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"""
The typing module: Support for gradual typing as defined by PEP 484 and subsequent PEPs.
Among other things, the module includes the following:
* Generic, Protocol, and internal machinery to support generic aliases.
All subscripted types like X[int], Union[int, str] are generic aliases.
* Various "special forms" that have unique meanings in type annotations:
Any, Never, ClassVar, Self, Concatenate, Unpack, and others.
* Classes whose instances can be type arguments to generic classes and functions:
TypeVar, ParamSpec, TypeVarTuple.
* Public helper functions: get_type_hints, overload, cast, final, and others.
* Several protocols to support duck-typing:
SupportsFloat, SupportsIndex, SupportsAbs, and others.
* Special types: NewType, NamedTuple, TypedDict.
* Deprecated aliases for builtin types and collections.abc ABCs.
Any name not present in __all__ is an implementation detail
that may be changed without notice. Use at your own risk!
"""
from abc import abstractmethod, ABCMeta
import collections
from collections import defaultdict
import collections.abc
import copyreg
import functools
import operator
import sys
import types
from types import WrapperDescriptorType, MethodWrapperType, MethodDescriptorType, GenericAlias
from _typing import (
_idfunc,
TypeVar,
ParamSpec,
TypeVarTuple,
ParamSpecArgs,
ParamSpecKwargs,
TypeAliasType,
Generic,
NoDefault,
)
# Please keep __all__ alphabetized within each category.
__all__ = [
# Super-special typing primitives.
'Annotated',
'Any',
'Callable',
'ClassVar',
'Concatenate',
'Final',
'ForwardRef',
'Generic',
'Literal',
'Optional',
'ParamSpec',
'Protocol',
'Tuple',
'Type',
'TypeVar',
'TypeVarTuple',
'Union',
# ABCs (from collections.abc).
'AbstractSet', # collections.abc.Set.
'ByteString',
'Container',
'ContextManager',
'Hashable',
'ItemsView',
'Iterable',
'Iterator',
'KeysView',
'Mapping',
'MappingView',
'MutableMapping',
'MutableSequence',
'MutableSet',
'Sequence',
'Sized',
'ValuesView',
'Awaitable',
'AsyncIterator',
'AsyncIterable',
'Coroutine',
'Collection',
'AsyncGenerator',
'AsyncContextManager',
# Structural checks, a.k.a. protocols.
'Reversible',
'SupportsAbs',
'SupportsBytes',
'SupportsComplex',
'SupportsFloat',
'SupportsIndex',
'SupportsInt',
'SupportsRound',
# Concrete collection types.
'ChainMap',
'Counter',
'Deque',
'Dict',
'DefaultDict',
'List',
'OrderedDict',
'Set',
'FrozenSet',
'NamedTuple', # Not really a type.
'TypedDict', # Not really a type.
'Generator',
# Other concrete types.
'BinaryIO',
'IO',
'Match',
'Pattern',
'TextIO',
# One-off things.
'AnyStr',
'assert_type',
'assert_never',
'cast',
'clear_overloads',
'dataclass_transform',
'final',
'get_args',
'get_origin',
'get_overloads',
'get_protocol_members',
'get_type_hints',
'is_protocol',
'is_typeddict',
'LiteralString',
'Never',
'NewType',
'no_type_check',
'no_type_check_decorator',
'NoDefault',
'NoReturn',
'NotRequired',
'overload',
'override',
'ParamSpecArgs',
'ParamSpecKwargs',
'ReadOnly',
'Required',
'reveal_type',
'runtime_checkable',
'Self',
'Text',
'TYPE_CHECKING',
'TypeAlias',
'TypeGuard',
'TypeIs',
'TypeAliasType',
'Unpack',
]
def _type_convert(arg, module=None, *, allow_special_forms=False):
"""For converting None to type(None), and strings to ForwardRef."""
if arg is None:
return type(None)
if isinstance(arg, str):
return ForwardRef(arg, module=module, is_class=allow_special_forms)
return arg
def _type_check(arg, msg, is_argument=True, module=None, *, allow_special_forms=False):
"""Check that the argument is a type, and return it (internal helper).
As a special case, accept None and return type(None) instead. Also wrap strings
into ForwardRef instances. Consider several corner cases, for example plain
special forms like Union are not valid, while Union[int, str] is OK, etc.
The msg argument is a human-readable error message, e.g.::
"Union[arg, ...]: arg should be a type."
We append the repr() of the actual value (truncated to 100 chars).
"""
invalid_generic_forms = (Generic, Protocol)
if not allow_special_forms:
invalid_generic_forms += (ClassVar,)
if is_argument:
invalid_generic_forms += (Final,)
arg = _type_convert(arg, module=module, allow_special_forms=allow_special_forms)
if (isinstance(arg, _GenericAlias) and
arg.__origin__ in invalid_generic_forms):
raise TypeError(f"{arg} is not valid as type argument")
if arg in (Any, LiteralString, NoReturn, Never, Self, TypeAlias):
return arg
if allow_special_forms and arg in (ClassVar, Final):
return arg
if isinstance(arg, _SpecialForm) or arg in (Generic, Protocol):
raise TypeError(f"Plain {arg} is not valid as type argument")
if type(arg) is tuple:
raise TypeError(f"{msg} Got {arg!r:.100}.")
return arg
def _is_param_expr(arg):
return arg is ... or isinstance(arg,
(tuple, list, ParamSpec, _ConcatenateGenericAlias))
def _should_unflatten_callable_args(typ, args):
"""Internal helper for munging collections.abc.Callable's __args__.
The canonical representation for a Callable's __args__ flattens the
argument types, see https://github.com/python/cpython/issues/86361.
For example::
>>> import collections.abc
>>> P = ParamSpec('P')
>>> collections.abc.Callable[[int, int], str].__args__ == (int, int, str)
True
>>> collections.abc.Callable[P, str].__args__ == (P, str)
True
As a result, if we need to reconstruct the Callable from its __args__,
we need to unflatten it.
"""
return (
typ.__origin__ is collections.abc.Callable
and not (len(args) == 2 and _is_param_expr(args[0]))
)
def _type_repr(obj):
"""Return the repr() of an object, special-casing types (internal helper).
If obj is a type, we return a shorter version than the default
type.__repr__, based on the module and qualified name, which is
typically enough to uniquely identify a type. For everything
else, we fall back on repr(obj).
"""
# When changing this function, don't forget about
# `_collections_abc._type_repr`, which does the same thing
# and must be consistent with this one.
if isinstance(obj, type):
if obj.__module__ == 'builtins':
return obj.__qualname__
return f'{obj.__module__}.{obj.__qualname__}'
if obj is ...:
return '...'
if isinstance(obj, types.FunctionType):
return obj.__name__
if isinstance(obj, tuple):
# Special case for `repr` of types with `ParamSpec`:
return '[' + ', '.join(_type_repr(t) for t in obj) + ']'
return repr(obj)
def _collect_type_parameters(args, *, enforce_default_ordering: bool = True):
"""Collect all type parameters in args
in order of first appearance (lexicographic order).
For example::
>>> P = ParamSpec('P')
>>> T = TypeVar('T')
>>> _collect_type_parameters((T, Callable[P, T]))
(~T, ~P)
"""
# required type parameter cannot appear after parameter with default
default_encountered = False
# or after TypeVarTuple
type_var_tuple_encountered = False
parameters = []
for t in args:
if isinstance(t, type):
# We don't want __parameters__ descriptor of a bare Python class.
pass
elif isinstance(t, tuple):
# `t` might be a tuple, when `ParamSpec` is substituted with
# `[T, int]`, or `[int, *Ts]`, etc.
for x in t:
for collected in _collect_type_parameters([x]):
if collected not in parameters:
parameters.append(collected)
elif hasattr(t, '__typing_subst__'):
if t not in parameters:
if enforce_default_ordering:
if type_var_tuple_encountered and t.has_default():
raise TypeError('Type parameter with a default'
' follows TypeVarTuple')
if t.has_default():
default_encountered = True
elif default_encountered:
raise TypeError(f'Type parameter {t!r} without a default'
' follows type parameter with a default')
parameters.append(t)
else:
if _is_unpacked_typevartuple(t):
type_var_tuple_encountered = True
for x in getattr(t, '__parameters__', ()):
if x not in parameters:
parameters.append(x)
return tuple(parameters)
def _check_generic_specialization(cls, arguments):
"""Check correct count for parameters of a generic cls (internal helper).
This gives a nice error message in case of count mismatch.
"""
expected_len = len(cls.__parameters__)
if not expected_len:
raise TypeError(f"{cls} is not a generic class")
actual_len = len(arguments)
if actual_len != expected_len:
# deal with defaults
if actual_len < expected_len:
# If the parameter at index `actual_len` in the parameters list
# has a default, then all parameters after it must also have
# one, because we validated as much in _collect_type_parameters().
# That means that no error needs to be raised here, despite
# the number of arguments being passed not matching the number
# of parameters: all parameters that aren't explicitly
# specialized in this call are parameters with default values.
if cls.__parameters__[actual_len].has_default():
return
expected_len -= sum(p.has_default() for p in cls.__parameters__)
expect_val = f"at least {expected_len}"
else:
expect_val = expected_len
raise TypeError(f"Too {'many' if actual_len > expected_len else 'few'} arguments"
f" for {cls}; actual {actual_len}, expected {expect_val}")
def _unpack_args(*args):
newargs = []
for arg in args:
subargs = getattr(arg, '__typing_unpacked_tuple_args__', None)
if subargs is not None and not (subargs and subargs[-1] is ...):
newargs.extend(subargs)
else:
newargs.append(arg)
return newargs
def _deduplicate(params, *, unhashable_fallback=False):
# Weed out strict duplicates, preserving the first of each occurrence.
try:
return dict.fromkeys(params)
except TypeError:
if not unhashable_fallback:
raise
# Happens for cases like `Annotated[dict, {'x': IntValidator()}]`
return _deduplicate_unhashable(params)
def _deduplicate_unhashable(unhashable_params):
new_unhashable = []
for t in unhashable_params:
if t not in new_unhashable:
new_unhashable.append(t)
return new_unhashable
def _compare_args_orderless(first_args, second_args):
first_unhashable = _deduplicate_unhashable(first_args)
second_unhashable = _deduplicate_unhashable(second_args)
t = list(second_unhashable)
try:
for elem in first_unhashable:
t.remove(elem)
except ValueError:
return False
return not t
def _remove_dups_flatten(parameters):
"""Internal helper for Union creation and substitution.
Flatten Unions among parameters, then remove duplicates.
"""
# Flatten out Union[Union[...], ...].
params = []
for p in parameters:
if isinstance(p, (_UnionGenericAlias, types.UnionType)):
params.extend(p.__args__)
else:
params.append(p)
return tuple(_deduplicate(params, unhashable_fallback=True))
def _flatten_literal_params(parameters):
"""Internal helper for Literal creation: flatten Literals among parameters."""
params = []
for p in parameters:
if isinstance(p, _LiteralGenericAlias):
params.extend(p.__args__)
else:
params.append(p)
return tuple(params)
_cleanups = []
_caches = {}
def _tp_cache(func=None, /, *, typed=False):
"""Internal wrapper caching __getitem__ of generic types.
For non-hashable arguments, the original function is used as a fallback.
"""
def decorator(func):
# The callback 'inner' references the newly created lru_cache
# indirectly by performing a lookup in the global '_caches' dictionary.
# This breaks a reference that can be problematic when combined with
# C API extensions that leak references to types. See GH-98253.
cache = functools.lru_cache(typed=typed)(func)
_caches[func] = cache
_cleanups.append(cache.cache_clear)
del cache
@functools.wraps(func)
def inner(*args, **kwds):
try:
return _caches[func](*args, **kwds)
except TypeError:
pass # All real errors (not unhashable args) are raised below.
return func(*args, **kwds)
return inner
if func is not None:
return decorator(func)
return decorator
def _deprecation_warning_for_no_type_params_passed(funcname: str) -> None:
import warnings
depr_message = (
f"Failing to pass a value to the 'type_params' parameter "
f"of {funcname!r} is deprecated, as it leads to incorrect behaviour "
f"when calling {funcname} on a stringified annotation "
f"that references a PEP 695 type parameter. "
f"It will be disallowed in Python 3.15."
)
warnings.warn(depr_message, category=DeprecationWarning, stacklevel=3)
class _Sentinel:
__slots__ = ()
def __repr__(self):
return '<sentinel>'
_sentinel = _Sentinel()
def _eval_type(t, globalns, localns, type_params=_sentinel, *, recursive_guard=frozenset()):
"""Evaluate all forward references in the given type t.
For use of globalns and localns see the docstring for get_type_hints().
recursive_guard is used to prevent infinite recursion with a recursive
ForwardRef.
"""
if type_params is _sentinel:
_deprecation_warning_for_no_type_params_passed("typing._eval_type")
type_params = ()
if isinstance(t, ForwardRef):
return t._evaluate(globalns, localns, type_params, recursive_guard=recursive_guard)
if isinstance(t, (_GenericAlias, GenericAlias, types.UnionType)):
if isinstance(t, GenericAlias):
args = tuple(
ForwardRef(arg) if isinstance(arg, str) else arg
for arg in t.__args__
)
is_unpacked = t.__unpacked__
if _should_unflatten_callable_args(t, args):
t = t.__origin__[(args[:-1], args[-1])]
else:
t = t.__origin__[args]
if is_unpacked:
t = Unpack[t]
ev_args = tuple(
_eval_type(
a, globalns, localns, type_params, recursive_guard=recursive_guard
)
for a in t.__args__
)
if ev_args == t.__args__:
return t
if isinstance(t, GenericAlias):
return GenericAlias(t.__origin__, ev_args)
if isinstance(t, types.UnionType):
return functools.reduce(operator.or_, ev_args)
else:
return t.copy_with(ev_args)
return t
class _Final:
"""Mixin to prohibit subclassing."""
__slots__ = ('__weakref__',)
def __init_subclass__(cls, /, *args, **kwds):
if '_root' not in kwds:
raise TypeError("Cannot subclass special typing classes")
class _NotIterable:
"""Mixin to prevent iteration, without being compatible with Iterable.
That is, we could do::
def __iter__(self): raise TypeError()
But this would make users of this mixin duck type-compatible with
collections.abc.Iterable - isinstance(foo, Iterable) would be True.
Luckily, we can instead prevent iteration by setting __iter__ to None, which
is treated specially.
"""
__slots__ = ()
__iter__ = None
# Internal indicator of special typing constructs.
# See __doc__ instance attribute for specific docs.
class _SpecialForm(_Final, _NotIterable, _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 'typing.' + 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 Union[self, other]
def __ror__(self, other):
return 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()")
@_tp_cache
def __getitem__(self, parameters):
return self._getitem(self, parameters)
class _TypedCacheSpecialForm(_SpecialForm, _root=True):
def __getitem__(self, parameters):
if not isinstance(parameters, tuple):
parameters = (parameters,)
return self._getitem(self, *parameters)
class _AnyMeta(type):
def __instancecheck__(self, obj):
if self is Any:
raise TypeError("typing.Any cannot be used with isinstance()")
return super().__instancecheck__(obj)
def __repr__(self):
if self is Any:
return "typing.Any"
return super().__repr__() # respect to subclasses
class Any(metaclass=_AnyMeta):
"""Special type indicating an unconstrained type.
- Any is assignable to every type.
- Any assumed to have all methods and attributes.
- All values are assignable to Any.
Note that all the above statements are true from the point of view of
static type checkers. At runtime, Any cannot be used with instance
checks.
"""
def __new__(cls, *args, **kwargs):
if cls is Any:
raise TypeError("Any cannot be instantiated")
return super().__new__(cls)
@_SpecialForm
def NoReturn(self, parameters):
"""Special type indicating functions that never return.
Example::
from typing import NoReturn
def stop() -> NoReturn:
raise Exception('no way')
NoReturn can also be used as a bottom type, a type that
has no values. Starting in Python 3.11, the Never type should
be used for this concept instead. Type checkers should treat the two
equivalently.
"""
raise TypeError(f"{self} is not subscriptable")
# This is semantically identical to NoReturn, but it is implemented
# separately so that type checkers can distinguish between the two
# if they want.
@_SpecialForm
def Never(self, parameters):
"""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 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")
@_SpecialForm
def Self(self, parameters):
"""Used to spell the type of "self" in classes.
Example::
from typing import Self
class Foo:
def return_self(self) -> Self:
...
return self
This is especially useful for:
- classmethods that are used as alternative constructors
- annotating an `__enter__` method which returns self
"""
raise TypeError(f"{self} is not subscriptable")
@_SpecialForm
def LiteralString(self, parameters):
"""Represents an arbitrary literal string.
Example::
from typing import LiteralString
def run_query(sql: LiteralString) -> None:
...
def caller(arbitrary_string: str, literal_string: LiteralString) -> None:
run_query("SELECT * FROM students") # OK
run_query(literal_string) # OK
run_query("SELECT * FROM " + literal_string) # OK
run_query(arbitrary_string) # type checker error
run_query( # type checker error
f"SELECT * FROM students WHERE name = {arbitrary_string}"
)
Only string literals and other LiteralStrings are compatible
with LiteralString. This provides a tool to help prevent
security issues such as SQL injection.
"""
raise TypeError(f"{self} is not subscriptable")
@_SpecialForm
def ClassVar(self, parameters):
"""Special 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 cannot
be used with isinstance() or issubclass().
"""
item = _type_check(parameters, f'{self} accepts only single type.', allow_special_forms=True)
return _GenericAlias(self, (item,))
@_SpecialForm
def Final(self, parameters):
"""Special typing construct to indicate final names to type checkers.
A final 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.
"""
item = _type_check(parameters, f'{self} accepts only single type.', allow_special_forms=True)
return _GenericAlias(self, (item,))
@_SpecialForm
def Union(self, parameters):
"""Union type; Union[X, Y] means either X or Y.
On Python 3.10 and higher, the | operator
can also be used to denote unions;
X | Y means the same thing to the type checker as Union[X, Y].
To define a union, use e.g. Union[int, str]. Details:
- The arguments must be types and there must be at least one.
- None as an argument is a special case and is replaced by
type(None).
- Unions of unions are flattened, e.g.::
assert Union[Union[int, str], float] == Union[int, str, float]
- Unions of a single argument vanish, e.g.::
assert Union[int] == int # The constructor actually returns int
- Redundant arguments are skipped, e.g.::
assert Union[int, str, int] == Union[int, str]
- When comparing unions, the argument order is ignored, e.g.::
assert Union[int, str] == Union[str, int]
- You cannot subclass or instantiate a union.
- You can use Optional[X] as a shorthand for Union[X, None].
"""
if parameters == ():
raise TypeError("Cannot take a Union of no types.")
if not isinstance(parameters, tuple):
parameters = (parameters,)
msg = "Union[arg, ...]: each arg must be a type."
parameters = tuple(_type_check(p, msg) for p in parameters)
parameters = _remove_dups_flatten(parameters)
if len(parameters) == 1:
return parameters[0]
if len(parameters) == 2 and type(None) in parameters:
return _UnionGenericAlias(self, parameters, name="Optional")
return _UnionGenericAlias(self, parameters)
def _make_union(left, right):
"""Used from the C implementation of TypeVar.
TypeVar.__or__ calls this instead of returning types.UnionType
because we want to allow unions between TypeVars and strings
(forward references).
"""
return Union[left, right]
@_SpecialForm
def Optional(self, parameters):
"""Optional[X] is equivalent to X | None."""
arg = _type_check(parameters, f"{self} requires a single type.")
return Union[arg, type(None)]
@_TypedCacheSpecialForm
@_tp_cache(typed=True)
def Literal(self, *parameters):
"""Special typing form to define literal types (a.k.a. value types).
This form 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)::
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.
"""
# There is no '_type_check' call because arguments to Literal[...] are
# values, not types.
parameters = _flatten_literal_params(parameters)
value_and_type_parameters = list(_value_and_type_iter(parameters))
deduplicated_parameters = tuple(
p
for p, _ in _deduplicate(
value_and_type_parameters,
unhashable_fallback=True,
)
)
return _LiteralGenericAlias(self, deduplicated_parameters)
@_SpecialForm
def TypeAlias(self, parameters):
"""Special form for marking type aliases.
TypeAlias can be used to indicate 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")
@_SpecialForm
def Concatenate(self, parameters):
"""Special form for annotating higher-order functions.
``Concatenate`` can be used in conjunction with ``ParamSpec`` and
``Callable`` to represent a higher-order function which adds, removes or
transforms the parameters of a callable.
For example::
Callable[Concatenate[int, P], int]
See PEP 612 for detailed information.
"""
if parameters == ():
raise TypeError("Cannot take a Concatenate of no types.")
if not isinstance(parameters, tuple):
parameters = (parameters,)
if not (parameters[-1] is ... or isinstance(parameters[-1], ParamSpec)):
raise TypeError("The last parameter to Concatenate should be a "
"ParamSpec variable or ellipsis.")
msg = "Concatenate[arg, ...]: each arg must be a type."
parameters = (*(_type_check(p, msg) for p in parameters[:-1]), parameters[-1])
return _ConcatenateGenericAlias(self, parameters)
@_SpecialForm
def TypeGuard(self, parameters):
"""Special typing construct for marking user-defined type predicate functions.
``TypeGuard`` can be used to annotate the return type of a user-defined
type predicate 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 predicate".
Sometimes it would be convenient to use a user-defined boolean function
as a type predicate. Such a function should use ``TypeGuard[...]`` or
``TypeIs[...]`` as its return type to alert static type checkers to
this intention. ``TypeGuard`` should be used over ``TypeIs`` when narrowing
from an incompatible type (e.g., ``list[object]`` to ``list[int]``) or when
the function does not return ``True`` for all instances of the narrowed type.
Using ``-> TypeGuard[NarrowedType]`` 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 ``NarrowedType``.
For example::
def is_str_list(val: list[object]) -> TypeGuard[list[str]]:
'''Determines whether all objects in the list are strings'''
return all(isinstance(x, str) for x in val)
def func1(val: list[object]):
if is_str_list(val):
# Type of ``val`` is narrowed to ``list[str]``.
print(" ".join(val))
else:
# Type of ``val`` remains as ``list[object]``.
print("Not a list of strings!")
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 predicates is left to the user.
``TypeGuard`` also works with type variables. For more information, see
PEP 647 (User-Defined Type Guards).
"""
item = _type_check(parameters, f'{self} accepts only single type.')
return _GenericAlias(self, (item,))
@_SpecialForm
def TypeIs(self, parameters):
"""Special typing construct for marking user-defined type predicate functions.
``TypeIs`` can be used to annotate the return type of a user-defined
type predicate function. ``TypeIs`` only accepts a single type argument.
At runtime, functions marked this way should return a boolean and accept
at least one argument.
``TypeIs`` 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 predicate".
Sometimes it would be convenient to use a user-defined boolean function
as a type predicate. Such a function should use ``TypeIs[...]`` or
``TypeGuard[...]`` as its return type to alert static type checkers to
this intention. ``TypeIs`` usually has more intuitive behavior than
``TypeGuard``, but it cannot be used when the input and output types
are incompatible (e.g., ``list[object]`` to ``list[int]``) or when the
function does not return ``True`` for all instances of the narrowed type.
Using ``-> TypeIs[NarrowedType]`` 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 intersection of the argument's original type and
``NarrowedType``.
3. If the return value is ``False``, the type of its argument
is narrowed to exclude ``NarrowedType``.
For example::
from typing import assert_type, final, TypeIs
class Parent: pass
class Child(Parent): pass
@final
class Unrelated: pass
def is_parent(val: object) -> TypeIs[Parent]:
return isinstance(val, Parent)
def run(arg: Child | Unrelated):
if is_parent(arg):
# Type of ``arg`` is narrowed to the intersection
# of ``Parent`` and ``Child``, which is equivalent to
# ``Child``.
assert_type(arg, Child)
else:
