Add support for nested generics (#1104)

* Add support for nested generics

* Allow instantiation of unparameterized generics

* Add better more partial instantiation tests

* Add changes

* Add docs

changes/1104-dmontagu.md

@@ -0,0 +1 @@
+Add support for nested generic models

docs/examples/models_generics_nested.py

@@ -0,0 +1,21 @@
+from typing import Generic, TypeVar
+
+from pydantic import ValidationError
+from pydantic.generics import GenericModel
+
+T = TypeVar('T')
+
+class InnerT(GenericModel, Generic[T]):
+    inner: T
+
+class OuterT(GenericModel, Generic[T]):
+    outer: T
+    nested: InnerT[T]
+
+nested = InnerT[int](inner=1)
+print(OuterT[int](outer=1, nested=nested))
+try:
+    nested = InnerT[str](inner='a')
+    print(OuterT[int](outer='a', nested=nested))
+except ValidationError as e:
+    print(e)

docs/examples/models_generics_typevars.py

@@ -0,0 +1,24 @@
+from typing import Generic, TypeVar
+
+from pydantic import ValidationError
+from pydantic.generics import GenericModel
+
+AT = TypeVar('AT')
+BT = TypeVar('BT')
+
+class Model(GenericModel, Generic[AT, BT]):
+    a: AT
+    b: BT
+
+print(Model(a='a', b='a'))
+
+IntT = TypeVar('IntT', bound=int)
+typevar_model = Model[int, IntT]
+print(typevar_model(a=1, b=1))
+try:
+    typevar_model(a='a', b='a')
+except ValidationError as exc:
+    print(exc)
+
+concrete_model = typevar_model[int]
+print(concrete_model(a=1, b=1))

docs/usage/models.md

@@ -303,6 +303,26 @@ If the name of the concrete subclasses is important, you can also override the d
 ```
 _(This script is complete, it should run "as is")_
 
+Using the same TypeVar in nested models allows you to enforce typing relationships at different points in your model:
+
+```py
+{!.tmp_examples/models_generics_nested.py!}
+```
+_(This script is complete, it should run "as is")_
+
+Pydantic also treats `GenericModel` similarly to how it treats built-in generic types like `List` and `Dict` when it
+comes to leaving them unparameterized, or using bounded `TypeVar` instances:    
+
+* If you don't specify parameters before instantiating the generic model, they will be treated as `Any`
+* You can parametrize models with one or more *bounded* parameters to add subclass checks
+
+Also, like `List` and `Dict`, any parameters specified using a `TypeVar` can later be substituted with concrete types.
+
+```py
+{!.tmp_examples/models_generics_typevars.py!}
+```
+_(This script is complete, it should run "as is")_
+
 ## Dynamic model creation
 
 There are some occasions where the shape of a model is not known until runtime. For this *pydantic* provides

pydantic/generics.py

@@ -1,21 +1,24 @@
-from typing import Any, ClassVar, Dict, Generic, Tuple, Type, TypeVar, Union, get_type_hints
+from typing import TYPE_CHECKING, Any, ClassVar, Dict, Tuple, Type, TypeVar, Union, cast, get_type_hints
 
 from .class_validators import gather_all_validators
+from .fields import FieldInfo, ModelField
 from .main import BaseModel, create_model
+from .utils import lenient_issubclass
 
 _generic_types_cache: Dict[Tuple[Type[Any], Union[Any, Tuple[Any, ...]]], Type[BaseModel]] = {}
 GenericModelT = TypeVar('GenericModelT', bound='GenericModel')
+TypeVarType = Any  # since mypy doesn't allow the use of TypeVar as a type
 
 
 class GenericModel(BaseModel):
     __slots__ = ()
     __concrete__: ClassVar[bool] = False
 
-    def __new__(cls, *args: Any, **kwargs: Any) -> Any:
-        if cls.__concrete__:
-            return super().__new__(cls)
-        else:
-            raise TypeError(f'Type {cls.__name__} cannot be used without generic parameters, e.g. {cls.__name__}[T]')
+    if TYPE_CHECKING:
+        # Putting this in a TYPE_CHECKING block allows us to replace `if Generic not in cls.__bases__` with
+        # `not hasattr(cls, "__parameters__")`. This means we don't need to force non-concrete subclasses of
+        # `GenericModel` to also inherit from `Generic`, which would require changes to the use of `create_model` below.
+        __parameters__: ClassVar[Tuple[TypeVarType, ...]]
 
     # Setting the return type as Type[Any] instead of Type[BaseModel] prevents PyCharm warnings
     def __class_getitem__(cls: Type[GenericModelT], params: Union[Type[Any], Tuple[Type[Any], ...]]) -> Type[Any]:
@@ -28,11 +31,11 @@ class GenericModel(BaseModel):
             params = (params,)
         if cls is GenericModel and any(isinstance(param, TypeVar) for param in params):  # type: ignore
             raise TypeError(f'Type parameters should be placed on typing.Generic, not GenericModel')
-        if Generic not in cls.__bases__:
+        if not hasattr(cls, '__parameters__'):
             raise TypeError(f'Type {cls.__name__} must inherit from typing.Generic before being parameterized')
 
         check_parameters_count(cls, params)
-        typevars_map: Dict[Any, Any] = dict(zip(cls.__parameters__, params))  # type: ignore
+        typevars_map: Dict[TypeVarType, Type[Any]] = dict(zip(cls.__parameters__, params))
         type_hints = get_type_hints(cls).items()
         instance_type_hints = {k: v for k, v in type_hints if getattr(v, '__origin__', None) is not ClassVar}
         concrete_type_hints: Dict[str, Type[Any]] = {
@@ -41,19 +44,25 @@ class GenericModel(BaseModel):
 
         model_name = cls.__concrete_name__(params)
         validators = gather_all_validators(cls)
-        fields: Dict[str, Tuple[Type[Any], Any]] = {
-            k: (v, cls.__fields__[k].field_info) for k, v in concrete_type_hints.items() if k in cls.__fields__
-        }
-        created_model = create_model(
-            model_name=model_name,
-            __module__=cls.__module__,
-            __base__=cls,
-            __config__=None,
-            __validators__=validators,
-            **fields,
+        fields = _build_generic_fields(cls.__fields__, concrete_type_hints, typevars_map)
+        created_model = cast(
+            Type[GenericModel],  # casting ensures mypy is aware of the __concrete__ and __parameters__ attributes
+            create_model(
+                model_name=model_name,
+                __module__=cls.__module__,
+                __base__=cls,
+                __config__=None,
+                __validators__=validators,
+                **fields,
+            ),
         )
         created_model.Config = cls.Config
-        created_model.__concrete__ = True  # type: ignore
+        concrete = all(not _is_typevar(v) for v in concrete_type_hints.values())
+        created_model.__concrete__ = concrete
+        if not concrete:
+            parameters = tuple(v for v in concrete_type_hints.values() if _is_typevar(v))
+            parameters = tuple({k: None for k in parameters}.keys())  # get unique params while maintaining order
+            created_model.__parameters__ = parameters
         _generic_types_cache[(cls, params)] = created_model
         if len(params) == 1:
             _generic_types_cache[(cls, params[0])] = created_model
@@ -78,7 +87,30 @@ def resolve_type_hint(type_: Any, typevars_map: Dict[Any, Any]) -> Type[Any]:
 
 def check_parameters_count(cls: Type[GenericModel], parameters: Tuple[Any, ...]) -> None:
     actual = len(parameters)
-    expected = len(cls.__parameters__)  # type: ignore
+    expected = len(cls.__parameters__)
     if actual != expected:
         description = 'many' if actual > expected else 'few'
         raise TypeError(f'Too {description} parameters for {cls.__name__}; actual {actual}, expected {expected}')
+
+
+def _build_generic_fields(
+    raw_fields: Dict[str, ModelField],
+    concrete_type_hints: Dict[str, Type[Any]],
+    typevars_map: Dict[TypeVarType, Type[Any]],
+) -> Dict[str, Tuple[Type[Any], FieldInfo]]:
+    return {
+        k: (_parameterize_generic_field(v, typevars_map), raw_fields[k].field_info)
+        for k, v in concrete_type_hints.items()
+        if k in raw_fields
+    }
+
+
+def _parameterize_generic_field(field_type: Type[Any], typevars_map: Dict[TypeVarType, Type[Any]]) -> Type[Any]:
+    if lenient_issubclass(field_type, GenericModel) and not field_type.__concrete__:
+        parameters = tuple(typevars_map.get(param, param) for param in field_type.__parameters__)
+        field_type = field_type[parameters]
+    return field_type
+
+
+def _is_typevar(v: Any) -> bool:
+    return isinstance(v, TypeVar)  # type: ignore

tests/test_generics.py

@@ -248,25 +248,6 @@ def test_generic_config():
         result.data = 2
 
 
-@skip_36
-def test_generic_instantiation_error():
-    with pytest.raises(TypeError) as exc_info:
-        GenericModel()
-    assert str(exc_info.value) == 'Type GenericModel cannot be used without generic parameters, e.g. GenericModel[T]'
-
-
-@skip_36
-def test_parameterized_generic_instantiation_error():
-    data_type = TypeVar('data_type')
-
-    class Result(GenericModel, Generic[data_type]):
-        data: data_type
-
-    with pytest.raises(TypeError) as exc_info:
-        Result(data=1)
-    assert str(exc_info.value) == 'Type Result cannot be used without generic parameters, e.g. Result[T]'
-
-
 @skip_36
 def test_deep_generic():
     T = TypeVar('T')
@@ -444,3 +425,157 @@ def test_custom_generic_naming():
 
     assert repr(MyModel[int](value=1)) == 'OptionalIntWrapper(value=1)'
     assert repr(MyModel[str](value=None)) == 'OptionalStrWrapper(value=None)'
+
+
+@skip_36
+def test_nested():
+    AT = TypeVar('AT')
+
+    class InnerT(GenericModel, Generic[AT]):
+        a: AT
+
+    inner_int = InnerT[int](a=8)
+    inner_str = InnerT[str](a='ate')
+    inner_dict_any = InnerT[Any](a={})
+    inner_int_any = InnerT[Any](a=7)
+
+    class OuterT_SameType(GenericModel, Generic[AT]):
+        i: InnerT[AT]
+
+    OuterT_SameType[int](i=inner_int)
+    OuterT_SameType[str](i=inner_str)
+    OuterT_SameType[int](i=inner_int_any)  # ensure parsing the broader inner type works
+
+    with pytest.raises(ValidationError) as exc_info:
+        OuterT_SameType[int](i=inner_str)
+    assert exc_info.value.errors() == [
+        {'loc': ('i', 'a'), 'msg': 'value is not a valid integer', 'type': 'type_error.integer'}
+    ]
+
+    with pytest.raises(ValidationError) as exc_info:
+        OuterT_SameType[int](i=inner_dict_any)
+    assert exc_info.value.errors() == [
+        {'loc': ('i', 'a'), 'msg': 'value is not a valid integer', 'type': 'type_error.integer'}
+    ]
+
+
+@skip_36
+def test_partial_specification():
+    AT = TypeVar('AT')
+    BT = TypeVar('BT')
+
+    class Model(GenericModel, Generic[AT, BT]):
+        a: AT
+        b: BT
+
+    partial_model = Model[int, BT]
+    concrete_model = partial_model[str]
+    concrete_model(a=1, b='abc')
+    with pytest.raises(ValidationError) as exc_info:
+        concrete_model(a='abc', b=None)
+    assert exc_info.value.errors() == [
+        {'loc': ('a',), 'msg': 'value is not a valid integer', 'type': 'type_error.integer'},
+        {'loc': ('b',), 'msg': 'none is not an allowed value', 'type': 'type_error.none.not_allowed'},
+    ]
+
+
+@skip_36
+def test_partial_specification_name():
+    AT = TypeVar('AT')
+    BT = TypeVar('BT')
+
+    class Model(GenericModel, Generic[AT, BT]):
+        a: AT
+        b: BT
+
+    partial_model = Model[int, BT]
+    assert partial_model.__name__ == 'Model[int, BT]'
+    concrete_model = partial_model[str]
+    assert concrete_model.__name__ == 'Model[int, BT][str]'
+
+
+@skip_36
+def test_partial_specification_instantiation():
+    AT = TypeVar('AT')
+    BT = TypeVar('BT')
+
+    class Model(GenericModel, Generic[AT, BT]):
+        a: AT
+        b: BT
+
+    partial_model = Model[int, BT]
+    partial_model(a=1, b=2)
+
+    partial_model(a=1, b='a')
+
+    with pytest.raises(ValidationError) as exc_info:
+        partial_model(a='a', b=2)
+    assert exc_info.value.errors() == [
+        {'loc': ('a',), 'msg': 'value is not a valid integer', 'type': 'type_error.integer'}
+    ]
+
+
+@skip_36
+def test_partial_specification_instantiation_bounded():
+    AT = TypeVar('AT')
+    BT = TypeVar('BT', bound=int)
+
+    class Model(GenericModel, Generic[AT, BT]):
+        a: AT
+        b: BT
+
+    Model(a=1, b=1)
+    with pytest.raises(ValidationError) as exc_info:
+        Model(a=1, b='a')
+    assert exc_info.value.errors() == [
+        {'loc': ('b',), 'msg': 'value is not a valid integer', 'type': 'type_error.integer'}
+    ]
+
+    partial_model = Model[int, BT]
+    partial_model(a=1, b=1)
+    with pytest.raises(ValidationError) as exc_info:
+        partial_model(a=1, b='a')
+    assert exc_info.value.errors() == [
+        {'loc': ('b',), 'msg': 'value is not a valid integer', 'type': 'type_error.integer'}
+    ]
+
+
+@skip_36
+def test_typevar_parametrization():
+    AT = TypeVar('AT')
+    BT = TypeVar('BT')
+
+    class Model(GenericModel, Generic[AT, BT]):
+        a: AT
+        b: BT
+
+    CT = TypeVar('CT', bound=int)
+    DT = TypeVar('DT', bound=int)
+
+    with pytest.raises(ValidationError) as exc_info:
+        Model[CT, DT](a='a', b='b')
+    assert exc_info.value.errors() == [
+        {'loc': ('a',), 'msg': 'value is not a valid integer', 'type': 'type_error.integer'},
+        {'loc': ('b',), 'msg': 'value is not a valid integer', 'type': 'type_error.integer'},
+    ]
+
+
+@skip_36
+def test_multiple_specification():
+    AT = TypeVar('AT')
+    BT = TypeVar('BT')
+
+    class Model(GenericModel, Generic[AT, BT]):
+        a: AT
+        b: BT
+
+    CT = TypeVar('CT')
+    partial_model = Model[CT, CT]
+    concrete_model = partial_model[str]
+
+    with pytest.raises(ValidationError) as exc_info:
+        concrete_model(a=None, b=None)
+    assert exc_info.value.errors() == [
+        {'loc': ('a',), 'msg': 'none is not an allowed value', 'type': 'type_error.none.not_allowed'},
+        {'loc': ('b',), 'msg': 'none is not an allowed value', 'type': 'type_error.none.not_allowed'},
+    ]