/
test_computed_fields.py
759 lines (588 loc) · 20.8 KB
/
test_computed_fields.py
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import random
import sys
from abc import ABC, abstractmethod
from typing import Any, Callable, ClassVar, Generic, List, Tuple, TypeVar
import pytest
from pydantic_core import ValidationError, core_schema
from typing_extensions import TypedDict
from pydantic import (
BaseModel,
Field,
GetCoreSchemaHandler,
PrivateAttr,
TypeAdapter,
computed_field,
dataclasses,
field_serializer,
field_validator,
)
from pydantic.alias_generators import to_camel
from pydantic.errors import PydanticUserError
try:
from functools import cached_property, lru_cache, singledispatchmethod
except ImportError:
cached_property = None
lru_cache = None
singledispatchmethod = None
def test_computed_fields_get():
class Rectangle(BaseModel):
width: int
length: int
@computed_field
def area(self) -> int:
"""An awesome area"""
return self.width * self.length
@computed_field(title='Pikarea', description='Another area')
@property
def area2(self) -> int:
return self.width * self.length
@property
def double_width(self) -> int:
return self.width * 2
rect = Rectangle(width=10, length=5)
assert set(rect.model_fields) == {'width', 'length'}
assert set(rect.model_computed_fields) == {'area', 'area2'}
assert rect.__dict__ == {'width': 10, 'length': 5}
assert rect.model_computed_fields['area'].description == 'An awesome area'
assert rect.model_computed_fields['area2'].title == 'Pikarea'
assert rect.model_computed_fields['area2'].description == 'Another area'
assert rect.area == 50
assert rect.double_width == 20
assert rect.model_dump() == {'width': 10, 'length': 5, 'area': 50, 'area2': 50}
assert rect.model_dump_json() == '{"width":10,"length":5,"area":50,"area2":50}'
def test_computed_fields_json_schema():
class Rectangle(BaseModel):
width: int
length: int
@computed_field
def area(self) -> int:
"""An awesome area"""
return self.width * self.length
@computed_field(title='Pikarea', description='Another area')
@property
def area2(self) -> int:
return self.width * self.length
@property
def double_width(self) -> int:
return self.width * 2
assert Rectangle.model_json_schema(mode='serialization') == {
'title': 'Rectangle',
'type': 'object',
'properties': {
'width': {
'title': 'Width',
'type': 'integer',
},
'length': {
'title': 'Length',
'type': 'integer',
},
'area': {
'title': 'Area',
'description': 'An awesome area',
'type': 'integer',
'readOnly': True,
},
'area2': {
'title': 'Pikarea',
'description': 'Another area',
'type': 'integer',
'readOnly': True,
},
},
'required': ['width', 'length', 'area', 'area2'],
}
def test_computed_fields_set():
class Square(BaseModel):
side: float
@computed_field
@property
def area(self) -> float:
return self.side**2
@computed_field
@property
def area_string(self) -> str:
return f'{self.area} square units'
@field_serializer('area_string')
def serialize_area_string(self, area_string):
return area_string.upper()
@area.setter
def area(self, new_area: int):
self.side = new_area**0.5
s = Square(side=10)
assert s.model_dump() == {'side': 10.0, 'area': 100.0, 'area_string': '100.0 SQUARE UNITS'}
s.area = 64
assert s.model_dump() == {'side': 8.0, 'area': 64.0, 'area_string': '64.0 SQUARE UNITS'}
def test_computed_fields_del():
class User(BaseModel):
first: str
last: str
@computed_field
def fullname(self) -> str:
return f'{self.first} {self.last}'
@fullname.setter
def fullname(self, new_fullname: str) -> None:
self.first, self.last = new_fullname.split()
@fullname.deleter
def fullname(self):
self.first = ''
self.last = ''
user = User(first='John', last='Smith')
assert user.model_dump() == {'first': 'John', 'last': 'Smith', 'fullname': 'John Smith'}
user.fullname = 'Pika Chu'
assert user.model_dump() == {'first': 'Pika', 'last': 'Chu', 'fullname': 'Pika Chu'}
del user.fullname
assert user.model_dump() == {'first': '', 'last': '', 'fullname': ' '}
@pytest.mark.skipif(cached_property is None, reason='cached_property not available')
def test_cached_property():
class Model(BaseModel):
minimum: int = Field(alias='min')
maximum: int = Field(alias='max')
@computed_field(alias='the magic number')
@cached_property
def random_number(self) -> int:
"""An awesome area"""
return random.randint(self.minimum, self.maximum)
@cached_property
def cached_property_2(self) -> int:
return 42
@cached_property
def _cached_property_3(self) -> int:
return 43
rect = Model(min=10, max=10_000)
assert rect.__private_attributes__ == {}
assert rect.cached_property_2 == 42
assert rect._cached_property_3 == 43
first_n = rect.random_number
second_n = rect.random_number
assert first_n == second_n
assert rect.model_dump() == {'minimum': 10, 'maximum': 10_000, 'random_number': first_n}
assert rect.model_dump(by_alias=True) == {'min': 10, 'max': 10_000, 'the magic number': first_n}
assert rect.model_dump(by_alias=True, exclude={'random_number'}) == {'min': 10, 'max': 10000}
def test_properties_and_computed_fields():
class Model(BaseModel):
x: str
_private_float: float = PrivateAttr(0)
@property
def public_int(self) -> int:
return int(self._private_float)
@public_int.setter
def public_int(self, v: float) -> None:
self._private_float = v
@computed_field
@property
def public_str(self) -> str:
return f'public {self.public_int}'
m = Model(x='pika')
assert m.model_dump() == {'x': 'pika', 'public_str': 'public 0'}
m._private_float = 3.1
assert m.model_dump() == {'x': 'pika', 'public_str': 'public 3'}
m.public_int = 2
assert m._private_float == 2.0
assert m.model_dump() == {'x': 'pika', 'public_str': 'public 2'}
def test_computed_fields_repr():
class Model(BaseModel):
x: int
@computed_field(repr=False)
@property
def double(self) -> int:
return self.x * 2
@computed_field # repr=True by default
@property
def triple(self) -> int:
return self.x * 3
assert repr(Model(x=2)) == 'Model(x=2, triple=6)'
@pytest.mark.skipif(singledispatchmethod is None, reason='singledispatchmethod not available')
def test_functools():
class Model(BaseModel, frozen=True):
x: int
@lru_cache
def x_pow(self, p):
return self.x**p
@singledispatchmethod
def neg(self, arg):
raise NotImplementedError('Cannot negate a')
@neg.register
def _(self, arg: int):
return -arg
@neg.register
def _(self, arg: bool):
return not arg
m = Model(x=2)
assert m.x_pow(1) == 2
assert m.x_pow(2) == 4
assert m.neg(1) == -1
assert m.neg(True) is False
def test_include_exclude():
class Model(BaseModel):
x: int
y: int
@computed_field
def x_list(self) -> List[int]:
return [self.x, self.x + 1]
@computed_field
def y_list(self) -> List[int]:
return [self.y, self.y + 1, self.y + 2]
m = Model(x=1, y=2)
assert m.model_dump() == {'x': 1, 'y': 2, 'x_list': [1, 2], 'y_list': [2, 3, 4]}
assert m.model_dump(include={'x'}) == {'x': 1}
assert m.model_dump(include={'x': None, 'x_list': {0}}) == {'x': 1, 'x_list': [1]}
assert m.model_dump(exclude={'x': ..., 'y_list': {2}}) == {'y': 2, 'x_list': [1, 2], 'y_list': [2, 3]}
def test_exclude_none():
class Model(BaseModel):
x: int
y: int
@computed_field
def sum(self) -> int:
return self.x + self.y
@computed_field
def none(self) -> None:
return None
m = Model(x=1, y=2)
assert m.model_dump(exclude_none=False) == {'x': 1, 'y': 2, 'sum': 3, 'none': None}
assert m.model_dump(exclude_none=True) == {'x': 1, 'y': 2, 'sum': 3}
assert m.model_dump(mode='json', exclude_none=False) == {'x': 1, 'y': 2, 'sum': 3, 'none': None}
assert m.model_dump(mode='json', exclude_none=True) == {'x': 1, 'y': 2, 'sum': 3}
def test_expected_type():
class Model(BaseModel):
x: int
y: int
@computed_field
def x_list(self) -> List[int]:
return [self.x, self.x + 1]
@computed_field
def y_str(self) -> bytes:
s = f'y={self.y}'
return s.encode()
m = Model(x=1, y=2)
assert m.model_dump() == {'x': 1, 'y': 2, 'x_list': [1, 2], 'y_str': b'y=2'}
assert m.model_dump(mode='json') == {'x': 1, 'y': 2, 'x_list': [1, 2], 'y_str': 'y=2'}
assert m.model_dump_json() == '{"x":1,"y":2,"x_list":[1,2],"y_str":"y=2"}'
def test_expected_type_wrong():
class Model(BaseModel):
x: int
@computed_field
def x_list(self) -> List[int]:
return 'not a list'
m = Model(x=1)
with pytest.warns(UserWarning, match=r'Expected `list\[int\]` but got `str`'):
m.model_dump()
with pytest.warns(UserWarning, match=r'Expected `list\[int\]` but got `str`'):
m.model_dump(mode='json')
with pytest.warns(UserWarning, match=r'Expected `list\[int\]` but got `str`'):
m.model_dump_json()
def test_inheritance():
class Base(BaseModel):
x: int
@computed_field
def double(self) -> int:
return self.x * 2
class Child(Base):
y: int
@computed_field
def triple(self) -> int:
return self.y * 3
c = Child(x=2, y=3)
assert c.double == 4
assert c.triple == 9
assert c.model_dump() == {'x': 2, 'y': 3, 'double': 4, 'triple': 9}
def test_dataclass():
@dataclasses.dataclass
class MyDataClass:
x: int
@computed_field
def double(self) -> int:
return self.x * 2
m = MyDataClass(x=2)
assert m.double == 4
assert TypeAdapter(MyDataClass).dump_python(m) == {'x': 2, 'double': 4}
def test_free_function():
@property
def double_func(self) -> int:
return self.x * 2
class MyModel(BaseModel):
x: int
double = computed_field(double_func)
m = MyModel(x=2)
assert set(m.model_fields) == {'x'}
assert m.__private_attributes__ == {}
assert m.double == 4
assert repr(m) == 'MyModel(x=2, double=4)'
assert m.model_dump() == {'x': 2, 'double': 4}
def test_private_computed_field():
class MyModel(BaseModel):
x: int
@computed_field(repr=True)
def _double(self) -> int:
return self.x * 2
m = MyModel(x=2)
assert repr(m) == 'MyModel(x=2, _double=4)'
assert m.__private_attributes__ == {}
assert m._double == 4
assert m.model_dump() == {'x': 2, '_double': 4}
@pytest.mark.skipif(sys.version_info < (3, 9), reason='@computed_field @classmethod @property only works in 3.9+')
def test_classmethod():
class MyModel(BaseModel):
x: int
y: ClassVar[int] = 4
@computed_field
@classmethod
@property
def two_y(cls) -> int:
return cls.y * 2
m = MyModel(x=1)
assert m.two_y == 8
assert m.model_dump() == {'x': 1, 'two_y': 8}
def test_frozen():
class Square(BaseModel, frozen=True):
side: float
@computed_field
@property
def area(self) -> float:
return self.side**2
@area.setter
def area(self, new_area: int):
self.side = new_area**0.5
m = Square(side=4)
assert m.area == 16.0
assert m.model_dump() == {'side': 4.0, 'area': 16.0}
with pytest.raises(ValidationError) as exc_info:
m.area = 4
assert exc_info.value.errors(include_url=False) == [
{'type': 'frozen_instance', 'loc': ('area',), 'msg': 'Instance is frozen', 'input': 4}
]
def test_validate_assignment():
class Square(BaseModel, validate_assignment=True):
side: float
@field_validator('side')
def small_side(cls, s):
if s < 2:
raise ValueError('must be >=2')
return float(round(s))
@computed_field
@property
def area(self) -> float:
return self.side**2
@area.setter
def area(self, new_area: int):
self.side = new_area**0.5
with pytest.raises(ValidationError, match=r'side\s+Value error, must be >=2'):
Square(side=1)
m = Square(side=4.0)
assert m.area == 16.0
assert m.model_dump() == {'side': 4.0, 'area': 16.0}
m.area = 10.0
assert m.side == 3.0
with pytest.raises(ValidationError, match=r'side\s+Value error, must be >=2'):
m.area = 3
def test_abstractmethod():
class AbstractSquare(BaseModel):
side: float
@computed_field
@property
@abstractmethod
def area(self) -> float:
raise NotImplementedError()
class Square(AbstractSquare):
@computed_field
@property
def area(self) -> float:
return self.side + 1
m = Square(side=4.0)
assert m.model_dump() == {'side': 4.0, 'area': 5.0}
@pytest.mark.skipif(sys.version_info < (3, 12), reason='error message is different on older versions')
@pytest.mark.parametrize(
'bases',
[
(BaseModel, ABC),
(ABC, BaseModel),
(BaseModel,),
],
)
def test_abstractmethod_missing(bases: Tuple[Any, ...]):
class AbstractSquare(*bases):
side: float
@computed_field
@property
@abstractmethod
def area(self) -> float:
raise NotImplementedError()
class Square(AbstractSquare):
pass
with pytest.raises(
TypeError, match="Can't instantiate abstract class Square without an implementation for abstract method 'area'"
):
Square(side=4.0)
class CustomType(str):
@classmethod
def __get_pydantic_core_schema__(cls, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
schema = handler(str)
schema['serialization'] = core_schema.plain_serializer_function_ser_schema(lambda x: '123')
return schema
def test_computed_fields_infer_return_type():
class Model(BaseModel):
@computed_field
def cfield(self) -> CustomType:
return CustomType('abc')
assert Model().model_dump() == {'cfield': '123'}
assert Model().model_dump_json() == '{"cfield":"123"}'
def test_computed_fields_missing_return_type():
with pytest.raises(PydanticUserError, match='Computed field is missing return type annotation'):
class _Model(BaseModel):
@computed_field
def cfield(self):
raise NotImplementedError
class Model(BaseModel):
@computed_field(return_type=CustomType)
def cfield(self):
return CustomType('abc')
assert Model().model_dump() == {'cfield': '123'}
assert Model().model_dump_json() == '{"cfield":"123"}'
def test_alias_generator():
class MyModel(BaseModel):
my_standard_field: int
@computed_field # *will* be overridden by alias generator
@property
def my_computed_field(self) -> int:
return self.my_standard_field + 1
@computed_field(alias='my_alias_none') # will *not* be overridden by alias generator
@property
def my_aliased_computed_field_none(self) -> int:
return self.my_standard_field + 2
@computed_field(alias='my_alias_1', alias_priority=1) # *will* be overridden by alias generator
@property
def my_aliased_computed_field_1(self) -> int:
return self.my_standard_field + 3
@computed_field(alias='my_alias_2', alias_priority=2) # will *not* be overridden by alias generator
@property
def my_aliased_computed_field_2(self) -> int:
return self.my_standard_field + 4
class MySubModel(MyModel):
model_config = dict(alias_generator=to_camel, populate_by_name=True)
model = MyModel(my_standard_field=1)
assert model.model_dump() == {
'my_standard_field': 1,
'my_computed_field': 2,
'my_aliased_computed_field_none': 3,
'my_aliased_computed_field_1': 4,
'my_aliased_computed_field_2': 5,
}
assert model.model_dump(by_alias=True) == {
'my_standard_field': 1,
'my_computed_field': 2,
'my_alias_none': 3,
'my_alias_1': 4,
'my_alias_2': 5,
}
submodel = MySubModel(my_standard_field=1)
assert submodel.model_dump() == {
'my_standard_field': 1,
'my_computed_field': 2,
'my_aliased_computed_field_none': 3,
'my_aliased_computed_field_1': 4,
'my_aliased_computed_field_2': 5,
}
assert submodel.model_dump(by_alias=True) == {
'myStandardField': 1,
'myComputedField': 2,
'my_alias_none': 3,
'myAliasedComputedField1': 4,
'my_alias_2': 5,
}
def make_base_model() -> Any:
class CompModel(BaseModel):
pass
class Model(BaseModel):
@computed_field
@property
def comp_1(self) -> CompModel:
return CompModel()
@computed_field
@property
def comp_2(self) -> CompModel:
return CompModel()
return Model
def make_dataclass() -> Any:
class CompModel(BaseModel):
pass
@dataclasses.dataclass
class Model:
@computed_field
@property
def comp_1(self) -> CompModel:
return CompModel()
@computed_field
@property
def comp_2(self) -> CompModel:
return CompModel()
return Model
def make_typed_dict() -> Any:
class CompModel(BaseModel):
pass
class Model(TypedDict):
@computed_field # type: ignore
@property
def comp_1(self) -> CompModel:
return CompModel()
@computed_field # type: ignore
@property
def comp_2(self) -> CompModel:
return CompModel()
return Model
@pytest.mark.parametrize(
'model_factory',
[
make_base_model,
pytest.param(
make_typed_dict,
marks=pytest.mark.xfail(
reason='computed fields do not work with TypedDict yet. See https://github.com/pydantic/pydantic-core/issues/657'
),
),
make_dataclass,
],
)
def test_multiple_references_to_schema(model_factory: Callable[[], Any]) -> None:
"""
https://github.com/pydantic/pydantic/issues/5980
"""
model = model_factory()
ta = TypeAdapter(model)
assert ta.dump_python(model()) == {'comp_1': {}, 'comp_2': {}}
assert ta.json_schema() == {'type': 'object', 'properties': {}, 'title': 'Model'}
assert ta.json_schema(mode='serialization') == {
'$defs': {'CompModel': {'properties': {}, 'title': 'CompModel', 'type': 'object'}},
'properties': {
'comp_1': {'allOf': [{'$ref': '#/$defs/CompModel'}], 'readOnly': True},
'comp_2': {'allOf': [{'$ref': '#/$defs/CompModel'}], 'readOnly': True},
},
'required': ['comp_1', 'comp_2'],
'title': 'Model',
'type': 'object',
}
def test_generic_computed_field():
T = TypeVar('T')
class A(BaseModel, Generic[T]):
x: T
@computed_field
@property
def double_x(self) -> T:
return self.x * 2
assert A[int](x=1).model_dump() == {'x': 1, 'double_x': 2}
assert A[str](x='abc').model_dump() == {'x': 'abc', 'double_x': 'abcabc'}
class B(BaseModel, Generic[T]):
@computed_field
@property
def double_x(self) -> T:
return 'abc' # this may not match the annotated return type, and will warn if not
with pytest.warns(UserWarning, match='Expected `int` but got `str` - serialized value may not be as expected'):
B[int]().model_dump()
def test_computed_field_override_raises():
class Model(BaseModel):
name: str = 'foo'
with pytest.raises(ValueError, match="you can't override a field with a computed field"):
class SubModel(Model):
@computed_field
@property
def name(self) -> str:
return 'bar'