# ------------------------------------------------------------------------
# Modified from ConvNeXt (https://github.com/facebookresearch/ConvNeXt)
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved.
# Licensed under the MIT License.
# ------------------------------------------------------------------------
# Modifications Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0

import argparse
import datetime
import numpy as np
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import json
import os

from pathlib import Path
import wandb

from timm.models import create_model
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from timm.utils import ModelEma
from util.optim_factory import create_optimizer, LayerDecayValueAssigner

from util.datasets import build_dataset_ssl
from engine_convnext import train_one_epoch_ssl, evaluate

from util.misc import NativeScalerWithGradNormCount as NativeScaler
import util.misc as utils
from util.lr_sched import cosine_scheduler
from util.mixup import get_mixup_func
import models.convnext


def str2bool(v):
    """
    Converts string to bool type; enables command line
    arguments in the format of '--arg1 true --arg2 false'
    """
    if isinstance(v, bool):
        return v
    if v.lower() in ('yes', 'true', 't', 'y', '1'):
        return True
    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
        return False
    else:
        raise argparse.ArgumentTypeError('Boolean value expected.')


def get_args_parser():
    parser = argparse.ArgumentParser('Semi-supervised ConvNeXt training and evaluation', add_help=False)
    parser.add_argument('--batch_size', default=64, type=int,
                        help='Per GPU batch size')
    parser.add_argument('--epochs', default=100, type=int)
    parser.add_argument('--update_freq', default=1, type=int,
                        help='gradient accumulation steps')

    # Model parameters
    parser.add_argument('--model', default='convnext_tiny', type=str, metavar='MODEL',
                        help='Name of model to train')
    parser.add_argument('--drop_path', type=float, default=0, metavar='PCT',
                        help='Drop path rate (default: 0.0)')
    parser.add_argument('--input_size', default=224, type=int,
                        help='image input size')
    parser.add_argument('--layer_scale_init_value', default=1e-6, type=float,
                        help="Layer scale initial values")

    # EMA related parameters
    parser.add_argument('--model_ema', type=str2bool, default=False)
    parser.add_argument('--model_ema_decay', type=float, default=0.9999, help='')
    parser.add_argument('--model_ema_force_cpu', type=str2bool, default=False, help='')

    # Optimization 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)')
    parser.add_argument('--weight_decay_end', type=float, default=None, help="""Final value of the
        weight decay. We use a cosine schedule for WD and using a larger decay by
        the end of training improves performance for ViTs.""")

    parser.add_argument('--lr', type=float, default=1e-3, metavar='LR',
                        help='learning rate (default: 1e-3), with total batch size 1024')
    parser.add_argument('--layer_decay', type=float, default=1.0)
    parser.add_argument('--min_lr', type=float, default=1e-6, metavar='LR',
                        help='lower lr bound for cyclic schedulers that hit 0 (1e-6)')
    parser.add_argument('--warmup_epochs', type=int, default=10, metavar='N',
                        help='epochs to warmup LR, if scheduler supports')
    parser.add_argument('--warmup_steps', type=int, default=-1, metavar='N',
                        help='num of steps to warmup LR, will overload warmup_epochs if set > 0')

    # 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")')

    # Evaluation parameters
    parser.add_argument('--crop_pct', type=float, default=None)

    # * 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', type=str2bool, 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.')
    parser.add_argument('--cutmix', type=float, default=1.0,
                        help='cutmix alpha, cutmix enabled if > 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"')

    # * Finetuning params
    parser.add_argument('--finetune', default='',
                        help='finetune from checkpoint')
    parser.add_argument('--head_init_scale', default=1.0, type=float,
                        help='classifier head initial scale, typically adjusted in fine-tuning')
    parser.add_argument('--model_key', default='model|module', type=str,
                        help='which key to load from saved state dict, usually model or model_ema')
    parser.add_argument('--model_prefix', default='', type=str)
    parser.add_argument('--finetune_ema', action='store_true')
    parser.set_defaults(finetune_ema=True)
    parser.add_argument('--no_finetune_ema', action='store_false', dest='finetune_ema')

    # Dataset parameters
    parser.add_argument('--data_path', default='~/data/imagenet/', type=str,
                        help='dataset path')
    parser.add_argument('--eval_data_path', default=None, type=str,
                        help='dataset path for evaluation')
    parser.add_argument('--nb_classes', default=1000, type=int,
                        help='number of the classification types')
    parser.add_argument('--imagenet_default_mean_and_std', type=str2bool, default=True)
    parser.add_argument('--trainindex_x', default=None, type=str, metavar='PATH',
                        help='path to train trainindex_x (default: None)')
    parser.add_argument('--trainindex_u', default=None, type=str, metavar='PATH',
                        help='path to train trainindex_u (default: None)')
    parser.add_argument('--anno_percent', type=float, default=1.0, help='number of labeled data')

    parser.add_argument('--output_dir', default='./save',
                        help='path where to save, empty for no saving')
    parser.add_argument('--log_dir', default=None,
                        help='path where to tensorboard log')
    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='',
                        help='resume from checkpoint')
    parser.add_argument('--auto_resume', type=str2bool, default=True)
    parser.add_argument('--save_ckpt', type=str2bool, default=True)
    parser.add_argument('--save_ckpt_freq', default=50, type=int)
    parser.add_argument('--save_ckpt_num', default=3, type=int)

    parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
                        help='start epoch')
    parser.add_argument('--eval', type=str2bool, default=False,
                        help='Perform evaluation only')
    parser.add_argument('--dist_eval', type=str2bool, default=True,
                        help='Enabling distributed evaluation')
    parser.add_argument('--eval_freq', default=1, type=int)
    parser.add_argument('--num_workers', default=6, type=int)
    parser.add_argument('--pin_mem', type=str2bool, default=True,
                        help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')

    # distributed training parameters
    parser.add_argument('--world_size', default=1, type=int,
                        help='number of distributed processes')
    parser.add_argument('--local_rank', default=-1, type=int)
    parser.add_argument('--dist_on_itp', type=str2bool, default=False)
    parser.add_argument('--dist_url', default='env://',
                        help='url used to set up distributed training')

    parser.add_argument('--use_amp', type=str2bool, default=True,
                        help="Use PyTorch's AMP (Automatic Mixed Precision) or not")

    parser.add_argument('--print_freq', default=40, type=int, help='print frequency')

    # Weights and Biases arguments
    parser.add_argument('--enable_wandb', type=str2bool, default=False,
                        help="enable logging to Weights and Biases")
    parser.add_argument('--project', default='convnext', type=str,
                        help="The name of the W&B project where you're sending the new run.")
    parser.add_argument('--wandb_ckpt', type=str2bool, default=False,
                        help="Save model checkpoints as W&B Artifacts.")
    parser.add_argument('--wandb', action='store_true', help='If use wandb for logging.')
    parser.add_argument('--model_id', default=None, help='model id')

    # FixMatch configs:
    parser.add_argument('--ema_teacher', action='store_true', default=False)
    parser.add_argument('--mu', default=5, type=int,
                        help='coefficient of unlabeled batch size')
    parser.add_argument('--lambda_u', default=10, type=float,
                        help='coefficient of unlabeled loss')
    parser.add_argument('--threshold', default=0.7, type=float,
                        help='pseudo label threshold')
    parser.add_argument('--burnin_epochs', default=0, type=int)
    parser.add_argument('--weak_aa', action='store_true', default=False,
                        help="using AutoAugment for weak augmentations")
    parser.add_argument('--weak_no_aug', action='store_true', default=False,
                        help="using disable any augmentation for weak augmentations")
    parser.add_argument('--strong_no_re', action='store_true', default=False,
                        help="disable random erasing for strong augmentations")
    parser.add_argument('--pseudo_mixup', action='store_true', default=False,
                        help="using mixup for unlabeled data")
    parser.add_argument('--disable_x_mixup', action='store_true', default=False,
                        help="disable mixup for labeled data")
    parser.add_argument('--mixup_func', type=str, default='Mixup',
                        help='How to apply mixup/cutmix params. Per "Mixup"')
    parser.add_argument('--pseudo_mixup_func', type=str, default='Mixup',
                        help='How to apply mixup/cutmix params. Per "Mixup", or "ProbPseudoMixup"')

    return parser

def main(args):
    utils.init_distributed_mode(args)
    args.print_freq = round(args.print_freq / (args.batch_size * args.mu * utils.get_world_size() / 1024.0))
    if args.ema_teacher:
        assert args.model_ema, "EMA teacher requires to enable model_ema"
    print(args)

    args.wandb = args.wandb and args.rank == 0
    if args.wandb:
        assert args.model_id is not None
        print("initialize wandb project")
        wandb.init(entity='YourName', project="semivit", name=args.model_id)
        wandb.config.update(args)

    device = torch.device(args.device)

    # fix the seed for reproducibility
    seed = args.seed + utils.get_rank()
    torch.manual_seed(seed)
    np.random.seed(seed)
    cudnn.benchmark = True

    dataset_train_x, dataset_train_u, dataset_val = build_dataset_ssl(args=args)

    num_tasks = utils.get_world_size()
    global_rank = utils.get_rank()

    sampler_train_x = torch.utils.data.DistributedSampler(
        dataset_train_x, num_replicas=num_tasks, rank=global_rank, shuffle=True
    )
    sampler_train_u = torch.utils.data.DistributedSampler(
        dataset_train_u, num_replicas=num_tasks, rank=global_rank, shuffle=True
    )
    print("Sampler_train_x = %s" % str(sampler_train_x))
    print("Sampler_train_u = %s" % str(sampler_train_u))
    if args.dist_eval:
        if len(dataset_val) % num_tasks != 0:
            print('Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. '
                    'This will slightly alter validation results as extra duplicate entries are added to achieve '
                    'equal num of samples per-process.')
        sampler_val = torch.utils.data.DistributedSampler(
            dataset_val, num_replicas=num_tasks, rank=global_rank, shuffle=False)
    else:
        sampler_val = torch.utils.data.SequentialSampler(dataset_val)

    if global_rank == 0 and args.log_dir is not None:
        os.makedirs(args.log_dir, exist_ok=True)
        log_writer = utils.TensorboardLogger(log_dir=args.log_dir)
    else:
        log_writer = None

    if global_rank == 0 and args.enable_wandb:
        wandb_logger = utils.WandbLogger(args)
    else:
        wandb_logger = None

    data_loader_train_x = torch.utils.data.DataLoader(
        dataset_train_x, sampler=sampler_train_x,
        batch_size=args.batch_size,
        num_workers=args.num_workers,
        pin_memory=args.pin_mem,
        drop_last=True,
    )
    data_loader_train_u = torch.utils.data.DataLoader(
        dataset_train_u, sampler=sampler_train_u,
        batch_size=args.batch_size * args.mu,
        num_workers=args.num_workers,
        pin_memory=args.pin_mem,
        drop_last=True,
    )

    data_loader_val = torch.utils.data.DataLoader(
        dataset_val, sampler=sampler_val,
        batch_size=int(args.mu * args.batch_size),
        num_workers=args.num_workers,
        pin_memory=args.pin_mem,
        drop_last=False
    )

    mixup_fn, pseudo_mixup_fn = None, None
    mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
    if mixup_active:
        print("Mixup is activated!")
        mixup_func = get_mixup_func(args.mixup_func)
        mixup_fn = mixup_func(
            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("mixup_fn: {}".format(mixup_fn))
        if args.pseudo_mixup:
            pseudo_mixup_func = get_mixup_func(args.pseudo_mixup_func)
            pseudo_mixup_fn = pseudo_mixup_func(
                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("pseudo_mixup_fn: {}".format(pseudo_mixup_fn))

    model = create_model(
        args.model,
        pretrained=False,
        num_classes=args.nb_classes,
        drop_path_rate=args.drop_path,
        layer_scale_init_value=args.layer_scale_init_value,
        head_init_scale=args.head_init_scale,
    )

    if args.finetune:
        checkpoint = torch.load(args.finetune, map_location='cpu')
        print("Load supervised pre-trained checkpoint from: %s" % args.finetune)
        checkpoint_model = checkpoint['model']
        # load pre-trained model
        msg = model.load_state_dict(checkpoint_model, strict=True)
        print(msg)

    model.to(device)

    model_ema = None
    if args.model_ema:
        # Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
        model_ema = ModelEma(
            model,
            decay=args.model_ema_decay,
            device='cpu' if args.model_ema_force_cpu else '',
            resume='')
        print("Using EMA with decay = %.8f" % args.model_ema_decay)
        if args.finetune and 'model_ema' in checkpoint and args.finetune_ema:
            print("Loading EMA model from supervised pre-trained checkpoint: %s" % args.finetune)
            checkpoint_ema = checkpoint['model_ema']
            msg = model_ema.ema.load_state_dict(checkpoint_ema, strict=True)
            print(msg)

    model_without_ddp = model
    n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
    n_parameters_tensors = sum(1 for p in model.parameters() if p.requires_grad)

    print("Model = %s" % str(model_without_ddp))
    print('number of params (M): %f' % (n_parameters / 1.e6))
    print('number of learnable param tensors: %d/%d' % (n_parameters_tensors, len(list(model.parameters()))))

    total_batch_size = args.batch_size * args.update_freq * utils.get_world_size()
    num_training_steps_per_epoch = len(dataset_train_u) // (total_batch_size * args.mu)
    args.lr = args.lr * total_batch_size / 256
    print("LR = %.8f" % args.lr)
    print("Labeled batch size = %d" % total_batch_size)
    print("Unlabeled batch size = %d" % (total_batch_size * args.mu))
    print("Update frequent = %d" % args.update_freq)
    print("Number of labeled training examples = %d" % len(dataset_train_x))
    print("Number of unlabeled training examples = %d" % len(dataset_train_u))
    print("Number of training training per epoch = %d" % num_training_steps_per_epoch)

    if args.layer_decay < 1.0 or args.layer_decay > 1.0:
        num_layers = 12 # convnext layers divided into 12 parts, each with a different decayed lr value.
        assert args.model in ['convnext_small', 'convnext_base', 'convnext_large', 'convnext_xlarge'], \
             "Layer Decay impl only supports convnext_small/base/large/xlarge"
        assigner = LayerDecayValueAssigner(list(args.layer_decay ** (num_layers + 1 - i) for i in range(num_layers + 2)))
    else:
        assigner = None

    if assigner is not None:
        print("Assigned values = %s" % str(assigner.values))

    if args.distributed:
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=False)
        model_without_ddp = model.module

    optimizer = create_optimizer(
        args, model_without_ddp, skip_list=None,
        get_num_layer=assigner.get_layer_id if assigner is not None else None,
        get_layer_scale=assigner.get_scale if assigner is not None else None)

    loss_scaler = NativeScaler() # if args.use_amp is False, this won't be used

    print("Use Cosine LR scheduler")
    lr_schedule_values = cosine_scheduler(
        args.lr, args.min_lr, args.epochs, num_training_steps_per_epoch,
        warmup_epochs=args.warmup_epochs, warmup_steps=args.warmup_steps,
    )

    if args.weight_decay_end is None:
        args.weight_decay_end = args.weight_decay
    wd_schedule_values = cosine_scheduler(
        args.weight_decay, args.weight_decay_end, args.epochs, num_training_steps_per_epoch)
    print("Max WD = %.7f, Min WD = %.7f" % (max(wd_schedule_values), min(wd_schedule_values)))

    if mixup_fn is not None and not args.disable_x_mixup:
        # smoothing is handled with mixup label transform
        criterion = SoftTargetCrossEntropy()
    elif args.smoothing > 0.:
        criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
    else:
        criterion = torch.nn.CrossEntropyLoss()

    print("criterion = %s" % str(criterion))

    utils.load_model(
        args=args, model_without_ddp=model_without_ddp,
        optimizer=optimizer, loss_scaler=loss_scaler, model_ema=model_ema)

    if args.eval:
        print(f"Eval only mode")
        test_stats = evaluate(data_loader_val, model, device, use_amp=args.use_amp)
        print(f"Accuracy of the network on {len(dataset_val)} test images: {test_stats['acc1']:.5f}%")
        if model_ema is not None:
            test_stats_ema = evaluate(data_loader_val, model_ema.ema, device, use_amp=args.use_amp)
            print(f"Accuracy of the EMA network on the {len(dataset_val)} test images: {test_stats_ema['acc1']:.1f}%")
        return

    print("Start training for %d epochs" % args.epochs)
    start_time = time.time()
    max_accuracy, max_accuracy_ema = 0.0, 0.0
    for epoch in range(args.start_epoch, args.epochs):
        if log_writer is not None:
            log_writer.set_step(epoch * num_training_steps_per_epoch * args.update_freq)
        if wandb_logger:
            wandb_logger.set_steps()
        train_stats = train_one_epoch_ssl(
            model, criterion, data_loader_train_x, data_loader_train_u, optimizer,
            device, epoch, loss_scaler, args.clip_grad, model_ema, mixup_fn, pseudo_mixup_fn=pseudo_mixup_fn,
            log_writer=log_writer, wandb_logger=wandb_logger, start_steps=epoch * num_training_steps_per_epoch,
            lr_schedule_values=lr_schedule_values, wd_schedule_values=wd_schedule_values,
            num_training_steps_per_epoch=num_training_steps_per_epoch, update_freq=args.update_freq,
            use_amp=args.use_amp, args=args
        )
        if args.output_dir and args.save_ckpt:
            save_names = ['checkpoint.pth']
            if (epoch + 1) % args.save_ckpt_freq == 0 or epoch + 1 == args.epochs:
                save_names.append('checkpoint-%s.pth' % str(epoch))
            utils.save_model(
                args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
                loss_scaler=loss_scaler, epoch=epoch, save_names=save_names, model_ema=model_ema)

        if (epoch + 1) % args.eval_freq == 0:
            test_stats = evaluate(data_loader_val, model, device, use_amp=args.use_amp)
            print(f"Accuracy of the model on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%")
            if args.wandb:
                wandb.log({'val_loss': test_stats['loss'], 'val_acc1': test_stats['acc1']}, commit=True)
            if max_accuracy < test_stats["acc1"]:
                max_accuracy = test_stats["acc1"]
                if args.output_dir and args.save_ckpt:
                    save_names = ['checkpoint-best.pth']
                    utils.save_model(
                        args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
                        loss_scaler=loss_scaler, epoch=epoch, save_names=save_names, model_ema=model_ema)
            print(f'Max accuracy: {max_accuracy:.2f}%')

            if log_writer is not None:
                log_writer.update(test_acc1=test_stats['acc1'], head="perf", step=epoch)
                log_writer.update(test_acc5=test_stats['acc5'], head="perf", step=epoch)
                log_writer.update(test_loss=test_stats['loss'], head="perf", step=epoch)

            log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
                         **{f'test_{k}': v for k, v in test_stats.items()},
                         'epoch': epoch,
                         'n_parameters': n_parameters}

            # repeat testing routines for EMA, if ema eval is turned on
            if model_ema is not None:
                test_stats_ema = evaluate(data_loader_val, model_ema.ema, device, use_amp=args.use_amp)
                print(f"Accuracy of the model EMA on {len(dataset_val)} test images: {test_stats_ema['acc1']:.1f}%")
                if args.wandb:
                    wandb.log({'val_loss_ema': test_stats_ema['loss'], 'val_acc1_ema': test_stats_ema['acc1']}, commit=True)
                if max_accuracy_ema < test_stats_ema["acc1"]:
                    max_accuracy_ema = test_stats_ema["acc1"]
                    if args.output_dir and args.save_ckpt:
                        save_names = ['checkpoint-best-ema.pth']
                        utils.save_model(
                            args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
                            loss_scaler=loss_scaler, epoch=epoch, save_names=save_names, model_ema=model_ema)
                    print(f'Max EMA accuracy: {max_accuracy_ema:.2f}%')
                if log_writer is not None:
                    log_writer.update(test_acc1_ema=test_stats_ema['acc1'], head="perf", step=epoch)
                log_stats.update({**{f'test_{k}_ema': v for k, v in test_stats_ema.items()}})
        else:
            log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
                         'epoch': epoch,
                         'n_parameters': n_parameters}

        if args.output_dir and utils.is_main_process():
            if log_writer is not None:
                log_writer.flush()
            with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f:
                f.write(json.dumps(log_stats) + "\n")

        if wandb_logger:
            wandb_logger.log_epoch_metrics(log_stats)

    if wandb_logger and args.wandb_ckpt and args.save_ckpt and args.output_dir:
        wandb_logger.log_checkpoints()


    total_time = time.time() - start_time
    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
    print('Training time {}'.format(total_time_str))


if __name__ == '__main__':
    parser = argparse.ArgumentParser('Semi-supervised ConvNeXt training and evaluation', parents=[get_args_parser()])
    args = parser.parse_args()
    if args.output_dir:
        Path(args.output_dir).mkdir(parents=True, exist_ok=True)
    main(args)
