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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.

```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
});
```

* 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.

RTD updated their build environment which broke the 1.8.14.dev build of
doxygen that we were using. The update also breaks the conda-forge build
of 1.8.13 (but that version has other issues).

Luckily, the RTD update did bring their doxygen version up to 1.8.11
which is enough to parse the C++11 code we need (ref qualifiers) and it
also avoids the segfault found in 1.8.13.

Since we're using the native doxygen, conda isn't required anymore and
we can simplify the RTD configuration.

[skip ci]

This commit largely rewrites the Eigen dense matrix support to avoid
copying in many cases: Eigen arguments can now reference numpy data, and
numpy objects can now reference Eigen data (given compatible types).

Eigen::Ref<...> arguments now also make use of the new `convert`
argument use (added in PR #634) to avoid conversion, allowing
`py::arg().noconvert()` to be used when binding a function to prohibit
copying when invoking the function.  Respecting `convert` also means
Eigen overloads that avoid copying will be preferred during overload
resolution to ones that require copying.

This commit also rewrites the Eigen documentation and test suite to
explain and test the new capabilities.

Now that only one shared metaclass is ever allocated, it's extremely
cheap to enable it for all pybind11 types.

* Deprecate the default py::metaclass() since it's not needed anymore.
* Allow users to specify a custom metaclass via py::metaclass(handle).

* Switch breathe to stable releases. It was previously pulling directly
  from master because a required bugfix was not in a stable release yet.

* Force update sphinx and RTD theme. When using conda, readthedocs pins
  sphinx==1.3.5 and sphinx_rtd_theme==0.1.7, which is a bit older than
  the ones used in the RTD regular (non-conda) build. The newer theme
  has nicer sidebar navigation (4-level depth vs. only 2-level on the
  older version). Note that the python==3.5 requirement must stay
  because RTD still installs the older sphinx at one point which isn't
  available with Python 3.6.

[skip ci]

* changed return_value:: to return_value_policy::

* Update functions.rst

Fixes #667

The sphinx version is pinned by readthedocs, but sphinx 1.3.5 is not
available with conda python 3.6. The workaround is to pin the python
version to 3.5 (it doesn't really matter for the docs build).

* Propagate unicode conversion failure

If returning a std::string with invalid utf-8 data, we currently fail
with an uninformative TypeError instead of propagating the
UnicodeDecodeError that Python sets on failure.

* Add support for u16/u32strings and literals

This adds support for wchar{16,32}_t character literals and the
associated std::u{16,32}string types.  It also folds the
character/string conversion into a single type_caster template, since
the type casters for string and wstring were mostly the same anyway.

* Added too-long and too-big character conversion errors

With this commit, when casting to a single character, as opposed to a
C-style string, we make sure the input wasn't a multi-character string
or a single character with codepoint too large for the character type.

This also changes the character cast op to CharT instead of CharT& (we
need to be able to return a temporary decoded char value, but also
because there's little gained by bothering with an lvalue return here).

Finally it changes the char caster to 'has-a-string-caster' instead of
'is-a-string-caster' because, with the cast_op change above, there's
nothing at all gained from inheritance.  This also lets us remove the
`success` from the string caster (which was only there for the char
caster) into the char caster itself.  (I also renamed it to 'none' and
inverted its value to better reflect its purpose).  The None -> nullptr
loading also now takes place only under a `convert = true` load pass.
Although it's unlikely that a function taking a char also has overloads
that can take a None, it seems marginally more correct to treat it as a
conversion.

This commit simplifies the size assumptions about character sizes with
static_asserts to back them up.

This changes the function dispatching code for overloaded functions into
a two-pass procedure where we first try all overloads with
`convert=false` for all arguments.  If no function calls succeeds in the
first pass, we then try a second pass where we allow arguments to have
`convert=true` (unless, of course, the argument was explicitly specified
with `py::arg().noconvert()`).

For non-overloaded methods, the two-pass procedure is skipped (we just
make the overload-allowed call).  The second pass is also skipped if it
would result in the same thing (i.e. where all arguments are
`.noconvert()` arguments).

This adds support for controlling the `convert` flag of arguments
through the py::arg annotation.  This then allows arguments to be
flagged as non-converting, which the type_caster is able to use to
request different behaviour.

Currently, AFAICS `convert` is only used for type converters of regular
pybind11-registered types; all of the other core type_casters ignore it.
We can, however, repurpose it to control internal conversion of
converters like Eigen and `array`: most usefully to give callers a way
to disable the conversion that would otherwise occur when a
`Eigen::Ref<const Eigen::Matrix>` argument is passed a numpy array that
requires conversion (either because it has an incompatible stride or the
wrong dtype).

Specifying a noconvert looks like one of these:

    m.def("f1", &f, "a"_a.noconvert() = "default"); // Named, default, noconvert
    m.def("f2", &f, "a"_a.noconvert()); // Named, no default, no converting
    m.def("f3", &f, py::arg().noconvert()); // Unnamed, no default, no converting

(The last part--being able to declare a py::arg without a name--is new:
previous py::arg() only accepted named keyword arguments).

Such an non-convert argument is then passed `convert = false` by the
type caster when loading the argument.  Whether this has an effect is up
to the type caster itself, but as mentioned above, this would be
extremely helpful for the Eigen support to give a nicer way to specify
a "no-copy" mode than the custom wrapper in the current PR, and
moreover isn't an Eigen-specific hack.

* Add documentation for strings and Unicode issues

* More Unicode documentation on character literals and wide characters

* Minor doc syntax fix

The numpy documentation had a bad :file: reference (was using double
backticks instead of single backticks).

* Changed long-outdated "example" -> "tests" wording

The ConstructorStats internal docs still had "from example import", and
the main testing cpp file still used "example" in the module
description.

This commit rewrites the function dispatcher code to support mixing
regular arguments with py::args/py::kwargs arguments.  It also
simplifies the argument loader noticeably as it no longer has to worry
about args/kwargs: all of that is now sorted out in the dispatcher,
which now simply appends a tuple/dict if the function takes
py::args/py::kwargs, then passes all the arguments in a vector.

When the argument loader hit a py::args or py::kwargs, it doesn't do
anything special: it just calls the appropriate type_caster just like it
does for any other argument (thus removing the previous special cases
for args/kwargs).

Switching to passing arguments in a single std::vector instead of a pair
of tuples also makes things simpler, both in the dispatch and the
argument_loader: since this argument list is strictly pybind-internal
(i.e. it never goes to Python) we have no particular reason to use a
Python tuple here.

Some (intentional) restrictions:
- you may not bind a function that has args/kwargs somewhere other than
  the end (this somewhat matches Python, and keeps the dispatch code a
  little cleaner by being able to not worry about where to inject the
  args/kwargs in the argument list).
- If you specify an argument both positionally and via a keyword
  argument, you get a TypeError alerting you to this (as you do in
  Python).

* Abstract away some holder functionality (resolve #585)

Custom holder types which don't have `.get()` can select the correct
function to call by specializing `holder_traits`.

* Add support for move-only holders (fix #605)

* Clarify PYBIND11_NUMPY_DTYPE documentation

The current documentation and example reads as though
PYBIND11_NUMPY_DTYPE is a declarative macro along the same lines as
PYBIND11_DECLARE_HOLDER_TYPE, but it isn't.  The changes the
documentation and docs example to make it clear that you need to "call"
the macro.

* Add satisfies_{all,any,none}_of<T, Preds>

`satisfies_all_of<T, Pred1, Pred2, Pred3>` is a nice legibility-enhanced
shortcut for `is_all<Pred1<T>, Pred2<T>, Pred3<T>>`.

* Give better error message for non-POD dtype attempts

If you try to use a non-POD data type, you get difficult-to-interpret
compilation errors (about ::name() not being a member of an internal
pybind11 struct, among others), for which isn't at all obvious what the
problem is.

This adds a static_assert for such cases.

It also changes the base case from an empty struct to the is_pod_struct
case by no longer using `enable_if<is_pod_struct>` but instead using a
static_assert: thus specializations avoid the base class, POD types
work, and non-POD types (and unimplemented POD types like std::array)
get a more informative static_assert failure.

* Prefix macros with PYBIND11_

numpy.h uses unprefixed macros, which seems undesirable.  This prefixes
them with PYBIND11_ to match all the other macros in numpy.h (and
elsewhere).

* Add long double support

This adds long double and std::complex<long double> support for numpy
arrays.

This allows some simplification of the code used to generate format
descriptors; the new code uses fewer macros, instead putting the code as
different templated options; the template conditions end up simpler with
this because we are now supporting all basic C++ arithmetic types (and
so can use is_arithmetic instead of is_integral + multiple
different specializations).

In addition to testing that it is indeed working in the test script, it
also adds various offset and size calculations there, which
fixes the test failures under x86 compilations.

* Make 'any' the default markup role for Sphinx docs

* Automate generation of reference docs with doxygen and breathe

* Improve reference docs coverage

* Some clarifications to section on virtual fns

Primarily, I made it clear that PYBIND11_OVERLOAD_PURE_NAME is not "useful" but required in renaming situations. Also clarified that one should not bind to the trampoline helper class which I found tempting since it seems more explicit.

* Remove :emphasize-lines: from cpp block, seems to suppress formatting

* docs: emphasize default policy, clarify keep_alive

Emphasize the default return value policy since this statement is hidden in a wall of text. 

Add a hint that call policies are probably required for container objects.

* [Doc] Fix several errors of examples from the doc

* Add missing operator def.

* Added missing `()`

* Add missing `namespace`.