VimPlug/jedi
1
0
Fork 1
mirror of https://github.com/davidhalter/jedi.git synced 2025-12-06 22:14:27 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

Extract nested function for common pattern

Browse Source 
This slightly simplifies the code, as well as providing a place
to put an explanation of what the moved block of code does.
This commit is contained in:
Peter Law
2020-03-07 17:06:22 +00:00
parent 3b4fa2aa9c
commit 95cec459a8
1 changed files with 55 additions and 39 deletions
Download Patch File Download Diff File Expand all files Collapse all files

94
jedi/inference/gradual/annotation.py
View File

@@ -356,6 +356,56 @@ def _infer_type_vars(annotation_value, value_set, is_class_value=False):
type_var_dict = {} type_var_dict = {}
annotation_name = annotation_value.py__name__() annotation_name = annotation_value.py__name__()
def merge_pairwise_generics(annotation_generics, actual_generics):
"""
Given iterables of the generics immediately within an annotation and
within an argument's type, match up the generic parameters with the
corrsponding actual types.
For example, given the following code:
def values(mapping: Mapping[K, V]) -> List[V]: ...
for val in values({1: 'a'}):
val
Then in this function we are given `K` & `V` and `int` & `str` in order
to determine that `K` is `int and `V` is `str`.
Parameters
----------
`annotation_generics`: an ordered collection of the immediately nested
generic parameters within the annotation being considered. In the
above example, the caller would be analysing `Mapping[K, V]` and
would give us an iterable yileding representations of the parameters
`K` and then `V`.
`actual_generics`: an ordered collection of the immediately nested
generic parameters within the type of the argument being considered.
These need to be the parameters at the level of the type for which
the annotation generics are given, rather than of the actual type of
the parameter.
In the above example, the caller would be analysing `{1: 'a'}`. The
caller must have already determined that this is an instance of a
type which implements `Mapping` and should pass us an iterable over
representations of the type parameters to that `Mapping`
implementation (here, `int` and `str` in that order).
"""
for annotation_generics_set, actual_generic_set in zip(annotation_generics, actual_generics):
for nested_annotation_value in annotation_generics_set:
_merge_type_var_dicts(
type_var_dict,
_infer_type_vars(
nested_annotation_value,
actual_generic_set,
# This is a note to ourselves that we have already
# converted the instance representation to its class.
is_class_value=True,
),
)
if isinstance(annotation_value, TypeVar): if isinstance(annotation_value, TypeVar):
if not is_class_value: if not is_class_value:
return {annotation_name: value_set.py__class__()} return {annotation_name: value_set.py__class__()}
@@ -371,19 +421,8 @@ def _infer_type_vars(annotation_value, value_set, is_class_value=False):
annotation_generics = annotation_value.get_generics() annotation_generics = annotation_value.get_generics()
actual_generics = element.get_generics() actual_generics = element.get_generics()
for annotation_generics_set, actual_generic_set in zip(annotation_generics, actual_generics): merge_pairwise_generics(annotation_generics, actual_generics)
for nested_annotation_value in annotation_generics_set:
_merge_type_var_dicts(
type_var_dict,
_infer_type_vars(
nested_annotation_value,
actual_generic_set,
# This is a note to ourselves that we
# have already converted the instance
# representation to its class.
is_class_value=True,
),
)
else: else:
for nested_annotation_value in given[0]: for nested_annotation_value in given[0]:
_merge_type_var_dicts( _merge_type_var_dicts(
@@ -431,19 +470,8 @@ def _infer_type_vars(annotation_value, value_set, is_class_value=False):
) )
else: else:
actual_generics = py_class.get_generics() actual_generics = py_class.get_generics()
for annotation_generics_set, actual_generic_set in zip(annotation_generics, actual_generics): merge_pairwise_generics(annotation_generics, actual_generics)
for nested_annotation_value in annotation_generics_set:
_merge_type_var_dicts(
type_var_dict,
_infer_type_vars(
nested_annotation_value,
actual_generic_set,
# This is a note to ourselves that we
# have already converted the instance
# representation to its class.
is_class_value=True,
),
)
elif isinstance(annotation_value, GenericClass): elif isinstance(annotation_value, GenericClass):
if annotation_name == 'Iterable' and not is_class_value: if annotation_name == 'Iterable' and not is_class_value:
given = annotation_value.get_generics() given = annotation_value.get_generics()
@@ -478,19 +506,7 @@ def _infer_type_vars(annotation_value, value_set, is_class_value=False):
annotation_generics = annotation_value.get_generics() annotation_generics = annotation_value.get_generics()
actual_generics = parent_class.get_generics() actual_generics = parent_class.get_generics()
for annotation_generics_set, actual_generic_set in zip(annotation_generics, actual_generics): merge_pairwise_generics(annotation_generics, actual_generics)
for nested_annotation_value in annotation_generics_set:
_merge_type_var_dicts(
type_var_dict,
_infer_type_vars(
nested_annotation_value,
actual_generic_set,
# This is a note to ourselves that we
# have already converted the instance
# representation to its class.
is_class_value=True,
),
)
break break