def transform_model(cls: ClassDef) -> None:
"""
Anything that uses the ModelMeta needs _meta and id.
Also keep track of relationships and make them in the related model class.
"""
if cls.name != "Model":
appname = "models"
for mcls in cls.get_children():
if isinstance(mcls, ClassDef):
for attr in mcls.get_children():
if isinstance(attr,
Assign) and attr.targets[0].name == "app":
appname = attr.value.value
mname = f"{appname}.{cls.name}"
MODELS[mname] = cls
for relname, relval in FUTURE_RELATIONS.get(mname, []):
cls.locals[relname] = relval
for attr in cls.get_children():
if isinstance(attr, (Assign, AnnAssign)):
try:
attrname = attr.value.func.attrname
except AttributeError:
pass
else:
if attrname in [
"OneToOneField", "ForeignKeyField",
"ManyToManyField"
]:
tomodel = attr.value.args[0].value
relname = ""
if attr.value.keywords:
for keyword in attr.value.keywords:
if keyword.arg == "related_name":
relname = keyword.value.value
if not relname:
relname = cls.name.lower() + "s"
# Injected model attributes need to also have the relation manager
if attrname == "ManyToManyField":
relval = [
# attr.value.func,
MANAGER.ast_from_module_name(
"tortoise.fields.relational").lookup(
"ManyToManyFieldInstance")[1][0],
MANAGER.ast_from_module_name(
"tortoise.fields.relational").lookup(
"ManyToManyRelation")[1][0],
]
elif attrname == "ForeignKeyField":
relval = [
MANAGER.ast_from_module_name(
"tortoise.fields.relational").lookup(
"ForeignKeyFieldInstance")[1][0],
MANAGER.ast_from_module_name(
"tortoise.fields.relational").lookup(
"ReverseRelation")[1][0],
]
elif attrname == "OneToOneField":
relval = [
MANAGER.ast_from_module_name(
"tortoise.fields.relational").lookup(
"OneToOneFieldInstance")[1][0],
MANAGER.ast_from_module_name(
"tortoise.fields.relational").lookup(
"OneToOneRelation")[1][0],
]
if tomodel in MODELS:
MODELS[tomodel].locals[relname] = relval
else:
FUTURE_RELATIONS.setdefault(tomodel, []).append(
(relname, relval))
cls.locals["_meta"] = [
MANAGER.ast_from_module_name("tortoise.models").lookup("MetaInfo")[1]
[0].instantiate_class()
]
if "id" not in cls.locals:
cls.locals["id"] = [nodes.ClassDef("id", None)]
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]