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Missing conformability check was causing Eigen to create a 0x0 matrix
with an error in debug mode and silent corruption in release mode.

Currently, `py::int_(1).cast<variant<double, int>>()` fills the `double`
slot of the variant. This commit switches the loader to a 2-pass scheme
in order to correctly fill the `int` slot.

Many of our `is_none()` checks in type caster loading return true, but
this should really be considered a deferral so that, for example, an
overload with a `py::none` argument would win over one that takes
`py::none` as a null option.

This keeps None-accepting for the `!convert` pass only for std::optional
and void casters.  (The `char` caster already deferred None; this just
extends that behaviour to other casters).

This exposed a few underlying issues:

1. is_pod_struct was too strict to allow this. I've relaxed it to
require only trivially copyable and standard layout, rather than POD
(which additionally requires a trivial constructor, which std::complex
violates).

2. format_descriptor<std::complex<T>>::format() returned numpy format
strings instead of PEP3118 format strings, but register_dtype
feeds format codes of its fields to _dtype_from_pep3118. I've changed it
to return PEP3118 format codes. format_descriptor is a public type, so
this may be considered an incompatible change.

3. register_structured_dtype tried to be smart about whether to mark
fields as unaligned (with ^). However, it's examining the C++ alignment,
rather than what numpy (or possibly PEP3118) thinks the alignment should
be. For complex values those are different. I've made it mark all fields
as ^ unconditionally, which should always be safe even if they are
aligned, because we explicitly mark the padding.

Resolves #800.

Both C++ arrays and std::array are supported, including mixtures like
std::array<int, 2>[4]. In a multi-dimensional array of char, the last
dimension is used to construct a numpy string type.

* Fix compilation error with std::nullptr_t

* Enable conversion from None to std::nullptr_t and std::nullopt_t

Fixes #839.

We're current copy by creating an Eigen::Map into the input numpy
array, then assigning that to the basic eigen type, effectively having
Eigen do the copy.  That doesn't work for negative strides, though:
Eigen doesn't allow them.

This commit makes numpy do the copying instead by allocating the eigen
type, then having numpy copy from the input array into a numpy reference
into the eigen object's data.  This also saves a copy when type
conversion is required: numpy can do the conversion on-the-fly as part
of the copy.

Finally this commit also makes non-reference parameters respect the
convert flag, declining the load when called in a noconvert pass with a
convertible, but non-array input or an array with the wrong dtype.

`EigenConformable::stride_compatible` returns false if the strides are
negative. In this case, do not use `EigenConformable::stride`, as it
is {0,0}. We cannot write negative strides in this element, as Eigen
will throw an assertion if we do.

The `type_caster` specialization for regular, dense Eigen matrices now
does a second `array_t::ensure` to copy data in case of negative strides.
I'm not sure that this is the best way to implement this.

I have added "TODO" tags linking these changes to Eigen bug #747, which,
when fixed, will allow Eigen to accept negative strides.

If a bound std::function is invoked with a bound method, the implicit
bound self is lost because we use `detail::get_function` to unbox the
function.  This commit amends the code to use py::function and only
unboxes in the special is-really-a-c-function case.  This makes bound
methods stay bound rather than unbinding them by forcing extraction of
the c function.

The added flag enables non-buffered console output when using Ninja

Enumerations on Python 2.7 were not always implicitly converted to
integers (depending on the target size). This patch adds a __long__
conversion function (only enabled on 2.7) which fixes this issue.

The attached test case fails without this patch.

This removes the convert-from-arithemtic-scalar constructor of
any_container as it can result in ambiguous calls, as in:

    py::array_t<float>({ 1, 2 })

which could be intepreted as either of:

    py::array_t<float>(py::array_t<float>(1, 2))
    py::array_t<float>(py::detail::any_container({ 1, 2 }))

Removing the convert-from-arithmetic constructor reduces the number of
implicit conversions, avoiding the ambiguity for array and array_t.
This also re-adds the array/array_t constructors taking a scalar
argument for backwards compatibility.

Python 3's `PyInstanceMethod_Type` hides itself via its `tp_descr_get`,
which prevents aliasing methods via `cls.attr("m2") = cls.attr("m1")`:
instead the `tp_descr_get` returns a plain function, when called on a
class, or a `PyMethod`, when called on an instance.  Override that
behaviour for pybind11 types with a special bypass for
`PyInstanceMethod_Types`.

The Unicode support added in 2.1 (PR #624) inadvertently broke accepting
`bytes` as std::string/char* arguments.  This restores it with a
separate path that does a plain conversion (i.e. completely bypassing
all the encoding/decoding code), but only for single-byte string types.

This commits adds base class pointers of offset base classes (i.e. due
to multiple inheritance) to `registered_instances` so that if such a
pointer is returned we properly recognize it as an existing instance.

Without this, returning a base class pointer will cast to the existing
instance if the pointer happens to coincide with the instance pointer,
but constructs a new instance (quite possibly with a segfault, if
ownership is applied) for unequal base class pointers due to multiple
inheritance.

When we are returned a base class pointer (either directly or via
shared_from_this()) we detect its runtime type (using `typeid`), then
end up essentially reinterpret_casting the pointer to the derived type.
This is invalid when the base class pointer was a non-first base, and we
end up with an invalid pointer.  We could dynamic_cast to the
most-derived type, but if *that* type isn't pybind11-registered, the
resulting pointer given to the base `cast` implementation isn't necessarily valid
to be reinterpret_cast'ed back to the backup type.

This commit removes the "backup" type argument from the many-argument
`cast(...)` and instead does the derived-or-pointer type decision and
type lookup in type_caster_base, where the dynamic_cast has to be to
correctly get the derived pointer, but also has to do the type lookup to
ensure that we don't pass the wrong (derived) pointer when the backup
type (i.e. the type caster intrinsic type) pointer is needed.

Since the lookup is needed before calling the base cast(), this also
changes the input type to a detail::type_info rather than doing a
(second) lookup in cast().

We currently fail at runtime when trying to call a method that is
overloaded with both static and non-static methods.  This is something
python won't allow: the object is either a function or an instance, and
can't be both.

Adding numpy to the pypy test exposed a segfault caused by the buffer
tests in test_stl_binders.py: the first such test was explicitly skipped
on pypy, but the second (test_vector_buffer_numpy) which also seems to
cause an occasional segfault was just marked as requiring numpy.

Explicitly skip it on pypy as well (until a workaround, fix, or pypy fix
are found).

Don't try to define these in the issues submodule, because that fails
if testing without issues compiled in (e.g. using
cmake -DPYBIND11_TEST_OVERRIDE=test_methods_and_attributes.cpp).

This adds support for constructing `buffer_info` and `array`s using
arbitrary containers or iterator pairs instead of requiring a vector.

This is primarily needed by PR #782 (which makes strides signed to
properly support negative strides, and will likely also make shape and
itemsize to avoid mixed integer issues), but also needs to preserve
backwards compatibility with 2.1 and earlier which accepts the strides
parameter as a vector of size_t's.

Rather than adding nearly duplicate constructors for each stride-taking
constructor, it seems nicer to simply allow any type of container (or
iterator pairs).  This works by replacing the existing vector arguments
with a new `detail::any_container` class that handles implicit
conversion of arbitrary containers into a vector of the desired type.
It can also be explicitly instantiated with a pair of iterators (e.g.
by passing {begin, end} instead of the container).

When attempting to get a raw array pointer we return nullptr if given a
nullptr, which triggers an error_already_set(), but we haven't set an
exception message, which results in "Unknown internal error".

Callers that want explicit allowing of a nullptr here already handle it
(by clearing the exception after the call).

Many of the Eigen type casters' name() methods weren't wrapping the type
description in a `type_descr` object, which thus wasn't adding the
"{...}" annotation used to identify an argument which broke the help
output by skipping eigen arguments.

The test code I had added even had some (unnoticed) broken output (with
the "arg0: " showing up in the return value).

This commit also adds test code to ensure that named eigen arguments
actually work properly, despite the invalid help output.  (The added
tests pass without the rest of this commit).

Fixes #775.

Assignments of the form `Type.static_prop = value` should be translated to
`Type.static_prop.__set__(value)` except when `isinstance(value, static_prop)`.

Besides appearing in the CMake GUI, the `:FILENAME` specifier changes
behavior as well:

cmake -DPYTHON_EXECUTABLE=python ..  # FAIL, can't find python
cmake -DPYTHON_EXECUTABLE=/path/to/python ..  # OK
cmake -DPYTHON_EXECUTABLE:FILENAME=python ..  # OK
cmake -DPYTHON_EXECUTABLE:FILENAME=/path/to/python ..  # OK

When make_tuple fails (for example, when print() is called with a
non-convertible argument, as in #778) the error message a less helpful
than it could be:

    make_tuple(): unable to convert arguments of types 'std::tuple<type1, type2>' to Python object

There is no actual std::tuple involved (only a parameter pack and a
Python tuple), but it also doesn't immediately reveal which type caused
the problem.

This commit changes the debugging mode output to show just the
problematic type:

    make_tuple(): unable to convert argument of type 'type2' to Python object

```c++
m.def("foo", foo, py::call_guard<T>());
```

is equivalent to:

```c++
m.def("foo", [](args...) {
    T scope_guard;
    return foo(args...); // forwarded arguments
});
```

This commit adds `error_already_set::matches()` convenience method to
check if the exception trapped by `error_already_set` matches a given
Python exception type. This will address #700 by providing a less
verbose way to check exceptions.

* Support raw string literals as input for py::eval
* Dedent only when needed

* nicer py::capsule destructor mechanism
* added destructor-only version of capsule & tests
* added documentation for module destructors (fixes #733)

The extends the previous unchecked support with the ability to
determine the dimensions at runtime.  This incurs a small performance
hit when used (versus the compile-time fixed alternative), but is still considerably
faster than the full checks on every call that happen with
`.at()`/`.mutable_at()`.

This adds bounds-unchecked access to arrays through a `a.unchecked<Type,
Dimensions>()` method.  (For `array_t<T>`, the `Type` template parameter
is omitted).  The mutable version (which requires the array have the
`writeable` flag) is available as `a.mutable_unchecked<...>()`.

Specifying the Dimensions as a template parameter allows storage of an
std::array; having the strides and sizes stored that way (as opposed to
storing a copy of the array's strides/shape pointers) allows the
compiler to make significant optimizations of the shape() method that it
can't make with a pointer; testing with nested loops of the form:

    for (size_t i0 = 0; i0 < r.shape(0); i0++)
        for (size_t i1 = 0; i1 < r.shape(1); i1++)
            ...
                r(i0, i1, ...) += 1;

over a 10 million element array gives around a 25% speedup (versus using
a pointer) for the 1D case, 33% for 2D, and runs more than twice as fast
with a 5D array.

Fixes #754.

This extends the trivial handling to support trivial handling for
Fortran-order arrays (i.e. column major): if inputs aren't all
C-contiguous, but *are* all F-contiguous, the resulting array will be
F-contiguous and we can do trivial processing.

For anything else (e.g. C-contiguous, or inputs requiring non-trivial
processing), the result is in (numpy-default) C-contiguous layout.