name: Build Executables

on:
  push:
    tags:
      - 'v*'
  workflow_dispatch:
    inputs:
      tag:
        description: '要上传资源的 Release 标签（如 v1.2.0），留空则上传到最新 Release'
        required: false
        default: ''

permissions:
  contents: write

jobs:
  build:
    strategy:
      fail-fast: false
      matrix:
        include:
          - os: windows-latest
            variant: CPU
            platform: Windows
            pytorch_url: https://download.pytorch.org/whl/cpu
            sep: ";"
            shell: pwsh
          - os: windows-latest
            variant: GPU
            platform: Windows
            pytorch_url: https://download.pytorch.org/whl/cu121
            sep: ";"
            shell: pwsh
          - os: ubuntu-latest
            variant: CPU
            platform: Linux
            pytorch_url: https://download.pytorch.org/whl/cpu
            sep: ":"
            shell: bash
          - os: ubuntu-latest
            variant: GPU
            platform: Linux
            pytorch_url: https://download.pytorch.org/whl/cu121
            sep: ":"
            shell: bash

    runs-on: ${{ matrix.os }}
    name: Build ${{ matrix.platform }}-${{ matrix.variant }}

    steps:
    - name: Checkout code
      uses: actions/checkout@v4

    - name: Set up Python
      uses: actions/setup-python@v5
      with:
        python-version: '3.10'

    - name: Install system dependencies (Linux)
      if: runner.os == 'Linux'
      run: |
        sudo apt-get update
        sudo apt-get install -y build-essential libsndfile1 ffmpeg portaudio19-dev

    - name: Install FFmpeg (Windows)
      if: runner.os == 'Windows'
      run: choco install ffmpeg -y

    - name: Install Python dependencies
      run: |
        python -m pip install --upgrade "pip<24.1"
        pip install pyinstaller
        pip install torch torchaudio --index-url ${{ matrix.pytorch_url }}
        pip install omegaconf==2.0.6 --no-deps
        pip install PyYAML antlr4-python3-runtime hydra-core
        pip install fairseq==0.12.2 --no-deps
        pip install "jinja2<3.1.5"
        pip install gradio==3.50.2 librosa soundfile scipy numpy praat-parselmouth pyworld faiss-cpu tqdm requests python-dotenv colorama huggingface_hub pedalboard ffmpeg-python av imageio-ffmpeg
        pip install demucs torchcrepe --no-deps
        pip install julius dora-search lameenc openunmix treetable
        python -c "import torch; print(f'PyTorch: {torch.__version__}, CUDA: {torch.cuda.is_available()}')"

    - name: Install audio-separator
      run: pip install audio-separator onnxruntime

    - name: Bundle FFmpeg runtime
      shell: bash
      run: |
        python - <<'PY'
        import os
        import shutil
        import stat
        import subprocess
        from pathlib import Path

        import imageio_ffmpeg

        def check_executable(executable: Path) -> None:
            result = subprocess.run([str(executable), "-version"], text=True, capture_output=True)
            if result.returncode != 0:
                details = "\n".join(
                    part.strip()
                    for part in (result.stdout, result.stderr)
                    if part and part.strip()
                )
                raise RuntimeError(f"{executable} failed -version:\n{details}")

        def resolve_ffprobe() -> Path:
            ffprobe_src = shutil.which("ffprobe")
            if not ffprobe_src:
                raise RuntimeError("ffprobe not found after installing FFmpeg")

            ffprobe_path = Path(ffprobe_src)
            if os.name == "nt":
                chocolatey_root = Path(os.environ.get("ChocolateyInstall", r"C:\ProgramData\chocolatey"))
                chocolatey_shim_dir = chocolatey_root / "bin"
                if ffprobe_path.parent.resolve() == chocolatey_shim_dir.resolve():
                    real_ffprobe = chocolatey_root / "lib" / "ffmpeg" / "tools" / "ffmpeg" / "bin" / "ffprobe.exe"
                    if not real_ffprobe.exists():
                        raise RuntimeError(f"Chocolatey ffprobe shim found, but real binary is missing: {real_ffprobe}")
                    return real_ffprobe

            return ffprobe_path

        bundle_dir = Path("tools/ffmpeg/bin")
        bundle_dir.mkdir(parents=True, exist_ok=True)

        ffmpeg_src = Path(imageio_ffmpeg.get_ffmpeg_exe())
        ffmpeg_name = "ffmpeg.exe" if os.name == "nt" else "ffmpeg"
        ffmpeg_dest = bundle_dir / ffmpeg_name
        shutil.copy2(ffmpeg_src, ffmpeg_dest)
        ffmpeg_dest.chmod(ffmpeg_dest.stat().st_mode | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH)
        check_executable(ffmpeg_dest)
        print(f"Bundled ffmpeg: {ffmpeg_dest}")

        ffprobe_src = resolve_ffprobe()
        ffprobe_name = "ffprobe.exe" if os.name == "nt" else "ffprobe"
        ffprobe_dest = bundle_dir / ffprobe_name
        shutil.copy2(ffprobe_src, ffprobe_dest)
        ffprobe_dest.chmod(ffprobe_dest.stat().st_mode | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH)
        subprocess.run([str(ffprobe_dest), "-version"], text=True, capture_output=True, check=True)
        print(f"Bundled ffprobe: {ffprobe_dest}")
        PY

    - name: Download all AI models
      shell: bash
      env:
        PYTHONIOENCODING: utf-8
      run: |
        echo "=== Download all base models (HuBERT, RMVPE, UVR5, DeEcho, pretrained) ==="
        python tools/download_models.py --all

        echo "=== Download Roformer separator models ==="
        python -c "
        from audio_separator.separator import Separator
        import os
        model_dir = os.path.join('assets', 'separator_models')
        os.makedirs(model_dir, exist_ok=True)
        # Vocal separation model
        sep = Separator(output_dir='.', model_file_dir=model_dir)
        sep.load_model('model_bs_roformer_ep_317_sdr_12.9755.ckpt')
        del sep
        # Karaoke SOTA ensemble models
        for karaoke_model in [
            'mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt',
            'mel_band_roformer_karaoke_gabox_v2.ckpt',
            'mel_band_roformer_karaoke_becruily.ckpt',
        ]:
            sep2 = Separator(output_dir='.', model_file_dir=model_dir)
            sep2.load_model(karaoke_model)
            del sep2
        print('Roformer models downloaded.')
        "

        echo "=== Verify downloaded models ==="
        find assets/ -name "*.pt" -o -name "*.pth" -o -name "*.ckpt" -o -name "*.onnx" | while read f; do
          SIZE=$(stat -c%s "$f" 2>/dev/null || stat -f%z "$f")
          echo "  $f ($(( SIZE / 1048576 )) MB)"
        done

    - name: Build executable
      shell: bash
      run: |
        SEP="${{ matrix.sep }}"
        NAME="AI-RVC-${{ matrix.platform }}-${{ matrix.variant }}"
        pyinstaller --name "${NAME}" \
          --onedir \
          --add-data "ui${SEP}ui" \
          --add-data "infer${SEP}infer" \
          --add-data "lib${SEP}lib" \
          --add-data "models${SEP}models" \
          --add-data "tools${SEP}tools" \
          --add-data "i18n${SEP}i18n" \
          --add-data "configs${SEP}configs" \
          --add-data "assets/hubert${SEP}assets/hubert" \
          --add-data "assets/rmvpe${SEP}assets/rmvpe" \
          --add-data "assets/uvr5_weights${SEP}assets/uvr5_weights" \
          --add-data "assets/pretrained_v2${SEP}assets/pretrained_v2" \
          --add-data "assets/separator_models${SEP}assets/separator_models" \
          --hidden-import=torch \
          --hidden-import=torchaudio \
          --hidden-import=gradio \
          --hidden-import=librosa \
          --hidden-import=soundfile \
          --hidden-import=fairseq \
          --hidden-import=audio_separator \
          --hidden-import=demucs \
          --hidden-import=pedalboard \
          --collect-all torch \
          --collect-all torchaudio \
          --collect-all gradio \
          --collect-all gradio_client \
          run.py

    - name: Create portable package
      shell: bash
      run: |
        NAME="AI-RVC-${{ matrix.platform }}-${{ matrix.variant }}"
        PKG="${NAME}-Portable"
        mkdir -p "${PKG}"
        # onedir 输出在 dist/NAME/ 目录下，复制全部内容
        cp -r dist/${NAME}/* "${PKG}/"
        cp README.md "${PKG}/"
        [ -f LICENSE ] && cp LICENSE "${PKG}/"

        if [ "${{ matrix.variant }}" = "GPU" ]; then
          VARIANT_NOTE="GPU 版（CUDA 12.1），支持 NVIDIA 显卡加速
        如果没有 NVIDIA 显卡，程序会自动回退到 CPU 推理"
        else
          VARIANT_NOTE="CPU 版，无需显卡即可运行
        如需 GPU 加速，请下载 GPU 版本或使用本地安装方式：python install.py"
        fi

        if [ "${{ matrix.platform }}" = "Windows" ]; then
          EXE_NAME="${NAME}.exe"
          cat > "${PKG}/使用说明.txt" << HEREDOC
        AI-RVC ${{ matrix.platform }} 便携版（${{ matrix.variant }}）

        使用方法：
          双击 ${EXE_NAME} 启动
          浏览器访问 http://127.0.0.1:7860

        ${VARIANT_NOTE}

        AI 模型已内置，无需额外下载
        无需安装 Python，解压即用
        HEREDOC
        else
          EXE_NAME="${NAME}"
          chmod +x "${PKG}/${EXE_NAME}"
          cat > "${PKG}/使用说明.txt" << HEREDOC
        AI-RVC ${{ matrix.platform }} 便携版（${{ matrix.variant }}）

        使用方法：
          chmod +x ${EXE_NAME}
          ./${EXE_NAME}
          浏览器访问 http://127.0.0.1:7860

        ${VARIANT_NOTE}

        AI 模型已内置，无需额外下载
        无需安装 Python，解压即用
        HEREDOC
        fi

    - name: Compress package
      shell: bash
      run: |
        NAME="AI-RVC-${{ matrix.platform }}-${{ matrix.variant }}"
        PKG="${NAME}-Portable"

        if [ "${{ matrix.platform }}" = "Windows" ]; then
          # 先压缩成 zip
          7z a -tzip "${PKG}.zip" "${PKG}"
          # 超过 1.9GB 时删除 zip 改用 7z（压缩率更高）
          FILE_SIZE=$(stat -c%s "${PKG}.zip" 2>/dev/null || wc -c < "${PKG}.zip" | tr -d ' ')
          if [ "$FILE_SIZE" -gt 1900000000 ]; then
            rm "${PKG}.zip"
            # 先尝试不分卷的 7z
            7z a "${PKG}.7z" "${PKG}"
            SEVENZ_SIZE=$(stat -c%s "${PKG}.7z" 2>/dev/null || wc -c < "${PKG}.7z" | tr -d ' ')
            if [ "$SEVENZ_SIZE" -gt 1900000000 ]; then
              # 7z 也超限，改用分卷
              rm "${PKG}.7z"
              7z a -v1900m "${PKG}.7z" "${PKG}"
            fi
          fi
        else
          tar -czf "${PKG}.tar.gz" "${PKG}"
          # 超过 1.9GB 时拆分
          FILE_SIZE=$(stat -c%s "${PKG}.tar.gz" 2>/dev/null || stat -f%z "${PKG}.tar.gz")
          if [ "$FILE_SIZE" -gt 1900000000 ]; then
            split -b 1900M "${PKG}.tar.gz" "${PKG}.tar.gz.part"
            rm "${PKG}.tar.gz"
          fi
        fi

    - name: Upload artifact
      uses: actions/upload-artifact@v4
      with:
        name: AI-RVC-${{ matrix.platform }}-${{ matrix.variant }}
        path: AI-RVC-${{ matrix.platform }}-${{ matrix.variant }}-Portable*

  upload-release:
    needs: [build]
    runs-on: ubuntu-latest

    steps:
    - name: Download all artifacts
      uses: actions/download-artifact@v4
      with:
        merge-multiple: true

    - name: List artifacts
      run: ls -lh AI-RVC-*

    - name: Determine target tag
      id: tag
      run: |
        if [ "${{ github.event_name }}" = "push" ]; then
          echo "tag=${GITHUB_REF#refs/tags/}" >> $GITHUB_OUTPUT
        elif [ -n "${{ github.event.inputs.tag }}" ]; then
          echo "tag=${{ github.event.inputs.tag }}" >> $GITHUB_OUTPUT
        else
          LATEST=$(gh release list --repo ${{ github.repository }} --limit 1 --json tagName -q '.[0].tagName')
          echo "tag=${LATEST}" >> $GITHUB_OUTPUT
        fi
      env:
        GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}

    - name: Upload assets to release
      run: |
        TAG="${{ steps.tag.outputs.tag }}"
        echo "上传资源到 Release: ${TAG}"
        # 收集所有构建产物（含分卷文件）
        mapfile -t FILES < <(find . -maxdepth 1 -name "AI-RVC-*-Portable*" -type f | sort)
        upload_asset_with_retry() {
          local tag="$1"
          local file="$2"
          local attempt
          local exit_code

          for attempt in 1 2 3; do
            echo "上传(${attempt}/3): ${file}"
            if timeout 30m gh release upload "${tag}" "${file}" \
              --repo ${{ github.repository }} \
              --clobber; then
              return 0
            fi

            exit_code=$?
            echo "上传失败(退出码=${exit_code}): ${file}"
            if [ "${attempt}" -lt 3 ]; then
              sleep $(( attempt * 20 ))
            fi
          done

          echo "上传最终失败: ${file}"
          return 1
        }
        echo "找到以下文件："
        for f in "${FILES[@]}"; do
          SIZE=$(stat -c%s "$f" 2>/dev/null || stat -f%z "$f")
          echo "  $f ($(( SIZE / 1048576 )) MB)"
        done
        # 逐个上传
        for f in "${FILES[@]}"; do
          upload_asset_with_retry "${TAG}" "$f"
        done
        echo "全部上传完成"
      env:
        GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}