# EvalScope Benchmark Docker Image (Python 3.12)
# 除 swe_bench 系列外，其他 benchmark 均可直接运行
# 使用国内镜像源解决网络问题
# 基于 python:3.12-slim，在容器内重新安装所有依赖

FROM python:3.12-slim-bookworm

LABEL maintainer="evalscope-benchmark"
LABEL description="EvalScope benchmark environment with Python 3.12, all dependencies including swe_bench"

ENV DEBIAN_FRONTEND=noninteractive
ENV PYTHONUNBUFFERED=1
ENV PIP_NO_CACHE_DIR=1
ENV PYTHONDONTWRITEBYTECODE=1

# 配置 apt 使用清华镜像源
RUN rm -f /etc/apt/sources.list.d/*.list && \
    echo 'deb https://mirrors.tuna.tsinghua.edu.cn/debian bookworm main contrib non-free' > /etc/apt/sources.list && \
    echo 'deb https://mirrors.tuna.tsinghua.edu.cn/debian bookworm-updates main contrib non-free' >> /etc/apt/sources.list && \
    echo 'deb https://mirrors.tuna.tsinghua.edu.cn/debian-security bookworm-security main contrib non-free' >> /etc/apt/sources.list && \
    apt-get update && \
    apt-get install -y --no-install-recommends \
        git wget curl ca-certificates \
        vim \
        build-essential libssl-dev libffi-dev zlib1g-dev \
        docker.io \
    && rm -rf /var/lib/apt/lists/*

# 配置 pip 使用清华镜像源
RUN mkdir -p /root/.config/pip && \
    cat > /root/.config/pip/pip.conf << 'EOF'
[global]
index-url = https://pypi.tuna.tsinghua.edu.cn/simple
trusted-host = pypi.tuna.tsinghua.edu.cn
timeout = 120
retries = 5
EOF

# 升级 pip
RUN pip install --upgrade pip setuptools wheel

# 先固定 numpy/scipy 版本（bfcl-eval 要求 numpy==1.26.4；新版 scipy 要求 numpy>=2.0）
RUN pip install numpy==1.26.4 scipy==1.13.1

# 安装 evalscope 依赖（先安装依赖，再用本地源码覆盖）
RUN pip install \
    openai pandas pyyaml requests tqdm \
    tiktoken transformers \
    scikit-learn matplotlib seaborn plotly \
    jieba nltk rouge-score sacrebleu \
    sympy latex2sympy2_extended pillow \
    docker pexpect pytest \
    tabulate rich jsonlines jsonschema \
    langdetect word2number zhconv \
    modelscope pydantic overrides \
    more_itertools pylatexenc \
    rouge-chinese markdown \
    editdistance dotenv docstring_parser \
    colorlog

# 安装 torch（CPU 版即可，主要用于 tokenizer 加载与部分 benchmark 的 tensor 操作）
# 当前 transformers 要求 torch>=2.4；numpy==1.26.4 仍兼容 torch 2.4.x
RUN pip install torch==2.4.0 --index-url https://download.pytorch.org/whl/cpu 2>/dev/null || \
    pip install torch==2.4.0 2>/dev/null || true

# 安装 sandbox 支持（ms-sandbox 等）
RUN pip install evalscope[sandbox] 2>/dev/null || \
    pip install ms-sandbox 2>/dev/null || true

# 安装 terminal_bench 依赖 (harbor)
RUN pip install "harbor>=0.8.0,<1.0.0" 2>/dev/null || true

# 安装 tau2-bench 依赖（不安装 torch/vllm，避免镜像过大）
RUN pip install \
    fastapi uvicorn psutil loguru \
    litellm tenacity deepdiff addict toml 2>/dev/null || true

# 安装 tau2-bench 依赖
RUN pip install git+https://github.com/sierra-research/tau2-bench@v0.2.0 2>/dev/null || true

# 安装 bigcodebench 核心评估依赖（不安装 torch/vllm/accelerate）
RUN pip install \
    tempdir termcolor wget \
    gradio-client 2>/dev/null || true

# 安装 bfcl_v3 依赖（--no-deps 避免 torch，再手动补必要依赖）
# tree-sitter 必须固定版本，bfcl-eval 要求 tree_sitter==0.21.3，
# 否则 Language() API 不兼容会报错
RUN pip install --no-deps bfcl-eval==2025.10.27.1 2>/dev/null || true
RUN pip install \
    anthropic cohere==5.18.0 datamodel-code-generator==0.25.7 \
    faiss-cpu==1.11.0 google-genai==1.24.0 mistralai==1.7.0 \
    networkx==3.3 google-search-results \
    rank_bm25 html2text boto3 qwen-agent writer-sdk \
    tree-sitter==0.21.3 \
    tree-sitter-python==0.21.0 \
    tree-sitter-javascript==0.21.4 \
    tree-sitter-java==0.21.0 \
    pathlib 2>/dev/null || true
# sentence-transformers 会强制拉 torch（~2GB+），如运行 bfcl_v3 的 embedding 类任务需手动安装

# 安装 soundfile / openpyxl
RUN pip install soundfile openpyxl 2>/dev/null || true

# 安装 SWE-bench 支持
RUN pip install evalscope[swe_bench] 2>/dev/null || \
    pip install swebench==4.1.0 2>/dev/null || true

# 创建 workspace
RUN mkdir -p /opt/evalscope
WORKDIR /opt/evalscope

# 复制本地 evalscope 源码、bash 脚本和 tau2-bench
# 注意：tau2-bench 在仓库里位于 tools/tau2-bench/
COPY evalscope/ /opt/evalscope/evalscope/
COPY bash/ /opt/evalscope/bash/
COPY tau2-bench/ /opt/evalscope/tools/tau2-bench/

# 用本地源码安装 evalscope（editable mode）
RUN pip install -e /opt/evalscope/evalscope/

# 安装本地 tau2-bench 到 Python 环境（先装依赖，避免与 bfcl-eval 冲突）
RUN pip install \
    rich tabulate fastapi uvicorn pandas psutil loguru \
    docstring-parser "litellm>=1.80.15,<1.82.7" "tenacity>=9.0.0" \
    deepdiff addict PyYAML toml python-dotenv typer requests httpx \
    2>/dev/null || true
RUN pip install -e /opt/evalscope/tools/tau2-bench/ --no-deps 2>/dev/null || true

# 最终固定 numpy/scipy 版本，确保 bfcl-eval 兼容且 scipy 不与 numpy 1.26.4 冲突
RUN pip install --force-reinstall numpy==1.26.4 scipy==1.13.1 2>/dev/null || true

# 设置权限
RUN chmod -R +x /opt/evalscope/bash/*.py 2>/dev/null || true

# 创建输出目录
RUN mkdir -p /opt/evalscope/output \
    /opt/evalscope/output_limit100 \
    /opt/evalscope/output_swe_bench \
    /opt/evalscope/datasets

# 验证安装
RUN python -c "import evalscope; print('evalscope ok')" && \
    python -c "import evalscope.api.agent; print('evalscope.api.agent ok')" && \
    python -c "import openai; print('openai ok')" && \
    python -c "import pandas; print('pandas ok')" && \
    python -c "import numpy; print('numpy ok')" && \
    python -c "import torch; print('torch ok')" && \
    python -c "from transformers import AutoTokenizer; print('transformers tokenizer ok')" && \
    python -c "import docker; print('docker ok')" && \
    python -c "import harbor; print('harbor ok')" && \
    python -c "import tau2; print('tau2 ok')" && \
    python -c "from tau2.runner.batch import run_single_task; print('tau2 runner ok')" && \
    python -c "import bfcl_eval; print('bfcl_eval ok')" && \
    python -c "import soundfile; print('soundfile ok')" && \
    python -c "import openpyxl; print('openpyxl ok')" && \
    docker --version

# 设置 PYTHONPATH，确保 editable install 的 evalscope 能被正确加载
ENV PYTHONPATH=/opt/evalscope/evalscope

# 默认命令
CMD ["/bin/bash"]