#!/bin/bash
# HumanEval+ / MBPP+ 评测 (EvalPlus, vLLM 后端)
# 官方项目: https://github.com/evalplus/evalplus
# 用法: bash run_evalplus.sh [MODEL_PATH] [TP]
#   TP 不传或传 auto 时自动使用当前可见的全部 GPU 数
# 结果输出到: /data/icto/mc56499/code_eval/evalplus_results/
#   samples:  evalplus_results/<dataset>/<model>_vllm_temp_0.0.jsonl
#   报告:     同目录 *_eval_results.json (含 pass@1 base/plus)

set -e

MODEL_PATH=${1:-/data/icto/mc56499/model/Qwen2.5-Coder-3B}
TP=${2:-auto}
if [ "$TP" = "auto" ] || [ -z "$TP" ]; then
    TP=$(nvidia-smi -L | wc -l)
fi
ROOT=/data/icto/mc56499/code_eval/evalplus_results
PARALLEL=16   # 评测进程数

mkdir -p "$ROOT"

for DATASET in humaneval mbpp; do
    echo "===== [EvalPlus] dataset=$DATASET model=$MODEL_PATH tp=$TP ====="
    evalplus.evaluate \
        --model "$MODEL_PATH" \
        --dataset "$DATASET" \
        --backend vllm \
        --tp "$TP" \
        --greedy \
        --force_base_prompt \
        --root "$ROOT" \
        --parallel "$PARALLEL"
done

echo "===== EvalPlus 结果汇总 ====="
for f in $(find "$ROOT" -name "*_eval_results.json" | sort); do
    echo "--- $f"
    python - "$f" <<'EOF'
import json, sys
with open(sys.argv[1]) as f:
    r = json.load(f)
tasks = r["eval"].values()
base = sum(1 for samples in tasks if any(s["base_status"] == "pass" for s in samples))
plus = sum(1 for samples in r["eval"].values() if any(
    s["base_status"] == "pass" and s["plus_status"] == "pass" for s in samples))
n = len(r["eval"])
print(f"  pass@1 (base tests): {base / n:.4f}  ({base}/{n})")
print(f"  pass@1 (base+extra): {plus / n:.4f}  ({plus}/{n})")
EOF
done