import logging
import time

import click
from celery import shared_task
from sqlalchemy import select

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_database import db
from extensions.ext_storage import storage
from models import WorkflowType
from models.dataset import (
    AppDatasetJoin,
    Dataset,
    DatasetMetadata,
    DatasetMetadataBinding,
    DatasetProcessRule,
    DatasetQuery,
    Document,
    DocumentSegment,
    Pipeline,
    SegmentAttachmentBinding,
)
from models.model import UploadFile
from models.workflow import Workflow

logger = logging.getLogger(__name__)


# Add import statement for ValueError
@shared_task(queue="dataset")
def clean_dataset_task(
    dataset_id: str,
    tenant_id: str,
    indexing_technique: str,
    index_struct: str,
    collection_binding_id: str,
    doc_form: str,
    pipeline_id: str | None = None,
):
    """
    Clean dataset when dataset deleted.
    :param dataset_id: dataset id
    :param tenant_id: tenant id
    :param indexing_technique: indexing technique
    :param index_struct: index struct dict
    :param collection_binding_id: collection binding id
    :param doc_form: dataset form

    Usage: clean_dataset_task.delay(dataset_id, tenant_id, indexing_technique, index_struct)
    """
    logger.info(click.style(f"Start clean dataset when dataset deleted: {dataset_id}", fg="green"))
    start_at = time.perf_counter()

    try:
        dataset = Dataset(
            id=dataset_id,
            tenant_id=tenant_id,
            indexing_technique=indexing_technique,
            index_struct=index_struct,
            collection_binding_id=collection_binding_id,
        )
        documents = db.session.scalars(select(Document).where(Document.dataset_id == dataset_id)).all()
        segments = db.session.scalars(select(DocumentSegment).where(DocumentSegment.dataset_id == dataset_id)).all()
        # Use JOIN to fetch attachments with bindings in a single query
        attachments_with_bindings = db.session.execute(
            select(SegmentAttachmentBinding, UploadFile)
            .join(UploadFile, UploadFile.id == SegmentAttachmentBinding.attachment_id)
            .where(SegmentAttachmentBinding.tenant_id == tenant_id, SegmentAttachmentBinding.dataset_id == dataset_id)
        ).all()

        # Enhanced validation: Check if doc_form is None, empty string, or contains only whitespace
        # This ensures all invalid doc_form values are properly handled
        if doc_form is None or (isinstance(doc_form, str) and not doc_form.strip()):
            # Use default paragraph index type for empty/invalid datasets to enable vector database cleanup
            from core.rag.index_processor.constant.index_type import IndexStructureType

            doc_form = IndexStructureType.PARAGRAPH_INDEX
            logger.info(
                click.style(f"Invalid doc_form detected, using default index type for cleanup: {doc_form}", fg="yellow")
            )

        # Add exception handling around IndexProcessorFactory.clean() to prevent single point of failure
        # This ensures Document/Segment deletion can continue even if vector database cleanup fails
        try:
            index_processor = IndexProcessorFactory(doc_form).init_index_processor()
            index_processor.clean(dataset, None, with_keywords=True, delete_child_chunks=True)
            logger.info(click.style(f"Successfully cleaned vector database for dataset: {dataset_id}", fg="green"))
        except Exception:
            logger.exception(click.style(f"Failed to clean vector database for dataset {dataset_id}", fg="red"))
            # Continue with document and segment deletion even if vector cleanup fails
            logger.info(
                click.style(f"Continuing with document and segment deletion for dataset: {dataset_id}", fg="yellow")
            )

        if documents is None or len(documents) == 0:
            logger.info(click.style(f"No documents found for dataset: {dataset_id}", fg="green"))
        else:
            logger.info(click.style(f"Cleaning documents for dataset: {dataset_id}", fg="green"))

            for document in documents:
                db.session.delete(document)
                # delete document file

            for segment in segments:
                image_upload_file_ids = get_image_upload_file_ids(segment.content)
                for upload_file_id in image_upload_file_ids:
                    image_file = db.session.query(UploadFile).where(UploadFile.id == upload_file_id).first()
                    if image_file is None:
                        continue
                    try:
                        storage.delete(image_file.key)
                    except Exception:
                        logger.exception(
                            "Delete image_files failed when storage deleted, \
                                          image_upload_file_is: %s",
                            upload_file_id,
                        )
                    db.session.delete(image_file)
                db.session.delete(segment)
        # delete segment attachments
        if attachments_with_bindings:
            for binding, attachment_file in attachments_with_bindings:
                try:
                    storage.delete(attachment_file.key)
                except Exception:
                    logger.exception(
                        "Delete attachment_file failed when storage deleted, \
                                        attachment_file_id: %s",
                        binding.attachment_id,
                    )
                db.session.delete(attachment_file)
                db.session.delete(binding)

        db.session.query(DatasetProcessRule).where(DatasetProcessRule.dataset_id == dataset_id).delete()
        db.session.query(DatasetQuery).where(DatasetQuery.dataset_id == dataset_id).delete()
        db.session.query(AppDatasetJoin).where(AppDatasetJoin.dataset_id == dataset_id).delete()
        # delete dataset metadata
        db.session.query(DatasetMetadata).where(DatasetMetadata.dataset_id == dataset_id).delete()
        db.session.query(DatasetMetadataBinding).where(DatasetMetadataBinding.dataset_id == dataset_id).delete()
        # delete pipeline and workflow
        if pipeline_id:
            db.session.query(Pipeline).where(Pipeline.id == pipeline_id).delete()
            db.session.query(Workflow).where(
                Workflow.tenant_id == tenant_id,
                Workflow.app_id == pipeline_id,
                Workflow.type == WorkflowType.RAG_PIPELINE,
            ).delete()
        # delete files
        if documents:
            for document in documents:
                try:
                    if document.data_source_type == "upload_file":
                        if document.data_source_info:
                            data_source_info = document.data_source_info_dict
                            if data_source_info and "upload_file_id" in data_source_info:
                                file_id = data_source_info["upload_file_id"]
                                file = (
                                    db.session.query(UploadFile)
                                    .where(UploadFile.tenant_id == document.tenant_id, UploadFile.id == file_id)
                                    .first()
                                )
                                if not file:
                                    continue
                                storage.delete(file.key)
                                db.session.delete(file)
                except Exception:
                    continue

        db.session.commit()
        end_at = time.perf_counter()
        logger.info(
            click.style(f"Cleaned dataset when dataset deleted: {dataset_id} latency: {end_at - start_at}", fg="green")
        )
    except Exception:
        # Add rollback to prevent dirty session state in case of exceptions
        # This ensures the database session is properly cleaned up
        try:
            db.session.rollback()
            logger.info(click.style(f"Rolled back database session for dataset: {dataset_id}", fg="yellow"))
        except Exception:
            logger.exception("Failed to rollback database session")

        logger.exception("Cleaned dataset when dataset deleted failed")
    finally:
        db.session.close()
