#!/bin/sh
#PJM -L rscgrp=b-batch
#PJM -L gpu=1
#PJM -L elapse=48:00:00
#PJM -N baseline_scdfm
#PJM -j
#PJM -o /home/hp250092/ku50001222/qian/aivc/lfj/GRN/baseline/baseline_%j.out

module load cuda/12.2.2
module load cudnn/8.9.7
module load gcc-toolset/12

source /home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/bin/activate
cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM

export PYTHONPATH=./
export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256

RESULT_PATH=/home/hp250092/ku50001222/qian/aivc/lfj/GRN/baseline

COMMON_ARGS="\
    --batch_size=48 \
    --devices='0' \
    --model_type=origin \
    --lr=5e-5 \
    --steps=200000 \
    --data_name=norman \
    --d_model=128 \
    --eta_min=1e-6 \
    --fusion_method=differential_perceiver \
    --infer_top_gene=1000 \
    --n_top_genes=5000 \
    --perturbation_function=crisper \
    --noise_type=Gaussian \
    --mode=predict_y \
    --gamma=0.5 \
    --split_method=additive \
    --use_mmd_loss \
    --fold=1 \
    --topk=30 \
    --use_negative_edge \
    --result_path=${RESULT_PATH}"

echo "=========================================="
echo "Job ID:    $PJM_JOBID"
echo "Job Name:  $PJM_JOBNAME"
echo "Start:     $(date)"
echo "Node:      $(hostname)"
echo "GPU:       $(nvidia-smi --query-gpu=name,memory.total --format=csv,noheader 2>/dev/null || echo 'N/A')"
echo "Run:       scDFM baseline (norman, original paper config)"
echo "=========================================="

# Step 1: Eval with random init (iteration 0)
echo "[Step 1] Evaluating random-init model..."
python src/script/run.py ${COMMON_ARGS} \
    --test_only \
    --print_every=50000

# Step 2: Train 200k steps, checkpoint every 50k, eval only at end
echo "[Step 2] Training 200k steps..."
python src/script/run.py ${COMMON_ARGS} \
    --print_every=50000

echo "=========================================="
echo "Finished:  $(date)"
echo "=========================================="