import logging
import time
from collections.abc import Sequence
from typing import Any, Protocol

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

from configs import dify_config
from core.db.session_factory import session_factory
from core.entities.document_task import DocumentTask
from core.indexing_runner import DocumentIsPausedError, IndexingRunner
from core.rag.index_processor.constant.index_type import IndexStructureType, IndexTechniqueType
from core.rag.pipeline.queue import TenantIsolatedTaskQueue
from enums.cloud_plan import CloudPlan
from libs.datetime_utils import naive_utc_now
from models.dataset import Dataset, Document
from models.enums import IndexingStatus
from services.feature_service import FeatureService
from tasks.generate_summary_index_task import generate_summary_index_task

logger = logging.getLogger(__name__)


class CeleryTaskLike(Protocol):
    def delay(self, *args: Any, **kwargs: Any) -> Any: ...

    def apply_async(self, *args: Any, **kwargs: Any) -> Any: ...


@shared_task(queue="dataset")
def document_indexing_task(dataset_id: str, document_ids: list):
    """
    Async process document
    :param dataset_id:
    :param document_ids:

    .. warning:: TO BE DEPRECATED
        This function will be deprecated and removed in a future version.
        Use normal_document_indexing_task or priority_document_indexing_task instead.

    Usage: document_indexing_task.delay(dataset_id, document_ids)
    """
    logger.warning("document indexing legacy mode received: %s - %s", dataset_id, document_ids)
    _document_indexing(dataset_id, document_ids)


def _document_indexing(dataset_id: str, document_ids: Sequence[str]):
    """
    Process document for tasks
    :param dataset_id:
    :param document_ids:

    Usage: _document_indexing(dataset_id, document_ids)
    """
    start_at = time.perf_counter()

    with session_factory.create_session() as session:
        dataset = session.scalar(select(Dataset).where(Dataset.id == dataset_id).limit(1))
        if not dataset:
            logger.info(click.style(f"Dataset is not found: {dataset_id}", fg="yellow"))
            return
        # check document limit
        features = FeatureService.get_features(dataset.tenant_id)
        try:
            if features.billing.enabled:
                vector_space = features.vector_space
                assert vector_space is not None
                count = len(document_ids)
                batch_upload_limit = int(dify_config.BATCH_UPLOAD_LIMIT)
                if features.billing.subscription.plan == CloudPlan.SANDBOX and count > 1:
                    raise ValueError("Your current plan does not support batch upload, please upgrade your plan.")
                if count > batch_upload_limit:
                    raise ValueError(f"You have reached the batch upload limit of {batch_upload_limit}.")
                if 0 < vector_space.limit <= vector_space.size:
                    raise ValueError(
                        "Your total number of documents plus the number of uploads have over the limit of "
                        "your subscription."
                    )
        except Exception as e:
            for document_id in document_ids:
                document = session.scalar(
                    select(Document).where(Document.id == document_id, Document.dataset_id == dataset_id).limit(1)
                )
                if document:
                    document.indexing_status = IndexingStatus.ERROR
                    document.error = str(e)
                    document.stopped_at = naive_utc_now()
                    session.add(document)
            session.commit()
            return

    # Phase 1: Update status to parsing (short transaction)
    with session_factory.create_session() as session, session.begin():
        documents: list[Document] = list(
            session.scalars(
                select(Document).where(Document.id.in_(document_ids), Document.dataset_id == dataset_id)
            ).all()
        )

        for document in documents:
            if document:
                document.indexing_status = IndexingStatus.PARSING
                document.processing_started_at = naive_utc_now()
                session.add(document)
    # Transaction committed and closed

    # Phase 2: Execute indexing (no transaction - IndexingRunner creates its own sessions)
    has_error = False
    try:
        indexing_runner = IndexingRunner()
        indexing_runner.run(documents)
        end_at = time.perf_counter()
        logger.info(click.style(f"Processed dataset: {dataset_id} latency: {end_at - start_at}", fg="green"))
    except DocumentIsPausedError as ex:
        logger.info(click.style(str(ex), fg="yellow"))
        has_error = True
    except Exception:
        logger.exception("Document indexing task failed, dataset_id: %s", dataset_id)
        has_error = True

    if not has_error:
        with session_factory.create_session() as session:
            # Trigger summary index generation for completed documents if enabled
            # Only generate for high_quality indexing technique and when summary_index_setting is enabled
            # Re-query dataset to get latest summary_index_setting (in case it was updated)
            dataset = session.scalar(select(Dataset).where(Dataset.id == dataset_id).limit(1))
            if not dataset:
                logger.warning("Dataset %s not found after indexing", dataset_id)
                return

            if dataset.indexing_technique == IndexTechniqueType.HIGH_QUALITY:
                summary_index_setting = dataset.summary_index_setting
                if summary_index_setting and summary_index_setting.get("enable"):
                    # expire all session to get latest document's indexing status
                    session.expire_all()
                    # Check each document's indexing status and trigger summary generation if completed

                    documents = list(
                        session.scalars(
                            select(Document).where(Document.id.in_(document_ids), Document.dataset_id == dataset_id)
                        ).all()
                    )

                    for document in documents:
                        if document:
                            logger.info(
                                "Checking document %s for summary generation: status=%s, doc_form=%s, need_summary=%s",
                                document.id,
                                document.indexing_status,
                                document.doc_form,
                                document.need_summary,
                            )
                            if (
                                document.indexing_status == IndexingStatus.COMPLETED
                                and document.doc_form != IndexStructureType.QA_INDEX
                                and document.need_summary is True
                            ):
                                try:
                                    generate_summary_index_task.delay(dataset.id, document.id, None)
                                    logger.info(
                                        "Queued summary index generation task for document %s in dataset %s "
                                        "after indexing completed",
                                        document.id,
                                        dataset.id,
                                    )
                                except Exception:
                                    logger.exception(
                                        "Failed to queue summary index generation task for document %s",
                                        document.id,
                                    )
                                    # Don't fail the entire indexing process if summary task queuing fails
                            else:
                                logger.info(
                                    "Skipping summary generation for document %s: "
                                    "status=%s, doc_form=%s, need_summary=%s",
                                    document.id,
                                    document.indexing_status,
                                    document.doc_form,
                                    document.need_summary,
                                )
                        else:
                            logger.warning("Document %s not found after indexing", document.id)
            else:
                logger.info(
                    "Summary index generation skipped for dataset %s: indexing_technique=%s (not 'high_quality')",
                    dataset.id,
                    dataset.indexing_technique,
                )


def _document_indexing_with_tenant_queue(
    tenant_id: str, dataset_id: str, document_ids: Sequence[str], task_func: CeleryTaskLike
) -> None:
    try:
        _document_indexing(dataset_id, document_ids)
    except Exception:
        logger.exception(
            "Error processing document indexing %s for tenant %s: %s",
            dataset_id,
            tenant_id,
            document_ids,
            exc_info=True,
        )
    finally:
        tenant_isolated_task_queue = TenantIsolatedTaskQueue(tenant_id, "document_indexing")

        # Check if there are waiting tasks in the queue
        # Use rpop to get the next task from the queue (FIFO order)
        next_tasks = tenant_isolated_task_queue.pull_tasks(count=dify_config.TENANT_ISOLATED_TASK_CONCURRENCY)

        logger.info("document indexing tenant isolation queue %s next tasks: %s", tenant_id, next_tasks)

        if next_tasks:
            with current_app.producer_or_acquire() as producer:  # type: ignore
                for next_task in next_tasks:
                    document_task = DocumentTask(**next_task)
                    # Keep the flag set to indicate a task is running
                    tenant_isolated_task_queue.set_task_waiting_time()
                    task_func.apply_async(
                        kwargs={
                            "tenant_id": document_task.tenant_id,
                            "dataset_id": document_task.dataset_id,
                            "document_ids": document_task.document_ids,
                        },
                        producer=producer,
                    )

        else:
            # No more waiting tasks, clear the flag
            tenant_isolated_task_queue.delete_task_key()


@shared_task(queue="dataset")
def normal_document_indexing_task(tenant_id: str, dataset_id: str, document_ids: Sequence[str]):
    """
    Async process document
    :param tenant_id:
    :param dataset_id:
    :param document_ids:

    Usage: normal_document_indexing_task.delay(tenant_id, dataset_id, document_ids)
    """
    logger.info("normal document indexing task received: %s - %s - %s", tenant_id, dataset_id, document_ids)
    _document_indexing_with_tenant_queue(tenant_id, dataset_id, document_ids, normal_document_indexing_task)


@shared_task(queue="priority_dataset")
def priority_document_indexing_task(tenant_id: str, dataset_id: str, document_ids: Sequence[str]):
    """
    Priority async process document
    :param tenant_id:
    :param dataset_id:
    :param document_ids:

    Usage: priority_document_indexing_task.delay(tenant_id, dataset_id, document_ids)
    """
    logger.info("priority document indexing task received: %s - %s - %s", tenant_id, dataset_id, document_ids)
    _document_indexing_with_tenant_queue(tenant_id, dataset_id, document_ids, priority_document_indexing_task)