380 lines
15 KiB
Python
380 lines
15 KiB
Python
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"""
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Like described in the :mod:`parso.python.tree` module,
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there's a need for an ast like module to represent the states of parsed
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modules.
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But now there are also structures in Python that need a little bit more than
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that. An ``Instance`` for example is only a ``Class`` before it is
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instantiated. This class represents these cases.
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So, why is there also a ``Class`` class here? Well, there are decorators and
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they change classes in Python 3.
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Representation modules also define "magic methods". Those methods look like
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``py__foo__`` and are typically mappable to the Python equivalents ``__call__``
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and others. Here's a list:
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====================================== ========================================
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**Method** **Description**
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-------------------------------------- ----------------------------------------
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py__call__(arguments: Array) On callable objects, returns types.
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py__bool__() Returns True/False/None; None means that
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there's no certainty.
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py__bases__() Returns a list of base classes.
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py__iter__() Returns a generator of a set of types.
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py__class__() Returns the class of an instance.
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py__simple_getitem__(index: int/str) Returns a a set of types of the index.
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Can raise an IndexError/KeyError.
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py__getitem__(indexes: ValueSet) Returns a a set of types of the index.
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py__file__() Only on modules. Returns None if does
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not exist.
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py__package__() -> List[str] Only on modules. For the import system.
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py__path__() Only on modules. For the import system.
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py__get__(call_object) Only on instances. Simulates
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descriptors.
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py__doc__() Returns the docstring for a value.
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====================================== ========================================
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"""
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from jedi import debug
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from jedi._compatibility import use_metaclass
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from jedi.parser_utils import get_cached_parent_scope, expr_is_dotted
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from jedi.inference.cache import inference_state_method_cache, CachedMetaClass, \
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inference_state_method_generator_cache
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from jedi.inference import compiled
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from jedi.inference.lazy_value import LazyKnownValues, LazyTreeValue
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from jedi.inference.filters import ParserTreeFilter
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from jedi.inference.names import TreeNameDefinition, ValueName
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from jedi.inference.arguments import unpack_arglist, ValuesArguments
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from jedi.inference.base_value import ValueSet, iterator_to_value_set, \
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NO_VALUES
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from jedi.inference.context import ClassContext
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from jedi.inference.value.function import FunctionAndClassBase
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from jedi.inference.gradual.generics import LazyGenericManager, TupleGenericManager
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from jedi.plugins import plugin_manager
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class ClassName(TreeNameDefinition):
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def __init__(self, class_value, tree_name, name_context, apply_decorators):
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super(ClassName, self).__init__(name_context, tree_name)
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self._apply_decorators = apply_decorators
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self._class_value = class_value
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@iterator_to_value_set
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def infer(self):
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# We're using a different value to infer, so we cannot call super().
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from jedi.inference.syntax_tree import tree_name_to_values
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inferred = tree_name_to_values(
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self.parent_context.inference_state, self.parent_context, self.tree_name)
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for result_value in inferred:
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if self._apply_decorators:
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for c in result_value.py__get__(instance=None, class_value=self._class_value):
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yield c
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else:
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yield result_value
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class ClassFilter(ParserTreeFilter):
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def __init__(self, class_value, node_context=None, until_position=None,
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origin_scope=None, is_instance=False):
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super(ClassFilter, self).__init__(
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class_value.as_context(), node_context,
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until_position=until_position,
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origin_scope=origin_scope,
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)
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self._class_value = class_value
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self._is_instance = is_instance
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def _convert_names(self, names):
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return [
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ClassName(
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class_value=self._class_value,
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tree_name=name,
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name_context=self._node_context,
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apply_decorators=not self._is_instance,
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) for name in names
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]
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def _equals_origin_scope(self):
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node = self._origin_scope
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while node is not None:
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if node == self._parser_scope or node == self.parent_context:
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return True
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node = get_cached_parent_scope(self._used_names, node)
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return False
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def _access_possible(self, name):
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# Filter for ClassVar variables
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# TODO this is not properly done, yet. It just checks for the string
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# ClassVar in the annotation, which can be quite imprecise. If we
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# wanted to do this correct, we would have to infer the ClassVar.
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if not self._is_instance:
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expr_stmt = name.get_definition()
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if expr_stmt is not None and expr_stmt.type == 'expr_stmt':
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annassign = expr_stmt.children[1]
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if annassign.type == 'annassign':
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# TODO this is not proper matching
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# If there is an =, the variable is obviously also
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# defined on the class.
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if 'ClassVar' not in annassign.children[1].get_code() \
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and '=' not in annassign.children:
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return False
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# Filter for name mangling of private variables like __foo
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return not name.value.startswith('__') or name.value.endswith('__') \
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or self._equals_origin_scope()
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def _filter(self, names):
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names = super(ClassFilter, self)._filter(names)
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return [name for name in names if self._access_possible(name)]
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class ClassMixin(object):
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def is_class(self):
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return True
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def py__call__(self, arguments=None):
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from jedi.inference.value import TreeInstance
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from jedi.inference.gradual.typing import TypedDict
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if self.is_typeddict():
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return ValueSet([TypedDict(self)])
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return ValueSet([TreeInstance(self.inference_state, self.parent_context, self, arguments)])
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def py__class__(self):
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return compiled.builtin_from_name(self.inference_state, u'type')
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@property
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def name(self):
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return ValueName(self, self.tree_node.name)
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def py__name__(self):
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return self.name.string_name
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@inference_state_method_generator_cache()
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def py__mro__(self):
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mro = [self]
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yield self
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# TODO Do a proper mro resolution. Currently we are just listing
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# classes. However, it's a complicated algorithm.
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for lazy_cls in self.py__bases__():
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# TODO there's multiple different mro paths possible if this yields
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# multiple possibilities. Could be changed to be more correct.
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for cls in lazy_cls.infer():
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# TODO detect for TypeError: duplicate base class str,
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# e.g. `class X(str, str): pass`
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try:
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mro_method = cls.py__mro__
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except AttributeError:
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# TODO add a TypeError like:
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"""
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>>> class Y(lambda: test): pass
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Traceback (most recent call last):
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File "<stdin>", line 1, in <module>
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TypeError: function() argument 1 must be code, not str
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>>> class Y(1): pass
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Traceback (most recent call last):
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File "<stdin>", line 1, in <module>
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TypeError: int() takes at most 2 arguments (3 given)
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"""
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debug.warning('Super class of %s is not a class: %s', self, cls)
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else:
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for cls_new in mro_method():
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if cls_new not in mro:
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mro.append(cls_new)
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yield cls_new
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def get_filters(self, origin_scope=None, is_instance=False):
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metaclasses = self.get_metaclasses()
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if metaclasses:
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for f in self.get_metaclass_filters(metaclasses):
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yield f
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for cls in self.py__mro__():
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if cls.is_compiled():
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for filter in cls.get_filters(is_instance=is_instance):
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yield filter
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else:
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yield ClassFilter(
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self, node_context=cls.as_context(),
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origin_scope=origin_scope,
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is_instance=is_instance
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)
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if not is_instance:
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from jedi.inference.compiled import builtin_from_name
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type_ = builtin_from_name(self.inference_state, u'type')
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assert isinstance(type_, ClassValue)
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if type_ != self:
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# We are not using execute_with_values here, because the
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# plugin function for type would get executed instead of an
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# instance creation.
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args = ValuesArguments([])
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for instance in type_.py__call__(args):
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instance_filters = instance.get_filters()
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# Filter out self filters
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next(instance_filters, None)
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next(instance_filters, None)
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x = next(instance_filters, None)
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assert x is not None
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yield x
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def get_signatures(self):
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# Since calling staticmethod without a function is illegal, the Jedi
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# plugin doesn't return anything. Therefore call directly and get what
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# we want: An instance of staticmethod.
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args = ValuesArguments([])
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init_funcs = self.py__call__(args).py__getattribute__('__init__')
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return [sig.bind(self) for sig in init_funcs.get_signatures()]
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def _as_context(self):
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return ClassContext(self)
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def get_type_hint(self, add_class_info=True):
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if add_class_info:
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return 'Type[%s]' % self.py__name__()
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return self.py__name__()
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@inference_state_method_cache(default=False)
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def is_typeddict(self):
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# TODO Do a proper mro resolution. Currently we are just listing
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# classes. However, it's a complicated algorithm.
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from jedi.inference.gradual.typing import TypedDictBase
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for lazy_cls in self.py__bases__():
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if not isinstance(lazy_cls, LazyTreeValue):
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return False
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tree_node = lazy_cls.data
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# Only resolve simple classes, stuff like Iterable[str] are more
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# intensive to resolve and if generics are involved, we know it's
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# not a TypedDict.
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if not expr_is_dotted(tree_node):
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return False
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for cls in lazy_cls.infer():
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if isinstance(cls, TypedDictBase):
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return True
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try:
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method = cls.is_typeddict
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except AttributeError:
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# We're only dealing with simple classes, so just returning
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# here should be fine. This only happens with e.g. compiled
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# classes.
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return False
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else:
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if method():
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return True
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return False
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class ClassValue(use_metaclass(CachedMetaClass, ClassMixin, FunctionAndClassBase)):
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api_type = u'class'
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@inference_state_method_cache()
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def list_type_vars(self):
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found = []
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arglist = self.tree_node.get_super_arglist()
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if arglist is None:
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return []
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for stars, node in unpack_arglist(arglist):
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if stars:
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continue # These are not relevant for this search.
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from jedi.inference.gradual.annotation import find_unknown_type_vars
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for type_var in find_unknown_type_vars(self.parent_context, node):
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if type_var not in found:
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# The order matters and it's therefore a list.
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found.append(type_var)
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return found
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def _get_bases_arguments(self):
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arglist = self.tree_node.get_super_arglist()
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if arglist:
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from jedi.inference import arguments
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return arguments.TreeArguments(self.inference_state, self.parent_context, arglist)
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return None
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@inference_state_method_cache(default=())
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def py__bases__(self):
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args = self._get_bases_arguments()
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if args is not None:
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lst = [value for key, value in args.unpack() if key is None]
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if lst:
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return lst
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if self.py__name__() == 'object' \
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and self.parent_context.is_builtins_module():
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return []
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return [LazyKnownValues(
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self.inference_state.builtins_module.py__getattribute__('object')
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)]
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def py__getitem__(self, index_value_set, contextualized_node):
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from jedi.inference.gradual.base import GenericClass
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if not index_value_set:
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return ValueSet([self])
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return ValueSet(
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GenericClass(
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self,
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LazyGenericManager(
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context_of_index=contextualized_node.context,
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index_value=index_value,
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)
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)
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for index_value in index_value_set
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)
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def with_generics(self, generics_tuple):
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from jedi.inference.gradual.base import GenericClass
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return GenericClass(
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self,
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TupleGenericManager(generics_tuple)
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)
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def define_generics(self, type_var_dict):
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from jedi.inference.gradual.base import GenericClass
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def remap_type_vars():
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"""
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The TypeVars in the resulting classes have sometimes different names
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and we need to check for that, e.g. a signature can be:
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def iter(iterable: Iterable[_T]) -> Iterator[_T]: ...
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However, the iterator is defined as Iterator[_T_co], which means it has
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a different type var name.
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"""
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for type_var in self.list_type_vars():
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yield type_var_dict.get(type_var.py__name__(), NO_VALUES)
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if type_var_dict:
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return ValueSet([GenericClass(
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self,
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TupleGenericManager(tuple(remap_type_vars()))
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)])
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return ValueSet({self})
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@plugin_manager.decorate()
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def get_metaclass_filters(self, metaclass):
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debug.dbg('Unprocessed metaclass %s', metaclass)
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return []
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@inference_state_method_cache(default=NO_VALUES)
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def get_metaclasses(self):
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|
args = self._get_bases_arguments()
|
||
|
|
if args is not None:
|
||
|
|
m = [value for key, value in args.unpack() if key == 'metaclass']
|
||
|
|
metaclasses = ValueSet.from_sets(lazy_value.infer() for lazy_value in m)
|
||
|
|
metaclasses = ValueSet(m for m in metaclasses if m.is_class())
|
||
|
|
if metaclasses:
|
||
|
|
return metaclasses
|
||
|
|
|
||
|
|
for lazy_base in self.py__bases__():
|
||
|
|
for value in lazy_base.infer():
|
||
|
|
if value.is_class():
|
||
|
|
values = value.get_metaclasses()
|
||
|
|
if values:
|
||
|
|
return values
|
||
|
|
return NO_VALUES
|