import yaml
import random as rnd


def parse_args(parser):
    # Dataset / Model parameters
    parser.add_argument('--data_dir', metavar='DIR', default='/imagenet',
                        help='path to dataset')
    parser.add_argument('--dataset', '-d', metavar='NAME', default='',
                        help='dataset type (default: ImageFolder/ImageTar if empty)')
    parser.add_argument('--train-split', metavar='NAME', default='train',
                        help='dataset train split (default: train)')
    parser.add_argument('--val-split', metavar='NAME', default='validation',
                        help='dataset validation split (default: validation)')
    parser.add_argument('--model', default='nf_resnet50', type=str, metavar='MODEL',
                        help='Name of model to train')
    parser.add_argument('--pretrained', action='store_true', default=False,
                        help='Start with pretrained version of specified network (if avail)')
    parser.add_argument('--initial-checkpoint', default='', type=str, metavar='PATH',
                        help='Initialize model from this checkpoint (default: none)')
    parser.add_argument('--resume', default='', type=str, metavar='PATH',
                        help='Resume full model and optimizer state from checkpoint (default: none)')
    parser.add_argument('--no-resume-opt', action='store_true', default=False,
                        help='prevent resume of optimizer state when resuming model')
    parser.add_argument('--num-classes', type=int, default=None, metavar='N',
                        help='number of label classes (Model default if None)')
    parser.add_argument('--gp', default=None, type=str, metavar='POOL',
                        help='Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.')
    parser.add_argument('--img-size', type=int, default=None, metavar='N',
                        help='Image patch size (default: None => model default)')
    parser.add_argument(
        '--input-size', default=None, nargs=3, type=int, metavar='N N N',
        help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty')
    parser.add_argument('--crop-pct', default=None, type=float,
                        metavar='N', help='Input image center crop percent (for validation only)')
    parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
                        help='Override mean pixel value of dataset')
    parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
                        help='Override std deviation of of dataset')
    parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
                        help='Image resize interpolation type (overrides model)')
    parser.add_argument('-b', '--batch-size', type=int, default=256, metavar='N',
                        help='input batch size for training (default: 256)')
    parser.add_argument('-vb', '--validation-batch-size-multiplier', type=int, default=1, metavar='N',
                        help='ratio of validation batch size to training batch size (default: 1)')

    # Optimizer parameters
    parser.add_argument('--opt', default='sgd', type=str, metavar='OPTIMIZER',
                        help='Optimizer (default: "sgd"')
    parser.add_argument('--opt-eps', default=None, type=float, metavar='EPSILON',
                        help='Optimizer Epsilon (default: None, use opt default)')
    parser.add_argument('--opt-betas', default=None, type=float, nargs='+', metavar='BETA',
                        help='Optimizer Betas (default: None, use opt default)')
    parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
                        help='Optimizer momentum (default: 0.9)')
    parser.add_argument('--weight-decay', type=float, default=0.0001,
                        help='weight decay (default: 0.0001)')
    parser.add_argument('--clip-grad', type=float, default=None, metavar='NORM',
                        help='Clip gradient norm (default: None, no clipping)')
    parser.add_argument('--clip-mode', type=str, default='norm',
                        help='Gradient clipping mode. One of ("norm", "value", "agc")')

    # 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=0.1, metavar='LR',
                        help='learning rate (default: 0.1)')
    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('--lr-cycle-mul', type=float, default=1.0, metavar='MULT',
                        help='learning rate cycle len multiplier (default: 1.0)')
    parser.add_argument('--lr-cycle-limit', type=int, default=1, metavar='N',
                        help='learning rate cycle limit')
    parser.add_argument('--warmup-lr', type=float, default=0.01, metavar='LR',
                        help='warmup learning rate (default: 0.01)')
    parser.add_argument('--min-lr', type=float, default=1e-4, metavar='LR',
                        help='lower lr bound for cyclic schedulers that hit 0 (1e-4)')
    parser.add_argument('--epochs', type=int, default=90, metavar='N',
                        help='number of epochs to train (default: 90)')
    parser.add_argument('--epoch-repeats', type=float, default=0., metavar='N',
                        help='epoch repeat multiplier (number of times to repeat dataset epoch per train epoch).')
    parser.add_argument('--start-epoch', default=None, type=int, metavar='N',
                        help='manual epoch number (useful on restarts)')
    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=0, metavar='N',
                        help='epochs to warmup LR, if scheduler supports')
    parser.add_argument('--cooldown-epochs', type=int, default=0, 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 & regularization parameters
    parser.add_argument('--no-aug', action='store_true', default=False,
                        help='Disable all training augmentation, override other train aug args')
    parser.add_argument('--scale', type=float, nargs='+', default=[0.08, 1.0], metavar='PCT',
                        help='Random resize scale (default: 0.08 1.0)')
    parser.add_argument('--ratio', type=float, nargs='+', default=[3./4., 4./3.], metavar='RATIO',
                        help='Random resize aspect ratio (default: 0.75 1.33)')
    parser.add_argument('--hflip', type=float, default=0.5,
                        help='Horizontal flip training aug probability')
    parser.add_argument('--vflip', type=float, default=0.,
                        help='Vertical flip training aug probability')
    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=None, metavar='NAME',
                        help='Use AutoAugment policy. "v0" or "original". (default: None)'),
    parser.add_argument('--aug-splits', type=int, default=0,
                        help='Number of augmentation splits (default: 0, valid: 0 or >=2)')
    parser.add_argument('--jsd', action='store_true', default=False,
                        help='Enable Jensen-Shannon Divergence + CE loss. Use with `--aug-splits`.')
    parser.add_argument('--reprob', type=float, default=0., metavar='PCT',
                        help='Random erase prob (default: 0.)')
    parser.add_argument('--remode', type=str, default='const',
                        help='Random erase mode (default: "const")')
    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')
    parser.add_argument('--mixup', type=float, default=0.0,
                        help='mixup alpha, mixup enabled if > 0. (default: 0.)')
    parser.add_argument('--cutmix', type=float, default=0.0,
                        help='cutmix alpha, cutmix enabled if > 0. (default: 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"')
    parser.add_argument('--mixup-off-epoch', default=0, type=int, metavar='N',
                        help='Turn off mixup after this epoch, disabled if 0 (default: 0)')
    parser.add_argument('--smoothing', type=float, default=0.0,
                        help='Label smoothing (default: 0.0)')
    parser.add_argument('--train-interpolation', type=str, default='random',
                        help='Training interpolation (random, bilinear, bicubic default: "random")')
    parser.add_argument('--drop', type=float, default=0.0, metavar='PCT',
                        help='Dropout rate (default: 0.)')
    parser.add_argument('--drop-connect', type=float, default=None, metavar='PCT',
                        help='Drop connect rate, DEPRECATED, use drop-path (default: None)')
    parser.add_argument('--drop-path', type=float, default=None, metavar='PCT',
                        help='Drop path rate (default: None)')
    parser.add_argument('--drop-block', type=float, default=None, metavar='PCT',
                        help='Drop block rate (default: None)')

    # Batch norm parameters (only works with gen_efficientnet based models currently)
    parser.add_argument('--bn-tf', action='store_true', default=False,
                        help='Use Tensorflow BatchNorm defaults for models that support it (default: False)')
    parser.add_argument('--bn-momentum', type=float, default=None,
                        help='BatchNorm momentum override (if not None)')
    parser.add_argument('--bn-eps', type=float, default=None,
                        help='BatchNorm epsilon override (if not None)')
    parser.add_argument('--sync-bn', action='store_true',
                        help='Enable NVIDIA Apex or Torch synchronized BatchNorm.')
    parser.add_argument('--dist-bn', type=str, default='',
                        help='Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")')
    parser.add_argument('--split-bn', action='store_true',
                        help='Enable separate BN layers per augmentation split.')

    # Model Exponential Moving Average
    parser.add_argument('--model-ema', action='store_true', default=False,
                        help='Enable tracking moving average of model weights')
    parser.add_argument('--model-ema-force-cpu', action='store_true', default=False,
                        help='Force ema to be tracked on CPU, rank=0 node only. Disables EMA validation.')
    parser.add_argument('--model-ema-decay', type=float, default=0.9998,
                        help='decay factor for model weights moving average (default: 0.9998)')

    # Misc
    parser.add_argument('--seed', type=int, default=rnd.randint(1, 1e6), metavar='S',
                        help='random seed (default: random)')
    parser.add_argument('--log-interval', type=int, default=20, metavar='N',
                        help='how many batches to wait before logging training status')
    parser.add_argument('--recovery-interval', type=int, default=0, metavar='N',
                        help='how many batches to wait before writing recovery checkpoint')
    parser.add_argument('--checkpoint-hist', type=int, default=2, metavar='N',
                        help='number of checkpoints to keep (default: 2)')
    parser.add_argument('-j', '--workers', type=int, default=14, metavar='N',
                        help='how many training processes to use (default: 14)')
    parser.add_argument('--save-images', action='store_true', default=False,
                        help='save images of input bathes every log interval for debugging')
    parser.add_argument('--amp', action='store_true', default=False,
                        help='use NVIDIA Apex AMP or Native AMP for mixed precision training')
    parser.add_argument('--apex-amp', action='store_true', default=False,
                        help='Use NVIDIA Apex AMP mixed precision')
    parser.add_argument('--native-amp', action='store_true', default=False,
                        help='Use Native Torch AMP mixed precision')
    parser.add_argument('--channels-last', action='store_true', default=True,
                        help='Use channels_last memory layout')
    parser.add_argument('--pin-mem', action='store_true', default=True,
                        help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
    parser.add_argument('--no-prefetcher', action='store_true', default=False,
                        help='disable fast prefetcher')
    parser.add_argument('--output', default='', type=str, metavar='PATH',
                        help='path to output folder (default: none, current dir)')
    parser.add_argument('--experiment', default='', type=str, metavar='NAME',
                        help='name of train experiment, name of sub-folder for output')
    parser.add_argument('--eval-metric', default='top1', type=str, metavar='EVAL_METRIC',
                        help='Best metric (default: "top1"')
    parser.add_argument('--tta', type=int, default=0, metavar='N',
                        help='Test/inference time augmentation (oversampling) factor. 0=None (default: 0)')
    parser.add_argument("--local_rank", default=0, type=int)
    parser.add_argument('--use-multi-epochs-loader', action='store_true', default=False,
                        help='use the multi-epochs-loader to save time at the beginning of every epoch')
    parser.add_argument('--torchscript', dest='torchscript', action='store_true',
                        help='convert model torchscript for inference')
    parser.add_argument('--log-wandb', action='store_true', default=False,
                        help='log training and validation metrics to wandb')

    # Useful additions
    parser.add_argument('--print-model', action='store_true', default=False,
                        help='Print model architecture and exit')
    parser.add_argument('--per-line', action='store_true', default=False,
                        help='Print logs per line')
    parser.add_argument("--train-continuous", type=int, default=1,
                        help='Defer all operations and train continuously for x epochs (default: 1)')
    parser.add_argument('-v', '--validate', action='store_true', default=False,
                        help='Perform validation only')
    parser.add_argument('--final-validate', action='store_true', default=False,
                        help='Final validate with logit stats')

    # Ext Dataloaders
    parser.add_argument('--dali', action='store_true', default=False,
                        help='Use DALI Dataloader')
    parser.add_argument('--dali-gpu', action='store_true', default=False,
                        help='Use DALI GPU')
    parser.add_argument('--cifar', default=0, type=int,
                        help='Create Dataloader for CIFAR 10/100')

    # Custom loss functions
    parser.add_argument('--custom-loss', action='store_true', default=False,
                        help='Use Custom Loss function')
    parser.add_argument('--logit-stats', action='store_true', default=False,
                        help='Print logit stats. Must load checkpoint first.')
    parser.add_argument('--grad-stats', action='store_true', default=False,
                        help='Print grad stats during training.')

    # Softmax gradient tampering
    parser.add_argument('--modify-softmax', default=0.0, type=float,
                        help='Gradient tampering for softmax')

    # The main arg parser parses the rest of the args, the usual
    # defaults will have been overridden if config file specified.
    args = parser.parse_args()

    # Cache the args as a text string to save them in the output dir later
    args_text = yaml.safe_dump(args.__dict__, default_flow_style=False)
    return args, args_text
