from __future__ import annotations

import datetime

from .. import stage, entities
from langbot_plugin.api.entities.builtin.provider import message as provider_message
import langbot_plugin.api.entities.events as events
import langbot_plugin.api.entities.builtin.platform.message as platform_message
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
import langbot_plugin.api.entities.builtin.platform.events as platform_events


@stage.stage_class('PreProcessor')
class PreProcessor(stage.PipelineStage):
    """Request pre-processing stage

    Check out session, prompt, context, model, and content functions.

    Rewrite:
        - session
        - prompt
        - messages
        - user_message
        - use_model
        - use_funcs
    """

    async def process(
        self,
        query: pipeline_query.Query,
        stage_inst_name: str,
    ) -> entities.StageProcessResult:
        """Process"""
        selected_runner = query.pipeline_config['ai']['runner']['runner']

        session = await self.ap.sess_mgr.get_session(query)

        # When not local-agent, llm_model is None
        llm_model = None
        if selected_runner == 'local-agent':
            # Read model config — new format is { primary: str, fallbacks: [str] },
            # but handle legacy plain string for backward compatibility
            model_config = query.pipeline_config['ai']['local-agent'].get('model', {})
            if isinstance(model_config, str):
                # Legacy format: plain UUID string
                primary_uuid = model_config
                fallback_uuids = []
            else:
                primary_uuid = model_config.get('primary', '')
                fallback_uuids = model_config.get('fallbacks', [])

            if primary_uuid:
                try:
                    llm_model = await self.ap.model_mgr.get_model_by_uuid(primary_uuid)
                except ValueError:
                    self.ap.logger.warning(f'LLM model {primary_uuid} not found or not configured')

            # Resolve fallback model UUIDs
            if fallback_uuids:
                valid_fallbacks = []
                for fb_uuid in fallback_uuids:
                    try:
                        await self.ap.model_mgr.get_model_by_uuid(fb_uuid)
                        valid_fallbacks.append(fb_uuid)
                    except ValueError:
                        self.ap.logger.warning(f'Fallback model {fb_uuid} not found, skipping')
                if valid_fallbacks:
                    query.variables['_fallback_model_uuids'] = valid_fallbacks

        conversation = await self.ap.sess_mgr.get_conversation(
            query,
            session,
            query.pipeline_config['ai']['local-agent']['prompt'],
            query.pipeline_uuid,
            query.bot_uuid,
        )

        # 设置query
        query.session = session
        query.prompt = conversation.prompt.copy()
        query.messages = conversation.messages.copy()

        if selected_runner == 'local-agent':
            query.use_funcs = []
            if llm_model:
                query.use_llm_model_uuid = llm_model.model_entity.uuid

                if llm_model.model_entity.abilities.__contains__('func_call'):
                    # Get bound plugins and MCP servers for filtering tools
                    bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
                    bound_mcp_servers = query.variables.get('_pipeline_bound_mcp_servers', None)
                    query.use_funcs = await self.ap.tool_mgr.get_all_tools(bound_plugins, bound_mcp_servers)

                    self.ap.logger.debug(f'Bound plugins: {bound_plugins}')
                    self.ap.logger.debug(f'Bound MCP servers: {bound_mcp_servers}')
                    self.ap.logger.debug(f'Use funcs: {query.use_funcs}')

            # If primary model doesn't support func_call but fallback models exist,
            # load tools anyway since fallback models may support them
            if not query.use_funcs and query.variables.get('_fallback_model_uuids'):
                bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
                bound_mcp_servers = query.variables.get('_pipeline_bound_mcp_servers', None)
                query.use_funcs = await self.ap.tool_mgr.get_all_tools(bound_plugins, bound_mcp_servers)

        sender_name = ''

        if isinstance(query.message_event, platform_events.GroupMessage):
            sender_name = query.message_event.sender.member_name
        elif isinstance(query.message_event, platform_events.FriendMessage):
            sender_name = query.message_event.sender.nickname

        variables = {
            'launcher_type': query.session.launcher_type.value,
            'launcher_id': query.session.launcher_id,
            'sender_id': query.sender_id,
            'session_id': f'{query.session.launcher_type.value}_{query.session.launcher_id}',
            'conversation_id': conversation.uuid,
            'msg_create_time': (
                int(query.message_event.time) if query.message_event.time else int(datetime.datetime.now().timestamp())
            ),
            'group_name': query.message_event.group.name
            if isinstance(query.message_event, platform_events.GroupMessage)
            else '',
            'sender_name': sender_name,
        }
        query.variables.update(variables)

        # Check if this model supports vision, if not, remove all images
        # TODO this checking should be performed in runner, and in this stage, the image should be reserved
        if (
            selected_runner == 'local-agent'
            and llm_model
            and not llm_model.model_entity.abilities.__contains__('vision')
        ):
            for msg in query.messages:
                if isinstance(msg.content, list):
                    for me in msg.content:
                        if me.type == 'image_url':
                            msg.content.remove(me)

        content_list: list[provider_message.ContentElement] = []

        plain_text = ''
        quote_msg = query.pipeline_config['trigger'].get('misc', '').get('combine-quote-message')

        for me in query.message_chain:
            if isinstance(me, platform_message.Plain):
                content_list.append(provider_message.ContentElement.from_text(me.text))
                plain_text += me.text
            elif isinstance(me, platform_message.Image):
                if selected_runner != 'local-agent' or (
                    llm_model and llm_model.model_entity.abilities.__contains__('vision')
                ):
                    if me.base64 is not None:
                        content_list.append(provider_message.ContentElement.from_image_base64(me.base64))
            elif isinstance(me, platform_message.Voice):
                # 转成文件链接，让下游 runner 上传到目标模型
                if me.base64:
                    content_list.append(provider_message.ContentElement.from_file_base64(me.base64, 'voice.silk'))
                elif me.url:
                    content_list.append(provider_message.ContentElement.from_file_url(me.url, 'voice'))
            elif isinstance(me, platform_message.File):
                content_list.append(provider_message.ContentElement.from_file_url(me.url, me.name))
            elif isinstance(me, platform_message.Quote) and quote_msg:
                for msg in me.origin:
                    if isinstance(msg, platform_message.Plain):
                        content_list.append(provider_message.ContentElement.from_text(msg.text))
                    elif isinstance(msg, platform_message.Image):
                        if selected_runner != 'local-agent' or (
                            llm_model and llm_model.model_entity.abilities.__contains__('vision')
                        ):
                            if msg.base64 is not None:
                                content_list.append(provider_message.ContentElement.from_image_base64(msg.base64))
                    elif isinstance(msg, platform_message.File):
                        content_list.append(provider_message.ContentElement.from_file_url(msg.url, msg.name))
                    elif isinstance(msg, platform_message.Voice):
                        if msg.base64:
                            content_list.append(
                                provider_message.ContentElement.from_file_base64(msg.base64, 'voice.silk')
                            )
                        elif msg.url:
                            content_list.append(provider_message.ContentElement.from_file_url(msg.url, 'voice'))

        query.variables['user_message_text'] = plain_text

        query.user_message = provider_message.Message(role='user', content=content_list)

        # Extract knowledge base UUIDs into query variables so plugins can modify them
        # during PromptPreProcessing before the runner performs retrieval.
        kb_uuids = query.pipeline_config['ai']['local-agent'].get('knowledge-bases', [])
        if not kb_uuids:
            old_kb_uuid = query.pipeline_config['ai']['local-agent'].get('knowledge-base', '')
            if old_kb_uuid and old_kb_uuid != '__none__':
                kb_uuids = [old_kb_uuid]
        query.variables['_knowledge_base_uuids'] = list(kb_uuids)

        # =========== 触发事件 PromptPreProcessing

        event = events.PromptPreProcessing(
            session_name=f'{query.session.launcher_type.value}_{query.session.launcher_id}',
            default_prompt=query.prompt.messages,
            prompt=query.messages,
            query=query,
        )

        # Get bound plugins for filtering
        bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
        event_ctx = await self.ap.plugin_connector.emit_event(event, bound_plugins)

        query.prompt.messages = event_ctx.event.default_prompt
        query.messages = event_ctx.event.prompt

        return entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
