astroid/brain/brain_builtin_inference.py

# Licensed under the LGPL: https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html
# For details: https://github.com/pylint-dev/astroid/blob/main/LICENSE
# Copyright (c) https://github.com/pylint-dev/astroid/blob/main/CONTRIBUTORS.txt

"""Astroid hooks for various builtins."""

from __future__ import annotations

import itertools
from collections.abc import Callable, Iterable, Iterator
from functools import partial
from typing import TYPE_CHECKING, Any, NoReturn, cast

from astroid import arguments, helpers, nodes, objects, util
from astroid.builder import AstroidBuilder
from astroid.context import InferenceContext
from astroid.exceptions import (
    AstroidTypeError,
    AttributeInferenceError,
    InferenceError,
    MroError,
    UseInferenceDefault,
)
from astroid.inference_tip import inference_tip
from astroid.manager import AstroidManager
from astroid.nodes import scoped_nodes
from astroid.typing import (
    ConstFactoryResult,
    InferenceResult,
    SuccessfulInferenceResult,
)

if TYPE_CHECKING:
    from astroid.bases import Instance

ContainerObjects = (
    objects.FrozenSet | objects.DictItems | objects.DictKeys | objects.DictValues
)

BuiltContainers = type[tuple] | type[list] | type[set] | type[frozenset]

CopyResult = nodes.Dict | nodes.List | nodes.Set | objects.FrozenSet

OBJECT_DUNDER_NEW = "object.__new__"

STR_CLASS = """
class whatever(object):
    def join(self, iterable):
        return {rvalue}
    def replace(self, old, new, count=None):
        return {rvalue}
    def format(self, *args, **kwargs):
        return {rvalue}
    def encode(self, encoding='ascii', errors=None):
        return b''
    def decode(self, encoding='ascii', errors=None):
        return u''
    def capitalize(self):
        return {rvalue}
    def title(self):
        return {rvalue}
    def lower(self):
        return {rvalue}
    def upper(self):
        return {rvalue}
    def swapcase(self):
        return {rvalue}
    def index(self, sub, start=None, end=None):
        return 0
    def find(self, sub, start=None, end=None):
        return 0
    def count(self, sub, start=None, end=None):
        return 0
    def strip(self, chars=None):
        return {rvalue}
    def lstrip(self, chars=None):
        return {rvalue}
    def rstrip(self, chars=None):
        return {rvalue}
    def rjust(self, width, fillchar=None):
        return {rvalue}
    def center(self, width, fillchar=None):
        return {rvalue}
    def ljust(self, width, fillchar=None):
        return {rvalue}
"""


BYTES_CLASS = """
class whatever(object):
    def join(self, iterable):
        return {rvalue}
    def replace(self, old, new, count=None):
        return {rvalue}
    def decode(self, encoding='ascii', errors=None):
        return u''
    def capitalize(self):
        return {rvalue}
    def title(self):
        return {rvalue}
    def lower(self):
        return {rvalue}
    def upper(self):
        return {rvalue}
    def swapcase(self):
        return {rvalue}
    def index(self, sub, start=None, end=None):
        return 0
    def find(self, sub, start=None, end=None):
        return 0
    def count(self, sub, start=None, end=None):
        return 0
    def strip(self, chars=None):
        return {rvalue}
    def lstrip(self, chars=None):
        return {rvalue}
    def rstrip(self, chars=None):
        return {rvalue}
    def rjust(self, width, fillchar=None):
        return {rvalue}
    def center(self, width, fillchar=None):
        return {rvalue}
    def ljust(self, width, fillchar=None):
        return {rvalue}
"""


def _use_default() -> NoReturn:  # pragma: no cover
    raise UseInferenceDefault()


def _extend_string_class(class_node, code, rvalue):
    """Function to extend builtin str/unicode class."""
    code = code.format(rvalue=rvalue)
    fake = AstroidBuilder(AstroidManager()).string_build(code)["whatever"]
    for method in fake.mymethods():
        method.parent = class_node
        method.lineno = None
        method.col_offset = None
        if "__class__" in method.locals:
            method.locals["__class__"] = [class_node]
        class_node.locals[method.name] = [method]
        method.parent = class_node


def _extend_builtins(class_transforms):
    builtin_ast = AstroidManager().builtins_module
    for class_name, transform in class_transforms.items():
        transform(builtin_ast[class_name])


def on_bootstrap():
    """Called by astroid_bootstrapping()."""
    _extend_builtins(
        {
            "bytes": partial(_extend_string_class, code=BYTES_CLASS, rvalue="b''"),
            "str": partial(_extend_string_class, code=STR_CLASS, rvalue="''"),
        }
    )


def _builtin_filter_predicate(node, builtin_name) -> bool:
    # pylint: disable = too-many-boolean-expressions
    if (
        builtin_name == "type"
        and node.root().name == "re"
        and isinstance(node.func, nodes.Name)
        and node.func.name == "type"
        and isinstance(node.parent, nodes.Assign)
        and len(node.parent.targets) == 1
        and isinstance(node.parent.targets[0], nodes.AssignName)
        and node.parent.targets[0].name in {"Pattern", "Match"}
    ):
        # Handle re.Pattern and re.Match in brain_re
        # Match these patterns from stdlib/re.py
        # ```py
        # Pattern = type(...)
        # Match = type(...)
        # ```
        return False
    if isinstance(node.func, nodes.Name):
        return node.func.name == builtin_name
    if isinstance(node.func, nodes.Attribute):
        return (
            node.func.attrname == "fromkeys"
            and isinstance(node.func.expr, nodes.Name)
            and node.func.expr.name == "dict"
        )
    return False


def register_builtin_transform(
    manager: AstroidManager, transform, builtin_name
) -> None:
    """Register a new transform function for the given *builtin_name*.

    The transform function must accept two parameters, a node and
    an optional context.
    """

    def _transform_wrapper(
        node: nodes.Call, context: InferenceContext | None = None, **kwargs: Any
    ) -> Iterator:
        result = transform(node, context=context)
        if result:
            if not result.parent:
                # Let the transformation function determine
                # the parent for its result. Otherwise,
                # we set it to be the node we transformed from.
                result.parent = node

            if result.lineno is None:
                result.lineno = node.lineno
            # Can be a 'Module' see https://github.com/pylint-dev/pylint/issues/4671
            # We don't have a regression test on this one: tread carefully
            if hasattr(result, "col_offset") and result.col_offset is None:
                result.col_offset = node.col_offset
        return iter([result])

    manager.register_transform(
        nodes.Call,
        inference_tip(_transform_wrapper),
        partial(_builtin_filter_predicate, builtin_name=builtin_name),
    )


def _container_generic_inference(
    node: nodes.Call,
    context: InferenceContext | None,
    node_type: type[nodes.BaseContainer],
    transform: Callable[[SuccessfulInferenceResult], nodes.BaseContainer | None],
) -> nodes.BaseContainer:
    args = node.args
    if not args:
        return node_type(
            lineno=node.lineno,
            col_offset=node.col_offset,
            parent=node.parent,
            end_lineno=node.end_lineno,
            end_col_offset=node.end_col_offset,
        )
    if len(node.args) > 1:
        raise UseInferenceDefault()

    (arg,) = args
    transformed = transform(arg)
    if not transformed:
        try:
            inferred = next(arg.infer(context=context))
        except (InferenceError, StopIteration) as exc:
            raise UseInferenceDefault from exc
        if isinstance(inferred, util.UninferableBase):
            raise UseInferenceDefault
        transformed = transform(inferred)
    if not transformed or isinstance(transformed, util.UninferableBase):
        raise UseInferenceDefault
    return transformed


def _container_generic_transform(
    arg: SuccessfulInferenceResult,
    context: InferenceContext | None,
    klass: type[nodes.BaseContainer],
    iterables: tuple[type[nodes.BaseContainer] | type[ContainerObjects], ...],
    build_elts: BuiltContainers,
) -> nodes.BaseContainer | None:
    elts: Iterable | str | bytes

    if isinstance(arg, klass):
        return arg
    if isinstance(arg, iterables):
        arg = cast((nodes.BaseContainer | ContainerObjects), arg)
        if all(isinstance(elt, nodes.Const) for elt in arg.elts):
            elts = [cast(nodes.Const, elt).value for elt in arg.elts]
        else:
            # TODO: Does not handle deduplication for sets.
            elts = []
            for element in arg.elts:
                if not element:
                    continue
                inferred = util.safe_infer(element, context=context)
                if inferred:
                    evaluated_object = nodes.EvaluatedObject(
                        original=element, value=inferred
                    )
                    elts.append(evaluated_object)
    elif isinstance(arg, nodes.Dict):
        # Dicts need to have consts as strings already.
        elts = [
            item[0].value if isinstance(item[0], nodes.Const) else _use_default()
            for item in arg.items
        ]
    elif isinstance(arg, nodes.Const) and isinstance(arg.value, (str, bytes)):
        elts = arg.value
    else:
        return None
    return klass.from_elements(elts=build_elts(elts))


def _infer_builtin_container(
    node: nodes.Call,
    context: InferenceContext | None,
    klass: type[nodes.BaseContainer],
    iterables: tuple[type[nodes.NodeNG] | type[ContainerObjects], ...],
    build_elts: BuiltContainers,
) -> nodes.BaseContainer:
    transform_func = partial(
        _container_generic_transform,
        context=context,
        klass=klass,
        iterables=iterables,
        build_elts=build_elts,
    )

    return _container_generic_inference(node, context, klass, transform_func)


# pylint: disable=invalid-name
infer_tuple = partial(
    _infer_builtin_container,
    klass=nodes.Tuple,
    iterables=(
        nodes.List,
        nodes.Set,
        objects.FrozenSet,
        objects.DictItems,
        objects.DictKeys,
        objects.DictValues,
    ),
    build_elts=tuple,
)

infer_list = partial(
    _infer_builtin_container,
    klass=nodes.List,
    iterables=(
        nodes.Tuple,
        nodes.Set,
        objects.FrozenSet,
        objects.DictItems,
        objects.DictKeys,
        objects.DictValues,
    ),
    build_elts=list,
)

infer_set = partial(
    _infer_builtin_container,
    klass=nodes.Set,
    iterables=(nodes.List, nodes.Tuple, objects.FrozenSet, objects.DictKeys),
    build_elts=set,
)

infer_frozenset = partial(
    _infer_builtin_container,
    klass=objects.FrozenSet,
    iterables=(nodes.List, nodes.Tuple, nodes.Set, objects.FrozenSet, objects.DictKeys),
    build_elts=frozenset,
)


def _get_elts(arg, context):
    def is_iterable(n) -> bool:
        return isinstance(n, (nodes.List, nodes.Tuple, nodes.Set))

    try:
        inferred = next(arg.infer(context))
    except (InferenceError, StopIteration) as exc:
        raise UseInferenceDefault from exc
    if isinstance(inferred, nodes.Dict):
        items = inferred.items
    elif is_iterable(inferred):
        items = []
        for elt in inferred.elts:
            # If an item is not a pair of two items,
            # then fallback to the default inference.
            # Also, take in consideration only hashable items,
            # tuples and consts. We are choosing Names as well.
            if not is_iterable(elt):
                raise UseInferenceDefault()
            if len(elt.elts) != 2:
                raise UseInferenceDefault()
            if not isinstance(elt.elts[0], (nodes.Tuple, nodes.Const, nodes.Name)):
                raise UseInferenceDefault()
            items.append(tuple(elt.elts))
    else:
        raise UseInferenceDefault()
    return items


def infer_dict(node: nodes.Call, context: InferenceContext | None = None) -> nodes.Dict:
    """Try to infer a dict call to a Dict node.

    The function treats the following cases:

        * dict()
        * dict(mapping)
        * dict(iterable)
        * dict(iterable, **kwargs)
        * dict(mapping, **kwargs)
        * dict(**kwargs)

    If a case can't be inferred, we'll fallback to default inference.
    """
    call = arguments.CallSite.from_call(node, context=context)
    if call.has_invalid_arguments() or call.has_invalid_keywords():
        raise UseInferenceDefault

    args = call.positional_arguments
    kwargs = list(call.keyword_arguments.items())

    items: list[tuple[InferenceResult, InferenceResult]]
    if not args and not kwargs:
        # dict()
        return nodes.Dict(
            lineno=node.lineno,
            col_offset=node.col_offset,
            parent=node.parent,
            end_lineno=node.end_lineno,
            end_col_offset=node.end_col_offset,
        )
    if kwargs and not args:
        # dict(a=1, b=2, c=4)
        items = [(nodes.Const(key), value) for key, value in kwargs]
    elif len(args) == 1 and kwargs:
        # dict(some_iterable, b=2, c=4)
        elts = _get_elts(args[0], context)
        keys = [(nodes.Const(key), value) for key, value in kwargs]
        items = elts + keys
    elif len(args) == 1:
        items = _get_elts(args[0], context)
    else:
        raise UseInferenceDefault()
    value = nodes.Dict(
        col_offset=node.col_offset,
        lineno=node.lineno,
        parent=node.parent,
        end_lineno=node.end_lineno,
        end_col_offset=node.end_col_offset,
    )
    value.postinit(items)
    return value


def infer_super(
    node: nodes.Call, context: InferenceContext | None = None
) -> objects.Super:
    """Understand super calls.

    There are some restrictions for what can be understood:

        * unbounded super (one argument form) is not understood.

        * if the super call is not inside a function (classmethod or method),
          then the default inference will be used.

        * if the super arguments can't be inferred, the default inference
          will be used.
    """
    if len(node.args) == 1:
        # Ignore unbounded super.
        raise UseInferenceDefault

    scope = node.scope()
    if not isinstance(scope, nodes.FunctionDef):
        # Ignore non-method uses of super.
        raise UseInferenceDefault
    if scope.type not in ("classmethod", "method"):
        # Not interested in staticmethods.
        raise UseInferenceDefault

    cls = scoped_nodes.get_wrapping_class(scope)
    assert cls is not None
    if not node.args:
        mro_pointer = cls
        # In we are in a classmethod, the interpreter will fill
        # automatically the class as the second argument, not an instance.
        if scope.type == "classmethod":
            mro_type = cls
        else:
            mro_type = cls.instantiate_class()
    else:
        try:
            mro_pointer = next(node.args[0].infer(context=context))
        except (InferenceError, StopIteration) as exc:
            raise UseInferenceDefault from exc
        try:
            mro_type = next(node.args[1].infer(context=context))
        except (InferenceError, StopIteration) as exc:
            raise UseInferenceDefault from exc

    if isinstance(mro_pointer, util.UninferableBase) or isinstance(
        mro_type, util.UninferableBase
    ):
        # No way we could understand this.
        raise UseInferenceDefault

    super_obj = objects.Super(
        mro_pointer=mro_pointer,
        mro_type=mro_type,
        self_class=cls,
        scope=scope,
        call=node,
    )
    super_obj.parent = node
    return super_obj


def _infer_getattr_args(node, context):
    if len(node.args) not in (2, 3):
        # Not a valid getattr call.
        raise UseInferenceDefault

    try:
        obj = next(node.args[0].infer(context=context))
        attr = next(node.args[1].infer(context=context))
    except (InferenceError, StopIteration) as exc:
        raise UseInferenceDefault from exc

    if isinstance(obj, util.UninferableBase) or isinstance(attr, util.UninferableBase):
        # If one of the arguments is something we can't infer,
        # then also make the result of the getattr call something
        # which is unknown.
        return util.Uninferable, util.Uninferable

    is_string = isinstance(attr, nodes.Const) and isinstance(attr.value, str)
    if not is_string:
        raise UseInferenceDefault

    return obj, attr.value


def infer_getattr(node, context: InferenceContext | None = None):
    """Understand getattr calls.

    If one of the arguments is an Uninferable object, then the
    result will be an Uninferable object. Otherwise, the normal attribute
    lookup will be done.
    """
    obj, attr = _infer_getattr_args(node, context)
    if (
        isinstance(obj, util.UninferableBase)
        or isinstance(attr, util.UninferableBase)
        or not hasattr(obj, "igetattr")
    ):
        return util.Uninferable

    try:
        return next(obj.igetattr(attr, context=context))
    except (StopIteration, InferenceError, AttributeInferenceError):
        if len(node.args) == 3:
            # Try to infer the default and return it instead.
            try:
                return next(node.args[2].infer(context=context))
            except (StopIteration, InferenceError) as exc:
                raise UseInferenceDefault from exc

    raise UseInferenceDefault


def infer_hasattr(node, context: InferenceContext | None = None):
    """Understand hasattr calls.

    This always guarantees three possible outcomes for calling
    hasattr: Const(False) when we are sure that the object
    doesn't have the intended attribute, Const(True) when
    we know that the object has the attribute and Uninferable
    when we are unsure of the outcome of the function call.
    """
    try:
        obj, attr = _infer_getattr_args(node, context)
        if (
            isinstance(obj, util.UninferableBase)
            or isinstance(attr, util.UninferableBase)
            or not hasattr(obj, "getattr")
        ):
            return util.Uninferable
        obj.getattr(attr, context=context)
    except UseInferenceDefault:
        # Can't infer something from this function call.
        return util.Uninferable
    except AttributeInferenceError:
        # Doesn't have it.
        return nodes.Const(False)
    return nodes.Const(True)


def infer_callable(node, context: InferenceContext | None = None):
    """Understand callable calls.

    This follows Python's semantics, where an object
    is callable if it provides an attribute __call__,
    even though that attribute is something which can't be
    called.
    """
    if len(node.args) != 1:
        # Invalid callable call.
        raise UseInferenceDefault

    argument = node.args[0]
    try:
        inferred = next(argument.infer(context=context))
    except (InferenceError, StopIteration):
        return util.Uninferable
    if isinstance(inferred, util.UninferableBase):
        return util.Uninferable
    return nodes.Const(inferred.callable())


def infer_property(
    node: nodes.Call, context: InferenceContext | None = None
) -> objects.Property:
    """Understand `property` class.

    This only infers the output of `property`
    call, not the arguments themselves.
    """
    if len(node.args) < 1:
        # Invalid property call.
        raise UseInferenceDefault

    getter = node.args[0]
    try:
        inferred = next(getter.infer(context=context))
    except (InferenceError, StopIteration) as exc:
        raise UseInferenceDefault from exc

    if not isinstance(inferred, (nodes.FunctionDef, nodes.Lambda)):
        raise UseInferenceDefault

    prop_func = objects.Property(
        function=inferred,
        name="<property>",
        lineno=node.lineno,
        col_offset=node.col_offset,
        # ↓ semantically, the definition of the class of property isn't within
        # node.frame. It's somewhere in the builtins module, but we are special
        # casing it for each "property()" call, so we are making up the
        # definition on the spot, ad-hoc.
        parent=scoped_nodes.SYNTHETIC_ROOT,
    )
    prop_func.postinit(
        body=[],
        args=inferred.args,
        doc_node=getattr(inferred, "doc_node", None),
    )
    return prop_func


def infer_bool(node, context: InferenceContext | None = None):
    """Understand bool calls."""
    if len(node.args) > 1:
        # Invalid bool call.
        raise UseInferenceDefault

    if not node.args:
        return nodes.Const(False)

    argument = node.args[0]
    try:
        inferred = next(argument.infer(context=context))
    except (InferenceError, StopIteration):
        return util.Uninferable
    if isinstance(inferred, util.UninferableBase):
        return util.Uninferable

    bool_value = inferred.bool_value(context=context)
    if isinstance(bool_value, util.UninferableBase):
        return util.Uninferable
    return nodes.Const(bool_value)


def infer_type(node, context: InferenceContext | None = None):
    """Understand the one-argument form of *type*."""
    if len(node.args) != 1:
        raise UseInferenceDefault

    return helpers.object_type(node.args[0], context)


def infer_slice(node, context: InferenceContext | None = None):
    """Understand `slice` calls."""
    args = node.args
    if not 0 < len(args) <= 3:
        raise UseInferenceDefault

    infer_func = partial(util.safe_infer, context=context)
    args = [infer_func(arg) for arg in args]
    for arg in args:
        if not arg or isinstance(arg, util.UninferableBase):
            raise UseInferenceDefault
        if not isinstance(arg, nodes.Const):
            raise UseInferenceDefault
        if not isinstance(arg.value, (type(None), int)):
            raise UseInferenceDefault

    if len(args) < 3:
        # Make sure we have 3 arguments.
        args.extend([None] * (3 - len(args)))

    slice_node = nodes.Slice(
        lineno=node.lineno,
        col_offset=node.col_offset,
        parent=node.parent,
        end_lineno=node.end_lineno,
        end_col_offset=node.end_col_offset,
    )
    slice_node.postinit(*args)
    return slice_node


def _infer_object__new__decorator(
    node: nodes.ClassDef, context: InferenceContext | None = None, **kwargs: Any
) -> Iterator[Instance]:
    # Instantiate class immediately
    # since that's what @object.__new__ does
    return iter((node.instantiate_class(),))


def _infer_object__new__decorator_check(node) -> bool:
    """Predicate before inference_tip.

    Check if the given ClassDef has an @object.__new__ decorator
    """
    if not node.decorators:
        return False

    for decorator in node.decorators.nodes:
        if isinstance(decorator, nodes.Attribute):
            if decorator.as_string() == OBJECT_DUNDER_NEW:
                return True
    return False


def infer_issubclass(callnode, context: InferenceContext | None = None):
    """Infer issubclass() calls.

    :param nodes.Call callnode: an `issubclass` call
    :param InferenceContext context: the context for the inference
    :rtype nodes.Const: Boolean Const value of the `issubclass` call
    :raises UseInferenceDefault: If the node cannot be inferred
    """
    call = arguments.CallSite.from_call(callnode, context=context)
    if call.keyword_arguments:
        # issubclass doesn't support keyword arguments
        raise UseInferenceDefault("TypeError: issubclass() takes no keyword arguments")
    if len(call.positional_arguments) != 2:
        raise UseInferenceDefault(
            f"Expected two arguments, got {len(call.positional_arguments)}"
        )
    # The left hand argument is the obj to be checked
    obj_node, class_or_tuple_node = call.positional_arguments

    try:
        obj_type = next(obj_node.infer(context=context))
    except (InferenceError, StopIteration) as exc:
        raise UseInferenceDefault from exc
    if not isinstance(obj_type, nodes.ClassDef):
        raise UseInferenceDefault(
            f"TypeError: arg 1 must be class, not {type(obj_type)!r}"
        )

    # The right hand argument is the class(es) that the given
    # object is to be checked against.
    try:
        class_container = _class_or_tuple_to_container(
            class_or_tuple_node, context=context
        )
    except InferenceError as exc:
        raise UseInferenceDefault from exc
    try:
        issubclass_bool = helpers.object_issubclass(obj_type, class_container, context)
    except AstroidTypeError as exc:
        raise UseInferenceDefault("TypeError: " + str(exc)) from exc
    except MroError as exc:
        raise UseInferenceDefault from exc
    return nodes.Const(issubclass_bool)


def infer_isinstance(
    callnode: nodes.Call, context: InferenceContext | None = None
) -> nodes.Const:
    """Infer isinstance calls.

    :param nodes.Call callnode: an isinstance call
    :raises UseInferenceDefault: If the node cannot be inferred
    """
    call = arguments.CallSite.from_call(callnode, context=context)
    if call.keyword_arguments:
        # isinstance doesn't support keyword arguments
        raise UseInferenceDefault("TypeError: isinstance() takes no keyword arguments")
    if len(call.positional_arguments) != 2:
        raise UseInferenceDefault(
            f"Expected two arguments, got {len(call.positional_arguments)}"
        )
    # The left hand argument is the obj to be checked
    obj_node, class_or_tuple_node = call.positional_arguments
    # The right hand argument is the class(es) that the given
    # obj is to be check is an instance of
    try:
        class_container = _class_or_tuple_to_container(
            class_or_tuple_node, context=context
        )
    except InferenceError as exc:
        raise UseInferenceDefault from exc
    try:
        isinstance_bool = helpers.object_isinstance(obj_node, class_container, context)
    except AstroidTypeError as exc:
        raise UseInferenceDefault("TypeError: " + str(exc)) from exc
    except MroError as exc:
        raise UseInferenceDefault from exc
    if isinstance(isinstance_bool, util.UninferableBase):
        raise UseInferenceDefault
    return nodes.Const(isinstance_bool)


def _class_or_tuple_to_container(
    node: InferenceResult, context: InferenceContext | None = None
) -> list[InferenceResult]:
    # Move inferences results into container
    # to simplify later logic
    # raises InferenceError if any of the inferences fall through
    try:
        node_infer = next(node.infer(context=context))
    except StopIteration as e:
        raise InferenceError(node=node, context=context) from e
    # arg2 MUST be a type or a TUPLE of types
    # for isinstance
    if isinstance(node_infer, nodes.Tuple):
        try:
            class_container = [
                next(node.infer(context=context)) for node in node_infer.elts
            ]
        except StopIteration as e:
            raise InferenceError(node=node, context=context) from e
    else:
        class_container = [node_infer]
    return class_container


def infer_len(node, context: InferenceContext | None = None) -> nodes.Const:
    """Infer length calls.

    :param nodes.Call node: len call to infer
    :param context.InferenceContext: node context
    :rtype nodes.Const: a Const node with the inferred length, if possible
    """
    call = arguments.CallSite.from_call(node, context=context)
    if call.keyword_arguments:
        raise UseInferenceDefault("TypeError: len() must take no keyword arguments")
    if len(call.positional_arguments) != 1:
        raise UseInferenceDefault(
            "TypeError: len() must take exactly one argument "
            "({len}) given".format(len=len(call.positional_arguments))
        )
    [argument_node] = call.positional_arguments

    try:
        return nodes.Const(helpers.object_len(argument_node, context=context))
    except (AstroidTypeError, InferenceError) as exc:
        raise UseInferenceDefault(str(exc)) from exc


def infer_str(node, context: InferenceContext | None = None) -> nodes.Const:
    """Infer str() calls.

    :param nodes.Call node: str() call to infer
    :param context.InferenceContext: node context
    :rtype nodes.Const: a Const containing an empty string
    """
    call = arguments.CallSite.from_call(node, context=context)
    if call.keyword_arguments:
        raise UseInferenceDefault("TypeError: str() must take no keyword arguments")
    try:
        return nodes.Const("")
    except (AstroidTypeError, InferenceError) as exc:
        raise UseInferenceDefault(str(exc)) from exc


def infer_int(node, context: InferenceContext | None = None):
    """Infer int() calls.

    :param nodes.Call node: int() call to infer
    :param context.InferenceContext: node context
    :rtype nodes.Const: a Const containing the integer value of the int() call
    """
    call = arguments.CallSite.from_call(node, context=context)
    if call.keyword_arguments:
        raise UseInferenceDefault("TypeError: int() must take no keyword arguments")

    if call.positional_arguments:
        try:
            first_value = next(call.positional_arguments[0].infer(context=context))
        except (InferenceError, StopIteration) as exc:
            raise UseInferenceDefault(str(exc)) from exc

        if isinstance(first_value, util.UninferableBase):
            raise UseInferenceDefault

        if isinstance(first_value, nodes.Const) and isinstance(
            first_value.value, (int, str)
        ):
            try:
                actual_value = int(first_value.value)
            except ValueError:
                return nodes.Const(0)
            return nodes.Const(actual_value)

    return nodes.Const(0)


def infer_dict_fromkeys(node, context: InferenceContext | None = None):
    """Infer dict.fromkeys.

    :param nodes.Call node: dict.fromkeys() call to infer
    :param context.InferenceContext context: node context
    :rtype nodes.Dict:
        a Dictionary containing the values that astroid was able to infer.
        In case the inference failed for any reason, an empty dictionary
        will be inferred instead.
    """

    def _build_dict_with_elements(elements: list) -> nodes.Dict:
        new_node = nodes.Dict(
            col_offset=node.col_offset,
            lineno=node.lineno,
            parent=node.parent,
            end_lineno=node.end_lineno,
            end_col_offset=node.end_col_offset,
        )
        new_node.postinit(elements)
        return new_node

    call = arguments.CallSite.from_call(node, context=context)
    if call.keyword_arguments:
        raise UseInferenceDefault("TypeError: int() must take no keyword arguments")
    if len(call.positional_arguments) not in {1, 2}:
        raise UseInferenceDefault(
            "TypeError: Needs between 1 and 2 positional arguments"
        )

    default = nodes.Const(None)
    values = call.positional_arguments[0]
    try:
        inferred_values = next(values.infer(context=context))
    except (InferenceError, StopIteration):
        return _build_dict_with_elements([])
    if inferred_values is util.Uninferable:
        return _build_dict_with_elements([])

    # Limit to a couple of potential values, as this can become pretty complicated
    accepted_iterable_elements = (nodes.Const,)
    if isinstance(inferred_values, (nodes.List, nodes.Set, nodes.Tuple)):
        elements = inferred_values.elts
        for element in elements:
            if not isinstance(element, accepted_iterable_elements):
                # Fallback to an empty dict
                return _build_dict_with_elements([])

        elements_with_value = [(element, default) for element in elements]
        return _build_dict_with_elements(elements_with_value)
    if isinstance(inferred_values, nodes.Const) and isinstance(
        inferred_values.value, (str, bytes)
    ):
        elements_with_value = [
            (nodes.Const(element), default) for element in inferred_values.value
        ]
        return _build_dict_with_elements(elements_with_value)
    if isinstance(inferred_values, nodes.Dict):
        keys = inferred_values.itered()
        for key in keys:
            if not isinstance(key, accepted_iterable_elements):
                # Fallback to an empty dict
                return _build_dict_with_elements([])

        elements_with_value = [(element, default) for element in keys]
        return _build_dict_with_elements(elements_with_value)

    # Fallback to an empty dictionary
    return _build_dict_with_elements([])


def _infer_copy_method(
    node: nodes.Call, context: InferenceContext | None = None, **kwargs: Any
) -> Iterator[CopyResult]:
    assert isinstance(node.func, nodes.Attribute)
    inferred_orig, inferred_copy = itertools.tee(node.func.expr.infer(context=context))
    if all(
        isinstance(
            inferred_node, (nodes.Dict, nodes.List, nodes.Set, objects.FrozenSet)
        )
        for inferred_node in inferred_orig
    ):
        return cast(Iterator[CopyResult], inferred_copy)

    raise UseInferenceDefault


def _is_str_format_call(node: nodes.Call) -> bool:
    """Catch calls to str.format()."""
    if not (isinstance(node.func, nodes.Attribute) and node.func.attrname == "format"):
        return False

    if isinstance(node.func.expr, nodes.Name):
        value = util.safe_infer(node.func.expr)
    else:
        value = node.func.expr

    return isinstance(value, nodes.Const) and isinstance(value.value, str)


def _infer_str_format_call(
    node: nodes.Call, context: InferenceContext | None = None, **kwargs: Any
) -> Iterator[ConstFactoryResult | util.UninferableBase]:
    """Return a Const node based on the template and passed arguments."""
    call = arguments.CallSite.from_call(node, context=context)
    assert isinstance(node.func, (nodes.Attribute, nodes.AssignAttr, nodes.DelAttr))

    value: nodes.Const
    if isinstance(node.func.expr, nodes.Name):
        if not (
            (inferred := util.safe_infer(node.func.expr))
            and isinstance(inferred, nodes.Const)
        ):
            return iter([util.Uninferable])
        value = inferred
    elif isinstance(node.func.expr, nodes.Const):
        value = node.func.expr
    else:  # pragma: no cover
        return iter([util.Uninferable])

    format_template = value.value

    # Get the positional arguments passed
    inferred_positional: list[nodes.Const] = []
    for i in call.positional_arguments:
        one_inferred = util.safe_infer(i, context)
        if not isinstance(one_inferred, nodes.Const):
            return iter([util.Uninferable])
        inferred_positional.append(one_inferred)

    pos_values: list[str] = [i.value for i in inferred_positional]

    # Get the keyword arguments passed
    inferred_keyword: dict[str, nodes.Const] = {}
    for k, v in call.keyword_arguments.items():
        one_inferred = util.safe_infer(v, context)
        if not isinstance(one_inferred, nodes.Const):
            return iter([util.Uninferable])
        inferred_keyword[k] = one_inferred

    keyword_values: dict[str, str] = {k: v.value for k, v in inferred_keyword.items()}

    try:
        formatted_string = format_template.format(*pos_values, **keyword_values)
    except (AttributeError, IndexError, KeyError, TypeError, ValueError):
        # AttributeError: named field in format string was not found in the arguments
        # IndexError: there are too few arguments to interpolate
        # TypeError: Unsupported format string
        # ValueError: Unknown format code
        return iter([util.Uninferable])

    return iter([nodes.const_factory(formatted_string)])


def register(manager: AstroidManager) -> None:
    # Builtins inference
    register_builtin_transform(manager, infer_bool, "bool")
    register_builtin_transform(manager, infer_super, "super")
    register_builtin_transform(manager, infer_callable, "callable")
    register_builtin_transform(manager, infer_property, "property")
    register_builtin_transform(manager, infer_getattr, "getattr")
    register_builtin_transform(manager, infer_hasattr, "hasattr")
    register_builtin_transform(manager, infer_tuple, "tuple")
    register_builtin_transform(manager, infer_set, "set")
    register_builtin_transform(manager, infer_list, "list")
    register_builtin_transform(manager, infer_dict, "dict")
    register_builtin_transform(manager, infer_frozenset, "frozenset")
    register_builtin_transform(manager, infer_type, "type")
    register_builtin_transform(manager, infer_slice, "slice")
    register_builtin_transform(manager, infer_isinstance, "isinstance")
    register_builtin_transform(manager, infer_issubclass, "issubclass")
    register_builtin_transform(manager, infer_len, "len")
    register_builtin_transform(manager, infer_str, "str")
    register_builtin_transform(manager, infer_int, "int")
    register_builtin_transform(manager, infer_dict_fromkeys, "dict.fromkeys")

    # Infer object.__new__ calls
    manager.register_transform(
        nodes.ClassDef,
        inference_tip(_infer_object__new__decorator),
        _infer_object__new__decorator_check,
    )

    manager.register_transform(
        nodes.Call,
        inference_tip(_infer_copy_method),
        lambda node: isinstance(node.func, nodes.Attribute)
        and node.func.attrname == "copy",
    )

    manager.register_transform(
        nodes.Call,
        inference_tip(_infer_str_format_call),
        _is_str_format_call,
    )