def test_instance_special_attributes(self):
for inst in (Instance(MODULE['YO']), nodes.List(), nodes.Const(1)):
self.assertRaises(NotFoundError, inst.getattr, '__mro__')
self.assertRaises(NotFoundError, inst.getattr, '__bases__')
self.assertRaises(NotFoundError, inst.getattr, '__name__')
self.assertEqual(len(inst.getattr('__dict__')), 1)
self.assertEqual(len(inst.getattr('__doc__')), 1)
def _inf_fn(node, context=None):
if hasattr(node, CODEWATCH_MODEL_INFERENCE_KEY):
# For the case where we already inferred the manager call
# We just need to infer the new call
# eg:
# users = DjangoUser.objects.all()
# users.first()
#
# users.first() is the node we are inferring
#
# Astroid has already taken care of calling `infer` on `users`
# Here we detected that `users` is an inference we returned
klass_obj = getattr(node, CODEWATCH_MODEL_INFERENCE_KEY)
else:
# Otherwise, it's a bare manager call, eg: DjangoUser.objects.all()
klass_def = node.func.expr.expr.inferred()[0]
klass_obj = klass_def.instantiate_class()
if node.func.attrname in DJANGO_MANAGER_METHODS_LIST:
# we infer a `List` node with a single `klass_obj` element
klass_obj_list = nodes.List(ctx=LoadContext)
klass_obj_list.elts = [klass_obj]
setattr(klass_obj_list, CODEWATCH_MODEL_INFERENCE_KEY, klass_obj)
return iter((klass_obj_list,))
# otherwise, just infer the `klass_obj`
return iter((klass_obj,))
def visit_list(self, node, parent):
"""visit a List node by returning a fresh instance of it"""
newnode = new.List()
_lineno_parent(node, newnode, parent)
newnode.elts = [self.visit(child, newnode) for child in node.elts]
newnode.set_line_info(newnode.last_child())
return newnode
def visit_list(self, node, parent, assign_ctx=None):
"""visit a List node by returning a fresh instance of it"""
newnode = new.List()
_lineno_parent(node, newnode, parent)
newnode.elts = [self.visit(child, newnode, assign_ctx)
for child in node.elts]
return newnode
def visit_list(self, node, parent):
"""visit a List node by returning a fresh instance of it"""
context = self._get_context(node)
newnode = nodes.List(
ctx=context, lineno=node.lineno, col_offset=node.col_offset, parent=parent
)
newnode.postinit([self.visit(child, newnode) for child in node.elts])
return newnode
def starred_assigned_stmts(self, node=None, context=None, asspath=None):
stmt = self.statement()
if not isinstance(stmt, (nodes.Assign, nodes.For)):
raise exceptions.InferenceError()
if isinstance(stmt, nodes.Assign):
value = stmt.value
lhs = stmt.targets[0]
if sum(1 for node in lhs.nodes_of_class(nodes.Starred)) > 1:
# Too many starred arguments in the expression.
raise exceptions.InferenceError()
if context is None:
context = contextmod.InferenceContext()
try:
rhs = next(value.infer(context))
except exceptions.InferenceError:
yield util.YES
return
if rhs is util.YES or not hasattr(rhs, 'elts'):
# Not interested in inferred values without elts.
yield util.YES
return
elts = collections.deque(rhs.elts[:])
if len(lhs.elts) > len(rhs.elts):
# a, *b, c = (1, 2)
raise exceptions.InferenceError()
# Unpack iteratively the values from the rhs of the assignment,
# until the find the starred node. What will remain will
# be the list of values which the Starred node will represent
# This is done in two steps, from left to right to remove
# anything before the starred node and from right to left
# to remvoe anything after the starred node.
for index, node in enumerate(lhs.elts):
if not isinstance(node, nodes.Starred):
elts.popleft()
continue
lhs_elts = collections.deque(reversed(lhs.elts[index:]))
for node in lhs_elts:
if not isinstance(node, nodes.Starred):
elts.pop()
continue
# We're done
packed = nodes.List()
packed.elts = elts
packed.parent = self
yield packed
break
def transform(cls):
if cls.name in CLASS_NAME_BLACKLIST:
return
if cls.name.endswith('API') or 'schema' in cls.locals:
# This is a class which defines attributes in "schema" variable using json schema.
# Those attributes are then assigned during run time inside the constructor
fqdn = cls.qname()
module_name, class_name = fqdn.rsplit('.', 1)
module = __import__(module_name, fromlist=[class_name])
actual_cls = getattr(module, class_name)
schema = actual_cls.schema
if not isinstance(schema, dict):
# Not a class we are interested in
return
properties = schema.get('properties', {})
for property_name, property_data in six.iteritems(properties):
property_name = property_name.replace(
'-', '_') # Note: We do the same in Python code
property_type = property_data.get('type', None)
if isinstance(property_type, (list, tuple)):
# Hack for attributes with multiple types (e.g. string, null)
property_type = property_type[0]
if property_type == 'object':
node = nodes.Dict()
elif property_type == 'array':
node = nodes.List()
elif property_type == 'integer':
node = scoped_nodes.builtin_lookup('int')[1][0]
elif property_type == 'number':
node = scoped_nodes.builtin_lookup('float')[1][0]
elif property_type == 'string':
node = scoped_nodes.builtin_lookup('str')[1][0]
elif property_type == 'boolean':
node = scoped_nodes.builtin_lookup('bool')[1][0]
elif property_type == 'null':
node = scoped_nodes.builtin_lookup('None')[1][0]
else:
# Unknown type
node = scoped_nodes.Class(property_name, None)
cls.locals[property_name] = [node]
def transform(cls):
if cls.name in CLASS_NAME_BLACKLIST:
return
if cls.name.endswith("API") or "schema" in cls.locals:
# This is a class which defines attributes in "schema" variable using json schema.
# Those attributes are then assigned during run time inside the constructor
fqdn = cls.qname()
module_name, class_name = fqdn.rsplit(".", 1)
module = __import__(module_name, fromlist=[class_name])
actual_cls = getattr(module, class_name)
schema = actual_cls.schema
if not isinstance(schema, dict):
# Not a class we are interested in
return
properties = schema.get("properties", {})
for property_name, property_data in six.iteritems(properties):
property_name = property_name.replace(
"-", "_") # Note: We do the same in Python code
property_type = property_data.get("type", None)
if isinstance(property_type, (list, tuple)):
# Hack for attributes with multiple types (e.g. string, null)
property_type = property_type[0]
if property_type == "object":
node = nodes.Dict()
elif property_type == "array":
node = nodes.List()
elif property_type == "integer":
node = scoped_nodes.builtin_lookup("int")[1][0]
elif property_type == "number":
node = scoped_nodes.builtin_lookup("float")[1][0]
elif property_type == "string":
node = scoped_nodes.builtin_lookup("str")[1][0]
elif property_type == "boolean":
node = scoped_nodes.builtin_lookup("bool")[1][0]
elif property_type == "null":
node = scoped_nodes.builtin_lookup("None")[1][0]
else:
# Unknown type
node = astroid.ClassDef(property_name, None)
cls.locals[property_name] = [node]
def _getattr(self, name, *args, **kw):
try:
return Module_getattr(self, name, *args, **kw)
except NotFoundError, e:
if self.name.startswith('erp5.'):
raise
real_module = __import__(self.name, fromlist=[self.name], level=0)
try:
attr = getattr(real_module, name)
except AttributeError:
raise e
# REQUEST object (or any object non acquisition-wrapped)
if (isinstance(attr, str)
and attr == '<Special Object Used to Force Acquisition>'):
raise e
try:
origin_module_name = attr.__module__
except AttributeError:
from astroid import nodes
if isinstance(attr, dict):
ast = nodes.Dict(attr)
elif isinstance(attr, list):
ast = nodes.List(attr)
elif isinstance(attr, tuple):
ast = nodes.Tuple(attr)
elif isinstance(attr, set):
ast = nodes.Set(attr)
else:
try:
ast = nodes.Const(attr)
except Exception:
raise e
else:
if self.name == origin_module_name:
raise
# ast_from_class() actually works for any attribute of a Module
try:
ast = MANAGER.ast_from_class(attr)
except AstroidBuildingException:
raise e
self.locals[name] = [ast]
return [ast]
def starred_assigned_stmts(self, node=None, context=None, asspath=None):
"""
Arguments:
self: nodes.Starred
node: TODO
context: TODO
asspath: TODO
"""
stmt = self.statement()
if not isinstance(stmt, (nodes.Assign, nodes.For)):
raise exceptions.InferenceError(
'Statement {stmt!r} enclosing {node!r} '
'must be an Assign or For node.',
node=self,
stmt=stmt,
unknown=node,
context=context)
if isinstance(stmt, nodes.Assign):
value = stmt.value
lhs = stmt.targets[0]
if sum(1 for node in lhs.nodes_of_class(nodes.Starred)) > 1:
raise exceptions.InferenceError(
'Too many starred arguments in the '
' assignment targets {lhs!r}.',
node=self,
targets=lhs,
unknown=node,
context=context)
if context is None:
context = contextmod.InferenceContext()
try:
rhs = next(value.infer(context))
except exceptions.InferenceError:
yield util.Uninferable
return
if rhs is util.Uninferable or not hasattr(rhs, 'elts'):
# Not interested in inferred values without elts.
yield util.Uninferable
return
elts = collections.deque(rhs.elts[:])
if len(lhs.elts) > len(rhs.elts):
raise exceptions.InferenceError(
'More targets, {targets!r}, than '
'values to unpack, {values!r}.',
node=self,
targets=lhs,
values=rhs,
unknown=node,
context=context)
# Unpack iteratively the values from the rhs of the assignment,
# until the find the starred node. What will remain will
# be the list of values which the Starred node will represent
# This is done in two steps, from left to right to remove
# anything before the starred node and from right to left
# to remove anything after the starred node.
for index, left_node in enumerate(lhs.elts):
if not isinstance(left_node, nodes.Starred):
elts.popleft()
continue
lhs_elts = collections.deque(reversed(lhs.elts[index:]))
for right_node in lhs_elts:
if not isinstance(right_node, nodes.Starred):
elts.pop()
continue
# We're done
packed = nodes.List()
packed.elts = elts
packed.parent = self
yield packed
break
def starred_assigned_stmts(self, node=None, context=None, assign_path=None):
"""
Arguments:
self: nodes.Starred
node: a node related to the current underlying Node.
context: Inference context used for caching already inferred objects
assign_path:
A list of indices, where each index specifies what item to fetch from
the inference results.
"""
# pylint: disable=too-many-locals,too-many-branches,too-many-statements
def _determine_starred_iteration_lookups(starred, target, lookups):
# Determine the lookups for the rhs of the iteration
itered = target.itered()
for index, element in enumerate(itered):
if (
isinstance(element, nodes.Starred)
and element.value.name == starred.value.name
):
lookups.append((index, len(itered)))
break
if isinstance(element, nodes.Tuple):
lookups.append((index, len(element.itered())))
_determine_starred_iteration_lookups(starred, element, lookups)
stmt = self.statement()
if not isinstance(stmt, (nodes.Assign, nodes.For)):
raise exceptions.InferenceError(
"Statement {stmt!r} enclosing {node!r} " "must be an Assign or For node.",
node=self,
stmt=stmt,
unknown=node,
context=context,
)
if context is None:
context = contextmod.InferenceContext()
if isinstance(stmt, nodes.Assign):
value = stmt.value
lhs = stmt.targets[0]
if sum(1 for _ in lhs.nodes_of_class(nodes.Starred)) > 1:
raise exceptions.InferenceError(
"Too many starred arguments in the " " assignment targets {lhs!r}.",
node=self,
targets=lhs,
unknown=node,
context=context,
)
try:
rhs = next(value.infer(context))
except exceptions.InferenceError:
yield util.Uninferable
return
if rhs is util.Uninferable or not hasattr(rhs, "itered"):
yield util.Uninferable
return
try:
elts = collections.deque(rhs.itered())
except TypeError:
yield util.Uninferable
return
# Unpack iteratively the values from the rhs of the assignment,
# until the find the starred node. What will remain will
# be the list of values which the Starred node will represent
# This is done in two steps, from left to right to remove
# anything before the starred node and from right to left
# to remove anything after the starred node.
for index, left_node in enumerate(lhs.elts):
if not isinstance(left_node, nodes.Starred):
if not elts:
break
elts.popleft()
continue
lhs_elts = collections.deque(reversed(lhs.elts[index:]))
for right_node in lhs_elts:
if not isinstance(right_node, nodes.Starred):
if not elts:
break
elts.pop()
continue
# We're done
packed = nodes.List(
ctx=Store, parent=self, lineno=lhs.lineno, col_offset=lhs.col_offset
)
packed.postinit(elts=elts)
yield packed
break
if isinstance(stmt, nodes.For):
try:
inferred_iterable = next(stmt.iter.infer(context=context))
except exceptions.InferenceError:
yield util.Uninferable
return
if inferred_iterable is util.Uninferable or not hasattr(
inferred_iterable, "itered"
):
yield util.Uninferable
return
try:
itered = inferred_iterable.itered()
except TypeError:
yield util.Uninferable
return
target = stmt.target
if not isinstance(target, nodes.Tuple):
raise exceptions.InferenceError(
"Could not make sense of this, the target must be a tuple",
context=context,
)
lookups = []
_determine_starred_iteration_lookups(self, target, lookups)
if not lookups:
raise exceptions.InferenceError(
"Could not make sense of this, needs at least a lookup", context=context
)
# Make the last lookup a slice, since that what we want for a Starred node
last_element_index, last_element_length = lookups[-1]
is_starred_last = last_element_index == (last_element_length - 1)
lookup_slice = slice(
last_element_index,
None if is_starred_last else (last_element_length - last_element_index),
)
lookups[-1] = lookup_slice
for element in itered:
# We probably want to infer the potential values *for each* element in an
# iterable, but we can't infer a list of all values, when only a list of
# step values are expected:
#
# for a, *b in [...]:
# b
#
# *b* should now point to just the elements at that particular iteration step,
# which astroid can't know about.
found_element = None
for lookup in lookups:
if not hasattr(element, "itered"):
break
if not isinstance(lookup, slice):
# Grab just the index, not the whole length
lookup = lookup[0]
try:
itered_inner_element = element.itered()
element = itered_inner_element[lookup]
except IndexError:
break
except TypeError:
# Most likely the itered() call failed, cannot make sense of this
yield util.Uninferable
return
else:
found_element = element
unpacked = nodes.List(
ctx=Store, parent=self, lineno=self.lineno, col_offset=self.col_offset
)
unpacked.postinit(elts=found_element or [])
yield unpacked
return
yield util.Uninferable
def list_node(draw, elt=const_node(), **kwargs):
"""Return a List node with elements drawn from elt."""
node = nodes.List()
node.postinit(draw(hs.lists(elt, **kwargs)))
return node
def transform(cls: nodes.ClassDef):
"""
Astroid (used by pylint) calls this function on each class definition it discovers.
cls is an Astroid AST representation of that class.
Our purpose here is to extract the schema dict from API model classes
so that we can inform pylint about all of the attributes on those models.
We do this by injecting attributes on the class for each property in the schema.
"""
# This is a class which defines attributes in "schema" variable using json schema.
# Those attributes are then assigned during run time inside the constructor
# Get the value node for the "schema =" assignment
schema_dict_node = next(cls.igetattr("schema"))
extra_schema_properties = {}
# If the "schema =" assignment's value node is not a simple type (like a dictionary),
# then pylint cannot infer exactly what it does. Most of the time, this is actually
# a function call to copy the schema from another class. So, let's find the dictionary.
if schema_dict_node is astroid.Uninferable:
# the assignment probably looks like this:
# schema = copy.deepcopy(ActionAPI.schema)
# so far we only have the value, but we need the actual assignment
assigns = [
n for n in cls.get_children() if isinstance(n, nodes.Assign)
]
schema_assign_name_node = cls.local_attr("schema")[0]
schema_assign_node = next(
assign for assign in assigns
if assign.targets[0] == schema_assign_name_node)
assigns.remove(schema_assign_node)
# We only care about "schema = copy.deepcopy(...)"
schema_dict_node = infer_copy_deepcopy(schema_assign_node.value)
if not schema_dict_node:
# This is not an API model class, as it doesn't have
# something we can resolve to a dictionary.
return
# OK, now we need to look for any properties that dynamically modify
# the dictionary that was just copied from somewhere else.
# See the note below for why we only care about "properties" here.
for assign_node in assigns:
# we're looking for assignments like this:
# schema["properties"]["ttl"] = {...}
target = assign_node.targets[0]
try:
if (isinstance(target, nodes.Subscript)
and target.value.value.name == "schema"
and target.value.slice.value.value == "properties"):
property_name_node = target.slice.value
else:
# not schema["properties"]
continue
except AttributeError:
continue
# schema["properties"]["execution"] = copy.deepcopy(ActionExecutionAPI.schema)
inferred_value = infer_copy_deepcopy(assign_node.value)
extra_schema_properties[property_name_node] = (
inferred_value if inferred_value else assign_node.value)
if not isinstance(schema_dict_node, nodes.Dict):
# Not a class we are interested in (like BaseAPI)
return
# We only care about "properties" in the schema because that's the only part of the schema
# that gets translated into dynamic attributes on the model API class.
properties_dict_node = None
for key_node, value_node in schema_dict_node.items:
if key_node.value == "properties":
properties_dict_node = value_node
break
if not properties_dict_node and not extra_schema_properties:
# Not a class we can do anything with
return
# Hooray! We have the schema properties dict now, so we can start processing
# each property and add an attribute for each one to the API model class node.
for property_name_node, property_data_node in properties_dict_node.items + list(
extra_schema_properties.items()):
property_name = property_name_node.value.replace(
"-", "_") # Note: We do the same in Python code
# Despite the processing above to extract the schema properties dictionary
# each property in the dictionary might also reference other variables,
# so we still need to resolve these to figure out each property's type.
# an indirect reference to copy.deepcopy() as in:
# REQUIRED_ATTR_SCHEMAS = {"action": copy.deepcopy(ActionAPI.schema)}
# schema = {"properties": {"action": REQUIRED_ATTR_SCHEMAS["action"]}}
if isinstance(property_data_node, nodes.Subscript):
var_name = property_data_node.value.name
subscript = property_data_node.slice.value.value
# lookup var by name (assume its at module level)
var_node = next(cls.root().igetattr(var_name))
# assume it is a dict at this point
data_node = None
for key_node, value_node in var_node.items:
if key_node.value == subscript:
# infer will resolve a Dict
data_node = next(value_node.infer())
if data_node is astroid.Uninferable:
data_node = infer_copy_deepcopy(value_node)
break
if data_node:
property_data_node = data_node
if not isinstance(property_data_node, nodes.Dict):
# if infer_copy_deepcopy already ran, we may need to resolve the dict
data_node = next(property_data_node.infer())
if data_node is not astroid.Uninferable:
property_data_node = data_node
property_type_node = None
if isinstance(property_data_node, nodes.Dict):
# We have a property schema, but we only care about the property's type.
for property_key_node, property_value_node in property_data_node.items:
if property_key_node.value == "type":
property_type_node = next(property_value_node.infer())
break
if property_type_node is None and isinstance(property_data_node,
nodes.Attribute):
# reference schema from another file like this:
# from ... import TriggerAPI
# schema = {"properties": {"trigger": TriggerAPI.schema}}
# We only pull a schema from another file when it is an "object" (a dict).
# So, we do not need to do any difficult cross-file processing.
property_type = "object"
elif property_type_node is None:
property_type = None
elif isinstance(property_type_node, nodes.Const):
property_type = property_type_node.value
elif isinstance(property_type_node, (nodes.List, nodes.Tuple)):
# Hack for attributes with multiple types (e.g. string, null)
property_type = property_type_node.elts[
0].value # elts has "elements" in the list/tuple
else:
# We should only hit this if someone has used a different approach
# for dynamically constructing the property's schema.
# Expose the AST at this point to facilitate handling that approach.
raise Exception(property_type_node.repr_tree())
# Hooray! We've got a property's name at this point.
# And we have the property's type, if that type was defined in the schema.
# Now, we can construct the AST node that we'll add to the API model class.
if property_type == "object":
node = nodes.Dict()
elif property_type == "array":
node = nodes.List()
elif property_type == "integer":
node = scoped_nodes.builtin_lookup("int")[1][0]
elif property_type == "number":
node = scoped_nodes.builtin_lookup("float")[1][0]
elif property_type == "string":
node = scoped_nodes.builtin_lookup("str")[1][0]
elif property_type == "boolean":
node = scoped_nodes.builtin_lookup("bool")[1][0]
elif property_type == "null":
node = scoped_nodes.builtin_lookup("None")[1][0]
else:
# Unknown type
node = astroid.ClassDef(property_name, None)
# Create a "property = node" assign node
assign_node = nodes.Assign(parent=cls)
assign_name_node = nodes.AssignName(property_name, parent=assign_node)
assign_node.postinit(targets=[assign_name_node], value=node)
# Finally, add the property node as an attribute on the class.
cls.locals[property_name] = [assign_name_node]
