#!/usr/bin/env python3
"""Atlas maintenance script.

Goal:
- automatically revisit stored entities
- enrich identifier coverage when Wikidata is present
- keep the claim supersession model authoritative

Operational rule:
- no manual subject list is required for normal runs
- --dry-run shows what would change, without writing
"""

from __future__ import annotations

import argparse
import asyncio
import json
import sys
from pathlib import Path
from dataclasses import asdict
from datetime import datetime, timezone
from typing import Any


ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
    sys.path.insert(0, str(ROOT))

import app.atlas as atlas_module
from app.atlas import resolve_entity
from app.ids import claim_hash
from app.models import AtlasClaim, AtlasClaimObject, AtlasEntity, AtlasProvenance
from app.storage_service import AtlasStorageService
from app.wikidata_lookup import lookup_wikidata

# High-confidence identifier properties we can mine from the full Wikidata entity.
# The goal is to enrich the entity with public identifiers and to reconcile the
# Google MID whenever Wikidata already exposes the same identity through another id.
#
# Note: provenance.retrieval_method describes the evidence/property source
# (for example "MusicBrainz artist ID"), not the name of this script.
WIKIDATA_IDENTIFIER_PROPERTIES: dict[str, tuple[str, str]] = {
    "P2671": ("mid", "Google Knowledge Graph ID"),
    "P434": ("musicbrainz-artist-id", "MusicBrainz artist ID"),
    "P435": ("musicbrainz-work-id", "MusicBrainz work ID"),
    "P436": ("musicbrainz-release-group-id", "MusicBrainz release group ID"),
    "P439": ("musicbrainz-release-id", "MusicBrainz release ID"),
    "P444": ("musicbrainz-recording-id", "MusicBrainz recording ID"),
    "P345": ("imdb-id", "IMDb ID"),
    "P214": ("viaf-id", "VIAF ID"),
    "P213": ("isni", "ISNI"),
    "P227": ("gnd-id", "GND ID"),
}

# Entity-type-specific Wikidata fields worth capturing early. These are the
# fields that help the most with disambiguation and downstream consolidation.
WIKIDATA_TYPE_FIELD_PLAN: dict[str, dict[str, tuple[str, str]]] = {
    "Person": {
        "P569": ("birth-date", "date of birth"),
        "P19": ("birth-place", "place of birth"),
        "P27": ("citizenship", "country of citizenship"),
    },
    "Organization": {
        "P571": ("inception", "inception"),
        "P159": ("headquarters", "headquarters location"),
        "P452": ("industry", "industry"),
    },
    "Location": {
        "P571": ("inception", "inception"),
        "P17": ("country", "country"),
        "P131": ("located-in", "located in the administrative territorial entity"),
    },
}


def _planned_claim_id(subject: str, predicate: str, value: str, layer: str = "raw") -> str:
    created_at = datetime.now(timezone.utc).date().isoformat()
    return f"clm_{layer}_{claim_hash(subject, predicate, value, layer, created_at=created_at)}"


def build_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser(description="Atlas maintenance / claim adjudication helper")
    parser.add_argument("--dry-run", action="store_true", help="Show planned claim updates without writing")
    parser.add_argument("--page-size", type=int, default=50, help="How many entities to scan per page")
    parser.add_argument("--start-after", default="", help="Resume scanning after this canonical label")
    parser.add_argument("--checkpoint-file", default=".atlas-maintenance.checkpoint", help="File storing the last processed label")
    parser.add_argument("--reset-checkpoint", action="store_true", help="Ignore any saved checkpoint and start from the beginning")
    parser.add_argument("--clear-checkpoint", action="store_true", help="Delete the checkpoint file and exit")
    return parser


async def _sparql_bindings(query: str) -> list[dict[str, Any]]:
    svc = AtlasStorageService()
    result = await svc._call_tool("sparql_query", {"query": query})
    if isinstance(result, list) and result:
        first = result[0]
        text = getattr(first, "text", None)
        result = json.loads(text) if text else {}
    return result.get("results", {}).get("bindings", []) if isinstance(result, dict) else []


async def discover_subjects(page_size: int, start_after: str = "") -> list[str]:
    """Ask Virtuoso for known Atlas entities and return their labels.

    This keeps the maintenance job automatic: we operate on the stored graph,
    not on a hand-entered subject list.
    """
    filter_clause = f'FILTER(STR(?label) > "{start_after.replace("\\", "\\\\").replace("\"", "\\\"")}")' if start_after else ""
    query = """
PREFIX atlas: <http://world.eu.org/atlas_ontology#>
SELECT DISTINCT ?label WHERE {{
  GRAPH <http://world.eu.org/atlas_data#> {{
    ?entity a atlas:Entity ;
            atlas:canonicalLabel ?label .
    {filter_clause}
  }}
}}
ORDER BY ?label
LIMIT {page_size}
""".format(filter_clause=filter_clause, page_size=page_size)
    bindings = await _sparql_bindings(query)
    return [b.get("label", {}).get("value", "") for b in bindings if b.get("label", {}).get("value")]


async def maintain_subject(subject: str, dry_run: bool) -> dict[str, Any]:
    # We resolve first so the maintenance run always starts from the current
    # canonical entity shape, then we layer on any new evidence.
    if dry_run:
        original_write = atlas_module._storage.write_entity

        async def _noop_write(entity):
            return {"status": "dry-run", "entity_id": entity.atlas_id}

        atlas_module._storage.write_entity = _noop_write
        try:
            entity = await resolve_entity(subject)
        finally:
            atlas_module._storage.write_entity = original_write
    else:
        entity = await resolve_entity(subject)
    report: dict[str, Any] = {
        "subject": subject,
        "atlas_id": entity.atlas_id,
        "planned": [],
        "written": False,
        "wikidata_status": "missing",
        "planned_identifier_claims": 0,
        "planned_identifier_types": [],
        "planned_type_field_claims": 0,
    }

    wikidata = entity.raw_payload.get("wikidata") if isinstance(entity.raw_payload, dict) else None
    if isinstance(wikidata, dict) and wikidata.get("wikidata_status") == "hit" and wikidata.get("qid"):
        report["wikidata_status"] = "hit"
        # If Wikidata already knows the entity, fetch the full object and mine
        # any additional identifiers we can safely attach as claims.
        full = await lookup_wikidata(subject)
        if full and isinstance(full.get("entity"), dict):
            report["wikidata_status"] = "enriched"
            entity_block = full["entity"]
            claims = entity_block.get("claims", {}) if isinstance(entity_block, dict) else {}

            # QID is always a known cross-reference and acts as a stable anchor.
            qid = full.get("qid")
            existing_qid = entity.active_identifier("qid")
            if qid and qid != existing_qid:
                claim = AtlasClaim(
                    claim_id=_planned_claim_id(entity.atlas_id, "atlas:hasIdentifier", qid),
                    subject=entity.atlas_id,
                    predicate="atlas:hasIdentifier",
                    object=AtlasClaimObject(kind="identifier", id_type="qid", value=qid),
                    layer="raw",
                    provenance=AtlasProvenance(
                        source="wikidata",
                        retrieval_method="atlas-maintenance-wikidata-enrichment",
                        confidence=0.99,
                        retrieved_at=full.get("retrieved_at"),
                        evidence_property="qid",
                    ),
                )
                report["planned"].append({"action": "add_identifier_claim", "claim": asdict(claim)})
                report["planned_identifier_claims"] += 1
                report["planned_identifier_types"].append("qid")

            for wikidata_property, (identifier_type, label) in WIKIDATA_IDENTIFIER_PROPERTIES.items():
                property_claims = claims.get(wikidata_property, []) if isinstance(claims, dict) else []
                for claim_node in property_claims:
                    mainsnak = claim_node.get("mainsnak", {})
                    datavalue = mainsnak.get("datavalue", {})
                    value = datavalue.get("value")
                    if not isinstance(value, str) or not value.strip():
                        continue
                    existing = entity.active_identifier(identifier_type)
                    if existing == value:
                        continue
                    claim = AtlasClaim(
                        claim_id=_planned_claim_id(entity.atlas_id, "atlas:hasIdentifier", value),
                        subject=entity.atlas_id,
                        predicate="atlas:hasIdentifier",
                        object=AtlasClaimObject(kind="identifier", id_type=identifier_type, value=value),
                        layer="raw",
                        provenance=AtlasProvenance(
                            source="wikidata",
                            retrieval_method="atlas-maintenance-wikidata-enrichment",
                            confidence=0.99,
                            retrieved_at=full.get("retrieved_at"),
                            evidence_property=wikidata_property,
                        ),
                    )
                    report["planned"].append({"action": "add_identifier_claim", "claim": asdict(claim)})
                    report["planned_identifier_claims"] += 1
                    report["planned_identifier_types"].append(identifier_type)

            # Type-specific enrichment: different entity kinds care about different fields.
            # We only plan claims for high-confidence public facts that are useful for
            # disambiguation and consolidation.
            type_plan = WIKIDATA_TYPE_FIELD_PLAN.get(entity.entity_type, {})
            for wikidata_property, (claim_type, label) in type_plan.items():
                property_claims = claims.get(wikidata_property, []) if isinstance(claims, dict) else []
                for claim_node in property_claims:
                    mainsnak = claim_node.get("mainsnak", {})
                    datavalue = mainsnak.get("datavalue", {})
                    value = datavalue.get("value")
                    if value in (None, "", {}):
                        continue
                    # For this first pass we capture these as literal payload claims;
                    # the exact ontology mapping can be tightened later.
                    if isinstance(value, dict):
                        # entity / place objects often carry an id and label
                        value = value.get("id") or value.get("time") or value.get("text") or value.get("amount")
                    if not isinstance(value, str):
                        continue
                    claim = AtlasClaim(
                        claim_id=_planned_claim_id(entity.atlas_id, f"atlas:has{claim_type.replace('-', ' ').title().replace(' ', '')}", value),
                        subject=entity.atlas_id,
                        predicate=f"atlas:has{claim_type.replace('-', ' ').title().replace(' ', '')}",
                        object=AtlasClaimObject(kind="literal", value=value),
                        layer="raw",
                        provenance=AtlasProvenance(
                            source="wikidata",
                            retrieval_method="atlas-maintenance-wikidata-enrichment",
                            confidence=0.95,
                            retrieved_at=full.get("retrieved_at"),
                            evidence_property=wikidata_property,
                        ),
                    )
                    report["planned"].append({"action": "add_type_field_claim", "claim": asdict(claim)})
                    report["planned_type_field_claims"] += 1
    if dry_run:
        return report

    # The script currently only reports planned updates.
    # Once the claim update path is wired, this is where write-back will happen.
    report["written"] = False
    return report


async def main() -> int:
    parser = build_parser()
    args = parser.parse_args()

    checkpoint_path = Path(args.checkpoint_file)
    if args.clear_checkpoint:
        if checkpoint_path.exists():
            checkpoint_path.unlink()
        print(json.dumps({"checkpoint_cleared": True, "checkpoint_file": str(checkpoint_path)}, indent=2, ensure_ascii=False))
        return 0

    start_after = args.start_after.strip()
    if args.reset_checkpoint:
        start_after = ""
    elif not start_after:
        checkpoint_path = Path(args.checkpoint_file)
        if checkpoint_path.exists():
            start_after = checkpoint_path.read_text(encoding="utf-8").strip()

    subjects = await discover_subjects(args.page_size, start_after)
    summaries = []
    for subject in subjects:
        summaries.append(await maintain_subject(subject, args.dry_run))

    if subjects and not args.dry_run:
        checkpoint_path.write_text(subjects[-1], encoding="utf-8")

    print(json.dumps({
        "dry_run": args.dry_run,
        "checkpoint_file": str(checkpoint_path),
        "checkpoint_start_after": start_after,
        "results": summaries,
    }, indent=2, ensure_ascii=False))
    return 0


if __name__ == "__main__":
    raise SystemExit(asyncio.run(main()))