from argparse import ArgumentParser

parser = ArgumentParser()

parser.add_argument(
    "--maxlen_train",
    type=int,
    default=30,
    help="Maximum number of tokens in the input sequence during training.",
)
parser.add_argument(
    "--maxlen_val",
    type=int,
    default=50,
    help="Maximum number of tokens in the input sequence during evaluation.",
)
parser.add_argument(
    "--batch_size", type=int, default=32, help="Batch size during training."
)
parser.add_argument("--lr", type=float, default=2e-5, help="Learning rate for Adam.")
parser.add_argument("--num_eps", type=int, default=2, help="Number of training epochs.")
parser.add_argument(
    "--num_threads",
    type=int,
    default=1,
    help="Number of threads for collecting the datasets.",
)
parser.add_argument(
    "--output_dir",
    type=str,
    default="my_model",
    help="Where to save the trained model, if relevant.",
)
parser.add_argument(
    "--model_name_or_path",
    type=str,
    default=None,
    help="""Name of or path to the pretrained/trained model.
            For training choose between bert-base-uncased, albert-base-v2, distilbert-base-uncased etc.
            For evaluating/analyzing/server choose between barissayil/bert-sentiment-analysis-sst and paths to the models you have trained previously.""",
)

args = parser.parse_args()
