#!/bin/bash
# v2 pipeline worker. Usage: worker_v2.sh <gpu_id>
# GPU1/GPU3: v2 readout shards then refits; GPU0: refits; GPU2: relearn then refits.
set -u
cd /workspace/jspace-unlearning
GPU=$1
PY=/workspace/venvs/openunlearn/bin/python
RETAIN_LOGS=/workspace/jspace-unlearning/refs/tofu_Llama-3.2-1B-Instruct_retain90__TOFU_EVAL.json
mkdir -p results/jocc_v2 results/jocc_v2_ownlens lenses/refit /workspace/models/relearn

jocc_v2() {  # $1 model_dir  $2 out_name  $3 lens
  [ -f "results/jocc_v2/$2.npz" ] && return
  echo "JOCC2 $2 $(date +%H:%M:%S)"
  CUDA_VISIBLE_DEVICES=$GPU python src/readout_jocc_v2.py --model_dir "$1" \
    --lens "$3" --out "results/jocc_v2/$2.npz" > "logs/jocc2_$2.log" 2>&1 \
    || echo "JOCC2-FAIL $2"
}

refit() {  # $1 short ckpt name
  local ckpt=/workspace/models/ckpts/$1
  if [ ! -f "lenses/refit/$1.pt" ]; then
    echo "REFIT $1 $(date +%H:%M:%S)"
    CUDA_VISIBLE_DEVICES=$GPU python scripts/fit_lens.py --model_dir "$ckpt" \
      --out "lenses/refit/$1.pt" --checkpoint "lenses/refit/$1.ckpt" --dim_batch 64 \
      > "logs/refit_$1.log" 2>&1 || { echo "REFIT-FAIL $1"; return; }
  fi
  if [ ! -f "results/jocc_v2_ownlens/$1.npz" ]; then
    echo "OWNLENS $1 $(date +%H:%M:%S)"
    CUDA_VISIBLE_DEVICES=$GPU python src/readout_jocc_v2.py --model_dir "$ckpt" \
      --lens "lenses/refit/$1.pt" --out "results/jocc_v2_ownlens/$1.npz" \
      > "logs/ownlens_$1.log" 2>&1 || echo "OWNLENS-FAIL $1"
  fi
}

relearn() {  # $1 short ckpt name or retain90; PREREG §5.3 settings
  local name=$1 src
  if [ "$name" = "retain90" ]; then src=/workspace/models/tofu_1b_retain90
  else src=/workspace/models/ckpts/$name; fi
  local outm=/workspace/models/relearn/$name
  if [ ! -f "$outm/model.safetensors" ]; then
    echo "RELEARN $name $(date +%H:%M:%S)"
    (cd /workspace/open-unlearning && CUDA_VISIBLE_DEVICES=$GPU \
      HF_DATASETS_CACHE=/workspace/hf_cache_ou HF_HUB_DISABLE_PROGRESS_BARS=1 \
      $PY src/train.py --config-name=train.yaml experiment=finetune/tofu/default.yaml \
      data/datasets@data.train=TOFU_QA_forget \
      model.model_args.pretrained_model_name_or_path="$src" \
      model.model_args.attn_implementation=sdpa \
      model.tokenizer_args.pretrained_model_name_or_path="$src" \
      trainer.args.num_train_epochs=1 trainer.args.learning_rate=1e-5 \
      trainer.args.optim=adamw_torch trainer.args.save_strategy=no \
      retain_logs_path=$RETAIN_LOGS \
      paths.output_dir="$outm" task_name=relearn_$name \
      > /workspace/jspace-unlearning/logs/relearn_$name.log 2>&1) \
      || { echo "RELEARN-FAIL $name"; return; }
  fi
  if [ ! -f "/workspace/open-unlearning/saves/eval/relearn_$name/TOFU_SUMMARY.json" ]; then
    echo "RELEVAL $name $(date +%H:%M:%S)"
    (cd /workspace/open-unlearning && CUDA_VISIBLE_DEVICES=$GPU \
      HF_DATASETS_CACHE=/workspace/hf_cache_ou HF_HUB_DISABLE_PROGRESS_BARS=1 \
      $PY src/eval.py --config-name=eval.yaml experiment=eval/tofu/default.yaml \
      model=Llama-3.2-1B-Instruct forget_split=forget10 holdout_split=holdout10 \
      model.model_args.pretrained_model_name_or_path="$outm" \
      model.model_args.attn_implementation=sdpa \
      model.tokenizer_args.pretrained_model_name_or_path="$src" \
      retain_logs_path=$RETAIN_LOGS task_name=relearn_$name \
      > /workspace/jspace-unlearning/logs/releval_$name.log 2>&1) \
      || echo "RELEVAL-FAIL $name"
  fi
}

LENS=lenses/full_1b.pt
ALL=$(ls /workspace/models/ckpts/)

case $GPU in
1)
  jocc_v2 /workspace/models/tofu_1b_retain90 retain90 $LENS
  i=0; for m in $ALL; do [ $((i % 2)) -eq 0 ] && jocc_v2 /workspace/models/ckpts/$m $m $LENS; i=$((i+1)); done
  refit NPO_lr5e-05_beta0.1_alpha1_epoch10
  refit AltPO_lr5e-05_beta0.1_alpha1_epoch10
  for m in $(ls /workspace/models/ckpts/); do jocc_v2 /workspace/models/ckpts/$m $m $LENS; done
  ;;
3)
  i=0; for m in $ALL; do [ $((i % 2)) -eq 1 ] && jocc_v2 /workspace/models/ckpts/$m $m $LENS; i=$((i+1)); done
  refit GradDiff_lr5e-05_alpha10_epoch10
  refit SimNPO_lr5e-05_b3.5_a1_d1_g0.125_ep10
  for m in $(ls /workspace/models/ckpts/); do jocc_v2 /workspace/models/ckpts/$m $m $LENS; done
  ;;
0)
  refit RMU_lr5e-05_layer5_scoeff100_epoch10
  refit UNDIAL_lr0.0003_beta10_alpha1_epoch10
  ;;
2)
  for m in NPO_lr5e-05_beta0.1_alpha1_epoch10 UNDIAL_lr0.0003_beta10_alpha1_epoch10 \
           IdkDPO_lr5e-05_beta0.05_alpha2_epoch10 GradDiff_lr5e-05_alpha10_epoch10 \
           RMU_lr5e-05_layer15_scoeff100_epoch10 retain90; do relearn $m; done
  refit IdkDPO_lr5e-05_beta0.05_alpha2_epoch10
  refit IdkNLL_lr5e-05_alpha5_epoch10
  ;;
esac
echo "WORKER$GPU ALL DONE $(date +%H:%M:%S)"