Merge branch 'master' into dataclasses

Author: ilevkivskyi

.travis.yml

@@ -16,6 +16,6 @@ install:
   - pip install .
 
 script:
-  - python runtests.py -j12 -x lint -x package
+  - python runtests.py -j12 -x lint -x self-check
   - if [[ $TRAVIS_PYTHON_VERSION == '3.6' ]]; then flake8; fi
   - if [[ $TRAVIS_PYTHON_VERSION == '3.5.1' ]]; then python runtests.py package; fi

appveyor.yml

@@ -21,7 +21,7 @@ build: off
 
 test_script:
     # Ignore lint and mypy self check (both run in Travis)
-    - "%PYTHON%\\python.exe runtests.py -x lint -x package -x pytest"
+    - "%PYTHON%\\python.exe runtests.py -x lint -x self-check -x pytest"
     - "%PYTHON%\\python.exe runtests.py pytest -p -k -p \"not (PythonEvaluationSuite and python2)\""
 
 skip_commits:

docs/source/additional_features.rst

@@ -1,7 +1,124 @@
 Additional features
 -------------------
 
-This section discusses various features outside core mypy features.
+This section discusses various features that did not fit in naturally in one
+of the previous sections.
+
+.. _function-overloading:
+
+Function overloading
+********************
+
+Sometimes the types in a function depend on each other in ways that
+can't be captured with a ``Union``.  For example, the ``__getitem__``
+(``[]`` bracket indexing) method can take an integer and return a
+single item, or take a ``slice`` and return a ``Sequence`` of items.
+You might be tempted to annotate it like so:
+
+.. code-block:: python
+
+    from typing import Sequence, TypeVar, Union
+    T = TypeVar('T')
+
+    class MyList(Sequence[T]):
+        def __getitem__(self, index: Union[int, slice]) -> Union[T, Sequence[T]]:
+            if isinstance(index, int):
+                ...  # Return a T here
+            elif isinstance(index, slice):
+                ...  # Return a sequence of Ts here
+            else:
+                raise TypeError(...)
+
+But this is too loose, as it implies that when you pass in an ``int``
+you might sometimes get out a single item and sometimes a sequence.
+The return type depends on the parameter type in a way that can't be
+expressed using a type variable.  Instead, we can use `overloading
+<https://www.python.org/dev/peps/pep-0484/#function-method-overloading>`_
+to give the same function multiple type annotations (signatures) and
+accurately describe the function's behavior.
+
+.. code-block:: python
+
+    from typing import overload, Sequence, TypeVar, Union
+    T = TypeVar('T')
+
+    class MyList(Sequence[T]):
+
+        # The @overload definitions are just for the type checker,
+        # and overwritten by the real implementation below.
+        @overload
+        def __getitem__(self, index: int) -> T:
+            pass  # Don't put code here
+
+        # All overloads and the implementation must be adjacent
+        # in the source file, and overload order may matter:
+        # when two overloads may overlap, the more specific one
+        # should come first.
+        @overload
+        def __getitem__(self, index: slice) -> Sequence[T]:
+            pass  # Don't put code here
+
+        # The implementation goes last, without @overload.
+        # It may or may not have type hints; if it does,
+        # these are checked against the overload definitions
+        # as well as against the implementation body.
+        def __getitem__(self, index: Union[int, slice]) -> Union[T, Sequence[T]]:
+            # This is exactly the same as before.
+            if isinstance(index, int):
+                ...  # Return a T here
+            elif isinstance(index, slice):
+                ...  # Return a sequence of Ts here
+            else:
+                raise TypeError(...)
+
+Calls to overloaded functions are type checked against the variants,
+not against the implementation. A call like ``my_list[5]`` would have
+type ``T``, not ``Union[T, Sequence[T]]`` because it matches the
+first overloaded definition, and ignores the type annotations on the
+implementation of ``__getitem__``. The code in the body of the
+definition of ``__getitem__`` is checked against the annotations on
+the corresponding declaration. In this case the body is checked
+with ``index: Union[int, slice]`` and a return type
+``Union[T, Sequence[T]]``. If there are no annotations on the
+corresponding definition, then code in the function body is not type
+checked.
+
+The annotations on the function body must be compatible with the
+types given for the overloaded variants listed above it. The type
+checker will verify that all the types for the overloaded variants
+are compatible with the types given for the implementation. In this
+case it checks that the parameter type ``int`` and the return type
+``T`` are compatible with ``Union[int, slice]`` and
+``Union[T, Sequence[T]]`` for the first variant. For the second
+variant it verifies that the parameter type ``slice`` and the return
+type ``Sequence[T]`` are compatible with ``Union[int, slice]`` and
+``Union[T, Sequence[T]]``.
+
+Overloaded function variants are still ordinary Python functions and
+they still define a single runtime object. There is no automatic
+dispatch happening, and you must manually handle the different types
+in the implementation (usually with :func:`isinstance` checks, as
+shown in the example).
+
+The overload variants must be adjacent in the code. This makes code
+clearer, as you don't have to hunt for overload variants across the
+file.
+
+Overloads in stub files are exactly the same, except there is no
+implementation.
+
+.. note::
+
+   As generic type variables are erased at runtime when constructing
+   instances of generic types, an overloaded function cannot have
+   variants that only differ in a generic type argument,
+   e.g. ``List[int]`` and ``List[str]``.
+
+.. note::
+
+   If you just need to constrain a type variable to certain types or
+   subtypes, you can use a :ref:`value restriction
+   <type-variable-value-restriction>`.
 
 .. _attrs_package:
 
@@ -17,6 +134,7 @@ Type annotations can be added as follows:
 .. code-block:: python
 
     import attr
+
     @attr.s
     class A:
         one: int = attr.ib()          # Variable annotation (Python 3.6+)
@@ -28,6 +146,7 @@ If you're using ``auto_attribs=True`` you must use variable annotations.
 .. code-block:: python
 
     import attr
+
     @attr.s(auto_attribs=True)
     class A:
         one: int
@@ -43,6 +162,7 @@ That enables this to work:
 
     import attr
     from typing import Dict
+
     @attr.s(auto_attribs=True)
     class A:
         one: int = attr.ib(8)
@@ -175,7 +295,7 @@ Caching with mypy daemon
 ========================
 
 You can also use remote caching with the :ref:`mypy daemon <mypy_daemon>`.
-The remote cache will significantly speed up the the first ``dmypy check``
+The remote cache will significantly speed up the first ``dmypy check``
 run after starting or restarting the daemon.
 
 The mypy daemon requires extra fine-grained dependency data in
@@ -231,3 +351,129 @@ at least if your codebase is hundreds of thousands of lines or more:
   mypy build to create the cache data, as repeatedly updating cache
   data incrementally could result in drift over a long time period (due
   to a mypy caching issue, perhaps).
+
+.. _extended_callable:
+
+Extended Callable types
+***********************
+
+As an experimental mypy extension, you can specify ``Callable`` types
+that support keyword arguments, optional arguments, and more.  When
+you specify the arguments of a Callable, you can choose to supply just
+the type of a nameless positional argument, or an "argument specifier"
+representing a more complicated form of argument.  This allows one to
+more closely emulate the full range of possibilities given by the
+``def`` statement in Python.
+
+As an example, here's a complicated function definition and the
+corresponding ``Callable``:
+
+.. code-block:: python
+
+   from typing import Callable
+   from mypy_extensions import (Arg, DefaultArg, NamedArg,
+                                DefaultNamedArg, VarArg, KwArg)
+
+   def func(__a: int,  # This convention is for nameless arguments
+            b: int,
+            c: int = 0,
+            *args: int,
+            d: int,
+            e: int = 0,
+            **kwargs: int) -> int:
+       ...
+
+   F = Callable[[int,  # Or Arg(int)
+                 Arg(int, 'b'),
+                 DefaultArg(int, 'c'),
+                 VarArg(int),
+                 NamedArg(int, 'd'),
+                 DefaultNamedArg(int, 'e'),
+                 KwArg(int)],
+                int]
+
+   f: F = func
+
+Argument specifiers are special function calls that can specify the
+following aspects of an argument:
+
+- its type (the only thing that the basic format supports)
+
+- its name (if it has one)
+
+- whether it may be omitted
+
+- whether it may or must be passed using a keyword
+
+- whether it is a ``*args`` argument (representing the remaining
+  positional arguments)
+
+- whether it is a ``**kwargs`` argument (representing the remaining
+  keyword arguments)
+
+The following functions are available in ``mypy_extensions`` for this
+purpose:
+
+.. code-block:: python
+
+   def Arg(type=Any, name=None):
+       # A normal, mandatory, positional argument.
+       # If the name is specified it may be passed as a keyword.
+
+   def DefaultArg(type=Any, name=None):
+       # An optional positional argument (i.e. with a default value).
+       # If the name is specified it may be passed as a keyword.
+
+   def NamedArg(type=Any, name=None):
+       # A mandatory keyword-only argument.
+
+   def DefaultNamedArg(type=Any, name=None):
+       # An optional keyword-only argument (i.e. with a default value).
+
+   def VarArg(type=Any):
+       # A *args-style variadic positional argument.
+       # A single VarArg() specifier represents all remaining
+       # positional arguments.
+
+   def KwArg(type=Any):
+       # A **kwargs-style variadic keyword argument.
+       # A single KwArg() specifier represents all remaining
+       # keyword arguments.
+
+In all cases, the ``type`` argument defaults to ``Any``, and if the
+``name`` argument is omitted the argument has no name (the name is
+required for ``NamedArg`` and ``DefaultNamedArg``).  A basic
+``Callable`` such as
+
+.. code-block:: python
+
+   MyFunc = Callable[[int, str, int], float]
+
+is equivalent to the following:
+
+.. code-block:: python
+
+   MyFunc = Callable[[Arg(int), Arg(str), Arg(int)], float]
+
+A ``Callable`` with unspecified argument types, such as
+
+.. code-block:: python
+
+   MyOtherFunc = Callable[..., int]
+
+is (roughly) equivalent to
+
+.. code-block:: python
+
+   MyOtherFunc = Callable[[VarArg(), KwArg()], int]
+
+.. note::
+
+   This feature is experimental.  Details of the implementation may
+   change and there may be unknown limitations. **IMPORTANT:**
+   Each of the functions above currently just returns its ``type``
+   argument, so the information contained in the argument specifiers
+   is not available at runtime.  This limitation is necessary for
+   backwards compatibility with the existing ``typing.py`` module as
+   present in the Python 3.5+ standard library and distributed via
+   PyPI.

docs/source/casts.rst

@@ -1,7 +1,7 @@
 .. _casts:
 
-Casts
-=====
+Casts and type assertions
+=========================
 
 Mypy supports type casts that are usually used to coerce a statically
 typed value to a subtype. Unlike languages such as Java or C#,
@@ -13,17 +13,27 @@ cast:
 
    from typing import cast, List
 
-   o = [1] # type: object
+   o: object = [1]
    x = cast(List[int], o)  # OK
    y = cast(List[str], o)  # OK (cast performs no actual runtime check)
 
 To support runtime checking of casts such as the above, we'd have to check
 the types of all list items, which would be very inefficient for large lists.
-Use assertions if you want to
-perform an actual runtime check. Casts are used to silence spurious
+Casts are used to silence spurious
 type checker warnings and give the type checker a little help when it can't
 quite understand what is going on.
 
+.. note::
+
+   You can use an assertion if you want to perform an actual runtime check:
+
+   .. code-block:: python
+
+      def foo(o: object) -> None:
+          print(o + 5)  # Error: can't add 'object' and 'int'
+          assert isinstance(o, int)
+          print(o + 5)  # OK: type of 'o' is 'int' here
+
 You don't need a cast for expressions with type ``Any``, or when
 assigning to a variable with type ``Any``, as was explained earlier.
 You can also use ``Any`` as the cast target type -- this lets you perform
@@ -34,6 +44,6 @@ any operations on the result. For example:
     from typing import cast, Any
 
     x = 1
-    x + 'x'   # Type check error
+    x.whatever()  # Type check error
     y = cast(Any, x)
-    y + 'x'   # Type check OK (runtime error)
+    y.whatever()  # Type check OK (runtime error)

docs/source/cheat_sheet.rst

@@ -1,7 +1,7 @@
 .. _cheat-sheet-py2:
 
-Mypy syntax cheat sheet (Python 2)
-==================================
+Type hints cheat sheet (Python 2)
+=================================
 
 This document is a quick cheat sheet showing how the `PEP 484 <https://www.python.org/dev/peps/pep-0484/>`_ type
 language represents various common types in Python 2.

docs/source/cheat_sheet_py3.rst

@@ -1,7 +1,7 @@
 .. _cheat-sheet-py3:
 
-Mypy syntax cheat sheet (Python 3)
-==================================
+Type hints cheat sheet (Python 3)
+=================================
 
 This document is a quick cheat sheet showing how the `PEP 484 <https://www.python.org/dev/peps/pep-0484/>`_ type
 language represents various common types in Python 3. Unless otherwise noted, the syntax is valid on all versions of Python 3.