648 lines
23 KiB
Python
648 lines
23 KiB
Python
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"""
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Contains all classes and functions to deal with lists, dicts, generators and
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iterators in general.
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"""
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from jedi.inference import compiled
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from jedi.inference import analysis
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from jedi.inference.lazy_value import LazyKnownValue, LazyKnownValues, \
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LazyTreeValue
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from jedi.inference.helpers import get_int_or_none, is_string, \
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reraise_getitem_errors, SimpleGetItemNotFound
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from jedi.inference.utils import safe_property, to_list
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from jedi.inference.cache import inference_state_method_cache
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from jedi.inference.filters import LazyAttributeOverwrite, publish_method
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from jedi.inference.base_value import ValueSet, Value, NO_VALUES, \
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ContextualizedNode, iterate_values, sentinel, \
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LazyValueWrapper
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from jedi.parser_utils import get_sync_comp_fors
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from jedi.inference.context import CompForContext
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from jedi.inference.value.dynamic_arrays import check_array_additions
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class IterableMixin:
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def py__next__(self, contextualized_node=None):
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return self.py__iter__(contextualized_node)
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def py__stop_iteration_returns(self):
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return ValueSet([compiled.builtin_from_name(self.inference_state, 'None')])
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# At the moment, safe values are simple values like "foo", 1 and not
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# lists/dicts. Therefore as a small speed optimization we can just do the
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# default instead of resolving the lazy wrapped values, that are just
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# doing this in the end as well.
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# This mostly speeds up patterns like `sys.version_info >= (3, 0)` in
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# typeshed.
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get_safe_value = Value.get_safe_value
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class GeneratorBase(LazyAttributeOverwrite, IterableMixin):
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array_type = None
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def _get_wrapped_value(self):
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instance, = self._get_cls().execute_annotation()
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return instance
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def _get_cls(self):
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generator, = self.inference_state.typing_module.py__getattribute__('Generator')
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return generator
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def py__bool__(self):
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return True
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@publish_method('__iter__')
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def _iter(self, arguments):
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return ValueSet([self])
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@publish_method('send')
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@publish_method('__next__')
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def _next(self, arguments):
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return ValueSet.from_sets(lazy_value.infer() for lazy_value in self.py__iter__())
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def py__stop_iteration_returns(self):
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return ValueSet([compiled.builtin_from_name(self.inference_state, 'None')])
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@property
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def name(self):
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return compiled.CompiledValueName(self, 'Generator')
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def get_annotated_class_object(self):
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from jedi.inference.gradual.generics import TupleGenericManager
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gen_values = self.merge_types_of_iterate().py__class__()
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gm = TupleGenericManager((gen_values, NO_VALUES, NO_VALUES))
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return self._get_cls().with_generics(gm)
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class Generator(GeneratorBase):
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"""Handling of `yield` functions."""
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def __init__(self, inference_state, func_execution_context):
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super().__init__(inference_state)
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self._func_execution_context = func_execution_context
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def py__iter__(self, contextualized_node=None):
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iterators = self._func_execution_context.infer_annotations()
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if iterators:
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return iterators.iterate(contextualized_node)
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return self._func_execution_context.get_yield_lazy_values()
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def py__stop_iteration_returns(self):
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return self._func_execution_context.get_return_values()
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def __repr__(self):
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return "<%s of %s>" % (type(self).__name__, self._func_execution_context)
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def comprehension_from_atom(inference_state, value, atom):
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bracket = atom.children[0]
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test_list_comp = atom.children[1]
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if bracket == '{':
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if atom.children[1].children[1] == ':':
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sync_comp_for = test_list_comp.children[3]
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if sync_comp_for.type == 'comp_for':
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sync_comp_for = sync_comp_for.children[1]
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return DictComprehension(
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inference_state,
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value,
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sync_comp_for_node=sync_comp_for,
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key_node=test_list_comp.children[0],
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value_node=test_list_comp.children[2],
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)
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else:
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cls = SetComprehension
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elif bracket == '(':
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cls = GeneratorComprehension
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elif bracket == '[':
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cls = ListComprehension
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sync_comp_for = test_list_comp.children[1]
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if sync_comp_for.type == 'comp_for':
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sync_comp_for = sync_comp_for.children[1]
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return cls(
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inference_state,
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defining_context=value,
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sync_comp_for_node=sync_comp_for,
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entry_node=test_list_comp.children[0],
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)
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class ComprehensionMixin:
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@inference_state_method_cache()
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def _get_comp_for_context(self, parent_context, comp_for):
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return CompForContext(parent_context, comp_for)
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def _nested(self, comp_fors, parent_context=None):
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comp_for = comp_fors[0]
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is_async = comp_for.parent.type == 'comp_for'
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input_node = comp_for.children[3]
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parent_context = parent_context or self._defining_context
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input_types = parent_context.infer_node(input_node)
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cn = ContextualizedNode(parent_context, input_node)
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iterated = input_types.iterate(cn, is_async=is_async)
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exprlist = comp_for.children[1]
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for i, lazy_value in enumerate(iterated):
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types = lazy_value.infer()
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dct = unpack_tuple_to_dict(parent_context, types, exprlist)
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context = self._get_comp_for_context(
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parent_context,
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comp_for,
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)
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with context.predefine_names(comp_for, dct):
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try:
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yield from self._nested(comp_fors[1:], context)
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except IndexError:
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iterated = context.infer_node(self._entry_node)
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if self.array_type == 'dict':
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yield iterated, context.infer_node(self._value_node)
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else:
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yield iterated
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@inference_state_method_cache(default=[])
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@to_list
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def _iterate(self):
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comp_fors = tuple(get_sync_comp_fors(self._sync_comp_for_node))
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yield from self._nested(comp_fors)
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def py__iter__(self, contextualized_node=None):
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for set_ in self._iterate():
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yield LazyKnownValues(set_)
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def __repr__(self):
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return "<%s of %s>" % (type(self).__name__, self._sync_comp_for_node)
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class _DictMixin:
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def _get_generics(self):
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return tuple(c_set.py__class__() for c_set in self.get_mapping_item_values())
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class Sequence(LazyAttributeOverwrite, IterableMixin):
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api_type = 'instance'
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@property
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def name(self):
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return compiled.CompiledValueName(self, self.array_type)
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def _get_generics(self):
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return (self.merge_types_of_iterate().py__class__(),)
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@inference_state_method_cache(default=())
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def _cached_generics(self):
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return self._get_generics()
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def _get_wrapped_value(self):
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from jedi.inference.gradual.base import GenericClass
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from jedi.inference.gradual.generics import TupleGenericManager
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klass = compiled.builtin_from_name(self.inference_state, self.array_type)
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c, = GenericClass(
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klass,
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TupleGenericManager(self._cached_generics())
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).execute_annotation()
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return c
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def py__bool__(self):
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return None # We don't know the length, because of appends.
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@safe_property
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def parent(self):
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return self.inference_state.builtins_module
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def py__getitem__(self, index_value_set, contextualized_node):
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if self.array_type == 'dict':
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return self._dict_values()
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return iterate_values(ValueSet([self]))
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class _BaseComprehension(ComprehensionMixin):
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def __init__(self, inference_state, defining_context, sync_comp_for_node, entry_node):
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assert sync_comp_for_node.type == 'sync_comp_for'
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super().__init__(inference_state)
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self._defining_context = defining_context
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self._sync_comp_for_node = sync_comp_for_node
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self._entry_node = entry_node
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class ListComprehension(_BaseComprehension, Sequence):
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array_type = 'list'
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def py__simple_getitem__(self, index):
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if isinstance(index, slice):
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return ValueSet([self])
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all_types = list(self.py__iter__())
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with reraise_getitem_errors(IndexError, TypeError):
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lazy_value = all_types[index]
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return lazy_value.infer()
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class SetComprehension(_BaseComprehension, Sequence):
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array_type = 'set'
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class GeneratorComprehension(_BaseComprehension, GeneratorBase):
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pass
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class _DictKeyMixin:
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# TODO merge with _DictMixin?
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def get_mapping_item_values(self):
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return self._dict_keys(), self._dict_values()
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def get_key_values(self):
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# TODO merge with _dict_keys?
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return self._dict_keys()
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class DictComprehension(ComprehensionMixin, Sequence, _DictKeyMixin):
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array_type = 'dict'
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def __init__(self, inference_state, defining_context, sync_comp_for_node, key_node, value_node):
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assert sync_comp_for_node.type == 'sync_comp_for'
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super().__init__(inference_state)
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self._defining_context = defining_context
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self._sync_comp_for_node = sync_comp_for_node
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self._entry_node = key_node
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self._value_node = value_node
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def py__iter__(self, contextualized_node=None):
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for keys, values in self._iterate():
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yield LazyKnownValues(keys)
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def py__simple_getitem__(self, index):
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for keys, values in self._iterate():
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for k in keys:
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# Be careful in the future if refactoring, index could be a
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# slice object.
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if k.get_safe_value(default=object()) == index:
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return values
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raise SimpleGetItemNotFound()
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def _dict_keys(self):
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return ValueSet.from_sets(keys for keys, values in self._iterate())
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def _dict_values(self):
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return ValueSet.from_sets(values for keys, values in self._iterate())
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@publish_method('values')
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def _imitate_values(self, arguments):
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lazy_value = LazyKnownValues(self._dict_values())
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return ValueSet([FakeList(self.inference_state, [lazy_value])])
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@publish_method('items')
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def _imitate_items(self, arguments):
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lazy_values = [
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LazyKnownValue(
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FakeTuple(
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self.inference_state,
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[LazyKnownValues(key),
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LazyKnownValues(value)]
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)
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)
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for key, value in self._iterate()
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]
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return ValueSet([FakeList(self.inference_state, lazy_values)])
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def exact_key_items(self):
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# NOTE: A smarter thing can probably done here to achieve better
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# completions, but at least like this jedi doesn't crash
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return []
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class SequenceLiteralValue(Sequence):
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_TUPLE_LIKE = 'testlist_star_expr', 'testlist', 'subscriptlist'
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mapping = {'(': 'tuple',
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'[': 'list',
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'{': 'set'}
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def __init__(self, inference_state, defining_context, atom):
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super().__init__(inference_state)
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self.atom = atom
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self._defining_context = defining_context
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if self.atom.type in self._TUPLE_LIKE:
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self.array_type = 'tuple'
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else:
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self.array_type = SequenceLiteralValue.mapping[atom.children[0]]
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"""The builtin name of the array (list, set, tuple or dict)."""
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def _get_generics(self):
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if self.array_type == 'tuple':
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return tuple(x.infer().py__class__() for x in self.py__iter__())
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return super()._get_generics()
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def py__simple_getitem__(self, index):
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"""Here the index is an int/str. Raises IndexError/KeyError."""
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if isinstance(index, slice):
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return ValueSet([self])
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else:
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with reraise_getitem_errors(TypeError, KeyError, IndexError):
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node = self.get_tree_entries()[index]
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if node == ':' or node.type == 'subscript':
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return NO_VALUES
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return self._defining_context.infer_node(node)
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def py__iter__(self, contextualized_node=None):
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"""
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While values returns the possible values for any array field, this
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function returns the value for a certain index.
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"""
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for node in self.get_tree_entries():
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if node == ':' or node.type == 'subscript':
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# TODO this should probably use at least part of the code
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# of infer_subscript_list.
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yield LazyKnownValue(Slice(self._defining_context, None, None, None))
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else:
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yield LazyTreeValue(self._defining_context, node)
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yield from check_array_additions(self._defining_context, self)
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def py__len__(self):
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# This function is not really used often. It's more of a try.
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return len(self.get_tree_entries())
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def get_tree_entries(self):
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||
|
|
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 __repr__(self):
|
||
|
|
return "<%s of %s>" % (self.__class__.__name__, self.atom)
|
||
|
|
|
||
|
|
|
||
|
|
class DictLiteralValue(_DictMixin, SequenceLiteralValue, _DictKeyMixin):
|
||
|
|
array_type = 'dict'
|
||
|
|
|
||
|
|
def __init__(self, inference_state, defining_context, atom):
|
||
|
|
# Intentionally don't call the super class. This is definitely a sign
|
||
|
|
# that the architecture is bad and we should refactor.
|
||
|
|
Sequence.__init__(self, 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, '=='):
|
||
|
|
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 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 _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().__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 = 'tuple'
|
||
|
|
|
||
|
|
|
||
|
|
class FakeList(_FakeSequence):
|
||
|
|
array_type = 'tuple'
|
||
|
|
|
||
|
|
|
||
|
|
class FakeDict(_DictMixin, Sequence, _DictKeyMixin):
|
||
|
|
array_type = 'dict'
|
||
|
|
|
||
|
|
def __init__(self, inference_state, dct):
|
||
|
|
super().__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):
|
||
|
|
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().__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:
|
||
|
|
yield from array.py__iter__()
|
||
|
|
|
||
|
|
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)
|