#!/usr/bin/env bash
# Independent C2 validation of bounded context-conditioned apical feedback.
# Usage: c2_context_validation.sh <gpu> "<model seeds>"
set -eu

cd "$(dirname "$0")/.."
GPU="${1:?GPU index required}"
MODEL_SEEDS="${2:-0 1 2 3 4}"
PYTHON="${PYTHON:-/home/yurenh2/miniconda3/envs/ep_pascal/bin/python3}"

for task_seed in 3 4 5; do
  for model_seed in $MODEL_SEEDS; do
    for mode in bp fa dfa sdil; do
      for depth in 1 4; do
        tag="c2_context_val_v1_tent_l2_w8_${mode}_d${depth}_t${task_seed}_s${model_seed}"
        out="results/${tag}.json"
        if [ -s "$out" ]; then
          echo "[$tag] exists; skipping"
          continue
        fi
        lesion=0
        if [ "$depth" -eq 4 ]; then
          lesion=0.3333333333333333
        fi
        vectorizer=linear
        eta_a=0.02
        if [ "$mode" = sdil ]; then
          vectorizer=context_gated
          eta_a=0.01
        fi
        CUDA_VISIBLE_DEVICES="$GPU" "$PYTHON" experiments/run.py \
          --mode "$mode" --dataset tentmap --device cuda \
          --depth "$depth" --width 8 --act relu --residual 1 \
          --residual_lesion_fraction "$lesion" \
          --vectorizer_mode "$vectorizer" \
          --epochs 80 --batch_size 256 --eta 0.03 --momentum 0.9 \
          --eta_A "$eta_a" --eta_P 0.002 \
          --pert_sigma 0.01 --pert_every 4 --pert_ndirs 1 \
          --pert_mode simultaneous \
          --traffic_mode none --nuis_rho 0 \
          --use_residual 1 --learn_A 1 --learn_P 1 --p_neutral 1 \
          --task_train_examples 10000 --task_test_examples 5000 \
          --task_levels 2 --task_n_in 1 --task_seed "$task_seed" \
          --val_examples 2000 --split_seed 2027 --eval_split validation --eval_every 0 \
          --diagnostics alignment --diagnostics_schedule final --probe_bs 512 \
          --seed "$model_seed" --log_every 100000 --tag "$tag"
      done
    done
  done
done