t2a_v2/tests/test_cpam_response.py

"""Tests pour la génération de contre-argumentation CPAM."""

from unittest.mock import patch, call

import pytest

from src.config import (
    ActeCCAM,
    BiologieCle,
    ControleCPAM,
    Diagnostic,
    DossierMedical,
    Imagerie,
    RAGSource,
    Sejour,
    Traitement,
)
from src.control.cpam_response import (
    _build_bio_summary,
    _build_correction_prompt,
    _build_cpam_prompt,
    _build_tagged_context,
    _check_das_bio_coherence,
    _extraction_pass,
    _format_response,
    _get_cim10_definitions,
    _get_code_label,
    _search_rag_for_control,
    _validate_adversarial,
    _validate_codes_in_response,
    _validate_grounding,
    _validate_references,
    generate_cpam_response,
)


def _make_dossier() -> DossierMedical:
    """Crée un dossier médical de test."""
    return DossierMedical(
        source_file="test.pdf",
        document_type="crh",
        sejour=Sejour(sexe="M", age=65, duree_sejour=5),
        diagnostic_principal=Diagnostic(
            texte="Cholécystite aiguë",
            cim10_suggestion="K81.0",
        ),
        diagnostics_associes=[
            Diagnostic(texte="Iléus réflexe", cim10_suggestion="K56.0"),
        ],
    )


def _make_dossier_complet() -> DossierMedical:
    """Crée un dossier médical enrichi avec traitements, imagerie, antécédents."""
    return DossierMedical(
        source_file="test.pdf",
        document_type="crh",
        sejour=Sejour(sexe="M", age=65, duree_sejour=5, imc=31.2),
        diagnostic_principal=Diagnostic(
            texte="Cholécystite aiguë",
            cim10_suggestion="K81.0",
        ),
        diagnostics_associes=[
            Diagnostic(texte="Iléus réflexe", cim10_suggestion="K56.0"),
        ],
        actes_ccam=[
            ActeCCAM(texte="Cholécystectomie", code_ccam_suggestion="HMFC004"),
        ],
        biologie_cle=[
            BiologieCle(test="CRP", valeur="180 mg/L", anomalie=True),
            BiologieCle(test="Créatinine", valeur="450 µmol/L", anomalie=True),
        ],
        imagerie=[
            Imagerie(type="Scanner abdominal", conclusion="Lithiase cholédocienne confirmée"),
        ],
        traitements_sortie=[
            Traitement(medicament="Augmentin IV", posologie="3g/j"),
            Traitement(medicament="Morphine SC"),
        ],
        antecedents=["HTA", "Diabète type 2"],
    )


def _make_controle() -> ControleCPAM:
    """Crée un contrôle CPAM de test."""
    return ControleCPAM(
        numero_ogc=17,
        titre="Désaccord sur les DAS",
        arg_ucr="L'UCR confirme l'avis des médecins contrôleurs au motif que le DAS K56.0 n'est pas justifié.",
        decision_ucr="UCR confirme avis médecins contrôleurs",
        dp_ucr=None,
        da_ucr="K56.0",
    )


class TestBuildPrompt:
    def test_prompt_contains_dossier_info(self):
        dossier = _make_dossier()
        controle = _make_controle()
        prompt, _ = _build_cpam_prompt(dossier, controle, [])

        assert "Cholécystite aiguë" in prompt
        assert "K81.0" in prompt
        assert "Iléus réflexe" in prompt
        assert "65 ans" in prompt

    def test_prompt_contains_cpam_argument(self):
        dossier = _make_dossier()
        controle = _make_controle()
        prompt, _ = _build_cpam_prompt(dossier, controle, [])

        assert controle.arg_ucr in prompt
        assert controle.decision_ucr in prompt

    def test_prompt_contains_codes_contestes(self):
        dossier = _make_dossier()
        controle = _make_controle()
        prompt, _ = _build_cpam_prompt(dossier, controle, [])

        assert "DA proposés par UCR : K56.0" in prompt

    def test_prompt_contains_rag_sources(self):
        dossier = _make_dossier()
        controle = _make_controle()
        sources = [
            {"document": "guide_methodo", "page": 64, "extrait": "Texte du guide..."},
            {"document": "cim10", "code": "K56.0", "extrait": "Iléus paralytique..."},
        ]
        prompt, _ = _build_cpam_prompt(dossier, controle, sources)

        assert "Guide Méthodologique MCO 2026" in prompt
        assert "CIM-10 FR 2026" in prompt
        assert "page 64" in prompt

    def test_prompt_contains_three_axes(self):
        dossier = _make_dossier()
        controle = _make_controle()
        prompt, _ = _build_cpam_prompt(dossier, controle, [])

        assert "AXE MÉDICAL" in prompt
        assert "AXE ASYMÉTRIE D'INFORMATION" in prompt
        assert "AXE RÉGLEMENTAIRE" in prompt

    def test_prompt_contains_traitements_imagerie_when_present(self):
        dossier = _make_dossier_complet()
        controle = _make_controle()
        prompt, _ = _build_cpam_prompt(dossier, controle, [])

        assert "Augmentin IV 3g/j" in prompt
        assert "Morphine SC" in prompt
        assert "Scanner abdominal" in prompt
        assert "Lithiase cholédocienne confirmée" in prompt
        assert "HTA" in prompt
        assert "Diabète type 2" in prompt
        assert "IMC : 31.2" in prompt

    def test_prompt_asymetrie_section_when_data_present(self):
        dossier = _make_dossier_complet()
        controle = _make_controle()
        prompt, _ = _build_cpam_prompt(dossier, controle, [])

        assert "ÉLÉMENTS DU DOSSIER NON TRANSMIS À LA CPAM" in prompt
        assert "CRP: 180 mg/L (anormale)" in prompt
        assert "Cholécystectomie (HMFC004)" in prompt

    def test_prompt_no_asymetrie_section_when_no_data(self):
        dossier = DossierMedical(
            source_file="test.pdf",
            document_type="crh",
            sejour=Sejour(),
            diagnostic_principal=Diagnostic(texte="Test", cim10_suggestion="Z00"),
        )
        controle = _make_controle()
        prompt, _ = _build_cpam_prompt(dossier, controle, [])

        assert "ÉLÉMENTS DU DOSSIER NON TRANSMIS À LA CPAM" not in prompt

    def test_prompt_json_format_new_fields(self):
        dossier = _make_dossier()
        controle = _make_controle()
        prompt, _ = _build_cpam_prompt(dossier, controle, [])

        assert "contre_arguments_medicaux" in prompt
        assert "contre_arguments_asymetrie" in prompt
        assert "contre_arguments_reglementaires" in prompt

    def test_prompt_contains_cite_exacts(self):
        """Le prompt renforcé demande des preuves exactes."""
        dossier = _make_dossier()
        controle = _make_controle()
        prompt, _ = _build_cpam_prompt(dossier, controle, [])

        assert "CITE" in prompt
        assert "EXACTS" in prompt

    def test_prompt_contains_interdiction(self):
        """Le prompt interdit les références inventées."""
        dossier = _make_dossier()
        controle = _make_controle()
        prompt, _ = _build_cpam_prompt(dossier, controle, [])

        assert "INTERDICTION ABSOLUE" in prompt

    def test_prompt_contains_preuves_dossier_field(self):
        """Le format JSON demandé inclut preuves_dossier."""
        dossier = _make_dossier()
        controle = _make_controle()
        prompt, _ = _build_cpam_prompt(dossier, controle, [])

        assert "preuves_dossier" in prompt

    @patch("src.control.cpam_context.validate_code", return_value=(True, "Iléus paralytique et obstruction intestinale"))
    @patch("src.control.cpam_context.normalize_code", return_value="K56.0")
    def test_prompt_codes_with_cim10_labels(self, mock_norm, mock_valid):
        """Les codes contestés affichent le libellé CIM-10."""
        dossier = _make_dossier()
        controle = _make_controle()  # da_ucr="K56.0"
        prompt, _ = _build_cpam_prompt(dossier, controle, [])

        assert "Iléus paralytique" in prompt
        assert "DA proposés par UCR" in prompt

    @patch("src.control.cpam_context.validate_code", return_value=(False, ""))
    @patch("src.control.cpam_context.normalize_code", return_value="Z99.9")
    def test_prompt_codes_invalid_graceful(self, mock_norm, mock_valid):
        """Les codes invalides ne crashent pas, juste pas de libellé."""
        dossier = _make_dossier()
        controle = ControleCPAM(
            numero_ogc=1, titre="Test", arg_ucr="Test",
            decision_ucr="Rejet", dp_ucr="Z99.9", da_ucr=None,
        )
        prompt, _ = _build_cpam_prompt(dossier, controle, [])

        assert "Z99.9" in prompt
        # Pas de crash

    @patch("src.control.cpam_context.validate_code", return_value=(True, "Ajustement et entretien d'un dispositif implantable"))
    @patch("src.control.cpam_context.normalize_code", return_value="Z45.8")
    def test_prompt_dp_fallback_from_ucr(self, mock_norm, mock_valid):
        """DP absent + dp_ucr → contexte injecté dans le prompt."""
        dossier = DossierMedical(
            source_file="test.pdf",
            document_type="crh",
            sejour=Sejour(),
            diagnostic_principal=None,
        )
        controle = ControleCPAM(
            numero_ogc=1, titre="Désaccord DP", arg_ucr="Test",
            decision_ucr="Rejet", dp_ucr="Z45.8", da_ucr=None,
        )
        prompt, _ = _build_cpam_prompt(dossier, controle, [])

        assert "codé par l'établissement" in prompt
        assert "contesté par la CPAM" in prompt
        assert "Z45.8" in prompt


class TestFormatResponse:
    def test_full_response_new_format(self):
        parsed = {
            "analyse_contestation": "La CPAM conteste le DAS K56.0",
            "points_accord": "Aucun",
            "contre_arguments_medicaux": "Le guide méthodologique précise...",
            "contre_arguments_asymetrie": "La biologie montre une CRP à 180...",
            "contre_arguments_reglementaires": "L'UCR interprète restrictivement...",
            "references": "Guide métho p.64, CIM-10 K56.0",
            "conclusion": "Le DAS est justifié",
        }
        text = _format_response(parsed)

        assert "ANALYSE DE LA CONTESTATION" in text
        assert "CONTRE-ARGUMENTS MÉDICAUX" in text
        assert "ASYMÉTRIE D'INFORMATION" in text
        assert "CONTRE-ARGUMENTS RÉGLEMENTAIRES" in text
        assert "REFERENCES" in text
        assert "CONCLUSION" in text
        # "Aucun" ne doit pas générer la section points d'accord
        assert "POINTS D'ACCORD" not in text
        # L'ancien champ ne doit pas apparaître
        assert "CONTRE-ARGUMENTS\n" not in text

    def test_fallback_old_format(self):
        """L'ancien champ contre_arguments est toujours géré (réponses en cache)."""
        parsed = {
            "analyse_contestation": "Analyse...",
            "contre_arguments": "Arguments anciens...",
            "conclusion": "Conclusion...",
        }
        text = _format_response(parsed)

        assert "CONTRE-ARGUMENTS\nArguments anciens..." in text
        assert "CONCLUSION" in text

    def test_new_fields_override_fallback(self):
        """Si les nouveaux champs existent, l'ancien contre_arguments est ignoré."""
        parsed = {
            "contre_arguments_medicaux": "Médicaux...",
            "contre_arguments": "Ancien fallback...",
            "conclusion": "Conclusion...",
        }
        text = _format_response(parsed)

        assert "CONTRE-ARGUMENTS MÉDICAUX" in text
        assert "Ancien fallback" not in text

    def test_partial_response(self):
        parsed = {
            "contre_arguments_medicaux": "Arguments médicaux...",
            "conclusion": "Conclusion...",
        }
        text = _format_response(parsed)

        assert "CONTRE-ARGUMENTS MÉDICAUX" in text
        assert "CONCLUSION" in text

    def test_empty_response(self):
        text = _format_response({})
        assert text == ""

    def test_preuves_dossier_formatting(self):
        """Le nouveau champ preuves_dossier est formaté correctement."""
        parsed = {
            "contre_arguments_medicaux": "Arguments...",
            "preuves_dossier": [
                {"element": "biologie", "valeur": "CRP 180 mg/L", "signification": "inflammation sévère"},
                {"element": "imagerie", "valeur": "lithiase cholédocienne", "signification": "confirme le diagnostic"},
            ],
            "conclusion": "Conclusion...",
        }
        text = _format_response(parsed)

        assert "PREUVES DU DOSSIER" in text
        assert "CRP 180 mg/L" in text
        assert "[biologie]" in text
        assert "[imagerie]" in text

    def test_structured_references_formatting(self):
        """Les références structurées sont formatées correctement."""
        parsed = {
            "contre_arguments_medicaux": "Arguments...",
            "references": [
                {"document": "Guide Méthodologique MCO 2026", "page": "64", "citation": "Le DAS doit être..."},
            ],
            "conclusion": "Conclusion...",
        }
        text = _format_response(parsed)

        assert "REFERENCES" in text
        assert "Guide Méthodologique MCO 2026" in text
        assert "p.64" in text
        assert "Le DAS doit être..." in text

    def test_ref_warnings_appended(self):
        """Les avertissements de références non vérifiées apparaissent."""
        parsed = {"conclusion": "Conclusion..."}
        warnings = ["Référence non vérifiable : Manuel Imaginaire 2025"]
        text = _format_response(parsed, ref_warnings=warnings)

        assert "AVERTISSEMENT" in text
        assert "Manuel Imaginaire 2025" in text


class TestValidateReferences:
    def test_valid_reference_no_warning(self):
        parsed = {
            "references": [
                {"document": "Guide Méthodologique MCO 2026", "page": "64", "citation": "..."},
            ]
        }
        sources = [{"document": "guide_methodo", "page": 64, "extrait": "..."}]
        warnings = _validate_references(parsed, sources)
        assert len(warnings) == 0

    def test_invented_reference_detected(self):
        parsed = {
            "references": [
                {"document": "Manuel Inventé 2025", "page": "12", "citation": "..."},
            ]
        }
        sources = [{"document": "guide_methodo", "page": 64, "extrait": "..."}]
        warnings = _validate_references(parsed, sources)
        assert len(warnings) == 1
        assert "Manuel Inventé" in warnings[0]

    def test_old_format_string_no_crash(self):
        """L'ancien format string pour references ne cause pas de crash."""
        parsed = {"references": "Guide méthodo p.64"}
        sources = [{"document": "guide_methodo"}]
        warnings = _validate_references(parsed, sources)
        assert len(warnings) == 0  # pas de validation sur l'ancien format

    def test_no_sources_no_validation(self):
        parsed = {
            "references": [
                {"document": "Quelque chose", "page": "1", "citation": "..."},
            ]
        }
        warnings = _validate_references(parsed, [])
        assert len(warnings) == 0


class TestGenerateResponse:
    @patch("src.control.cpam_validation.call_ollama")
    @patch("src.control.cpam_response.call_ollama")
    @patch("src.control.cpam_response.call_anthropic")
    @patch("src.control.cpam_response._search_rag_for_control")
    def test_generate_success_ollama_cpam(self, mock_rag, mock_anthropic, mock_ollama, mock_val_ollama):
        """Ollama disponible → 3 passes (extraction + argumentation + validation)."""
        mock_rag.return_value = [
            {"document": "guide_methodo", "page": 64, "extrait": "Texte guide"},
        ]
        call_count = {"n": 0}

        def ollama_side_effect(prompt, temperature=0.1, max_tokens=4000, **kwargs):
            call_count["n"] += 1
            if call_count["n"] == 1:
                return {"comprehension_contestation": "Extraction...", "elements_cliniques_pertinents": [], "points_accord_potentiels": [], "codes_en_jeu": {}}
            elif call_count["n"] == 2:
                return {
                    "analyse_contestation": "Analyse...",
                    "contre_arguments_medicaux": "Contre-arguments médicaux...",
                    "contre_arguments_asymetrie": "Asymétrie...",
                    "conclusion": "Conclusion...",
                }
            else:
                return {"coherent": True, "erreurs": [], "score_confiance": 9}

        mock_ollama.side_effect = ollama_side_effect
        mock_val_ollama.side_effect = ollama_side_effect

        dossier = _make_dossier()
        controle = _make_controle()

        text, response_data, sources = generate_cpam_response(dossier, controle)

        assert "Contre-arguments médicaux..." in text
        assert response_data is not None
        assert response_data["analyse_contestation"] == "Analyse..."
        assert len(sources) == 1
        assert sources[0].document == "guide_methodo"
        # 3 appels Ollama : extraction + argumentation + validation
        assert call_count["n"] == 3
        mock_anthropic.assert_not_called()

    @patch("src.control.cpam_validation.call_anthropic")
    @patch("src.control.cpam_validation.call_ollama")
    @patch("src.control.cpam_response.call_ollama")
    @patch("src.control.cpam_response.call_anthropic")
    @patch("src.control.cpam_response._search_rag_for_control")
    def test_generate_fallback_haiku(self, mock_rag, mock_anthropic, mock_ollama, mock_val_ollama, mock_val_anthropic):
        """Ollama indisponible → fallback Haiku pour les 3 passes."""
        mock_rag.return_value = [
            {"document": "guide_methodo", "page": 64, "extrait": "Texte guide"},
        ]
        mock_ollama.return_value = None
        mock_val_ollama.return_value = None
        call_count = {"n": 0}

        def anthropic_side_effect(prompt, temperature=0.1, max_tokens=4000, **kwargs):
            call_count["n"] += 1
            if call_count["n"] == 1:
                return {"comprehension_contestation": "Extraction Haiku...", "elements_cliniques_pertinents": [], "points_accord_potentiels": [], "codes_en_jeu": {}}
            elif call_count["n"] == 2:
                return {
                    "analyse_contestation": "Analyse Haiku...",
                    "contre_arguments_medicaux": "Contre-args Haiku...",
                    "conclusion": "Conclusion Haiku...",
                }
            else:
                return {"coherent": True, "erreurs": [], "score_confiance": 8}

        mock_anthropic.side_effect = anthropic_side_effect
        mock_val_anthropic.side_effect = anthropic_side_effect

        dossier = _make_dossier()
        controle = _make_controle()

        text, response_data, sources = generate_cpam_response(dossier, controle)

        assert "Contre-args Haiku..." in text
        assert response_data is not None
        # 3 appels Ollama (retourne None) + 3 Anthropic en fallback
        assert call_count["n"] == 3

    @patch("src.control.cpam_validation.call_anthropic", return_value=None)
    @patch("src.control.cpam_validation.call_ollama", return_value=None)
    @patch("src.control.cpam_response.call_ollama")
    @patch("src.control.cpam_response.call_anthropic")
    @patch("src.control.cpam_response._search_rag_for_control")
    def test_generate_all_unavailable(self, mock_rag, mock_anthropic, mock_ollama, _mock_val_ollama, _mock_val_anthropic):
        """Tous LLMs indisponibles → texte vide, response_data None."""
        mock_rag.return_value = []
        mock_anthropic.return_value = None
        mock_ollama.return_value = None

        dossier = _make_dossier()
        controle = _make_controle()

        text, response_data, sources = generate_cpam_response(dossier, controle)

        assert text == ""
        assert response_data is None
        assert sources == []


class TestSearchRagForControl:
    """Tests pour la logique de recherche RAG multi-requêtes."""

    @patch("src.medical.rag_search.search_similar_cpam")
    def test_multiple_queries_with_da_ucr(self, mock_search):
        """Avec da_ucr, on doit avoir au moins 2 requêtes (codes + argument)."""
        mock_search.return_value = [
            {"document": "guide_methodo", "page": 10, "code": None, "score": 0.6,
             "extrait": "Texte guide"},
        ]

        dossier = _make_dossier()
        controle = _make_controle()  # da_ucr="K56.0"

        results = _search_rag_for_control(controle, dossier)

        # Au moins 2 appels : codes contestés + argument CPAM
        assert mock_search.call_count >= 2

    @patch("src.medical.rag_search.search_similar_cpam")
    def test_query_codes_contains_cma(self, mock_search):
        """La requête codes contestés doit contenir 'CMA' pour un DA."""
        mock_search.return_value = []

        dossier = _make_dossier()
        controle = _make_controle()  # da_ucr="K56.0"

        _search_rag_for_control(controle, dossier)

        # Premier appel = requête codes
        first_call_query = mock_search.call_args_list[0][0][0]
        assert "K56.0" in first_call_query
        assert "CMA" in first_call_query

    @patch("src.medical.rag_search.search_similar_cpam")
    def test_query_argument_contains_titre(self, mock_search):
        """La requête argument doit contenir le titre du contrôle."""
        mock_search.return_value = []

        dossier = _make_dossier()
        controle = _make_controle()

        _search_rag_for_control(controle, dossier)

        # Deuxième appel = requête argument
        second_call_query = mock_search.call_args_list[1][0][0]
        assert controle.titre in second_call_query

    @patch("src.medical.rag_search.search_similar_cpam")
    def test_deduplication_by_document_code_page(self, mock_search):
        """Les résultats dupliqués (même document/code/page) sont fusionnés."""
        # Les deux requêtes retournent le même résultat
        shared_result = {
            "document": "guide_methodo", "page": 64, "code": None,
            "score": 0.55, "extrait": "Texte partagé",
        }
        mock_search.return_value = [shared_result.copy()]

        dossier = _make_dossier()
        controle = _make_controle()

        results = _search_rag_for_control(controle, dossier)

        # Le résultat ne doit apparaître qu'une seule fois malgré les requêtes multiples
        guide_results = [r for r in results if r["document"] == "guide_methodo" and r.get("page") == 64]
        assert len(guide_results) == 1

    @patch("src.medical.rag_search.search_similar_cpam")
    def test_dedup_keeps_best_score(self, mock_search):
        """La déduplication garde le meilleur score."""
        def side_effect(query, top_k=8):
            if "CMA" in query:
                return [{"document": "cim10", "code": "K56.0", "page": None,
                         "score": 0.5, "extrait": "Iléus"}]
            else:
                return [{"document": "cim10", "code": "K56.0", "page": None,
                         "score": 0.7, "extrait": "Iléus"}]
        mock_search.side_effect = side_effect

        dossier = _make_dossier()
        controle = _make_controle()

        results = _search_rag_for_control(controle, dossier)

        k56_results = [r for r in results if r.get("code") == "K56.0"]
        assert len(k56_results) == 1
        assert k56_results[0]["score"] == 0.7

    @patch("src.medical.rag_search.search_similar_cpam")
    def test_no_codes_contestes_only_argument_query(self, mock_search):
        """Sans codes contestés, seule la requête argument est lancée."""
        mock_search.return_value = []

        dossier = _make_dossier()
        controle = ControleCPAM(
            numero_ogc=1,
            titre="Désaccord sur la durée",
            arg_ucr="Séjour trop long selon l'UCR.",
            decision_ucr="Rejet",
            dp_ucr=None,
            da_ucr=None,
        )

        _search_rag_for_control(controle, dossier)

        # Un seul appel : requête argument (pas de codes contestés)
        assert mock_search.call_count == 1

    @patch("src.medical.rag_search.search_similar_cpam")
    def test_dp_ucr_query_contains_diagnostic_principal(self, mock_search):
        """Avec dp_ucr, la requête codes mentionne 'diagnostic principal'."""
        mock_search.return_value = []

        dossier = _make_dossier()
        controle = ControleCPAM(
            numero_ogc=2,
            titre="Désaccord sur le DP",
            arg_ucr="Le DP devrait être K80.1",
            decision_ucr="Rejet",
            dp_ucr="K81.0",
            da_ucr=None,
        )

        _search_rag_for_control(controle, dossier)

        first_call_query = mock_search.call_args_list[0][0][0]
        assert "K81.0" in first_call_query
        assert "diagnostic principal" in first_call_query

    @patch("src.medical.rag_search.search_similar_cpam")
    def test_max_12_results(self, mock_search):
        """Le résultat final est limité à 12 entrées."""
        mock_search.return_value = [
            {"document": "guide_methodo", "page": i, "code": None,
             "score": 0.9 - i * 0.01, "extrait": f"Texte {i}"}
            for i in range(8)
        ]

        dossier = _make_dossier()
        controle = _make_controle()

        results = _search_rag_for_control(controle, dossier)

        assert len(results) <= 12

    @patch("src.medical.rag_search.search_similar_cpam")
    def test_arg_ucr_not_truncated_200(self, mock_search):
        """La requête RAG argument utilise jusqu'à 500 chars, pas 200."""
        mock_search.return_value = []

        dossier = _make_dossier()
        long_arg = "A" * 400
        controle = ControleCPAM(
            numero_ogc=1, titre="Test", arg_ucr=long_arg,
            decision_ucr="Rejet", dp_ucr=None, da_ucr=None,
        )

        _search_rag_for_control(controle, dossier)

        # La requête argument doit contenir les 400 chars (pas tronquée à 200)
        arg_call_query = mock_search.call_args_list[0][0][0]
        assert len(arg_call_query) > 200

    @patch("src.control.cpam_rag.validate_code", return_value=(True, "Iléus paralytique"))
    @patch("src.control.cpam_rag.normalize_code", return_value="K56.0")
    @patch("src.medical.rag_search.search_similar_cpam")
    def test_query_cim10_definitions(self, mock_search, mock_norm, mock_valid):
        """Requête 4 exécutée quand codes contestés présents."""
        mock_search.return_value = []

        dossier = _make_dossier()
        controle = _make_controle()  # da_ucr="K56.0"

        _search_rag_for_control(controle, dossier)

        # Chercher la requête contenant "CIM-10" et "définition"
        cim10_queries = [
            c[0][0] for c in mock_search.call_args_list
            if "CIM-10" in c[0][0] and "définition" in c[0][0]
        ]
        assert len(cim10_queries) >= 1
        assert "K56.0" in cim10_queries[0]

    @patch("src.medical.rag_search.search_similar_cpam")
    def test_query_rule_extraction(self, mock_search):
        """Requête 5 exécutée quand arg_ucr contient une règle nommée."""
        mock_search.return_value = []

        dossier = _make_dossier()
        controle = ControleCPAM(
            numero_ogc=1, titre="Désaccord DAS",
            arg_ucr="Selon la RègleT7 et l'Annexe-4B, le DAS n'est pas justifié.",
            decision_ucr="Rejet", dp_ucr=None, da_ucr=None,
        )

        _search_rag_for_control(controle, dossier)

        # Chercher la requête contenant les règles extraites
        rule_queries = [
            c[0][0] for c in mock_search.call_args_list
            if "guide méthodologique" in c[0][0]
        ]
        assert len(rule_queries) >= 1
        assert "RègleT7" in rule_queries[0] or "Annexe" in rule_queries[0]

    @patch("src.medical.rag_search.search_similar_cpam")
    def test_clinical_query_when_das_match(self, mock_search):
        """Requête clinique lancée quand da_ucr matche un DAS du dossier."""
        mock_search.return_value = []

        dossier = _make_dossier()  # DAS K56.0 "Iléus réflexe"
        controle = _make_controle()  # da_ucr="K56.0"

        _search_rag_for_control(controle, dossier)

        # Au moins 4 appels : codes + argument + clinique + CIM-10 définitions
        assert mock_search.call_count >= 4
        # La requête clinique contient DP + DAS textes
        clinique_queries = [
            c[0][0] for c in mock_search.call_args_list
            if "Iléus réflexe" in c[0][0] and "Cholécystite aiguë" in c[0][0]
        ]
        assert len(clinique_queries) >= 1


class TestGetCim10Definitions:
    """Tests pour l'injection déterministe des définitions CIM-10."""

    @patch("src.control.cpam_context.validate_code")
    @patch("src.control.cpam_context.normalize_code", side_effect=lambda c: c.upper())
    def test_definitions_injected_in_prompt(self, mock_norm, mock_valid):
        """La section DÉFINITIONS CIM-10 apparaît dans le prompt avec les libellés."""
        mock_valid.side_effect = lambda c: {
            "K81.0": (True, "Cholécystite aiguë"),
            "K56.0": (True, "Iléus paralytique et obstruction intestinale"),
        }.get(c, (False, ""))

        dossier = _make_dossier()  # DP=K81.0, DAS=K56.0
        controle = _make_controle()  # da_ucr="K56.0"

        prompt, _ = _build_cpam_prompt(dossier, controle, [])

        assert "DÉFINITIONS CIM-10" in prompt
        assert "dictionnaire officiel" in prompt
        assert "Cholécystite aiguë" in prompt
        assert "Iléus paralytique" in prompt
        assert "DP établissement" in prompt

    @patch("src.control.cpam_context.validate_code")
    @patch("src.control.cpam_context.normalize_code", side_effect=lambda c: c.upper())
    def test_definitions_include_dp_and_ucr_codes(self, mock_norm, mock_valid):
        """Les codes du dossier ET de l'UCR sont tous inclus."""
        mock_valid.side_effect = lambda c: {
            "K81.0": (True, "Cholécystite aiguë"),
            "K56.0": (True, "Iléus paralytique"),
            "Z45.8": (True, "Ajustement d'un dispositif implantable"),
        }.get(c, (False, ""))

        dossier = _make_dossier()  # DP=K81.0, DAS=K56.0
        controle = ControleCPAM(
            numero_ogc=1, titre="Test", arg_ucr="Test",
            decision_ucr="Rejet", dp_ucr="Z45.8", da_ucr="K56.0",
        )

        result = _get_cim10_definitions(dossier, controle)

        # Codes dossier
        assert "K81.0" in result
        assert "DP établissement" in result
        assert "K56.0" in result
        # Codes UCR
        assert "Z45.8" in result
        assert "DP proposé UCR" in result
        assert "DA proposé UCR" in result or "DAS établissement" in result

    @patch("src.control.cpam_context.validate_code", return_value=(False, ""))
    @patch("src.control.cpam_context.normalize_code", side_effect=lambda c: c.upper())
    def test_definitions_graceful_when_code_unknown(self, mock_norm, mock_valid):
        """Un code inconnu ne crashe pas, affiche un message explicite."""
        dossier = DossierMedical(
            source_file="test.pdf",
            diagnostic_principal=None,
        )
        controle = ControleCPAM(
            numero_ogc=1, titre="Test", arg_ucr="Test",
            decision_ucr="Rejet", dp_ucr="Z99.9", da_ucr=None,
        )

        result = _get_cim10_definitions(dossier, controle)

        assert "Z99.9" in result
        assert "non trouvé" in result

    def test_definitions_empty_when_no_codes(self):
        """Aucun code → chaîne vide."""
        dossier = DossierMedical(
            source_file="test.pdf",
            diagnostic_principal=None,
        )
        controle = ControleCPAM(
            numero_ogc=1, titre="Test", arg_ucr="Test",
            decision_ucr="Rejet", dp_ucr=None, da_ucr=None,
        )

        result = _get_cim10_definitions(dossier, controle)

        assert result == ""


class TestBuildTaggedContext:
    """Tests pour le contexte clinique tagué (grounding)."""

    def test_tagged_context_bio_img_trt(self):
        """Les tags BIO, IMG, TRT, ACTE sont correctement générés."""
        dossier = _make_dossier_complet()
        text, tag_map = _build_tagged_context(dossier)

        assert "[BIO-1]" in text
        assert "CRP" in text
        assert "BIO-1" in tag_map
        assert "[IMG-1]" in text
        assert "Scanner abdominal" in text
        assert "IMG-1" in tag_map
        assert "[TRT-1]" in text
        assert "Augmentin IV" in text
        assert "TRT-1" in tag_map
        assert "[ACTE-1]" in text
        assert "Cholécystectomie" in text
        assert "ACTE-1" in tag_map

    def test_tagged_context_bio_norms_annotated(self):
        """Les valeurs bio sont annotées avec les normes de référence."""
        dossier = DossierMedical(
            source_file="test.pdf",
            biologie_cle=[
                BiologieCle(test="CRP", valeur="5", anomalie=False),
                BiologieCle(test="CRP", valeur="180", anomalie=True),
                BiologieCle(test="Hémoglobine", valeur="8.5", anomalie=True),
            ],
        )
        text, tag_map = _build_tagged_context(dossier)

        # CRP 5 = normal (norme 0-5)
        assert "NORMAL" in tag_map.get("BIO-1", "")
        # CRP 180 = élevé
        assert "ÉLEVÉ" in tag_map.get("BIO-2", "")
        # Hb 8.5 = bas (norme 12-17)
        assert "BAS" in tag_map.get("BIO-3", "")

    def test_tagged_context_empty_dossier(self):
        """Dossier sans données cliniques → texte vide, tag_map vide."""
        dossier = DossierMedical(
            source_file="test.pdf",
            diagnostic_principal=Diagnostic(texte="Test", cim10_suggestion="Z00"),
        )
        text, tag_map = _build_tagged_context(dossier)

        assert text == ""
        assert tag_map == {}

    def test_tagged_context_in_prompt(self):
        """Le contexte tagué apparaît dans le prompt généré."""
        dossier = _make_dossier_complet()
        controle = _make_controle()
        prompt, tag_map = _build_cpam_prompt(dossier, controle, [])

        assert "ÉLÉMENTS CLINIQUES RÉFÉRENCÉS" in prompt
        assert "[BIO-1]" in prompt
        assert "[IMG-1]" in prompt
        assert len(tag_map) > 0

    def test_poor_dossier_warning_in_prompt(self):
        """Dossier sans bio/imagerie → avertissement dans le prompt."""
        dossier = DossierMedical(
            source_file="test.pdf",
            diagnostic_principal=Diagnostic(texte="Test", cim10_suggestion="Z00"),
            sejour=Sejour(sexe="M", age=70),
        )
        controle = _make_controle()
        prompt, tag_map = _build_cpam_prompt(dossier, controle, [])

        assert "DOSSIER PAUVRE" in prompt
        assert "Ne spécule PAS" in prompt
        assert len(tag_map) == 0


class TestValidateGrounding:
    """Tests pour la validation des preuves grounded."""

    def test_grounding_valid_refs(self):
        """Toutes les refs existent → 0 warnings."""
        tag_map = {"BIO-1": "CRP: 180 mg/L", "IMG-1": "Scanner abdominal"}
        response_data = {
            "preuves_dossier": [
                {"ref": "BIO-1", "element": "biologie", "valeur": "CRP 180 mg/L", "signification": "inflammation"},
                {"ref": "IMG-1", "element": "imagerie", "valeur": "Scanner", "signification": "confirme"},
            ]
        }
        warnings = _validate_grounding(response_data, tag_map)
        assert len(warnings) == 0

    def test_grounding_invented_ref(self):
        """Ref inventée [BIO-99] → warning détecté."""
        tag_map = {"BIO-1": "CRP: 180 mg/L"}
        response_data = {
            "preuves_dossier": [
                {"ref": "BIO-99", "element": "biologie", "valeur": "Albumine 15 g/L", "signification": "inventé"},
            ]
        }
        warnings = _validate_grounding(response_data, tag_map)
        assert len(warnings) == 1
        assert "BIO-99" in warnings[0]

    def test_grounding_no_tag_map_no_validation(self):
        """Pas de tag_map (dossier vide) → pas de validation."""
        response_data = {
            "preuves_dossier": [
                {"ref": "BIO-1", "element": "biologie", "valeur": "test", "signification": "test"},
            ]
        }
        warnings = _validate_grounding(response_data, {})
        assert len(warnings) == 0

    def test_grounding_no_ref_field_ok(self):
        """Preuves sans champ ref (ancien format) → pas de warning."""
        tag_map = {"BIO-1": "CRP: 180 mg/L"}
        response_data = {
            "preuves_dossier": [
                {"element": "biologie", "valeur": "CRP 180 mg/L", "signification": "inflammation"},
            ]
        }
        warnings = _validate_grounding(response_data, tag_map)
        assert len(warnings) == 0

    def test_format_response_with_ref(self):
        """Le formatage inclut le tag ref dans les preuves."""
        parsed = {
            "contre_arguments_medicaux": "Arguments...",
            "preuves_dossier": [
                {"ref": "BIO-1", "element": "biologie", "valeur": "CRP 180 mg/L", "signification": "inflammation"},
            ],
            "conclusion": "Conclusion...",
        }
        text = _format_response(parsed)

        assert "[BIO-1]" in text
        assert "[biologie]" in text
        assert "CRP 180 mg/L" in text


class TestCheckDasBioCoherence:
    """Tests pour la vérification cohérence DAS / biologie."""

    def test_leucocytose_with_low_leucocytes(self):
        """DAS 'leucocytose' mais leucocytes bas → incohérence détectée."""
        dossier = DossierMedical(
            source_file="test.pdf",
            diagnostics_associes=[
                Diagnostic(texte="Leucocytose", cim10_suggestion="D72.8"),
            ],
            biologie_cle=[
                BiologieCle(test="Leucocytes", valeur="3", anomalie=True),
            ],
        )
        warnings = _check_das_bio_coherence(dossier)

        assert len(warnings) == 1
        assert "Leucocytose" in warnings[0]
        assert "NORMAL" in warnings[0]

    def test_anemie_with_normal_hb(self):
        """DAS 'anémie' mais Hb normale → incohérence détectée."""
        dossier = DossierMedical(
            source_file="test.pdf",
            diagnostics_associes=[
                Diagnostic(texte="Anémie ferriprive", cim10_suggestion="D50.9"),
            ],
            biologie_cle=[
                BiologieCle(test="Hémoglobine", valeur="14.5", anomalie=False),
            ],
        )
        warnings = _check_das_bio_coherence(dossier)

        assert len(warnings) == 1
        assert "anémie" in warnings[0].lower() or "Anémie" in warnings[0]

    def test_coherent_das_bio_no_warnings(self):
        """DAS 'anémie' avec Hb basse → pas d'incohérence."""
        dossier = DossierMedical(
            source_file="test.pdf",
            diagnostics_associes=[
                Diagnostic(texte="Anémie", cim10_suggestion="D64.9"),
            ],
            biologie_cle=[
                BiologieCle(test="Hémoglobine", valeur="8.5", anomalie=True),
            ],
        )
        warnings = _check_das_bio_coherence(dossier)

        assert len(warnings) == 0

    def test_no_bio_no_crash(self):
        """Pas de biologie → pas de crash, pas de warnings."""
        dossier = DossierMedical(
            source_file="test.pdf",
            diagnostics_associes=[
                Diagnostic(texte="Leucocytose", cim10_suggestion="D72.8"),
            ],
        )
        warnings = _check_das_bio_coherence(dossier)

        assert len(warnings) == 0

    def test_coherence_warnings_in_prompt(self):
        """Les incohérences DAS/bio apparaissent dans le prompt."""
        dossier = DossierMedical(
            source_file="test.pdf",
            sejour=Sejour(sexe="M", age=65),
            diagnostic_principal=Diagnostic(texte="Test", cim10_suggestion="Z00"),
            diagnostics_associes=[
                Diagnostic(texte="Thrombocytose", cim10_suggestion="D75.9"),
            ],
            biologie_cle=[
                BiologieCle(test="Plaquettes", valeur="200", anomalie=False),
            ],
        )
        controle = _make_controle()
        prompt, _ = _build_cpam_prompt(dossier, controle, [])

        assert "ALERTES COHÉRENCE DAS / BIOLOGIE" in prompt
        assert "Thrombocytose" in prompt
        assert "NORMAL" in prompt


class TestPatientContext:
    """Tests pour le contexte patient dans le prompt."""

    def test_pediatric_flag(self):
        """Patient < 18 ans → mention pédiatrie dans le prompt."""
        dossier = DossierMedical(
            source_file="test.pdf",
            sejour=Sejour(sexe="F", age=9),
            diagnostic_principal=Diagnostic(texte="Appendicite", cim10_suggestion="K35.8"),
        )
        controle = _make_controle()
        prompt, _ = _build_cpam_prompt(dossier, controle, [])

        assert "PÉDIATRIE" in prompt
        assert "9 ans" in prompt

    def test_elderly_flag(self):
        """Patient >= 80 ans → mention patient âgé."""
        dossier = DossierMedical(
            source_file="test.pdf",
            sejour=Sejour(sexe="M", age=85),
            diagnostic_principal=Diagnostic(texte="Test", cim10_suggestion="Z00"),
        )
        controle = _make_controle()
        prompt, _ = _build_cpam_prompt(dossier, controle, [])

        assert "patient âgé" in prompt
        assert "85 ans" in prompt

    def test_emergency_admission(self):
        """Admission en urgence → flag dans le prompt."""
        dossier = DossierMedical(
            source_file="test.pdf",
            sejour=Sejour(sexe="M", age=50, mode_entree="Autres admissions urgentes"),
            diagnostic_principal=Diagnostic(texte="Test", cim10_suggestion="Z00"),
        )
        controle = _make_controle()
        prompt, _ = _build_cpam_prompt(dossier, controle, [])

        assert "ADMISSION EN URGENCE" in prompt

    def test_context_consigne_in_prompt(self):
        """Le prompt contient une consigne sur le contexte clinique."""
        dossier = _make_dossier()
        controle = _make_controle()
        prompt, _ = _build_cpam_prompt(dossier, controle, [])

        assert "CONTEXTE CLINIQUE" in prompt
        assert "ÂGE" in prompt
        assert "MODE D'ENTRÉE" in prompt


class TestExtractionPass:
    """Tests pour la passe 1 — extraction structurée."""

    @patch("src.control.cpam_response.call_ollama")
    def test_extraction_pass_returns_structured_json(self, mock_ollama):
        """Passe 1 retourne les champs attendus."""
        mock_ollama.return_value = {
            "comprehension_contestation": "La CPAM conteste le DAS K56.0",
            "elements_cliniques_pertinents": [
                {"tag": "BIO-1", "pertinence": "CRP élevée confirme inflammation"}
            ],
            "points_accord_potentiels": ["Le CRH est succinct"],
            "codes_en_jeu": {
                "dp_etablissement": "K81.0 — Cholécystite aiguë",
                "dp_ucr": "",
                "difference_cle": "contestation porte sur le DAS, pas le DP",
            },
        }

        dossier = _make_dossier()
        controle = _make_controle()
        result = _extraction_pass(dossier, controle)

        assert result is not None
        assert "comprehension_contestation" in result
        assert len(result["elements_cliniques_pertinents"]) == 1
        mock_ollama.assert_called_once()

    @patch("src.control.cpam_response.call_anthropic", return_value=None)
    @patch("src.control.cpam_response.call_ollama", return_value=None)
    def test_extraction_pass_failure_returns_none(self, mock_ollama, mock_anthropic):
        """Passe 1 échoue → retourne None (fallback single-pass)."""
        dossier = _make_dossier()
        controle = _make_controle()
        result = _extraction_pass(dossier, controle)

        assert result is None

    @patch("src.control.cpam_response.call_ollama")
    def test_extraction_injected_in_prompt(self, mock_ollama):
        """Le résultat de passe 1 est injecté dans le prompt de passe 2."""
        extraction = {
            "comprehension_contestation": "La CPAM conteste le DAS K56.0",
            "elements_cliniques_pertinents": [
                {"tag": "BIO-1", "pertinence": "CRP élevée"}
            ],
            "points_accord_potentiels": ["Le CRH est succinct"],
            "codes_en_jeu": {
                "difference_cle": "contestation porte sur le DAS",
            },
        }

        dossier = _make_dossier()
        controle = _make_controle()
        prompt, _ = _build_cpam_prompt(dossier, controle, [], extraction)

        assert "PRÉ-ANALYSE" in prompt
        assert "La CPAM conteste le DAS K56.0" in prompt
        assert "CRP élevée" in prompt
        assert "contestation porte sur le DAS" in prompt

    def test_prompt_without_extraction(self):
        """Sans extraction, pas de section PRÉ-ANALYSE."""
        dossier = _make_dossier()
        controle = _make_controle()
        prompt, _ = _build_cpam_prompt(dossier, controle, [], None)

        assert "PRÉ-ANALYSE" not in prompt

    @patch("src.control.cpam_validation.call_ollama")
    @patch("src.control.cpam_response.call_ollama")
    @patch("src.control.cpam_response._search_rag_for_control")
    def test_generate_calls_three_passes(self, mock_rag, mock_ollama, mock_val_ollama):
        """L'orchestrateur appelle extraction + argumentation + validation."""
        call_count = {"n": 0}

        def ollama_side_effect(prompt, temperature=0.1, max_tokens=4000, **kwargs):
            call_count["n"] += 1
            if call_count["n"] == 1:
                return {
                    "comprehension_contestation": "Contestation DAS",
                    "elements_cliniques_pertinents": [],
                    "points_accord_potentiels": [],
                    "codes_en_jeu": {},
                }
            elif call_count["n"] == 2:
                return {
                    "analyse_contestation": "Analyse...",
                    "contre_arguments_medicaux": "Arguments...",
                    "conclusion": "Conclusion...",
                }
            else:
                return {"coherent": True, "erreurs": [], "score_confiance": 9}

        mock_ollama.side_effect = ollama_side_effect
        mock_val_ollama.side_effect = ollama_side_effect
        mock_rag.return_value = []

        dossier = _make_dossier()
        controle = _make_controle()
        text, response_data, sources = generate_cpam_response(dossier, controle)

        # 3 appels Ollama : extraction + argumentation + validation
        assert call_count["n"] == 3
        assert response_data is not None
        assert "Arguments..." in text


class TestValidateAdversarial:
    """Tests pour la validation adversariale."""

    @patch("src.control.cpam_validation.call_ollama")
    def test_coherent_response_no_warnings(self, mock_ollama):
        """Réponse cohérente → coherent=true, pas de warnings dans le texte."""
        mock_ollama.return_value = {"coherent": True, "erreurs": [], "score_confiance": 9}

        tag_map = {"BIO-1": "CRP: 180 mg/L"}
        response_data = {
            "analyse_contestation": "Analyse...",
            "preuves_dossier": [{"ref": "BIO-1", "valeur": "CRP 180 mg/L"}],
            "conclusion": "Conclusion...",
        }
        controle = _make_controle()

        result = _validate_adversarial(response_data, tag_map, controle)

        assert result is not None
        assert result["coherent"] is True
        assert len(result["erreurs"]) == 0

    @patch("src.control.cpam_validation.call_ollama")
    def test_hallucinated_bio_detected(self, mock_ollama):
        """Valeur bio halluccinée → coherent=false avec erreur."""
        mock_ollama.return_value = {
            "coherent": False,
            "erreurs": ["CRP citée à 250 mg/L mais le dossier indique 180 mg/L"],
            "score_confiance": 3,
        }

        tag_map = {"BIO-1": "CRP: 180 mg/L"}
        response_data = {
            "preuves_dossier": [{"ref": "BIO-1", "valeur": "CRP 250 mg/L"}],
            "conclusion": "Conclusion...",
        }
        controle = _make_controle()

        result = _validate_adversarial(response_data, tag_map, controle)

        assert result is not None
        assert result["coherent"] is False
        assert len(result["erreurs"]) == 1
        assert "CRP" in result["erreurs"][0]

    @patch("src.control.cpam_validation.call_anthropic", return_value=None)
    @patch("src.control.cpam_validation.call_ollama", return_value=None)
    def test_adversarial_failure_graceful(self, mock_ollama, mock_anthropic):
        """LLM indisponible → retourne None, pas de crash."""
        tag_map = {"BIO-1": "CRP: 180 mg/L"}
        response_data = {"conclusion": "Conclusion..."}
        controle = _make_controle()

        result = _validate_adversarial(response_data, tag_map, controle)

        assert result is None

    @patch("src.control.cpam_validation.call_ollama")
    @patch("src.control.cpam_response.call_ollama")
    @patch("src.control.cpam_response._search_rag_for_control")
    def test_adversarial_warnings_in_output(self, mock_rag, mock_ollama, mock_val_ollama):
        """Incohérences détectées → avertissements dans le texte formaté."""
        call_count = {"n": 0}

        def ollama_side_effect(prompt, temperature=0.1, max_tokens=4000, **kwargs):
            call_count["n"] += 1
            if call_count["n"] == 1:
                return {"comprehension_contestation": "Extraction", "elements_cliniques_pertinents": [], "points_accord_potentiels": [], "codes_en_jeu": {}}
            elif call_count["n"] == 2:
                return {
                    "analyse_contestation": "Analyse...",
                    "contre_arguments_medicaux": "Arguments...",
                    "conclusion": "Conclusion...",
                }
            else:
                return {
                    "coherent": False,
                    "erreurs": ["Antibiotiques mentionnés mais absents du dossier"],
                    "score_confiance": 4,
                }

        mock_ollama.side_effect = ollama_side_effect
        mock_val_ollama.side_effect = ollama_side_effect
        mock_rag.return_value = []

        dossier = _make_dossier()
        controle = _make_controle()
        text, response_data, sources = generate_cpam_response(dossier, controle)

        assert "Antibiotiques mentionnés" in text
        assert "Score de confiance" in text

    def test_adversarial_empty_tag_map(self):
        """Dossier sans tags → validation fonctionne quand même."""
        with patch("src.control.cpam_validation.call_ollama") as mock_ollama:
            mock_ollama.return_value = {"coherent": True, "erreurs": [], "score_confiance": 7}

            result = _validate_adversarial(
                {"conclusion": "Test"}, {}, _make_controle()
            )

            assert result is not None
            assert result["coherent"] is True


class TestValidateCodesInResponse:
    """Tests pour la validation codes fermée (périmètre dossier + UCR)."""

    def test_code_in_dossier_no_warning(self):
        """Code du dossier cité → pas de warning."""
        parsed = {"conclusion": "Le code K81.0 est justifié par la cholécystite."}
        dossier = _make_dossier()  # DP K81.0, DAS K56.0
        controle = _make_controle()
        warnings = _validate_codes_in_response(parsed, dossier, controle)
        assert len(warnings) == 0

    def test_code_from_ucr_no_warning(self):
        """Code proposé par l'UCR cité → pas de warning."""
        parsed = {"conclusion": "Le code K56.0 contesté par l'UCR est bien justifié."}
        dossier = _make_dossier()
        controle = _make_controle()  # da_ucr="K56.0"
        warnings = _validate_codes_in_response(parsed, dossier, controle)
        assert len(warnings) == 0

    def test_invented_code_detected(self):
        """Code absent du dossier et de l'UCR → warning."""
        parsed = {"conclusion": "Le code Z45.8 confirme la nécessité du séjour."}
        dossier = _make_dossier()  # DP K81.0, DAS K56.0
        controle = _make_controle()  # da_ucr=K56.0
        warnings = _validate_codes_in_response(parsed, dossier, controle)
        assert len(warnings) >= 1
        assert any("Z45" in w for w in warnings)

    def test_subcode_tolerated(self):
        """K81.09 toléré quand K81.0 est dans la whitelist (même préfixe 3 chars)."""
        parsed = {"contre_arguments_medicaux": "Le sous-code K81.09 est une précision de K81.0."}
        dossier = _make_dossier()  # DP K81.0
        controle = _make_controle()
        warnings = _validate_codes_in_response(parsed, dossier, controle)
        # K81.09 partage le préfixe K81 avec K81.0 → toléré
        assert len(warnings) == 0

    def test_codes_in_citations_excluded(self):
        """Codes dans references[].citation → pas de validation."""
        parsed = {
            "conclusion": "Le codage est justifié.",
            "references": [
                {"document": "CIM-10", "citation": "Z45.8 — Ajustement d'un dispositif"},
            ],
        }
        dossier = _make_dossier()
        controle = _make_controle()
        warnings = _validate_codes_in_response(parsed, dossier, controle)
        # Z45.8 est dans references, pas dans les champs textuels → pas flaggé
        assert len(warnings) == 0

    def test_no_codes_in_response_no_warning(self):
        """Réponse sans codes CIM-10 → 0 warnings."""
        parsed = {"conclusion": "Le séjour est justifié par la gravité clinique."}
        dossier = _make_dossier()
        controle = _make_controle()
        warnings = _validate_codes_in_response(parsed, dossier, controle)
        assert len(warnings) == 0

    def test_multiple_invented_codes(self):
        """Plusieurs codes hors périmètre → autant de warnings."""
        parsed = {
            "contre_arguments_medicaux": "Les codes Z45.8 et E11.9 confirment le diagnostic.",
        }
        dossier = _make_dossier()  # K81.0, K56.0
        controle = _make_controle()
        warnings = _validate_codes_in_response(parsed, dossier, controle)
        assert len(warnings) >= 2

    def test_no_whitelist_no_validation(self):
        """Aucun code dans le dossier ni l'UCR → pas de validation (0 warnings)."""
        parsed = {"conclusion": "Le code Z45.8 est justifié."}
        dossier = DossierMedical(source_file="test.pdf", diagnostic_principal=None)
        controle = ControleCPAM(
            numero_ogc=1, titre="Test", arg_ucr="Test",
            decision_ucr="Rejet", dp_ucr=None, da_ucr=None,
        )
        warnings = _validate_codes_in_response(parsed, dossier, controle)
        assert len(warnings) == 0


class TestBuildBioSummary:
    """Tests pour le résumé biologique déterministe."""

    def test_bio_summary_interpretation(self):
        """CRP élevée, Hb basse → résumé correct avec interprétations cliniques."""
        dossier = DossierMedical(
            source_file="test.pdf",
            biologie_cle=[
                BiologieCle(test="CRP", valeur="180 mg/L", anomalie=True),
                BiologieCle(test="Hémoglobine", valeur="8.5 g/dL", anomalie=True),
            ],
        )
        summary = _build_bio_summary(dossier)

        assert "CRP" in summary
        assert "ÉLEVÉ" in summary
        assert "infection/inflammation active" in summary
        assert "Hémoglobine" in summary
        assert "BAS" in summary
        assert "anémie" in summary

    def test_bio_summary_normal_values(self):
        """Valeurs normales → interprétation 'normal' affichée."""
        dossier = DossierMedical(
            source_file="test.pdf",
            biologie_cle=[
                BiologieCle(test="Plaquettes", valeur="250 G/L", anomalie=False),
            ],
        )
        summary = _build_bio_summary(dossier)

        assert "NORMAL" in summary
        assert "numération normale" in summary

    def test_bio_summary_in_prompt(self):
        """Le résumé bio apparaît dans le prompt CPAM."""
        dossier = _make_dossier_complet()  # CRP 180, Créatinine 450
        controle = _make_controle()
        prompt, _ = _build_cpam_prompt(dossier, controle, [])

        assert "FAITS BIOLOGIQUES VÉRIFIÉS" in prompt
        assert "NE PAS MODIFIER" in prompt
        assert "RÈGLE STRICTE" in prompt

    def test_bio_summary_empty_no_bio(self):
        """Pas de biologie → résumé vide."""
        dossier = DossierMedical(source_file="test.pdf")
        summary = _build_bio_summary(dossier)
        assert summary == ""

    def test_bio_summary_unknown_test(self):
        """Test bio non reconnu (hors BIO_NORMALS) → omis du résumé."""
        dossier = DossierMedical(
            source_file="test.pdf",
            biologie_cle=[
                BiologieCle(test="Ferritine", valeur="15 µg/L", anomalie=True),
            ],
        )
        summary = _build_bio_summary(dossier)
        assert summary == ""

    def test_bio_summary_unparseable_value(self):
        """Valeur bio non parseable → omise sans crash."""
        dossier = DossierMedical(
            source_file="test.pdf",
            biologie_cle=[
                BiologieCle(test="CRP", valeur="positif", anomalie=True),
                BiologieCle(test="Hémoglobine", valeur="8.5 g/dL", anomalie=True),
            ],
        )
        summary = _build_bio_summary(dossier)
        # CRP "positif" non parseable → omis, mais Hb présente
        assert "Hémoglobine" in summary
        assert "CRP" not in summary


class TestCorrectionLoop:
    """Tests pour la boucle de correction adversariale."""

    @patch("src.control.cpam_response.rule_enabled", return_value=True)
    @patch("src.control.cpam_validation.call_ollama")
    @patch("src.control.cpam_response.call_ollama")
    @patch("src.control.cpam_response.call_anthropic")
    @patch("src.control.cpam_response._search_rag_for_control")
    def test_correction_triggered_when_score_low(self, mock_rag, mock_anthropic, mock_ollama, mock_val_ollama, mock_rule):
        """Score adversarial ≤ 5 → correction relancée (5 appels LLM total)."""
        mock_rag.return_value = []
        call_count = {"n": 0}

        def ollama_side_effect(prompt, temperature=0.1, max_tokens=4000, **kwargs):
            call_count["n"] += 1
            if call_count["n"] == 1:
                # Passe 1 extraction
                return {"comprehension_contestation": "Extraction", "elements_cliniques_pertinents": [], "points_accord_potentiels": [], "codes_en_jeu": {}}
            elif call_count["n"] == 2:
                # Passe 2 argumentation
                return {
                    "analyse_contestation": "Analyse...",
                    "contre_arguments_medicaux": "Arguments erronés...",
                    "conclusion": "Conclusion avec erreurs...",
                }
            elif call_count["n"] == 3:
                # Passe 3 validation adversariale → score bas
                return {"coherent": False, "erreurs": ["CRP citée à 250 mais vaut 180"], "score_confiance": 3}
            elif call_count["n"] == 4:
                # Passe 4 correction
                return {
                    "analyse_contestation": "Analyse corrigée...",
                    "contre_arguments_medicaux": "Arguments corrigés...",
                    "conclusion": "Conclusion corrigée...",
                }
            else:
                # Passe 5 re-validation
                return {"coherent": True, "erreurs": [], "score_confiance": 8}

        mock_ollama.side_effect = ollama_side_effect
        mock_val_ollama.side_effect = ollama_side_effect

        dossier = _make_dossier()
        controle = _make_controle()
        text, response_data, sources = generate_cpam_response(dossier, controle)

        # 5 appels Ollama : extraction + argumentation + validation + correction + re-validation
        assert call_count["n"] == 5
        # La correction a été acceptée (score 8 > 3)
        assert "corrigé" in text.lower()

    @patch("src.control.cpam_response.rule_enabled", return_value=True)
    @patch("src.control.cpam_validation.call_ollama")
    @patch("src.control.cpam_response.call_ollama")
    @patch("src.control.cpam_response.call_anthropic")
    @patch("src.control.cpam_response._search_rag_for_control")
    def test_no_correction_when_score_high(self, mock_rag, mock_anthropic, mock_ollama, mock_val_ollama, mock_rule):
        """Score adversarial > 5 → pas de correction (3 appels LLM)."""
        mock_rag.return_value = []
        call_count = {"n": 0}

        def ollama_side_effect(prompt, temperature=0.1, max_tokens=4000, **kwargs):
            call_count["n"] += 1
            if call_count["n"] == 1:
                return {"comprehension_contestation": "Extraction", "elements_cliniques_pertinents": [], "points_accord_potentiels": [], "codes_en_jeu": {}}
            elif call_count["n"] == 2:
                return {
                    "analyse_contestation": "Analyse...",
                    "contre_arguments_medicaux": "Arguments...",
                    "conclusion": "Conclusion...",
                }
            else:
                return {"coherent": True, "erreurs": [], "score_confiance": 8}

        mock_ollama.side_effect = ollama_side_effect
        mock_val_ollama.side_effect = ollama_side_effect

        dossier = _make_dossier()
        controle = _make_controle()
        text, response_data, sources = generate_cpam_response(dossier, controle)

        # Seulement 3 appels : extraction + argumentation + validation
        assert call_count["n"] == 3

    @patch("src.control.cpam_response.rule_enabled", return_value=True)
    @patch("src.control.cpam_validation.call_ollama")
    @patch("src.control.cpam_response.call_ollama")
    @patch("src.control.cpam_response.call_anthropic")
    @patch("src.control.cpam_response._search_rag_for_control")
    def test_correction_accepted_when_score_improves(self, mock_rag, mock_anthropic, mock_ollama, mock_val_ollama, mock_rule):
        """Score passe de 3 à 7 → correction acceptée."""
        mock_rag.return_value = []
        call_count = {"n": 0}

        def ollama_side_effect(prompt, temperature=0.1, max_tokens=4000, **kwargs):
            call_count["n"] += 1
            if call_count["n"] == 1:
                return {"comprehension_contestation": "Extraction", "elements_cliniques_pertinents": [], "points_accord_potentiels": [], "codes_en_jeu": {}}
            elif call_count["n"] == 2:
                return {
                    "analyse_contestation": "Analyse originale...",
                    "contre_arguments_medicaux": "Arguments originaux...",
                    "conclusion": "Conclusion originale...",
                }
            elif call_count["n"] == 3:
                return {"coherent": False, "erreurs": ["Erreur bio"], "score_confiance": 3}
            elif call_count["n"] == 4:
                return {
                    "analyse_contestation": "Analyse améliorée...",
                    "contre_arguments_medicaux": "Arguments améliorés...",
                    "conclusion": "Conclusion améliorée...",
                }
            else:
                return {"coherent": True, "erreurs": [], "score_confiance": 7}

        mock_ollama.side_effect = ollama_side_effect
        mock_val_ollama.side_effect = ollama_side_effect

        dossier = _make_dossier()
        controle = _make_controle()
        text, response_data, sources = generate_cpam_response(dossier, controle)

        # Le résultat final est la correction
        assert response_data["conclusion"] == "Conclusion améliorée..."

    @patch("src.control.cpam_response.rule_enabled", return_value=True)
    @patch("src.control.cpam_validation.call_ollama")
    @patch("src.control.cpam_response.call_ollama")
    @patch("src.control.cpam_response.call_anthropic")
    @patch("src.control.cpam_response._search_rag_for_control")
    def test_correction_rejected_when_score_same(self, mock_rag, mock_anthropic, mock_ollama, mock_val_ollama, mock_rule):
        """Score ne s'améliore pas → original conservé."""
        mock_rag.return_value = []
        call_count = {"n": 0}

        def ollama_side_effect(prompt, temperature=0.1, max_tokens=4000, **kwargs):
            call_count["n"] += 1
            if call_count["n"] == 1:
                return {"comprehension_contestation": "Extraction", "elements_cliniques_pertinents": [], "points_accord_potentiels": [], "codes_en_jeu": {}}
            elif call_count["n"] == 2:
                return {
                    "analyse_contestation": "Analyse originale...",
                    "contre_arguments_medicaux": "Arguments originaux...",
                    "conclusion": "Conclusion originale...",
                }
            elif call_count["n"] == 3:
                return {"coherent": False, "erreurs": ["Erreur bio"], "score_confiance": 4}
            elif call_count["n"] == 4:
                return {
                    "analyse_contestation": "Correction pire...",
                    "contre_arguments_medicaux": "Arguments pires...",
                    "conclusion": "Conclusion pire...",
                }
            else:
                return {"coherent": False, "erreurs": ["Encore des erreurs"], "score_confiance": 3}

        mock_ollama.side_effect = ollama_side_effect
        mock_val_ollama.side_effect = ollama_side_effect

        dossier = _make_dossier()
        controle = _make_controle()
        text, response_data, sources = generate_cpam_response(dossier, controle)

        # Score correction (3) <= score original (4) → original conservé
        assert response_data["conclusion"] == "Conclusion originale..."

    @patch("src.control.cpam_response.rule_enabled", return_value=False)
    @patch("src.control.cpam_validation.call_ollama")
    @patch("src.control.cpam_response.call_ollama")
    @patch("src.control.cpam_response.call_anthropic")
    @patch("src.control.cpam_response._search_rag_for_control")
    def test_correction_disabled_by_rule(self, mock_rag, mock_anthropic, mock_ollama, mock_val_ollama, mock_rule):
        """RULE-CPAM-CORRECTION-LOOP désactivée → pas de retry."""
        mock_rag.return_value = []
        call_count = {"n": 0}

        def ollama_side_effect(prompt, temperature=0.1, max_tokens=4000, **kwargs):
            call_count["n"] += 1
            if call_count["n"] == 1:
                return {"comprehension_contestation": "Extraction", "elements_cliniques_pertinents": [], "points_accord_potentiels": [], "codes_en_jeu": {}}
            elif call_count["n"] == 2:
                return {
                    "analyse_contestation": "Analyse...",
                    "contre_arguments_medicaux": "Arguments...",
                    "conclusion": "Conclusion...",
                }
            else:
                return {"coherent": False, "erreurs": ["Erreur bio"], "score_confiance": 2}

        mock_ollama.side_effect = ollama_side_effect
        mock_val_ollama.side_effect = ollama_side_effect

        dossier = _make_dossier()
        controle = _make_controle()
        text, response_data, sources = generate_cpam_response(dossier, controle)

        # Seulement 3 appels, pas de correction (règle désactivée)
        assert call_count["n"] == 3

    def test_build_correction_prompt_format(self):
        """Le prompt de correction contient les erreurs et la réponse originale."""
        original_prompt = "Prompt d'argumentation original..."
        original_response = {
            "analyse_contestation": "Analyse avec erreur CRP 250",
            "conclusion": "Conclusion erronée",
        }
        adversarial_result = {
            "coherent": False,
            "erreurs": ["CRP citée à 250 mg/L mais le dossier indique 180 mg/L"],
            "score_confiance": 3,
        }

        correction = _build_correction_prompt(original_prompt, original_response, adversarial_result)

        assert "CORRECTION REQUISE" in correction
        assert "CRP citée à 250" in correction
        assert "Prompt d'argumentation original" in correction
        assert "Corrige UNIQUEMENT" in correction