# SPDX-License-Identifier: BSD-2-Clause
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# Copyright (c) 2021, Pl@ntNet
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import torch


def add_all_parsers(parser):
    _add_loss_parser(parser)
    _add_training_parser(parser)
    _add_model_parser(parser)
    _add_hardware_parser(parser)
    _add_misc_parser(parser)


def _add_loss_parser(parser):
    group_loss = parser.add_argument_group("Loss parameters")
    group_loss.add_argument(
        "--mu", type=float, default=0.0001, help="weight decay parameter"
    )


def _add_training_parser(parser):
    group_training = parser.add_argument_group("Training parameters")
    group_training.add_argument("--lr", type=float, help="learning rate to use")
    group_training.add_argument(
        "--batch_size", type=int, default=32, help="default is 32"
    )
    group_training.add_argument("--n_epochs", type=int)
    group_training.add_argument("--pretrained", action="store_true")
    group_training.add_argument("--image_size", type=int, default=256)
    group_training.add_argument("--crop_size", type=int, default=224)
    group_training.add_argument("--epoch_decay", nargs="+", type=int, default=[])
    group_training.add_argument(
        "--k",
        nargs="+",
        help="value of k for computing the topk loss and computing topk accuracy",
        required=True,
        type=int,
    )


def _add_model_parser(parser):
    group_model = parser.add_argument_group("Model parameters")
    group_model.add_argument(
        "--model",
        choices=[
            "resnet18",
            "resnet34",
            "resnet50",
            "resnet101",
            "resnet152",
            "densenet121",
            "densenet161",
            "densenet169",
            "densenet201",
            "mobilenet_v2",
            "inception_v3",
            "alexnet",
            "squeezenet",
            "shufflenet",
            "wide_resnet50_2",
            "wide_resnet101_2",
            "vgg11",
            "mobilenet_v3_large",
            "mobilenet_v3_small",
            "inception_resnet_v2",
            "inception_v4",
            "efficientnet_b0",
            "efficientnet_b1",
            "efficientnet_b2",
            "efficientnet_b3",
            "efficientnet_b4",
            "vit_base_patch16_224",
        ],
        default="resnet50",
        help="choose the model you want to train on",
    )


def _add_hardware_parser(parser):
    group_hardware = parser.add_argument_group("Hardware parameters")
    group_hardware.add_argument(
        "--use_gpu", type=int, choices=[0, 1], default=torch.cuda.is_available()
    )


def _add_misc_parser(parser):
    group_misc = parser.add_argument_group("Miscellaneous parameters")
    group_misc.add_argument(
        "--seed", type=int, help="set the seed for reproductible experiments"
    )
    group_misc.add_argument(
        "--num_workers",
        type=int,
        default=4,
        help="number of workers for the data loader. Default is one. You can bring it up. "
        "If you have memory errors go back to one",
    )
    group_misc.add_argument(
        "--root", help="location of the train val and test directories"
    )
    group_misc.add_argument("--save_name_xp", help="name of the saving file")