forked from VimPlug/jedi
823 lines
29 KiB
Python
823 lines
29 KiB
Python
"""
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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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Array modifications
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*******************
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If the content of an array (``set``/``list``) is requested somewhere, the
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current module will be checked for appearances of ``arr.append``,
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``arr.insert``, etc. If the ``arr`` name points to an actual array, the
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content will be added
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This can be really cpu intensive, as you can imagine. Because |jedi| has to
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follow **every** ``append`` and check wheter it's the right array. However this
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works pretty good, because in *slow* cases, the recursion detector and other
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settings will stop this process.
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It is important to note that:
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1. Array modfications work only in the current module.
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2. Jedi only checks Array additions; ``list.pop``, etc are ignored.
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"""
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import sys
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from jedi import debug
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from jedi import settings
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from jedi._compatibility import force_unicode, is_py3
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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 import recursion
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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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predefine_names, infer_call_of_leaf, reraise_getitem_errors, \
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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 ParserTreeFilter, LazyAttributeOverwrite, \
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publish_method
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from jedi.inference.base_value import ValueSet, Value, NO_VALUES, \
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TreeValue, ValueualizedNode, iterate_values, HelperValueMixin, _sentinel
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from jedi.parser_utils import get_sync_comp_fors
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class IterableMixin(object):
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def py__stop_iteration_returns(self):
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return ValueSet([compiled.builtin_from_name(self.inference_state, u'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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if sys.version_info[0] == 2:
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# Python 2...........
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def get_safe_value(self, default=_sentinel):
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if default is _sentinel:
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raise ValueError("There exists no safe value for value %s" % self)
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return default
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else:
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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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generator, = self.inference_state.typing_module \
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.py__getattribute__('Generator') \
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.execute_annotation()
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return generator
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def is_instance(self):
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return False
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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 py__iter__(self, valueualized_node=None):
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return ValueSet([self])
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@publish_method('send')
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@publish_method('next', python_version_match=2)
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@publish_method('__next__', python_version_match=3)
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def py__next__(self):
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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, u'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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class Generator(GeneratorBase):
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"""Handling of `yield` functions."""
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def __init__(self, inference_state, func_execution_value):
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super(Generator, self).__init__(inference_state)
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self._func_execution_value = func_execution_value
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def py__iter__(self, valueualized_node=None):
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return self._func_execution_value.get_yield_lazy_values()
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def py__stop_iteration_returns(self):
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return self._func_execution_value.get_return_values()
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def __repr__(self):
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return "<%s of %s>" % (type(self).__name__, self._func_execution_value)
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class CompForValue(TreeValue):
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@classmethod
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def from_comp_for(cls, parent_context, comp_for):
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return cls(parent_context.inference_state, parent_context, comp_for)
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def get_filters(self, until_position=None, origin_scope=None):
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yield ParserTreeFilter(self)
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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_value=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(object):
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@inference_state_method_cache()
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def _get_comp_for_value(self, parent_context, comp_for):
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return CompForValue.from_comp_for(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_value
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input_types = parent_context.infer_node(input_node)
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# TODO: simulate await if self.is_async
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cn = ValueualizedNode(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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value_ = self._get_comp_for_value(
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parent_context,
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comp_for,
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)
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with predefine_names(value_, comp_for, dct):
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try:
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for result in self._nested(comp_fors[1:], value_):
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yield result
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except IndexError:
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iterated = value_.infer_node(self._entry_node)
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if self.array_type == 'dict':
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yield iterated, value_.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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for result in self._nested(comp_fors):
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yield result
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def py__iter__(self, valueualized_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(object):
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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 = u'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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def _get_wrapped_value(self):
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from jedi.inference.gradual.typing import GenericClass
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klass = compiled.builtin_from_name(self.inference_state, self.array_type)
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c, = GenericClass(klass, self._get_generics()).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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def py__class__(self):
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return compiled.builtin_from_name(self.inference_state, self.array_type)
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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, valueualized_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_value, sync_comp_for_node, entry_node):
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assert sync_comp_for_node.type == 'sync_comp_for'
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super(_BaseComprehension, self).__init__(inference_state)
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self._defining_value = defining_value
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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 = u'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 = u'set'
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class GeneratorComprehension(_BaseComprehension, GeneratorBase):
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pass
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class DictComprehension(ComprehensionMixin, Sequence):
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array_type = u'dict'
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def __init__(self, inference_state, defining_value, 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(DictComprehension, self).__init__(inference_state)
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self._defining_value = defining_value
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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, valueualized_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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if isinstance(k, compiled.CompiledObject):
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# Be careful in the future if refactoring, index could be a
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# slice.
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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):
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lazy_value = LazyKnownValues(self._dict_values())
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return ValueSet([FakeSequence(self.inference_state, u'list', [lazy_value])])
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@publish_method('items')
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def _imitate_items(self):
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lazy_values = [
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LazyKnownValue(
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FakeSequence(
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self.inference_state,
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u'tuple',
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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([FakeSequence(self.inference_state, u'list', lazy_values)])
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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 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 = {'(': u'tuple',
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'[': u'list',
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'{': u'set'}
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def __init__(self, inference_state, defining_value, atom):
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super(SequenceLiteralValue, self).__init__(inference_state)
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self.atom = atom
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self._defining_value = defining_value
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if self.atom.type in self._TUPLE_LIKE:
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self.array_type = u'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 py__simple_getitem__(self, index):
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"""Here the index is an int/str. Raises IndexError/KeyError."""
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if self.array_type == u'dict':
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compiled_obj_index = compiled.create_simple_object(self.inference_state, index)
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for key, value in self.get_tree_entries():
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for k in self._defining_value.infer_node(key):
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try:
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method = k.execute_operation
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except AttributeError:
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pass
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else:
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if method(compiled_obj_index, u'==').get_safe_value():
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return self._defining_value.infer_node(value)
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raise SimpleGetItemNotFound('No key found in dictionary %s.' % self)
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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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return self._defining_value.infer_node(node)
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def py__iter__(self, valueualized_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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if self.array_type == u'dict':
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# Get keys.
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types = NO_VALUES
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for k, _ in self.get_tree_entries():
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types |= self._defining_value.infer_node(k)
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# We don't know which dict index comes first, therefore always
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# yield all the types.
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for _ in types:
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yield LazyKnownValues(types)
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else:
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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_value, None, None, None))
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else:
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yield LazyTreeValue(self._defining_value, node)
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for addition in check_array_additions(self._defining_value, self):
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yield addition
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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 _dict_values(self):
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return ValueSet.from_sets(
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self._defining_value.infer_node(v)
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for k, v in self.get_tree_entries()
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)
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def get_tree_entries(self):
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c = self.atom.children
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|
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if self.atom.type in self._TUPLE_LIKE:
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return c[::2]
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array_node = c[1]
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if array_node in (']', '}', ')'):
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return [] # Direct closing bracket, doesn't contain items.
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if array_node.type == 'testlist_comp':
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# filter out (for now) pep 448 single-star unpacking
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return [value for value in array_node.children[::2]
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if value.type != "star_expr"]
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elif array_node.type == 'dictorsetmaker':
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kv = []
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iterator = iter(array_node.children)
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for key in iterator:
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if key == "**":
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# dict with pep 448 double-star unpacking
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# for now ignoring the values imported by **
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next(iterator)
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next(iterator, None) # Possible comma.
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else:
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op = next(iterator, None)
|
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if op is None or op == ',':
|
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if key.type == "star_expr":
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# pep 448 single-star unpacking
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# for now ignoring values imported by *
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pass
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else:
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kv.append(key) # A set.
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else:
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assert op == ':' # A dict.
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kv.append((key, next(iterator)))
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next(iterator, None) # Possible comma.
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return kv
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else:
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if array_node.type == "star_expr":
|
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# pep 448 single-star unpacking
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# for now ignoring values imported by *
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return []
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else:
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return [array_node]
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def exact_key_items(self):
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"""
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|
Returns a generator of tuples like dict.items(), where the key is
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resolved (as a string) and the values are still lazy values.
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|
"""
|
|
for key_node, value in self.get_tree_entries():
|
|
for key in self._defining_value.infer_node(key_node):
|
|
if is_string(key):
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yield key.get_safe_value(), LazyTreeValue(self._defining_value, value)
|
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|
|
def __repr__(self):
|
|
return "<%s of %s>" % (self.__class__.__name__, self.atom)
|
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|
|
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class DictLiteralValue(_DictMixin, SequenceLiteralValue):
|
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array_type = u'dict'
|
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|
def __init__(self, inference_state, defining_value, atom):
|
|
super(SequenceLiteralValue, self).__init__(inference_state)
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|
self._defining_value = defining_value
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self.atom = atom
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|
|
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@publish_method('values')
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|
def _imitate_values(self):
|
|
lazy_value = LazyKnownValues(self._dict_values())
|
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return ValueSet([FakeSequence(self.inference_state, u'list', [lazy_value])])
|
|
|
|
@publish_method('items')
|
|
def _imitate_items(self):
|
|
lazy_values = [
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|
LazyKnownValue(FakeSequence(
|
|
self.inference_state, u'tuple',
|
|
(LazyTreeValue(self._defining_value, key_node),
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|
LazyTreeValue(self._defining_value, value_node))
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|
)) for key_node, value_node in self.get_tree_entries()
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|
]
|
|
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return ValueSet([FakeSequence(self.inference_state, u'list', lazy_values)])
|
|
|
|
def _dict_keys(self):
|
|
return ValueSet.from_sets(
|
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self._defining_value.infer_node(k)
|
|
for k, v in self.get_tree_entries()
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|
)
|
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|
|
def get_mapping_item_values(self):
|
|
return self._dict_keys(), self._dict_values()
|
|
|
|
|
|
class _FakeArray(SequenceLiteralValue):
|
|
def __init__(self, inference_state, container, type):
|
|
super(SequenceLiteralValue, self).__init__(inference_state)
|
|
self.array_type = type
|
|
self.atom = container
|
|
# TODO is this class really needed?
|
|
|
|
|
|
class FakeSequence(_FakeArray):
|
|
def __init__(self, inference_state, array_type, lazy_value_list):
|
|
"""
|
|
type should be one of "tuple", "list"
|
|
"""
|
|
super(FakeSequence, self).__init__(inference_state, None, array_type)
|
|
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, valueualized_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 FakeDict(_DictMixin, _FakeArray):
|
|
def __init__(self, inference_state, dct):
|
|
super(FakeDict, self).__init__(inference_state, dct, u'dict')
|
|
self._dct = dct
|
|
|
|
def py__iter__(self, valueualized_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):
|
|
return ValueSet([FakeSequence(
|
|
self.inference_state, u'tuple',
|
|
[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 get_mapping_item_values(self):
|
|
return self._dict_keys(), self._dict_values()
|
|
|
|
def exact_key_items(self):
|
|
return self._dct.items()
|
|
|
|
|
|
class MergedArray(_FakeArray):
|
|
def __init__(self, inference_state, arrays):
|
|
super(MergedArray, self).__init__(inference_state, arrays, arrays[-1].array_type)
|
|
self._arrays = arrays
|
|
|
|
def py__iter__(self, valueualized_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 get_tree_entries(self):
|
|
for array in self._arrays:
|
|
for a in array.get_tree_entries():
|
|
yield a
|
|
|
|
def __len__(self):
|
|
return sum(len(a) for a in self._arrays)
|
|
|
|
|
|
def unpack_tuple_to_dict(value, 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(value, 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(exprlist):
|
|
n += 1
|
|
try:
|
|
part = next(parts)
|
|
except StopIteration:
|
|
# TODO this value is probably not right.
|
|
analysis.add(value, 'value-error-too-many-values', part,
|
|
message="ValueError: too many values to unpack (expected %s)" % n)
|
|
else:
|
|
dct.update(unpack_tuple_to_dict(value, lazy_value.infer(), part))
|
|
has_parts = next(parts, None)
|
|
if types and has_parts is not None:
|
|
# TODO this value is probably not right.
|
|
analysis.add(value, '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
|
|
|
|
|
|
def check_array_additions(value, sequence):
|
|
""" Just a mapper function for the internal _check_array_additions """
|
|
if sequence.array_type not in ('list', 'set'):
|
|
# TODO also check for dict updates
|
|
return NO_VALUES
|
|
|
|
return _check_array_additions(value, sequence)
|
|
|
|
|
|
@inference_state_method_cache(default=NO_VALUES)
|
|
@debug.increase_indent
|
|
def _check_array_additions(value, 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 = value.get_root_context()
|
|
if not settings.dynamic_array_additions or isinstance(module_context, compiled.CompiledObject):
|
|
debug.dbg('Dynamic array search aborted.', color='MAGENTA')
|
|
return NO_VALUES
|
|
|
|
def find_additions(value, arglist, add_name):
|
|
params = list(arguments.TreeArguments(value.inference_state, value, 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 = value.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
|
|
|
|
raise NotImplementedError
|
|
random_context = value.create_context(name)
|
|
|
|
with recursion.execution_allowed(value.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 = _ArrayInstance(instance, arguments)
|
|
from jedi.inference import arguments
|
|
return arguments.ValuesArguments([ValueSet([ai])])
|
|
|
|
|
|
class _ArrayInstance(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.
|
|
"""
|
|
def __init__(self, instance, var_args):
|
|
self.instance = instance
|
|
self.var_args = var_args
|
|
|
|
def py__class__(self):
|
|
tuple_, = self.instance.inference_state.builtins_module.py__getattribute__('tuple')
|
|
return tuple_
|
|
|
|
def py__iter__(self, valueualized_node=None):
|
|
var_args = self.var_args
|
|
try:
|
|
_, lazy_value = next(var_args.unpack())
|
|
except StopIteration:
|
|
pass
|
|
else:
|
|
for lazy in lazy_value.infer().iterate():
|
|
yield lazy
|
|
|
|
from jedi.inference import arguments
|
|
if isinstance(var_args, arguments.TreeArguments):
|
|
additions = _check_array_additions(var_args.value, self.instance)
|
|
for addition in additions:
|
|
yield addition
|
|
|
|
def iterate(self, valueualized_node=None, is_async=False):
|
|
return self.py__iter__(valueualized_node)
|
|
|
|
|
|
class Slice(object):
|
|
def __init__(self, value, start, stop, step):
|
|
self._value = value
|
|
self._slice_object = None
|
|
# All of them are either a Precedence or None.
|
|
self._start = start
|
|
self._stop = stop
|
|
self._step = step
|
|
|
|
def __getattr__(self, name):
|
|
if self._slice_object is None:
|
|
value = compiled.builtin_from_name(self._value.inference_state, 'slice')
|
|
self._slice_object, = value.execute_with_values()
|
|
return getattr(self._slice_object, name)
|
|
|
|
@property
|
|
def obj(self):
|
|
"""
|
|
Imitate CompiledObject.obj behavior and return a ``builtin.slice()``
|
|
object.
|
|
"""
|
|
def get(element):
|
|
if element is None:
|
|
return None
|
|
|
|
result = self._value.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)
|