Vehicle-Anti-Theft-Face-Rec.../venv/Lib/site-packages/jedi/inference/param.py

258 lines
10 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.value import iterable
from jedi._compatibility import Parameter
from jedi.inference.names import ParamName
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 ExecutedParamName(ParamName):
def __init__(self, function_value, arguments, param_node, lazy_value, is_default=False):
super(ExecutedParamName, self).__init__(
function_value, param_node.name, arguments=arguments)
self._lazy_value = lazy_value
self._is_default = is_default
def infer(self):
return self._lazy_value.infer()
def matches_signature(self):
if self._is_default:
return True
argument_values = self.infer().py__class__()
if self.get_kind() in (Parameter.VAR_POSITIONAL, Parameter.VAR_KEYWORD):
return True
annotations = self.infer_annotation(execute_annotation=False)
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("param compare %s: %s <=> %s",
matches, argument_values, annotations, color='BLUE')
return matches
def __repr__(self):
return '<%s: %s>' % (self.__class__.__name__, self.string_name)
def get_executed_param_names_and_issues(function_value, arguments):
"""
Return a tuple of:
- a list of `ExecutedParamName`s corresponding to the arguments of the
function execution `function_value`, containing the inferred value of
those arguments (whether explicit or default)
- a list of the issues encountered while building that list
For example, given:
```
def foo(a, b, c=None, d='d'): ...
foo(42, c='c')
```
Then for the execution of `foo`, this will return a tuple containing:
- a list with entries for each parameter a, b, c & d; the entries for a,
c, & d will have their values (42, 'c' and 'd' respectively) included.
- a list with a single entry about the lack of a value for `b`
"""
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)
debug.warning('non-public warning: %s', m)
issues = [] # List[Optional[analysis issue]]
result_params = []
param_dict = {}
funcdef = function_value.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 = 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 contextualized_node in arguments.get_calling_nodes():
issues.append(
analysis.add(contextualized_node.context,
'type-error-multiple-values',
contextualized_node.node, message=m)
)
else:
keys_used[key] = ExecutedParamName(
function_value, arguments, 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.FakeTuple(function_value.inference_state, 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(function_value.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 contextualized_node in arguments.get_calling_nodes():
m = _error_argument_count(funcdef, len(unpacked_va))
issues.append(
analysis.add(
contextualized_node.context,
'type-error-too-few-arguments',
contextualized_node.node,
message=m,
)
)
else:
result_arg = LazyTreeValue(default_param_context, param.default)
is_default = True
else:
result_arg = argument
result_params.append(ExecutedParamName(
function_value, arguments, 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 contextualized_node in arguments.get_calling_nodes():
m = _error_argument_count(funcdef, len(unpacked_va))
issues.append(
analysis.add(contextualized_node.context,
'type-error-too-few-arguments',
contextualized_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 get_executed_param_names(function_value, arguments):
"""
Return a list of `ExecutedParamName`s corresponding to the arguments of the
function execution `function_value`, containing the inferred value of those
arguments (whether explicit or default). Any issues building this list (for
example required arguments which are missing in the invocation) are ignored.
For example, given:
```
def foo(a, b, c=None, d='d'): ...
foo(42, c='c')
```
Then for the execution of `foo`, this will return a list containing entries
for each parameter a, b, c & d; the entries for a, c, & d will have their
values (42, 'c' and 'd' respectively) included.
"""
return get_executed_param_names_and_issues(function_value, arguments)[0]
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))