# Loss + optimizer + sampler — symmetric InfoNCE, AdamW, mutex sampler.
TrainingConfig.loss_type = 'symmetric'
TrainingConfig.tau_init = 0.07
TrainingConfig.tau_min = 0.01
TrainingConfig.tau_max = 0.1
TrainingConfig.learnable_temperature = True
TrainingConfig.label_smoothing = 0.1

TrainingConfig.tau_final = 0.01
TrainingConfig.weight_q2g = 0.6
TrainingConfig.weight_g2q = 0.4
TrainingConfig.hard_mining_k = 0
TrainingConfig.neg_bank_size = 0

# WeightedInfoNCE-only (unused when loss_type='symmetric').
TrainingConfig.weighted_loss_k = 5.0

TrainingConfig.learning_rate = 1e-4
TrainingConfig.text_lr_factor = 0.1
TrainingConfig.weight_decay = 1e-4
TrainingConfig.grad_clip = 1.0

TrainingConfig.sampler_type = 'mutex'
TrainingConfig.dss_warmup_epochs = 1
TrainingConfig.dss_reembed_every = 1
TrainingConfig.dss_knn_device = 'cuda'
TrainingConfig.dss_use_lsh = False
TrainingConfig.dss_lsh_num_tables = 8
TrainingConfig.dss_lsh_num_bits = 14
TrainingConfig.dss_cache_dir = None
TrainingConfig.use_mutex_sampler = True