def matches_signature(self, arguments):
from medi.inference.param import get_executed_param_names_and_issues
executed_param_names, issues = \
get_executed_param_names_and_issues(self._function_value, arguments)
if issues:
return False
matches = all(executed_param_name.matches_signature()
for executed_param_name in executed_param_names)
if debug.enable_notice:
tree_node = self._function_value.tree_node
signature = parser_utils.get_signature(tree_node)
if matches:
debug.dbg("Overloading match: %s@%s (%s)",
signature,
tree_node.start_pos[0],
arguments,
color='BLUE')
else:
debug.dbg("Overloading no match: %s@%s (%s)",
signature,
tree_node.start_pos[0],
arguments,
color='BLUE')
return matches
def _get_annotated_class_object(self):
from medi.inference.gradual.annotation import py__annotations__, \
infer_type_vars_for_execution
args = InstanceArguments(self, self._arguments)
for signature in self.class_value.py__getattribute__(
'__init__').get_signatures():
# Just take the first result, it should always be one, because we
# control the typeshed code.
funcdef = signature.value.tree_node
if funcdef is None or funcdef.type != 'funcdef' \
or not signature.matches_signature(args):
# First check if the signature even matches, if not we don't
# need to infer anything.
continue
bound_method = BoundMethod(self, self.class_value.as_context(),
signature.value)
all_annotations = py__annotations__(funcdef)
type_var_dict = infer_type_vars_for_execution(
bound_method, args, all_annotations)
if type_var_dict:
defined, = self.class_value.define_generics(
infer_type_vars_for_execution(signature.value, args,
all_annotations), )
debug.dbg('Inferred instance value as %s',
defined,
color='BLUE')
return defined
return None
def _parse_function_doc(doc):
"""
Takes a function and returns the params and return value as a tuple.
This is nothing more than a docstring parser.
TODO docstrings like utime(path, (atime, mtime)) and a(b [, b]) -> None
TODO docstrings like 'tuple of integers'
"""
doc = force_unicode(doc)
# parse round parentheses: def func(a, (b,c))
try:
count = 0
start = doc.index('(')
for i, s in enumerate(doc[start:]):
if s == '(':
count += 1
elif s == ')':
count -= 1
if count == 0:
end = start + i
break
param_str = doc[start + 1:end]
except (ValueError, UnboundLocalError):
# ValueError for doc.index
# UnboundLocalError for undefined end in last line
debug.dbg('no brackets found - no param')
end = 0
param_str = u''
else:
# remove square brackets, that show an optional param ( = None)
def change_options(m):
args = m.group(1).split(',')
for i, a in enumerate(args):
if a and '=' not in a:
args[i] += '=None'
return ','.join(args)
while True:
param_str, changes = re.subn(r' ?\[([^\[\]]+)\]', change_options,
param_str)
if changes == 0:
break
param_str = param_str.replace('-', '_') # see: isinstance.__doc__
# parse return value
r = re.search(u'-[>-]* ', doc[end:end + 7])
if r is None:
ret = u''
else:
index = end + r.end()
# get result type, which can contain newlines
pattern = re.compile(r'(,\n|[^\n-])+')
ret_str = pattern.match(doc, index).group(0).strip()
# New object -> object()
ret_str = re.sub(r'[nN]ew (.*)', r'\1()', ret_str)
ret = docstr_defaults.get(ret_str, ret_str)
return param_str, ret
def py__getattribute__(self,
name_or_str,
name_context=None,
position=None,
analysis_errors=True):
"""
:param position: Position of the last statement -> tuple of line, column
"""
if name_context is None:
name_context = self
names = self.goto(name_or_str, position)
string_name = name_or_str.value if isinstance(name_or_str,
Name) else name_or_str
# This paragraph is currently needed for proper branch type inference
# (static analysis).
found_predefined_types = None
if self.predefined_names and isinstance(name_or_str, Name):
node = name_or_str
while node is not None and not parser_utils.is_scope(node):
node = node.parent
if node.type in ("if_stmt", "for_stmt", "comp_for",
'sync_comp_for'):
try:
name_dict = self.predefined_names[node]
types = name_dict[string_name]
except KeyError:
continue
else:
found_predefined_types = types
break
if found_predefined_types is not None and names:
from medi.inference import flow_analysis
check = flow_analysis.reachability_check(
context=self,
value_scope=self.tree_node,
node=name_or_str,
)
if check is flow_analysis.UNREACHABLE:
values = NO_VALUES
else:
values = found_predefined_types
else:
values = ValueSet.from_sets(name.infer() for name in names)
if not names and not values and analysis_errors:
if isinstance(name_or_str, Name):
from medi.inference import analysis
message = ("NameError: name '%s' is not defined." %
string_name)
analysis.add(name_context, 'name-error', name_or_str, message)
debug.dbg('context.names_to_types: %s -> %s', names, values)
if values:
return values
return self._check_for_additional_knowledge(name_or_str, name_context,
position)
def py__simple_getitem__(self, index):
if self._is_homogenous():
return self._generics_manager.get_index_and_execute(0)
else:
if isinstance(index, int):
return self._generics_manager.get_index_and_execute(index)
debug.dbg('The getitem type on Tuple was %s' % index)
return NO_VALUES
def goto(self, name_or_str, position):
from medi.inference import finder
filters = _get_global_filters_for_name(
self,
name_or_str if isinstance(name_or_str, Name) else None,
position,
)
names = finder.filter_name(filters, name_or_str)
debug.dbg('context.goto %s in (%s): %s', name_or_str, self, names)
return names
def extract_variable(inference_state, path, module_node, name, pos, until_pos):
nodes = _find_nodes(module_node, pos, until_pos)
debug.dbg('Extracting nodes: %s', nodes)
is_expression, message = _is_expression_with_error(nodes)
if not is_expression:
raise RefactoringError(message)
generated_code = name + ' = ' + _expression_nodes_to_string(nodes)
file_to_node_changes = {path: _replace(nodes, name, generated_code, pos)}
return Refactoring(inference_state, file_to_node_changes)
def iterate(self, contextualized_node=None, is_async=False):
debug.dbg('iterate %s', self)
if is_async:
from medi.inference.lazy_value import LazyKnownValues
# TODO if no __aiter__ values are there, error should be:
# TypeError: 'async for' requires an object with __aiter__ method, got int
return iter([
LazyKnownValues(
self.py__getattribute__('__aiter__').execute_with_values(
).py__getattribute__('__anext__').execute_with_values(
).py__getattribute__('__await__').execute_with_values().
py__stop_iteration_returns()) # noqa
])
return self.py__iter__(contextualized_node)
def _infer_for_statement_string(module_context, string):
code = dedent(u("""
def pseudo_docstring_stuff():
'''
Create a pseudo function for docstring statements.
Need this docstring so that if the below part is not valid Python this
is still a function.
'''
{}
"""))
if string is None:
return []
for element in re.findall(r'((?:\w+\.)*\w+)\.', string):
# Try to import module part in dotted name.
# (e.g., 'threading' in 'threading.Thread').
string = 'import %s\n' % element + string
# Take the default grammar here, if we load the Python 2.7 grammar here, it
# will be impossible to use `...` (Ellipsis) as a token. Docstring types
# don't need to conform with the current grammar.
debug.dbg('Parse docstring code %s', string, color='BLUE')
grammar = module_context.inference_state.latest_grammar
try:
module = grammar.parse(code.format(indent_block(string)), error_recovery=False)
except ParserSyntaxError:
return []
try:
funcdef = next(module.iter_funcdefs())
# First pick suite, then simple_stmt and then the node,
# which is also not the last item, because there's a newline.
stmt = funcdef.children[-1].children[-1].children[-2]
except (AttributeError, IndexError):
return []
if stmt.type not in ('name', 'atom', 'atom_expr'):
return []
from medi.inference.value import FunctionValue
function_value = FunctionValue(
module_context.inference_state,
module_context,
funcdef
)
func_execution_context = function_value.as_context()
# Use the module of the param.
# TODO this module is not the module of the param in case of a function
# call. In that case it's the module of the function call.
# stuffed with content from a function call.
return list(_execute_types_in_stmt(func_execution_context, stmt))
def py__call__(self, arguments):
debug.dbg("Execute overloaded function %s",
self._wrapped_value,
color='BLUE')
function_executions = []
for signature in self.get_signatures():
function_execution = signature.value.as_context(arguments)
function_executions.append(function_execution)
if signature.matches_signature(arguments):
return function_execution.infer()
if self.inference_state.is_analysis:
# In this case we want precision.
return NO_VALUES
return ValueSet.from_sets(fe.infer() for fe in function_executions)
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 infer_import(context, tree_name):
module_context = context.get_root_context()
from_import_name, import_path, level, values = \
_prepare_infer_import(module_context, tree_name)
if values:
if from_import_name is not None:
values = values.py__getattribute__(from_import_name,
name_context=context,
analysis_errors=False)
if not values:
path = import_path + (from_import_name, )
importer = Importer(context.inference_state, path,
module_context, level)
values = importer.follow()
debug.dbg('after import: %s', values)
return values
def infer_param(function_value, param):
def infer_docstring(docstring):
return ValueSet(
p
for param_str in _search_param_in_docstr(docstring, param.name.value)
for p in _infer_for_statement_string(module_context, param_str)
)
module_context = function_value.get_root_context()
func = param.get_parent_function()
if func.type == 'lambdef':
return NO_VALUES
types = infer_docstring(function_value.py__doc__())
if function_value.is_bound_method() \
and function_value.py__name__() == '__init__':
types |= infer_docstring(function_value.class_context.py__doc__())
debug.dbg('Found param types for docstring: %s', types, color='BLUE')
return types
def dynamic_param_lookup(function_value, param_index):
"""
A dynamic search for param values. If you try to complete a type:
>>> def func(foo):
... foo
>>> func(1)
>>> func("")
It is not known what the type ``foo`` without analysing the whole code. You
have to look for all calls to ``func`` to find out what ``foo`` possibly
is.
"""
funcdef = function_value.tree_node
if not settings.dynamic_params:
return NO_VALUES
path = function_value.get_root_context().py__file__()
if path is not None and is_stdlib_path(path):
# We don't want to search for references in the stdlib. Usually people
# don't work with it (except if you are a core maintainer, sorry).
# This makes everything slower. Just disable it and run the tests,
# you will see the slowdown, especially in 3.6.
return NO_VALUES
if funcdef.type == 'lambdef':
string_name = _get_lambda_name(funcdef)
if string_name is None:
return NO_VALUES
else:
string_name = funcdef.name.value
debug.dbg('Dynamic param search in %s.', string_name, color='MAGENTA')
module_context = function_value.get_root_context()
arguments_list = _search_function_arguments(module_context, funcdef,
string_name)
values = ValueSet.from_sets(
get_executed_param_names(function_value, arguments)
[param_index].infer() for arguments in arguments_list)
debug.dbg('Dynamic param result finished', color='MAGENTA')
return values
def definition(correct, correct_start, path):
should_be = set()
for match in re.finditer('(?:[^ ]+)', correct):
string = match.group(0)
parser = grammar37.parse(string, start_symbol='eval_input', error_recovery=False)
parser_utils.move(parser.get_root_node(), self.line_nr)
node = parser.get_root_node()
module_context = script._get_module_context()
user_context = get_user_context(module_context, (self.line_nr, 0))
node.parent = user_context.tree_node
results = convert_values(user_context.infer_node(node))
if not results:
raise Exception('Could not resolve %s on line %s'
% (match.string, self.line_nr - 1))
should_be |= set(Name(inference_state, r.name) for r in results)
debug.dbg('Finished getting types', color='YELLOW')
# Because the objects have different ids, `repr`, then compare.
should = set(comparison(r) for r in should_be)
return should
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 search_in_file_ios(inference_state,
file_io_iterator,
name,
limit_reduction=1):
parse_limit = _PARSED_FILE_LIMIT / limit_reduction
open_limit = _OPENED_FILE_LIMIT / limit_reduction
file_io_count = 0
parsed_file_count = 0
regex = re.compile(r'\b' + re.escape(name) + r'\b')
for file_io in file_io_iterator:
file_io_count += 1
m = _check_fs(inference_state, file_io, regex)
if m is not None:
parsed_file_count += 1
yield m
if parsed_file_count >= parse_limit:
dbg('Hit limit of parsed files: %s', parse_limit)
break
if file_io_count >= open_limit:
dbg('Hit limit of opened files: %s', open_limit)
break
def infer_trailer(context, atom_values, trailer):
trailer_op, node = trailer.children[:2]
if node == ')': # `arglist` is optional.
node = None
if trailer_op == '[':
trailer_op, node, _ = trailer.children
return atom_values.get_item(
_infer_subscript_list(context, node),
ContextualizedNode(context, trailer)
)
else:
debug.dbg('infer_trailer: %s in %s', trailer, atom_values)
if trailer_op == '.':
return atom_values.py__getattribute__(
name_context=context,
name_or_str=node
)
else:
assert trailer_op == '(', 'trailer_op is actually %s' % trailer_op
args = arguments.TreeArguments(context.inference_state, context, node, trailer)
return atom_values.execute(args)
def get_return_values(self, check_yields=False):
funcdef = self.tree_node
if funcdef.type == 'lambdef':
return self.infer_node(funcdef.children[-1])
if check_yields:
value_set = NO_VALUES
returns = get_yield_exprs(self.inference_state, funcdef)
else:
value_set = self._infer_annotations()
if value_set:
# If there are annotations, prefer them over anything else.
# This will make it faster.
return value_set
value_set |= docstrings.infer_return_types(self._value)
returns = funcdef.iter_return_stmts()
for r in returns:
if check_yields:
value_set |= ValueSet.from_sets(
lazy_value.infer()
for lazy_value in self._get_yield_lazy_value(r))
else:
check = flow_analysis.reachability_check(self, funcdef, r)
if check is flow_analysis.UNREACHABLE:
debug.dbg('Return unreachable: %s', r)
else:
try:
children = r.children
except AttributeError:
ctx = compiled.builtin_from_name(
self.inference_state, u'None')
value_set |= ValueSet([ctx])
else:
value_set |= self.infer_node(children[1])
if check is flow_analysis.REACHABLE:
debug.dbg('Return reachable: %s', r)
break
return value_set
def _getitem(value, index_values, contextualized_node):
# The actual getitem call.
result = NO_VALUES
unused_values = set()
for index_value in index_values:
index = index_value.get_safe_value(default=None)
if type(index) in (float, int, str, unicode, slice, bytes):
try:
result |= value.py__simple_getitem__(index)
continue
except SimpleGetItemNotFound:
pass
unused_values.add(index_value)
# The index was somehow not good enough or simply a wrong type.
# Therefore we now iterate through all the values and just take
# all results.
if unused_values or not index_values:
result |= value.py__getitem__(ValueSet(unused_values),
contextualized_node)
debug.dbg('py__getitem__ result: %s', result)
return result
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
def py__getattribute__(self,
name_or_str,
name_context=None,
position=None,
analysis_errors=True):
"""
:param position: Position of the last statement -> tuple of line, column
"""
if name_context is None:
name_context = self
names = self.goto(name_or_str, name_context, analysis_errors)
values = ValueSet.from_sets(name.infer() for name in names)
if not values:
n = name_or_str.value if isinstance(name_or_str,
Name) else name_or_str
values = self.py__getattribute__alternatives(n)
if not names and not values and analysis_errors:
if isinstance(name_or_str, Name):
from medi.inference import analysis
analysis.add_attribute_error(name_context, self, name_or_str)
debug.dbg('context.names_to_types: %s -> %s', names, values)
return values
def goto(self, name_or_str, name_context=None, analysis_errors=True):
from medi.inference import finder
filters = self._get_value_filters(name_or_str)
names = finder.filter_name(filters, name_or_str)
debug.dbg('context.goto %s in (%s): %s', name_or_str, self, names)
return names
def _internal_check_array_additions(context, sequence):
"""
Checks if a `Array` has "add" (append, insert, extend) statements:
>>> a = [""]
>>> a.append(1)
"""
from medi.inference import arguments
debug.dbg('Dynamic array search for %s' % sequence, color='MAGENTA')
module_context = context.get_root_context()
if not settings.dynamic_array_additions or module_context.is_compiled():
debug.dbg('Dynamic array search aborted.', color='MAGENTA')
return NO_VALUES
def find_additions(context, arglist, add_name):
params = list(
arguments.TreeArguments(context.inference_state, context,
arglist).unpack())
result = set()
if add_name in ['insert']:
params = params[1:]
if add_name in ['append', 'add', 'insert']:
for key, lazy_value in params:
result.add(lazy_value)
elif add_name in ['extend', 'update']:
for key, lazy_value in params:
result |= set(lazy_value.infer().iterate())
return result
temp_param_add, settings.dynamic_params_for_other_modules = \
settings.dynamic_params_for_other_modules, False
is_list = sequence.name.string_name == 'list'
search_names = (['append', 'extend', 'insert']
if is_list else ['add', 'update'])
added_types = set()
for add_name in search_names:
try:
possible_names = module_context.tree_node.get_used_names(
)[add_name]
except KeyError:
continue
else:
for name in possible_names:
value_node = context.tree_node
if not (value_node.start_pos < name.start_pos <
value_node.end_pos):
continue
trailer = name.parent
power = trailer.parent
trailer_pos = power.children.index(trailer)
try:
execution_trailer = power.children[trailer_pos + 1]
except IndexError:
continue
else:
if execution_trailer.type != 'trailer' \
or execution_trailer.children[0] != '(' \
or execution_trailer.children[1] == ')':
continue
random_context = context.create_context(name)
with recursion.execution_allowed(context.inference_state,
power) as allowed:
if allowed:
found = infer_call_of_leaf(random_context,
name,
cut_own_trailer=True)
if sequence in found:
# The arrays match. Now add the results
added_types |= find_additions(
random_context, execution_trailer.children[1],
add_name)
# reset settings
settings.dynamic_params_for_other_modules = temp_param_add
debug.dbg('Dynamic array result %s', added_types, color='MAGENTA')
return added_types
def test_simple():
medi.set_debug_function()
debug.speed('foo')
debug.dbg('bar')
debug.warning('baz')
medi.set_debug_function(None, False, False, False)
def execute(value, arguments):
debug.dbg('execute: %s %s', value, arguments)
with debug.increase_indent_cm():
value_set = value.py__call__(arguments=arguments)
debug.dbg('execute result: %s in %s', value_set, value)
return value_set
def infer_node(context, element):
if isinstance(context, CompForContext):
return _infer_node(context, element)
if_stmt = element
while if_stmt is not None:
if_stmt = if_stmt.parent
if if_stmt.type in ('if_stmt', 'for_stmt'):
break
if parser_utils.is_scope(if_stmt):
if_stmt = None
break
predefined_if_name_dict = context.predefined_names.get(if_stmt)
# TODO there's a lot of issues with this one. We actually should do
# this in a different way. Caching should only be active in certain
# cases and this all sucks.
if predefined_if_name_dict is None and if_stmt \
and if_stmt.type == 'if_stmt' and context.inference_state.is_analysis:
if_stmt_test = if_stmt.children[1]
name_dicts = [{}]
# If we already did a check, we don't want to do it again -> If
# value.predefined_names is filled, we stop.
# We don't want to check the if stmt itself, it's just about
# the content.
if element.start_pos > if_stmt_test.end_pos:
# Now we need to check if the names in the if_stmt match the
# names in the suite.
if_names = get_names_of_node(if_stmt_test)
element_names = get_names_of_node(element)
str_element_names = [e.value for e in element_names]
if any(i.value in str_element_names for i in if_names):
for if_name in if_names:
definitions = context.inference_state.infer(context, if_name)
# Every name that has multiple different definitions
# causes the complexity to rise. The complexity should
# never fall below 1.
if len(definitions) > 1:
if len(name_dicts) * len(definitions) > 16:
debug.dbg('Too many options for if branch inference %s.', if_stmt)
# There's only a certain amount of branches
# Medi can infer, otherwise it will take to
# long.
name_dicts = [{}]
break
original_name_dicts = list(name_dicts)
name_dicts = []
for definition in definitions:
new_name_dicts = list(original_name_dicts)
for i, name_dict in enumerate(new_name_dicts):
new_name_dicts[i] = name_dict.copy()
new_name_dicts[i][if_name.value] = ValueSet([definition])
name_dicts += new_name_dicts
else:
for name_dict in name_dicts:
name_dict[if_name.value] = definitions
if len(name_dicts) > 1:
result = NO_VALUES
for name_dict in name_dicts:
with context.predefine_names(if_stmt, name_dict):
result |= _infer_node(context, element)
return result
else:
return _infer_node_if_inferred(context, element)
else:
if predefined_if_name_dict:
return _infer_node(context, element)
else:
return _infer_node_if_inferred(context, element)
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 Medi 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
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_node(context, element):
debug.dbg('infer_node %s@%s in %s', element, element.start_pos, context)
inference_state = context.inference_state
typ = element.type
if typ in ('name', 'number', 'string', 'atom', 'strings', 'keyword', 'fstring'):
return infer_atom(context, element)
elif typ == 'lambdef':
return ValueSet([FunctionValue.from_context(context, element)])
elif typ == 'expr_stmt':
return infer_expr_stmt(context, element)
elif typ in ('power', 'atom_expr'):
first_child = element.children[0]
children = element.children[1:]
had_await = False
if first_child.type == 'keyword' and first_child.value == 'await':
had_await = True
first_child = children.pop(0)
value_set = context.infer_node(first_child)
for (i, trailer) in enumerate(children):
if trailer == '**': # has a power operation.
right = context.infer_node(children[i + 1])
value_set = _infer_comparison(
context,
value_set,
trailer,
right
)
break
value_set = infer_trailer(context, value_set, trailer)
if had_await:
return value_set.py__await__().py__stop_iteration_returns()
return value_set
elif typ in ('testlist_star_expr', 'testlist',):
# The implicit tuple in statements.
return ValueSet([iterable.SequenceLiteralValue(inference_state, context, element)])
elif typ in ('not_test', 'factor'):
value_set = context.infer_node(element.children[-1])
for operator in element.children[:-1]:
value_set = infer_factor(value_set, operator)
return value_set
elif typ == 'test':
# `x if foo else y` case.
return (context.infer_node(element.children[0])
| context.infer_node(element.children[-1]))
elif typ == 'operator':
# Must be an ellipsis, other operators are not inferred.
# In Python 2 ellipsis is coded as three single dot tokens, not
# as one token 3 dot token.
if element.value not in ('.', '...'):
origin = element.parent
raise AssertionError("unhandled operator %s in %s " % (repr(element.value), origin))
return ValueSet([compiled.builtin_from_name(inference_state, u'Ellipsis')])
elif typ == 'dotted_name':
value_set = infer_atom(context, element.children[0])
for next_name in element.children[2::2]:
value_set = value_set.py__getattribute__(next_name, name_context=context)
return value_set
elif typ == 'eval_input':
return context.infer_node(element.children[0])
elif typ == 'annassign':
return annotation.infer_annotation(context, element.children[1]) \
.execute_annotation()
elif typ == 'yield_expr':
if len(element.children) and element.children[1].type == 'yield_arg':
# Implies that it's a yield from.
element = element.children[1].children[1]
generators = context.infer_node(element) \
.py__getattribute__('__iter__').execute_with_values()
return generators.py__stop_iteration_returns()
# Generator.send() is not implemented.
return NO_VALUES
elif typ == 'namedexpr_test':
return context.infer_node(element.children[2])
else:
return infer_or_test(context, element)
