mirror of
https://github.com/RYDE-WORK/ballistica.git
synced 2026-01-21 22:33:58 +08:00
1069 lines
31 KiB
Python
1069 lines
31 KiB
Python
# Released under the MIT License. See LICENSE for details.
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#
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"""Testing dataclasses functionality."""
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# pylint: disable=too-many-lines
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from __future__ import annotations
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from enum import Enum
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import datetime
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from dataclasses import field, dataclass
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from typing import TYPE_CHECKING, Any, Sequence, Annotated
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import pytest
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from efro.util import utc_now
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from efro.dataclassio import (
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dataclass_validate,
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dataclass_from_dict,
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dataclass_to_dict,
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ioprepped,
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ioprep,
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IOAttrs,
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Codec,
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DataclassFieldLookup,
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IOExtendedData,
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)
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if TYPE_CHECKING:
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pass
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class _EnumTest(Enum):
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TEST1 = 'test1'
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TEST2 = 'test2'
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class _GoodEnum(Enum):
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VAL1 = 'val1'
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VAL2 = 'val2'
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class _GoodEnum2(Enum):
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VAL1 = 1
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VAL2 = 2
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class _BadEnum1(Enum):
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VAL1 = 1.23
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class _BadEnum2(Enum):
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VAL1 = 1
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VAL2 = 'val2'
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@dataclass
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class _NestedClass:
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ival: int = 0
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sval: str = 'foo'
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dval: dict[int, str] = field(default_factory=dict)
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def test_assign() -> None:
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"""Testing various assignments."""
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# pylint: disable=too-many-statements
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@ioprepped
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@dataclass
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class _TestClass:
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ival: int = 0
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sval: str = ''
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bval: bool = True
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fval: float = 1.0
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nval: _NestedClass = field(default_factory=_NestedClass)
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enval: _EnumTest = _EnumTest.TEST1
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oival: int | None = None
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oival2: int | None = None
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osval: str | None = None
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obval: bool | None = None
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ofval: float | None = None
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oenval: _EnumTest | None = _EnumTest.TEST1
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lsval: list[str] = field(default_factory=list)
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lival: list[int] = field(default_factory=list)
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lbval: list[bool] = field(default_factory=list)
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lfval: list[float] = field(default_factory=list)
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lenval: list[_EnumTest] = field(default_factory=list)
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ssval: set[str] = field(default_factory=set)
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anyval: Any = 1
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dictval: dict[int, str] = field(default_factory=dict)
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tupleval: tuple[int, str, bool] = (1, 'foo', False)
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datetimeval: datetime.datetime | None = None
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class _TestClass2:
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pass
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# Attempting to use with non-dataclass should fail.
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass2, {})
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# Attempting to pass non-dicts should fail.
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, []) # type: ignore
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, None) # type: ignore
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now = utc_now()
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# A dict containing *ALL* values should match what we
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# get when creating a dataclass and then converting back
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# to a dict.
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dict1 = {
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'ival': 1,
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'sval': 'foo',
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'bval': True,
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'fval': 2.0,
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'nval': {
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'ival': 1,
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'sval': 'bar',
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'dval': {'1': 'foof'},
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},
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'enval': 'test1',
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'oival': 1,
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'oival2': 1,
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'osval': 'foo',
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'obval': True,
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'ofval': 1.0,
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'oenval': 'test2',
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'lsval': ['foo'],
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'lival': [10],
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'lbval': [False],
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'lfval': [1.0],
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'lenval': ['test1', 'test2'],
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'ssval': ['foo'],
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'dval': {'k': 123},
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'anyval': {'foo': [1, 2, {'bar': 'eep', 'rah': 1}]},
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'dictval': {'1': 'foo'},
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'tupleval': [2, 'foof', True],
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'datetimeval': [
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now.year,
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now.month,
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now.day,
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now.hour,
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now.minute,
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now.second,
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now.microsecond,
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],
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}
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dc1 = dataclass_from_dict(_TestClass, dict1)
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assert dataclass_to_dict(dc1) == dict1
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# A few other assignment checks.
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assert isinstance(
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dataclass_from_dict(
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_TestClass,
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{
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'oival': None,
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'oival2': None,
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'osval': None,
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'obval': None,
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'ofval': None,
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'lsval': [],
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'lival': [],
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'lbval': [],
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'lfval': [],
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'ssval': [],
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},
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),
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_TestClass,
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)
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assert isinstance(
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dataclass_from_dict(
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_TestClass,
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{
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'oival': 1,
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'oival2': 1,
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'osval': 'foo',
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'obval': True,
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'ofval': 2.0,
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'lsval': ['foo', 'bar', 'eep'],
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'lival': [10, 11, 12],
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'lbval': [False, True],
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'lfval': [1.0, 2.0, 3.0],
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},
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),
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_TestClass,
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)
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# Attr assigns mismatched with their value types should fail.
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'ival': 'foo'})
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'sval': 1})
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'bval': 2})
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'oival': 'foo'})
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'oival2': 'foo'})
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dataclass_from_dict(_TestClass, {'oival2': None})
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dataclass_from_dict(_TestClass, {'oival2': 123})
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'osval': 1})
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'obval': 2})
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'ofval': 'blah'})
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with pytest.raises(ValueError):
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dataclass_from_dict(_TestClass, {'oenval': 'test3'})
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'lsval': 'blah'})
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'lsval': ['blah', None]})
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'lsval': [1]})
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'lsval': (1,)})
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'lbval': [None]})
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'lival': ['foo']})
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'lfval': [True]})
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with pytest.raises(ValueError):
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dataclass_from_dict(_TestClass, {'lenval': ['test1', 'test3']})
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'ssval': [True]})
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'ssval': {}})
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'ssval': set()})
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with pytest.raises(ValueError):
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dataclass_from_dict(_TestClass, {'tupleval': []})
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'tupleval': [1, 1, 1]})
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with pytest.raises(ValueError):
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dataclass_from_dict(_TestClass, {'tupleval': [2, 'foof', True, True]})
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# Fields with type Any should accept all types which are directly
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# supported by json, but not ones such as tuples or non-string dict keys
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# which get implicitly translated by python's json module.
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dataclass_from_dict(_TestClass, {'anyval': {}})
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dataclass_from_dict(_TestClass, {'anyval': None})
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dataclass_from_dict(_TestClass, {'anyval': []})
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dataclass_from_dict(_TestClass, {'anyval': [True, {'foo': 'bar'}, None]})
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'anyval': {1: 'foo'}})
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'anyval': set()})
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'anyval': (1, 2, 3)})
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# More subtle attr/type mismatches that should fail
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# (we currently require EXACT type matches).
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'ival': True})
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'fval': 2}, coerce_to_float=False)
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'bval': 1})
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'ofval': 1}, coerce_to_float=False)
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'lfval': [1]}, coerce_to_float=False)
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# Coerce-to-float should only work on ints; not bools or other types.
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dataclass_from_dict(_TestClass, {'fval': 1}, coerce_to_float=True)
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'fval': 1}, coerce_to_float=False)
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'fval': True}, coerce_to_float=True)
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'fval': None}, coerce_to_float=True)
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'fval': []}, coerce_to_float=True)
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# Datetime values should only be allowed with timezone set as utc.
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dataclass_to_dict(_TestClass(datetimeval=utc_now()))
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with pytest.raises(ValueError):
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dataclass_to_dict(_TestClass(datetimeval=datetime.datetime.now()))
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with pytest.raises(ValueError):
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# This doesn't actually set timezone on the datetime obj.
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dataclass_to_dict(_TestClass(datetimeval=datetime.datetime.utcnow()))
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def test_coerce() -> None:
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"""Test value coercion."""
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@ioprepped
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@dataclass
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class _TestClass:
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ival: int = 0
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fval: float = 0.0
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# Float value present for int should never work.
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obj = _TestClass()
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# noinspection PyTypeHints
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obj.ival = 1.0 # type: ignore
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with pytest.raises(TypeError):
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dataclass_validate(obj, coerce_to_float=True)
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with pytest.raises(TypeError):
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dataclass_validate(obj, coerce_to_float=False)
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# Int value present for float should work only with coerce on.
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obj = _TestClass()
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obj.fval = 1
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dataclass_validate(obj, coerce_to_float=True)
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with pytest.raises(TypeError):
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dataclass_validate(obj, coerce_to_float=False)
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# Likewise, passing in an int for a float field should work only
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# with coerce on.
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dataclass_from_dict(_TestClass, {'fval': 1}, coerce_to_float=True)
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'fval': 1}, coerce_to_float=False)
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# Passing in floats for an int field should never work.
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'ival': 1.0}, coerce_to_float=True)
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClass, {'ival': 1.0}, coerce_to_float=False)
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def test_prep() -> None:
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"""Test the prepping process."""
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# We currently don't support Sequence; can revisit if there is
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# a strong use case.
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with pytest.raises(TypeError):
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@ioprepped
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@dataclass
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class _TestClass:
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ival: Sequence[int]
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# We currently only support Unions with exactly 2 members; one of
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# which is None. (Optional types get transformed into this by
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# get_type_hints() so we need to support at least that).
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with pytest.raises(TypeError):
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@ioprepped
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@dataclass
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class _TestClass2:
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ival: int | str
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@ioprepped
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@dataclass
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class _TestClass3:
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uval: int | None
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with pytest.raises(TypeError):
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@ioprepped
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@dataclass
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class _TestClass4:
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ival: int | str
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# This will get simplified down to simply int by get_type_hints so is ok.
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@ioprepped
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@dataclass
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class _TestClass5:
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ival: int | int
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# This will get simplified down to a valid 2 member union so is ok
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@ioprepped
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@dataclass
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class _TestClass6:
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ival: int | None | int | None
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# Disallow dict entries with types other than str, int, or enums
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# having those value types.
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with pytest.raises(TypeError):
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@ioprepped
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@dataclass
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class _TestClass7:
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dval: dict[float, int]
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@ioprepped
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@dataclass
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class _TestClass8:
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dval: dict[str, int]
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@ioprepped
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@dataclass
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class _TestClass9:
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dval: dict[_GoodEnum, int]
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@ioprepped
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@dataclass
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class _TestClass10:
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dval: dict[_GoodEnum2, int]
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with pytest.raises(TypeError):
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@ioprepped
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@dataclass
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class _TestClass11:
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dval: dict[_BadEnum1, int]
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with pytest.raises(TypeError):
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@ioprepped
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@dataclass
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class _TestClass12:
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dval: dict[_BadEnum2, int]
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def test_validate() -> None:
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"""Testing validation."""
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@ioprepped
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@dataclass
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class _TestClass:
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ival: int = 0
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sval: str = ''
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bval: bool = True
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fval: float = 1.0
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oival: int | None = None
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osval: str | None = None
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obval: bool | None = None
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ofval: float | None = None
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# Should pass by default.
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tclass = _TestClass()
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dataclass_validate(tclass)
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# No longer valid (without coerce)
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tclass.fval = 1
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with pytest.raises(TypeError):
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dataclass_validate(tclass, coerce_to_float=False)
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# Should pass by default.
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tclass = _TestClass()
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dataclass_validate(tclass)
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# No longer valid.
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# noinspection PyTypeHints
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tclass.ival = None # type: ignore
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with pytest.raises(TypeError):
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dataclass_validate(tclass)
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def test_extra_data() -> None:
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"""Test handling of data that doesn't map to dataclass attrs."""
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@ioprepped
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@dataclass
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class _TestClass:
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ival: int = 0
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sval: str = ''
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# Passing an attr not in the dataclass should fail if we ask it to.
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with pytest.raises(AttributeError):
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dataclass_from_dict(
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_TestClass, {'nonexistent': 'foo'}, allow_unknown_attrs=False
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)
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# But normally it should be preserved and present in re-export.
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obj = dataclass_from_dict(_TestClass, {'nonexistent': 'foo'})
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assert isinstance(obj, _TestClass)
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out = dataclass_to_dict(obj)
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assert out.get('nonexistent') == 'foo'
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# But not if we ask it to discard unknowns.
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obj = dataclass_from_dict(
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_TestClass, {'nonexistent': 'foo'}, discard_unknown_attrs=True
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)
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assert isinstance(obj, _TestClass)
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out = dataclass_to_dict(obj)
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assert 'nonexistent' not in out
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def test_ioattrs() -> None:
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"""Testing ioattrs annotations."""
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@ioprepped
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@dataclass
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class _TestClass:
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dval: Annotated[dict, IOAttrs('d')]
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obj = _TestClass(dval={'foo': 'bar'})
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# Make sure key is working.
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assert dataclass_to_dict(obj) == {'d': {'foo': 'bar'}}
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# Setting store_default False without providing a default or
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# default_factory should fail.
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with pytest.raises(TypeError):
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@ioprepped
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@dataclass
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class _TestClass2:
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dval: Annotated[dict, IOAttrs('d', store_default=False)]
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@ioprepped
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@dataclass
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class _TestClass3:
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dval: Annotated[dict, IOAttrs('d', store_default=False)] = field(
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default_factory=dict
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)
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ival: Annotated[int, IOAttrs('i', store_default=False)] = 123
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# Both attrs are default; should get stripped out.
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obj3 = _TestClass3()
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assert dataclass_to_dict(obj3) == {}
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|
|
# Both attrs are non-default vals; should remain in output.
|
|
obj3 = _TestClass3(dval={'foo': 'bar'}, ival=124)
|
|
assert dataclass_to_dict(obj3) == {'d': {'foo': 'bar'}, 'i': 124}
|
|
|
|
# Test going the other way.
|
|
obj3 = dataclass_from_dict(
|
|
_TestClass3,
|
|
{'d': {'foo': 'barf'}, 'i': 125},
|
|
allow_unknown_attrs=False,
|
|
)
|
|
assert obj3.dval == {'foo': 'barf'}
|
|
assert obj3.ival == 125
|
|
|
|
|
|
def test_codecs() -> None:
|
|
"""Test differences with codecs."""
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClass:
|
|
bval: bytes
|
|
|
|
# bytes to/from JSON (goes through base64)
|
|
obj = _TestClass(bval=b'foo')
|
|
out = dataclass_to_dict(obj, codec=Codec.JSON)
|
|
assert isinstance(out['bval'], str) and out['bval'] == 'Zm9v'
|
|
obj = dataclass_from_dict(_TestClass, out, codec=Codec.JSON)
|
|
assert obj.bval == b'foo'
|
|
|
|
# bytes to/from FIRESTORE (passed as-is)
|
|
obj = _TestClass(bval=b'foo')
|
|
out = dataclass_to_dict(obj, codec=Codec.FIRESTORE)
|
|
assert isinstance(out['bval'], bytes) and out['bval'] == b'foo'
|
|
obj = dataclass_from_dict(_TestClass, out, codec=Codec.FIRESTORE)
|
|
assert obj.bval == b'foo'
|
|
|
|
now = utc_now()
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClass2:
|
|
dval: datetime.datetime
|
|
|
|
# datetime to/from JSON (turns into a list of values)
|
|
obj2 = _TestClass2(dval=now)
|
|
out = dataclass_to_dict(obj2, codec=Codec.JSON)
|
|
assert (
|
|
isinstance(out['dval'], list)
|
|
and len(out['dval']) == 7
|
|
and all(isinstance(val, int) for val in out['dval'])
|
|
)
|
|
obj2 = dataclass_from_dict(_TestClass2, out, codec=Codec.JSON)
|
|
assert obj2.dval == now
|
|
|
|
# datetime to/from FIRESTORE (passed through as-is)
|
|
obj2 = _TestClass2(dval=now)
|
|
out = dataclass_to_dict(obj2, codec=Codec.FIRESTORE)
|
|
assert isinstance(out['dval'], datetime.datetime)
|
|
obj2 = dataclass_from_dict(_TestClass2, out, codec=Codec.FIRESTORE)
|
|
assert obj2.dval == now
|
|
|
|
|
|
def test_dict() -> None:
|
|
"""Test various dict related bits."""
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClass:
|
|
dval: dict
|
|
|
|
obj = _TestClass(dval={})
|
|
|
|
# 'Any' dicts should only support values directly compatible with json.
|
|
obj.dval['foo'] = 5
|
|
dataclass_to_dict(obj)
|
|
with pytest.raises(TypeError):
|
|
obj.dval[5] = 5
|
|
dataclass_to_dict(obj)
|
|
with pytest.raises(TypeError):
|
|
obj.dval['foo'] = _GoodEnum.VAL1
|
|
dataclass_to_dict(obj)
|
|
|
|
# Int dict-keys should actually be stored as strings internally
|
|
# (for json compatibility).
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClass2:
|
|
dval: dict[int, float]
|
|
|
|
obj2 = _TestClass2(dval={1: 2.34})
|
|
out = dataclass_to_dict(obj2)
|
|
assert '1' in out['dval']
|
|
assert 1 not in out['dval']
|
|
out['dval']['1'] = 2.35
|
|
obj2 = dataclass_from_dict(_TestClass2, out)
|
|
assert isinstance(obj2, _TestClass2)
|
|
assert obj2.dval[1] == 2.35
|
|
|
|
# Same with enum keys (we support enums with str and int values)
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClass3:
|
|
dval: dict[_GoodEnum, int]
|
|
|
|
obj3 = _TestClass3(dval={_GoodEnum.VAL1: 123})
|
|
out = dataclass_to_dict(obj3)
|
|
assert out['dval']['val1'] == 123
|
|
out['dval']['val1'] = 124
|
|
obj3 = dataclass_from_dict(_TestClass3, out)
|
|
assert obj3.dval[_GoodEnum.VAL1] == 124
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClass4:
|
|
dval: dict[_GoodEnum2, int]
|
|
|
|
obj4 = _TestClass4(dval={_GoodEnum2.VAL1: 125})
|
|
out = dataclass_to_dict(obj4)
|
|
assert out['dval']['1'] == 125
|
|
out['dval']['1'] = 126
|
|
obj4 = dataclass_from_dict(_TestClass4, out)
|
|
assert obj4.dval[_GoodEnum2.VAL1] == 126
|
|
|
|
# The wrong enum type as a key should error.
|
|
obj4.dval = {_GoodEnum.VAL1: 999} # type: ignore
|
|
with pytest.raises(TypeError):
|
|
dataclass_to_dict(obj4)
|
|
|
|
|
|
def test_name_clashes() -> None:
|
|
"""Make sure we catch name clashes since we can remap attr names."""
|
|
|
|
with pytest.raises(TypeError):
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClass:
|
|
ival: Annotated[int, IOAttrs('i')] = 4
|
|
ival2: Annotated[int, IOAttrs('i')] = 5
|
|
|
|
with pytest.raises(TypeError):
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClass2:
|
|
ival: int = 4
|
|
ival2: Annotated[int, IOAttrs('ival')] = 5
|
|
|
|
with pytest.raises(TypeError):
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClass3:
|
|
ival: Annotated[int, IOAttrs(store_default=False)] = 4
|
|
ival2: Annotated[int, IOAttrs('ival')] = 5
|
|
|
|
|
|
@dataclass
|
|
class _RecursiveTest:
|
|
val: int
|
|
child: _RecursiveTest | None = None
|
|
|
|
|
|
def test_recursive() -> None:
|
|
"""Test recursive classes."""
|
|
|
|
# Can't use ioprepped on this since it refers to its own name which
|
|
# doesn't exist yet. Have to explicitly prep it after.
|
|
ioprep(_RecursiveTest)
|
|
|
|
rtest = _RecursiveTest(val=1)
|
|
rtest.child = _RecursiveTest(val=2)
|
|
rtest.child.child = _RecursiveTest(val=3)
|
|
expected_output = {
|
|
'val': 1,
|
|
'child': {'val': 2, 'child': {'val': 3, 'child': None}},
|
|
}
|
|
assert dataclass_to_dict(rtest) == expected_output
|
|
assert dataclass_from_dict(_RecursiveTest, expected_output) == rtest
|
|
|
|
|
|
def test_any() -> None:
|
|
"""Test data included with type Any."""
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClass:
|
|
anyval: Any
|
|
|
|
obj = _TestClass(anyval=b'bytes')
|
|
|
|
# JSON output doesn't allow bytes or datetime objects
|
|
# included in 'Any' data.
|
|
with pytest.raises(TypeError):
|
|
dataclass_validate(obj, codec=Codec.JSON)
|
|
|
|
obj.anyval = datetime.datetime.now()
|
|
with pytest.raises(TypeError):
|
|
dataclass_validate(obj, codec=Codec.JSON)
|
|
|
|
# Firestore, however, does.
|
|
obj.anyval = b'bytes'
|
|
dataclass_validate(obj, codec=Codec.FIRESTORE)
|
|
obj.anyval = datetime.datetime.now()
|
|
dataclass_validate(obj, codec=Codec.FIRESTORE)
|
|
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _SPTestClass1:
|
|
barf: int = 5
|
|
eep: str = 'blah'
|
|
barf2: Annotated[int, IOAttrs('b')] = 5
|
|
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _SPTestClass2:
|
|
rah: bool = False
|
|
subc: _SPTestClass1 = field(default_factory=_SPTestClass1)
|
|
subc2: Annotated[_SPTestClass1, IOAttrs('s')] = field(
|
|
default_factory=_SPTestClass1
|
|
)
|
|
|
|
|
|
def test_datetime_limits() -> None:
|
|
"""Test limiting datetime values in various ways."""
|
|
from efro.util import utc_today, utc_this_hour
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClass:
|
|
tval: Annotated[datetime.datetime, IOAttrs(whole_hours=True)]
|
|
|
|
# Check whole-hour limit when validating/exporting.
|
|
obj = _TestClass(tval=utc_this_hour() + datetime.timedelta(minutes=1))
|
|
with pytest.raises(ValueError):
|
|
dataclass_validate(obj)
|
|
obj.tval = utc_this_hour()
|
|
dataclass_validate(obj)
|
|
|
|
# Check whole-days limit when importing.
|
|
out = dataclass_to_dict(obj)
|
|
out['tval'][-1] += 1
|
|
with pytest.raises(ValueError):
|
|
dataclass_from_dict(_TestClass, out)
|
|
|
|
# Check whole-days limit when validating/exporting.
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClass2:
|
|
tval: Annotated[datetime.datetime, IOAttrs(whole_days=True)]
|
|
|
|
obj2 = _TestClass2(tval=utc_today() + datetime.timedelta(hours=1))
|
|
with pytest.raises(ValueError):
|
|
dataclass_validate(obj2)
|
|
obj2.tval = utc_today()
|
|
dataclass_validate(obj2)
|
|
|
|
# Check whole-days limit when importing.
|
|
out = dataclass_to_dict(obj2)
|
|
out['tval'][-1] += 1
|
|
with pytest.raises(ValueError):
|
|
dataclass_from_dict(_TestClass2, out)
|
|
|
|
|
|
def test_field_paths() -> None:
|
|
"""Test type-safe field path evaluations."""
|
|
|
|
# Define a few nested dataclass types, some of which
|
|
# have storage names differing from their field names.
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClass:
|
|
@dataclass
|
|
class _TestSubClass:
|
|
val1: int = 0
|
|
val2: Annotated[int, IOAttrs('v2')] = 0
|
|
|
|
sub1: _TestSubClass = field(default_factory=_TestSubClass)
|
|
sub2: Annotated[_TestSubClass, IOAttrs('s2')] = field(
|
|
default_factory=_TestSubClass
|
|
)
|
|
|
|
# Now let's lookup various storage paths.
|
|
lookup = DataclassFieldLookup(_TestClass)
|
|
|
|
# Make sure lookups are returning correct storage paths.
|
|
assert lookup.path(lambda obj: obj.sub1) == 'sub1'
|
|
assert lookup.path(lambda obj: obj.sub1.val1) == 'sub1.val1'
|
|
assert lookup.path(lambda obj: obj.sub1.val2) == 'sub1.v2'
|
|
assert lookup.path(lambda obj: obj.sub2.val1) == 's2.val1'
|
|
assert lookup.path(lambda obj: obj.sub2.val2) == 's2.v2'
|
|
|
|
# Attempting to return fields that aren't there should fail
|
|
# in both type-checking and runtime.
|
|
with pytest.raises(AttributeError):
|
|
lookup.path(lambda obj: obj.sub1.val3) # type: ignore
|
|
|
|
# Returning non-field objects will fail at runtime
|
|
# even if type-checking evaluates them as valid values.
|
|
with pytest.raises(TypeError):
|
|
lookup.path(lambda obj: 1)
|
|
|
|
with pytest.raises(TypeError):
|
|
lookup.path(lambda obj: obj.sub1.val1.real)
|
|
|
|
|
|
def test_nested() -> None:
|
|
"""Test nesting dataclasses."""
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClass:
|
|
class _TestEnum(Enum):
|
|
VAL1 = 'val1'
|
|
VAL2 = 'val2'
|
|
|
|
@dataclass
|
|
class _TestSubClass:
|
|
ival: int = 0
|
|
|
|
subval: _TestSubClass = field(default_factory=_TestSubClass)
|
|
enval: _TestEnum = _TestEnum.VAL1
|
|
|
|
|
|
def test_extended_data() -> None:
|
|
"""Test IOExtendedData functionality."""
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClass:
|
|
vals: tuple[int, int]
|
|
|
|
# This data lines up.
|
|
indata = {'vals': [0, 0]}
|
|
_obj = dataclass_from_dict(_TestClass, indata)
|
|
|
|
# This data doesn't.
|
|
indata = {'vals': [0, 0, 0]}
|
|
with pytest.raises(ValueError):
|
|
_obj = dataclass_from_dict(_TestClass, indata)
|
|
|
|
# Now define the same data but give it an adapter
|
|
# so it can work with our incorrectly-formatted data.
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClass2(IOExtendedData):
|
|
vals: tuple[int, int]
|
|
|
|
@classmethod
|
|
def will_input(cls, data: dict) -> None:
|
|
data['vals'] = data['vals'][:2]
|
|
|
|
def will_output(self) -> None:
|
|
self.vals = (0, 0)
|
|
|
|
# This data lines up.
|
|
indata = {'vals': [0, 0]}
|
|
_obj2 = dataclass_from_dict(_TestClass2, indata)
|
|
|
|
# Now this data will too via our custom input filter.
|
|
indata = {'vals': [0, 0, 0]}
|
|
_obj2 = dataclass_from_dict(_TestClass2, indata)
|
|
|
|
# Ok, now test output:
|
|
|
|
# Does the expected thing.
|
|
assert dataclass_to_dict(_TestClass(vals=(1, 2))) == {'vals': [1, 2]}
|
|
|
|
# Uses our output filter.
|
|
assert dataclass_to_dict(_TestClass2(vals=(1, 2))) == {'vals': [0, 0]}
|
|
|
|
|
|
def test_soft_default() -> None:
|
|
"""Test soft_default IOAttr value."""
|
|
# pylint: disable=too-many-locals
|
|
# pylint: disable=too-many-statements
|
|
|
|
# Try both of these with and without storage_name to make sure
|
|
# soft_default interacts correctly with both cases.
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClassA:
|
|
ival: int
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClassA2:
|
|
ival: Annotated[int, IOAttrs('i')]
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClassB:
|
|
ival: Annotated[int, IOAttrs(soft_default=0)]
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClassB2:
|
|
ival: Annotated[int, IOAttrs('i', soft_default=0)]
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClassB3:
|
|
ival: Annotated[int, IOAttrs('i', soft_default_factory=lambda: 0)]
|
|
|
|
# These should fail because there's no value for ival.
|
|
with pytest.raises(ValueError):
|
|
dataclass_from_dict(_TestClassA, {})
|
|
|
|
with pytest.raises(ValueError):
|
|
dataclass_from_dict(_TestClassA2, {})
|
|
|
|
# These should succeed because it has a soft-default value to
|
|
# fall back on.
|
|
dataclass_from_dict(_TestClassB, {})
|
|
dataclass_from_dict(_TestClassB2, {})
|
|
dataclass_from_dict(_TestClassB3, {})
|
|
|
|
# soft_default should also allow using store_default=False without
|
|
# requiring the dataclass to contain a default or default_factory
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClassC:
|
|
ival: Annotated[int, IOAttrs(store_default=False)] = 0
|
|
|
|
assert dataclass_to_dict(_TestClassC()) == {}
|
|
|
|
# This should fail since store_default would be meaningless without
|
|
# any source for the default value.
|
|
with pytest.raises(TypeError):
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClassC2:
|
|
ival: Annotated[int, IOAttrs(store_default=False)]
|
|
|
|
# However with our shiny soft_default it should work.
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClassC3:
|
|
ival: Annotated[int, IOAttrs(store_default=False, soft_default=0)]
|
|
|
|
assert dataclass_to_dict(_TestClassC3(0)) == {}
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClassC3b:
|
|
ival: Annotated[
|
|
int, IOAttrs(store_default=False, soft_default_factory=lambda: 0)
|
|
]
|
|
|
|
assert dataclass_to_dict(_TestClassC3b(0)) == {}
|
|
|
|
# We disallow passing a few mutable types as soft_defaults
|
|
# just as dataclass does with regular defaults.
|
|
with pytest.raises(TypeError):
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClassD:
|
|
lval: Annotated[list, IOAttrs(soft_default=[])]
|
|
|
|
with pytest.raises(TypeError):
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClassD2:
|
|
# noinspection PyTypeHints
|
|
lval: Annotated[set, IOAttrs(soft_default=set())]
|
|
|
|
with pytest.raises(TypeError):
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClassD3:
|
|
lval: Annotated[dict, IOAttrs(soft_default={})]
|
|
|
|
# soft_defaults are not static-type-checked, but we do try to
|
|
# catch basic type mismatches at prep time. Make sure that's working.
|
|
# (we also do full value validation during input, but the more we catch
|
|
# early the better)
|
|
with pytest.raises(TypeError):
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClassE:
|
|
lval: Annotated[int, IOAttrs(soft_default='')]
|
|
|
|
with pytest.raises(TypeError):
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClassE2:
|
|
lval: Annotated[str, IOAttrs(soft_default=45)]
|
|
|
|
with pytest.raises(TypeError):
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClassE3:
|
|
lval: Annotated[list, IOAttrs(soft_default_factory=set)]
|
|
|
|
# Make sure Unions/Optionals go through ok.
|
|
# (note that mismatches currently aren't caught at prep time; just
|
|
# checking the negative case here).
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClassE4:
|
|
lval: Annotated[str | None, IOAttrs(soft_default=None)]
|
|
|
|
@ioprepped
|
|
@dataclass
|
|
class _TestClassE5:
|
|
lval: Annotated[str | None, IOAttrs(soft_default='foo')]
|
|
|
|
# Now try more in-depth examples: nested type mismatches like this
|
|
# are currently not caught at prep-time but ARE caught during inputting.
|
|
@ioprepped
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@dataclass
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class _TestClassE6:
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lval: Annotated[tuple[int, int], IOAttrs(soft_default=('foo', 'bar'))]
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClassE6, {})
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@ioprepped
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@dataclass
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class _TestClassE7:
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lval: Annotated[bool | None, IOAttrs(soft_default=12)]
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|
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with pytest.raises(TypeError):
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dataclass_from_dict(_TestClassE7, {})
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# If both a soft_default and regular field default are present,
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|
# make sure soft_default takes precedence (it applies before
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|
# data even hits the dataclass constructor).
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|
@ioprepped
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|
@dataclass
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class _TestClassE8:
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ival: Annotated[int, IOAttrs(soft_default=1, store_default=False)] = 2
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|
|
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assert dataclass_from_dict(_TestClassE8, {}).ival == 1
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|
|
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# Make sure soft_default gets used both when determining when
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|
# to omit values from output and what to recreate missing values as.
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|
orig = _TestClassE8(ival=1)
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|
todict = dataclass_to_dict(orig)
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assert todict == {}
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|
assert dataclass_from_dict(_TestClassE8, todict) == orig
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|
|
|
# Instantiate with the dataclass default and it should still get
|
|
# explicitly despite the store_default=False because soft_default
|
|
# takes precedence.
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|
orig = _TestClassE8()
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|
todict = dataclass_to_dict(orig)
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|
assert todict == {'ival': 2}
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assert dataclass_from_dict(_TestClassE8, todict) == orig
|