from numpy import require

import colossalai

__all__ = ['parse_args']


def parse_args():
    parser = colossalai.get_default_parser()

    parser.add_argument(
        "--distplan",
        type=str,
        default='CAI_Gemini',
        help="The distributed plan [colossalai, zero1, zero2, torch_ddp, torch_zero].",
    )
    parser.add_argument(
        "--tp_degree",
        type=int,
        default=1,
        help="Tensor Parallelism Degree. Valid when using colossalai as dist plan.",
    )
    parser.add_argument(
        "--placement",
        type=str,
        default='cpu',
        help="Placement Policy for Gemini. Valid when using colossalai as dist plan.",
    )
    parser.add_argument(
        "--shardinit",
        action='store_true',
        help=
        "Shard the tensors when init the model to shrink peak memory size on the assigned device. Valid when using colossalai as dist plan.",
    )

    parser.add_argument('--lr', type=float, required=True, help='initial learning rate')
    parser.add_argument('--epoch', type=int, required=True, help='number of epoch')
    parser.add_argument('--data_path_prefix', type=str, required=True, help="location of the train data corpus")
    parser.add_argument('--eval_data_path_prefix',
                        type=str,
                        required=True,
                        help='location of the evaluation data corpus')
    parser.add_argument('--tokenizer_path', type=str, required=True, help='location of the tokenizer')
    parser.add_argument('--max_seq_length', type=int, default=512, help='sequence length')
    parser.add_argument('--refresh_bucket_size',
                        type=int,
                        default=1,
                        help="This param makes sure that a certain task is repeated for this time steps to \
        optimize on the back propagation speed with APEX's DistributedDataParallel")
    parser.add_argument("--max_predictions_per_seq",
                        "--max_pred",
                        default=80,
                        type=int,
                        help="The maximum number of masked tokens in a sequence to be predicted.")
    parser.add_argument("--gradient_accumulation_steps", default=1, type=int, help="accumulation_steps")
    parser.add_argument("--train_micro_batch_size_per_gpu", default=2, type=int, required=True, help="train batch size")
    parser.add_argument("--eval_micro_batch_size_per_gpu", default=2, type=int, required=True, help="eval batch size")
    parser.add_argument("--num_workers", default=8, type=int, help="")
    parser.add_argument("--async_worker", action='store_true', help="")
    parser.add_argument("--bert_config", required=True, type=str, help="location of config.json")
    parser.add_argument("--wandb", action='store_true', help="use wandb to watch model")
    parser.add_argument("--wandb_project_name", default='roberta', help="wandb project name")
    parser.add_argument("--log_interval", default=100, type=int, help="report interval")
    parser.add_argument("--log_path", type=str, required=True, help="log file which records train step")
    parser.add_argument("--tensorboard_path", type=str, required=True, help="location of tensorboard file")
    parser.add_argument("--colossal_config",
                        type=str,
                        required=True,
                        help="colossal config, which contains zero config and so on")
    parser.add_argument("--ckpt_path",
                        type=str,
                        required=True,
                        help="location of saving checkpoint, which contains model and optimizer")
    parser.add_argument('--seed', type=int, default=42, help="random seed for initialization")
    parser.add_argument('--vscode_debug', action='store_true', help="use vscode to debug")
    parser.add_argument('--load_pretrain_model', default='', type=str, help="location of model's checkpoint")
    parser.add_argument(
        '--load_optimizer_lr',
        default='',
        type=str,
        help="location of checkpoint, which contains optimizer, learning rate, epoch, shard and global_step")
    parser.add_argument('--resume_train', action='store_true', help="whether resume training from a early checkpoint")
    parser.add_argument('--mlm', default='bert', type=str, help="model type, bert or deberta")
    parser.add_argument('--checkpoint_activations', action='store_true', help="whether to use gradient checkpointing")

    args = parser.parse_args()
    return args