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Diffstat (limited to '.venv/lib/python3.12/site-packages/pydantic/mypy.py')
| -rw-r--r-- | .venv/lib/python3.12/site-packages/pydantic/mypy.py | 1320 |
1 files changed, 1320 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/pydantic/mypy.py b/.venv/lib/python3.12/site-packages/pydantic/mypy.py new file mode 100644 index 00000000..9ad3a3ff --- /dev/null +++ b/.venv/lib/python3.12/site-packages/pydantic/mypy.py @@ -0,0 +1,1320 @@ +"""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) |