add pybind11 for compile

CMakeLists.txt

@@ -137,6 +137,8 @@ add_subdirectory(sdk-cpp)
 if(CLIENT_ONLY)
 add_subdirectory(general-client)
 add_subdirectory(python)
+set(PYTHON_INCLUDE_DIR ${PYTHON_INCLUDE})
+set(PYTHON_LIBRARIES ${PYTHON_LIB})
 endif()
 
 if (NOT CLIENT_ONLY)

general-client/pybind11/.appveyor.yml

+version: 1.0.{build}
+image:
+- Visual Studio 2017
+- Visual Studio 2015
+test: off
+build:
+  parallel: true
+platform:
+- x64
+- x86
+environment:
+  matrix:
+  - PYTHON: 36
+    CPP: 14
+    CONFIG: Debug
+  - PYTHON: 27
+    CPP: 14
+    CONFIG: Debug
+  - CONDA: 36
+    CPP: latest
+    CONFIG: Release
+matrix:
+  exclude:
+    - image: Visual Studio 2015
+      platform: x86
+    - image: Visual Studio 2015
+      CPP: latest
+    - image: Visual Studio 2017
+      CPP: latest
+      platform: x86
+install:
+- ps: |
+    if ($env:PLATFORM -eq "x64") { $env:CMAKE_ARCH = "x64" }
+    if ($env:APPVEYOR_JOB_NAME -like "*Visual Studio 2017*") {
+      $env:CMAKE_GENERATOR = "Visual Studio 15 2017"
+      $env:CMAKE_INCLUDE_PATH = "C:\Libraries\boost_1_64_0"
+      $env:CXXFLAGS = "-permissive-"
+    } else {
+      $env:CMAKE_GENERATOR = "Visual Studio 14 2015"
+    }
+    if ($env:PYTHON) {
+      if ($env:PLATFORM -eq "x64") { $env:PYTHON = "$env:PYTHON-x64" }
+      $env:PATH = "C:\Python$env:PYTHON\;C:\Python$env:PYTHON\Scripts\;$env:PATH"
+      python -m pip install --upgrade pip wheel
+      python -m pip install pytest numpy --no-warn-script-location
+    } elseif ($env:CONDA) {
+      if ($env:CONDA -eq "27") { $env:CONDA = "" }
+      if ($env:PLATFORM -eq "x64") { $env:CONDA = "$env:CONDA-x64" }
+      $env:PATH = "C:\Miniconda$env:CONDA\;C:\Miniconda$env:CONDA\Scripts\;$env:PATH"
+      $env:PYTHONHOME = "C:\Miniconda$env:CONDA"
+      conda install -y -q pytest numpy scipy
+    }
+- ps: |
+    Start-FileDownload 'http://bitbucket.org/eigen/eigen/get/3.3.3.zip'
+    7z x 3.3.3.zip -y > $null
+    $env:CMAKE_INCLUDE_PATH = "eigen-eigen-67e894c6cd8f;$env:CMAKE_INCLUDE_PATH"
+build_script:
+- cmake -G "%CMAKE_GENERATOR%" -A "%CMAKE_ARCH%"
+    -DPYBIND11_CPP_STANDARD=/std:c++%CPP%
+    -DPYBIND11_WERROR=ON
+    -DDOWNLOAD_CATCH=ON
+    -DCMAKE_SUPPRESS_REGENERATION=1
+- set MSBuildLogger="C:\Program Files\AppVeyor\BuildAgent\Appveyor.MSBuildLogger.dll"
+- cmake --build . --config %CONFIG% --target pytest -- /m /v:m /logger:%MSBuildLogger%
+- cmake --build . --config %CONFIG% --target cpptest -- /m /v:m /logger:%MSBuildLogger%
+- if "%CPP%"=="latest" (cmake --build . --config %CONFIG% --target test_cmake_build -- /m /v:m /logger:%MSBuildLogger%)
+on_failure: if exist "tests\test_cmake_build" type tests\test_cmake_build\*.log*

general-client/pybind11/.gitignore

+CMakeCache.txt
+CMakeFiles
+Makefile
+cmake_install.cmake
+.DS_Store
+*.so
+*.pyd
+*.dll
+*.sln
+*.sdf
+*.opensdf
+*.vcxproj
+*.filters
+example.dir
+Win32
+x64
+Release
+Debug
+.vs
+CTestTestfile.cmake
+Testing
+autogen
+MANIFEST
+/.ninja_*
+/*.ninja
+/docs/.build
+*.py[co]
+*.egg-info
+*~
+.*.swp
+.DS_Store
+/dist
+/build
+/cmake/
+.cache/
+sosize-*.txt
+pybind11Config*.cmake
+pybind11Targets.cmake

general-client/pybind11/.gitmodules

+[submodule "tools/clang"]
+	path = tools/clang
+	url = https://github.com/wjakob/clang-cindex-python3

general-client/pybind11/.readthedocs.yml

+python:
+  version: 3
+requirements_file: docs/requirements.txt

general-client/pybind11/.travis.yml

+language: cpp
+dist: trusty
+sudo: false
+matrix:
+  include:
+  # This config does a few things:
+  # - Checks C++ and Python code styles (check-style.sh and flake8).
+  # - Makes sure sphinx can build the docs without any errors or warnings.
+  # - Tests setup.py sdist and install (all header files should be present).
+  # - Makes sure that everything still works without optional deps (numpy/scipy/eigen) and
+  #   also tests the automatic discovery functions in CMake (Python version, C++ standard).
+  - os: linux
+    env: STYLE DOCS PIP
+    cache: false
+    before_install:
+    - pyenv global $(pyenv whence 2to3)  # activate all python versions
+    - PY_CMD=python3
+    - $PY_CMD -m pip install --user --upgrade pip wheel
+    install:
+    - $PY_CMD -m pip install --user --upgrade sphinx sphinx_rtd_theme breathe flake8 pep8-naming pytest
+    - curl -fsSL ftp://ftp.stack.nl/pub/users/dimitri/doxygen-1.8.12.linux.bin.tar.gz | tar xz
+    - export PATH="$PWD/doxygen-1.8.12/bin:$PATH"
+    script:
+    - tools/check-style.sh
+    - flake8
+    - $PY_CMD -m sphinx -W -b html docs docs/.build
+    - |
+      # Make sure setup.py distributes and installs all the headers
+      $PY_CMD setup.py sdist
+      $PY_CMD -m pip install --user -U ./dist/*
+      installed=$($PY_CMD -c "import pybind11; print(pybind11.get_include(True) + '/pybind11')")
+      diff -rq $installed ./include/pybind11
+    - |
+      # Barebones build
+      cmake -DCMAKE_BUILD_TYPE=Debug -DPYBIND11_WERROR=ON -DDOWNLOAD_CATCH=ON
+      make pytest -j 2
+      make cpptest -j 2
+  # The following are regular test configurations, including optional dependencies.
+  # With regard to each other they differ in Python version, C++ standard and compiler.
+  - os: linux
+    env: PYTHON=2.7 CPP=11 GCC=4.8
+    addons:
+      apt:
+        packages: [cmake=2.\*, cmake-data=2.\*]
+  - os: linux
+    env: PYTHON=3.6 CPP=11 GCC=4.8
+    addons:
+      apt:
+        sources: [deadsnakes]
+        packages: [python3.6-dev python3.6-venv, cmake=2.\*, cmake-data=2.\*]
+  - sudo: true
+    services: docker
+    env: PYTHON=2.7 CPP=14 GCC=6 CMAKE=1
+  - sudo: true
+    services: docker
+    env: PYTHON=3.5 CPP=14 GCC=6 DEBUG=1
+  - sudo: true
+    services: docker
+    env: PYTHON=3.6 CPP=17 GCC=7
+  - os: linux
+    env: PYTHON=3.6 CPP=17 CLANG=5.0
+    addons:
+      apt:
+        sources: [deadsnakes, llvm-toolchain-trusty-5.0, ubuntu-toolchain-r-test]
+        packages: [python3.6-dev python3.6-venv clang-5.0 llvm-5.0-dev, lld-5.0]
+  - os: osx
+    osx_image: xcode7.3
+    env: PYTHON=2.7 CPP=14 CLANG CMAKE=1
+  - os: osx
+    osx_image: xcode9
+    env: PYTHON=3.7 CPP=14 CLANG DEBUG=1
+  # Test a PyPy 2.7 build
+  - os: linux
+    env: PYPY=5.8 PYTHON=2.7 CPP=11 GCC=4.8
+    addons:
+      apt:
+        packages: [libblas-dev, liblapack-dev, gfortran]
+  # Build in 32-bit mode and tests against the CMake-installed version
+  - sudo: true
+    services: docker
+    env: ARCH=i386 PYTHON=3.5 CPP=14 GCC=6 INSTALL=1
+    script:
+      - |
+        $SCRIPT_RUN_PREFIX sh -c "set -e
+        cmake ${CMAKE_EXTRA_ARGS} -DPYBIND11_INSTALL=1 -DPYBIND11_TEST=0
+        make install
+        cp -a tests /pybind11-tests
+        mkdir /build-tests && cd /build-tests
+        cmake ../pybind11-tests ${CMAKE_EXTRA_ARGS} -DPYBIND11_WERROR=ON
+        make pytest -j 2"
+cache:
+  directories:
+  - $HOME/.local/bin
+  - $HOME/.local/lib
+  - $HOME/.local/include
+  - $HOME/Library/Python
+before_install:
+- |
+  # Configure build variables
+  if [ "$TRAVIS_OS_NAME" = "linux" ]; then
+    if [ -n "$CLANG" ]; then
+      export CXX=clang++-$CLANG CC=clang-$CLANG
+      EXTRA_PACKAGES+=" clang-$CLANG llvm-$CLANG-dev"
+    else
+      if [ -z "$GCC" ]; then GCC=4.8
+      else EXTRA_PACKAGES+=" g++-$GCC"
+      fi
+      export CXX=g++-$GCC CC=gcc-$GCC
+    fi
+    if [ "$GCC" = "6" ]; then DOCKER=${ARCH:+$ARCH/}debian:stretch
+    elif [ "$GCC" = "7" ]; then DOCKER=debian:buster EXTRA_PACKAGES+=" catch python3-distutils" DOWNLOAD_CATCH=OFF
+    fi
+  elif [ "$TRAVIS_OS_NAME" = "osx" ]; then
+    export CXX=clang++ CC=clang;
+  fi
+  if [ -n "$CPP" ]; then CPP=-std=c++$CPP; fi
+  if [ "${PYTHON:0:1}" = "3" ]; then PY=3; fi
+  if [ -n "$DEBUG" ]; then CMAKE_EXTRA_ARGS+=" -DCMAKE_BUILD_TYPE=Debug"; fi
+- |
+  # Initialize environment
+  set -e
+  if [ -n "$DOCKER" ]; then
+    docker pull $DOCKER
+
+    containerid=$(docker run --detach --tty \
+      --volume="$PWD":/pybind11 --workdir=/pybind11 \
+      --env="CC=$CC" --env="CXX=$CXX" --env="DEBIAN_FRONTEND=$DEBIAN_FRONTEND" \
+      --env=GCC_COLORS=\  \
+      $DOCKER)
+    SCRIPT_RUN_PREFIX="docker exec --tty $containerid"
+    $SCRIPT_RUN_PREFIX sh -c 'for s in 0 15; do sleep $s; apt-get update && apt-get -qy dist-upgrade && break; done'
+  else
+    if [ "$PYPY" = "5.8" ]; then
+      curl -fSL https://bitbucket.org/pypy/pypy/downloads/pypy2-v5.8.0-linux64.tar.bz2 | tar xj
+      PY_CMD=$(echo `pwd`/pypy2-v5.8.0-linux64/bin/pypy)
+      CMAKE_EXTRA_ARGS+=" -DPYTHON_EXECUTABLE:FILEPATH=$PY_CMD"
+    else
+      PY_CMD=python$PYTHON
+      if [ "$TRAVIS_OS_NAME" = "osx" ]; then
+        if [ "$PY" = "3" ]; then
+          brew install python$PY;
+        else
+          curl -fsSL https://bootstrap.pypa.io/get-pip.py | $PY_CMD - --user
+        fi
+      fi
+    fi
+    if [ "$PY" = 3 ] || [ -n "$PYPY" ]; then
+      $PY_CMD -m ensurepip --user
+    fi
+    $PY_CMD -m pip install --user --upgrade pip wheel
+  fi
+  set +e
+install:
+- |
+  # Install dependencies
+  set -e
+  if [ -n "$DOCKER" ]; then
+    if [ -n "$DEBUG" ]; then
+      PY_DEBUG="python$PYTHON-dbg python$PY-scipy-dbg"
+      CMAKE_EXTRA_ARGS+=" -DPYTHON_EXECUTABLE=/usr/bin/python${PYTHON}dm"
+    fi
+    $SCRIPT_RUN_PREFIX sh -c "for s in 0 15; do sleep \$s; \
+      apt-get -qy --no-install-recommends install \
+        $PY_DEBUG python$PYTHON-dev python$PY-pytest python$PY-scipy \
+        libeigen3-dev libboost-dev cmake make ${EXTRA_PACKAGES} && break; done"
+  else
+
+    if [ "$CLANG" = "5.0" ]; then
+      if ! [ -d ~/.local/include/c++/v1 ]; then
+        # Neither debian nor llvm provide a libc++ 5.0 deb; luckily it's fairly quick
+        # to build, install (and cache), so do it ourselves:
+        git clone --depth=1 https://github.com/llvm-mirror/llvm.git llvm-source
+        git clone https://github.com/llvm-mirror/libcxx.git llvm-source/projects/libcxx -b release_50
+        git clone https://github.com/llvm-mirror/libcxxabi.git llvm-source/projects/libcxxabi -b release_50
+        mkdir llvm-build && cd llvm-build
+        # Building llvm requires a newer cmake than is provided by the trusty container:
+        CMAKE_VER=cmake-3.8.0-Linux-x86_64
+        curl https://cmake.org/files/v3.8/$CMAKE_VER.tar.gz | tar xz
+        ./$CMAKE_VER/bin/cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=~/.local ../llvm-source
+        make -j2 install-cxxabi install-cxx
+        cp -a include/c++/v1/*cxxabi*.h ~/.local/include/c++/v1
+        cd ..
+      fi
+      export CXXFLAGS="-isystem $HOME/.local/include/c++/v1 -stdlib=libc++"
+      export LDFLAGS="-L$HOME/.local/lib -fuse-ld=lld-$CLANG"
+      export LD_LIBRARY_PATH="$HOME/.local/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}"
+      if [ "$CPP" = "-std=c++17" ]; then CPP="-std=c++1z"; fi
+    fi
+
+    export NPY_NUM_BUILD_JOBS=2
+    echo "Installing pytest, numpy, scipy..."
+    ${PYPY:+travis_wait 30} $PY_CMD -m pip install --user --upgrade pytest numpy scipy \
+        ${PYPY:+--extra-index-url https://imaginary.ca/trusty-pypi}
+    echo "done."
+
+    wget -q -O eigen.tar.gz https://bitbucket.org/eigen/eigen/get/3.3.3.tar.gz
+    tar xzf eigen.tar.gz
+    export CMAKE_INCLUDE_PATH="${CMAKE_INCLUDE_PATH:+$CMAKE_INCLUDE_PATH:}$PWD/eigen-eigen-67e894c6cd8f"
+  fi
+  set +e
+script:
+- $SCRIPT_RUN_PREFIX cmake ${CMAKE_EXTRA_ARGS}
+    -DPYBIND11_PYTHON_VERSION=$PYTHON
+    -DPYBIND11_CPP_STANDARD=$CPP
+    -DPYBIND11_WERROR=${WERROR:-ON}
+    -DDOWNLOAD_CATCH=${DOWNLOAD_CATCH:-ON}
+- $SCRIPT_RUN_PREFIX make pytest -j 2
+- $SCRIPT_RUN_PREFIX make cpptest -j 2
+- if [ -n "$CMAKE" ]; then $SCRIPT_RUN_PREFIX make test_cmake_build; fi
+after_failure: cat tests/test_cmake_build/*.log*
+after_script:
+- if [ -n "$DOCKER" ]; then docker stop "$containerid"; docker rm "$containerid"; fi

general-client/pybind11/CMakeLists.txt

+# CMakeLists.txt -- Build system for the pybind11 modules
+#
+# Copyright (c) 2015 Wenzel Jakob <wenzel@inf.ethz.ch>
+#
+# All rights reserved. Use of this source code is governed by a
+# BSD-style license that can be found in the LICENSE file.
+
+cmake_minimum_required(VERSION 2.8.12)
+
+if (POLICY CMP0048)
+  # cmake warns if loaded from a min-3.0-required parent dir, so silence the warning:
+  cmake_policy(SET CMP0048 NEW)
+endif()
+
+# CMake versions < 3.4.0 do not support try_compile/pthread checks without C as active language.
+if(CMAKE_VERSION VERSION_LESS 3.4.0)
+  project(pybind11)
+else()
+  project(pybind11 CXX)
+endif()
+
+# Check if pybind11 is being used directly or via add_subdirectory
+set(PYBIND11_MASTER_PROJECT OFF)
+if (CMAKE_CURRENT_SOURCE_DIR STREQUAL CMAKE_SOURCE_DIR)
+  set(PYBIND11_MASTER_PROJECT ON)
+endif()
+
+option(PYBIND11_INSTALL "Install pybind11 header files?" ${PYBIND11_MASTER_PROJECT})
+option(PYBIND11_TEST    "Build pybind11 test suite?"     ${PYBIND11_MASTER_PROJECT})
+
+list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/tools")
+
+include(pybind11Tools)
+
+# Cache variables so pybind11_add_module can be used in parent projects
+set(PYBIND11_INCLUDE_DIR "${CMAKE_CURRENT_LIST_DIR}/include" CACHE INTERNAL "")
+set(PYTHON_INCLUDE_DIRS ${PYTHON_INCLUDE_DIRS} CACHE INTERNAL "")
+set(PYTHON_LIBRARIES ${PYTHON_LIBRARIES} CACHE INTERNAL "")
+set(PYTHON_MODULE_PREFIX ${PYTHON_MODULE_PREFIX} CACHE INTERNAL "")
+set(PYTHON_MODULE_EXTENSION ${PYTHON_MODULE_EXTENSION} CACHE INTERNAL "")
+set(PYTHON_VERSION_MAJOR ${PYTHON_VERSION_MAJOR} CACHE INTERNAL "")
+set(PYTHON_VERSION_MINOR ${PYTHON_VERSION_MINOR} CACHE INTERNAL "")
+
+# NB: when adding a header don't forget to also add it to setup.py
+set(PYBIND11_HEADERS
+  include/pybind11/detail/class.h
+  include/pybind11/detail/common.h
+  include/pybind11/detail/descr.h
+  include/pybind11/detail/init.h
+  include/pybind11/detail/internals.h
+  include/pybind11/detail/typeid.h
+  include/pybind11/attr.h
+  include/pybind11/buffer_info.h
+  include/pybind11/cast.h
+  include/pybind11/chrono.h
+  include/pybind11/common.h
+  include/pybind11/complex.h
+  include/pybind11/options.h
+  include/pybind11/eigen.h
+  include/pybind11/embed.h
+  include/pybind11/eval.h
+  include/pybind11/functional.h
+  include/pybind11/numpy.h
+  include/pybind11/operators.h
+  include/pybind11/pybind11.h
+  include/pybind11/pytypes.h
+  include/pybind11/stl.h
+  include/pybind11/stl_bind.h
+)
+string(REPLACE "include/" "${CMAKE_CURRENT_SOURCE_DIR}/include/"
+       PYBIND11_HEADERS "${PYBIND11_HEADERS}")
+
+if (PYBIND11_TEST)
+  add_subdirectory(tests)
+endif()
+
+include(GNUInstallDirs)
+include(CMakePackageConfigHelpers)
+
+# extract project version from source
+file(STRINGS "${PYBIND11_INCLUDE_DIR}/pybind11/detail/common.h" pybind11_version_defines
+     REGEX "#define PYBIND11_VERSION_(MAJOR|MINOR|PATCH) ")
+foreach(ver ${pybind11_version_defines})
+  if (ver MATCHES "#define PYBIND11_VERSION_(MAJOR|MINOR|PATCH) +([^ ]+)$")
+    set(PYBIND11_VERSION_${CMAKE_MATCH_1} "${CMAKE_MATCH_2}" CACHE INTERNAL "")
+  endif()
+endforeach()
+set(${PROJECT_NAME}_VERSION ${PYBIND11_VERSION_MAJOR}.${PYBIND11_VERSION_MINOR}.${PYBIND11_VERSION_PATCH})
+message(STATUS "pybind11 v${${PROJECT_NAME}_VERSION}")
+
+option (USE_PYTHON_INCLUDE_DIR "Install pybind11 headers in Python include directory instead of default installation prefix" OFF)
+if (USE_PYTHON_INCLUDE_DIR)
+    file(RELATIVE_PATH CMAKE_INSTALL_INCLUDEDIR ${CMAKE_INSTALL_PREFIX} ${PYTHON_INCLUDE_DIRS})
+endif()
+
+if(NOT (CMAKE_VERSION VERSION_LESS 3.0))  # CMake >= 3.0
+  # Build an interface library target:
+  add_library(pybind11 INTERFACE)
+  add_library(pybind11::pybind11 ALIAS pybind11)  # to match exported target
+  target_include_directories(pybind11 INTERFACE $<BUILD_INTERFACE:${PYBIND11_INCLUDE_DIR}>
+                                                $<BUILD_INTERFACE:${PYTHON_INCLUDE_DIRS}>
+                                                $<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}>)
+  target_compile_options(pybind11 INTERFACE $<BUILD_INTERFACE:${PYBIND11_CPP_STANDARD}>)
+
+  add_library(module INTERFACE)
+  add_library(pybind11::module ALIAS module)
+  if(NOT MSVC)
+    target_compile_options(module INTERFACE -fvisibility=hidden)
+  endif()
+  target_link_libraries(module INTERFACE pybind11::pybind11)
+  if(WIN32 OR CYGWIN)
+    target_link_libraries(module INTERFACE $<BUILD_INTERFACE:${PYTHON_LIBRARIES}>)
+  elseif(APPLE)
+    target_link_libraries(module INTERFACE "-undefined dynamic_lookup")
+  endif()
+
+  add_library(embed INTERFACE)
+  add_library(pybind11::embed ALIAS embed)
+  target_link_libraries(embed INTERFACE pybind11::pybind11 $<BUILD_INTERFACE:${PYTHON_LIBRARIES}>)
+endif()
+
+if (PYBIND11_INSTALL)
+  install(DIRECTORY ${PYBIND11_INCLUDE_DIR}/pybind11 DESTINATION ${CMAKE_INSTALL_INCLUDEDIR})
+  # GNUInstallDirs "DATADIR" wrong here; CMake search path wants "share".
+  set(PYBIND11_CMAKECONFIG_INSTALL_DIR "share/cmake/${PROJECT_NAME}" CACHE STRING "install path for pybind11Config.cmake")
+
+  configure_package_config_file(tools/${PROJECT_NAME}Config.cmake.in
+                                "${CMAKE_CURRENT_BINARY_DIR}/${PROJECT_NAME}Config.cmake"
+                                INSTALL_DESTINATION ${PYBIND11_CMAKECONFIG_INSTALL_DIR})
+  # Remove CMAKE_SIZEOF_VOID_P from ConfigVersion.cmake since the library does
+  # not depend on architecture specific settings or libraries.
+  set(_PYBIND11_CMAKE_SIZEOF_VOID_P ${CMAKE_SIZEOF_VOID_P})
+  unset(CMAKE_SIZEOF_VOID_P)
+  write_basic_package_version_file(${CMAKE_CURRENT_BINARY_DIR}/${PROJECT_NAME}ConfigVersion.cmake
+                                   VERSION ${${PROJECT_NAME}_VERSION}
+                                   COMPATIBILITY AnyNewerVersion)
+  set(CMAKE_SIZEOF_VOID_P ${_PYBIND11_CMAKE_SIZEOF_VOID_P})
+  install(FILES ${CMAKE_CURRENT_BINARY_DIR}/${PROJECT_NAME}Config.cmake
+                ${CMAKE_CURRENT_BINARY_DIR}/${PROJECT_NAME}ConfigVersion.cmake
+                tools/FindPythonLibsNew.cmake
+                tools/pybind11Tools.cmake
+          DESTINATION ${PYBIND11_CMAKECONFIG_INSTALL_DIR})
+
+  if(NOT (CMAKE_VERSION VERSION_LESS 3.0))
+    if(NOT PYBIND11_EXPORT_NAME)
+      set(PYBIND11_EXPORT_NAME "${PROJECT_NAME}Targets")
+    endif()
+
+    install(TARGETS pybind11 module embed
+            EXPORT "${PYBIND11_EXPORT_NAME}")
+    if(PYBIND11_MASTER_PROJECT)
+      install(EXPORT "${PYBIND11_EXPORT_NAME}"
+              NAMESPACE "${PROJECT_NAME}::"
+              DESTINATION ${PYBIND11_CMAKECONFIG_INSTALL_DIR})
+    endif()
+  endif()
+endif()

general-client/pybind11/CONTRIBUTING.md

+Thank you for your interest in this project! Please refer to the following
+sections on how to contribute code and bug reports.
+
+### Reporting bugs
+
+At the moment, this project is run in the spare time of a single person
+([Wenzel Jakob](http://rgl.epfl.ch/people/wjakob)) with very limited resources
+for issue tracker tickets. Thus, before submitting a question or bug report,
+please take a moment of your time and ensure that your issue isn't already
+discussed in the project documentation provided at
+[http://pybind11.readthedocs.org/en/latest](http://pybind11.readthedocs.org/en/latest).
+
+Assuming that you have identified a previously unknown problem or an important
+question, it's essential that you submit a self-contained and minimal piece of
+code that reproduces the problem. In other words: no external dependencies,
+isolate the function(s) that cause breakage, submit matched and complete C++
+and Python snippets that can be easily compiled and run on my end.
+
+## Pull requests
+Contributions are submitted, reviewed, and accepted using Github pull requests.
+Please refer to [this
+article](https://help.github.com/articles/using-pull-requests) for details and
+adhere to the following rules to make the process as smooth as possible:
+
+* Make a new branch for every feature you're working on.
+* Make small and clean pull requests that are easy to review but make sure they
+  do add value by themselves.
+* Add tests for any new functionality and run the test suite (``make pytest``)
+  to ensure that no existing features break.
+* This project has a strong focus on providing general solutions using a
+  minimal amount of code, thus small pull requests are greatly preferred.
+
+### Licensing of contributions
+
+pybind11 is provided under a BSD-style license that can be found in the
+``LICENSE`` file. By using, distributing, or contributing to this project, you
+agree to the terms and conditions of this license.
+
+You are under no obligation whatsoever to provide any bug fixes, patches, or
+upgrades to the features, functionality or performance of the source code
+("Enhancements") to anyone; however, if you choose to make your Enhancements
+available either publicly, or directly to the author of this software, without
+imposing a separate written license agreement for such Enhancements, then you
+hereby grant the following license: a non-exclusive, royalty-free perpetual
+license to install, use, modify, prepare derivative works, incorporate into
+other computer software, distribute, and sublicense such enhancements or
+derivative works thereof, in binary and source code form.

general-client/pybind11/ISSUE_TEMPLATE.md

+Make sure you've completed the following steps before submitting your issue -- thank you!
+
+1. Check if your question has already been answered in the [FAQ](http://pybind11.readthedocs.io/en/latest/faq.html) section.
+2. Make sure you've read the [documentation](http://pybind11.readthedocs.io/en/latest/). Your issue may be addressed there.
+3. If those resources didn't help and you only have a short question (not a bug report), consider asking in the [Gitter chat room](https://gitter.im/pybind/Lobby).
+4. If you have a genuine bug report or a more complex question which is not answered in the previous items (or not suitable for chat), please fill in the details below.
+5. Include a self-contained and minimal piece of code that reproduces the problem. If that's not possible, try to make the description as clear as possible.
+
+*After reading, remove this checklist and the template text in parentheses below.*
+
+## Issue description
+
+(Provide a short description, state the expected behavior and what actually happens.)
+
+## Reproducible example code
+
+(The code should be minimal, have no external dependencies, isolate the function(s) that cause breakage. Submit matched and complete C++ and Python snippets that can be easily compiled and run to diagnose the issue.)

general-client/pybind11/LICENSE

+Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>, All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its contributors
+   may be used to endorse or promote products derived from this software
+   without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+Please also refer to the file CONTRIBUTING.md, which clarifies licensing of
+external contributions to this project including patches, pull requests, etc.

general-client/pybind11/MANIFEST.in

+recursive-include include/pybind11 *.h
+include LICENSE README.md CONTRIBUTING.md

general-client/pybind11/README.md

+![pybind11 logo](https://github.com/pybind/pybind11/raw/master/docs/pybind11-logo.png)
+
+# pybind11 — Seamless operability between C++11 and Python
+
+[![Documentation Status](https://readthedocs.org/projects/pybind11/badge/?version=master)](http://pybind11.readthedocs.org/en/master/?badge=master)
+[![Documentation Status](https://readthedocs.org/projects/pybind11/badge/?version=stable)](http://pybind11.readthedocs.org/en/stable/?badge=stable)
+[![Gitter chat](https://img.shields.io/gitter/room/gitterHQ/gitter.svg)](https://gitter.im/pybind/Lobby)
+[![Build Status](https://travis-ci.org/pybind/pybind11.svg?branch=master)](https://travis-ci.org/pybind/pybind11)
+[![Build status](https://ci.appveyor.com/api/projects/status/riaj54pn4h08xy40?svg=true)](https://ci.appveyor.com/project/wjakob/pybind11)
+
+**pybind11** is a lightweight header-only library that exposes C++ types in Python
+and vice versa, mainly to create Python bindings of existing C++ code. Its
+goals and syntax are similar to the excellent
+[Boost.Python](http://www.boost.org/doc/libs/1_58_0/libs/python/doc/) library
+by David Abrahams: to minimize boilerplate code in traditional extension
+modules by inferring type information using compile-time introspection.
+
+The main issue with Boost.Python—and the reason for creating such a similar
+project—is Boost. Boost is an enormously large and complex suite of utility
+libraries that works with almost every C++ compiler in existence. This
+compatibility has its cost: arcane template tricks and workarounds are
+necessary to support the oldest and buggiest of compiler specimens. Now that
+C++11-compatible compilers are widely available, this heavy machinery has
+become an excessively large and unnecessary dependency.
+
+Think of this library as a tiny self-contained version of Boost.Python with
+everything stripped away that isn't relevant for binding generation. Without
+comments, the core header files only require ~4K lines of code and depend on
+Python (2.7 or 3.x, or PyPy2.7 >= 5.7) and the C++ standard library. This
+compact implementation was possible thanks to some of the new C++11 language
+features (specifically: tuples, lambda functions and variadic templates). Since
+its creation, this library has grown beyond Boost.Python in many ways, leading
+to dramatically simpler binding code in many common situations.
+
+Tutorial and reference documentation is provided at
+[http://pybind11.readthedocs.org/en/master](http://pybind11.readthedocs.org/en/master).
+A PDF version of the manual is available
+[here](https://media.readthedocs.org/pdf/pybind11/master/pybind11.pdf).
+
+## Core features
+pybind11 can map the following core C++ features to Python
+
+- Functions accepting and returning custom data structures per value, reference, or pointer
+- Instance methods and static methods
+- Overloaded functions
+- Instance attributes and static attributes
+- Arbitrary exception types
+- Enumerations
+- Callbacks
+- Iterators and ranges
+- Custom operators
+- Single and multiple inheritance
+- STL data structures
+- Iterators and ranges
+- Smart pointers with reference counting like ``std::shared_ptr``
+- Internal references with correct reference counting
+- C++ classes with virtual (and pure virtual) methods can be extended in Python
+
+## Goodies
+In addition to the core functionality, pybind11 provides some extra goodies:
+
+- Python 2.7, 3.x, and PyPy (PyPy2.7 >= 5.7) are supported with an
+  implementation-agnostic interface.
+
+- It is possible to bind C++11 lambda functions with captured variables. The
+  lambda capture data is stored inside the resulting Python function object.
+
+- pybind11 uses C++11 move constructors and move assignment operators whenever
+  possible to efficiently transfer custom data types.
+
+- It's easy to expose the internal storage of custom data types through
+  Pythons' buffer protocols. This is handy e.g. for fast conversion between
+  C++ matrix classes like Eigen and NumPy without expensive copy operations.
+
+- pybind11 can automatically vectorize functions so that they are transparently
+  applied to all entries of one or more NumPy array arguments.
+
+- Python's slice-based access and assignment operations can be supported with
+  just a few lines of code.
+
+- Everything is contained in just a few header files; there is no need to link
+  against any additional libraries.
+
+- Binaries are generally smaller by a factor of at least 2 compared to
+  equivalent bindings generated by Boost.Python. A recent pybind11 conversion
+  of PyRosetta, an enormous Boost.Python binding project,
+  [reported](http://graylab.jhu.edu/RosettaCon2016/PyRosetta-4.pdf) a binary
+  size reduction of **5.4x** and compile time reduction by **5.8x**.
+
+- When supported by the compiler, two new C++14 features (relaxed constexpr and
+  return value deduction) are used to precompute function signatures at compile
+  time, leading to smaller binaries.
+
+- With little extra effort, C++ types can be pickled and unpickled similar to
+  regular Python objects.
+
+## Supported compilers
+
+1. Clang/LLVM 3.3 or newer (for Apple Xcode's clang, this is 5.0.0 or newer)
+2. GCC 4.8 or newer
+3. Microsoft Visual Studio 2015 Update 3 or newer
+4. Intel C++ compiler 17 or newer (16 with pybind11 v2.0 and 15 with pybind11 v2.0 and a [workaround](https://github.com/pybind/pybind11/issues/276))
+5. Cygwin/GCC (tested on 2.5.1)
+
+## About
+
+This project was created by [Wenzel Jakob](http://rgl.epfl.ch/people/wjakob).
+Significant features and/or improvements to the code were contributed by
+Jonas Adler,
+Sylvain Corlay,
+Trent Houliston,
+Axel Huebl,
+@hulucc,
+Sergey Lyskov
+Johan Mabille,
+Tomasz Miąsko,
+Dean Moldovan,
+Ben Pritchard,
+Jason Rhinelander,
+Boris Schäling,
+Pim Schellart,
+Ivan Smirnov, and
+Patrick Stewart.
+
+### License
+
+pybind11 is provided under a BSD-style license that can be found in the
+``LICENSE`` file. By using, distributing, or contributing to this project,
+you agree to the terms and conditions of this license.

general-client/pybind11/docs/Doxyfile

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general-client/pybind11/docs/advanced/cast/chrono.rst

+Chrono
+======
+
+When including the additional header file :file:`pybind11/chrono.h` conversions
+from C++11 chrono datatypes to python datetime objects are automatically enabled.
+This header also enables conversions of python floats (often from sources such
+as ``time.monotonic()``, ``time.perf_counter()`` and ``time.process_time()``)
+into durations.
+
+An overview of clocks in C++11
+------------------------------
+
+A point of confusion when using these conversions is the differences between
+clocks provided in C++11. There are three clock types defined by the C++11
+standard and users can define their own if needed. Each of these clocks have
+different properties and when converting to and from python will give different
+results.
+
+The first clock defined by the standard is ``std::chrono::system_clock``. This
+clock measures the current date and time. However, this clock changes with to
+updates to the operating system time. For example, if your time is synchronised
+with a time server this clock will change. This makes this clock a poor choice
+for timing purposes but good for measuring the wall time.
+
+The second clock defined in the standard is ``std::chrono::steady_clock``.
+This clock ticks at a steady rate and is never adjusted. This makes it excellent
+for timing purposes, however the value in this clock does not correspond to the
+current date and time. Often this clock will be the amount of time your system
+has been on, although it does not have to be. This clock will never be the same
+clock as the system clock as the system clock can change but steady clocks
+cannot.
+
+The third clock defined in the standard is ``std::chrono::high_resolution_clock``.
+This clock is the clock that has the highest resolution out of the clocks in the
+system. It is normally a typedef to either the system clock or the steady clock
+but can be its own independent clock. This is important as when using these
+conversions as the types you get in python for this clock might be different
+depending on the system.
+If it is a typedef of the system clock, python will get datetime objects, but if
+it is a different clock they will be timedelta objects.
+
+Provided conversions
+--------------------
+
+.. rubric:: C++ to Python
+
+- ``std::chrono::system_clock::time_point`` → ``datetime.datetime``
+    System clock times are converted to python datetime instances. They are
+    in the local timezone, but do not have any timezone information attached
+    to them (they are naive datetime objects).
+
+- ``std::chrono::duration`` → ``datetime.timedelta``
+    Durations are converted to timedeltas, any precision in the duration
+    greater than microseconds is lost by rounding towards zero.
+
+- ``std::chrono::[other_clocks]::time_point`` → ``datetime.timedelta``
+    Any clock time that is not the system clock is converted to a time delta.
+    This timedelta measures the time from the clocks epoch to now.
+
+.. rubric:: Python to C++
+
+- ``datetime.datetime`` → ``std::chrono::system_clock::time_point``
+    Date/time objects are converted into system clock timepoints. Any
+    timezone information is ignored and the type is treated as a naive
+    object.
+
+- ``datetime.timedelta`` → ``std::chrono::duration``
+    Time delta are converted into durations with microsecond precision.
+
+- ``datetime.timedelta`` → ``std::chrono::[other_clocks]::time_point``
+    Time deltas that are converted into clock timepoints are treated as
+    the amount of time from the start of the clocks epoch.
+
+- ``float`` → ``std::chrono::duration``
+    Floats that are passed to C++ as durations be interpreted as a number of
+    seconds. These will be converted to the duration using ``duration_cast``
+    from the float.
+
+- ``float`` → ``std::chrono::[other_clocks]::time_point``
+    Floats that are passed to C++ as time points will be interpreted as the
+    number of seconds from the start of the clocks epoch.

general-client/pybind11/docs/advanced/cast/custom.rst

+Custom type casters
+===================
+
+In very rare cases, applications may require custom type casters that cannot be
+expressed using the abstractions provided by pybind11, thus requiring raw
+Python C API calls. This is fairly advanced usage and should only be pursued by
+experts who are familiar with the intricacies of Python reference counting.
+
+The following snippets demonstrate how this works for a very simple ``inty``
+type that that should be convertible from Python types that provide a
+``__int__(self)`` method.
+
+.. code-block:: cpp
+
+    struct inty { long long_value; };
+
+    void print(inty s) {
+        std::cout << s.long_value << std::endl;
+    }
+
+The following Python snippet demonstrates the intended usage from the Python side:
+
+.. code-block:: python
+
+    class A:
+        def __int__(self):
+            return 123
+
+    from example import print
+    print(A())
+
+To register the necessary conversion routines, it is necessary to add
+a partial overload to the ``pybind11::detail::type_caster<T>`` template.
+Although this is an implementation detail, adding partial overloads to this
+type is explicitly allowed.
+
+.. code-block:: cpp
+
+    namespace pybind11 { namespace detail {
+        template <> struct type_caster<inty> {
+        public:
+            /**
+             * This macro establishes the name 'inty' in
+             * function signatures and declares a local variable
+             * 'value' of type inty
+             */
+            PYBIND11_TYPE_CASTER(inty, _("inty"));
+
+            /**
+             * Conversion part 1 (Python->C++): convert a PyObject into a inty
+             * instance or return false upon failure. The second argument
+             * indicates whether implicit conversions should be applied.
+             */
+            bool load(handle src, bool) {
+                /* Extract PyObject from handle */
+                PyObject *source = src.ptr();
+                /* Try converting into a Python integer value */
+                PyObject *tmp = PyNumber_Long(source);
+                if (!tmp)
+                    return false;
+                /* Now try to convert into a C++ int */
+                value.long_value = PyLong_AsLong(tmp);
+                Py_DECREF(tmp);
+                /* Ensure return code was OK (to avoid out-of-range errors etc) */
+                return !(value.long_value == -1 && !PyErr_Occurred());
+            }
+
+            /**
+             * Conversion part 2 (C++ -> Python): convert an inty instance into
+             * a Python object. The second and third arguments are used to
+             * indicate the return value policy and parent object (for
+             * ``return_value_policy::reference_internal``) and are generally
+             * ignored by implicit casters.
+             */
+            static handle cast(inty src, return_value_policy /* policy */, handle /* parent */) {
+                return PyLong_FromLong(src.long_value);
+            }
+        };
+    }} // namespace pybind11::detail
+
+.. note::
+
+    A ``type_caster<T>`` defined with ``PYBIND11_TYPE_CASTER(T, ...)`` requires
+    that ``T`` is default-constructible (``value`` is first default constructed
+    and then ``load()`` assigns to it).
+
+.. warning::
+
+    When using custom type casters, it's important to declare them consistently
+    in every compilation unit of the Python extension module. Otherwise,
+    undefined behavior can ensue.

general-client/pybind11/docs/advanced/cast/eigen.rst

+Eigen
+#####
+
+`Eigen <http://eigen.tuxfamily.org>`_ is C++ header-based library for dense and
+sparse linear algebra. Due to its popularity and widespread adoption, pybind11
+provides transparent conversion and limited mapping support between Eigen and
+Scientific Python linear algebra data types.
+
+To enable the built-in Eigen support you must include the optional header file
+:file:`pybind11/eigen.h`.
+
+Pass-by-value
+=============
+
+When binding a function with ordinary Eigen dense object arguments (for
+example, ``Eigen::MatrixXd``), pybind11 will accept any input value that is
+already (or convertible to) a ``numpy.ndarray`` with dimensions compatible with
+the Eigen type, copy its values into a temporary Eigen variable of the
+appropriate type, then call the function with this temporary variable.
+
+Sparse matrices are similarly copied to or from
+``scipy.sparse.csr_matrix``/``scipy.sparse.csc_matrix`` objects.
+
+Pass-by-reference
+=================
+
+One major limitation of the above is that every data conversion implicitly
+involves a copy, which can be both expensive (for large matrices) and disallows
+binding functions that change their (Matrix) arguments.  Pybind11 allows you to
+work around this by using Eigen's ``Eigen::Ref<MatrixType>`` class much as you
+would when writing a function taking a generic type in Eigen itself (subject to
+some limitations discussed below).
+
+When calling a bound function accepting a ``Eigen::Ref<const MatrixType>``
+type, pybind11 will attempt to avoid copying by using an ``Eigen::Map`` object
+that maps into the source ``numpy.ndarray`` data: this requires both that the
+data types are the same (e.g. ``dtype='float64'`` and ``MatrixType::Scalar`` is
+``double``); and that the storage is layout compatible.  The latter limitation
+is discussed in detail in the section below, and requires careful
+consideration: by default, numpy matrices and eigen matrices are *not* storage
+compatible.
+
+If the numpy matrix cannot be used as is (either because its types differ, e.g.
+passing an array of integers to an Eigen parameter requiring doubles, or
+because the storage is incompatible), pybind11 makes a temporary copy and
+passes the copy instead.
+
+When a bound function parameter is instead ``Eigen::Ref<MatrixType>`` (note the
+lack of ``const``), pybind11 will only allow the function to be called if it
+can be mapped *and* if the numpy array is writeable (that is
+``a.flags.writeable`` is true).  Any access (including modification) made to
+the passed variable will be transparently carried out directly on the
+``numpy.ndarray``.
+
+This means you can can write code such as the following and have it work as
+expected:
+
+.. code-block:: cpp
+
+    void scale_by_2(Eigen::Ref<Eigen::VectorXd> v) {
+        v *= 2;
+    }
+
+Note, however, that you will likely run into limitations due to numpy and
+Eigen's difference default storage order for data; see the below section on
+:ref:`storage_orders` for details on how to bind code that won't run into such
+limitations.
+
+.. note::
+
+    Passing by reference is not supported for sparse types.
+
+Returning values to Python
+==========================
+
+When returning an ordinary dense Eigen matrix type to numpy (e.g.
+``Eigen::MatrixXd`` or ``Eigen::RowVectorXf``) pybind11 keeps the matrix and
+returns a numpy array that directly references the Eigen matrix: no copy of the
+data is performed.  The numpy array will have ``array.flags.owndata`` set to
+``False`` to indicate that it does not own the data, and the lifetime of the
+stored Eigen matrix will be tied to the returned ``array``.
+
+If you bind a function with a non-reference, ``const`` return type (e.g.
+``const Eigen::MatrixXd``), the same thing happens except that pybind11 also
+sets the numpy array's ``writeable`` flag to false.
+
+If you return an lvalue reference or pointer, the usual pybind11 rules apply,
+as dictated by the binding function's return value policy (see the
+documentation on :ref:`return_value_policies` for full details).  That means,
+without an explicit return value policy, lvalue references will be copied and
+pointers will be managed by pybind11.  In order to avoid copying, you should
+explicitly specify an appropriate return value policy, as in the following
+example:
+
+.. code-block:: cpp
+
+    class MyClass {
+        Eigen::MatrixXd big_mat = Eigen::MatrixXd::Zero(10000, 10000);
+    public:
+        Eigen::MatrixXd &getMatrix() { return big_mat; }
+        const Eigen::MatrixXd &viewMatrix() { return big_mat; }
+    };
+
+    // Later, in binding code:
+    py::class_<MyClass>(m, "MyClass")
+        .def(py::init<>())
+        .def("copy_matrix", &MyClass::getMatrix) // Makes a copy!
+        .def("get_matrix", &MyClass::getMatrix, py::return_value_policy::reference_internal)
+        .def("view_matrix", &MyClass::viewMatrix, py::return_value_policy::reference_internal)
+        ;
+
+.. code-block:: python
+
+    a = MyClass()
+    m = a.get_matrix()   # flags.writeable = True,  flags.owndata = False
+    v = a.view_matrix()  # flags.writeable = False, flags.owndata = False
+    c = a.copy_matrix()  # flags.writeable = True,  flags.owndata = True
+    # m[5,6] and v[5,6] refer to the same element, c[5,6] does not.
+
+Note in this example that ``py::return_value_policy::reference_internal`` is
+used to tie the life of the MyClass object to the life of the returned arrays.
+
+You may also return an ``Eigen::Ref``, ``Eigen::Map`` or other map-like Eigen
+object (for example, the return value of ``matrix.block()`` and related
+methods) that map into a dense Eigen type.  When doing so, the default
+behaviour of pybind11 is to simply reference the returned data: you must take
+care to ensure that this data remains valid!  You may ask pybind11 to
+explicitly *copy* such a return value by using the
+``py::return_value_policy::copy`` policy when binding the function.  You may
+also use ``py::return_value_policy::reference_internal`` or a
+``py::keep_alive`` to ensure the data stays valid as long as the returned numpy
+array does.
+
+When returning such a reference of map, pybind11 additionally respects the
+readonly-status of the returned value, marking the numpy array as non-writeable
+if the reference or map was itself read-only.
+
+.. note::
+
+    Sparse types are always copied when returned.
+
+.. _storage_orders:
+
+Storage orders
+==============
+
+Passing arguments via ``Eigen::Ref`` has some limitations that you must be
+aware of in order to effectively pass matrices by reference.  First and
+foremost is that the default ``Eigen::Ref<MatrixType>`` class requires
+contiguous storage along columns (for column-major types, the default in Eigen)
+or rows if ``MatrixType`` is specifically an ``Eigen::RowMajor`` storage type.
+The former, Eigen's default, is incompatible with ``numpy``'s default row-major
+storage, and so you will not be able to pass numpy arrays to Eigen by reference
+without making one of two changes.
+
+(Note that this does not apply to vectors (or column or row matrices): for such
+types the "row-major" and "column-major" distinction is meaningless).
+
+The first approach is to change the use of ``Eigen::Ref<MatrixType>`` to the
+more general ``Eigen::Ref<MatrixType, 0, Eigen::Stride<Eigen::Dynamic,
+Eigen::Dynamic>>`` (or similar type with a fully dynamic stride type in the
+third template argument).  Since this is a rather cumbersome type, pybind11
+provides a ``py::EigenDRef<MatrixType>`` type alias for your convenience (along
+with EigenDMap for the equivalent Map, and EigenDStride for just the stride
+type).
+
+This type allows Eigen to map into any arbitrary storage order.  This is not
+the default in Eigen for performance reasons: contiguous storage allows
+vectorization that cannot be done when storage is not known to be contiguous at
+compile time.  The default ``Eigen::Ref`` stride type allows non-contiguous
+storage along the outer dimension (that is, the rows of a column-major matrix
+or columns of a row-major matrix), but not along the inner dimension.
+
+This type, however, has the added benefit of also being able to map numpy array
+slices.  For example, the following (contrived) example uses Eigen with a numpy
+slice to multiply by 2 all coefficients that are both on even rows (0, 2, 4,
+...) and in columns 2, 5, or 8:
+
+.. code-block:: cpp
+
+    m.def("scale", [](py::EigenDRef<Eigen::MatrixXd> m, double c) { m *= c; });
+
+.. code-block:: python
+
+    # a = np.array(...)
+    scale_by_2(myarray[0::2, 2:9:3])
+
+The second approach to avoid copying is more intrusive: rearranging the
+underlying data types to not run into the non-contiguous storage problem in the
+first place.  In particular, that means using matrices with ``Eigen::RowMajor``
+storage, where appropriate, such as:
+
+.. code-block:: cpp
+
+    using RowMatrixXd = Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
+    // Use RowMatrixXd instead of MatrixXd
+
+Now bound functions accepting ``Eigen::Ref<RowMatrixXd>`` arguments will be
+callable with numpy's (default) arrays without involving a copying.
+
+You can, alternatively, change the storage order that numpy arrays use by
+adding the ``order='F'`` option when creating an array:
+
+.. code-block:: python
+
+    myarray = np.array(source, order='F')
+
+Such an object will be passable to a bound function accepting an
+``Eigen::Ref<MatrixXd>`` (or similar column-major Eigen type).
+
+One major caveat with this approach, however, is that it is not entirely as
+easy as simply flipping all Eigen or numpy usage from one to the other: some
+operations may alter the storage order of a numpy array.  For example, ``a2 =
+array.transpose()`` results in ``a2`` being a view of ``array`` that references
+the same data, but in the opposite storage order!
+
+While this approach allows fully optimized vectorized calculations in Eigen, it
+cannot be used with array slices, unlike the first approach.
+
+When *returning* a matrix to Python (either a regular matrix, a reference via
+``Eigen::Ref<>``, or a map/block into a matrix), no special storage
+consideration is required: the created numpy array will have the required
+stride that allows numpy to properly interpret the array, whatever its storage
+order.
+
+Failing rather than copying
+===========================
+
+The default behaviour when binding ``Eigen::Ref<const MatrixType>`` eigen
+references is to copy matrix values when passed a numpy array that does not
+conform to the element type of ``MatrixType`` or does not have a compatible
+stride layout.  If you want to explicitly avoid copying in such a case, you
+should bind arguments using the ``py::arg().noconvert()`` annotation (as
+described in the :ref:`nonconverting_arguments` documentation).
+
+The following example shows an example of arguments that don't allow data
+copying to take place:
+
+.. code-block:: cpp
+
+    // The method and function to be bound:
+    class MyClass {
+        // ...
+        double some_method(const Eigen::Ref<const MatrixXd> &matrix) { /* ... */ }
+    };
+    float some_function(const Eigen::Ref<const MatrixXf> &big,
+                        const Eigen::Ref<const MatrixXf> &small) {
+        // ...
+    }
+
+    // The associated binding code:
+    using namespace pybind11::literals; // for "arg"_a
+    py::class_<MyClass>(m, "MyClass")
+        // ... other class definitions
+        .def("some_method", &MyClass::some_method, py::arg().noconvert());
+
+    m.def("some_function", &some_function,
+        "big"_a.noconvert(), // <- Don't allow copying for this arg
+        "small"_a            // <- This one can be copied if needed
+    );
+
+With the above binding code, attempting to call the the ``some_method(m)``
+method on a ``MyClass`` object, or attempting to call ``some_function(m, m2)``
+will raise a ``RuntimeError`` rather than making a temporary copy of the array.
+It will, however, allow the ``m2`` argument to be copied into a temporary if
+necessary.
+
+Note that explicitly specifying ``.noconvert()`` is not required for *mutable*
+Eigen references (e.g. ``Eigen::Ref<MatrixXd>`` without ``const`` on the
+``MatrixXd``): mutable references will never be called with a temporary copy.
+
+Vectors versus column/row matrices
+==================================
+
+Eigen and numpy have fundamentally different notions of a vector.  In Eigen, a
+vector is simply a matrix with the number of columns or rows set to 1 at
+compile time (for a column vector or row vector, respectively).  Numpy, in
+contrast, has comparable 2-dimensional 1xN and Nx1 arrays, but *also* has
+1-dimensional arrays of size N.
+
+When passing a 2-dimensional 1xN or Nx1 array to Eigen, the Eigen type must
+have matching dimensions: That is, you cannot pass a 2-dimensional Nx1 numpy
+array to an Eigen value expecting a row vector, or a 1xN numpy array as a
+column vector argument.
+
+On the other hand, pybind11 allows you to pass 1-dimensional arrays of length N
+as Eigen parameters.  If the Eigen type can hold a column vector of length N it
+will be passed as such a column vector.  If not, but the Eigen type constraints
+will accept a row vector, it will be passed as a row vector.  (The column
+vector takes precedence when both are supported, for example, when passing a
+1D numpy array to a MatrixXd argument).  Note that the type need not be
+expicitly a vector: it is permitted to pass a 1D numpy array of size 5 to an
+Eigen ``Matrix<double, Dynamic, 5>``: you would end up with a 1x5 Eigen matrix.
+Passing the same to an ``Eigen::MatrixXd`` would result in a 5x1 Eigen matrix.
+
+When returning an eigen vector to numpy, the conversion is ambiguous: a row
+vector of length 4 could be returned as either a 1D array of length 4, or as a
+2D array of size 1x4.  When encoutering such a situation, pybind11 compromises
+by considering the returned Eigen type: if it is a compile-time vector--that
+is, the type has either the number of rows or columns set to 1 at compile
+time--pybind11 converts to a 1D numpy array when returning the value.  For
+instances that are a vector only at run-time (e.g. ``MatrixXd``,
+``Matrix<float, Dynamic, 4>``), pybind11 returns the vector as a 2D array to
+numpy.  If this isn't want you want, you can use ``array.reshape(...)`` to get
+a view of the same data in the desired dimensions.
+
+.. seealso::
+
+    The file :file:`tests/test_eigen.cpp` contains a complete example that
+    shows how to pass Eigen sparse and dense data types in more detail.

general-client/pybind11/docs/advanced/cast/functional.rst

+Functional
+##########
+
+The following features must be enabled by including :file:`pybind11/functional.h`.
+
+
+Callbacks and passing anonymous functions
+=========================================
+
+The C++11 standard brought lambda functions and the generic polymorphic
+function wrapper ``std::function<>`` to the C++ programming language, which
+enable powerful new ways of working with functions. Lambda functions come in
+two flavors: stateless lambda function resemble classic function pointers that
+link to an anonymous piece of code, while stateful lambda functions
+additionally depend on captured variables that are stored in an anonymous
+*lambda closure object*.
+
+Here is a simple example of a C++ function that takes an arbitrary function
+(stateful or stateless) with signature ``int -> int`` as an argument and runs
+it with the value 10.
+
+.. code-block:: cpp
+
+    int func_arg(const std::function<int(int)> &f) {
+        return f(10);
+    }
+
+The example below is more involved: it takes a function of signature ``int -> int``
+and returns another function of the same kind. The return value is a stateful
+lambda function, which stores the value ``f`` in the capture object and adds 1 to
+its return value upon execution.
+
+.. code-block:: cpp
+
+    std::function<int(int)> func_ret(const std::function<int(int)> &f) {
+        return [f](int i) {
+            return f(i) + 1;
+        };
+    }
+
+This example demonstrates using python named parameters in C++ callbacks which
+requires using ``py::cpp_function`` as a wrapper. Usage is similar to defining
+methods of classes:
+
+.. code-block:: cpp
+
+    py::cpp_function func_cpp() {
+        return py::cpp_function([](int i) { return i+1; },
+           py::arg("number"));
+    }
+
+After including the extra header file :file:`pybind11/functional.h`, it is almost
+trivial to generate binding code for all of these functions.
+
+.. code-block:: cpp
+
+    #include <pybind11/functional.h>
+
+    PYBIND11_MODULE(example, m) {
+        m.def("func_arg", &func_arg);
+        m.def("func_ret", &func_ret);
+        m.def("func_cpp", &func_cpp);
+    }
+
+The following interactive session shows how to call them from Python.
+
+.. code-block:: pycon
+
+    $ python
+    >>> import example
+    >>> def square(i):
+    ...     return i * i
+    ...
+    >>> example.func_arg(square)
+    100L
+    >>> square_plus_1 = example.func_ret(square)
+    >>> square_plus_1(4)
+    17L
+    >>> plus_1 = func_cpp()
+    >>> plus_1(number=43)
+    44L
+
+.. warning::
+
+    Keep in mind that passing a function from C++ to Python (or vice versa)
+    will instantiate a piece of wrapper code that translates function
+    invocations between the two languages. Naturally, this translation
+    increases the computational cost of each function call somewhat. A
+    problematic situation can arise when a function is copied back and forth
+    between Python and C++ many times in a row, in which case the underlying
+    wrappers will accumulate correspondingly. The resulting long sequence of
+    C++ -> Python -> C++ -> ... roundtrips can significantly decrease
+    performance.
+
+    There is one exception: pybind11 detects case where a stateless function
+    (i.e. a function pointer or a lambda function without captured variables)
+    is passed as an argument to another C++ function exposed in Python. In this
+    case, there is no overhead. Pybind11 will extract the underlying C++
+    function pointer from the wrapped function to sidestep a potential C++ ->
+    Python -> C++ roundtrip. This is demonstrated in :file:`tests/test_callbacks.cpp`.
+
+.. note::
+
+    This functionality is very useful when generating bindings for callbacks in
+    C++ libraries (e.g. GUI libraries, asynchronous networking libraries, etc.).
+
+    The file :file:`tests/test_callbacks.cpp` contains a complete example
+    that demonstrates how to work with callbacks and anonymous functions in
+    more detail.

general-client/pybind11/docs/advanced/cast/index.rst

+Type conversions
+################
+
+Apart from enabling cross-language function calls, a fundamental problem
+that a binding tool like pybind11 must address is to provide access to
+native Python types in C++ and vice versa. There are three fundamentally
+different ways to do this—which approach is preferable for a particular type
+depends on the situation at hand.
+
+1. Use a native C++ type everywhere. In this case, the type must be wrapped
+   using pybind11-generated bindings so that Python can interact with it.
+
+2. Use a native Python type everywhere. It will need to be wrapped so that
+   C++ functions can interact with it.
+
+3. Use a native C++ type on the C++ side and a native Python type on the
+   Python side. pybind11 refers to this as a *type conversion*.
+
+   Type conversions are the most "natural" option in the sense that native
+   (non-wrapped) types are used everywhere. The main downside is that a copy
+   of the data must be made on every Python ↔ C++ transition: this is
+   needed since the C++ and Python versions of the same type generally won't
+   have the same memory layout.
+
+   pybind11 can perform many kinds of conversions automatically. An overview
+   is provided in the table ":ref:`conversion_table`".
+
+The following subsections discuss the differences between these options in more
+detail. The main focus in this section is on type conversions, which represent
+the last case of the above list.
+
+.. toctree::
+   :maxdepth: 1
+
+   overview
+   strings
+   stl
+   functional
+   chrono
+   eigen
+   custom
+

general-client/pybind11/docs/advanced/cast/overview.rst

+Overview
+########
+
+.. rubric:: 1. Native type in C++, wrapper in Python
+
+Exposing a custom C++ type using :class:`py::class_` was covered in detail
+in the :doc:`/classes` section. There, the underlying data structure is
+always the original C++ class while the :class:`py::class_` wrapper provides
+a Python interface. Internally, when an object like this is sent from C++ to
+Python, pybind11 will just add the outer wrapper layer over the native C++
+object. Getting it back from Python is just a matter of peeling off the
+wrapper.
+
+.. rubric:: 2. Wrapper in C++, native type in Python
+
+This is the exact opposite situation. Now, we have a type which is native to
+Python, like a ``tuple`` or a ``list``. One way to get this data into C++ is
+with the :class:`py::object` family of wrappers. These are explained in more
+detail in the :doc:`/advanced/pycpp/object` section. We'll just give a quick
+example here:
+
+.. code-block:: cpp
+
+    void print_list(py::list my_list) {
+        for (auto item : my_list)
+            std::cout << item << " ";
+    }
+
+.. code-block:: pycon
+
+    >>> print_list([1, 2, 3])
+    1 2 3
+
+The Python ``list`` is not converted in any way -- it's just wrapped in a C++
+:class:`py::list` class. At its core it's still a Python object. Copying a
+:class:`py::list` will do the usual reference-counting like in Python.
+Returning the object to Python will just remove the thin wrapper.
+
+.. rubric:: 3. Converting between native C++ and Python types
+
+In the previous two cases we had a native type in one language and a wrapper in
+the other. Now, we have native types on both sides and we convert between them.
+
+.. code-block:: cpp
+
+    void print_vector(const std::vector<int> &v) {
+        for (auto item : v)
+            std::cout << item << "\n";
+    }
+
+.. code-block:: pycon
+
+    >>> print_vector([1, 2, 3])
+    1 2 3
+
+In this case, pybind11 will construct a new ``std::vector<int>`` and copy each
+element from the Python ``list``. The newly constructed object will be passed
+to ``print_vector``. The same thing happens in the other direction: a new
+``list`` is made to match the value returned from C++.
+
+Lots of these conversions are supported out of the box, as shown in the table
+below. They are very convenient, but keep in mind that these conversions are
+fundamentally based on copying data. This is perfectly fine for small immutable
+types but it may become quite expensive for large data structures. This can be
+avoided by overriding the automatic conversion with a custom wrapper (i.e. the
+above-mentioned approach 1). This requires some manual effort and more details
+are available in the :ref:`opaque` section.
+
+.. _conversion_table:
+
+List of all builtin conversions
+-------------------------------
+
+The following basic data types are supported out of the box (some may require
+an additional extension header to be included). To pass other data structures
+as arguments and return values, refer to the section on binding :ref:`classes`.
+
++------------------------------------+---------------------------+-------------------------------+
+|  Data type                         |  Description              | Header file                   |
++====================================+===========================+===============================+
+| ``int8_t``, ``uint8_t``            | 8-bit integers            | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``int16_t``, ``uint16_t``          | 16-bit integers           | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``int32_t``, ``uint32_t``          | 32-bit integers           | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``int64_t``, ``uint64_t``          | 64-bit integers           | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``ssize_t``, ``size_t``            | Platform-dependent size   | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``float``, ``double``              | Floating point types      | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``bool``                           | Two-state Boolean type    | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``char``                           | Character literal         | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``char16_t``                       | UTF-16 character literal  | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``char32_t``                       | UTF-32 character literal  | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``wchar_t``                        | Wide character literal    | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``const char *``                   | UTF-8 string literal      | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``const char16_t *``               | UTF-16 string literal     | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``const char32_t *``               | UTF-32 string literal     | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``const wchar_t *``                | Wide string literal       | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``std::string``                    | STL dynamic UTF-8 string  | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``std::u16string``                 | STL dynamic UTF-16 string | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``std::u32string``                 | STL dynamic UTF-32 string | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``std::wstring``                   | STL dynamic wide string   | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``std::string_view``,              | STL C++17 string views    | :file:`pybind11/pybind11.h`   |
+| ``std::u16string_view``, etc.      |                           |                               |
++------------------------------------+---------------------------+-------------------------------+
+| ``std::pair<T1, T2>``              | Pair of two custom types  | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``std::tuple<...>``                | Arbitrary tuple of types  | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``std::reference_wrapper<...>``    | Reference type wrapper    | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``std::complex<T>``                | Complex numbers           | :file:`pybind11/complex.h`    |
++------------------------------------+---------------------------+-------------------------------+
+| ``std::array<T, Size>``            | STL static array          | :file:`pybind11/stl.h`        |
++------------------------------------+---------------------------+-------------------------------+
+| ``std::vector<T>``                 | STL dynamic array         | :file:`pybind11/stl.h`        |
++------------------------------------+---------------------------+-------------------------------+
+| ``std::valarray<T>``               | STL value array           | :file:`pybind11/stl.h`        |
++------------------------------------+---------------------------+-------------------------------+
+| ``std::list<T>``                   | STL linked list           | :file:`pybind11/stl.h`        |
++------------------------------------+---------------------------+-------------------------------+
+| ``std::map<T1, T2>``               | STL ordered map           | :file:`pybind11/stl.h`        |
++------------------------------------+---------------------------+-------------------------------+
+| ``std::unordered_map<T1, T2>``     | STL unordered map         | :file:`pybind11/stl.h`        |
++------------------------------------+---------------------------+-------------------------------+
+| ``std::set<T>``                    | STL ordered set           | :file:`pybind11/stl.h`        |
++------------------------------------+---------------------------+-------------------------------+
+| ``std::unordered_set<T>``          | STL unordered set         | :file:`pybind11/stl.h`        |
++------------------------------------+---------------------------+-------------------------------+
+| ``std::optional<T>``               | STL optional type (C++17) | :file:`pybind11/stl.h`        |
++------------------------------------+---------------------------+-------------------------------+
+| ``std::experimental::optional<T>`` | STL optional type (exp.)  | :file:`pybind11/stl.h`        |
++------------------------------------+---------------------------+-------------------------------+
+| ``std::variant<...>``              | Type-safe union (C++17)   | :file:`pybind11/stl.h`        |
++------------------------------------+---------------------------+-------------------------------+
+| ``std::function<...>``             | STL polymorphic function  | :file:`pybind11/functional.h` |
++------------------------------------+---------------------------+-------------------------------+
+| ``std::chrono::duration<...>``     | STL time duration         | :file:`pybind11/chrono.h`     |
++------------------------------------+---------------------------+-------------------------------+
+| ``std::chrono::time_point<...>``   | STL date/time             | :file:`pybind11/chrono.h`     |
++------------------------------------+---------------------------+-------------------------------+
+| ``Eigen::Matrix<...>``             | Eigen: dense matrix       | :file:`pybind11/eigen.h`      |
++------------------------------------+---------------------------+-------------------------------+
+| ``Eigen::Map<...>``                | Eigen: mapped memory      | :file:`pybind11/eigen.h`      |
++------------------------------------+---------------------------+-------------------------------+
+| ``Eigen::SparseMatrix<...>``       | Eigen: sparse matrix      | :file:`pybind11/eigen.h`      |
++------------------------------------+---------------------------+-------------------------------+

general-client/pybind11/docs/advanced/cast/stl.rst

+STL containers
+##############
+
+Automatic conversion
+====================
+
+When including the additional header file :file:`pybind11/stl.h`, conversions
+between ``std::vector<>``/``std::list<>``/``std::array<>``,
+``std::set<>``/``std::unordered_set<>``, and
+``std::map<>``/``std::unordered_map<>`` and the Python ``list``, ``set`` and
+``dict`` data structures are automatically enabled. The types ``std::pair<>``
+and ``std::tuple<>`` are already supported out of the box with just the core
+:file:`pybind11/pybind11.h` header.
+
+The major downside of these implicit conversions is that containers must be
+converted (i.e. copied) on every Python->C++ and C++->Python transition, which
+can have implications on the program semantics and performance. Please read the
+next sections for more details and alternative approaches that avoid this.
+
+.. note::
+
+    Arbitrary nesting of any of these types is possible.
+
+.. seealso::
+
+    The file :file:`tests/test_stl.cpp` contains a complete
+    example that demonstrates how to pass STL data types in more detail.
+
+.. _cpp17_container_casters:
+
+C++17 library containers
+========================
+
+The :file:`pybind11/stl.h` header also includes support for ``std::optional<>``
+and ``std::variant<>``. These require a C++17 compiler and standard library.
+In C++14 mode, ``std::experimental::optional<>`` is supported if available.
+
+Various versions of these containers also exist for C++11 (e.g. in Boost).
+pybind11 provides an easy way to specialize the ``type_caster`` for such
+types:
+
+.. code-block:: cpp
+
+    // `boost::optional` as an example -- can be any `std::optional`-like container
+    namespace pybind11 { namespace detail {
+        template <typename T>
+        struct type_caster<boost::optional<T>> : optional_caster<boost::optional<T>> {};
+    }}
+
+The above should be placed in a header file and included in all translation units
+where automatic conversion is needed. Similarly, a specialization can be provided
+for custom variant types:
+
+.. code-block:: cpp
+
+    // `boost::variant` as an example -- can be any `std::variant`-like container
+    namespace pybind11 { namespace detail {
+        template <typename... Ts>
+        struct type_caster<boost::variant<Ts...>> : variant_caster<boost::variant<Ts...>> {};
+
+        // Specifies the function used to visit the variant -- `apply_visitor` instead of `visit`
+        template <>
+        struct visit_helper<boost::variant> {
+            template <typename... Args>
+            static auto call(Args &&...args) -> decltype(boost::apply_visitor(args...)) {
+                return boost::apply_visitor(args...);
+            }
+        };
+    }} // namespace pybind11::detail
+
+The ``visit_helper`` specialization is not required if your ``name::variant`` provides
+a ``name::visit()`` function. For any other function name, the specialization must be
+included to tell pybind11 how to visit the variant.
+
+.. note::
+
+    pybind11 only supports the modern implementation of ``boost::variant``
+    which makes use of variadic templates. This requires Boost 1.56 or newer.
+    Additionally, on Windows, MSVC 2017 is required because ``boost::variant``
+    falls back to the old non-variadic implementation on MSVC 2015.
+
+.. _opaque:
+
+Making opaque types
+===================
+
+pybind11 heavily relies on a template matching mechanism to convert parameters
+and return values that are constructed from STL data types such as vectors,
+linked lists, hash tables, etc. This even works in a recursive manner, for
+instance to deal with lists of hash maps of pairs of elementary and custom
+types, etc.
+
+However, a fundamental limitation of this approach is that internal conversions
+between Python and C++ types involve a copy operation that prevents
+pass-by-reference semantics. What does this mean?
+
+Suppose we bind the following function
+
+.. code-block:: cpp
+
+    void append_1(std::vector<int> &v) {
+       v.push_back(1);
+    }
+
+and call it from Python, the following happens:
+
+.. code-block:: pycon
+
+   >>> v = [5, 6]
+   >>> append_1(v)
+   >>> print(v)
+   [5, 6]
+
+As you can see, when passing STL data structures by reference, modifications
+are not propagated back the Python side. A similar situation arises when
+exposing STL data structures using the ``def_readwrite`` or ``def_readonly``
+functions:
+
+.. code-block:: cpp
+
+    /* ... definition ... */
+
+    class MyClass {
+        std::vector<int> contents;
+    };
+
+    /* ... binding code ... */
+
+    py::class_<MyClass>(m, "MyClass")
+        .def(py::init<>())
+        .def_readwrite("contents", &MyClass::contents);
+
+In this case, properties can be read and written in their entirety. However, an
+``append`` operation involving such a list type has no effect:
+
+.. code-block:: pycon
+
+   >>> m = MyClass()
+   >>> m.contents = [5, 6]
+   >>> print(m.contents)
+   [5, 6]
+   >>> m.contents.append(7)
+   >>> print(m.contents)
+   [5, 6]
+
+Finally, the involved copy operations can be costly when dealing with very
+large lists. To deal with all of the above situations, pybind11 provides a
+macro named ``PYBIND11_MAKE_OPAQUE(T)`` that disables the template-based
+conversion machinery of types, thus rendering them *opaque*. The contents of
+opaque objects are never inspected or extracted, hence they *can* be passed by
+reference. For instance, to turn ``std::vector<int>`` into an opaque type, add
+the declaration
+
+.. code-block:: cpp
+
+    PYBIND11_MAKE_OPAQUE(std::vector<int>);
+
+before any binding code (e.g. invocations to ``class_::def()``, etc.). This
+macro must be specified at the top level (and outside of any namespaces), since
+it instantiates a partial template overload. If your binding code consists of
+multiple compilation units, it must be present in every file (typically via a
+common header) preceding any usage of ``std::vector<int>``. Opaque types must
+also have a corresponding ``class_`` declaration to associate them with a name
+in Python, and to define a set of available operations, e.g.:
+
+.. code-block:: cpp
+
+    py::class_<std::vector<int>>(m, "IntVector")
+        .def(py::init<>())
+        .def("clear", &std::vector<int>::clear)
+        .def("pop_back", &std::vector<int>::pop_back)
+        .def("__len__", [](const std::vector<int> &v) { return v.size(); })
+        .def("__iter__", [](std::vector<int> &v) {
+           return py::make_iterator(v.begin(), v.end());
+        }, py::keep_alive<0, 1>()) /* Keep vector alive while iterator is used */
+        // ....
+
+Please take a look at the :ref:`macro_notes` before using the
+``PYBIND11_MAKE_OPAQUE`` macro.
+
+.. seealso::
+
+    The file :file:`tests/test_opaque_types.cpp` contains a complete
+    example that demonstrates how to create and expose opaque types using
+    pybind11 in more detail.
+
+.. _stl_bind:
+
+Binding STL containers
+======================
+
+The ability to expose STL containers as native Python objects is a fairly
+common request, hence pybind11 also provides an optional header file named
+:file:`pybind11/stl_bind.h` that does exactly this. The mapped containers try
+to match the behavior of their native Python counterparts as much as possible.
+
+The following example showcases usage of :file:`pybind11/stl_bind.h`:
+
+.. code-block:: cpp
+
+    // Don't forget this
+    #include <pybind11/stl_bind.h>
+
+    PYBIND11_MAKE_OPAQUE(std::vector<int>);
+    PYBIND11_MAKE_OPAQUE(std::map<std::string, double>);
+
+    // ...
+
+    // later in binding code:
+    py::bind_vector<std::vector<int>>(m, "VectorInt");
+    py::bind_map<std::map<std::string, double>>(m, "MapStringDouble");
+
+When binding STL containers pybind11 considers the types of the container's
+elements to decide whether the container should be confined to the local module
+(via the :ref:`module_local` feature).  If the container element types are
+anything other than already-bound custom types bound without
+``py::module_local()`` the container binding will have ``py::module_local()``
+applied.  This includes converting types such as numeric types, strings, Eigen
+types; and types that have not yet been bound at the time of the stl container
+binding.  This module-local binding is designed to avoid potential conflicts
+between module bindings (for example, from two separate modules each attempting
+to bind ``std::vector<int>`` as a python type).
+
+It is possible to override this behavior to force a definition to be either
+module-local or global.  To do so, you can pass the attributes
+``py::module_local()`` (to make the binding module-local) or
+``py::module_local(false)`` (to make the binding global) into the
+``py::bind_vector`` or ``py::bind_map`` arguments:
+
+.. code-block:: cpp
+
+    py::bind_vector<std::vector<int>>(m, "VectorInt", py::module_local(false));
+
+Note, however, that such a global binding would make it impossible to load this
+module at the same time as any other pybind module that also attempts to bind
+the same container type (``std::vector<int>`` in the above example).
+
+See :ref:`module_local` for more details on module-local bindings.
+
+.. seealso::
+
+    The file :file:`tests/test_stl_binders.cpp` shows how to use the
+    convenience STL container wrappers.

general-client/pybind11/docs/advanced/cast/strings.rst

+Strings, bytes and Unicode conversions
+######################################
+
+.. note::
+
+    This section discusses string handling in terms of Python 3 strings. For
+    Python 2.7, replace all occurrences of ``str`` with ``unicode`` and
+    ``bytes`` with ``str``.  Python 2.7 users may find it best to use ``from
+    __future__ import unicode_literals`` to avoid unintentionally using ``str``
+    instead of ``unicode``.
+
+Passing Python strings to C++
+=============================
+
+When a Python ``str`` is passed from Python to a C++ function that accepts
+``std::string`` or ``char *`` as arguments, pybind11 will encode the Python
+string to UTF-8. All Python ``str`` can be encoded in UTF-8, so this operation
+does not fail.
+
+The C++ language is encoding agnostic. It is the responsibility of the
+programmer to track encodings. It's often easiest to simply `use UTF-8
+everywhere <http://utf8everywhere.org/>`_.
+
+.. code-block:: c++
+
+    m.def("utf8_test",
+        [](const std::string &s) {
+            cout << "utf-8 is icing on the cake.\n";
+            cout << s;
+        }
+    );
+    m.def("utf8_charptr",
+        [](const char *s) {
+            cout << "My favorite food is\n";
+            cout << s;
+        }
+    );
+
+.. code-block:: python
+
+    >>> utf8_test('🎂')
+    utf-8 is icing on the cake.
+    🎂
+
+    >>> utf8_charptr('🍕')
+    My favorite food is
+    🍕
+
+.. note::
+
+    Some terminal emulators do not support UTF-8 or emoji fonts and may not
+    display the example above correctly.
+
+The results are the same whether the C++ function accepts arguments by value or
+reference, and whether or not ``const`` is used.
+
+Passing bytes to C++
+--------------------
+
+A Python ``bytes`` object will be passed to C++ functions that accept
+``std::string`` or ``char*`` *without* conversion.  On Python 3, in order to
+make a function *only* accept ``bytes`` (and not ``str``), declare it as taking
+a ``py::bytes`` argument.
+
+
+Returning C++ strings to Python
+===============================
+
+When a C++ function returns a ``std::string`` or ``char*`` to a Python caller,
+**pybind11 will assume that the string is valid UTF-8** and will decode it to a
+native Python ``str``, using the same API as Python uses to perform
+``bytes.decode('utf-8')``. If this implicit conversion fails, pybind11 will
+raise a ``UnicodeDecodeError``.
+
+.. code-block:: c++
+
+    m.def("std_string_return",
+        []() {
+            return std::string("This string needs to be UTF-8 encoded");
+        }
+    );
+
+.. code-block:: python
+
+    >>> isinstance(example.std_string_return(), str)
+    True
+
+
+Because UTF-8 is inclusive of pure ASCII, there is never any issue with
+returning a pure ASCII string to Python. If there is any possibility that the
+string is not pure ASCII, it is necessary to ensure the encoding is valid
+UTF-8.
+
+.. warning::
+
+    Implicit conversion assumes that a returned ``char *`` is null-terminated.
+    If there is no null terminator a buffer overrun will occur.
+
+Explicit conversions
+--------------------
+
+If some C++ code constructs a ``std::string`` that is not a UTF-8 string, one
+can perform a explicit conversion and return a ``py::str`` object. Explicit
+conversion has the same overhead as implicit conversion.
+
+.. code-block:: c++
+
+    // This uses the Python C API to convert Latin-1 to Unicode
+    m.def("str_output",
+        []() {
+            std::string s = "Send your r\xe9sum\xe9 to Alice in HR"; // Latin-1
+            py::str py_s = PyUnicode_DecodeLatin1(s.data(), s.length());
+            return py_s;
+        }
+    );
+
+.. code-block:: python
+
+    >>> str_output()
+    'Send your résumé to Alice in HR'
+
+The `Python C API
+<https://docs.python.org/3/c-api/unicode.html#built-in-codecs>`_ provides
+several built-in codecs.
+
+
+One could also use a third party encoding library such as libiconv to transcode
+to UTF-8.
+
+Return C++ strings without conversion
+-------------------------------------
+
+If the data in a C++ ``std::string`` does not represent text and should be
+returned to Python as ``bytes``, then one can return the data as a
+``py::bytes`` object.
+
+.. code-block:: c++
+
+    m.def("return_bytes",
+        []() {
+            std::string s("\xba\xd0\xba\xd0");  // Not valid UTF-8
+            return py::bytes(s);  // Return the data without transcoding
+        }
+    );
+
+.. code-block:: python
+
+    >>> example.return_bytes()
+    b'\xba\xd0\xba\xd0'
+
+
+Note the asymmetry: pybind11 will convert ``bytes`` to ``std::string`` without
+encoding, but cannot convert ``std::string`` back to ``bytes`` implicitly.
+
+.. code-block:: c++
+
+    m.def("asymmetry",
+        [](std::string s) {  // Accepts str or bytes from Python
+            return s;  // Looks harmless, but implicitly converts to str
+        }
+    );
+
+.. code-block:: python
+
+    >>> isinstance(example.asymmetry(b"have some bytes"), str)
+    True
+
+    >>> example.asymmetry(b"\xba\xd0\xba\xd0")  # invalid utf-8 as bytes
+    UnicodeDecodeError: 'utf-8' codec can't decode byte 0xba in position 0: invalid start byte
+
+
+Wide character strings
+======================
+
+When a Python ``str`` is passed to a C++ function expecting ``std::wstring``,
+``wchar_t*``, ``std::u16string`` or ``std::u32string``, the ``str`` will be
+encoded to UTF-16 or UTF-32 depending on how the C++ compiler implements each
+type, in the platform's native endianness. When strings of these types are
+returned, they are assumed to contain valid UTF-16 or UTF-32, and will be
+decoded to Python ``str``.
+
+.. code-block:: c++
+
+    #define UNICODE
+    #include <windows.h>
+
+    m.def("set_window_text",
+        [](HWND hwnd, std::wstring s) {
+            // Call SetWindowText with null-terminated UTF-16 string
+            ::SetWindowText(hwnd, s.c_str());
+        }
+    );
+    m.def("get_window_text",
+        [](HWND hwnd) {
+            const int buffer_size = ::GetWindowTextLength(hwnd) + 1;
+            auto buffer = std::make_unique< wchar_t[] >(buffer_size);
+
+            ::GetWindowText(hwnd, buffer.data(), buffer_size);
+
+            std::wstring text(buffer.get());
+
+            // wstring will be converted to Python str
+            return text;
+        }
+    );
+
+.. warning::
+
+    Wide character strings may not work as described on Python 2.7 or Python
+    3.3 compiled with ``--enable-unicode=ucs2``.
+
+Strings in multibyte encodings such as Shift-JIS must transcoded to a
+UTF-8/16/32 before being returned to Python.
+
+
+Character literals
+==================
+
+C++ functions that accept character literals as input will receive the first
+character of a Python ``str`` as their input. If the string is longer than one
+Unicode character, trailing characters will be ignored.
+
+When a character literal is returned from C++ (such as a ``char`` or a
+``wchar_t``), it will be converted to a ``str`` that represents the single
+character.
+
+.. code-block:: c++
+
+    m.def("pass_char", [](char c) { return c; });
+    m.def("pass_wchar", [](wchar_t w) { return w; });
+
+.. code-block:: python
+
+    >>> example.pass_char('A')
+    'A'
+
+While C++ will cast integers to character types (``char c = 0x65;``), pybind11
+does not convert Python integers to characters implicitly. The Python function
+``chr()`` can be used to convert integers to characters.
+
+.. code-block:: python
+
+    >>> example.pass_char(0x65)
+    TypeError
+
+    >>> example.pass_char(chr(0x65))
+    'A'
+
+If the desire is to work with an 8-bit integer, use ``int8_t`` or ``uint8_t``
+as the argument type.
+
+Grapheme clusters
+-----------------
+
+A single grapheme may be represented by two or more Unicode characters. For
+example 'é' is usually represented as U+00E9 but can also be expressed as the
+combining character sequence U+0065 U+0301 (that is, the letter 'e' followed by
+a combining acute accent). The combining character will be lost if the
+two-character sequence is passed as an argument, even though it renders as a
+single grapheme.
+
+.. code-block:: python
+
+    >>> example.pass_wchar('é')
+    'é'
+
+    >>> combining_e_acute = 'e' + '\u0301'
+
+    >>> combining_e_acute
+    'é'
+
+    >>> combining_e_acute == 'é'
+    False
+
+    >>> example.pass_wchar(combining_e_acute)
+    'e'
+
+Normalizing combining characters before passing the character literal to C++
+may resolve *some* of these issues:
+
+.. code-block:: python
+
+    >>> example.pass_wchar(unicodedata.normalize('NFC', combining_e_acute))
+    'é'
+
+In some languages (Thai for example), there are `graphemes that cannot be
+expressed as a single Unicode code point
+<http://unicode.org/reports/tr29/#Grapheme_Cluster_Boundaries>`_, so there is
+no way to capture them in a C++ character type.
+
+
+C++17 string views
+==================
+
+C++17 string views are automatically supported when compiling in C++17 mode.
+They follow the same rules for encoding and decoding as the corresponding STL
+string type (for example, a ``std::u16string_view`` argument will be passed
+UTF-16-encoded data, and a returned ``std::string_view`` will be decoded as
+UTF-8).
+
+References
+==========
+
+* `The Absolute Minimum Every Software Developer Absolutely, Positively Must Know About Unicode and Character Sets (No Excuses!) <https://www.joelonsoftware.com/2003/10/08/the-absolute-minimum-every-software-developer-absolutely-positively-must-know-about-unicode-and-character-sets-no-excuses/>`_
+* `C++ - Using STL Strings at Win32 API Boundaries <https://msdn.microsoft.com/en-ca/magazine/mt238407.aspx>`_

general-client/pybind11/docs/advanced/embedding.rst

+.. _embedding:
+
+Embedding the interpreter
+#########################
+
+While pybind11 is mainly focused on extending Python using C++, it's also
+possible to do the reverse: embed the Python interpreter into a C++ program.
+All of the other documentation pages still apply here, so refer to them for
+general pybind11 usage. This section will cover a few extra things required
+for embedding.
+
+Getting started
+===============
+
+A basic executable with an embedded interpreter can be created with just a few
+lines of CMake and the ``pybind11::embed`` target, as shown below. For more
+information, see :doc:`/compiling`.
+
+.. code-block:: cmake
+
+    cmake_minimum_required(VERSION 3.0)
+    project(example)
+
+    find_package(pybind11 REQUIRED)  # or `add_subdirectory(pybind11)`
+
+    add_executable(example main.cpp)
+    target_link_libraries(example PRIVATE pybind11::embed)
+
+The essential structure of the ``main.cpp`` file looks like this:
+
+.. code-block:: cpp
+
+    #include <pybind11/embed.h> // everything needed for embedding
+    namespace py = pybind11;
+
+    int main() {
+        py::scoped_interpreter guard{}; // start the interpreter and keep it alive
+
+        py::print("Hello, World!"); // use the Python API
+    }
+
+The interpreter must be initialized before using any Python API, which includes
+all the functions and classes in pybind11. The RAII guard class `scoped_interpreter`
+takes care of the interpreter lifetime. After the guard is destroyed, the interpreter
+shuts down and clears its memory. No Python functions can be called after this.
+
+Executing Python code
+=====================
+
+There are a few different ways to run Python code. One option is to use `eval`,
+`exec` or `eval_file`, as explained in :ref:`eval`. Here is a quick example in
+the context of an executable with an embedded interpreter:
+
+.. code-block:: cpp
+
+    #include <pybind11/embed.h>
+    namespace py = pybind11;
+
+    int main() {
+        py::scoped_interpreter guard{};
+
+        py::exec(R"(
+            kwargs = dict(name="World", number=42)
+            message = "Hello, {name}! The answer is {number}".format(**kwargs)
+            print(message)
+        )");
+    }
+
+Alternatively, similar results can be achieved using pybind11's API (see
+:doc:`/advanced/pycpp/index` for more details).
+
+.. code-block:: cpp
+
+    #include <pybind11/embed.h>
+    namespace py = pybind11;
+    using namespace py::literals;
+
+    int main() {
+        py::scoped_interpreter guard{};
+
+        auto kwargs = py::dict("name"_a="World", "number"_a=42);
+        auto message = "Hello, {name}! The answer is {number}"_s.format(**kwargs);
+        py::print(message);
+    }
+
+The two approaches can also be combined:
+
+.. code-block:: cpp
+
+    #include <pybind11/embed.h>
+    #include <iostream>
+
+    namespace py = pybind11;
+    using namespace py::literals;
+
+    int main() {
+        py::scoped_interpreter guard{};
+
+        auto locals = py::dict("name"_a="World", "number"_a=42);
+        py::exec(R"(
+            message = "Hello, {name}! The answer is {number}".format(**locals())
+        )", py::globals(), locals);
+
+        auto message = locals["message"].cast<std::string>();
+        std::cout << message;
+    }
+
+Importing modules
+=================
+
+Python modules can be imported using `module::import()`:
+
+.. code-block:: cpp
+
+    py::module sys = py::module::import("sys");
+    py::print(sys.attr("path"));
+
+For convenience, the current working directory is included in ``sys.path`` when
+embedding the interpreter. This makes it easy to import local Python files:
+
+.. code-block:: python
+
+    """calc.py located in the working directory"""
+
+    def add(i, j):
+        return i + j
+
+
+.. code-block:: cpp
+
+    py::module calc = py::module::import("calc");
+    py::object result = calc.attr("add")(1, 2);
+    int n = result.cast<int>();
+    assert(n == 3);
+
+Modules can be reloaded using `module::reload()` if the source is modified e.g.
+by an external process. This can be useful in scenarios where the application
+imports a user defined data processing script which needs to be updated after
+changes by the user. Note that this function does not reload modules recursively.
+
+.. _embedding_modules:
+
+Adding embedded modules
+=======================
+
+Embedded binary modules can be added using the `PYBIND11_EMBEDDED_MODULE` macro.
+Note that the definition must be placed at global scope. They can be imported
+like any other module.
+
+.. code-block:: cpp
+
+    #include <pybind11/embed.h>
+    namespace py = pybind11;
+
+    PYBIND11_EMBEDDED_MODULE(fast_calc, m) {
+        // `m` is a `py::module` which is used to bind functions and classes
+        m.def("add", [](int i, int j) {
+            return i + j;
+        });
+    }
+
+    int main() {
+        py::scoped_interpreter guard{};
+
+        auto fast_calc = py::module::import("fast_calc");
+        auto result = fast_calc.attr("add")(1, 2).cast<int>();
+        assert(result == 3);
+    }
+
+Unlike extension modules where only a single binary module can be created, on
+the embedded side an unlimited number of modules can be added using multiple
+`PYBIND11_EMBEDDED_MODULE` definitions (as long as they have unique names).
+
+These modules are added to Python's list of builtins, so they can also be
+imported in pure Python files loaded by the interpreter. Everything interacts
+naturally:
+
+.. code-block:: python
+
+    """py_module.py located in the working directory"""
+    import cpp_module
+
+    a = cpp_module.a
+    b = a + 1
+
+
+.. code-block:: cpp
+
+    #include <pybind11/embed.h>
+    namespace py = pybind11;
+
+    PYBIND11_EMBEDDED_MODULE(cpp_module, m) {
+        m.attr("a") = 1;
+    }
+
+    int main() {
+        py::scoped_interpreter guard{};
+
+        auto py_module = py::module::import("py_module");
+
+        auto locals = py::dict("fmt"_a="{} + {} = {}", **py_module.attr("__dict__"));
+        assert(locals["a"].cast<int>() == 1);
+        assert(locals["b"].cast<int>() == 2);
+
+        py::exec(R"(
+            c = a + b
+            message = fmt.format(a, b, c)
+        )", py::globals(), locals);
+
+        assert(locals["c"].cast<int>() == 3);
+        assert(locals["message"].cast<std::string>() == "1 + 2 = 3");
+    }
+
+
+Interpreter lifetime
+====================
+
+The Python interpreter shuts down when `scoped_interpreter` is destroyed. After
+this, creating a new instance will restart the interpreter. Alternatively, the
+`initialize_interpreter` / `finalize_interpreter` pair of functions can be used
+to directly set the state at any time.
+
+Modules created with pybind11 can be safely re-initialized after the interpreter
+has been restarted. However, this may not apply to third-party extension modules.
+The issue is that Python itself cannot completely unload extension modules and
+there are several caveats with regard to interpreter restarting. In short, not
+all memory may be freed, either due to Python reference cycles or user-created
+global data. All the details can be found in the CPython documentation.
+
+.. warning::
+
+    Creating two concurrent `scoped_interpreter` guards is a fatal error. So is
+    calling `initialize_interpreter` for a second time after the interpreter
+    has already been initialized.
+
+    Do not use the raw CPython API functions ``Py_Initialize`` and
+    ``Py_Finalize`` as these do not properly handle the lifetime of
+    pybind11's internal data.
+
+
+Sub-interpreter support
+=======================
+
+Creating multiple copies of `scoped_interpreter` is not possible because it
+represents the main Python interpreter. Sub-interpreters are something different
+and they do permit the existence of multiple interpreters. This is an advanced
+feature of the CPython API and should be handled with care. pybind11 does not
+currently offer a C++ interface for sub-interpreters, so refer to the CPython
+documentation for all the details regarding this feature.
+
+We'll just mention a couple of caveats the sub-interpreters support in pybind11:
+
+ 1. Sub-interpreters will not receive independent copies of embedded modules.
+    Instead, these are shared and modifications in one interpreter may be
+    reflected in another.
+
+ 2. Managing multiple threads, multiple interpreters and the GIL can be
+    challenging and there are several caveats here, even within the pure
+    CPython API (please refer to the Python docs for details). As for
+    pybind11, keep in mind that `gil_scoped_release` and `gil_scoped_acquire`
+    do not take sub-interpreters into account.

general-client/pybind11/docs/advanced/exceptions.rst

+Exceptions
+##########
+
+Built-in exception translation
+==============================
+
+When C++ code invoked from Python throws an ``std::exception``, it is
+automatically converted into a Python ``Exception``. pybind11 defines multiple
+special exception classes that will map to different types of Python
+exceptions:
+
+.. tabularcolumns:: |p{0.5\textwidth}|p{0.45\textwidth}|
+
++--------------------------------------+------------------------------+
+|  C++ exception type                  |  Python exception type       |
++======================================+==============================+
+| :class:`std::exception`              | ``RuntimeError``             |
++--------------------------------------+------------------------------+
+| :class:`std::bad_alloc`              | ``MemoryError``              |
++--------------------------------------+------------------------------+
+| :class:`std::domain_error`           | ``ValueError``               |
++--------------------------------------+------------------------------+
+| :class:`std::invalid_argument`       | ``ValueError``               |
++--------------------------------------+------------------------------+
+| :class:`std::length_error`           | ``ValueError``               |
++--------------------------------------+------------------------------+
+| :class:`std::out_of_range`           | ``ValueError``               |
++--------------------------------------+------------------------------+
+| :class:`std::range_error`            | ``ValueError``               |
++--------------------------------------+------------------------------+
+| :class:`pybind11::stop_iteration`    | ``StopIteration`` (used to   |
+|                                      | implement custom iterators)  |
++--------------------------------------+------------------------------+
+| :class:`pybind11::index_error`       | ``IndexError`` (used to      |
+|                                      | indicate out of bounds       |
+|                                      | accesses in ``__getitem__``, |
+|                                      | ``__setitem__``, etc.)       |
++--------------------------------------+------------------------------+
+| :class:`pybind11::value_error`       | ``ValueError`` (used to      |
+|                                      | indicate wrong value passed  |
+|                                      | in ``container.remove(...)`` |
++--------------------------------------+------------------------------+
+| :class:`pybind11::key_error`         | ``KeyError`` (used to        |
+|                                      | indicate out of bounds       |
+|                                      | accesses in ``__getitem__``, |
+|                                      | ``__setitem__`` in dict-like |
+|                                      | objects, etc.)               |
++--------------------------------------+------------------------------+
+| :class:`pybind11::error_already_set` | Indicates that the Python    |
+|                                      | exception flag has already   |
+|                                      | been initialized             |
++--------------------------------------+------------------------------+
+
+When a Python function invoked from C++ throws an exception, it is converted
+into a C++ exception of type :class:`error_already_set` whose string payload
+contains a textual summary.
+
+There is also a special exception :class:`cast_error` that is thrown by
+:func:`handle::call` when the input arguments cannot be converted to Python
+objects.
+
+Registering custom translators
+==============================
+
+If the default exception conversion policy described above is insufficient,
+pybind11 also provides support for registering custom exception translators.
+To register a simple exception conversion that translates a C++ exception into
+a new Python exception using the C++ exception's ``what()`` method, a helper
+function is available:
+
+.. code-block:: cpp
+
+    py::register_exception<CppExp>(module, "PyExp");
+
+This call creates a Python exception class with the name ``PyExp`` in the given
+module and automatically converts any encountered exceptions of type ``CppExp``
+into Python exceptions of type ``PyExp``.
+
+When more advanced exception translation is needed, the function
+``py::register_exception_translator(translator)`` can be used to register
+functions that can translate arbitrary exception types (and which may include
+additional logic to do so).  The function takes a stateless callable (e.g.  a
+function pointer or a lambda function without captured variables) with the call
+signature ``void(std::exception_ptr)``.
+
+When a C++ exception is thrown, the registered exception translators are tried
+in reverse order of registration (i.e. the last registered translator gets the
+first shot at handling the exception).
+
+Inside the translator, ``std::rethrow_exception`` should be used within
+a try block to re-throw the exception.  One or more catch clauses to catch
+the appropriate exceptions should then be used with each clause using
+``PyErr_SetString`` to set a Python exception or ``ex(string)`` to set
+the python exception to a custom exception type (see below).
+
+To declare a custom Python exception type, declare a ``py::exception`` variable
+and use this in the associated exception translator (note: it is often useful
+to make this a static declaration when using it inside a lambda expression
+without requiring capturing).
+
+
+The following example demonstrates this for a hypothetical exception classes
+``MyCustomException`` and ``OtherException``: the first is translated to a
+custom python exception ``MyCustomError``, while the second is translated to a
+standard python RuntimeError:
+
+.. code-block:: cpp
+
+    static py::exception<MyCustomException> exc(m, "MyCustomError");
+    py::register_exception_translator([](std::exception_ptr p) {
+        try {
+            if (p) std::rethrow_exception(p);
+        } catch (const MyCustomException &e) {
+            exc(e.what());
+        } catch (const OtherException &e) {
+            PyErr_SetString(PyExc_RuntimeError, e.what());
+        }
+    });
+
+Multiple exceptions can be handled by a single translator, as shown in the
+example above. If the exception is not caught by the current translator, the
+previously registered one gets a chance.
+
+If none of the registered exception translators is able to handle the
+exception, it is handled by the default converter as described in the previous
+section.
+
+.. seealso::
+
+    The file :file:`tests/test_exceptions.cpp` contains examples
+    of various custom exception translators and custom exception types.
+
+.. note::
+
+    You must call either ``PyErr_SetString`` or a custom exception's call
+    operator (``exc(string)``) for every exception caught in a custom exception
+    translator.  Failure to do so will cause Python to crash with ``SystemError:
+    error return without exception set``.
+
+    Exceptions that you do not plan to handle should simply not be caught, or
+    may be explicitly (re-)thrown to delegate it to the other,
+    previously-declared existing exception translators.

general-client/pybind11/docs/advanced/misc.rst

+Miscellaneous
+#############
+
+.. _macro_notes:
+
+General notes regarding convenience macros
+==========================================
+
+pybind11 provides a few convenience macros such as
+:func:`PYBIND11_MAKE_OPAQUE` and :func:`PYBIND11_DECLARE_HOLDER_TYPE`, and
+``PYBIND11_OVERLOAD_*``. Since these are "just" macros that are evaluated
+in the preprocessor (which has no concept of types), they *will* get confused
+by commas in a template argument such as ``PYBIND11_OVERLOAD(MyReturnValue<T1,
+T2>, myFunc)``. In this case, the preprocessor assumes that the comma indicates
+the beginning of the next parameter. Use a ``typedef`` to bind the template to
+another name and use it in the macro to avoid this problem.
+
+.. _gil:
+
+Global Interpreter Lock (GIL)
+=============================
+
+When calling a C++ function from Python, the GIL is always held.
+The classes :class:`gil_scoped_release` and :class:`gil_scoped_acquire` can be
+used to acquire and release the global interpreter lock in the body of a C++
+function call. In this way, long-running C++ code can be parallelized using
+multiple Python threads. Taking :ref:`overriding_virtuals` as an example, this
+could be realized as follows (important changes highlighted):
+
+.. code-block:: cpp
+    :emphasize-lines: 8,9,31,32
+
+    class PyAnimal : public Animal {
+    public:
+        /* Inherit the constructors */
+        using Animal::Animal;
+
+        /* Trampoline (need one for each virtual function) */
+        std::string go(int n_times) {
+            /* Acquire GIL before calling Python code */
+            py::gil_scoped_acquire acquire;
+
+            PYBIND11_OVERLOAD_PURE(
+                std::string, /* Return type */
+                Animal,      /* Parent class */
+                go,          /* Name of function */
+                n_times      /* Argument(s) */
+            );
+        }
+    };
+
+    PYBIND11_MODULE(example, m) {
+        py::class_<Animal, PyAnimal> animal(m, "Animal");
+        animal
+            .def(py::init<>())
+            .def("go", &Animal::go);
+
+        py::class_<Dog>(m, "Dog", animal)
+            .def(py::init<>());
+
+        m.def("call_go", [](Animal *animal) -> std::string {
+            /* Release GIL before calling into (potentially long-running) C++ code */
+            py::gil_scoped_release release;
+            return call_go(animal);
+        });
+    }
+
+The ``call_go`` wrapper can also be simplified using the `call_guard` policy
+(see :ref:`call_policies`) which yields the same result:
+
+.. code-block:: cpp
+
+    m.def("call_go", &call_go, py::call_guard<py::gil_scoped_release>());
+
+
+Binding sequence data types, iterators, the slicing protocol, etc.
+==================================================================
+
+Please refer to the supplemental example for details.
+
+.. seealso::
+
+    The file :file:`tests/test_sequences_and_iterators.cpp` contains a
+    complete example that shows how to bind a sequence data type, including
+    length queries (``__len__``), iterators (``__iter__``), the slicing
+    protocol and other kinds of useful operations.
+
+
+Partitioning code over multiple extension modules
+=================================================
+
+It's straightforward to split binding code over multiple extension modules,
+while referencing types that are declared elsewhere. Everything "just" works
+without any special precautions. One exception to this rule occurs when
+extending a type declared in another extension module. Recall the basic example
+from Section :ref:`inheritance`.
+
+.. code-block:: cpp
+
+    py::class_<Pet> pet(m, "Pet");
+    pet.def(py::init<const std::string &>())
+       .def_readwrite("name", &Pet::name);
+
+    py::class_<Dog>(m, "Dog", pet /* <- specify parent */)
+        .def(py::init<const std::string &>())
+        .def("bark", &Dog::bark);
+
+Suppose now that ``Pet`` bindings are defined in a module named ``basic``,
+whereas the ``Dog`` bindings are defined somewhere else. The challenge is of
+course that the variable ``pet`` is not available anymore though it is needed
+to indicate the inheritance relationship to the constructor of ``class_<Dog>``.
+However, it can be acquired as follows:
+
+.. code-block:: cpp
+
+    py::object pet = (py::object) py::module::import("basic").attr("Pet");
+
+    py::class_<Dog>(m, "Dog", pet)
+        .def(py::init<const std::string &>())
+        .def("bark", &Dog::bark);
+
+Alternatively, you can specify the base class as a template parameter option to
+``class_``, which performs an automated lookup of the corresponding Python
+type. Like the above code, however, this also requires invoking the ``import``
+function once to ensure that the pybind11 binding code of the module ``basic``
+has been executed:
+
+.. code-block:: cpp
+
+    py::module::import("basic");
+
+    py::class_<Dog, Pet>(m, "Dog")
+        .def(py::init<const std::string &>())
+        .def("bark", &Dog::bark);
+
+Naturally, both methods will fail when there are cyclic dependencies.
+
+Note that pybind11 code compiled with hidden-by-default symbol visibility (e.g.
+via the command line flag ``-fvisibility=hidden`` on GCC/Clang), which is
+required proper pybind11 functionality, can interfere with the ability to
+access types defined in another extension module.  Working around this requires
+manually exporting types that are accessed by multiple extension modules;
+pybind11 provides a macro to do just this:
+
+.. code-block:: cpp
+
+    class PYBIND11_EXPORT Dog : public Animal {
+        ...
+    };
+
+Note also that it is possible (although would rarely be required) to share arbitrary
+C++ objects between extension modules at runtime. Internal library data is shared
+between modules using capsule machinery [#f6]_ which can be also utilized for
+storing, modifying and accessing user-defined data. Note that an extension module
+will "see" other extensions' data if and only if they were built with the same
+pybind11 version. Consider the following example:
+
+.. code-block:: cpp
+
+    auto data = (MyData *) py::get_shared_data("mydata");
+    if (!data)
+        data = (MyData *) py::set_shared_data("mydata", new MyData(42));
+
+If the above snippet was used in several separately compiled extension modules,
+the first one to be imported would create a ``MyData`` instance and associate
+a ``"mydata"`` key with a pointer to it. Extensions that are imported later
+would be then able to access the data behind the same pointer.
+
+.. [#f6] https://docs.python.org/3/extending/extending.html#using-capsules
+
+Module Destructors
+==================
+
+pybind11 does not provide an explicit mechanism to invoke cleanup code at
+module destruction time. In rare cases where such functionality is required, it
+is possible to emulate it using Python capsules or weak references with a
+destruction callback.
+
+.. code-block:: cpp
+
+    auto cleanup_callback = []() {
+        // perform cleanup here -- this function is called with the GIL held
+    };
+
+    m.add_object("_cleanup", py::capsule(cleanup_callback));
+
+This approach has the potential downside that instances of classes exposed
+within the module may still be alive when the cleanup callback is invoked
+(whether this is acceptable will generally depend on the application).
+
+Alternatively, the capsule may also be stashed within a type object, which
+ensures that it not called before all instances of that type have been
+collected:
+
+.. code-block:: cpp
+
+    auto cleanup_callback = []() { /* ... */ };
+    m.attr("BaseClass").attr("_cleanup") = py::capsule(cleanup_callback);
+
+Both approaches also expose a potentially dangerous ``_cleanup`` attribute in
+Python, which may be undesirable from an API standpoint (a premature explicit
+call from Python might lead to undefined behavior). Yet another approach that 
+avoids this issue involves weak reference with a cleanup callback:
+
+.. code-block:: cpp
+
+    // Register a callback function that is invoked when the BaseClass object is colelcted
+    py::cpp_function cleanup_callback(
+        [](py::handle weakref) {
+            // perform cleanup here -- this function is called with the GIL held
+
+            weakref.dec_ref(); // release weak reference
+        }
+    );
+
+    // Create a weak reference with a cleanup callback and initially leak it
+    (void) py::weakref(m.attr("BaseClass"), cleanup_callback).release();
+
+
+Generating documentation using Sphinx
+=====================================
+
+Sphinx [#f4]_ has the ability to inspect the signatures and documentation
+strings in pybind11-based extension modules to automatically generate beautiful
+documentation in a variety formats. The python_example repository [#f5]_ contains a
+simple example repository which uses this approach.
+
+There are two potential gotchas when using this approach: first, make sure that
+the resulting strings do not contain any :kbd:`TAB` characters, which break the
+docstring parsing routines. You may want to use C++11 raw string literals,
+which are convenient for multi-line comments. Conveniently, any excess
+indentation will be automatically be removed by Sphinx. However, for this to
+work, it is important that all lines are indented consistently, i.e.:
+
+.. code-block:: cpp
+
+    // ok
+    m.def("foo", &foo, R"mydelimiter(
+        The foo function
+
+        Parameters
+        ----------
+    )mydelimiter");
+
+    // *not ok*
+    m.def("foo", &foo, R"mydelimiter(The foo function
+
+        Parameters
+        ----------
+    )mydelimiter");
+
+By default, pybind11 automatically generates and prepends a signature to the docstring of a function 
+registered with ``module::def()`` and ``class_::def()``. Sometimes this
+behavior is not desirable, because you want to provide your own signature or remove 
+the docstring completely to exclude the function from the Sphinx documentation.
+The class ``options`` allows you to selectively suppress auto-generated signatures:
+
+.. code-block:: cpp
+
+    PYBIND11_MODULE(example, m) {
+        py::options options;
+        options.disable_function_signatures();
+
+        m.def("add", [](int a, int b) { return a + b; }, "A function which adds two numbers");
+    }
+
+Note that changes to the settings affect only function bindings created during the 
+lifetime of the ``options`` instance. When it goes out of scope at the end of the module's init function, 
+the default settings are restored to prevent unwanted side effects.
+
+.. [#f4] http://www.sphinx-doc.org
+.. [#f5] http://github.com/pybind/python_example

general-client/pybind11/docs/advanced/pycpp/index.rst

+Python C++ interface
+####################
+
+pybind11 exposes Python types and functions using thin C++ wrappers, which
+makes it possible to conveniently call Python code from C++ without resorting
+to Python's C API.
+
+.. toctree::
+   :maxdepth: 2
+
+   object
+   numpy
+   utilities

general-client/pybind11/docs/advanced/pycpp/numpy.rst

+.. _numpy:
+
+NumPy
+#####
+
+Buffer protocol
+===============
+
+Python supports an extremely general and convenient approach for exchanging
+data between plugin libraries. Types can expose a buffer view [#f2]_, which
+provides fast direct access to the raw internal data representation. Suppose we
+want to bind the following simplistic Matrix class:
+
+.. code-block:: cpp
+
+    class Matrix {
+    public:
+        Matrix(size_t rows, size_t cols) : m_rows(rows), m_cols(cols) {
+            m_data = new float[rows*cols];
+        }
+        float *data() { return m_data; }
+        size_t rows() const { return m_rows; }
+        size_t cols() const { return m_cols; }
+    private:
+        size_t m_rows, m_cols;
+        float *m_data;
+    };
+
+The following binding code exposes the ``Matrix`` contents as a buffer object,
+making it possible to cast Matrices into NumPy arrays. It is even possible to
+completely avoid copy operations with Python expressions like
+``np.array(matrix_instance, copy = False)``.
+
+.. code-block:: cpp
+
+    py::class_<Matrix>(m, "Matrix", py::buffer_protocol())
+       .def_buffer([](Matrix &m) -> py::buffer_info {
+            return py::buffer_info(
+                m.data(),                               /* Pointer to buffer */
+                sizeof(float),                          /* Size of one scalar */
+                py::format_descriptor<float>::format(), /* Python struct-style format descriptor */
+                2,                                      /* Number of dimensions */
+                { m.rows(), m.cols() },                 /* Buffer dimensions */
+                { sizeof(float) * m.rows(),             /* Strides (in bytes) for each index */
+                  sizeof(float) }
+            );
+        });
+
+Supporting the buffer protocol in a new type involves specifying the special
+``py::buffer_protocol()`` tag in the ``py::class_`` constructor and calling the
+``def_buffer()`` method with a lambda function that creates a
+``py::buffer_info`` description record on demand describing a given matrix
+instance. The contents of ``py::buffer_info`` mirror the Python buffer protocol
+specification.
+
+.. code-block:: cpp
+
+    struct buffer_info {
+        void *ptr;
+        ssize_t itemsize;
+        std::string format;
+        ssize_t ndim;
+        std::vector<ssize_t> shape;
+        std::vector<ssize_t> strides;
+    };
+
+To create a C++ function that can take a Python buffer object as an argument,
+simply use the type ``py::buffer`` as one of its arguments. Buffers can exist
+in a great variety of configurations, hence some safety checks are usually
+necessary in the function body. Below, you can see an basic example on how to
+define a custom constructor for the Eigen double precision matrix
+(``Eigen::MatrixXd``) type, which supports initialization from compatible
+buffer objects (e.g. a NumPy matrix).
+
+.. code-block:: cpp
+
+    /* Bind MatrixXd (or some other Eigen type) to Python */
+    typedef Eigen::MatrixXd Matrix;
+
+    typedef Matrix::Scalar Scalar;
+    constexpr bool rowMajor = Matrix::Flags & Eigen::RowMajorBit;
+
+    py::class_<Matrix>(m, "Matrix", py::buffer_protocol())
+        .def("__init__", [](Matrix &m, py::buffer b) {
+            typedef Eigen::Stride<Eigen::Dynamic, Eigen::Dynamic> Strides;
+
+            /* Request a buffer descriptor from Python */
+            py::buffer_info info = b.request();
+
+            /* Some sanity checks ... */
+            if (info.format != py::format_descriptor<Scalar>::format())
+                throw std::runtime_error("Incompatible format: expected a double array!");
+
+            if (info.ndim != 2)
+                throw std::runtime_error("Incompatible buffer dimension!");
+
+            auto strides = Strides(
+                info.strides[rowMajor ? 0 : 1] / (py::ssize_t)sizeof(Scalar),
+                info.strides[rowMajor ? 1 : 0] / (py::ssize_t)sizeof(Scalar));
+
+            auto map = Eigen::Map<Matrix, 0, Strides>(
+                static_cast<Scalar *>(info.ptr), info.shape[0], info.shape[1], strides);
+
+            new (&m) Matrix(map);
+        });
+
+For reference, the ``def_buffer()`` call for this Eigen data type should look
+as follows:
+
+.. code-block:: cpp
+
+    .def_buffer([](Matrix &m) -> py::buffer_info {
+        return py::buffer_info(
+            m.data(),                                /* Pointer to buffer */
+            sizeof(Scalar),                          /* Size of one scalar */
+            py::format_descriptor<Scalar>::format(), /* Python struct-style format descriptor */
+            2,                                       /* Number of dimensions */
+            { m.rows(), m.cols() },                  /* Buffer dimensions */
+            { sizeof(Scalar) * (rowMajor ? m.cols() : 1),
+              sizeof(Scalar) * (rowMajor ? 1 : m.rows()) }
+                                                     /* Strides (in bytes) for each index */
+        );
+     })
+
+For a much easier approach of binding Eigen types (although with some
+limitations), refer to the section on :doc:`/advanced/cast/eigen`.
+
+.. seealso::
+
+    The file :file:`tests/test_buffers.cpp` contains a complete example
+    that demonstrates using the buffer protocol with pybind11 in more detail.
+
+.. [#f2] http://docs.python.org/3/c-api/buffer.html
+
+Arrays
+======
+
+By exchanging ``py::buffer`` with ``py::array`` in the above snippet, we can
+restrict the function so that it only accepts NumPy arrays (rather than any
+type of Python object satisfying the buffer protocol).
+
+In many situations, we want to define a function which only accepts a NumPy
+array of a certain data type. This is possible via the ``py::array_t<T>``
+template. For instance, the following function requires the argument to be a
+NumPy array containing double precision values.
+
+.. code-block:: cpp
+
+    void f(py::array_t<double> array);
+
+When it is invoked with a different type (e.g. an integer or a list of
+integers), the binding code will attempt to cast the input into a NumPy array
+of the requested type. Note that this feature requires the
+:file:`pybind11/numpy.h` header to be included.
+
+Data in NumPy arrays is not guaranteed to packed in a dense manner;
+furthermore, entries can be separated by arbitrary column and row strides.
+Sometimes, it can be useful to require a function to only accept dense arrays
+using either the C (row-major) or Fortran (column-major) ordering. This can be
+accomplished via a second template argument with values ``py::array::c_style``
+or ``py::array::f_style``.
+
+.. code-block:: cpp
+
+    void f(py::array_t<double, py::array::c_style | py::array::forcecast> array);
+
+The ``py::array::forcecast`` argument is the default value of the second
+template parameter, and it ensures that non-conforming arguments are converted
+into an array satisfying the specified requirements instead of trying the next
+function overload.
+
+Structured types
+================
+
+In order for ``py::array_t`` to work with structured (record) types, we first
+need to register the memory layout of the type. This can be done via
+``PYBIND11_NUMPY_DTYPE`` macro, called in the plugin definition code, which
+expects the type followed by field names:
+
+.. code-block:: cpp
+
+    struct A {
+        int x;
+        double y;
+    };
+
+    struct B {
+        int z;
+        A a;
+    };
+
+    // ...
+    PYBIND11_MODULE(test, m) {
+        // ...
+
+        PYBIND11_NUMPY_DTYPE(A, x, y);
+        PYBIND11_NUMPY_DTYPE(B, z, a);
+        /* now both A and B can be used as template arguments to py::array_t */
+    }
+
+The structure should consist of fundamental arithmetic types, ``std::complex``,
+previously registered substructures, and arrays of any of the above. Both C++
+arrays and ``std::array`` are supported. While there is a static assertion to
+prevent many types of unsupported structures, it is still the user's
+responsibility to use only "plain" structures that can be safely manipulated as
+raw memory without violating invariants.
+
+Vectorizing functions
+=====================
+
+Suppose we want to bind a function with the following signature to Python so
+that it can process arbitrary NumPy array arguments (vectors, matrices, general
+N-D arrays) in addition to its normal arguments:
+
+.. code-block:: cpp
+
+    double my_func(int x, float y, double z);
+
+After including the ``pybind11/numpy.h`` header, this is extremely simple:
+
+.. code-block:: cpp
+
+    m.def("vectorized_func", py::vectorize(my_func));
+
+Invoking the function like below causes 4 calls to be made to ``my_func`` with
+each of the array elements. The significant advantage of this compared to
+solutions like ``numpy.vectorize()`` is that the loop over the elements runs
+entirely on the C++ side and can be crunched down into a tight, optimized loop
+by the compiler. The result is returned as a NumPy array of type
+``numpy.dtype.float64``.
+
+.. code-block:: pycon
+
+    >>> x = np.array([[1, 3],[5, 7]])
+    >>> y = np.array([[2, 4],[6, 8]])
+    >>> z = 3
+    >>> result = vectorized_func(x, y, z)
+
+The scalar argument ``z`` is transparently replicated 4 times.  The input
+arrays ``x`` and ``y`` are automatically converted into the right types (they
+are of type  ``numpy.dtype.int64`` but need to be ``numpy.dtype.int32`` and
+``numpy.dtype.float32``, respectively).
+
+.. note::
+
+    Only arithmetic, complex, and POD types passed by value or by ``const &``
+    reference are vectorized; all other arguments are passed through as-is.
+    Functions taking rvalue reference arguments cannot be vectorized.
+
+In cases where the computation is too complicated to be reduced to
+``vectorize``, it will be necessary to create and access the buffer contents
+manually. The following snippet contains a complete example that shows how this
+works (the code is somewhat contrived, since it could have been done more
+simply using ``vectorize``).
+
+.. code-block:: cpp
+
+    #include <pybind11/pybind11.h>
+    #include <pybind11/numpy.h>
+
+    namespace py = pybind11;
+
+    py::array_t<double> add_arrays(py::array_t<double> input1, py::array_t<double> input2) {
+        py::buffer_info buf1 = input1.request(), buf2 = input2.request();
+
+        if (buf1.ndim != 1 || buf2.ndim != 1)
+            throw std::runtime_error("Number of dimensions must be one");
+
+        if (buf1.size != buf2.size)
+            throw std::runtime_error("Input shapes must match");
+
+        /* No pointer is passed, so NumPy will allocate the buffer */
+        auto result = py::array_t<double>(buf1.size);
+
+        py::buffer_info buf3 = result.request();
+
+        double *ptr1 = (double *) buf1.ptr,
+               *ptr2 = (double *) buf2.ptr,
+               *ptr3 = (double *) buf3.ptr;
+
+        for (size_t idx = 0; idx < buf1.shape[0]; idx++)
+            ptr3[idx] = ptr1[idx] + ptr2[idx];
+
+        return result;
+    }
+
+    PYBIND11_MODULE(test, m) {
+        m.def("add_arrays", &add_arrays, "Add two NumPy arrays");
+    }
+
+.. seealso::
+
+    The file :file:`tests/test_numpy_vectorize.cpp` contains a complete
+    example that demonstrates using :func:`vectorize` in more detail.
+
+Direct access
+=============
+
+For performance reasons, particularly when dealing with very large arrays, it
+is often desirable to directly access array elements without internal checking
+of dimensions and bounds on every access when indices are known to be already
+valid.  To avoid such checks, the ``array`` class and ``array_t<T>`` template
+class offer an unchecked proxy object that can be used for this unchecked
+access through the ``unchecked<N>`` and ``mutable_unchecked<N>`` methods,
+where ``N`` gives the required dimensionality of the array:
+
+.. code-block:: cpp
+
+    m.def("sum_3d", [](py::array_t<double> x) {
+        auto r = x.unchecked<3>(); // x must have ndim = 3; can be non-writeable
+        double sum = 0;
+        for (ssize_t i = 0; i < r.shape(0); i++)
+            for (ssize_t j = 0; j < r.shape(1); j++)
+                for (ssize_t k = 0; k < r.shape(2); k++)
+                    sum += r(i, j, k);
+        return sum;
+    });
+    m.def("increment_3d", [](py::array_t<double> x) {
+        auto r = x.mutable_unchecked<3>(); // Will throw if ndim != 3 or flags.writeable is false
+        for (ssize_t i = 0; i < r.shape(0); i++)
+            for (ssize_t j = 0; j < r.shape(1); j++)
+                for (ssize_t k = 0; k < r.shape(2); k++)
+                    r(i, j, k) += 1.0;
+    }, py::arg().noconvert());
+
+To obtain the proxy from an ``array`` object, you must specify both the data
+type and number of dimensions as template arguments, such as ``auto r =
+myarray.mutable_unchecked<float, 2>()``.
+
+If the number of dimensions is not known at compile time, you can omit the
+dimensions template parameter (i.e. calling ``arr_t.unchecked()`` or
+``arr.unchecked<T>()``.  This will give you a proxy object that works in the
+same way, but results in less optimizable code and thus a small efficiency
+loss in tight loops.
+
+Note that the returned proxy object directly references the array's data, and
+only reads its shape, strides, and writeable flag when constructed.  You must
+take care to ensure that the referenced array is not destroyed or reshaped for
+the duration of the returned object, typically by limiting the scope of the
+returned instance.
+
+The returned proxy object supports some of the same methods as ``py::array`` so
+that it can be used as a drop-in replacement for some existing, index-checked
+uses of ``py::array``:
+
+- ``r.ndim()`` returns the number of dimensions
+
+- ``r.data(1, 2, ...)`` and ``r.mutable_data(1, 2, ...)``` returns a pointer to
+  the ``const T`` or ``T`` data, respectively, at the given indices.  The
+  latter is only available to proxies obtained via ``a.mutable_unchecked()``.
+
+- ``itemsize()`` returns the size of an item in bytes, i.e. ``sizeof(T)``.
+
+- ``ndim()`` returns the number of dimensions.
+
+- ``shape(n)`` returns the size of dimension ``n``
+
+- ``size()`` returns the total number of elements (i.e. the product of the shapes).
+
+- ``nbytes()`` returns the number of bytes used by the referenced elements
+  (i.e. ``itemsize()`` times ``size()``).
+
+.. seealso::
+
+    The file :file:`tests/test_numpy_array.cpp` contains additional examples
+    demonstrating the use of this feature.

general-client/pybind11/docs/advanced/pycpp/object.rst

+Python types
+############
+
+Available wrappers
+==================
+
+All major Python types are available as thin C++ wrapper classes. These
+can also be used as function parameters -- see :ref:`python_objects_as_args`.
+
+Available types include :class:`handle`, :class:`object`, :class:`bool_`,
+:class:`int_`, :class:`float_`, :class:`str`, :class:`bytes`, :class:`tuple`,
+:class:`list`, :class:`dict`, :class:`slice`, :class:`none`, :class:`capsule`,
+:class:`iterable`, :class:`iterator`, :class:`function`, :class:`buffer`,
+:class:`array`, and :class:`array_t`.
+
+Casting back and forth
+======================
+
+In this kind of mixed code, it is often necessary to convert arbitrary C++
+types to Python, which can be done using :func:`py::cast`:
+
+.. code-block:: cpp
+
+    MyClass *cls = ..;
+    py::object obj = py::cast(cls);
+
+The reverse direction uses the following syntax:
+
+.. code-block:: cpp
+
+    py::object obj = ...;
+    MyClass *cls = obj.cast<MyClass *>();
+
+When conversion fails, both directions throw the exception :class:`cast_error`.
+
+.. _python_libs:
+
+Accessing Python libraries from C++
+===================================
+
+It is also possible to import objects defined in the Python standard
+library or available in the current Python environment (``sys.path``) and work
+with these in C++.
+
+This example obtains a reference to the Python ``Decimal`` class.
+
+.. code-block:: cpp
+
+    // Equivalent to "from decimal import Decimal"
+    py::object Decimal = py::module::import("decimal").attr("Decimal");
+
+.. code-block:: cpp
+
+    // Try to import scipy
+    py::object scipy = py::module::import("scipy");
+    return scipy.attr("__version__");
+
+.. _calling_python_functions:
+
+Calling Python functions
+========================
+
+It is also possible to call Python classes, functions and methods 
+via ``operator()``.
+
+.. code-block:: cpp
+
+    // Construct a Python object of class Decimal
+    py::object pi = Decimal("3.14159");
+
+.. code-block:: cpp
+
+    // Use Python to make our directories
+    py::object os = py::module::import("os");
+    py::object makedirs = os.attr("makedirs");
+    makedirs("/tmp/path/to/somewhere");
+
+One can convert the result obtained from Python to a pure C++ version 
+if a ``py::class_`` or type conversion is defined.
+
+.. code-block:: cpp
+
+    py::function f = <...>;
+    py::object result_py = f(1234, "hello", some_instance);
+    MyClass &result = result_py.cast<MyClass>();
+
+.. _calling_python_methods:
+
+Calling Python methods
+========================
+
+To call an object's method, one can again use ``.attr`` to obtain access to the
+Python method.
+
+.. code-block:: cpp
+
+    // Calculate e^π in decimal
+    py::object exp_pi = pi.attr("exp")();
+    py::print(py::str(exp_pi));
+
+In the example above ``pi.attr("exp")`` is a *bound method*: it will always call
+the method for that same instance of the class. Alternately one can create an 
+*unbound method* via the Python class (instead of instance) and pass the ``self`` 
+object explicitly, followed by other arguments.
+
+.. code-block:: cpp
+
+    py::object decimal_exp = Decimal.attr("exp");
+
+    // Compute the e^n for n=0..4
+    for (int n = 0; n < 5; n++) {
+        py::print(decimal_exp(Decimal(n));
+    }
+
+Keyword arguments
+=================
+
+Keyword arguments are also supported. In Python, there is the usual call syntax:
+
+.. code-block:: python
+
+    def f(number, say, to):
+        ...  # function code
+
+    f(1234, say="hello", to=some_instance)  # keyword call in Python
+
+In C++, the same call can be made using:
+
+.. code-block:: cpp
+
+    using namespace pybind11::literals; // to bring in the `_a` literal
+    f(1234, "say"_a="hello", "to"_a=some_instance); // keyword call in C++
+
+Unpacking arguments
+===================
+
+Unpacking of ``*args`` and ``**kwargs`` is also possible and can be mixed with
+other arguments:
+
+.. code-block:: cpp
+
+    // * unpacking
+    py::tuple args = py::make_tuple(1234, "hello", some_instance);
+    f(*args);
+
+    // ** unpacking
+    py::dict kwargs = py::dict("number"_a=1234, "say"_a="hello", "to"_a=some_instance);
+    f(**kwargs);
+
+    // mixed keywords, * and ** unpacking
+    py::tuple args = py::make_tuple(1234);
+    py::dict kwargs = py::dict("to"_a=some_instance);
+    f(*args, "say"_a="hello", **kwargs);
+
+Generalized unpacking according to PEP448_ is also supported:
+
+.. code-block:: cpp
+
+    py::dict kwargs1 = py::dict("number"_a=1234);
+    py::dict kwargs2 = py::dict("to"_a=some_instance);
+    f(**kwargs1, "say"_a="hello", **kwargs2);
+
+.. seealso::
+
+    The file :file:`tests/test_pytypes.cpp` contains a complete
+    example that demonstrates passing native Python types in more detail. The
+    file :file:`tests/test_callbacks.cpp` presents a few examples of calling
+    Python functions from C++, including keywords arguments and unpacking.
+
+.. _PEP448: https://www.python.org/dev/peps/pep-0448/

general-client/pybind11/docs/advanced/pycpp/utilities.rst

+Utilities
+#########
+
+Using Python's print function in C++
+====================================
+
+The usual way to write output in C++ is using ``std::cout`` while in Python one
+would use ``print``. Since these methods use different buffers, mixing them can
+lead to output order issues. To resolve this, pybind11 modules can use the
+:func:`py::print` function which writes to Python's ``sys.stdout`` for consistency.
+
+Python's ``print`` function is replicated in the C++ API including optional
+keyword arguments ``sep``, ``end``, ``file``, ``flush``. Everything works as
+expected in Python:
+
+.. code-block:: cpp
+
+    py::print(1, 2.0, "three"); // 1 2.0 three
+    py::print(1, 2.0, "three", "sep"_a="-"); // 1-2.0-three
+
+    auto args = py::make_tuple("unpacked", true);
+    py::print("->", *args, "end"_a="<-"); // -> unpacked True <-
+
+.. _ostream_redirect:
+
+Capturing standard output from ostream
+======================================
+
+Often, a library will use the streams ``std::cout`` and ``std::cerr`` to print,
+but this does not play well with Python's standard ``sys.stdout`` and ``sys.stderr``
+redirection. Replacing a library's printing with `py::print <print>` may not
+be feasible. This can be fixed using a guard around the library function that
+redirects output to the corresponding Python streams:
+
+.. code-block:: cpp
+
+    #include <pybind11/iostream.h>
+
+    ...
+
+    // Add a scoped redirect for your noisy code
+    m.def("noisy_func", []() {
+        py::scoped_ostream_redirect stream(
+            std::cout,                               // std::ostream&
+            py::module::import("sys").attr("stdout") // Python output
+        );
+        call_noisy_func();
+    });
+
+This method respects flushes on the output streams and will flush if needed
+when the scoped guard is destroyed. This allows the output to be redirected in
+real time, such as to a Jupyter notebook. The two arguments, the C++ stream and
+the Python output, are optional, and default to standard output if not given. An
+extra type, `py::scoped_estream_redirect <scoped_estream_redirect>`, is identical
+except for defaulting to ``std::cerr`` and ``sys.stderr``; this can be useful with
+`py::call_guard`, which allows multiple items, but uses the default constructor:
+
+.. code-block:: py
+
+    // Alternative: Call single function using call guard
+    m.def("noisy_func", &call_noisy_function,
+          py::call_guard<py::scoped_ostream_redirect,
+                         py::scoped_estream_redirect>());
+
+The redirection can also be done in Python with the addition of a context
+manager, using the `py::add_ostream_redirect() <add_ostream_redirect>` function:
+
+.. code-block:: cpp
+
+    py::add_ostream_redirect(m, "ostream_redirect");
+
+The name in Python defaults to ``ostream_redirect`` if no name is passed.  This
+creates the following context manager in Python:
+
+.. code-block:: python
+
+    with ostream_redirect(stdout=True, stderr=True):
+        noisy_function()
+
+It defaults to redirecting both streams, though you can use the keyword
+arguments to disable one of the streams if needed.
+
+.. note::
+
+    The above methods will not redirect C-level output to file descriptors, such
+    as ``fprintf``. For those cases, you'll need to redirect the file
+    descriptors either directly in C or with Python's ``os.dup2`` function
+    in an operating-system dependent way.
+
+.. _eval:
+
+Evaluating Python expressions from strings and files
+====================================================
+
+pybind11 provides the `eval`, `exec` and `eval_file` functions to evaluate
+Python expressions and statements. The following example illustrates how they
+can be used.
+
+.. code-block:: cpp
+
+    // At beginning of file
+    #include <pybind11/eval.h>
+
+    ...
+
+    // Evaluate in scope of main module
+    py::object scope = py::module::import("__main__").attr("__dict__");
+
+    // Evaluate an isolated expression
+    int result = py::eval("my_variable + 10", scope).cast<int>();
+
+    // Evaluate a sequence of statements
+    py::exec(
+        "print('Hello')\n"
+        "print('world!');",
+        scope);
+
+    // Evaluate the statements in an separate Python file on disk
+    py::eval_file("script.py", scope);
+
+C++11 raw string literals are also supported and quite handy for this purpose.
+The only requirement is that the first statement must be on a new line following
+the raw string delimiter ``R"(``, ensuring all lines have common leading indent:
+
+.. code-block:: cpp
+
+    py::exec(R"(
+        x = get_answer()
+        if x == 42:
+            print('Hello World!')
+        else:
+            print('Bye!')
+        )", scope
+    );
+
+.. note::
+
+    `eval` and `eval_file` accept a template parameter that describes how the
+    string/file should be interpreted. Possible choices include ``eval_expr``
+    (isolated expression), ``eval_single_statement`` (a single statement, return
+    value is always ``none``), and ``eval_statements`` (sequence of statements,
+    return value is always ``none``). `eval` defaults to  ``eval_expr``,
+    `eval_file` defaults to ``eval_statements`` and `exec` is just a shortcut
+    for ``eval<eval_statements>``.

general-client/pybind11/docs/limitations.rst

+Limitations
+###########
+
+pybind11 strives to be a general solution to binding generation, but it also has
+certain limitations:
+
+- pybind11 casts away ``const``-ness in function arguments and return values.
+  This is in line with the Python language, which has no concept of ``const``
+  values. This means that some additional care is needed to avoid bugs that
+  would be caught by the type checker in a traditional C++ program.
+
+- The NumPy interface ``pybind11::array`` greatly simplifies accessing
+  numerical data from C++ (and vice versa), but it's not a full-blown array
+  class like ``Eigen::Array`` or ``boost.multi_array``.
+
+These features could be implemented but would lead to a significant increase in
+complexity. I've decided to draw the line here to keep this project simple and
+compact. Users who absolutely require these features are encouraged to fork
+pybind11.
+

general-client/pybind11/docs/requirements.txt

+breathe == 4.5.0

general-client/pybind11/include/pybind11/common.h

+#include "detail/common.h"
+#warning "Including 'common.h' is deprecated. It will be removed in v3.0. Use 'pybind11.h'."

general-client/pybind11/pybind11/_version.py

+version_info = (2, 2, 4)
+__version__ = '.'.join(map(str, version_info))

general-client/pybind11/setup.cfg

+[bdist_wheel]
+universal=1
+
+[flake8]
+max-line-length = 99
+show_source = True
+exclude = .git, __pycache__, build, dist, docs, tools, venv
+ignore =
+    # required for pretty matrix formatting: multiple spaces after `,` and `[`
+    E201, E241