885 lines
34 KiB
Python
885 lines
34 KiB
Python
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"""
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Functions inferring the syntax tree.
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"""
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import copy
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from parso.python import tree
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from jedi._compatibility import force_unicode, unicode
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from jedi import debug
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from jedi import parser_utils
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from jedi.inference.base_value import ValueSet, NO_VALUES, ContextualizedNode, \
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iterator_to_value_set, iterate_values
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from jedi.inference.lazy_value import LazyTreeValue
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from jedi.inference import compiled
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from jedi.inference import recursion
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from jedi.inference import analysis
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from jedi.inference import imports
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from jedi.inference import arguments
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from jedi.inference.value import ClassValue, FunctionValue
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from jedi.inference.value import iterable
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from jedi.inference.value.dynamic_arrays import ListModification, DictModification
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from jedi.inference.value import TreeInstance
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from jedi.inference.helpers import is_string, is_literal, is_number, \
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get_names_of_node, is_big_annoying_library
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from jedi.inference.compiled.access import COMPARISON_OPERATORS
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from jedi.inference.cache import inference_state_method_cache
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from jedi.inference.gradual.stub_value import VersionInfo
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from jedi.inference.gradual import annotation
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from jedi.inference.names import TreeNameDefinition
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from jedi.inference.context import CompForContext
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from jedi.inference.value.decorator import Decoratee
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from jedi.plugins import plugin_manager
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operator_to_magic_method = {
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'+': '__add__',
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'-': '__sub__',
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'*': '__mul__',
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'@': '__matmul__',
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'/': '__truediv__',
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'//': '__floordiv__',
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'%': '__mod__',
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'**': '__pow__',
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'<<': '__lshift__',
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'>>': '__rshift__',
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'&': '__and__',
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'|': '__or__',
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'^': '__xor__',
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}
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reverse_operator_to_magic_method = {
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k: '__r' + v[2:] for k, v in operator_to_magic_method.items()
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}
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def _limit_value_infers(func):
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"""
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This is for now the way how we limit type inference going wild. There are
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other ways to ensure recursion limits as well. This is mostly necessary
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because of instance (self) access that can be quite tricky to limit.
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I'm still not sure this is the way to go, but it looks okay for now and we
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can still go anther way in the future. Tests are there. ~ dave
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"""
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def wrapper(context, *args, **kwargs):
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n = context.tree_node
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inference_state = context.inference_state
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try:
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inference_state.inferred_element_counts[n] += 1
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maximum = 300
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if context.parent_context is None \
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and context.get_value() is inference_state.builtins_module:
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# Builtins should have a more generous inference limit.
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# It is important that builtins can be executed, otherwise some
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# functions that depend on certain builtins features would be
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# broken, see e.g. GH #1432
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maximum *= 100
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if inference_state.inferred_element_counts[n] > maximum:
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debug.warning('In value %s there were too many inferences.', n)
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return NO_VALUES
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except KeyError:
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inference_state.inferred_element_counts[n] = 1
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return func(context, *args, **kwargs)
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return wrapper
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def infer_node(context, element):
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if isinstance(context, CompForContext):
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return _infer_node(context, element)
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if_stmt = element
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while if_stmt is not None:
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if_stmt = if_stmt.parent
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if if_stmt.type in ('if_stmt', 'for_stmt'):
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break
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if parser_utils.is_scope(if_stmt):
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if_stmt = None
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break
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predefined_if_name_dict = context.predefined_names.get(if_stmt)
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# TODO there's a lot of issues with this one. We actually should do
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# this in a different way. Caching should only be active in certain
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# cases and this all sucks.
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if predefined_if_name_dict is None and if_stmt \
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and if_stmt.type == 'if_stmt' and context.inference_state.is_analysis:
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if_stmt_test = if_stmt.children[1]
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name_dicts = [{}]
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# If we already did a check, we don't want to do it again -> If
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# value.predefined_names is filled, we stop.
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# We don't want to check the if stmt itself, it's just about
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# the content.
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if element.start_pos > if_stmt_test.end_pos:
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# Now we need to check if the names in the if_stmt match the
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# names in the suite.
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if_names = get_names_of_node(if_stmt_test)
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element_names = get_names_of_node(element)
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str_element_names = [e.value for e in element_names]
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if any(i.value in str_element_names for i in if_names):
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for if_name in if_names:
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definitions = context.inference_state.infer(context, if_name)
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# Every name that has multiple different definitions
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# causes the complexity to rise. The complexity should
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# never fall below 1.
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if len(definitions) > 1:
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if len(name_dicts) * len(definitions) > 16:
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debug.dbg('Too many options for if branch inference %s.', if_stmt)
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# There's only a certain amount of branches
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# Jedi can infer, otherwise it will take to
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# long.
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name_dicts = [{}]
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break
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original_name_dicts = list(name_dicts)
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name_dicts = []
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for definition in definitions:
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new_name_dicts = list(original_name_dicts)
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for i, name_dict in enumerate(new_name_dicts):
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new_name_dicts[i] = name_dict.copy()
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new_name_dicts[i][if_name.value] = ValueSet([definition])
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name_dicts += new_name_dicts
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else:
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for name_dict in name_dicts:
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name_dict[if_name.value] = definitions
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if len(name_dicts) > 1:
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result = NO_VALUES
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for name_dict in name_dicts:
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with context.predefine_names(if_stmt, name_dict):
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result |= _infer_node(context, element)
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return result
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else:
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return _infer_node_if_inferred(context, element)
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else:
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if predefined_if_name_dict:
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return _infer_node(context, element)
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else:
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return _infer_node_if_inferred(context, element)
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def _infer_node_if_inferred(context, element):
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"""
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TODO This function is temporary: Merge with infer_node.
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"""
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parent = element
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while parent is not None:
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parent = parent.parent
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predefined_if_name_dict = context.predefined_names.get(parent)
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if predefined_if_name_dict is not None:
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return _infer_node(context, element)
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return _infer_node_cached(context, element)
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@inference_state_method_cache(default=NO_VALUES)
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def _infer_node_cached(context, element):
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return _infer_node(context, element)
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@debug.increase_indent
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@_limit_value_infers
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def _infer_node(context, element):
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debug.dbg('infer_node %s@%s in %s', element, element.start_pos, context)
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inference_state = context.inference_state
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typ = element.type
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if typ in ('name', 'number', 'string', 'atom', 'strings', 'keyword', 'fstring'):
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return infer_atom(context, element)
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elif typ == 'lambdef':
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return ValueSet([FunctionValue.from_context(context, element)])
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elif typ == 'expr_stmt':
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return infer_expr_stmt(context, element)
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elif typ in ('power', 'atom_expr'):
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first_child = element.children[0]
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children = element.children[1:]
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had_await = False
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if first_child.type == 'keyword' and first_child.value == 'await':
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had_await = True
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first_child = children.pop(0)
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value_set = context.infer_node(first_child)
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for (i, trailer) in enumerate(children):
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if trailer == '**': # has a power operation.
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right = context.infer_node(children[i + 1])
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value_set = _infer_comparison(
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context,
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value_set,
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trailer,
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right
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)
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break
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value_set = infer_trailer(context, value_set, trailer)
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if had_await:
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return value_set.py__await__().py__stop_iteration_returns()
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return value_set
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elif typ in ('testlist_star_expr', 'testlist',):
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# The implicit tuple in statements.
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return ValueSet([iterable.SequenceLiteralValue(inference_state, context, element)])
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elif typ in ('not_test', 'factor'):
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value_set = context.infer_node(element.children[-1])
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for operator in element.children[:-1]:
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value_set = infer_factor(value_set, operator)
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return value_set
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elif typ == 'test':
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# `x if foo else y` case.
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return (context.infer_node(element.children[0])
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| context.infer_node(element.children[-1]))
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elif typ == 'operator':
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# Must be an ellipsis, other operators are not inferred.
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# In Python 2 ellipsis is coded as three single dot tokens, not
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# as one token 3 dot token.
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if element.value not in ('.', '...'):
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origin = element.parent
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raise AssertionError("unhandled operator %s in %s " % (repr(element.value), origin))
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return ValueSet([compiled.builtin_from_name(inference_state, u'Ellipsis')])
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elif typ == 'dotted_name':
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value_set = infer_atom(context, element.children[0])
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for next_name in element.children[2::2]:
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value_set = value_set.py__getattribute__(next_name, name_context=context)
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return value_set
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elif typ == 'eval_input':
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return context.infer_node(element.children[0])
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elif typ == 'annassign':
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return annotation.infer_annotation(context, element.children[1]) \
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.execute_annotation()
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elif typ == 'yield_expr':
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if len(element.children) and element.children[1].type == 'yield_arg':
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# Implies that it's a yield from.
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element = element.children[1].children[1]
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generators = context.infer_node(element) \
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.py__getattribute__('__iter__').execute_with_values()
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return generators.py__stop_iteration_returns()
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# Generator.send() is not implemented.
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return NO_VALUES
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elif typ == 'namedexpr_test':
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return context.infer_node(element.children[2])
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else:
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return infer_or_test(context, element)
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def infer_trailer(context, atom_values, trailer):
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trailer_op, node = trailer.children[:2]
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if node == ')': # `arglist` is optional.
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node = None
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if trailer_op == '[':
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trailer_op, node, _ = trailer.children
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return atom_values.get_item(
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_infer_subscript_list(context, node),
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ContextualizedNode(context, trailer)
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)
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else:
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debug.dbg('infer_trailer: %s in %s', trailer, atom_values)
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if trailer_op == '.':
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return atom_values.py__getattribute__(
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name_context=context,
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name_or_str=node
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)
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else:
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assert trailer_op == '(', 'trailer_op is actually %s' % trailer_op
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args = arguments.TreeArguments(context.inference_state, context, node, trailer)
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return atom_values.execute(args)
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def infer_atom(context, atom):
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"""
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Basically to process ``atom`` nodes. The parser sometimes doesn't
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generate the node (because it has just one child). In that case an atom
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might be a name or a literal as well.
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"""
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state = context.inference_state
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if atom.type == 'name':
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if atom.value in ('True', 'False', 'None'):
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# Python 2...
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return ValueSet([compiled.builtin_from_name(state, atom.value)])
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# This is the first global lookup.
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stmt = tree.search_ancestor(
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atom, 'expr_stmt', 'lambdef'
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) or atom
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if stmt.type == 'lambdef':
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stmt = atom
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position = stmt.start_pos
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if _is_annotation_name(atom):
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# Since Python 3.7 (with from __future__ import annotations),
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# annotations are essentially strings and can reference objects
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# that are defined further down in code. Therefore just set the
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# position to None, so the finder will not try to stop at a certain
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# position in the module.
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position = None
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return context.py__getattribute__(atom, position=position)
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elif atom.type == 'keyword':
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# For False/True/None
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if atom.value in ('False', 'True', 'None'):
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return ValueSet([compiled.builtin_from_name(state, atom.value)])
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elif atom.value == 'print':
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# print e.g. could be inferred like this in Python 2.7
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return NO_VALUES
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elif atom.value == 'yield':
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# Contrary to yield from, yield can just appear alone to return a
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# value when used with `.send()`.
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return NO_VALUES
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assert False, 'Cannot infer the keyword %s' % atom
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elif isinstance(atom, tree.Literal):
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string = state.compiled_subprocess.safe_literal_eval(atom.value)
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return ValueSet([compiled.create_simple_object(state, string)])
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elif atom.type == 'strings':
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# Will be multiple string.
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value_set = infer_atom(context, atom.children[0])
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for string in atom.children[1:]:
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right = infer_atom(context, string)
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value_set = _infer_comparison(context, value_set, u'+', right)
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return value_set
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elif atom.type == 'fstring':
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return compiled.get_string_value_set(state)
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else:
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c = atom.children
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# Parentheses without commas are not tuples.
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if c[0] == '(' and not len(c) == 2 \
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and not(c[1].type == 'testlist_comp'
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and len(c[1].children) > 1):
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return context.infer_node(c[1])
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try:
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comp_for = c[1].children[1]
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except (IndexError, AttributeError):
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pass
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else:
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if comp_for == ':':
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|
|
# Dict comprehensions have a colon at the 3rd index.
|
||
|
|
try:
|
||
|
|
comp_for = c[1].children[3]
|
||
|
|
except IndexError:
|
||
|
|
pass
|
||
|
|
|
||
|
|
if comp_for.type in ('comp_for', 'sync_comp_for'):
|
||
|
|
return ValueSet([iterable.comprehension_from_atom(
|
||
|
|
state, context, atom
|
||
|
|
)])
|
||
|
|
|
||
|
|
# It's a dict/list/tuple literal.
|
||
|
|
array_node = c[1]
|
||
|
|
try:
|
||
|
|
array_node_c = array_node.children
|
||
|
|
except AttributeError:
|
||
|
|
array_node_c = []
|
||
|
|
if c[0] == '{' and (array_node == '}' or ':' in array_node_c
|
||
|
|
or '**' in array_node_c):
|
||
|
|
new_value = iterable.DictLiteralValue(state, context, atom)
|
||
|
|
else:
|
||
|
|
new_value = iterable.SequenceLiteralValue(state, context, atom)
|
||
|
|
return ValueSet([new_value])
|
||
|
|
|
||
|
|
|
||
|
|
@_limit_value_infers
|
||
|
|
def infer_expr_stmt(context, stmt, seek_name=None):
|
||
|
|
with recursion.execution_allowed(context.inference_state, stmt) as allowed:
|
||
|
|
if allowed:
|
||
|
|
if seek_name is not None:
|
||
|
|
pep0484_values = \
|
||
|
|
annotation.find_type_from_comment_hint_assign(context, stmt, seek_name)
|
||
|
|
if pep0484_values:
|
||
|
|
return pep0484_values
|
||
|
|
|
||
|
|
return _infer_expr_stmt(context, stmt, seek_name)
|
||
|
|
return NO_VALUES
|
||
|
|
|
||
|
|
|
||
|
|
@debug.increase_indent
|
||
|
|
def _infer_expr_stmt(context, stmt, seek_name=None):
|
||
|
|
"""
|
||
|
|
The starting point of the completion. A statement always owns a call
|
||
|
|
list, which are the calls, that a statement does. In case multiple
|
||
|
|
names are defined in the statement, `seek_name` returns the result for
|
||
|
|
this name.
|
||
|
|
|
||
|
|
expr_stmt: testlist_star_expr (annassign | augassign (yield_expr|testlist) |
|
||
|
|
('=' (yield_expr|testlist_star_expr))*)
|
||
|
|
annassign: ':' test ['=' test]
|
||
|
|
augassign: ('+=' | '-=' | '*=' | '@=' | '/=' | '%=' | '&=' | '|=' | '^=' |
|
||
|
|
'<<=' | '>>=' | '**=' | '//=')
|
||
|
|
|
||
|
|
:param stmt: A `tree.ExprStmt`.
|
||
|
|
"""
|
||
|
|
def check_setitem(stmt):
|
||
|
|
atom_expr = stmt.children[0]
|
||
|
|
if atom_expr.type not in ('atom_expr', 'power'):
|
||
|
|
return False, None
|
||
|
|
name = atom_expr.children[0]
|
||
|
|
if name.type != 'name' or len(atom_expr.children) != 2:
|
||
|
|
return False, None
|
||
|
|
trailer = atom_expr.children[-1]
|
||
|
|
return trailer.children[0] == '[', trailer.children[1]
|
||
|
|
|
||
|
|
debug.dbg('infer_expr_stmt %s (%s)', stmt, seek_name)
|
||
|
|
rhs = stmt.get_rhs()
|
||
|
|
|
||
|
|
value_set = context.infer_node(rhs)
|
||
|
|
|
||
|
|
if seek_name:
|
||
|
|
n = TreeNameDefinition(context, seek_name)
|
||
|
|
value_set = check_tuple_assignments(n, value_set)
|
||
|
|
|
||
|
|
first_operator = next(stmt.yield_operators(), None)
|
||
|
|
is_setitem, subscriptlist = check_setitem(stmt)
|
||
|
|
is_annassign = first_operator not in ('=', None) and first_operator.type == 'operator'
|
||
|
|
if is_annassign or is_setitem:
|
||
|
|
# `=` is always the last character in aug assignments -> -1
|
||
|
|
name = stmt.get_defined_names(include_setitem=True)[0].value
|
||
|
|
left_values = context.py__getattribute__(name, position=stmt.start_pos)
|
||
|
|
|
||
|
|
if is_setitem:
|
||
|
|
def to_mod(v):
|
||
|
|
c = ContextualizedSubscriptListNode(context, subscriptlist)
|
||
|
|
if v.array_type == 'dict':
|
||
|
|
return DictModification(v, value_set, c)
|
||
|
|
elif v.array_type == 'list':
|
||
|
|
return ListModification(v, value_set, c)
|
||
|
|
return v
|
||
|
|
|
||
|
|
value_set = ValueSet(to_mod(v) for v in left_values)
|
||
|
|
else:
|
||
|
|
operator = copy.copy(first_operator)
|
||
|
|
operator.value = operator.value[:-1]
|
||
|
|
for_stmt = tree.search_ancestor(stmt, 'for_stmt')
|
||
|
|
if for_stmt is not None and for_stmt.type == 'for_stmt' and value_set \
|
||
|
|
and parser_utils.for_stmt_defines_one_name(for_stmt):
|
||
|
|
# Iterate through result and add the values, that's possible
|
||
|
|
# only in for loops without clutter, because they are
|
||
|
|
# predictable. Also only do it, if the variable is not a tuple.
|
||
|
|
node = for_stmt.get_testlist()
|
||
|
|
cn = ContextualizedNode(context, node)
|
||
|
|
ordered = list(cn.infer().iterate(cn))
|
||
|
|
|
||
|
|
for lazy_value in ordered:
|
||
|
|
dct = {for_stmt.children[1].value: lazy_value.infer()}
|
||
|
|
with context.predefine_names(for_stmt, dct):
|
||
|
|
t = context.infer_node(rhs)
|
||
|
|
left_values = _infer_comparison(context, left_values, operator, t)
|
||
|
|
value_set = left_values
|
||
|
|
else:
|
||
|
|
value_set = _infer_comparison(context, left_values, operator, value_set)
|
||
|
|
debug.dbg('infer_expr_stmt result %s', value_set)
|
||
|
|
return value_set
|
||
|
|
|
||
|
|
|
||
|
|
def infer_or_test(context, or_test):
|
||
|
|
iterator = iter(or_test.children)
|
||
|
|
types = context.infer_node(next(iterator))
|
||
|
|
for operator in iterator:
|
||
|
|
right = next(iterator)
|
||
|
|
if operator.type == 'comp_op': # not in / is not
|
||
|
|
operator = ' '.join(c.value for c in operator.children)
|
||
|
|
|
||
|
|
# handle type inference of and/or here.
|
||
|
|
if operator in ('and', 'or'):
|
||
|
|
left_bools = set(left.py__bool__() for left in types)
|
||
|
|
if left_bools == {True}:
|
||
|
|
if operator == 'and':
|
||
|
|
types = context.infer_node(right)
|
||
|
|
elif left_bools == {False}:
|
||
|
|
if operator != 'and':
|
||
|
|
types = context.infer_node(right)
|
||
|
|
# Otherwise continue, because of uncertainty.
|
||
|
|
else:
|
||
|
|
types = _infer_comparison(context, types, operator,
|
||
|
|
context.infer_node(right))
|
||
|
|
debug.dbg('infer_or_test types %s', types)
|
||
|
|
return types
|
||
|
|
|
||
|
|
|
||
|
|
@iterator_to_value_set
|
||
|
|
def infer_factor(value_set, operator):
|
||
|
|
"""
|
||
|
|
Calculates `+`, `-`, `~` and `not` prefixes.
|
||
|
|
"""
|
||
|
|
for value in value_set:
|
||
|
|
if operator == '-':
|
||
|
|
if is_number(value):
|
||
|
|
yield value.negate()
|
||
|
|
elif operator == 'not':
|
||
|
|
b = value.py__bool__()
|
||
|
|
if b is None: # Uncertainty.
|
||
|
|
return
|
||
|
|
yield compiled.create_simple_object(value.inference_state, not b)
|
||
|
|
else:
|
||
|
|
yield value
|
||
|
|
|
||
|
|
|
||
|
|
def _literals_to_types(inference_state, result):
|
||
|
|
# Changes literals ('a', 1, 1.0, etc) to its type instances (str(),
|
||
|
|
# int(), float(), etc).
|
||
|
|
new_result = NO_VALUES
|
||
|
|
for typ in result:
|
||
|
|
if is_literal(typ):
|
||
|
|
# Literals are only valid as long as the operations are
|
||
|
|
# correct. Otherwise add a value-free instance.
|
||
|
|
cls = compiled.builtin_from_name(inference_state, typ.name.string_name)
|
||
|
|
new_result |= cls.execute_with_values()
|
||
|
|
else:
|
||
|
|
new_result |= ValueSet([typ])
|
||
|
|
return new_result
|
||
|
|
|
||
|
|
|
||
|
|
def _infer_comparison(context, left_values, operator, right_values):
|
||
|
|
state = context.inference_state
|
||
|
|
if not left_values or not right_values:
|
||
|
|
# illegal slices e.g. cause left/right_result to be None
|
||
|
|
result = (left_values or NO_VALUES) | (right_values or NO_VALUES)
|
||
|
|
return _literals_to_types(state, result)
|
||
|
|
else:
|
||
|
|
# I don't think there's a reasonable chance that a string
|
||
|
|
# operation is still correct, once we pass something like six
|
||
|
|
# objects.
|
||
|
|
if len(left_values) * len(right_values) > 6:
|
||
|
|
return _literals_to_types(state, left_values | right_values)
|
||
|
|
else:
|
||
|
|
return ValueSet.from_sets(
|
||
|
|
_infer_comparison_part(state, context, left, operator, right)
|
||
|
|
for left in left_values
|
||
|
|
for right in right_values
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def _is_annotation_name(name):
|
||
|
|
ancestor = tree.search_ancestor(name, 'param', 'funcdef', 'expr_stmt')
|
||
|
|
if ancestor is None:
|
||
|
|
return False
|
||
|
|
|
||
|
|
if ancestor.type in ('param', 'funcdef'):
|
||
|
|
ann = ancestor.annotation
|
||
|
|
if ann is not None:
|
||
|
|
return ann.start_pos <= name.start_pos < ann.end_pos
|
||
|
|
elif ancestor.type == 'expr_stmt':
|
||
|
|
c = ancestor.children
|
||
|
|
if len(c) > 1 and c[1].type == 'annassign':
|
||
|
|
return c[1].start_pos <= name.start_pos < c[1].end_pos
|
||
|
|
return False
|
||
|
|
|
||
|
|
|
||
|
|
def _is_list(value):
|
||
|
|
return value.array_type == 'list'
|
||
|
|
|
||
|
|
|
||
|
|
def _is_tuple(value):
|
||
|
|
return value.array_type == 'tuple'
|
||
|
|
|
||
|
|
|
||
|
|
def _bool_to_value(inference_state, bool_):
|
||
|
|
return compiled.builtin_from_name(inference_state, force_unicode(str(bool_)))
|
||
|
|
|
||
|
|
|
||
|
|
def _get_tuple_ints(value):
|
||
|
|
if not isinstance(value, iterable.SequenceLiteralValue):
|
||
|
|
return None
|
||
|
|
numbers = []
|
||
|
|
for lazy_value in value.py__iter__():
|
||
|
|
if not isinstance(lazy_value, LazyTreeValue):
|
||
|
|
return None
|
||
|
|
node = lazy_value.data
|
||
|
|
if node.type != 'number':
|
||
|
|
return None
|
||
|
|
try:
|
||
|
|
numbers.append(int(node.value))
|
||
|
|
except ValueError:
|
||
|
|
return None
|
||
|
|
return numbers
|
||
|
|
|
||
|
|
|
||
|
|
def _infer_comparison_part(inference_state, context, left, operator, right):
|
||
|
|
l_is_num = is_number(left)
|
||
|
|
r_is_num = is_number(right)
|
||
|
|
if isinstance(operator, unicode):
|
||
|
|
str_operator = operator
|
||
|
|
else:
|
||
|
|
str_operator = force_unicode(str(operator.value))
|
||
|
|
|
||
|
|
if str_operator == '*':
|
||
|
|
# for iterables, ignore * operations
|
||
|
|
if isinstance(left, iterable.Sequence) or is_string(left):
|
||
|
|
return ValueSet([left])
|
||
|
|
elif isinstance(right, iterable.Sequence) or is_string(right):
|
||
|
|
return ValueSet([right])
|
||
|
|
elif str_operator == '+':
|
||
|
|
if l_is_num and r_is_num or is_string(left) and is_string(right):
|
||
|
|
return left.execute_operation(right, str_operator)
|
||
|
|
elif _is_list(left) and _is_list(right) or _is_tuple(left) and _is_tuple(right):
|
||
|
|
return ValueSet([iterable.MergedArray(inference_state, (left, right))])
|
||
|
|
elif str_operator == '-':
|
||
|
|
if l_is_num and r_is_num:
|
||
|
|
return left.execute_operation(right, str_operator)
|
||
|
|
elif str_operator == '%':
|
||
|
|
# With strings and numbers the left type typically remains. Except for
|
||
|
|
# `int() % float()`.
|
||
|
|
return ValueSet([left])
|
||
|
|
elif str_operator in COMPARISON_OPERATORS:
|
||
|
|
if left.is_compiled() and right.is_compiled():
|
||
|
|
# Possible, because the return is not an option. Just compare.
|
||
|
|
result = left.execute_operation(right, str_operator)
|
||
|
|
if result:
|
||
|
|
return result
|
||
|
|
else:
|
||
|
|
if str_operator in ('is', '!=', '==', 'is not'):
|
||
|
|
operation = COMPARISON_OPERATORS[str_operator]
|
||
|
|
bool_ = operation(left, right)
|
||
|
|
# Only if == returns True or != returns False, we can continue.
|
||
|
|
# There's no guarantee that they are not equal. This can help
|
||
|
|
# in some cases, but does not cover everything.
|
||
|
|
if (str_operator in ('is', '==')) == bool_:
|
||
|
|
return ValueSet([_bool_to_value(inference_state, bool_)])
|
||
|
|
|
||
|
|
if isinstance(left, VersionInfo):
|
||
|
|
version_info = _get_tuple_ints(right)
|
||
|
|
if version_info is not None:
|
||
|
|
bool_result = compiled.access.COMPARISON_OPERATORS[operator](
|
||
|
|
inference_state.environment.version_info,
|
||
|
|
tuple(version_info)
|
||
|
|
)
|
||
|
|
return ValueSet([_bool_to_value(inference_state, bool_result)])
|
||
|
|
|
||
|
|
return ValueSet([
|
||
|
|
_bool_to_value(inference_state, True),
|
||
|
|
_bool_to_value(inference_state, False)
|
||
|
|
])
|
||
|
|
elif str_operator in ('in', 'not in'):
|
||
|
|
return NO_VALUES
|
||
|
|
|
||
|
|
def check(obj):
|
||
|
|
"""Checks if a Jedi object is either a float or an int."""
|
||
|
|
return isinstance(obj, TreeInstance) and \
|
||
|
|
obj.name.string_name in ('int', 'float')
|
||
|
|
|
||
|
|
# Static analysis, one is a number, the other one is not.
|
||
|
|
if str_operator in ('+', '-') and l_is_num != r_is_num \
|
||
|
|
and not (check(left) or check(right)):
|
||
|
|
message = "TypeError: unsupported operand type(s) for +: %s and %s"
|
||
|
|
analysis.add(context, 'type-error-operation', operator,
|
||
|
|
message % (left, right))
|
||
|
|
|
||
|
|
if left.is_class() or right.is_class():
|
||
|
|
return NO_VALUES
|
||
|
|
|
||
|
|
method_name = operator_to_magic_method[str_operator]
|
||
|
|
magic_methods = left.py__getattribute__(method_name)
|
||
|
|
if magic_methods:
|
||
|
|
result = magic_methods.execute_with_values(right)
|
||
|
|
if result:
|
||
|
|
return result
|
||
|
|
|
||
|
|
if not magic_methods:
|
||
|
|
reverse_method_name = reverse_operator_to_magic_method[str_operator]
|
||
|
|
magic_methods = right.py__getattribute__(reverse_method_name)
|
||
|
|
|
||
|
|
result = magic_methods.execute_with_values(left)
|
||
|
|
if result:
|
||
|
|
return result
|
||
|
|
|
||
|
|
result = ValueSet([left, right])
|
||
|
|
debug.dbg('Used operator %s resulting in %s', operator, result)
|
||
|
|
return result
|
||
|
|
|
||
|
|
|
||
|
|
@plugin_manager.decorate()
|
||
|
|
def tree_name_to_values(inference_state, context, tree_name):
|
||
|
|
value_set = NO_VALUES
|
||
|
|
module_node = context.get_root_context().tree_node
|
||
|
|
# First check for annotations, like: `foo: int = 3`
|
||
|
|
if module_node is not None:
|
||
|
|
names = module_node.get_used_names().get(tree_name.value, [])
|
||
|
|
found_annotation = False
|
||
|
|
for name in names:
|
||
|
|
expr_stmt = name.parent
|
||
|
|
|
||
|
|
if expr_stmt.type == "expr_stmt" and expr_stmt.children[1].type == "annassign":
|
||
|
|
correct_scope = parser_utils.get_parent_scope(name) == context.tree_node
|
||
|
|
if correct_scope:
|
||
|
|
found_annotation = True
|
||
|
|
value_set |= annotation.infer_annotation(
|
||
|
|
context, expr_stmt.children[1].children[1]
|
||
|
|
).execute_annotation()
|
||
|
|
if found_annotation:
|
||
|
|
return value_set
|
||
|
|
|
||
|
|
types = []
|
||
|
|
node = tree_name.get_definition(import_name_always=True, include_setitem=True)
|
||
|
|
if node is None:
|
||
|
|
node = tree_name.parent
|
||
|
|
if node.type == 'global_stmt':
|
||
|
|
c = context.create_context(tree_name)
|
||
|
|
if c.is_module():
|
||
|
|
# In case we are already part of the module, there is no point
|
||
|
|
# in looking up the global statement anymore, because it's not
|
||
|
|
# valid at that point anyway.
|
||
|
|
return NO_VALUES
|
||
|
|
# For global_stmt lookups, we only need the first possible scope,
|
||
|
|
# which means the function itself.
|
||
|
|
filter = next(c.get_filters())
|
||
|
|
names = filter.get(tree_name.value)
|
||
|
|
return ValueSet.from_sets(name.infer() for name in names)
|
||
|
|
elif node.type not in ('import_from', 'import_name'):
|
||
|
|
c = context.create_context(tree_name)
|
||
|
|
return infer_atom(c, tree_name)
|
||
|
|
|
||
|
|
typ = node.type
|
||
|
|
if typ == 'for_stmt':
|
||
|
|
types = annotation.find_type_from_comment_hint_for(context, node, tree_name)
|
||
|
|
if types:
|
||
|
|
return types
|
||
|
|
if typ == 'with_stmt':
|
||
|
|
types = annotation.find_type_from_comment_hint_with(context, node, tree_name)
|
||
|
|
if types:
|
||
|
|
return types
|
||
|
|
|
||
|
|
if typ in ('for_stmt', 'comp_for', 'sync_comp_for'):
|
||
|
|
try:
|
||
|
|
types = context.predefined_names[node][tree_name.value]
|
||
|
|
except KeyError:
|
||
|
|
cn = ContextualizedNode(context, node.children[3])
|
||
|
|
for_types = iterate_values(
|
||
|
|
cn.infer(),
|
||
|
|
contextualized_node=cn,
|
||
|
|
is_async=node.parent.type == 'async_stmt',
|
||
|
|
)
|
||
|
|
n = TreeNameDefinition(context, tree_name)
|
||
|
|
types = check_tuple_assignments(n, for_types)
|
||
|
|
elif typ == 'expr_stmt':
|
||
|
|
types = infer_expr_stmt(context, node, tree_name)
|
||
|
|
elif typ == 'with_stmt':
|
||
|
|
value_managers = context.infer_node(node.get_test_node_from_name(tree_name))
|
||
|
|
enter_methods = value_managers.py__getattribute__(u'__enter__')
|
||
|
|
return enter_methods.execute_with_values()
|
||
|
|
elif typ in ('import_from', 'import_name'):
|
||
|
|
types = imports.infer_import(context, tree_name)
|
||
|
|
elif typ in ('funcdef', 'classdef'):
|
||
|
|
types = _apply_decorators(context, node)
|
||
|
|
elif typ == 'try_stmt':
|
||
|
|
# TODO an exception can also be a tuple. Check for those.
|
||
|
|
# TODO check for types that are not classes and add it to
|
||
|
|
# the static analysis report.
|
||
|
|
exceptions = context.infer_node(tree_name.get_previous_sibling().get_previous_sibling())
|
||
|
|
types = exceptions.execute_with_values()
|
||
|
|
elif typ == 'param':
|
||
|
|
types = NO_VALUES
|
||
|
|
elif typ == 'del_stmt':
|
||
|
|
types = NO_VALUES
|
||
|
|
else:
|
||
|
|
raise ValueError("Should not happen. type: %s" % typ)
|
||
|
|
return types
|
||
|
|
|
||
|
|
|
||
|
|
# We don't want to have functions/classes that are created by the same
|
||
|
|
# tree_node.
|
||
|
|
@inference_state_method_cache()
|
||
|
|
def _apply_decorators(context, node):
|
||
|
|
"""
|
||
|
|
Returns the function, that should to be executed in the end.
|
||
|
|
This is also the places where the decorators are processed.
|
||
|
|
"""
|
||
|
|
if node.type == 'classdef':
|
||
|
|
decoratee_value = ClassValue(
|
||
|
|
context.inference_state,
|
||
|
|
parent_context=context,
|
||
|
|
tree_node=node
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
decoratee_value = FunctionValue.from_context(context, node)
|
||
|
|
initial = values = ValueSet([decoratee_value])
|
||
|
|
|
||
|
|
if is_big_annoying_library(context):
|
||
|
|
return values
|
||
|
|
|
||
|
|
for dec in reversed(node.get_decorators()):
|
||
|
|
debug.dbg('decorator: %s %s', dec, values, color="MAGENTA")
|
||
|
|
with debug.increase_indent_cm():
|
||
|
|
dec_values = context.infer_node(dec.children[1])
|
||
|
|
trailer_nodes = dec.children[2:-1]
|
||
|
|
if trailer_nodes:
|
||
|
|
# Create a trailer and infer it.
|
||
|
|
trailer = tree.PythonNode('trailer', trailer_nodes)
|
||
|
|
trailer.parent = dec
|
||
|
|
dec_values = infer_trailer(context, dec_values, trailer)
|
||
|
|
|
||
|
|
if not len(dec_values):
|
||
|
|
code = dec.get_code(include_prefix=False)
|
||
|
|
# For the short future, we don't want to hear about the runtime
|
||
|
|
# decorator in typing that was intentionally omitted. This is not
|
||
|
|
# "correct", but helps with debugging.
|
||
|
|
if code != '@runtime\n':
|
||
|
|
debug.warning('decorator not found: %s on %s', dec, node)
|
||
|
|
return initial
|
||
|
|
|
||
|
|
values = dec_values.execute(arguments.ValuesArguments([values]))
|
||
|
|
if not len(values):
|
||
|
|
debug.warning('not possible to resolve wrappers found %s', node)
|
||
|
|
return initial
|
||
|
|
|
||
|
|
debug.dbg('decorator end %s', values, color="MAGENTA")
|
||
|
|
if values != initial:
|
||
|
|
return ValueSet([Decoratee(c, decoratee_value) for c in values])
|
||
|
|
return values
|
||
|
|
|
||
|
|
|
||
|
|
def check_tuple_assignments(name, value_set):
|
||
|
|
"""
|
||
|
|
Checks if tuples are assigned.
|
||
|
|
"""
|
||
|
|
lazy_value = None
|
||
|
|
for index, node in name.assignment_indexes():
|
||
|
|
cn = ContextualizedNode(name.parent_context, node)
|
||
|
|
iterated = value_set.iterate(cn)
|
||
|
|
if isinstance(index, slice):
|
||
|
|
# For no star unpacking is not possible.
|
||
|
|
return NO_VALUES
|
||
|
|
i = 0
|
||
|
|
while i <= index:
|
||
|
|
try:
|
||
|
|
lazy_value = next(iterated)
|
||
|
|
except StopIteration:
|
||
|
|
# We could do this with the default param in next. But this
|
||
|
|
# would allow this loop to run for a very long time if the
|
||
|
|
# index number is high. Therefore break if the loop is
|
||
|
|
# finished.
|
||
|
|
return NO_VALUES
|
||
|
|
else:
|
||
|
|
i += lazy_value.max
|
||
|
|
value_set = lazy_value.infer()
|
||
|
|
return value_set
|
||
|
|
|
||
|
|
|
||
|
|
class ContextualizedSubscriptListNode(ContextualizedNode):
|
||
|
|
def infer(self):
|
||
|
|
return _infer_subscript_list(self.context, self.node)
|
||
|
|
|
||
|
|
|
||
|
|
def _infer_subscript_list(context, index):
|
||
|
|
"""
|
||
|
|
Handles slices in subscript nodes.
|
||
|
|
"""
|
||
|
|
if index == ':':
|
||
|
|
# Like array[:]
|
||
|
|
return ValueSet([iterable.Slice(context, None, None, None)])
|
||
|
|
|
||
|
|
elif index.type == 'subscript' and not index.children[0] == '.':
|
||
|
|
# subscript basically implies a slice operation, except for Python 2's
|
||
|
|
# Ellipsis.
|
||
|
|
# e.g. array[:3]
|
||
|
|
result = []
|
||
|
|
for el in index.children:
|
||
|
|
if el == ':':
|
||
|
|
if not result:
|
||
|
|
result.append(None)
|
||
|
|
elif el.type == 'sliceop':
|
||
|
|
if len(el.children) == 2:
|
||
|
|
result.append(el.children[1])
|
||
|
|
else:
|
||
|
|
result.append(el)
|
||
|
|
result += [None] * (3 - len(result))
|
||
|
|
|
||
|
|
return ValueSet([iterable.Slice(context, *result)])
|
||
|
|
elif index.type == 'subscriptlist':
|
||
|
|
return ValueSet([iterable.SequenceLiteralValue(context.inference_state, context, index)])
|
||
|
|
|
||
|
|
# No slices
|
||
|
|
return context.infer_node(index)
|