#!/usr/bin/env bash
set -euo pipefail

# Run a sampled zero-shot baseline for publication comparison.
# Default evaluates Qwen/Qwen3-8B without any adapter on 200 examples per split.
# Usage:
#   EVAL_BATCH_SIZE=4 BASELINE_MAX_SAMPLES=200 bash scripts/run_zero_shot_baseline.sh outputs/baselines/qwen3-8b-zero-shot

OUT_DIR="${1:-outputs/baselines/qwen3-8b-zero-shot}"
MODEL_ID="${BASELINE_MODEL:-Qwen/Qwen3-8B}"
MAX_SAMPLES="${BASELINE_MAX_SAMPLES:-200}"

source .venv/bin/activate
export CUDA_VISIBLE_DEVICES="${CUDA_VISIBLE_DEVICES:-0}"
export PYTHONPATH="$PWD/src:${PYTHONPATH:-}"
export TOKENIZERS_PARALLELISM=false

python scripts/check_gpu.py
mkdir -p "$OUT_DIR"

python scripts/evaluate_model.py \
  --model "$MODEL_ID" \
  --dataset nraptisss/TMF921-intent-to-config-research-sota \
  --output_dir "$OUT_DIR" \
  --batch_size "${EVAL_BATCH_SIZE:-4}" \
  --max_samples_per_split "$MAX_SAMPLES" \
  --max_new_tokens "${EVAL_MAX_NEW_TOKENS:-1536}" \
  --gold_length_buffer "${EVAL_GOLD_LENGTH_BUFFER:-96}" \
  --save_every "${EVAL_SAVE_EVERY:-25}"

python scripts/normalize_eval_metrics.py --eval_dir "$OUT_DIR"

python - <<PY
import json
from pathlib import Path
p = Path("$OUT_DIR") / "all_normalized_metrics.json"
m = json.loads(p.read_text())
print("Zero-shot baseline:", "$MODEL_ID")
for split, s in m.items():
    print(f"{split}: n={s.get('num_examples')} parse={s.get('parse_json'):.4f} norm_field_f1={s.get('norm_field_f1'):.4f} norm_key_f1={s.get('norm_key_f1'):.4f} norm_exact={s.get('norm_exact_match'):.4f}")
PY