#!/usr/bin/env bash
set -e
ROOT=/lustre/fsw/portfolios/nvr/users/jtremblay/yu
V5CODE=$ROOT/1027_sim_tasks/share_v5_sim2real_20260929/code
CONDA=$ROOT/miniconda3; UPY=$CONDA/envs/ur5dp/bin/python
NAME=plug_v1
DATA_DIR=$ROOT/1027_sim_tasks/yu_parts_tasks/data/plug/$NAME
RUN=$ROOT/1027_run
source $CONDA/etc/profile.d/conda.sh
kept=$($UPY -c "import json; d=json.load(open('$DATA_DIR/collect_meta.json')); print(d['n_kept'])" 2>/dev/null || echo 50)
echo "== Training DP $NAME ($kept demos)"
cd $V5CODE/ur5_lab_code && export PYTHONPATH=src:.
$UPY train.py --config-name=train_baseline_diffusion_unet task=sim_baseline \
    task_name=$NAME dataset_path=$DATA_DIR n_demo=$kept \
    training.device=cuda:0 training.seed=0 training.num_epochs=130 \
    training.checkpoint_every=129 checkpoint.topk.k=0 \
    training.resume=True checkpoint.save_last_ckpt=True \
    dataloader.batch_size=256 dataloader.num_workers=16 logging.mode=offline \
    "policy._target_=algorithms.policy.diffusion_unet_baseline_aug_policy.DiffusionUnetBaselineAugPolicy" \
    +policy.cam1_dropout=0.1 +policy.cam1_aug_p=0.5 +policy.cam1_erase_p=0.3 +policy.cam0_aug_p=0.3 \
    "hydra.run.dir=$V5CODE/train_runs/${NAME}_aug_s0" \
    2>&1 | tee $RUN/logs/${NAME}_train.log
echo "== DONE: $V5CODE/train_runs/${NAME}_aug_s0/checkpoints/"