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