Current File : //opt/cloudlinux/venv/lib64/python3.11/site-packages/pydantic/mypy.py
"""This module includes classes and functions designed specifically for use with the mypy plugin."""
from __future__ import annotations
import sys
from configparser import ConfigParser
from typing import Any, Callable, Iterator
from mypy.errorcodes import ErrorCode
from mypy.expandtype import expand_type, expand_type_by_instance
from mypy.nodes import (
ARG_NAMED,
ARG_NAMED_OPT,
ARG_OPT,
ARG_POS,
ARG_STAR2,
MDEF,
Argument,
AssignmentStmt,
Block,
CallExpr,
ClassDef,
Context,
Decorator,
DictExpr,
EllipsisExpr,
Expression,
FuncDef,
IfStmt,
JsonDict,
MemberExpr,
NameExpr,
PassStmt,
PlaceholderNode,
RefExpr,
Statement,
StrExpr,
SymbolTableNode,
TempNode,
TypeAlias,
TypeInfo,
Var,
)
from mypy.options import Options
from mypy.plugin import (
CheckerPluginInterface,
ClassDefContext,
FunctionContext,
MethodContext,
Plugin,
ReportConfigContext,
SemanticAnalyzerPluginInterface,
)
from mypy.plugins import dataclasses
from mypy.plugins.common import (
deserialize_and_fixup_type,
)
from mypy.semanal import set_callable_name
from mypy.server.trigger import make_wildcard_trigger
from mypy.state import state
from mypy.typeops import map_type_from_supertype
from mypy.types import (
AnyType,
CallableType,
Instance,
NoneType,
Overloaded,
Type,
TypeOfAny,
TypeType,
TypeVarType,
UnionType,
get_proper_type,
)
from mypy.typevars import fill_typevars
from mypy.util import get_unique_redefinition_name
from mypy.version import __version__ as mypy_version
from pydantic._internal import _fields
from pydantic.version import parse_mypy_version
try:
from mypy.types import TypeVarDef # type: ignore[attr-defined]
except ImportError: # pragma: no cover
# Backward-compatible with TypeVarDef from Mypy 0.930.
from mypy.types import TypeVarType as TypeVarDef
CONFIGFILE_KEY = 'pydantic-mypy'
METADATA_KEY = 'pydantic-mypy-metadata'
BASEMODEL_FULLNAME = 'pydantic.main.BaseModel'
BASESETTINGS_FULLNAME = 'pydantic_settings.main.BaseSettings'
MODEL_METACLASS_FULLNAME = 'pydantic._internal._model_construction.ModelMetaclass'
FIELD_FULLNAME = 'pydantic.fields.Field'
DATACLASS_FULLNAME = 'pydantic.dataclasses.dataclass'
MODEL_VALIDATOR_FULLNAME = 'pydantic.functional_validators.model_validator'
DECORATOR_FULLNAMES = {
'pydantic.functional_validators.field_validator',
'pydantic.functional_validators.model_validator',
'pydantic.functional_serializers.serializer',
'pydantic.functional_serializers.model_serializer',
'pydantic.deprecated.class_validators.validator',
'pydantic.deprecated.class_validators.root_validator',
}
MYPY_VERSION_TUPLE = parse_mypy_version(mypy_version)
BUILTINS_NAME = 'builtins' if MYPY_VERSION_TUPLE >= (0, 930) else '__builtins__'
# Increment version if plugin changes and mypy caches should be invalidated
__version__ = 2
def plugin(version: str) -> type[Plugin]:
"""`version` is the mypy version string.
We might want to use this to print a warning if the mypy version being used is
newer, or especially older, than we expect (or need).
Args:
version: The mypy version string.
Return:
The Pydantic mypy plugin type.
"""
return PydanticPlugin
class PydanticPlugin(Plugin):
"""The Pydantic mypy plugin."""
def __init__(self, options: Options) -> None:
self.plugin_config = PydanticPluginConfig(options)
self._plugin_data = self.plugin_config.to_data()
super().__init__(options)
def get_base_class_hook(self, fullname: str) -> Callable[[ClassDefContext], bool] | None:
"""Update Pydantic model class."""
sym = self.lookup_fully_qualified(fullname)
if sym and isinstance(sym.node, TypeInfo): # pragma: no branch
# No branching may occur if the mypy cache has not been cleared
if any(base.fullname == BASEMODEL_FULLNAME for base in sym.node.mro):
return self._pydantic_model_class_maker_callback
return None
def get_metaclass_hook(self, fullname: str) -> Callable[[ClassDefContext], None] | None:
"""Update Pydantic `ModelMetaclass` definition."""
if fullname == MODEL_METACLASS_FULLNAME:
return self._pydantic_model_metaclass_marker_callback
return None
def get_function_hook(self, fullname: str) -> Callable[[FunctionContext], Type] | None:
"""Adjust the return type of the `Field` function."""
sym = self.lookup_fully_qualified(fullname)
if sym and sym.fullname == FIELD_FULLNAME:
return self._pydantic_field_callback
return None
def get_method_hook(self, fullname: str) -> Callable[[MethodContext], Type] | None:
"""Adjust return type of `from_orm` method call."""
if fullname.endswith('.from_orm'):
return from_attributes_callback
return None
def get_class_decorator_hook(self, fullname: str) -> Callable[[ClassDefContext], None] | None:
"""Mark pydantic.dataclasses as dataclass.
Mypy version 1.1.1 added support for `@dataclass_transform` decorator.
"""
if fullname == DATACLASS_FULLNAME and MYPY_VERSION_TUPLE < (1, 1):
return dataclasses.dataclass_class_maker_callback # type: ignore[return-value]
return None
def report_config_data(self, ctx: ReportConfigContext) -> dict[str, Any]:
"""Return all plugin config data.
Used by mypy to determine if cache needs to be discarded.
"""
return self._plugin_data
def _pydantic_model_class_maker_callback(self, ctx: ClassDefContext) -> bool:
transformer = PydanticModelTransformer(ctx.cls, ctx.reason, ctx.api, self.plugin_config)
return transformer.transform()
def _pydantic_model_metaclass_marker_callback(self, ctx: ClassDefContext) -> None:
"""Reset dataclass_transform_spec attribute of ModelMetaclass.
Let the plugin handle it. This behavior can be disabled
if 'debug_dataclass_transform' is set to True', for testing purposes.
"""
if self.plugin_config.debug_dataclass_transform:
return
info_metaclass = ctx.cls.info.declared_metaclass
assert info_metaclass, "callback not passed from 'get_metaclass_hook'"
if getattr(info_metaclass.type, 'dataclass_transform_spec', None):
info_metaclass.type.dataclass_transform_spec = None
def _pydantic_field_callback(self, ctx: FunctionContext) -> Type:
"""Extract the type of the `default` argument from the Field function, and use it as the return type.
In particular:
* Check whether the default and default_factory argument is specified.
* Output an error if both are specified.
* Retrieve the type of the argument which is specified, and use it as return type for the function.
"""
default_any_type = ctx.default_return_type
assert ctx.callee_arg_names[0] == 'default', '"default" is no longer first argument in Field()'
assert ctx.callee_arg_names[1] == 'default_factory', '"default_factory" is no longer second argument in Field()'
default_args = ctx.args[0]
default_factory_args = ctx.args[1]
if default_args and default_factory_args:
error_default_and_default_factory_specified(ctx.api, ctx.context)
return default_any_type
if default_args:
default_type = ctx.arg_types[0][0]
default_arg = default_args[0]
# Fallback to default Any type if the field is required
if not isinstance(default_arg, EllipsisExpr):
return default_type
elif default_factory_args:
default_factory_type = ctx.arg_types[1][0]
# Functions which use `ParamSpec` can be overloaded, exposing the callable's types as a parameter
# Pydantic calls the default factory without any argument, so we retrieve the first item
if isinstance(default_factory_type, Overloaded):
default_factory_type = default_factory_type.items[0]
if isinstance(default_factory_type, CallableType):
ret_type = default_factory_type.ret_type
# mypy doesn't think `ret_type` has `args`, you'd think mypy should know,
# add this check in case it varies by version
args = getattr(ret_type, 'args', None)
if args:
if all(isinstance(arg, TypeVarType) for arg in args):
# Looks like the default factory is a type like `list` or `dict`, replace all args with `Any`
ret_type.args = tuple(default_any_type for _ in args) # type: ignore[attr-defined]
return ret_type
return default_any_type
class PydanticPluginConfig:
"""A Pydantic mypy plugin config holder.
Attributes:
init_forbid_extra: Whether to add a `**kwargs` at the end of the generated `__init__` signature.
init_typed: Whether to annotate fields in the generated `__init__`.
warn_required_dynamic_aliases: Whether to raise required dynamic aliases error.
debug_dataclass_transform: Whether to not reset `dataclass_transform_spec` attribute
of `ModelMetaclass` for testing purposes.
"""
__slots__ = (
'init_forbid_extra',
'init_typed',
'warn_required_dynamic_aliases',
'debug_dataclass_transform',
)
init_forbid_extra: bool
init_typed: bool
warn_required_dynamic_aliases: bool
debug_dataclass_transform: bool # undocumented
def __init__(self, options: Options) -> None:
if options.config_file is None: # pragma: no cover
return
toml_config = parse_toml(options.config_file)
if toml_config is not None:
config = toml_config.get('tool', {}).get('pydantic-mypy', {})
for key in self.__slots__:
setting = config.get(key, False)
if not isinstance(setting, bool):
raise ValueError(f'Configuration value must be a boolean for key: {key}')
setattr(self, key, setting)
else:
plugin_config = ConfigParser()
plugin_config.read(options.config_file)
for key in self.__slots__:
setting = plugin_config.getboolean(CONFIGFILE_KEY, key, fallback=False)
setattr(self, key, setting)
def to_data(self) -> dict[str, Any]:
"""Returns a dict of config names to their values."""
return {key: getattr(self, key) for key in self.__slots__}
def from_attributes_callback(ctx: MethodContext) -> Type:
"""Raise an error if from_attributes is not enabled."""
model_type: Instance
ctx_type = ctx.type
if isinstance(ctx_type, TypeType):
ctx_type = ctx_type.item
if isinstance(ctx_type, CallableType) and isinstance(ctx_type.ret_type, Instance):
model_type = ctx_type.ret_type # called on the class
elif isinstance(ctx_type, Instance):
model_type = ctx_type # called on an instance (unusual, but still valid)
else: # pragma: no cover
detail = f'ctx.type: {ctx_type} (of type {ctx_type.__class__.__name__})'
error_unexpected_behavior(detail, ctx.api, ctx.context)
return ctx.default_return_type
pydantic_metadata = model_type.type.metadata.get(METADATA_KEY)
if pydantic_metadata is None:
return ctx.default_return_type
from_attributes = pydantic_metadata.get('config', {}).get('from_attributes')
if from_attributes is not True:
error_from_attributes(model_type.type.name, ctx.api, ctx.context)
return ctx.default_return_type
class PydanticModelField:
"""Based on mypy.plugins.dataclasses.DataclassAttribute."""
def __init__(
self,
name: str,
alias: str | None,
has_dynamic_alias: bool,
has_default: bool,
line: int,
column: int,
type: Type | None,
info: TypeInfo,
):
self.name = name
self.alias = alias
self.has_dynamic_alias = has_dynamic_alias
self.has_default = has_default
self.line = line
self.column = column
self.type = type
self.info = info
def to_argument(self, current_info: TypeInfo, typed: bool, force_optional: bool, use_alias: bool) -> Argument:
"""Based on mypy.plugins.dataclasses.DataclassAttribute.to_argument."""
return Argument(
variable=self.to_var(current_info, use_alias),
type_annotation=self.expand_type(current_info) if typed else AnyType(TypeOfAny.explicit),
initializer=None,
kind=ARG_NAMED_OPT if force_optional or self.has_default else ARG_NAMED,
)
def expand_type(self, current_info: TypeInfo) -> Type | None:
"""Based on mypy.plugins.dataclasses.DataclassAttribute.expand_type."""
if self.type is not None and self.info.self_type is not None:
# In general, it is not safe to call `expand_type()` during semantic analyzis,
# however this plugin is called very late, so all types should be fully ready.
# Also, it is tricky to avoid eager expansion of Self types here (e.g. because
# we serialize attributes).
return expand_type(self.type, {self.info.self_type.id: fill_typevars(current_info)})
return self.type
def to_var(self, current_info: TypeInfo, use_alias: bool) -> Var:
"""Based on mypy.plugins.dataclasses.DataclassAttribute.to_var."""
if use_alias and self.alias is not None:
name = self.alias
else:
name = self.name
return Var(name, self.expand_type(current_info))
def serialize(self) -> JsonDict:
"""Based on mypy.plugins.dataclasses.DataclassAttribute.serialize."""
assert self.type
return {
'name': self.name,
'alias': self.alias,
'has_dynamic_alias': self.has_dynamic_alias,
'has_default': self.has_default,
'line': self.line,
'column': self.column,
'type': self.type.serialize(),
}
@classmethod
def deserialize(cls, info: TypeInfo, data: JsonDict, api: SemanticAnalyzerPluginInterface) -> PydanticModelField:
"""Based on mypy.plugins.dataclasses.DataclassAttribute.deserialize."""
data = data.copy()
typ = deserialize_and_fixup_type(data.pop('type'), api)
return cls(type=typ, info=info, **data)
def expand_typevar_from_subtype(self, sub_type: TypeInfo) -> None:
"""Expands type vars in the context of a subtype when an attribute is inherited
from a generic super type.
"""
if self.type is not None:
self.type = map_type_from_supertype(self.type, sub_type, self.info)
class PydanticModelTransformer:
"""Transform the BaseModel subclass according to the plugin settings.
Attributes:
tracked_config_fields: A set of field configs that the plugin has to track their value.
"""
tracked_config_fields: set[str] = {
'extra',
'frozen',
'from_attributes',
'populate_by_name',
'alias_generator',
}
def __init__(
self,
cls: ClassDef,
reason: Expression | Statement,
api: SemanticAnalyzerPluginInterface,
plugin_config: PydanticPluginConfig,
) -> None:
self._cls = cls
self._reason = reason
self._api = api
self.plugin_config = plugin_config
def transform(self) -> bool:
"""Configures the BaseModel subclass according to the plugin settings.
In particular:
* determines the model config and fields,
* adds a fields-aware signature for the initializer and construct methods
* freezes the class if frozen = True
* stores the fields, config, and if the class is settings in the mypy metadata for access by subclasses
"""
info = self._cls.info
config = self.collect_config()
fields = self.collect_fields(config)
if fields is None:
# Some definitions are not ready. We need another pass.
return False
for field in fields:
if field.type is None:
return False
is_settings = any(base.fullname == BASESETTINGS_FULLNAME for base in info.mro[:-1])
self.add_initializer(fields, config, is_settings)
self.add_model_construct_method(fields, config, is_settings)
self.set_frozen(fields, frozen=config.frozen is True)
self.adjust_decorator_signatures()
info.metadata[METADATA_KEY] = {
'fields': {field.name: field.serialize() for field in fields},
'config': config.get_values_dict(),
}
return True
def adjust_decorator_signatures(self) -> None:
"""When we decorate a function `f` with `pydantic.validator(...)`, `pydantic.field_validator`
or `pydantic.serializer(...)`, mypy sees `f` as a regular method taking a `self` instance,
even though pydantic internally wraps `f` with `classmethod` if necessary.
Teach mypy this by marking any function whose outermost decorator is a `validator()`,
`field_validator()` or `serializer()` call as a `classmethod`.
"""
for name, sym in self._cls.info.names.items():
if isinstance(sym.node, Decorator):
first_dec = sym.node.original_decorators[0]
if (
isinstance(first_dec, CallExpr)
and isinstance(first_dec.callee, NameExpr)
and first_dec.callee.fullname in DECORATOR_FULLNAMES
# @model_validator(mode="after") is an exception, it expects a regular method
and not (
first_dec.callee.fullname == MODEL_VALIDATOR_FULLNAME
and any(
first_dec.arg_names[i] == 'mode' and isinstance(arg, StrExpr) and arg.value == 'after'
for i, arg in enumerate(first_dec.args)
)
)
):
# TODO: Only do this if the first argument of the decorated function is `cls`
sym.node.func.is_class = True
def collect_config(self) -> ModelConfigData: # noqa: C901 (ignore complexity)
"""Collects the values of the config attributes that are used by the plugin, accounting for parent classes."""
cls = self._cls
config = ModelConfigData()
has_config_kwargs = False
has_config_from_namespace = False
# Handle `class MyModel(BaseModel, <name>=<expr>, ...):`
for name, expr in cls.keywords.items():
config_data = self.get_config_update(name, expr)
if config_data:
has_config_kwargs = True
config.update(config_data)
# Handle `model_config`
stmt: Statement | None = None
for stmt in cls.defs.body:
if not isinstance(stmt, (AssignmentStmt, ClassDef)):
continue
if isinstance(stmt, AssignmentStmt):
lhs = stmt.lvalues[0]
if not isinstance(lhs, NameExpr) or lhs.name != 'model_config':
continue
if isinstance(stmt.rvalue, CallExpr): # calls to `dict` or `ConfigDict`
for arg_name, arg in zip(stmt.rvalue.arg_names, stmt.rvalue.args):
if arg_name is None:
continue
config.update(self.get_config_update(arg_name, arg))
elif isinstance(stmt.rvalue, DictExpr): # dict literals
for key_expr, value_expr in stmt.rvalue.items:
if not isinstance(key_expr, StrExpr):
continue
config.update(self.get_config_update(key_expr.value, value_expr))
elif isinstance(stmt, ClassDef):
if stmt.name != 'Config': # 'deprecated' Config-class
continue
for substmt in stmt.defs.body:
if not isinstance(substmt, AssignmentStmt):
continue
lhs = substmt.lvalues[0]
if not isinstance(lhs, NameExpr):
continue
config.update(self.get_config_update(lhs.name, substmt.rvalue))
if has_config_kwargs:
self._api.fail(
'Specifying config in two places is ambiguous, use either Config attribute or class kwargs',
cls,
)
break
has_config_from_namespace = True
if has_config_kwargs or has_config_from_namespace:
if (
stmt
and config.has_alias_generator
and not config.populate_by_name
and self.plugin_config.warn_required_dynamic_aliases
):
error_required_dynamic_aliases(self._api, stmt)
for info in cls.info.mro[1:]: # 0 is the current class
if METADATA_KEY not in info.metadata:
continue
# Each class depends on the set of fields in its ancestors
self._api.add_plugin_dependency(make_wildcard_trigger(info.fullname))
for name, value in info.metadata[METADATA_KEY]['config'].items():
config.setdefault(name, value)
return config
def collect_fields(self, model_config: ModelConfigData) -> list[PydanticModelField] | None:
"""Collects the fields for the model, accounting for parent classes."""
cls = self._cls
# First, collect fields belonging to any class in the MRO, ignoring duplicates.
#
# We iterate through the MRO in reverse because attrs defined in the parent must appear
# earlier in the attributes list than attrs defined in the child. See:
# https://docs.python.org/3/library/dataclasses.html#inheritance
#
# However, we also want fields defined in the subtype to override ones defined
# in the parent. We can implement this via a dict without disrupting the attr order
# because dicts preserve insertion order in Python 3.7+.
found_fields: dict[str, PydanticModelField] = {}
for info in reversed(cls.info.mro[1:-1]): # 0 is the current class, -2 is BaseModel, -1 is object
# if BASEMODEL_METADATA_TAG_KEY in info.metadata and BASEMODEL_METADATA_KEY not in info.metadata:
# # We haven't processed the base class yet. Need another pass.
# return None
if METADATA_KEY not in info.metadata:
continue
# Each class depends on the set of attributes in its dataclass ancestors.
self._api.add_plugin_dependency(make_wildcard_trigger(info.fullname))
for name, data in info.metadata[METADATA_KEY]['fields'].items():
field = PydanticModelField.deserialize(info, data, self._api)
# (The following comment comes directly from the dataclasses plugin)
# TODO: We shouldn't be performing type operations during the main
# semantic analysis pass, since some TypeInfo attributes might
# still be in flux. This should be performed in a later phase.
with state.strict_optional_set(self._api.options.strict_optional):
field.expand_typevar_from_subtype(cls.info)
found_fields[name] = field
sym_node = cls.info.names.get(name)
if sym_node and sym_node.node and not isinstance(sym_node.node, Var):
self._api.fail(
'BaseModel field may only be overridden by another field',
sym_node.node,
)
# Second, collect fields belonging to the current class.
current_field_names: set[str] = set()
for stmt in self._get_assignment_statements_from_block(cls.defs):
maybe_field = self.collect_field_from_stmt(stmt, model_config)
if maybe_field is not None:
lhs = stmt.lvalues[0]
current_field_names.add(lhs.name)
found_fields[lhs.name] = maybe_field
return list(found_fields.values())
def _get_assignment_statements_from_if_statement(self, stmt: IfStmt) -> Iterator[AssignmentStmt]:
for body in stmt.body:
if not body.is_unreachable:
yield from self._get_assignment_statements_from_block(body)
if stmt.else_body is not None and not stmt.else_body.is_unreachable:
yield from self._get_assignment_statements_from_block(stmt.else_body)
def _get_assignment_statements_from_block(self, block: Block) -> Iterator[AssignmentStmt]:
for stmt in block.body:
if isinstance(stmt, AssignmentStmt):
yield stmt
elif isinstance(stmt, IfStmt):
yield from self._get_assignment_statements_from_if_statement(stmt)
def collect_field_from_stmt( # noqa C901
self, stmt: AssignmentStmt, model_config: ModelConfigData
) -> PydanticModelField | None:
"""Get pydantic model field from statement.
Args:
stmt: The statement.
model_config: Configuration settings for the model.
Returns:
A pydantic model field if it could find the field in statement. Otherwise, `None`.
"""
cls = self._cls
lhs = stmt.lvalues[0]
if not isinstance(lhs, NameExpr) or not _fields.is_valid_field_name(lhs.name) or lhs.name == 'model_config':
return None
if not stmt.new_syntax:
if (
isinstance(stmt.rvalue, CallExpr)
and isinstance(stmt.rvalue.callee, CallExpr)
and isinstance(stmt.rvalue.callee.callee, NameExpr)
and stmt.rvalue.callee.callee.fullname in DECORATOR_FULLNAMES
):
# This is a (possibly-reused) validator or serializer, not a field
# In particular, it looks something like: my_validator = validator('my_field')(f)
# Eventually, we may want to attempt to respect model_config['ignored_types']
return None
# The assignment does not have an annotation, and it's not anything else we recognize
error_untyped_fields(self._api, stmt)
return None
lhs = stmt.lvalues[0]
if not isinstance(lhs, NameExpr):
return None
if not _fields.is_valid_field_name(lhs.name) or lhs.name == 'model_config':
return None
sym = cls.info.names.get(lhs.name)
if sym is None: # pragma: no cover
# This is likely due to a star import (see the dataclasses plugin for a more detailed explanation)
# This is the same logic used in the dataclasses plugin
return None
node = sym.node
if isinstance(node, PlaceholderNode): # pragma: no cover
# See the PlaceholderNode docstring for more detail about how this can occur
# Basically, it is an edge case when dealing with complex import logic
# The dataclasses plugin now asserts this cannot happen, but I'd rather not error if it does..
return None
if isinstance(node, TypeAlias):
self._api.fail(
'Type aliases inside BaseModel definitions are not supported at runtime',
node,
)
# Skip processing this node. This doesn't match the runtime behaviour,
# but the only alternative would be to modify the SymbolTable,
# and it's a little hairy to do that in a plugin.
return None
if not isinstance(node, Var): # pragma: no cover
# Don't know if this edge case still happens with the `is_valid_field` check above
# but better safe than sorry
# The dataclasses plugin now asserts this cannot happen, but I'd rather not error if it does..
return None
# x: ClassVar[int] is not a field
if node.is_classvar:
return None
# x: InitVar[int] is not supported in BaseModel
node_type = get_proper_type(node.type)
if isinstance(node_type, Instance) and node_type.type.fullname == 'dataclasses.InitVar':
self._api.fail(
'InitVar is not supported in BaseModel',
node,
)
has_default = self.get_has_default(stmt)
if sym.type is None and node.is_final and node.is_inferred:
# This follows the logic from the dataclasses plugin. The following comment is taken verbatim:
#
# This is a special case, assignment like x: Final = 42 is classified
# annotated above, but mypy strips the `Final` turning it into x = 42.
# We do not support inferred types in dataclasses, so we can try inferring
# type for simple literals, and otherwise require an explicit type
# argument for Final[...].
typ = self._api.analyze_simple_literal_type(stmt.rvalue, is_final=True)
if typ:
node.type = typ
else:
self._api.fail(
'Need type argument for Final[...] with non-literal default in BaseModel',
stmt,
)
node.type = AnyType(TypeOfAny.from_error)
alias, has_dynamic_alias = self.get_alias_info(stmt)
if has_dynamic_alias and not model_config.populate_by_name and self.plugin_config.warn_required_dynamic_aliases:
error_required_dynamic_aliases(self._api, stmt)
init_type = self._infer_dataclass_attr_init_type(sym, lhs.name, stmt)
return PydanticModelField(
name=lhs.name,
has_dynamic_alias=has_dynamic_alias,
has_default=has_default,
alias=alias,
line=stmt.line,
column=stmt.column,
type=init_type,
info=cls.info,
)
def _infer_dataclass_attr_init_type(self, sym: SymbolTableNode, name: str, context: Context) -> Type | None:
"""Infer __init__ argument type for an attribute.
In particular, possibly use the signature of __set__.
"""
default = sym.type
if sym.implicit:
return default
t = get_proper_type(sym.type)
# Perform a simple-minded inference from the signature of __set__, if present.
# We can't use mypy.checkmember here, since this plugin runs before type checking.
# We only support some basic scanerios here, which is hopefully sufficient for
# the vast majority of use cases.
if not isinstance(t, Instance):
return default
setter = t.type.get('__set__')
if setter:
if isinstance(setter.node, FuncDef):
super_info = t.type.get_containing_type_info('__set__')
assert super_info
if setter.type:
setter_type = get_proper_type(map_type_from_supertype(setter.type, t.type, super_info))
else:
return AnyType(TypeOfAny.unannotated)
if isinstance(setter_type, CallableType) and setter_type.arg_kinds == [
ARG_POS,
ARG_POS,
ARG_POS,
]:
return expand_type_by_instance(setter_type.arg_types[2], t)
else:
self._api.fail(f'Unsupported signature for "__set__" in "{t.type.name}"', context)
else:
self._api.fail(f'Unsupported "__set__" in "{t.type.name}"', context)
return default
def add_initializer(self, fields: list[PydanticModelField], config: ModelConfigData, is_settings: bool) -> None:
"""Adds a fields-aware `__init__` method to the class.
The added `__init__` will be annotated with types vs. all `Any` depending on the plugin settings.
"""
if '__init__' in self._cls.info.names and not self._cls.info.names['__init__'].plugin_generated:
return # Don't generate an __init__ if one already exists
typed = self.plugin_config.init_typed
use_alias = config.populate_by_name is not True
requires_dynamic_aliases = bool(config.has_alias_generator and not config.populate_by_name)
with state.strict_optional_set(self._api.options.strict_optional):
args = self.get_field_arguments(
fields,
typed=typed,
requires_dynamic_aliases=requires_dynamic_aliases,
use_alias=use_alias,
is_settings=is_settings,
)
if is_settings:
base_settings_node = self._api.lookup_fully_qualified(BASESETTINGS_FULLNAME).node
if '__init__' in base_settings_node.names:
base_settings_init_node = base_settings_node.names['__init__'].node
if base_settings_init_node is not None and base_settings_init_node.type is not None:
func_type = base_settings_init_node.type
for arg_idx, arg_name in enumerate(func_type.arg_names):
if arg_name.startswith('__') or not arg_name.startswith('_'):
continue
analyzed_variable_type = self._api.anal_type(func_type.arg_types[arg_idx])
variable = Var(arg_name, analyzed_variable_type)
args.append(Argument(variable, analyzed_variable_type, None, ARG_OPT))
if not self.should_init_forbid_extra(fields, config):
var = Var('kwargs')
args.append(Argument(var, AnyType(TypeOfAny.explicit), None, ARG_STAR2))
add_method(self._api, self._cls, '__init__', args=args, return_type=NoneType())
def add_model_construct_method(
self, fields: list[PydanticModelField], config: ModelConfigData, is_settings: bool
) -> None:
"""Adds a fully typed `model_construct` classmethod to the class.
Similar to the fields-aware __init__ method, but always uses the field names (not aliases),
and does not treat settings fields as optional.
"""
set_str = self._api.named_type(f'{BUILTINS_NAME}.set', [self._api.named_type(f'{BUILTINS_NAME}.str')])
optional_set_str = UnionType([set_str, NoneType()])
fields_set_argument = Argument(Var('_fields_set', optional_set_str), optional_set_str, None, ARG_OPT)
with state.strict_optional_set(self._api.options.strict_optional):
args = self.get_field_arguments(
fields, typed=True, requires_dynamic_aliases=False, use_alias=False, is_settings=is_settings
)
if not self.should_init_forbid_extra(fields, config):
var = Var('kwargs')
args.append(Argument(var, AnyType(TypeOfAny.explicit), None, ARG_STAR2))
args = [fields_set_argument] + args
add_method(
self._api,
self._cls,
'model_construct',
args=args,
return_type=fill_typevars(self._cls.info),
is_classmethod=True,
)
def set_frozen(self, fields: list[PydanticModelField], frozen: bool) -> None:
"""Marks all fields as properties so that attempts to set them trigger mypy errors.
This is the same approach used by the attrs and dataclasses plugins.
"""
info = self._cls.info
for field in fields:
sym_node = info.names.get(field.name)
if sym_node is not None:
var = sym_node.node
if isinstance(var, Var):
var.is_property = frozen
elif isinstance(var, PlaceholderNode) and not self._api.final_iteration:
# See https://github.com/pydantic/pydantic/issues/5191 to hit this branch for test coverage
self._api.defer()
else: # pragma: no cover
# I don't know whether it's possible to hit this branch, but I've added it for safety
try:
var_str = str(var)
except TypeError:
# This happens for PlaceholderNode; perhaps it will happen for other types in the future..
var_str = repr(var)
detail = f'sym_node.node: {var_str} (of type {var.__class__})'
error_unexpected_behavior(detail, self._api, self._cls)
else:
var = field.to_var(info, use_alias=False)
var.info = info
var.is_property = frozen
var._fullname = info.fullname + '.' + var.name
info.names[var.name] = SymbolTableNode(MDEF, var)
def get_config_update(self, name: str, arg: Expression) -> ModelConfigData | None:
"""Determines the config update due to a single kwarg in the ConfigDict definition.
Warns if a tracked config attribute is set to a value the plugin doesn't know how to interpret (e.g., an int)
"""
if name not in self.tracked_config_fields:
return None
if name == 'extra':
if isinstance(arg, StrExpr):
forbid_extra = arg.value == 'forbid'
elif isinstance(arg, MemberExpr):
forbid_extra = arg.name == 'forbid'
else:
error_invalid_config_value(name, self._api, arg)
return None
return ModelConfigData(forbid_extra=forbid_extra)
if name == 'alias_generator':
has_alias_generator = True
if isinstance(arg, NameExpr) and arg.fullname == 'builtins.None':
has_alias_generator = False
return ModelConfigData(has_alias_generator=has_alias_generator)
if isinstance(arg, NameExpr) and arg.fullname in ('builtins.True', 'builtins.False'):
return ModelConfigData(**{name: arg.fullname == 'builtins.True'})
error_invalid_config_value(name, self._api, arg)
return None
@staticmethod
def get_has_default(stmt: AssignmentStmt) -> bool:
"""Returns a boolean indicating whether the field defined in `stmt` is a required field."""
expr = stmt.rvalue
if isinstance(expr, TempNode):
# TempNode means annotation-only, so has no default
return False
if isinstance(expr, CallExpr) and isinstance(expr.callee, RefExpr) and expr.callee.fullname == FIELD_FULLNAME:
# The "default value" is a call to `Field`; at this point, the field has a default if and only if:
# * there is a positional argument that is not `...`
# * there is a keyword argument named "default" that is not `...`
# * there is a "default_factory" that is not `None`
for arg, name in zip(expr.args, expr.arg_names):
# If name is None, then this arg is the default because it is the only positional argument.
if name is None or name == 'default':
return arg.__class__ is not EllipsisExpr
if name == 'default_factory':
return not (isinstance(arg, NameExpr) and arg.fullname == 'builtins.None')
return False
# Has no default if the "default value" is Ellipsis (i.e., `field_name: Annotation = ...`)
return not isinstance(expr, EllipsisExpr)
@staticmethod
def get_alias_info(stmt: AssignmentStmt) -> tuple[str | None, bool]:
"""Returns a pair (alias, has_dynamic_alias), extracted from the declaration of the field defined in `stmt`.
`has_dynamic_alias` is True if and only if an alias is provided, but not as a string literal.
If `has_dynamic_alias` is True, `alias` will be None.
"""
expr = stmt.rvalue
if isinstance(expr, TempNode):
# TempNode means annotation-only
return None, False
if not (
isinstance(expr, CallExpr) and isinstance(expr.callee, RefExpr) and expr.callee.fullname == FIELD_FULLNAME
):
# Assigned value is not a call to pydantic.fields.Field
return None, False
for i, arg_name in enumerate(expr.arg_names):
if arg_name != 'alias':
continue
arg = expr.args[i]
if isinstance(arg, StrExpr):
return arg.value, False
else:
return None, True
return None, False
def get_field_arguments(
self,
fields: list[PydanticModelField],
typed: bool,
use_alias: bool,
requires_dynamic_aliases: bool,
is_settings: bool,
) -> list[Argument]:
"""Helper function used during the construction of the `__init__` and `model_construct` method signatures.
Returns a list of mypy Argument instances for use in the generated signatures.
"""
info = self._cls.info
arguments = [
field.to_argument(
info, typed=typed, force_optional=requires_dynamic_aliases or is_settings, use_alias=use_alias
)
for field in fields
if not (use_alias and field.has_dynamic_alias)
]
return arguments
def should_init_forbid_extra(self, fields: list[PydanticModelField], config: ModelConfigData) -> bool:
"""Indicates whether the generated `__init__` should get a `**kwargs` at the end of its signature.
We disallow arbitrary kwargs if the extra config setting is "forbid", or if the plugin config says to,
*unless* a required dynamic alias is present (since then we can't determine a valid signature).
"""
if not config.populate_by_name:
if self.is_dynamic_alias_present(fields, bool(config.has_alias_generator)):
return False
if config.forbid_extra:
return True
return self.plugin_config.init_forbid_extra
@staticmethod
def is_dynamic_alias_present(fields: list[PydanticModelField], has_alias_generator: bool) -> bool:
"""Returns whether any fields on the model have a "dynamic alias", i.e., an alias that cannot be
determined during static analysis.
"""
for field in fields:
if field.has_dynamic_alias:
return True
if has_alias_generator:
for field in fields:
if field.alias is None:
return True
return False
class ModelConfigData:
"""Pydantic mypy plugin model config class."""
def __init__(
self,
forbid_extra: bool | None = None,
frozen: bool | None = None,
from_attributes: bool | None = None,
populate_by_name: bool | None = None,
has_alias_generator: bool | None = None,
):
self.forbid_extra = forbid_extra
self.frozen = frozen
self.from_attributes = from_attributes
self.populate_by_name = populate_by_name
self.has_alias_generator = has_alias_generator
def get_values_dict(self) -> dict[str, Any]:
"""Returns a dict of Pydantic model config names to their values.
It includes the config if config value is not `None`.
"""
return {k: v for k, v in self.__dict__.items() if v is not None}
def update(self, config: ModelConfigData | None) -> None:
"""Update Pydantic model config values."""
if config is None:
return
for k, v in config.get_values_dict().items():
setattr(self, k, v)
def setdefault(self, key: str, value: Any) -> None:
"""Set default value for Pydantic model config if config value is `None`."""
if getattr(self, key) is None:
setattr(self, key, value)
ERROR_ORM = ErrorCode('pydantic-orm', 'Invalid from_attributes call', 'Pydantic')
ERROR_CONFIG = ErrorCode('pydantic-config', 'Invalid config value', 'Pydantic')
ERROR_ALIAS = ErrorCode('pydantic-alias', 'Dynamic alias disallowed', 'Pydantic')
ERROR_UNEXPECTED = ErrorCode('pydantic-unexpected', 'Unexpected behavior', 'Pydantic')
ERROR_UNTYPED = ErrorCode('pydantic-field', 'Untyped field disallowed', 'Pydantic')
ERROR_FIELD_DEFAULTS = ErrorCode('pydantic-field', 'Invalid Field defaults', 'Pydantic')
def error_from_attributes(model_name: str, api: CheckerPluginInterface, context: Context) -> None:
"""Emits an error when the model does not have `from_attributes=True`."""
api.fail(f'"{model_name}" does not have from_attributes=True', context, code=ERROR_ORM)
def error_invalid_config_value(name: str, api: SemanticAnalyzerPluginInterface, context: Context) -> None:
"""Emits an error when the config value is invalid."""
api.fail(f'Invalid value for "Config.{name}"', context, code=ERROR_CONFIG)
def error_required_dynamic_aliases(api: SemanticAnalyzerPluginInterface, context: Context) -> None:
"""Emits required dynamic aliases error.
This will be called when `warn_required_dynamic_aliases=True`.
"""
api.fail('Required dynamic aliases disallowed', context, code=ERROR_ALIAS)
def error_unexpected_behavior(
detail: str, api: CheckerPluginInterface | SemanticAnalyzerPluginInterface, context: Context
) -> None: # pragma: no cover
"""Emits unexpected behavior error."""
# Can't think of a good way to test this, but I confirmed it renders as desired by adding to a non-error path
link = 'https://github.com/pydantic/pydantic/issues/new/choose'
full_message = f'The pydantic mypy plugin ran into unexpected behavior: {detail}\n'
full_message += f'Please consider reporting this bug at {link} so we can try to fix it!'
api.fail(full_message, context, code=ERROR_UNEXPECTED)
def error_untyped_fields(api: SemanticAnalyzerPluginInterface, context: Context) -> None:
"""Emits an error when there is an untyped field in the model."""
api.fail('Untyped fields disallowed', context, code=ERROR_UNTYPED)
def error_default_and_default_factory_specified(api: CheckerPluginInterface, context: Context) -> None:
"""Emits an error when `Field` has both `default` and `default_factory` together."""
api.fail('Field default and default_factory cannot be specified together', context, code=ERROR_FIELD_DEFAULTS)
def add_method(
api: SemanticAnalyzerPluginInterface | CheckerPluginInterface,
cls: ClassDef,
name: str,
args: list[Argument],
return_type: Type,
self_type: Type | None = None,
tvar_def: TypeVarDef | None = None,
is_classmethod: bool = False,
) -> None:
"""Very closely related to `mypy.plugins.common.add_method_to_class`, with a few pydantic-specific changes."""
info = cls.info
# First remove any previously generated methods with the same name
# to avoid clashes and problems in the semantic analyzer.
if name in info.names:
sym = info.names[name]
if sym.plugin_generated and isinstance(sym.node, FuncDef):
cls.defs.body.remove(sym.node) # pragma: no cover
if isinstance(api, SemanticAnalyzerPluginInterface):
function_type = api.named_type('builtins.function')
else:
function_type = api.named_generic_type('builtins.function', [])
if is_classmethod:
self_type = self_type or TypeType(fill_typevars(info))
first = [Argument(Var('_cls'), self_type, None, ARG_POS, True)]
else:
self_type = self_type or fill_typevars(info)
first = [Argument(Var('__pydantic_self__'), self_type, None, ARG_POS)]
args = first + args
arg_types, arg_names, arg_kinds = [], [], []
for arg in args:
assert arg.type_annotation, 'All arguments must be fully typed.'
arg_types.append(arg.type_annotation)
arg_names.append(arg.variable.name)
arg_kinds.append(arg.kind)
signature = CallableType(arg_types, arg_kinds, arg_names, return_type, function_type)
if tvar_def:
signature.variables = [tvar_def]
func = FuncDef(name, args, Block([PassStmt()]))
func.info = info
func.type = set_callable_name(signature, func)
func.is_class = is_classmethod
func._fullname = info.fullname + '.' + name
func.line = info.line
# NOTE: we would like the plugin generated node to dominate, but we still
# need to keep any existing definitions so they get semantically analyzed.
if name in info.names:
# Get a nice unique name instead.
r_name = get_unique_redefinition_name(name, info.names)
info.names[r_name] = info.names[name]
# Add decorator for is_classmethod
# The dataclasses plugin claims this is unnecessary for classmethods, but not including it results in a
# signature incompatible with the superclass, which causes mypy errors to occur for every subclass of BaseModel.
if is_classmethod:
func.is_decorated = True
v = Var(name, func.type)
v.info = info
v._fullname = func._fullname
v.is_classmethod = True
dec = Decorator(func, [NameExpr('classmethod')], v)
dec.line = info.line
sym = SymbolTableNode(MDEF, dec)
else:
sym = SymbolTableNode(MDEF, func)
sym.plugin_generated = True
info.names[name] = sym
info.defn.defs.body.append(func)
def parse_toml(config_file: str) -> dict[str, Any] | None:
"""Returns a dict of config keys to values.
It reads configs from toml file and returns `None` if the file is not a toml file.
"""
if not config_file.endswith('.toml'):
return None
if sys.version_info >= (3, 11):
import tomllib as toml_
else:
try:
import tomli as toml_
except ImportError: # pragma: no cover
import warnings
warnings.warn('No TOML parser installed, cannot read configuration from `pyproject.toml`.')
return None
with open(config_file, 'rb') as rf:
return toml_.load(rf)
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