259 lines
10 KiB
Python
259 lines
10 KiB
Python
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from collections import defaultdict
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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, \
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LazyTreeValue, LazyUnknownValue
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from jedi.inference.value import iterable
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from jedi._compatibility import Parameter
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from jedi.inference.names import ParamName
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def _add_argument_issue(error_name, lazy_value, message):
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if isinstance(lazy_value, LazyTreeValue):
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node = lazy_value.data
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if node.parent.type == 'argument':
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node = node.parent
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return analysis.add(lazy_value.context, error_name, node, message)
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class ExecutedParamName(ParamName):
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def __init__(self, function_value, arguments, param_node, lazy_value, is_default=False):
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super(ExecutedParamName, self).__init__(
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function_value, param_node.name, arguments=arguments)
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self._lazy_value = lazy_value
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self._is_default = is_default
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def infer(self):
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return self._lazy_value.infer()
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def matches_signature(self):
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if self._is_default:
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return True
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argument_values = self.infer().py__class__()
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if self.get_kind() in (Parameter.VAR_POSITIONAL, Parameter.VAR_KEYWORD):
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return True
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annotations = self.infer_annotation(execute_annotation=False)
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if not annotations:
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# If we cannot infer annotations - or there aren't any - pretend
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# that the signature matches.
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return True
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matches = any(c1.is_sub_class_of(c2)
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for c1 in argument_values
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for c2 in annotations.gather_annotation_classes())
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debug.dbg("param compare %s: %s <=> %s",
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matches, argument_values, annotations, color='BLUE')
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return matches
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def __repr__(self):
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return '<%s: %s>' % (self.__class__.__name__, self.string_name)
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def get_executed_param_names_and_issues(function_value, arguments):
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"""
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Return a tuple of:
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- a list of `ExecutedParamName`s corresponding to the arguments of the
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function execution `function_value`, containing the inferred value of
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those arguments (whether explicit or default)
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- a list of the issues encountered while building that list
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For example, given:
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```
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def foo(a, b, c=None, d='d'): ...
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foo(42, c='c')
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```
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Then for the execution of `foo`, this will return a tuple containing:
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- a list with entries for each parameter a, b, c & d; the entries for a,
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c, & d will have their values (42, 'c' and 'd' respectively) included.
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- a list with a single entry about the lack of a value for `b`
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"""
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def too_many_args(argument):
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m = _error_argument_count(funcdef, len(unpacked_va))
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# Just report an error for the first param that is not needed (like
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# cPython).
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if arguments.get_calling_nodes():
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# There might not be a valid calling node so check for that first.
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issues.append(
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_add_argument_issue(
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'type-error-too-many-arguments',
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argument,
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message=m
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)
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)
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else:
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issues.append(None)
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debug.warning('non-public warning: %s', m)
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issues = [] # List[Optional[analysis issue]]
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result_params = []
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param_dict = {}
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funcdef = function_value.tree_node
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# Default params are part of the value where the function was defined.
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# This means that they might have access on class variables that the
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# function itself doesn't have.
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default_param_context = function_value.get_default_param_context()
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for param in funcdef.get_params():
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param_dict[param.name.value] = param
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unpacked_va = list(arguments.unpack(funcdef))
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var_arg_iterator = PushBackIterator(iter(unpacked_va))
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non_matching_keys = defaultdict(lambda: [])
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keys_used = {}
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keys_only = False
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had_multiple_value_error = False
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for param in funcdef.get_params():
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# The value and key can both be null. There, the defaults apply.
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# args / kwargs will just be empty arrays / dicts, respectively.
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# Wrong value count is just ignored. If you try to test cases that are
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# not allowed in Python, Jedi will maybe not show any completions.
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is_default = False
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key, argument = next(var_arg_iterator, (None, None))
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while key is not None:
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keys_only = True
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try:
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key_param = param_dict[key]
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except KeyError:
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non_matching_keys[key] = argument
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else:
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if key in keys_used:
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had_multiple_value_error = True
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m = ("TypeError: %s() got multiple values for keyword argument '%s'."
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% (funcdef.name, key))
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for contextualized_node in arguments.get_calling_nodes():
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issues.append(
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analysis.add(contextualized_node.context,
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'type-error-multiple-values',
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contextualized_node.node, message=m)
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)
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else:
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keys_used[key] = ExecutedParamName(
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function_value, arguments, key_param, argument)
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key, argument = next(var_arg_iterator, (None, None))
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try:
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result_params.append(keys_used[param.name.value])
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continue
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except KeyError:
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pass
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if param.star_count == 1:
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# *args param
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lazy_value_list = []
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if argument is not None:
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lazy_value_list.append(argument)
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for key, argument in var_arg_iterator:
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# Iterate until a key argument is found.
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if key:
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var_arg_iterator.push_back((key, argument))
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break
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lazy_value_list.append(argument)
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seq = iterable.FakeTuple(function_value.inference_state, lazy_value_list)
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result_arg = LazyKnownValue(seq)
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elif param.star_count == 2:
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if argument is not None:
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too_many_args(argument)
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# **kwargs param
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dct = iterable.FakeDict(function_value.inference_state, dict(non_matching_keys))
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result_arg = LazyKnownValue(dct)
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non_matching_keys = {}
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else:
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# normal param
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if argument is None:
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# No value: Return an empty container
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if param.default is None:
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result_arg = LazyUnknownValue()
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if not keys_only:
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for contextualized_node in arguments.get_calling_nodes():
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m = _error_argument_count(funcdef, len(unpacked_va))
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issues.append(
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analysis.add(
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contextualized_node.context,
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'type-error-too-few-arguments',
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contextualized_node.node,
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message=m,
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)
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)
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else:
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result_arg = LazyTreeValue(default_param_context, param.default)
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is_default = True
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else:
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result_arg = argument
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result_params.append(ExecutedParamName(
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function_value, arguments, param, result_arg, is_default=is_default
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))
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if not isinstance(result_arg, LazyUnknownValue):
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keys_used[param.name.value] = result_params[-1]
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if keys_only:
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# All arguments should be handed over to the next function. It's not
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# about the values inside, it's about the names. Jedi needs to now that
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# there's nothing to find for certain names.
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for k in set(param_dict) - set(keys_used):
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param = param_dict[k]
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if not (non_matching_keys or had_multiple_value_error
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or param.star_count or param.default):
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# add a warning only if there's not another one.
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for contextualized_node in arguments.get_calling_nodes():
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m = _error_argument_count(funcdef, len(unpacked_va))
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issues.append(
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analysis.add(contextualized_node.context,
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'type-error-too-few-arguments',
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contextualized_node.node, message=m)
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)
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for key, lazy_value in non_matching_keys.items():
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m = "TypeError: %s() got an unexpected keyword argument '%s'." \
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% (funcdef.name, key)
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issues.append(
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_add_argument_issue(
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'type-error-keyword-argument',
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lazy_value,
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message=m
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)
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)
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remaining_arguments = list(var_arg_iterator)
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if remaining_arguments:
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first_key, lazy_value = remaining_arguments[0]
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too_many_args(lazy_value)
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return result_params, issues
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def get_executed_param_names(function_value, arguments):
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"""
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Return a list of `ExecutedParamName`s corresponding to the arguments of the
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function execution `function_value`, containing the inferred value of those
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arguments (whether explicit or default). Any issues building this list (for
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example required arguments which are missing in the invocation) are ignored.
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For example, given:
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```
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def foo(a, b, c=None, d='d'): ...
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foo(42, c='c')
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```
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Then for the execution of `foo`, this will return a list containing entries
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for each parameter a, b, c & d; the entries for a, c, & d will have their
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values (42, 'c' and 'd' respectively) included.
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"""
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return get_executed_param_names_and_issues(function_value, arguments)[0]
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def _error_argument_count(funcdef, actual_count):
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params = funcdef.get_params()
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default_arguments = sum(1 for p in params if p.default or p.star_count)
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if default_arguments == 0:
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before = 'exactly '
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else:
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before = 'from %s to ' % (len(params) - default_arguments)
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return ('TypeError: %s() takes %s%s arguments (%s given).'
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% (funcdef.name, before, len(params), actual_count))
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