Current File : //opt/cloudlinux/venv/lib64/python3.11/site-packages/astroid/inference.py
# Licensed under the LGPL: https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html
# For details: https://github.com/PyCQA/astroid/blob/main/LICENSE
# Copyright (c) https://github.com/PyCQA/astroid/blob/main/CONTRIBUTORS.txt
"""This module contains a set of functions to handle inference on astroid trees."""
from __future__ import annotations
import ast
import functools
import itertools
import operator
import typing
from collections.abc import Callable, Generator, Iterable, Iterator
from typing import TYPE_CHECKING, Any, Optional, TypeVar, Union
from astroid import bases, constraint, decorators, helpers, nodes, protocols, util
from astroid.const import PY310_PLUS
from astroid.context import (
CallContext,
InferenceContext,
bind_context_to_node,
copy_context,
)
from astroid.exceptions import (
AstroidBuildingError,
AstroidError,
AstroidIndexError,
AstroidTypeError,
AstroidValueError,
AttributeInferenceError,
InferenceError,
NameInferenceError,
_NonDeducibleTypeHierarchy,
)
from astroid.interpreter import dunder_lookup
from astroid.manager import AstroidManager
from astroid.typing import (
InferenceErrorInfo,
InferenceResult,
SuccessfulInferenceResult,
)
if TYPE_CHECKING:
from astroid.objects import Property
# Prevents circular imports
objects = util.lazy_import("objects")
_T = TypeVar("_T")
_BaseContainerT = TypeVar("_BaseContainerT", bound=nodes.BaseContainer)
_FunctionDefT = TypeVar("_FunctionDefT", bound=nodes.FunctionDef)
GetFlowFactory = typing.Callable[
[
InferenceResult,
Optional[InferenceResult],
Union[nodes.AugAssign, nodes.BinOp],
InferenceResult,
Optional[InferenceResult],
InferenceContext,
InferenceContext,
],
"list[functools.partial[Generator[InferenceResult, None, None]]]",
]
# .infer method ###############################################################
def infer_end(
self: _T, context: InferenceContext | None = None, **kwargs: Any
) -> Iterator[_T]:
"""Inference's end for nodes that yield themselves on inference.
These are objects for which inference does not have any semantic,
such as Module or Consts.
"""
yield self
# We add ignores to all assignments to methods
# See https://github.com/python/mypy/issues/2427
nodes.Module._infer = infer_end
nodes.ClassDef._infer = infer_end
nodes.Lambda._infer = infer_end # type: ignore[assignment]
nodes.Const._infer = infer_end # type: ignore[assignment]
nodes.Slice._infer = infer_end # type: ignore[assignment]
def _infer_sequence_helper(
node: _BaseContainerT, context: InferenceContext | None = None
) -> list[SuccessfulInferenceResult]:
"""Infer all values based on _BaseContainer.elts."""
values = []
for elt in node.elts:
if isinstance(elt, nodes.Starred):
starred = helpers.safe_infer(elt.value, context)
if not starred:
raise InferenceError(node=node, context=context)
if not hasattr(starred, "elts"):
raise InferenceError(node=node, context=context)
values.extend(_infer_sequence_helper(starred))
elif isinstance(elt, nodes.NamedExpr):
value = helpers.safe_infer(elt.value, context)
if not value:
raise InferenceError(node=node, context=context)
values.append(value)
else:
values.append(elt)
return values
@decorators.raise_if_nothing_inferred
def infer_sequence(
self: _BaseContainerT,
context: InferenceContext | None = None,
**kwargs: Any,
) -> Iterator[_BaseContainerT]:
has_starred_named_expr = any(
isinstance(e, (nodes.Starred, nodes.NamedExpr)) for e in self.elts
)
if has_starred_named_expr:
values = _infer_sequence_helper(self, context)
new_seq = type(self)(
lineno=self.lineno, col_offset=self.col_offset, parent=self.parent
)
new_seq.postinit(values)
yield new_seq
else:
yield self
nodes.List._infer = infer_sequence # type: ignore[assignment]
nodes.Tuple._infer = infer_sequence # type: ignore[assignment]
nodes.Set._infer = infer_sequence # type: ignore[assignment]
def infer_map(
self: nodes.Dict, context: InferenceContext | None = None
) -> Iterator[nodes.Dict]:
if not any(isinstance(k, nodes.DictUnpack) for k, _ in self.items):
yield self
else:
items = _infer_map(self, context)
new_seq = type(self)(self.lineno, self.col_offset, self.parent)
new_seq.postinit(list(items.items()))
yield new_seq
def _update_with_replacement(
lhs_dict: dict[SuccessfulInferenceResult, SuccessfulInferenceResult],
rhs_dict: dict[SuccessfulInferenceResult, SuccessfulInferenceResult],
) -> dict[SuccessfulInferenceResult, SuccessfulInferenceResult]:
"""Delete nodes that equate to duplicate keys.
Since an astroid node doesn't 'equal' another node with the same value,
this function uses the as_string method to make sure duplicate keys
don't get through
Note that both the key and the value are astroid nodes
Fixes issue with DictUnpack causing duplicate keys
in inferred Dict items
:param lhs_dict: Dictionary to 'merge' nodes into
:param rhs_dict: Dictionary with nodes to pull from
:return : merged dictionary of nodes
"""
combined_dict = itertools.chain(lhs_dict.items(), rhs_dict.items())
# Overwrite keys which have the same string values
string_map = {key.as_string(): (key, value) for key, value in combined_dict}
# Return to dictionary
return dict(string_map.values())
def _infer_map(
node: nodes.Dict, context: InferenceContext | None
) -> dict[SuccessfulInferenceResult, SuccessfulInferenceResult]:
"""Infer all values based on Dict.items."""
values: dict[SuccessfulInferenceResult, SuccessfulInferenceResult] = {}
for name, value in node.items:
if isinstance(name, nodes.DictUnpack):
double_starred = helpers.safe_infer(value, context)
if not double_starred:
raise InferenceError
if not isinstance(double_starred, nodes.Dict):
raise InferenceError(node=node, context=context)
unpack_items = _infer_map(double_starred, context)
values = _update_with_replacement(values, unpack_items)
else:
key = helpers.safe_infer(name, context=context)
safe_value = helpers.safe_infer(value, context=context)
if any(not elem for elem in (key, safe_value)):
raise InferenceError(node=node, context=context)
# safe_value is SuccessfulInferenceResult as bool(Uninferable) == False
values = _update_with_replacement(values, {key: safe_value})
return values
nodes.Dict._infer = infer_map # type: ignore[assignment]
def _higher_function_scope(node: nodes.NodeNG) -> nodes.FunctionDef | None:
"""Search for the first function which encloses the given
scope. This can be used for looking up in that function's
scope, in case looking up in a lower scope for a particular
name fails.
:param node: A scope node.
:returns:
``None``, if no parent function scope was found,
otherwise an instance of :class:`astroid.nodes.scoped_nodes.Function`,
which encloses the given node.
"""
current = node
while current.parent and not isinstance(current.parent, nodes.FunctionDef):
current = current.parent
if current and current.parent:
return current.parent # type: ignore[no-any-return]
return None
def infer_name(
self: nodes.Name | nodes.AssignName,
context: InferenceContext | None = None,
**kwargs: Any,
) -> Generator[InferenceResult, None, None]:
"""Infer a Name: use name lookup rules."""
frame, stmts = self.lookup(self.name)
if not stmts:
# Try to see if the name is enclosed in a nested function
# and use the higher (first function) scope for searching.
parent_function = _higher_function_scope(self.scope())
if parent_function:
_, stmts = parent_function.lookup(self.name)
if not stmts:
raise NameInferenceError(
name=self.name, scope=self.scope(), context=context
)
context = copy_context(context)
context.lookupname = self.name
context.constraints[self.name] = constraint.get_constraints(self, frame)
return bases._infer_stmts(stmts, context, frame)
# pylint: disable=no-value-for-parameter
# The order of the decorators here is important
# See https://github.com/PyCQA/astroid/commit/0a8a75db30da060a24922e05048bc270230f5
nodes.Name._infer = decorators.raise_if_nothing_inferred(
decorators.path_wrapper(infer_name)
)
nodes.AssignName.infer_lhs = infer_name # won't work with a path wrapper
@decorators.raise_if_nothing_inferred
@decorators.path_wrapper
def infer_call(
self: nodes.Call, context: InferenceContext | None = None, **kwargs: Any
) -> Generator[InferenceResult, None, InferenceErrorInfo]:
"""Infer a Call node by trying to guess what the function returns."""
callcontext = copy_context(context)
callcontext.boundnode = None
if context is not None:
callcontext.extra_context = _populate_context_lookup(self, context.clone())
for callee in self.func.infer(context):
if isinstance(callee, util.UninferableBase):
yield callee
continue
try:
if hasattr(callee, "infer_call_result"):
callcontext.callcontext = CallContext(
args=self.args, keywords=self.keywords, callee=callee
)
yield from callee.infer_call_result(caller=self, context=callcontext)
except InferenceError:
continue
return InferenceErrorInfo(node=self, context=context)
nodes.Call._infer = infer_call # type: ignore[assignment]
@decorators.raise_if_nothing_inferred
@decorators.path_wrapper
def infer_import(
self: nodes.Import,
context: InferenceContext | None = None,
asname: bool = True,
**kwargs: Any,
) -> Generator[nodes.Module, None, None]:
"""Infer an Import node: return the imported module/object."""
context = context or InferenceContext()
name = context.lookupname
if name is None:
raise InferenceError(node=self, context=context)
try:
if asname:
yield self.do_import_module(self.real_name(name))
else:
yield self.do_import_module(name)
except AstroidBuildingError as exc:
raise InferenceError(node=self, context=context) from exc
nodes.Import._infer = infer_import
@decorators.raise_if_nothing_inferred
@decorators.path_wrapper
def infer_import_from(
self: nodes.ImportFrom,
context: InferenceContext | None = None,
asname: bool = True,
**kwargs: Any,
) -> Generator[InferenceResult, None, None]:
"""Infer a ImportFrom node: return the imported module/object."""
context = context or InferenceContext()
name = context.lookupname
if name is None:
raise InferenceError(node=self, context=context)
if asname:
try:
name = self.real_name(name)
except AttributeInferenceError as exc:
# See https://github.com/PyCQA/pylint/issues/4692
raise InferenceError(node=self, context=context) from exc
try:
module = self.do_import_module()
except AstroidBuildingError as exc:
raise InferenceError(node=self, context=context) from exc
try:
context = copy_context(context)
context.lookupname = name
stmts = module.getattr(name, ignore_locals=module is self.root())
return bases._infer_stmts(stmts, context)
except AttributeInferenceError as error:
raise InferenceError(
str(error), target=self, attribute=name, context=context
) from error
nodes.ImportFrom._infer = infer_import_from # type: ignore[assignment]
def infer_attribute(
self: nodes.Attribute | nodes.AssignAttr,
context: InferenceContext | None = None,
**kwargs: Any,
) -> Generator[InferenceResult, None, InferenceErrorInfo]:
"""Infer an Attribute node by using getattr on the associated object."""
for owner in self.expr.infer(context):
if isinstance(owner, util.UninferableBase):
yield owner
continue
context = copy_context(context)
old_boundnode = context.boundnode
try:
context.boundnode = owner
if isinstance(owner, (nodes.ClassDef, bases.Instance)):
frame = owner if isinstance(owner, nodes.ClassDef) else owner._proxied
context.constraints[self.attrname] = constraint.get_constraints(
self, frame=frame
)
yield from owner.igetattr(self.attrname, context)
except (
AttributeInferenceError,
InferenceError,
AttributeError,
):
pass
finally:
context.boundnode = old_boundnode
return InferenceErrorInfo(node=self, context=context)
# The order of the decorators here is important
# See https://github.com/PyCQA/astroid/commit/0a8a75db30da060a24922e05048bc270230f5
nodes.Attribute._infer = decorators.raise_if_nothing_inferred(
decorators.path_wrapper(infer_attribute)
)
# won't work with a path wrapper
nodes.AssignAttr.infer_lhs = decorators.raise_if_nothing_inferred(infer_attribute)
@decorators.raise_if_nothing_inferred
@decorators.path_wrapper
def infer_global(
self: nodes.Global, context: InferenceContext | None = None, **kwargs: Any
) -> Generator[InferenceResult, None, None]:
if context is None or context.lookupname is None:
raise InferenceError(node=self, context=context)
try:
return bases._infer_stmts(self.root().getattr(context.lookupname), context)
except AttributeInferenceError as error:
raise InferenceError(
str(error), target=self, attribute=context.lookupname, context=context
) from error
nodes.Global._infer = infer_global # type: ignore[assignment]
_SUBSCRIPT_SENTINEL = object()
def infer_subscript(
self: nodes.Subscript, context: InferenceContext | None = None, **kwargs: Any
) -> Generator[InferenceResult, None, InferenceErrorInfo | None]:
"""Inference for subscripts.
We're understanding if the index is a Const
or a slice, passing the result of inference
to the value's `getitem` method, which should
handle each supported index type accordingly.
"""
found_one = False
for value in self.value.infer(context):
if isinstance(value, util.UninferableBase):
yield util.Uninferable
return None
for index in self.slice.infer(context):
if isinstance(index, util.UninferableBase):
yield util.Uninferable
return None
# Try to deduce the index value.
index_value = _SUBSCRIPT_SENTINEL
if value.__class__ == bases.Instance:
index_value = index
elif index.__class__ == bases.Instance:
instance_as_index = helpers.class_instance_as_index(index)
if instance_as_index:
index_value = instance_as_index
else:
index_value = index
if index_value is _SUBSCRIPT_SENTINEL:
raise InferenceError(node=self, context=context)
try:
assigned = value.getitem(index_value, context)
except (
AstroidTypeError,
AstroidIndexError,
AstroidValueError,
AttributeInferenceError,
AttributeError,
) as exc:
raise InferenceError(node=self, context=context) from exc
# Prevent inferring if the inferred subscript
# is the same as the original subscripted object.
if self is assigned or isinstance(assigned, util.UninferableBase):
yield util.Uninferable
return None
yield from assigned.infer(context)
found_one = True
if found_one:
return InferenceErrorInfo(node=self, context=context)
return None
# The order of the decorators here is important
# See https://github.com/PyCQA/astroid/commit/0a8a75db30da060a24922e05048bc270230f5
nodes.Subscript._infer = decorators.raise_if_nothing_inferred( # type: ignore[assignment]
decorators.path_wrapper(infer_subscript)
)
nodes.Subscript.infer_lhs = decorators.raise_if_nothing_inferred(infer_subscript)
@decorators.raise_if_nothing_inferred
@decorators.path_wrapper
def _infer_boolop(
self: nodes.BoolOp, context: InferenceContext | None = None, **kwargs: Any
) -> Generator[InferenceResult, None, InferenceErrorInfo | None]:
"""Infer a boolean operation (and / or / not).
The function will calculate the boolean operation
for all pairs generated through inference for each component
node.
"""
values = self.values
if self.op == "or":
predicate = operator.truth
else:
predicate = operator.not_
try:
inferred_values = [value.infer(context=context) for value in values]
except InferenceError:
yield util.Uninferable
return None
for pair in itertools.product(*inferred_values):
if any(isinstance(item, util.UninferableBase) for item in pair):
# Can't infer the final result, just yield Uninferable.
yield util.Uninferable
continue
bool_values = [item.bool_value() for item in pair]
if any(isinstance(item, util.UninferableBase) for item in bool_values):
# Can't infer the final result, just yield Uninferable.
yield util.Uninferable
continue
# Since the boolean operations are short circuited operations,
# this code yields the first value for which the predicate is True
# and if no value respected the predicate, then the last value will
# be returned (or Uninferable if there was no last value).
# This is conforming to the semantics of `and` and `or`:
# 1 and 0 -> 1
# 0 and 1 -> 0
# 1 or 0 -> 1
# 0 or 1 -> 1
value = util.Uninferable
for value, bool_value in zip(pair, bool_values):
if predicate(bool_value):
yield value
break
else:
yield value
return InferenceErrorInfo(node=self, context=context)
nodes.BoolOp._infer = _infer_boolop
# UnaryOp, BinOp and AugAssign inferences
def _filter_operation_errors(
self: _T,
infer_callable: Callable[
[_T, InferenceContext | None],
Generator[InferenceResult | util.BadOperationMessage, None, None],
],
context: InferenceContext | None,
error: type[util.BadOperationMessage],
) -> Generator[InferenceResult, None, None]:
for result in infer_callable(self, context):
if isinstance(result, error):
# For the sake of .infer(), we don't care about operation
# errors, which is the job of pylint. So return something
# which shows that we can't infer the result.
yield util.Uninferable
else:
yield result
def _infer_unaryop(
self: nodes.UnaryOp, context: InferenceContext | None = None
) -> Generator[InferenceResult | util.BadUnaryOperationMessage, None, None]:
"""Infer what an UnaryOp should return when evaluated."""
for operand in self.operand.infer(context):
try:
yield operand.infer_unary_op(self.op)
except TypeError as exc:
# The operand doesn't support this operation.
yield util.BadUnaryOperationMessage(operand, self.op, exc)
except AttributeError as exc:
meth = protocols.UNARY_OP_METHOD[self.op]
if meth is None:
# `not node`. Determine node's boolean
# value and negate its result, unless it is
# Uninferable, which will be returned as is.
bool_value = operand.bool_value()
if not isinstance(bool_value, util.UninferableBase):
yield nodes.const_factory(not bool_value)
else:
yield util.Uninferable
else:
if not isinstance(operand, (bases.Instance, nodes.ClassDef)):
# The operation was used on something which
# doesn't support it.
yield util.BadUnaryOperationMessage(operand, self.op, exc)
continue
try:
try:
methods = dunder_lookup.lookup(operand, meth)
except AttributeInferenceError:
yield util.BadUnaryOperationMessage(operand, self.op, exc)
continue
meth = methods[0]
inferred = next(meth.infer(context=context), None)
if (
isinstance(inferred, util.UninferableBase)
or not inferred.callable()
):
continue
context = copy_context(context)
context.boundnode = operand
context.callcontext = CallContext(args=[], callee=inferred)
call_results = inferred.infer_call_result(self, context=context)
result = next(call_results, None)
if result is None:
# Failed to infer, return the same type.
yield operand
else:
yield result
except AttributeInferenceError as inner_exc:
# The unary operation special method was not found.
yield util.BadUnaryOperationMessage(operand, self.op, inner_exc)
except InferenceError:
yield util.Uninferable
@decorators.raise_if_nothing_inferred
@decorators.path_wrapper
def infer_unaryop(
self: nodes.UnaryOp, context: InferenceContext | None = None, **kwargs: Any
) -> Generator[InferenceResult, None, InferenceErrorInfo]:
"""Infer what an UnaryOp should return when evaluated."""
yield from _filter_operation_errors(
self, _infer_unaryop, context, util.BadUnaryOperationMessage
)
return InferenceErrorInfo(node=self, context=context)
nodes.UnaryOp._infer_unaryop = _infer_unaryop
nodes.UnaryOp._infer = infer_unaryop
def _is_not_implemented(const) -> bool:
"""Check if the given const node is NotImplemented."""
return isinstance(const, nodes.Const) and const.value is NotImplemented
def _infer_old_style_string_formatting(
instance: nodes.Const, other: nodes.NodeNG, context: InferenceContext
) -> tuple[util.UninferableBase | nodes.Const]:
"""Infer the result of '"string" % ...'.
TODO: Instead of returning Uninferable we should rely
on the call to '%' to see if the result is actually uninferable.
"""
if isinstance(other, nodes.Tuple):
if util.Uninferable in other.elts:
return (util.Uninferable,)
inferred_positional = [helpers.safe_infer(i, context) for i in other.elts]
if all(isinstance(i, nodes.Const) for i in inferred_positional):
values = tuple(i.value for i in inferred_positional)
else:
values = None
elif isinstance(other, nodes.Dict):
values: dict[Any, Any] = {}
for pair in other.items:
key = helpers.safe_infer(pair[0], context)
if not isinstance(key, nodes.Const):
return (util.Uninferable,)
value = helpers.safe_infer(pair[1], context)
if not isinstance(value, nodes.Const):
return (util.Uninferable,)
values[key.value] = value.value
elif isinstance(other, nodes.Const):
values = other.value
else:
return (util.Uninferable,)
try:
return (nodes.const_factory(instance.value % values),)
except (TypeError, KeyError, ValueError):
return (util.Uninferable,)
def _invoke_binop_inference(
instance: InferenceResult,
opnode: nodes.AugAssign | nodes.BinOp,
op: str,
other: InferenceResult,
context: InferenceContext,
method_name: str,
) -> Generator[InferenceResult, None, None]:
"""Invoke binary operation inference on the given instance."""
methods = dunder_lookup.lookup(instance, method_name)
context = bind_context_to_node(context, instance)
method = methods[0]
context.callcontext.callee = method
if (
isinstance(instance, nodes.Const)
and isinstance(instance.value, str)
and op == "%"
):
return iter(_infer_old_style_string_formatting(instance, other, context))
try:
inferred = next(method.infer(context=context))
except StopIteration as e:
raise InferenceError(node=method, context=context) from e
if isinstance(inferred, util.UninferableBase):
raise InferenceError
if not isinstance(
instance, (nodes.Const, nodes.Tuple, nodes.List, nodes.ClassDef, bases.Instance)
):
raise InferenceError # pragma: no cover # Used as a failsafe
return instance.infer_binary_op(opnode, op, other, context, inferred)
def _aug_op(
instance: InferenceResult,
opnode: nodes.AugAssign,
op: str,
other: InferenceResult,
context: InferenceContext,
reverse: bool = False,
) -> functools.partial[Generator[InferenceResult, None, None]]:
"""Get an inference callable for an augmented binary operation."""
method_name = protocols.AUGMENTED_OP_METHOD[op]
return functools.partial(
_invoke_binop_inference,
instance=instance,
op=op,
opnode=opnode,
other=other,
context=context,
method_name=method_name,
)
def _bin_op(
instance: InferenceResult,
opnode: nodes.AugAssign | nodes.BinOp,
op: str,
other: InferenceResult,
context: InferenceContext,
reverse: bool = False,
) -> functools.partial[Generator[InferenceResult, None, None]]:
"""Get an inference callable for a normal binary operation.
If *reverse* is True, then the reflected method will be used instead.
"""
if reverse:
method_name = protocols.REFLECTED_BIN_OP_METHOD[op]
else:
method_name = protocols.BIN_OP_METHOD[op]
return functools.partial(
_invoke_binop_inference,
instance=instance,
op=op,
opnode=opnode,
other=other,
context=context,
method_name=method_name,
)
def _bin_op_or_union_type(
left: bases.UnionType | nodes.ClassDef | nodes.Const,
right: bases.UnionType | nodes.ClassDef | nodes.Const,
) -> Generator[InferenceResult, None, None]:
"""Create a new UnionType instance for binary or, e.g. int | str."""
yield bases.UnionType(left, right)
def _get_binop_contexts(context, left, right):
"""Get contexts for binary operations.
This will return two inference contexts, the first one
for x.__op__(y), the other one for y.__rop__(x), where
only the arguments are inversed.
"""
# The order is important, since the first one should be
# left.__op__(right).
for arg in (right, left):
new_context = context.clone()
new_context.callcontext = CallContext(args=[arg])
new_context.boundnode = None
yield new_context
def _same_type(type1, type2) -> bool:
"""Check if type1 is the same as type2."""
return type1.qname() == type2.qname()
def _get_binop_flow(
left: InferenceResult,
left_type: InferenceResult | None,
binary_opnode: nodes.AugAssign | nodes.BinOp,
right: InferenceResult,
right_type: InferenceResult | None,
context: InferenceContext,
reverse_context: InferenceContext,
) -> list[functools.partial[Generator[InferenceResult, None, None]]]:
"""Get the flow for binary operations.
The rules are a bit messy:
* if left and right have the same type, then only one
method will be called, left.__op__(right)
* if left and right are unrelated typewise, then first
left.__op__(right) is tried and if this does not exist
or returns NotImplemented, then right.__rop__(left) is tried.
* if left is a subtype of right, then only left.__op__(right)
is tried.
* if left is a supertype of right, then right.__rop__(left)
is first tried and then left.__op__(right)
"""
op = binary_opnode.op
if _same_type(left_type, right_type):
methods = [_bin_op(left, binary_opnode, op, right, context)]
elif helpers.is_subtype(left_type, right_type):
methods = [_bin_op(left, binary_opnode, op, right, context)]
elif helpers.is_supertype(left_type, right_type):
methods = [
_bin_op(right, binary_opnode, op, left, reverse_context, reverse=True),
_bin_op(left, binary_opnode, op, right, context),
]
else:
methods = [
_bin_op(left, binary_opnode, op, right, context),
_bin_op(right, binary_opnode, op, left, reverse_context, reverse=True),
]
if (
PY310_PLUS
and op == "|"
and (
isinstance(left, (bases.UnionType, nodes.ClassDef))
or isinstance(left, nodes.Const)
and left.value is None
)
and (
isinstance(right, (bases.UnionType, nodes.ClassDef))
or isinstance(right, nodes.Const)
and right.value is None
)
):
methods.extend([functools.partial(_bin_op_or_union_type, left, right)])
return methods
def _get_aug_flow(
left: InferenceResult,
left_type: InferenceResult | None,
aug_opnode: nodes.AugAssign,
right: InferenceResult,
right_type: InferenceResult | None,
context: InferenceContext,
reverse_context: InferenceContext,
) -> list[functools.partial[Generator[InferenceResult, None, None]]]:
"""Get the flow for augmented binary operations.
The rules are a bit messy:
* if left and right have the same type, then left.__augop__(right)
is first tried and then left.__op__(right).
* if left and right are unrelated typewise, then
left.__augop__(right) is tried, then left.__op__(right)
is tried and then right.__rop__(left) is tried.
* if left is a subtype of right, then left.__augop__(right)
is tried and then left.__op__(right).
* if left is a supertype of right, then left.__augop__(right)
is tried, then right.__rop__(left) and then
left.__op__(right)
"""
bin_op = aug_opnode.op.strip("=")
aug_op = aug_opnode.op
if _same_type(left_type, right_type):
methods = [
_aug_op(left, aug_opnode, aug_op, right, context),
_bin_op(left, aug_opnode, bin_op, right, context),
]
elif helpers.is_subtype(left_type, right_type):
methods = [
_aug_op(left, aug_opnode, aug_op, right, context),
_bin_op(left, aug_opnode, bin_op, right, context),
]
elif helpers.is_supertype(left_type, right_type):
methods = [
_aug_op(left, aug_opnode, aug_op, right, context),
_bin_op(right, aug_opnode, bin_op, left, reverse_context, reverse=True),
_bin_op(left, aug_opnode, bin_op, right, context),
]
else:
methods = [
_aug_op(left, aug_opnode, aug_op, right, context),
_bin_op(left, aug_opnode, bin_op, right, context),
_bin_op(right, aug_opnode, bin_op, left, reverse_context, reverse=True),
]
return methods
def _infer_binary_operation(
left: InferenceResult,
right: InferenceResult,
binary_opnode: nodes.AugAssign | nodes.BinOp,
context: InferenceContext,
flow_factory: GetFlowFactory,
) -> Generator[InferenceResult | util.BadBinaryOperationMessage, None, None]:
"""Infer a binary operation between a left operand and a right operand.
This is used by both normal binary operations and augmented binary
operations, the only difference is the flow factory used.
"""
context, reverse_context = _get_binop_contexts(context, left, right)
left_type = helpers.object_type(left)
right_type = helpers.object_type(right)
methods = flow_factory(
left, left_type, binary_opnode, right, right_type, context, reverse_context
)
for method in methods:
try:
results = list(method())
except AttributeError:
continue
except AttributeInferenceError:
continue
except InferenceError:
yield util.Uninferable
return
else:
if any(isinstance(result, util.UninferableBase) for result in results):
yield util.Uninferable
return
if all(map(_is_not_implemented, results)):
continue
not_implemented = sum(
1 for result in results if _is_not_implemented(result)
)
if not_implemented and not_implemented != len(results):
# Can't infer yet what this is.
yield util.Uninferable
return
yield from results
return
# The operation doesn't seem to be supported so let the caller know about it
yield util.BadBinaryOperationMessage(left_type, binary_opnode.op, right_type)
def _infer_binop(
self: nodes.BinOp, context: InferenceContext | None = None
) -> Generator[InferenceResult | util.BadBinaryOperationMessage, None, None]:
"""Binary operation inference logic."""
left = self.left
right = self.right
# we use two separate contexts for evaluating lhs and rhs because
# 1. evaluating lhs may leave some undesired entries in context.path
# which may not let us infer right value of rhs
context = context or InferenceContext()
lhs_context = copy_context(context)
rhs_context = copy_context(context)
lhs_iter = left.infer(context=lhs_context)
rhs_iter = right.infer(context=rhs_context)
for lhs, rhs in itertools.product(lhs_iter, rhs_iter):
if any(isinstance(value, util.UninferableBase) for value in (rhs, lhs)):
# Don't know how to process this.
yield util.Uninferable
return
try:
yield from _infer_binary_operation(lhs, rhs, self, context, _get_binop_flow)
except _NonDeducibleTypeHierarchy:
yield util.Uninferable
@decorators.yes_if_nothing_inferred
@decorators.path_wrapper
def infer_binop(
self: nodes.BinOp, context: InferenceContext | None = None, **kwargs: Any
) -> Generator[InferenceResult, None, None]:
return _filter_operation_errors(
self, _infer_binop, context, util.BadBinaryOperationMessage
)
nodes.BinOp._infer_binop = _infer_binop
nodes.BinOp._infer = infer_binop
COMPARE_OPS: dict[str, Callable[[Any, Any], bool]] = {
"==": operator.eq,
"!=": operator.ne,
"<": operator.lt,
"<=": operator.le,
">": operator.gt,
">=": operator.ge,
"in": lambda a, b: a in b,
"not in": lambda a, b: a not in b,
}
UNINFERABLE_OPS = {
"is",
"is not",
}
def _to_literal(node: nodes.NodeNG) -> Any:
# Can raise SyntaxError or ValueError from ast.literal_eval
# Can raise AttributeError from node.as_string() as not all nodes have a visitor
# Is this the stupidest idea or the simplest idea?
return ast.literal_eval(node.as_string())
def _do_compare(
left_iter: Iterable[nodes.NodeNG], op: str, right_iter: Iterable[nodes.NodeNG]
) -> bool | util.UninferableBase:
"""
If all possible combinations are either True or False, return that:
>>> _do_compare([1, 2], '<=', [3, 4])
True
>>> _do_compare([1, 2], '==', [3, 4])
False
If any item is uninferable, or if some combinations are True and some
are False, return Uninferable:
>>> _do_compare([1, 3], '<=', [2, 4])
util.Uninferable
"""
retval: bool | None = None
if op in UNINFERABLE_OPS:
return util.Uninferable
op_func = COMPARE_OPS[op]
for left, right in itertools.product(left_iter, right_iter):
if isinstance(left, util.UninferableBase) or isinstance(
right, util.UninferableBase
):
return util.Uninferable
try:
left, right = _to_literal(left), _to_literal(right)
except (SyntaxError, ValueError, AttributeError):
return util.Uninferable
try:
expr = op_func(left, right)
except TypeError as exc:
raise AstroidTypeError from exc
if retval is None:
retval = expr
elif retval != expr:
return util.Uninferable
# (or both, but "True | False" is basically the same)
assert retval is not None
return retval # it was all the same value
def _infer_compare(
self: nodes.Compare, context: InferenceContext | None = None, **kwargs: Any
) -> Generator[nodes.Const | util.UninferableBase, None, None]:
"""Chained comparison inference logic."""
retval: bool | util.UninferableBase = True
ops = self.ops
left_node = self.left
lhs = list(left_node.infer(context=context))
# should we break early if first element is uninferable?
for op, right_node in ops:
# eagerly evaluate rhs so that values can be re-used as lhs
rhs = list(right_node.infer(context=context))
try:
retval = _do_compare(lhs, op, rhs)
except AstroidTypeError:
retval = util.Uninferable
break
if retval is not True:
break # short-circuit
lhs = rhs # continue
if retval is util.Uninferable:
yield retval # type: ignore[misc]
else:
yield nodes.Const(retval)
nodes.Compare._infer = _infer_compare # type: ignore[assignment]
def _infer_augassign(
self: nodes.AugAssign, context: InferenceContext | None = None
) -> Generator[InferenceResult | util.BadBinaryOperationMessage, None, None]:
"""Inference logic for augmented binary operations."""
context = context or InferenceContext()
rhs_context = context.clone()
lhs_iter = self.target.infer_lhs(context=context)
rhs_iter = self.value.infer(context=rhs_context)
for lhs, rhs in itertools.product(lhs_iter, rhs_iter):
if any(isinstance(value, util.UninferableBase) for value in (rhs, lhs)):
# Don't know how to process this.
yield util.Uninferable
return
try:
yield from _infer_binary_operation(
left=lhs,
right=rhs,
binary_opnode=self,
context=context,
flow_factory=_get_aug_flow,
)
except _NonDeducibleTypeHierarchy:
yield util.Uninferable
@decorators.raise_if_nothing_inferred
@decorators.path_wrapper
def infer_augassign(
self: nodes.AugAssign, context: InferenceContext | None = None, **kwargs: Any
) -> Generator[InferenceResult, None, None]:
return _filter_operation_errors(
self, _infer_augassign, context, util.BadBinaryOperationMessage
)
nodes.AugAssign._infer_augassign = _infer_augassign
nodes.AugAssign._infer = infer_augassign
# End of binary operation inference.
@decorators.raise_if_nothing_inferred
def infer_arguments(
self: nodes.Arguments, context: InferenceContext | None = None, **kwargs: Any
) -> Generator[InferenceResult, None, None]:
if context is None or context.lookupname is None:
raise InferenceError(node=self, context=context)
return protocols._arguments_infer_argname(self, context.lookupname, context)
nodes.Arguments._infer = infer_arguments # type: ignore[assignment]
@decorators.raise_if_nothing_inferred
@decorators.path_wrapper
def infer_assign(
self: nodes.AssignName | nodes.AssignAttr,
context: InferenceContext | None = None,
**kwargs: Any,
) -> Generator[InferenceResult, None, None]:
"""Infer a AssignName/AssignAttr: need to inspect the RHS part of the
assign node.
"""
if isinstance(self.parent, nodes.AugAssign):
return self.parent.infer(context)
stmts = list(self.assigned_stmts(context=context))
return bases._infer_stmts(stmts, context)
nodes.AssignName._infer = infer_assign
nodes.AssignAttr._infer = infer_assign
@decorators.raise_if_nothing_inferred
@decorators.path_wrapper
def infer_empty_node(
self: nodes.EmptyNode, context: InferenceContext | None = None, **kwargs: Any
) -> Generator[InferenceResult, None, None]:
if not self.has_underlying_object():
yield util.Uninferable
else:
try:
yield from AstroidManager().infer_ast_from_something(
self.object, context=context
)
except AstroidError:
yield util.Uninferable
nodes.EmptyNode._infer = infer_empty_node # type: ignore[assignment]
def _populate_context_lookup(call: nodes.Call, context: InferenceContext | None):
# Allows context to be saved for later
# for inference inside a function
context_lookup: dict[InferenceResult, InferenceContext] = {}
if context is None:
return context_lookup
for arg in call.args:
if isinstance(arg, nodes.Starred):
context_lookup[arg.value] = context
else:
context_lookup[arg] = context
keywords = call.keywords if call.keywords is not None else []
for keyword in keywords:
context_lookup[keyword.value] = context
return context_lookup
@decorators.raise_if_nothing_inferred
def infer_ifexp(
self: nodes.IfExp, context: InferenceContext | None = None, **kwargs: Any
) -> Generator[InferenceResult, None, None]:
"""Support IfExp inference.
If we can't infer the truthiness of the condition, we default
to inferring both branches. Otherwise, we infer either branch
depending on the condition.
"""
both_branches = False
# We use two separate contexts for evaluating lhs and rhs because
# evaluating lhs may leave some undesired entries in context.path
# which may not let us infer right value of rhs.
context = context or InferenceContext()
lhs_context = copy_context(context)
rhs_context = copy_context(context)
try:
test = next(self.test.infer(context=context.clone()))
except (InferenceError, StopIteration):
both_branches = True
else:
if not isinstance(test, util.UninferableBase):
if test.bool_value():
yield from self.body.infer(context=lhs_context)
else:
yield from self.orelse.infer(context=rhs_context)
else:
both_branches = True
if both_branches:
yield from self.body.infer(context=lhs_context)
yield from self.orelse.infer(context=rhs_context)
nodes.IfExp._infer = infer_ifexp # type: ignore[assignment]
def infer_functiondef(
self: _FunctionDefT, context: InferenceContext | None = None, **kwargs: Any
) -> Generator[Property | _FunctionDefT, None, InferenceErrorInfo]:
if not self.decorators or not bases._is_property(self):
yield self
return InferenceErrorInfo(node=self, context=context)
# When inferring a property, we instantiate a new `objects.Property` object,
# which in turn, because it inherits from `FunctionDef`, sets itself in the locals
# of the wrapping frame. This means that every time we infer a property, the locals
# are mutated with a new instance of the property. To avoid this, we detect this
# scenario and avoid passing the `parent` argument to the constructor.
parent_frame = self.parent.frame(future=True)
property_already_in_parent_locals = self.name in parent_frame.locals and any(
isinstance(val, objects.Property) for val in parent_frame.locals[self.name]
)
# We also don't want to pass parent if the definition is within a Try node
if isinstance(self.parent, (nodes.TryExcept, nodes.TryFinally, nodes.If)):
property_already_in_parent_locals = True
prop_func = objects.Property(
function=self,
name=self.name,
lineno=self.lineno,
parent=self.parent if not property_already_in_parent_locals else None,
col_offset=self.col_offset,
)
if property_already_in_parent_locals:
prop_func.parent = self.parent
prop_func.postinit(body=[], args=self.args, doc_node=self.doc_node)
yield prop_func
return InferenceErrorInfo(node=self, context=context)
nodes.FunctionDef._infer = infer_functiondef