This adds automatic casting when assigning to python types like dict,
list, and attributes.  Instead of:

    dict["key"] = py::cast(val);
    m.attr("foo") = py::cast(true);
    list.append(py::cast(42));

you can now simply write:

    dict["key"] = val;
    m.attr("foo") = true;
    list.append(42);

Casts needing extra parameters (e.g. for a non-default rvp) still
require the py::cast() call. set::add() is also supported.

All usage is channeled through a SFINAE implementation which either just returns or casts. 

Combined non-converting handle and autocasting template methods via a
helper method that either just returns (handle) or casts (C++ type).

https://github.com/sphinx-doc/sphinx/issues/3212

* Added ternary support with descr args

Current the `_<bool>(a, b)` ternary support only works for `char[]` `a`
and `b`; this commit allows it to work for `descr` `a` and `b` arguments
as well.

* Add support for std::valarray to stl.h

This abstracts the std::array into a `array_caster` which can then be
used with either std::array or std::valarray, the main difference being
that std::valarray is resizable.  (It also lets the array_caster be
potentially used for other std::array-like interfaces, much as the
list_caster and map_caster currently provide).

* Small stl.h cleanups

- Remove redundant `type` typedefs
- make internal list_caster methods private

Newer standard libraries use compiler intrinsics for std::index_sequence
which makes it ‘free’. This prevents hitting instantiation limits for
recursive templates (-ftemplate-depth).

This is more Pythonic and compliments the std::vector and std::list
casters which also accept sequences.

This is needed in order to allow the tuple caster to accept any sequence
while keeping the argument loader fast. There is also very little overlap
between the two classes which makes the separation clean. It’s also good
practice not to have completely new functionality in a specialization.

Using a complicated declval here was pointlessly complicated: we
already know the type, because that's what cast_op_type<T> is in the
first place.  (The declval also broke MSVC).

This adds a `detail::cast_op<T>(caster)` function which handles the
rather verbose:

    caster.operator typename CasterType::template cast_op_type<T>()

which allows various places to use the shorter and clearer:

    cast_op<T>(caster)

instead of the full verbose cast operator invocation.

stl casters were using a value cast to (Value) or (Key), but that isn't
always appropriate.  This changes it to use the appropriate value
converter's cast_op_type.

C++ exceptions are destructed in the context of the code that catches
them. At this point, the Python GIL may not be held, which could lead
to crashes with the previous implementation.

PyErr_Fetch and PyErr_Restore should always occur in pairs, which was
not the case for the previous implementation. To clear the exception,
the new approach uses PyErr_Restore && PyErr_Clear instead of simply
decreasing the reference counts of the exception objects.

* Use LIBDIR and MULTIARCH on linux to find python library

* Remove apple-specific setting; the non-windows one should work fine on OS X

* Default LIBDIR/MULTIARCH to '' (to avoid getting None)

* Remove trailing whitespace from FindPythonLibsNew

Previously all types are marked unaligned in buffer format strings,
now we test for alignment before adding the '=' marker.

This gives more informative output, often including the type (or at
least some hint about the type).

A flake8 configuration is included in setup.cfg and the checks are
executed automatically on Travis:

* Ensures a consistent PEP8 code style
* Does basic linting to prevent possible bugs

Fixes #509.

The move policy was already set for rvalues in PR #473, but this only
applied to directly cast user-defined types. The problem is that STL
containers cast values indirectly and the rvalue information is lost.
Therefore the move policy was not set correctly. This commit fixes it.

This also makes an additional adjustment to remove the `copy` policy
exception: rvalues now always use the `move` policy. This is also safe
for copy-only rvalues because the `move` policy has an internal fallback
to copying.

Following commit 90d278, the object code generated by the python
bindings of nanogui (github.com/wjakob/nanogui) went up by a whopping
12%. It turns out that that project has quite a few enums where we don't
really care about arithmetic operators.

This commit thus partially reverts the effects of #503 by introducing
an additional attribute py::arithmetic() that must be specified if the
arithmetic operators are desired.

* `array_t(const object &)` now throws on error
* `array_t::ensure()` is intended for casters —- old constructor is
  deprecated
* `array` and `array_t` get default constructors (empty array)
* `array` gets a converting constructor
* `py::isinstance<array_T<T>>()` checks the type (but not flags)

There is only one special thing which must remain: `array_t` gets
its own `type_caster` specialization which uses `ensure` instead
of a simple check.

The pytype converting constructors are convenient and safe for user
code, but for library internals the additional type checks and possible
conversions are sometimes not desired. `reinterpret_borrow<T>()` and
`reinterpret_steal<T>()` serve as the low-level unsafe counterparts
of `cast<T>()`.

This deprecates the `object(handle, bool)` constructor.

Renamed `borrowed` parameter to `is_borrowed` to avoid shadowing
warnings on MSVC.

* Deprecate the `py::object::str()` member function since `py::str(obj)`
  is now equivalent and preferred

* Make `py::repr()` a free function

* Make sure obj.cast<T>() works as expected when T is a Python type

`obj.cast<T>()` should be the same as `T(obj)`, i.e. it should convert
the given object to a different Python type. However, `obj.cast<T>()`
usually calls `type_caster::load()` which only checks the type without
doing any actual conversion. That causes a very unexpected `cast_error`.
This commit makes it so that `obj.cast<T>()` and `T(obj)` are the same
when T is a Python type.

* Simplify pytypes converting constructor implementation

It's not necessary to maintain a full set of converting constructors
and assignment operators + const& and &&. A single converting const&
constructor will work and there is no impact on binary size. On the
other hand, the conversion functions can be significantly simplified.