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