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Edit File: typing_extensions.py
import abc import collections import collections.abc import functools import operator import sys import types as _types import typing __all__ = [ # Super-special typing primitives. 'Any', 'ClassVar', 'Concatenate', 'Final', 'LiteralString', 'ParamSpec', 'ParamSpecArgs', 'ParamSpecKwargs', 'Self', 'Type', 'TypeVar', 'TypeVarTuple', 'Unpack', # ABCs (from collections.abc). 'Awaitable', 'AsyncIterator', 'AsyncIterable', 'Coroutine', 'AsyncGenerator', 'AsyncContextManager', 'ChainMap', # Concrete collection types. 'ContextManager', 'Counter', 'Deque', 'DefaultDict', 'NamedTuple', 'OrderedDict', 'TypedDict', # Structural checks, a.k.a. protocols. 'SupportsIndex', # One-off things. 'Annotated', 'assert_never', 'assert_type', 'clear_overloads', 'dataclass_transform', 'get_overloads', 'final', 'get_args', 'get_origin', 'get_type_hints', 'IntVar', 'is_typeddict', 'Literal', 'NewType', 'overload', 'override', 'Protocol', 'reveal_type', 'runtime', 'runtime_checkable', 'Text', 'TypeAlias', 'TypeGuard', 'TYPE_CHECKING', 'Never', 'NoReturn', 'Required', 'NotRequired', ] # for backward compatibility PEP_560 = True GenericMeta = type # The functions below are modified copies of typing internal helpers. # They are needed by _ProtocolMeta and they provide support for PEP 646. _marker = object() def _check_generic(cls, parameters, elen=_marker): """Check correct count for parameters of a generic cls (internal helper). This gives a nice error message in case of count mismatch. """ if not elen: raise TypeError(f"{cls} is not a generic class") if elen is _marker: if not hasattr(cls, "__parameters__") or not cls.__parameters__: raise TypeError(f"{cls} is not a generic class") elen = len(cls.__parameters__) alen = len(parameters) if alen != elen: if hasattr(cls, "__parameters__"): parameters = [p for p in cls.__parameters__ if not _is_unpack(p)] num_tv_tuples = sum(isinstance(p, TypeVarTuple) for p in parameters) if (num_tv_tuples > 0) and (alen >= elen - num_tv_tuples): return raise TypeError(f"Too {'many' if alen > elen else 'few'} parameters for {cls};" f" actual {alen}, expected {elen}") if sys.version_info >= (3, 10): def _should_collect_from_parameters(t): return isinstance( t, (typing._GenericAlias, _types.GenericAlias, _types.UnionType) ) elif sys.version_info >= (3, 9): def _should_collect_from_parameters(t): return isinstance(t, (typing._GenericAlias, _types.GenericAlias)) else: def _should_collect_from_parameters(t): return isinstance(t, typing._GenericAlias) and not t._special def _collect_type_vars(types, typevar_types=None): """Collect all type variable contained in types in order of first appearance (lexicographic order). For example:: _collect_type_vars((T, List[S, T])) == (T, S) """ if typevar_types is None: typevar_types = typing.TypeVar tvars = [] for t in types: if ( isinstance(t, typevar_types) and t not in tvars and not _is_unpack(t) ): tvars.append(t) if _should_collect_from_parameters(t): tvars.extend([t for t in t.__parameters__ if t not in tvars]) return tuple(tvars) NoReturn = typing.NoReturn # Some unconstrained type variables. These are used by the container types. # (These are not for export.) T = typing.TypeVar('T') # Any type. KT = typing.TypeVar('KT') # Key type. VT = typing.TypeVar('VT') # Value type. T_co = typing.TypeVar('T_co', covariant=True) # Any type covariant containers. T_contra = typing.TypeVar('T_contra', contravariant=True) # Ditto contravariant. if sys.version_info >= (3, 11): from typing import Any else: class _AnyMeta(type): def __instancecheck__(self, obj): if self is Any: raise TypeError("typing_extensions.Any cannot be used with isinstance()") return super().__instancecheck__(obj) def __repr__(self): if self is Any: return "typing_extensions.Any" return super().__repr__() class Any(metaclass=_AnyMeta): """Special type indicating an unconstrained type. - Any is compatible with every type. - Any assumed to have all methods. - All values assumed to be instances of Any. 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 checks. """ def __new__(cls, *args, **kwargs): if cls is Any: raise TypeError("Any cannot be instantiated") return super().__new__(cls, *args, **kwargs) ClassVar = typing.ClassVar # On older versions of typing there is an internal class named "Final". # 3.8+ if hasattr(typing, 'Final') and sys.version_info[:2] >= (3, 7): Final = typing.Final # 3.7 else: class _FinalForm(typing._SpecialForm, _root=True): def __repr__(self): return 'typing_extensions.' + self._name def __getitem__(self, parameters): item = typing._type_check(parameters, f'{self._name} accepts only a single type.') return typing._GenericAlias(self, (item,)) Final = _FinalForm('Final', doc="""A special typing construct to indicate 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.""") if sys.version_info >= (3, 11): final = typing.final else: # @final exists in 3.8+, but we backport it for all versions # before 3.11 to keep support for the __final__ attribute. # See https://bugs.python.org/issue46342 def final(f): """This decorator can be used 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. The decorator sets the ``__final__`` attribute to ``True`` on the decorated object to allow runtime introspection. """ try: f.__final__ = True except (AttributeError, TypeError): # Skip the attribute silently if it is not writable. # AttributeError happens if the object has __slots__ or a # read-only property, TypeError if it's a builtin class. pass return f def IntVar(name): return typing.TypeVar(name) # 3.8+: if hasattr(typing, 'Literal'): Literal = typing.Literal # 3.7: else: class _LiteralForm(typing._SpecialForm, _root=True): def __repr__(self): return 'typing_extensions.' + self._name def __getitem__(self, parameters): return typing._GenericAlias(self, parameters) Literal = _LiteralForm('Literal', doc="""A type that can be used to indicate to type checkers that the corresponding value has a value literally equivalent to the provided parameter. For example: var: Literal[4] = 4 The type checker understands that 'var' is literally equal to the value 4 and no other value. Literal[...] cannot be subclassed. There is no runtime checking verifying that the parameter is actually a value instead of a type.""") _overload_dummy = typing._overload_dummy # noqa if hasattr(typing, "get_overloads"): # 3.11+ overload = typing.overload get_overloads = typing.get_overloads clear_overloads = typing.clear_overloads else: # {module: {qualname: {firstlineno: func}}} _overload_registry = collections.defaultdict( functools.partial(collections.defaultdict, dict) ) def overload(func): """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 The overloads for a function can be retrieved at runtime using the get_overloads() function. """ # classmethod and staticmethod f = getattr(func, "__func__", func) try: _overload_registry[f.__module__][f.__qualname__][ f.__code__.co_firstlineno ] = func except AttributeError: # Not a normal function; ignore. pass return _overload_dummy def get_overloads(func): """Return all defined overloads for *func* as a sequence.""" # classmethod and staticmethod f = getattr(func, "__func__", func) if f.__module__ not in _overload_registry: return [] mod_dict = _overload_registry[f.__module__] if f.__qualname__ not in mod_dict: return [] return list(mod_dict[f.__qualname__].values()) def clear_overloads(): """Clear all overloads in the registry.""" _overload_registry.clear() # This is not a real generic class. Don't use outside annotations. Type = typing.Type # Various ABCs mimicking those in collections.abc. # A few are simply re-exported for completeness. Awaitable = typing.Awaitable Coroutine = typing.Coroutine AsyncIterable = typing.AsyncIterable AsyncIterator = typing.AsyncIterator Deque = typing.Deque ContextManager = typing.ContextManager AsyncContextManager = typing.AsyncContextManager DefaultDict = typing.DefaultDict # 3.7.2+ if hasattr(typing, 'OrderedDict'): OrderedDict = typing.OrderedDict # 3.7.0-3.7.2 else: OrderedDict = typing._alias(collections.OrderedDict, (KT, VT)) Counter = typing.Counter ChainMap = typing.ChainMap AsyncGenerator = typing.AsyncGenerator NewType = typing.NewType Text = typing.Text TYPE_CHECKING = typing.TYPE_CHECKING _PROTO_WHITELIST = ['Callable', 'Awaitable', 'Iterable', 'Iterator', 'AsyncIterable', 'AsyncIterator', 'Hashable', 'Sized', 'Container', 'Collection', 'Reversible', 'ContextManager', 'AsyncContextManager'] def _get_protocol_attrs(cls): attrs = set() for base in cls.__mro__[:-1]: # without object if base.__name__ in ('Protocol', 'Generic'): continue annotations = getattr(base, '__annotations__', {}) for attr in list(base.__dict__.keys()) + list(annotations.keys()): if (not attr.startswith('_abc_') and attr not in ( '__abstractmethods__', '__annotations__', '__weakref__', '_is_protocol', '_is_runtime_protocol', '__dict__', '__args__', '__slots__', '__next_in_mro__', '__parameters__', '__origin__', '__orig_bases__', '__extra__', '__tree_hash__', '__doc__', '__subclasshook__', '__init__', '__new__', '__module__', '_MutableMapping__marker', '_gorg')): attrs.add(attr) return attrs def _is_callable_members_only(cls): return all(callable(getattr(cls, attr, None)) for attr in _get_protocol_attrs(cls)) def _maybe_adjust_parameters(cls): """Helper function used in Protocol.__init_subclass__ and _TypedDictMeta.__new__. The contents of this function are very similar to logic found in typing.Generic.__init_subclass__ on the CPython main branch. """ tvars = [] if '__orig_bases__' in cls.__dict__: tvars = typing._collect_type_vars(cls.__orig_bases__) # Look for Generic[T1, ..., Tn] or Protocol[T1, ..., Tn]. # If found, tvars must be a subset of it. # If not found, tvars is it. # Also check for and reject plain Generic, # and reject multiple Generic[...] and/or Protocol[...]. gvars = None for base in cls.__orig_bases__: if (isinstance(base, typing._GenericAlias) and base.__origin__ in (typing.Generic, Protocol)): # for error messages the_base = base.__origin__.__name__ if gvars is not None: raise TypeError( "Cannot inherit from Generic[...]" " and/or Protocol[...] multiple types.") gvars = base.__parameters__ if gvars is None: gvars = tvars else: tvarset = set(tvars) gvarset = set(gvars) if not tvarset <= gvarset: s_vars = ', '.join(str(t) for t in tvars if t not in gvarset) s_args = ', '.join(str(g) for g in gvars) raise TypeError(f"Some type variables ({s_vars}) are" f" not listed in {the_base}[{s_args}]") tvars = gvars cls.__parameters__ = tuple(tvars) # 3.8+ if hasattr(typing, 'Protocol'): Protocol = typing.Protocol # 3.7 else: def _no_init(self, *args, **kwargs): if type(self)._is_protocol: raise TypeError('Protocols cannot be instantiated') class _ProtocolMeta(abc.ABCMeta): # noqa: B024 # This metaclass is a bit unfortunate and exists only because of the lack # of __instancehook__. def __instancecheck__(cls, instance): # We need this method for situations where attributes are # assigned in __init__. if ((not getattr(cls, '_is_protocol', False) or _is_callable_members_only(cls)) and issubclass(instance.__class__, cls)): return True if cls._is_protocol: if all(hasattr(instance, attr) and (not callable(getattr(cls, attr, None)) or getattr(instance, attr) is not None) for attr in _get_protocol_attrs(cls)): return True return super().__instancecheck__(instance) class Protocol(metaclass=_ProtocolMeta): # There is quite a lot of overlapping code with typing.Generic. # Unfortunately it is hard to avoid this while these live in two different # modules. The duplicated code will be removed when Protocol is moved to typing. """Base class for protocol classes. Protocol classes are defined as:: 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 @typing_extensions.runtime act as simple-minded runtime protocol that checks only the presence of given attributes, ignoring their type signatures. Protocol classes can be generic, they are defined as:: class GenProto(Protocol[T]): def meth(self) -> T: ... """ __slots__ = () _is_protocol = True def __new__(cls, *args, **kwds): if cls is Protocol: raise TypeError("Type Protocol cannot be instantiated; " "it can only be used as a base class") return super().__new__(cls) @typing._tp_cache def __class_getitem__(cls, params): if not isinstance(params, tuple): params = (params,) if not params and cls is not typing.Tuple: raise TypeError( f"Parameter list to {cls.__qualname__}[...] cannot be empty") msg = "Parameters to generic types must be types." params = tuple(typing._type_check(p, msg) for p in params) # noqa if cls is Protocol: # Generic can only be subscripted with unique type variables. if not all(isinstance(p, typing.TypeVar) for p in params): i = 0 while isinstance(params[i], typing.TypeVar): i += 1 raise TypeError( "Parameters to Protocol[...] must all be type variables." f" Parameter {i + 1} is {params[i]}") if len(set(params)) != len(params): raise TypeError( "Parameters to Protocol[...] must all be unique") else: # Subscripting a regular Generic subclass. _check_generic(cls, params, len(cls.__parameters__)) return typing._GenericAlias(cls, params) def __init_subclass__(cls, *args, **kwargs): if '__orig_bases__' in cls.__dict__: error = typing.Generic in cls.__orig_bases__ else: error = typing.Generic in cls.__bases__ if error: raise TypeError("Cannot inherit from plain Generic") _maybe_adjust_parameters(cls) # Determine if this is a protocol or a concrete subclass. if not cls.__dict__.get('_is_protocol', None): cls._is_protocol = any(b is Protocol for b in cls.__bases__) # Set (or override) the protocol subclass hook. def _proto_hook(other): if not cls.__dict__.get('_is_protocol', None): return NotImplemented if not getattr(cls, '_is_runtime_protocol', False): if sys._getframe(2).f_globals['__name__'] in ['abc', 'functools']: return NotImplemented raise TypeError("Instance and class checks can only be used with" " @runtime protocols") if not _is_callable_members_only(cls): if sys._getframe(2).f_globals['__name__'] in ['abc', 'functools']: return NotImplemented raise TypeError("Protocols with non-method members" " don't support issubclass()") if not isinstance(other, type): # Same error as for issubclass(1, int) raise TypeError('issubclass() arg 1 must be a class') for attr in _get_protocol_attrs(cls): for base in other.__mro__: if attr in base.__dict__: if base.__dict__[attr] is None: return NotImplemented break annotations = getattr(base, '__annotations__', {}) if (isinstance(annotations, typing.Mapping) and attr in annotations and isinstance(other, _ProtocolMeta) and other._is_protocol): break else: return NotImplemented return True if '__subclasshook__' not in cls.__dict__: cls.__subclasshook__ = _proto_hook # We have nothing more to do for non-protocols. if not cls._is_protocol: return # Check consistency of bases. for base in cls.__bases__: if not (base in (object, typing.Generic) or base.__module__ == 'collections.abc' and base.__name__ in _PROTO_WHITELIST or isinstance(base, _ProtocolMeta) and base._is_protocol): raise TypeError('Protocols can only inherit from other' f' protocols, got {repr(base)}') cls.__init__ = _no_init # 3.8+ if hasattr(typing, 'runtime_checkable'): runtime_checkable = typing.runtime_checkable # 3.7 else: def runtime_checkable(cls): """Mark a protocol class as a runtime protocol, so that it can be used with isinstance() and issubclass(). Raise TypeError if applied to a non-protocol class. This allows a simple-minded structural check very similar to the one-offs in collections.abc such as Hashable. """ if not isinstance(cls, _ProtocolMeta) or not cls._is_protocol: raise TypeError('@runtime_checkable can be only applied to protocol classes,' f' got {cls!r}') cls._is_runtime_protocol = True return cls # Exists for backwards compatibility. runtime = runtime_checkable # 3.8+ if hasattr(typing, 'SupportsIndex'): SupportsIndex = typing.SupportsIndex # 3.7 else: @runtime_checkable class SupportsIndex(Protocol): __slots__ = () @abc.abstractmethod def __index__(self) -> int: pass if hasattr(typing, "Required"): # The standard library TypedDict in Python 3.8 does not store runtime information # about which (if any) keys are optional. See https://bugs.python.org/issue38834 # The standard library TypedDict in Python 3.9.0/1 does not honour the "total" # keyword with old-style TypedDict(). See https://bugs.python.org/issue42059 # The standard library TypedDict below Python 3.11 does not store runtime # information about optional and required keys when using Required or NotRequired. # Generic TypedDicts are also impossible using typing.TypedDict on Python <3.11. TypedDict = typing.TypedDict _TypedDictMeta = typing._TypedDictMeta is_typeddict = typing.is_typeddict else: def _check_fails(cls, other): try: if sys._getframe(1).f_globals['__name__'] not in ['abc', 'functools', 'typing']: # Typed dicts are only for static structural subtyping. raise TypeError('TypedDict does not support instance and class checks') except (AttributeError, ValueError): pass return False def _dict_new(*args, **kwargs): if not args: raise TypeError('TypedDict.__new__(): not enough arguments') _, args = args[0], args[1:] # allow the "cls" keyword be passed return dict(*args, **kwargs) _dict_new.__text_signature__ = '($cls, _typename, _fields=None, /, **kwargs)' def _typeddict_new(*args, total=True, **kwargs): if not args: raise TypeError('TypedDict.__new__(): not enough arguments') _, args = args[0], args[1:] # allow the "cls" keyword be passed if args: typename, args = args[0], args[1:] # allow the "_typename" keyword be passed elif '_typename' in kwargs: typename = kwargs.pop('_typename') import warnings warnings.warn("Passing '_typename' as keyword argument is deprecated", DeprecationWarning, stacklevel=2) else: raise TypeError("TypedDict.__new__() missing 1 required positional " "argument: '_typename'") if args: try: fields, = args # allow the "_fields" keyword be passed except ValueError: raise TypeError('TypedDict.__new__() takes from 2 to 3 ' f'positional arguments but {len(args) + 2} ' 'were given') elif '_fields' in kwargs and len(kwargs) == 1: fields = kwargs.pop('_fields') import warnings warnings.warn("Passing '_fields' as keyword argument is deprecated", DeprecationWarning, stacklevel=2) else: fields = None if fields is None: fields = kwargs elif kwargs: raise TypeError("TypedDict takes either a dict or keyword arguments," " but not both") ns = {'__annotations__': dict(fields)} try: # Setting correct module is necessary to make typed dict classes pickleable. ns['__module__'] = sys._getframe(1).f_globals.get('__name__', '__main__') except (AttributeError, ValueError): pass return _TypedDictMeta(typename, (), ns, total=total) _typeddict_new.__text_signature__ = ('($cls, _typename, _fields=None,' ' /, *, total=True, **kwargs)') class _TypedDictMeta(type): def __init__(cls, name, bases, ns, total=True): super().__init__(name, bases, ns) def __new__(cls, name, bases, ns, total=True): # Create new typed dict class object. # This method is called directly when TypedDict is subclassed, # or via _typeddict_new when TypedDict is instantiated. This way # TypedDict supports all three syntaxes described in its docstring. # Subclasses and instances of TypedDict return actual dictionaries # via _dict_new. ns['__new__'] = _typeddict_new if name == 'TypedDict' else _dict_new # Don't insert typing.Generic into __bases__ here, # or Generic.__init_subclass__ will raise TypeError # in the super().__new__() call. # Instead, monkey-patch __bases__ onto the class after it's been created. tp_dict = super().__new__(cls, name, (dict,), ns) if any(issubclass(base, typing.Generic) for base in bases): tp_dict.__bases__ = (typing.Generic, dict) _maybe_adjust_parameters(tp_dict) annotations = {} own_annotations = ns.get('__annotations__', {}) msg = "TypedDict('Name', {f0: t0, f1: t1, ...}); each t must be a type" own_annotations = { n: typing._type_check(tp, msg) for n, tp in own_annotations.items() } required_keys = set() optional_keys = set() for base in bases: annotations.update(base.__dict__.get('__annotations__', {})) required_keys.update(base.__dict__.get('__required_keys__', ())) optional_keys.update(base.__dict__.get('__optional_keys__', ())) annotations.update(own_annotations) for annotation_key, annotation_type in own_annotations.items(): annotation_origin = get_origin(annotation_type) if annotation_origin is Annotated: annotation_args = get_args(annotation_type) if annotation_args: annotation_type = annotation_args[0] annotation_origin = get_origin(annotation_type) if annotation_origin is Required: required_keys.add(annotation_key) elif annotation_origin is NotRequired: optional_keys.add(annotation_key) elif total: required_keys.add(annotation_key) else: optional_keys.add(annotation_key) tp_dict.__annotations__ = annotations tp_dict.__required_keys__ = frozenset(required_keys) tp_dict.__optional_keys__ = frozenset(optional_keys) if not hasattr(tp_dict, '__total__'): tp_dict.__total__ = total return tp_dict __instancecheck__ = __subclasscheck__ = _check_fails TypedDict = _TypedDictMeta('TypedDict', (dict,), {}) TypedDict.__module__ = __name__ TypedDict.__doc__ = \ """A simple typed name space. 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, with each key 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 the Point2D.__annotations__ dict, and the Point2D.__required_keys__ and Point2D.__optional_keys__ frozensets. TypedDict supports two additional equivalent forms:: Point2D = TypedDict('Point2D', x=int, y=int, label=str) Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': str}) The class syntax is only supported in Python 3.6+, while two other syntax forms work for Python 2.7 and 3.2+ """ if hasattr(typing, "_TypedDictMeta"): _TYPEDDICT_TYPES = (typing._TypedDictMeta, _TypedDictMeta) else: _TYPEDDICT_TYPES = (_TypedDictMeta,) def is_typeddict(tp): """Check if an annotation is a TypedDict class For example:: class Film(TypedDict): title: str year: int is_typeddict(Film) # => True is_typeddict(Union[list, str]) # => False """ return isinstance(tp, tuple(_TYPEDDICT_TYPES)) if hasattr(typing, "assert_type"): assert_type = typing.assert_type else: def assert_type(__val, __typ): """Assert (to the type checker) that the value is of the given type. When the type checker encounters a call to assert_type(), it emits an error if the value is not of the specified type:: def greet(name: str) -> None: assert_type(name, str) # ok assert_type(name, int) # type checker error At runtime this returns the first argument unchanged and otherwise does nothing. """ return __val if hasattr(typing, "Required"): get_type_hints = typing.get_type_hints else: import functools import types # replaces _strip_annotations() def _strip_extras(t): """Strips Annotated, Required and NotRequired from a given type.""" if isinstance(t, _AnnotatedAlias): return _strip_extras(t.__origin__) if hasattr(t, "__origin__") and t.__origin__ in (Required, NotRequired): return _strip_extras(t.__args__[0]) if isinstance(t, typing._GenericAlias): stripped_args = tuple(_strip_extras(a) for a in t.__args__) if stripped_args == t.__args__: return t return t.copy_with(stripped_args) if hasattr(types, "GenericAlias") and isinstance(t, types.GenericAlias): stripped_args = tuple(_strip_extras(a) for a in t.__args__) if stripped_args == t.__args__: return t return types.GenericAlias(t.__origin__, stripped_args) if hasattr(types, "UnionType") and isinstance(t, types.UnionType): stripped_args = tuple(_strip_extras(a) for a in t.__args__) if stripped_args == t.__args__: return t return functools.reduce(operator.or_, stripped_args) return t def get_type_hints(obj, globalns=None, localns=None, include_extras=False): """Return type hints for an object. This is often the same as obj.__annotations__, but it handles forward references encoded as string literals, adds Optional[t] if a default value equal to None is set and recursively replaces all 'Annotated[T, ...]', 'Required[T]' or 'NotRequired[T]' with 'T' (unless 'include_extras=True'). The argument may be a module, class, method, or function. The annotations are returned as a dictionary. For classes, annotations include also inherited members. TypeError is raised if the argument is not of a type that can contain annotations, and an empty dictionary is returned if no annotations are present. BEWARE -- the behavior of globalns and localns is counterintuitive (unless you are familiar with how eval() and exec() work). The search order is locals first, then globals. - If no dict arguments are passed, an attempt is made to use the globals from obj (or the respective module's globals for classes), and these are also used as the locals. If the object does not appear to have globals, an empty dictionary is used. - If one dict argument is passed, it is used for both globals and locals. - If two dict arguments are passed, they specify globals and locals, respectively. """ if hasattr(typing, "Annotated"): hint = typing.get_type_hints( obj, globalns=globalns, localns=localns, include_extras=True ) else: hint = typing.get_type_hints(obj, globalns=globalns, localns=localns) if include_extras: return hint return {k: _strip_extras(t) for k, t in hint.items()} # Python 3.9+ has PEP 593 (Annotated) if hasattr(typing, 'Annotated'): Annotated = typing.Annotated # Not exported and not a public API, but needed for get_origin() and get_args() # to work. _AnnotatedAlias = typing._AnnotatedAlias # 3.7-3.8 else: class _AnnotatedAlias(typing._GenericAlias, _root=True): """Runtime representation of an annotated type. At its core 'Annotated[t, dec1, dec2, ...]' is an alias for the type 't' with extra annotations. The alias behaves like a normal typing alias, instantiating is the same as instantiating the underlying type, binding it to types is also the same. """ def __init__(self, origin, metadata): if isinstance(origin, _AnnotatedAlias): metadata = origin.__metadata__ + metadata origin = origin.__origin__ super().__init__(origin, origin) self.__metadata__ = metadata def copy_with(self, params): assert len(params) == 1 new_type = params[0] return _AnnotatedAlias(new_type, self.__metadata__) def __repr__(self): return (f"typing_extensions.Annotated[{typing._type_repr(self.__origin__)}, " f"{', '.join(repr(a) for a in self.__metadata__)}]") def __reduce__(self): return operator.getitem, ( Annotated, (self.__origin__,) + self.__metadata__ ) def __eq__(self, other): if not isinstance(other, _AnnotatedAlias): return NotImplemented if self.__origin__ != other.__origin__: return False return self.__metadata__ == other.__metadata__ def __hash__(self): return hash((self.__origin__, self.__metadata__)) class Annotated: """Add context specific metadata to a type. Example: Annotated[int, runtime_check.Unsigned] indicates to the hypothetical runtime_check module that this type is an unsigned int. Every other consumer of this type can ignore this metadata and treat this type as int. The first argument to Annotated must be a valid type (and will be in the __origin__ field), the remaining arguments are kept as a tuple in the __extra__ field. Details: - It's an error to call `Annotated` with less than two arguments. - Nested Annotated are flattened:: Annotated[Annotated[T, Ann1, Ann2], Ann3] == Annotated[T, Ann1, Ann2, Ann3] - Instantiating an annotated type is equivalent to instantiating the underlying type:: Annotated[C, Ann1](5) == C(5) - Annotated can be used as a generic type alias:: Optimized = Annotated[T, runtime.Optimize()] Optimized[int] == Annotated[int, runtime.Optimize()] OptimizedList = Annotated[List[T], runtime.Optimize()] OptimizedList[int] == Annotated[List[int], runtime.Optimize()] """ __slots__ = () def __new__(cls, *args, **kwargs): raise TypeError("Type Annotated cannot be instantiated.") @typing._tp_cache def __class_getitem__(cls, params): if not isinstance(params, tuple) or len(params) < 2: raise TypeError("Annotated[...] should be used " "with at least two arguments (a type and an " "annotation).") allowed_special_forms = (ClassVar, Final) if get_origin(params[0]) in allowed_special_forms: origin = params[0] else: msg = "Annotated[t, ...]: t must be a type." origin = typing._type_check(params[0], msg) metadata = tuple(params[1:]) return _AnnotatedAlias(origin, metadata) def __init_subclass__(cls, *args, **kwargs): raise TypeError( f"Cannot subclass {cls.__module__}.Annotated" ) # Python 3.8 has get_origin() and get_args() but those implementations aren't # Annotated-aware, so we can't use those. Python 3.9's versions don't support # ParamSpecArgs and ParamSpecKwargs, so only Python 3.10's versions will do. if sys.version_info[:2] >= (3, 10): get_origin = typing.get_origin get_args = typing.get_args # 3.7-3.9 else: try: # 3.9+ from typing import _BaseGenericAlias except ImportError: _BaseGenericAlias = typing._GenericAlias try: # 3.9+ from typing import GenericAlias as _typing_GenericAlias except ImportError: _typing_GenericAlias = typing._GenericAlias def get_origin(tp): """Get the unsubscripted version of a type. This supports generic types, Callable, Tuple, Union, Literal, Final, ClassVar and Annotated. Return None for unsupported types. 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 get_origin(P.args) is P """ if isinstance(tp, _AnnotatedAlias): return Annotated if isinstance(tp, (typing._GenericAlias, _typing_GenericAlias, _BaseGenericAlias, ParamSpecArgs, ParamSpecKwargs)): return tp.__origin__ if tp is typing.Generic: return typing.Generic return None def get_args(tp): """Get 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) """ if isinstance(tp, _AnnotatedAlias): return (tp.__origin__,) + tp.__metadata__ if isinstance(tp, (typing._GenericAlias, _typing_GenericAlias)): if getattr(tp, "_special", False): return () res = tp.__args__ if get_origin(tp) is collections.abc.Callable and res[0] is not Ellipsis: res = (list(res[:-1]), res[-1]) return res return () # 3.10+ if hasattr(typing, 'TypeAlias'): TypeAlias = typing.TypeAlias # 3.9 elif sys.version_info[:2] >= (3, 9): class _TypeAliasForm(typing._SpecialForm, _root=True): def __repr__(self): return 'typing_extensions.' + self._name @_TypeAliasForm def TypeAlias(self, parameters): """Special marker indicating that an assignment should be recognized as a proper type alias definition by type checkers. For example:: Predicate: TypeAlias = Callable[..., bool] It's invalid when used anywhere except as in the example above. """ raise TypeError(f"{self} is not subscriptable") # 3.7-3.8 else: class _TypeAliasForm(typing._SpecialForm, _root=True): def __repr__(self): return 'typing_extensions.' + self._name TypeAlias = _TypeAliasForm('TypeAlias', doc="""Special marker indicating that an assignment should be recognized as a proper type alias definition by type checkers. For example:: Predicate: TypeAlias = Callable[..., bool] It's invalid when used anywhere except as in the example above.""") class _DefaultMixin: """Mixin for TypeVarLike defaults.""" __slots__ = () def __init__(self, default): if isinstance(default, (tuple, list)): self.__default__ = tuple((typing._type_check(d, "Default must be a type") for d in default)) elif default: self.__default__ = typing._type_check(default, "Default must be a type") else: self.__default__ = None # Add default and infer_variance parameters from PEP 696 and 695 class TypeVar(typing.TypeVar, _DefaultMixin, _root=True): """Type variable.""" __module__ = 'typing' def __init__(self, name, *constraints, bound=None, covariant=False, contravariant=False, default=None, infer_variance=False): super().__init__(name, *constraints, bound=bound, covariant=covariant, contravariant=contravariant) _DefaultMixin.__init__(self, default) self.__infer_variance__ = infer_variance # for pickling: try: def_mod = sys._getframe(1).f_globals.get('__name__', '__main__') except (AttributeError, ValueError): def_mod = None if def_mod != 'typing_extensions': self.__module__ = def_mod # Python 3.10+ has PEP 612 if hasattr(typing, 'ParamSpecArgs'): ParamSpecArgs = typing.ParamSpecArgs ParamSpecKwargs = typing.ParamSpecKwargs # 3.7-3.9 else: class _Immutable: """Mixin to indicate that object should not be copied.""" __slots__ = () def __copy__(self): return self def __deepcopy__(self, memo): return self class ParamSpecArgs(_Immutable): """The args for a ParamSpec object. Given a ParamSpec object P, P.args is an instance of ParamSpecArgs. ParamSpecArgs objects have a reference back to their ParamSpec: P.args.__origin__ is P This type is meant for runtime introspection and has no special meaning to static type checkers. """ def __init__(self, origin): self.__origin__ = origin def __repr__(self): return f"{self.__origin__.__name__}.args" def __eq__(self, other): if not isinstance(other, ParamSpecArgs): return NotImplemented return self.__origin__ == other.__origin__ class ParamSpecKwargs(_Immutable): """The kwargs for a ParamSpec object. Given a ParamSpec object P, P.kwargs is an instance of ParamSpecKwargs. ParamSpecKwargs objects have a reference back to their ParamSpec: P.kwargs.__origin__ is P This type is meant for runtime introspection and has no special meaning to static type checkers. """ def __init__(self, origin): self.__origin__ = origin def __repr__(self): return f"{self.__origin__.__name__}.kwargs" def __eq__(self, other): if not isinstance(other, ParamSpecKwargs): return NotImplemented return self.__origin__ == other.__origin__ # 3.10+ if hasattr(typing, 'ParamSpec'): # Add default Parameter - PEP 696 class ParamSpec(typing.ParamSpec, _DefaultMixin, _root=True): """Parameter specification variable.""" __module__ = 'typing' def __init__(self, name, *, bound=None, covariant=False, contravariant=False, default=None): super().__init__(name, bound=bound, covariant=covariant, contravariant=contravariant) _DefaultMixin.__init__(self, default) # for pickling: try: def_mod = sys._getframe(1).f_globals.get('__name__', '__main__') except (AttributeError, ValueError): def_mod = None if def_mod != 'typing_extensions': self.__module__ = def_mod # 3.7-3.9 else: # Inherits from list as a workaround for Callable checks in Python < 3.9.2. class ParamSpec(list, _DefaultMixin): """Parameter specification variable. Usage:: P = ParamSpec('P') Parameter specification variables exist primarily for the benefit of static type checkers. They are used to forward the parameter types of one callable to another callable, a pattern commonly found in higher order functions and decorators. They are only valid when used in ``Concatenate``, or s the first argument to ``Callable``. In Python 3.10 and higher, they are also supported in user-defined Generics at runtime. See class Generic for more information on generic types. An example for annotating a decorator:: T = TypeVar('T') P = ParamSpec('P') def add_logging(f: Callable[P, T]) -> Callable[P, T]: '''A type-safe decorator to add logging to a function.''' def inner(*args: P.args, **kwargs: P.kwargs) -> T: logging.info(f'{f.__name__} was called') return f(*args, **kwargs) return inner @add_logging def add_two(x: float, y: float) -> float: '''Add two numbers together.''' return x + y Parameter specification variables defined with covariant=True or contravariant=True can be used to declare covariant or contravariant generic types. These keyword arguments are valid, but their actual semantics are yet to be decided. See PEP 612 for details. Parameter specification variables can be introspected. e.g.: P.__name__ == 'T' P.__bound__ == None P.__covariant__ == False P.__contravariant__ == False Note that only parameter specification variables defined in global scope can be pickled. """ # Trick Generic __parameters__. __class__ = typing.TypeVar @property def args(self): return ParamSpecArgs(self) @property def kwargs(self): return ParamSpecKwargs(self) def __init__(self, name, *, bound=None, covariant=False, contravariant=False, default=None): super().__init__([self]) self.__name__ = name self.__covariant__ = bool(covariant) self.__contravariant__ = bool(contravariant) if bound: self.__bound__ = typing._type_check(bound, 'Bound must be a type.') else: self.__bound__ = None _DefaultMixin.__init__(self, default) # for pickling: try: def_mod = sys._getframe(1).f_globals.get('__name__', '__main__') except (AttributeError, ValueError): def_mod = None if def_mod != 'typing_extensions': self.__module__ = def_mod def __repr__(self): if self.__covariant__: prefix = '+' elif self.__contravariant__: prefix = '-' else: prefix = '~' return prefix + self.__name__ def __hash__(self): return object.__hash__(self) def __eq__(self, other): return self is other def __reduce__(self): return self.__name__ # Hack to get typing._type_check to pass. def __call__(self, *args, **kwargs): pass # 3.7-3.9 if not hasattr(typing, 'Concatenate'): # Inherits from list as a workaround for Callable checks in Python < 3.9.2. class _ConcatenateGenericAlias(list): # Trick Generic into looking into this for __parameters__. __class__ = typing._GenericAlias # Flag in 3.8. _special = False def __init__(self, origin, args): super().__init__(args) self.__origin__ = origin self.__args__ = args def __repr__(self): _type_repr = typing._type_repr return (f'{_type_repr(self.__origin__)}' f'[{", ".join(_type_repr(arg) for arg in self.__args__)}]') def __hash__(self): return hash((self.__origin__, self.__args__)) # Hack to get typing._type_check to pass in Generic. def __call__(self, *args, **kwargs): pass @property def __parameters__(self): return tuple( tp for tp in self.__args__ if isinstance(tp, (typing.TypeVar, ParamSpec)) ) # 3.7-3.9 @typing._tp_cache def _concatenate_getitem(self, parameters): if parameters == (): raise TypeError("Cannot take a Concatenate of no types.") if not isinstance(parameters, tuple): parameters = (parameters,) if not isinstance(parameters[-1], ParamSpec): raise TypeError("The last parameter to Concatenate should be a " "ParamSpec variable.") msg = "Concatenate[arg, ...]: each arg must be a type." parameters = tuple(typing._type_check(p, msg) for p in parameters) return _ConcatenateGenericAlias(self, parameters) # 3.10+ if hasattr(typing, 'Concatenate'): Concatenate = typing.Concatenate _ConcatenateGenericAlias = typing._ConcatenateGenericAlias # noqa # 3.9 elif sys.version_info[:2] >= (3, 9): @_TypeAliasForm def Concatenate(self, parameters): """Used in conjunction with ``ParamSpec`` and ``Callable`` to represent a higher order function which adds, removes or transforms parameters of a callable. For example:: Callable[Concatenate[int, P], int] See PEP 612 for detailed information. """ return _concatenate_getitem(self, parameters) # 3.7-8 else: class _ConcatenateForm(typing._SpecialForm, _root=True): def __repr__(self): return 'typing_extensions.' + self._name def __getitem__(self, parameters): return _concatenate_getitem(self, parameters) Concatenate = _ConcatenateForm( 'Concatenate', doc="""Used in conjunction with ``ParamSpec`` and ``Callable`` to represent a higher order function which adds, removes or transforms parameters of a callable. For example:: Callable[Concatenate[int, P], int] See PEP 612 for detailed information. """) # 3.10+ if hasattr(typing, 'TypeGuard'): TypeGuard = typing.TypeGuard # 3.9 elif sys.version_info[:2] >= (3, 9): class _TypeGuardForm(typing._SpecialForm, _root=True): def __repr__(self): return 'typing_extensions.' + self._name @_TypeGuardForm def TypeGuard(self, parameters): """Special typing form used to annotate the return type of a user-defined type guard function. ``TypeGuard`` only accepts a single type argument. At runtime, functions marked this way should return a boolean. ``TypeGuard`` aims to benefit *type narrowing* -- a technique used by static type checkers to determine a more precise type of an expression within a program's code flow. Usually type narrowing is done by analyzing conditional code flow and applying the narrowing to a block of code. The conditional expression here is sometimes referred to as a "type guard". Sometimes it would be convenient to use a user-defined boolean function as a type guard. Such a function should use ``TypeGuard[...]`` as its return type to alert static type checkers to this intention. Using ``-> TypeGuard`` tells the static type checker that for a given function: 1. The return value is a boolean. 2. If the return value is ``True``, the type of its argument is the type inside ``TypeGuard``. For example:: def is_str(val: Union[str, float]): # "isinstance" type guard if isinstance(val, str): # Type of ``val`` is narrowed to ``str`` ... else: # Else, type of ``val`` is narrowed to ``float``. ... Strict type narrowing is not enforced -- ``TypeB`` need not be a narrower form of ``TypeA`` (it can even be a wider form) and this may lead to type-unsafe results. The main reason is to allow for things like narrowing ``List[object]`` to ``List[str]`` even though the latter is not a subtype of the former, since ``List`` is invariant. The responsibility of writing type-safe type guards is left to the user. ``TypeGuard`` also works with type variables. For more information, see PEP 647 (User-Defined Type Guards). """ item = typing._type_check(parameters, f'{self} accepts only a single type.') return typing._GenericAlias(self, (item,)) # 3.7-3.8 else: class _TypeGuardForm(typing._SpecialForm, _root=True): def __repr__(self): return 'typing_extensions.' + self._name def __getitem__(self, parameters): item = typing._type_check(parameters, f'{self._name} accepts only a single type') return typing._GenericAlias(self, (item,)) TypeGuard = _TypeGuardForm( 'TypeGuard', doc="""Special typing form used to annotate the return type of a user-defined type guard function. ``TypeGuard`` only accepts a single type argument. At runtime, functions marked this way should return a boolean. ``TypeGuard`` aims to benefit *type narrowing* -- a technique used by static type checkers to determine a more precise type of an expression within a program's code flow. Usually type narrowing is done by analyzing conditional code flow and applying the narrowing to a block of code. The conditional expression here is sometimes referred to as a "type guard". Sometimes it would be convenient to use a user-defined boolean function as a type guard. Such a function should use ``TypeGuard[...]`` as its return type to alert static type checkers to this intention. Using ``-> TypeGuard`` tells the static type checker that for a given function: 1. The return value is a boolean. 2. If the return value is ``True``, the type of its argument is the type inside ``TypeGuard``. For example:: def is_str(val: Union[str, float]): # "isinstance" type guard if isinstance(val, str): # Type of ``val`` is narrowed to ``str`` ... else: # Else, type of ``val`` is narrowed to ``float``. ... Strict type narrowing is not enforced -- ``TypeB`` need not be a narrower form of ``TypeA`` (it can even be a wider form) and this may lead to type-unsafe results. The main reason is to allow for things like narrowing ``List[object]`` to ``List[str]`` even though the latter is not a subtype of the former, since ``List`` is invariant. The responsibility of writing type-safe type guards is left to the user. ``TypeGuard`` also works with type variables. For more information, see PEP 647 (User-Defined Type Guards). """) # Vendored from cpython typing._SpecialFrom class _SpecialForm(typing._Final, _root=True): __slots__ = ('_name', '__doc__', '_getitem') def __init__(self, getitem): self._getitem = getitem self._name = getitem.__name__ self.__doc__ = getitem.__doc__ def __getattr__(self, item): if item in {'__name__', '__qualname__'}: return self._name raise AttributeError(item) def __mro_entries__(self, bases): raise TypeError(f"Cannot subclass {self!r}") def __repr__(self): return f'typing_extensions.{self._name}' def __reduce__(self): return self._name def __call__(self, *args, **kwds): raise TypeError(f"Cannot instantiate {self!r}") def __or__(self, other): return typing.Union[self, other] def __ror__(self, other): return typing.Union[other, self] def __instancecheck__(self, obj): raise TypeError(f"{self} cannot be used with isinstance()") def __subclasscheck__(self, cls): raise TypeError(f"{self} cannot be used with issubclass()") @typing._tp_cache def __getitem__(self, parameters): return self._getitem(self, parameters) if hasattr(typing, "LiteralString"): LiteralString = typing.LiteralString else: @_SpecialForm def LiteralString(self, params): """Represents an arbitrary literal string. Example:: from typing_extensions import LiteralString def query(sql: LiteralString) -> ...: ... query("SELECT * FROM table") # ok query(f"SELECT * FROM {input()}") # not ok See PEP 675 for details. """ raise TypeError(f"{self} is not subscriptable") if hasattr(typing, "Self"): Self = typing.Self else: @_SpecialForm def Self(self, params): """Used to spell the type of "self" in classes. Example:: from typing import Self class ReturnsSelf: def parse(self, data: bytes) -> Self: ... return self """ raise TypeError(f"{self} is not subscriptable") if hasattr(typing, "Never"): Never = typing.Never else: @_SpecialForm def Never(self, params): """The bottom type, a type that has no members. This can be used to define a function that should never be called, or a function that never returns:: from typing_extensions import Never def never_call_me(arg: Never) -> None: pass def int_or_str(arg: int | str) -> None: never_call_me(arg) # type checker error match arg: case int(): print("It's an int") case str(): print("It's a str") case _: never_call_me(arg) # ok, arg is of type Never """ raise TypeError(f"{self} is not subscriptable") if hasattr(typing, 'Required'): Required = typing.Required NotRequired = typing.NotRequired elif sys.version_info[:2] >= (3, 9): class _ExtensionsSpecialForm(typing._SpecialForm, _root=True): def __repr__(self): return 'typing_extensions.' + self._name @_ExtensionsSpecialForm def Required(self, parameters): """A special typing construct to mark a key of a total=False TypedDict as required. For example: class Movie(TypedDict, total=False): title: Required[str] year: int m = Movie( title='The Matrix', # typechecker error if key is omitted year=1999, ) There is no runtime checking that a required key is actually provided when instantiating a related TypedDict. """ item = typing._type_check(parameters, f'{self._name} accepts only a single type.') return typing._GenericAlias(self, (item,)) @_ExtensionsSpecialForm def NotRequired(self, parameters): """A special typing construct to mark a key of a TypedDict as potentially missing. For example: class Movie(TypedDict): title: str year: NotRequired[int] m = Movie( title='The Matrix', # typechecker error if key is omitted year=1999, ) """ item = typing._type_check(parameters, f'{self._name} accepts only a single type.') return typing._GenericAlias(self, (item,)) else: class _RequiredForm(typing._SpecialForm, _root=True): def __repr__(self): return 'typing_extensions.' + self._name def __getitem__(self, parameters): item = typing._type_check(parameters, f'{self._name} accepts only a single type.') return typing._GenericAlias(self, (item,)) Required = _RequiredForm( 'Required', doc="""A special typing construct to mark a key of a total=False TypedDict as required. For example: class Movie(TypedDict, total=False): title: Required[str] year: int m = Movie( title='The Matrix', # typechecker error if key is omitted year=1999, ) There is no runtime checking that a required key is actually provided when instantiating a related TypedDict. """) NotRequired = _RequiredForm( 'NotRequired', doc="""A special typing construct to mark a key of a TypedDict as potentially missing. For example: class Movie(TypedDict): title: str year: NotRequired[int] m = Movie( title='The Matrix', # typechecker error if key is omitted year=1999, ) """) if hasattr(typing, "Unpack"): # 3.11+ Unpack = typing.Unpack elif sys.version_info[:2] >= (3, 9): class _UnpackSpecialForm(typing._SpecialForm, _root=True): def __repr__(self): return 'typing_extensions.' + self._name class _UnpackAlias(typing._GenericAlias, _root=True): __class__ = typing.TypeVar @_UnpackSpecialForm def Unpack(self, parameters): """A special typing construct to unpack a variadic type. For example: Shape = TypeVarTuple('Shape') Batch = NewType('Batch', int) def add_batch_axis( x: Array[Unpack[Shape]] ) -> Array[Batch, Unpack[Shape]]: ... """ item = typing._type_check(parameters, f'{self._name} accepts only a single type.') return _UnpackAlias(self, (item,)) def _is_unpack(obj): return isinstance(obj, _UnpackAlias) else: class _UnpackAlias(typing._GenericAlias, _root=True): __class__ = typing.TypeVar class _UnpackForm(typing._SpecialForm, _root=True): def __repr__(self): return 'typing_extensions.' + self._name def __getitem__(self, parameters): item = typing._type_check(parameters, f'{self._name} accepts only a single type.') return _UnpackAlias(self, (item,)) Unpack = _UnpackForm( 'Unpack', doc="""A special typing construct to unpack a variadic type. For example: Shape = TypeVarTuple('Shape') Batch = NewType('Batch', int) def add_batch_axis( x: Array[Unpack[Shape]] ) -> Array[Batch, Unpack[Shape]]: ... """) def _is_unpack(obj): return isinstance(obj, _UnpackAlias) if hasattr(typing, "TypeVarTuple"): # 3.11+ # Add default Parameter - PEP 696 class TypeVarTuple(typing.TypeVarTuple, _DefaultMixin, _root=True): """Type variable tuple.""" def __init__(self, name, *, default=None): super().__init__(name) _DefaultMixin.__init__(self, default) # for pickling: try: def_mod = sys._getframe(1).f_globals.get('__name__', '__main__') except (AttributeError, ValueError): def_mod = None if def_mod != 'typing_extensions': self.__module__ = def_mod else: class TypeVarTuple(_DefaultMixin): """Type variable tuple. Usage:: Ts = TypeVarTuple('Ts') In the same way that a normal type variable is a stand-in for a single type such as ``int``, a type variable *tuple* is a stand-in for a *tuple* type such as ``Tuple[int, str]``. Type variable tuples can be used in ``Generic`` declarations. Consider the following example:: class Array(Generic[*Ts]): ... The ``Ts`` type variable tuple here behaves like ``tuple[T1, T2]``, where ``T1`` and ``T2`` are type variables. To use these type variables as type parameters of ``Array``, we must *unpack* the type variable tuple using the star operator: ``*Ts``. The signature of ``Array`` then behaves as if we had simply written ``class Array(Generic[T1, T2]): ...``. In contrast to ``Generic[T1, T2]``, however, ``Generic[*Shape]`` allows us to parameterise the class with an *arbitrary* number of type parameters. Type variable tuples can be used anywhere a normal ``TypeVar`` can. This includes class definitions, as shown above, as well as function signatures and variable annotations:: class Array(Generic[*Ts]): def __init__(self, shape: Tuple[*Ts]): self._shape: Tuple[*Ts] = shape def get_shape(self) -> Tuple[*Ts]: return self._shape shape = (Height(480), Width(640)) x: Array[Height, Width] = Array(shape) y = abs(x) # Inferred type is Array[Height, Width] z = x + x # ... is Array[Height, Width] x.get_shape() # ... is tuple[Height, Width] """ # Trick Generic __parameters__. __class__ = typing.TypeVar def __iter__(self): yield self.__unpacked__ def __init__(self, name, *, default=None): self.__name__ = name _DefaultMixin.__init__(self, default) # for pickling: try: def_mod = sys._getframe(1).f_globals.get('__name__', '__main__') except (AttributeError, ValueError): def_mod = None if def_mod != 'typing_extensions': self.__module__ = def_mod self.__unpacked__ = Unpack[self] def __repr__(self): return self.__name__ def __hash__(self): return object.__hash__(self) def __eq__(self, other): return self is other def __reduce__(self): return self.__name__ def __init_subclass__(self, *args, **kwds): if '_root' not in kwds: raise TypeError("Cannot subclass special typing classes") if hasattr(typing, "reveal_type"): reveal_type = typing.reveal_type else: def reveal_type(__obj: T) -> T: """Reveal the inferred type of a variable. When a static type checker encounters a call to ``reveal_type()``, it will emit the inferred type of the argument:: x: int = 1 reveal_type(x) Running a static type checker (e.g., ``mypy``) on this example will produce output similar to 'Revealed type is "builtins.int"'. At runtime, the function prints the runtime type of the argument and returns it unchanged. """ print(f"Runtime type is {type(__obj).__name__!r}", file=sys.stderr) return __obj if hasattr(typing, "assert_never"): assert_never = typing.assert_never else: def assert_never(__arg: Never) -> Never: """Assert to the type checker that a line of code is unreachable. Example:: def int_or_str(arg: int | str) -> None: match arg: case int(): print("It's an int") case str(): print("It's a str") case _: assert_never(arg) If a type checker finds that a call to assert_never() is reachable, it will emit an error. At runtime, this throws an exception when called. """ raise AssertionError("Expected code to be unreachable") if hasattr(typing, 'dataclass_transform'): dataclass_transform = typing.dataclass_transform else: def dataclass_transform( *, eq_default: bool = True, order_default: bool = False, kw_only_default: bool = False, field_specifiers: typing.Tuple[ typing.Union[typing.Type[typing.Any], typing.Callable[..., typing.Any]], ... ] = (), **kwargs: typing.Any, ) -> typing.Callable[[T], T]: """Decorator that marks a function, class, or metaclass as providing dataclass-like behavior. Example: from typing_extensions import dataclass_transform _T = TypeVar("_T") # Used on a decorator function @dataclass_transform() def create_model(cls: type[_T]) -> type[_T]: ... return cls @create_model class CustomerModel: id: int name: str # Used on a base class @dataclass_transform() class ModelBase: ... class CustomerModel(ModelBase): id: int name: str # Used on a metaclass @dataclass_transform() class ModelMeta(type): ... class ModelBase(metaclass=ModelMeta): ... class CustomerModel(ModelBase): id: int name: str Each of the ``CustomerModel`` classes defined in this example will now behave similarly to a dataclass created with the ``@dataclasses.dataclass`` decorator. For example, the type checker will synthesize an ``__init__`` method. The arguments to this decorator can be used to customize this behavior: - ``eq_default`` indicates whether the ``eq`` parameter is assumed to be True or False if it is omitted by the caller. - ``order_default`` indicates whether the ``order`` parameter is assumed to be True or False if it is omitted by the caller. - ``kw_only_default`` indicates whether the ``kw_only`` parameter is assumed to be True or False if it is omitted by the caller. - ``field_specifiers`` specifies a static list of supported classes or functions that describe fields, similar to ``dataclasses.field()``. At runtime, this decorator records its arguments in the ``__dataclass_transform__`` attribute on the decorated object. See PEP 681 for details. """ def decorator(cls_or_fn): cls_or_fn.__dataclass_transform__ = { "eq_default": eq_default, "order_default": order_default, "kw_only_default": kw_only_default, "field_specifiers": field_specifiers, "kwargs": kwargs, } return cls_or_fn return decorator if hasattr(typing, "override"): override = typing.override else: _F = typing.TypeVar("_F", bound=typing.Callable[..., typing.Any]) def override(__arg: _F) -> _F: """Indicate that a method is intended to override a method in a base class. Usage: class Base: def method(self) -> None: ... pass class Child(Base): @override def method(self) -> None: super().method() When this decorator is applied to a method, the type checker will validate that it overrides a method with the same name on a base class. This helps prevent bugs that may occur when a base class is changed without an equivalent change to a child class. See PEP 698 for details. """ return __arg # We have to do some monkey patching to deal with the dual nature of # Unpack/TypeVarTuple: # - We want Unpack to be a kind of TypeVar so it gets accepted in # Generic[Unpack[Ts]] # - We want it to *not* be treated as a TypeVar for the purposes of # counting generic parameters, so that when we subscript a generic, # the runtime doesn't try to substitute the Unpack with the subscripted type. if not hasattr(typing, "TypeVarTuple"): typing._collect_type_vars = _collect_type_vars typing._check_generic = _check_generic # Backport typing.NamedTuple as it exists in Python 3.11. # In 3.11, the ability to define generic `NamedTuple`s was supported. # This was explicitly disallowed in 3.9-3.10, and only half-worked in <=3.8. if sys.version_info >= (3, 11): NamedTuple = typing.NamedTuple else: def _caller(): try: return sys._getframe(2).f_globals.get('__name__', '__main__') except (AttributeError, ValueError): # For platforms without _getframe() return None def _make_nmtuple(name, types, module, defaults=()): fields = [n for n, t in types] annotations = {n: typing._type_check(t, f"field {n} annotation must be a type") for n, t in types} nm_tpl = collections.namedtuple(name, fields, defaults=defaults, module=module) nm_tpl.__annotations__ = nm_tpl.__new__.__annotations__ = annotations # The `_field_types` attribute was removed in 3.9; # in earlier versions, it is the same as the `__annotations__` attribute if sys.version_info < (3, 9): nm_tpl._field_types = annotations return nm_tpl _prohibited_namedtuple_fields = typing._prohibited _special_namedtuple_fields = frozenset({'__module__', '__name__', '__annotations__'}) class _NamedTupleMeta(type): def __new__(cls, typename, bases, ns): assert _NamedTuple in bases for base in bases: if base is not _NamedTuple and base is not typing.Generic: raise TypeError( 'can only inherit from a NamedTuple type and Generic') bases = tuple(tuple if base is _NamedTuple else base for base in bases) types = ns.get('__annotations__', {}) default_names = [] for field_name in types: if field_name in ns: default_names.append(field_name) elif default_names: raise TypeError(f"Non-default namedtuple field {field_name} " f"cannot follow default field" f"{'s' if len(default_names) > 1 else ''} " f"{', '.join(default_names)}") nm_tpl = _make_nmtuple( typename, types.items(), defaults=[ns[n] for n in default_names], module=ns['__module__'] ) nm_tpl.__bases__ = bases if typing.Generic in bases: class_getitem = typing.Generic.__class_getitem__.__func__ nm_tpl.__class_getitem__ = classmethod(class_getitem) # update from user namespace without overriding special namedtuple attributes for key in ns: if key in _prohibited_namedtuple_fields: raise AttributeError("Cannot overwrite NamedTuple attribute " + key) elif key not in _special_namedtuple_fields and key not in nm_tpl._fields: setattr(nm_tpl, key, ns[key]) if typing.Generic in bases: nm_tpl.__init_subclass__() return nm_tpl def NamedTuple(__typename, __fields=None, **kwargs): if __fields is None: __fields = kwargs.items() elif kwargs: raise TypeError("Either list of fields or keywords" " can be provided to NamedTuple, not both") return _make_nmtuple(__typename, __fields, module=_caller()) NamedTuple.__doc__ = typing.NamedTuple.__doc__ _NamedTuple = type.__new__(_NamedTupleMeta, 'NamedTuple', (), {}) # On 3.8+, alter the signature so that it matches typing.NamedTuple. # The signature of typing.NamedTuple on >=3.8 is invalid syntax in Python 3.7, # so just leave the signature as it is on 3.7. if sys.version_info >= (3, 8): NamedTuple.__text_signature__ = '(typename, fields=None, /, **kwargs)' def _namedtuple_mro_entries(bases): assert NamedTuple in bases return (_NamedTuple,) NamedTuple.__mro_entries__ = _namedtuple_mro_entries