from __future__ import annotations

from datetime import timedelta


METRIC_FIELDS = (
    "job_states",
    "duration_seconds_p95",
    "failure_stage",
    "candidate_acceptance_ratio",
    "review_time_seconds_p95",
    "ontology_coverage_ratio",
    "projection_lag_seconds",
)


class DataResearchOperations:
    def __init__(self, repository, *, max_concurrent_jobs=10):
        if int(max_concurrent_jobs) < 1:
            raise ValueError("max_concurrent_jobs must be positive")
        self.repository = repository
        self.max_concurrent_jobs = int(max_concurrent_jobs)

    def can_start_job(self):
        return int(self.repository.running_job_count()) < self.max_concurrent_jobs

    def recover_stale_jobs(self, *, now, stale_after_seconds=900):
        before = now - timedelta(seconds=int(stale_after_seconds))
        recovered = []
        recoverable = {"extracting", "normalizing", "matching"}
        for job in self.repository.list_stale_jobs(before):
            if job.get("status") not in recoverable:
                continue
            self.repository.recover_job(
                str(job["uid"]),
                "queued",
                f"recovered stale {job['status']} job",
            )
            recovered.append(str(job["uid"]))
        return recovered

    def health(self):
        raw = dict(self.repository.aggregate_metrics() or {})
        return {
            "can_start_job": self.can_start_job(),
            "running_job_count": int(self.repository.running_job_count()),
            "max_concurrent_jobs": self.max_concurrent_jobs,
            "dead_letter_count": int(self.repository.dead_letter_count()),
            "metrics": {key: raw.get(key) for key in METRIC_FIELDS},
        }


class DataResearchReconciler:
    def __init__(self, control_plane, graph, storage, knowledge):
        self.control_plane = control_plane
        self.graph = graph
        self.storage = storage
        self.knowledge = knowledge

    def reconcile(self, *, repair=False):
        issues = []
        active = self.graph.active_versions()
        versions = self.control_plane.published_versions()
        for version in versions:
            if active.get(version["ontology_uid"]) != version["version_uid"]:
                issues.append({"type": "projection_mismatch", **version})
        for artifact in self.control_plane.artifacts():
            if not self.storage.exists(artifact["storage_ref"]):
                issues.append({"type": "artifact_missing", **artifact})
        documents = {
            (item["object_uid"], int(item["object_version"])): item
            for item in self.control_plane.governance_documents()
        }
        for version in versions:
            key = (version["ontology_uid"], int(version.get("version", 2)))
            document = documents.get(key)
            expected = self.knowledge.expected_hash(
                version["ontology_uid"], version["version_uid"]
            )
            if document is None or document.get("content_hash") != expected:
                issues.append(
                    {
                        "type": "knowledge_mismatch",
                        **version,
                        "expected_hash": expected,
                    }
                )
        repair_plan = [
            issue
            for issue in issues
            if issue["type"] in {"projection_mismatch", "knowledge_mismatch"}
        ]
        repaired = 0
        if repair:
            for issue in repair_plan:
                if issue["type"] == "projection_mismatch":
                    self.graph.repair_projection(issue)
                else:
                    self.knowledge.repair_document(issue)
                repaired += 1
        return {
            "repair_mode": bool(repair),
            "issues": issues,
            "repair_plan": repair_plan,
            "repaired": repaired,
        }


class SqlAlchemyOperationsRepository:
    def __init__(self, session):
        self.session = session

    def running_job_count(self):
        from app.models.data_research import IngestionJob

        return self.session.query(IngestionJob).filter(
            IngestionJob.status.in_(("queued", "extracting", "normalizing", "matching"))
        ).count()

    def list_stale_jobs(self, before):
        from app.models.data_research import IngestionJob

        return [model.to_dict() for model in self.session.query(IngestionJob).filter(
            IngestionJob.updated_at < before,
            IngestionJob.status.in_(("extracting", "normalizing", "matching")),
        ).all()]

    def recover_job(self, uid, status, safe_error):
        from app.models.data_research import IngestionJob

        model = self.session.get(IngestionJob, str(uid))
        model.status = status
        model.last_error = safe_error
        self.session.flush()

    def aggregate_metrics(self):
        from app.models.data_research import IngestionJob

        states = {}
        for model in self.session.query(IngestionJob.status).all():
            states[model[0]] = states.get(model[0], 0) + 1
        return {
            "job_states": states,
            "duration_seconds_p95": None,
            "failure_stage": {},
            "candidate_acceptance_ratio": None,
            "review_time_seconds_p95": None,
            "ontology_coverage_ratio": None,
            "projection_lag_seconds": None,
        }

    def dead_letter_count(self):
        from sqlalchemy import text

        return self.session.execute(
            text("SELECT COUNT(*) FROM public.outbox_events WHERE status = 'dead_letter'")
        ).scalar() or 0