"typing" --- Support for type hints
***********************************

3.5 版新加入.

**Source code:** Lib/typing.py

備註:

  The Python runtime does not enforce function and variable type
  annotations. They can be used by third party tools such as type
  checkers, IDEs, linters, etc.

======================================================================

This module provides runtime support for type hints as specified by
**PEP 484**, **PEP 526**, **PEP 544**, **PEP 586**, **PEP 589**, and
**PEP 591**. The most fundamental support consists of the types "Any",
"Union", "Tuple", "Callable", "TypeVar", and "Generic".  For full
specification please see **PEP 484**.  For a simplified introduction
to type hints see **PEP 483**.

The function below takes and returns a string and is annotated as
follows:

   def greeting(name: str) -> str:
       return 'Hello ' + name

In the function "greeting", the argument "name" is expected to be of
type "str" and the return type "str". Subtypes are accepted as
arguments.


Type aliases
============

A type alias is defined by assigning the type to the alias. In this
example, "Vector" and "List[float]" will be treated as interchangeable
synonyms:

   from typing import List
   Vector = List[float]

   def scale(scalar: float, vector: Vector) -> Vector:
       return [scalar * num for num in vector]

   # typechecks; a list of floats qualifies as a Vector.
   new_vector = scale(2.0, [1.0, -4.2, 5.4])

Type aliases are useful for simplifying complex type signatures. For
example:

   from typing import Dict, Tuple, Sequence

   ConnectionOptions = Dict[str, str]
   Address = Tuple[str, int]
   Server = Tuple[Address, ConnectionOptions]

   def broadcast_message(message: str, servers: Sequence[Server]) -> None:
       ...

   # The static type checker will treat the previous type signature as
   # being exactly equivalent to this one.
   def broadcast_message(
           message: str,
           servers: Sequence[Tuple[Tuple[str, int], Dict[str, str]]]) -> None:
       ...

Note that "None" as a type hint is a special case and is replaced by
"type(None)".


NewType
=======

Use the "NewType()" helper function to create distinct types:

   from typing import NewType

   UserId = NewType('UserId', int)
   some_id = UserId(524313)

The static type checker will treat the new type as if it were a
subclass of the original type. This is useful in helping catch logical
errors:

   def get_user_name(user_id: UserId) -> str:
       ...

   # typechecks
   user_a = get_user_name(UserId(42351))

   # does not typecheck; an int is not a UserId
   user_b = get_user_name(-1)

You may still perform all "int" operations on a variable of type
"UserId", but the result will always be of type "int". This lets you
pass in a "UserId" wherever an "int" might be expected, but will
prevent you from accidentally creating a "UserId" in an invalid way:

   # 'output' is of type 'int', not 'UserId'
   output = UserId(23413) + UserId(54341)

Note that these checks are enforced only by the static type checker.
At runtime, the statement "Derived = NewType('Derived', Base)" will
make "Derived" a function that immediately returns whatever parameter
you pass it. That means the expression "Derived(some_value)" does not
create a new class or introduce any overhead beyond that of a regular
function call.

More precisely, the expression "some_value is Derived(some_value)" is
always true at runtime.

This also means that it is not possible to create a subtype of
"Derived" since it is an identity function at runtime, not an actual
type:

   from typing import NewType

   UserId = NewType('UserId', int)

   # Fails at runtime and does not typecheck
   class AdminUserId(UserId): pass

However, it is possible to create a "NewType()" based on a 'derived'
"NewType":

   from typing import NewType

   UserId = NewType('UserId', int)

   ProUserId = NewType('ProUserId', UserId)

and typechecking for "ProUserId" will work as expected.

See **PEP 484** for more details.

備註:

  Recall that the use of a type alias declares two types to be
  *equivalent* to one another. Doing "Alias = Original" will make the
  static type checker treat "Alias" as being *exactly equivalent* to
  "Original" in all cases. This is useful when you want to simplify
  complex type signatures.In contrast, "NewType" declares one type to
  be a *subtype* of another. Doing "Derived = NewType('Derived',
  Original)" will make the static type checker treat "Derived" as a
  *subclass* of "Original", which means a value of type "Original"
  cannot be used in places where a value of type "Derived" is
  expected. This is useful when you want to prevent logic errors with
  minimal runtime cost.

3.5.2 版新加入.


Callable
========

Frameworks expecting callback functions of specific signatures might
be type hinted using "Callable[[Arg1Type, Arg2Type], ReturnType]".

For example:

   from typing import Callable

   def feeder(get_next_item: Callable[[], str]) -> None:
       # Body

   def async_query(on_success: Callable[[int], None],
                   on_error: Callable[[int, Exception], None]) -> None:
       # Body

It is possible to declare the return type of a callable without
specifying the call signature by substituting a literal ellipsis for
the list of arguments in the type hint: "Callable[..., ReturnType]".


Generics
========

Since type information about objects kept in containers cannot be
statically inferred in a generic way, abstract base classes have been
extended to support subscription to denote expected types for
container elements.

   from typing import Mapping, Sequence

   def notify_by_email(employees: Sequence[Employee],
                       overrides: Mapping[str, str]) -> None: ...

Generics can be parameterized by using a new factory available in
typing called "TypeVar".

   from typing import Sequence, TypeVar

   T = TypeVar('T')      # Declare type variable

   def first(l: Sequence[T]) -> T:   # Generic function
       return l[0]


User-defined generic types
==========================

A user-defined class can be defined as a generic class.

   from typing import TypeVar, Generic
   from logging import Logger

   T = TypeVar('T')

   class LoggedVar(Generic[T]):
       def __init__(self, value: T, name: str, logger: Logger) -> None:
           self.name = name
           self.logger = logger
           self.value = value

       def set(self, new: T) -> None:
           self.log('Set ' + repr(self.value))
           self.value = new

       def get(self) -> T:
           self.log('Get ' + repr(self.value))
           return self.value

       def log(self, message: str) -> None:
           self.logger.info('%s: %s', self.name, message)

"Generic[T]" as a base class defines that the class "LoggedVar" takes
a single type parameter "T" . This also makes "T" valid as a type
within the class body.

The "Generic" base class defines "__class_getitem__()" so that
"LoggedVar[t]" is valid as a type:

   from typing import Iterable

   def zero_all_vars(vars: Iterable[LoggedVar[int]]) -> None:
       for var in vars:
           var.set(0)

A generic type can have any number of type variables, and type
variables may be constrained:

   from typing import TypeVar, Generic
   ...

   T = TypeVar('T')
   S = TypeVar('S', int, str)

   class StrangePair(Generic[T, S]):
       ...

Each type variable argument to "Generic" must be distinct. This is
thus invalid:

   from typing import TypeVar, Generic
   ...

   T = TypeVar('T')

   class Pair(Generic[T, T]):   # INVALID
       ...

You can use multiple inheritance with "Generic":

   from typing import TypeVar, Generic, Sized

   T = TypeVar('T')

   class LinkedList(Sized, Generic[T]):
       ...

When inheriting from generic classes, some type variables could be
fixed:

   from typing import TypeVar, Mapping

   T = TypeVar('T')

   class MyDict(Mapping[str, T]):
       ...

In this case "MyDict" has a single parameter, "T".

Using a generic class without specifying type parameters assumes "Any"
for each position. In the following example, "MyIterable" is not
generic but implicitly inherits from "Iterable[Any]":

   from typing import Iterable

   class MyIterable(Iterable): # Same as Iterable[Any]

User defined generic type aliases are also supported. Examples:

   from typing import TypeVar, Iterable, Tuple, Union
   S = TypeVar('S')
   Response = Union[Iterable[S], int]

   # Return type here is same as Union[Iterable[str], int]
   def response(query: str) -> Response[str]:
       ...

   T = TypeVar('T', int, float, complex)
   Vec = Iterable[Tuple[T, T]]

   def inproduct(v: Vec[T]) -> T: # Same as Iterable[Tuple[T, T]]
       return sum(x*y for x, y in v)

3.7 版更變: "Generic" no longer has a custom metaclass.

A user-defined generic class can have ABCs as base classes without a
metaclass conflict. Generic metaclasses are not supported. The outcome
of parameterizing generics is cached, and most types in the typing
module are hashable and comparable for equality.


The "Any" type
==============

A special kind of type is "Any". A static type checker will treat
every type as being compatible with "Any" and "Any" as being
compatible with every type.

This means that it is possible to perform any operation or method call
on a value of type "Any" and assign it to any variable:

   from typing import Any

   a = None    # type: Any
   a = []      # OK
   a = 2       # OK

   s = ''      # type: str
   s = a       # OK

   def foo(item: Any) -> int:
       # Typechecks; 'item' could be any type,
       # and that type might have a 'bar' method
       item.bar()
       ...

Notice that no typechecking is performed when assigning a value of
type "Any" to a more precise type. For example, the static type
checker did not report an error when assigning "a" to "s" even though
"s" was declared to be of type "str" and receives an "int" value at
runtime!

Furthermore, all functions without a return type or parameter types
will implicitly default to using "Any":

   def legacy_parser(text):
       ...
       return data

   # A static type checker will treat the above
   # as having the same signature as:
   def legacy_parser(text: Any) -> Any:
       ...
       return data

This behavior allows "Any" to be used as an *escape hatch* when you
need to mix dynamically and statically typed code.

Contrast the behavior of "Any" with the behavior of "object". Similar
to "Any", every type is a subtype of "object". However, unlike "Any",
the reverse is not true: "object" is *not* a subtype of every other
type.

That means when the type of a value is "object", a type checker will
reject almost all operations on it, and assigning it to a variable (or
using it as a return value) of a more specialized type is a type
error. For example:

   def hash_a(item: object) -> int:
       # Fails; an object does not have a 'magic' method.
       item.magic()
       ...

   def hash_b(item: Any) -> int:
       # Typechecks
       item.magic()
       ...

   # Typechecks, since ints and strs are subclasses of object
   hash_a(42)
   hash_a("foo")

   # Typechecks, since Any is compatible with all types
   hash_b(42)
   hash_b("foo")

Use "object" to indicate that a value could be any type in a typesafe
manner. Use "Any" to indicate that a value is dynamically typed.


Nominal vs structural subtyping
===============================

Initially **PEP 484** defined Python static type system as using
*nominal subtyping*. This means that a class "A" is allowed where a
class "B" is expected if and only if "A" is a subclass of "B".

This requirement previously also applied to abstract base classes,
such as "Iterable". The problem with this approach is that a class had
to be explicitly marked to support them, which is unpythonic and
unlike what one would normally do in idiomatic dynamically typed
Python code. For example, this conforms to **PEP 484**:

   from typing import Sized, Iterable, Iterator

   class Bucket(Sized, Iterable[int]):
       ...
       def __len__(self) -> int: ...
       def __iter__(self) -> Iterator[int]: ...

**PEP 544** allows to solve this problem by allowing users to write
the above code without explicit base classes in the class definition,
allowing "Bucket" to be implicitly considered a subtype of both
"Sized" and "Iterable[int]" by static type checkers. This is known as
*structural subtyping* (or static duck-typing):

   from typing import Iterator, Iterable

   class Bucket:  # Note: no base classes
       ...
       def __len__(self) -> int: ...
       def __iter__(self) -> Iterator[int]: ...

   def collect(items: Iterable[int]) -> int: ...
   result = collect(Bucket())  # Passes type check

Moreover, by subclassing a special class "Protocol", a user can define
new custom protocols to fully enjoy structural subtyping (see examples
below).


Classes, functions, and decorators
==================================

The module defines the following classes, functions and decorators:

class typing.TypeVar

   Type variable.

   Usage:

      T = TypeVar('T')  # Can be anything
      A = TypeVar('A', str, bytes)  # Must be str or bytes

   Type variables exist primarily for the benefit of static type
   checkers.  They serve as the parameters for generic types as well
   as for generic function definitions.  See class Generic for more
   information on generic types.  Generic functions work as follows:

      def repeat(x: T, n: int) -> Sequence[T]:
          """Return a list containing n references to x."""
          return [x]*n

      def longest(x: A, y: A) -> A:
          """Return the longest of two strings."""
          return x if len(x) >= len(y) else y

   The latter example's signature is essentially the overloading of
   "(str, str) -> str" and "(bytes, bytes) -> bytes".  Also note that
   if the arguments are instances of some subclass of "str", the
   return type is still plain "str".

   At runtime, "isinstance(x, T)" will raise "TypeError".  In general,
   "isinstance()" and "issubclass()" should not be used with types.

   Type variables may be marked covariant or contravariant by passing
   "covariant=True" or "contravariant=True".  See **PEP 484** for more
   details.  By default type variables are invariant.  Alternatively,
   a type variable may specify an upper bound using "bound=<type>".
   This means that an actual type substituted (explicitly or
   implicitly) for the type variable must be a subclass of the
   boundary type, see **PEP 484**.

class typing.Generic

   Abstract base class for generic types.

   A generic type is typically declared by inheriting from an
   instantiation of this class with one or more type variables. For
   example, a generic mapping type might be defined as:

      class Mapping(Generic[KT, VT]):
          def __getitem__(self, key: KT) -> VT:
              ...
              # Etc.

   This class can then be used as follows:

      X = TypeVar('X')
      Y = TypeVar('Y')

      def lookup_name(mapping: Mapping[X, Y], key: X, default: Y) -> Y:
          try:
              return mapping[key]
          except KeyError:
              return default

class typing.Protocol(Generic)

   Base class for protocol classes. Protocol classes are defined like
   this:

      class Proto(Protocol):
          def meth(self) -> int:
              ...

   Such classes are primarily used with static type checkers that
   recognize structural subtyping (static duck-typing), for example:

      class C:
          def meth(self) -> int:
              return 0

      def func(x: Proto) -> int:
          return x.meth()

      func(C())  # Passes static type check

   See **PEP 544** for details. Protocol classes decorated with
   "runtime_checkable()" (described later) act as simple-minded
   runtime protocols that check only the presence of given attributes,
   ignoring their type signatures.

   Protocol classes can be generic, for example:

      class GenProto(Protocol[T]):
          def meth(self) -> T:
              ...

   3.8 版新加入.

class typing.Type(Generic[CT_co])

   A variable annotated with "C" may accept a value of type "C". In
   contrast, a variable annotated with "Type[C]" may accept values
   that are classes themselves -- specifically, it will accept the
   *class object* of "C". For example:

      a = 3         # Has type 'int'
      b = int       # Has type 'Type[int]'
      c = type(a)   # Also has type 'Type[int]'

   Note that "Type[C]" is covariant:

      class User: ...
      class BasicUser(User): ...
      class ProUser(User): ...
      class TeamUser(User): ...

      # Accepts User, BasicUser, ProUser, TeamUser, ...
      def make_new_user(user_class: Type[User]) -> User:
          # ...
          return user_class()

   The fact that "Type[C]" is covariant implies that all subclasses of
   "C" should implement the same constructor signature and class
   method signatures as "C". The type checker should flag violations
   of this, but should also allow constructor calls in subclasses that
   match the constructor calls in the indicated base class. How the
   type checker is required to handle this particular case may change
   in future revisions of **PEP 484**.

   The only legal parameters for "Type" are classes, "Any", type
   variables, and unions of any of these types. For example:

      def new_non_team_user(user_class: Type[Union[BasicUser, ProUser]]): ...

   "Type[Any]" is equivalent to "Type" which in turn is equivalent to
   "type", which is the root of Python's metaclass hierarchy.

   3.5.2 版新加入.

class typing.Iterable(Generic[T_co])

   A generic version of "collections.abc.Iterable".

class typing.Iterator(Iterable[T_co])

   A generic version of "collections.abc.Iterator".

class typing.Reversible(Iterable[T_co])

   A generic version of "collections.abc.Reversible".

class typing.SupportsInt

   An ABC with one abstract method "__int__".

class typing.SupportsFloat

   An ABC with one abstract method "__float__".

class typing.SupportsComplex

   An ABC with one abstract method "__complex__".

class typing.SupportsBytes

   An ABC with one abstract method "__bytes__".

class typing.SupportsIndex

   An ABC with one abstract method "__index__".

   3.8 版新加入.

class typing.SupportsAbs

   An ABC with one abstract method "__abs__" that is covariant in its
   return type.

class typing.SupportsRound

   An ABC with one abstract method "__round__" that is covariant in
   its return type.

class typing.Container(Generic[T_co])

   A generic version of "collections.abc.Container".

class typing.Hashable

   An alias to "collections.abc.Hashable"

class typing.Sized

   An alias to "collections.abc.Sized"

class typing.Collection(Sized, Iterable[T_co], Container[T_co])

   A generic version of "collections.abc.Collection"

   3.6.0 版新加入.

class typing.AbstractSet(Sized, Collection[T_co])

   A generic version of "collections.abc.Set".

class typing.MutableSet(AbstractSet[T])

   A generic version of "collections.abc.MutableSet".

class typing.Mapping(Sized, Collection[KT], Generic[VT_co])

   A generic version of "collections.abc.Mapping". This type can be
   used as follows:

      def get_position_in_index(word_list: Mapping[str, int], word: str) -> int:
          return word_list[word]

class typing.MutableMapping(Mapping[KT, VT])

   A generic version of "collections.abc.MutableMapping".

class typing.Sequence(Reversible[T_co], Collection[T_co])

   A generic version of "collections.abc.Sequence".

class typing.MutableSequence(Sequence[T])

   A generic version of "collections.abc.MutableSequence".

class typing.ByteString(Sequence[int])

   A generic version of "collections.abc.ByteString".

   This type represents the types "bytes", "bytearray", and
   "memoryview" of byte sequences.

   As a shorthand for this type, "bytes" can be used to annotate
   arguments of any of the types mentioned above.

class typing.Deque(deque, MutableSequence[T])

   A generic version of "collections.deque".

   3.5.4 版新加入.

   3.6.1 版新加入.

class typing.List(list, MutableSequence[T])

   Generic version of "list". Useful for annotating return types. To
   annotate arguments it is preferred to use an abstract collection
   type such as "Sequence" or "Iterable".

   This type may be used as follows:

      T = TypeVar('T', int, float)

      def vec2(x: T, y: T) -> List[T]:
          return [x, y]

      def keep_positives(vector: Sequence[T]) -> List[T]:
          return [item for item in vector if item > 0]

class typing.Set(set, MutableSet[T])

   A generic version of "builtins.set". Useful for annotating return
   types. To annotate arguments it is preferred to use an abstract
   collection type such as "AbstractSet".

class typing.FrozenSet(frozenset, AbstractSet[T_co])

   A generic version of "builtins.frozenset".

class typing.MappingView(Sized, Iterable[T_co])

   A generic version of "collections.abc.MappingView".

class typing.KeysView(MappingView[KT_co], AbstractSet[KT_co])

   A generic version of "collections.abc.KeysView".

class typing.ItemsView(MappingView, Generic[KT_co, VT_co])

   A generic version of "collections.abc.ItemsView".

class typing.ValuesView(MappingView[VT_co])

   A generic version of "collections.abc.ValuesView".

class typing.Awaitable(Generic[T_co])

   A generic version of "collections.abc.Awaitable".

   3.5.2 版新加入.

class typing.Coroutine(Awaitable[V_co], Generic[T_co, T_contra, V_co])

   A generic version of "collections.abc.Coroutine". The variance and
   order of type variables correspond to those of "Generator", for
   example:

      from typing import List, Coroutine
      c = None # type: Coroutine[List[str], str, int]
      ...
      x = c.send('hi') # type: List[str]
      async def bar() -> None:
          x = await c # type: int

   3.5.3 版新加入.

class typing.AsyncIterable(Generic[T_co])

   A generic version of "collections.abc.AsyncIterable".

   3.5.2 版新加入.

class typing.AsyncIterator(AsyncIterable[T_co])

   A generic version of "collections.abc.AsyncIterator".

   3.5.2 版新加入.

class typing.ContextManager(Generic[T_co])

   A generic version of "contextlib.AbstractContextManager".

   3.5.4 版新加入.

   3.6.0 版新加入.

class typing.AsyncContextManager(Generic[T_co])

   A generic version of "contextlib.AbstractAsyncContextManager".

   3.5.4 版新加入.

   3.6.2 版新加入.

class typing.Dict(dict, MutableMapping[KT, VT])

   A generic version of "dict". Useful for annotating return types. To
   annotate arguments it is preferred to use an abstract collection
   type such as "Mapping".

   This type can be used as follows:

      def count_words(text: str) -> Dict[str, int]:
          ...

class typing.DefaultDict(collections.defaultdict, MutableMapping[KT, VT])

   A generic version of "collections.defaultdict".

   3.5.2 版新加入.

class typing.OrderedDict(collections.OrderedDict, MutableMapping[KT, VT])

   A generic version of "collections.OrderedDict".

   3.7.2 版新加入.

class typing.Counter(collections.Counter, Dict[T, int])

   A generic version of "collections.Counter".

   3.5.4 版新加入.

   3.6.1 版新加入.

class typing.ChainMap(collections.ChainMap, MutableMapping[KT, VT])

   A generic version of "collections.ChainMap".

   3.5.4 版新加入.

   3.6.1 版新加入.

class typing.Generator(Iterator[T_co], Generic[T_co, T_contra, V_co])

   A generator can be annotated by the generic type
   "Generator[YieldType, SendType, ReturnType]". For example:

      def echo_round() -> Generator[int, float, str]:
          sent = yield 0
          while sent >= 0:
              sent = yield round(sent)
          return 'Done'

   Note that unlike many other generics in the typing module, the
   "SendType" of "Generator" behaves contravariantly, not covariantly
   or invariantly.

   If your generator will only yield values, set the "SendType" and
   "ReturnType" to "None":

      def infinite_stream(start: int) -> Generator[int, None, None]:
          while True:
              yield start
              start += 1

   Alternatively, annotate your generator as having a return type of
   either "Iterable[YieldType]" or "Iterator[YieldType]":

      def infinite_stream(start: int) -> Iterator[int]:
          while True:
              yield start
              start += 1

class typing.AsyncGenerator(AsyncIterator[T_co], Generic[T_co, T_contra])

   An async generator can be annotated by the generic type
   "AsyncGenerator[YieldType, SendType]". For example:

      async def echo_round() -> AsyncGenerator[int, float]:
          sent = yield 0
          while sent >= 0.0:
              rounded = await round(sent)
              sent = yield rounded

   Unlike normal generators, async generators cannot return a value,
   so there is no "ReturnType" type parameter. As with "Generator",
   the "SendType" behaves contravariantly.

   If your generator will only yield values, set the "SendType" to
   "None":

      async def infinite_stream(start: int) -> AsyncGenerator[int, None]:
          while True:
              yield start
              start = await increment(start)

   Alternatively, annotate your generator as having a return type of
   either "AsyncIterable[YieldType]" or "AsyncIterator[YieldType]":

      async def infinite_stream(start: int) -> AsyncIterator[int]:
          while True:
              yield start
              start = await increment(start)

   3.6.1 版新加入.

class typing.Text

   "Text" is an alias for "str". It is provided to supply a forward
   compatible path for Python 2 code: in Python 2, "Text" is an alias
   for "unicode".

   Use "Text" to indicate that a value must contain a unicode string
   in a manner that is compatible with both Python 2 and Python 3:

      def add_unicode_checkmark(text: Text) -> Text:
          return text + u' \u2713'

   3.5.2 版新加入.

class typing.IO
class typing.TextIO
class typing.BinaryIO

   Generic type "IO[AnyStr]" and its subclasses "TextIO(IO[str])" and
   "BinaryIO(IO[bytes])" represent the types of I/O streams such as
   returned by "open()".

class typing.Pattern
class typing.Match

   These type aliases correspond to the return types from
   "re.compile()" and "re.match()".  These types (and the
   corresponding functions) are generic in "AnyStr" and can be made
   specific by writing "Pattern[str]", "Pattern[bytes]", "Match[str]",
   or "Match[bytes]".

class typing.NamedTuple

   Typed version of "collections.namedtuple()".

   Usage:

      class Employee(NamedTuple):
          name: str
          id: int

   This is equivalent to:

      Employee = collections.namedtuple('Employee', ['name', 'id'])

   To give a field a default value, you can assign to it in the class
   body:

      class Employee(NamedTuple):
          name: str
          id: int = 3

      employee = Employee('Guido')
      assert employee.id == 3

   Fields with a default value must come after any fields without a
   default.

   The resulting class has an extra attribute "__annotations__" giving
   a dict that maps the field names to the field types.  (The field
   names are in the "_fields" attribute and the default values are in
   the "_field_defaults" attribute both of which are part of the
   namedtuple API.)

   "NamedTuple" subclasses can also have docstrings and methods:

      class Employee(NamedTuple):
          """Represents an employee."""
          name: str
          id: int = 3

          def __repr__(self) -> str:
              return f'<Employee {self.name}, id={self.id}>'

   Backward-compatible usage:

      Employee = NamedTuple('Employee', [('name', str), ('id', int)])

   3.6 版更變: Added support for **PEP 526** variable annotation
   syntax.

   3.6.1 版更變: Added support for default values, methods, and
   docstrings.

   Deprecated since version 3.8, will be removed in version 3.9:
   Deprecated the "_field_types" attribute in favor of the more
   standard "__annotations__" attribute which has the same
   information.

   3.8 版更變: The "_field_types" and "__annotations__" attributes are
   now regular dictionaries instead of instances of "OrderedDict".

class typing.TypedDict(dict)

   A simple typed namespace. At runtime it is equivalent to a plain
   "dict".

   "TypedDict" creates a dictionary type that expects all of its
   instances to have a certain set of keys, where each key is
   associated with a value of a consistent type. This expectation is
   not checked at runtime but is only enforced by type checkers.
   Usage:

      class Point2D(TypedDict):
          x: int
          y: int
          label: str

      a: Point2D = {'x': 1, 'y': 2, 'label': 'good'}  # OK
      b: Point2D = {'z': 3, 'label': 'bad'}           # Fails type check

      assert Point2D(x=1, y=2, label='first') == dict(x=1, y=2, label='first')

   The type info for introspection can be accessed via
   "Point2D.__annotations__" and "Point2D.__total__".  To allow using
   this feature with older versions of Python that do not support
   **PEP 526**, "TypedDict" supports two additional equivalent
   syntactic forms:

      Point2D = TypedDict('Point2D', x=int, y=int, label=str)
      Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': str})

   By default, all keys must be present in a TypedDict. It is possible
   to override this by specifying totality. Usage:

      class point2D(TypedDict, total=False):
          x: int
          y: int

   This means that a point2D TypedDict can have any of the keys
   omitted. A type checker is only expected to support a literal False
   or True as the value of the total argument. True is the default,
   and makes all items defined in the class body be required.

   See **PEP 589** for more examples and detailed rules of using
   "TypedDict".

   3.8 版新加入.

class typing.ForwardRef

   A class used for internal typing representation of string forward
   references. For example, "List["SomeClass"]" is implicitly
   transformed into "List[ForwardRef("SomeClass")]".  This class
   should not be instantiated by a user, but may be used by
   introspection tools.

   3.7.4 版新加入.

typing.NewType(name, tp)

   A helper function to indicate a distinct type to a typechecker, see
   NewType. At runtime it returns a function that returns its
   argument. Usage:

      UserId = NewType('UserId', int)
      first_user = UserId(1)

   3.5.2 版新加入.

typing.cast(typ, val)

   Cast a value to a type.

   This returns the value unchanged.  To the type checker this signals
   that the return value has the designated type, but at runtime we
   intentionally don't check anything (we want this to be as fast as
   possible).

typing.get_type_hints(obj[, globals[, locals]])

   Return a dictionary containing type hints for a function, method,
   module or class object.

   This is often the same as "obj.__annotations__". In addition,
   forward references encoded as string literals are handled by
   evaluating them in "globals" and "locals" namespaces. If necessary,
   "Optional[t]" is added for function and method annotations if a
   default value equal to "None" is set. For a class "C", return a
   dictionary constructed by merging all the "__annotations__" along
   "C.__mro__" in reverse order.

typing.get_origin(tp)

typing.get_args(tp)

   Provide basic introspection for generic types and special typing
   forms.

   For a typing object of the form "X[Y, Z, ...]" these functions
   return "X" and "(Y, Z, ...)". If "X" is a generic alias for a
   builtin or "collections" class, it gets normalized to the original
   class. For unsupported objects return "None" and "()"
   correspondingly. Examples:

      assert get_origin(Dict[str, int]) is dict
      assert get_args(Dict[int, str]) == (int, str)

      assert get_origin(Union[int, str]) is Union
      assert get_args(Union[int, str]) == (int, str)

   3.8 版新加入.

@typing.overload

   The "@overload" decorator allows describing functions and methods
   that support multiple different combinations of argument types. A
   series of "@overload"-decorated definitions must be followed by
   exactly one non-"@overload"-decorated definition (for the same
   function/method). The "@overload"-decorated definitions are for the
   benefit of the type checker only, since they will be overwritten by
   the non-"@overload"-decorated definition, while the latter is used
   at runtime but should be ignored by a type checker.  At runtime,
   calling a "@overload"-decorated function directly will raise
   "NotImplementedError". An example of overload that gives a more
   precise type than can be expressed using a union or a type
   variable:

      @overload
      def process(response: None) -> None:
          ...
      @overload
      def process(response: int) -> Tuple[int, str]:
          ...
      @overload
      def process(response: bytes) -> str:
          ...
      def process(response):
          <actual implementation>

   See **PEP 484** for details and comparison with other typing
   semantics.

@typing.final

   A decorator to indicate to type checkers that the decorated method
   cannot be overridden, and the decorated class cannot be subclassed.
   For example:

      class Base:
          @final
          def done(self) -> None:
              ...
      class Sub(Base):
          def done(self) -> None:  # Error reported by type checker
                ...

      @final
      class Leaf:
          ...
      class Other(Leaf):  # Error reported by type checker
          ...

   There is no runtime checking of these properties. See **PEP 591**
   for more details.

   3.8 版新加入.

@typing.no_type_check

   Decorator to indicate that annotations are not type hints.

   This works as class or function *decorator*.  With a class, it
   applies recursively to all methods defined in that class (but not
   to methods defined in its superclasses or subclasses).

   This mutates the function(s) in place.

@typing.no_type_check_decorator

   Decorator to give another decorator the "no_type_check()" effect.

   This wraps the decorator with something that wraps the decorated
   function in "no_type_check()".

@typing.type_check_only

   Decorator to mark a class or function to be unavailable at runtime.

   This decorator is itself not available at runtime. It is mainly
   intended to mark classes that are defined in type stub files if an
   implementation returns an instance of a private class:

      @type_check_only
      class Response:  # private or not available at runtime
          code: int
          def get_header(self, name: str) -> str: ...

      def fetch_response() -> Response: ...

   Note that returning instances of private classes is not
   recommended. It is usually preferable to make such classes public.

@typing.runtime_checkable

   Mark a protocol class as a runtime protocol.

   Such a protocol can be used with "isinstance()" and "issubclass()".
   This raises "TypeError" when applied to a non-protocol class.  This
   allows a simple-minded structural check, very similar to "one trick
   ponies" in "collections.abc" such as "Iterable".  For example:

      @runtime_checkable
      class Closable(Protocol):
          def close(self): ...

      assert isinstance(open('/some/file'), Closable)

   **Warning:** this will check only the presence of the required
   methods, not their type signatures!

   3.8 版新加入.

typing.Any

   Special type indicating an unconstrained type.

   * Every type is compatible with "Any".

   * "Any" is compatible with every type.

typing.NoReturn

   Special type indicating that a function never returns. For example:

      from typing import NoReturn

      def stop() -> NoReturn:
          raise RuntimeError('no way')

   3.5.4 版新加入.

   3.6.2 版新加入.

typing.Union

   Union type; "Union[X, Y]" means either X or Y.

   To define a union, use e.g. "Union[int, str]".  Details:

   * The arguments must be types and there must be at least one.

   * Unions of unions are flattened, e.g.:

        Union[Union[int, str], float] == Union[int, str, float]

   * Unions of a single argument vanish, e.g.:

        Union[int] == int  # The constructor actually returns int

   * Redundant arguments are skipped, e.g.:

        Union[int, str, int] == Union[int, str]

   * When comparing unions, the argument order is ignored, e.g.:

        Union[int, str] == Union[str, int]

   * You cannot subclass or instantiate a union.

   * You cannot write "Union[X][Y]".

   * You can use "Optional[X]" as a shorthand for "Union[X, None]".

   3.7 版更變: Don't remove explicit subclasses from unions at
   runtime.

typing.Optional

   Optional type.

   "Optional[X]" is equivalent to "Union[X, None]".

   Note that this is not the same concept as an optional argument,
   which is one that has a default.  An optional argument with a
   default does not require the "Optional" qualifier on its type
   annotation just because it is optional. For example:

      def foo(arg: int = 0) -> None:
          ...

   On the other hand, if an explicit value of "None" is allowed, the
   use of "Optional" is appropriate, whether the argument is optional
   or not. For example:

      def foo(arg: Optional[int] = None) -> None:
          ...

typing.Tuple

   Tuple type; "Tuple[X, Y]" is the type of a tuple of two items with
   the first item of type X and the second of type Y. The type of the
   empty tuple can be written as "Tuple[()]".

   Example: "Tuple[T1, T2]" is a tuple of two elements corresponding
   to type variables T1 and T2.  "Tuple[int, float, str]" is a tuple
   of an int, a float and a string.

   To specify a variable-length tuple of homogeneous type, use literal
   ellipsis, e.g. "Tuple[int, ...]". A plain "Tuple" is equivalent to
   "Tuple[Any, ...]", and in turn to "tuple".

typing.Callable

   Callable type; "Callable[[int], str]" is a function of (int) ->
   str.

   The subscription syntax must always be used with exactly two
   values: the argument list and the return type.  The argument list
   must be a list of types or an ellipsis; the return type must be a
   single type.

   There is no syntax to indicate optional or keyword arguments; such
   function types are rarely used as callback types. "Callable[...,
   ReturnType]" (literal ellipsis) can be used to type hint a callable
   taking any number of arguments and returning "ReturnType".  A plain
   "Callable" is equivalent to "Callable[..., Any]", and in turn to
   "collections.abc.Callable".

typing.Literal

   A type that can be used to indicate to type checkers that the
   corresponding variable or function parameter has a value equivalent
   to the provided literal (or one of several literals). For example:

      def validate_simple(data: Any) -> Literal[True]:  # always returns True
          ...

      MODE = Literal['r', 'rb', 'w', 'wb']
      def open_helper(file: str, mode: MODE) -> str:
          ...

      open_helper('/some/path', 'r')  # Passes type check
      open_helper('/other/path', 'typo')  # Error in type checker

   "Literal[...]" cannot be subclassed. At runtime, an arbitrary value
   is allowed as type argument to "Literal[...]", but type checkers
   may impose restrictions. See **PEP 586** for more details about
   literal types.

   3.8 版新加入.

typing.ClassVar

   Special type construct to mark class variables.

   As introduced in **PEP 526**, a variable annotation wrapped in
   ClassVar indicates that a given attribute is intended to be used as
   a class variable and should not be set on instances of that class.
   Usage:

      class Starship:
          stats: ClassVar[Dict[str, int]] = {} # class variable
          damage: int = 10                     # instance variable

   "ClassVar" accepts only types and cannot be further subscribed.

   "ClassVar" is not a class itself, and should not be used with
   "isinstance()" or "issubclass()". "ClassVar" does not change Python
   runtime behavior, but it can be used by third-party type checkers.
   For example, a type checker might flag the following code as an
   error:

      enterprise_d = Starship(3000)
      enterprise_d.stats = {} # Error, setting class variable on instance
      Starship.stats = {}     # This is OK

   3.5.3 版新加入.

typing.Final

   A special typing construct to indicate to type checkers that a name
   cannot be re-assigned or overridden in a subclass. For example:

      MAX_SIZE: Final = 9000
      MAX_SIZE += 1  # Error reported by type checker

      class Connection:
          TIMEOUT: Final[int] = 10

      class FastConnector(Connection):
          TIMEOUT = 1  # Error reported by type checker

   There is no runtime checking of these properties. See **PEP 591**
   for more details.

   3.8 版新加入.

typing.AnyStr

   "AnyStr" is a type variable defined as "AnyStr = TypeVar('AnyStr',
   str, bytes)".

   It is meant to be used for functions that may accept any kind of
   string without allowing different kinds of strings to mix. For
   example:

      def concat(a: AnyStr, b: AnyStr) -> AnyStr:
          return a + b

      concat(u"foo", u"bar")  # Ok, output has type 'unicode'
      concat(b"foo", b"bar")  # Ok, output has type 'bytes'
      concat(u"foo", b"bar")  # Error, cannot mix unicode and bytes

typing.TYPE_CHECKING

   A special constant that is assumed to be "True" by 3rd party static
   type checkers. It is "False" at runtime. Usage:

      if TYPE_CHECKING:
          import expensive_mod

      def fun(arg: 'expensive_mod.SomeType') -> None:
          local_var: expensive_mod.AnotherType = other_fun()

   Note that the first type annotation must be enclosed in quotes,
   making it a "forward reference", to hide the "expensive_mod"
   reference from the interpreter runtime.  Type annotations for local
   variables are not evaluated, so the second annotation does not need
   to be enclosed in quotes.

   3.5.2 版新加入.
