"""RED contracts for privacy-minimizing nutrition-onboarding value models.""" from __future__ import annotations import importlib.util from datetime import date from decimal import Decimal from enum import StrEnum import pytest from pydantic import ValidationError MODULE = "checkin_cli.nutrition_onboarding_models" assert importlib.util.find_spec(MODULE) is not None, ( "nutrition onboarding contract missing: " "checkin_cli.nutrition_onboarding_models" ) from checkin_cli.nutrition_onboarding_models import ( # noqa: E402 ActivityCategory, EquationSexBasis, GoalType, NutritionOnboardingBaseline, OnboardingSessionStatus, PublicationStatus, ReviewDecision, StructuredItemStatus, StructuredItems, build_onboarding_baseline, canonical_equation_sex_basis, derive_adult_age, ) def _none() -> StructuredItems: return StructuredItems(status=StructuredItemStatus.NONE, items=()) def _baseline(**overrides: object) -> NutritionOnboardingBaseline: values: dict[str, object] = { "schema_version": "1.0", "customer_key": "client_001", "adult_age": 30, "equation_sex_basis": EquationSexBasis.MALE, "height_cm": Decimal("180.50"), "weight_kg": Decimal("80.00"), "activity_category": ActivityCategory.MODERATE, "goal_type": GoalType.MAINTAIN, "target_weight_kg": None, "target_date": None, "dietary_preferences": _none(), "disliked_foods": _none(), "allergies": _none(), "intolerances": _none(), "religious_ethical_exclusions": _none(), "conditions": _none(), "medications": _none(), "session_status": OnboardingSessionStatus.COMPLETED, "review_decision": ReviewDecision.PENDING, "publication_status": PublicationStatus.UNPUBLISHED, } values.update(overrides) return NutritionOnboardingBaseline(**values) def _raw_answers(**overrides: object) -> dict[str, object]: values: dict[str, object] = { "schema_version": "1.0", "customer_key": "client_001", "date_of_birth": "1996-08-01", "equation_sex_basis": "female", "height_cm": "165.50", "weight_kg": "65.00", "activity_category": "light", "goal_type": "maintain", "target_weight_kg": None, "target_date": None, "dietary_preferences": {"status": "none", "items": []}, "disliked_foods": {"status": "none", "items": []}, "allergies": {"status": "none", "items": []}, "intolerances": {"status": "none", "items": []}, "religious_ethical_exclusions": {"status": "none", "items": []}, "conditions": {"status": "none", "items": []}, "medications": {"status": "none", "items": []}, "session_status": "completed", "review_decision": "pending", "publication_status": "unpublished", } values.update(overrides) return values def test_workflow_enums_are_closed_string_contracts() -> None: assert issubclass(OnboardingSessionStatus, StrEnum) assert [item.value for item in OnboardingSessionStatus] == [ "in_progress", "completed", "cancelled", ] assert [item.value for item in ReviewDecision] == [ "pending", "approved", "rejected", ] assert [item.value for item in PublicationStatus] == [ "unpublished", "published", "withdrawn", ] assert [item.value for item in EquationSexBasis] == [ "male", "female", "decline", ] @pytest.mark.parametrize( ("value", "expected"), ( ("male", "male"), ("Female", "female"), ("decline", "decline"), ("남성", "male"), ("여성입니다", "female"), ("선택 안 합니다", "decline"), ("넵 남성입니다", "male"), ("네, 여성입니다", "female"), ("Yes, male", "male"), ("Yep female", "female"), ("I prefer not to say", "decline"), ), ) def test_equation_sex_basis_normalizes_only_exact_supported_answers( value: str, expected: str, ) -> None: assert canonical_equation_sex_basis(value) == expected @pytest.mark.parametrize( "value", ( "남성이라는 단어가 포함됨", "female-ish", "maybe male", "선택 안 함이라고 쓰인 설명", 1, None, ), ) def test_equation_sex_basis_does_not_guess_from_substrings(value: object) -> None: assert canonical_equation_sex_basis(value) is None def test_every_public_schema_is_frozen_and_forbids_extra_fields() -> None: baseline = _baseline() structured = _none() with pytest.raises(ValidationError, match="Extra inputs are not permitted"): NutritionOnboardingBaseline(**baseline.model_dump(), surprise=True) with pytest.raises(ValidationError, match="Extra inputs are not permitted"): StructuredItems(status="none", items=(), surprise=True) with pytest.raises(ValidationError, match="frozen"): baseline.adult_age = 31 with pytest.raises(ValidationError, match="frozen"): structured.items = ("changed",) def test_decimal_fields_serialize_as_canonical_decimal_strings() -> None: payload = _baseline().model_dump(mode="json") assert payload["height_cm"] == "180.5" assert payload["weight_kg"] == "80" assert payload["target_weight_kg"] is None assert not isinstance(payload["height_cm"], float) assert not isinstance(payload["weight_kg"], float) def test_adult_age_uses_a_supplied_date_and_raw_dob_is_discarded() -> None: assert derive_adult_age( date(2008, 8, 1), as_of=date(2026, 8, 1), ) == 18 assert derive_adult_age( date(1996, 8, 2), as_of=date(2026, 8, 1), ) == 29 baseline = build_onboarding_baseline( _raw_answers(), as_of=date(2026, 8, 1), ) assert baseline.adult_age == 30 assert "date_of_birth" not in type(baseline).model_fields assert "date_of_birth" not in baseline.model_dump(mode="json") assert "1996-08-01" not in baseline.model_dump_json() def test_minor_and_implausible_adult_ages_are_rejected() -> None: with pytest.raises(ValueError, match="adult"): derive_adult_age( date(2008, 8, 2), as_of=date(2026, 8, 1), ) with pytest.raises(ValidationError): _baseline(adult_age=17) with pytest.raises(ValidationError): _baseline(adult_age=121) def test_raw_dob_cannot_be_reintroduced_into_the_canonical_schema() -> None: with pytest.raises(ValidationError, match="Extra inputs are not permitted"): NutritionOnboardingBaseline( **_baseline().model_dump(), date_of_birth="1996-08-01", ) def test_structured_none_is_explicit_and_not_an_empty_or_ambiguous_answer() -> None: assert _none().model_dump(mode="json") == { "status": "none", "items": [], } with pytest.raises(ValidationError, match="none"): StructuredItems(status="none", items=("peanut",)) with pytest.raises(ValidationError, match="provided"): StructuredItems(status="provided", items=()) with pytest.raises(ValidationError): StructuredItems(items=()) assert StructuredItems( status="unknown", items=(), ).model_dump(mode="json") == { "status": "unknown", "items": [], } with pytest.raises(ValidationError, match="unknown"): StructuredItems(status="unknown", items=("uncertain",)) def test_structured_items_trim_nfc_normalize_and_deduplicate_in_order() -> None: answer = StructuredItems( status="provided", items=(" Café ", "Cafe\u0301", "두부", " 두부 "), ) assert answer.items == ("Café", "두부") assert all( item == __import__("unicodedata").normalize("NFC", item) for item in answer.items ) @pytest.mark.parametrize("bad_item", ["", " ", "x" * 201]) def test_structured_item_text_bounds_are_enforced(bad_item: str) -> None: with pytest.raises(ValidationError): StructuredItems(status="provided", items=(bad_item,)) def test_structured_item_count_bound_is_enforced_after_normalization() -> None: with pytest.raises(ValidationError): StructuredItems( status="provided", items=tuple(f"item-{index}" for index in range(41)), ) def test_anthropometric_and_goal_bounds_are_closed() -> None: for field, value in ( ("height_cm", "119.9"), ("height_cm", "250.1"), ("weight_kg", "34.9"), ("weight_kg", "300.1"), ): with pytest.raises(ValidationError): _baseline(**{field: Decimal(value)}) with pytest.raises(ValidationError, match="target"): _baseline(goal_type=GoalType.LOSS) with pytest.raises(ValidationError, match="maintain"): _baseline( goal_type=GoalType.MAINTAIN, target_weight_kg=Decimal("75"), target_date=date(2026, 10, 1), ) def test_unknown_enum_values_are_rejected_instead_of_coerced() -> None: for field in ( "equation_sex_basis", "activity_category", "goal_type", "session_status", "review_decision", "publication_status", ): with pytest.raises(ValidationError): _baseline(**{field: "other"})