate(data_loader_val, model, device)
        print(f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%")
        max_accuracy = max(max_accuracy, test_stats["acc1"])
        print(f'Max accuracy: {max_accuracy:.2f}%')

        if utils.is_main_process():
            tbd_writer.add_scalars('data/loss', {'trn_loss': train_stats['loss'], 'test_loss': test_stats['loss']}, epoch+1)
            tbd_writer.add_scalars('data/acc1', {'test_acc1': test_stats['acc1']}, epoch+1)
            tbd_writer.add_scalars('data/acc5', {'test_acc5': test_stats['acc5']}, epoch+1)
            tbd_writer.add_scalars('data/lr', {'lr': train_stats['lr']}, epoch+1)
            tbd_writer.flush()

    if utils.is_main_process():
        total_time = time.time() - start_time
        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
        tbd_writer.close()
        print('Training time {}'.format(total_time_str))

if __name__ == '__main__':
    parser = argparse.ArgumentParser('PerViT training and evaluation script', parents=[get_args_parser()])
    args = parser.parse_args()
    if args.output_dir:
        Path(args.output_dir).mkdir(parents=True, exist_ok=True)
        Path(os.path.join(args.output_dir, 'tbd/runs')).mkdir(parents=True, exist_ok=True)
    main(args)

