Current File : //opt/cloudlinux/venv/lib/python3.11/site-packages/astroid/brain/brain_functools.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
"""Astroid hooks for understanding functools library module."""
from __future__ import annotations
from collections.abc import Iterator
from functools import partial
from itertools import chain
from astroid import BoundMethod, arguments, extract_node, helpers, nodes, objects
from astroid.context import InferenceContext
from astroid.exceptions import InferenceError, UseInferenceDefault
from astroid.inference_tip import inference_tip
from astroid.interpreter import objectmodel
from astroid.manager import AstroidManager
from astroid.nodes.node_classes import AssignName, Attribute, Call, Name
from astroid.nodes.scoped_nodes import FunctionDef
from astroid.util import UninferableBase
LRU_CACHE = "functools.lru_cache"
class LruWrappedModel(objectmodel.FunctionModel):
"""Special attribute model for functions decorated with functools.lru_cache.
The said decorators patches at decoration time some functions onto
the decorated function.
"""
@property
def attr___wrapped__(self):
return self._instance
@property
def attr_cache_info(self):
cache_info = extract_node(
"""
from functools import _CacheInfo
_CacheInfo(0, 0, 0, 0)
"""
)
class CacheInfoBoundMethod(BoundMethod):
def infer_call_result(
self, caller, context: InferenceContext | None = None
):
yield helpers.safe_infer(cache_info)
return CacheInfoBoundMethod(proxy=self._instance, bound=self._instance)
@property
def attr_cache_clear(self):
node = extract_node("""def cache_clear(self): pass""")
return BoundMethod(proxy=node, bound=self._instance.parent.scope())
def _transform_lru_cache(node, context: InferenceContext | None = None) -> None:
# TODO: this is not ideal, since the node should be immutable,
# but due to https://github.com/PyCQA/astroid/issues/354,
# there's not much we can do now.
# Replacing the node would work partially, because,
# in pylint, the old node would still be available, leading
# to spurious false positives.
node.special_attributes = LruWrappedModel()(node)
def _functools_partial_inference(
node: nodes.Call, context: InferenceContext | None = None
) -> Iterator[objects.PartialFunction]:
call = arguments.CallSite.from_call(node, context=context)
number_of_positional = len(call.positional_arguments)
if number_of_positional < 1:
raise UseInferenceDefault("functools.partial takes at least one argument")
if number_of_positional == 1 and not call.keyword_arguments:
raise UseInferenceDefault(
"functools.partial needs at least to have some filled arguments"
)
partial_function = call.positional_arguments[0]
try:
inferred_wrapped_function = next(partial_function.infer(context=context))
except (InferenceError, StopIteration) as exc:
raise UseInferenceDefault from exc
if isinstance(inferred_wrapped_function, UninferableBase):
raise UseInferenceDefault("Cannot infer the wrapped function")
if not isinstance(inferred_wrapped_function, FunctionDef):
raise UseInferenceDefault("The wrapped function is not a function")
# Determine if the passed keywords into the callsite are supported
# by the wrapped function.
if not inferred_wrapped_function.args:
function_parameters = []
else:
function_parameters = chain(
inferred_wrapped_function.args.args or (),
inferred_wrapped_function.args.posonlyargs or (),
inferred_wrapped_function.args.kwonlyargs or (),
)
parameter_names = {
param.name for param in function_parameters if isinstance(param, AssignName)
}
if set(call.keyword_arguments) - parameter_names:
raise UseInferenceDefault("wrapped function received unknown parameters")
partial_function = objects.PartialFunction(
call,
name=inferred_wrapped_function.name,
lineno=inferred_wrapped_function.lineno,
col_offset=inferred_wrapped_function.col_offset,
parent=node.parent,
)
partial_function.postinit(
args=inferred_wrapped_function.args,
body=inferred_wrapped_function.body,
decorators=inferred_wrapped_function.decorators,
returns=inferred_wrapped_function.returns,
type_comment_returns=inferred_wrapped_function.type_comment_returns,
type_comment_args=inferred_wrapped_function.type_comment_args,
doc_node=inferred_wrapped_function.doc_node,
)
return iter((partial_function,))
def _looks_like_lru_cache(node) -> bool:
"""Check if the given function node is decorated with lru_cache."""
if not node.decorators:
return False
for decorator in node.decorators.nodes:
if not isinstance(decorator, Call):
continue
if _looks_like_functools_member(decorator, "lru_cache"):
return True
return False
def _looks_like_functools_member(node, member) -> bool:
"""Check if the given Call node is a functools.partial call."""
if isinstance(node.func, Name):
return node.func.name == member
if isinstance(node.func, Attribute):
return (
node.func.attrname == member
and isinstance(node.func.expr, Name)
and node.func.expr.name == "functools"
)
return False
_looks_like_partial = partial(_looks_like_functools_member, member="partial")
AstroidManager().register_transform(
FunctionDef, _transform_lru_cache, _looks_like_lru_cache
)
AstroidManager().register_transform(
Call,
inference_tip(_functools_partial_inference),
_looks_like_partial,
)