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Current Directory: /opt/imh-python/lib/python3.9/site-packages/astroid
Viewing File: /opt/imh-python/lib/python3.9/site-packages/astroid/objects.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 """ Inference objects are a way to represent composite AST nodes, which are used only as inference results, so they can't be found in the original AST tree. For instance, inferring the following frozenset use, leads to an inferred FrozenSet: Call(func=Name('frozenset'), args=Tuple(...)) """ from __future__ import annotations from collections.abc import Generator, Iterator from functools import cached_property from typing import Any, Literal, NoReturn, TypeVar from astroid import bases, util from astroid.context import InferenceContext from astroid.exceptions import ( AttributeInferenceError, InferenceError, MroError, SuperError, ) from astroid.interpreter import objectmodel from astroid.manager import AstroidManager from astroid.nodes import node_classes, scoped_nodes from astroid.typing import InferenceResult, SuccessfulInferenceResult _T = TypeVar("_T") class FrozenSet(node_classes.BaseContainer): """Class representing a FrozenSet composite node.""" def pytype(self) -> Literal["builtins.frozenset"]: return "builtins.frozenset" def _infer(self, context: InferenceContext | None = None, **kwargs: Any): yield self @cached_property def _proxied(self): # pylint: disable=method-hidden ast_builtins = AstroidManager().builtins_module return ast_builtins.getattr("frozenset")[0] class Super(node_classes.NodeNG): """Proxy class over a super call. This class offers almost the same behaviour as Python's super, which is MRO lookups for retrieving attributes from the parents. The *mro_pointer* is the place in the MRO from where we should start looking, not counting it. *mro_type* is the object which provides the MRO, it can be both a type or an instance. *self_class* is the class where the super call is, while *scope* is the function where the super call is. """ special_attributes = objectmodel.SuperModel() def __init__( self, mro_pointer: SuccessfulInferenceResult, mro_type: SuccessfulInferenceResult, self_class: scoped_nodes.ClassDef, scope: scoped_nodes.FunctionDef, call: node_classes.Call, ) -> None: self.type = mro_type self.mro_pointer = mro_pointer self._class_based = False self._self_class = self_class self._scope = scope super().__init__( parent=scope, lineno=scope.lineno, col_offset=scope.col_offset, end_lineno=scope.end_lineno, end_col_offset=scope.end_col_offset, ) def _infer(self, context: InferenceContext | None = None, **kwargs: Any): yield self def super_mro(self): """Get the MRO which will be used to lookup attributes in this super.""" if not isinstance(self.mro_pointer, scoped_nodes.ClassDef): raise SuperError( "The first argument to super must be a subtype of " "type, not {mro_pointer}.", super_=self, ) if isinstance(self.type, scoped_nodes.ClassDef): # `super(type, type)`, most likely in a class method. self._class_based = True mro_type = self.type else: mro_type = getattr(self.type, "_proxied", None) if not isinstance(mro_type, (bases.Instance, scoped_nodes.ClassDef)): raise SuperError( "The second argument to super must be an " "instance or subtype of type, not {type}.", super_=self, ) if not mro_type.newstyle: raise SuperError("Unable to call super on old-style classes.", super_=self) mro = mro_type.mro() if self.mro_pointer not in mro: raise SuperError( "The second argument to super must be an " "instance or subtype of type, not {type}.", super_=self, ) index = mro.index(self.mro_pointer) return mro[index + 1 :] @cached_property def _proxied(self): ast_builtins = AstroidManager().builtins_module return ast_builtins.getattr("super")[0] def pytype(self) -> Literal["builtins.super"]: return "builtins.super" def display_type(self) -> str: return "Super of" @property def name(self): """Get the name of the MRO pointer.""" return self.mro_pointer.name def qname(self) -> Literal["super"]: return "super" def igetattr( # noqa: C901 self, name: str, context: InferenceContext | None = None ) -> Iterator[InferenceResult]: """Retrieve the inferred values of the given attribute name.""" # '__class__' is a special attribute that should be taken directly # from the special attributes dict if name == "__class__": yield self.special_attributes.lookup(name) return try: mro = self.super_mro() # Don't let invalid MROs or invalid super calls # leak out as is from this function. except SuperError as exc: raise AttributeInferenceError( ( "Lookup for {name} on {target!r} because super call {super!r} " "is invalid." ), target=self, attribute=name, context=context, super_=exc.super_, ) from exc except MroError as exc: raise AttributeInferenceError( ( "Lookup for {name} on {target!r} failed because {cls!r} has an " "invalid MRO." ), target=self, attribute=name, context=context, mros=exc.mros, cls=exc.cls, ) from exc found = False for cls in mro: if name not in cls.locals: continue found = True for inferred in bases._infer_stmts([cls[name]], context, frame=self): if not isinstance(inferred, scoped_nodes.FunctionDef): yield inferred continue # We can obtain different descriptors from a super depending # on what we are accessing and where the super call is. if inferred.type == "classmethod": yield bases.BoundMethod(inferred, cls) elif self._scope.type == "classmethod" and inferred.type == "method": yield inferred elif self._class_based or inferred.type == "staticmethod": yield inferred elif isinstance(inferred, Property): function = inferred.function try: yield from function.infer_call_result( caller=self, context=context ) except InferenceError: yield util.Uninferable elif bases._is_property(inferred): # TODO: support other descriptors as well. try: yield from inferred.infer_call_result(self, context) except InferenceError: yield util.Uninferable else: yield bases.BoundMethod(inferred, cls) # Only if we haven't found any explicit overwrites for the # attribute we look it up in the special attributes if not found and name in self.special_attributes: yield self.special_attributes.lookup(name) return if not found: raise AttributeInferenceError(target=self, attribute=name, context=context) def getattr(self, name, context: InferenceContext | None = None): return list(self.igetattr(name, context=context)) class ExceptionInstance(bases.Instance): """Class for instances of exceptions. It has special treatment for some of the exceptions's attributes, which are transformed at runtime into certain concrete objects, such as the case of .args. """ @cached_property def special_attributes(self): qname = self.qname() instance = objectmodel.BUILTIN_EXCEPTIONS.get( qname, objectmodel.ExceptionInstanceModel ) return instance()(self) class DictInstance(bases.Instance): """Special kind of instances for dictionaries. This instance knows the underlying object model of the dictionaries, which means that methods such as .values or .items can be properly inferred. """ special_attributes = objectmodel.DictModel() # Custom objects tailored for dictionaries, which are used to # disambiguate between the types of Python 2 dict's method returns # and Python 3 (where they return set like objects). class DictItems(bases.Proxy): __str__ = node_classes.NodeNG.__str__ __repr__ = node_classes.NodeNG.__repr__ class DictKeys(bases.Proxy): __str__ = node_classes.NodeNG.__str__ __repr__ = node_classes.NodeNG.__repr__ class DictValues(bases.Proxy): __str__ = node_classes.NodeNG.__str__ __repr__ = node_classes.NodeNG.__repr__ class PartialFunction(scoped_nodes.FunctionDef): """A class representing partial function obtained via functools.partial.""" def __init__(self, call, name=None, lineno=None, col_offset=None, parent=None): # TODO: Pass end_lineno, end_col_offset and parent as well super().__init__( name, lineno=lineno, col_offset=col_offset, parent=node_classes.Unknown(), end_col_offset=0, end_lineno=0, ) # A typical FunctionDef automatically adds its name to the parent scope, # but a partial should not, so defer setting parent until after init self.parent = parent self.filled_args = call.positional_arguments[1:] self.filled_keywords = call.keyword_arguments wrapped_function = call.positional_arguments[0] inferred_wrapped_function = next(wrapped_function.infer()) if isinstance(inferred_wrapped_function, PartialFunction): self.filled_args = inferred_wrapped_function.filled_args + self.filled_args self.filled_keywords = { **inferred_wrapped_function.filled_keywords, **self.filled_keywords, } self.filled_positionals = len(self.filled_args) def infer_call_result( self, caller: SuccessfulInferenceResult | None, context: InferenceContext | None = None, ) -> Iterator[InferenceResult]: if context: assert ( context.callcontext ), "CallContext should be set before inferring call result" current_passed_keywords = { keyword for (keyword, _) in context.callcontext.keywords } for keyword, value in self.filled_keywords.items(): if keyword not in current_passed_keywords: context.callcontext.keywords.append((keyword, value)) call_context_args = context.callcontext.args or [] context.callcontext.args = self.filled_args + call_context_args return super().infer_call_result(caller=caller, context=context) def qname(self) -> str: return self.__class__.__name__ # TODO: Hack to solve the circular import problem between node_classes and objects # This is not needed in 2.0, which has a cleaner design overall node_classes.Dict.__bases__ = (node_classes.NodeNG, DictInstance) class Property(scoped_nodes.FunctionDef): """Class representing a Python property.""" def __init__(self, function, name=None, lineno=None, col_offset=None, parent=None): self.function = function super().__init__( name, lineno=lineno, col_offset=col_offset, parent=parent, end_col_offset=function.end_col_offset, end_lineno=function.end_lineno, ) special_attributes = objectmodel.PropertyModel() type = "property" def pytype(self) -> Literal["builtins.property"]: return "builtins.property" def infer_call_result( self, caller: SuccessfulInferenceResult | None, context: InferenceContext | None = None, ) -> NoReturn: raise InferenceError("Properties are not callable") def _infer( self: _T, context: InferenceContext | None = None, **kwargs: Any ) -> Generator[_T]: yield self