refactor: module -> module_ with typedef (#2544)

* WIP: module -> module_ without typedef

* refactor: allow py::module to work again

docs/advanced/embedding.rst

@@ -108,11 +108,11 @@ The two approaches can also be combined:
 Importing modules
 =================
 
-Python modules can be imported using `module::import()`:
+Python modules can be imported using `module_::import()`:
 
 .. code-block:: cpp
 
-    py::module sys = py::module::import("sys");
+    py::module_ sys = py::module_::import("sys");
     py::print(sys.attr("path"));
 
 For convenience, the current working directory is included in ``sys.path`` when
@@ -128,12 +128,12 @@ embedding the interpreter. This makes it easy to import local Python files:
 
 .. code-block:: cpp
 
-    py::module calc = py::module::import("calc");
+    py::module_ calc = py::module_::import("calc");
     py::object result = calc.attr("add")(1, 2);
     int n = result.cast<int>();
     assert(n == 3);
 
-Modules can be reloaded using `module::reload()` if the source is modified e.g.
+Modules can be reloaded using `module_::reload()` if the source is modified e.g.
 by an external process. This can be useful in scenarios where the application
 imports a user defined data processing script which needs to be updated after
 changes by the user. Note that this function does not reload modules recursively.
@@ -153,7 +153,7 @@ like any other module.
     namespace py = pybind11;
 
     PYBIND11_EMBEDDED_MODULE(fast_calc, m) {
-        // `m` is a `py::module` which is used to bind functions and classes
+        // `m` is a `py::module_` which is used to bind functions and classes
         m.def("add", [](int i, int j) {
             return i + j;
         });
@@ -162,7 +162,7 @@ like any other module.
     int main() {
         py::scoped_interpreter guard{};
 
-        auto fast_calc = py::module::import("fast_calc");
+        auto fast_calc = py::module_::import("fast_calc");
         auto result = fast_calc.attr("add")(1, 2).cast<int>();
         assert(result == 3);
     }
@@ -196,7 +196,7 @@ naturally:
     int main() {
         py::scoped_interpreter guard{};
 
-        auto py_module = py::module::import("py_module");
+        auto py_module = py::module_::import("py_module");
 
         auto locals = py::dict("fmt"_a="{} + {} = {}", **py_module.attr("__dict__"));
         assert(locals["a"].cast<int>() == 1);

docs/advanced/exceptions.rst

@@ -182,7 +182,7 @@ For example:
 
     try {
         // open("missing.txt", "r")
-        auto file = py::module::import("io").attr("open")("missing.txt", "r");
+        auto file = py::module_::import("io").attr("open")("missing.txt", "r");
         auto text = file.attr("read")();
         file.attr("close")();
     } catch (py::error_already_set &e) {

docs/advanced/functions.rst

@@ -17,7 +17,7 @@ bindings for functions that return a non-trivial type. Just by looking at the
 type information, it is not clear whether Python should take charge of the
 returned value and eventually free its resources, or if this is handled on the
 C++ side. For this reason, pybind11 provides a several *return value policy*
-annotations that can be passed to the :func:`module::def` and
+annotations that can be passed to the :func:`module_::def` and
 :func:`class_::def` functions. The default policy is
 :enum:`return_value_policy::automatic`.
 

docs/advanced/misc.rst

@@ -132,7 +132,7 @@ However, it can be acquired as follows:
 
 .. code-block:: cpp
 
-    py::object pet = (py::object) py::module::import("basic").attr("Pet");
+    py::object pet = (py::object) py::module_::import("basic").attr("Pet");
 
     py::class_<Dog>(m, "Dog", pet)
         .def(py::init<const std::string &>())
@@ -146,7 +146,7 @@ has been executed:
 
 .. code-block:: cpp
 
-    py::module::import("basic");
+    py::module_::import("basic");
 
     py::class_<Dog, Pet>(m, "Dog")
         .def(py::init<const std::string &>())
@@ -243,7 +243,7 @@ avoids this issue involves weak reference with a cleanup callback:
 
     .. code-block:: cpp
 
-        auto atexit = py::module::import("atexit");
+        auto atexit = py::module_::import("atexit");
         atexit.attr("register")(py::cpp_function([]() {
             // perform cleanup here -- this function is called with the GIL held
         }));
@@ -284,7 +284,7 @@ work, it is important that all lines are indented consistently, i.e.:
     )mydelimiter");
 
 By default, pybind11 automatically generates and prepends a signature to the docstring of a function
-registered with ``module::def()`` and ``class_::def()``. Sometimes this
+registered with ``module_::def()`` and ``class_::def()``. Sometimes this
 behavior is not desirable, because you want to provide your own signature or remove
 the docstring completely to exclude the function from the Sphinx documentation.
 The class ``options`` allows you to selectively suppress auto-generated signatures:

docs/advanced/pycpp/object.rst

@@ -56,12 +56,12 @@ This example obtains a reference to the Python ``Decimal`` class.
 .. code-block:: cpp
 
     // Equivalent to "from decimal import Decimal"
-    py::object Decimal = py::module::import("decimal").attr("Decimal");
+    py::object Decimal = py::module_::import("decimal").attr("Decimal");
 
 .. code-block:: cpp
 
     // Try to import scipy
-    py::object scipy = py::module::import("scipy");
+    py::object scipy = py::module_::import("scipy");
     return scipy.attr("__version__");
 
 
@@ -81,7 +81,7 @@ via ``operator()``.
 .. code-block:: cpp
 
     // Use Python to make our directories
-    py::object os = py::module::import("os");
+    py::object os = py::module_::import("os");
     py::object makedirs = os.attr("makedirs");
     makedirs("/tmp/path/to/somewhere");
 
@@ -196,9 +196,9 @@ C++ functions that require a specific subtype rather than a generic :class:`obje
     #include <pybind11/numpy.h>
     using namespace pybind11::literals;
 
-    py::module os = py::module::import("os");
-    py::module path = py::module::import("os.path");  // like 'import os.path as path'
-    py::module np = py::module::import("numpy");  // like 'import numpy as np'
+    py::module_ os = py::module_::import("os");
+    py::module_ path = py::module_::import("os.path");  // like 'import os.path as path'
+    py::module_ np = py::module_::import("numpy");  // like 'import numpy as np'
 
     py::str curdir_abs = path.attr("abspath")(path.attr("curdir"));
     py::print(py::str("Current directory: ") + curdir_abs);

docs/advanced/pycpp/utilities.rst

@@ -42,7 +42,7 @@ redirects output to the corresponding Python streams:
     m.def("noisy_func", []() {
         py::scoped_ostream_redirect stream(
             std::cout,                               // std::ostream&
-            py::module::import("sys").attr("stdout") // Python output
+            py::module_::import("sys").attr("stdout") // Python output
         );
         call_noisy_func();
     });
@@ -104,7 +104,7 @@ can be used.
     ...
 
     // Evaluate in scope of main module
-    py::object scope = py::module::import("__main__").attr("__dict__");
+    py::object scope = py::module_::import("__main__").attr("__dict__");
 
     // Evaluate an isolated expression
     int result = py::eval("my_variable + 10", scope).cast<int>();