Uploaded Test files

venv/Lib/site-packages/jedi/inference/gradual/__init__.py

@@ -0,0 +1,4 @@
+"""
+It is unfortunately not well documented how stubs and annotations work in Jedi.
+If somebody needs an introduction, please let me know.
+"""

venv/Lib/site-packages/jedi/inference/gradual/annotation.py

@@ -0,0 +1,439 @@
+"""
+PEP 0484 ( https://www.python.org/dev/peps/pep-0484/ ) describes type hints
+through function annotations. There is a strong suggestion in this document
+that only the type of type hinting defined in PEP0484 should be allowed
+as annotations in future python versions.
+"""
+
+import re
+
+from parso import ParserSyntaxError, parse
+
+from jedi._compatibility import force_unicode, Parameter
+from jedi.inference.cache import inference_state_method_cache
+from jedi.inference.base_value import ValueSet, NO_VALUES
+from jedi.inference.gradual.base import DefineGenericBaseClass, GenericClass
+from jedi.inference.gradual.generics import TupleGenericManager
+from jedi.inference.gradual.type_var import TypeVar
+from jedi.inference.helpers import is_string
+from jedi.inference.compiled import builtin_from_name
+from jedi.inference.param import get_executed_param_names
+from jedi import debug
+from jedi import parser_utils
+
+
+def infer_annotation(context, annotation):
+    """
+    Inferes an annotation node. This means that it inferes the part of
+    `int` here:
+
+        foo: int = 3
+
+    Also checks for forward references (strings)
+    """
+    value_set = context.infer_node(annotation)
+    if len(value_set) != 1:
+        debug.warning("Inferred typing index %s should lead to 1 object, "
+                      " not %s" % (annotation, value_set))
+        return value_set
+
+    inferred_value = list(value_set)[0]
+    if is_string(inferred_value):
+        result = _get_forward_reference_node(context, inferred_value.get_safe_value())
+        if result is not None:
+            return context.infer_node(result)
+    return value_set
+
+
+def _infer_annotation_string(context, string, index=None):
+    node = _get_forward_reference_node(context, string)
+    if node is None:
+        return NO_VALUES
+
+    value_set = context.infer_node(node)
+    if index is not None:
+        value_set = value_set.filter(
+            lambda value: value.array_type == u'tuple'  # noqa
+                            and len(list(value.py__iter__())) >= index
+        ).py__simple_getitem__(index)
+    return value_set
+
+
+def _get_forward_reference_node(context, string):
+    try:
+        new_node = context.inference_state.grammar.parse(
+            force_unicode(string),
+            start_symbol='eval_input',
+            error_recovery=False
+        )
+    except ParserSyntaxError:
+        debug.warning('Annotation not parsed: %s' % string)
+        return None
+    else:
+        module = context.tree_node.get_root_node()
+        parser_utils.move(new_node, module.end_pos[0])
+        new_node.parent = context.tree_node
+        return new_node
+
+
+def _split_comment_param_declaration(decl_text):
+    """
+    Split decl_text on commas, but group generic expressions
+    together.
+
+    For example, given "foo, Bar[baz, biz]" we return
+    ['foo', 'Bar[baz, biz]'].
+
+    """
+    try:
+        node = parse(decl_text, error_recovery=False).children[0]
+    except ParserSyntaxError:
+        debug.warning('Comment annotation is not valid Python: %s' % decl_text)
+        return []
+
+    if node.type in ['name', 'atom_expr', 'power']:
+        return [node.get_code().strip()]
+
+    params = []
+    try:
+        children = node.children
+    except AttributeError:
+        return []
+    else:
+        for child in children:
+            if child.type in ['name', 'atom_expr', 'power']:
+                params.append(child.get_code().strip())
+
+    return params
+
+
+@inference_state_method_cache()
+def infer_param(function_value, param, ignore_stars=False):
+    values = _infer_param(function_value, param)
+    if ignore_stars or not values:
+        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,)),
+        )])
+    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),
+        )])
+    return values
+
+
+def _infer_param(function_value, param):
+    """
+    Infers the type of a function parameter, using type annotations.
+    """
+    annotation = param.annotation
+    if annotation is None:
+        # If no Python 3-style annotation, look for a Python 2-style comment
+        # annotation.
+        # Identify parameters to function in the same sequence as they would
+        # appear in a type comment.
+        all_params = [child for child in param.parent.children
+                      if child.type == 'param']
+
+        node = param.parent.parent
+        comment = parser_utils.get_following_comment_same_line(node)
+        if comment is None:
+            return NO_VALUES
+
+        match = re.match(r"^#\s*type:\s*\(([^#]*)\)\s*->", comment)
+        if not match:
+            return NO_VALUES
+        params_comments = _split_comment_param_declaration(match.group(1))
+
+        # Find the specific param being investigated
+        index = all_params.index(param)
+        # If the number of parameters doesn't match length of type comment,
+        # ignore first parameter (assume it's self).
+        if len(params_comments) != len(all_params):
+            debug.warning(
+                "Comments length != Params length %s %s",
+                params_comments, all_params
+            )
+        if function_value.is_bound_method():
+            if index == 0:
+                # Assume it's self, which is already handled
+                return NO_VALUES
+            index -= 1
+        if index >= len(params_comments):
+            return NO_VALUES
+
+        param_comment = params_comments[index]
+        return _infer_annotation_string(
+            function_value.get_default_param_context(),
+            param_comment
+        )
+    # Annotations are like default params and resolve in the same way.
+    context = function_value.get_default_param_context()
+    return infer_annotation(context, annotation)
+
+
+def py__annotations__(funcdef):
+    dct = {}
+    for function_param in funcdef.get_params():
+        param_annotation = function_param.annotation
+        if param_annotation is not None:
+            dct[function_param.name.value] = param_annotation
+
+    return_annotation = funcdef.annotation
+    if return_annotation:
+        dct['return'] = return_annotation
+    return dct
+
+
+@inference_state_method_cache()
+def infer_return_types(function, arguments):
+    """
+    Infers the type of a function's return value,
+    according to type annotations.
+    """
+    all_annotations = py__annotations__(function.tree_node)
+    annotation = all_annotations.get("return", None)
+    if annotation is None:
+        # If there is no Python 3-type annotation, look for a Python 2-type annotation
+        node = function.tree_node
+        comment = parser_utils.get_following_comment_same_line(node)
+        if comment is None:
+            return NO_VALUES
+
+        match = re.match(r"^#\s*type:\s*\([^#]*\)\s*->\s*([^#]*)", comment)
+        if not match:
+            return NO_VALUES
+
+        return _infer_annotation_string(
+            function.get_default_param_context(),
+            match.group(1).strip()
+        ).execute_annotation()
+
+    context = function.get_default_param_context()
+    unknown_type_vars = find_unknown_type_vars(context, annotation)
+    annotation_values = infer_annotation(context, annotation)
+    if not unknown_type_vars:
+        return annotation_values.execute_annotation()
+
+    type_var_dict = infer_type_vars_for_execution(function, arguments, all_annotations)
+
+    return ValueSet.from_sets(
+        ann.define_generics(type_var_dict)
+        if isinstance(ann, (DefineGenericBaseClass, TypeVar)) else ValueSet({ann})
+        for ann in annotation_values
+    ).execute_annotation()
+
+
+def infer_type_vars_for_execution(function, arguments, annotation_dict):
+    """
+    Some functions use type vars that are not defined by the class, but rather
+    only defined in the function. See for example `iter`. In those cases we
+    want to:
+
+    1. Search for undefined type vars.
+    2. Infer type vars with the execution state we have.
+    3. Return the union of all type vars that have been found.
+    """
+    context = function.get_default_param_context()
+
+    annotation_variable_results = {}
+    executed_param_names = get_executed_param_names(function, arguments)
+    for executed_param_name in executed_param_names:
+        try:
+            annotation_node = annotation_dict[executed_param_name.string_name]
+        except KeyError:
+            continue
+
+        annotation_variables = find_unknown_type_vars(context, annotation_node)
+        if annotation_variables:
+            # Infer unknown type var
+            annotation_value_set = context.infer_node(annotation_node)
+            kind = executed_param_name.get_kind()
+            actual_value_set = executed_param_name.infer()
+            if kind is Parameter.VAR_POSITIONAL:
+                actual_value_set = actual_value_set.merge_types_of_iterate()
+            elif kind is Parameter.VAR_KEYWORD:
+                # TODO _dict_values is not public.
+                actual_value_set = actual_value_set.try_merge('_dict_values')
+            merge_type_var_dicts(
+                annotation_variable_results,
+                annotation_value_set.infer_type_vars(actual_value_set),
+            )
+    return annotation_variable_results
+
+
+def infer_return_for_callable(arguments, param_values, result_values):
+    all_type_vars = {}
+    for pv in param_values:
+        if pv.array_type == 'list':
+            type_var_dict = _infer_type_vars_for_callable(arguments, pv.py__iter__())
+            all_type_vars.update(type_var_dict)
+
+    return ValueSet.from_sets(
+        v.define_generics(all_type_vars)
+        if isinstance(v, (DefineGenericBaseClass, TypeVar))
+        else ValueSet({v})
+        for v in result_values
+    ).execute_annotation()
+
+
+def _infer_type_vars_for_callable(arguments, lazy_params):
+    """
+    Infers type vars for the Calllable class:
+
+        def x() -> Callable[[Callable[..., _T]], _T]: ...
+    """
+    annotation_variable_results = {}
+    for (_, lazy_value), lazy_callable_param in zip(arguments.unpack(), lazy_params):
+        callable_param_values = lazy_callable_param.infer()
+        # Infer unknown type var
+        actual_value_set = lazy_value.infer()
+        merge_type_var_dicts(
+            annotation_variable_results,
+            callable_param_values.infer_type_vars(actual_value_set),
+        )
+    return annotation_variable_results
+
+
+def merge_type_var_dicts(base_dict, new_dict):
+    for type_var_name, values in new_dict.items():
+        if values:
+            try:
+                base_dict[type_var_name] |= values
+            except KeyError:
+                base_dict[type_var_name] = values
+
+
+def merge_pairwise_generics(annotation_value, annotated_argument_class):
+    """
+    Match up the generic parameters from the given argument class to the
+    target annotation.
+
+    This walks the generic parameters immediately within the annotation and
+    argument's type, in order to determine the concrete values of the
+    annotation's parameters for the current case.
+
+    For example, given the following code:
+
+        def values(mapping: Mapping[K, V]) -> List[V]: ...
+
+        for val in values({1: 'a'}):
+            val
+
+    Then this function should be given representations of `Mapping[K, V]`
+    and `Mapping[int, str]`, so that it can determine that `K` is `int and
+    `V` is `str`.
+
+    Note that it is responsibility of the caller to traverse the MRO of the
+    argument type as needed in order to find the type matching the
+    annotation (in this case finding `Mapping[int, str]` as a parent of
+    `Dict[int, str]`).
+
+    Parameters
+    ----------
+
+    `annotation_value`: represents the annotation to infer the concrete
+        parameter types of.
+
+    `annotated_argument_class`: represents the annotated class of the
+        argument being passed to the object annotated by `annotation_value`.
+    """
+
+    type_var_dict = {}
+
+    if not isinstance(annotated_argument_class, DefineGenericBaseClass):
+        return type_var_dict
+
+    annotation_generics = annotation_value.get_generics()
+    actual_generics = annotated_argument_class.get_generics()
+
+    for annotation_generics_set, actual_generic_set in zip(annotation_generics, actual_generics):
+        merge_type_var_dicts(
+            type_var_dict,
+            annotation_generics_set.infer_type_vars(actual_generic_set.execute_annotation()),
+        )
+
+    return type_var_dict
+
+
+def find_type_from_comment_hint_for(context, node, name):
+    return _find_type_from_comment_hint(context, node, node.children[1], name)
+
+
+def find_type_from_comment_hint_with(context, node, name):
+    assert len(node.children[1].children) == 3, \
+        "Can only be here when children[1] is 'foo() as f'"
+    varlist = node.children[1].children[2]
+    return _find_type_from_comment_hint(context, node, varlist, name)
+
+
+def find_type_from_comment_hint_assign(context, node, name):
+    return _find_type_from_comment_hint(context, node, node.children[0], name)
+
+
+def _find_type_from_comment_hint(context, node, varlist, name):
+    index = None
+    if varlist.type in ("testlist_star_expr", "exprlist", "testlist"):
+        # something like "a, b = 1, 2"
+        index = 0
+        for child in varlist.children:
+            if child == name:
+                break
+            if child.type == "operator":
+                continue
+            index += 1
+        else:
+            return []
+
+    comment = parser_utils.get_following_comment_same_line(node)
+    if comment is None:
+        return []
+    match = re.match(r"^#\s*type:\s*([^#]*)", comment)
+    if match is None:
+        return []
+    return _infer_annotation_string(
+        context, match.group(1).strip(), index
+    ).execute_annotation()
+
+
+def find_unknown_type_vars(context, node):
+    def check_node(node):
+        if node.type in ('atom_expr', 'power'):
+            trailer = node.children[-1]
+            if trailer.type == 'trailer' and trailer.children[0] == '[':
+                for subscript_node in _unpack_subscriptlist(trailer.children[1]):
+                    check_node(subscript_node)
+        else:
+            found[:] = _filter_type_vars(context.infer_node(node), found)
+
+    found = []  # We're not using a set, because the order matters.
+    check_node(node)
+    return found
+
+
+def _filter_type_vars(value_set, found=()):
+    new_found = list(found)
+    for type_var in value_set:
+        if isinstance(type_var, TypeVar) and type_var not in found:
+            new_found.append(type_var)
+    return new_found
+
+
+def _unpack_subscriptlist(subscriptlist):
+    if subscriptlist.type == 'subscriptlist':
+        for subscript in subscriptlist.children[::2]:
+            if subscript.type != 'subscript':
+                yield subscript
+    else:
+        if subscriptlist.type != 'subscript':
+            yield subscriptlist

venv/Lib/site-packages/jedi/inference/gradual/base.py

@@ -0,0 +1,430 @@
+from jedi.inference.cache import inference_state_method_cache
+from jedi.inference.base_value import ValueSet, NO_VALUES, Value, \
+    iterator_to_value_set, LazyValueWrapper, ValueWrapper
+from jedi.inference.compiled import builtin_from_name
+from jedi.inference.value.klass import ClassFilter
+from jedi.inference.value.klass import ClassMixin
+from jedi.inference.utils import to_list
+from jedi.inference.names import AbstractNameDefinition, ValueName
+from jedi.inference.context import ClassContext
+from jedi.inference.gradual.generics import TupleGenericManager
+
+
+class _BoundTypeVarName(AbstractNameDefinition):
+    """
+    This type var was bound to a certain type, e.g. int.
+    """
+    def __init__(self, type_var, value_set):
+        self._type_var = type_var
+        self.parent_context = type_var.parent_context
+        self._value_set = value_set
+
+    def infer(self):
+        def iter_():
+            for value in self._value_set:
+                # Replace any with the constraints if they are there.
+                from jedi.inference.gradual.typing import AnyClass
+                if isinstance(value, AnyClass):
+                    for constraint in self._type_var.constraints:
+                        yield constraint
+                else:
+                    yield value
+        return ValueSet(iter_())
+
+    def py__name__(self):
+        return self._type_var.py__name__()
+
+    def __repr__(self):
+        return '<%s %s -> %s>' % (self.__class__.__name__, self.py__name__(), self._value_set)
+
+
+class _TypeVarFilter(object):
+    """
+    A filter for all given variables in a class.
+
+        A = TypeVar('A')
+        B = TypeVar('B')
+        class Foo(Mapping[A, B]):
+            ...
+
+    In this example we would have two type vars given: A and B
+    """
+    def __init__(self, generics, type_vars):
+        self._generics = generics
+        self._type_vars = type_vars
+
+    def get(self, name):
+        for i, type_var in enumerate(self._type_vars):
+            if type_var.py__name__() == name:
+                try:
+                    return [_BoundTypeVarName(type_var, self._generics[i])]
+                except IndexError:
+                    return [type_var.name]
+        return []
+
+    def values(self):
+        # The values are not relevant. If it's not searched exactly, the type
+        # vars are just global and should be looked up as that.
+        return []
+
+
+class _AnnotatedClassContext(ClassContext):
+    def get_filters(self, *args, **kwargs):
+        filters = super(_AnnotatedClassContext, self).get_filters(
+            *args, **kwargs
+        )
+        for f in filters:
+            yield f
+
+        # The type vars can only be looked up if it's a global search and
+        # not a direct lookup on the class.
+        yield self._value.get_type_var_filter()
+
+
+class DefineGenericBaseClass(LazyValueWrapper):
+    def __init__(self, generics_manager):
+        self._generics_manager = generics_manager
+
+    def _create_instance_with_generics(self, generics_manager):
+        raise NotImplementedError
+
+    @inference_state_method_cache()
+    def get_generics(self):
+        return self._generics_manager.to_tuple()
+
+    def define_generics(self, type_var_dict):
+        from jedi.inference.gradual.type_var import TypeVar
+        changed = False
+        new_generics = []
+        for generic_set in self.get_generics():
+            values = NO_VALUES
+            for generic in generic_set:
+                if isinstance(generic, (DefineGenericBaseClass, TypeVar)):
+                    result = generic.define_generics(type_var_dict)
+                    values |= result
+                    if result != ValueSet({generic}):
+                        changed = True
+                else:
+                    values |= ValueSet([generic])
+            new_generics.append(values)
+
+        if not changed:
+            # There might not be any type vars that change. In that case just
+            # return itself, because it does not make sense to potentially lose
+            # cached results.
+            return ValueSet([self])
+
+        return ValueSet([self._create_instance_with_generics(
+            TupleGenericManager(tuple(new_generics))
+        )])
+
+    def is_same_class(self, other):
+        if not isinstance(other, DefineGenericBaseClass):
+            return False
+
+        if self.tree_node != other.tree_node:
+            # TODO not sure if this is nice.
+            return False
+        given_params1 = self.get_generics()
+        given_params2 = other.get_generics()
+
+        if len(given_params1) != len(given_params2):
+            # If the amount of type vars doesn't match, the class doesn't
+            # match.
+            return False
+
+        # Now compare generics
+        return all(
+            any(
+                # TODO why is this ordering the correct one?
+                cls2.is_same_class(cls1)
+                # TODO I'm still not sure gather_annotation_classes is a good
+                # idea. They are essentially here to avoid comparing Tuple <=>
+                # tuple and instead compare tuple <=> tuple, but at the moment
+                # the whole `is_same_class` and `is_sub_class` matching is just
+                # not in the best shape.
+                for cls1 in class_set1.gather_annotation_classes()
+                for cls2 in class_set2.gather_annotation_classes()
+            ) for class_set1, class_set2 in zip(given_params1, given_params2)
+        )
+
+    def __repr__(self):
+        return '<%s: %s%s>' % (
+            self.__class__.__name__,
+            self._wrapped_value,
+            list(self.get_generics()),
+        )
+
+
+class GenericClass(DefineGenericBaseClass, ClassMixin):
+    """
+    A class that is defined with generics, might be something simple like:
+
+        class Foo(Generic[T]): ...
+        my_foo_int_cls = Foo[int]
+    """
+    def __init__(self, class_value, generics_manager):
+        super(GenericClass, self).__init__(generics_manager)
+        self._class_value = class_value
+
+    def _get_wrapped_value(self):
+        return self._class_value
+
+    def get_type_hint(self, add_class_info=True):
+        n = self.py__name__()
+        # Not sure if this is the best way to do this, but all of these types
+        # are a bit special in that they have type aliases and other ways to
+        # become lower case. It's probably better to make them upper case,
+        # because that's what you can use in annotations.
+        n = dict(list="List", dict="Dict", set="Set", tuple="Tuple").get(n, n)
+        s = n + self._generics_manager.get_type_hint()
+        if add_class_info:
+            return 'Type[%s]' % s
+        return s
+
+    def get_type_var_filter(self):
+        return _TypeVarFilter(self.get_generics(), self.list_type_vars())
+
+    def py__call__(self, arguments):
+        instance, = super(GenericClass, self).py__call__(arguments)
+        return ValueSet([_GenericInstanceWrapper(instance)])
+
+    def _as_context(self):
+        return _AnnotatedClassContext(self)
+
+    @to_list
+    def py__bases__(self):
+        for base in self._wrapped_value.py__bases__():
+            yield _LazyGenericBaseClass(self, base, self._generics_manager)
+
+    def _create_instance_with_generics(self, generics_manager):
+        return GenericClass(self._class_value, generics_manager)
+
+    def is_sub_class_of(self, class_value):
+        if super(GenericClass, self).is_sub_class_of(class_value):
+            return True
+        return self._class_value.is_sub_class_of(class_value)
+
+    def with_generics(self, generics_tuple):
+        return self._class_value.with_generics(generics_tuple)
+
+    def infer_type_vars(self, value_set):
+        # Circular
+        from jedi.inference.gradual.annotation import merge_pairwise_generics, merge_type_var_dicts
+
+        annotation_name = self.py__name__()
+        type_var_dict = {}
+        if annotation_name == 'Iterable':
+            annotation_generics = self.get_generics()
+            if annotation_generics:
+                return annotation_generics[0].infer_type_vars(
+                    value_set.merge_types_of_iterate(),
+                )
+        else:
+            # Note: we need to handle the MRO _in order_, so we need to extract
+            # the elements from the set first, then handle them, even if we put
+            # them back in a set afterwards.
+            for py_class in value_set:
+                if py_class.is_instance() and not py_class.is_compiled():
+                    py_class = py_class.get_annotated_class_object()
+                else:
+                    continue
+
+                if py_class.api_type != u'class':
+                    # Functions & modules don't have an MRO and we're not
+                    # expecting a Callable (those are handled separately within
+                    # TypingClassValueWithIndex).
+                    continue
+
+                for parent_class in py_class.py__mro__():
+                    class_name = parent_class.py__name__()
+                    if annotation_name == class_name:
+                        merge_type_var_dicts(
+                            type_var_dict,
+                            merge_pairwise_generics(self, parent_class),
+                        )
+                        break
+
+        return type_var_dict
+
+
+class _LazyGenericBaseClass(object):
+    def __init__(self, class_value, lazy_base_class, generics_manager):
+        self._class_value = class_value
+        self._lazy_base_class = lazy_base_class
+        self._generics_manager = generics_manager
+
+    @iterator_to_value_set
+    def infer(self):
+        for base in self._lazy_base_class.infer():
+            if isinstance(base, GenericClass):
+                # Here we have to recalculate the given types.
+                yield GenericClass.create_cached(
+                    base.inference_state,
+                    base._wrapped_value,
+                    TupleGenericManager(tuple(self._remap_type_vars(base))),
+                )
+            else:
+                if base.is_class_mixin():
+                    # This case basically allows classes like `class Foo(List)`
+                    # to be used like `Foo[int]`. The generics are not
+                    # necessary and can be used later.
+                    yield GenericClass.create_cached(
+                        base.inference_state,
+                        base,
+                        self._generics_manager,
+                    )
+                else:
+                    yield base
+
+    def _remap_type_vars(self, base):
+        from jedi.inference.gradual.type_var import TypeVar
+        filter = self._class_value.get_type_var_filter()
+        for type_var_set in base.get_generics():
+            new = NO_VALUES
+            for type_var in type_var_set:
+                if isinstance(type_var, TypeVar):
+                    names = filter.get(type_var.py__name__())
+                    new |= ValueSet.from_sets(
+                        name.infer() for name in names
+                    )
+                else:
+                    # Mostly will be type vars, except if in some cases
+                    # a concrete type will already be there. In that
+                    # case just add it to the value set.
+                    new |= ValueSet([type_var])
+            yield new
+
+    def __repr__(self):
+        return '<%s: %s>' % (self.__class__.__name__, self._lazy_base_class)
+
+
+class _GenericInstanceWrapper(ValueWrapper):
+    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, u'None')])
+        return self._wrapped_value.py__stop_iteration_returns()
+
+    def get_type_hint(self, add_class_info=True):
+        return self._wrapped_value.class_value.get_type_hint(add_class_info=False)
+
+
+class _PseudoTreeNameClass(Value):
+    """
+    In typeshed, some classes are defined like this:
+
+        Tuple: _SpecialForm = ...
+
+    Now this is not a real class, therefore we have to do some workarounds like
+    this class. Essentially this class makes it possible to goto that `Tuple`
+    name, without affecting anything else negatively.
+    """
+    api_type = u'class'
+
+    def __init__(self, parent_context, tree_name):
+        super(_PseudoTreeNameClass, self).__init__(
+            parent_context.inference_state,
+            parent_context
+        )
+        self._tree_name = tree_name
+
+    @property
+    def tree_node(self):
+        return self._tree_name
+
+    def get_filters(self, *args, **kwargs):
+        # TODO this is obviously wrong. Is it though?
+        class EmptyFilter(ClassFilter):
+            def __init__(self):
+                pass
+
+            def get(self, name, **kwargs):
+                return []
+
+            def values(self, **kwargs):
+                return []
+
+        yield EmptyFilter()
+
+    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')
+
+    @property
+    def name(self):
+        return ValueName(self, self._tree_name)
+
+    def get_qualified_names(self):
+        return (self._tree_name.value,)
+
+    def __repr__(self):
+        return '%s(%s)' % (self.__class__.__name__, self._tree_name.value)
+
+
+class BaseTypingValue(LazyValueWrapper):
+    def __init__(self, parent_context, tree_name):
+        self.inference_state = parent_context.inference_state
+        self.parent_context = parent_context
+        self._tree_name = tree_name
+
+    @property
+    def name(self):
+        return ValueName(self, self._tree_name)
+
+    def _get_wrapped_value(self):
+        return _PseudoTreeNameClass(self.parent_context, self._tree_name)
+
+    def __repr__(self):
+        return '%s(%s)' % (self.__class__.__name__, self._tree_name.value)
+
+
+class BaseTypingClassWithGenerics(DefineGenericBaseClass):
+    def __init__(self, parent_context, tree_name, generics_manager):
+        super(BaseTypingClassWithGenerics, self).__init__(generics_manager)
+        self.inference_state = parent_context.inference_state
+        self.parent_context = parent_context
+        self._tree_name = tree_name
+
+    def _get_wrapped_value(self):
+        return _PseudoTreeNameClass(self.parent_context, self._tree_name)
+
+    def __repr__(self):
+        return '%s(%s%s)' % (self.__class__.__name__, self._tree_name.value,
+                             self._generics_manager)
+
+
+class BaseTypingInstance(LazyValueWrapper):
+    def __init__(self, parent_context, class_value, tree_name, generics_manager):
+        self.inference_state = class_value.inference_state
+        self.parent_context = parent_context
+        self._class_value = class_value
+        self._tree_name = tree_name
+        self._generics_manager = generics_manager
+
+    def py__class__(self):
+        return self._class_value
+
+    def get_annotated_class_object(self):
+        return self._class_value
+
+    def get_qualified_names(self):
+        return (self.py__name__(),)
+
+    @property
+    def name(self):
+        return ValueName(self, self._tree_name)
+
+    def _get_wrapped_value(self):
+        object_, = builtin_from_name(self.inference_state, u'object').execute_annotation()
+        return object_
+
+    def __repr__(self):
+        return '<%s: %s>' % (self.__class__.__name__, self._generics_manager)

venv/Lib/site-packages/jedi/inference/gradual/conversion.py

@@ -0,0 +1,210 @@
+from jedi import debug
+from jedi.inference.base_value import ValueSet, \
+    NO_VALUES
+from jedi.inference.utils import to_list
+from jedi.inference.gradual.stub_value import StubModuleValue
+from jedi.inference.gradual.typeshed import try_to_load_stub_cached
+from jedi.inference.value.decorator import Decoratee
+
+
+def _stub_to_python_value_set(stub_value, ignore_compiled=False):
+    stub_module_context = stub_value.get_root_context()
+    if not stub_module_context.is_stub():
+        return ValueSet([stub_value])
+
+    decorates = None
+    if isinstance(stub_value, Decoratee):
+        decorates = stub_value._original_value
+
+    was_instance = stub_value.is_instance()
+    if was_instance:
+        arguments = getattr(stub_value, '_arguments', None)
+        stub_value = stub_value.py__class__()
+
+    qualified_names = stub_value.get_qualified_names()
+    if qualified_names is None:
+        return NO_VALUES
+
+    was_bound_method = stub_value.is_bound_method()
+    if was_bound_method:
+        # Infer the object first. We can infer the method later.
+        method_name = qualified_names[-1]
+        qualified_names = qualified_names[:-1]
+        was_instance = True
+        arguments = None
+
+    values = _infer_from_stub(stub_module_context, qualified_names, ignore_compiled)
+    if was_instance:
+        values = ValueSet.from_sets(
+            c.execute_with_values() if arguments is None else c.execute(arguments)
+            for c in values
+            if c.is_class()
+        )
+    if was_bound_method:
+        # Now that the instance has been properly created, we can simply get
+        # the method.
+        values = values.py__getattribute__(method_name)
+    if decorates is not None:
+        values = ValueSet(Decoratee(v, decorates) for v in values)
+    return values
+
+
+def _infer_from_stub(stub_module_context, qualified_names, ignore_compiled):
+    from jedi.inference.compiled.mixed import MixedObject
+    stub_module = stub_module_context.get_value()
+    assert isinstance(stub_module, (StubModuleValue, MixedObject)), stub_module_context
+    non_stubs = stub_module.non_stub_value_set
+    if ignore_compiled:
+        non_stubs = non_stubs.filter(lambda c: not c.is_compiled())
+    for name in qualified_names:
+        non_stubs = non_stubs.py__getattribute__(name)
+    return non_stubs
+
+
+@to_list
+def _try_stub_to_python_names(names, prefer_stub_to_compiled=False):
+    for name in names:
+        module_context = name.get_root_context()
+        if not module_context.is_stub():
+            yield name
+            continue
+
+        if name.api_type == 'module':
+            values = convert_values(name.infer(), ignore_compiled=prefer_stub_to_compiled)
+            if values:
+                for v in values:
+                    yield v.name
+                continue
+        else:
+            v = name.get_defining_qualified_value()
+            if v is not None:
+                converted = _stub_to_python_value_set(v, ignore_compiled=prefer_stub_to_compiled)
+                if converted:
+                    converted_names = converted.goto(name.get_public_name())
+                    if converted_names:
+                        for n in converted_names:
+                            if n.get_root_context().is_stub():
+                                # If it's a stub again, it means we're going in
+                                # a circle. Probably some imports make it a
+                                # stub again.
+                                yield name
+                            else:
+                                yield n
+                        continue
+        yield name
+
+
+def _load_stub_module(module):
+    if module.is_stub():
+        return module
+    return try_to_load_stub_cached(
+        module.inference_state,
+        import_names=module.string_names,
+        python_value_set=ValueSet([module]),
+        parent_module_value=None,
+        sys_path=module.inference_state.get_sys_path(),
+    )
+
+
+@to_list
+def _python_to_stub_names(names, fallback_to_python=False):
+    for name in names:
+        module_context = name.get_root_context()
+        if module_context.is_stub():
+            yield name
+            continue
+
+        if name.api_type == 'module':
+            found_name = False
+            for n in name.goto():
+                if n.api_type == 'module':
+                    values = convert_values(n.infer(), only_stubs=True)
+                    for v in values:
+                        yield v.name
+                        found_name = True
+                else:
+                    for x in _python_to_stub_names([n], fallback_to_python=fallback_to_python):
+                        yield x
+                        found_name = True
+            if found_name:
+                continue
+        else:
+            v = name.get_defining_qualified_value()
+            if v is not None:
+                converted = to_stub(v)
+                if converted:
+                    converted_names = converted.goto(name.get_public_name())
+                    if converted_names:
+                        for n in converted_names:
+                            yield n
+                        continue
+        if fallback_to_python:
+            # This is the part where if we haven't found anything, just return
+            # the stub name.
+            yield name
+
+
+def convert_names(names, only_stubs=False, prefer_stubs=False, prefer_stub_to_compiled=True):
+    if only_stubs and prefer_stubs:
+        raise ValueError("You cannot use both of only_stubs and prefer_stubs.")
+
+    with debug.increase_indent_cm('convert names'):
+        if only_stubs or prefer_stubs:
+            return _python_to_stub_names(names, fallback_to_python=prefer_stubs)
+        else:
+            return _try_stub_to_python_names(
+                names, prefer_stub_to_compiled=prefer_stub_to_compiled)
+
+
+def convert_values(values, only_stubs=False, prefer_stubs=False, ignore_compiled=True):
+    assert not (only_stubs and prefer_stubs)
+    with debug.increase_indent_cm('convert values'):
+        if only_stubs or prefer_stubs:
+            return ValueSet.from_sets(
+                to_stub(value)
+                or (ValueSet({value}) if prefer_stubs else NO_VALUES)
+                for value in values
+            )
+        else:
+            return ValueSet.from_sets(
+                _stub_to_python_value_set(stub_value, ignore_compiled=ignore_compiled)
+                or ValueSet({stub_value})
+                for stub_value in values
+            )
+
+
+def to_stub(value):
+    if value.is_stub():
+        return ValueSet([value])
+
+    was_instance = value.is_instance()
+    if was_instance:
+        value = value.py__class__()
+
+    qualified_names = value.get_qualified_names()
+    stub_module = _load_stub_module(value.get_root_context().get_value())
+    if stub_module is None or qualified_names is None:
+        return NO_VALUES
+
+    was_bound_method = value.is_bound_method()
+    if was_bound_method:
+        # Infer the object first. We can infer the method later.
+        method_name = qualified_names[-1]
+        qualified_names = qualified_names[:-1]
+        was_instance = True
+
+    stub_values = ValueSet([stub_module])
+    for name in qualified_names:
+        stub_values = stub_values.py__getattribute__(name)
+
+    if was_instance:
+        stub_values = ValueSet.from_sets(
+            c.execute_with_values()
+            for c in stub_values
+            if c.is_class()
+        )
+    if was_bound_method:
+        # Now that the instance has been properly created, we can simply get
+        # the method.
+        stub_values = stub_values.py__getattribute__(method_name)
+    return stub_values

venv/Lib/site-packages/jedi/inference/gradual/generics.py

@@ -0,0 +1,101 @@
+"""
+This module is about generics, like the `int` in `List[int]`. It's not about
+the Generic class.
+"""
+
+from jedi import debug
+from jedi.cache import memoize_method
+from jedi.inference.utils import to_tuple
+from jedi.inference.base_value import ValueSet, NO_VALUES
+from jedi.inference.value.iterable import SequenceLiteralValue
+from jedi.inference.helpers import is_string
+
+
+def _resolve_forward_references(context, value_set):
+    for value in value_set:
+        if is_string(value):
+            from jedi.inference.gradual.annotation import _get_forward_reference_node
+            node = _get_forward_reference_node(context, value.get_safe_value())
+            if node is not None:
+                for c in context.infer_node(node):
+                    yield c
+        else:
+            yield value
+
+
+class _AbstractGenericManager(object):
+    def get_index_and_execute(self, index):
+        try:
+            return self[index].execute_annotation()
+        except IndexError:
+            debug.warning('No param #%s found for annotation %s', index, self)
+            return NO_VALUES
+
+    def get_type_hint(self):
+        return '[%s]' % ', '.join(t.get_type_hint(add_class_info=False) for t in self.to_tuple())
+
+
+class LazyGenericManager(_AbstractGenericManager):
+    def __init__(self, context_of_index, index_value):
+        self._context_of_index = context_of_index
+        self._index_value = index_value
+
+    @memoize_method
+    def __getitem__(self, index):
+        return self._tuple()[index]()
+
+    def __len__(self):
+        return len(self._tuple())
+
+    @memoize_method
+    @to_tuple
+    def _tuple(self):
+        def lambda_scoping_in_for_loop_sucks(lazy_value):
+            return lambda: ValueSet(_resolve_forward_references(
+                self._context_of_index,
+                lazy_value.infer()
+            ))
+
+        if isinstance(self._index_value, SequenceLiteralValue):
+            for lazy_value in self._index_value.py__iter__(contextualized_node=None):
+                yield lambda_scoping_in_for_loop_sucks(lazy_value)
+        else:
+            yield lambda: ValueSet(_resolve_forward_references(
+                self._context_of_index,
+                ValueSet([self._index_value])
+            ))
+
+    @to_tuple
+    def to_tuple(self):
+        for callable_ in self._tuple():
+            yield callable_()
+
+    def is_homogenous_tuple(self):
+        if isinstance(self._index_value, SequenceLiteralValue):
+            entries = self._index_value.get_tree_entries()
+            if len(entries) == 2 and entries[1] == '...':
+                return True
+        return False
+
+    def __repr__(self):
+        return '<LazyG>[%s]' % (', '.join(repr(x) for x in self.to_tuple()))
+
+
+class TupleGenericManager(_AbstractGenericManager):
+    def __init__(self, tup):
+        self._tuple = tup
+
+    def __getitem__(self, index):
+        return self._tuple[index]
+
+    def __len__(self):
+        return len(self._tuple)
+
+    def to_tuple(self):
+        return self._tuple
+
+    def is_homogenous_tuple(self):
+        return False
+
+    def __repr__(self):
+        return '<TupG>[%s]' % (', '.join(repr(x) for x in self.to_tuple()))

venv/Lib/site-packages/jedi/inference/gradual/stub_value.py

@@ -0,0 +1,105 @@
+from jedi.inference.base_value import ValueWrapper
+from jedi.inference.value.module import ModuleValue
+from jedi.inference.filters import ParserTreeFilter
+from jedi.inference.names import StubName, StubModuleName
+from jedi.inference.gradual.typing import TypingModuleFilterWrapper
+from jedi.inference.context import ModuleContext
+
+
+class StubModuleValue(ModuleValue):
+    _module_name_class = StubModuleName
+
+    def __init__(self, non_stub_value_set, *args, **kwargs):
+        super(StubModuleValue, self).__init__(*args, **kwargs)
+        self.non_stub_value_set = non_stub_value_set
+
+    def is_stub(self):
+        return True
+
+    def sub_modules_dict(self):
+        """
+        We have to overwrite this, because it's possible to have stubs that
+        don't have code for all the child modules. At the time of writing this
+        there are for example no stubs for `json.tool`.
+        """
+        names = {}
+        for value in self.non_stub_value_set:
+            try:
+                method = value.sub_modules_dict
+            except AttributeError:
+                pass
+            else:
+                names.update(method())
+        names.update(super(StubModuleValue, self).sub_modules_dict())
+        return names
+
+    def _get_stub_filters(self, origin_scope):
+        return [StubFilter(
+            parent_context=self.as_context(),
+            origin_scope=origin_scope
+        )] + list(self.iter_star_filters())
+
+    def get_filters(self, origin_scope=None):
+        filters = super(StubModuleValue, self).get_filters(origin_scope)
+        next(filters, None)  # Ignore the first filter and replace it with our own
+        stub_filters = self._get_stub_filters(origin_scope=origin_scope)
+        for f in stub_filters:
+            yield f
+
+        for f in filters:
+            yield f
+
+    def _as_context(self):
+        return StubModuleContext(self)
+
+
+class StubModuleContext(ModuleContext):
+    def get_filters(self, until_position=None, origin_scope=None):
+        # Make sure to ignore the position, because positions are not relevant
+        # for stubs.
+        return super(StubModuleContext, self).get_filters(origin_scope=origin_scope)
+
+
+class TypingModuleWrapper(StubModuleValue):
+    def get_filters(self, *args, **kwargs):
+        filters = super(TypingModuleWrapper, self).get_filters(*args, **kwargs)
+        f = next(filters, None)
+        assert f is not None
+        yield TypingModuleFilterWrapper(f)
+        for f in filters:
+            yield f
+
+    def _as_context(self):
+        return TypingModuleContext(self)
+
+
+class TypingModuleContext(ModuleContext):
+    def get_filters(self, *args, **kwargs):
+        filters = super(TypingModuleContext, self).get_filters(*args, **kwargs)
+        yield TypingModuleFilterWrapper(next(filters, None))
+        for f in filters:
+            yield f
+
+
+class StubFilter(ParserTreeFilter):
+    name_class = StubName
+
+    def _is_name_reachable(self, name):
+        if not super(StubFilter, self)._is_name_reachable(name):
+            return False
+
+        # Imports in stub files are only public if they have an "as"
+        # export.
+        definition = name.get_definition()
+        if definition.type in ('import_from', 'import_name'):
+            if name.parent.type not in ('import_as_name', 'dotted_as_name'):
+                return False
+        n = name.value
+        # TODO rewrite direct return
+        if n.startswith('_') and not (n.startswith('__') and n.endswith('__')):
+            return False
+        return True
+
+
+class VersionInfo(ValueWrapper):
+    pass

venv/Lib/site-packages/jedi/inference/gradual/type_var.py

@@ -0,0 +1,131 @@
+from jedi._compatibility import unicode, force_unicode
+from jedi import debug
+from jedi.inference.base_value import ValueSet, NO_VALUES, ValueWrapper
+from jedi.inference.gradual.base import BaseTypingValue
+
+
+class TypeVarClass(BaseTypingValue):
+    def py__call__(self, arguments):
+        unpacked = arguments.unpack()
+
+        key, lazy_value = next(unpacked, (None, None))
+        var_name = self._find_string_name(lazy_value)
+        # The name must be given, otherwise it's useless.
+        if var_name is None or key is not None:
+            debug.warning('Found a variable without a name %s', arguments)
+            return NO_VALUES
+
+        return ValueSet([TypeVar.create_cached(
+            self.inference_state,
+            self.parent_context,
+            self._tree_name,
+            var_name,
+            unpacked
+        )])
+
+    def _find_string_name(self, lazy_value):
+        if lazy_value is None:
+            return None
+
+        value_set = lazy_value.infer()
+        if not value_set:
+            return None
+        if len(value_set) > 1:
+            debug.warning('Found multiple values for a type variable: %s', value_set)
+
+        name_value = next(iter(value_set))
+        try:
+            method = name_value.get_safe_value
+        except AttributeError:
+            return None
+        else:
+            safe_value = method(default=None)
+            if self.inference_state.environment.version_info.major == 2:
+                if isinstance(safe_value, bytes):
+                    return force_unicode(safe_value)
+            if isinstance(safe_value, (str, unicode)):
+                return safe_value
+            return None
+
+
+class TypeVar(BaseTypingValue):
+    def __init__(self, parent_context, tree_name, var_name, unpacked_args):
+        super(TypeVar, self).__init__(parent_context, tree_name)
+        self._var_name = var_name
+
+        self._constraints_lazy_values = []
+        self._bound_lazy_value = None
+        self._covariant_lazy_value = None
+        self._contravariant_lazy_value = None
+        for key, lazy_value in unpacked_args:
+            if key is None:
+                self._constraints_lazy_values.append(lazy_value)
+            else:
+                if key == 'bound':
+                    self._bound_lazy_value = lazy_value
+                elif key == 'covariant':
+                    self._covariant_lazy_value = lazy_value
+                elif key == 'contravariant':
+                    self._contra_variant_lazy_value = lazy_value
+                else:
+                    debug.warning('Invalid TypeVar param name %s', key)
+
+    def py__name__(self):
+        return self._var_name
+
+    def get_filters(self, *args, **kwargs):
+        return iter([])
+
+    def _get_classes(self):
+        if self._bound_lazy_value is not None:
+            return self._bound_lazy_value.infer()
+        if self._constraints_lazy_values:
+            return self.constraints
+        debug.warning('Tried to infer the TypeVar %s without a given type', self._var_name)
+        return NO_VALUES
+
+    def is_same_class(self, other):
+        # Everything can match an undefined type var.
+        return True
+
+    @property
+    def constraints(self):
+        return ValueSet.from_sets(
+            lazy.infer() for lazy in self._constraints_lazy_values
+        )
+
+    def define_generics(self, type_var_dict):
+        try:
+            found = type_var_dict[self.py__name__()]
+        except KeyError:
+            pass
+        else:
+            if found:
+                return found
+        return ValueSet({self})
+
+    def execute_annotation(self):
+        return self._get_classes().execute_annotation()
+
+    def infer_type_vars(self, value_set):
+        def iterate():
+            for v in value_set:
+                cls = v.py__class__()
+                if v.is_function() or v.is_class():
+                    cls = TypeWrapper(cls, v)
+                yield cls
+
+        annotation_name = self.py__name__()
+        return {annotation_name: ValueSet(iterate())}
+
+    def __repr__(self):
+        return '<%s: %s>' % (self.__class__.__name__, self.py__name__())
+
+
+class TypeWrapper(ValueWrapper):
+    def __init__(self, wrapped_value, original_value):
+        super(TypeWrapper, self).__init__(wrapped_value)
+        self._original_value = original_value
+
+    def execute_annotation(self):
+        return ValueSet({self._original_value})

venv/Lib/site-packages/jedi/inference/gradual/typeshed.py

@@ -0,0 +1,312 @@
+import os
+import re
+from functools import wraps
+from collections import namedtuple
+
+from jedi import settings
+from jedi.file_io import FileIO
+from jedi._compatibility import FileNotFoundError, cast_path
+from jedi.parser_utils import get_cached_code_lines
+from jedi.inference.base_value import ValueSet, NO_VALUES
+from jedi.inference.gradual.stub_value import TypingModuleWrapper, StubModuleValue
+from jedi.inference.value import ModuleValue
+
+_jedi_path = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+TYPESHED_PATH = os.path.join(_jedi_path, 'third_party', 'typeshed')
+DJANGO_INIT_PATH = os.path.join(_jedi_path, 'third_party', 'django-stubs',
+                                'django-stubs', '__init__.pyi')
+
+_IMPORT_MAP = dict(
+    _collections='collections',
+    _socket='socket',
+)
+
+PathInfo = namedtuple('PathInfo', 'path is_third_party')
+
+
+def _merge_create_stub_map(path_infos):
+    map_ = {}
+    for directory_path_info in path_infos:
+        map_.update(_create_stub_map(directory_path_info))
+    return map_
+
+
+def _create_stub_map(directory_path_info):
+    """
+    Create a mapping of an importable name in Python to a stub file.
+    """
+    def generate():
+        try:
+            listed = os.listdir(directory_path_info.path)
+        except (FileNotFoundError, OSError):
+            # OSError is Python 2
+            return
+
+        for entry in listed:
+            entry = cast_path(entry)
+            path = os.path.join(directory_path_info.path, entry)
+            if os.path.isdir(path):
+                init = os.path.join(path, '__init__.pyi')
+                if os.path.isfile(init):
+                    yield entry, PathInfo(init, directory_path_info.is_third_party)
+            elif entry.endswith('.pyi') and os.path.isfile(path):
+                name = entry[:-4]
+                if name != '__init__':
+                    yield name, PathInfo(path, directory_path_info.is_third_party)
+
+    # Create a dictionary from the tuple generator.
+    return dict(generate())
+
+
+def _get_typeshed_directories(version_info):
+    check_version_list = ['2and3', str(version_info.major)]
+    for base in ['stdlib', 'third_party']:
+        base_path = os.path.join(TYPESHED_PATH, base)
+        base_list = os.listdir(base_path)
+        for base_list_entry in base_list:
+            match = re.match(r'(\d+)\.(\d+)$', base_list_entry)
+            if match is not None:
+                if int(match.group(1)) == version_info.major \
+                        and int(match.group(2)) <= version_info.minor:
+                    check_version_list.append(base_list_entry)
+
+        for check_version in check_version_list:
+            is_third_party = base != 'stdlib'
+            yield PathInfo(os.path.join(base_path, check_version), is_third_party)
+
+
+_version_cache = {}
+
+
+def _cache_stub_file_map(version_info):
+    """
+    Returns a map of an importable name in Python to a stub file.
+    """
+    # TODO this caches the stub files indefinitely, maybe use a time cache
+    # for that?
+    version = version_info[:2]
+    try:
+        return _version_cache[version]
+    except KeyError:
+        pass
+
+    _version_cache[version] = file_set = \
+        _merge_create_stub_map(_get_typeshed_directories(version_info))
+    return file_set
+
+
+def import_module_decorator(func):
+    @wraps(func)
+    def wrapper(inference_state, import_names, parent_module_value, sys_path, prefer_stubs):
+        python_value_set = inference_state.module_cache.get(import_names)
+        if python_value_set is None:
+            if parent_module_value is not None and parent_module_value.is_stub():
+                parent_module_values = parent_module_value.non_stub_value_set
+            else:
+                parent_module_values = [parent_module_value]
+            if import_names == ('os', 'path'):
+                # This is a huge exception, we follow a nested import
+                # ``os.path``, because it's a very important one in Python
+                # that is being achieved by messing with ``sys.modules`` in
+                # ``os``.
+                python_value_set = ValueSet.from_sets(
+                    func(inference_state, (n,), None, sys_path,)
+                    for n in [u'posixpath', u'ntpath', u'macpath', u'os2emxpath']
+                )
+            else:
+                python_value_set = ValueSet.from_sets(
+                    func(inference_state, import_names, p, sys_path,)
+                    for p in parent_module_values
+                )
+            inference_state.module_cache.add(import_names, python_value_set)
+
+        if not prefer_stubs:
+            return python_value_set
+
+        stub = try_to_load_stub_cached(inference_state, import_names, python_value_set,
+                                       parent_module_value, sys_path)
+        if stub is not None:
+            return ValueSet([stub])
+        return python_value_set
+
+    return wrapper
+
+
+def try_to_load_stub_cached(inference_state, import_names, *args, **kwargs):
+    if import_names is None:
+        return None
+
+    try:
+        return inference_state.stub_module_cache[import_names]
+    except KeyError:
+        pass
+
+    # TODO is this needed? where are the exceptions coming from that make this
+    # necessary? Just remove this line.
+    inference_state.stub_module_cache[import_names] = None
+    inference_state.stub_module_cache[import_names] = result = \
+        _try_to_load_stub(inference_state, import_names, *args, **kwargs)
+    return result
+
+
+def _try_to_load_stub(inference_state, import_names, python_value_set,
+                      parent_module_value, sys_path):
+    """
+    Trying to load a stub for a set of import_names.
+
+    This is modelled to work like "PEP 561 -- Distributing and Packaging Type
+    Information", see https://www.python.org/dev/peps/pep-0561.
+    """
+    if parent_module_value is None and len(import_names) > 1:
+        try:
+            parent_module_value = try_to_load_stub_cached(
+                inference_state, import_names[:-1], NO_VALUES,
+                parent_module_value=None, sys_path=sys_path)
+        except KeyError:
+            pass
+
+    # 1. Try to load foo-stubs folders on path for import name foo.
+    if len(import_names) == 1:
+        # foo-stubs
+        for p in sys_path:
+            p = cast_path(p)
+            init = os.path.join(p, *import_names) + '-stubs' + os.path.sep + '__init__.pyi'
+            m = _try_to_load_stub_from_file(
+                inference_state,
+                python_value_set,
+                file_io=FileIO(init),
+                import_names=import_names,
+            )
+            if m is not None:
+                return m
+        if import_names[0] == 'django' and python_value_set:
+            return _try_to_load_stub_from_file(
+                inference_state,
+                python_value_set,
+                file_io=FileIO(DJANGO_INIT_PATH),
+                import_names=import_names,
+            )
+
+    # 2. Try to load pyi files next to py files.
+    for c in python_value_set:
+        try:
+            method = c.py__file__
+        except AttributeError:
+            pass
+        else:
+            file_path = method()
+            file_paths = []
+            if c.is_namespace():
+                file_paths = [os.path.join(p, '__init__.pyi') for p in c.py__path__()]
+            elif file_path is not None and file_path.endswith('.py'):
+                file_paths = [file_path + 'i']
+
+            for file_path in file_paths:
+                m = _try_to_load_stub_from_file(
+                    inference_state,
+                    python_value_set,
+                    # The file path should end with .pyi
+                    file_io=FileIO(file_path),
+                    import_names=import_names,
+                )
+                if m is not None:
+                    return m
+
+    # 3. Try to load typeshed
+    m = _load_from_typeshed(inference_state, python_value_set, parent_module_value, import_names)
+    if m is not None:
+        return m
+
+    # 4. Try to load pyi file somewhere if python_value_set was not defined.
+    if not python_value_set:
+        if parent_module_value is not None:
+            check_path = parent_module_value.py__path__() or []
+            # In case import_names
+            names_for_path = (import_names[-1],)
+        else:
+            check_path = sys_path
+            names_for_path = import_names
+
+        for p in check_path:
+            m = _try_to_load_stub_from_file(
+                inference_state,
+                python_value_set,
+                file_io=FileIO(os.path.join(p, *names_for_path) + '.pyi'),
+                import_names=import_names,
+            )
+            if m is not None:
+                return m
+
+    # If no stub is found, that's fine, the calling function has to deal with
+    # it.
+    return None
+
+
+def _load_from_typeshed(inference_state, python_value_set, parent_module_value, import_names):
+    import_name = import_names[-1]
+    map_ = None
+    if len(import_names) == 1:
+        map_ = _cache_stub_file_map(inference_state.grammar.version_info)
+        import_name = _IMPORT_MAP.get(import_name, import_name)
+    elif isinstance(parent_module_value, ModuleValue):
+        if not parent_module_value.is_package():
+            # Only if it's a package (= a folder) something can be
+            # imported.
+            return None
+        paths = parent_module_value.py__path__()
+        # Once the initial package has been loaded, the sub packages will
+        # always be loaded, regardless if they are there or not. This makes
+        # sense, IMO, because stubs take preference, even if the original
+        # library doesn't provide a module (it could be dynamic). ~dave
+        map_ = _merge_create_stub_map([PathInfo(p, is_third_party=False) for p in paths])
+
+    if map_ is not None:
+        path_info = map_.get(import_name)
+        if path_info is not None and (not path_info.is_third_party or python_value_set):
+            return _try_to_load_stub_from_file(
+                inference_state,
+                python_value_set,
+                file_io=FileIO(path_info.path),
+                import_names=import_names,
+            )
+
+
+def _try_to_load_stub_from_file(inference_state, python_value_set, file_io, import_names):
+    try:
+        stub_module_node = parse_stub_module(inference_state, file_io)
+    except (OSError, IOError):  # IOError is Python 2 only
+        # The file that you're looking for doesn't exist (anymore).
+        return None
+    else:
+        return create_stub_module(
+            inference_state, python_value_set, stub_module_node, file_io,
+            import_names
+        )
+
+
+def parse_stub_module(inference_state, file_io):
+    return inference_state.parse(
+        file_io=file_io,
+        cache=True,
+        diff_cache=settings.fast_parser,
+        cache_path=settings.cache_directory,
+        use_latest_grammar=True
+    )
+
+
+def create_stub_module(inference_state, python_value_set, stub_module_node, file_io, import_names):
+    if import_names == ('typing',):
+        module_cls = TypingModuleWrapper
+    else:
+        module_cls = StubModuleValue
+    file_name = os.path.basename(file_io.path)
+    stub_module_value = module_cls(
+        python_value_set, inference_state, stub_module_node,
+        file_io=file_io,
+        string_names=import_names,
+        # The code was loaded with latest_grammar, so use
+        # that.
+        code_lines=get_cached_code_lines(inference_state.latest_grammar, file_io.path),
+        is_package=file_name == '__init__.pyi',
+    )
+    return stub_module_value

venv/Lib/site-packages/jedi/inference/gradual/typing.py

@@ -0,0 +1,485 @@
+"""
+We need to somehow work with the typing objects. Since the typing objects are
+pretty bare we need to add all the Jedi customizations to make them work as
+values.
+
+This file deals with all the typing.py cases.
+"""
+import itertools
+
+from jedi._compatibility import unicode
+from jedi import debug
+from jedi.inference.compiled import builtin_from_name, create_simple_object
+from jedi.inference.base_value import ValueSet, NO_VALUES, Value, \
+    LazyValueWrapper
+from jedi.inference.lazy_value import LazyKnownValues
+from jedi.inference.arguments import repack_with_argument_clinic
+from jedi.inference.filters import FilterWrapper
+from jedi.inference.names import NameWrapper, ValueName
+from jedi.inference.value.klass import ClassMixin
+from jedi.inference.gradual.base import BaseTypingValue, \
+    BaseTypingClassWithGenerics, BaseTypingInstance
+from jedi.inference.gradual.type_var import TypeVarClass
+from jedi.inference.gradual.generics import LazyGenericManager, TupleGenericManager
+
+_PROXY_CLASS_TYPES = 'Tuple Generic Protocol Callable Type'.split()
+_TYPE_ALIAS_TYPES = {
+    'List': 'builtins.list',
+    'Dict': 'builtins.dict',
+    'Set': 'builtins.set',
+    'FrozenSet': 'builtins.frozenset',
+    'ChainMap': 'collections.ChainMap',
+    'Counter': 'collections.Counter',
+    'DefaultDict': 'collections.defaultdict',
+    'Deque': 'collections.deque',
+}
+_PROXY_TYPES = 'Optional Union ClassVar'.split()
+
+
+class TypingModuleName(NameWrapper):
+    def infer(self):
+        return ValueSet(self._remap())
+
+    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, u'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.
+            for c in self._wrapped_name.infer():  # Fuck my life Python 2
+                yield c
+        else:
+            # Everything else shouldn't be relevant for type checking.
+            for c in self._wrapped_name.infer():  # Fuck my life Python 2
+                yield c
+
+
+class TypingModuleFilterWrapper(FilterWrapper):
+    name_wrapper_class = TypingModuleName
+
+
+class ProxyWithGenerics(BaseTypingClassWithGenerics):
+    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 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 gather_annotation_classes(self):
+        return ValueSet.from_sets(self._generics_manager.to_tuple())
+
+    def _create_instance_with_generics(self, generics_manager):
+        return ProxyWithGenerics(
+            self.parent_context,
+            self._tree_name,
+            generics_manager
+        )
+
+    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, u'None')
+            return annotation_generics[0].infer_type_vars(
+                value_set.filter(lambda x: x != none),
+            )
+
+        return {}
+
+
+class ProxyTypingValue(BaseTypingValue):
+    index_class = ProxyWithGenerics
+
+    def with_generics(self, generics_tuple):
+        return self.index_class.create_cached(
+            self.inference_state,
+            self.parent_context,
+            self._tree_name,
+            generics_manager=TupleGenericManager(generics_tuple)
+        )
+
+    def py__getitem__(self, index_value_set, contextualized_node):
+        return ValueSet(
+            self.index_class.create_cached(
+                self.inference_state,
+                self.parent_context,
+                self._tree_name,
+                generics_manager=LazyGenericManager(
+                    context_of_index=contextualized_node.context,
+                    index_value=index_value,
+                )
+            ) for index_value in index_value_set
+        )
+
+
+class _TypingClassMixin(ClassMixin):
+    def py__bases__(self):
+        return [LazyKnownValues(
+            self.inference_state.builtins_module.py__getattribute__('object')
+        )]
+
+    def get_metaclasses(self):
+        return []
+
+    @property
+    def name(self):
+        return ValueName(self, self._tree_name)
+
+
+class TypingClassWithGenerics(ProxyWithGenerics, _TypingClassMixin):
+    def infer_type_vars(self, value_set):
+        type_var_dict = {}
+        annotation_generics = self.get_generics()
+
+        if not annotation_generics:
+            return type_var_dict
+
+        annotation_name = self.py__name__()
+        if annotation_name == 'Type':
+            return annotation_generics[0].infer_type_vars(
+                # This is basically a trick to avoid extra code: We execute the
+                # incoming classes to be able to use the normal code for type
+                # var inference.
+                value_set.execute_annotation(),
+            )
+
+        elif annotation_name == 'Callable':
+            if len(annotation_generics) == 2:
+                return annotation_generics[1].infer_type_vars(
+                    value_set.execute_annotation(),
+                )
+
+        elif annotation_name == 'Tuple':
+            tuple_annotation, = self.execute_annotation()
+            return tuple_annotation.infer_type_vars(value_set)
+
+        return type_var_dict
+
+    def _create_instance_with_generics(self, generics_manager):
+        return TypingClassWithGenerics(
+            self.parent_context,
+            self._tree_name,
+            generics_manager
+        )
+
+
+class ProxyTypingClassValue(ProxyTypingValue, _TypingClassMixin):
+    index_class = TypingClassWithGenerics
+
+
+class TypeAlias(LazyValueWrapper):
+    def __init__(self, parent_context, origin_tree_name, actual):
+        self.inference_state = parent_context.inference_state
+        self.parent_context = parent_context
+        self._origin_tree_name = origin_tree_name
+        self._actual = actual  # e.g. builtins.list
+
+    @property
+    def name(self):
+        return ValueName(self, self._origin_tree_name)
+
+    def py__name__(self):
+        return self.name.string_name
+
+    def __repr__(self):
+        return '<%s: %s>' % (self.__class__.__name__, self._actual)
+
+    def _get_wrapped_value(self):
+        module_name, class_name = self._actual.split('.')
+        if self.inference_state.environment.version_info.major == 2 and module_name == 'builtins':
+            module_name = '__builtin__'
+
+        # TODO use inference_state.import_module?
+        from jedi.inference.imports import Importer
+        module, = Importer(
+            self.inference_state, [module_name], self.inference_state.builtins_module
+        ).follow()
+        classes = module.py__getattribute__(class_name)
+        # There should only be one, because it's code that we control.
+        assert len(classes) == 1, classes
+        cls = next(iter(classes))
+        return cls
+
+    def gather_annotation_classes(self):
+        return ValueSet([self._get_wrapped_value()])
+
+
+class Callable(BaseTypingInstance):
+    def py__call__(self, arguments):
+        """
+            def x() -> Callable[[Callable[..., _T]], _T]: ...
+        """
+        # The 0th index are the arguments.
+        try:
+            param_values = self._generics_manager[0]
+            result_values = self._generics_manager[1]
+        except IndexError:
+            debug.warning('Callable[...] defined without two arguments')
+            return NO_VALUES
+        else:
+            from jedi.inference.gradual.annotation import infer_return_for_callable
+            return infer_return_for_callable(arguments, param_values, result_values)
+
+
+class Tuple(BaseTypingInstance):
+    def _is_homogenous(self):
+        # To specify a variable-length tuple of homogeneous type, Tuple[T, ...]
+        # is used.
+        return self._generics_manager.is_homogenous_tuple()
+
+    def py__simple_getitem__(self, index):
+        if self._is_homogenous():
+            return self._generics_manager.get_index_and_execute(0)
+        else:
+            if isinstance(index, int):
+                return self._generics_manager.get_index_and_execute(index)
+
+            debug.dbg('The getitem type on Tuple was %s' % index)
+            return NO_VALUES
+
+    def py__iter__(self, contextualized_node=None):
+        if self._is_homogenous():
+            yield LazyKnownValues(self._generics_manager.get_index_and_execute(0))
+        else:
+            for v in self._generics_manager.to_tuple():
+                yield LazyKnownValues(v.execute_annotation())
+
+    def py__getitem__(self, index_value_set, contextualized_node):
+        if self._is_homogenous():
+            return self._generics_manager.get_index_and_execute(0)
+
+        return ValueSet.from_sets(
+            self._generics_manager.to_tuple()
+        ).execute_annotation()
+
+    def _get_wrapped_value(self):
+        tuple_, = self.inference_state.builtins_module \
+            .py__getattribute__('tuple').execute_annotation()
+        return tuple_
+
+    @property
+    def name(self):
+        return self._wrapped_value.name
+
+    def infer_type_vars(self, value_set):
+        # Circular
+        from jedi.inference.gradual.annotation import merge_pairwise_generics, merge_type_var_dicts
+
+        value_set = value_set.filter(
+            lambda x: x.py__name__().lower() == 'tuple',
+        )
+
+        if self._is_homogenous():
+            # The parameter annotation is of the form `Tuple[T, ...]`,
+            # so we treat the incoming tuple like a iterable sequence
+            # rather than a positional container of elements.
+            return self._class_value.get_generics()[0].infer_type_vars(
+                value_set.merge_types_of_iterate(),
+            )
+
+        else:
+            # The parameter annotation has only explicit type parameters
+            # (e.g: `Tuple[T]`, `Tuple[T, U]`, `Tuple[T, U, V]`, etc.) so we
+            # treat the incoming values as needing to match the annotation
+            # exactly, just as we would for non-tuple annotations.
+
+            type_var_dict = {}
+            for element in value_set:
+                try:
+                    method = element.get_annotated_class_object
+                except AttributeError:
+                    # This might still happen, because the tuple name matching
+                    # above is not 100% correct, so just catch the remaining
+                    # cases here.
+                    continue
+
+                py_class = method()
+                merge_type_var_dicts(
+                    type_var_dict,
+                    merge_pairwise_generics(self._class_value, py_class),
+                )
+
+            return type_var_dict
+
+
+class Generic(BaseTypingInstance):
+    pass
+
+
+class Protocol(BaseTypingInstance):
+    pass
+
+
+class AnyClass(BaseTypingValue):
+    def execute_annotation(self):
+        debug.warning('Used Any - returned no results')
+        return NO_VALUES
+
+
+class OverloadFunction(BaseTypingValue):
+    @repack_with_argument_clinic('func, /')
+    def py__call__(self, func_value_set):
+        # Just pass arguments through.
+        return func_value_set
+
+
+class NewTypeFunction(BaseTypingValue):
+    def py__call__(self, arguments):
+        ordered_args = arguments.unpack()
+        next(ordered_args, (None, None))
+        _, second_arg = next(ordered_args, (None, None))
+        if second_arg is None:
+            return NO_VALUES
+        return ValueSet(
+            NewType(
+                self.inference_state,
+                contextualized_node.context,
+                contextualized_node.node,
+                second_arg.infer(),
+            ) for contextualized_node in arguments.get_calling_nodes())
+
+
+class NewType(Value):
+    def __init__(self, inference_state, parent_context, tree_node, type_value_set):
+        super(NewType, self).__init__(inference_state, parent_context)
+        self._type_value_set = type_value_set
+        self.tree_node = tree_node
+
+    def py__class__(self):
+        c, = self._type_value_set.py__class__()
+        return c
+
+    def py__call__(self, arguments):
+        return self._type_value_set.execute_annotation()
+
+    @property
+    def name(self):
+        from jedi.inference.compiled.value import CompiledValueName
+        return CompiledValueName(self, 'NewType')
+
+
+class CastFunction(BaseTypingValue):
+    @repack_with_argument_clinic('type, object, /')
+    def py__call__(self, type_value_set, object_value_set):
+        return type_value_set.execute_annotation()
+
+
+class TypedDictClass(BaseTypingValue):
+    """
+    This class has no responsibilities and is just here to make sure that typed
+    dicts can be identified.
+    """
+
+
+class TypedDict(LazyValueWrapper):
+    """Represents the instance version of ``TypedDictClass``."""
+    def __init__(self, definition_class):
+        self.inference_state = definition_class.inference_state
+        self.parent_context = definition_class.parent_context
+        self.tree_node = definition_class.tree_node
+        self._definition_class = definition_class
+
+    @property
+    def name(self):
+        return ValueName(self, self.tree_node.name)
+
+    def py__simple_getitem__(self, index):
+        if isinstance(index, unicode):
+            return ValueSet.from_sets(
+                name.infer()
+                for filter in self._definition_class.get_filters(is_instance=True)
+                for name in filter.get(index)
+            )
+        return NO_VALUES
+
+    def get_key_values(self):
+        filtered_values = itertools.chain.from_iterable((
+            f.values()
+            for f in self._definition_class.get_filters(is_instance=True)
+        ))
+        return ValueSet({
+            create_simple_object(self.inference_state, v.string_name)
+            for v in filtered_values
+        })
+
+    def _get_wrapped_value(self):
+        d, = self.inference_state.builtins_module.py__getattribute__('dict')
+        result, = d.execute_with_values()
+        return result

venv/Lib/site-packages/jedi/inference/gradual/utils.py

@@ -0,0 +1,30 @@
+import os
+
+from jedi.inference.gradual.typeshed import TYPESHED_PATH, create_stub_module
+
+
+def load_proper_stub_module(inference_state, file_io, import_names, module_node):
+    """
+    This function is given a random .pyi file and should return the proper
+    module.
+    """
+    path = file_io.path
+    assert path.endswith('.pyi')
+    if path.startswith(TYPESHED_PATH):
+        # /foo/stdlib/3/os/__init__.pyi -> stdlib/3/os/__init__
+        rest = path[len(TYPESHED_PATH) + 1: -4]
+        split_paths = tuple(rest.split(os.path.sep))
+        # Remove the stdlib/3 or third_party/3.5 part
+        import_names = split_paths[2:]
+        if import_names[-1] == '__init__':
+            import_names = import_names[:-1]
+
+    if import_names is not None:
+        actual_value_set = inference_state.import_module(import_names, prefer_stubs=False)
+
+        stub = create_stub_module(
+            inference_state, actual_value_set, module_node, file_io, import_names
+        )
+        inference_state.stub_module_cache[import_names] = stub
+        return stub
+    return None