defaults:
  - _self_
  - /callbacks: [checkpoint_every_n_steps, checkpoint_monitor, learning_rate_monitor]
  - /data: protein
  - /model: small
  - /strategy: ddp
  - /noise: loglinear
  - /lr_scheduler: cosine_decay_warmup  # constant_warmup
  - /classifier_model: null
  - /guidance: null

mode: ppl_eval  # train / train_classifier / ppl_eval
diffusion: uniform  # absorbing_state / uniform
backbone: dit  # dit / dimamba / ar
classifier_backbone: null
parameterization: d3pm  # subs / d3pm / ar
time_conditioning: True  # UDLM is conditioned on time
subs_masking: False
zero_recon_loss: True  # Use for UDLM
T: 0  # 0 (continuous time) / 1000

is_vision: False
seed: 42

loader:
  global_batch_size: 512
  eval_global_batch_size: ${.global_batch_size}
  # Note: batch_size and eval_batch_size are **per machine**
  batch_size: ${div_up:${.global_batch_size}, ${eval:${trainer.devices} * ${trainer.num_nodes}}}
  eval_batch_size: ${div_up:${.eval_global_batch_size}, ${eval:${trainer.devices} * ${trainer.num_nodes}}}
  num_workers: 0 # ${eval:"len(__import__('os').sched_getaffinity(0))"}
  pin_memory: True
  persistent_workers: False # True

sampling:
  use_cache: True
  steps: 32
  # Note: batch_size is **per machine**
  batch_size: 1 # ${loader.eval_batch_size}
  num_sample_batches: 10  # Total samples: `num_gpus` * `batch_size` * `num_sample_batches`
  use_float64: False

eval:
  checkpoint_path: '/home/tc415/discrete-diffusion-guidance/outputs/peptide/2024.12.31/122818/checkpoints/best.ckpt'  # Used to evaluate a checkpoint after training.
  target_sequence: 'MLQTKDLIWTLFFLGTAVSLQVDIVPSQGEISVGESKFFLCQVAGDAKDKDISWFSPNGEKLTPNQQRISVVWNDDSSSTLTIYNANIDDAGIYKCVVTGEDGSESEATVNVKIFQKLMFKNAPTPQEFREGEDAVIVCDVVSSLPPTIIWKHKGRDVILKKDVRFIVLSNNYLQIRGIKKTDEGTYRCEGRILARGEINFKDIQVIVNVPPTIQARQNIVNATANLGQSVTLVCDAEGFPEPTMSWTKDGEQIEQEEDDEKYIFSDDSSQLTIKKVDKNDEAEYICIAENKAGEQDATIHLKVFAKPKITYVENQTAMELEEQVTLTCEASGDPIPSITWRTSTRNISSEEKASWTRPEKQETLDGHMVVRSHARVSSLTLKSIQYTDAGEYICTASNTIGQDSQSMYLEVQYAPKLQGPVAVYTWEGNQVNITCEVFAYPSATISWFRDGQLLPSSNYSNIKIYNTPSASYLEVTPDSENDFGNYNCTAVNRIGQESL'
  target_motifs: '415-430' # NCAM1_IG
  # target_sequence: 'TPSSPSIDQVEPYSSTAQVQFDEPEATGGVPILKYKAEWRAVGEEVWHSKWYDAKEASMEGIVTIVGLKPETTYAVRLAALNGKGLGEISAASEFKTQPVQGEPSAPKLEGQMGEDGNSIKVNLIKQDDGGSPIRHYLVRYRALSSEWKPEIRLPSGSDHVMLKSLDWNAEYEVYVVAENQQGKSKAAHFVFRTSAQP'
  # target_motifs: '98-108' # NCAM1_FN3

  disable_ema: False
  generate_samples: True
  generated_samples_path: ''
  max_samples: 50_000

training:
  ema: 0.9999
  antithetic_sampling: True
  importance_sampling: False
  sampling_eps: 1e-3
  change_of_variables: False
  compute_loss_on_pad_tokens: True
  use_simple_ce_loss: False  # Ignore ELBO; just use CE
  guidance: null # Can turn off with `training.guidance: null`
    # cond_dropout: 0.0

optim:
  weight_decay: 1e-4
  lr: 1e-5
  beta1: 0.9
  beta2: 0.999
  eps: 1e-8

trainer:
  _target_: lightning.Trainer
  accelerator: cuda
  num_nodes: 1
  devices: 2 # ${device_count:}
  accumulate_grad_batches: 1 # ${div_up:${loader.global_batch_size}, ${eval:${trainer.devices} * ${loader.batch_size} * ${trainer.num_nodes}}}
  gradient_clip_val: 1.0
  precision: 'bf16-mixed'
  num_sanity_val_steps: 2
  # max_epochs: 10
  max_steps: 1652000
  log_every_n_steps: 100
  limit_train_batches: 1.0   # train on full dataset, can be used to toggle quick run
  limit_val_batches: 1.0     # validate on full dataset, can be used to toggle quick run
  val_check_interval: 16520 # 2545

wandb:
  project: moPPIt-v2
  job_type: model-training
  name: protein_medium_100epochs_lr1e-5_gradclip1_wd1e-4_dropout0.1 #epochs10_lr3e-4_bsz8_64-true_all-params_gradclip1_beta-one0.9_beta-two0.999
  id: ${.name}

hydra:
  run:
    dir: ./outputs/${wandb.name} # ./outputs/${data.train}/${now:%Y.%m.%d}/${now:%H%M%S}
  job:
    chdir: true

checkpointing:
  # Use custom `save_dir` if, e.g., saving to S3 bucket, otherwise leave this parameter as is
  save_dir: ${cwd:}
  # Note: `checkpoints` path should correspond to `checkpoint_every_n_steps.dirpath`
  resume_from_ckpt: False
  resume_ckpt_path: ${.save_dir}/checkpoints/last.ckpt


  # target_sequence: 'MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPPVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRVEYLDDRNTFRHSVVVPYEPPEVGSDCTTIHYNYMCNSSCMGGMNRRPILTIITLEDSSGNLLGRNSFEVRVCACPGRDRRTEEENLRKKGEPHHELPPGSTKRALPNNTSSSPQPKKKPLDGEYFTLQIRGRERFEMFRELNEALELKDAQAGKEPGGSRAHSSHLKSKKGQSTSRHKKLMFKTEGPDSD'
  # target_motifs: '305-313' # P53_1
  # target_motifs: '371-382' # P53_2
  # target_motifs: '351-393' # P53_3
  # target_motifs: '210-230' # P53_4
  # target_sequence: 'MLQTKDLIWTLFFLGTAVSLQVDIVPSQGEISVGESKFFLCQVAGDAKDKDISWFSPNGEKLTPNQQRISVVWNDDSSSTLTIYNANIDDAGIYKCVVTGEDGSESEATVNVKIFQKLMFKNAPTPQEFREGEDAVIVCDVVSSLPPTIIWKHKGRDVILKKDVRFIVLSNNYLQIRGIKKTDEGTYRCEGRILARGEINFKDIQVIVNVPPTIQARQNIVNATANLGQSVTLVCDAEGFPEPTMSWTKDGEQIEQEEDDEKYIFSDDSSQLTIKKVDKNDEAEYICIAENKAGEQDATIHLKVFAKPKITYVENQTAMELEEQVTLTCEASGDPIPSITWRTSTRNISSEEKTLDGHMVVRSHARVSSLTLKSIQYTDAGEYICTASNTIGQDSQSMYLEVQYAPKLQGPVAVYTWEGNQVNITCEVFAYPSATISWFRDGQLLPSSNYSNIKIYNTPSASYLEVTPDSENDFGNYNCTAVNRIGQESLEFILVQADTPSSPSIDQVEPYSSTAQVQFDEPEATGGVPILKYKAEWRAVGEEVWHSKWYDAKEASMEGIVTIVGLKPETTYAVRLAALNGKGLGEISAASEFKTQPVHSPPP'
  # target_motifs: '28-39' # NCAM1_ECD