"""
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:
  NoReturn, 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 wrapper submodules for re and io related types.
* 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!
"""

# The pseudo-submodules 're' and 'io' are part of the public
# namespace, but excluded from __all__ because they might stomp on
# legitimate imports of those modules.

"""For converting None to type(None), and strings to ForwardRef."""

"""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).
    """

"""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 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.
    
        # Special case for `repr` of types with `ParamSpec`:
        
"""Collect all type variables and parameter specifications in args
    in order of first appearance (lexicographic order).

        >>> T = TypeVar('T')
        >>> _collect_parameters((T, Callable[P, T]))
        (~T, ~P)
    """

            # We don't want __parameters__ descriptor of a bare Python class.
            
            # `t` might be a tuple, when `ParamSpec` is substituted with
            # `[T, int]`, or `[int, *Ts]`, etc.
            
"""Check correct count for parameters of a generic cls (internal helper).

    This gives a nice error message in case of count mismatch.
    """

    # Weed out strict duplicates, preserving the first of each occurrence.
    
        # Happens for cases like `Annotated[dict, {'x': IntValidator()}]`
        
"""Internal helper for Union creation and substitution.

    Flatten Unions among parameters, then remove duplicates.
    """

"""Internal helper for Literal creation: flatten Literals among parameters."""

"""Internal wrapper caching __getitem__ of generic types.

    For non-hashable arguments, the original function is used as a fallback.
    """

        # 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.

"""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.
    """

"""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.
    """

# Internal indicator of special typing constructs.
# See __doc__ instance attribute for specific docs.

"""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.
    """

"""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.
    """

# This is semantically identical to NoReturn, but it is implemented
# separately so that type checkers can distinguish between the two
# if they want.

"""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
    """

"""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
    """

"""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.
    """

"""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 should not
    be used with isinstance() or issubclass().
    """

"""Special typing construct to indicate final names to type checkers.

    A final name cannot be re-assigned or overridden in a subclass.

        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.
    """

"""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].
    """

"""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).
    """

"""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.
    
"""Special form for marking type aliases.

    Use TypeAlias to indicate that an assignment should
    be recognized as a proper type alias definition by type
    checkers.

        Predicate: TypeAlias = Callable[..., bool]

    It's invalid when used anywhere except as in the example above.
    """

"""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.

        Callable[Concatenate[int, P], int]

    See PEP 612 for detailed information.
    """

"""Special typing construct for marking user-defined type guard functions.

    ``TypeGuard`` can be 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``.

         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 guards is left to the user.

    ``TypeGuard`` also works with type variables.  For more information, see
    PEP 647 (User-Defined Type Guards).
    """

        # If we do `def f(*args: *Ts)`, then we'll have `arg = '*Ts'`.
        # Unfortunately, this isn't a valid expression on its own, so we
        # do the unpacking manually.
        
    # Special case where Z[[int, str, bool]] == Z[int, str, bool] in PEP 612.
    
    # Convert lists to tuples to help other libraries cache the results.
    
"""Parameterizes a generic class.

    At least, parameterizing a generic class is the *main* thing this method
    does. For example, for some generic class `Foo`, this is called when we
    do `Foo[int]` - there, with `cls=Foo` and `params=int`.

    However, note that this method is also called when defining generic
    classes in the first place with `class Foo(Generic[T]): ...`.
    """

        # Generic and Protocol can only be subscripted with unique type variables.
        
        # Look for Generic[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[...].
        
"""The central part of the internal API.

    This represents a generic version of type 'origin' with type arguments 'params'.
    There are two kind of these aliases: user defined and special. The special ones
    are wrappers around builtin collections and ABCs in collections.abc. These must
    have 'name' always set. If 'inst' is False, then the alias can't be instantiated;
    this is used by e.g. typing.List and typing.Dict.
    """

        # Some objects raise TypeError (or something even more exotic)
        # if you try to set attributes on them; we guard against that here
        
        # We are careful for copy and pickle.
        # Also for simplicity we don't relay any dunder names
        
# Special typing constructs Union, Optional, Generic, Callable and Tuple
# use three special attributes for internal bookkeeping of generic types:
# * __parameters__ is a tuple of unique free type parameters of a generic
#   type, for example, Dict[T, T].__parameters__ == (T,);
# * __origin__ keeps a reference to a type that was subscripted,
#   e.g., Union[T, int].__origin__ == Union, or the non-generic version of
#   the type.
# * __args__ is a tuple of all arguments used in subscripting,
#   e.g., Dict[T, int].__args__ == (T, int).

    # The type of parameterized generics.
    #
    # That is, for example, `type(List[int])` is `_GenericAlias`.
    #
    # Objects which are instances of this class include:
    # * Parameterized container types, e.g. `Tuple[int]`, `List[int]`.
    #  * Note that native container types, e.g. `tuple`, `list`, use
    #    `types.GenericAlias` instead.
    # * Parameterized classes:
    #     class C[T]: pass
    #     # C[int] is a _GenericAlias
    # * `Callable` aliases, generic `Callable` aliases, and
    #   parameterized `Callable` aliases:
    #     T = TypeVar('T')
    #     # _CallableGenericAlias inherits from _GenericAlias.
    #     A = Callable[[], None]  # _CallableGenericAlias
    #     B = Callable[[T], None]  # _CallableGenericAlias
    #     C = B[int]  # _CallableGenericAlias
    # * Parameterized `Final`, `ClassVar` and `TypeGuard`:
    #     # All _GenericAlias
    #     Final[int]
    #     ClassVar[float]
    #     TypeVar[bool]

        # Parameterizes an already-parameterized object.
        #
        # For example, we arrive here doing something like:
        #   T1 = TypeVar('T1')
        #   T2 = TypeVar('T2')
        #   T3 = TypeVar('T3')
        #   class A(Generic[T1]): pass
        #   B = A[T2]  # B is a _GenericAlias
        #   C = B[T3]  # Invokes _GenericAlias.__getitem__
        #
        # We also arrive here when parameterizing a generic `Callable` alias:
        #   T = TypeVar('T')
        #   C = Callable[[T], None]
        #   C[int]  # Invokes _GenericAlias.__getitem__

            # Can't subscript Generic[...] or Protocol[...].
            
        # Determines new __args__ for __getitem__.
        #
        # For example, suppose we had:
        #   class A(Generic[T1, T2]): pass
        #   B = A[int, T3]
        #   C = B[str]
        # `B.__args__` is `(int, T3)`, so `C.__args__` should be `(int, str)`.
        # Unfortunately, this is harder than it looks, because if `T3` is
        # anything more exotic than a plain `TypeVar`, we need to consider
        # edge cases.

        # In the example above, this would be {T3: str}
        
                # Consider the following `Callable`.
                #   C = Callable[[int], str]
                # Here, `C.__args__` should be (int, str) - NOT ([int], str).
                # That means that if we had something like...
                #   P = ParamSpec('P')
                #   T = TypeVar('T')
                #   C = Callable[P, T]
                #   D = C[[int, str], float]
                # ...we need to be careful; `new_args` should end up as
                # `(int, str, float)` rather than `([int, str], float)`.
                
                # Consider the following `_GenericAlias`, `B`:
                #   class A(Generic[*Ts]): ...
                #   B = A[T, *Ts]
                # If we then do:
                #   B[float, int, str]
                # The `new_arg` corresponding to `T` will be `float`, and the
                # `new_arg` corresponding to `*Ts` will be `(int, str)`. We
                # should join all these types together in a flat list
                # `(float, int, str)` - so again, we should `extend`.
                
                # Corner case:
                #    P = ParamSpec('P')
                #    T = TypeVar('T')
                #    class Base(Generic[P]): ...
                # Can be substituted like this:
                #    X = Base[[int, T]]
                # In this case, `old_arg` will be a tuple:
                
# _nparams is the number of accepted parameters, e.g. 0 for Hashable,
# 1 for List and 2 for Dict.  It may be -1 if variable number of
# parameters are accepted (needs custom __getitem__).

        # This relaxes what args can be on purpose to allow things like
        # PEP 612 ParamSpec.  Responsibility for whether a user is using
        # Callable[...] properly is deferred to static type checkers.
        
"""Type unpack operator.

    The type unpack operator takes the child types from some container type,
    such as `tuple[int, str]` or a `TypeVarTuple`, and 'pulls them out'.

        # For some generic class `Foo`:
        Foo[Unpack[tuple[int, str]]]  # Equivalent to Foo[int, str]

        Ts = TypeVarTuple('Ts')
        # Specifies that `Bar` is generic in an arbitrary number of types.
        # (Think of `Ts` as a tuple of an arbitrary number of individual
        #  `TypeVar`s, which the `Unpack` is 'pulling out' directly into the
        #  `Generic[]`.)
        class Bar(Generic[Unpack[Ts]]): ...
        Bar[int]  # Valid
        Bar[int, str]  # Also valid

    From Python 3.11, this can also be done using the `*` operator::

        Foo[*tuple[int, str]]
        class Bar(Generic[*Ts]): ...

    And from Python 3.12, it can be done using built-in syntax for generics::

        class Bar[*Ts]: ...

    The operator can also be used along with a `TypedDict` to annotate
    `**kwargs` in a function signature::

        class Movie(TypedDict):
            name: str
            year: int

        # This function expects two keyword arguments - *name* of type `str` and
        # *year* of type `int`.
        def foo(**kwargs: Unpack[Movie]): ...

    Note that there is only some runtime checking of this operator. Not
    everything the runtime allows may be accepted by static type checkers.

    For more information, see PEPs 646 and 692.
    """

        # `Unpack` only takes one argument, so __args__ should contain only
        # a single item.
        
# These special attributes will be not collected as protocol members.

"""Collect protocol members from a protocol class objects.

    This includes names actually defined in the class dictionary, as well
    as names that appear in annotations. Special names (above) are skipped.
    """

    # Already using a custom `__init__`. No need to calculate correct
    # `__init__` to call. This can lead to RecursionError. See bpo-45121.
    
    # Initially, `__init__` of a protocol subclass is set to `_no_init_or_replace_init`.
    # The first instantiation of the subclass will call `_no_init_or_replace_init` which
    # searches for a proper new `__init__` in the MRO. The new `__init__`
    # replaces the subclass' old `__init__` (ie `_no_init_or_replace_init`). Subsequent
    # instantiation of the protocol subclass will thus use the new
    # `__init__` and no longer call `_no_init_or_replace_init`.
    
"""Allow instance and class checks for special stdlib modules.

    The abc and functools modules indiscriminately call isinstance() and
    issubclass() on the whole MRO of a user class, which may contain protocols.
    """

    # Import getattr_static lazily so as not to slow down the import of typing.py
    # Cache the result so we don't slow down _ProtocolMeta.__instancecheck__ unnecessarily
    
    # This metaclass is somewhat unfortunate,
    # but is necessary for several reasons...
    
                # Same error message as for issubclass(1, int).
                
                # this attribute is set by @runtime_checkable:
                
        # We need this method for situations where attributes are
        # assigned in __init__.
        
            # i.e., it's a concrete subclass of a protocol
            
            # this attribute is set by @runtime_checkable:
            
            # Check if the members appears in the class dictionary...
            
            # ...or in annotations, if it is a sub-protocol.
            
"""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).

        class C:
                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.runtime_checkable act as simple-minded runtime protocols that check
    only the presence of given attributes, ignoring their type signatures.
    Protocol classes can be generic, they are defined as::

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

        # Determine if this is a protocol or a concrete subclass.
        
        # Set (or override) the protocol subclass hook.
        
"""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.

    The metadata itself is stored in a '__metadata__' attribute as a tuple.
    """

"""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.

    Details:

    - It's an error to call `Annotated` with less than two arguments.
    - Access the metadata via the ``__metadata__`` attribute::

        assert Annotated[int, '$'].__metadata__ == ('$',)

    - Nested Annotated types are flattened::

        assert Annotated[Annotated[T, Ann1, Ann2], Ann3] == Annotated[T, Ann1, Ann2, Ann3]

    - Instantiating an annotated type is equivalent to instantiating the
    underlying type::

        assert Annotated[C, Ann1](5) == C(5)

    - Annotated can be used as a generic type alias::

        type Optimized[T] = Annotated[T, runtime.Optimize()]
        # type checker will treat Optimized[int]
        # as equivalent to Annotated[int, runtime.Optimize()]

        type OptimizedList[T] = Annotated[list[T], runtime.Optimize()]
        # type checker will treat OptimizedList[int]
        # as equivalent to Annotated[list[int], runtime.Optimize()]

    - Annotated cannot be used with an unpacked TypeVarTuple::

        type Variadic[*Ts] = Annotated[*Ts, Ann1]  # NOT valid

      This would be equivalent to::

        Annotated[T1, T2, T3, ..., Ann1]

      where T1, T2 etc. are TypeVars, which would be invalid, because
      only one type should be passed to Annotated.
    """

"""Mark a protocol class as a runtime protocol.

    Such protocol 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
    one trick ponies in collections.abc such as Iterable.

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

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

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

    # PEP 544 prohibits using issubclass()
    # with protocols that have non-method members.
    # See gh-113320 for why we compute this attribute here,
    # rather than in `_ProtocolMeta.__init__`
    
"""Cast a value to a type.

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

"""Ask a static type checker to confirm that the value is of the given type.

    At runtime this does nothing: it returns the first argument unchanged with no
    checks or side effects, no matter the actual type of the argument.

    When a static 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
    """

"""Return type hints for an object.

    This is often the same as obj.__annotations__, but it handles
    forward references encoded as string literals and recursively replaces all
    'Annotated[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.  For classes, the search
      order is globals first then locals.

    - 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.
    """

                # This is surprising, but required.  Before Python 3.10,
                # get_type_hints only evaluated the globalns of
                # a class.  To maintain backwards compatibility, we reverse
                # the globalns and localns order so that eval() looks into
                # *base_globals* first rather than *base_locals*.
                # This only affects ForwardRefs.
                
            # Find globalns for the unwrapped object.
            
        # Return empty annotations for something that _could_ have them.
        
            # class-level forward refs were handled above, this must be either
            # a module-level annotation or a function argument annotation
            
"""Get the unsubscripted version of a type.

    This supports generic types, Callable, Tuple, Union, Literal, Final, ClassVar,
    Annotated, and others. Return None for unsupported types.

    Examples::

        >>> assert get_origin(Literal[42]) is Literal
        >>> assert get_origin(int) is None
        >>> assert get_origin(ClassVar[int]) is ClassVar
        >>> assert get_origin(Generic) is Generic
        >>> assert get_origin(Generic[T]) is Generic
        >>> assert get_origin(Union[T, int]) is Union
        >>> assert get_origin(List[Tuple[T, T]][int]) is list
        >>> assert get_origin(P.args) is P
    """

"""Get type arguments with all substitutions performed.

    For unions, basic simplifications used by Union constructor are performed.

        >>> assert get_args(Dict[str, int]) == (str, int)
        >>> assert get_args(int) == ()
        >>> assert get_args(Union[int, Union[T, int], str][int]) == (int, str)
        >>> assert get_args(Union[int, Tuple[T, int]][str]) == (int, Tuple[str, int])
        >>> assert get_args(Callable[[], T][int]) == ([], int)
    """

"""Check if an annotation is a TypedDict class.

        >>> from typing import TypedDict
        >>> class Film(TypedDict):
        ...     title: str
        ...     year: int
        ...
        >>> is_typeddict(Film)
        True
        >>> is_typeddict(dict)
        False
    """

"""Statically assert that a line of code is unreachable.

    Example::

            match arg:
                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.
    """

"""Decorator 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.
    """

                # We only modify objects that are defined in this type directly.
                # If classes / methods are nested in multiple layers,
                # we will modify them when processing their direct holders.
                
"""Decorator to give another decorator the @no_type_check effect.

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

"""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.

        @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::

        @overload
        @overload
        @overload
        def utf8(value):
            ...  # implementation goes here

    The overloads for a function can be retrieved at runtime using the
    get_overloads() function.
    """

"""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.

        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
    attempts to set the ``__final__`` attribute to ``True`` on the decorated
    object to allow runtime introspection.
    """

        # 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.
        
# Some unconstrained type variables.  These were initially used by the container types.
# They were never meant for export and are now unused, but we keep them around to
# avoid breaking compatibility with users who import them.

# A useful type variable with constraints.  This represents string types.
# (This one *is* for export!)

"""Deprecated alias to collections.abc.Callable.

    Callable[[int], str] signifies a function that takes a single
    parameter of type int and returns a 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, a ParamSpec,
    Concatenate 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.
    """

"""Deprecated alias to builtins.tuple.

    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.

    To specify a variable-length tuple of homogeneous type, use Tuple[T, ...].
    """

"""Deprecated alias to builtins.type.

    builtins.type or typing.Type can be used to annotate class objects.
    For example, suppose we have the following classes::

        class User: ...  # Abstract base for User classes
        class BasicUser(User): ...
        class ProUser(User): ...
        class TeamUser(User): ...

    And a function that takes a class argument that's a subclass of
    User and returns an instance of the corresponding class::

        def new_user[U](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.
    """

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

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

        # update from user namespace without overriding special namedtuple attributes
        
"""Typed version of namedtuple.

    Usage::

        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.)
    An alternative equivalent functional syntax is also accepted::

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

"""Create a new typed dict class object.

        This method is called when TypedDict is subclassed,
        or when TypedDict is instantiated. This way
        TypedDict supports all three syntax forms described in its docstring.
        Subclasses and instances of TypedDict return actual dictionaries.
        """

"TypedDict('Name', {f0: t0, f1: t1, ...}); each t must be a type"

        # Typed dicts are only for static structural subtyping.
        
"""A simple typed namespace. At runtime it is equivalent to a plain dict.

    TypedDict creates a dictionary type such that a type checker will expect all
    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.

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

    The type info can be accessed via the Point2D.__annotations__ dict, and
    the Point2D.__required_keys__ and Point2D.__optional_keys__ frozensets.
    TypedDict supports an additional equivalent form::

        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::

        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 Required and NotRequired special forms can also be used to mark
    individual keys as being required or not required::

        class Point2D(TypedDict):
            x: int               # the "x" key must always be present (Required is the default)
            y: NotRequired[int]  # the "y" key can be omitted

    See PEP 655 for more details on Required and NotRequired.
    """

"The kwargs-based syntax for TypedDict definitions is deprecated "

"in Python 3.11, will be removed in Python 3.13, and may not be "

        # Setting correct module is necessary to make typed dict classes pickleable.
        
"""Special typing construct to mark a TypedDict key as required.

    This is mainly useful for total=False TypedDicts.

        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.
    """

"""Special typing construct to mark a TypedDict key as potentially missing.

            title: str
            year: NotRequired[int]

        m = Movie(
        )
    """

"""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 callable that simply returns its argument.

    Usage::

        UserId = NewType('UserId', int)

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

        UserId('user')          # Fails type check

        name_by_id(42)          # Fails type check
        name_by_id(UserId(42))  # OK

        num = UserId(5) + 1     # type: int
    """

        # We defined __mro_entries__ to get a better error message
        # if a user attempts to subclass a NewType instance. bpo-46170
        
Cannot subclass an instance of NewType. Perhaps you were looking for: 

# Python-version-specific alias (Python 2: unicode; Python 3: str)

"""Generic base class for TextIO and BinaryIO.

    This is an abstract, generic version of the return of open().

    NOTE: This does not distinguish between the different possible
    classes (text vs. binary, read vs. write vs. read/write,
    append-only, unbuffered).  The TextIO and BinaryIO subclasses
    below capture the distinctions between text vs. binary, which is
    pervasive in the interface; however we currently do not offer a
    way to track the other distinctions in the type system.
    """

"""Ask a static type checker to reveal the inferred type of an expression.

    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 the argument unchanged.
    """

"""Decorator to mark an object as providing dataclass-like behaviour.

    The decorator can be applied to a function, class, or metaclass.

    Example usage with a decorator function::

        @dataclass_transform()
        def create_model[T](cls: type[T]) -> type[T]:
            ...
            return cls

        @create_model
        class CustomerModel:
            id: int
            name: str

    On a base class::

        class ModelBase: ...

        class CustomerModel(ModelBase):
            id: int
            name: str

    On a metaclass::

        class ModelMeta(type): ...

        class ModelBase(metaclass=ModelMeta): ...

            id: int
            name: str

    The ``CustomerModel`` classes defined above will
    be treated by type checkers similarly to classes created with
    ``@dataclasses.dataclass``.
    For example, type checkers will assume these classes have
    ``__init__`` methods that accept ``id`` and ``name``.

    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
    - ``frozen_default`` indicates whether the ``frozen`` parameter is
    - ``field_specifiers`` specifies a static list of supported classes
        or functions that describe fields, similar to ``dataclasses.field()``.
    - Arbitrary other keyword arguments are accepted in order to allow for
        possible future extensions.

    At runtime, this decorator records its arguments in the
    ``__dataclass_transform__`` attribute on the decorated object.
    It has no other runtime effect.

    See PEP 681 for more details.
    """

"""Indicate that a method is intended to override a method in a base class.

    Usage::

            def method(self) -> None:
                pass

        class Child(Base):
            @override
                super().method()

    When this decorator is applied to a method, the type checker will
    validate that it overrides a method or attribute 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.

    There is no runtime checking of this property. The decorator attempts to
    set the ``__override__`` attribute to ``True`` on the decorated object to
    allow runtime introspection.

    See PEP 698 for details.
    """

import re as stdlib_re  # Avoid confusion with the re we export.

from types import WrapperDescriptorType, MethodWrapperType, MethodDescriptorType, GenericAlias

def _type_convert(arg, module=None, *, allow_special_forms=False):

    """For converting None to type(None), and strings to ForwardRef."""

        return ForwardRef(arg, module=module, is_class=allow_special_forms)

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).

    arg = _type_convert(arg, module=module, allow_special_forms=allow_special_forms)

        raise TypeError(f"{arg} is not valid as type argument")

    if arg in (Any, LiteralString, NoReturn, Never, Self, TypeAlias):

    if isinstance(arg, _SpecialForm) or arg in (Generic, Protocol):

        raise TypeError(f"Plain {arg} is not valid as type argument")

            (tuple, list, ParamSpec, _ConcatenateGenericAlias))

    """Internal helper for munging collections.abc.Callable's __args__.

    """Return the repr() of an object, special-casing types (internal helper).

        return '[' + ', '.join(_type_repr(t) for t in obj) + ']'

    """Collect all type variables and parameter specifications in args

    """Check correct count for parameters of a generic cls (internal helper).

        raise TypeError(f"Too {'many' if alen > elen else 'few'} arguments for {cls};"

        subargs = getattr(arg, '__typing_unpacked_tuple_args__', None)

        if subargs is not None and not (subargs and subargs[-1] is ...):

        if isinstance(p, (_UnionGenericAlias, types.UnionType)):

    return tuple(_deduplicate(params, unhashable_fallback=True))

    """Internal helper for Literal creation: flatten Literals among parameters."""

                pass  # All real errors (not unhashable args) are raised below.

def _eval_type(t, globalns, localns, recursive_guard=frozenset()):

    if isinstance(t, (_GenericAlias, GenericAlias, types.UnionType)):

                ForwardRef(arg) if isinstance(arg, str) else arg

        ev_args = tuple(_eval_type(a, globalns, localns, recursive_guard) for a in t.__args__)

            raise TypeError("Cannot subclass special typing classes")

    """Mixin to prevent iteration, without being compatible with Iterable.

        raise TypeError(f"{self} cannot be used with isinstance()")

        raise TypeError(f"{self} cannot be used with issubclass()")

            raise TypeError("typing.Any cannot be used with isinstance()")

    item = _type_check(parameters, f'{self} accepts only single type.')

    """Special typing construct to indicate final names to type checkers.

    parameters = tuple(_type_check(p, msg) for p in parameters)

        return _UnionGenericAlias(self, parameters, name="Optional")

    arg = _type_check(parameters, f"{self} requires a single type.")

    """Special typing form to define literal types (a.k.a. value types).

        parameters = tuple(p for p, _ in _deduplicate(list(_value_and_type_iter(parameters))))

        raise TypeError("Cannot take a Concatenate of no types.")

    if not (parameters[-1] is ... or isinstance(parameters[-1], ParamSpec)):

        raise TypeError("The last parameter to Concatenate should be a "

    parameters = (*(_type_check(p, msg) for p in parameters[:-1]), parameters[-1])

    """Special typing construct for marking user-defined type guard functions.

                 '__forward_is_argument__', '__forward_is_class__',

    def __init__(self, arg, is_argument=True, module=None, *, is_class=False):

            raise TypeError(f"Forward reference must be a string -- got {arg!r}")

            arg_to_compile = f'({arg},)[0]'  # E.g. (*Ts,)[0] or (*tuple[int, int],)[0]

            raise SyntaxError(f"Forward reference must be an expression -- got {arg!r}")

        if not self.__forward_evaluated__ or localns is not globalns:

                    sys.modules.get(self.__forward_module__, None), '__dict__', globalns

                eval(self.__forward_code__, globalns, localns),

                type_, globalns, localns, recursive_guard | {self.__forward_arg__}

        if self.__forward_evaluated__ and other.__forward_evaluated__:

            return (self.__forward_arg__ == other.__forward_arg__ and

                    self.__forward_value__ == other.__forward_value__)

        return (self.__forward_arg__ == other.__forward_arg__ and

                self.__forward_module__ == other.__forward_module__)

        return hash((self.__forward_arg__, self.__forward_module__))

            module_repr = f', module={self.__forward_module__!r}'

        return f'ForwardRef({self.__forward_arg__!r}{module_repr})'

            getattr(x, '__typing_is_unpacked_typevartuple__', False))

    return isinstance(x, (TypeVar, ParamSpec)) or _is_unpacked_typevartuple(x)

    if ((isinstance(arg, _GenericAlias) and arg.__origin__ is Unpack) or

        (isinstance(arg, GenericAlias) and getattr(arg, '__unpacked__', False))):

            raise TypeError(f"More than one TypeVarTuple parameter in {alias}")

            subargs = getattr(arg, '__typing_unpacked_tuple_args__', None)

            if subargs and len(subargs) == 2 and subargs[-1] is ...:

                    raise TypeError("More than one unpacked arbitrary-length tuple argument")

                        f" actual {alen}, expected at least {plen-1}")

        *([fillarg]*(plen - right - left - typevartuple_index - 1)),

        arg = tuple(_type_check(a, "Expected a type.") for a in arg)

        raise TypeError(f"Expected a list of types, an ellipsis, "

                        f"ParamSpec, or Concatenate. Got {arg}")

                f"Parameter list to {cls.__qualname__}[...] cannot be empty"

                f"Parameters to {cls.__name__}[...] must all be type variables "

                f"Parameters to {cls.__name__}[...] must all be unique")

            prepare = getattr(param, '__typing_prepare_subst__', None)

                        "Cannot inherit from Generic[...] multiple times.")

                s_vars = ', '.join(str(t) for t in tvars if t not in gvarset)

                raise TypeError(f"Some type variables ({s_vars}) are"

                                f" not listed in Generic[{s_args}]")

            raise TypeError(f"Type {self._name} cannot be instantiated; "

                            f"use {self.__origin__.__name__}() instead")

            if isinstance(b, _BaseGenericAlias) or issubclass(b, Generic):

        if '__origin__' in self.__dict__ and not _is_dunder(attr):

        if _is_dunder(attr) or attr in {'_name', '_inst', '_nparams'}:

        raise TypeError("Subscripted generics cannot be used with"

                + [attr for attr in dir(self.__origin__) if not _is_dunder(attr)]))

            raise TypeError(f"Cannot subscript already-subscripted {self}")

            raise TypeError(f"Too {'many' if alen > plen else 'few'} arguments for {self};"

                            f" actual {alen}, expected {plen}")

        return tuple(self._make_substitution(self.__args__, new_arg_by_param))

            substfunc = getattr(old_arg, '__typing_subst__', None)

                subparams = getattr(old_arg, '__parameters__', ())

                            subargs.extend(new_arg_by_param[x])

                            subargs.append(new_arg_by_param[x])

            if self.__origin__ == collections.abc.Callable and isinstance(new_arg, tuple):

                    tuple(self._make_substitution(old_arg, new_arg_by_param)),

        return self.__class__(self.__origin__, args, name=self._name, inst=self._inst)

            args = ", ".join([_type_repr(a) for a in self.__args__])

        if self._name:  # generic version of an ABC or built-in class

                if isinstance(b, _BaseGenericAlias) and b is not self:

class _SpecialGenericAlias(_NotIterable, _BaseGenericAlias, _root=True):

    def __init__(self, origin, nparams, *, inst=True, name=None):

            self.__doc__ = f'A generic version of {origin.__qualname__}.'

            self.__doc__ = f'A generic version of {origin.__module__}.{origin.__qualname__}.'

class _DeprecatedGenericAlias(_SpecialGenericAlias, _root=True):

        self, origin, nparams, *, removal_version, inst=True, name=None

        super().__init__(origin, nparams, inst=inst, name=name)

            f"{self.__module__}.{self._name}", remove=self._removal_version

class _CallableGenericAlias(_NotIterable, _GenericAlias, _root=True):

                f'[[{", ".join([_type_repr(a) for a in args[:-1]])}], '

                                     name=self._name, inst=self._inst)

            params = tuple(_type_check(p, msg) for p in params[:-1])

class _UnionGenericAlias(_NotIterable, _GenericAlias, _root=True):

        if not isinstance(other, (_UnionGenericAlias, types.UnionType)):

            return _compare_args_orderless(self.__args__, other.__args__)

                return f'typing.Optional[{_type_repr(args[1])}]'

                return f'typing.Optional[{_type_repr(args[0])}]'

        return set(_value_and_type_iter(self.__args__)) == set(_value_and_type_iter(other.__args__))

        return hash(frozenset(_value_and_type_iter(self.__args__)))

        return f'typing.Unpack[{_type_repr(self.__args__[0])}]'

    '_is_protocol', '_is_runtime_protocol', '__protocol_attrs__',

    '__abstractmethods__', '__annotations__', '__dict__', '__doc__',

EXCLUDED_ATTRIBUTES = _TYPING_INTERNALS | _SPECIAL_NAMES | {'_MutableMapping__marker'}

            if not attr.startswith('_abc_') and attr not in EXCLUDED_ATTRIBUTES:

        init = base.__dict__.get('__init__', _no_init_or_replace_init)

    except AttributeError:  # For platforms without _getframemodulename()

        return sys._getframe(depth + 1).f_globals.get('__name__', default)

    except (AttributeError, ValueError):  # For platforms without _getframe()

    """Allow instance and class checks for special stdlib modules.

        'Callable', 'Awaitable', 'Iterable', 'Iterator', 'AsyncIterable',

        'Hashable', 'Sized', 'Container', 'Collection', 'Reversible', 'Buffer',

    'contextlib': ['AbstractContextManager', 'AbstractAsyncContextManager'],

                    or base.__name__ in _PROTO_ALLOWLIST.get(base.__module__, [])

                        and getattr(base, "_is_protocol", False)

                        f"Protocols can only inherit from other protocols, "

        return super().__new__(mcls, name, bases, namespace, **kwargs)

                raise TypeError('issubclass() arg 1 must be a class')

            if not getattr(cls, '_is_runtime_protocol', False):

                    "Instance and class checks can only be used with "

                and cls.__dict__.get("__subclasshook__") is _proto_hook

                    "Protocols with non-method members don't support issubclass()"

            not getattr(cls, '_is_runtime_protocol', False) and

            raise TypeError("Instance and class checks can only be used with"

            if val is None and attr not in cls.__non_callable_proto_members__:

            if (isinstance(annotations, collections.abc.Mapping) and

                    issubclass(other, Generic) and getattr(other, '_is_protocol', False)):

            cls._is_protocol = any(b is Protocol for b in cls.__bases__)

        if cls._is_protocol and cls.__init__ is Protocol.__init__:

class _AnnotatedAlias(_NotIterable, _GenericAlias, _root=True):

        raise TypeError("Type Annotated cannot be instantiated.")

                            "with at least two arguments (a type and an "

            raise TypeError("Annotated[...] should not be used with an "

        origin = _type_check(params[0], msg, allow_special_forms=True)

            "Cannot subclass {}.Annotated".format(cls.__module__)

    if not issubclass(cls, Generic) or not getattr(cls, '_is_protocol', False):

        raise TypeError('@runtime_checkable can be only applied to protocol classes,'

                f"Failed to determine whether protocol member {attr!r} "

    """Ask a static type checker to confirm that the value is of the given type.

_allowed_types = (types.FunctionType, types.BuiltinFunctionType,

                  WrapperDescriptorType, MethodWrapperType, MethodDescriptorType)

def get_type_hints(obj, globalns=None, localns=None, include_extras=False):

                base_globals = getattr(sys.modules.get(base.__module__, None), '__dict__', {})

            base_locals = dict(vars(base)) if localns is None else localns

                base_globals, base_locals = base_locals, base_globals

                    value = ForwardRef(value, is_argument=False, is_class=True)

                value = _eval_type(value, base_globals, base_locals)

        return hints if include_extras else {k: _strip_annotations(t) for k, t in hints.items()}

            raise TypeError('{!r} is not a module, class, method, '

                is_argument=not isinstance(obj, types.ModuleType),

    return hints if include_extras else {k: _strip_annotations(t) for k, t in hints.items()}

    if hasattr(t, "__origin__") and t.__origin__ in (Required, NotRequired):

        stripped_args = tuple(_strip_annotations(a) for a in t.__args__)

    raise AssertionError(f"Expected code to be unreachable, but got: {value}")

    """Decorator to indicate that annotations are not type hints.

                or obj.__qualname__ != f'{arg.__qualname__}.{obj.__name__}'

                or getattr(obj, '__module__', None) != arg.__module__

    """Decorator to give another decorator the @no_type_check effect.

_overload_registry = defaultdict(functools.partial(defaultdict, dict))

        _overload_registry[f.__module__][f.__qualname__][f.__code__.co_firstlineno] = func

    """Return all defined overloads for *func* as a sequence."""

T_co = TypeVar('T_co', covariant=True)  # Any type covariant containers.

V_co = TypeVar('V_co', covariant=True)  # Any type covariant containers.

VT_co = TypeVar('VT_co', covariant=True)  # Value type covariant containers.

T_contra = TypeVar('T_contra', contravariant=True)  # Ditto contravariant.

AbstractSet = _alias(collections.abc.Set, 1, name='AbstractSet')

    collections.abc.ByteString, 0, removal_version=(3, 14)  # Not generic.

ContextManager = _alias(contextlib.AbstractContextManager, 1, name='ContextManager')

AsyncContextManager = _alias(contextlib.AbstractAsyncContextManager, 1, name='AsyncContextManager')

DefaultDict = _alias(collections.defaultdict, 2, name='DefaultDict')

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

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

    types = {n: _type_check(t, f"field {n} annotation must be a type")

                                    defaults=defaults, module=module)

    nm_tpl.__annotations__ = nm_tpl.__new__.__annotations__ = types

_prohibited = frozenset({'__new__', '__init__', '__slots__', '__getnewargs__',

                         '_make', '_replace', '_asdict', '_source'})

_special = frozenset({'__module__', '__name__', '__annotations__'})

            if base is not _NamedTuple and base is not Generic:

                    'can only inherit from a NamedTuple type and Generic')

        bases = tuple(tuple if base is _NamedTuple else base for base in bases)

                raise TypeError(f"Non-default namedtuple field {field_name} "

                                f"{'s' if len(default_names) > 1 else ''} "

                               defaults=[ns[n] for n in default_names],

            nm_tpl.__class_getitem__ = classmethod(class_getitem)

                raise AttributeError("Cannot overwrite NamedTuple attribute " + key)

            elif key not in _special and key not in nm_tpl._fields:

                        " can be provided to NamedTuple, not both")

_NamedTuple = type.__new__(NamedTupleMeta, 'NamedTuple', (), {})

            if type(base) is not _TypedDictMeta and base is not Generic:

                raise TypeError('cannot inherit from both a TypedDict type '

                                'and a non-TypedDict base class')

        tp_dict = type.__new__(_TypedDictMeta, name, (*generic_base, dict), ns)

        msg = "TypedDict('Name', {f0: t0, f1: t1, ...}); each t must be a type"

            annotations.update(base.__dict__.get('__annotations__', {}))

            base_required = base.__dict__.get('__required_keys__', set())

            base_optional = base.__dict__.get('__optional_keys__', set())

        for annotation_key, annotation_type in own_annotations.items():

                    annotation_origin = get_origin(annotation_type)

            f"Required keys overlap with optional keys in {name}:"

        raise TypeError('TypedDict does not support instance and class checks')

def TypedDict(typename, fields=None, /, *, total=True, **kwargs):

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

        raise TypeError("TypedDict takes either a dict or keyword arguments,"

            "The kwargs-based syntax for TypedDict definitions is deprecated "

            "in Python 3.11, will be removed in Python 3.13, and may not be "

    """Special typing construct to mark a TypedDict key as required.

    item = _type_check(parameters, f'{self._name} accepts only a single type.')

    """Special typing construct to mark a TypedDict key as potentially missing.

    """NewType creates simple unique types with almost zero runtime overhead.

                    f"Cannot subclass an instance of NewType. Perhaps you were looking for: "

                    f"`{subclass_name} = NewType({subclass_name!r}, {superclass_name})`"

    """Typed version of the return of open() in binary mode."""

        if name not in {"__dict__", "__module__", "__doc__"} and name in cls.__dict__:

                f"{cls.__name__} is deprecated, import directly "

                f"from typing instead. {cls.__name__} will be removed "

    """Ask a static type checker to reveal the inferred type of an expression.

    print(f"Runtime type is {type(obj).__name__!r}", file=sys.stderr)

    field_specifiers: tuple[type[Any] | Callable[..., Any], ...] = (),

    """Decorator to mark an object as providing dataclass-like behaviour.

    """Indicate that a method is intended to override a method in a base class.