import contextvars
import logging
import threading
import uuid
from collections.abc import Generator, Mapping
from typing import Any, Literal, Optional, Union, overload

from flask import Flask, current_app
from pydantic import ValidationError

import contexts
from configs import dify_config
from constants import UUID_NIL
from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
from core.app.apps.advanced_chat.app_config_manager import AdvancedChatAppConfigManager
from core.app.apps.advanced_chat.app_runner import AdvancedChatAppRunner
from core.app.apps.advanced_chat.generate_response_converter import AdvancedChatAppGenerateResponseConverter
from core.app.apps.advanced_chat.generate_task_pipeline import AdvancedChatAppGenerateTaskPipeline
from core.app.apps.base_app_queue_manager import AppQueueManager, GenerateTaskStoppedError, PublishFrom
from core.app.apps.message_based_app_generator import MessageBasedAppGenerator
from core.app.apps.message_based_app_queue_manager import MessageBasedAppQueueManager
from core.app.entities.app_invoke_entities import AdvancedChatAppGenerateEntity, InvokeFrom
from core.app.entities.task_entities import ChatbotAppBlockingResponse, ChatbotAppStreamResponse
from core.model_runtime.errors.invoke import InvokeAuthorizationError
from core.ops.ops_trace_manager import TraceQueueManager
from core.prompt.utils.get_thread_messages_length import get_thread_messages_length
from extensions.ext_database import db
from factories import file_factory
from models.account import Account
from models.model import App, Conversation, EndUser, Message
from models.workflow import Workflow
from services.errors.message import MessageNotExistsError

logger = logging.getLogger(__name__)


class AdvancedChatAppGenerator(MessageBasedAppGenerator):
    _dialogue_count: int

    @overload
    def generate(
        self,
        app_model: App,
        workflow: Workflow,
        user: Union[Account, EndUser],
        args: Mapping[str, Any],
        invoke_from: InvokeFrom,
        streaming: Literal[True],
    ) -> Generator[str, None, None]: ...

    @overload
    def generate(
        self,
        app_model: App,
        workflow: Workflow,
        user: Union[Account, EndUser],
        args: Mapping[str, Any],
        invoke_from: InvokeFrom,
        streaming: Literal[False],
    ) -> Mapping[str, Any]: ...

    @overload
    def generate(
        self,
        app_model: App,
        workflow: Workflow,
        user: Union[Account, EndUser],
        args: Mapping[str, Any],
        invoke_from: InvokeFrom,
        streaming: bool = True,
    ) -> Union[Mapping[str, Any], Generator[str, None, None]]: ...

    def generate(
        self,
        app_model: App,
        workflow: Workflow,
        user: Union[Account, EndUser],
        args: Mapping[str, Any],
        invoke_from: InvokeFrom,
        streaming: bool = True,
    ):
        """
        Generate App response.

        :param app_model: App
        :param workflow: Workflow
        :param user: account or end user
        :param args: request args
        :param invoke_from: invoke from source
        :param stream: is stream
        """
        if not args.get("query"):
            raise ValueError("query is required")

        query = args["query"]
        if not isinstance(query, str):
            raise ValueError("query must be a string")

        query = query.replace("\x00", "")
        inputs = args["inputs"]

        extras = {"auto_generate_conversation_name": args.get("auto_generate_name", False)}

        # get conversation
        conversation = None
        conversation_id = args.get("conversation_id")
        if conversation_id:
            conversation = self._get_conversation_by_user(
                app_model=app_model, conversation_id=conversation_id, user=user
            )

        # parse files
        files = args["files"] if args.get("files") else []
        file_extra_config = FileUploadConfigManager.convert(workflow.features_dict, is_vision=False)
        if file_extra_config:
            file_objs = file_factory.build_from_mappings(
                mappings=files,
                tenant_id=app_model.tenant_id,
                config=file_extra_config,
            )
        else:
            file_objs = []

        # convert to app config
        app_config = AdvancedChatAppConfigManager.get_app_config(app_model=app_model, workflow=workflow)

        # get tracing instance
        trace_manager = TraceQueueManager(
            app_id=app_model.id, user_id=user.id if isinstance(user, Account) else user.session_id
        )

        if invoke_from == InvokeFrom.DEBUGGER:
            # always enable retriever resource in debugger mode
            app_config.additional_features.show_retrieve_source = True

        workflow_run_id = str(uuid.uuid4())
        # init application generate entity
        application_generate_entity = AdvancedChatAppGenerateEntity(
            task_id=str(uuid.uuid4()),
            app_config=app_config,
            file_upload_config=file_extra_config,
            conversation_id=conversation.id if conversation else None,
            inputs=conversation.inputs
            if conversation
            else self._prepare_user_inputs(
                user_inputs=inputs, variables=app_config.variables, tenant_id=app_model.tenant_id
            ),
            query=query,
            files=list(file_objs),
            parent_message_id=args.get("parent_message_id") if invoke_from != InvokeFrom.SERVICE_API else UUID_NIL,
            user_id=user.id,
            stream=streaming,
            invoke_from=invoke_from,
            extras=extras,
            trace_manager=trace_manager,
            workflow_run_id=workflow_run_id,
        )
        contexts.tenant_id.set(application_generate_entity.app_config.tenant_id)

        return self._generate(
            workflow=workflow,
            user=user,
            invoke_from=invoke_from,
            application_generate_entity=application_generate_entity,
            conversation=conversation,
            stream=streaming,
        )

    def single_iteration_generate(
        self, app_model: App, workflow: Workflow, node_id: str, user: Account, args: dict, streaming: bool = True
    ) -> Mapping[str, Any] | Generator[str, None, None]:
        """
        Generate App response.

        :param app_model: App
        :param workflow: Workflow
        :param user: account or end user
        :param args: request args
        :param invoke_from: invoke from source
        :param stream: is stream
        """
        if not node_id:
            raise ValueError("node_id is required")

        if args.get("inputs") is None:
            raise ValueError("inputs is required")

        # convert to app config
        app_config = AdvancedChatAppConfigManager.get_app_config(app_model=app_model, workflow=workflow)

        # init application generate entity
        application_generate_entity = AdvancedChatAppGenerateEntity(
            task_id=str(uuid.uuid4()),
            app_config=app_config,
            conversation_id=None,
            inputs={},
            query="",
            files=[],
            user_id=user.id,
            stream=streaming,
            invoke_from=InvokeFrom.DEBUGGER,
            extras={"auto_generate_conversation_name": False},
            single_iteration_run=AdvancedChatAppGenerateEntity.SingleIterationRunEntity(
                node_id=node_id, inputs=args["inputs"]
            ),
        )
        contexts.tenant_id.set(application_generate_entity.app_config.tenant_id)

        return self._generate(
            workflow=workflow,
            user=user,
            invoke_from=InvokeFrom.DEBUGGER,
            application_generate_entity=application_generate_entity,
            conversation=None,
            stream=streaming,
        )

    def _generate(
        self,
        *,
        workflow: Workflow,
        user: Union[Account, EndUser],
        invoke_from: InvokeFrom,
        application_generate_entity: AdvancedChatAppGenerateEntity,
        conversation: Optional[Conversation] = None,
        stream: bool = True,
    ) -> Mapping[str, Any] | Generator[str, None, None]:
        """
        Generate App response.

        :param workflow: Workflow
        :param user: account or end user
        :param invoke_from: invoke from source
        :param application_generate_entity: application generate entity
        :param conversation: conversation
        :param stream: is stream
        """
        is_first_conversation = False
        if not conversation:
            is_first_conversation = True

        # init generate records
        (conversation, message) = self._init_generate_records(application_generate_entity, conversation)

        if is_first_conversation:
            # update conversation features
            conversation.override_model_configs = workflow.features
            db.session.commit()
            db.session.refresh(conversation)

        # get conversation dialogue count
        self._dialogue_count = get_thread_messages_length(conversation.id)

        # init queue manager
        queue_manager = MessageBasedAppQueueManager(
            task_id=application_generate_entity.task_id,
            user_id=application_generate_entity.user_id,
            invoke_from=application_generate_entity.invoke_from,
            conversation_id=conversation.id,
            app_mode=conversation.mode,
            message_id=message.id,
        )

        # new thread
        worker_thread = threading.Thread(
            target=self._generate_worker,
            kwargs={
                "flask_app": current_app._get_current_object(),  # type: ignore
                "application_generate_entity": application_generate_entity,
                "queue_manager": queue_manager,
                "conversation_id": conversation.id,
                "message_id": message.id,
                "context": contextvars.copy_context(),
            },
        )

        worker_thread.start()

        # return response or stream generator
        response = self._handle_advanced_chat_response(
            application_generate_entity=application_generate_entity,
            workflow=workflow,
            queue_manager=queue_manager,
            conversation=conversation,
            message=message,
            user=user,
            stream=stream,
        )

        return AdvancedChatAppGenerateResponseConverter.convert(response=response, invoke_from=invoke_from)

    def _generate_worker(
        self,
        flask_app: Flask,
        application_generate_entity: AdvancedChatAppGenerateEntity,
        queue_manager: AppQueueManager,
        conversation_id: str,
        message_id: str,
        context: contextvars.Context,
    ) -> None:
        """
        Generate worker in a new thread.
        :param flask_app: Flask app
        :param application_generate_entity: application generate entity
        :param queue_manager: queue manager
        :param conversation_id: conversation ID
        :param message_id: message ID
        :return:
        """
        for var, val in context.items():
            var.set(val)
        with flask_app.app_context():
            try:
                # get conversation and message
                conversation = self._get_conversation(conversation_id)
                message = self._get_message(message_id)
                if message is None:
                    raise MessageNotExistsError("Message not exists")

                # chatbot app
                runner = AdvancedChatAppRunner(
                    application_generate_entity=application_generate_entity,
                    queue_manager=queue_manager,
                    conversation=conversation,
                    message=message,
                    dialogue_count=self._dialogue_count,
                )

                runner.run()
            except GenerateTaskStoppedError:
                pass
            except InvokeAuthorizationError:
                queue_manager.publish_error(
                    InvokeAuthorizationError("Incorrect API key provided"), PublishFrom.APPLICATION_MANAGER
                )
            except ValidationError as e:
                logger.exception("Validation Error when generating")
                queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
            except ValueError as e:
                if dify_config.DEBUG:
                    logger.exception("Error when generating")
                queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
            except Exception as e:
                logger.exception("Unknown Error when generating")
                queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
            finally:
                db.session.close()

    def _handle_advanced_chat_response(
        self,
        *,
        application_generate_entity: AdvancedChatAppGenerateEntity,
        workflow: Workflow,
        queue_manager: AppQueueManager,
        conversation: Conversation,
        message: Message,
        user: Union[Account, EndUser],
        stream: bool = False,
    ) -> Union[ChatbotAppBlockingResponse, Generator[ChatbotAppStreamResponse, None, None]]:
        """
        Handle response.
        :param application_generate_entity: application generate entity
        :param workflow: workflow
        :param queue_manager: queue manager
        :param conversation: conversation
        :param message: message
        :param user: account or end user
        :param stream: is stream
        :return:
        """
        # init generate task pipeline
        generate_task_pipeline = AdvancedChatAppGenerateTaskPipeline(
            application_generate_entity=application_generate_entity,
            workflow=workflow,
            queue_manager=queue_manager,
            conversation=conversation,
            message=message,
            user=user,
            stream=stream,
            dialogue_count=self._dialogue_count,
        )

        try:
            return generate_task_pipeline.process()
        except ValueError as e:
            if len(e.args) > 0 and e.args[0] == "I/O operation on closed file.":  # ignore this error
                raise GenerateTaskStoppedError()
            else:
                logger.exception(f"Failed to process generate task pipeline, conversation_id: {conversation.id}")
                raise e
