946 lines
38 KiB
Python
946 lines
38 KiB
Python
import sys
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from configparser import ConfigParser
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from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type as TypingType, Union
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from mypy.errorcodes import ErrorCode
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from mypy.nodes import (
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ARG_NAMED,
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ARG_NAMED_OPT,
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ARG_OPT,
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ARG_POS,
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ARG_STAR2,
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MDEF,
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Argument,
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AssignmentStmt,
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Block,
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CallExpr,
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ClassDef,
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Context,
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Decorator,
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EllipsisExpr,
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FuncBase,
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FuncDef,
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JsonDict,
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MemberExpr,
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NameExpr,
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PassStmt,
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PlaceholderNode,
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RefExpr,
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StrExpr,
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SymbolNode,
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SymbolTableNode,
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TempNode,
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TypeInfo,
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TypeVarExpr,
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Var,
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)
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from mypy.options import Options
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from mypy.plugin import (
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CheckerPluginInterface,
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ClassDefContext,
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FunctionContext,
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MethodContext,
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Plugin,
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ReportConfigContext,
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SemanticAnalyzerPluginInterface,
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)
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from mypy.plugins import dataclasses
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from mypy.semanal import set_callable_name # type: ignore
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from mypy.server.trigger import make_wildcard_trigger
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from mypy.types import (
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AnyType,
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CallableType,
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Instance,
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NoneType,
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Overloaded,
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ProperType,
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Type,
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TypeOfAny,
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TypeType,
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TypeVarId,
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TypeVarType,
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UnionType,
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get_proper_type,
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)
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from mypy.typevars import fill_typevars
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from mypy.util import get_unique_redefinition_name
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from mypy.version import __version__ as mypy_version
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from pydantic.v1.utils import is_valid_field
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try:
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from mypy.types import TypeVarDef # type: ignore[attr-defined]
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except ImportError: # pragma: no cover
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# Backward-compatible with TypeVarDef from Mypy 0.910.
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from mypy.types import TypeVarType as TypeVarDef
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CONFIGFILE_KEY = 'pydantic-mypy'
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METADATA_KEY = 'pydantic-mypy-metadata'
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_NAMESPACE = __name__[:-5] # 'pydantic' in 1.10.X, 'pydantic.v1' in v2.X
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BASEMODEL_FULLNAME = f'{_NAMESPACE}.main.BaseModel'
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BASESETTINGS_FULLNAME = f'{_NAMESPACE}.env_settings.BaseSettings'
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MODEL_METACLASS_FULLNAME = f'{_NAMESPACE}.main.ModelMetaclass'
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FIELD_FULLNAME = f'{_NAMESPACE}.fields.Field'
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DATACLASS_FULLNAME = f'{_NAMESPACE}.dataclasses.dataclass'
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def parse_mypy_version(version: str) -> Tuple[int, ...]:
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return tuple(map(int, version.partition('+')[0].split('.')))
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MYPY_VERSION_TUPLE = parse_mypy_version(mypy_version)
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BUILTINS_NAME = 'builtins' if MYPY_VERSION_TUPLE >= (0, 930) else '__builtins__'
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# Increment version if plugin changes and mypy caches should be invalidated
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__version__ = 2
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def plugin(version: str) -> 'TypingType[Plugin]':
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"""
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`version` is the mypy version string
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We might want to use this to print a warning if the mypy version being used is
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newer, or especially older, than we expect (or need).
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"""
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return PydanticPlugin
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class PydanticPlugin(Plugin):
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def __init__(self, options: Options) -> None:
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self.plugin_config = PydanticPluginConfig(options)
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self._plugin_data = self.plugin_config.to_data()
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super().__init__(options)
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def get_base_class_hook(self, fullname: str) -> 'Optional[Callable[[ClassDefContext], None]]':
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sym = self.lookup_fully_qualified(fullname)
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if sym and isinstance(sym.node, TypeInfo): # pragma: no branch
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# No branching may occur if the mypy cache has not been cleared
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if any(get_fullname(base) == BASEMODEL_FULLNAME for base in sym.node.mro):
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return self._pydantic_model_class_maker_callback
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return None
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def get_metaclass_hook(self, fullname: str) -> Optional[Callable[[ClassDefContext], None]]:
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if fullname == MODEL_METACLASS_FULLNAME:
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return self._pydantic_model_metaclass_marker_callback
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return None
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def get_function_hook(self, fullname: str) -> 'Optional[Callable[[FunctionContext], Type]]':
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sym = self.lookup_fully_qualified(fullname)
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if sym and sym.fullname == FIELD_FULLNAME:
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return self._pydantic_field_callback
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return None
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def get_method_hook(self, fullname: str) -> Optional[Callable[[MethodContext], Type]]:
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if fullname.endswith('.from_orm'):
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return from_orm_callback
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return None
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def get_class_decorator_hook(self, fullname: str) -> Optional[Callable[[ClassDefContext], None]]:
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"""Mark pydantic.dataclasses as dataclass.
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Mypy version 1.1.1 added support for `@dataclass_transform` decorator.
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"""
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if fullname == DATACLASS_FULLNAME and MYPY_VERSION_TUPLE < (1, 1):
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return dataclasses.dataclass_class_maker_callback # type: ignore[return-value]
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return None
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def report_config_data(self, ctx: ReportConfigContext) -> Dict[str, Any]:
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"""Return all plugin config data.
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Used by mypy to determine if cache needs to be discarded.
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"""
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return self._plugin_data
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def _pydantic_model_class_maker_callback(self, ctx: ClassDefContext) -> None:
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transformer = PydanticModelTransformer(ctx, self.plugin_config)
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transformer.transform()
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def _pydantic_model_metaclass_marker_callback(self, ctx: ClassDefContext) -> None:
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"""Reset dataclass_transform_spec attribute of ModelMetaclass.
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Let the plugin handle it. This behavior can be disabled
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if 'debug_dataclass_transform' is set to True', for testing purposes.
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"""
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if self.plugin_config.debug_dataclass_transform:
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return
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info_metaclass = ctx.cls.info.declared_metaclass
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assert info_metaclass, "callback not passed from 'get_metaclass_hook'"
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if getattr(info_metaclass.type, 'dataclass_transform_spec', None):
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info_metaclass.type.dataclass_transform_spec = None # type: ignore[attr-defined]
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def _pydantic_field_callback(self, ctx: FunctionContext) -> 'Type':
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"""
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Extract the type of the `default` argument from the Field function, and use it as the return type.
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In particular:
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* Check whether the default and default_factory argument is specified.
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* Output an error if both are specified.
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* Retrieve the type of the argument which is specified, and use it as return type for the function.
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"""
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default_any_type = ctx.default_return_type
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assert ctx.callee_arg_names[0] == 'default', '"default" is no longer first argument in Field()'
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assert ctx.callee_arg_names[1] == 'default_factory', '"default_factory" is no longer second argument in Field()'
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default_args = ctx.args[0]
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default_factory_args = ctx.args[1]
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if default_args and default_factory_args:
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error_default_and_default_factory_specified(ctx.api, ctx.context)
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return default_any_type
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if default_args:
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default_type = ctx.arg_types[0][0]
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default_arg = default_args[0]
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# Fallback to default Any type if the field is required
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if not isinstance(default_arg, EllipsisExpr):
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return default_type
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elif default_factory_args:
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default_factory_type = ctx.arg_types[1][0]
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# Functions which use `ParamSpec` can be overloaded, exposing the callable's types as a parameter
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# Pydantic calls the default factory without any argument, so we retrieve the first item
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if isinstance(default_factory_type, Overloaded):
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if MYPY_VERSION_TUPLE > (0, 910):
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default_factory_type = default_factory_type.items[0]
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else:
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# Mypy0.910 exposes the items of overloaded types in a function
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default_factory_type = default_factory_type.items()[0] # type: ignore[operator]
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if isinstance(default_factory_type, CallableType):
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ret_type = default_factory_type.ret_type
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# mypy doesn't think `ret_type` has `args`, you'd think mypy should know,
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# add this check in case it varies by version
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args = getattr(ret_type, 'args', None)
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if args:
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if all(isinstance(arg, TypeVarType) for arg in args):
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# Looks like the default factory is a type like `list` or `dict`, replace all args with `Any`
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ret_type.args = tuple(default_any_type for _ in args) # type: ignore[attr-defined]
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return ret_type
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return default_any_type
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class PydanticPluginConfig:
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__slots__ = (
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'init_forbid_extra',
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'init_typed',
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'warn_required_dynamic_aliases',
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'warn_untyped_fields',
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'debug_dataclass_transform',
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)
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init_forbid_extra: bool
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init_typed: bool
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warn_required_dynamic_aliases: bool
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warn_untyped_fields: bool
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debug_dataclass_transform: bool # undocumented
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def __init__(self, options: Options) -> None:
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if options.config_file is None: # pragma: no cover
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return
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toml_config = parse_toml(options.config_file)
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if toml_config is not None:
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config = toml_config.get('tool', {}).get('pydantic-mypy', {})
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for key in self.__slots__:
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setting = config.get(key, False)
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if not isinstance(setting, bool):
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raise ValueError(f'Configuration value must be a boolean for key: {key}')
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setattr(self, key, setting)
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else:
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plugin_config = ConfigParser()
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plugin_config.read(options.config_file)
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for key in self.__slots__:
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setting = plugin_config.getboolean(CONFIGFILE_KEY, key, fallback=False)
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setattr(self, key, setting)
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def to_data(self) -> Dict[str, Any]:
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return {key: getattr(self, key) for key in self.__slots__}
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def from_orm_callback(ctx: MethodContext) -> Type:
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"""
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Raise an error if orm_mode is not enabled
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"""
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model_type: Instance
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ctx_type = ctx.type
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if isinstance(ctx_type, TypeType):
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ctx_type = ctx_type.item
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if isinstance(ctx_type, CallableType) and isinstance(ctx_type.ret_type, Instance):
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model_type = ctx_type.ret_type # called on the class
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elif isinstance(ctx_type, Instance):
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model_type = ctx_type # called on an instance (unusual, but still valid)
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else: # pragma: no cover
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detail = f'ctx.type: {ctx_type} (of type {ctx_type.__class__.__name__})'
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error_unexpected_behavior(detail, ctx.api, ctx.context)
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return ctx.default_return_type
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pydantic_metadata = model_type.type.metadata.get(METADATA_KEY)
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if pydantic_metadata is None:
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return ctx.default_return_type
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orm_mode = pydantic_metadata.get('config', {}).get('orm_mode')
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if orm_mode is not True:
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error_from_orm(get_name(model_type.type), ctx.api, ctx.context)
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return ctx.default_return_type
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class PydanticModelTransformer:
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tracked_config_fields: Set[str] = {
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'extra',
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'allow_mutation',
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'frozen',
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'orm_mode',
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'allow_population_by_field_name',
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'alias_generator',
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}
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def __init__(self, ctx: ClassDefContext, plugin_config: PydanticPluginConfig) -> None:
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self._ctx = ctx
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self.plugin_config = plugin_config
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def transform(self) -> None:
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"""
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Configures the BaseModel subclass according to the plugin settings.
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In particular:
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* determines the model config and fields,
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* adds a fields-aware signature for the initializer and construct methods
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* freezes the class if allow_mutation = False or frozen = True
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* stores the fields, config, and if the class is settings in the mypy metadata for access by subclasses
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"""
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ctx = self._ctx
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info = ctx.cls.info
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self.adjust_validator_signatures()
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config = self.collect_config()
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fields = self.collect_fields(config)
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is_settings = any(get_fullname(base) == BASESETTINGS_FULLNAME for base in info.mro[:-1])
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self.add_initializer(fields, config, is_settings)
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self.add_construct_method(fields)
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self.set_frozen(fields, frozen=config.allow_mutation is False or config.frozen is True)
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info.metadata[METADATA_KEY] = {
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'fields': {field.name: field.serialize() for field in fields},
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'config': config.set_values_dict(),
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}
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def adjust_validator_signatures(self) -> None:
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"""When we decorate a function `f` with `pydantic.validator(...), mypy sees
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`f` as a regular method taking a `self` instance, even though pydantic
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internally wraps `f` with `classmethod` if necessary.
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Teach mypy this by marking any function whose outermost decorator is a
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`validator()` call as a classmethod.
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"""
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for name, sym in self._ctx.cls.info.names.items():
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if isinstance(sym.node, Decorator):
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first_dec = sym.node.original_decorators[0]
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if (
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isinstance(first_dec, CallExpr)
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and isinstance(first_dec.callee, NameExpr)
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and first_dec.callee.fullname == f'{_NAMESPACE}.class_validators.validator'
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):
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sym.node.func.is_class = True
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def collect_config(self) -> 'ModelConfigData':
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"""
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Collects the values of the config attributes that are used by the plugin, accounting for parent classes.
|
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"""
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ctx = self._ctx
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cls = ctx.cls
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config = ModelConfigData()
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for stmt in cls.defs.body:
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if not isinstance(stmt, ClassDef):
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continue
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if stmt.name == 'Config':
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for substmt in stmt.defs.body:
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if not isinstance(substmt, AssignmentStmt):
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continue
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config.update(self.get_config_update(substmt))
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if (
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config.has_alias_generator
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and not config.allow_population_by_field_name
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and self.plugin_config.warn_required_dynamic_aliases
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):
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error_required_dynamic_aliases(ctx.api, stmt)
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for info in cls.info.mro[1:]: # 0 is the current class
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if METADATA_KEY not in info.metadata:
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continue
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# Each class depends on the set of fields in its ancestors
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ctx.api.add_plugin_dependency(make_wildcard_trigger(get_fullname(info)))
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for name, value in info.metadata[METADATA_KEY]['config'].items():
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config.setdefault(name, value)
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return config
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def collect_fields(self, model_config: 'ModelConfigData') -> List['PydanticModelField']:
|
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"""
|
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Collects the fields for the model, accounting for parent classes
|
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"""
|
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# First, collect fields belonging to the current class.
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ctx = self._ctx
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cls = self._ctx.cls
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fields = [] # type: List[PydanticModelField]
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known_fields = set() # type: Set[str]
|
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for stmt in cls.defs.body:
|
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if not isinstance(stmt, AssignmentStmt): # `and stmt.new_syntax` to require annotation
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|
continue
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|
|
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lhs = stmt.lvalues[0]
|
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if not isinstance(lhs, NameExpr) or not is_valid_field(lhs.name):
|
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continue
|
|
|
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if not stmt.new_syntax and self.plugin_config.warn_untyped_fields:
|
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error_untyped_fields(ctx.api, stmt)
|
|
|
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# if lhs.name == '__config__': # BaseConfig not well handled; I'm not sure why yet
|
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# continue
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|
|
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sym = cls.info.names.get(lhs.name)
|
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if sym is None: # pragma: no cover
|
|
# This is likely due to a star import (see the dataclasses plugin for a more detailed explanation)
|
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# This is the same logic used in the dataclasses plugin
|
|
continue
|
|
|
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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
|
|
# This is the same logic used in the dataclasses plugin
|
|
continue
|
|
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
|
|
continue
|
|
|
|
# x: ClassVar[int] is ignored by dataclasses.
|
|
if node.is_classvar:
|
|
continue
|
|
|
|
is_required = self.get_is_required(cls, stmt, lhs)
|
|
alias, has_dynamic_alias = self.get_alias_info(stmt)
|
|
if (
|
|
has_dynamic_alias
|
|
and not model_config.allow_population_by_field_name
|
|
and self.plugin_config.warn_required_dynamic_aliases
|
|
):
|
|
error_required_dynamic_aliases(ctx.api, stmt)
|
|
fields.append(
|
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PydanticModelField(
|
|
name=lhs.name,
|
|
is_required=is_required,
|
|
alias=alias,
|
|
has_dynamic_alias=has_dynamic_alias,
|
|
line=stmt.line,
|
|
column=stmt.column,
|
|
)
|
|
)
|
|
known_fields.add(lhs.name)
|
|
all_fields = fields.copy()
|
|
for info in cls.info.mro[1:]: # 0 is the current class, -2 is BaseModel, -1 is object
|
|
if METADATA_KEY not in info.metadata:
|
|
continue
|
|
|
|
superclass_fields = []
|
|
# Each class depends on the set of fields in its ancestors
|
|
ctx.api.add_plugin_dependency(make_wildcard_trigger(get_fullname(info)))
|
|
|
|
for name, data in info.metadata[METADATA_KEY]['fields'].items():
|
|
if name not in known_fields:
|
|
field = PydanticModelField.deserialize(info, data)
|
|
known_fields.add(name)
|
|
superclass_fields.append(field)
|
|
else:
|
|
(field,) = (a for a in all_fields if a.name == name)
|
|
all_fields.remove(field)
|
|
superclass_fields.append(field)
|
|
all_fields = superclass_fields + all_fields
|
|
return all_fields
|
|
|
|
def add_initializer(self, fields: List['PydanticModelField'], config: 'ModelConfigData', is_settings: bool) -> None:
|
|
"""
|
|
Adds a fields-aware `__init__` method to the class.
|
|
|
|
The added `__init__` will be annotated with types vs. all `Any` depending on the plugin settings.
|
|
"""
|
|
ctx = self._ctx
|
|
typed = self.plugin_config.init_typed
|
|
use_alias = config.allow_population_by_field_name is not True
|
|
force_all_optional = is_settings or bool(
|
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config.has_alias_generator and not config.allow_population_by_field_name
|
|
)
|
|
init_arguments = self.get_field_arguments(
|
|
fields, typed=typed, force_all_optional=force_all_optional, use_alias=use_alias
|
|
)
|
|
if not self.should_init_forbid_extra(fields, config):
|
|
var = Var('kwargs')
|
|
init_arguments.append(Argument(var, AnyType(TypeOfAny.explicit), None, ARG_STAR2))
|
|
|
|
if '__init__' not in ctx.cls.info.names:
|
|
add_method(ctx, '__init__', init_arguments, NoneType())
|
|
|
|
def add_construct_method(self, fields: List['PydanticModelField']) -> None:
|
|
"""
|
|
Adds a fully typed `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.
|
|
"""
|
|
ctx = self._ctx
|
|
set_str = ctx.api.named_type(f'{BUILTINS_NAME}.set', [ctx.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)
|
|
construct_arguments = self.get_field_arguments(fields, typed=True, force_all_optional=False, use_alias=False)
|
|
construct_arguments = [fields_set_argument] + construct_arguments
|
|
|
|
obj_type = ctx.api.named_type(f'{BUILTINS_NAME}.object')
|
|
self_tvar_name = '_PydanticBaseModel' # Make sure it does not conflict with other names in the class
|
|
tvar_fullname = ctx.cls.fullname + '.' + self_tvar_name
|
|
if MYPY_VERSION_TUPLE >= (1, 4):
|
|
tvd = TypeVarType(
|
|
self_tvar_name,
|
|
tvar_fullname,
|
|
TypeVarId(-1),
|
|
[],
|
|
obj_type,
|
|
AnyType(TypeOfAny.from_omitted_generics), # type: ignore[arg-type]
|
|
)
|
|
self_tvar_expr = TypeVarExpr(
|
|
self_tvar_name,
|
|
tvar_fullname,
|
|
[],
|
|
obj_type,
|
|
AnyType(TypeOfAny.from_omitted_generics), # type: ignore[arg-type]
|
|
)
|
|
else:
|
|
tvd = TypeVarDef(self_tvar_name, tvar_fullname, -1, [], obj_type)
|
|
self_tvar_expr = TypeVarExpr(self_tvar_name, tvar_fullname, [], obj_type)
|
|
ctx.cls.info.names[self_tvar_name] = SymbolTableNode(MDEF, self_tvar_expr)
|
|
|
|
# Backward-compatible with TypeVarDef from Mypy 0.910.
|
|
if isinstance(tvd, TypeVarType):
|
|
self_type = tvd
|
|
else:
|
|
self_type = TypeVarType(tvd)
|
|
|
|
add_method(
|
|
ctx,
|
|
'construct',
|
|
construct_arguments,
|
|
return_type=self_type,
|
|
self_type=self_type,
|
|
tvar_def=tvd,
|
|
is_classmethod=True,
|
|
)
|
|
|
|
def set_frozen(self, fields: List['PydanticModelField'], frozen: bool) -> None:
|
|
"""
|
|
Marks all fields as properties so that attempts to set them trigger mypy errors.
|
|
|
|
This is the same approach used by the attrs and dataclasses plugins.
|
|
"""
|
|
ctx = self._ctx
|
|
info = ctx.cls.info
|
|
for field in fields:
|
|
sym_node = info.names.get(field.name)
|
|
if sym_node is not None:
|
|
var = sym_node.node
|
|
if isinstance(var, Var):
|
|
var.is_property = frozen
|
|
elif isinstance(var, PlaceholderNode) and not ctx.api.final_iteration:
|
|
# See https://github.com/pydantic/pydantic/issues/5191 to hit this branch for test coverage
|
|
ctx.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, ctx.api, ctx.cls)
|
|
else:
|
|
var = field.to_var(info, use_alias=False)
|
|
var.info = info
|
|
var.is_property = frozen
|
|
var._fullname = get_fullname(info) + '.' + get_name(var)
|
|
info.names[get_name(var)] = SymbolTableNode(MDEF, var)
|
|
|
|
def get_config_update(self, substmt: AssignmentStmt) -> Optional['ModelConfigData']:
|
|
"""
|
|
Determines the config update due to a single statement in the Config class definition.
|
|
|
|
Warns if a tracked config attribute is set to a value the plugin doesn't know how to interpret (e.g., an int)
|
|
"""
|
|
lhs = substmt.lvalues[0]
|
|
if not (isinstance(lhs, NameExpr) and lhs.name in self.tracked_config_fields):
|
|
return None
|
|
if lhs.name == 'extra':
|
|
if isinstance(substmt.rvalue, StrExpr):
|
|
forbid_extra = substmt.rvalue.value == 'forbid'
|
|
elif isinstance(substmt.rvalue, MemberExpr):
|
|
forbid_extra = substmt.rvalue.name == 'forbid'
|
|
else:
|
|
error_invalid_config_value(lhs.name, self._ctx.api, substmt)
|
|
return None
|
|
return ModelConfigData(forbid_extra=forbid_extra)
|
|
if lhs.name == 'alias_generator':
|
|
has_alias_generator = True
|
|
if isinstance(substmt.rvalue, NameExpr) and substmt.rvalue.fullname == 'builtins.None':
|
|
has_alias_generator = False
|
|
return ModelConfigData(has_alias_generator=has_alias_generator)
|
|
if isinstance(substmt.rvalue, NameExpr) and substmt.rvalue.fullname in ('builtins.True', 'builtins.False'):
|
|
return ModelConfigData(**{lhs.name: substmt.rvalue.fullname == 'builtins.True'})
|
|
error_invalid_config_value(lhs.name, self._ctx.api, substmt)
|
|
return None
|
|
|
|
@staticmethod
|
|
def get_is_required(cls: ClassDef, stmt: AssignmentStmt, lhs: NameExpr) -> 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 only non-required if Optional
|
|
value_type = get_proper_type(cls.info[lhs.name].type)
|
|
return not PydanticModelTransformer.type_has_implicit_default(value_type)
|
|
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 is
|
|
# only required if default is Ellipsis (i.e., `field_name: Annotation = Field(...)`) or if default_factory
|
|
# is specified.
|
|
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 EllipsisExpr
|
|
if name == 'default_factory':
|
|
return False
|
|
# In this case, default and default_factory are not specified, so we need to look at the annotation
|
|
value_type = get_proper_type(cls.info[lhs.name].type)
|
|
return not PydanticModelTransformer.type_has_implicit_default(value_type)
|
|
# Only required if the "default value" is Ellipsis (i.e., `field_name: Annotation = ...`)
|
|
return isinstance(expr, EllipsisExpr)
|
|
|
|
@staticmethod
|
|
def type_has_implicit_default(type_: Optional[ProperType]) -> bool:
|
|
"""
|
|
Returns True if the passed type will be given an implicit default value.
|
|
|
|
In pydantic v1, this is the case for Optional types and Any (with default value None).
|
|
"""
|
|
if isinstance(type_, AnyType):
|
|
# Annotated as Any
|
|
return True
|
|
if isinstance(type_, UnionType) and any(
|
|
isinstance(item, NoneType) or isinstance(item, AnyType) for item in type_.items
|
|
):
|
|
# Annotated as Optional, or otherwise having NoneType or AnyType in the union
|
|
return True
|
|
return False
|
|
|
|
@staticmethod
|
|
def get_alias_info(stmt: AssignmentStmt) -> Tuple[Optional[str], bool]:
|
|
"""
|
|
Returns a pair (alias, has_dynamic_alias), extracted from the declaration of the field defined in `stmt`.
|
|
|
|
`has_dynamic_alias` is True if and only if an alias is provided, but not as a string literal.
|
|
If `has_dynamic_alias` is True, `alias` will be None.
|
|
"""
|
|
expr = stmt.rvalue
|
|
if isinstance(expr, TempNode):
|
|
# TempNode means annotation-only
|
|
return None, False
|
|
|
|
if not (
|
|
isinstance(expr, CallExpr) and isinstance(expr.callee, RefExpr) and expr.callee.fullname == FIELD_FULLNAME
|
|
):
|
|
# Assigned value is not a call to pydantic.fields.Field
|
|
return None, False
|
|
|
|
for i, arg_name in enumerate(expr.arg_names):
|
|
if arg_name != 'alias':
|
|
continue
|
|
arg = expr.args[i]
|
|
if isinstance(arg, StrExpr):
|
|
return arg.value, False
|
|
else:
|
|
return None, True
|
|
return None, False
|
|
|
|
def get_field_arguments(
|
|
self, fields: List['PydanticModelField'], typed: bool, force_all_optional: bool, use_alias: bool
|
|
) -> List[Argument]:
|
|
"""
|
|
Helper function used during the construction of the `__init__` and `construct` method signatures.
|
|
|
|
Returns a list of mypy Argument instances for use in the generated signatures.
|
|
"""
|
|
info = self._ctx.cls.info
|
|
arguments = [
|
|
field.to_argument(info, typed=typed, force_optional=force_all_optional, use_alias=use_alias)
|
|
for field in fields
|
|
if not (use_alias and field.has_dynamic_alias)
|
|
]
|
|
return arguments
|
|
|
|
def should_init_forbid_extra(self, fields: List['PydanticModelField'], config: 'ModelConfigData') -> bool:
|
|
"""
|
|
Indicates whether the generated `__init__` should get a `**kwargs` at the end of its signature
|
|
|
|
We disallow arbitrary kwargs if the extra config setting is "forbid", or if the plugin config says to,
|
|
*unless* a required dynamic alias is present (since then we can't determine a valid signature).
|
|
"""
|
|
if not config.allow_population_by_field_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 PydanticModelField:
|
|
def __init__(
|
|
self, name: str, is_required: bool, alias: Optional[str], has_dynamic_alias: bool, line: int, column: int
|
|
):
|
|
self.name = name
|
|
self.is_required = is_required
|
|
self.alias = alias
|
|
self.has_dynamic_alias = has_dynamic_alias
|
|
self.line = line
|
|
self.column = column
|
|
|
|
def to_var(self, info: TypeInfo, use_alias: bool) -> Var:
|
|
name = self.name
|
|
if use_alias and self.alias is not None:
|
|
name = self.alias
|
|
return Var(name, info[self.name].type)
|
|
|
|
def to_argument(self, info: TypeInfo, typed: bool, force_optional: bool, use_alias: bool) -> Argument:
|
|
if typed and info[self.name].type is not None:
|
|
type_annotation = info[self.name].type
|
|
else:
|
|
type_annotation = AnyType(TypeOfAny.explicit)
|
|
return Argument(
|
|
variable=self.to_var(info, use_alias),
|
|
type_annotation=type_annotation,
|
|
initializer=None,
|
|
kind=ARG_NAMED_OPT if force_optional or not self.is_required else ARG_NAMED,
|
|
)
|
|
|
|
def serialize(self) -> JsonDict:
|
|
return self.__dict__
|
|
|
|
@classmethod
|
|
def deserialize(cls, info: TypeInfo, data: JsonDict) -> 'PydanticModelField':
|
|
return cls(**data)
|
|
|
|
|
|
class ModelConfigData:
|
|
def __init__(
|
|
self,
|
|
forbid_extra: Optional[bool] = None,
|
|
allow_mutation: Optional[bool] = None,
|
|
frozen: Optional[bool] = None,
|
|
orm_mode: Optional[bool] = None,
|
|
allow_population_by_field_name: Optional[bool] = None,
|
|
has_alias_generator: Optional[bool] = None,
|
|
):
|
|
self.forbid_extra = forbid_extra
|
|
self.allow_mutation = allow_mutation
|
|
self.frozen = frozen
|
|
self.orm_mode = orm_mode
|
|
self.allow_population_by_field_name = allow_population_by_field_name
|
|
self.has_alias_generator = has_alias_generator
|
|
|
|
def set_values_dict(self) -> Dict[str, Any]:
|
|
return {k: v for k, v in self.__dict__.items() if v is not None}
|
|
|
|
def update(self, config: Optional['ModelConfigData']) -> None:
|
|
if config is None:
|
|
return
|
|
for k, v in config.set_values_dict().items():
|
|
setattr(self, k, v)
|
|
|
|
def setdefault(self, key: str, value: Any) -> None:
|
|
if getattr(self, key) is None:
|
|
setattr(self, key, value)
|
|
|
|
|
|
ERROR_ORM = ErrorCode('pydantic-orm', 'Invalid from_orm call', 'Pydantic')
|
|
ERROR_CONFIG = ErrorCode('pydantic-config', 'Invalid config value', 'Pydantic')
|
|
ERROR_ALIAS = ErrorCode('pydantic-alias', 'Dynamic alias disallowed', 'Pydantic')
|
|
ERROR_UNEXPECTED = ErrorCode('pydantic-unexpected', 'Unexpected behavior', 'Pydantic')
|
|
ERROR_UNTYPED = ErrorCode('pydantic-field', 'Untyped field disallowed', 'Pydantic')
|
|
ERROR_FIELD_DEFAULTS = ErrorCode('pydantic-field', 'Invalid Field defaults', 'Pydantic')
|
|
|
|
|
|
def error_from_orm(model_name: str, api: CheckerPluginInterface, context: Context) -> None:
|
|
api.fail(f'"{model_name}" does not have orm_mode=True', context, code=ERROR_ORM)
|
|
|
|
|
|
def error_invalid_config_value(name: str, api: SemanticAnalyzerPluginInterface, context: Context) -> None:
|
|
api.fail(f'Invalid value for "Config.{name}"', context, code=ERROR_CONFIG)
|
|
|
|
|
|
def error_required_dynamic_aliases(api: SemanticAnalyzerPluginInterface, context: Context) -> None:
|
|
api.fail('Required dynamic aliases disallowed', context, code=ERROR_ALIAS)
|
|
|
|
|
|
def error_unexpected_behavior(
|
|
detail: str, api: Union[CheckerPluginInterface, SemanticAnalyzerPluginInterface], context: Context
|
|
) -> None: # pragma: no cover
|
|
# 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:
|
|
api.fail('Untyped fields disallowed', context, code=ERROR_UNTYPED)
|
|
|
|
|
|
def error_default_and_default_factory_specified(api: CheckerPluginInterface, context: Context) -> None:
|
|
api.fail('Field default and default_factory cannot be specified together', context, code=ERROR_FIELD_DEFAULTS)
|
|
|
|
|
|
def add_method(
|
|
ctx: ClassDefContext,
|
|
name: str,
|
|
args: List[Argument],
|
|
return_type: Type,
|
|
self_type: Optional[Type] = None,
|
|
tvar_def: Optional[TypeVarDef] = None,
|
|
is_classmethod: bool = False,
|
|
is_new: bool = False,
|
|
# is_staticmethod: bool = False,
|
|
) -> None:
|
|
"""
|
|
Adds a new method to a class.
|
|
|
|
This can be dropped if/when https://github.com/python/mypy/issues/7301 is merged
|
|
"""
|
|
info = ctx.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):
|
|
ctx.cls.defs.body.remove(sym.node) # pragma: no cover
|
|
|
|
self_type = self_type or fill_typevars(info)
|
|
if is_classmethod or is_new:
|
|
first = [Argument(Var('_cls'), TypeType.make_normalized(self_type), None, ARG_POS)]
|
|
# elif is_staticmethod:
|
|
# first = []
|
|
else:
|
|
self_type = self_type or fill_typevars(info)
|
|
first = [Argument(Var('__pydantic_self__'), self_type, None, ARG_POS)]
|
|
args = first + args
|
|
arg_types, arg_names, arg_kinds = [], [], []
|
|
for arg in args:
|
|
assert arg.type_annotation, 'All arguments must be fully typed.'
|
|
arg_types.append(arg.type_annotation)
|
|
arg_names.append(get_name(arg.variable))
|
|
arg_kinds.append(arg.kind)
|
|
|
|
function_type = ctx.api.named_type(f'{BUILTINS_NAME}.function')
|
|
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.is_static = is_staticmethod
|
|
func._fullname = get_fullname(info) + '.' + 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]
|
|
|
|
if is_classmethod: # or is_staticmethod:
|
|
func.is_decorated = True
|
|
v = Var(name, func.type)
|
|
v.info = info
|
|
v._fullname = func._fullname
|
|
# if is_classmethod:
|
|
v.is_classmethod = True
|
|
dec = Decorator(func, [NameExpr('classmethod')], v)
|
|
# else:
|
|
# v.is_staticmethod = True
|
|
# dec = Decorator(func, [NameExpr('staticmethod')], 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 get_fullname(x: Union[FuncBase, SymbolNode]) -> str:
|
|
"""
|
|
Used for compatibility with mypy 0.740; can be dropped once support for 0.740 is dropped.
|
|
"""
|
|
fn = x.fullname
|
|
if callable(fn): # pragma: no cover
|
|
return fn()
|
|
return fn
|
|
|
|
|
|
def get_name(x: Union[FuncBase, SymbolNode]) -> str:
|
|
"""
|
|
Used for compatibility with mypy 0.740; can be dropped once support for 0.740 is dropped.
|
|
"""
|
|
fn = x.name
|
|
if callable(fn): # pragma: no cover
|
|
return fn()
|
|
return fn
|
|
|
|
|
|
def parse_toml(config_file: str) -> Optional[Dict[str, Any]]:
|
|
if not config_file.endswith('.toml'):
|
|
return None
|
|
|
|
read_mode = 'rb'
|
|
if sys.version_info >= (3, 11):
|
|
import tomllib as toml_
|
|
else:
|
|
try:
|
|
import tomli as toml_
|
|
except ImportError:
|
|
# older versions of mypy have toml as a dependency, not tomli
|
|
read_mode = 'r'
|
|
try:
|
|
import toml as toml_ # type: ignore[no-redef]
|
|
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, read_mode) as rf:
|
|
return toml_.load(rf) # type: ignore[arg-type]
|