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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.

The numpy API constants can check past the end of the API array if the
numpy version is too old thus causing a segfault.  The current list of
functions requires numpy >= 1.7.0, so this adds a check and exception if
numpy is too old.

The added feature version API element was added in numpy 1.4.0, so this
could still segfault if loaded in 1.3.0 or earlier, but given that
1.4.0 was released at the end of 2009, it seems reasonable enough to
not worry about that case.  (1.7.0 was released in early 2013).

This further reduces the constructors required in buffer_info/numpy by
removing the need for the constructors that take a single size_t and
just forward it on via an initializer_list to the container-accepting
constructor.

Unfortunately, in `array` one of the constructors runs into an ambiguity
problem with the deprecated `array(handle, bool)` constructor (because
both the bool constructor and the any_container constructor involve an
implicit conversion, so neither has precedence), so a forwarding
constructor is kept there (until the deprecated constructor is
eventually removed).

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).

The constexpr static instances can cause linking failures if the
compiler doesn't optimize away the reference, as reported in #770.

There's no particularly nice way of fixing this in C++11/14: we can't
inline definitions to match the declaration aren't permitted for
non-templated static variables (C++17 *does* allows "inline" on
variables, but that obviously doesn't help us.)

One solution that could work around it is to add an extra inherited
subclass to `object`'s hierarchy, but that's a bit of a messy solution
and was decided against in #771 in favour of just deprecating (and
eventually dropping) the constexpr statics.

Fixes #770.

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.

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.

The only part of the vectorize code that actually needs c-contiguous is
the "trivial" broadcast; for non-trivial arguments, the code already
uses strides properly (and so handles C-style, F-style, neither, slices,
etc.)

This commit rewrites `broadcast` to additionally check for C-contiguous
storage, then takes off the `c_style` flag for the arguments, which
will keep the functionality more or less the same, except for no longer
requiring an array copy for non-c-contiguous input arrays.

Additionally, if we're given a singleton slice (e.g. a[0::4, 0::4] for a
4x4 or smaller array), we no longer fail triviality because the trivial
code path never actually uses the strides on a singleton.

Allows equivalent integral types and numpy dtypes

* Add value_type member alias to py::array_t (resolve #632)

* Use numpy scalar name in py::array_t function signatures (e.g. float32/64 instead of just float)

This makes array_t respect overload resolution and noconvert by failing
to load when `convert = false` if the src isn't already an array of the
correct type.

Numpy raises ValueError when attempting to modify an array, while
py::array is raising a RuntimeError.  This changes the exception to a
std::domain_error, which gets mapped to the expected ValueError in
python.

numpy arrays aren't currently properly setting base: by setting `->base`
directly, the base doesn't follow what numpy expects and documents (that
is, following chained array bases to the root array).

This fixes the behaviour by using numpy's PyArray_SetBaseObject to set
the base instead, and then updates the tests to reflect the fixed
behaviour.

A few of pybind's numpy constants are using the numpy-deprecated names
(without "ARRAY_" in them); updated our names to be consistent with
current numpy code.

noexcept deduction, added in PR #555, doesn't work with clang's
-std=c++1z; and while it works with g++, it isn't entirely clear to me
that it is required to work in C++17.

What should work, however, is that C++17 allows implicit conversion of a
`noexcept(true)` function pointer to a `noexcept(false)` (i.e.  default,
noexcept-not-specified) function pointer.  That was breaking in pybind11
because the cpp_function template used for lambdas provided a better
match (i.e. without requiring an implicit conversion), but it then
failed.

This commit takes a different approach of using SFINAE on the lambda
function to prevent it from matching a non-lambda object, which then
gets implicit conversion from a `noexcept` function pointer to a
`noexcept(false)` function pointer.  This much nicer solution also gets
rid of the C++17 NOEXCEPT macros, and works in both clang and g++.

This reverts commit bee8827a.

* Avoid C-style const casts

Replace C-style casts that discard `const` with `const_cast` (and, where
necessary, `reinterpret_cast` as well).

* Warn about C-style const-discarding casts

Change pybind11_enable_warnings to also enable `-Wcast-qual` (warn if a
C-style cast discards `const`) by default. The previous commit should
have gotten rid of all of these (at least, all the ones that tripped in
my build, which included the tests), and this should discourage more
from newly appearing.

(Identifiers starting with underscores are reserved by the standard)
Also fixed a typo in a comment.

* 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.

When compiling in C++17 mode the noexcept specifier is part of the
function type.  This causes a failure in pybind11 because, by omitting
a noexcept specifier when deducing function return and argument types,
we are implicitly making `noexcept(false)` part of the type.

This means that functions with `noexcept` fail to match the function
templates in cpp_function (and other places), and we get compilation
failure (we end up trying to fit it into the lambda function version,
which fails since a function pointer has no `operator()`).

We can, however, deduce the true/false `B` in noexcept(B), so we don't
need to add a whole other set of overloads, but need to deduce the extra
argument when under C++17.  That will *not* work under pre-C++17,
however.

This commit adds two macros to fix the problem: under C++17 (with the
appropriate feature macro set) they provide an extra `bool NoExceptions`
template argument and provide the `noexcept(NoExceptions)` deduced
specifier.  Under pre-C++17 they expand to nothing.

This is needed to compile pybind11 with gcc7 under -std=c++17.

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

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

* `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.

Allows checking the Python types before creating an object instead of
after. For example:
```c++
auto l = list(ptr, true);
if (l.check())
   // ...
```
The above is replaced with:
```c++
if (isinstance<list>(ptr)) {
    auto l = reinterpret_borrow(ptr);
    // ...
}
```

This deprecates `py::object::check()`. `py::isinstance()` covers the
same use case, but it can also check for user-defined types:
```c++
class Pet { ... };
py::class_<Pet>(...);

m.def("is_pet", [](py::object obj) {
    return py::isinstance<Pet>(obj); // works as expected
});
```

* Also added unsafe version without checks

(avoid code bloat if possible)

NumPy internals are stored under "_numpy_internals" key.