from __future__ import annotations

import uuid
import typing
import traceback
import time
from datetime import datetime


from .. import handler
from ... import entities
from ... import plugin_diagnostics
from ....provider import runner as runner_module

import langbot_plugin.api.entities.events as events
from ....utils import importutil, constants, runner as runner_utils
from ....telemetry import features as telemetry_features
from ....telemetry.identity import workspace_identity
from ....provider import runners
import langbot_plugin.api.entities.builtin.provider.session as provider_session
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
import langbot_plugin.api.entities.builtin.provider.message as provider_message
from ...pool import get_query_execution_context


importutil.import_modules_in_pkg(runners)


class ChatMessageHandler(handler.MessageHandler):
    def _response_limit(self, name: str, default: int) -> int:
        instance_config = getattr(self.ap, 'instance_config', None)
        data = getattr(instance_config, 'data', {})
        if not isinstance(data, dict):
            return default
        value = data.get('system', {}).get('response_limits', {}).get(name, default)
        try:
            return max(int(value), 1)
        except (TypeError, ValueError):
            return default

    def _check_response_size(
        self,
        result: provider_message.Message | provider_message.MessageChunk,
    ) -> None:
        content = result.content
        if isinstance(content, str) and len(content) > self._response_limit('max_generated_chars', 1024 * 1024):
            raise RuntimeError('Provider response exceeds the configured limit')

    async def handle(
        self,
        query: pipeline_query.Query,
    ) -> typing.AsyncGenerator[entities.StageProcessResult, None]:
        """处理"""
        # 调API
        #   生成器

        # 触发插件事件
        event_class = (
            events.PersonNormalMessageReceived
            if query.launcher_type == provider_session.LauncherTypes.PERSON
            else events.GroupNormalMessageReceived
        )

        event = event_class(
            launcher_type=query.launcher_type.value,
            launcher_id=query.launcher_id,
            sender_id=query.sender_id,
            text_message=str(query.message_chain),
            message_event=query.message_event,
            message_chain=query.message_chain,
            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)

        is_create_card = False  # 判断下是否需要创建流式卡片

        if event_ctx.is_prevented_default():
            if event_ctx.event.reply_message_chain is not None:
                mc = event_ctx.event.reply_message_chain
                plugin_diagnostics.record_pending_plugin_response_source(
                    query,
                    mc,
                    plugin_diagnostics.get_response_sources(event_ctx),
                    plugin_diagnostics.get_emitted_plugins(event_ctx),
                    event.event_name,
                )
                query.resp_messages.append(mc)

                yield entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
            else:
                self.ap.logger.debug(
                    f'NormalMessageReceived event prevented default for query {query.query_id} without reply'
                )
                yield entities.StageProcessResult(result_type=entities.ResultType.INTERRUPT, new_query=query)
        else:
            if event_ctx.event.user_message_alter is not None:
                if isinstance(event_ctx.event.user_message_alter, list):
                    query.user_message.content = event_ctx.event.user_message_alter
                elif isinstance(event_ctx.event.user_message_alter, str):
                    query.user_message.content = [
                        provider_message.ContentElement.from_text(event_ctx.event.user_message_alter)
                    ]
                elif isinstance(event_ctx.event.user_message_alter, provider_message.ContentElement):
                    query.user_message.content = [event_ctx.event.user_message_alter]

            text_length = 0
            runner = None
            try:
                is_stream = await query.adapter.is_stream_output_supported()
            except AttributeError:
                is_stream = False

            try:
                for r in runner_module.preregistered_runners:
                    if r.name == query.pipeline_config['ai']['runner']['runner']:
                        runner = r(self.ap, query.pipeline_config)
                        break
                else:
                    raise ValueError(f'Request Runner not found: {query.pipeline_config["ai"]["runner"]["runner"]}')
                # Mark start time for telemetry
                start_ts = time.time()

                if is_stream:
                    resp_message_id = uuid.uuid4()
                    chunk_count = 0  # Track streaming chunks to reduce excessive logging

                    async for result in runner.run(query):
                        self._check_response_size(result)
                        result.resp_message_id = str(resp_message_id)
                        if query.resp_messages:
                            query.resp_messages.pop()
                        if query.resp_message_chain:
                            query.resp_message_chain.pop()
                        # 此时连接外部 AI 服务正常,创建卡片
                        if not is_create_card:  # 只有不是第一次才创建卡片
                            await query.adapter.create_message_card(str(resp_message_id), query.message_event)
                            is_create_card = True
                        query.resp_messages.append(result)

                        chunk_count += 1
                        if chunk_count > self._response_limit(
                            'max_stream_chunks',
                            100_000,
                        ):
                            raise RuntimeError('Provider stream exceeds the configured event limit')
                        # Only log every 10th chunk to reduce excessive logging during streaming
                        # This prevents memory overflow from thousands of log entries per conversation
                        # First chunk uses INFO level to confirm connection establishment
                        if chunk_count == 1:
                            summary = self.format_result_log(result)
                            if summary is not None:
                                self.ap.logger.info(f'Conversation({query.query_id}) Streaming started: {summary}')
                            else:
                                self.ap.logger.info(f'Conversation({query.query_id}) Streaming started')
                        elif chunk_count % 10 == 0:
                            self.ap.logger.debug(
                                f'Conversation({query.query_id}) Streaming chunk {chunk_count}: {self.cut_str(result.readable_str())}'
                            )

                        if result.content is not None:
                            text_length += len(result.content)

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

                    # Log final summary after streaming completes
                    self.ap.logger.info(
                        f'Conversation({query.query_id}) Streaming completed: {chunk_count} chunks, {text_length} chars'
                    )

                else:
                    async for result in runner.run(query):
                        self._check_response_size(result)
                        query.resp_messages.append(result)

                        summary = self.format_result_log(result)
                        if summary is not None:
                            self.ap.logger.info(f'Conversation({query.query_id}) Response: {summary}')

                        if result.content is not None:
                            text_length += len(result.content)

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

                query.session.using_conversation.messages.append(query.user_message)

                query.session.using_conversation.messages.extend(query.resp_messages)
                self.ap.sess_mgr.trim_conversation_messages(
                    query.session.using_conversation,
                    max_rounds=query.pipeline_config['ai']['local-agent'].get('max-round', 10),
                )
            except Exception as e:
                error_info = f'{traceback.format_exc()}'
                self.ap.logger.error(f'Conversation({query.query_id}) Request Failed: {error_info}')
                traceback.print_exc()

                exception_handling = query.pipeline_config['output']['misc'].get('exception-handling', 'show-hint')

                if exception_handling == 'show-error':
                    user_notice = f'{e}'
                elif exception_handling == 'show-hint':
                    user_notice = query.pipeline_config['output']['misc'].get('failure-hint', 'Request failed.')
                else:  # hide
                    user_notice = None

                yield entities.StageProcessResult(
                    result_type=entities.ResultType.INTERRUPT,
                    new_query=query,
                    user_notice=user_notice,
                    error_notice=f'{e}',
                    debug_notice=traceback.format_exc(),
                )
            finally:
                if runner is not None:
                    try:
                        close_runner = getattr(runner, 'aclose', None)
                        if close_runner is not None:
                            await close_runner()
                    except Exception as ex:
                        self.ap.logger.warning(f'Failed to close request runner: {ex}')
                # Telemetry reporting: collect minimal per-query execution info and send asynchronously
                try:
                    end_ts = time.time()
                    duration_ms = None
                    if 'start_ts' in locals():
                        duration_ms = int((end_ts - start_ts) * 1000)

                    adapter_name = query.adapter.__class__.__name__ if hasattr(query, 'adapter') else None
                    runner_name = (
                        query.pipeline_config.get('ai', {}).get('runner', {}).get('runner')
                        if query.pipeline_config
                        else None
                    )

                    # Model name if using localagent
                    model_name = None
                    try:
                        if runner_name == 'local-agent' and getattr(query, 'use_llm_model_uuid', None):
                            m = await self.ap.model_mgr.get_model_by_uuid(
                                get_query_execution_context(query),
                                query.use_llm_model_uuid,
                            )
                            if m and getattr(m, 'model_entity', None):
                                model_name = getattr(m.model_entity, 'name', None)
                    except Exception:
                        model_name = None

                    pipeline_plugins = query.variables.get('_pipeline_bound_plugins', None)

                    runner_category = runner_utils.get_runner_category_from_runner(
                        runner_name, runner, query.pipeline_config
                    )

                    # Feature usage collected during query processing (tool calls,
                    # knowledge base usage, sandbox executions, activated skills, ...)
                    features = telemetry_features.collect_features(query)

                    payload = {
                        'event_type': 'query',
                        'query_id': query.query_id,
                        'adapter': adapter_name,
                        'runner': runner_name,
                        'runner_category': runner_category,
                        'duration_ms': duration_ms,
                        'model_name': model_name,
                        'version': constants.semantic_version,
                        **workspace_identity(get_query_execution_context(query)),
                        'runtime_instance_id': constants.instance_id,
                        'edition': constants.edition,
                        'pipeline_plugins': pipeline_plugins,
                        'features': features,
                        'error': locals().get('error_info', None),
                        'timestamp': datetime.utcnow().isoformat(),
                    }

                    # Send telemetry asynchronously and do not block pipeline via app's telemetry manager
                    await self.ap.telemetry.start_send_task(payload)

                    # Trigger survey events on successful non-WebSocket responses
                    if not locals().get('error_info') and adapter_name and 'WebSocket' not in adapter_name:
                        if self.ap.survey:
                            await self.ap.survey.trigger_event('first_bot_response_success')
                            # Counts toward the bot_response_success_100 milestone event
                            await self.ap.survey.record_bot_response_success()
                except Exception as ex:
                    # Ensure telemetry issues do not affect normal flow
                    self.ap.logger.warning(f'Failed to send telemetry: {ex}')
