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test: implement basic tests for FlatInitModel
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‎tests/tests_pydantic_tools/tests_flat_init_model/test_flat_init_model_basic.py‎

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"""Pytest entry point for for basic lupl.FlatInitModel test."""
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from typing import Any, NamedTuple
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from lupl import ConfigDict, FlatInitModel
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from pydantic import BaseModel
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import pytest
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class FlatInitTestParameter(NamedTuple):
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model: type[BaseModel]
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kwargs: dict[str, Any]
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expected: dict[str, Any]
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class DeeplyNestedModel1(BaseModel):
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z: int
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class NestedModel1(BaseModel):
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y: int
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deeply_nested: DeeplyNestedModel1
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class NestedModel2(NestedModel1):
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model_config = ConfigDict(model_bool="y")
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class NestedModel3(NestedModel1):
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model_config = ConfigDict(model_bool=lambda model: model.y < 0)
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class NestedModel4(NestedModel1):
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model_config = ConfigDict(model_bool={"y", "y2"})
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y2: int = 0
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class Model1(BaseModel):
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x: int
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nested: NestedModel1
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class Model2(Model1):
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model_config = ConfigDict(extra="forbid")
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class Model3(Model1):
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x: int = 42
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class Model4(BaseModel):
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x: int
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nested: NestedModel2 | str = "default"
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class Model5(BaseModel):
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x: int
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nested: NestedModel3 | str = "default"
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class Model6(BaseModel):
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x: int
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nested: NestedModel4 | str = "default"
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class Model7(BaseModel):
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x: int
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nested: NestedModel1 | None = None
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class Model8(BaseModel):
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x: int
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nested: DeeplyNestedModel1 | None = None
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params: list[FlatInitTestParameter] = [
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FlatInitTestParameter(
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model=Model1,
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kwargs={"x": 1, "y": 2, "z": 3},
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expected={"x": 1, "nested": {"y": 2, "deeply_nested": {"z": 3}}},
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),
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FlatInitTestParameter(
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model=Model2,
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kwargs={"x": 1, "y": 2, "z": 3},
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expected={"x": 1, "nested": {"y": 2, "deeply_nested": {"z": 3}}},
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),
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FlatInitTestParameter(
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model=Model1,
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kwargs={"x": 1.0, "y": 2.0, "z": 3.0},
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expected={"x": 1, "nested": {"y": 2, "deeply_nested": {"z": 3}}},
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),
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FlatInitTestParameter(
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model=Model3,
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kwargs={"x": 1, "y": 2, "z": 3},
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expected={"x": 1, "nested": {"y": 2, "deeply_nested": {"z": 3}}},
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),
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FlatInitTestParameter(
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model=Model3,
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kwargs={"y": 2, "z": 3},
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expected={"x": 42, "nested": {"y": 2, "deeply_nested": {"z": 3}}},
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),
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FlatInitTestParameter(
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model=Model4,
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kwargs={"x": 1, "y": 2, "z": 3},
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expected={"x": 1, "nested": {"y": 2, "deeply_nested": {"z": 3}}},
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),
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FlatInitTestParameter(
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model=Model4,
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kwargs={"x": 1, "y": 0, "z": 3},
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expected={"x": 1, "nested": "default"},
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),
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FlatInitTestParameter(
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model=Model5,
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kwargs={"x": 1, "y": 0, "z": 3},
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expected={"x": 1, "nested": "default"},
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),
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FlatInitTestParameter(
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model=Model5,
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kwargs={"x": 1, "y": -2, "z": 3},
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expected={"x": 1, "nested": {"y": -2, "deeply_nested": {"z": 3}}},
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),
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FlatInitTestParameter(
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model=Model6,
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kwargs={"x": 1, "y": 2, "z": 3},
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expected={"x": 1, "nested": "default"},
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),
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FlatInitTestParameter(
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model=Model7,
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kwargs={"x": 1, "y": 2, "z": 3},
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expected={"x": 1, "nested": {"y": 2, "deeply_nested": {"z": 3}}},
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),
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FlatInitTestParameter(
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model=Model8,
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kwargs={"x": 1, "z": 3},
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expected={"x": 1, "nested": {"z": 3}},
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),
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FlatInitTestParameter(
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model=Model8,
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kwargs={"x": 1, "z": 0},
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expected={"x": 1, "nested": None},
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),
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]
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@pytest.mark.parametrize("param", params)
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def test_basic_flat_init_model(param):
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constructor = FlatInitModel(model=param.model)
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instance = constructor(**param.kwargs)
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assert instance.model_dump() == param.expected

‎tests/tests_pydantic_tools/tests_flat_init_model/test_flat_init_model_model_bool_fail.py‎

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"""Pytest entry point for lupl.FlatInitModel model_bool failure tests."""
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from lupl import ConfigDict, FlatInitModel
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from pydantic import BaseModel
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import pytest
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class NestedModel1(BaseModel):
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model_config = ConfigDict(model_bool=["x"])
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class NestedModel2(BaseModel):
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model_config = ConfigDict(model_bool=object())
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class NestedModel3(BaseModel):
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model_config = ConfigDict(model_bool=None)
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class Model1(BaseModel):
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nested: NestedModel1 | None = None
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class Model2(BaseModel):
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nested: NestedModel2 | None = None
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class Model3(BaseModel):
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nested: NestedModel3 | None = None
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@pytest.mark.parametrize("model", [Model1, Model2, Model3])
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def test_flat_init_model_model_bool_fail(model):
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constructor = FlatInitModel(model=model)
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with pytest.raises(ValueError):
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constructor()

‎tests/tests_pydantic_tools/tests_flat_init_model/test_flat_init_model_validation_fail.py‎

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"""Pytest entry point for lupl.FlatInitBase fail tests."""
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from typing import Any, NamedTuple
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from lupl import FlatInitModel
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from pydantic import BaseModel, ValidationError
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import pytest
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class FlatInitFailParameter(NamedTuple):
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model: type[BaseModel]
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kwargs: dict[str, Any]
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errors: list[dict[str, Any]]
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fail_fast: bool = True
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class DeeplyNestedModel(BaseModel):
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z: int
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class NestedModel(BaseModel):
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y: int
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deeply_nested: DeeplyNestedModel
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class Model(BaseModel):
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x: int
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nested: NestedModel
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class SimpleModel(BaseModel):
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x: int
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y: int
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params = [
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FlatInitFailParameter(
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model=Model,
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kwargs={"x": 1, "y": 2.1, "z": 3},
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errors=[
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{
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"type": "int_from_float",
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"loc": ("y",),
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"msg": "Input should be a valid integer, got a number with a fractional part",
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"input": 2.1,
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"url": "https://errors.pydantic.dev/2.12/v/int_from_float",
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}
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],
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),
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FlatInitFailParameter(
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model=Model,
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kwargs={"x": 1, "y": 2, "z": 3.1},
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errors=[
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{
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"type": "int_from_float",
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"loc": ("z",),
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"msg": "Input should be a valid integer, got a number with a fractional part",
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"input": 3.1,
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"url": "https://errors.pydantic.dev/2.12/v/int_from_float",
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}
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],
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),
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FlatInitFailParameter(
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model=Model,
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kwargs={"x": 1, "y": 2.1, "z": 3.1},
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errors=[
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{
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"type": "int_from_float",
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"loc": ("y",),
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"msg": "Input should be a valid integer, got a number with a fractional part",
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"input": 2.1,
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"url": "https://errors.pydantic.dev/2.12/v/int_from_float",
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}
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],
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fail_fast=True,
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),
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FlatInitFailParameter(
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model=Model,
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kwargs={"x": 1, "y": 2.1, "z": 3.1},
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errors=[
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{
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"type": "int_from_float",
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"loc": ("z",),
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"msg": "Input should be a valid integer, got a number with a fractional part",
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"input": 3.1,
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"url": "https://errors.pydantic.dev/2.12/v/int_from_float",
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}
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],
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fail_fast=False,
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),
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FlatInitFailParameter(
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model=SimpleModel,
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kwargs={"x": 1.1, "y": 2.1},
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errors=[
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{
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"type": "int_from_float",
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"loc": ("x",),
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"msg": "Input should be a valid integer, got a number with a fractional part",
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"input": 1.1,
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"url": "https://errors.pydantic.dev/2.12/v/int_from_float",
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},
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{
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"type": "int_from_float",
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"loc": ("y",),
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"msg": "Input should be a valid integer, got a number with a fractional part",
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"input": 2.1,
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"url": "https://errors.pydantic.dev/2.12/v/int_from_float",
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},
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],
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fail_fast=False,
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),
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FlatInitFailParameter(
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model=SimpleModel,
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kwargs={"x": 1.1, "y": 2.1},
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errors=[
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{
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"type": "int_from_float",
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"loc": ("x",),
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"msg": "Input should be a valid integer, got a number with a fractional part",
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"input": 1.1,
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"url": "https://errors.pydantic.dev/2.12/v/int_from_float",
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}
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],
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fail_fast=True,
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),
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]
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@pytest.mark.parametrize("param", params)
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def test_flat_init_model_validation_fail(param):
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constructor = FlatInitModel(model=param.model, fail_fast=param.fail_fast)
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with pytest.raises(ValidationError) as excinfo:
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constructor(**param.kwargs)
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errors = excinfo.value.errors()
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assert param.errors == errors

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