def infer_type_vars(self, value_set, is_class_value=False):
# Circular
from jedi.inference.gradual.annotation import merge_pairwise_generics, merge_type_var_dicts
from jedi.inference.gradual.base import GenericClass
if self._is_homogenous():
# The parameter annotation is of the form `Tuple[T, ...]`,
# so we treat the incoming tuple like a iterable sequence
# rather than a positional container of elements.
return self.get_generics()[0].infer_type_vars(
value_set.merge_types_of_iterate(), )
else:
# The parameter annotation has only explicit type parameters
# (e.g: `Tuple[T]`, `Tuple[T, U]`, `Tuple[T, U, V]`, etc.) so we
# treat the incoming values as needing to match the annotation
# exactly, just as we would for non-tuple annotations.
type_var_dict = {}
for element in value_set:
py_class = element.get_annotated_class_object()
if not isinstance(py_class, GenericClass):
py_class = element
merge_type_var_dicts(
type_var_dict,
merge_pairwise_generics(self, py_class),
)
return type_var_dict
def infer_type_vars(self, value_set, is_class_value=False):
# Circular
from jedi.inference.gradual.annotation import merge_pairwise_generics, merge_type_var_dicts
type_var_dict = {}
annotation_generics = self.get_generics()
if not annotation_generics:
return type_var_dict
annotation_name = self.py__name__()
if annotation_name == 'Type':
if is_class_value:
# This only applies if we are comparing something like
# List[Type[int]] with Iterable[Type[int]]. First, Jedi tries to
# match List/Iterable. After that we will land here, because
# is_class_value will be True at that point. Obviously we also
# compare below that both sides are `Type`.
for element in value_set:
element_name = element.py__name__()
if element_name == 'Type':
merge_type_var_dicts(
type_var_dict,
merge_pairwise_generics(self, element),
)
else:
return annotation_generics[0].infer_type_vars(
value_set,
is_class_value=True,
)
elif annotation_name == 'Callable':
if len(annotation_generics) == 2:
if is_class_value:
# This only applies if we are comparing something like
# List[Callable[..., T]] with Iterable[Callable[..., T]].
# First, Jedi tries to match List/Iterable. After that we
# will land here, because is_class_value will be True at
# that point. Obviously we also compare below that both
# sides are `Callable`.
for element in value_set:
element_name = element.py__name__()
if element_name == 'Callable':
merge_type_var_dicts(
type_var_dict,
merge_pairwise_generics(self, element),
)
else:
return annotation_generics[1].infer_type_vars(
value_set.execute_annotation(), )
elif annotation_name == 'Tuple':
tuple_annotation, = self.execute_annotation()
return tuple_annotation.infer_type_vars(value_set, is_class_value)
return type_var_dict
def infer_type_vars(self, value_set, is_class_value=False):
# Circular
from jedi.inference.gradual.annotation import merge_type_var_dicts
type_var_dict = {}
for value in self._set:
merge_type_var_dicts(
type_var_dict,
value.infer_type_vars(value_set, is_class_value),
)
return type_var_dict
def infer_type_vars(self, value_set, is_class_value=False):
# Circular
from jedi.inference.gradual.annotation import merge_pairwise_generics, merge_type_var_dicts
from jedi.inference.gradual.base import GenericClass
value_set = value_set.filter(
lambda x: x.py__name__().lower() == 'tuple', )
# Somewhat unusually, this `infer_type_vars` method is on an instance
# representation of a type, rather than the annotation or class
# representation. This means that as a starting point, we need to
# convert the incoming values to their instance style if they're
# classes, rather than the reverse.
if is_class_value:
value_set = value_set.execute_annotation()
if self._is_homogenous():
# The parameter annotation is of the form `Tuple[T, ...]`,
# so we treat the incoming tuple like a iterable sequence
# rather than a positional container of elements.
return self.get_generics()[0].infer_type_vars(
value_set.merge_types_of_iterate(), )
else:
# The parameter annotation has only explicit type parameters
# (e.g: `Tuple[T]`, `Tuple[T, U]`, `Tuple[T, U, V]`, etc.) so we
# treat the incoming values as needing to match the annotation
# exactly, just as we would for non-tuple annotations.
type_var_dict = {}
for element in value_set:
if not is_class_value:
py_class = element.get_annotated_class_object()
if not isinstance(py_class, GenericClass):
py_class = element
else:
py_class = element
merge_type_var_dicts(
type_var_dict,
merge_pairwise_generics(self, py_class),
)
return type_var_dict
def infer_type_vars(self, value_set):
# Circular
from jedi.inference.gradual.annotation import merge_pairwise_generics, merge_type_var_dicts
value_set = value_set.filter(
lambda x: x.py__name__().lower() == 'tuple',
)
if self._is_homogenous():
# The parameter annotation is of the form `Tuple[T, ...]`,
# so we treat the incoming tuple like a iterable sequence
# rather than a positional container of elements.
return self._class_value.get_generics()[0].infer_type_vars(
value_set.merge_types_of_iterate(),
)
else:
# The parameter annotation has only explicit type parameters
# (e.g: `Tuple[T]`, `Tuple[T, U]`, `Tuple[T, U, V]`, etc.) so we
# treat the incoming values as needing to match the annotation
# exactly, just as we would for non-tuple annotations.
type_var_dict = {}
for element in value_set:
try:
method = element.get_annotated_class_object
except AttributeError:
# This might still happen, because the tuple name matching
# above is not 100% correct, so just catch the remaining
# cases here.
continue
py_class = method()
merge_type_var_dicts(
type_var_dict,
merge_pairwise_generics(self._class_value, py_class),
)
return type_var_dict
def infer_type_vars(self, value_set, is_class_value=False):
# Circular
from jedi.inference.gradual.annotation import merge_pairwise_generics, merge_type_var_dicts
annotation_name = self.py__name__()
type_var_dict = {}
if annotation_name == 'Iterable' and not is_class_value:
annotation_generics = self.get_generics()
if annotation_generics:
return annotation_generics[0].infer_type_vars(
value_set.merge_types_of_iterate(), )
else:
# Note: we need to handle the MRO _in order_, so we need to extract
# the elements from the set first, then handle them, even if we put
# them back in a set afterwards.
for py_class in value_set:
if not is_class_value:
if py_class.is_instance() and not py_class.is_compiled():
py_class = py_class.get_annotated_class_object()
else:
continue
if py_class.api_type != u'class':
# Functions & modules don't have an MRO and we're not
# expecting a Callable (those are handled separately within
# TypingClassValueWithIndex).
continue
for parent_class in py_class.py__mro__():
class_name = parent_class.py__name__()
if annotation_name == class_name:
merge_type_var_dicts(
type_var_dict,
merge_pairwise_generics(self, parent_class),
)
break
return type_var_dict
