import logging
import time

import click
from celery import shared_task
from sqlalchemy import delete, select

from core.db.session_factory import session_factory
from core.rag.index_processor.index_processor_factory import IndexProcessorFactory
from core.tools.utils.web_reader_tool import get_image_upload_file_ids
from extensions.ext_storage import storage
from models.dataset import Dataset, DatasetMetadataBinding, DocumentSegment, SegmentAttachmentBinding
from models.model import UploadFile
from tasks.refresh_billing_vector_space_task import schedule_billing_vector_space_refresh

logger = logging.getLogger(__name__)


@shared_task(queue="dataset")
def clean_document_task(
    document_id: str,
    dataset_id: str,
    doc_form: str,
    file_id: str | None,
) -> None:
    """
    Clean document when document deleted.
    :param document_id: document id
    :param dataset_id: dataset id
    :param doc_form: doc_form
    :param file_id: file id

    Usage: clean_document_task.delay(document_id, dataset_id)
    """
    logger.info(click.style(f"Start clean document when document deleted: {document_id}", fg="green"))
    start_at = time.perf_counter()
    total_attachment_files = []
    vector_cleanup_succeeded = False

    with session_factory.create_session() as session:
        try:
            dataset = session.scalar(select(Dataset).where(Dataset.id == dataset_id).limit(1))

            if not dataset:
                raise Exception("Document has no dataset")

            dataset_tenant_id = dataset.tenant_id
            segments = session.scalars(select(DocumentSegment).where(DocumentSegment.document_id == document_id)).all()
            # Use JOIN to fetch attachments with bindings in a single query
            attachments_with_bindings = session.execute(
                select(SegmentAttachmentBinding, UploadFile)
                .join(UploadFile, UploadFile.id == SegmentAttachmentBinding.attachment_id)
                .where(
                    SegmentAttachmentBinding.tenant_id == dataset.tenant_id,
                    SegmentAttachmentBinding.dataset_id == dataset_id,
                    SegmentAttachmentBinding.document_id == document_id,
                )
            ).all()

            attachment_ids = [attachment_file.id for _, attachment_file in attachments_with_bindings]
            binding_ids = [binding.id for binding, _ in attachments_with_bindings]
            total_attachment_files.extend([attachment_file.key for _, attachment_file in attachments_with_bindings])

            index_node_ids = [segment.index_node_id for segment in segments if segment.index_node_id]
            segment_contents = [segment.content for segment in segments]
        except Exception:
            logger.exception("Cleaned document when document deleted failed")
            return

    # check segment is exist
    if index_node_ids:
        # Wrap vector / keyword index cleanup in try/except so that a transient
        # failure here (e.g. billing API hiccup propagated via FeatureService when
        # ModelManager is initialized inside ``Vector(dataset)``) does not abort
        # the entire task and leave document_segments / child_chunks / image_files
        # / metadata bindings stranded in PG. Mirrors the pattern already used in
        # ``clean_dataset_task`` so the document row's hard delete (already
        # committed by the caller) does not produce orphan PG rows just because
        # the vector backend or one of its transitive dependencies was unhappy.
        try:
            index_processor = IndexProcessorFactory(doc_form).init_index_processor()
            with session_factory.create_session() as session, session.begin():
                dataset = session.scalar(select(Dataset).where(Dataset.id == dataset_id).limit(1))
                if dataset:
                    index_processor.clean(
                        dataset,
                        index_node_ids,
                        with_keywords=True,
                        delete_child_chunks=True,
                        delete_summaries=True,
                        session=session,
                    )
                    vector_cleanup_succeeded = True
        except Exception:
            logger.exception(
                "Failed to clean vector / keyword index in clean_document_task, "
                "document_id=%s, dataset_id=%s, index_node_ids_count=%d. "
                "Continuing with PG / storage cleanup; vector orphans can be reaped later.",
                document_id,
                dataset_id,
                len(index_node_ids),
            )

    total_image_files = []
    with session_factory.create_session() as session, session.begin():
        for segment_content in segment_contents:
            image_upload_file_ids = get_image_upload_file_ids(segment_content)
            image_files = session.scalars(select(UploadFile).where(UploadFile.id.in_(image_upload_file_ids))).all()
            total_image_files.extend([image_file.key for image_file in image_files])
            image_file_delete_stmt = delete(UploadFile).where(UploadFile.id.in_(image_upload_file_ids))
            session.execute(image_file_delete_stmt)

    with session_factory.create_session() as session, session.begin():
        segment_delete_stmt = delete(DocumentSegment).where(DocumentSegment.document_id == document_id)
        session.execute(segment_delete_stmt)

    for image_file_key in total_image_files:
        try:
            storage.delete(image_file_key)
        except Exception:
            logger.exception(
                "Delete image_files failed when storage deleted, \
                                          image_upload_file_is: %s",
                image_file_key,
            )

    with session_factory.create_session() as session, session.begin():
        if file_id:
            file = session.scalar(select(UploadFile).where(UploadFile.id == file_id).limit(1))
            if file:
                try:
                    storage.delete(file.key)
                except Exception:
                    logger.exception("Delete file failed when document deleted, file_id: %s", file_id)
                session.delete(file)

    with session_factory.create_session() as session, session.begin():
        # delete segment attachments
        if attachment_ids:
            attachment_file_delete_stmt = delete(UploadFile).where(UploadFile.id.in_(attachment_ids))
            session.execute(attachment_file_delete_stmt)

        if binding_ids:
            binding_delete_stmt = delete(SegmentAttachmentBinding).where(SegmentAttachmentBinding.id.in_(binding_ids))
            session.execute(binding_delete_stmt)

    for attachment_file_key in total_attachment_files:
        try:
            storage.delete(attachment_file_key)
        except Exception:
            logger.exception(
                "Delete attachment_file failed when storage deleted, \
                                    attachment_file_id: %s",
                attachment_file_key,
            )

    with session_factory.create_session() as session, session.begin():
        # delete dataset metadata binding
        session.execute(
            delete(DatasetMetadataBinding).where(
                DatasetMetadataBinding.dataset_id == dataset_id,
                DatasetMetadataBinding.document_id == document_id,
            )
        )

    if vector_cleanup_succeeded:
        schedule_billing_vector_space_refresh(dataset_tenant_id)

    end_at = time.perf_counter()
    logger.info(
        click.style(
            f"Cleaned document when document deleted: {document_id} latency: {end_at - start_at}",
            fg="green",
        )
    )
