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Note that all the above statements are true from the point of view of static type checkers. At runtime, Any should not be used with instance or class checks. )raSpecial type indicating functions that never return. Example:: from typing import NoReturn def stop() -> NoReturn: raise Exception('no way') This type is invalid in other positions, e.g., ``List[NoReturn]`` will fail in static type checkers. a3Special type construct to mark class variables. An 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. Note that ClassVar is not a class itself, and should not be used with isinstance() or issubclass(). aSpecial typing construct to indicate final names to type checkers. 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Examples:: get_origin(Literal[42]) is Literal get_origin(int) is None get_origin(ClassVar[int]) is ClassVar get_origin(Generic) is Generic get_origin(Generic[T]) is Generic get_origin(Union[T, int]) is Union get_origin(List[Tuple[T, T]][int]) == list N)rPrRrSr )rwrZrZr[rDs  cCsRt|trN|jsN|j}t|tjjkrJ|dtk rJt |dd|df}|SdS)aGet type arguments with all substitutions performed. For unions, basic simplifications used by Union constructor are performed. Examples:: get_args(Dict[str, int]) == (str, int) get_args(int) == () get_args(Union[int, Union[T, int], str][int]) == (int, str) get_args(Union[int, Tuple[T, int]][str]) == (int, Tuple[str, int]) get_args(Callable[[], T][int]) == ([], int) rNrrZ) rPrRrmrsrDrrrrrr)rwrrZrZr[rC s cCst|trt|j}|jD]"\}}||j|fkr||q|D](}t|tj r`d|_ t|trJt |qJz d|_ Wnt k rYnX|S)aIDecorator to indicate that annotations are not type hints. The argument must be a class or function; if it is a class, it applies recursively to all methods and classes defined in that class (but not to methods defined in its superclasses or subclasses). This mutates the function(s) or class(es) in place. T) rPrOr copyrPr!popvaluesr`rarKrGrT)rWZ arg_attrsrrrdrZrZr[rGs        cstfdd}|S)zDecorator to give another decorator the @no_type_check effect. This wraps the decorator with something that wraps the decorated function in @no_type_check. cs||}t|}|Sr)rG)rrr decoratorrZr[wrapped_decorator@s z2no_type_check_decorator..wrapped_decorator)rr)rYrZrZrXr[rH9scOs tddS)z*Helper for @overload to raise when called.zYou should not call an overloaded function. A series of @overload-decorated functions outside a stub module should always be followed by an implementation that is not @overload-ed.N)NotImplementedErrorrrZrZr[_overload_dummyIsr\cCstS)a Decorator for overloaded functions/methods. In a stub file, place two or more stub definitions for the same function in a row, each decorated with @overload. For example: @overload def utf8(value: None) -> None: ... @overload def utf8(value: bytes) -> bytes: ... @overload def utf8(value: str) -> bytes: ... In a non-stub file (i.e. a regular .py file), do the same but follow it with an implementation. The implementation should *not* be decorated with @overload. For example: @overload def utf8(value: None) -> None: ... @overload def utf8(value: bytes) -> bytes: ... @overload def utf8(value: str) -> bytes: ... def utf8(value): # implementation goes here )r\)rrZrZr[rJRscCs|S)aVA decorator to indicate final methods and final classes. Use this decorator to indicate to type checkers that the decorated method cannot be overridden, and 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. rZ)frZrZr[rBosTKTVTT_co)rV_coVT_coT_contra)rCT_co)rrcCst||d|dS)NT)rr)rR)rrrrZrZr[_aliassrfrZ)raCallable 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 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. F)rra@Tuple type; Tuple[X, Y] is the cross-product type of X and Y. 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. 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And a function that takes a class argument that's a subclass of User and returns an instance of the corresponding class:: U = TypeVar('U', bound=User) def new_user(user_class: Type[U]) -> U: user = user_class() # (Here we could write the user object to a database) return user joe = new_user(BasicUser) At this point the type checker knows that joe has type BasicUser. c@s&eZdZdZdZeedddZdS)r2z(An ABC with one abstract method __int__.rZreturncCsdSrrZrrZrZr[__int__szSupportsInt.__int__N)rbr^r_rrrintrirZrZrZr[r2sc@s&eZdZdZdZeedddZdS)r0z*An ABC with one abstract method __float__.rZrgcCsdSrrZrrZrZr[ __float__szSupportsFloat.__float__N)rbr^r_rrrfloatrkrZrZrZr[r0sc@s&eZdZdZdZeedddZdS)r/z,An ABC with one abstract method __complex__.rZrgcCsdSrrZrrZrZr[ __complex__ szSupportsComplex.__complex__N)rbr^r_rrrcomplexrmrZrZrZr[r/sc@s&eZdZdZdZeedddZdS)r.z*An ABC with one abstract method __bytes__.rZrgcCsdSrrZrrZrZr[ __bytes__szSupportsBytes.__bytes__N)rbr^r_rrrbytesrorZrZrZr[r.sc@s&eZdZdZdZeedddZdS)r1z*An ABC with one abstract method __index__.rZrgcCsdSrrZrrZrZr[ __index__szSupportsIndex.__index__N)rbr^r_rrrrjrqrZrZrZr[r1sc@s&eZdZdZdZeedddZdS)r-zMAn ABC with one abstract method __abs__ that is covariant in its return type.rZrgcCsdSrrZrrZrZr[__abs__(szSupportsAbs.__abs__N)rbr^r_rrrrarrrZrZrZr[r-#sc@s*eZdZdZdZedeedddZdS) r3zOAn ABC with one abstract method __round__ that is covariant in its return type.rZr)ndigitsrhcCsdSrrZ)rrsrZrZr[ __round__2szSupportsRound.__round__N)r) rbr^r_rrrrjrartrZrZrZr[r3-sc stdfdd|D}t|dd|D}t||_|_ztdjdd|_ Wnt t fk rnYnX|S)NzDNamedTuple('Name', [(f0, t0), (f1, t1), ...]); each t must be a typecsg|]\}}|t|fqSrZrrgnrhrrZr[rk9sz!_make_nmtuple..cSsg|] \}}|qSrZrZrurZrZr[rk:srrbr) r namedtuplerr( _field_typesrrrrr^rr)rr`nm_tplrZrr[ _make_nmtuple7srz) rrr__getnewargs___fields_field_defaultsrx_make_replace_asdictZ_source)r^rbr(cseZdZfddZZS)NamedTupleMetac s|ddrt||||S|di}t||}g}i}|D]H}||krl||} || | ||<qD|rDtdj|d| dqDt ||j_ t ||j_ ||_|D]<} | tkrtd| q| tkr| |jkrt|| || q|S)NrFr(zXNon-default namedtuple field {field_name} cannot follow default field(s) {default_names}r) field_nameZ default_namesz&Cannot overwrite NamedTuple attribute )rrrrzrPrlrTrSrr,rr(rprEr} _prohibitedrrmr|r) r{typenamer nsr`ryrHZ defaults_dictrZ default_valuekeyrrZr[rOs2       zNamedTupleMeta.__new__)rbr^r_rrrZrZrr[rMsrc@s"eZdZdZdZddZde_dS)r=aTyped version of namedtuple. Usage in Python versions >= 3.6:: class Employee(NamedTuple): name: str id: int This is equivalent to:: Employee = collections.namedtuple('Employee', ['name', 'id']) The resulting class has an extra __annotations__ attribute, giving a dict that maps field names to types. (The field names are also in the _fields attribute, which is part of the namedtuple API.) Alternative equivalent keyword syntax is also accepted:: Employee = NamedTuple('Employee', name=str, id=int) In Python versions <= 3.5 use:: Employee = NamedTuple('Employee', [('name', str), ('id', int)]) TcOs|s td|^}}|r"|^}}n4d|krN|d}ddl}|jdtddntd|rz |\}Wqtk rtdt|dd dYqXnrr(z?TypedDict('Name', {f0: t0, f1: t1, ...}); each t must be a typecsi|]\}}|t|qSrZr)rgrvrwrrZr[ sz*_TypedDictMeta.__new__..r) rrrrrrrrPupdater r(r;r)r{rr rrZtp_dictZannsr#rrr[rs   z_TypedDictMeta.__new__)T)rbr^r_rrrrrrZrZrr[rsrc@seZdZdZdS)r>aA 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 can be accessed via Point2D.__annotations__. TypedDict supports two additional equivalent 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. The class syntax is only supported in Python 3.6+, while two other syntax forms work for Python 2.7 and 3.2+ Nr%rZrZrZr[r>scCsdd}||_||_|S)a%NewType creates simple unique types with almost zero runtime overhead. NewType(name, tp) is considered a subtype of tp by static type checkers. At runtime, NewType(name, tp) returns a dummy function that simply returns its argument. Usage:: UserId = NewType('UserId', int) def name_by_id(user_id: UserId) -> str: ... 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