#!/bin/bash
# ClawBenchPro base100/hard100 后处理评分。
# 可选在本机启动 OpenAI-compatible vLLM Judge，然后调用每题自带 verifier。

set -Eeo pipefail
set -x

SCRIPT_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
BUNDLE_ROOT=/opt/huawei/dataset/zyr_yuyin/lyf/datasets/testClawBenchPro/upload_clawbenchpro_base100_hard100_npu

export WORK_DIR=${WORK_DIR:-${BUNDLE_ROOT}/verl}
export BASE_TASKS=${BASE_TASKS:-${BUNDLE_ROOT}/data/ClawBenchPro_base100_hard100_quality}
export INFERENCE_ROOT=${INFERENCE_ROOT:-/opt/huawei/dataset/zyr_yuyin/lyf/datasets/testClawBenchPro/output/qwen35_2b}
export SCORE_OUTPUT_ROOT=${SCORE_OUTPUT_ROOT:-${INFERENCE_ROOT}/scores}
export SETUP_ENVIRONMENT=${SETUP_ENVIRONMENT:-1}

# 留空表示评测 INFERENCE_ROOT 下所有含 step_1/ 的模型目录；也可逗号分隔。
export MODELS=${MODELS:-}

# JUDGE_ENABLED=0 只评 66 条规则任务，134 条 LLM Judge 任务会明确标记为 skipped。
export JUDGE_ENABLED=${JUDGE_ENABLED:-1}
# START_JUDGE_SERVER=1 在本机启动 vLLM；设为 0 时连接已有的兼容服务。
export START_JUDGE_SERVER=${START_JUDGE_SERVER:-1}
export JUDGE_MODEL_PATH=${JUDGE_MODEL_PATH:-/opt/huawei/dataset/zyr_yuyin/models/Qwen/Qwen3___5-9B}
export JUDGE_SERVED_MODEL_NAME=${JUDGE_SERVED_MODEL_NAME:-qwen35_9b_judge}
export JUDGE_BIND_HOST=${JUDGE_BIND_HOST:-127.0.0.1}
export JUDGE_PORT=${JUDGE_PORT:-8000}
export JUDGE_BASE_URL=${JUDGE_BASE_URL:-http://${JUDGE_BIND_HOST}:${JUDGE_PORT}/v1}
export JUDGE_API_KEY=${JUDGE_API_KEY:-dummy_key}
export JUDGE_DEVICES=${JUDGE_DEVICES:-0,1,2,3,4,5,6,7}
export JUDGE_TP=${JUDGE_TP:-8}
export JUDGE_DTYPE=${JUDGE_DTYPE:-bfloat16}
export JUDGE_MAX_MODEL_LEN=${JUDGE_MAX_MODEL_LEN:-32768}
export JUDGE_MAX_NUM_BATCHED_TOKENS=${JUDGE_MAX_NUM_BATCHED_TOKENS:-32768}
export JUDGE_MAX_NUM_SEQS=${JUDGE_MAX_NUM_SEQS:-128}
export JUDGE_GPU_MEMORY_UTILIZATION=${JUDGE_GPU_MEMORY_UTILIZATION:-0.80}
export JUDGE_STARTUP_TIMEOUT=${JUDGE_STARTUP_TIMEOUT:-1800}
export JUDGE_LOG=${JUDGE_LOG:-${SCORE_OUTPUT_ROOT}/judge.log}

export PARALLEL=${PARALLEL:-128}
export VERIFIER_TIMEOUT=${VERIFIER_TIMEOUT:-600}
export PASS_THRESHOLD=${PASS_THRESHOLD:-0.75}
export RESUME=${RESUME:-1}
export OVERWRITE=${OVERWRITE:-0}
export FAIL_ON_ERROR=${FAIL_ON_ERROR:-1}

if [ ! -f "${WORK_DIR}/recipe/nanoclaw/score_clawbenchpro.py" ]; then
    echo "ERROR: scorer not found: ${WORK_DIR}/recipe/nanoclaw/score_clawbenchpro.py" >&2
    exit 2
fi
if [ ! -f "${BASE_TASKS}/benchmark_manifest.json" ]; then
    echo "ERROR: benchmark manifest not found: ${BASE_TASKS}/benchmark_manifest.json" >&2
    exit 2
fi
if [ ! -d "${INFERENCE_ROOT}" ]; then
    echo "ERROR: inference root not found: ${INFERENCE_ROOT}" >&2
    exit 2
fi
if [ ! -f "${BUNDLE_ROOT}/scripts/setup_full_npu_environment.sh" ]; then
    echo "ERROR: full NPU environment installer not found: ${BUNDLE_ROOT}/scripts/setup_full_npu_environment.sh" >&2
    exit 2
fi

mkdir -p "${SCORE_OUTPUT_ROOT}" "$(dirname "${JUDGE_LOG}")"

# 只把完整通过磁盘审计的 checkpoint 交给评分器。残缺模型不能用部分题目
# 计算均分；它们由推理续跑脚本补齐后，再次运行本脚本即可自动纳入评分。
MODELS=$(python3 - "${INFERENCE_ROOT}" "${BASE_TASKS}/benchmark_manifest.json" "${MODELS}" <<'PY'
import json
import sys
from pathlib import Path

inference_root = Path(sys.argv[1])
manifest_path = Path(sys.argv[2])
requested_raw = sys.argv[3]
requested = {item.strip() for item in requested_raw.split(",") if item.strip()} or None

try:
    manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
    task_ids = {str(item["task_id"]) for item in manifest["tasks"]}
except Exception as exc:
    print(
        f"ERROR: failed to load benchmark manifest {manifest_path}: {type(exc).__name__}: {exc}",
        file=sys.stderr,
    )
    raise SystemExit(2)

def load_json(path: Path):
    try:
        value = json.loads(path.read_text(encoding="utf-8"))
    except Exception:
        return None
    return value if isinstance(value, dict) else None

complete = []
discovered_names = set()
for model_dir in sorted(path for path in inference_root.iterdir() if path.is_dir()):
    if not (model_dir / "step_1").is_dir():
        continue
    model_name = model_dir.name
    discovered_names.add(model_name)
    if requested is not None and model_name not in requested:
        continue

    result_dirs = sorted(
        path for path in (model_dir / "step_1").glob("data_*_sample_*") if path.is_dir()
    )
    discovered_task_ids = {
        path.name.rsplit("_sample_", 1)[0]
        for path in result_dirs
        if "_sample_" in path.name
    }
    missing = sorted(task_ids - discovered_task_ids)
    extra = sorted(discovered_task_ids - task_ids)
    issues = []
    if missing:
        issues.append(f"missing_tasks={len(missing)} first={missing[:5]}")
    if extra:
        issues.append(f"extra_tasks={len(extra)} first={extra[:5]}")

    for result_dir in result_dirs:
        if not (result_dir / "workspace_after").is_dir():
            issues.append(f"missing_workspace={result_dir.name}")
        metadata = load_json(result_dir / "nanoclaw_metadata.json")
        if metadata is None:
            issues.append(f"invalid_metadata={result_dir.name}")
        elif metadata.get("status") != "ready":
            issues.append(f"metadata_status={metadata.get('status')}:{result_dir.name}")
        if load_json(result_dir / "conversation_history.json") is None:
            issues.append(f"invalid_conversation={result_dir.name}")
        if load_json(result_dir / "trajectory.json") is None:
            issues.append(f"invalid_trajectory={result_dir.name}")
        if len(issues) >= 20:
            break

    if issues:
        print(
            f"[score_skip_incomplete] model={model_name} issues={len(issues)} "
            f"preview={' | '.join(issues[:8])}",
            file=sys.stderr,
        )
        continue

    complete.append(model_name)
    print(
        f"[score_include_complete] model={model_name} tasks={len(task_ids)} samples={len(result_dirs)}",
        file=sys.stderr,
    )

if requested is not None:
    unknown = sorted(requested - discovered_names)
    if unknown:
        print(f"ERROR: requested model directories not found: {unknown}", file=sys.stderr)
        raise SystemExit(2)

print(",".join(complete))
PY
)
export MODELS
if [ -z "${MODELS}" ]; then
    echo "NO_COMPLETE_MODELS_TO_SCORE: incomplete checkpoints must finish inference before scoring."
    exit 0
fi
echo "COMPLETE_MODELS_TO_SCORE=${MODELS}"

# Triton/vLLM 在 Judge 初始化 KV cache 前会通过 tempfile 创建 hivmc
# 探测文件。训练环境安装脚本历史上默认使用 /cache/ray_tmp，但该目录在
# 某些独立评分节点上不存在、不可写或已耗尽配额。评分任务使用独立临时
# 目录，允许通过 JUDGE_TMPDIR 覆盖，避免把 Judge 启动失败误报成评分失败。
export JUDGE_TMPDIR=${JUDGE_TMPDIR:-/tmp/clawbenchpro_judge_${USER:-unknown}_$$}
if [ -z "${TMPDIR:-}" ]; then
    export TMPDIR="${JUDGE_TMPDIR}"
fi
if ! mkdir -p "${TMPDIR}" 2>/dev/null || [ ! -d "${TMPDIR}" ] || [ ! -w "${TMPDIR}" ]; then
    echo "WARNING: configured TMPDIR is unavailable: ${TMPDIR}; falling back to ${JUDGE_TMPDIR}" >&2
    export TMPDIR="${JUDGE_TMPDIR}"
    mkdir -p "${TMPDIR}"
fi
if [ ! -d "${TMPDIR}" ] || [ ! -w "${TMPDIR}" ]; then
    echo "ERROR: Judge temporary directory is not writable: ${TMPDIR}" >&2
    df -h "$(dirname "${TMPDIR}")" >&2 || true
    df -i "$(dirname "${TMPDIR}")" >&2 || true
    exit 2
fi
export TEMP="${TMPDIR}"
export TMP="${TMPDIR}"
echo "Judge temporary directory: ${TMPDIR}"

# 默认与训练/推理一样完整安装 GCC、CANN、torch-npu、vLLM-Ascend、Triton、
# VERL 和 verifier 依赖。使用 source，确保导出的动态库和 Python 路径对 Judge 生效。
source "${BUNDLE_ROOT}/scripts/setup_full_npu_environment.sh"
cd "${BUNDLE_ROOT}"

# Probe the same Python tempfile path used by Triton before launching all TP
# workers. This produces a short actionable error instead of a long vLLM
# multiprocess traceback.
python3 - "${TMPDIR}" <<'PY'
import shutil
import sys
import tempfile
from pathlib import Path

tmpdir = Path(sys.argv[1])
try:
    probe = Path(tempfile.mkdtemp(prefix="clawbenchpro_probe_", dir=str(tmpdir)))
    (probe / "probe").write_text("ok", encoding="utf-8")
    shutil.rmtree(probe)
except Exception as exc:
    print(f"ERROR: Python/Triton temporary-file probe failed in {tmpdir}: {type(exc).__name__}: {exc}", file=sys.stderr)
    raise SystemExit(2)
PY

judge_pid=""
cleanup() {
    exit_code=$?
    if [ -n "${judge_pid}" ]; then
        kill "${judge_pid}" 2>/dev/null || true
        wait "${judge_pid}" 2>/dev/null || true
    fi
    exit "${exit_code}"
}
trap cleanup EXIT INT TERM

check_judge() {
    python3 -c 'import json,sys,urllib.request; req=urllib.request.Request(sys.argv[1].rstrip("/")+"/models",headers={"Authorization":"Bearer "+sys.argv[2]}); data=json.load(urllib.request.urlopen(req,timeout=10)); ids={str(x.get("id")) for x in data.get("data",[])}; raise SystemExit(0 if sys.argv[3] in ids else 1)' \
        "${JUDGE_BASE_URL}" "${JUDGE_API_KEY}" "${JUDGE_SERVED_MODEL_NAME}" 2>/dev/null
}

if [ "${JUDGE_ENABLED}" = "1" ]; then
    if [ "${START_JUDGE_SERVER}" = "1" ]; then
        if [ -z "${JUDGE_MODEL_PATH}" ] || [ ! -d "${JUDGE_MODEL_PATH}" ]; then
            echo "ERROR: set JUDGE_MODEL_PATH to a local Hugging Face Judge model directory." >&2
            exit 2
        fi
        visible_count=$(awk -F, '{print NF}' <<< "${JUDGE_DEVICES}")
        if [ "${JUDGE_TP}" -gt "${visible_count}" ]; then
            echo "ERROR: JUDGE_TP=${JUDGE_TP} exceeds JUDGE_DEVICES count=${visible_count}." >&2
            exit 2
        fi
        export ASCEND_RT_VISIBLE_DEVICES=${JUDGE_DEVICES}
        judge_args=(
            --model "${JUDGE_MODEL_PATH}"
            --tokenizer "${JUDGE_MODEL_PATH}"
            --served-model-name "${JUDGE_SERVED_MODEL_NAME}"
            --host "${JUDGE_BIND_HOST}"
            --port "${JUDGE_PORT}"
            --tensor-parallel-size "${JUDGE_TP}"
            --dtype "${JUDGE_DTYPE}"
            --max-model-len "${JUDGE_MAX_MODEL_LEN}"
            --max-num-batched-tokens "${JUDGE_MAX_NUM_BATCHED_TOKENS}"
            --max-num-seqs "${JUDGE_MAX_NUM_SEQS}"
            --gpu-memory-utilization "${JUDGE_GPU_MEMORY_UTILIZATION}"
        )
        echo "Starting Judge model: ${JUDGE_MODEL_PATH}"
        python3 -m vllm.entrypoints.openai.api_server "${judge_args[@]}" >"${JUDGE_LOG}" 2>&1 &
        judge_pid=$!
    fi

    started=$(date +%s)
    until check_judge; do
        if [ -n "${judge_pid}" ] && ! kill -0 "${judge_pid}" 2>/dev/null; then
            echo "ERROR: Judge process exited before becoming ready. Log: ${JUDGE_LOG}" >&2
            tail -n 160 "${JUDGE_LOG}" >&2 || true
            exit 2
        fi
        elapsed=$(($(date +%s) - started))
        if [ "${elapsed}" -ge "${JUDGE_STARTUP_TIMEOUT}" ]; then
            echo "ERROR: Judge API did not become ready within ${JUDGE_STARTUP_TIMEOUT}s." >&2
            tail -n 160 "${JUDGE_LOG}" >&2 || true
            exit 2
        fi
        echo "Waiting for Judge API ${JUDGE_BASE_URL}; elapsed=${elapsed}s"
        sleep 5
    done
    echo "Judge API ready: ${JUDGE_BASE_URL}, model=${JUDGE_SERVED_MODEL_NAME}"
else
    echo "WARNING: JUDGE_ENABLED=0; 134 LLM Judge tasks will be skipped explicitly."
fi

score_args=(
    python3 "${WORK_DIR}/recipe/nanoclaw/score_clawbenchpro.py"
    --inference-root "${INFERENCE_ROOT}"
    --base-tasks "${BASE_TASKS}"
    --output-root "${SCORE_OUTPUT_ROOT}"
    --models "${MODELS}"
    --judge-enabled "${JUDGE_ENABLED}"
    --judge-base-url "${JUDGE_BASE_URL}"
    --judge-api-key "${JUDGE_API_KEY}"
    --judge-model "${JUDGE_SERVED_MODEL_NAME}"
    --parallel "${PARALLEL}"
    --timeout "${VERIFIER_TIMEOUT}"
    --pass-threshold "${PASS_THRESHOLD}"
    --resume "${RESUME}"
    --overwrite "${OVERWRITE}"
)
if [ "${FAIL_ON_ERROR}" = "1" ]; then
    score_args+=(--fail-on-error)
fi
score_rc=0
"${score_args[@]}" || score_rc=$?
if [ "${score_rc}" -ne 0 ]; then
    echo "ERROR: ClawBenchPro scoring failed with exit code ${score_rc}." >&2
    exit "${score_rc}"
fi

echo "Scoring completed:"
echo "  ${SCORE_OUTPUT_ROOT}/summary.json"
echo "  ${SCORE_OUTPUT_ROOT}/leaderboard.csv"
echo "  ${SCORE_OUTPUT_ROOT}/<model>/scoring_summary.json"