ext: Update pybind11 to v2.8.1
Change-Id: Ia1c7081377f53fd470addf35526f8b28a949a7b0 Signed-off-by: Jason Lowe-Power <jason@lowepower.com> Reviewed-on: https://gem5-review.googlesource.com/c/public/gem5/+/52523 Maintainer: Bobby R. Bruce <bbruce@ucdavis.edu> Tested-by: kokoro <noreply+kokoro@google.com> Reviewed-by: Gabe Black <gabe.black@gmail.com>
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Jason Lowe-Power
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@@ -171,6 +171,31 @@ template parameter, and it ensures that non-conforming arguments are converted
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into an array satisfying the specified requirements instead of trying the next
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function overload.
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There are several methods on arrays; the methods listed below under references
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work, as well as the following functions based on the NumPy API:
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- ``.dtype()`` returns the type of the contained values.
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- ``.strides()`` returns a pointer to the strides of the array (optionally pass
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an integer axis to get a number).
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- ``.flags()`` returns the flag settings. ``.writable()`` and ``.owndata()``
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are directly available.
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- ``.offset_at()`` returns the offset (optionally pass indices).
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- ``.squeeze()`` returns a view with length-1 axes removed.
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- ``.view(dtype)`` returns a view of the array with a different dtype.
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- ``.reshape({i, j, ...})`` returns a view of the array with a different shape.
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``.resize({...})`` is also available.
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- ``.index_at(i, j, ...)`` gets the count from the beginning to a given index.
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There are also several methods for getting references (described below).
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Structured types
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================
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@@ -233,8 +258,8 @@ by the compiler. The result is returned as a NumPy array of type
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.. code-block:: pycon
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>>> x = np.array([[1, 3],[5, 7]])
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>>> y = np.array([[2, 4],[6, 8]])
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>>> x = np.array([[1, 3], [5, 7]])
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>>> y = np.array([[2, 4], [6, 8]])
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>>> z = 3
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>>> result = vectorized_func(x, y, z)
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@@ -345,21 +370,21 @@ The returned proxy object supports some of the same methods as ``py::array`` so
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that it can be used as a drop-in replacement for some existing, index-checked
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uses of ``py::array``:
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- ``r.ndim()`` returns the number of dimensions
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- ``.ndim()`` returns the number of dimensions
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- ``r.data(1, 2, ...)`` and ``r.mutable_data(1, 2, ...)``` returns a pointer to
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- ``.data(1, 2, ...)`` and ``r.mutable_data(1, 2, ...)``` returns a pointer to
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the ``const T`` or ``T`` data, respectively, at the given indices. The
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latter is only available to proxies obtained via ``a.mutable_unchecked()``.
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- ``itemsize()`` returns the size of an item in bytes, i.e. ``sizeof(T)``.
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- ``.itemsize()`` returns the size of an item in bytes, i.e. ``sizeof(T)``.
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- ``ndim()`` returns the number of dimensions.
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- ``.ndim()`` returns the number of dimensions.
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- ``shape(n)`` returns the size of dimension ``n``
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- ``.shape(n)`` returns the size of dimension ``n``
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- ``size()`` returns the total number of elements (i.e. the product of the shapes).
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- ``.size()`` returns the total number of elements (i.e. the product of the shapes).
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- ``nbytes()`` returns the number of bytes used by the referenced elements
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- ``.nbytes()`` returns the number of bytes used by the referenced elements
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(i.e. ``itemsize()`` times ``size()``).
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.. seealso::
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@@ -378,7 +403,7 @@ In Python 2, the syntactic sugar ``...`` is not available, but the singleton
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.. code-block:: python
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a = # a NumPy array
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a = ... # a NumPy array
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b = a[0, ..., 0]
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The function ``py::ellipsis()`` function can be used to perform the same
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@@ -390,7 +415,7 @@ operation on the C++ side:
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py::array b = a[py::make_tuple(0, py::ellipsis(), 0)];
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.. versionchanged:: 2.6
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``py::ellipsis()`` is now also avaliable in Python 2.
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``py::ellipsis()`` is now also available in Python 2.
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Memory view
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===========
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@@ -20,6 +20,40 @@ Available types include :class:`handle`, :class:`object`, :class:`bool_`,
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Be sure to review the :ref:`pytypes_gotchas` before using this heavily in
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your C++ API.
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.. _instantiating_compound_types:
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Instantiating compound Python types from C++
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============================================
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Dictionaries can be initialized in the :class:`dict` constructor:
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.. code-block:: cpp
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using namespace pybind11::literals; // to bring in the `_a` literal
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py::dict d("spam"_a=py::none(), "eggs"_a=42);
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A tuple of python objects can be instantiated using :func:`py::make_tuple`:
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.. code-block:: cpp
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py::tuple tup = py::make_tuple(42, py::none(), "spam");
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Each element is converted to a supported Python type.
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A `simple namespace`_ can be instantiated using
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.. code-block:: cpp
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using namespace pybind11::literals; // to bring in the `_a` literal
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py::object SimpleNamespace = py::module_::import("types").attr("SimpleNamespace");
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py::object ns = SimpleNamespace("spam"_a=py::none(), "eggs"_a=42);
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Attributes on a namespace can be modified with the :func:`py::delattr`,
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:func:`py::getattr`, and :func:`py::setattr` functions. Simple namespaces can
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be useful as lightweight stand-ins for class instances.
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.. _simple namespace: https://docs.python.org/3/library/types.html#types.SimpleNamespace
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.. _casting_back_and_forth:
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Casting back and forth
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@@ -30,7 +64,7 @@ types to Python, which can be done using :func:`py::cast`:
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.. code-block:: cpp
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MyClass *cls = ..;
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MyClass *cls = ...;
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py::object obj = py::cast(cls);
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The reverse direction uses the following syntax:
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@@ -132,6 +166,7 @@ Keyword arguments are also supported. In Python, there is the usual call syntax:
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def f(number, say, to):
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... # function code
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f(1234, say="hello", to=some_instance) # keyword call in Python
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In C++, the same call can be made using:
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@@ -28,7 +28,7 @@ Capturing standard output from ostream
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Often, a library will use the streams ``std::cout`` and ``std::cerr`` to print,
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but this does not play well with Python's standard ``sys.stdout`` and ``sys.stderr``
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redirection. Replacing a library's printing with `py::print <print>` may not
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redirection. Replacing a library's printing with ``py::print <print>`` may not
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be feasible. This can be fixed using a guard around the library function that
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redirects output to the corresponding Python streams:
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@@ -47,15 +47,26 @@ redirects output to the corresponding Python streams:
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call_noisy_func();
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});
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.. warning::
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The implementation in ``pybind11/iostream.h`` is NOT thread safe. Multiple
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threads writing to a redirected ostream concurrently cause data races
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and potentially buffer overflows. Therefore it is currently a requirement
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that all (possibly) concurrent redirected ostream writes are protected by
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a mutex. #HelpAppreciated: Work on iostream.h thread safety. For more
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background see the discussions under
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`PR #2982 <https://github.com/pybind/pybind11/pull/2982>`_ and
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`PR #2995 <https://github.com/pybind/pybind11/pull/2995>`_.
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This method respects flushes on the output streams and will flush if needed
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when the scoped guard is destroyed. This allows the output to be redirected in
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real time, such as to a Jupyter notebook. The two arguments, the C++ stream and
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the Python output, are optional, and default to standard output if not given. An
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extra type, `py::scoped_estream_redirect <scoped_estream_redirect>`, is identical
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extra type, ``py::scoped_estream_redirect <scoped_estream_redirect>``, is identical
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except for defaulting to ``std::cerr`` and ``sys.stderr``; this can be useful with
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`py::call_guard`, which allows multiple items, but uses the default constructor:
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``py::call_guard``, which allows multiple items, but uses the default constructor:
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.. code-block:: py
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.. code-block:: cpp
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// Alternative: Call single function using call guard
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m.def("noisy_func", &call_noisy_function,
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@@ -63,7 +74,7 @@ except for defaulting to ``std::cerr`` and ``sys.stderr``; this can be useful wi
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py::scoped_estream_redirect>());
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The redirection can also be done in Python with the addition of a context
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manager, using the `py::add_ostream_redirect() <add_ostream_redirect>` function:
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manager, using the ``py::add_ostream_redirect() <add_ostream_redirect>`` function:
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.. code-block:: cpp
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@@ -92,7 +103,7 @@ arguments to disable one of the streams if needed.
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Evaluating Python expressions from strings and files
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====================================================
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pybind11 provides the `eval`, `exec` and `eval_file` functions to evaluate
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pybind11 provides the ``eval``, ``exec`` and ``eval_file`` functions to evaluate
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Python expressions and statements. The following example illustrates how they
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can be used.
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