import contextvars
import json
import logging
import time
import uuid
from collections.abc import Mapping
from concurrent.futures import ThreadPoolExecutor
from typing import Any

import click
from celery import shared_task  # type: ignore
from flask import current_app, g
from sqlalchemy.orm import Session, sessionmaker

from configs import dify_config
from core.app.entities.app_invoke_entities import InvokeFrom, RagPipelineGenerateEntity
from core.app.entities.rag_pipeline_invoke_entities import RagPipelineInvokeEntity
from core.rag.pipeline.queue import TenantIsolatedTaskQueue
from core.repositories.factory import DifyCoreRepositoryFactory
from extensions.ext_database import db
from models import Account, Tenant
from models.dataset import Pipeline
from models.enums import WorkflowRunTriggeredFrom
from models.workflow import Workflow, WorkflowNodeExecutionTriggeredFrom
from services.file_service import FileService

logger = logging.getLogger(__name__)


@shared_task(queue="pipeline")
def rag_pipeline_run_task(
    rag_pipeline_invoke_entities_file_id: str,
    tenant_id: str,
):
    """
    Async Run rag pipeline task using regular priority queue.

    :param rag_pipeline_invoke_entities_file_id: File ID containing serialized RAG pipeline invoke entities
    :param tenant_id: Tenant ID for the pipeline execution
    """
    # run with threading, thread pool size is 10

    try:
        start_at = time.perf_counter()
        rag_pipeline_invoke_entities_content = FileService(db.engine).get_file_content(
            rag_pipeline_invoke_entities_file_id
        )
        rag_pipeline_invoke_entities = json.loads(rag_pipeline_invoke_entities_content)

        # Get Flask app object for thread context
        flask_app = current_app._get_current_object()  # type: ignore

        with ThreadPoolExecutor(max_workers=10) as executor:
            futures = []
            for rag_pipeline_invoke_entity in rag_pipeline_invoke_entities:
                # Submit task to thread pool with Flask app
                future = executor.submit(run_single_rag_pipeline_task, rag_pipeline_invoke_entity, flask_app)
                futures.append(future)

            # Wait for all tasks to complete
            for future in futures:
                try:
                    future.result()  # This will raise any exceptions that occurred in the thread
                except Exception:
                    logging.exception("Error in pipeline task")
        end_at = time.perf_counter()
        logging.info(
            click.style(
                f"tenant_id: {tenant_id} , Rag pipeline run completed. Latency: {end_at - start_at}s", fg="green"
            )
        )
    except Exception:
        logging.exception(click.style(f"Error running rag pipeline, tenant_id: {tenant_id}", fg="red"))
        raise
    finally:
        tenant_isolated_task_queue = TenantIsolatedTaskQueue(tenant_id, "pipeline")

        # Check if there are waiting tasks in the queue
        # Use rpop to get the next task from the queue (FIFO order)
        next_file_ids = tenant_isolated_task_queue.pull_tasks(count=dify_config.TENANT_ISOLATED_TASK_CONCURRENCY)
        logger.info("rag pipeline tenant isolation queue next files: %s", next_file_ids)

        if next_file_ids:
            for next_file_id in next_file_ids:
                # Process the next waiting task
                # Keep the flag set to indicate a task is running
                tenant_isolated_task_queue.set_task_waiting_time()
                rag_pipeline_run_task.delay(  # type: ignore
                    rag_pipeline_invoke_entities_file_id=next_file_id.decode("utf-8")
                    if isinstance(next_file_id, bytes)
                    else next_file_id,
                    tenant_id=tenant_id,
                )
        else:
            # No more waiting tasks, clear the flag
            tenant_isolated_task_queue.delete_task_key()
        file_service = FileService(db.engine)
        file_service.delete_file(rag_pipeline_invoke_entities_file_id)
        db.session.close()


def run_single_rag_pipeline_task(rag_pipeline_invoke_entity: Mapping[str, Any], flask_app):
    """Run a single RAG pipeline task within Flask app context."""
    # Create Flask application context for this thread
    with flask_app.app_context():
        try:
            rag_pipeline_invoke_entity_model = RagPipelineInvokeEntity.model_validate(rag_pipeline_invoke_entity)
            user_id = rag_pipeline_invoke_entity_model.user_id
            tenant_id = rag_pipeline_invoke_entity_model.tenant_id
            pipeline_id = rag_pipeline_invoke_entity_model.pipeline_id
            workflow_id = rag_pipeline_invoke_entity_model.workflow_id
            streaming = rag_pipeline_invoke_entity_model.streaming
            workflow_execution_id = rag_pipeline_invoke_entity_model.workflow_execution_id
            workflow_thread_pool_id = rag_pipeline_invoke_entity_model.workflow_thread_pool_id
            application_generate_entity = rag_pipeline_invoke_entity_model.application_generate_entity

            with Session(db.engine) as session:
                # Load required entities
                account = session.query(Account).where(Account.id == user_id).first()
                if not account:
                    raise ValueError(f"Account {user_id} not found")

                tenant = session.query(Tenant).where(Tenant.id == tenant_id).first()
                if not tenant:
                    raise ValueError(f"Tenant {tenant_id} not found")
                account.current_tenant = tenant

                pipeline = session.query(Pipeline).where(Pipeline.id == pipeline_id).first()
                if not pipeline:
                    raise ValueError(f"Pipeline {pipeline_id} not found")

                workflow = session.query(Workflow).where(Workflow.id == pipeline.workflow_id).first()
                if not workflow:
                    raise ValueError(f"Workflow {pipeline.workflow_id} not found")

                if workflow_execution_id is None:
                    workflow_execution_id = str(uuid.uuid4())

                # Create application generate entity from dict
                entity = RagPipelineGenerateEntity.model_validate(application_generate_entity)

                # Create workflow repositories
                session_factory = sessionmaker(bind=db.engine, expire_on_commit=False)
                workflow_execution_repository = DifyCoreRepositoryFactory.create_workflow_execution_repository(
                    session_factory=session_factory,
                    user=account,
                    app_id=entity.app_config.app_id,
                    triggered_from=WorkflowRunTriggeredFrom.RAG_PIPELINE_RUN,
                )

                workflow_node_execution_repository = (
                    DifyCoreRepositoryFactory.create_workflow_node_execution_repository(
                        session_factory=session_factory,
                        user=account,
                        app_id=entity.app_config.app_id,
                        triggered_from=WorkflowNodeExecutionTriggeredFrom.RAG_PIPELINE_RUN,
                    )
                )

                # Set the user directly in g for preserve_flask_contexts
                g._login_user = account

                # Copy context for passing to pipeline generator
                context = contextvars.copy_context()

                # Direct execution without creating another thread
                # Since we're already in a thread pool, no need for nested threading
                from core.app.apps.pipeline.pipeline_generator import PipelineGenerator

                pipeline_generator = PipelineGenerator()
                # Using protected method intentionally for async execution
                pipeline_generator._generate(  # type: ignore[attr-defined]
                    flask_app=flask_app,
                    context=context,
                    pipeline=pipeline,
                    workflow_id=workflow_id,
                    user=account,
                    application_generate_entity=entity,
                    invoke_from=InvokeFrom.PUBLISHED,
                    workflow_execution_repository=workflow_execution_repository,
                    workflow_node_execution_repository=workflow_node_execution_repository,
                    streaming=streaming,
                    workflow_thread_pool_id=workflow_thread_pool_id,
                )
        except Exception:
            logging.exception("Error in pipeline task")
            raise