336 lines
12 KiB
Python
336 lines
12 KiB
Python
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import re
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from itertools import zip_longest
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from parso.python import tree
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from jedi import debug
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from jedi.inference.utils import PushBackIterator
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from jedi.inference import analysis
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from jedi.inference.lazy_value import LazyKnownValue, LazyKnownValues, \
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LazyTreeValue, get_merged_lazy_value
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from jedi.inference.names import ParamName, TreeNameDefinition, AnonymousParamName
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from jedi.inference.base_value import NO_VALUES, ValueSet, ContextualizedNode
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from jedi.inference.value import iterable
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from jedi.inference.cache import inference_state_as_method_param_cache
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def try_iter_content(types, depth=0):
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"""Helper method for static analysis."""
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if depth > 10:
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# It's possible that a loop has references on itself (especially with
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# CompiledValue). Therefore don't loop infinitely.
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return
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for typ in types:
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try:
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f = typ.py__iter__
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except AttributeError:
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pass
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else:
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for lazy_value in f():
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try_iter_content(lazy_value.infer(), depth + 1)
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class ParamIssue(Exception):
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pass
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def repack_with_argument_clinic(clinic_string):
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"""
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Transforms a function or method with arguments to the signature that is
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given as an argument clinic notation.
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Argument clinic is part of CPython and used for all the functions that are
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implemented in C (Python 3.7):
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str.split.__text_signature__
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# Results in: '($self, /, sep=None, maxsplit=-1)'
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"""
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def decorator(func):
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def wrapper(value, arguments):
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try:
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args = tuple(iterate_argument_clinic(
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value.inference_state,
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arguments,
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clinic_string,
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))
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except ParamIssue:
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return NO_VALUES
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else:
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return func(value, *args)
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return wrapper
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return decorator
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def iterate_argument_clinic(inference_state, arguments, clinic_string):
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"""Uses a list with argument clinic information (see PEP 436)."""
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clinic_args = list(_parse_argument_clinic(clinic_string))
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iterator = PushBackIterator(arguments.unpack())
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for i, (name, optional, allow_kwargs, stars) in enumerate(clinic_args):
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if stars == 1:
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lazy_values = []
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for key, argument in iterator:
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if key is not None:
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iterator.push_back((key, argument))
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break
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lazy_values.append(argument)
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yield ValueSet([iterable.FakeTuple(inference_state, lazy_values)])
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lazy_values
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continue
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elif stars == 2:
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raise NotImplementedError()
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key, argument = next(iterator, (None, None))
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if key is not None:
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debug.warning('Keyword arguments in argument clinic are currently not supported.')
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raise ParamIssue
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if argument is None and not optional:
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debug.warning('TypeError: %s expected at least %s arguments, got %s',
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name, len(clinic_args), i)
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raise ParamIssue
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value_set = NO_VALUES if argument is None else argument.infer()
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if not value_set and not optional:
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# For the stdlib we always want values. If we don't get them,
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# that's ok, maybe something is too hard to resolve, however,
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# we will not proceed with the type inference of that function.
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debug.warning('argument_clinic "%s" not resolvable.', name)
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raise ParamIssue
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yield value_set
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def _parse_argument_clinic(string):
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allow_kwargs = False
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optional = False
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while string:
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# Optional arguments have to begin with a bracket. And should always be
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# at the end of the arguments. This is therefore not a proper argument
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# clinic implementation. `range()` for exmple allows an optional start
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# value at the beginning.
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match = re.match(r'(?:(?:(\[),? ?|, ?|)(\**\w+)|, ?/)\]*', string)
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string = string[len(match.group(0)):]
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if not match.group(2): # A slash -> allow named arguments
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allow_kwargs = True
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continue
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optional = optional or bool(match.group(1))
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word = match.group(2)
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stars = word.count('*')
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word = word[stars:]
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yield (word, optional, allow_kwargs, stars)
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if stars:
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allow_kwargs = True
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class _AbstractArgumentsMixin:
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def unpack(self, funcdef=None):
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raise NotImplementedError
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def get_calling_nodes(self):
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return []
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class AbstractArguments(_AbstractArgumentsMixin):
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context = None
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argument_node = None
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trailer = None
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def unpack_arglist(arglist):
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if arglist is None:
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return
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if arglist.type != 'arglist' and not (
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arglist.type == 'argument' and arglist.children[0] in ('*', '**')):
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yield 0, arglist
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return
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iterator = iter(arglist.children)
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for child in iterator:
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if child == ',':
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continue
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elif child in ('*', '**'):
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c = next(iterator, None)
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assert c is not None
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yield len(child.value), c
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elif child.type == 'argument' and \
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child.children[0] in ('*', '**'):
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assert len(child.children) == 2
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yield len(child.children[0].value), child.children[1]
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else:
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yield 0, child
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class TreeArguments(AbstractArguments):
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def __init__(self, inference_state, context, argument_node, trailer=None):
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"""
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:param argument_node: May be an argument_node or a list of nodes.
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"""
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self.argument_node = argument_node
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self.context = context
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self._inference_state = inference_state
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self.trailer = trailer # Can be None, e.g. in a class definition.
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@classmethod
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@inference_state_as_method_param_cache()
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def create_cached(cls, *args, **kwargs):
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return cls(*args, **kwargs)
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def unpack(self, funcdef=None):
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named_args = []
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for star_count, el in unpack_arglist(self.argument_node):
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if star_count == 1:
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arrays = self.context.infer_node(el)
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iterators = [_iterate_star_args(self.context, a, el, funcdef)
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for a in arrays]
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for values in list(zip_longest(*iterators)):
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yield None, get_merged_lazy_value(
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[v for v in values if v is not None]
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)
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elif star_count == 2:
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arrays = self.context.infer_node(el)
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for dct in arrays:
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yield from _star_star_dict(self.context, dct, el, funcdef)
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else:
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if el.type == 'argument':
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c = el.children
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if len(c) == 3: # Keyword argument.
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named_args.append((c[0].value, LazyTreeValue(self.context, c[2]),))
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else: # Generator comprehension.
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# Include the brackets with the parent.
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sync_comp_for = el.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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comp = iterable.GeneratorComprehension(
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self._inference_state,
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defining_context=self.context,
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sync_comp_for_node=sync_comp_for,
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entry_node=el.children[0],
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)
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yield None, LazyKnownValue(comp)
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else:
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yield None, LazyTreeValue(self.context, el)
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# Reordering arguments is necessary, because star args sometimes appear
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# after named argument, but in the actual order it's prepended.
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yield from named_args
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def _as_tree_tuple_objects(self):
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for star_count, argument in unpack_arglist(self.argument_node):
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default = None
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if argument.type == 'argument':
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if len(argument.children) == 3: # Keyword argument.
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argument, default = argument.children[::2]
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yield argument, default, star_count
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def iter_calling_names_with_star(self):
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for name, default, star_count in self._as_tree_tuple_objects():
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# TODO this function is a bit strange. probably refactor?
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if not star_count or not isinstance(name, tree.Name):
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continue
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yield TreeNameDefinition(self.context, name)
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def __repr__(self):
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return '<%s: %s>' % (self.__class__.__name__, self.argument_node)
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def get_calling_nodes(self):
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old_arguments_list = []
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arguments = self
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while arguments not in old_arguments_list:
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if not isinstance(arguments, TreeArguments):
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break
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old_arguments_list.append(arguments)
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for calling_name in reversed(list(arguments.iter_calling_names_with_star())):
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names = calling_name.goto()
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if len(names) != 1:
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break
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if isinstance(names[0], AnonymousParamName):
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# Dynamic parameters should not have calling nodes, because
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# they are dynamic and extremely random.
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return []
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if not isinstance(names[0], ParamName):
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break
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executed_param_name = names[0].get_executed_param_name()
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arguments = executed_param_name.arguments
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break
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if arguments.argument_node is not None:
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return [ContextualizedNode(arguments.context, arguments.argument_node)]
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if arguments.trailer is not None:
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return [ContextualizedNode(arguments.context, arguments.trailer)]
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return []
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class ValuesArguments(AbstractArguments):
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def __init__(self, values_list):
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self._values_list = values_list
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def unpack(self, funcdef=None):
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for values in self._values_list:
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yield None, LazyKnownValues(values)
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def __repr__(self):
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return '<%s: %s>' % (self.__class__.__name__, self._values_list)
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class TreeArgumentsWrapper(_AbstractArgumentsMixin):
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def __init__(self, arguments):
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self._wrapped_arguments = arguments
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@property
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def context(self):
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return self._wrapped_arguments.context
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@property
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def argument_node(self):
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return self._wrapped_arguments.argument_node
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@property
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def trailer(self):
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return self._wrapped_arguments.trailer
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def unpack(self, func=None):
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raise NotImplementedError
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def get_calling_nodes(self):
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return self._wrapped_arguments.get_calling_nodes()
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def __repr__(self):
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return '<%s: %s>' % (self.__class__.__name__, self._wrapped_arguments)
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def _iterate_star_args(context, array, input_node, funcdef=None):
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if not array.py__getattribute__('__iter__'):
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if funcdef is not None:
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# TODO this funcdef should not be needed.
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m = "TypeError: %s() argument after * must be a sequence, not %s" \
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% (funcdef.name.value, array)
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analysis.add(context, 'type-error-star', input_node, message=m)
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try:
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iter_ = array.py__iter__
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except AttributeError:
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pass
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else:
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yield from iter_()
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def _star_star_dict(context, array, input_node, funcdef):
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from jedi.inference.value.instance import CompiledInstance
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if isinstance(array, CompiledInstance) and array.name.string_name == 'dict':
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# For now ignore this case. In the future add proper iterators and just
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# make one call without crazy isinstance checks.
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return {}
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elif isinstance(array, iterable.Sequence) and array.array_type == 'dict':
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return array.exact_key_items()
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else:
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if funcdef is not None:
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m = "TypeError: %s argument after ** must be a mapping, not %s" \
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% (funcdef.name.value, array)
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analysis.add(context, 'type-error-star-star', input_node, message=m)
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return {}
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