def infer_param(function_value, param, ignore_stars=False):
values = _infer_param(function_value, param)
if ignore_stars:
return values
inference_state = function_value.inference_state
if param.star_count == 1:
tuple_ = builtin_from_name(inference_state, 'tuple')
return ValueSet([
GenericClass(
tuple_,
TupleGenericManager((values, )),
) for c in values
])
elif param.star_count == 2:
dct = builtin_from_name(inference_state, 'dict')
generics = (ValueSet([builtin_from_name(inference_state,
'str')]), values)
return ValueSet([
GenericClass(
dct,
TupleGenericManager(generics),
) for c in values
])
pass
return values
def execute_annotation(self):
string_name = self._tree_name.value
if string_name == 'Union':
# This is kind of a special case, because we have Unions (in Jedi
# ValueSets).
return self.gather_annotation_classes().execute_annotation()
elif string_name == 'Optional':
# Optional is basically just saying it's either None or the actual
# type.
return self.gather_annotation_classes().execute_annotation() \
| ValueSet([builtin_from_name(self.inference_state, u'None')])
elif string_name == 'Type':
# The type is actually already given in the index_value
return ValueSet([self._index_value])
elif string_name == 'ClassVar':
# For now don't do anything here, ClassVars are always used.
return self._index_value.execute_annotation()
cls = globals()[string_name]
return ValueSet([cls(
self.inference_state,
self.parent_context,
self._tree_name,
self._index_value,
self._context_of_index
)])
def execute_annotation(self):
string_name = self._tree_name.value
if string_name == 'Union':
# This is kind of a special case, because we have Unions (in Jedi
# ValueSets).
return self.gather_annotation_classes().execute_annotation()
elif string_name == 'Optional':
# Optional is basically just saying it's either None or the actual
# type.
return self.gather_annotation_classes().execute_annotation() \
| ValueSet([builtin_from_name(self.inference_state, 'None')])
elif string_name == 'Type':
# The type is actually already given in the index_value
return self._generics_manager[0]
elif string_name == 'ClassVar':
# For now don't do anything here, ClassVars are always used.
return self._generics_manager[0].execute_annotation()
mapped = {
'Tuple': Tuple,
'Generic': Generic,
'Protocol': Protocol,
'Callable': Callable,
}
cls = mapped[string_name]
return ValueSet([
cls(
self.parent_context,
self,
self._tree_name,
generics_manager=self._generics_manager,
)
])
def _remap(self):
name = self.string_name
inference_state = self.parent_context.inference_state
try:
actual = _TYPE_ALIAS_TYPES[name]
except KeyError:
pass
else:
yield TypeAlias.create_cached(inference_state, self.parent_context,
self.tree_name, actual)
return
if name in _PROXY_CLASS_TYPES:
yield ProxyTypingClassValue.create_cached(inference_state,
self.parent_context,
self.tree_name)
elif name in _PROXY_TYPES:
yield ProxyTypingValue.create_cached(inference_state,
self.parent_context,
self.tree_name)
elif name == 'runtime':
# We don't want anything here, not sure what this function is
# supposed to do, since it just appears in the stubs and shouldn't
# have any effects there (because it's never executed).
return
elif name == 'TypeVar':
yield TypeVarClass.create_cached(inference_state,
self.parent_context,
self.tree_name)
elif name == 'Any':
yield AnyClass.create_cached(inference_state, self.parent_context,
self.tree_name)
elif name == 'TYPE_CHECKING':
# This is needed for e.g. imports that are only available for type
# checking or are in cycles. The user can then check this variable.
yield builtin_from_name(inference_state, 'True')
elif name == 'overload':
yield OverloadFunction.create_cached(inference_state,
self.parent_context,
self.tree_name)
elif name == 'NewType':
yield NewTypeFunction.create_cached(inference_state,
self.parent_context,
self.tree_name)
elif name == 'cast':
yield CastFunction.create_cached(inference_state,
self.parent_context,
self.tree_name)
elif name == 'TypedDict':
# TODO doesn't even exist in typeshed/typing.py, yet. But will be
# added soon.
yield TypedDictClass.create_cached(inference_state,
self.parent_context,
self.tree_name)
elif name in ('no_type_check', 'no_type_check_decorator'):
# This is not necessary, as long as we are not doing type checking.
yield from self._wrapped_name.infer()
else:
# Everything else shouldn't be relevant for type checking.
yield from self._wrapped_name.infer()
def get_filters(self, origin_scope=None, is_instance=False, include_metaclasses=True):
if include_metaclasses:
metaclasses = self.get_metaclasses()
if metaclasses:
for f in self.get_metaclass_filters(metaclasses):
yield f
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:
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_filters(self, origin_scope=None, is_instance=False):
metaclasses = self.get_metaclasses()
if metaclasses:
for f in self.get_metaclass_filters(metaclasses):
yield f
for cls in self.py__mro__():
if isinstance(cls, compiled.CompiledObject):
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:
from jedi.inference.compiled import builtin_from_name
type_ = builtin_from_name(self.inference_state, u'type')
assert isinstance(type_, ClassValue)
if type_ != self:
for instance in type_.py__call__():
instance_filters = instance.get_filters()
# Filter out self filters
next(instance_filters)
next(instance_filters)
yield next(instance_filters)
def _get_wrapped_value(self):
from jedi.inference.gradual.base import GenericClass
from jedi.inference.gradual.generics import TupleGenericManager
klass = compiled.builtin_from_name(self.inference_state,
self.array_type)
c, = GenericClass(klass, TupleGenericManager(
self._cached_generics())).execute_annotation()
return c
def py__stop_iteration_returns(self):
for cls in self._wrapped_value.class_value.py__mro__():
if cls.py__name__() == 'Generator':
generics = cls.get_generics()
try:
return generics[2].execute_annotation()
except IndexError:
pass
elif cls.py__name__() == 'Iterator':
return ValueSet([builtin_from_name(self.inference_state, 'None')])
return self._wrapped_value.py__stop_iteration_returns()
def _literals_to_types(inference_state, result):
# Changes literals ('a', 1, 1.0, etc) to its type instances (str(),
# int(), float(), etc).
new_result = NO_VALUES
for typ in result:
if is_literal(typ):
# Literals are only valid as long as the operations are
# correct. Otherwise add a value-free instance.
cls = compiled.builtin_from_name(inference_state, typ.name.string_name)
new_result |= cls.execute_with_values()
else:
new_result |= ValueSet([typ])
return new_result
def _get_yield_lazy_value(self, yield_expr):
if yield_expr.type == 'keyword':
# `yield` just yields None.
ctx = compiled.builtin_from_name(self.inference_state, 'None')
yield LazyKnownValue(ctx)
return
node = yield_expr.children[1]
if node.type == 'yield_arg': # It must be a yield from.
cn = ContextualizedNode(self, node.children[1])
yield from cn.infer().iterate(cn)
else:
yield LazyTreeValue(self, node)
def py__get__(self, obj, class_value):
"""
obj may be None.
"""
# Arguments in __get__ descriptors are obj, class.
# `method` is the new parent of the array, don't know if that's good.
names = self.get_function_slot_names(u'__get__')
if names:
if obj is None:
obj = compiled.builtin_from_name(self.inference_state, u'None')
return self.execute_function_slots(names, obj, class_value)
else:
return ValueSet([self])
def infer_type_vars(self, value_set):
annotation_generics = self.get_generics()
if not annotation_generics:
return {}
annotation_name = self.py__name__()
if annotation_name == 'Optional':
# Optional[T] is equivalent to Union[T, None]. In Jedi unions
# are represented by members within a ValueSet, so we extract
# the T from the Optional[T] by removing the None value.
none = builtin_from_name(self.inference_state, 'None')
return annotation_generics[0].infer_type_vars(
value_set.filter(lambda x: x != none), )
return {}
def values(self):
from jedi.inference.compiled import builtin_from_name
names = []
needs_type_completions, dir_infos = self.compiled_object.access_handle.get_dir_infos()
for name in dir_infos:
names += self._get(
name,
lambda: dir_infos[name],
lambda: dir_infos.keys(),
)
# ``dir`` doesn't include the type names.
if not self.is_instance and needs_type_completions:
for filter in builtin_from_name(self._inference_state, u'type').get_filters():
names += filter.values()
return names
def py__get__(self, instance, class_value):
"""
obj may be None.
"""
# Arguments in __get__ descriptors are obj, class.
# `method` is the new parent of the array, don't know if that's good.
for cls in self.class_value.py__mro__():
result = cls.py__get__on_class(self, instance, class_value)
if result is not NotImplemented:
return result
names = self.get_function_slot_names('__get__')
if names:
if instance is None:
instance = compiled.builtin_from_name(self.inference_state, 'None')
return self.execute_function_slots(names, instance, class_value)
else:
return ValueSet([self])
def _execute_function(self, params):
from jedi.inference import docstrings
from jedi.inference.compiled import builtin_from_name
if self.api_type != 'function':
return
for name in self._parse_function_doc()[1].split():
try:
# TODO wtf is this? this is exactly the same as the thing
# below. It uses getattr as well.
self.inference_state.builtins_module.access_handle.getattr_paths(
name)
except AttributeError:
continue
else:
bltn_obj = builtin_from_name(self.inference_state, name)
yield from self.inference_state.execute(bltn_obj, params)
yield from docstrings.infer_return_types(self)
def values(self):
from jedi.inference.compiled import builtin_from_name
names = []
needs_type_completions, dir_infos = self.compiled_value.access_handle.get_dir_infos()
# We could use `unsafe` here as well, especially as a parameter to
# get_dir_infos. But this would lead to a lot of property executions
# that are probably not wanted. The drawback for this is that we
# have a different name for `get` and `values`. For `get` we always
# execute.
for name in dir_infos:
names += self._get(
name,
lambda name, unsafe: dir_infos[name],
lambda name: name in dir_infos,
)
# ``dir`` doesn't include the type names.
if not self.is_instance and needs_type_completions:
for filter in builtin_from_name(self._inference_state, u'type').get_filters():
names += filter.values()
return names
def builtins_isinstance(objects, types, arguments, inference_state):
bool_results = set()
for o in objects:
cls = o.py__class__()
try:
cls.py__bases__
except AttributeError:
# This is temporary. Everything should have a class attribute in
# Python?! Maybe we'll leave it here, because some numpy objects or
# whatever might not.
bool_results = set([True, False])
break
mro = list(cls.py__mro__())
for cls_or_tup in types:
if cls_or_tup.is_class():
bool_results.add(cls_or_tup in mro)
elif cls_or_tup.name.string_name == 'tuple' \
and cls_or_tup.get_root_context().is_builtins_module():
# Check for tuples.
classes = ValueSet.from_sets(
lazy_value.infer()
for lazy_value in cls_or_tup.iterate()
)
bool_results.add(any(cls in mro for cls in classes))
else:
_, lazy_value = list(arguments.unpack())[1]
if isinstance(lazy_value, LazyTreeValue):
node = lazy_value.data
message = 'TypeError: isinstance() arg 2 must be a ' \
'class, type, or tuple of classes and types, ' \
'not %s.' % cls_or_tup
analysis.add(lazy_value.context, 'type-error-isinstance', node, message)
return ValueSet(
compiled.builtin_from_name(inference_state, str(b))
for b in bool_results
)
def get_return_values(self, check_yields=False):
funcdef = self.tree_node
if funcdef.type == 'lambdef':
return self.infer_node(funcdef.children[-1])
if check_yields:
value_set = NO_VALUES
returns = get_yield_exprs(self.inference_state, funcdef)
else:
value_set = self._infer_annotations()
if value_set:
# If there are annotations, prefer them over anything else.
# This will make it faster.
return value_set
value_set |= docstrings.infer_return_types(self._value)
returns = funcdef.iter_return_stmts()
for r in returns:
check = flow_analysis.reachability_check(self, funcdef, r)
if check is flow_analysis.UNREACHABLE:
debug.dbg('Return unreachable: %s', r)
else:
if check_yields:
value_set |= ValueSet.from_sets(
lazy_value.infer()
for lazy_value in self._get_yield_lazy_value(r))
else:
try:
children = r.children
except AttributeError:
ctx = compiled.builtin_from_name(
self.inference_state, u'None')
value_set |= ValueSet([ctx])
else:
value_set |= self.infer_node(children[1])
if check is flow_analysis.REACHABLE:
debug.dbg('Return reachable: %s', r)
break
return value_set
def py__stop_iteration_returns(self):
return ValueSet(
[compiled.builtin_from_name(self.inference_state, u'None')])
def _bool_to_value(inference_state, bool_):
return compiled.builtin_from_name(inference_state, str(bool_))
def infer_atom(context, atom):
"""
Basically to process ``atom`` nodes. The parser sometimes doesn't
generate the node (because it has just one child). In that case an atom
might be a name or a literal as well.
"""
state = context.inference_state
if atom.type == 'name':
# This is the first global lookup.
stmt = tree.search_ancestor(atom, 'expr_stmt', 'lambdef',
'if_stmt') or atom
if stmt.type == 'if_stmt':
if not any(n.start_pos <= atom.start_pos < n.end_pos
for n in stmt.get_test_nodes()):
stmt = atom
elif stmt.type == 'lambdef':
stmt = atom
position = stmt.start_pos
if _is_annotation_name(atom):
# Since Python 3.7 (with from __future__ import annotations),
# annotations are essentially strings and can reference objects
# that are defined further down in code. Therefore just set the
# position to None, so the finder will not try to stop at a certain
# position in the module.
position = None
return context.py__getattribute__(atom, position=position)
elif atom.type == 'keyword':
# For False/True/None
if atom.value in ('False', 'True', 'None'):
return ValueSet([compiled.builtin_from_name(state, atom.value)])
elif atom.value == 'yield':
# Contrary to yield from, yield can just appear alone to return a
# value when used with `.send()`.
return NO_VALUES
assert False, 'Cannot infer the keyword %s' % atom
elif isinstance(atom, tree.Literal):
string = state.compiled_subprocess.safe_literal_eval(atom.value)
return ValueSet([compiled.create_simple_object(state, string)])
elif atom.type == 'strings':
# Will be multiple string.
value_set = infer_atom(context, atom.children[0])
for string in atom.children[1:]:
right = infer_atom(context, string)
value_set = _infer_comparison(context, value_set, '+', right)
return value_set
elif atom.type == 'fstring':
return compiled.get_string_value_set(state)
else:
c = atom.children
# Parentheses without commas are not tuples.
if c[0] == '(' and not len(c) == 2 \
and not(c[1].type == 'testlist_comp'
and len(c[1].children) > 1):
return context.infer_node(c[1])
try:
comp_for = c[1].children[1]
except (IndexError, AttributeError):
pass
else:
if comp_for == ':':
# Dict comprehensions have a colon at the 3rd index.
try:
comp_for = c[1].children[3]
except IndexError:
pass
if comp_for.type in ('comp_for', 'sync_comp_for'):
return ValueSet(
[iterable.comprehension_from_atom(state, context, atom)])
# It's a dict/list/tuple literal.
array_node = c[1]
try:
array_node_c = array_node.children
except AttributeError:
array_node_c = []
if c[0] == '{' and (array_node == '}' or ':' in array_node_c
or '**' in array_node_c):
new_value = iterable.DictLiteralValue(state, context, atom)
else:
new_value = iterable.SequenceLiteralValue(state, context, atom)
return ValueSet([new_value])
def _infer_node(context, element):
debug.dbg('infer_node %s@%s in %s', element, element.start_pos, context)
inference_state = context.inference_state
typ = element.type
if typ in ('name', 'number', 'string', 'atom', 'strings', 'keyword',
'fstring'):
return infer_atom(context, element)
elif typ == 'lambdef':
return ValueSet([FunctionValue.from_context(context, element)])
elif typ == 'expr_stmt':
return infer_expr_stmt(context, element)
elif typ in ('power', 'atom_expr'):
first_child = element.children[0]
children = element.children[1:]
had_await = False
if first_child.type == 'keyword' and first_child.value == 'await':
had_await = True
first_child = children.pop(0)
value_set = context.infer_node(first_child)
for (i, trailer) in enumerate(children):
if trailer == '**': # has a power operation.
right = context.infer_node(children[i + 1])
value_set = _infer_comparison(context, value_set, trailer,
right)
break
value_set = infer_trailer(context, value_set, trailer)
if had_await:
return value_set.py__await__().py__stop_iteration_returns()
return value_set
elif typ in (
'testlist_star_expr',
'testlist',
):
# The implicit tuple in statements.
return ValueSet(
[iterable.SequenceLiteralValue(inference_state, context, element)])
elif typ in ('not_test', 'factor'):
value_set = context.infer_node(element.children[-1])
for operator in element.children[:-1]:
value_set = infer_factor(value_set, operator)
return value_set
elif typ == 'test':
# `x if foo else y` case.
return (context.infer_node(element.children[0])
| context.infer_node(element.children[-1]))
elif typ == 'operator':
# Must be an ellipsis, other operators are not inferred.
if element.value != '...':
origin = element.parent
raise AssertionError("unhandled operator %s in %s " %
(repr(element.value), origin))
return ValueSet(
[compiled.builtin_from_name(inference_state, 'Ellipsis')])
elif typ == 'dotted_name':
value_set = infer_atom(context, element.children[0])
for next_name in element.children[2::2]:
value_set = value_set.py__getattribute__(next_name,
name_context=context)
return value_set
elif typ == 'eval_input':
return context.infer_node(element.children[0])
elif typ == 'annassign':
return annotation.infer_annotation(context, element.children[1]) \
.execute_annotation()
elif typ == 'yield_expr':
if len(element.children) and element.children[1].type == 'yield_arg':
# Implies that it's a yield from.
element = element.children[1].children[1]
generators = context.infer_node(element) \
.py__getattribute__('__iter__').execute_with_values()
return generators.py__stop_iteration_returns()
# Generator.send() is not implemented.
return NO_VALUES
elif typ == 'namedexpr_test':
return context.infer_node(element.children[2])
else:
return infer_or_test(context, element)
def _get_wrapped_value(self):
object_, = builtin_from_name(self.inference_state,
u'object').execute_annotation()
return object_
def test_next_docstr(inference_state):
next_ = compiled.builtin_from_name(inference_state, u'next')
assert next_.tree_node is not None
assert next_.py__doc__() == '' # It's a stub
for non_stub in _stub_to_python_value_set(next_):
assert non_stub.py__doc__() == next.__doc__
def py__class__(self):
return compiled.builtin_from_name(self.inference_state, u'type')
def py__class__(self):
# This might not be 100% correct, but it is good enough. The details of
# the typing library are not really an issue for Jedi.
return builtin_from_name(self.inference_state, u'type')
def py__class__(self):
# TODO this is obviously not correct, but at least gives us a class if
# we have none. Some of these objects don't really have a base class in
# typeshed.
return builtin_from_name(self.inference_state, u'object')
def _get_wrapped_value(self):
value = compiled.builtin_from_name(self._context.inference_state,
'slice')
slice_value, = value.execute_with_values()
return slice_value
def _get_wrapped_value(self):
from jedi.inference.gradual.typing import GenericClass
klass = compiled.builtin_from_name(self.inference_state,
self.array_type)
c, = GenericClass(klass, self._get_generics()).execute_annotation()
return c