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import argparse
def parse_args():
parser = argparse.ArgumentParser(description="Run REARM.")
parser.add_argument('--seed', type=int, default=2025, help='Seed init.')
parser.add_argument('--model_name', default='REARM', help='Model name.')
parser.add_argument('--use_gpu', type=bool, default=True, help='enable CUDA training.')
parser.add_argument('--gpu_id', type=int, default=0, help='The model of the device running the program')
parser.add_argument('--dataset', nargs='?', default='baby',
help='Choose a dataset from {baby, sports, clothing}')
parser.add_argument('--batch_size', type=int, default=2048, help='Batch size.')
parser.add_argument('--eval_batch_size', type=int, default=8192, help='The data size of batch evaluation')
parser.add_argument('--metrics', type=list, default=["Precision", "Recall", "NDCG"],
help='Choose some from {"Precision", "Recall", "NDCG", "MAP"}')
parser.add_argument('--topk', type=list, default=[10, 20], help='Metrics scale')
parser.add_argument('--embedding_dim', type=int, default=64, help='Latent dimension 64.')
parser.add_argument('--num_epoch', type=int, default=2000, help='Epoch number.')
parser.add_argument('--num_workers', type=int, default=8, help='Workers number.')
parser.add_argument('--stopping_step', type=int, default=20, help='early stopping strategy.')
parser.add_argument('--valid_metric', type=str, default="Recall@20", help='valid metric')
parser.add_argument('--with_tensorboard', action='store_true', default=False, help='with tensorboard analysis ')
parser.add_argument('--l_r', type=float, default=5e-5, help='Learning rate.')
parser.add_argument('--learning_rate_scheduler', type=list, default=[1.0, 50], help='learning rate scheduler.')
parser.add_argument('--reg_weight', type=float, default=5e-4, help='regularization weight.')
parser.add_argument('--num_layer', type=int, default=4, help='Layer number.')
parser.add_argument('--s_drop', type=float, default=0.4, help='self_attention_dropout.')
parser.add_argument('--m_drop', type=float, default=0.6, help='mutual_attention_dropout.')
parser.add_argument('--cl_tmp', type=float, default=0.6, help='Contrast learning temperature coefficient')
parser.add_argument('--cl_loss_weight', type=float, default=5e-6, help='contrast loss weight.')
parser.add_argument('--diff_loss_weight', type=float, default=1e-4, help='Structure contrast loss weight.')
parser.add_argument('--user_knn_k', type=int, default=40,
help='Select the 10 users most similar to the target users to build the users graph')
parser.add_argument('--item_knn_k', type=int, default=10,
help='Select the 10 items most similar to the target item to build the item graph')
parser.add_argument('--i_mm_image_weight', type=float, default=0,
help='The proportion of visual feat in item graph.')
parser.add_argument('--u_mm_image_weight', type=float, default=0.2,
help='The proportion of visual feat in user graph.')
parser.add_argument('--n_ii_layers', type=int, default=1,
help='Number of layers of item feature propagation in the item graph')
parser.add_argument('--n_uu_layers', type=int, default=1,
help='Number of layers of user feature propagation in the user graph')
parser.add_argument('--user_aggr_mode', type=str, default='softmax',
help='Choose a modedataset from {softmax, mean}')
parser.add_argument('--rank', type=int, default=3, help='the dimension of low rank matrix decomposition')
parser.add_argument('--uu_co_weight', type=float, default=0.4,
help='the proportion of user co-occurrence graphs to user homographs')
parser.add_argument('--ii_co_weight', type=float, default=0.2,
help='the proportion of item co-occurrence graphs to user homographs')
return parser.parse_args()
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