ext: Add pybind rev f4b81b3

Change-Id: I52e4fc9ebf2f59da57d8cf8f3e37cc79598c2f5f
Signed-off-by: Andreas Sandberg <andreas.sandberg@arm.com>
Reviewed-by: Andreas Hansson <andreas.hansson@arm.com>
Reviewed-by: Curtis Dunham <curtis.dunham@arm.com>
Reviewed-on: https://gem5-review.googlesource.com/2229
Reviewed-by: Tony Gutierrez <anthony.gutierrez@amd.com>
Reviewed-by: Jason Lowe-Power <jason@lowepower.com>
Reviewed-by: Pierre-Yves Péneau <pierre-yves.peneau@lirmm.fr>

35 files changed, 5702 insertions, 0 deletions

diff --git a/ext/pybind11/docs/_static/theme_overrides.css b/ext/pybind11/docs/_static/theme_overrides.css
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+.wy-table-responsive table td,
+.wy-table-responsive table th {
+    white-space: initial !important;
+}
+.rst-content table.docutils td {
+    vertical-align: top !important;
+}
+div[class^='highlight'] pre {
+    white-space: pre;
+    white-space: pre-wrap;
+}
diff --git a/ext/pybind11/docs/advanced/cast/chrono.rst b/ext/pybind11/docs/advanced/cast/chrono.rst
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+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.
diff --git a/ext/pybind11/docs/advanced/cast/custom.rst b/ext/pybind11/docs/advanced/cast/custom.rst
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+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
+
+.. 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.
diff --git a/ext/pybind11/docs/advanced/cast/eigen.rst b/ext/pybind11/docs/advanced/cast/eigen.rst
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+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 support between Eigen and Scientific Python linear
+algebra data types.
+
+Specifically, when including the optional header file :file:`pybind11/eigen.h`,
+pybind11 will automatically and transparently convert
+
+1. Static and dynamic Eigen dense vectors and matrices to instances of
+   ``numpy.ndarray`` (and vice versa).
+
+2. Returned matrix expressions such as blocks (including columns or rows) and
+   diagonals will be converted to ``numpy.ndarray`` of the expression
+   values.
+
+3. Returned matrix-like objects such as Eigen::DiagonalMatrix or
+   Eigen::SelfAdjointView will be converted to ``numpy.ndarray`` containing the
+   expressed value.
+
+4. Eigen sparse vectors and matrices to instances of
+   ``scipy.sparse.csr_matrix``/``scipy.sparse.csc_matrix`` (and vice versa).
+
+This makes it possible to bind most kinds of functions that rely on these types.
+One major caveat are functions that take Eigen matrices *by reference* and modify
+them somehow, in which case the information won't be propagated to the caller.
+
+.. code-block:: cpp
+
+    /* The Python bindings of these functions won't replicate
+       the intended effect of modifying the function arguments */
+    void scale_by_2(Eigen::Vector3f &v) {
+        v *= 2;
+    }
+    void scale_by_2(Eigen::Ref<Eigen::MatrixXd> &v) {
+        v *= 2;
+    }
+
+To see why this is, refer to the section on :ref:`opaque` (although that
+section specifically covers STL data types, the underlying issue is the same).
+The :ref:`numpy` sections discuss an efficient alternative for exposing the
+underlying native Eigen types as opaque objects in a way that still integrates
+with NumPy and SciPy.
+
+.. 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.
diff --git a/ext/pybind11/docs/advanced/cast/functional.rst b/ext/pybind11/docs/advanced/cast/functional.rst
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+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_PLUGIN(example) {
+        py::module m("example", "pybind11 example plugin");
+
+        m.def("func_arg", &func_arg);
+        m.def("func_ret", &func_ret);
+        m.def("func_cpp", &func_cpp);
+
+        return m.ptr();
+    }
+
+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.
diff --git a/ext/pybind11/docs/advanced/cast/index.rst b/ext/pybind11/docs/advanced/cast/index.rst
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+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
+   stl
+   functional
+   chrono
+   eigen
+   custom
+
diff --git a/ext/pybind11/docs/advanced/cast/overview.rst b/ext/pybind11/docs/advanced/cast/overview.rst
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+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`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``wchar_t``                        | Wide character literal    | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``const char *``                   | UTF-8 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::wstring``                   | STL dynamic wide string   | :file:`pybind11/pybind11.h`   |
++------------------------------------+---------------------------+-------------------------------+
+| ``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::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`      |
++------------------------------------+---------------------------+-------------------------------+
diff --git a/ext/pybind11/docs/advanced/cast/stl.rst b/ext/pybind11/docs/advanced/cast/stl.rst
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index 000000000..bbd23732b
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+STL containers
+##############
+
+Automatic conversion
+====================
+
+When including the additional header file :file:`pybind11/stl.h`, conversions
+between ``std::vector<>``, ``std::list<>``, ``std::set<>``, and ``std::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_python_types.cpp` contains a complete
+    example that demonstrates how to pass STL data types in more detail.
+
+.. _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 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 */
+        // ....
+
+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");
+
+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.
+
+    The file :file:`tests/test_stl_binders.cpp` shows how to use the
+    convenience STL container wrappers.
diff --git a/ext/pybind11/docs/advanced/classes.rst b/ext/pybind11/docs/advanced/classes.rst
new file mode 100644
index 000000000..4a423b578
--- /dev/null
+++ b/ext/pybind11/docs/advanced/classes.rst
@@ -0,0 +1,634 @@
+Classes
+#######
+
+This section presents advanced binding code for classes and it is assumed
+that you are already familiar with the basics from :doc:`/classes`.
+
+.. _overriding_virtuals:
+
+Overriding virtual functions in Python
+======================================
+
+Suppose that a C++ class or interface has a virtual function that we'd like to
+to override from within Python (we'll focus on the class ``Animal``; ``Dog`` is
+given as a specific example of how one would do this with traditional C++
+code).
+
+.. code-block:: cpp
+
+    class Animal {
+    public:
+        virtual ~Animal() { }
+        virtual std::string go(int n_times) = 0;
+    };
+
+    class Dog : public Animal {
+    public:
+        std::string go(int n_times) override {
+            std::string result;
+            for (int i=0; i<n_times; ++i)
+                result += "woof! ";
+            return result;
+        }
+    };
+
+Let's also suppose that we are given a plain function which calls the
+function ``go()`` on an arbitrary ``Animal`` instance.
+
+.. code-block:: cpp
+
+    std::string call_go(Animal *animal) {
+        return animal->go(3);
+    }
+
+Normally, the binding code for these classes would look as follows:
+
+.. code-block:: cpp
+
+    PYBIND11_PLUGIN(example) {
+        py::module m("example", "pybind11 example plugin");
+
+        py::class_<Animal> animal(m, "Animal");
+        animal
+            .def("go", &Animal::go);
+
+        py::class_<Dog>(m, "Dog", animal)
+            .def(py::init<>());
+
+        m.def("call_go", &call_go);
+
+        return m.ptr();
+    }
+
+However, these bindings are impossible to extend: ``Animal`` is not
+constructible, and we clearly require some kind of "trampoline" that
+redirects virtual calls back to Python.
+
+Defining a new type of ``Animal`` from within Python is possible but requires a
+helper class that is defined as follows:
+
+.. code-block:: cpp
+
+    class PyAnimal : public Animal {
+    public:
+        /* Inherit the constructors */
+        using Animal::Animal;
+
+        /* Trampoline (need one for each virtual function) */
+        std::string go(int n_times) override {
+            PYBIND11_OVERLOAD_PURE(
+                std::string, /* Return type */
+                Animal,      /* Parent class */
+                go,          /* Name of function */
+                n_times      /* Argument(s) */
+            );
+        }
+    };
+
+The macro :func:`PYBIND11_OVERLOAD_PURE` should be used for pure virtual
+functions, and :func:`PYBIND11_OVERLOAD` should be used for functions which have
+a default implementation.  There are also two alternate macros
+:func:`PYBIND11_OVERLOAD_PURE_NAME` and :func:`PYBIND11_OVERLOAD_NAME` which
+take a string-valued name argument between the *Parent class* and *Name of the
+function* slots. This is useful when the C++ and Python versions of the
+function have different names, e.g.  ``operator()`` vs ``__call__``.
+
+The binding code also needs a few minor adaptations (highlighted):
+
+.. code-block:: cpp
+    :emphasize-lines: 4,6,7
+
+    PYBIND11_PLUGIN(example) {
+        py::module m("example", "pybind11 example plugin");
+
+        py::class_<Animal, PyAnimal /* <--- trampoline*/> animal(m, "Animal");
+        animal
+            .def(py::init<>())
+            .def("go", &Animal::go);
+
+        py::class_<Dog>(m, "Dog", animal)
+            .def(py::init<>());
+
+        m.def("call_go", &call_go);
+
+        return m.ptr();
+    }
+
+Importantly, pybind11 is made aware of the trampoline helper class by
+specifying it as an extra template argument to :class:`class_`.  (This can also
+be combined with other template arguments such as a custom holder type; the
+order of template types does not matter).  Following this, we are able to
+define a constructor as usual.
+
+Note, however, that the above is sufficient for allowing python classes to
+extend ``Animal``, but not ``Dog``: see ref:`virtual_and_inheritance` for the
+necessary steps required to providing proper overload support for inherited
+classes.
+
+The Python session below shows how to override ``Animal::go`` and invoke it via
+a virtual method call.
+
+.. code-block:: pycon
+
+    >>> from example import *
+    >>> d = Dog()
+    >>> call_go(d)
+    u'woof! woof! woof! '
+    >>> class Cat(Animal):
+    ...     def go(self, n_times):
+    ...             return "meow! " * n_times
+    ...
+    >>> c = Cat()
+    >>> call_go(c)
+    u'meow! meow! meow! '
+
+Please take a look at the :ref:`macro_notes` before using this feature.
+
+.. note::
+
+    When the overridden type returns a reference or pointer to a type that
+    pybind11 converts from Python (for example, numeric values, std::string,
+    and other built-in value-converting types), there are some limitations to
+    be aware of:
+
+    - because in these cases there is no C++ variable to reference (the value
+      is stored in the referenced Python variable), pybind11 provides one in
+      the PYBIND11_OVERLOAD macros (when needed) with static storage duration.
+      Note that this means that invoking the overloaded method on *any*
+      instance will change the referenced value stored in *all* instances of
+      that type.
+
+    - Attempts to modify a non-const reference will not have the desired
+      effect: it will change only the static cache variable, but this change
+      will not propagate to underlying Python instance, and the change will be
+      replaced the next time the overload is invoked.
+
+.. seealso::
+
+    The file :file:`tests/test_virtual_functions.cpp` contains a complete
+    example that demonstrates how to override virtual functions using pybind11
+    in more detail.
+
+.. _virtual_and_inheritance:
+
+Combining virtual functions and inheritance
+===========================================
+
+When combining virtual methods with inheritance, you need to be sure to provide
+an override for each method for which you want to allow overrides from derived
+python classes.  For example, suppose we extend the above ``Animal``/``Dog``
+example as follows:
+
+.. code-block:: cpp
+
+    class Animal {
+    public:
+        virtual std::string go(int n_times) = 0;
+        virtual std::string name() { return "unknown"; }
+    };
+    class Dog : public class Animal {
+    public:
+        std::string go(int n_times) override {
+            std::string result;
+            for (int i=0; i<n_times; ++i)
+                result += bark() + " ";
+            return result;
+        }
+        virtual std::string bark() { return "woof!"; }
+    };
+
+then the trampoline class for ``Animal`` must, as described in the previous
+section, override ``go()`` and ``name()``, but in order to allow python code to
+inherit properly from ``Dog``, we also need a trampoline class for ``Dog`` that
+overrides both the added ``bark()`` method *and* the ``go()`` and ``name()``
+methods inherited from ``Animal`` (even though ``Dog`` doesn't directly
+override the ``name()`` method):
+
+.. code-block:: cpp
+
+    class PyAnimal : public Animal {
+    public:
+        using Animal::Animal; // Inherit constructors
+        std::string go(int n_times) override { PYBIND11_OVERLOAD_PURE(std::string, Animal, go, n_times); }
+        std::string name() override { PYBIND11_OVERLOAD(std::string, Animal, name, ); }
+    };
+    class PyDog : public Dog {
+    public:
+        using Dog::Dog; // Inherit constructors
+        std::string go(int n_times) override { PYBIND11_OVERLOAD_PURE(std::string, Dog, go, n_times); }
+        std::string name() override { PYBIND11_OVERLOAD(std::string, Dog, name, ); }
+        std::string bark() override { PYBIND11_OVERLOAD(std::string, Dog, bark, ); }
+    };
+
+A registered class derived from a pybind11-registered class with virtual
+methods requires a similar trampoline class, *even if* it doesn't explicitly
+declare or override any virtual methods itself:
+
+.. code-block:: cpp
+
+    class Husky : public Dog {};
+    class PyHusky : public Husky {
+        using Dog::Dog; // Inherit constructors
+        std::string go(int n_times) override { PYBIND11_OVERLOAD_PURE(std::string, Husky, go, n_times); }
+        std::string name() override { PYBIND11_OVERLOAD(std::string, Husky, name, ); }
+        std::string bark() override { PYBIND11_OVERLOAD(std::string, Husky, bark, ); }
+    };
+
+There is, however, a technique that can be used to avoid this duplication
+(which can be especially helpful for a base class with several virtual
+methods).  The technique involves using template trampoline classes, as
+follows:
+
+.. code-block:: cpp
+
+    template <class AnimalBase = Animal> class PyAnimal : public AnimalBase {
+        using AnimalBase::AnimalBase; // Inherit constructors
+        std::string go(int n_times) override { PYBIND11_OVERLOAD_PURE(std::string, AnimalBase, go, n_times); }
+        std::string name() override { PYBIND11_OVERLOAD(std::string, AnimalBase, name, ); }
+    };
+    template <class DogBase = Dog> class PyDog : public PyAnimal<DogBase> {
+        using PyAnimal<DogBase>::PyAnimal; // Inherit constructors
+        // Override PyAnimal's pure virtual go() with a non-pure one:
+        std::string go(int n_times) override { PYBIND11_OVERLOAD(std::string, DogBase, go, n_times); }
+        std::string bark() override { PYBIND11_OVERLOAD(std::string, DogBase, bark, ); }
+    };
+
+This technique has the advantage of requiring just one trampoline method to be
+declared per virtual method and pure virtual method override.  It does,
+however, require the compiler to generate at least as many methods (and
+possibly more, if both pure virtual and overridden pure virtual methods are
+exposed, as above).
+
+The classes are then registered with pybind11 using:
+
+.. code-block:: cpp
+
+    py::class_<Animal, PyAnimal<>> animal(m, "Animal");
+    py::class_<Dog, PyDog<>> dog(m, "Dog");
+    py::class_<Husky, PyDog<Husky>> husky(m, "Husky");
+    // ... add animal, dog, husky definitions
+
+Note that ``Husky`` did not require a dedicated trampoline template class at
+all, since it neither declares any new virtual methods nor provides any pure
+virtual method implementations.
+
+With either the repeated-virtuals or templated trampoline methods in place, you
+can now create a python class that inherits from ``Dog``:
+
+.. code-block:: python
+
+    class ShihTzu(Dog):
+        def bark(self):
+            return "yip!"
+
+.. seealso::
+
+    See the file :file:`tests/test_virtual_functions.cpp` for complete examples
+    using both the duplication and templated trampoline approaches.
+
+Extended trampoline class functionality
+=======================================
+
+The trampoline classes described in the previous sections are, by default, only
+initialized when needed.  More specifically, they are initialized when a python
+class actually inherits from a registered type (instead of merely creating an
+instance of the registered type), or when a registered constructor is only
+valid for the trampoline class but not the registered class.  This is primarily
+for performance reasons: when the trampoline class is not needed for anything
+except virtual method dispatching, not initializing the trampoline class
+improves performance by avoiding needing to do a run-time check to see if the
+inheriting python instance has an overloaded method.
+
+Sometimes, however, it is useful to always initialize a trampoline class as an
+intermediate class that does more than just handle virtual method dispatching.
+For example, such a class might perform extra class initialization, extra
+destruction operations, and might define new members and methods to enable a
+more python-like interface to a class.
+
+In order to tell pybind11 that it should *always* initialize the trampoline
+class when creating new instances of a type, the class constructors should be
+declared using ``py::init_alias<Args, ...>()`` instead of the usual
+``py::init<Args, ...>()``.  This forces construction via the trampoline class,
+ensuring member initialization and (eventual) destruction.
+
+.. seealso::
+
+    See the file :file:`tests/test_alias_initialization.cpp` for complete examples
+    showing both normal and forced trampoline instantiation.
+
+.. _custom_constructors:
+
+Custom constructors
+===================
+
+The syntax for binding constructors was previously introduced, but it only
+works when a constructor with the given parameters actually exists on the C++
+side. To extend this to more general cases, let's take a look at what actually
+happens under the hood: the following statement
+
+.. code-block:: cpp
+
+    py::class_<Example>(m, "Example")
+        .def(py::init<int>());
+
+is short hand notation for
+
+.. code-block:: cpp
+
+    py::class_<Example>(m, "Example")
+        .def("__init__",
+            [](Example &instance, int arg) {
+                new (&instance) Example(arg);
+            }
+        );
+
+In other words, :func:`init` creates an anonymous function that invokes an
+in-place constructor. Memory allocation etc. is already take care of beforehand
+within pybind11.
+
+.. _classes_with_non_public_destructors:
+
+Non-public destructors
+======================
+
+If a class has a private or protected destructor (as might e.g. be the case in
+a singleton pattern), a compile error will occur when creating bindings via
+pybind11. The underlying issue is that the ``std::unique_ptr`` holder type that
+is responsible for managing the lifetime of instances will reference the
+destructor even if no deallocations ever take place. In order to expose classes
+with private or protected destructors, it is possible to override the holder
+type via a holder type argument to ``class_``. Pybind11 provides a helper class
+``py::nodelete`` that disables any destructor invocations. In this case, it is
+crucial that instances are deallocated on the C++ side to avoid memory leaks.
+
+.. code-block:: cpp
+
+    /* ... definition ... */
+
+    class MyClass {
+    private:
+        ~MyClass() { }
+    };
+
+    /* ... binding code ... */
+
+    py::class_<MyClass, std::unique_ptr<MyClass, py::nodelete>>(m, "MyClass")
+        .def(py::init<>)
+
+Implicit conversions
+====================
+
+Suppose that instances of two types ``A`` and ``B`` are used in a project, and
+that an ``A`` can easily be converted into an instance of type ``B`` (examples of this
+could be a fixed and an arbitrary precision number type).
+
+.. code-block:: cpp
+
+    py::class_<A>(m, "A")
+        /// ... members ...
+
+    py::class_<B>(m, "B")
+        .def(py::init<A>())
+        /// ... members ...
+
+    m.def("func",
+        [](const B &) { /* .... */ }
+    );
+
+To invoke the function ``func`` using a variable ``a`` containing an ``A``
+instance, we'd have to write ``func(B(a))`` in Python. On the other hand, C++
+will automatically apply an implicit type conversion, which makes it possible
+to directly write ``func(a)``.
+
+In this situation (i.e. where ``B`` has a constructor that converts from
+``A``), the following statement enables similar implicit conversions on the
+Python side:
+
+.. code-block:: cpp
+
+    py::implicitly_convertible<A, B>();
+
+.. note::
+
+    Implicit conversions from ``A`` to ``B`` only work when ``B`` is a custom
+    data type that is exposed to Python via pybind11.
+
+.. _static_properties:
+
+Static properties
+=================
+
+The section on :ref:`properties` discussed the creation of instance properties
+that are implemented in terms of C++ getters and setters.
+
+Static properties can also be created in a similar way to expose getters and
+setters of static class attributes. It is important to note that the implicit
+``self`` argument also exists in this case and is used to pass the Python
+``type`` subclass instance. This parameter will often not be needed by the C++
+side, and the following example illustrates how to instantiate a lambda getter
+function that ignores it:
+
+.. code-block:: cpp
+
+    py::class_<Foo>(m, "Foo")
+        .def_property_readonly_static("foo", [](py::object /* self */) { return Foo(); });
+
+Operator overloading
+====================
+
+Suppose that we're given the following ``Vector2`` class with a vector addition
+and scalar multiplication operation, all implemented using overloaded operators
+in C++.
+
+.. code-block:: cpp
+
+    class Vector2 {
+    public:
+        Vector2(float x, float y) : x(x), y(y) { }
+
+        Vector2 operator+(const Vector2 &v) const { return Vector2(x + v.x, y + v.y); }
+        Vector2 operator*(float value) const { return Vector2(x * value, y * value); }
+        Vector2& operator+=(const Vector2 &v) { x += v.x; y += v.y; return *this; }
+        Vector2& operator*=(float v) { x *= v; y *= v; return *this; }
+
+        friend Vector2 operator*(float f, const Vector2 &v) {
+            return Vector2(f * v.x, f * v.y);
+        }
+
+        std::string toString() const {
+            return "[" + std::to_string(x) + ", " + std::to_string(y) + "]";
+        }
+    private:
+        float x, y;
+    };
+
+The following snippet shows how the above operators can be conveniently exposed
+to Python.
+
+.. code-block:: cpp
+
+    #include <pybind11/operators.h>
+
+    PYBIND11_PLUGIN(example) {
+        py::module m("example", "pybind11 example plugin");
+
+        py::class_<Vector2>(m, "Vector2")
+            .def(py::init<float, float>())
+            .def(py::self + py::self)
+            .def(py::self += py::self)
+            .def(py::self *= float())
+            .def(float() * py::self)
+            .def("__repr__", &Vector2::toString);
+
+        return m.ptr();
+    }
+
+Note that a line like
+
+.. code-block:: cpp
+
+            .def(py::self * float())
+
+is really just short hand notation for
+
+.. code-block:: cpp
+
+    .def("__mul__", [](const Vector2 &a, float b) {
+        return a * b;
+    }, py::is_operator())
+
+This can be useful for exposing additional operators that don't exist on the
+C++ side, or to perform other types of customization. The ``py::is_operator``
+flag marker is needed to inform pybind11 that this is an operator, which
+returns ``NotImplemented`` when invoked with incompatible arguments rather than
+throwing a type error.
+
+.. note::
+
+    To use the more convenient ``py::self`` notation, the additional
+    header file :file:`pybind11/operators.h` must be included.
+
+.. seealso::
+
+    The file :file:`tests/test_operator_overloading.cpp` contains a
+    complete example that demonstrates how to work with overloaded operators in
+    more detail.
+
+Pickling support
+================
+
+Python's ``pickle`` module provides a powerful facility to serialize and
+de-serialize a Python object graph into a binary data stream. To pickle and
+unpickle C++ classes using pybind11, two additional functions must be provided.
+Suppose the class in question has the following signature:
+
+.. code-block:: cpp
+
+    class Pickleable {
+    public:
+        Pickleable(const std::string &value) : m_value(value) { }
+        const std::string &value() const { return m_value; }
+
+        void setExtra(int extra) { m_extra = extra; }
+        int extra() const { return m_extra; }
+    private:
+        std::string m_value;
+        int m_extra = 0;
+    };
+
+The binding code including the requisite ``__setstate__`` and ``__getstate__`` methods [#f3]_
+looks as follows:
+
+.. code-block:: cpp
+
+    py::class_<Pickleable>(m, "Pickleable")
+        .def(py::init<std::string>())
+        .def("value", &Pickleable::value)
+        .def("extra", &Pickleable::extra)
+        .def("setExtra", &Pickleable::setExtra)
+        .def("__getstate__", [](const Pickleable &p) {
+            /* Return a tuple that fully encodes the state of the object */
+            return py::make_tuple(p.value(), p.extra());
+        })
+        .def("__setstate__", [](Pickleable &p, py::tuple t) {
+            if (t.size() != 2)
+                throw std::runtime_error("Invalid state!");
+
+            /* Invoke the in-place constructor. Note that this is needed even
+               when the object just has a trivial default constructor */
+            new (&p) Pickleable(t[0].cast<std::string>());
+
+            /* Assign any additional state */
+            p.setExtra(t[1].cast<int>());
+        });
+
+An instance can now be pickled as follows:
+
+.. code-block:: python
+
+    try:
+        import cPickle as pickle  # Use cPickle on Python 2.7
+    except ImportError:
+        import pickle
+
+    p = Pickleable("test_value")
+    p.setExtra(15)
+    data = pickle.dumps(p, 2)
+
+Note that only the cPickle module is supported on Python 2.7. The second
+argument to ``dumps`` is also crucial: it selects the pickle protocol version
+2, since the older version 1 is not supported. Newer versions are also fine—for
+instance, specify ``-1`` to always use the latest available version. Beware:
+failure to follow these instructions will cause important pybind11 memory
+allocation routines to be skipped during unpickling, which will likely lead to
+memory corruption and/or segmentation faults.
+
+.. seealso::
+
+    The file :file:`tests/test_pickling.cpp` contains a complete example
+    that demonstrates how to pickle and unpickle types using pybind11 in more
+    detail.
+
+.. [#f3] http://docs.python.org/3/library/pickle.html#pickling-class-instances
+
+Multiple Inheritance
+====================
+
+pybind11 can create bindings for types that derive from multiple base types
+(aka. *multiple inheritance*). To do so, specify all bases in the template
+arguments of the ``class_`` declaration:
+
+.. code-block:: cpp
+
+    py::class_<MyType, BaseType1, BaseType2, BaseType3>(m, "MyType")
+       ...
+
+The base types can be specified in arbitrary order, and they can even be
+interspersed with alias types and holder types (discussed earlier in this
+document)---pybind11 will automatically find out which is which. The only
+requirement is that the first template argument is the type to be declared.
+
+There are two caveats regarding the implementation of this feature:
+
+1. When only one base type is specified for a C++ type that actually has
+   multiple bases, pybind11 will assume that it does not participate in
+   multiple inheritance, which can lead to undefined behavior. In such cases,
+   add the tag ``multiple_inheritance``:
+
+    .. code-block:: cpp
+
+        py::class_<MyType, BaseType2>(m, "MyType", py::multiple_inheritance());
+
+   The tag is redundant and does not need to be specified when multiple base
+   types are listed.
+
+2. As was previously discussed in the section on :ref:`overriding_virtuals`, it
+   is easy to create Python types that derive from C++ classes. It is even
+   possible to make use of multiple inheritance to declare a Python class which
+   has e.g. a C++ and a Python class as bases. However, any attempt to create a
+   type that has *two or more* C++ classes in its hierarchy of base types will
+   fail with a fatal error message: ``TypeError: multiple bases have instance
+   lay-out conflict``. Core Python types that are implemented in C (e.g.
+   ``dict``, ``list``, ``Exception``, etc.) also fall under this combination
+   and cannot be combined with C++ types bound using pybind11 via multiple
+   inheritance.
diff --git a/ext/pybind11/docs/advanced/exceptions.rst b/ext/pybind11/docs/advanced/exceptions.rst
new file mode 100644
index 000000000..348337916
--- /dev/null
+++ b/ext/pybind11/docs/advanced/exceptions.rst
@@ -0,0 +1,142 @@
+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 explicity (re-)thrown to delegate it to the other,
+    previously-declared existing exception translators.
diff --git a/ext/pybind11/docs/advanced/functions.rst b/ext/pybind11/docs/advanced/functions.rst
new file mode 100644
index 000000000..f291e8222
--- /dev/null
+++ b/ext/pybind11/docs/advanced/functions.rst
@@ -0,0 +1,311 @@
+Functions
+#########
+
+Before proceeding with this section, make sure that you are already familiar
+with the basics of binding functions and classes, as explained in :doc:`/basics`
+and :doc:`/classes`. The following guide is applicable to both free and member
+functions, i.e. *methods* in Python.
+
+Return value policies
+=====================
+
+Python and C++ use fundamentally different ways of managing the memory and
+lifetime of objects managed by them. This can lead to issues when creating
+bindings for functions that return a non-trivial type. Just by looking at the
+type information, it is not clear whether Python should take charge of the
+returned value and eventually free its resources, or if this is handled on the
+C++ side. For this reason, pybind11 provides a several `return value policy`
+annotations that can be passed to the :func:`module::def` and
+:func:`class_::def` functions. The default policy is
+:enum:`return_value_policy::automatic`.
+
+Return value policies are tricky, and it's very important to get them right.
+Just to illustrate what can go wrong, consider the following simple example:
+
+.. code-block:: cpp
+
+    /* Function declaration */ 
+    Data *get_data() { return _data; /* (pointer to a static data structure) */ }
+    ...
+
+    /* Binding code */ 
+    m.def("get_data", &get_data); // <-- KABOOM, will cause crash when called from Python
+
+What's going on here? When ``get_data()`` is called from Python, the return
+value (a native C++ type) must be wrapped to turn it into a usable Python type.
+In this case, the default return value policy (:enum:`return_value_policy::automatic`)
+causes pybind11 to assume ownership of the static ``_data`` instance.
+
+When Python's garbage collector eventually deletes the Python
+wrapper, pybind11 will also attempt to delete the C++ instance (via ``operator
+delete()``) due to the implied ownership. At this point, the entire application
+will come crashing down, though errors could also be more subtle and involve
+silent data corruption.
+
+In the above example, the policy :enum:`return_value_policy::reference` should have
+been specified so that the global data instance is only *referenced* without any
+implied transfer of ownership, i.e.: 
+
+.. code-block:: cpp
+
+    m.def("get_data", &get_data, return_value_policy::reference);
+
+On the other hand, this is not the right policy for many other situations,
+where ignoring ownership could lead to resource leaks.
+As a developer using pybind11, it's important to be familiar with the different
+return value policies, including which situation calls for which one of them.
+The following table provides an overview of available policies:
+
+.. tabularcolumns:: |p{0.5\textwidth}|p{0.45\textwidth}|
+
++--------------------------------------------------+----------------------------------------------------------------------------+
+| Return value policy                              | Description                                                                |
++==================================================+============================================================================+
+| :enum:`return_value_policy::take_ownership`      | Reference an existing object (i.e. do not create a new copy) and take      |
+|                                                  | ownership. Python will call the destructor and delete operator when the    |
+|                                                  | object's reference count reaches zero. Undefined behavior ensues when the  |
+|                                                  | C++ side does the same, or when the data was not dynamically allocated.    |
++--------------------------------------------------+----------------------------------------------------------------------------+
+| :enum:`return_value_policy::copy`                | Create a new copy of the returned object, which will be owned by Python.   |
+|                                                  | This policy is comparably safe because the lifetimes of the two instances  |
+|                                                  | are decoupled.                                                             |
++--------------------------------------------------+----------------------------------------------------------------------------+
+| :enum:`return_value_policy::move`                | Use ``std::move`` to move the return value contents into a new instance    |
+|                                                  | that will be owned by Python. This policy is comparably safe because the   |
+|                                                  | lifetimes of the two instances (move source and destination) are decoupled.|
++--------------------------------------------------+----------------------------------------------------------------------------+
+| :enum:`return_value_policy::reference`           | Reference an existing object, but do not take ownership. The C++ side is   |
+|                                                  | responsible for managing the object's lifetime and deallocating it when    |
+|                                                  | it is no longer used. Warning: undefined behavior will ensue when the C++  |
+|                                                  | side deletes an object that is still referenced and used by Python.        |
++--------------------------------------------------+----------------------------------------------------------------------------+
+| :enum:`return_value_policy::reference_internal`  | Indicates that the lifetime of the return value is tied to the lifetime    |
+|                                                  | of a parent object, namely the implicit ``this``, or ``self`` argument of  |
+|                                                  | the called method or property. Internally, this policy works just like     |
+|                                                  | :enum:`return_value_policy::reference` but additionally applies a          |
+|                                                  | ``keep_alive<0, 1>`` *call policy* (described in the next section) that    |
+|                                                  | prevents the parent object from being garbage collected as long as the     |
+|                                                  | return value is referenced by Python. This is the default policy for       |
+|                                                  | property getters created via ``def_property``, ``def_readwrite``, etc.     |
++--------------------------------------------------+----------------------------------------------------------------------------+
+| :enum:`return_value_policy::automatic`           | This is the default return value policy, which falls back to the policy    |
+|                                                  | :enum:`return_value_policy::take_ownership` when the return value is a     |
+|                                                  | pointer. Otherwise, it uses :enum:`return_value::move` or                  |
+|                                                  | :enum:`return_value::copy` for rvalue and lvalue references, respectively. |
+|                                                  | See above for a description of what all of these different policies do.    |
++--------------------------------------------------+----------------------------------------------------------------------------+
+| :enum:`return_value_policy::automatic_reference` | As above, but use policy :enum:`return_value_policy::reference` when the   |
+|                                                  | return value is a pointer. This is the default conversion policy for       |
+|                                                  | function arguments when calling Python functions manually from C++ code    |
+|                                                  | (i.e. via handle::operator()). You probably won't need to use this.        |
++--------------------------------------------------+----------------------------------------------------------------------------+
+
+Return value policies can also be applied to properties:
+
+.. code-block:: cpp
+
+    class_<MyClass>(m, "MyClass")
+        .def_property("data", &MyClass::getData, &MyClass::setData,
+                      py::return_value_policy::copy);
+
+Technically, the code above applies the policy to both the getter and the
+setter function, however, the setter doesn't really care about *return*
+value policies which makes this a convenient terse syntax. Alternatively,
+targeted arguments can be passed through the :class:`cpp_function` constructor:
+
+.. code-block:: cpp
+
+    class_<MyClass>(m, "MyClass")
+        .def_property("data"
+            py::cpp_function(&MyClass::getData, py::return_value_policy::copy),
+            py::cpp_function(&MyClass::setData)
+        );
+
+.. warning::
+
+    Code with invalid return value policies might access unitialized memory or
+    free data structures multiple times, which can lead to hard-to-debug
+    non-determinism and segmentation faults, hence it is worth spending the
+    time to understand all the different options in the table above.
+
+.. note::
+
+    One important aspect of the above policies is that they only apply to
+    instances which pybind11 has *not* seen before, in which case the policy
+    clarifies essential questions about the return value's lifetime and
+    ownership.  When pybind11 knows the instance already (as identified by its
+    type and address in memory), it will return the existing Python object
+    wrapper rather than creating a new copy.
+
+.. note::
+
+    The next section on :ref:`call_policies` discusses *call policies* that can be
+    specified *in addition* to a return value policy from the list above. Call
+    policies indicate reference relationships that can involve both return values
+    and parameters of functions.
+
+.. note::
+
+   As an alternative to elaborate call policies and lifetime management logic,
+   consider using smart pointers (see the section on :ref:`smart_pointers` for
+   details). Smart pointers can tell whether an object is still referenced from
+   C++ or Python, which generally eliminates the kinds of inconsistencies that
+   can lead to crashes or undefined behavior. For functions returning smart
+   pointers, it is not necessary to specify a return value policy.
+
+.. _call_policies:
+
+Additional call policies
+========================
+
+In addition to the above return value policies, further `call policies` can be
+specified to indicate dependencies between parameters. There is currently just
+one policy named ``keep_alive<Nurse, Patient>``, which indicates that the
+argument with index ``Patient`` should be kept alive at least until the
+argument with index ``Nurse`` is freed by the garbage collector. Argument
+indices start at one, while zero refers to the return value. For methods, index
+``1`` refers to the implicit ``this`` pointer, while regular arguments begin at
+index ``2``. Arbitrarily many call policies can be specified. When a ``Nurse``
+with value ``None`` is detected at runtime, the call policy does nothing.
+
+This feature internally relies on the ability to create a *weak reference* to
+the nurse object, which is permitted by all classes exposed via pybind11. When
+the nurse object does not support weak references, an exception will be thrown.
+
+Consider the following example: here, the binding code for a list append
+operation ties the lifetime of the newly added element to the underlying
+container:
+
+.. code-block:: cpp
+
+    py::class_<List>(m, "List")
+        .def("append", &List::append, py::keep_alive<1, 2>());
+
+.. note::
+
+    ``keep_alive`` is analogous to the ``with_custodian_and_ward`` (if Nurse,
+    Patient != 0) and ``with_custodian_and_ward_postcall`` (if Nurse/Patient ==
+    0) policies from Boost.Python.
+
+.. seealso::
+
+    The file :file:`tests/test_keep_alive.cpp` contains a complete example
+    that demonstrates using :class:`keep_alive` in more detail.
+
+.. _python_objects_as_args:
+
+Python objects as arguments
+===========================
+
+pybind11 exposes all major Python types using thin C++ wrapper classes. These
+wrapper classes can also be used as parameters of functions in bindings, which
+makes it possible to directly work with native Python types on the C++ side.
+For instance, the following statement iterates over a Python ``dict``:
+
+.. code-block:: cpp
+
+    void print_dict(py::dict dict) {
+        /* Easily interact with Python types */
+        for (auto item : dict)
+            std::cout << "key=" << item.first << ", "
+                      << "value=" << item.second << std::endl;
+    }
+
+It can be exported:
+
+.. code-block:: cpp
+
+    m.def("print_dict", &print_dict);
+
+And used in Python as usual:
+
+.. code-block:: pycon
+
+    >>> print_dict({'foo': 123, 'bar': 'hello'})
+    key=foo, value=123
+    key=bar, value=hello
+
+For more information on using Python objects in C++, see :doc:`/advanced/pycpp/index`.
+
+Accepting \*args and \*\*kwargs
+===============================
+
+Python provides a useful mechanism to define functions that accept arbitrary
+numbers of arguments and keyword arguments:
+
+.. code-block:: python
+
+   def generic(*args, **kwargs):
+       ...  # do something with args and kwargs
+
+Such functions can also be created using pybind11:
+
+.. code-block:: cpp
+
+   void generic(py::args args, py::kwargs kwargs) {
+       /// .. do something with args
+       if (kwargs)
+           /// .. do something with kwargs
+   }
+
+   /// Binding code
+   m.def("generic", &generic);
+
+The class ``py::args`` derives from ``py::tuple`` and ``py::kwargs`` derives
+from ``py::dict``. Note that the ``kwargs`` argument is invalid if no keyword
+arguments were actually provided. Please refer to the other examples for
+details on how to iterate over these, and on how to cast their entries into
+C++ objects. A demonstration is also available in
+``tests/test_kwargs_and_defaults.cpp``.
+
+.. warning::
+
+   Unlike Python, pybind11 does not allow combining normal parameters with the
+   ``args`` / ``kwargs`` special parameters.
+
+Default arguments revisited
+===========================
+
+The section on :ref:`default_args` previously discussed basic usage of default
+arguments using pybind11. One noteworthy aspect of their implementation is that
+default arguments are converted to Python objects right at declaration time.
+Consider the following example:
+
+.. code-block:: cpp
+
+    py::class_<MyClass>("MyClass")
+        .def("myFunction", py::arg("arg") = SomeType(123));
+
+In this case, pybind11 must already be set up to deal with values of the type
+``SomeType`` (via a prior instantiation of ``py::class_<SomeType>``), or an
+exception will be thrown.
+
+Another aspect worth highlighting is that the "preview" of the default argument
+in the function signature is generated using the object's ``__repr__`` method.
+If not available, the signature may not be very helpful, e.g.:
+
+.. code-block:: pycon
+
+    FUNCTIONS
+    ...
+    |  myFunction(...)
+    |      Signature : (MyClass, arg : SomeType = <SomeType object at 0x101b7b080>) -> NoneType
+    ...
+
+The first way of addressing this is by defining ``SomeType.__repr__``.
+Alternatively, it is possible to specify the human-readable preview of the
+default argument manually using the ``arg_v`` notation:
+
+.. code-block:: cpp
+
+    py::class_<MyClass>("MyClass")
+        .def("myFunction", py::arg_v("arg", SomeType(123), "SomeType(123)"));
+
+Sometimes it may be necessary to pass a null pointer value as a default
+argument. In this case, remember to cast it to the underlying type in question,
+like so:
+
+.. code-block:: cpp
+
+    py::class_<MyClass>("MyClass")
+        .def("myFunction", py::arg("arg") = (SomeType *) nullptr);
diff --git a/ext/pybind11/docs/advanced/misc.rst b/ext/pybind11/docs/advanced/misc.rst
new file mode 100644
index 000000000..c13df7bf8
--- /dev/null
+++ b/ext/pybind11/docs/advanced/misc.rst
@@ -0,0 +1,229 @@
+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.
+
+
+Global Interpreter Lock (GIL)
+=============================
+
+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: 8,9,33,34
+
+    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_PLUGIN(example) {
+        py::module m("example", "pybind11 example plugin");
+
+        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);
+        });
+
+        return m.ptr();
+    }
+
+
+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 compiling code which has its default symbol visibility set to
+*hidden* (e.g. via the command line flag ``-fvisibility=hidden`` on GCC/Clang) can interfere with the
+ability to access types defined in another extension module. Workarounds
+include changing the global symbol visibility (not recommended, because it will
+lead unnecessarily large binaries) or manually exporting types that are
+accessed by multiple extension modules:
+
+.. code-block:: cpp
+
+    #ifdef _WIN32
+    #  define EXPORT_TYPE __declspec(dllexport)
+    #else
+    #  define EXPORT_TYPE __attribute__ ((visibility("default")))
+    #endif
+
+    class EXPORT_TYPE 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
+
+
+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_PLUGIN(example) {
+        py::module m("example", "pybind11 example plugin");
+
+        py::options options;
+        options.disable_function_signatures();
+        
+        m.def("add", [](int a, int b) { return a + b; }, "A function which adds two numbers");
+        
+        return m.ptr();
+    }
+
+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
diff --git a/ext/pybind11/docs/advanced/pycpp/index.rst b/ext/pybind11/docs/advanced/pycpp/index.rst
new file mode 100644
index 000000000..6885bdcff
--- /dev/null
+++ b/ext/pybind11/docs/advanced/pycpp/index.rst
@@ -0,0 +1,13 @@
+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
diff --git a/ext/pybind11/docs/advanced/pycpp/numpy.rst b/ext/pybind11/docs/advanced/pycpp/numpy.rst
new file mode 100644
index 000000000..8b46b7c83
--- /dev/null
+++ b/ext/pybind11/docs/advanced/pycpp/numpy.rst
@@ -0,0 +1,299 @@
+.. _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")
+       .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) }
+            );
+        });
+
+The snippet above binds a lambda function, which can create ``py::buffer_info``
+description records on demand describing a given matrix. The contents of
+``py::buffer_info`` mirror the Python buffer protocol specification.
+
+.. code-block:: cpp
+
+    struct buffer_info {
+        void *ptr;
+        size_t itemsize;
+        std::string format;
+        int ndim;
+        std::vector<size_t> shape;
+        std::vector<size_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")
+        .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] / sizeof(Scalar),
+                info.strides[rowMajor ? 1 : 0] / sizeof(Scalar));
+
+            auto map = Eigen::Map<Matrix, 0, Strides>(
+                static_cat<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 */
+            /* Python struct-style format descriptor */
+            py::format_descriptor<Scalar>::format(),
+            /* Number of dimensions */
+            2,
+            /* Buffer dimensions */
+            { (size_t) m.rows(),
+              (size_t) m.cols() },
+            /* Strides (in bytes) for each index */
+            { sizeof(Scalar) * (rowMajor ? m.cols() : 1),
+              sizeof(Scalar) * (rowMajor ? 1 : m.rows()) }
+        );
+     })
+
+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 which expects the type followed by field names:
+
+.. code-block:: cpp
+
+    struct A {
+        int x;
+        double y;
+    };
+
+    struct B {
+        int z;
+        A a;
+    };
+
+    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 */
+
+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)
+
+Sometimes we might want to explicitly exclude an argument from the vectorization
+because it makes little sense to wrap it in a NumPy array. For instance,
+suppose the function signature was
+
+.. code-block:: cpp
+
+    double my_func(int x, float y, my_custom_type *z);
+
+This can be done with a stateful Lambda closure:
+
+.. code-block:: cpp
+
+    // Vectorize a lambda function with a capture object (e.g. to exclude some arguments from the vectorization)
+    m.def("vectorized_func",
+        [](py::array_t<int> x, py::array_t<float> y, my_custom_type *z) {
+            auto stateful_closure = [z](int x, float y) { return my_func(x, y, z); };
+            return py::vectorize(stateful_closure)(x, y);
+        }
+    );
+
+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) {
+        auto 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);
+
+        auto 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_PLUGIN(test) {
+        py::module m("test");
+        m.def("add_arrays", &add_arrays, "Add two NumPy arrays");
+        return m.ptr();
+    }
+
+.. seealso::
+
+    The file :file:`tests/test_numpy_vectorize.cpp` contains a complete
+    example that demonstrates using :func:`vectorize` in more detail.
diff --git a/ext/pybind11/docs/advanced/pycpp/object.rst b/ext/pybind11/docs/advanced/pycpp/object.rst
new file mode 100644
index 000000000..8fc165d16
--- /dev/null
+++ b/ext/pybind11/docs/advanced/pycpp/object.rst
@@ -0,0 +1,96 @@
+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`.
+
+Calling Python functions
+========================
+
+It is also possible to call python functions via ``operator()``.
+
+.. code-block:: cpp
+
+    py::function f = <...>;
+    py::object result_py = f(1234, "hello", some_instance);
+    MyClass &result = result_py.cast<MyClass>();
+
+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 pybind11::literals; // to bring in the `_a` literal
+    f(1234, "say"_a="hello", "to"_a=some_instance); // keyword call in C++
+
+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_python_types.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/
diff --git a/ext/pybind11/docs/advanced/pycpp/utilities.rst b/ext/pybind11/docs/advanced/pycpp/utilities.rst
new file mode 100644
index 000000000..ba0dbef88
--- /dev/null
+++ b/ext/pybind11/docs/advanced/pycpp/utilities.rst
@@ -0,0 +1,57 @@
+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 <-
+
+Evaluating Python expressions from strings and files
+====================================================
+
+pybind11 provides the :func:`eval` and :func:`eval_file` functions to evaluate
+Python expressions and statements. The following example illustrates how they
+can be used.
+
+Both functions accept a template parameter that describes how the argument
+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``).
+
+.. 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::eval<py::eval_statements>(
+        "print('Hello')\n"
+        "print('world!');",
+        scope);
+
+    // Evaluate the statements in an separate Python file on disk
+    py::eval_file("script.py", scope);
diff --git a/ext/pybind11/docs/advanced/smart_ptrs.rst b/ext/pybind11/docs/advanced/smart_ptrs.rst
new file mode 100644
index 000000000..23072b6bf
--- /dev/null
+++ b/ext/pybind11/docs/advanced/smart_ptrs.rst
@@ -0,0 +1,143 @@
+Smart pointers
+##############
+
+std::unique_ptr
+===============
+
+Given a class ``Example`` with Python bindings, it's possible to return
+instances wrapped in C++11 unique pointers, like so
+
+.. code-block:: cpp
+
+    std::unique_ptr<Example> create_example() { return std::unique_ptr<Example>(new Example()); }
+
+.. code-block:: cpp
+
+    m.def("create_example", &create_example);
+
+In other words, there is nothing special that needs to be done. While returning
+unique pointers in this way is allowed, it is *illegal* to use them as function
+arguments. For instance, the following function signature cannot be processed
+by pybind11.
+
+.. code-block:: cpp
+
+    void do_something_with_example(std::unique_ptr<Example> ex) { ... }
+
+The above signature would imply that Python needs to give up ownership of an
+object that is passed to this function, which is generally not possible (for
+instance, the object might be referenced elsewhere).
+
+std::shared_ptr
+===============
+
+The binding generator for classes, :class:`class_`, can be passed a template
+type that denotes a special *holder* type that is used to manage references to
+the object.  If no such holder type template argument is given, the default for
+a type named ``Type`` is ``std::unique_ptr<Type>``, which means that the object
+is deallocated when Python's reference count goes to zero.
+
+It is possible to switch to other types of reference counting wrappers or smart
+pointers, which is useful in codebases that rely on them. For instance, the
+following snippet causes ``std::shared_ptr`` to be used instead.
+
+.. code-block:: cpp
+
+    py::class_<Example, std::shared_ptr<Example> /* <- holder type */> obj(m, "Example");
+
+Note that any particular class can only be associated with a single holder type.
+
+One potential stumbling block when using holder types is that they need to be
+applied consistently. Can you guess what's broken about the following binding
+code?
+
+.. code-block:: cpp
+
+    class Child { };
+
+    class Parent {
+    public:
+       Parent() : child(std::make_shared<Child>()) { }
+       Child *get_child() { return child.get(); }  /* Hint: ** DON'T DO THIS ** */
+    private:
+        std::shared_ptr<Child> child;
+    };
+
+    PYBIND11_PLUGIN(example) {
+        py::module m("example");
+
+        py::class_<Child, std::shared_ptr<Child>>(m, "Child");
+
+        py::class_<Parent, std::shared_ptr<Parent>>(m, "Parent")
+           .def(py::init<>())
+           .def("get_child", &Parent::get_child);
+
+        return m.ptr();
+    }
+
+The following Python code will cause undefined behavior (and likely a
+segmentation fault).
+
+.. code-block:: python
+
+   from example import Parent
+   print(Parent().get_child())
+
+The problem is that ``Parent::get_child()`` returns a pointer to an instance of
+``Child``, but the fact that this instance is already managed by
+``std::shared_ptr<...>`` is lost when passing raw pointers. In this case,
+pybind11 will create a second independent ``std::shared_ptr<...>`` that also
+claims ownership of the pointer. In the end, the object will be freed **twice**
+since these shared pointers have no way of knowing about each other.
+
+There are two ways to resolve this issue:
+
+1. For types that are managed by a smart pointer class, never use raw pointers
+   in function arguments or return values. In other words: always consistently
+   wrap pointers into their designated holder types (such as
+   ``std::shared_ptr<...>``). In this case, the signature of ``get_child()``
+   should be modified as follows:
+
+.. code-block:: cpp
+
+    std::shared_ptr<Child> get_child() { return child; }
+
+2. Adjust the definition of ``Child`` by specifying
+   ``std::enable_shared_from_this<T>`` (see cppreference_ for details) as a
+   base class. This adds a small bit of information to ``Child`` that allows
+   pybind11 to realize that there is already an existing
+   ``std::shared_ptr<...>`` and communicate with it. In this case, the
+   declaration of ``Child`` should look as follows:
+
+.. _cppreference: http://en.cppreference.com/w/cpp/memory/enable_shared_from_this
+
+.. code-block:: cpp
+
+    class Child : public std::enable_shared_from_this<Child> { };
+
+.. _smart_pointers:
+
+Custom smart pointers
+=====================
+
+pybind11 supports ``std::unique_ptr`` and ``std::shared_ptr`` right out of the
+box. For any other custom smart pointer, transparent conversions can be enabled
+using a macro invocation similar to the following. It must be declared at the
+level before any binding code:
+
+.. code-block:: cpp
+
+    PYBIND11_DECLARE_HOLDER_TYPE(T, SmartPtr<T>);
+
+The first argument of :func:`PYBIND11_DECLARE_HOLDER_TYPE` should be a
+placeholder name that is used as a template parameter of the second argument.
+Thus, feel free to use any identifier, but use it consistently on both sides;
+also, don't use the name of a type that already exists in your codebase.
+
+Please take a look at the :ref:`macro_notes` before using this feature.
+
+.. seealso::
+
+    The file :file:`tests/test_smart_ptr.cpp` contains a complete example
+    that demonstrates how to work with custom reference-counting holder types
+    in more detail.
diff --git a/ext/pybind11/docs/basics.rst b/ext/pybind11/docs/basics.rst
new file mode 100644
index 000000000..c1dd47412
--- /dev/null
+++ b/ext/pybind11/docs/basics.rst
@@ -0,0 +1,289 @@
+.. _basics:
+
+First steps
+###########
+
+This sections demonstrates the basic features of pybind11. Before getting
+started, make sure that development environment is set up to compile the
+included set of test cases.
+
+
+Compiling the test cases
+========================
+
+Linux/MacOS
+-----------
+
+On Linux  you'll need to install the **python-dev** or **python3-dev** packages as
+well as **cmake**. On Mac OS, the included python version works out of the box,
+but **cmake** must still be installed.
+
+After installing the prerequisites, run
+
+.. code-block:: bash
+
+   mkdir build
+   cd build
+   cmake ..
+   make pytest -j 4
+
+The last line will both compile and run the tests.
+
+Windows
+-------
+
+On Windows, only **Visual Studio 2015** and newer are supported since pybind11 relies
+on various C++11 language features that break older versions of Visual Studio.
+
+To compile and run the tests:
+
+.. code-block:: batch
+
+   mkdir build
+   cd build
+   cmake ..
+   cmake --build . --config Release --target pytest
+
+This will create a Visual Studio project, compile and run the target, all from the
+command line.
+
+.. Note::
+
+    If all tests fail, make sure that the Python binary and the testcases are compiled
+    for the same processor type and bitness (i.e. either **i386** or **x86_64**). You
+    can specify **x86_64** as the target architecture for the generated Visual Studio
+    project using ``cmake -A x64 ..``.
+
+.. seealso::
+
+    Advanced users who are already familiar with Boost.Python may want to skip
+    the tutorial and look at the test cases in the :file:`tests` directory,
+    which exercise all features of pybind11.
+
+Header and namespace conventions
+================================
+
+For brevity, all code examples assume that the following two lines are present:
+
+.. code-block:: cpp
+
+    #include <pybind11/pybind11.h>
+
+    namespace py = pybind11;
+
+Some features may require additional headers, but those will be specified as needed.
+
+Creating bindings for a simple function
+=======================================
+
+Let's start by creating Python bindings for an extremely simple function, which
+adds two numbers and returns their result:
+
+.. code-block:: cpp
+
+    int add(int i, int j) {
+        return i + j;
+    }
+
+For simplicity [#f1]_, we'll put both this function and the binding code into
+a file named :file:`example.cpp` with the following contents:
+
+.. code-block:: cpp
+
+    #include <pybind11/pybind11.h>
+
+    int add(int i, int j) {
+        return i + j;
+    }
+
+    namespace py = pybind11;
+
+    PYBIND11_PLUGIN(example) {
+        py::module m("example", "pybind11 example plugin");
+
+        m.def("add", &add, "A function which adds two numbers");
+
+        return m.ptr();
+    }
+
+.. [#f1] In practice, implementation and binding code will generally be located
+         in separate files.
+
+The :func:`PYBIND11_PLUGIN` macro creates a function that will be called when an
+``import`` statement is issued from within Python. The next line creates a
+module named ``example`` (with the supplied docstring). The method
+:func:`module::def` generates binding code that exposes the
+``add()`` function to Python. The last line returns the internal Python object
+associated with ``m`` to the Python interpreter.
+
+.. note::
+
+    Notice how little code was needed to expose our function to Python: all
+    details regarding the function's parameters and return value were
+    automatically inferred using template metaprogramming. This overall
+    approach and the used syntax are borrowed from Boost.Python, though the
+    underlying implementation is very different.
+
+pybind11 is a header-only-library, hence it is not necessary to link against
+any special libraries (other than Python itself). On Windows, use the CMake
+build file discussed in section :ref:`cmake`. On Linux and Mac OS, the above
+example can be compiled using the following command
+
+.. code-block:: bash
+
+    $ c++ -O3 -shared -std=c++11 -I <path-to-pybind11>/include `python-config --cflags --ldflags` example.cpp -o example.so
+
+In general, it is advisable to include several additional build parameters
+that can considerably reduce the size of the created binary. Refer to section
+:ref:`cmake` for a detailed example of a suitable cross-platform CMake-based
+build system.
+
+Assuming that the created file :file:`example.so` (:file:`example.pyd` on Windows)
+is located in the current directory, the following interactive Python session
+shows how to load and execute the example.
+
+.. code-block:: pycon
+
+    $ python
+    Python 2.7.10 (default, Aug 22 2015, 20:33:39)
+    [GCC 4.2.1 Compatible Apple LLVM 7.0.0 (clang-700.0.59.1)] on darwin
+    Type "help", "copyright", "credits" or "license" for more information.
+    >>> import example
+    >>> example.add(1, 2)
+    3L
+    >>>
+
+.. _keyword_args:
+
+Keyword arguments
+=================
+
+With a simple modification code, it is possible to inform Python about the
+names of the arguments ("i" and "j" in this case).
+
+.. code-block:: cpp
+
+    m.def("add", &add, "A function which adds two numbers",
+          py::arg("i"), py::arg("j"));
+
+:class:`arg` is one of several special tag classes which can be used to pass
+metadata into :func:`module::def`. With this modified binding code, we can now
+call the function using keyword arguments, which is a more readable alternative
+particularly for functions taking many parameters:
+
+.. code-block:: pycon
+
+    >>> import example
+    >>> example.add(i=1, j=2)
+    3L
+
+The keyword names also appear in the function signatures within the documentation.
+
+.. code-block:: pycon
+
+    >>> help(example)
+
+    ....
+
+    FUNCTIONS
+        add(...)
+            Signature : (i: int, j: int) -> int
+
+            A function which adds two numbers
+
+A shorter notation for named arguments is also available:
+
+.. code-block:: cpp
+
+    // regular notation
+    m.def("add1", &add, py::arg("i"), py::arg("j"));
+    // shorthand
+    using namespace pybind11::literals;
+    m.def("add2", &add, "i"_a, "j"_a);
+
+The :var:`_a` suffix forms a C++11 literal which is equivalent to :class:`arg`.
+Note that the literal operator must first be made visible with the directive
+``using namespace pybind11::literals``. This does not bring in anything else
+from the ``pybind11`` namespace except for literals.
+
+.. _default_args:
+
+Default arguments
+=================
+
+Suppose now that the function to be bound has default arguments, e.g.:
+
+.. code-block:: cpp
+
+    int add(int i = 1, int j = 2) {
+        return i + j;
+    }
+
+Unfortunately, pybind11 cannot automatically extract these parameters, since they
+are not part of the function's type information. However, they are simple to specify
+using an extension of :class:`arg`:
+
+.. code-block:: cpp
+
+    m.def("add", &add, "A function which adds two numbers",
+          py::arg("i") = 1, py::arg("j") = 2);
+
+The default values also appear within the documentation.
+
+.. code-block:: pycon
+
+    >>> help(example)
+
+    ....
+
+    FUNCTIONS
+        add(...)
+            Signature : (i: int = 1, j: int = 2) -> int
+
+            A function which adds two numbers
+
+The shorthand notation is also available for default arguments:
+
+.. code-block:: cpp
+
+    // regular notation
+    m.def("add1", &add, py::arg("i") = 1, py::arg("j") = 2);
+    // shorthand
+    m.def("add2", &add, "i"_a=1, "j"_a=2);
+
+Exporting variables
+===================
+
+To expose a value from C++, use the ``attr`` function to register it in a
+module as shown below. Built-in types and general objects (more on that later)
+are automatically converted when assigned as attributes, and can be explicitly
+converted using the function ``py::cast``.
+
+.. code-block:: cpp
+
+    PYBIND11_PLUGIN(example) {
+        py::module m("example", "pybind11 example plugin");
+        m.attr("the_answer") = 42;
+        py::object world = py::cast("World");
+        m.attr("what") = world;
+        return m.ptr();
+    }
+
+These are then accessible from Python:
+
+.. code-block:: pycon
+
+    >>> import example
+    >>> example.the_answer
+    42
+    >>> example.what
+    'World'
+
+.. _supported_types:
+
+Supported data types
+====================
+
+A large number of data types are supported out of the box and can be used
+seamlessly as functions arguments, return values or with ``py::cast`` in general.
+For a full overview, see the :doc:`advanced/cast/index` section.
diff --git a/ext/pybind11/docs/benchmark.py b/ext/pybind11/docs/benchmark.py
new file mode 100644
index 000000000..6f02e92ff
--- /dev/null
+++ b/ext/pybind11/docs/benchmark.py
@@ -0,0 +1,90 @@
+import random
+import os
+import time
+import datetime as dt
+
+nfns = 4  # Functions per class
+nargs = 4  # Arguments per function
+
+
+def generate_dummy_code_pybind11(nclasses=10):
+    decl = ""
+    bindings = ""
+
+    for cl in range(nclasses):
+        decl += "class cl%03i;\n" % cl
+    decl += '\n'
+
+    for cl in range(nclasses):
+        decl += "class cl%03i {\n" % cl
+        decl += "public:\n"
+        bindings += '    py::class_<cl%03i>(m, "cl%03i")\n' % (cl, cl)
+        for fn in range(nfns):
+            ret = random.randint(0, nclasses - 1)
+            params  = [random.randint(0, nclasses - 1) for i in range(nargs)]
+            decl += "    cl%03i *fn_%03i(" % (ret, fn)
+            decl += ", ".join("cl%03i *" % p for p in params)
+            decl += ");\n"
+            bindings += '        .def("fn_%03i", &cl%03i::fn_%03i)\n' % \
+                (fn, cl, fn)
+        decl += "};\n\n"
+        bindings += '        ;\n'
+
+    result = "#include <pybind11/pybind11.h>\n\n"
+    result += "namespace py = pybind11;\n\n"
+    result += decl + '\n'
+    result += "PYBIND11_PLUGIN(example) {\n"
+    result += "    py::module m(\"example\");"
+    result += bindings
+    result += "    return m.ptr();"
+    result += "}"
+    return result
+
+
+def generate_dummy_code_boost(nclasses=10):
+    decl = ""
+    bindings = ""
+
+    for cl in range(nclasses):
+        decl += "class cl%03i;\n" % cl
+    decl += '\n'
+
+    for cl in range(nclasses):
+        decl += "class cl%03i {\n" % cl
+        decl += "public:\n"
+        bindings += '    py::class_<cl%03i>("cl%03i")\n' % (cl, cl)
+        for fn in range(nfns):
+            ret = random.randint(0, nclasses - 1)
+            params  = [random.randint(0, nclasses - 1) for i in range(nargs)]
+            decl += "    cl%03i *fn_%03i(" % (ret, fn)
+            decl += ", ".join("cl%03i *" % p for p in params)
+            decl += ");\n"
+            bindings += '        .def("fn_%03i", &cl%03i::fn_%03i, py::return_value_policy<py::manage_new_object>())\n' % \
+                (fn, cl, fn)
+        decl += "};\n\n"
+        bindings += '        ;\n'
+
+    result = "#include <boost/python.hpp>\n\n"
+    result += "namespace py = boost::python;\n\n"
+    result += decl + '\n'
+    result += "BOOST_PYTHON_MODULE(example) {\n"
+    result += bindings
+    result += "}"
+    return result
+
+
+for codegen in [generate_dummy_code_pybind11, generate_dummy_code_boost]:
+    print ("{")
+    for i in range(0, 10):
+        nclasses = 2 ** i
+        with open("test.cpp", "w") as f:
+            f.write(codegen(nclasses))
+        n1 = dt.datetime.now()
+        os.system("g++ -Os -shared -rdynamic -undefined dynamic_lookup "
+            "-fvisibility=hidden -std=c++14 test.cpp -I include "
+            "-I /System/Library/Frameworks/Python.framework/Headers -o test.so")
+        n2 = dt.datetime.now()
+        elapsed = (n2 - n1).total_seconds()
+        size = os.stat('test.so').st_size
+        print("   {%i, %f, %i}," % (nclasses * nfns, elapsed, size))
+    print ("}")
diff --git a/ext/pybind11/docs/benchmark.rst b/ext/pybind11/docs/benchmark.rst
new file mode 100644
index 000000000..8babaa319
--- /dev/null
+++ b/ext/pybind11/docs/benchmark.rst
@@ -0,0 +1,99 @@
+Benchmark
+=========
+
+The following is the result of a synthetic benchmark comparing both compilation
+time and module size of pybind11 against Boost.Python. A detailed report about a
+Boost.Python to pybind11 conversion of a real project is available here: [#f1]_.
+
+.. [#f1] http://graylab.jhu.edu/RosettaCon2016/PyRosetta-4.pdf
+
+Setup
+-----
+
+A python script (see the ``docs/benchmark.py`` file) was used to generate a set
+of files with dummy classes whose count increases for each successive benchmark
+(between 1 and 2048 classes in powers of two). Each class has four methods with
+a randomly generated signature with a return value and four arguments. (There
+was no particular reason for this setup other than the desire to generate many
+unique function signatures whose count could be controlled in a simple way.)
+
+Here is an example of the binding code for one class:
+
+.. code-block:: cpp
+
+    ...
+    class cl034 {
+    public:
+        cl279 *fn_000(cl084 *, cl057 *, cl065 *, cl042 *);
+        cl025 *fn_001(cl098 *, cl262 *, cl414 *, cl121 *);
+        cl085 *fn_002(cl445 *, cl297 *, cl145 *, cl421 *);
+        cl470 *fn_003(cl200 *, cl323 *, cl332 *, cl492 *);
+    };
+    ...
+
+    PYBIND11_PLUGIN(example) {
+        py::module m("example");
+        ...
+        py::class_<cl034>(m, "cl034")
+            .def("fn_000", &cl034::fn_000)
+            .def("fn_001", &cl034::fn_001)
+            .def("fn_002", &cl034::fn_002)
+            .def("fn_003", &cl034::fn_003)
+        ...
+        return m.ptr();
+    }
+
+The Boost.Python version looks almost identical except that a return value
+policy had to be specified as an argument to ``def()``. For both libraries,
+compilation was done with
+
+.. code-block:: bash
+
+    Apple LLVM version 7.0.2 (clang-700.1.81)
+
+and the following compilation flags
+
+.. code-block:: bash
+
+    g++ -Os -shared -rdynamic -undefined dynamic_lookup -fvisibility=hidden -std=c++14
+
+Compilation time
+----------------
+
+The following log-log plot shows how the compilation time grows for an
+increasing number of class and function declarations. pybind11 includes many
+fewer headers, which initially leads to shorter compilation times, but the
+performance is ultimately fairly similar (pybind11 is 19.8 seconds faster for
+the largest largest file with 2048 classes and a total of 8192 methods -- a
+modest **1.2x** speedup relative to Boost.Python, which required 116.35
+seconds).
+
+.. only:: not latex
+
+    .. image:: pybind11_vs_boost_python1.svg
+
+.. only:: latex
+
+    .. image:: pybind11_vs_boost_python1.png
+
+Module size
+-----------
+
+Differences between the two libraries become much more pronounced when
+considering the file size of the generated Python plugin: for the largest file,
+the binary generated by Boost.Python required 16.8 MiB, which was **2.17
+times** / **9.1 megabytes** larger than the output generated by pybind11. For
+very small inputs, Boost.Python has an edge in the plot below -- however, note
+that it stores many definitions in an external library, whose size was not
+included here, hence the comparison is slightly shifted in Boost.Python's
+favor.
+
+.. only:: not latex
+
+    .. image:: pybind11_vs_boost_python2.svg
+
+.. only:: latex
+
+    .. image:: pybind11_vs_boost_python2.png
+
+
diff --git a/ext/pybind11/docs/changelog.rst b/ext/pybind11/docs/changelog.rst
new file mode 100644
index 000000000..15cf8d959
--- /dev/null
+++ b/ext/pybind11/docs/changelog.rst
@@ -0,0 +1,222 @@
+.. _changelog:
+
+Changelog
+#########
+
+Starting with version 1.8, pybind11 releases use a
+[semantic versioning](http://semver.org) policy.
+
+Breaking changes queued for v2.0.0 (Not yet released)
+-----------------------------------------------------
+* Redesigned virtual call mechanism and user-facing syntax (see
+  https://github.com/pybind/pybind11/commit/86d825f3302701d81414ddd3d38bcd09433076bc)
+
+* Remove ``handle.call()`` method
+
+1.9.0 (Not yet released)
+------------------------
+* Queued changes: map indexing suite, documentation for indexing suites.
+* Mapping a stateless C++ function to Python and back is now "for free" (i.e. no call overheads)
+* Support for translation of arbitrary C++ exceptions to Python counterparts
+* Added ``eval`` and ``eval_file`` functions for evaluating expressions and
+  statements from a string or file
+* eigen.h type converter fixed for non-contiguous arrays (e.g. slices)
+* Print more informative error messages when ``make_tuple()`` or ``cast()`` fail
+* ``std::enable_shared_from_this<>`` now also works for ``const`` values
+* A return value policy can now be passed to ``handle::operator()``
+* ``make_iterator()`` improvements for better compatibility with various types
+  (now uses prefix increment operator); it now also accepts iterators with
+  different begin/end types as long as they are equality comparable.
+* ``arg()`` now accepts a wider range of argument types for default values
+* Added ``py::repr()`` function which is equivalent to Python's builtin ``repr()``.
+* Added support for registering structured dtypes via ``PYBIND11_NUMPY_DTYPE()`` macro.
+* Added ``PYBIND11_STR_TYPE`` macro which maps to the ``builtins.str`` type.
+* Added a simplified ``buffer_info`` constructor for 1-dimensional buffers.
+* Format descriptor strings should now be accessed via ``format_descriptor::format()``
+  (for compatibility purposes, the old syntax ``format_descriptor::value`` will still
+  work for non-structured data types).
+* Added a class wrapping NumPy array descriptors: ``dtype``.
+* Added buffer/NumPy support for ``char[N]`` and ``std::array<char, N>`` types.
+* ``array`` gained new constructors accepting dtype objects.
+* Added constructors for ``array`` and ``array_t`` explicitly accepting shape and
+  strides; if strides are not provided, they are deduced assuming C-contiguity.
+  Also added simplified constructors for 1-dimensional case.
+* Added constructors for ``str`` from ``bytes`` and for ``bytes`` from ``str``.
+  This will do the UTF-8 decoding/encoding as required.
+* Added constructors for ``str`` and ``bytes`` from zero-terminated char pointers,
+  and from char pointers and length.
+* Added ``memoryview`` wrapper type which is constructible from ``buffer_info``.
+* New syntax to call a Python function from C++ using keyword arguments and unpacking,
+  e.g. ``foo(1, 2, "z"_a=3)`` or ``bar(1, *args, "z"_a=3, **kwargs)``.
+* Added ``py::print()`` function which replicates Python's API and writes to Python's
+  ``sys.stdout`` by default (as opposed to C's ``stdout`` like ``std::cout``).
+* Added ``py::dict`` keyword constructor:``auto d = dict("number"_a=42, "name"_a="World");``
+* Added ``py::str::format()`` method and ``_s`` literal:
+  ``py::str s = "1 + 2 = {}"_s.format(3);``
+* Attribute and item accessors now have a more complete interface which makes it possible
+  to chain attributes ``obj.attr("a")[key].attr("b").attr("method")(1, 2, 3)```.
+* Added built-in support for ``std::shared_ptr`` holder type. There is no more need
+  to do it manually via ``PYBIND11_DECLARE_HOLDER_TYPE(T, std::shared_ptr<T>)``.
+* Default return values policy changes: non-static properties now use ``reference_internal``
+  and static properties use ``reference`` (previous default was ``automatic``, i.e. ``copy``).
+* Support for ``std::experimental::optional<T>`` and ``std::optional<T>`` (C++17).
+* Various minor improvements of library internals (no user-visible changes)
+
+1.8.1 (July 12, 2016)
+----------------------
+* Fixed a rare but potentially very severe issue when the garbage collector ran
+  during pybind11 type creation.
+
+1.8.0 (June 14, 2016)
+----------------------
+* Redesigned CMake build system which exports a convenient
+  ``pybind11_add_module`` function to parent projects.
+* ``std::vector<>`` type bindings analogous to Boost.Python's ``indexing_suite``
+* Transparent conversion of sparse and dense Eigen matrices and vectors (``eigen.h``)
+* Added an ``ExtraFlags`` template argument to the NumPy ``array_t<>`` wrapper
+  to disable an enforced cast that may lose precision, e.g. to create overloads
+  for different precisions and complex vs real-valued matrices.
+* Prevent implicit conversion of floating point values to integral types in
+  function arguments
+* Fixed incorrect default return value policy for functions returning a shared
+  pointer
+* Don't allow registering a type via ``class_`` twice
+* Don't allow casting a ``None`` value into a C++ lvalue reference
+* Fixed a crash in ``enum_::operator==`` that was triggered by the ``help()`` command
+* Improved detection of whether or not custom C++ types can be copy/move-constructed
+* Extended ``str`` type to also work with ``bytes`` instances
+* Added a ``"name"_a`` user defined string literal that is equivalent to ``py::arg("name")``.
+* When specifying function arguments via ``py::arg``, the test that verifies
+  the number of arguments now runs at compile time.
+* Added ``[[noreturn]]`` attribute to ``pybind11_fail()`` to quench some
+  compiler warnings
+* List function arguments in exception text when the dispatch code cannot find
+  a matching overload
+* Added ``PYBIND11_OVERLOAD_NAME`` and ``PYBIND11_OVERLOAD_PURE_NAME`` macros which
+  can be used to override virtual methods whose name differs in C++ and Python
+  (e.g. ``__call__`` and ``operator()``)
+* Various minor ``iterator`` and ``make_iterator()`` improvements
+* Transparently support ``__bool__`` on Python 2.x and Python 3.x
+* Fixed issue with destructor of unpickled object not being called
+* Minor CMake build system improvements on Windows
+* New ``pybind11::args`` and ``pybind11::kwargs`` types to create functions which
+  take an arbitrary number of arguments and keyword arguments
+* New syntax to call a Python function from C++ using ``*args`` and ``*kwargs``
+* The functions ``def_property_*`` now correctly process docstring arguments (these
+  formerly caused a segmentation fault)
+* Many ``mkdoc.py`` improvements (enumerations, template arguments, ``DOC()``
+  macro accepts more arguments)
+* Cygwin support
+* Documentation improvements (pickling support, ``keep_alive``, macro usage)
+
+1.7 (April 30, 2016)
+----------------------
+* Added a new ``move`` return value policy that triggers C++11 move semantics.
+  The automatic return value policy falls back to this case whenever a rvalue
+  reference is encountered
+* Significantly more general GIL state routines that are used instead of
+  Python's troublesome ``PyGILState_Ensure`` and ``PyGILState_Release`` API
+* Redesign of opaque types that drastically simplifies their usage
+* Extended ability to pass values of type ``[const] void *``
+* ``keep_alive`` fix: don't fail when there is no patient
+* ``functional.h``: acquire the GIL before calling a Python function
+* Added Python RAII type wrappers ``none`` and ``iterable``
+* Added ``*args`` and ``*kwargs`` pass-through parameters to
+  ``pybind11.get_include()`` function
+* Iterator improvements and fixes
+* Documentation on return value policies and opaque types improved
+
+1.6 (April 30, 2016)
+----------------------
+* Skipped due to upload to PyPI gone wrong and inability to recover
+  (https://github.com/pypa/packaging-problems/issues/74)
+
+1.5 (April 21, 2016)
+----------------------
+* For polymorphic types, use RTTI to try to return the closest type registered with pybind11
+* Pickling support for serializing and unserializing C++ instances to a byte stream in Python
+* Added a convenience routine ``make_iterator()`` which turns a range indicated
+  by a pair of C++ iterators into a iterable Python object
+* Added ``len()`` and a variadic ``make_tuple()`` function
+* Addressed a rare issue that could confuse the current virtual function
+  dispatcher and another that could lead to crashes in multi-threaded
+  applications
+* Added a ``get_include()`` function to the Python module that returns the path
+  of the directory containing the installed pybind11 header files
+* Documentation improvements: import issues, symbol visibility, pickling, limitations
+* Added casting support for ``std::reference_wrapper<>``
+
+1.4 (April 7, 2016)
+--------------------------
+* Transparent type conversion for ``std::wstring`` and ``wchar_t``
+* Allow passing ``nullptr``-valued strings
+* Transparent passing of ``void *`` pointers using capsules
+* Transparent support for returning values wrapped in ``std::unique_ptr<>``
+* Improved docstring generation for compatibility with Sphinx
+* Nicer debug error message when default parameter construction fails
+* Support for "opaque" types that bypass the transparent conversion layer for STL containers
+* Redesigned type casting interface to avoid ambiguities that could occasionally cause compiler errors
+* Redesigned property implementation; fixes crashes due to an unfortunate default return value policy
+* Anaconda package generation support
+
+1.3 (March 8, 2016)
+--------------------------
+
+* Added support for the Intel C++ compiler (v15+)
+* Added support for the STL unordered set/map data structures
+* Added support for the STL linked list data structure
+* NumPy-style broadcasting support in ``pybind11::vectorize``
+* pybind11 now displays more verbose error messages when ``arg::operator=()`` fails
+* pybind11 internal data structures now live in a version-dependent namespace to avoid ABI issues
+* Many, many bugfixes involving corner cases and advanced usage
+
+1.2 (February 7, 2016)
+--------------------------
+
+* Optional: efficient generation of function signatures at compile time using C++14
+* Switched to a simpler and more general way of dealing with function default
+  arguments. Unused keyword arguments in function calls are now detected and
+  cause errors as expected
+* New ``keep_alive`` call policy analogous to Boost.Python's ``with_custodian_and_ward``
+* New ``pybind11::base<>`` attribute to indicate a subclass relationship
+* Improved interface for RAII type wrappers in ``pytypes.h``
+* Use RAII type wrappers consistently within pybind11 itself. This
+  fixes various potential refcount leaks when exceptions occur
+* Added new ``bytes`` RAII type wrapper (maps to ``string`` in Python 2.7)
+* Made handle and related RAII classes const correct, using them more
+  consistently everywhere now
+* Got rid of the ugly ``__pybind11__`` attributes on the Python side---they are
+  now stored in a C++ hash table that is not visible in Python
+* Fixed refcount leaks involving NumPy arrays and bound functions
+* Vastly improved handling of shared/smart pointers
+* Removed an unnecessary copy operation in ``pybind11::vectorize``
+* Fixed naming clashes when both pybind11 and NumPy headers are included
+* Added conversions for additional exception types
+* Documentation improvements (using multiple extension modules, smart pointers,
+  other minor clarifications)
+* unified infrastructure for parsing variadic arguments in ``class_`` and cpp_function
+* Fixed license text (was: ZLIB, should have been: 3-clause BSD)
+* Python 3.2 compatibility
+* Fixed remaining issues when accessing types in another plugin module
+* Added enum comparison and casting methods
+* Improved SFINAE-based detection of whether types are copy-constructible
+* Eliminated many warnings about unused variables and the use of ``offsetof()``
+* Support for ``std::array<>`` conversions
+
+1.1 (December 7, 2015)
+--------------------------
+
+* Documentation improvements (GIL, wrapping functions, casting, fixed many typos)
+* Generalized conversion of integer types
+* Improved support for casting function objects
+* Improved support for ``std::shared_ptr<>`` conversions
+* Initial support for ``std::set<>`` conversions
+* Fixed type resolution issue for types defined in a separate plugin module
+* Cmake build system improvements
+* Factored out generic functionality to non-templated code (smaller code size)
+* Added a code size / compile time benchmark vs Boost.Python
+* Added an appveyor CI script
+
+1.0 (October 15, 2015)
+------------------------
+* Initial release
diff --git a/ext/pybind11/docs/classes.rst b/ext/pybind11/docs/classes.rst
new file mode 100644
index 000000000..872977684
--- /dev/null
+++ b/ext/pybind11/docs/classes.rst
@@ -0,0 +1,439 @@
+.. _classes:
+
+Object-oriented code
+####################
+
+Creating bindings for a custom type
+===================================
+
+Let's now look at a more complex example where we'll create bindings for a
+custom C++ data structure named ``Pet``. Its definition is given below:
+
+.. code-block:: cpp
+
+    struct Pet {
+        Pet(const std::string &name) : name(name) { }
+        void setName(const std::string &name_) { name = name_; }
+        const std::string &getName() const { return name; }
+
+        std::string name;
+    };
+
+The binding code for ``Pet`` looks as follows:
+
+.. code-block:: cpp
+
+    #include <pybind11/pybind11.h>
+
+    namespace py = pybind11;
+
+    PYBIND11_PLUGIN(example) {
+        py::module m("example", "pybind11 example plugin");
+
+        py::class_<Pet>(m, "Pet")
+            .def(py::init<const std::string &>())
+            .def("setName", &Pet::setName)
+            .def("getName", &Pet::getName);
+
+        return m.ptr();
+    }
+
+:class:`class_` creates bindings for a C++ `class` or `struct`-style data
+structure. :func:`init` is a convenience function that takes the types of a
+constructor's parameters as template arguments and wraps the corresponding
+constructor (see the :ref:`custom_constructors` section for details). An
+interactive Python session demonstrating this example is shown below:
+
+.. code-block:: pycon
+
+    % python
+    >>> import example
+    >>> p = example.Pet('Molly')
+    >>> print(p)
+    <example.Pet object at 0x10cd98060>
+    >>> p.getName()
+    u'Molly'
+    >>> p.setName('Charly')
+    >>> p.getName()
+    u'Charly'
+
+.. seealso::
+
+    Static member functions can be bound in the same way using
+    :func:`class_::def_static`.
+
+Keyword and default arguments
+=============================
+It is possible to specify keyword and default arguments using the syntax
+discussed in the previous chapter. Refer to the sections :ref:`keyword_args`
+and :ref:`default_args` for details.
+
+Binding lambda functions
+========================
+
+Note how ``print(p)`` produced a rather useless summary of our data structure in the example above:
+
+.. code-block:: pycon
+
+    >>> print(p)
+    <example.Pet object at 0x10cd98060>
+
+To address this, we could bind an utility function that returns a human-readable
+summary to the special method slot named ``__repr__``. Unfortunately, there is no
+suitable functionality in the ``Pet`` data structure, and it would be nice if
+we did not have to change it. This can easily be accomplished by binding a
+Lambda function instead:
+
+.. code-block:: cpp
+
+        py::class_<Pet>(m, "Pet")
+            .def(py::init<const std::string &>())
+            .def("setName", &Pet::setName)
+            .def("getName", &Pet::getName)
+            .def("__repr__",
+                [](const Pet &a) {
+                    return "<example.Pet named '" + a.name + "'>";
+                }
+            );
+
+Both stateless [#f1]_ and stateful lambda closures are supported by pybind11.
+With the above change, the same Python code now produces the following output:
+
+.. code-block:: pycon
+
+    >>> print(p)
+    <example.Pet named 'Molly'>
+
+.. [#f1] Stateless closures are those with an empty pair of brackets ``[]`` as the capture object.
+
+.. _properties:
+
+Instance and static fields
+==========================
+
+We can also directly expose the ``name`` field using the
+:func:`class_::def_readwrite` method. A similar :func:`class_::def_readonly`
+method also exists for ``const`` fields.
+
+.. code-block:: cpp
+
+        py::class_<Pet>(m, "Pet")
+            .def(py::init<const std::string &>())
+            .def_readwrite("name", &Pet::name)
+            // ... remainder ...
+
+This makes it possible to write
+
+.. code-block:: pycon
+
+    >>> p = example.Pet('Molly')
+    >>> p.name
+    u'Molly'
+    >>> p.name = 'Charly'
+    >>> p.name
+    u'Charly'
+
+Now suppose that ``Pet::name`` was a private internal variable
+that can only be accessed via setters and getters.
+
+.. code-block:: cpp
+
+    class Pet {
+    public:
+        Pet(const std::string &name) : name(name) { }
+        void setName(const std::string &name_) { name = name_; }
+        const std::string &getName() const { return name; }
+    private:
+        std::string name;
+    };
+
+In this case, the method :func:`class_::def_property`
+(:func:`class_::def_property_readonly` for read-only data) can be used to
+provide a field-like interface within Python that will transparently call
+the setter and getter functions:
+
+.. code-block:: cpp
+
+        py::class_<Pet>(m, "Pet")
+            .def(py::init<const std::string &>())
+            .def_property("name", &Pet::getName, &Pet::setName)
+            // ... remainder ...
+
+.. seealso::
+
+    Similar functions :func:`class_::def_readwrite_static`,
+    :func:`class_::def_readonly_static` :func:`class_::def_property_static`,
+    and :func:`class_::def_property_readonly_static` are provided for binding
+    static variables and properties. Please also see the section on
+    :ref:`static_properties` in the advanced part of the documentation.
+
+Dynamic attributes
+==================
+
+Native Python classes can pick up new attributes dynamically:
+
+.. code-block:: pycon
+
+    >>> class Pet:
+    ...     name = 'Molly'
+    ...
+    >>> p = Pet()
+    >>> p.name = 'Charly'  # overwrite existing
+    >>> p.age = 2  # dynamically add a new attribute
+
+By default, classes exported from C++ do not support this and the only writable
+attributes are the ones explicitly defined using :func:`class_::def_readwrite`
+or :func:`class_::def_property`.
+
+.. code-block:: cpp
+
+    py::class_<Pet>(m, "Pet")
+        .def(py::init<>())
+        .def_readwrite("name", &Pet::name);
+
+Trying to set any other attribute results in an error:
+
+.. code-block:: pycon
+
+    >>> p = example.Pet()
+    >>> p.name = 'Charly'  # OK, attribute defined in C++
+    >>> p.age = 2  # fail
+    AttributeError: 'Pet' object has no attribute 'age'
+
+To enable dynamic attributes for C++ classes, the :class:`py::dynamic_attr` tag
+must be added to the :class:`py::class_` constructor:
+
+.. code-block:: cpp
+
+    py::class_<Pet>(m, "Pet", py::dynamic_attr())
+        .def(py::init<>())
+        .def_readwrite("name", &Pet::name);
+
+Now everything works as expected:
+
+.. code-block:: pycon
+
+    >>> p = example.Pet()
+    >>> p.name = 'Charly'  # OK, overwrite value in C++
+    >>> p.age = 2  # OK, dynamically add a new attribute
+    >>> p.__dict__  # just like a native Python class
+    {'age': 2}
+
+Note that there is a small runtime cost for a class with dynamic attributes.
+Not only because of the addition of a ``__dict__``, but also because of more
+expensive garbage collection tracking which must be activated to resolve
+possible circular references. Native Python classes incur this same cost by
+default, so this is not anything to worry about. By default, pybind11 classes
+are more efficient than native Python classes. Enabling dynamic attributes
+just brings them on par.
+
+.. _inheritance:
+
+Inheritance
+===========
+
+Suppose now that the example consists of two data structures with an
+inheritance relationship:
+
+.. code-block:: cpp
+
+    struct Pet {
+        Pet(const std::string &name) : name(name) { }
+        std::string name;
+    };
+
+    struct Dog : Pet {
+        Dog(const std::string &name) : Pet(name) { }
+        std::string bark() const { return "woof!"; }
+    };
+
+There are two different ways of indicating a hierarchical relationship to
+pybind11: the first specifies the C++ base class as an extra template
+parameter of the :class:`class_`:
+
+.. code-block:: cpp
+
+    py::class_<Pet>(m, "Pet")
+       .def(py::init<const std::string &>())
+       .def_readwrite("name", &Pet::name);
+
+    // Method 1: template parameter:
+    py::class_<Dog, Pet /* <- specify C++ parent type */>(m, "Dog")
+        .def(py::init<const std::string &>())
+        .def("bark", &Dog::bark);
+
+Alternatively, we can also assign a name to the previously bound ``Pet``
+:class:`class_` object and reference it when binding the ``Dog`` class:
+
+.. code-block:: cpp
+
+    py::class_<Pet> pet(m, "Pet");
+    pet.def(py::init<const std::string &>())
+       .def_readwrite("name", &Pet::name);
+
+    // Method 2: pass parent class_ object:
+    py::class_<Dog>(m, "Dog", pet /* <- specify Python parent type */)
+        .def(py::init<const std::string &>())
+        .def("bark", &Dog::bark);
+
+Functionality-wise, both approaches are equivalent. Afterwards, instances will
+expose fields and methods of both types:
+
+.. code-block:: pycon
+
+    >>> p = example.Dog('Molly')
+    >>> p.name
+    u'Molly'
+    >>> p.bark()
+    u'woof!'
+
+Overloaded methods
+==================
+
+Sometimes there are several overloaded C++ methods with the same name taking
+different kinds of input arguments:
+
+.. code-block:: cpp
+
+    struct Pet {
+        Pet(const std::string &name, int age) : name(name), age(age) { }
+
+        void set(int age) { age = age; }
+        void set(const std::string &name) { name = name; }
+
+        std::string name;
+        int age;
+    };
+
+Attempting to bind ``Pet::set`` will cause an error since the compiler does not
+know which method the user intended to select. We can disambiguate by casting
+them to function pointers. Binding multiple functions to the same Python name
+automatically creates a chain of function overloads that will be tried in
+sequence.
+
+.. code-block:: cpp
+
+    py::class_<Pet>(m, "Pet")
+       .def(py::init<const std::string &, int>())
+       .def("set", (void (Pet::*)(int)) &Pet::set, "Set the pet's age")
+       .def("set", (void (Pet::*)(const std::string &)) &Pet::set, "Set the pet's name");
+
+The overload signatures are also visible in the method's docstring:
+
+.. code-block:: pycon
+
+    >>> help(example.Pet)
+
+    class Pet(__builtin__.object)
+     |  Methods defined here:
+     |
+     |  __init__(...)
+     |      Signature : (Pet, str, int) -> NoneType
+     |
+     |  set(...)
+     |      1. Signature : (Pet, int) -> NoneType
+     |
+     |      Set the pet's age
+     |
+     |      2. Signature : (Pet, str) -> NoneType
+     |
+     |      Set the pet's name
+
+If you have a C++14 compatible compiler [#cpp14]_, you can use an alternative
+syntax to cast the overloaded function:
+
+.. code-block:: cpp
+
+    py::class_<Pet>(m, "Pet")
+        .def("set", py::overload_cast<int>(&Pet::set), "Set the pet's age")
+        .def("set", py::overload_cast<const std::string &>(&Pet::set), "Set the pet's name");
+
+Here, ``py::overload_cast`` only requires the parameter types to be specified.
+The return type and class are deduced. This avoids the additional noise of
+``void (Pet::*)()`` as seen in the raw cast. If a function is overloaded based
+on constness, the ``py::const_`` tag should be used:
+
+.. code-block:: cpp
+
+    struct Widget {
+        int foo(int x, float y);
+        int foo(int x, float y) const;
+    };
+
+    py::class_<Widget>(m, "Widget")
+       .def("foo_mutable", py::overload_cast<int, float>(&Widget::foo))
+       .def("foo_const",   py::overload_cast<int, float>(&Widget::foo, py::const_));
+
+
+.. [#cpp14] A compiler which supports the ``-std=c++14`` flag
+            or Visual Studio 2015 Update 2 and newer.
+
+.. note::
+
+    To define multiple overloaded constructors, simply declare one after the
+    other using the ``.def(py::init<...>())`` syntax. The existing machinery
+    for specifying keyword and default arguments also works.
+
+Enumerations and internal types
+===============================
+
+Let's now suppose that the example class contains an internal enumeration type,
+e.g.:
+
+.. code-block:: cpp
+
+    struct Pet {
+        enum Kind {
+            Dog = 0,
+            Cat
+        };
+
+        Pet(const std::string &name, Kind type) : name(name), type(type) { }
+
+        std::string name;
+        Kind type;
+    };
+
+The binding code for this example looks as follows:
+
+.. code-block:: cpp
+
+    py::class_<Pet> pet(m, "Pet");
+
+    pet.def(py::init<const std::string &, Pet::Kind>())
+        .def_readwrite("name", &Pet::name)
+        .def_readwrite("type", &Pet::type);
+
+    py::enum_<Pet::Kind>(pet, "Kind")
+        .value("Dog", Pet::Kind::Dog)
+        .value("Cat", Pet::Kind::Cat)
+        .export_values();
+
+To ensure that the ``Kind`` type is created within the scope of ``Pet``, the
+``pet`` :class:`class_` instance must be supplied to the :class:`enum_`.
+constructor. The :func:`enum_::export_values` function exports the enum entries
+into the parent scope, which should be skipped for newer C++11-style strongly
+typed enums.
+
+.. code-block:: pycon
+
+    >>> p = Pet('Lucy', Pet.Cat)
+    >>> p.type
+    Kind.Cat
+    >>> int(p.type)
+    1L
+
+
+.. note::
+
+    When the special tag ``py::arithmetic()`` is specified to the ``enum_``
+    constructor, pybind11 creates an enumeration that also supports rudimentary
+    arithmetic and bit-level operations like comparisons, and, or, xor, negation,
+    etc.
+
+    .. code-block:: cpp
+
+        py::enum_<Pet::Kind>(pet, "Kind", py::arithmetic())
+           ...
+
+    By default, these are omitted to conserve space.
diff --git a/ext/pybind11/docs/compiling.rst b/ext/pybind11/docs/compiling.rst
new file mode 100644
index 000000000..35768fe45
--- /dev/null
+++ b/ext/pybind11/docs/compiling.rst
@@ -0,0 +1,116 @@
+Build systems
+#############
+
+Building with setuptools
+========================
+
+For projects on PyPI, building with setuptools is the way to go. Sylvain Corlay
+has kindly provided an example project which shows how to set up everything,
+including automatic generation of documentation using Sphinx. Please refer to
+the [python_example]_ repository.
+
+.. [python_example] https://github.com/pybind/python_example
+
+Building with cppimport
+========================
+
+ cppimport is a small Python import hook that determines whether there is a C++
+ source file whose name matches the requested module. If there is, the file is
+ compiled as a Python extension using pybind11 and placed in the same folder as
+ the C++ source file. Python is then able to find the module and load it.
+
+.. [cppimport] https://github.com/tbenthompson/cppimport
+
+.. _cmake:
+
+Building with CMake
+===================
+
+For C++ codebases that have an existing CMake-based build system, a Python
+extension module can be created with just a few lines of code:
+
+.. code-block:: cmake
+
+    cmake_minimum_required(VERSION 2.8.12)
+    project(example)
+
+    add_subdirectory(pybind11)
+    pybind11_add_module(example example.cpp)
+
+This assumes that the pybind11 repository is located in a subdirectory named
+:file:`pybind11` and that the code is located in a file named :file:`example.cpp`.
+The CMake command ``add_subdirectory`` will import a function with the signature
+``pybind11_add_module(<name> source1 [source2 ...])``. It will take care of all
+the details needed to build a Python extension module on any platform.
+
+The target Python version can be selected by setting the ``PYBIND11_PYTHON_VERSION``
+variable before adding the pybind11 subdirectory. Alternatively, an exact Python
+installation can be specified by setting ``PYTHON_EXECUTABLE``.
+
+A working sample project, including a way to invoke CMake from :file:`setup.py` for
+PyPI integration, can be found in the [cmake_example]_  repository.
+
+.. [cmake_example] https://github.com/pybind/cmake_example
+
+For CMake-based projects that don't include the pybind11
+repository internally, an external installation can be detected
+through `find_package(pybind11 ... CONFIG ...)`. See the `Config file
+<https://github.com/pybind/pybind11/blob/master/tools/pybind11Config.cmake.in>`_
+docstring for details of relevant CMake variables.
+
+Once detected, and after setting any variables to guide Python and
+C++ standard detection, the aforementioned ``pybind11_add_module``
+wrapper to ``add_library`` can be employed as described above (after
+``include(pybind11Tools)``). This procedure is available when using CMake
+>= 2.8.12. A working example can be found at [test_installed_module]_ .
+
+.. code-block:: cmake
+
+    cmake_minimum_required(VERSION 2.8.12)
+    project(example)
+
+    find_package(pybind11 REQUIRED)
+    pybind11_add_module(example example.cpp)
+
+.. [test_installed_module] https://github.com/pybind/pybind11/blob/master/tests/test_installed_module/CMakeLists.txt
+
+When using a version of CMake greater than 3.0, pybind11 can
+additionally be used as a special *interface library* following the
+call to ``find_package``. CMake variables to guide Python and C++
+standard detection should be set *before* ``find_package``. When
+``find_package`` returns, the target ``pybind11::pybind11`` is
+available with pybind11 headers, Python headers and libraries as
+needed, and C++ compile definitions attached. This target is suitable
+for linking to an independently constructed (through ``add_library``,
+not ``pybind11_add_module``) target in the consuming project. A working
+example can be found at [test_installed_target]_ .
+
+.. code-block:: cmake
+
+    cmake_minimum_required(VERSION 3.0)
+    project(example)
+
+    add_library(example MODULE main.cpp)
+
+    find_package(pybind11 REQUIRED)
+    target_link_libraries(example PRIVATE pybind11::pybind11)
+    set_target_properties(example PROPERTIES PREFIX "${PYTHON_MODULE_PREFIX}"
+                                             SUFFIX "${PYTHON_MODULE_EXTENSION}")
+
+.. warning::
+
+    Since pybind11 is a metatemplate library, it is crucial that certain
+    compiler flags are provided to ensure high quality code generation. In
+    contrast to the ``pybind11_add_module()`` command, the CMake interface
+    library only provides the *minimal* set of parameters to ensure that the
+    code using pybind11 compiles, but it does **not** pass these extra compiler
+    flags (i.e. this is up to you).
+
+    These include Link Time Optimization (``-flto`` on GCC/Clang/ICPC, ``/GL``
+    and ``/LTCG`` on Visual Studio). Default-hidden symbols on GCC/Clang/ICPC
+    (``-fvisibility=hidden``) and .OBJ files with many sections on Visual Studio
+    (``/bigobj``). The :ref:`FAQ <faq:symhidden>` contains an
+    explanation on why these are needed.
+
+.. [test_installed_target] https://github.com/pybind/pybind11/blob/master/tests/test_installed_target/CMakeLists.txt
+
diff --git a/ext/pybind11/docs/conf.py b/ext/pybind11/docs/conf.py
new file mode 100644
index 000000000..69f0cb337
--- /dev/null
+++ b/ext/pybind11/docs/conf.py
@@ -0,0 +1,308 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+#
+# pybind11 documentation build configuration file, created by
+# sphinx-quickstart on Sun Oct 11 19:23:48 2015.
+#
+# This file is execfile()d with the current directory set to its
+# containing dir.
+#
+# Note that not all possible configuration values are present in this
+# autogenerated file.
+#
+# All configuration values have a default; values that are commented out
+# serve to show the default.
+
+import sys
+import os
+import shlex
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#sys.path.insert(0, os.path.abspath('.'))
+
+# -- General configuration ------------------------------------------------
+
+# If your documentation needs a minimal Sphinx version, state it here.
+#needs_sphinx = '1.0'
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = []
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['.templates']
+
+# The suffix(es) of source filenames.
+# You can specify multiple suffix as a list of string:
+# source_suffix = ['.rst', '.md']
+source_suffix = '.rst'
+
+# The encoding of source files.
+#source_encoding = 'utf-8-sig'
+
+# The master toctree document.
+master_doc = 'index'
+
+# General information about the project.
+project = 'pybind11'
+copyright = '2015, Wenzel Jakob'
+author = 'Wenzel Jakob'
+
+# The version info for the project you're documenting, acts as replacement for
+# |version| and |release|, also used in various other places throughout the
+# built documents.
+#
+# The short X.Y version.
+version = '1.9'
+# The full version, including alpha/beta/rc tags.
+release = '1.9.dev0'
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#
+# This is also used if you do content translation via gettext catalogs.
+# Usually you set "language" from the command line for these cases.
+language = None
+
+# There are two options for replacing |today|: either, you set today to some
+# non-false value, then it is used:
+#today = ''
+# Else, today_fmt is used as the format for a strftime call.
+#today_fmt = '%B %d, %Y'
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+exclude_patterns = ['.build', 'release.rst']
+
+# The reST default role (used for this markup: `text`) to use for all
+# documents.
+#default_role = None
+
+# If true, '()' will be appended to :func: etc. cross-reference text.
+#add_function_parentheses = True
+
+# If true, the current module name will be prepended to all description
+# unit titles (such as .. function::).
+#add_module_names = True
+
+# If true, sectionauthor and moduleauthor directives will be shown in the
+# output. They are ignored by default.
+#show_authors = False
+
+# The name of the Pygments (syntax highlighting) style to use.
+#pygments_style = 'monokai'
+
+# A list of ignored prefixes for module index sorting.
+#modindex_common_prefix = []
+
+# If true, keep warnings as "system message" paragraphs in the built documents.
+#keep_warnings = False
+
+# If true, `todo` and `todoList` produce output, else they produce nothing.
+todo_include_todos = False
+
+
+# -- Options for HTML output ----------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+
+on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
+
+if not on_rtd:  # only import and set the theme if we're building docs locally
+    import sphinx_rtd_theme
+    html_theme = 'sphinx_rtd_theme'
+    html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
+
+    html_context = {
+        'css_files': [
+            '_static/theme_overrides.css'
+        ]
+    }
+else:
+    html_context = {
+        'css_files': [
+            '//media.readthedocs.org/css/sphinx_rtd_theme.css',            
+            '//media.readthedocs.org/css/readthedocs-doc-embed.css',    
+            '_static/theme_overrides.css'
+        ]
+    }
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
+#html_theme_options = {}
+
+# Add any paths that contain custom themes here, relative to this directory.
+#html_theme_path = []
+
+# The name for this set of Sphinx documents.  If None, it defaults to
+# "<project> v<release> documentation".
+#html_title = None
+
+# A shorter title for the navigation bar.  Default is the same as html_title.
+#html_short_title = None
+
+# The name of an image file (relative to this directory) to place at the top
+# of the sidebar.
+#html_logo = None
+
+# The name of an image file (within the static path) to use as favicon of the
+# docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
+# pixels large.
+#html_favicon = None
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+
+# Add any extra paths that contain custom files (such as robots.txt or
+# .htaccess) here, relative to this directory. These files are copied
+# directly to the root of the documentation.
+#html_extra_path = []
+
+# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
+# using the given strftime format.
+#html_last_updated_fmt = '%b %d, %Y'
+
+# If true, SmartyPants will be used to convert quotes and dashes to
+# typographically correct entities.
+#html_use_smartypants = True
+
+# Custom sidebar templates, maps document names to template names.
+#html_sidebars = {}
+
+# Additional templates that should be rendered to pages, maps page names to
+# template names.
+#html_additional_pages = {}
+
+# If false, no module index is generated.
+#html_domain_indices = True
+
+# If false, no index is generated.
+#html_use_index = True
+
+# If true, the index is split into individual pages for each letter.
+#html_split_index = False
+
+# If true, links to the reST sources are added to the pages.
+#html_show_sourcelink = True
+
+# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
+#html_show_sphinx = True
+
+# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
+#html_show_copyright = True
+
+# If true, an OpenSearch description file will be output, and all pages will
+# contain a <link> tag referring to it.  The value of this option must be the
+# base URL from which the finished HTML is served.
+#html_use_opensearch = ''
+
+# This is the file name suffix for HTML files (e.g. ".xhtml").
+#html_file_suffix = None
+
+# Language to be used for generating the HTML full-text search index.
+# Sphinx supports the following languages:
+#   'da', 'de', 'en', 'es', 'fi', 'fr', 'h', 'it', 'ja'
+#   'nl', 'no', 'pt', 'ro', 'r', 'sv', 'tr'
+#html_search_language = 'en'
+
+# A dictionary with options for the search language support, empty by default.
+# Now only 'ja' uses this config value
+#html_search_options = {'type': 'default'}
+
+# The name of a javascript file (relative to the configuration directory) that
+# implements a search results scorer. If empty, the default will be used.
+#html_search_scorer = 'scorer.js'
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = 'pybind11doc'
+
+# -- Options for LaTeX output ---------------------------------------------
+
+latex_elements = {
+# The paper size ('letterpaper' or 'a4paper').
+#'papersize': 'letterpaper',
+
+# The font size ('10pt', '11pt' or '12pt').
+#'pointsize': '10pt',
+
+# Additional stuff for the LaTeX preamble.
+'preamble': '\DeclareUnicodeCharacter{00A0}{}',
+
+# Latex figure (float) alignment
+#'figure_align': 'htbp',
+}
+
+# Grouping the document tree into LaTeX files. List of tuples
+# (source start file, target name, title,
+#  author, documentclass [howto, manual, or own class]).
+latex_documents = [
+  (master_doc, 'pybind11.tex', 'pybind11 Documentation',
+   'Wenzel Jakob', 'manual'),
+]
+
+# The name of an image file (relative to this directory) to place at the top of
+# the title page.
+# latex_logo = 'pybind11-logo.png'
+
+# For "manual" documents, if this is true, then toplevel headings are parts,
+# not chapters.
+#latex_use_parts = False
+
+# If true, show page references after internal links.
+#latex_show_pagerefs = False
+
+# If true, show URL addresses after external links.
+#latex_show_urls = False
+
+# Documents to append as an appendix to all manuals.
+#latex_appendices = []
+
+# If false, no module index is generated.
+#latex_domain_indices = True
+
+
+# -- Options for manual page output ---------------------------------------
+
+# One entry per manual page. List of tuples
+# (source start file, name, description, authors, manual section).
+man_pages = [
+    (master_doc, 'pybind11', 'pybind11 Documentation',
+     [author], 1)
+]
+
+# If true, show URL addresses after external links.
+#man_show_urls = False
+
+
+# -- Options for Texinfo output -------------------------------------------
+
+# Grouping the document tree into Texinfo files. List of tuples
+# (source start file, target name, title, author,
+#  dir menu entry, description, category)
+texinfo_documents = [
+  (master_doc, 'pybind11', 'pybind11 Documentation',
+   author, 'pybind11', 'One line description of project.',
+   'Miscellaneous'),
+]
+
+# Documents to append as an appendix to all manuals.
+#texinfo_appendices = []
+
+# If false, no module index is generated.
+#texinfo_domain_indices = True
+
+# How to display URL addresses: 'footnote', 'no', or 'inline'.
+#texinfo_show_urls = 'footnote'
+
+# If true, do not generate a @detailmenu in the "Top" node's menu.
+#texinfo_no_detailmenu = False
+
+primary_domain = 'cpp'
+highlight_language = 'cpp'
diff --git a/ext/pybind11/docs/faq.rst b/ext/pybind11/docs/faq.rst
new file mode 100644
index 000000000..34002b42d
--- /dev/null
+++ b/ext/pybind11/docs/faq.rst
@@ -0,0 +1,253 @@
+Frequently asked questions
+##########################
+
+"ImportError: dynamic module does not define init function"
+===========================================================
+
+1. Make sure that the name specified in ``pybind::module`` and
+   ``PYBIND11_PLUGIN`` is consistent and identical to the filename of the
+   extension library. The latter should not contain any extra prefixes (e.g.
+   ``test.so`` instead of ``libtest.so``).
+
+2. If the above did not fix your issue, then you are likely using an
+   incompatible version of Python (for instance, the extension library was
+   compiled against Python 2, while the interpreter is running on top of some
+   version of Python 3, or vice versa)
+
+"Symbol not found: ``__Py_ZeroStruct`` / ``_PyInstanceMethod_Type``"
+========================================================================
+
+See item 2 of the first answer.
+
+"SystemError: dynamic module not initialized properly"
+======================================================
+
+See item 2 of the first answer.
+
+The Python interpreter immediately crashes when importing my module
+===================================================================
+
+See item 2 of the first answer.
+
+CMake doesn't detect the right Python version
+=============================================
+
+The CMake-based build system will try to automatically detect the installed
+version of Python and link against that. When this fails, or when there are
+multiple versions of Python and it finds the wrong one, delete
+``CMakeCache.txt`` and then invoke CMake as follows:
+
+.. code-block:: bash
+
+    cmake -DPYTHON_EXECUTABLE:FILEPATH=<path-to-python-executable> .
+
+Limitations involving reference arguments
+=========================================
+
+In C++, it's fairly common to pass arguments using mutable references or
+mutable pointers, which allows both read and write access to the value
+supplied by the caller. This is sometimes done for efficiency reasons, or to
+realize functions that have multiple return values. Here are two very basic
+examples:
+
+.. code-block:: cpp
+
+    void increment(int &i) { i++; }
+    void increment_ptr(int *i) { (*i)++; }
+
+In Python, all arguments are passed by reference, so there is no general
+issue in binding such code from Python.
+
+However, certain basic Python types (like ``str``, ``int``, ``bool``,
+``float``, etc.) are **immutable**. This means that the following attempt
+to port the function to Python doesn't have the same effect on the value
+provided by the caller -- in fact, it does nothing at all.
+
+.. code-block:: python
+
+    def increment(i):
+        i += 1 # nope..
+
+pybind11 is also affected by such language-level conventions, which means that
+binding ``increment`` or ``increment_ptr`` will also create Python functions
+that don't modify their arguments.
+
+Although inconvenient, one workaround is to encapsulate the immutable types in
+a custom type that does allow modifications.
+
+An other alternative involves binding a small wrapper lambda function that
+returns a tuple with all output arguments (see the remainder of the
+documentation for examples on binding lambda functions). An example:
+
+.. code-block:: cpp
+
+    int foo(int &i) { i++; return 123; }
+
+and the binding code
+
+.. code-block:: cpp
+
+   m.def("foo", [](int i) { int rv = foo(i); return std::make_tuple(rv, i); });
+
+
+How can I reduce the build time?
+================================
+
+It's good practice to split binding code over multiple files, as in the
+following example:
+
+:file:`example.cpp`:
+
+.. code-block:: cpp
+
+    void init_ex1(py::module &);
+    void init_ex2(py::module &);
+    /* ... */
+
+    PYBIND11_PLUGIN(example) {
+        py::module m("example", "pybind example plugin");
+
+        init_ex1(m);
+        init_ex2(m);
+        /* ... */
+
+        return m.ptr();
+    }
+
+:file:`ex1.cpp`:
+
+.. code-block:: cpp
+
+    void init_ex1(py::module &m) {
+        m.def("add", [](int a, int b) { return a + b; });
+    }
+
+:file:`ex2.cpp`:
+
+.. code-block:: cpp
+
+    void init_ex1(py::module &m) {
+        m.def("sub", [](int a, int b) { return a - b; });
+    }
+
+:command:`python`:
+
+.. code-block:: pycon
+
+    >>> import example
+    >>> example.add(1, 2)
+    3
+    >>> example.sub(1, 1)
+    0
+
+As shown above, the various ``init_ex`` functions should be contained in
+separate files that can be compiled independently from one another, and then
+linked together into the same final shared object.  Following this approach
+will:
+
+1. reduce memory requirements per compilation unit.
+
+2. enable parallel builds (if desired).
+
+3. allow for faster incremental builds. For instance, when a single class
+   definition is changed, only a subset of the binding code will generally need
+   to be recompiled.
+
+"recursive template instantiation exceeded maximum depth of 256"
+================================================================
+
+If you receive an error about excessive recursive template evaluation, try
+specifying a larger value, e.g. ``-ftemplate-depth=1024`` on GCC/Clang. The
+culprit is generally the generation of function signatures at compile time
+using C++14 template metaprogramming.
+
+
+.. _`faq:symhidden`:
+
+How can I create smaller binaries?
+==================================
+
+To do its job, pybind11 extensively relies on a programming technique known as
+*template metaprogramming*, which is a way of performing computation at compile
+time using type information. Template metaprogamming usually instantiates code
+involving significant numbers of deeply nested types that are either completely
+removed or reduced to just a few instructions during the compiler's optimization
+phase. However, due to the nested nature of these types, the resulting symbol
+names in the compiled extension library can be extremely long. For instance,
+the included test suite contains the following symbol:
+
+.. only:: html
+
+    .. code-block:: none
+
+        _​_​Z​N​8​p​y​b​i​n​d​1​1​1​2​c​p​p​_​f​u​n​c​t​i​o​n​C​1​I​v​8​E​x​a​m​p​l​e​2​J​R​N​S​t​3​_​_​1​6​v​e​c​t​o​r​I​N​S​3​_​1​2​b​a​s​i​c​_​s​t​r​i​n​g​I​w​N​S​3​_​1​1​c​h​a​r​_​t​r​a​i​t​s​I​w​E​E​N​S​3​_​9​a​l​l​o​c​a​t​o​r​I​w​E​E​E​E​N​S​8​_​I​S​A​_​E​E​E​E​E​J​N​S​_​4​n​a​m​e​E​N​S​_​7​s​i​b​l​i​n​g​E​N​S​_​9​i​s​_​m​e​t​h​o​d​E​A​2​8​_​c​E​E​E​M​T​0​_​F​T​_​D​p​T​1​_​E​D​p​R​K​T​2​_
+
+.. only:: not html
+
+    .. code-block:: cpp
+
+        __ZN8pybind1112cpp_functionC1Iv8Example2JRNSt3__16vectorINS3_12basic_stringIwNS3_11char_traitsIwEENS3_9allocatorIwEEEENS8_ISA_EEEEEJNS_4nameENS_7siblingENS_9is_methodEA28_cEEEMT0_FT_DpT1_EDpRKT2_
+
+which is the mangled form of the following function type:
+
+.. code-block:: cpp
+
+    pybind11::cpp_function::cpp_function<void, Example2, std::__1::vector<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> >, std::__1::allocator<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> > > >&, pybind11::name, pybind11::sibling, pybind11::is_method, char [28]>(void (Example2::*)(std::__1::vector<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> >, std::__1::allocator<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> > > >&), pybind11::name const&, pybind11::sibling const&, pybind11::is_method const&, char const (&) [28])
+
+The memory needed to store just the mangled name of this function (196 bytes)
+is larger than the actual piece of code (111 bytes) it represents! On the other
+hand, it's silly to even give this function a name -- after all, it's just a
+tiny cog in a bigger piece of machinery that is not exposed to the outside
+world. So we'll generally only want to export symbols for those functions which
+are actually called from the outside.
+
+This can be achieved by specifying the parameter ``-fvisibility=hidden`` to GCC
+and Clang, which sets the default symbol visibility to *hidden*. It's best to
+do this only for release builds, since the symbol names can be helpful in
+debugging sessions. On Visual Studio, symbols are already hidden by default, so
+nothing needs to be done there. Needless to say, this has a tremendous impact
+on the final binary size of the resulting extension library.
+
+Another aspect that can require a fair bit of code are function signature
+descriptions. pybind11 automatically generates human-readable function
+signatures for docstrings, e.g.:
+
+.. code-block:: none
+
+     |  __init__(...)
+     |      __init__(*args, **kwargs)
+     |      Overloaded function.
+     |
+     |      1. __init__(example.Example1) -> NoneType
+     |
+     |      Docstring for overload #1 goes here
+     |
+     |      2. __init__(example.Example1, int) -> NoneType
+     |
+     |      Docstring for overload #2 goes here
+     |
+     |      3. __init__(example.Example1, example.Example1) -> NoneType
+     |
+     |      Docstring for overload #3 goes here
+
+
+In C++11 mode, these are generated at run time using string concatenation,
+which can amount to 10-20% of the size of the resulting binary. If you can,
+enable C++14 language features (using ``-std=c++14`` for GCC/Clang), in which
+case signatures are efficiently pre-generated at compile time. Unfortunately,
+Visual Studio's C++14 support (``constexpr``) is not good enough as of April
+2016, so it always uses the more expensive run-time approach.
+
+Working with ancient Visual Studio 2009 builds on Windows
+=========================================================
+
+The official Windows distributions of Python are compiled using truly
+ancient versions of Visual Studio that lack good C++11 support. Some users
+implicitly assume that it would be impossible to load a plugin built with
+Visual Studio 2015 into a Python distribution that was compiled using Visual
+Studio 2009. However, no such issue exists: it's perfectly legitimate to
+interface DLLs that are built with different compilers and/or C libraries.
+Common gotchas to watch out for involve not ``free()``-ing memory region
+that that were ``malloc()``-ed in another shared library, using data
+structures with incompatible ABIs, and so on. pybind11 is very careful not
+to make these types of mistakes.
diff --git a/ext/pybind11/docs/index.rst b/ext/pybind11/docs/index.rst
new file mode 100644
index 000000000..cedf65209
--- /dev/null
+++ b/ext/pybind11/docs/index.rst
@@ -0,0 +1,45 @@
+.. only: not latex
+
+    .. image:: pybind11-logo.png
+
+pybind11 --- Seamless operability between C++11 and Python
+==========================================================
+
+.. only: not latex
+
+    Contents:
+
+.. toctree::
+   :maxdepth: 1
+
+   intro
+   changelog
+
+.. toctree::
+   :caption: The Basics
+   :maxdepth: 2
+
+   basics
+   classes
+   compiling
+
+.. toctree::
+   :caption: Advanced Topics
+   :maxdepth: 2
+
+   advanced/functions
+   advanced/classes
+   advanced/exceptions
+   advanced/smart_ptrs
+   advanced/cast/index
+   advanced/pycpp/index
+   advanced/misc
+
+.. toctree::
+   :caption: Extra Information
+   :maxdepth: 1
+
+   faq
+   benchmark
+   limitations
+   reference
diff --git a/ext/pybind11/docs/intro.rst b/ext/pybind11/docs/intro.rst
new file mode 100644
index 000000000..429a01cdd
--- /dev/null
+++ b/ext/pybind11/docs/intro.rst
@@ -0,0 +1,93 @@
+.. image:: pybind11-logo.png
+
+About this project
+==================
+**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`_ library by David
+Abrahams: to minimize boilerplate code in traditional extension modules by
+inferring type information using compile-time introspection.
+
+.. _Boost.Python: http://www.boost.org/doc/libs/release/libs/python/doc/index.html
+
+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 ~2.5K lines of code and depend on
+Python (2.7 or 3.x) 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.
+
+Core features
+*************
+The following core C++ features can be mapped 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:
+
+- 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 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.
+
+.. _PyRosetta: http://graylab.jhu.edu/RosettaCon2016/PyRosetta-4.pdf
+
+Supported compilers
+*******************
+
+1. Clang/LLVM (any non-ancient version with C++11 support)
+2. GCC (any non-ancient version with C++11 support)
+3. Microsoft Visual Studio 2015 or newer
+4. Intel C++ compiler v15 or newer
diff --git a/ext/pybind11/docs/limitations.rst b/ext/pybind11/docs/limitations.rst
new file mode 100644
index 000000000..a1a4f1aff
--- /dev/null
+++ b/ext/pybind11/docs/limitations.rst
@@ -0,0 +1,20 @@
+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.
+
diff --git a/ext/pybind11/docs/pybind11-logo.png b/ext/pybind11/docs/pybind11-logo.png
new file mode 100644
index 000000000..4cbad54f7
--- /dev/null
+++ b/ext/pybind11/docs/pybind11-logo.png
diff --git a/ext/pybind11/docs/pybind11_vs_boost_python1.png b/ext/pybind11/docs/pybind11_vs_boost_python1.png
new file mode 100644
index 000000000..833231f24
--- /dev/null
+++ b/ext/pybind11/docs/pybind11_vs_boost_python1.png
diff --git a/ext/pybind11/docs/pybind11_vs_boost_python1.svg b/ext/pybind11/docs/pybind11_vs_boost_python1.svg
new file mode 100644
index 000000000..5bf950e6f
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diff --git a/ext/pybind11/docs/reference.rst b/ext/pybind11/docs/reference.rst
new file mode 100644
index 000000000..542259eba
--- /dev/null
+++ b/ext/pybind11/docs/reference.rst
@@ -0,0 +1,247 @@
+.. _reference:
+
+.. warning::
+
+    Please be advised that the reference documentation discussing pybind11
+    internals is currently incomplete. Please refer to the previous sections
+    and the pybind11 header files for the nitty gritty details.
+
+Reference
+#########
+
+Macros
+======
+
+.. function:: PYBIND11_PLUGIN(const char *name)
+
+    This macro creates the entry point that will be invoked when the Python
+    interpreter imports a plugin library. Please create a
+    :class:`module` in the function body and return the pointer to its
+    underlying Python object at the end.
+
+    .. code-block:: cpp
+
+        PYBIND11_PLUGIN(example) {
+            pybind11::module m("example", "pybind11 example plugin");
+            /// Set up bindings here
+            return m.ptr();
+        }
+
+.. _core_types:
+
+Convenience classes for arbitrary Python types
+==============================================
+
+Without reference counting
+--------------------------
+
+.. class:: handle
+
+    The :class:`handle` class is a thin wrapper around an arbitrary Python
+    object (i.e. a ``PyObject *`` in Python's C API). It does not perform any
+    automatic reference counting and merely provides a basic C++ interface to
+    various Python API functions.
+
+.. seealso::
+
+    The :class:`object` class inherits from :class:`handle` and adds automatic
+    reference counting features.
+
+.. function:: handle::handle()
+
+    The default constructor creates a handle with a ``nullptr``-valued pointer.
+
+.. function:: handle::handle(const handle&)
+
+    Copy constructor
+
+.. function:: handle::handle(PyObject *)
+
+    Creates a :class:`handle` from the given raw Python object pointer.
+
+.. function:: PyObject * handle::ptr() const
+
+    Return the ``PyObject *`` underlying a :class:`handle`.
+
+.. function:: const handle& handle::inc_ref() const
+
+    Manually increase the reference count of the Python object. Usually, it is
+    preferable to use the :class:`object` class which derives from
+    :class:`handle` and calls this function automatically. Returns a reference
+    to itself.
+
+.. function:: const handle& handle::dec_ref() const
+
+    Manually decrease the reference count of the Python object. Usually, it is
+    preferable to use the :class:`object` class which derives from
+    :class:`handle` and calls this function automatically. Returns a reference
+    to itself.
+
+.. function:: void handle::ref_count() const
+
+    Return the object's current reference count
+
+.. function:: handle handle::get_type() const
+
+    Return a handle to the Python type object underlying the instance
+
+.. function detail::accessor handle::operator[](handle key) const
+
+    Return an internal functor to invoke the object's sequence protocol.
+    Casting the returned ``detail::accessor`` instance to a :class:`handle` or
+    :class:`object` subclass causes a corresponding call to ``__getitem__``.
+    Assigning a :class:`handle` or :class:`object` subclass causes a call to
+    ``__setitem__``.
+
+.. function detail::accessor handle::operator[](const char *key) const
+
+    See the above function (the only difference is that they key is provided as
+    a string literal).
+
+.. function detail::accessor handle::attr(handle key) const
+
+    Return an internal functor to access the object's attributes.
+    Casting the returned ``detail::accessor`` instance to a :class:`handle` or
+    :class:`object` subclass causes a corresponding call to ``__getattr``.
+    Assigning a :class:`handle` or :class:`object` subclass causes a call to
+    ``__setattr``.
+
+.. function detail::accessor handle::attr(const char *key) const
+
+    See the above function (the only difference is that they key is provided as
+    a string literal).
+
+.. function operator handle::bool() const
+
+    Return ``true`` when the :class:`handle` wraps a valid Python object.
+
+.. function str handle::str() const
+
+    Return a string representation of the object. This is analogous to
+    the ``str()`` function in Python.
+
+.. function:: template <typename T> T handle::cast() const
+
+    Attempt to cast the Python object into the given C++ type. A
+    :class:`cast_error` will be throw upon failure.
+
+.. function:: template <typename ... Args> object handle::call(Args&&... args) const
+
+    Assuming the Python object is a function or implements the ``__call__``
+    protocol, ``call()`` invokes the underlying function, passing an arbitrary
+    set of parameters. The result is returned as a :class:`object` and may need
+    to be converted back into a Python object using :func:`handle::cast`.
+
+    When some of the arguments cannot be converted to Python objects, the
+    function will throw a :class:`cast_error` exception. When the Python
+    function call fails, a :class:`error_already_set` exception is thrown.
+
+With reference counting
+-----------------------
+
+.. class:: object : public handle
+
+    Like :class:`handle`, the object class is a thin wrapper around an
+    arbitrary Python object (i.e. a ``PyObject *`` in Python's C API). In
+    contrast to :class:`handle`, it optionally increases the object's reference
+    count upon construction, and it *always* decreases the reference count when
+    the :class:`object` instance goes out of scope and is destructed. When
+    using :class:`object` instances consistently, it is much easier to get
+    reference counting right at the first attempt.
+
+.. function:: object::object(const object &o)
+
+    Copy constructor; always increases the reference count
+
+.. function:: object::object(const handle &h, bool borrowed)
+
+    Creates a :class:`object` from the given :class:`handle`. The reference
+    count is only increased if the ``borrowed`` parameter is set to ``true``.
+
+.. function:: object::object(PyObject *ptr, bool borrowed)
+
+    Creates a :class:`object` from the given raw Python object pointer. The
+    reference  count is only increased if the ``borrowed`` parameter is set to
+    ``true``.
+
+.. function:: object::object(object &&other)
+
+    Move constructor; steals the object from ``other`` and preserves its
+    reference count.
+
+.. function:: handle object::release()
+
+    Resets the internal pointer to ``nullptr`` without without decreasing the
+    object's reference count. The function returns a raw handle to the original
+    Python object.
+
+.. function:: object::~object()
+
+    Destructor, which automatically calls :func:`handle::dec_ref()`.
+
+Convenience classes for specific Python types
+=============================================
+
+
+.. class:: module : public object
+
+.. function:: module::module(const char *name, const char *doc = nullptr)
+
+    Create a new top-level Python module with the given name and docstring
+
+.. function:: module module::def_submodule(const char *name, const char *doc = nullptr)
+
+    Create and return a new Python submodule with the given name and docstring.
+    This also works recursively, i.e.
+
+    .. code-block:: cpp
+
+        pybind11::module m("example", "pybind11 example plugin");
+        pybind11::module m2 = m.def_submodule("sub", "A submodule of 'example'");
+        pybind11::module m3 = m2.def_submodule("subsub", "A submodule of 'example.sub'");
+
+.. cpp:function:: template <typename Func, typename ... Extra> module& module::def(const char *name, Func && f, Extra && ... extra)
+
+    Create Python binding for a new function within the module scope. ``Func``
+    can be a plain C++ function, a function pointer, or a lambda function. For
+    details on the ``Extra&& ... extra`` argument, see section :ref:`extras`.
+
+.. _extras:
+
+Passing extra arguments to the def function
+===========================================
+
+.. class:: arg
+
+.. function:: arg::arg(const char *name)
+
+.. function:: template <typename T> arg_v arg::operator=(T &&value)
+
+.. class:: arg_v : public arg
+
+    Represents a named argument with a default value
+
+.. class:: sibling
+
+    Used to specify a handle to an existing sibling function; used internally
+    to implement function overloading in :func:`module::def` and
+    :func:`class_::def`.
+
+.. function:: sibling::sibling(handle handle)
+
+.. class doc
+
+    This is class is internally used by pybind11.
+
+.. function:: doc::doc(const char *value)
+
+    Create a new docstring with the specified value
+
+.. class name
+
+    This is class is internally used by pybind11.
+
+.. function:: name::name(const char *value)
+
+    Used to specify the function name
+
diff --git a/ext/pybind11/docs/release.rst b/ext/pybind11/docs/release.rst
new file mode 100644
index 000000000..a15504ea8
--- /dev/null
+++ b/ext/pybind11/docs/release.rst
@@ -0,0 +1,22 @@
+To release a new version of pybind11:
+
+- Update the version number and push to pypi
+    - Update ``pybind11/_version.py`` (set release version, remove 'dev')
+    - Update version in ``docs/conf.py``
+    - Tag release date in ``docs/changelog.rst``.
+    - ``git add`` and ``git commit``.
+    - if new minor version: ``git checkout -b vX.Y``, ``git push -u origin vX.Y``
+    - ``git tag -a vX.Y.Z -m 'vX.Y.Z release'``.
+    - ``git push``
+    - ``git push --tags``.
+    - ``python setup.py sdist upload``.
+    - ``python setup.py bdist_wheel upload``.
+- Update conda-forge (https://github.com/conda-forge/pybind11-feedstock) via PR
+    - change version number in ``recipe/meta.yml``
+    - update checksum to match the one computed by pypi
+- Get back to work
+    - Update ``_version.py`` (add 'dev' and increment minor).
+    - Update version in ``docs/conf.py``
+    - Update version macros in ``include/pybind11/common.h``
+    - ``git add`` and ``git commit``.
+      ``git push``