from __future__ import annotations
import asyncio, inspect, json, math, random
from datetime import date, datetime
from typing import Any, Awaitable, Callable
from evaluator.collectors.base import ConversationCollector
from evaluator.collectors.langfuse import LangfuseCollector
from evaluator.collectors.agent_framework import AgentFrameworkCollector
from evaluator.collectors.mock import MockCollector
from evaluator.config.agents import AgentConfig
from evaluator.config.settings import settings
from evaluator.core.models import ConversationRecord, RunStatus
from evaluator.judges.llm_judge import TIMStyleLLMJudge
from evaluator.output.legacy_exporter import export_legacy_txt_gz
from evaluator.persistence.repository import EvaluationRepository
from evaluator.publishers.langfuse_scores import LangfuseScorePublisher
from evaluator.publishers.langfuse_job_audit import LangfuseJobAuditPublisher
from evaluator.publishers.gcs_export import upload_evaluation_file

ProgressCallback = Callable[[dict[str, Any]], Awaitable[None] | None]


class EvaluationEngine:
    def __init__(self, repository: EvaluationRepository | None = None, progress_callback: ProgressCallback | None = None):
        self.repository = repository or EvaluationRepository(auto_init_schema=False)
        self.progress_callback = progress_callback
        self.judge = TIMStyleLLMJudge()
        self.langfuse_publisher = LangfuseScorePublisher()
        self.job_audit = LangfuseJobAuditPublisher()

    async def _emit(self, progress_run_id: str, stage: str, message: str = "", **details):
        details.pop("run_id", None)
        await self.repository.arecord_progress(progress_run_id, stage, message, details)
        event = {"run_id": progress_run_id, "stage": stage, "message": message, "details": details}
        if self.progress_callback:
            r = self.progress_callback(event)
            if inspect.isawaitable(r):
                await r

    def collector_for(self, source: str, agent_id: str | None = None) -> ConversationCollector:
        if source == 'langfuse': return LangfuseCollector(agent_id=agent_id)
        if source == 'agent_framework': return AgentFrameworkCollector()
        if source == 'mock': return MockCollector()
        raise ValueError('source must be langfuse, agent_framework or mock')

    async def _audit(self, progress_run_id: str, audit_trace_id: str, agent_id: str, status: str, period_start: datetime, period_end: datetime, *, required: bool = True, **details) -> bool:
        try:
            result = await self.job_audit.publish(
                audit_trace_id, agent_id, status, period_start=period_start, period_end=period_end, details=details
            )
            if not result or not result.get("ok"):
                raise RuntimeError(f"Langfuse audit did not confirm persistence: {result!r}")
            await self._emit(progress_run_id, 'LANGFUSE_JOB_AUDIT', f'judge-job-{agent_id} status={status}', audit_status=status, audit_result=result)
            return True
        except Exception as exc:
            await self._emit(progress_run_id, 'LANGFUSE_JOB_AUDIT_FAILED', f'Langfuse job audit failed: {exc}', audit_status=status, error=str(exc))
            if required:
                raise
            return False

    async def run_agent(self, agent: AgentConfig, period_start: datetime, period_end: datetime, source: str = 'langfuse', limit: int | None = None, reference_date: date | None = None) -> dict:
        run_id = await self.repository.acreate_run(period_start, period_end, source, agent.agent_id)
        reference_date = reference_date or period_start.date()
        audit_trace_id = self.job_audit.new_trace_id()
        try:
            # Emit RUN_CREATED immediately after persistence so CLI/monitor can expose
            # the RUN_ID before any external Langfuse audit/network call is performed.
            await self._emit(run_id, 'RUN_CREATED', f'Agent run created: {agent.agent_id}', agent_id=agent.agent_id, source=source, reference_date=reference_date.isoformat())
            await self._audit(run_id, audit_trace_id, agent.agent_id, 'created', period_start, period_end, run_id=run_id, source=source)
            collector = self.collector_for(source, agent.agent_id)
            await self._emit(run_id, 'COLLECTING', 'Collecting sessions and conversations')
            seed = f'{agent.agent_id}:{reference_date.isoformat()}'
            if isinstance(collector, LangfuseCollector):
                records, sampled_sessions, collection_stats = await collector.collect_sampled(
                    period_start, period_end, agent_aliases=agent.aliases, percentage=agent.percentage, seed=seed, limit=limit
                )
                await self._emit(
                    run_id, 'COLLECTED', f'Collected {len(records)} traces from {len(sampled_sessions)} sampled sessions',
                    **collection_stats
                )
            else:
                records = await collector.collect(period_start, period_end, agent_aliases=agent.aliases, limit=limit)
                records, sampled_sessions = self._sample_sessions(records, agent.percentage, agent.agent_id, reference_date)
                collection_stats = {}
                await self._emit(run_id, 'COLLECTED', f'Collected {len(records)} traces before session sampling')
            await self._emit(
                run_id,
                'SAMPLED',
                f'Kept {len(sampled_sessions)} sessions / {len(records)} traces',
                percentage=agent.percentage,
                seed=seed,
                sampled_sessions=sampled_sessions,
            )
            inserted = await self.repository.ainsert_items(run_id, records)
            await self._emit(run_id, 'ITEMS_INSERTED', f'Inserted {inserted} items')
            summary = await self._process(run_id)
            output_path = export_legacy_txt_gz(self.repository, run_id, agent.agent_id)
            await self._emit(run_id, 'EXPORTED', f'Exported {output_path}', output_file=str(output_path))
            if not settings.enable_gcs_upload:
                raise RuntimeError("TIM specification requires GCS upload for run-agents; set ENABLE_GCS_UPLOAD=true")
            uploaded_to = await asyncio.to_thread(upload_evaluation_file, output_path, agent.gcs_prefix)
            await self._emit(run_id, 'GCS_UPLOADED', f'Uploaded {uploaded_to}', uploaded_to=uploaded_to)
            output_path.unlink()
            await self._emit(run_id, 'LOCAL_FILE_DELETED', 'Local export deleted after successful GCS upload')
            result = {**summary, 'agent_id': agent.agent_id, 'output_file': str(output_path), 'uploaded_to': uploaded_to}
            # _audit() receives status as an explicit positional argument. The run
            # summary also contains a "status" key (e.g. COMPLETED), so forwarding
            # **result unchanged would pass status twice and raise:
            #   TypeError: _audit() got multiple values for argument 'status'
            audit_details = dict(result)
            # Never forward names that are explicit _audit() parameters.
            for reserved_key in ('progress_run_id', 'audit_trace_id', 'agent_id', 'status', 'period_start', 'period_end'):
                audit_details.pop(reserved_key, None)
            await self._audit(run_id, audit_trace_id, agent.agent_id, 'completed', period_start, period_end, **audit_details)
            return result
        except Exception as exc:
            await self.repository.amark_run_status(run_id, RunStatus.FAILED, str(exc))
            await self._emit(run_id, 'FAILED', f'Agent run failed: {exc}', error=str(exc))
            await self._audit(run_id, audit_trace_id, agent.agent_id, 'failed', period_start, period_end, required=False, run_id=run_id, error=str(exc))
            return {'status': 'FAILED', 'run_id': run_id, 'agent_id': agent.agent_id, 'error': str(exc)}

    async def run(self, period_start: datetime, period_end: datetime, source: str = 'langfuse', limit: int | None = None) -> dict:
        run_id = await self.repository.acreate_run(period_start, period_end, source, None)
        try:
            collector = self.collector_for(source)
            await self._emit(run_id, 'COLLECTING', 'Collecting conversations')
            records = await collector.collect(period_start, period_end, limit=limit)
            await self._emit(run_id, 'COLLECTED', f'Collected {len(records)} records')
            await self.repository.ainsert_items(run_id, records)
            return await self._process(run_id)
        except Exception as exc:
            await self.repository.amark_run_status(run_id, RunStatus.PARTIAL, str(exc))
            await self._emit(run_id, 'PARTIAL', f'Run failed: {exc}', error=str(exc))
            return {'status': 'PARTIAL', 'run_id': run_id, 'error': str(exc)}

    async def _process(self, run_id: str) -> dict:
        processed_records: list[ConversationRecord] = []
        while True:
            items = await self.repository.afetch_next_items(run_id, settings.batch_size)
            if not items: break
            await self._emit(run_id, 'BATCH_STARTED', f'Processing {len(items)} items')
            for item in items:
                item_id = item['item_id']
                await self.repository.amark_item_processing(item_id)
                try:
                    raw = item['raw_json']
                    if hasattr(raw, 'read'): raw = raw.read()
                    record = ConversationRecord.model_validate(json.loads(raw))
                    result = await self.judge.judge_trace(record)
                    await self.repository.asave_trace_result(run_id, item_id, record, result)
                    await self.langfuse_publisher.publish_trace_score(record, result)
                    await self.repository.amark_item_completed(run_id, item_id)
                    processed_records.append(record)
                    await self._emit(
                        run_id,
                        "ITEM_COMPLETED",
                        f"Item completed {item_id}",
                        trace_id=record.trace_id,
                        session_id=record.session_id,
                        judgeScore=result.judgeScore,
                        accuracyScore=result.accuracyScore,
                        alucinationScore=result.alucinationScore,
                        rationale=result.rationale,
                    )
                except Exception as exc:
                    await self.repository.amark_item_failed(run_id, item_id, str(exc))
                    await self._emit(run_id, 'ITEM_FAILED', f'Item failed {item_id}', error=str(exc))
        if processed_records:
            sessions = await self.judge.judge_sessions(processed_records)
            for sid, result in sessions.items():
                agent_id = next((r.agent_id for r in processed_records if r.session_id == sid), None)
                await self.repository.asave_session_result(run_id, sid, agent_id, result)
            await self._emit(run_id, 'SESSION_JUDGE_COMPLETED', f'Evaluated {len(sessions)} sessions')
        await self.repository.amark_run_status(run_id, RunStatus.COMPLETED)
        summary = await self.repository.asummarize_run(run_id)
        await self._emit(run_id, 'COMPLETED', 'Run completed', **summary)
        return {'status': 'COMPLETED', **summary}

    def _sample_sessions(self, records: list[ConversationRecord], percentage: float, agent_id: str, reference_date: date) -> tuple[list[ConversationRecord], list[str]]:
        """Deterministic session sampling; all traces from selected sessions are retained."""
        if not records:
            return [], []
        session_ids = sorted({r.session_id for r in records})
        if percentage >= 1:
            selected = session_ids
        elif percentage <= 0:
            selected = []
        else:
            count = min(len(session_ids), max(1, math.ceil(len(session_ids) * percentage)))
            rng = random.Random(f"{agent_id}:{reference_date.isoformat()}")
            selected = sorted(rng.sample(session_ids, count))
        selected_set = set(selected)
        return [r for r in records if r.session_id in selected_set], selected