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+"""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,
+ INVARIANT,
+ 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,
+ MethodContext,
+ Plugin,
+ ReportConfigContext,
+ SemanticAnalyzerPluginInterface,
+)
+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,
+ 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
+
+CONFIGFILE_KEY = 'pydantic-mypy'
+METADATA_KEY = 'pydantic-mypy-metadata'
+BASEMODEL_FULLNAME = 'pydantic.main.BaseModel'
+BASESETTINGS_FULLNAME = 'pydantic_settings.main.BaseSettings'
+ROOT_MODEL_FULLNAME = 'pydantic.root_model.RootModel'
+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'
+
+# 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], None] | 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_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 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) -> None:
+ transformer = PydanticModelTransformer(ctx.cls, ctx.reason, ctx.api, self.plugin_config)
+ 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
+
+
+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
+ if not any(base.fullname == BASEMODEL_FULLNAME for base in model_type.type.mro):
+ # not a Pydantic v2 model
+ 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,
+ is_frozen: bool,
+ has_dynamic_alias: bool,
+ has_default: bool,
+ strict: bool | None,
+ line: int,
+ column: int,
+ type: Type | None,
+ info: TypeInfo,
+ ):
+ self.name = name
+ self.alias = alias
+ self.is_frozen = is_frozen
+ self.has_dynamic_alias = has_dynamic_alias
+ self.has_default = has_default
+ self.strict = strict
+ self.line = line
+ self.column = column
+ self.type = type
+ self.info = info
+
+ def to_argument(
+ self,
+ current_info: TypeInfo,
+ typed: bool,
+ model_strict: bool,
+ force_optional: bool,
+ use_alias: bool,
+ api: SemanticAnalyzerPluginInterface,
+ force_typevars_invariant: bool,
+ is_root_model_root: bool,
+ ) -> Argument:
+ """Based on mypy.plugins.dataclasses.DataclassAttribute.to_argument."""
+ variable = self.to_var(current_info, api, use_alias, force_typevars_invariant)
+
+ strict = model_strict if self.strict is None else self.strict
+ if typed or strict:
+ type_annotation = self.expand_type(current_info, api)
+ else:
+ type_annotation = AnyType(TypeOfAny.explicit)
+
+ return Argument(
+ variable=variable,
+ type_annotation=type_annotation,
+ initializer=None,
+ kind=ARG_OPT
+ if is_root_model_root
+ else (ARG_NAMED_OPT if force_optional or self.has_default else ARG_NAMED),
+ )
+
+ def expand_type(
+ self, current_info: TypeInfo, api: SemanticAnalyzerPluginInterface, force_typevars_invariant: bool = False
+ ) -> Type | None:
+ """Based on mypy.plugins.dataclasses.DataclassAttribute.expand_type."""
+ if force_typevars_invariant:
+ # In some cases, mypy will emit an error "Cannot use a covariant type variable as a parameter"
+ # To prevent that, we add an option to replace typevars with invariant ones while building certain
+ # method signatures (in particular, `__init__`). There may be a better way to do this, if this causes
+ # us problems in the future, we should look into why the dataclasses plugin doesn't have this issue.
+ if isinstance(self.type, TypeVarType):
+ modified_type = self.type.copy_modified()
+ modified_type.variance = INVARIANT
+ self.type = modified_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 analysis,
+ # 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).
+ with state.strict_optional_set(api.options.strict_optional):
+ filled_with_typevars = fill_typevars(current_info)
+ # Cannot be TupleType as current_info represents a Pydantic model:
+ assert isinstance(filled_with_typevars, Instance)
+ if force_typevars_invariant:
+ for arg in filled_with_typevars.args:
+ if isinstance(arg, TypeVarType):
+ arg.variance = INVARIANT
+ return expand_type(self.type, {self.info.self_type.id: filled_with_typevars})
+ return self.type
+
+ def to_var(
+ self,
+ current_info: TypeInfo,
+ api: SemanticAnalyzerPluginInterface,
+ use_alias: bool,
+ force_typevars_invariant: bool = False,
+ ) -> 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, api, force_typevars_invariant))
+
+ def serialize(self) -> JsonDict:
+ """Based on mypy.plugins.dataclasses.DataclassAttribute.serialize."""
+ assert self.type
+ return {
+ 'name': self.name,
+ 'alias': self.alias,
+ 'is_frozen': self.is_frozen,
+ 'has_dynamic_alias': self.has_dynamic_alias,
+ 'has_default': self.has_default,
+ 'strict': self.strict,
+ '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, api: SemanticAnalyzerPluginInterface) -> 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:
+ with state.strict_optional_set(api.options.strict_optional):
+ self.type = map_type_from_supertype(self.type, sub_type, self.info)
+
+
+class PydanticModelClassVar:
+ """Based on mypy.plugins.dataclasses.DataclassAttribute.
+
+ ClassVars are ignored by subclasses.
+
+ Attributes:
+ name: the ClassVar name
+ """
+
+ def __init__(self, name):
+ self.name = name
+
+ @classmethod
+ def deserialize(cls, data: JsonDict) -> PydanticModelClassVar:
+ """Based on mypy.plugins.dataclasses.DataclassAttribute.deserialize."""
+ data = data.copy()
+ return cls(**data)
+
+ def serialize(self) -> JsonDict:
+ """Based on mypy.plugins.dataclasses.DataclassAttribute.serialize."""
+ return {
+ 'name': self.name,
+ }
+
+
+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',
+ 'strict',
+ }
+
+ 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
+ is_root_model = any(ROOT_MODEL_FULLNAME in base.fullname for base in info.mro[:-1])
+ config = self.collect_config()
+ fields, class_vars = self.collect_fields_and_class_vars(config, is_root_model)
+ if fields is None or class_vars 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, is_root_model)
+ self.add_model_construct_method(fields, config, is_settings, is_root_model)
+ self.set_frozen(fields, self._api, frozen=config.frozen is True)
+
+ self.adjust_decorator_signatures()
+
+ info.metadata[METADATA_KEY] = {
+ 'fields': {field.name: field.serialize() for field in fields},
+ 'class_vars': {class_var.name: class_var.serialize() for class_var in class_vars},
+ '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 sym in self._cls.info.names.values():
+ 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, lax_extra=True))
+ 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_and_class_vars(
+ self, model_config: ModelConfigData, is_root_model: bool
+ ) -> tuple[list[PydanticModelField] | None, list[PydanticModelClassVar] | None]:
+ """Collects the fields for the model, accounting for parent classes."""
+ cls = self._cls
+
+ # First, collect fields and ClassVars 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] = {}
+ found_class_vars: dict[str, PydanticModelClassVar] = {}
+ 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, 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.
+ field.expand_typevar_from_subtype(cls.info, self._api)
+ 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,
+ )
+ # Collect ClassVars
+ for name, data in info.metadata[METADATA_KEY]['class_vars'].items():
+ found_class_vars[name] = PydanticModelClassVar.deserialize(data)
+
+ # Second, collect fields and ClassVars belonging to the current class.
+ current_field_names: set[str] = set()
+ current_class_vars_names: set[str] = set()
+ for stmt in self._get_assignment_statements_from_block(cls.defs):
+ maybe_field = self.collect_field_or_class_var_from_stmt(stmt, model_config, found_class_vars)
+ if maybe_field is None:
+ continue
+
+ lhs = stmt.lvalues[0]
+ assert isinstance(lhs, NameExpr) # collect_field_or_class_var_from_stmt guarantees this
+ if isinstance(maybe_field, PydanticModelField):
+ if is_root_model and lhs.name != 'root':
+ error_extra_fields_on_root_model(self._api, stmt)
+ else:
+ current_field_names.add(lhs.name)
+ found_fields[lhs.name] = maybe_field
+ elif isinstance(maybe_field, PydanticModelClassVar):
+ current_class_vars_names.add(lhs.name)
+ found_class_vars[lhs.name] = maybe_field
+
+ return list(found_fields.values()), list(found_class_vars.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_or_class_var_from_stmt( # noqa C901
+ self, stmt: AssignmentStmt, model_config: ModelConfigData, class_vars: dict[str, PydanticModelClassVar]
+ ) -> PydanticModelField | PydanticModelClassVar | None:
+ """Get pydantic model field from statement.
+
+ Args:
+ stmt: The statement.
+ model_config: Configuration settings for the model.
+ class_vars: ClassVars already known to be defined on 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
+
+ if lhs.name in class_vars:
+ # Class vars are not fields and are not required to be annotated
+ 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 PydanticModelClassVar(lhs.name)
+
+ # 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)
+ strict = self.get_strict(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)
+ is_frozen = self.is_field_frozen(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,
+ strict=strict,
+ alias=alias,
+ is_frozen=is_frozen,
+ 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, is_root_model: 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
+ model_strict = bool(config.strict)
+ use_alias = config.populate_by_name is not True
+ requires_dynamic_aliases = bool(config.has_alias_generator and not config.populate_by_name)
+ args = self.get_field_arguments(
+ fields,
+ typed=typed,
+ model_strict=model_strict,
+ requires_dynamic_aliases=requires_dynamic_aliases,
+ use_alias=use_alias,
+ is_settings=is_settings,
+ is_root_model=is_root_model,
+ force_typevars_invariant=True,
+ )
+
+ if is_settings:
+ base_settings_node = self._api.lookup_fully_qualified(BASESETTINGS_FULLNAME).node
+ assert isinstance(base_settings_node, TypeInfo)
+ if '__init__' in base_settings_node.names:
+ base_settings_init_node = base_settings_node.names['__init__'].node
+ assert isinstance(base_settings_init_node, FuncDef)
+ if base_settings_init_node is not None and base_settings_init_node.type is not None:
+ func_type = base_settings_init_node.type
+ assert isinstance(func_type, CallableType)
+ for arg_idx, arg_name in enumerate(func_type.arg_names):
+ if arg_name is None or 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,
+ is_root_model: 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,
+ model_strict=bool(config.strict),
+ requires_dynamic_aliases=False,
+ use_alias=False,
+ is_settings=is_settings,
+ is_root_model=is_root_model,
+ )
+ if not self.should_init_forbid_extra(fields, config):
+ var = Var('kwargs')
+ args.append(Argument(var, AnyType(TypeOfAny.explicit), None, ARG_STAR2))
+
+ args = args + [fields_set_argument] if is_root_model else [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], api: SemanticAnalyzerPluginInterface, 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 or field.is_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, api, 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, lax_extra: bool = False) -> 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:
+ if not lax_extra:
+ # Only emit an error for other types of `arg` (e.g., `NameExpr`, `ConditionalExpr`, etc.) when
+ # reading from a config class, etc. If a ConfigDict is used, then we don't want to emit an error
+ # because you'll get type checking from the ConfigDict itself.
+ #
+ # It would be nice if we could introspect the types better otherwise, but I don't know what the API
+ # is to evaluate an expr into its type and then check if that type is compatible with the expected
+ # type. Note that you can still get proper type checking via: `model_config = ConfigDict(...)`, just
+ # if you don't use an explicit string, the plugin won't be able to infer whether extra is forbidden.
+ 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_strict(stmt: AssignmentStmt) -> bool | None:
+ """Returns a the `strict` value of a field if defined, otherwise `None`."""
+ expr = stmt.rvalue
+ if isinstance(expr, CallExpr) and isinstance(expr.callee, RefExpr) and expr.callee.fullname == FIELD_FULLNAME:
+ for arg, name in zip(expr.args, expr.arg_names):
+ if name != 'strict':
+ continue
+ if isinstance(arg, NameExpr):
+ if arg.fullname == 'builtins.True':
+ return True
+ elif arg.fullname == 'builtins.False':
+ return False
+ return None
+ return None
+
+ @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
+
+ @staticmethod
+ def is_field_frozen(stmt: AssignmentStmt) -> bool:
+ """Returns whether the field is frozen, extracted from the declaration of the field defined in `stmt`.
+
+ Note that this is only whether the field was declared to be frozen in a `<field_name> = Field(frozen=True)`
+ sense; this does not determine whether the field is frozen because the entire model is frozen; that is
+ handled separately.
+ """
+ expr = stmt.rvalue
+ if isinstance(expr, TempNode):
+ # TempNode means annotation-only
+ return 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 False
+
+ for i, arg_name in enumerate(expr.arg_names):
+ if arg_name == 'frozen':
+ arg = expr.args[i]
+ return isinstance(arg, NameExpr) and arg.fullname == 'builtins.True'
+ return False
+
+ def get_field_arguments(
+ self,
+ fields: list[PydanticModelField],
+ typed: bool,
+ model_strict: bool,
+ use_alias: bool,
+ requires_dynamic_aliases: bool,
+ is_settings: bool,
+ is_root_model: bool,
+ force_typevars_invariant: bool = False,
+ ) -> 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,
+ model_strict=model_strict,
+ force_optional=requires_dynamic_aliases or is_settings,
+ use_alias=use_alias,
+ api=self._api,
+ force_typevars_invariant=force_typevars_invariant,
+ is_root_model_root=is_root_model and field.name == 'root',
+ )
+ 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,
+ strict: 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
+ self.strict = strict
+
+ 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')
+ERROR_EXTRA_FIELD_ROOT_MODEL = ErrorCode('pydantic-field', 'Extra field on RootModel subclass', '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_extra_fields_on_root_model(api: CheckerPluginInterface, context: Context) -> None:
+ """Emits an error when there is more than just a root field defined for a subclass of RootModel."""
+ api.fail('Only `root` is allowed as a field of a `RootModel`', context, code=ERROR_EXTRA_FIELD_ROOT_MODEL)
+
+
+def add_method(
+ api: SemanticAnalyzerPluginInterface | CheckerPluginInterface,
+ cls: ClassDef,
+ name: str,
+ args: list[Argument],
+ return_type: Type,
+ self_type: Type | None = None,
+ tvar_def: TypeVarType | 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)
+ # `self` is positional *ONLY* here, but this can't be expressed
+ # fully in the mypy internal API. ARG_POS is the closest we can get.
+ # Using ARG_POS will, however, give mypy errors if a `self` field
+ # is present on a model:
+ #
+ # Name "self" already defined (possibly by an import) [no-redef]
+ #
+ # As a workaround, we give this argument a name that will
+ # never conflict. By its positional nature, this name will not
+ # be used or exposed to users.
+ 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)