File size: 15,940 Bytes
d1ceb73 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 |
"""
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.parser_utils import get_cached_parent_scope, expr_is_dotted, \
function_is_property
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().__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:
yield from result_value.py__get__(instance=None, class_value=self._class_value)
else:
yield result_value
@property
def api_type(self):
type_ = super().api_type
if type_ == 'function':
definition = self.tree_name.get_definition()
if definition is None:
return type_
if function_is_property(definition):
# This essentially checks if there is an @property before
# the function. @property could be something different, but
# any programmer that redefines property as something that
# is not really a property anymore, should be shot. (i.e.
# this is a heuristic).
return 'property'
return type_
class ClassFilter(ParserTreeFilter):
def __init__(self, class_value, node_context=None, until_position=None,
origin_scope=None, is_instance=False):
super().__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._parso_cache_node, node)
return False
def _access_possible(self, name):
# 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()._filter(names)
return [name for name in names if self._access_possible(name)]
class ClassMixin:
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, '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:
yield from self.get_metaclass_filters(metaclasses, is_instance)
for cls in self.py__mro__():
if cls.is_compiled():
yield from cls.get_filters(is_instance=is_instance)
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, '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(ClassMixin, FunctionAndClassBase, metaclass=CachedMetaClass):
api_type = '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 []
|