forked from VimPlug/jedi
254 lines
9.9 KiB
Python
254 lines
9.9 KiB
Python
from collections import defaultdict
|
|
|
|
from jedi import debug
|
|
from jedi.inference.utils import PushBackIterator
|
|
from jedi.inference import analysis
|
|
from jedi.inference.lazy_value import LazyKnownValue, \
|
|
LazyTreeValue, LazyUnknownValue
|
|
from jedi.inference import docstrings
|
|
from jedi.inference.value import iterable
|
|
|
|
|
|
def _add_argument_issue(error_name, lazy_value, message):
|
|
if isinstance(lazy_value, LazyTreeValue):
|
|
node = lazy_value.data
|
|
if node.parent.type == 'argument':
|
|
node = node.parent
|
|
return analysis.add(lazy_value.context, error_name, node, message)
|
|
|
|
|
|
class ExecutedParam(object):
|
|
"""Fake a param and give it values."""
|
|
def __init__(self, execution_context, param_node, lazy_value, is_default=False):
|
|
self._execution_context = execution_context
|
|
self._param_node = param_node
|
|
self._lazy_value = lazy_value
|
|
self.string_name = param_node.name.value
|
|
self._is_default = is_default
|
|
|
|
def infer_annotations(self):
|
|
from jedi.inference.gradual.annotation import infer_param
|
|
return infer_param(self._execution_context, self._param_node)
|
|
|
|
def infer(self, use_hints=True):
|
|
if use_hints:
|
|
doc_params = docstrings.infer_param(self._execution_context, self._param_node)
|
|
ann = self.infer_annotations().execute_annotation()
|
|
if ann or doc_params:
|
|
return ann | doc_params
|
|
|
|
return self._lazy_value.infer()
|
|
|
|
def matches_signature(self):
|
|
if self._is_default:
|
|
return True
|
|
argument_values = self.infer(use_hints=False).py__class__()
|
|
if self._param_node.star_count:
|
|
return True
|
|
annotations = self.infer_annotations()
|
|
if not annotations:
|
|
# If we cannot infer annotations - or there aren't any - pretend
|
|
# that the signature matches.
|
|
return True
|
|
matches = any(c1.is_sub_class_of(c2)
|
|
for c1 in argument_values
|
|
for c2 in annotations.gather_annotation_classes())
|
|
debug.dbg("signature compare %s: %s <=> %s",
|
|
matches, argument_values, annotations, color='BLUE')
|
|
return matches
|
|
|
|
@property
|
|
def var_args(self):
|
|
return self._execution_context.var_args
|
|
|
|
def __repr__(self):
|
|
return '<%s: %s>' % (self.__class__.__name__, self.string_name)
|
|
|
|
|
|
def get_executed_params_and_issues(execution_context, arguments):
|
|
def too_many_args(argument):
|
|
m = _error_argument_count(funcdef, len(unpacked_va))
|
|
# Just report an error for the first param that is not needed (like
|
|
# cPython).
|
|
if arguments.get_calling_nodes():
|
|
# There might not be a valid calling node so check for that first.
|
|
issues.append(
|
|
_add_argument_issue(
|
|
'type-error-too-many-arguments',
|
|
argument,
|
|
message=m
|
|
)
|
|
)
|
|
else:
|
|
issues.append(None)
|
|
|
|
issues = [] # List[Optional[analysis issue]]
|
|
result_params = []
|
|
param_dict = {}
|
|
funcdef = execution_context.tree_node
|
|
# Default params are part of the value where the function was defined.
|
|
# This means that they might have access on class variables that the
|
|
# function itself doesn't have.
|
|
default_param_context = execution_context.function_value.get_default_param_context()
|
|
|
|
for param in funcdef.get_params():
|
|
param_dict[param.name.value] = param
|
|
unpacked_va = list(arguments.unpack(funcdef))
|
|
var_arg_iterator = PushBackIterator(iter(unpacked_va))
|
|
|
|
non_matching_keys = defaultdict(lambda: [])
|
|
keys_used = {}
|
|
keys_only = False
|
|
had_multiple_value_error = False
|
|
for param in funcdef.get_params():
|
|
# The value and key can both be null. There, the defaults apply.
|
|
# args / kwargs will just be empty arrays / dicts, respectively.
|
|
# Wrong value count is just ignored. If you try to test cases that are
|
|
# not allowed in Python, Jedi will maybe not show any completions.
|
|
is_default = False
|
|
key, argument = next(var_arg_iterator, (None, None))
|
|
while key is not None:
|
|
keys_only = True
|
|
try:
|
|
key_param = param_dict[key]
|
|
except KeyError:
|
|
non_matching_keys[key] = argument
|
|
else:
|
|
if key in keys_used:
|
|
had_multiple_value_error = True
|
|
m = ("TypeError: %s() got multiple values for keyword argument '%s'."
|
|
% (funcdef.name, key))
|
|
for valueualized_node in arguments.get_calling_nodes():
|
|
issues.append(
|
|
analysis.add(valueualized_node.context,
|
|
'type-error-multiple-values',
|
|
valueualized_node.node, message=m)
|
|
)
|
|
else:
|
|
keys_used[key] = ExecutedParam(execution_context, key_param, argument)
|
|
key, argument = next(var_arg_iterator, (None, None))
|
|
|
|
try:
|
|
result_params.append(keys_used[param.name.value])
|
|
continue
|
|
except KeyError:
|
|
pass
|
|
|
|
if param.star_count == 1:
|
|
# *args param
|
|
lazy_value_list = []
|
|
if argument is not None:
|
|
lazy_value_list.append(argument)
|
|
for key, argument in var_arg_iterator:
|
|
# Iterate until a key argument is found.
|
|
if key:
|
|
var_arg_iterator.push_back((key, argument))
|
|
break
|
|
lazy_value_list.append(argument)
|
|
seq = iterable.FakeSequence(execution_context.inference_state, u'tuple', lazy_value_list)
|
|
result_arg = LazyKnownValue(seq)
|
|
elif param.star_count == 2:
|
|
if argument is not None:
|
|
too_many_args(argument)
|
|
# **kwargs param
|
|
dct = iterable.FakeDict(execution_context.inference_state, dict(non_matching_keys))
|
|
result_arg = LazyKnownValue(dct)
|
|
non_matching_keys = {}
|
|
else:
|
|
# normal param
|
|
if argument is None:
|
|
# No value: Return an empty container
|
|
if param.default is None:
|
|
result_arg = LazyUnknownValue()
|
|
if not keys_only:
|
|
for valueualized_node in arguments.get_calling_nodes():
|
|
m = _error_argument_count(funcdef, len(unpacked_va))
|
|
issues.append(
|
|
analysis.add(
|
|
valueualized_node.context,
|
|
'type-error-too-few-arguments',
|
|
valueualized_node.node,
|
|
message=m,
|
|
)
|
|
)
|
|
else:
|
|
result_arg = LazyTreeValue(default_param_context, param.default)
|
|
is_default = True
|
|
else:
|
|
result_arg = argument
|
|
|
|
result_params.append(ExecutedParam(
|
|
execution_context, param, result_arg,
|
|
is_default=is_default
|
|
))
|
|
if not isinstance(result_arg, LazyUnknownValue):
|
|
keys_used[param.name.value] = result_params[-1]
|
|
|
|
if keys_only:
|
|
# All arguments should be handed over to the next function. It's not
|
|
# about the values inside, it's about the names. Jedi needs to now that
|
|
# there's nothing to find for certain names.
|
|
for k in set(param_dict) - set(keys_used):
|
|
param = param_dict[k]
|
|
|
|
if not (non_matching_keys or had_multiple_value_error or
|
|
param.star_count or param.default):
|
|
# add a warning only if there's not another one.
|
|
for valueualized_node in arguments.get_calling_nodes():
|
|
m = _error_argument_count(funcdef, len(unpacked_va))
|
|
issues.append(
|
|
analysis.add(valueualized_node.context,
|
|
'type-error-too-few-arguments',
|
|
valueualized_node.node, message=m)
|
|
)
|
|
|
|
for key, lazy_value in non_matching_keys.items():
|
|
m = "TypeError: %s() got an unexpected keyword argument '%s'." \
|
|
% (funcdef.name, key)
|
|
issues.append(
|
|
_add_argument_issue(
|
|
'type-error-keyword-argument',
|
|
lazy_value,
|
|
message=m
|
|
)
|
|
)
|
|
|
|
remaining_arguments = list(var_arg_iterator)
|
|
if remaining_arguments:
|
|
first_key, lazy_value = remaining_arguments[0]
|
|
too_many_args(lazy_value)
|
|
return result_params, issues
|
|
|
|
|
|
def _error_argument_count(funcdef, actual_count):
|
|
params = funcdef.get_params()
|
|
default_arguments = sum(1 for p in params if p.default or p.star_count)
|
|
|
|
if default_arguments == 0:
|
|
before = 'exactly '
|
|
else:
|
|
before = 'from %s to ' % (len(params) - default_arguments)
|
|
return ('TypeError: %s() takes %s%s arguments (%s given).'
|
|
% (funcdef.name, before, len(params), actual_count))
|
|
|
|
|
|
def _create_default_param(execution_context, param):
|
|
if param.star_count == 1:
|
|
result_arg = LazyKnownValue(
|
|
iterable.FakeSequence(execution_context.inference_state, u'tuple', [])
|
|
)
|
|
elif param.star_count == 2:
|
|
result_arg = LazyKnownValue(
|
|
iterable.FakeDict(execution_context.inference_state, {})
|
|
)
|
|
elif param.default is None:
|
|
result_arg = LazyUnknownValue()
|
|
else:
|
|
result_arg = LazyTreeValue(execution_context.parent_context, param.default)
|
|
return ExecutedParam(execution_context, param, result_arg)
|
|
|
|
|
|
def create_default_params(execution_context, funcdef):
|
|
return [_create_default_param(execution_context, p)
|
|
for p in funcdef.get_params()]
|