Uploaded Test files

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

@@ -0,0 +1,6 @@
+from jedi.inference.value.module import ModuleValue
+from jedi.inference.value.klass import ClassValue
+from jedi.inference.value.function import FunctionValue, \
+    MethodValue
+from jedi.inference.value.instance import AnonymousInstance, BoundMethod, \
+    CompiledInstance, AbstractInstanceValue, TreeInstance

venv/Lib/site-packages/jedi/inference/value/decorator.py

@@ -0,0 +1,21 @@
+'''
+Decorators are not really values, however we need some wrappers to improve
+docstrings and other things around decorators.
+'''
+
+from jedi.inference.base_value import ValueWrapper, ValueSet
+
+
+class Decoratee(ValueWrapper):
+    def __init__(self, wrapped_value, original_value):
+        super(Decoratee, self).__init__(wrapped_value)
+        self._original_value = original_value
+
+    def py__doc__(self):
+        return self._original_value.py__doc__()
+
+    def py__get__(self, instance, class_value):
+        return ValueSet(
+            Decoratee(v, self._original_value)
+            for v in self._wrapped_value.py__get__(instance, class_value)
+        )

venv/Lib/site-packages/jedi/inference/value/dynamic_arrays.py

@@ -0,0 +1,204 @@
+"""
+A module to deal with stuff like `list.append` and `set.add`.
+
+Array modifications
+*******************
+
+If the content of an array (``set``/``list``) is requested somewhere, the
+current module will be checked for appearances of ``arr.append``,
+``arr.insert``, etc.  If the ``arr`` name points to an actual array, the
+content will be added
+
+This can be really cpu intensive, as you can imagine. Because |jedi| has to
+follow **every** ``append`` and check whether it's the right array. However this
+works pretty good, because in *slow* cases, the recursion detector and other
+settings will stop this process.
+
+It is important to note that:
+
+1. Array modfications work only in the current module.
+2. Jedi only checks Array additions; ``list.pop``, etc are ignored.
+"""
+from jedi import debug
+from jedi import settings
+from jedi.inference import recursion
+from jedi.inference.base_value import ValueSet, NO_VALUES, HelperValueMixin, \
+    ValueWrapper
+from jedi.inference.lazy_value import LazyKnownValues
+from jedi.inference.helpers import infer_call_of_leaf
+from jedi.inference.cache import inference_state_method_cache
+
+_sentinel = object()
+
+
+def check_array_additions(context, sequence):
+    """ Just a mapper function for the internal _internal_check_array_additions """
+    if sequence.array_type not in ('list', 'set'):
+        # TODO also check for dict updates
+        return NO_VALUES
+
+    return _internal_check_array_additions(context, sequence)
+
+
+@inference_state_method_cache(default=NO_VALUES)
+@debug.increase_indent
+def _internal_check_array_additions(context, sequence):
+    """
+    Checks if a `Array` has "add" (append, insert, extend) statements:
+
+    >>> a = [""]
+    >>> a.append(1)
+    """
+    from jedi.inference import arguments
+
+    debug.dbg('Dynamic array search for %s' % sequence, color='MAGENTA')
+    module_context = context.get_root_context()
+    if not settings.dynamic_array_additions or module_context.is_compiled():
+        debug.dbg('Dynamic array search aborted.', color='MAGENTA')
+        return NO_VALUES
+
+    def find_additions(context, arglist, add_name):
+        params = list(arguments.TreeArguments(context.inference_state, context, arglist).unpack())
+        result = set()
+        if add_name in ['insert']:
+            params = params[1:]
+        if add_name in ['append', 'add', 'insert']:
+            for key, lazy_value in params:
+                result.add(lazy_value)
+        elif add_name in ['extend', 'update']:
+            for key, lazy_value in params:
+                result |= set(lazy_value.infer().iterate())
+        return result
+
+    temp_param_add, settings.dynamic_params_for_other_modules = \
+        settings.dynamic_params_for_other_modules, False
+
+    is_list = sequence.name.string_name == 'list'
+    search_names = (['append', 'extend', 'insert'] if is_list else ['add', 'update'])
+
+    added_types = set()
+    for add_name in search_names:
+        try:
+            possible_names = module_context.tree_node.get_used_names()[add_name]
+        except KeyError:
+            continue
+        else:
+            for name in possible_names:
+                value_node = context.tree_node
+                if not (value_node.start_pos < name.start_pos < value_node.end_pos):
+                    continue
+                trailer = name.parent
+                power = trailer.parent
+                trailer_pos = power.children.index(trailer)
+                try:
+                    execution_trailer = power.children[trailer_pos + 1]
+                except IndexError:
+                    continue
+                else:
+                    if execution_trailer.type != 'trailer' \
+                            or execution_trailer.children[0] != '(' \
+                            or execution_trailer.children[1] == ')':
+                        continue
+
+                random_context = context.create_context(name)
+
+                with recursion.execution_allowed(context.inference_state, power) as allowed:
+                    if allowed:
+                        found = infer_call_of_leaf(
+                            random_context,
+                            name,
+                            cut_own_trailer=True
+                        )
+                        if sequence in found:
+                            # The arrays match. Now add the results
+                            added_types |= find_additions(
+                                random_context,
+                                execution_trailer.children[1],
+                                add_name
+                            )
+
+    # reset settings
+    settings.dynamic_params_for_other_modules = temp_param_add
+    debug.dbg('Dynamic array result %s', added_types, color='MAGENTA')
+    return added_types
+
+
+def get_dynamic_array_instance(instance, arguments):
+    """Used for set() and list() instances."""
+    ai = _DynamicArrayAdditions(instance, arguments)
+    from jedi.inference import arguments
+    return arguments.ValuesArguments([ValueSet([ai])])
+
+
+class _DynamicArrayAdditions(HelperValueMixin):
+    """
+    Used for the usage of set() and list().
+    This is definitely a hack, but a good one :-)
+    It makes it possible to use set/list conversions.
+
+    This is not a proper context, because it doesn't have to be. It's not used
+    in the wild, it's just used within typeshed as an argument to `__init__`
+    for set/list and never used in any other place.
+    """
+    def __init__(self, instance, arguments):
+        self._instance = instance
+        self._arguments = arguments
+
+    def py__class__(self):
+        tuple_, = self._instance.inference_state.builtins_module.py__getattribute__('tuple')
+        return tuple_
+
+    def py__iter__(self, contextualized_node=None):
+        arguments = self._arguments
+        try:
+            _, lazy_value = next(arguments.unpack())
+        except StopIteration:
+            pass
+        else:
+            for lazy in lazy_value.infer().iterate():
+                yield lazy
+
+        from jedi.inference.arguments import TreeArguments
+        if isinstance(arguments, TreeArguments):
+            additions = _internal_check_array_additions(arguments.context, self._instance)
+            for addition in additions:
+                yield addition
+
+    def iterate(self, contextualized_node=None, is_async=False):
+        return self.py__iter__(contextualized_node)
+
+
+class _Modification(ValueWrapper):
+    def __init__(self, wrapped_value, assigned_values, contextualized_key):
+        super(_Modification, self).__init__(wrapped_value)
+        self._assigned_values = assigned_values
+        self._contextualized_key = contextualized_key
+
+    def py__getitem__(self, *args, **kwargs):
+        return self._wrapped_value.py__getitem__(*args, **kwargs) | self._assigned_values
+
+    def py__simple_getitem__(self, index):
+        actual = [
+            v.get_safe_value(_sentinel)
+            for v in self._contextualized_key.infer()
+        ]
+        if index in actual:
+            return self._assigned_values
+        return self._wrapped_value.py__simple_getitem__(index)
+
+
+class DictModification(_Modification):
+    def py__iter__(self, contextualized_node=None):
+        for lazy_context in self._wrapped_value.py__iter__(contextualized_node):
+            yield lazy_context
+        yield self._contextualized_key
+
+    def get_key_values(self):
+        return self._wrapped_value.get_key_values() | self._contextualized_key.infer()
+
+
+class ListModification(_Modification):
+    def py__iter__(self, contextualized_node=None):
+        for lazy_context in self._wrapped_value.py__iter__(contextualized_node):
+            yield lazy_context
+        yield LazyKnownValues(self._assigned_values)

venv/Lib/site-packages/jedi/inference/value/function.py

@@ -0,0 +1,470 @@
+from parso.python import tree
+
+from jedi._compatibility import use_metaclass
+from jedi import debug
+from jedi.inference.cache import inference_state_method_cache, CachedMetaClass
+from jedi.inference import compiled
+from jedi.inference import recursion
+from jedi.inference import docstrings
+from jedi.inference import flow_analysis
+from jedi.inference.signature import TreeSignature
+from jedi.inference.filters import ParserTreeFilter, FunctionExecutionFilter, \
+    AnonymousFunctionExecutionFilter
+from jedi.inference.names import ValueName, AbstractNameDefinition, \
+    AnonymousParamName, ParamName, NameWrapper
+from jedi.inference.base_value import ContextualizedNode, NO_VALUES, \
+    ValueSet, TreeValue, ValueWrapper
+from jedi.inference.lazy_value import LazyKnownValues, LazyKnownValue, \
+    LazyTreeValue
+from jedi.inference.context import ValueContext, TreeContextMixin
+from jedi.inference.value import iterable
+from jedi import parser_utils
+from jedi.inference.parser_cache import get_yield_exprs
+from jedi.inference.helpers import values_from_qualified_names
+from jedi.inference.gradual.generics import TupleGenericManager
+
+
+class LambdaName(AbstractNameDefinition):
+    string_name = '<lambda>'
+    api_type = u'function'
+
+    def __init__(self, lambda_value):
+        self._lambda_value = lambda_value
+        self.parent_context = lambda_value.parent_context
+
+    @property
+    def start_pos(self):
+        return self._lambda_value.tree_node.start_pos
+
+    def infer(self):
+        return ValueSet([self._lambda_value])
+
+
+class FunctionAndClassBase(TreeValue):
+    def get_qualified_names(self):
+        if self.parent_context.is_class():
+            n = self.parent_context.get_qualified_names()
+            if n is None:
+                # This means that the parent class lives within a function.
+                return None
+            return n + (self.py__name__(),)
+        elif self.parent_context.is_module():
+            return (self.py__name__(),)
+        else:
+            return None
+
+
+class FunctionMixin(object):
+    api_type = u'function'
+
+    def get_filters(self, origin_scope=None):
+        cls = self.py__class__()
+        for instance in cls.execute_with_values():
+            for filter in instance.get_filters(origin_scope=origin_scope):
+                yield filter
+
+    def py__get__(self, instance, class_value):
+        from jedi.inference.value.instance import BoundMethod
+        if instance is None:
+            # Calling the Foo.bar results in the original bar function.
+            return ValueSet([self])
+        return ValueSet([BoundMethod(instance, class_value.as_context(), self)])
+
+    def get_param_names(self):
+        return [AnonymousParamName(self, param.name)
+                for param in self.tree_node.get_params()]
+
+    @property
+    def name(self):
+        if self.tree_node.type == 'lambdef':
+            return LambdaName(self)
+        return ValueName(self, self.tree_node.name)
+
+    def is_function(self):
+        return True
+
+    def py__name__(self):
+        return self.name.string_name
+
+    def get_type_hint(self, add_class_info=True):
+        return_annotation = self.tree_node.annotation
+        if return_annotation is None:
+            def param_name_to_str(n):
+                s = n.string_name
+                annotation = n.infer().get_type_hint()
+                if annotation is not None:
+                    s += ': ' + annotation
+                if n.default_node is not None:
+                    s += '=' + n.default_node.get_code(include_prefix=False)
+                return s
+
+            function_execution = self.as_context()
+            result = function_execution.infer()
+            return_hint = result.get_type_hint()
+            body = self.py__name__() + '(%s)' % ', '.join([
+                param_name_to_str(n)
+                for n in function_execution.get_param_names()
+            ])
+            if return_hint is None:
+                return body
+        else:
+            return_hint = return_annotation.get_code(include_prefix=False)
+            body = self.py__name__() + self.tree_node.children[2].get_code(include_prefix=False)
+
+        return body + ' -> ' + return_hint
+
+    def py__call__(self, arguments):
+        function_execution = self.as_context(arguments)
+        return function_execution.infer()
+
+    def _as_context(self, arguments=None):
+        if arguments is None:
+            return AnonymousFunctionExecution(self)
+        return FunctionExecutionContext(self, arguments)
+
+    def get_signatures(self):
+        return [TreeSignature(f) for f in self.get_signature_functions()]
+
+
+class FunctionValue(use_metaclass(CachedMetaClass, FunctionMixin, FunctionAndClassBase)):
+    @classmethod
+    def from_context(cls, context, tree_node):
+        def create(tree_node):
+            if context.is_class():
+                return MethodValue(
+                    context.inference_state,
+                    context,
+                    parent_context=parent_context,
+                    tree_node=tree_node
+                )
+            else:
+                return cls(
+                    context.inference_state,
+                    parent_context=parent_context,
+                    tree_node=tree_node
+                )
+
+        overloaded_funcs = list(_find_overload_functions(context, tree_node))
+
+        parent_context = context
+        while parent_context.is_class() or parent_context.is_instance():
+            parent_context = parent_context.parent_context
+
+        function = create(tree_node)
+
+        if overloaded_funcs:
+            return OverloadedFunctionValue(
+                function,
+                # Get them into the correct order: lower line first.
+                list(reversed([create(f) for f in overloaded_funcs]))
+            )
+        return function
+
+    def py__class__(self):
+        c, = values_from_qualified_names(self.inference_state, u'types', u'FunctionType')
+        return c
+
+    def get_default_param_context(self):
+        return self.parent_context
+
+    def get_signature_functions(self):
+        return [self]
+
+
+class FunctionNameInClass(NameWrapper):
+    def __init__(self, class_context, name):
+        super(FunctionNameInClass, self).__init__(name)
+        self._class_context = class_context
+
+    def get_defining_qualified_value(self):
+        return self._class_context.get_value()  # Might be None.
+
+
+class MethodValue(FunctionValue):
+    def __init__(self, inference_state, class_context, *args, **kwargs):
+        super(MethodValue, self).__init__(inference_state, *args, **kwargs)
+        self.class_context = class_context
+
+    def get_default_param_context(self):
+        return self.class_context
+
+    def get_qualified_names(self):
+        # Need to implement this, because the parent value of a method
+        # value is not the class value but the module.
+        names = self.class_context.get_qualified_names()
+        if names is None:
+            return None
+        return names + (self.py__name__(),)
+
+    @property
+    def name(self):
+        return FunctionNameInClass(self.class_context, super(MethodValue, self).name)
+
+
+class BaseFunctionExecutionContext(ValueContext, TreeContextMixin):
+    def infer_annotations(self):
+        raise NotImplementedError
+
+    @inference_state_method_cache(default=NO_VALUES)
+    @recursion.execution_recursion_decorator()
+    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:
+            if check_yields:
+                value_set |= ValueSet.from_sets(
+                    lazy_value.infer()
+                    for lazy_value in self._get_yield_lazy_value(r)
+                )
+            else:
+                check = flow_analysis.reachability_check(self, funcdef, r)
+                if check is flow_analysis.UNREACHABLE:
+                    debug.dbg('Return unreachable: %s', 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 _get_yield_lazy_value(self, yield_expr):
+        if yield_expr.type == 'keyword':
+            # `yield` just yields None.
+            ctx = compiled.builtin_from_name(self.inference_state, u'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])
+            for lazy_value in cn.infer().iterate(cn):
+                yield lazy_value
+        else:
+            yield LazyTreeValue(self, node)
+
+    @recursion.execution_recursion_decorator(default=iter([]))
+    def get_yield_lazy_values(self, is_async=False):
+        # TODO: if is_async, wrap yield statements in Awaitable/async_generator_asend
+        for_parents = [(y, tree.search_ancestor(y, 'for_stmt', 'funcdef',
+                                                'while_stmt', 'if_stmt'))
+                       for y in get_yield_exprs(self.inference_state, self.tree_node)]
+
+        # Calculate if the yields are placed within the same for loop.
+        yields_order = []
+        last_for_stmt = None
+        for yield_, for_stmt in for_parents:
+            # For really simple for loops we can predict the order. Otherwise
+            # we just ignore it.
+            parent = for_stmt.parent
+            if parent.type == 'suite':
+                parent = parent.parent
+            if for_stmt.type == 'for_stmt' and parent == self.tree_node \
+                    and parser_utils.for_stmt_defines_one_name(for_stmt):  # Simplicity for now.
+                if for_stmt == last_for_stmt:
+                    yields_order[-1][1].append(yield_)
+                else:
+                    yields_order.append((for_stmt, [yield_]))
+            elif for_stmt == self.tree_node:
+                yields_order.append((None, [yield_]))
+            else:
+                types = self.get_return_values(check_yields=True)
+                if types:
+                    yield LazyKnownValues(types, min=0, max=float('inf'))
+                return
+            last_for_stmt = for_stmt
+
+        for for_stmt, yields in yields_order:
+            if for_stmt is None:
+                # No for_stmt, just normal yields.
+                for yield_ in yields:
+                    for result in self._get_yield_lazy_value(yield_):
+                        yield result
+            else:
+                input_node = for_stmt.get_testlist()
+                cn = ContextualizedNode(self, input_node)
+                ordered = cn.infer().iterate(cn)
+                ordered = list(ordered)
+                for lazy_value in ordered:
+                    dct = {str(for_stmt.children[1].value): lazy_value.infer()}
+                    with self.predefine_names(for_stmt, dct):
+                        for yield_in_same_for_stmt in yields:
+                            for result in self._get_yield_lazy_value(yield_in_same_for_stmt):
+                                yield result
+
+    def merge_yield_values(self, is_async=False):
+        return ValueSet.from_sets(
+            lazy_value.infer()
+            for lazy_value in self.get_yield_lazy_values()
+        )
+
+    def is_generator(self):
+        return bool(get_yield_exprs(self.inference_state, self.tree_node))
+
+    def infer(self):
+        """
+        Created to be used by inheritance.
+        """
+        inference_state = self.inference_state
+        is_coroutine = self.tree_node.parent.type in ('async_stmt', 'async_funcdef')
+        from jedi.inference.gradual.base import GenericClass
+
+        if is_coroutine:
+            if self.is_generator():
+                if inference_state.environment.version_info < (3, 6):
+                    return NO_VALUES
+                async_generator_classes = inference_state.typing_module \
+                    .py__getattribute__('AsyncGenerator')
+
+                yield_values = self.merge_yield_values(is_async=True)
+                # The contravariant doesn't seem to be defined.
+                generics = (yield_values.py__class__(), NO_VALUES)
+                return ValueSet(
+                    # In Python 3.6 AsyncGenerator is still a class.
+                    GenericClass(c, TupleGenericManager(generics))
+                    for c in async_generator_classes
+                ).execute_annotation()
+            else:
+                if inference_state.environment.version_info < (3, 5):
+                    return NO_VALUES
+                async_classes = inference_state.typing_module.py__getattribute__('Coroutine')
+                return_values = self.get_return_values()
+                # Only the first generic is relevant.
+                generics = (return_values.py__class__(), NO_VALUES, NO_VALUES)
+                return ValueSet(
+                    GenericClass(c, TupleGenericManager(generics)) for c in async_classes
+                ).execute_annotation()
+        else:
+            if self.is_generator():
+                return ValueSet([iterable.Generator(inference_state, self)])
+            else:
+                return self.get_return_values()
+
+
+class FunctionExecutionContext(BaseFunctionExecutionContext):
+    def __init__(self, function_value, arguments):
+        super(FunctionExecutionContext, self).__init__(function_value)
+        self._arguments = arguments
+
+    def get_filters(self, until_position=None, origin_scope=None):
+        yield FunctionExecutionFilter(
+            self, self._value,
+            until_position=until_position,
+            origin_scope=origin_scope,
+            arguments=self._arguments
+        )
+
+    def infer_annotations(self):
+        from jedi.inference.gradual.annotation import infer_return_types
+        return infer_return_types(self._value, self._arguments)
+
+    def get_param_names(self):
+        return [
+            ParamName(self._value, param.name, self._arguments)
+            for param in self._value.tree_node.get_params()
+        ]
+
+
+class AnonymousFunctionExecution(BaseFunctionExecutionContext):
+    def infer_annotations(self):
+        # I don't think inferring anonymous executions is a big thing.
+        # Anonymous contexts are mostly there for the user to work in. ~ dave
+        return NO_VALUES
+
+    def get_filters(self, until_position=None, origin_scope=None):
+        yield AnonymousFunctionExecutionFilter(
+            self, self._value,
+            until_position=until_position,
+            origin_scope=origin_scope,
+        )
+
+    def get_param_names(self):
+        return self._value.get_param_names()
+
+
+class OverloadedFunctionValue(FunctionMixin, ValueWrapper):
+    def __init__(self, function, overloaded_functions):
+        super(OverloadedFunctionValue, self).__init__(function)
+        self._overloaded_functions = overloaded_functions
+
+    def py__call__(self, arguments):
+        debug.dbg("Execute overloaded function %s", self._wrapped_value, color='BLUE')
+        function_executions = []
+        for signature in self.get_signatures():
+            function_execution = signature.value.as_context(arguments)
+            function_executions.append(function_execution)
+            if signature.matches_signature(arguments):
+                return function_execution.infer()
+
+        if self.inference_state.is_analysis:
+            # In this case we want precision.
+            return NO_VALUES
+        return ValueSet.from_sets(fe.infer() for fe in function_executions)
+
+    def get_signature_functions(self):
+        return self._overloaded_functions
+
+    def get_type_hint(self, add_class_info=True):
+        return 'Union[%s]' % ', '.join(f.get_type_hint() for f in self._overloaded_functions)
+
+
+def _find_overload_functions(context, tree_node):
+    def _is_overload_decorated(funcdef):
+        if funcdef.parent.type == 'decorated':
+            decorators = funcdef.parent.children[0]
+            if decorators.type == 'decorator':
+                decorators = [decorators]
+            else:
+                decorators = decorators.children
+            for decorator in decorators:
+                dotted_name = decorator.children[1]
+                if dotted_name.type == 'name' and dotted_name.value == 'overload':
+                    # TODO check with values if it's the right overload
+                    return True
+        return False
+
+    if tree_node.type == 'lambdef':
+        return
+
+    if _is_overload_decorated(tree_node):
+        yield tree_node
+
+    while True:
+        filter = ParserTreeFilter(
+            context,
+            until_position=tree_node.start_pos
+        )
+        names = filter.get(tree_node.name.value)
+        assert isinstance(names, list)
+        if not names:
+            break
+
+        found = False
+        for name in names:
+            funcdef = name.tree_name.parent
+            if funcdef.type == 'funcdef' and _is_overload_decorated(funcdef):
+                tree_node = funcdef
+                found = True
+                yield funcdef
+
+        if not found:
+            break

venv/Lib/site-packages/jedi/inference/value/instance.py

@@ -0,0 +1,625 @@
+from abc import abstractproperty
+
+from parso.python.tree import search_ancestor
+
+from jedi import debug
+from jedi import settings
+from jedi.inference import compiled
+from jedi.inference.compiled.value import CompiledValueFilter
+from jedi.inference.helpers import values_from_qualified_names, is_big_annoying_library
+from jedi.inference.filters import AbstractFilter, AnonymousFunctionExecutionFilter
+from jedi.inference.names import ValueName, TreeNameDefinition, ParamName, \
+    NameWrapper
+from jedi.inference.base_value import Value, NO_VALUES, ValueSet, \
+    iterator_to_value_set, ValueWrapper
+from jedi.inference.lazy_value import LazyKnownValue, LazyKnownValues
+from jedi.inference.cache import inference_state_method_cache
+from jedi.inference.arguments import ValuesArguments, TreeArgumentsWrapper
+from jedi.inference.value.function import \
+    FunctionValue, FunctionMixin, OverloadedFunctionValue, \
+    BaseFunctionExecutionContext, FunctionExecutionContext, FunctionNameInClass
+from jedi.inference.value.klass import ClassFilter
+from jedi.inference.value.dynamic_arrays import get_dynamic_array_instance
+from jedi.parser_utils import function_is_staticmethod, function_is_classmethod
+
+
+class InstanceExecutedParamName(ParamName):
+    def __init__(self, instance, function_value, tree_name):
+        super(InstanceExecutedParamName, self).__init__(
+            function_value, tree_name, arguments=None)
+        self._instance = instance
+
+    def infer(self):
+        return ValueSet([self._instance])
+
+    def matches_signature(self):
+        return True
+
+
+class AnonymousMethodExecutionFilter(AnonymousFunctionExecutionFilter):
+    def __init__(self, instance, *args, **kwargs):
+        super(AnonymousMethodExecutionFilter, self).__init__(*args, **kwargs)
+        self._instance = instance
+
+    def _convert_param(self, param, name):
+        if param.position_index == 0:
+            if function_is_classmethod(self._function_value.tree_node):
+                return InstanceExecutedParamName(
+                    self._instance.py__class__(),
+                    self._function_value,
+                    name
+                )
+            elif not function_is_staticmethod(self._function_value.tree_node):
+                return InstanceExecutedParamName(
+                    self._instance,
+                    self._function_value,
+                    name
+                )
+        return super(AnonymousMethodExecutionFilter, self)._convert_param(param, name)
+
+
+class AnonymousMethodExecutionContext(BaseFunctionExecutionContext):
+    def __init__(self, instance, value):
+        super(AnonymousMethodExecutionContext, self).__init__(value)
+        self.instance = instance
+
+    def get_filters(self, until_position=None, origin_scope=None):
+        yield AnonymousMethodExecutionFilter(
+            self.instance, self, self._value,
+            until_position=until_position,
+            origin_scope=origin_scope,
+        )
+
+    def get_param_names(self):
+        param_names = list(self._value.get_param_names())
+        # set the self name
+        param_names[0] = InstanceExecutedParamName(
+            self.instance,
+            self._value,
+            param_names[0].tree_name
+        )
+        return param_names
+
+
+class MethodExecutionContext(FunctionExecutionContext):
+    def __init__(self, instance, *args, **kwargs):
+        super(MethodExecutionContext, self).__init__(*args, **kwargs)
+        self.instance = instance
+
+
+class AbstractInstanceValue(Value):
+    api_type = u'instance'
+
+    def __init__(self, inference_state, parent_context, class_value):
+        super(AbstractInstanceValue, self).__init__(inference_state, parent_context)
+        # Generated instances are classes that are just generated by self
+        # (No arguments) used.
+        self.class_value = class_value
+
+    def is_instance(self):
+        return True
+
+    def get_qualified_names(self):
+        return self.class_value.get_qualified_names()
+
+    def get_annotated_class_object(self):
+        return self.class_value  # This is the default.
+
+    def py__class__(self):
+        return self.class_value
+
+    def py__bool__(self):
+        # Signalize that we don't know about the bool type.
+        return None
+
+    @abstractproperty
+    def name(self):
+        raise NotImplementedError
+
+    def get_signatures(self):
+        call_funcs = self.py__getattribute__('__call__').py__get__(self, self.class_value)
+        return [s.bind(self) for s in call_funcs.get_signatures()]
+
+    def get_function_slot_names(self, name):
+        # Searches for Python functions in classes.
+        return []
+
+    def execute_function_slots(self, names, *inferred_args):
+        return ValueSet.from_sets(
+            name.infer().execute_with_values(*inferred_args)
+            for name in names
+        )
+
+    def get_type_hint(self, add_class_info=True):
+        return self.py__name__()
+
+    def __repr__(self):
+        return "<%s of %s>" % (self.__class__.__name__, self.class_value)
+
+
+class CompiledInstance(AbstractInstanceValue):
+    # This is not really a compiled class, it's just an instance from a
+    # compiled class.
+    def __init__(self, inference_state, parent_context, class_value, arguments):
+        super(CompiledInstance, self).__init__(inference_state, parent_context,
+                                               class_value)
+        self._arguments = arguments
+
+    def get_filters(self, origin_scope=None, include_self_names=True):
+        class_value = self.get_annotated_class_object()
+        class_filters = class_value.get_filters(
+            origin_scope=origin_scope,
+            is_instance=True,
+        )
+        for f in class_filters:
+            yield CompiledInstanceClassFilter(self, f)
+
+    @property
+    def name(self):
+        return compiled.CompiledValueName(self, self.class_value.name.string_name)
+
+    def is_stub(self):
+        return False
+
+
+class _BaseTreeInstance(AbstractInstanceValue):
+    @property
+    def array_type(self):
+        name = self.class_value.py__name__()
+        if name in ['list', 'set', 'dict'] \
+                and self.parent_context.get_root_context().is_builtins_module():
+            return name
+        return None
+
+    @property
+    def name(self):
+        return ValueName(self, self.class_value.name.tree_name)
+
+    def get_filters(self, origin_scope=None, include_self_names=True):
+        class_value = self.get_annotated_class_object()
+        if include_self_names:
+            for cls in class_value.py__mro__():
+                if not cls.is_compiled():
+                    # In this case we're excluding compiled objects that are
+                    # not fake objects. It doesn't make sense for normal
+                    # compiled objects to search for self variables.
+                    yield SelfAttributeFilter(self, class_value, cls.as_context(), origin_scope)
+
+        class_filters = class_value.get_filters(
+            origin_scope=origin_scope,
+            is_instance=True,
+        )
+        for f in class_filters:
+            if isinstance(f, ClassFilter):
+                yield InstanceClassFilter(self, f)
+            elif isinstance(f, CompiledValueFilter):
+                yield CompiledInstanceClassFilter(self, f)
+            else:
+                # Propably from the metaclass.
+                yield f
+
+    @inference_state_method_cache()
+    def create_instance_context(self, class_context, node):
+        new = node
+        while True:
+            func_node = new
+            new = search_ancestor(new, 'funcdef', 'classdef')
+            if class_context.tree_node is new:
+                func = FunctionValue.from_context(class_context, func_node)
+                bound_method = BoundMethod(self, class_context, func)
+                if func_node.name.value == '__init__':
+                    context = bound_method.as_context(self._arguments)
+                else:
+                    context = bound_method.as_context()
+                break
+        return context.create_context(node)
+
+    def py__getattribute__alternatives(self, string_name):
+        '''
+        Since nothing was inferred, now check the __getattr__ and
+        __getattribute__ methods. Stubs don't need to be checked, because
+        they don't contain any logic.
+        '''
+        if self.is_stub():
+            return NO_VALUES
+
+        name = compiled.create_simple_object(self.inference_state, string_name)
+
+        # This is a little bit special. `__getattribute__` is in Python
+        # executed before `__getattr__`. But: I know no use case, where
+        # this could be practical and where Jedi would return wrong types.
+        # If you ever find something, let me know!
+        # We are inversing this, because a hand-crafted `__getattribute__`
+        # could still call another hand-crafted `__getattr__`, but not the
+        # other way around.
+        if is_big_annoying_library(self.parent_context):
+            return NO_VALUES
+        names = (self.get_function_slot_names(u'__getattr__')
+                 or self.get_function_slot_names(u'__getattribute__'))
+        return self.execute_function_slots(names, name)
+
+    def py__getitem__(self, index_value_set, contextualized_node):
+        names = self.get_function_slot_names(u'__getitem__')
+        if not names:
+            return super(_BaseTreeInstance, self).py__getitem__(
+                index_value_set,
+                contextualized_node,
+            )
+
+        args = ValuesArguments([index_value_set])
+        return ValueSet.from_sets(name.infer().execute(args) for name in names)
+
+    def py__iter__(self, contextualized_node=None):
+        iter_slot_names = self.get_function_slot_names(u'__iter__')
+        if not iter_slot_names:
+            return super(_BaseTreeInstance, self).py__iter__(contextualized_node)
+
+        def iterate():
+            for generator in self.execute_function_slots(iter_slot_names):
+                for lazy_value in generator.py__next__(contextualized_node):
+                    yield lazy_value
+        return iterate()
+
+    def py__next__(self, contextualized_node=None):
+        # `__next__` logic.
+        if self.inference_state.environment.version_info.major == 2:
+            name = u'next'
+        else:
+            name = u'__next__'
+        next_slot_names = self.get_function_slot_names(name)
+        if next_slot_names:
+            yield LazyKnownValues(
+                self.execute_function_slots(next_slot_names)
+            )
+        else:
+            debug.warning('Instance has no __next__ function in %s.', self)
+
+    def py__call__(self, arguments):
+        names = self.get_function_slot_names(u'__call__')
+        if not names:
+            # Means the Instance is not callable.
+            return super(_BaseTreeInstance, self).py__call__(arguments)
+
+        return ValueSet.from_sets(name.infer().execute(arguments) for name in 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(u'__get__')
+        if names:
+            if instance is None:
+                instance = compiled.builtin_from_name(self.inference_state, u'None')
+            return self.execute_function_slots(names, instance, class_value)
+        else:
+            return ValueSet([self])
+
+    def get_function_slot_names(self, name):
+        # Python classes don't look at the dictionary of the instance when
+        # looking up `__call__`. This is something that has to do with Python's
+        # internal slot system (note: not __slots__, but C slots).
+        for filter in self.get_filters(include_self_names=False):
+            names = filter.get(name)
+            if names:
+                return names
+        return []
+
+
+class TreeInstance(_BaseTreeInstance):
+    def __init__(self, inference_state, parent_context, class_value, arguments):
+        # I don't think that dynamic append lookups should happen here. That
+        # sounds more like something that should go to py__iter__.
+        if class_value.py__name__() in ['list', 'set'] \
+                and parent_context.get_root_context().is_builtins_module():
+            # compare the module path with the builtin name.
+            if settings.dynamic_array_additions:
+                arguments = get_dynamic_array_instance(self, arguments)
+
+        super(TreeInstance, self).__init__(inference_state, parent_context, class_value)
+        self._arguments = arguments
+        self.tree_node = class_value.tree_node
+
+    # This can recurse, if the initialization of the class includes a reference
+    # to itself.
+    @inference_state_method_cache(default=None)
+    def _get_annotated_class_object(self):
+        from jedi.inference.gradual.annotation import py__annotations__, \
+            infer_type_vars_for_execution
+
+        args = InstanceArguments(self, self._arguments)
+        for signature in self.class_value.py__getattribute__('__init__').get_signatures():
+            # Just take the first result, it should always be one, because we
+            # control the typeshed code.
+            funcdef = signature.value.tree_node
+            if funcdef is None or funcdef.type != 'funcdef' \
+                    or not signature.matches_signature(args):
+                # First check if the signature even matches, if not we don't
+                # need to infer anything.
+                continue
+            bound_method = BoundMethod(self, self.class_value.as_context(), signature.value)
+            all_annotations = py__annotations__(funcdef)
+            type_var_dict = infer_type_vars_for_execution(bound_method, args, all_annotations)
+            if type_var_dict:
+                defined, = self.class_value.define_generics(
+                    infer_type_vars_for_execution(signature.value, args, all_annotations),
+                )
+                debug.dbg('Inferred instance value as %s', defined, color='BLUE')
+                return defined
+        return None
+
+    def get_annotated_class_object(self):
+        return self._get_annotated_class_object() or self.class_value
+
+    def get_key_values(self):
+        values = NO_VALUES
+        if self.array_type == 'dict':
+            for i, (key, instance) in enumerate(self._arguments.unpack()):
+                if key is None and i == 0:
+                    values |= ValueSet.from_sets(
+                        v.get_key_values()
+                        for v in instance.infer()
+                        if v.array_type == 'dict'
+                    )
+                if key:
+                    values |= ValueSet([compiled.create_simple_object(
+                        self.inference_state,
+                        key,
+                    )])
+
+        return values
+
+    def py__simple_getitem__(self, index):
+        if self.array_type == 'dict':
+            # Logic for dict({'foo': bar}) and dict(foo=bar)
+            # reversed, because:
+            # >>> dict({'a': 1}, a=3)
+            # {'a': 3}
+            # TODO tuple initializations
+            # >>> dict([('a', 4)])
+            # {'a': 4}
+            for key, lazy_context in reversed(list(self._arguments.unpack())):
+                if key is None:
+                    values = ValueSet.from_sets(
+                        dct_value.py__simple_getitem__(index)
+                        for dct_value in lazy_context.infer()
+                        if dct_value.array_type == 'dict'
+                    )
+                    if values:
+                        return values
+                else:
+                    if key == index:
+                        return lazy_context.infer()
+        return super(TreeInstance, self).py__simple_getitem__(index)
+
+    def __repr__(self):
+        return "<%s of %s(%s)>" % (self.__class__.__name__, self.class_value,
+                                   self._arguments)
+
+
+class AnonymousInstance(_BaseTreeInstance):
+    _arguments = None
+
+
+class CompiledInstanceName(compiled.CompiledName):
+    def __init__(self, inference_state, instance, klass, name):
+        parent_value = klass.parent_context.get_value()
+        assert parent_value is not None, "How? Please reproduce and report"
+        super(CompiledInstanceName, self).__init__(
+            inference_state,
+            parent_value,
+            name.string_name
+        )
+        self._instance = instance
+        self._class_member_name = name
+
+    @iterator_to_value_set
+    def infer(self):
+        for result_value in self._class_member_name.infer():
+            if result_value.api_type == 'function':
+                yield CompiledBoundMethod(result_value)
+            else:
+                yield result_value
+
+
+class CompiledInstanceClassFilter(AbstractFilter):
+    def __init__(self, instance, f):
+        self._instance = instance
+        self._class_filter = f
+
+    def get(self, name):
+        return self._convert(self._class_filter.get(name))
+
+    def values(self):
+        return self._convert(self._class_filter.values())
+
+    def _convert(self, names):
+        klass = self._class_filter.compiled_value
+        return [
+            CompiledInstanceName(self._instance.inference_state, self._instance, klass, n)
+            for n in names
+        ]
+
+
+class BoundMethod(FunctionMixin, ValueWrapper):
+    def __init__(self, instance, class_context, function):
+        super(BoundMethod, self).__init__(function)
+        self.instance = instance
+        self._class_context = class_context
+
+    def is_bound_method(self):
+        return True
+
+    @property
+    def name(self):
+        return FunctionNameInClass(
+            self._class_context,
+            super(BoundMethod, self).name
+        )
+
+    def py__class__(self):
+        c, = values_from_qualified_names(self.inference_state, u'types', u'MethodType')
+        return c
+
+    def _get_arguments(self, arguments):
+        assert arguments is not None
+        return InstanceArguments(self.instance, arguments)
+
+    def _as_context(self, arguments=None):
+        if arguments is None:
+            return AnonymousMethodExecutionContext(self.instance, self)
+
+        arguments = self._get_arguments(arguments)
+        return MethodExecutionContext(self.instance, self, arguments)
+
+    def py__call__(self, arguments):
+        if isinstance(self._wrapped_value, OverloadedFunctionValue):
+            return self._wrapped_value.py__call__(self._get_arguments(arguments))
+
+        function_execution = self.as_context(arguments)
+        return function_execution.infer()
+
+    def get_signature_functions(self):
+        return [
+            BoundMethod(self.instance, self._class_context, f)
+            for f in self._wrapped_value.get_signature_functions()
+        ]
+
+    def get_signatures(self):
+        return [sig.bind(self) for sig in super(BoundMethod, self).get_signatures()]
+
+    def __repr__(self):
+        return '<%s: %s>' % (self.__class__.__name__, self._wrapped_value)
+
+
+class CompiledBoundMethod(ValueWrapper):
+    def is_bound_method(self):
+        return True
+
+    def get_signatures(self):
+        return [sig.bind(self) for sig in self._wrapped_value.get_signatures()]
+
+
+class SelfName(TreeNameDefinition):
+    """
+    This name calculates the parent_context lazily.
+    """
+    def __init__(self, instance, class_context, tree_name):
+        self._instance = instance
+        self.class_context = class_context
+        self.tree_name = tree_name
+
+    @property
+    def parent_context(self):
+        return self._instance.create_instance_context(self.class_context, self.tree_name)
+
+    def get_defining_qualified_value(self):
+        return self._instance
+
+
+class LazyInstanceClassName(NameWrapper):
+    def __init__(self, instance, class_member_name):
+        super(LazyInstanceClassName, self).__init__(class_member_name)
+        self._instance = instance
+
+    @iterator_to_value_set
+    def infer(self):
+        for result_value in self._wrapped_name.infer():
+            for c in result_value.py__get__(self._instance, self._instance.py__class__()):
+                yield c
+
+    def get_signatures(self):
+        return self.infer().get_signatures()
+
+    def get_defining_qualified_value(self):
+        return self._instance
+
+
+class InstanceClassFilter(AbstractFilter):
+    """
+    This filter is special in that it uses the class filter and wraps the
+    resulting names in LazyInstanceClassName. The idea is that the class name
+    filtering can be very flexible and always be reflected in instances.
+    """
+    def __init__(self, instance, class_filter):
+        self._instance = instance
+        self._class_filter = class_filter
+
+    def get(self, name):
+        return self._convert(self._class_filter.get(name))
+
+    def values(self):
+        return self._convert(self._class_filter.values())
+
+    def _convert(self, names):
+        return [
+            LazyInstanceClassName(self._instance, n)
+            for n in names
+        ]
+
+    def __repr__(self):
+        return '<%s for %s>' % (self.__class__.__name__, self._class_filter)
+
+
+class SelfAttributeFilter(ClassFilter):
+    """
+    This class basically filters all the use cases where `self.*` was assigned.
+    """
+    def __init__(self, instance, instance_class, node_context, origin_scope):
+        super(SelfAttributeFilter, self).__init__(
+            class_value=instance_class,
+            node_context=node_context,
+            origin_scope=origin_scope,
+            is_instance=True,
+        )
+        self._instance = instance
+
+    def _filter(self, names):
+        start, end = self._parser_scope.start_pos, self._parser_scope.end_pos
+        names = [n for n in names if start < n.start_pos < end]
+        return self._filter_self_names(names)
+
+    def _filter_self_names(self, names):
+        for name in names:
+            trailer = name.parent
+            if trailer.type == 'trailer' \
+                    and len(trailer.parent.children) == 2 \
+                    and trailer.children[0] == '.':
+                if name.is_definition() and self._access_possible(name):
+                    # TODO filter non-self assignments instead of this bad
+                    #      filter.
+                    if self._is_in_right_scope(trailer.parent.children[0], name):
+                        yield name
+
+    def _is_in_right_scope(self, self_name, name):
+        self_context = self._node_context.create_context(self_name)
+        names = self_context.goto(self_name, position=self_name.start_pos)
+        return any(
+            n.api_type == 'param'
+            and n.tree_name.get_definition().position_index == 0
+            and n.parent_context.tree_node is self._parser_scope
+            for n in names
+        )
+
+    def _convert_names(self, names):
+        return [SelfName(self._instance, self._node_context, name) for name in names]
+
+    def _check_flows(self, names):
+        return names
+
+
+class InstanceArguments(TreeArgumentsWrapper):
+    def __init__(self, instance, arguments):
+        super(InstanceArguments, self).__init__(arguments)
+        self.instance = instance
+
+    def unpack(self, func=None):
+        yield None, LazyKnownValue(self.instance)
+        for values in self._wrapped_arguments.unpack(func):
+            yield values

venv/Lib/site-packages/jedi/inference/value/iterable.py

@@ -0,0 +1,673 @@
+"""
+Contains all classes and functions to deal with lists, dicts, generators and
+iterators in general.
+"""
+import sys
+
+from jedi._compatibility import force_unicode, is_py3
+from jedi.inference import compiled
+from jedi.inference import analysis
+from jedi.inference.lazy_value import LazyKnownValue, LazyKnownValues, \
+    LazyTreeValue
+from jedi.inference.helpers import get_int_or_none, is_string, \
+    reraise_getitem_errors, SimpleGetItemNotFound
+from jedi.inference.utils import safe_property, to_list
+from jedi.inference.cache import inference_state_method_cache
+from jedi.inference.filters import LazyAttributeOverwrite, publish_method
+from jedi.inference.base_value import ValueSet, Value, NO_VALUES, \
+    ContextualizedNode, iterate_values, sentinel, \
+    LazyValueWrapper
+from jedi.parser_utils import get_sync_comp_fors
+from jedi.inference.context import CompForContext
+from jedi.inference.value.dynamic_arrays import check_array_additions
+
+
+class IterableMixin(object):
+    def py__next__(self, contextualized_node=None):
+        return self.py__iter__(contextualized_node)
+
+    def py__stop_iteration_returns(self):
+        return ValueSet([compiled.builtin_from_name(self.inference_state, u'None')])
+
+    # At the moment, safe values are simple values like "foo", 1 and not
+    # lists/dicts. Therefore as a small speed optimization we can just do the
+    # default instead of resolving the lazy wrapped values, that are just
+    # doing this in the end as well.
+    # This mostly speeds up patterns like `sys.version_info >= (3, 0)` in
+    # typeshed.
+    if sys.version_info[0] == 2:
+        # Python 2...........
+        def get_safe_value(self, default=sentinel):
+            if default is sentinel:
+                raise ValueError("There exists no safe value for value %s" % self)
+            return default
+    else:
+        get_safe_value = Value.get_safe_value
+
+
+class GeneratorBase(LazyAttributeOverwrite, IterableMixin):
+    array_type = None
+
+    def _get_wrapped_value(self):
+        instance, = self._get_cls().execute_annotation()
+        return instance
+
+    def _get_cls(self):
+        generator, = self.inference_state.typing_module.py__getattribute__('Generator')
+        return generator
+
+    def py__bool__(self):
+        return True
+
+    @publish_method('__iter__')
+    def _iter(self, arguments):
+        return ValueSet([self])
+
+    @publish_method('send')
+    @publish_method('next', python_version_match=2)
+    @publish_method('__next__', python_version_match=3)
+    def _next(self, arguments):
+        return ValueSet.from_sets(lazy_value.infer() for lazy_value in self.py__iter__())
+
+    def py__stop_iteration_returns(self):
+        return ValueSet([compiled.builtin_from_name(self.inference_state, u'None')])
+
+    @property
+    def name(self):
+        return compiled.CompiledValueName(self, 'Generator')
+
+    def get_annotated_class_object(self):
+        from jedi.inference.gradual.generics import TupleGenericManager
+        gen_values = self.merge_types_of_iterate().py__class__()
+        gm = TupleGenericManager((gen_values, NO_VALUES, NO_VALUES))
+        return self._get_cls().with_generics(gm)
+
+
+class Generator(GeneratorBase):
+    """Handling of `yield` functions."""
+    def __init__(self, inference_state, func_execution_context):
+        super(Generator, self).__init__(inference_state)
+        self._func_execution_context = func_execution_context
+
+    def py__iter__(self, contextualized_node=None):
+        iterators = self._func_execution_context.infer_annotations()
+        if iterators:
+            return iterators.iterate(contextualized_node)
+        return self._func_execution_context.get_yield_lazy_values()
+
+    def py__stop_iteration_returns(self):
+        return self._func_execution_context.get_return_values()
+
+    def __repr__(self):
+        return "<%s of %s>" % (type(self).__name__, self._func_execution_context)
+
+
+def comprehension_from_atom(inference_state, value, atom):
+    bracket = atom.children[0]
+    test_list_comp = atom.children[1]
+
+    if bracket == '{':
+        if atom.children[1].children[1] == ':':
+            sync_comp_for = test_list_comp.children[3]
+            if sync_comp_for.type == 'comp_for':
+                sync_comp_for = sync_comp_for.children[1]
+
+            return DictComprehension(
+                inference_state,
+                value,
+                sync_comp_for_node=sync_comp_for,
+                key_node=test_list_comp.children[0],
+                value_node=test_list_comp.children[2],
+            )
+        else:
+            cls = SetComprehension
+    elif bracket == '(':
+        cls = GeneratorComprehension
+    elif bracket == '[':
+        cls = ListComprehension
+
+    sync_comp_for = test_list_comp.children[1]
+    if sync_comp_for.type == 'comp_for':
+        sync_comp_for = sync_comp_for.children[1]
+
+    return cls(
+        inference_state,
+        defining_context=value,
+        sync_comp_for_node=sync_comp_for,
+        entry_node=test_list_comp.children[0],
+    )
+
+
+class ComprehensionMixin(object):
+    @inference_state_method_cache()
+    def _get_comp_for_context(self, parent_context, comp_for):
+        return CompForContext(parent_context, comp_for)
+
+    def _nested(self, comp_fors, parent_context=None):
+        comp_for = comp_fors[0]
+
+        is_async = comp_for.parent.type == 'comp_for'
+
+        input_node = comp_for.children[3]
+        parent_context = parent_context or self._defining_context
+        input_types = parent_context.infer_node(input_node)
+
+        cn = ContextualizedNode(parent_context, input_node)
+        iterated = input_types.iterate(cn, is_async=is_async)
+        exprlist = comp_for.children[1]
+        for i, lazy_value in enumerate(iterated):
+            types = lazy_value.infer()
+            dct = unpack_tuple_to_dict(parent_context, types, exprlist)
+            context = self._get_comp_for_context(
+                parent_context,
+                comp_for,
+            )
+            with context.predefine_names(comp_for, dct):
+                try:
+                    for result in self._nested(comp_fors[1:], context):
+                        yield result
+                except IndexError:
+                    iterated = context.infer_node(self._entry_node)
+                    if self.array_type == 'dict':
+                        yield iterated, context.infer_node(self._value_node)
+                    else:
+                        yield iterated
+
+    @inference_state_method_cache(default=[])
+    @to_list
+    def _iterate(self):
+        comp_fors = tuple(get_sync_comp_fors(self._sync_comp_for_node))
+        for result in self._nested(comp_fors):
+            yield result
+
+    def py__iter__(self, contextualized_node=None):
+        for set_ in self._iterate():
+            yield LazyKnownValues(set_)
+
+    def __repr__(self):
+        return "<%s of %s>" % (type(self).__name__, self._sync_comp_for_node)
+
+
+class _DictMixin(object):
+    def _get_generics(self):
+        return tuple(c_set.py__class__() for c_set in self.get_mapping_item_values())
+
+
+class Sequence(LazyAttributeOverwrite, IterableMixin):
+    api_type = u'instance'
+
+    @property
+    def name(self):
+        return compiled.CompiledValueName(self, self.array_type)
+
+    def _get_generics(self):
+        return (self.merge_types_of_iterate().py__class__(),)
+
+    @inference_state_method_cache(default=())
+    def _cached_generics(self):
+        return self._get_generics()
+
+    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__bool__(self):
+        return None  # We don't know the length, because of appends.
+
+    @safe_property
+    def parent(self):
+        return self.inference_state.builtins_module
+
+    def py__getitem__(self, index_value_set, contextualized_node):
+        if self.array_type == 'dict':
+            return self._dict_values()
+        return iterate_values(ValueSet([self]))
+
+
+class _BaseComprehension(ComprehensionMixin):
+    def __init__(self, inference_state, defining_context, sync_comp_for_node, entry_node):
+        assert sync_comp_for_node.type == 'sync_comp_for'
+        super(_BaseComprehension, self).__init__(inference_state)
+        self._defining_context = defining_context
+        self._sync_comp_for_node = sync_comp_for_node
+        self._entry_node = entry_node
+
+
+class ListComprehension(_BaseComprehension, Sequence):
+    array_type = u'list'
+
+    def py__simple_getitem__(self, index):
+        if isinstance(index, slice):
+            return ValueSet([self])
+
+        all_types = list(self.py__iter__())
+        with reraise_getitem_errors(IndexError, TypeError):
+            lazy_value = all_types[index]
+        return lazy_value.infer()
+
+
+class SetComprehension(_BaseComprehension, Sequence):
+    array_type = u'set'
+
+
+class GeneratorComprehension(_BaseComprehension, GeneratorBase):
+    pass
+
+
+class _DictKeyMixin(object):
+    # TODO merge with _DictMixin?
+    def get_mapping_item_values(self):
+        return self._dict_keys(), self._dict_values()
+
+    def get_key_values(self):
+        # TODO merge with _dict_keys?
+        return self._dict_keys()
+
+
+class DictComprehension(ComprehensionMixin, Sequence, _DictKeyMixin):
+    array_type = u'dict'
+
+    def __init__(self, inference_state, defining_context, sync_comp_for_node, key_node, value_node):
+        assert sync_comp_for_node.type == 'sync_comp_for'
+        super(DictComprehension, self).__init__(inference_state)
+        self._defining_context = defining_context
+        self._sync_comp_for_node = sync_comp_for_node
+        self._entry_node = key_node
+        self._value_node = value_node
+
+    def py__iter__(self, contextualized_node=None):
+        for keys, values in self._iterate():
+            yield LazyKnownValues(keys)
+
+    def py__simple_getitem__(self, index):
+        for keys, values in self._iterate():
+            for k in keys:
+                # Be careful in the future if refactoring, index could be a
+                # slice object.
+                if k.get_safe_value(default=object()) == index:
+                    return values
+        raise SimpleGetItemNotFound()
+
+    def _dict_keys(self):
+        return ValueSet.from_sets(keys for keys, values in self._iterate())
+
+    def _dict_values(self):
+        return ValueSet.from_sets(values for keys, values in self._iterate())
+
+    @publish_method('values')
+    def _imitate_values(self, arguments):
+        lazy_value = LazyKnownValues(self._dict_values())
+        return ValueSet([FakeList(self.inference_state, [lazy_value])])
+
+    @publish_method('items')
+    def _imitate_items(self, arguments):
+        lazy_values = [
+            LazyKnownValue(
+                FakeTuple(
+                    self.inference_state,
+                    [LazyKnownValues(key),
+                     LazyKnownValues(value)]
+                )
+            )
+            for key, value in self._iterate()
+        ]
+
+        return ValueSet([FakeList(self.inference_state, lazy_values)])
+
+    def exact_key_items(self):
+        # NOTE: A smarter thing can probably done here to achieve better
+        # completions, but at least like this jedi doesn't crash
+        return []
+
+
+class SequenceLiteralValue(Sequence):
+    _TUPLE_LIKE = 'testlist_star_expr', 'testlist', 'subscriptlist'
+    mapping = {'(': u'tuple',
+               '[': u'list',
+               '{': u'set'}
+
+    def __init__(self, inference_state, defining_context, atom):
+        super(SequenceLiteralValue, self).__init__(inference_state)
+        self.atom = atom
+        self._defining_context = defining_context
+
+        if self.atom.type in self._TUPLE_LIKE:
+            self.array_type = u'tuple'
+        else:
+            self.array_type = SequenceLiteralValue.mapping[atom.children[0]]
+            """The builtin name of the array (list, set, tuple or dict)."""
+
+    def _get_generics(self):
+        if self.array_type == u'tuple':
+            return tuple(x.infer().py__class__() for x in self.py__iter__())
+        return super(SequenceLiteralValue, self)._get_generics()
+
+    def py__simple_getitem__(self, index):
+        """Here the index is an int/str. Raises IndexError/KeyError."""
+        if isinstance(index, slice):
+            return ValueSet([self])
+        else:
+            with reraise_getitem_errors(TypeError, KeyError, IndexError):
+                node = self.get_tree_entries()[index]
+            return self._defining_context.infer_node(node)
+
+    def py__iter__(self, contextualized_node=None):
+        """
+        While values returns the possible values for any array field, this
+        function returns the value for a certain index.
+        """
+        for node in self.get_tree_entries():
+            if node == ':' or node.type == 'subscript':
+                # TODO this should probably use at least part of the code
+                #      of infer_subscript_list.
+                yield LazyKnownValue(Slice(self._defining_context, None, None, None))
+            else:
+                yield LazyTreeValue(self._defining_context, node)
+        for addition in check_array_additions(self._defining_context, self):
+            yield addition
+
+    def py__len__(self):
+        # This function is not really used often. It's more of a try.
+        return len(self.get_tree_entries())
+
+    def get_tree_entries(self):
+        c = self.atom.children
+
+        if self.atom.type in self._TUPLE_LIKE:
+            return c[::2]
+
+        array_node = c[1]
+        if array_node in (']', '}', ')'):
+            return []  # Direct closing bracket, doesn't contain items.
+
+        if array_node.type == 'testlist_comp':
+            # filter out (for now) pep 448 single-star unpacking
+            return [value for value in array_node.children[::2]
+                    if value.type != "star_expr"]
+        elif array_node.type == 'dictorsetmaker':
+            kv = []
+            iterator = iter(array_node.children)
+            for key in iterator:
+                if key == "**":
+                    # dict with pep 448 double-star unpacking
+                    # for now ignoring the values imported by **
+                    next(iterator)
+                    next(iterator, None)  # Possible comma.
+                else:
+                    op = next(iterator, None)
+                    if op is None or op == ',':
+                        if key.type == "star_expr":
+                            # pep 448 single-star unpacking
+                            # for now ignoring values imported by *
+                            pass
+                        else:
+                            kv.append(key)  # A set.
+                    else:
+                        assert op == ':'  # A dict.
+                        kv.append((key, next(iterator)))
+                        next(iterator, None)  # Possible comma.
+            return kv
+        else:
+            if array_node.type == "star_expr":
+                # pep 448 single-star unpacking
+                # for now ignoring values imported by *
+                return []
+            else:
+                return [array_node]
+
+    def exact_key_items(self):
+        """
+        Returns a generator of tuples like dict.items(), where the key is
+        resolved (as a string) and the values are still lazy values.
+        """
+        for key_node, value in self.get_tree_entries():
+            for key in self._defining_context.infer_node(key_node):
+                if is_string(key):
+                    yield key.get_safe_value(), LazyTreeValue(self._defining_context, value)
+
+    def __repr__(self):
+        return "<%s of %s>" % (self.__class__.__name__, self.atom)
+
+
+class DictLiteralValue(_DictMixin, SequenceLiteralValue, _DictKeyMixin):
+    array_type = u'dict'
+
+    def __init__(self, inference_state, defining_context, atom):
+        super(SequenceLiteralValue, self).__init__(inference_state)
+        self._defining_context = defining_context
+        self.atom = atom
+
+    def py__simple_getitem__(self, index):
+        """Here the index is an int/str. Raises IndexError/KeyError."""
+        compiled_value_index = compiled.create_simple_object(self.inference_state, index)
+        for key, value in self.get_tree_entries():
+            for k in self._defining_context.infer_node(key):
+                for key_v in k.execute_operation(compiled_value_index, u'=='):
+                    if key_v.get_safe_value():
+                        return self._defining_context.infer_node(value)
+        raise SimpleGetItemNotFound('No key found in dictionary %s.' % self)
+
+    def py__iter__(self, contextualized_node=None):
+        """
+        While values returns the possible values for any array field, this
+        function returns the value for a certain index.
+        """
+        # Get keys.
+        types = NO_VALUES
+        for k, _ in self.get_tree_entries():
+            types |= self._defining_context.infer_node(k)
+        # We don't know which dict index comes first, therefore always
+        # yield all the types.
+        for _ in types:
+            yield LazyKnownValues(types)
+
+    @publish_method('values')
+    def _imitate_values(self, arguments):
+        lazy_value = LazyKnownValues(self._dict_values())
+        return ValueSet([FakeList(self.inference_state, [lazy_value])])
+
+    @publish_method('items')
+    def _imitate_items(self, arguments):
+        lazy_values = [
+            LazyKnownValue(FakeTuple(
+                self.inference_state,
+                (LazyTreeValue(self._defining_context, key_node),
+                 LazyTreeValue(self._defining_context, value_node))
+            )) for key_node, value_node in self.get_tree_entries()
+        ]
+
+        return ValueSet([FakeList(self.inference_state, lazy_values)])
+
+    def _dict_values(self):
+        return ValueSet.from_sets(
+            self._defining_context.infer_node(v)
+            for k, v in self.get_tree_entries()
+        )
+
+    def _dict_keys(self):
+        return ValueSet.from_sets(
+            self._defining_context.infer_node(k)
+            for k, v in self.get_tree_entries()
+        )
+
+
+class _FakeSequence(Sequence):
+    def __init__(self, inference_state, lazy_value_list):
+        """
+        type should be one of "tuple", "list"
+        """
+        super(_FakeSequence, self).__init__(inference_state)
+        self._lazy_value_list = lazy_value_list
+
+    def py__simple_getitem__(self, index):
+        if isinstance(index, slice):
+            return ValueSet([self])
+
+        with reraise_getitem_errors(IndexError, TypeError):
+            lazy_value = self._lazy_value_list[index]
+        return lazy_value.infer()
+
+    def py__iter__(self, contextualized_node=None):
+        return self._lazy_value_list
+
+    def py__bool__(self):
+        return bool(len(self._lazy_value_list))
+
+    def __repr__(self):
+        return "<%s of %s>" % (type(self).__name__, self._lazy_value_list)
+
+
+class FakeTuple(_FakeSequence):
+    array_type = u'tuple'
+
+
+class FakeList(_FakeSequence):
+    array_type = u'tuple'
+
+
+class FakeDict(_DictMixin, Sequence, _DictKeyMixin):
+    array_type = u'dict'
+
+    def __init__(self, inference_state, dct):
+        super(FakeDict, self).__init__(inference_state)
+        self._dct = dct
+
+    def py__iter__(self, contextualized_node=None):
+        for key in self._dct:
+            yield LazyKnownValue(compiled.create_simple_object(self.inference_state, key))
+
+    def py__simple_getitem__(self, index):
+        if is_py3 and self.inference_state.environment.version_info.major == 2:
+            # In Python 2 bytes and unicode compare.
+            if isinstance(index, bytes):
+                index_unicode = force_unicode(index)
+                try:
+                    return self._dct[index_unicode].infer()
+                except KeyError:
+                    pass
+            elif isinstance(index, str):
+                index_bytes = index.encode('utf-8')
+                try:
+                    return self._dct[index_bytes].infer()
+                except KeyError:
+                    pass
+
+        with reraise_getitem_errors(KeyError, TypeError):
+            lazy_value = self._dct[index]
+        return lazy_value.infer()
+
+    @publish_method('values')
+    def _values(self, arguments):
+        return ValueSet([FakeTuple(
+            self.inference_state,
+            [LazyKnownValues(self._dict_values())]
+        )])
+
+    def _dict_values(self):
+        return ValueSet.from_sets(lazy_value.infer() for lazy_value in self._dct.values())
+
+    def _dict_keys(self):
+        return ValueSet.from_sets(lazy_value.infer() for lazy_value in self.py__iter__())
+
+    def exact_key_items(self):
+        return self._dct.items()
+
+    def __repr__(self):
+        return '<%s: %s>' % (self.__class__.__name__, self._dct)
+
+
+class MergedArray(Sequence):
+    def __init__(self, inference_state, arrays):
+        super(MergedArray, self).__init__(inference_state)
+        self.array_type = arrays[-1].array_type
+        self._arrays = arrays
+
+    def py__iter__(self, contextualized_node=None):
+        for array in self._arrays:
+            for lazy_value in array.py__iter__():
+                yield lazy_value
+
+    def py__simple_getitem__(self, index):
+        return ValueSet.from_sets(lazy_value.infer() for lazy_value in self.py__iter__())
+
+
+def unpack_tuple_to_dict(context, types, exprlist):
+    """
+    Unpacking tuple assignments in for statements and expr_stmts.
+    """
+    if exprlist.type == 'name':
+        return {exprlist.value: types}
+    elif exprlist.type == 'atom' and exprlist.children[0] in ('(', '['):
+        return unpack_tuple_to_dict(context, types, exprlist.children[1])
+    elif exprlist.type in ('testlist', 'testlist_comp', 'exprlist',
+                           'testlist_star_expr'):
+        dct = {}
+        parts = iter(exprlist.children[::2])
+        n = 0
+        for lazy_value in types.iterate(ContextualizedNode(context, exprlist)):
+            n += 1
+            try:
+                part = next(parts)
+            except StopIteration:
+                analysis.add(context, 'value-error-too-many-values', part,
+                             message="ValueError: too many values to unpack (expected %s)" % n)
+            else:
+                dct.update(unpack_tuple_to_dict(context, lazy_value.infer(), part))
+        has_parts = next(parts, None)
+        if types and has_parts is not None:
+            analysis.add(context, 'value-error-too-few-values', has_parts,
+                         message="ValueError: need more than %s values to unpack" % n)
+        return dct
+    elif exprlist.type == 'power' or exprlist.type == 'atom_expr':
+        # Something like ``arr[x], var = ...``.
+        # This is something that is not yet supported, would also be difficult
+        # to write into a dict.
+        return {}
+    elif exprlist.type == 'star_expr':  # `a, *b, c = x` type unpackings
+        # Currently we're not supporting them.
+        return {}
+    raise NotImplementedError
+
+
+class Slice(LazyValueWrapper):
+    def __init__(self, python_context, start, stop, step):
+        self.inference_state = python_context.inference_state
+        self._context = python_context
+        # All of them are either a Precedence or None.
+        self._start = start
+        self._stop = stop
+        self._step = step
+
+    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_safe_value(self, default=sentinel):
+        """
+        Imitate CompiledValue.obj behavior and return a ``builtin.slice()``
+        object.
+        """
+        def get(element):
+            if element is None:
+                return None
+
+            result = self._context.infer_node(element)
+            if len(result) != 1:
+                # For simplicity, we want slices to be clear defined with just
+                # one type.  Otherwise we will return an empty slice object.
+                raise IndexError
+
+            value, = result
+            return get_int_or_none(value)
+
+        try:
+            return slice(get(self._start), get(self._stop), get(self._step))
+        except IndexError:
+            return slice(None, None, None)

venv/Lib/site-packages/jedi/inference/value/klass.py

@@ -0,0 +1,392 @@
+"""
+Like described in the :mod:`parso.python.tree` module,
+there's a need for an ast like module to represent the states of parsed
+modules.
+
+But now there are also structures in Python that need a little bit more than
+that. An ``Instance`` for example is only a ``Class`` before it is
+instantiated. This class represents these cases.
+
+So, why is there also a ``Class`` class here? Well, there are decorators and
+they change classes in Python 3.
+
+Representation modules also define "magic methods". Those methods look like
+``py__foo__`` and are typically mappable to the Python equivalents ``__call__``
+and others. Here's a list:
+
+====================================== ========================================
+**Method**                             **Description**
+-------------------------------------- ----------------------------------------
+py__call__(arguments: Array)           On callable objects, returns types.
+py__bool__()                           Returns True/False/None; None means that
+                                       there's no certainty.
+py__bases__()                          Returns a list of base classes.
+py__iter__()                           Returns a generator of a set of types.
+py__class__()                          Returns the class of an instance.
+py__simple_getitem__(index: int/str)   Returns a a set of types of the index.
+                                       Can raise an IndexError/KeyError.
+py__getitem__(indexes: ValueSet)       Returns a a set of types of the index.
+py__file__()                           Only on modules. Returns None if does
+                                       not exist.
+py__package__() -> List[str]           Only on modules. For the import system.
+py__path__()                           Only on modules. For the import system.
+py__get__(call_object)                 Only on instances. Simulates
+                                       descriptors.
+py__doc__()                            Returns the docstring for a value.
+====================================== ========================================
+
+"""
+from jedi import debug
+from jedi._compatibility import use_metaclass
+from jedi.parser_utils import get_cached_parent_scope, expr_is_dotted
+from jedi.inference.cache import inference_state_method_cache, CachedMetaClass, \
+    inference_state_method_generator_cache
+from jedi.inference import compiled
+from jedi.inference.lazy_value import LazyKnownValues, LazyTreeValue
+from jedi.inference.filters import ParserTreeFilter
+from jedi.inference.names import TreeNameDefinition, ValueName
+from jedi.inference.arguments import unpack_arglist, ValuesArguments
+from jedi.inference.base_value import ValueSet, iterator_to_value_set, \
+    NO_VALUES
+from jedi.inference.context import ClassContext
+from jedi.inference.value.function import FunctionAndClassBase
+from jedi.inference.gradual.generics import LazyGenericManager, TupleGenericManager
+from jedi.plugins import plugin_manager
+
+
+class ClassName(TreeNameDefinition):
+    def __init__(self, class_value, tree_name, name_context, apply_decorators):
+        super(ClassName, self).__init__(name_context, tree_name)
+        self._apply_decorators = apply_decorators
+        self._class_value = class_value
+
+    @iterator_to_value_set
+    def infer(self):
+        # We're using a different value to infer, so we cannot call super().
+        from jedi.inference.syntax_tree import tree_name_to_values
+        inferred = tree_name_to_values(
+            self.parent_context.inference_state, self.parent_context, self.tree_name)
+
+        for result_value in inferred:
+            if self._apply_decorators:
+                for c in result_value.py__get__(instance=None, class_value=self._class_value):
+                    yield c
+            else:
+                yield result_value
+
+
+class ClassFilter(ParserTreeFilter):
+    def __init__(self, class_value, node_context=None, until_position=None,
+                 origin_scope=None, is_instance=False):
+        super(ClassFilter, self).__init__(
+            class_value.as_context(), node_context,
+            until_position=until_position,
+            origin_scope=origin_scope,
+        )
+        self._class_value = class_value
+        self._is_instance = is_instance
+
+    def _convert_names(self, names):
+        return [
+            ClassName(
+                class_value=self._class_value,
+                tree_name=name,
+                name_context=self._node_context,
+                apply_decorators=not self._is_instance,
+            ) for name in names
+        ]
+
+    def _equals_origin_scope(self):
+        node = self._origin_scope
+        while node is not None:
+            if node == self._parser_scope or node == self.parent_context:
+                return True
+            node = get_cached_parent_scope(self._used_names, node)
+        return False
+
+    def _access_possible(self, name):
+        # Filter for ClassVar variables
+        # TODO this is not properly done, yet. It just checks for the string
+        # ClassVar in the annotation, which can be quite imprecise. If we
+        # wanted to do this correct, we would have to infer the ClassVar.
+        if not self._is_instance:
+            expr_stmt = name.get_definition()
+            if expr_stmt is not None and expr_stmt.type == 'expr_stmt':
+                annassign = expr_stmt.children[1]
+                if annassign.type == 'annassign':
+                    # If there is an =, the variable is obviously also
+                    # defined on the class.
+                    if 'ClassVar' not in annassign.children[1].get_code() \
+                            and '=' not in annassign.children:
+                        return False
+
+        # Filter for name mangling of private variables like __foo
+        return not name.value.startswith('__') or name.value.endswith('__') \
+            or self._equals_origin_scope()
+
+    def _filter(self, names):
+        names = super(ClassFilter, self)._filter(names)
+        return [name for name in names if self._access_possible(name)]
+
+
+class ClassMixin(object):
+    def is_class(self):
+        return True
+
+    def is_class_mixin(self):
+        return True
+
+    def py__call__(self, arguments):
+        from jedi.inference.value import TreeInstance
+
+        from jedi.inference.gradual.typing import TypedDict
+        if self.is_typeddict():
+            return ValueSet([TypedDict(self)])
+        return ValueSet([TreeInstance(self.inference_state, self.parent_context, self, arguments)])
+
+    def py__class__(self):
+        return compiled.builtin_from_name(self.inference_state, u'type')
+
+    @property
+    def name(self):
+        return ValueName(self, self.tree_node.name)
+
+    def py__name__(self):
+        return self.name.string_name
+
+    @inference_state_method_generator_cache()
+    def py__mro__(self):
+        mro = [self]
+        yield self
+        # TODO Do a proper mro resolution. Currently we are just listing
+        # classes. However, it's a complicated algorithm.
+        for lazy_cls in self.py__bases__():
+            # TODO there's multiple different mro paths possible if this yields
+            # multiple possibilities. Could be changed to be more correct.
+            for cls in lazy_cls.infer():
+                # TODO detect for TypeError: duplicate base class str,
+                # e.g.  `class X(str, str): pass`
+                try:
+                    mro_method = cls.py__mro__
+                except AttributeError:
+                    # TODO add a TypeError like:
+                    """
+                    >>> class Y(lambda: test): pass
+                    Traceback (most recent call last):
+                      File "<stdin>", line 1, in <module>
+                    TypeError: function() argument 1 must be code, not str
+                    >>> class Y(1): pass
+                    Traceback (most recent call last):
+                      File "<stdin>", line 1, in <module>
+                    TypeError: int() takes at most 2 arguments (3 given)
+                    """
+                    debug.warning('Super class of %s is not a class: %s', self, cls)
+                else:
+                    for cls_new in mro_method():
+                        if cls_new not in mro:
+                            mro.append(cls_new)
+                            yield cls_new
+
+    def get_filters(self, origin_scope=None, is_instance=False,
+                    include_metaclasses=True, include_type_when_class=True):
+        if include_metaclasses:
+            metaclasses = self.get_metaclasses()
+            if metaclasses:
+                for f in self.get_metaclass_filters(metaclasses, is_instance):
+                    yield f  # Python 2..
+
+        for cls in self.py__mro__():
+            if cls.is_compiled():
+                for filter in cls.get_filters(is_instance=is_instance):
+                    yield filter
+            else:
+                yield ClassFilter(
+                    self, node_context=cls.as_context(),
+                    origin_scope=origin_scope,
+                    is_instance=is_instance
+                )
+        if not is_instance and include_type_when_class:
+            from jedi.inference.compiled import builtin_from_name
+            type_ = builtin_from_name(self.inference_state, u'type')
+            assert isinstance(type_, ClassValue)
+            if type_ != self:
+                # We are not using execute_with_values here, because the
+                # plugin function for type would get executed instead of an
+                # instance creation.
+                args = ValuesArguments([])
+                for instance in type_.py__call__(args):
+                    instance_filters = instance.get_filters()
+                    # Filter out self filters
+                    next(instance_filters, None)
+                    next(instance_filters, None)
+                    x = next(instance_filters, None)
+                    assert x is not None
+                    yield x
+
+    def get_signatures(self):
+        # Since calling staticmethod without a function is illegal, the Jedi
+        # plugin doesn't return anything. Therefore call directly and get what
+        # we want: An instance of staticmethod.
+        metaclasses = self.get_metaclasses()
+        if metaclasses:
+            sigs = self.get_metaclass_signatures(metaclasses)
+            if sigs:
+                return sigs
+        args = ValuesArguments([])
+        init_funcs = self.py__call__(args).py__getattribute__('__init__')
+        return [sig.bind(self) for sig in init_funcs.get_signatures()]
+
+    def _as_context(self):
+        return ClassContext(self)
+
+    def get_type_hint(self, add_class_info=True):
+        if add_class_info:
+            return 'Type[%s]' % self.py__name__()
+        return self.py__name__()
+
+    @inference_state_method_cache(default=False)
+    def is_typeddict(self):
+        # TODO Do a proper mro resolution. Currently we are just listing
+        # classes. However, it's a complicated algorithm.
+        from jedi.inference.gradual.typing import TypedDictClass
+        for lazy_cls in self.py__bases__():
+            if not isinstance(lazy_cls, LazyTreeValue):
+                return False
+            tree_node = lazy_cls.data
+            # Only resolve simple classes, stuff like Iterable[str] are more
+            # intensive to resolve and if generics are involved, we know it's
+            # not a TypedDict.
+            if not expr_is_dotted(tree_node):
+                return False
+
+            for cls in lazy_cls.infer():
+                if isinstance(cls, TypedDictClass):
+                    return True
+                try:
+                    method = cls.is_typeddict
+                except AttributeError:
+                    # We're only dealing with simple classes, so just returning
+                    # here should be fine. This only happens with e.g. compiled
+                    # classes.
+                    return False
+                else:
+                    if method():
+                        return True
+        return False
+
+    def py__getitem__(self, index_value_set, contextualized_node):
+        from jedi.inference.gradual.base import GenericClass
+        if not index_value_set:
+            debug.warning('Class indexes inferred to nothing. Returning class instead')
+            return ValueSet([self])
+        return ValueSet(
+            GenericClass(
+                self,
+                LazyGenericManager(
+                    context_of_index=contextualized_node.context,
+                    index_value=index_value,
+                )
+            )
+            for index_value in index_value_set
+        )
+
+    def with_generics(self, generics_tuple):
+        from jedi.inference.gradual.base import GenericClass
+        return GenericClass(
+            self,
+            TupleGenericManager(generics_tuple)
+        )
+
+    def define_generics(self, type_var_dict):
+        from jedi.inference.gradual.base import GenericClass
+
+        def remap_type_vars():
+            """
+            The TypeVars in the resulting classes have sometimes different names
+            and we need to check for that, e.g. a signature can be:
+
+            def iter(iterable: Iterable[_T]) -> Iterator[_T]: ...
+
+            However, the iterator is defined as Iterator[_T_co], which means it has
+            a different type var name.
+            """
+            for type_var in self.list_type_vars():
+                yield type_var_dict.get(type_var.py__name__(), NO_VALUES)
+
+        if type_var_dict:
+            return ValueSet([GenericClass(
+                self,
+                TupleGenericManager(tuple(remap_type_vars()))
+            )])
+        return ValueSet({self})
+
+
+class ClassValue(use_metaclass(CachedMetaClass, ClassMixin, FunctionAndClassBase)):
+    api_type = u'class'
+
+    @inference_state_method_cache()
+    def list_type_vars(self):
+        found = []
+        arglist = self.tree_node.get_super_arglist()
+        if arglist is None:
+            return []
+
+        for stars, node in unpack_arglist(arglist):
+            if stars:
+                continue  # These are not relevant for this search.
+
+            from jedi.inference.gradual.annotation import find_unknown_type_vars
+            for type_var in find_unknown_type_vars(self.parent_context, node):
+                if type_var not in found:
+                    # The order matters and it's therefore a list.
+                    found.append(type_var)
+        return found
+
+    def _get_bases_arguments(self):
+        arglist = self.tree_node.get_super_arglist()
+        if arglist:
+            from jedi.inference import arguments
+            return arguments.TreeArguments(self.inference_state, self.parent_context, arglist)
+        return None
+
+    @inference_state_method_cache(default=())
+    def py__bases__(self):
+        args = self._get_bases_arguments()
+        if args is not None:
+            lst = [value for key, value in args.unpack() if key is None]
+            if lst:
+                return lst
+
+        if self.py__name__() == 'object' \
+                and self.parent_context.is_builtins_module():
+            return []
+        return [LazyKnownValues(
+            self.inference_state.builtins_module.py__getattribute__('object')
+        )]
+
+    @plugin_manager.decorate()
+    def get_metaclass_filters(self, metaclasses, is_instance):
+        debug.warning('Unprocessed metaclass %s', metaclasses)
+        return []
+
+    @inference_state_method_cache(default=NO_VALUES)
+    def get_metaclasses(self):
+        args = self._get_bases_arguments()
+        if args is not None:
+            m = [value for key, value in args.unpack() if key == 'metaclass']
+            metaclasses = ValueSet.from_sets(lazy_value.infer() for lazy_value in m)
+            metaclasses = ValueSet(m for m in metaclasses if m.is_class())
+            if metaclasses:
+                return metaclasses
+
+        for lazy_base in self.py__bases__():
+            for value in lazy_base.infer():
+                if value.is_class():
+                    values = value.get_metaclasses()
+                    if values:
+                        return values
+        return NO_VALUES
+
+    @plugin_manager.decorate()
+    def get_metaclass_signatures(self, metaclasses):
+        return []

venv/Lib/site-packages/jedi/inference/value/module.py

@@ -0,0 +1,229 @@
+import os
+
+from jedi.inference.cache import inference_state_method_cache
+from jedi.inference.names import AbstractNameDefinition, ModuleName
+from jedi.inference.filters import GlobalNameFilter, ParserTreeFilter, DictFilter, MergedFilter
+from jedi.inference import compiled
+from jedi.inference.base_value import TreeValue
+from jedi.inference.names import SubModuleName
+from jedi.inference.helpers import values_from_qualified_names
+from jedi.inference.compiled import create_simple_object
+from jedi.inference.base_value import ValueSet
+from jedi.inference.context import ModuleContext
+
+
+class _ModuleAttributeName(AbstractNameDefinition):
+    """
+    For module attributes like __file__, __str__ and so on.
+    """
+    api_type = u'instance'
+
+    def __init__(self, parent_module, string_name, string_value=None):
+        self.parent_context = parent_module
+        self.string_name = string_name
+        self._string_value = string_value
+
+    def infer(self):
+        if self._string_value is not None:
+            s = self._string_value
+            if self.parent_context.inference_state.environment.version_info.major == 2 \
+                    and not isinstance(s, bytes):
+                s = s.encode('utf-8')
+            return ValueSet([
+                create_simple_object(self.parent_context.inference_state, s)
+            ])
+        return compiled.get_string_value_set(self.parent_context.inference_state)
+
+
+class SubModuleDictMixin(object):
+    @inference_state_method_cache()
+    def sub_modules_dict(self):
+        """
+        Lists modules in the directory of this module (if this module is a
+        package).
+        """
+        names = {}
+        if self.is_package():
+            mods = self.inference_state.compiled_subprocess.iter_module_names(
+                self.py__path__()
+            )
+            for name in mods:
+                # It's obviously a relative import to the current module.
+                names[name] = SubModuleName(self.as_context(), name)
+
+        # In the case of an import like `from x.` we don't need to
+        # add all the variables, this is only about submodules.
+        return names
+
+
+class ModuleMixin(SubModuleDictMixin):
+    _module_name_class = ModuleName
+
+    def get_filters(self, origin_scope=None):
+        yield MergedFilter(
+            ParserTreeFilter(
+                parent_context=self.as_context(),
+                origin_scope=origin_scope
+            ),
+            GlobalNameFilter(self.as_context(), self.tree_node),
+        )
+        yield DictFilter(self.sub_modules_dict())
+        yield DictFilter(self._module_attributes_dict())
+        for star_filter in self.iter_star_filters():
+            yield star_filter
+
+    def py__class__(self):
+        c, = values_from_qualified_names(self.inference_state, u'types', u'ModuleType')
+        return c
+
+    def is_module(self):
+        return True
+
+    def is_stub(self):
+        return False
+
+    @property
+    @inference_state_method_cache()
+    def name(self):
+        return self._module_name_class(self, self.string_names[-1])
+
+    @inference_state_method_cache()
+    def _module_attributes_dict(self):
+        names = ['__package__', '__doc__', '__name__']
+        # All the additional module attributes are strings.
+        dct = dict((n, _ModuleAttributeName(self, n)) for n in names)
+        file = self.py__file__()
+        if file is not None:
+            dct['__file__'] = _ModuleAttributeName(self, '__file__', file)
+        return dct
+
+    def iter_star_filters(self):
+        for star_module in self.star_imports():
+            f = next(star_module.get_filters(), None)
+            assert f is not None
+            yield f
+
+    # I'm not sure if the star import cache is really that effective anymore
+    # with all the other really fast import caches. Recheck. Also we would need
+    # to push the star imports into InferenceState.module_cache, if we reenable this.
+    @inference_state_method_cache([])
+    def star_imports(self):
+        from jedi.inference.imports import Importer
+
+        modules = []
+        module_context = self.as_context()
+        for i in self.tree_node.iter_imports():
+            if i.is_star_import():
+                new = Importer(
+                    self.inference_state,
+                    import_path=i.get_paths()[-1],
+                    module_context=module_context,
+                    level=i.level
+                ).follow()
+
+                for module in new:
+                    if isinstance(module, ModuleValue):
+                        modules += module.star_imports()
+                modules += new
+        return modules
+
+    def get_qualified_names(self):
+        """
+        A module doesn't have a qualified name, but it's important to note that
+        it's reachable and not `None`. With this information we can add
+        qualified names on top for all value children.
+        """
+        return ()
+
+
+class ModuleValue(ModuleMixin, TreeValue):
+    api_type = u'module'
+
+    def __init__(self, inference_state, module_node, code_lines, file_io=None,
+                 string_names=None, is_package=False):
+        super(ModuleValue, self).__init__(
+            inference_state,
+            parent_context=None,
+            tree_node=module_node
+        )
+        self.file_io = file_io
+        if file_io is None:
+            self._path = None
+        else:
+            self._path = file_io.path
+        self.string_names = string_names  # Optional[Tuple[str, ...]]
+        self.code_lines = code_lines
+        self._is_package = is_package
+
+    def is_stub(self):
+        if self._path is not None and self._path.endswith('.pyi'):
+            # Currently this is the way how we identify stubs when e.g. goto is
+            # used in them. This could be changed if stubs would be identified
+            # sooner and used as StubModuleValue.
+            return True
+        return super(ModuleValue, self).is_stub()
+
+    def py__name__(self):
+        if self.string_names is None:
+            return None
+        return '.'.join(self.string_names)
+
+    def py__file__(self):
+        """
+        In contrast to Python's __file__ can be None.
+        """
+        if self._path is None:
+            return None
+
+        return os.path.abspath(self._path)
+
+    def is_package(self):
+        return self._is_package
+
+    def py__package__(self):
+        if self._is_package:
+            return self.string_names
+        return self.string_names[:-1]
+
+    def py__path__(self):
+        """
+        In case of a package, this returns Python's __path__ attribute, which
+        is a list of paths (strings).
+        Returns None if the module is not a package.
+        """
+        if not self._is_package:
+            return None
+
+        # A namespace package is typically auto generated and ~10 lines long.
+        first_few_lines = ''.join(self.code_lines[:50])
+        # these are strings that need to be used for namespace packages,
+        # the first one is ``pkgutil``, the second ``pkg_resources``.
+        options = ('declare_namespace(__name__)', 'extend_path(__path__')
+        if options[0] in first_few_lines or options[1] in first_few_lines:
+            # It is a namespace, now try to find the rest of the
+            # modules on sys_path or whatever the search_path is.
+            paths = set()
+            for s in self.inference_state.get_sys_path():
+                other = os.path.join(s, self.name.string_name)
+                if os.path.isdir(other):
+                    paths.add(other)
+            if paths:
+                return list(paths)
+            # Nested namespace packages will not be supported. Nobody ever
+            # asked for it and in Python 3 they are there without using all the
+            # crap above.
+
+        # Default to the of this file.
+        file = self.py__file__()
+        assert file is not None  # Shouldn't be a package in the first place.
+        return [os.path.dirname(file)]
+
+    def _as_context(self):
+        return ModuleContext(self)
+
+    def __repr__(self):
+        return "<%s: %s@%s-%s is_stub=%s>" % (
+            self.__class__.__name__, self.py__name__(),
+            self.tree_node.start_pos[0], self.tree_node.end_pos[0],
+            self.is_stub()
+        )

venv/Lib/site-packages/jedi/inference/value/namespace.py

@@ -0,0 +1,74 @@
+from jedi.inference.cache import inference_state_method_cache
+from jedi.inference.filters import DictFilter
+from jedi.inference.names import ValueNameMixin, AbstractNameDefinition
+from jedi.inference.base_value import Value
+from jedi.inference.value.module import SubModuleDictMixin
+from jedi.inference.context import NamespaceContext
+
+
+class ImplicitNSName(ValueNameMixin, AbstractNameDefinition):
+    """
+    Accessing names for implicit namespace packages should infer to nothing.
+    This object will prevent Jedi from raising exceptions
+    """
+    def __init__(self, implicit_ns_value, string_name):
+        self._value = implicit_ns_value
+        self.string_name = string_name
+
+
+class ImplicitNamespaceValue(Value, SubModuleDictMixin):
+    """
+    Provides support for implicit namespace packages
+    """
+    # Is a module like every other module, because if you import an empty
+    # folder foobar it will be available as an object:
+    # <module 'foobar' (namespace)>.
+    api_type = u'module'
+    parent_context = None
+
+    def __init__(self, inference_state, string_names, paths):
+        super(ImplicitNamespaceValue, self).__init__(inference_state, parent_context=None)
+        self.inference_state = inference_state
+        self.string_names = string_names
+        self._paths = paths
+
+    def get_filters(self, origin_scope=None):
+        yield DictFilter(self.sub_modules_dict())
+
+    def get_qualified_names(self):
+        return ()
+
+    @property
+    @inference_state_method_cache()
+    def name(self):
+        string_name = self.py__package__()[-1]
+        return ImplicitNSName(self, string_name)
+
+    def py__file__(self):
+        return None
+
+    def py__package__(self):
+        """Return the fullname
+        """
+        return self.string_names
+
+    def py__path__(self):
+        return self._paths
+
+    def py__name__(self):
+        return '.'.join(self.string_names)
+
+    def is_namespace(self):
+        return True
+
+    def is_stub(self):
+        return False
+
+    def is_package(self):
+        return True
+
+    def as_context(self):
+        return NamespaceContext(self)
+
+    def __repr__(self):
+        return '<%s: %s>' % (self.__class__.__name__, self.py__name__())