feat(missao-8): bump Hub 1.3.15, restauracao de cfop_ippt_auto_apply e elevacao de cobertura

2026-09-28 22:14:39 -03:00
parent 98754da1c1
commit 26405b0144
8 changed files with 498 additions and 39 deletions
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+"""Missão 8 — Bloco C: Testes direcionados para elevar cobertura de reference_registry e classification."""
+import pytest
+from sqlalchemy import create_engine
+from sqlalchemy.orm import Session
+
+from central.app.models import Base, Dataset
+from central.app.reference_registry import (
+    register_draft, approve_draft, reject_draft, list_registry,
+    diff_against_vigente, technical_checklist, RegistryError
+)
+from central.app.classification import Classifier
+
+
+@pytest.fixture
+def memory_db():
+    engine = create_engine("sqlite:///:memory:")
+    Base.metadata.create_all(engine)
+    with Session(engine) as session:
+        yield session
+
+
+# ---------------- C.2: Reference Registry Edge Cases ----------------
+
+def test_reference_registry_approval_rejections_and_conflicts(memory_db):
+    """Testa aprovação com dado inválido, erros de parse e concorrência."""
+    # 1. Tentar aprovar dataset inexistente
+    with pytest.raises(RegistryError, match="dataset not found"):
+        approve_draft(memory_db, 9999, admin_name="Admin")
+
+    # 2. Tentar rejeitar dataset inexistente
+    with pytest.raises(RegistryError, match="dataset not found"):
+        reject_draft(memory_db, 9999, admin_name="Admin")
+
+    # 3. Registrar DRAFT válido
+    d1 = register_draft(
+        memory_db,
+        kind="CFOP",
+        provider="Portal NF-e",
+        source_url="https://nfe.fazenda.gov.br",
+        sha256="1" * 64,
+        version_label="cfop-v1",
+        record_count=619,
+    )
+
+    # 4. Primeira aprovação tem sucesso
+    app1 = approve_draft(memory_db, d1.id, admin_name="Admin 1")
+    assert app1.status == "VIGENTE"
+
+    # 5. Concorrência / Segunda aprovação simultânea da mesma versão deve falhar
+    with pytest.raises(RegistryError, match="only a DRAFT dataset can be approved"):
+        approve_draft(memory_db, d1.id, admin_name="Admin 2")
+
+    # 6. Tentar rejeitar dataset que já é VIGENTE deve falhar
+    with pytest.raises(RegistryError, match="only a DRAFT dataset can be rejected"):
+        reject_draft(memory_db, d1.id, admin_name="Admin")
+
+    # 7. Registrar DRAFT com erro de parse
+    d_err = register_draft(
+        memory_db,
+        kind="CFOP",
+        provider="Portal NF-e",
+        source_url="https://nfe.fazenda.gov.br",
+        sha256="2" * 64,
+        version_label="cfop-v2-err",
+        record_count=100,
+        parse_errors={"row_15": "syntax error in sheet"},
+    )
+    # Bloqueio estrito de aprovação com parse_errors
+    with pytest.raises(RegistryError, match="dataset has recorded parse errors"):
+        approve_draft(memory_db, d_err.id, admin_name="Admin")
+
+    # Rejeitar DRAFT com erro tem sucesso
+    rej = reject_draft(memory_db, d_err.id, admin_name="Admin")
+    assert rej.status == "REJECTED"
+
+    # Rejeitar novamente falha
+    with pytest.raises(RegistryError, match="only a DRAFT dataset can be rejected"):
+        reject_draft(memory_db, d_err.id, admin_name="Admin")
+
+
+def test_reference_registry_diff_with_conflicts_and_volume_change(memory_db):
+    """Testa diff contra vigente com alteração de sha256 e volume."""
+    # V1 vigente
+    v1 = register_draft(
+        memory_db,
+        kind="TIPI_IPI",
+        provider="Receita Federal",
+        source_url="https://receita.gov.br",
+        sha256="a" * 64,
+        version_label="tipi-v1",
+        record_count=11000,
+    )
+    approve_draft(memory_db, v1.id, admin_name="Admin")
+
+    # V2 com novo hash e contagem menor (possível deleção/mudança)
+    v2 = register_draft(
+        memory_db,
+        kind="TIPI_IPI",
+        provider="Receita Federal",
+        source_url="https://receita.gov.br",
+        sha256="b" * 64,
+        version_label="tipi-v2",
+        record_count=10950,
+    )
+
+    diff = diff_against_vigente(memory_db, v2)
+    assert diff["is_first_version"] is False
+    assert diff["sha256_unchanged"] is False
+    assert diff["record_count_delta"] == -50
+    assert diff["current_vigente"]["id"] == v1.id
+
+
+# ---------------- C.2: Classification Ambiguous / Edge Cases ----------------
+
+def test_classification_candidate_search_and_ambiguity():
+    """Testa busca heurística de NCM por descrição quando não há NCM informado."""
+    class MockEntry:
+        def __init__(self, code, desc):
+            self.code = code
+            self.description = desc
+
+    class MockRepo:
+        def active_entries(self):
+            return [
+                MockEntry("22030000", "CERVEJA DE MALTE EM LATA"),
+                MockEntry("22021000", "AGUA MINERAL NATURAL GASEIFICADA"),
+                MockEntry("22021001", "AGUA MINERAL NATURAL SEM GAS"),
+            ]
+        def dataset_versions(self):
+            return {}
+
+    classifier = Classifier(MockRepo())
+
+    # 1. Sem NCM e sem correspondência textual -> NOT_FOUND
+    res_not_found = classifier.classify({"current_ncm": None, "description": "PRODUTO DESCONHECIDO XYZ 123"})
+    assert res_not_found["status"] == "NOT_FOUND"
+    assert res_not_found["classification"]["ncm"] is None
+
+    # 2. Sem NCM e com candidato de altíssima confiança inequívoca -> HIGH_CONFIDENCE_CANDIDATE
+    res_high = classifier.classify({"current_ncm": None, "description": "CERVEJA DE MALTE EM LATA"})
+    assert res_high["status"] == "HIGH_CONFIDENCE_CANDIDATE"
+    assert res_high["classification"]["ncm"] == "22030000"
+
+    # 3. Sem NCM e com dois candidatos muito próximos (ambíguo) -> AMBIGUOUS
+    res_amb = classifier.classify({"current_ncm": "", "description": "AGUA MINERAL NATURAL"})
+    assert res_amb["status"] == "AMBIGUOUS"
+    assert res_amb["classification"]["ncm"] is None
+
+
+def test_classification_cest_and_mva_resolution():
+    """Testa desempate de CEST por descrição e resolução de MVA por CEST."""
+    class MockCest:
+        def __init__(self, code, desc):
+            self.code = code
+            self.description = desc
+
+    class MockMva:
+        def __init__(self, item, cest, mva_orig, mva_adj, desc):
+            self.item = item
+            self.cest = cest
+            self.mva_original = mva_orig
+            self.mva_adjusted = mva_adj
+            self.description = desc
+
+    class MockRepo:
+        def active_entries(self):
+            class Entry:
+                code = "22030000"
+                description = "Cervejas de malte"
+            return [Entry()]
+
+        def cest_for_ncm(self, code):
+            if code == "22030000":
+                return [
+                    MockCest("03.021.01", "Cerveja em garrafa de vidro"),
+                    MockCest("03.021.03", "Cerveja em lata descartavel"),
+                ]
+            return []
+
+        def ba_st_mva_for_ncm(self, code):
+            if code == "22030000":
+                return [
+                    MockMva("1", "03.021.01", 50.0, 70.0, "Cerveja garrafa"),
+                    MockMva("2", "03.021.03", 60.0, 80.0, "Cerveja lata"),
+                ]
+            return []
+
+        def dataset_versions(self):
+            return {"ncm": "v1", "cest": "v1"}
+
+    classifier = Classifier(MockRepo())
+
+    # 1. Produto NCM 22030000 com descrição que casa especificamente com CEST 03.021.03 (lata)
+    res_lata = classifier.classify({"current_ncm": "22030000", "description": "CERVEJA EM LATA DESCARTAVEL"})
+    assert res_lata["status"] == "EXACT_NCM"
+    # Quando o desempate tem score >= 0.5 e separa as opções
+    # Verifica resolução de MVA associada ao CEST confirmado
+    assert res_lata["ba_st_mva_status"] in ("MVA_RESOLVED", "MVA_CANDIDATES")
+
+    # 2. Produto NCM com descrição totalmente genérica -> CEST_CANDIDATES
+    res_gen = classifier.classify({"current_ncm": "22030000", "description": "BEBIDA"})
+    assert res_gen["cest_status"] == "CEST_DESCRIPTION_MISMATCH"