config.py

import argparse

def parse_options():

    # tox and set params
    parser = argparse.ArgumentParser()
    parser.add_argument("--test_file", type=str)
    parser.add_argument("--save_path", type=str)
    parser.add_argument("--prompt_set", type=str, default="bold")
    parser.add_argument("--prompt_domain", type=str, default="gender")
    parser.add_argument("--category", type=str, default=None)
    parser.add_argument("--summarize", action="store_true")
    parser.add_argument("--regard", action="store_true")

    if parser.parse_known_args()[0].regard == True:
        # Required parameters for regard
        parser.add_argument(
            "--data_dir",
            default="",
            type=str,
            required=True,
            help="The input data dir. Should contain the training files for the regard classification task.",
        )
        parser.add_argument(
            "--model_version",
            default=2,
            type=int,
            required=False,
            help="1 or 2.",
        )
        parser.add_argument(
            "--model_type",
            default=None,
            type=str,
            required=True,
        )
        parser.add_argument(
            "--model_name_or_path",
            default=None,
            type=str,
            required=True,
        )
        parser.add_argument(
            "--output_dir",
            default=None,
            type=str,
            required=True,
            help="The output directory where the model predictions and checkpoints will be written.",
        )

        # Other parameters
        parser.add_argument(
            "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name"
        )
        parser.add_argument(
            "--tokenizer_name",
            default="",
            type=str,
            help="Pretrained tokenizer name or path if not the same as model_name",
        )
        parser.add_argument(
            "--cache_dir",
            default="",
            type=str,
            help="Where do you want to store the pre-trained models downloaded from s3",
        )
        parser.add_argument(
            "--max_seq_length",
            default=128,
            type=int,
            help="The maximum total input sequence length after tokenization. Sequences longer "
            "than this will be truncated, sequences shorter will be padded.",
        )
        parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
        parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
        parser.add_argument("--do_predict", action="store_true", help="Whether to run predictions on the test set.")
        parser.add_argument(
            "--evaluate_during_training",
            action="store_true",
            help="Whether to run evaluation during training at each logging step.",
        )
        parser.add_argument(
            "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model."
        )

        parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.")
        parser.add_argument(
            "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation."
        )
        parser.add_argument(
            "--gradient_accumulation_steps",
            type=int,
            default=1,
            help="Number of updates steps to accumulate before performing a backward/update pass.",
        )
        parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
        parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
        parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
        parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
        parser.add_argument(
            "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform."
        )
        parser.add_argument(
            "--max_steps",
            default=-1,
            type=int,
            help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
        )
        parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")

        parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
        parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
        parser.add_argument(
            "--eval_all_checkpoints",
            action="store_true",
            help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
        )
        parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
        parser.add_argument(
            "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory"
        )
        parser.add_argument(
            "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
        )
        parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")

        parser.add_argument(
            "--fp16",
            action="store_true",
            help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
        )
        parser.add_argument(
            "--fp16_opt_level",
            type=str,
            default="O1",
            help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
            "See details at https://nvidia.github.io/apex/amp.html",
        )
        parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
        parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.")
        parser.add_argument("--server_port", type=str, default="", help="For distant debugging.")
    args = parser.parse_args()
    return args