from __future__ import annotations

from dataclasses import dataclass
from datetime import UTC, datetime
from typing import Any

from app.case_epicrisis.application.cost_ordering import COST_ORDER_VERSION, apply_factura_cost_ordering
from app.case_epicrisis.application.curation import (
    curation_context_payload,
    reconcile_case_objective_data,
)
from app.case_epicrisis.application.medication_pertinence import (
    apply_clinical_pertinence_reviews,
    build_medication_glosa_findings,
)
from app.case_epicrisis.application.utils import (
    build_ayudas_diagnosticas,
    build_medication_display_list,
    coerce_pdf_draft,
    extract_recomendaciones_medicas,
    extraer_imagenes_diagnosticas,
    extraer_procedimientos_factura,
    format_ayuda_diagnostica_presentacion,
    merge_soat_results,
    normalize_context_payload,
    normalize_factura_medicamentos,
    serialize_doc,
    strip_html_tags,
)
from app.case_epicrisis.domain.models import (
    CaseEpicrisisDocuments,
    ClinicalSnapshot,
    CodingSnapshot,
    EpicrisisEvidenceSnapshot,
    EpicrisisGenerationBlockedError,
    EpicrisisRuleEvaluation,
    EpicrisisRuleEvaluationMode,
    EpicrisisRuleStatus,
    EpicrisisSummarySourceUnit,
    PreservedEpicrisisState,
)
from app.case_epicrisis.domain.ports import (
    CaseDocumentsReader,
    CaseEpicrisisCacheRepository,
    EpicrisisCodingGateway,
    EpicrisisPreGenerationRule,
    EpicrisisSummaryComposer,
    MedicationPertinenceClinicalReviewer,
)
from app.services.clinical_document_projection import (
    get_structured_document_model,
    render_document_analysis_html,
)
from app.services.clinical_processing import (
    extraer_antecedentes_historia,
    extraer_antecedentes_historia_estructurados,
    extraer_medicamentos_historia,
    extraer_metadatos_historia,
    extraer_procedimientos_historia,
    extraer_secciones_quirurgicas,
)
from app.services.historia_medicamentos import consolidate_medication_items, medication_from_legacy


_SUMMARY_COMPOSITION_WARNING = (
    "No fue posible integrar cronológicamente los resúmenes. Se muestran por separado sin pérdida de "
    "contenido. Puedes regenerar la epicrisis para volver a intentarlo."
)


def _summary_source_identifier(document_type: str, document: dict[str, Any] | None) -> str:
    raw_identifier = ""
    if isinstance(document, dict):
        raw_identifier = str(
            document.get("_id")
            or document.get("document_id")
            or document.get("nombre_archivo")
            or document.get("case_key")
            or ""
        ).strip()
    return f"{document_type}:{raw_identifier or 'sin-identificador'}"


def build_integrated_summary_context(
    *,
    username: str,
    historia: dict[str, Any] | None,
    quirurgico: dict[str, Any] | None,
    metadatos_hc: dict[str, Any],
    summary_composer: EpicrisisSummaryComposer | None,
) -> dict[str, Any]:
    historia_summary = str(metadatos_hc.get("resumen") or "").strip()
    historia_date = str(
        metadatos_hc.get("fecha_ingreso")
        or (historia or {}).get("fecha_analisis")
        or ""
    ).strip()
    sources_metadata: list[dict[str, Any]] = []
    historia_source: EpicrisisSummarySourceUnit | None = None
    if historia_summary:
        historia_source = EpicrisisSummarySourceUnit(
            identifier=_summary_source_identifier("historia_clinica", historia),
            document_type="historia_clinica",
            summary=historia_summary,
            available_date=historia_date,
        )
        sources_metadata.append(
            {
                "identificador": historia_source.identifier,
                "tipo_documento": historia_source.document_type,
                "fecha_disponible": historia_source.available_date,
                "resumen_disponible": True,
            }
        )

    if not quirurgico:
        return {
            "estado": "solo_historia",
            "texto": historia_summary,
            "parrafos": [historia_summary] if historia_summary else [],
            "fuentes": sources_metadata,
            "advertencia": None,
        }

    structured_qx = get_structured_document_model(quirurgico)
    surgical_summary = str(getattr(structured_qx, "resumen_clinico", "") or "").strip()
    surgical_date = str(
        getattr(structured_qx, "fecha_procedimiento", "")
        or quirurgico.get("fecha_analisis")
        or ""
    ).strip()
    qx_identifier = _summary_source_identifier("quirurgico", quirurgico)
    sources_metadata.append(
        {
            "identificador": qx_identifier,
            "tipo_documento": "quirurgico",
            "fecha_disponible": surgical_date,
            "resumen_disponible": bool(surgical_summary),
        }
    )
    if not historia_source or not surgical_summary:
        return {
            "estado": "solo_historia",
            "texto": historia_summary,
            "parrafos": [historia_summary] if historia_summary else [],
            "fuentes": sources_metadata,
            "advertencia": None,
        }

    qx_source = EpicrisisSummarySourceUnit(
        identifier=qx_identifier,
        document_type="quirurgico",
        summary=surgical_summary,
        available_date=surgical_date,
    )
    sources = [historia_source, qx_source]
    try:
        if summary_composer is None:
            raise RuntimeError("Compositor no configurado")
        composition = summary_composer.compose(username=username, sources=sources)
        return {
            "estado": "integrado",
            "texto": composition.narrative,
            "parrafos": [composition.narrative],
            "fuentes": sources_metadata,
            "advertencia": None,
        }
    except Exception:
        paragraphs = [historia_summary, surgical_summary]
        return {
            "estado": "fuentes_separadas",
            "texto": "\n\n".join(paragraphs),
            "parrafos": paragraphs,
            "fuentes": sources_metadata,
            "advertencia": _SUMMARY_COMPOSITION_WARNING,
        }


def _clean_unique_texts(values: list[str]) -> list[str]:
    seen: set[str] = set()
    unique: list[str] = []
    for raw in values:
        value = " ".join(str(raw or "").split()).strip()
        key = value.casefold()
        if not value or key in seen:
            continue
        seen.add(key)
        unique.append(value)
    return unique


def _generic_document_snapshot(documents: list[dict[str, Any]]) -> tuple[list[str], list[str]]:
    summaries: list[str] = []
    references: list[str] = []
    for doc in documents:
        structured = get_structured_document_model(doc)
        if structured is not None:
            summary = getattr(structured, "resumen", "") or getattr(structured, "observaciones", "")
            if summary:
                summaries.append(str(summary))
            for item in getattr(structured, "referencias_diagnosticas", []) or []:
                nombre = str(getattr(item, "nombre", "") or "").strip()
                concepto = str(getattr(item, "concepto", "") or "").strip()
                references.append(" - ".join([part for part in (nombre, concepto) if part]))
            continue
        raw_summary = str(doc.get("descripcion") or "").strip()
        if raw_summary:
            summaries.append(raw_summary)
        html_summary = strip_html_tags(
            str(doc.get("analisis_html") or render_document_analysis_html(doc) or "")
        )
        if html_summary:
            summaries.append(html_summary[:400])
    return _clean_unique_texts(summaries), _clean_unique_texts(references)


def _document_types_available(documents: CaseEpicrisisDocuments) -> list[str]:
    available: list[str] = []
    if documents.historia:
        available.append("historia_clinica")
    if documents.quirurgico:
        available.append("quirurgico")
    if documents.factura:
        available.append("factura")
    if documents.radiologia:
        available.append("radiologia")
    if documents.laboratorio:
        available.append("laboratorio")
    if documents.generico:
        available.append("generico")
    if documents.prescripcion:
        available.append("prescripcion")
    return available


def _extract_prescription_medications(documents: list[dict[str, Any]]) -> list[dict[str, Any]]:
    medications: list[dict[str, Any]] = []
    for document in documents:
        structured = get_structured_document_model(document)
        for item in getattr(structured, "medicamentos", []) or []:
            medication = medication_from_legacy(
                {
                    "codigo_referencia": getattr(item, "codigo", "") or "",
                    "nombre": getattr(item, "nombre", "") or "",
                    "dosis": getattr(item, "dosis", "") or "",
                    "posologia": getattr(item, "posologia", "") or "",
                    "via": getattr(item, "via", "") or "",
                    "frecuencia": getattr(item, "frecuencia", "") or "",
                    "duracion": getattr(item, "duracion", "") or "",
                    "cantidad": getattr(item, "cantidad", "") or "",
                    "tipo_uso": "formulado",
                    "fuente": "prescripcion",
                    "texto_original": getattr(item, "diagnostico_contexto", "") or "",
                },
                fuente="prescripcion",
            )
            if medication:
                medications.append(medication)
    return consolidate_medication_items(medications)


def _build_default_rule_evaluation() -> EpicrisisRuleEvaluation:
    return EpicrisisRuleEvaluation(
        status=EpicrisisRuleStatus.PASSED,
        generation_allowed=True,
        ready_for_epicrisis=True,
        findings=[],
        required_documents=[],
        missing_documents=[],
        evaluated_at=datetime.now(UTC),
        evaluation_mode=EpicrisisRuleEvaluationMode.DETERMINISTIC_ONLY,
    )


@dataclass
class EvaluateEpicrisisRulesUseCase:
    rules: list[EpicrisisPreGenerationRule]

    def execute(self, snapshot: EpicrisisEvidenceSnapshot) -> EpicrisisRuleEvaluation:
        if not self.rules:
            return _build_default_rule_evaluation()

        evaluations = [rule.evaluate(snapshot) for rule in self.rules]
        findings = [item for evaluation in evaluations for item in evaluation.findings]
        required_documents = _clean_unique_texts(
            [item for evaluation in evaluations for item in evaluation.required_documents]
        )
        missing_documents = _clean_unique_texts(
            [item for evaluation in evaluations for item in evaluation.missing_documents]
        )
        status = EpicrisisRuleStatus.PASSED
        generation_allowed = True
        ready_for_epicrisis = True
        if any(item.status == EpicrisisRuleStatus.BLOCKED for item in evaluations):
            status = EpicrisisRuleStatus.BLOCKED
            generation_allowed = False
            ready_for_epicrisis = False
        elif any(item.status == EpicrisisRuleStatus.WARNING for item in evaluations):
            status = EpicrisisRuleStatus.WARNING

        evaluation_mode = (
            EpicrisisRuleEvaluationMode.DETERMINISTIC_PLUS_LLM
            if any(
                item.evaluation_mode == EpicrisisRuleEvaluationMode.DETERMINISTIC_PLUS_LLM
                for item in evaluations
            )
            else EpicrisisRuleEvaluationMode.DETERMINISTIC_ONLY
        )
        evaluated_at = max(item.evaluated_at for item in evaluations)
        return EpicrisisRuleEvaluation(
            status=status,
            generation_allowed=generation_allowed,
            ready_for_epicrisis=ready_for_epicrisis,
            findings=findings,
            required_documents=required_documents,
            missing_documents=missing_documents,
            evaluated_at=evaluated_at,
            evaluation_mode=evaluation_mode,
        )


@dataclass
class BuildCaseEpicrisisContextUseCase:
    documents_reader: CaseDocumentsReader
    coding_gateway: EpicrisisCodingGateway
    rules_evaluator: EvaluateEpicrisisRulesUseCase | None = None
    medication_pertinence_reviewer: MedicationPertinenceClinicalReviewer | None = None
    summary_composer: EpicrisisSummaryComposer | None = None

    def _load_documents(self, username: str, case_key: str) -> CaseEpicrisisDocuments:
        documents = CaseEpicrisisDocuments.from_payload(
            self.documents_reader.get_case_documents(username, case_key)
        )
        if not documents.has_clinical_content():
            raise ValueError("No se encontraron documentos clinicos para el caso.")
        return documents

    def _build_evidence_snapshot(
        self,
        documents: CaseEpicrisisDocuments,
        case_key: str,
    ) -> EpicrisisEvidenceSnapshot:
        historia_doc = documents.historia
        historia_html = documents.historia_html
        historia_metadatos = extraer_metadatos_historia(historia_doc or historia_html)
        antecedentes_historia = extraer_antecedentes_historia(historia_doc or historia_html)
        procedimientos_historia = extraer_procedimientos_historia(historia_doc or historia_html)
        procedimientos_factura = [
            str(item.get("descripcion") or item.get("procedimiento") or "").strip()
            for item in extraer_procedimientos_factura(documents.factura)
            if str(item.get("descripcion") or item.get("procedimiento") or "").strip()
        ]
        hallazgos_qx, descripcion_qx = extraer_secciones_quirurgicas(
            documents.quirurgico or documents.quirurgico_html
        )
        ayudas_diagnosticas = [
            format_ayuda_diagnostica_presentacion(item)
            for item in build_ayudas_diagnosticas(
                {
                    "factura": documents.factura,
                    "radiologia": documents.radiologia,
                    "laboratorio": documents.laboratorio,
                    "generico": documents.generico,
                    "historia": documents.historia,
                    "quirurgico": documents.quirurgico,
                    "hallazgos_quirurgicos": hallazgos_qx,
                    "descripcion_procedimiento": descripcion_qx,
                }
            )
        ]
        generico_resumenes, generico_referencias = _generic_document_snapshot(documents.generico)
        weak_signals = _clean_unique_texts(
            [
                *[
                    format_ayuda_diagnostica_presentacion(item)
                    for item in build_ayudas_diagnosticas(
                        {
                            "factura": None,
                            "radiologia": documents.radiologia,
                            "laboratorio": documents.laboratorio,
                            "generico": [],
                            "historia": None,
                            "quirurgico": None,
                            "hallazgos_quirurgicos": "",
                            "descripcion_procedimiento": "",
                        }
                    )
                ],
                *generico_resumenes,
            ]
        )
        return EpicrisisEvidenceSnapshot(
            case_key=case_key,
            patient_name=documents.nombre_paciente,
            case_number=documents.case_number,
            has_quirurgico_document=bool(documents.quirurgico),
            document_types_available=_document_types_available(documents),
            historia_metadatos=historia_metadatos,
            procedimientos_factura=procedimientos_factura,
            procedimientos_historia=_clean_unique_texts([str(item) for item in procedimientos_historia]),
            antecedentes_historia=_clean_unique_texts([str(item) for item in antecedentes_historia]),
            historia_resumen=str(historia_metadatos.get("resumen") or "").strip(),
            historia_motivo_consulta=str(historia_metadatos.get("motivo_consulta") or "").strip(),
            generico_resumenes=generico_resumenes,
            generico_referencias=generico_referencias,
            ayudas_diagnosticas=_clean_unique_texts(ayudas_diagnosticas),
            hallazgos_quirurgicos=str(hallazgos_qx or "").strip(),
            descripcion_procedimiento=str(descripcion_qx or "").strip(),
            support_signals=generico_referencias,
            weak_signals=weak_signals,
        )

    def _build_preserved_state(
        self,
        preserved_context: dict[str, Any] | None,
    ) -> PreservedEpicrisisState:
        preserved = normalize_context_payload(preserved_context or {})
        return PreservedEpicrisisState(
            manual_soat_results=preserved.get("manual_soat_results") or [],
            pdf_draft=coerce_pdf_draft(preserved.get("pdf_draft")),
        )

    def _resolve_blocking_reason(self, evaluation: EpicrisisRuleEvaluation | None) -> str:
        if evaluation is None:
            return ""
        for item in evaluation.findings:
            if item.blocking:
                return item.message
        return evaluation.findings[0].message if evaluation.findings else ""

    def _build_base_context(
        self,
        *,
        documents: CaseEpicrisisDocuments,
        case_key: str,
        preserved_state: PreservedEpicrisisState,
        rule_evaluation: EpicrisisRuleEvaluation,
        evidence_snapshot: EpicrisisEvidenceSnapshot,
    ) -> dict[str, Any]:
        evaluation_payload = rule_evaluation.model_dump(mode="python", exclude_none=True)
        blocking_reason = self._resolve_blocking_reason(rule_evaluation)
        return {
            "nombre_paciente": documents.nombre_paciente,
            "case_key": case_key,
            "case_number": documents.case_number,
            "historia": serialize_doc(documents.historia),
            "quirurgico": serialize_doc(documents.quirurgico),
            "factura": normalize_factura_medicamentos(serialize_doc(documents.factura)),
            "radiologia": [serialize_doc(doc) for doc in documents.radiologia],
            "laboratorio": [serialize_doc(doc) for doc in documents.laboratorio],
            "generico": [serialize_doc(doc) for doc in documents.generico],
            "prescripcion": [serialize_doc(doc) for doc in documents.prescripcion],
            "metadatos_hc": evidence_snapshot.historia_metadatos,
            "antecedentes_hc": list(evidence_snapshot.antecedentes_historia),
            "procedimientos_hc": list(evidence_snapshot.procedimientos_historia),
            "medicamentos_hc": [],
            "medicamentos_hc_display": [],
            "recomendaciones_medicas": [],
            "ayudas_diagnosticas": [],
            "imagenes_diagnosticas": [],
            "hallazgos_quirurgicos": evidence_snapshot.hallazgos_quirurgicos,
            "descripcion_procedimiento": evidence_snapshot.descripcion_procedimiento,
            "soat_resultados": [],
            "glosa_analisis": "",
            "procedimientos_factura": [
                {"codigo_soat": "", "descripcion": item} for item in evidence_snapshot.procedimientos_factura
            ],
            "auto_soat_results": [],
            "qx_agent_results": [],
            "manual_soat_results": preserved_state.manual_soat_results,
            "pdf_draft": preserved_state.pdf_draft,
            "codigos_desde_soat": merge_soat_results([], preserved_state.manual_soat_results),
            "regen_url": f"/epicrisis?case_key={case_key}&regen=1",
            "epicrisis_cached": False,
            "orden_costo_version": COST_ORDER_VERSION,
            "rule_evaluation": evaluation_payload,
            "epicrisis_generation_allowed": rule_evaluation.generation_allowed,
            "required_documents": list(rule_evaluation.required_documents),
            "missing_documents": list(rule_evaluation.missing_documents),
            "blocking_reason": blocking_reason,
        }

    def _build_clinical_snapshot(self, documents: CaseEpicrisisDocuments) -> ClinicalSnapshot:
        historia_doc = documents.historia
        quirurgico_doc = documents.quirurgico
        historia_html = documents.historia_html
        quirurgico_html = documents.quirurgico_html
        medicamentos_hc = extraer_medicamentos_historia(historia_doc or historia_html)
        medicamentos_prescripcion = _extract_prescription_medications(documents.prescripcion)
        hallazgos_qx, descripcion_qx = extraer_secciones_quirurgicas(quirurgico_doc or quirurgico_html)
        ayudas_diagnosticas = build_ayudas_diagnosticas(
            {
                "factura": documents.factura,
                "radiologia": documents.radiologia,
                "laboratorio": documents.laboratorio,
                "generico": documents.generico,
                "historia": documents.historia,
                "quirurgico": documents.quirurgico,
                "hallazgos_quirurgicos": hallazgos_qx,
                "descripcion_procedimiento": descripcion_qx,
            }
        )
        return ClinicalSnapshot(
            metadatos_hc=extraer_metadatos_historia(historia_doc or historia_html),
            antecedentes_hc=extraer_antecedentes_historia(historia_doc or historia_html),
            antecedentes_hc_estructurados=extraer_antecedentes_historia_estructurados(
                historia_doc or historia_html
            ),
            procedimientos_hc=extraer_procedimientos_historia(historia_doc or historia_html),
            medicamentos_hc=medicamentos_hc,
            medicamentos_hc_display=build_medication_display_list(medicamentos_hc),
            recomendaciones_medicas=extract_recomendaciones_medicas(historia_doc),
            medicamentos_prescripcion=medicamentos_prescripcion,
            hallazgos_qx=hallazgos_qx,
            descripcion_qx=descripcion_qx,
            procedimientos_factura=extraer_procedimientos_factura(documents.factura),
            ayudas_diagnosticas=ayudas_diagnosticas,
            imagenes_diagnosticas=[
                format_ayuda_diagnostica_presentacion(item)
                for item in ayudas_diagnosticas
                if item.get("tipo") == "imagen"
            ]
            or extraer_imagenes_diagnosticas(documents.factura, documents.radiologia),
        )

    def _build_auto_soat_results(
        self,
        soat_resultados: list[dict[str, Any]],
        procedimientos_factura: list[dict[str, str]],
    ) -> list[dict[str, Any]]:
        auto_soat_results: list[dict[str, Any]] = []
        descripciones_soat = [
            {
                "codigo_soat": str(item.get("codigo_soat") or "").strip(),
                "descripcion": str(item.get("descripcion") or "").strip(),
            }
            for item in soat_resultados[:5]
            if str(item.get("descripcion") or "").strip()
        ]
        if descripciones_soat:
            auto_soat_results = merge_soat_results(
                auto_soat_results,
                self.coding_gateway.generate_codes_from_descriptions(descripciones_soat),
            )
        if procedimientos_factura:
            auto_soat_results = merge_soat_results(
                auto_soat_results,
                self.coding_gateway.generate_codes_from_descriptions(procedimientos_factura),
            )
        return auto_soat_results

    def _build_coding_snapshot(
        self,
        documents: CaseEpicrisisDocuments,
        clinical_snapshot: ClinicalSnapshot,
    ) -> CodingSnapshot:
        soat_resultados, glosa_html = self.coding_gateway.build_soat_and_glosa(
            historia_html=documents.historia_html,
            qx_html=documents.quirurgico_html,
        )
        return CodingSnapshot(
            soat_resultados=soat_resultados,
            glosa_html=glosa_html,
            auto_soat_results=self._build_auto_soat_results(
                soat_resultados,
                clinical_snapshot.procedimientos_factura,
            ),
            qx_agent_results=self.coding_gateway.generate_qx_results(
                hallazgos=clinical_snapshot.hallazgos_qx,
                descripcion_procedimiento=clinical_snapshot.descripcion_qx,
            ),
        )

    def execute(
        self,
        username: str,
        case_key: str,
        *,
        preserved_context: dict[str, Any] | None = None,
    ) -> dict[str, Any]:
        documents = self._load_documents(username, case_key)
        preserved_state = self._build_preserved_state(preserved_context)
        evidence_snapshot = self._build_evidence_snapshot(documents, case_key)
        rule_evaluation = (
            self.rules_evaluator.execute(evidence_snapshot)
            if self.rules_evaluator is not None
            else _build_default_rule_evaluation()
        )
        context = self._build_base_context(
            documents=documents,
            case_key=case_key,
            preserved_state=preserved_state,
            rule_evaluation=rule_evaluation,
            evidence_snapshot=evidence_snapshot,
        )
        if not rule_evaluation.generation_allowed:
            raise EpicrisisGenerationBlockedError(
                self._resolve_blocking_reason(rule_evaluation)
                or "No se puede generar la epicrisis sin documento quirúrgico requerido.",
                evaluation=rule_evaluation,
                context=context,
            )

        clinical_snapshot = self._build_clinical_snapshot(documents)
        coding_snapshot = self._build_coding_snapshot(documents, clinical_snapshot)
        integrated_summary = build_integrated_summary_context(
            username=username,
            historia=documents.historia,
            quirurgico=documents.quirurgico,
            metadatos_hc=clinical_snapshot.metadatos_hc,
            summary_composer=self.summary_composer,
        )
        metadatos_hc = dict(clinical_snapshot.metadatos_hc)
        metadatos_hc["resumen"] = integrated_summary["texto"]
        context.update(
            {
                "metadatos_hc": metadatos_hc,
                "resumen_clinico_integrado": integrated_summary,
                "antecedentes_hc": clinical_snapshot.antecedentes_hc,
                "antecedentes_hc_estructurados": clinical_snapshot.antecedentes_hc_estructurados,
                "procedimientos_hc": clinical_snapshot.procedimientos_hc,
                "medicamentos_hc": clinical_snapshot.medicamentos_hc,
                "medicamentos_hc_display": clinical_snapshot.medicamentos_hc_display,
                "recomendaciones_medicas": clinical_snapshot.recomendaciones_medicas,
                "medicamentos_prescripcion": clinical_snapshot.medicamentos_prescripcion,
                "ayudas_diagnosticas": clinical_snapshot.ayudas_diagnosticas,
                "imagenes_diagnosticas": clinical_snapshot.imagenes_diagnosticas,
                "hallazgos_quirurgicos": clinical_snapshot.hallazgos_qx,
                "descripcion_procedimiento": clinical_snapshot.descripcion_qx,
                "soat_resultados": coding_snapshot.soat_resultados,
                "glosa_analisis": coding_snapshot.glosa_html,
                "procedimientos_factura": clinical_snapshot.procedimientos_factura,
                "auto_soat_results": coding_snapshot.auto_soat_results,
                "qx_agent_results": coding_snapshot.qx_agent_results,
                "codigos_desde_soat": merge_soat_results(
                    merge_soat_results(
                        coding_snapshot.auto_soat_results,
                        coding_snapshot.qx_agent_results,
                    ),
                    preserved_state.manual_soat_results,
                ),
            }
        )
        context = normalize_context_payload(context)
        context = self.coding_gateway.refresh_diagnostico_context(context)
        curation = reconcile_case_objective_data(
            [
                documents.historia,
                documents.quirurgico,
                documents.factura,
                *documents.radiologia,
                *documents.laboratorio,
                *documents.generico,
                *documents.prescripcion,
            ],
            preserved_context=preserved_context,
        )
        if curation is not None:
            context.update(curation_context_payload(curation))
            apply_factura_cost_ordering(context)
        if self.medication_pertinence_reviewer is not None:
            reviews = self.medication_pertinence_reviewer.review(
                medications=context.get("medicamentos_caso") or [],
                diagnoses=context.get("diagnosticos_consolidados") or [],
                procedures=context.get("procedimientos_clinicos") or [],
            )
            context["medicamentos_caso"] = apply_clinical_pertinence_reviews(
                context.get("medicamentos_caso") or [],
                reviews,
            )
            context["medicamentos_glosa"] = build_medication_glosa_findings(context["medicamentos_caso"])
        return context


@dataclass
class GetCaseEpicrisisContextUseCase:
    cache_repository: CaseEpicrisisCacheRepository
    build_case_context_use_case: BuildCaseEpicrisisContextUseCase

    def execute(
        self,
        username: str,
        case_key: str,
        *,
        regen: bool = False,
    ) -> dict[str, Any]:
        existing_doc = self.cache_repository.get(username, case_key)
        existing_context = existing_doc.get("contexto") if existing_doc else None
        if not regen and isinstance(existing_context, dict):
            return normalize_context_payload(existing_context)

        preserved_context = (
            normalize_context_payload(existing_context) if isinstance(existing_context, dict) else None
        )
        try:
            context = self.build_case_context_use_case.execute(
                username,
                case_key,
                preserved_context=preserved_context,
            )
        except EpicrisisGenerationBlockedError as exc:
            self.cache_repository.upsert(
                username=username,
                case_key=case_key,
                context=exc.context,
                regen_requested=regen,
            )
            raise
        self.cache_repository.upsert(
            username=username,
            case_key=case_key,
            context=context,
            regen_requested=regen,
        )
        return context


@dataclass
class UpdateCaseEpicrisisDraftUseCase:
    cache_repository: CaseEpicrisisCacheRepository
    get_case_context_use_case: GetCaseEpicrisisContextUseCase

    def execute(
        self,
        username: str,
        case_key: str,
        *,
        payload: dict[str, Any],
    ) -> dict[str, Any]:
        context = normalize_context_payload(
            self.get_case_context_use_case.execute(username, case_key, regen=False)
        )

        incoming_manual = payload.get("manual_soat_results")
        if incoming_manual is not None:
            context["manual_soat_results"] = merge_soat_results([], incoming_manual)

        current_draft = coerce_pdf_draft(context.get("pdf_draft"))
        draft_payload = payload.get("pdf_draft") if isinstance(payload.get("pdf_draft"), dict) else payload
        merged_draft = {**current_draft, **coerce_pdf_draft(draft_payload)}
        context["codigos_desde_soat"] = merge_soat_results(
            merge_soat_results(context.get("auto_soat_results"), context.get("qx_agent_results")),
            context.get("manual_soat_results"),
        )
        context["pdf_draft"] = merged_draft
        refreshed_context = self.get_case_context_use_case.build_case_context_use_case.coding_gateway.refresh_diagnostico_context(
            context
        )

        self.cache_repository.upsert(
            username=username,
            case_key=case_key,
            context=refreshed_context,
            regen_requested=False,
        )
        return refreshed_context
