"""Async significance pass with auto-pin on score 3 (T22).

After ``assistant_turn`` lands the turn flow enqueues a SignificanceJob on
a background asyncio worker. The worker calls a classifier (per §11.1,
score 0-3) and writes a ``memory_significance_set`` event. On score 3 the
memory is auto-pinned and a soft cap of 8 pins per owner is enforced —
when the cap is exceeded the oldest auto-pin (excluding the just-pinned
row) is unpinned via another ``memory_pin_changed`` event.
"""

from __future__ import annotations

import asyncio
import json

import pytest

from chat.config import load_settings
from chat.db.connection import open_db
from chat.db.migrate import apply_migrations
from chat.eventlog.log import append_event
from chat.eventlog.projector import project
from chat.llm.mock import MockLLMClient
from chat.services.background import BackgroundWorker, SignificanceJob
from chat.services.significance import compute_significance

# Trigger handler registration for projection.
import chat.state.entities  # noqa: F401
import chat.state.memory  # noqa: F401
import chat.state.world  # noqa: F401


async def test_compute_significance_parses_score():
    canned = json.dumps({"score": 2, "reason": "notable"})
    mock = MockLLMClient(canned=[canned])
    score = await compute_significance(
        mock,
        model="x",
        narrative_text="...",
        prior_dialogue=[],
    )
    assert score == 2


async def test_compute_significance_default_on_failure():
    # Both attempts return non-JSON text; the classify wrapper falls back
    # to the SignificanceVerdict default (score=1, "fallback").
    mock = MockLLMClient(canned=["nope", "still nope", "nope3"])
    score = await compute_significance(
        mock,
        model="x",
        narrative_text="...",
        prior_dialogue=[],
    )
    assert score == 1


async def test_background_worker_processes_job_and_updates_significance(
    tmp_path, monkeypatch
):
    cfg = tmp_path / "config.toml"
    cfg.write_text('featherless_api_key = "test"\n')
    monkeypatch.setenv("CHAT_CONFIG_PATH", str(cfg))
    db = tmp_path / "test.db"
    monkeypatch.setenv("CHAT_DB_PATH", str(db))
    apply_migrations(db)
    settings = load_settings()

    # Seed bot, chat, memory.
    with open_db(db) as conn:
        append_event(
            conn,
            kind="bot_authored",
            payload={
                "id": "bot_a",
                "name": "BotA",
                "persona": "...",
                "voice_samples": [],
                "traits": [],
                "backstory": "",
                "initial_relationship_to_you": "",
                "kickoff_prose": "",
            },
        )
        append_event(
            conn,
            kind="chat_created",
            payload={
                "id": "chat_bot_a",
                "host_bot_id": "bot_a",
                "initial_time": "2026-04-26T20:00:00+00:00",
                "narrative_anchor": "Day 1",
                "weather": "",
            },
        )
        append_event(
            conn,
            kind="memory_written",
            payload={
                "owner_id": "bot_a",
                "chat_id": "chat_bot_a",
                "pov_summary": "Some scene",
                "witness_you": 1,
                "witness_host": 1,
                "witness_guest": 0,
                "source": "direct",
                "reliability": 1.0,
                "significance": 1,
                "pinned": 0,
                "auto_pinned": 0,
            },
        )
        project(conn)
        memory_id = conn.execute(
            "SELECT id FROM memories WHERE owner_id = 'bot_a'"
        ).fetchone()[0]

    # Worker with mock LLM that returns score=3 (pivotal).
    canned = [json.dumps({"score": 3, "reason": "pivotal"})]
    factory = lambda: MockLLMClient(canned=list(canned))
    worker = BackgroundWorker(settings, llm_client_factory=factory)
    await worker.start()
    worker.enqueue(
        SignificanceJob(
            memory_id=memory_id,
            narrative_text="...",
            prior_dialogue=[],
            host_bot_id="bot_a",
        )
    )
    # Drain via stop sentinel — guarantees the prior job completed.
    await worker.stop()

    # Verify significance updated AND memory auto-pinned.
    with open_db(db) as conn:
        row = conn.execute(
            "SELECT significance, pinned, auto_pinned FROM memories "
            "WHERE id = ?",
            (memory_id,),
        ).fetchone()
        assert row[0] == 3
        assert row[1] == 1  # pinned
        assert row[2] == 1  # auto_pinned


async def test_auto_pin_evicts_oldest_when_over_cap(tmp_path, monkeypatch):
    """Pin 9 memories with score 3; verify only 8 are pinned at the end."""
    cfg = tmp_path / "config.toml"
    cfg.write_text('featherless_api_key = "test"\n')
    monkeypatch.setenv("CHAT_CONFIG_PATH", str(cfg))
    db = tmp_path / "test.db"
    monkeypatch.setenv("CHAT_DB_PATH", str(db))
    apply_migrations(db)
    settings = load_settings()

    with open_db(db) as conn:
        append_event(
            conn,
            kind="bot_authored",
            payload={
                "id": "bot_a",
                "name": "BotA",
                "persona": "...",
                "voice_samples": [],
                "traits": [],
                "backstory": "",
                "initial_relationship_to_you": "",
                "kickoff_prose": "",
            },
        )
        append_event(
            conn,
            kind="chat_created",
            payload={
                "id": "chat_bot_a",
                "host_bot_id": "bot_a",
                "initial_time": "2026-04-26T20:00:00+00:00",
                "narrative_anchor": "Day 1",
                "weather": "",
            },
        )
        for i in range(9):
            append_event(
                conn,
                kind="memory_written",
                payload={
                    "owner_id": "bot_a",
                    "chat_id": "chat_bot_a",
                    "pov_summary": f"memory {i}",
                    "witness_you": 1,
                    "witness_host": 1,
                    "witness_guest": 0,
                    "source": "direct",
                    "reliability": 1.0,
                    "significance": 1,
                    "pinned": 0,
                    "auto_pinned": 0,
                },
            )
        project(conn)
        memory_ids = [
            r[0]
            for r in conn.execute(
                "SELECT id FROM memories WHERE owner_id = 'bot_a' ORDER BY id"
            ).fetchall()
        ]

    # Each job runs through its own MockLLMClient with one canned response.
    factory = lambda: MockLLMClient(
        canned=[json.dumps({"score": 3, "reason": "pivotal"})]
    )
    worker = BackgroundWorker(settings, llm_client_factory=factory)
    await worker.start()
    for mid in memory_ids:
        worker.enqueue(
            SignificanceJob(
                memory_id=mid,
                narrative_text="...",
                prior_dialogue=[],
                host_bot_id="bot_a",
            )
        )
    await worker.stop()

    with open_db(db) as conn:
        pinned_count = conn.execute(
            "SELECT COUNT(*) FROM memories "
            "WHERE owner_id = 'bot_a' AND pinned = 1"
        ).fetchone()[0]
        assert pinned_count == 8

        # The oldest should have been evicted.
        first_id = memory_ids[0]
        first_pinned = conn.execute(
            "SELECT pinned FROM memories WHERE id = ?", (first_id,)
        ).fetchone()[0]
        assert first_pinned == 0