"""Per-document end-to-end pipeline: OCR -> Docling -> chunk -> persist -> index.

Called by the Celery worker. Idempotent: re-running on the same document deletes
existing chunks for that document and re-creates them, then re-indexes in
OpenSearch and Qdrant.
"""

from __future__ import annotations

import json
import uuid
from datetime import datetime, timezone
from pathlib import Path
from typing import Any

from sqlalchemy import delete, select

from app.config import settings
from app.db.models import (
    ArtifactType,
    Chunk,
    Document,
    DocumentArtifact,
    DocumentStatus,
    Page,
    ProcessingEvent,
)
from app.db.session import session_scope
from app.indexing import opensearch_client, qdrant_client
from app.indexing.embeddings import get_embedder
from app.ingestion.chunker import ChunkRecord, chunk_extraction
from app.ingestion.docling_extractor import ExtractionResult, extract
from app.ingestion.figure_processor import persist_figures
from app.ingestion.ocr import run_ocr
from app.ingestion.table_processor import persist_tables
from app.logging_config import get_logger
from app.storage.local_paths import (
    key_docling_json,
    key_markdown,
    key_ocr_pdf,
    work_dir_for,
)
from app.storage.minio_client import get_storage
from app.utils.language import detect_language

logger = get_logger(__name__)


def process_document_id(document_id: uuid.UUID, run_id: uuid.UUID | None = None) -> dict[str, Any]:
    """Top-level entry called by the Celery task. Wraps the pipeline in
    error handling so the task always either succeeds or marks the document FAILED.
    """
    storage = get_storage()
    storage.ensure_buckets()

    with session_scope() as db:
        doc = db.get(Document, document_id)
        if doc is None:
            logger.warning("pipeline.document_missing", document_id=str(document_id))
            return {"status": "missing"}

        source_path = Path(doc.source_path)
        sha = doc.sha256
        original_artifact = db.execute(
            select(DocumentArtifact).where(
                DocumentArtifact.document_id == doc.id,
                DocumentArtifact.artifact_type == ArtifactType.ORIGINAL_PDF,
            )
        ).scalar_one_or_none()

    work_dir = work_dir_for(document_id)
    local_pdf = work_dir / f"{sha}.pdf"
    if not local_pdf.exists():
        if source_path.exists():
            local_pdf.write_bytes(source_path.read_bytes())
        elif original_artifact:
            storage.get_to_path(original_artifact.storage_bucket, original_artifact.storage_key, local_pdf)
        else:
            return _fail(document_id, run_id, "OCR_FAILED", "Original PDF not available locally or in MinIO")

    # ---------------- OCR ----------------
    ocr_pdf = work_dir / "ocr.pdf"
    try:
        _emit_event(document_id, run_id, DocumentStatus.OCR_STARTED, "OCR started")
        ocr_result = run_ocr(local_pdf, ocr_pdf, languages=settings.ocr_languages)
    except Exception as exc:  # noqa: BLE001
        logger.exception("pipeline.ocr_failed", document_id=str(document_id))
        return _fail(document_id, run_id, DocumentStatus.OCR_FAILED, f"OCR failed: {exc}")

    # Upload OCR PDF (even if we 'skipped' it - OCR PDF is the canonical input to Docling).
    ocr_key = key_ocr_pdf(document_id)
    storage.put_file(
        bucket=storage.derived_bucket,
        key=ocr_key,
        path=ocr_result.output_path,
        content_type="application/pdf",
    )
    with session_scope() as db:
        _ensure_artifact(db, document_id, ArtifactType.OCR_PDF, storage.derived_bucket, ocr_key)
        doc = db.get(Document, document_id)
        if doc is not None:
            doc.status = DocumentStatus.OCR_COMPLETED
        db.add(
            ProcessingEvent(
                run_id=run_id,
                document_id=document_id,
                stage=DocumentStatus.OCR_COMPLETED,
                level="INFO",
                message=f"OCR finished ({ocr_result.reason})",
                data={"skipped": ocr_result.skipped, "languages": ocr_result.languages},
            )
        )

    # ---------------- Docling ----------------
    try:
        _emit_event(document_id, run_id, DocumentStatus.EXTRACTION_STARTED, "Docling extraction started")
        extraction = extract(ocr_result.output_path)
    except Exception as exc:  # noqa: BLE001
        logger.exception("pipeline.docling_failed", document_id=str(document_id))
        return _fail(document_id, run_id, DocumentStatus.EXTRACTION_FAILED, f"Docling failed: {exc}")

    # Persist Markdown + JSON to MinIO.
    md_key = key_markdown(document_id)
    json_key = key_docling_json(document_id)
    storage.put_bytes(
        bucket=storage.derived_bucket,
        key=md_key,
        data=extraction.markdown.encode("utf-8"),
        content_type="text/markdown",
    )
    storage.put_bytes(
        bucket=storage.derived_bucket,
        key=json_key,
        data=json.dumps(extraction.json_payload, ensure_ascii=False).encode("utf-8"),
        content_type="application/json",
    )

    # ---------------- Persist pages, chunks, tables, figures ----------------
    chunk_records = chunk_extraction(extraction)
    sample_text = "\n".join(p.text for p in extraction.pages[:3] if p.text)
    lang = detect_language(sample_text)

    with session_scope() as db:
        _ensure_artifact(db, document_id, ArtifactType.MARKDOWN, storage.derived_bucket, md_key)
        _ensure_artifact(db, document_id, ArtifactType.DOCLING_JSON, storage.derived_bucket, json_key)

        doc = db.get(Document, document_id)
        if doc is None:
            return {"status": "missing"}
        doc.status = DocumentStatus.EXTRACTION_COMPLETED
        if lang and not doc.language_hint:
            doc.language_hint = lang

        page_id_by_number = _upsert_pages(db, document_id, extraction)
        persist_tables(db, storage, document_id, extraction.tables, page_id_by_number)
        persist_figures(db, storage, document_id, extraction.figures, page_id_by_number)

        # Replace chunks idempotently: drop all and re-insert.
        db.execute(delete(Chunk).where(Chunk.document_id == document_id))
        for cr in chunk_records:
            db.add(_to_chunk_row(document_id, page_id_by_number, cr))

        doc.status = DocumentStatus.CHUNKING_COMPLETED
        db.add(
            ProcessingEvent(
                run_id=run_id,
                document_id=document_id,
                stage=DocumentStatus.CHUNKING_COMPLETED,
                level="INFO",
                message="Chunking complete",
                data={"chunks": len(chunk_records)},
            )
        )

    # ---------------- Indexing (OpenSearch + Qdrant) ----------------
    try:
        opensearch_client.ensure_index()
        qdrant_client.ensure_collection()
        opensearch_client.delete_by_document(str(document_id))
        qdrant_client.delete_by_document(str(document_id))

        os_docs, qdrant_points = _build_index_payloads(document_id, chunk_records, extraction, lang)
        if os_docs:
            opensearch_client.index_chunks(os_docs)
        if qdrant_points:
            embedder = get_embedder()
            texts_to_embed = [text for _, text, _ in qdrant_points]
            vectors = embedder.encode(texts_to_embed)
            triples = [
                (chunk_id, vec, payload)
                for (chunk_id, _text, payload), vec in zip(qdrant_points, vectors, strict=True)
            ]
            qdrant_client.upsert_chunks(triples)
    except Exception as exc:  # noqa: BLE001
        logger.exception("pipeline.indexing_failed", document_id=str(document_id))
        return _fail(document_id, run_id, DocumentStatus.FAILED, f"Indexing failed: {exc}")

    with session_scope() as db:
        doc = db.get(Document, document_id)
        if doc is not None:
            doc.status = DocumentStatus.INDEXING_COMPLETED
            doc.error_message = None
        db.add(
            ProcessingEvent(
                run_id=run_id,
                document_id=document_id,
                stage=DocumentStatus.INDEXING_COMPLETED,
                level="INFO",
                message="Indexing complete",
                data={"chunks": len(chunk_records)},
            )
        )

    return {"status": DocumentStatus.INDEXING_COMPLETED, "chunks": len(chunk_records)}


# ---------------- helpers ----------------

def _to_chunk_row(
    document_id: uuid.UUID, page_id_by_number: dict[int, uuid.UUID], cr: ChunkRecord
) -> Chunk:
    return Chunk(
        document_id=document_id,
        page_id=page_id_by_number.get(cr.page_number),
        page_number=cr.page_number,
        block_id=cr.block_id,
        chunk_index=cr.chunk_index,
        block_type=cr.block_type,
        text=cr.text,
        normalized_text=cr.normalized_text,
        token_count=cr.token_count,
        ocr_confidence=None,
        quality_flags=cr.quality_flags,
        chunk_metadata=cr.metadata,
    )


def _upsert_pages(db, document_id: uuid.UUID, extraction: ExtractionResult) -> dict[int, uuid.UUID]:
    existing = {
        p.page_number: p
        for p in db.execute(select(Page).where(Page.document_id == document_id)).scalars()
    }
    out: dict[int, uuid.UUID] = {}
    for ep in extraction.pages:
        page = existing.get(ep.page_number)
        if page is None:
            page = Page(
                document_id=document_id,
                page_number=ep.page_number,
                text=ep.text,
                ocr_confidence=ep.ocr_confidence,
                has_tables=ep.has_tables,
                has_figures=ep.has_figures,
                has_handwriting=ep.has_handwriting,
            )
            db.add(page)
            db.flush()
        else:
            page.text = ep.text
            page.has_tables = ep.has_tables
            page.has_figures = ep.has_figures
            page.has_handwriting = ep.has_handwriting
        out[ep.page_number] = page.id
    return out


def _build_index_payloads(
    document_id: uuid.UUID,
    chunks: list[ChunkRecord],
    extraction: ExtractionResult,
    language_hint: str | None,
) -> tuple[list[dict[str, Any]], list[tuple[str, str, dict[str, Any]]]]:
    with session_scope() as db:
        doc = db.get(Document, document_id)
        if doc is None:
            return [], []
        original_file_name = doc.original_file_name
        source_path = doc.source_path

        chunk_rows = (
            db.execute(select(Chunk).where(Chunk.document_id == document_id))
            .scalars()
            .all()
        )

    os_docs: list[dict[str, Any]] = []
    qdrant: list[tuple[str, str, dict[str, Any]]] = []

    for row in chunk_rows:
        chunk_id = str(row.id)
        text = row.text or ""
        os_docs.append(
            {
                "chunk_id": chunk_id,
                "document_id": str(document_id),
                "source_path": source_path,
                "original_file_name": original_file_name,
                "page_number": row.page_number,
                "block_type": row.block_type,
                "block_id": row.block_id,
                "text": text,
                "normalized_text": row.normalized_text,
                "ocr_confidence": row.ocr_confidence,
                "language_hint": language_hint,
                "metadata": row.chunk_metadata or {},
                "quality_flags": row.quality_flags or {},
                "created_at": (row.created_at or datetime.now(tz=timezone.utc)).isoformat(),
            }
        )
        text_preview = text[:512]
        qdrant.append(
            (
                chunk_id,
                text,
                {
                    "document_id": str(document_id),
                    "source_path": source_path,
                    "original_file_name": original_file_name,
                    "page_number": row.page_number,
                    "block_type": row.block_type,
                    "block_id": row.block_id,
                    "text_preview": text_preview,
                    "ocr_confidence": row.ocr_confidence,
                    "quality_flags": row.quality_flags or {},
                    "metadata": row.chunk_metadata or {},
                },
            )
        )
    return os_docs, qdrant


def _ensure_artifact(db, document_id: uuid.UUID, artifact_type: str, bucket: str, key: str) -> None:
    existing = db.execute(
        select(DocumentArtifact).where(
            DocumentArtifact.document_id == document_id,
            DocumentArtifact.storage_key == key,
        )
    ).scalar_one_or_none()
    if existing:
        return
    db.add(
        DocumentArtifact(
            document_id=document_id,
            artifact_type=artifact_type,
            storage_bucket=bucket,
            storage_key=key,
        )
    )


def _emit_event(document_id: uuid.UUID, run_id: uuid.UUID | None, stage: str, message: str) -> None:
    with session_scope() as db:
        db.add(
            ProcessingEvent(
                run_id=run_id,
                document_id=document_id,
                stage=stage,
                level="INFO",
                message=message,
                data={},
            )
        )


def _fail(
    document_id: uuid.UUID, run_id: uuid.UUID | None, stage: str, message: str
) -> dict[str, Any]:
    with session_scope() as db:
        doc = db.get(Document, document_id)
        if doc is not None:
            doc.status = stage
            doc.error_message = message[:2000]
        db.add(
            ProcessingEvent(
                run_id=run_id,
                document_id=document_id,
                stage=stage,
                level="ERROR",
                message=message,
                data={},
            )
        )
    logger.error("pipeline.failed", document_id=str(document_id), stage=stage, message=message)
    return {"status": stage, "error": message}