"""Tests for turnstone ChatSession and server endpoints.

Mock-based tests verify streaming, tool calling, multi-turn conversation,
and session configuration WITHOUT a running LLM backend.  The mocks replace
only the OpenAI streaming layer -- tool execution (bash, read_file)
still runs real subprocesses.

The TestBackendConnectivity class is marked @pytest.mark.live and requires a
running llama-server (or compatible OpenAI API) on localhost:8000.

The TestServerHealthMetrics class spins up an in-process HTTP server and
needs no LLM backend at all.

Run all non-live tests:
    pytest tests/test_server_live.py -v -m "not live"

Run everything (needs backend):
    pytest tests/test_server_live.py -v --timeout=120
"""

import json
import os
import queue
import tempfile
import threading
from types import SimpleNamespace
from unittest.mock import MagicMock

import httpx
import pytest
from openai import OpenAI

from tests._session_helpers import replace_session_lane
from turnstone.core.session import ChatSession
from turnstone.core.storage import init_storage, reset_storage

# ---------------------------------------------------------------------------
# Fixtures
# ---------------------------------------------------------------------------

BASE_URL = os.environ.get("TURNSTONE_TEST_BASE_URL", "http://localhost:8000/v1")


@pytest.fixture(scope="module")
def live_client():
    """Create an OpenAI client pointed at the local backend (live tests only)."""
    return OpenAI(
        base_url=BASE_URL,
        api_key=os.environ.get("TURNSTONE_TEST_API_KEY", "not-needed"),
    )


@pytest.fixture(scope="module")
def live_model_id(live_client):
    """Auto-detect the model name from the backend (live tests only)."""
    models = live_client.models.list()
    ids = [m.id for m in models.data]
    assert len(ids) > 0, "No models found on the backend"
    return ids[0]


class RecordingUI:
    """Minimal SessionUI that captures events for assertions."""

    def __init__(self):
        self.events: list[tuple[str, ...]] = []
        self.content_tokens: list[str] = []
        self.reasoning_tokens: list[str] = []
        self.tool_results: list[tuple[str, str, str]] = []
        self.tool_chunks: list[tuple[str, str]] = []
        self.errors: list[str] = []
        self.infos: list[str] = []

    def on_turn_start(self):
        self.events.append(("turn_start",))

    def on_turn_committed(self):
        self.events.append(("turn_committed",))

    def on_stream_discarded(self):
        # A live-wire blip entering the retry arm must not crash the fake.
        self.events.append(("stream_discarded",))

    def on_thinking_start(self):
        self.events.append(("thinking_start",))

    def on_thinking_stop(self):
        self.events.append(("thinking_stop",))

    def on_reasoning_token(self, text):
        self.reasoning_tokens.append(text)

    def on_content_token(self, text):
        self.content_tokens.append(text)

    def on_stream_end(self):
        self.events.append(("stream_end",))

    def approve_tools(self, items):
        return True, None  # auto-approve everything

    def on_tool_result(self, call_id, name, output, **kwargs):
        self.tool_results.append((call_id, name, output))

    def on_tool_output_chunk(self, call_id, chunk):
        self.tool_chunks.append((call_id, chunk))

    def on_status(self, usage, context_window, effort):
        self.events.append(("status",))

    def on_info(self, message):
        self.infos.append(message)

    def on_error(self, message):
        self.errors.append(message)

    def on_state_change(self, state):
        self.events.append(("state_change", state))

    def on_rename(self, name: str):
        self.events.append(("rename", name))

    def on_output_warning(self, call_id, assessment):
        pass

    def record_output_assessment(
        self,
        call_id,
        assessment,
        *,
        tier="heuristic",
        reasoning="",
        judge_model="",
        latency_ms=0,
        confidence=0.0,
    ):
        pass

    @property
    def full_content(self) -> str:
        return "".join(self.content_tokens)

    @property
    def full_reasoning(self) -> str:
        return "".join(self.reasoning_tokens)


@pytest.fixture
def tmp_db():
    """Temp DB to avoid polluting real conversation history."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        path = f.name
    reset_storage()
    init_storage("sqlite", path=path, run_migrations=False)
    yield path
    reset_storage()
    os.unlink(path)


def _make_session(client, model_id, tmp_db, **kwargs) -> tuple[ChatSession, RecordingUI]:
    """Create a ChatSession with RecordingUI and sensible test defaults."""
    from turnstone.core.memory import register_workstream
    from turnstone.core.providers._openai_chat import OpenAIChatCompletionsProvider

    ui = RecordingUI()
    defaults = dict(
        client=client,
        model=model_id,
        ui=ui,
        instructions=None,
        temperature=0.3,
        max_tokens=2048,
        tool_timeout=30,
        reasoning_effort="low",
    )
    defaults.update(kwargs)
    session = ChatSession(**defaults)
    # Production creates the parent workstream before admitting any keyed
    # conversation row.  These direct-session tests mirror that ordering so
    # the storage orphan-write fence remains exercised rather than bypassed.
    register_workstream(session.ws_id, user_id=kwargs.get("user_id"))
    # Mock-based tests use Chat Completions format (client.chat.completions)
    replace_session_lane(session, provider=OpenAIChatCompletionsProvider())
    session.auto_approve = True
    return session, ui


# ---------------------------------------------------------------------------
# Mock streaming helpers
# ---------------------------------------------------------------------------


def _make_chunk(
    *,
    content=None,
    reasoning_content=None,
    tool_calls=None,
    finish_reason=None,
    usage=None,
):
    """Build a single mock streaming chunk matching the OpenAI format.

    The chunk structure mirrors openai.types.chat.ChatCompletionChunk:
      chunk.choices[0].delta.content
      chunk.choices[0].delta.reasoning_content
      chunk.choices[0].delta.tool_calls
      chunk.choices[0].finish_reason
      chunk.usage
    """
    delta = SimpleNamespace(
        content=content,
        reasoning_content=reasoning_content,
        reasoning=None,
        tool_calls=tool_calls,
        role=None,
        model_extra=None,
    )
    choice = SimpleNamespace(delta=delta, finish_reason=finish_reason)
    chunk = SimpleNamespace(choices=[choice], usage=usage)
    return chunk


def _make_tool_call_deltas(call_id, name, arguments):
    """Build a list of tool_call delta objects for a single tool call.

    Returns a list with one element (single tool call at index 0).
    """
    fn = SimpleNamespace(name=name, arguments=arguments)
    return [SimpleNamespace(index=0, id=call_id, function=fn)]


def _usage(prompt=100, completion=50, total=None):
    """Build a mock usage object."""
    return SimpleNamespace(
        prompt_tokens=prompt,
        completion_tokens=completion,
        total_tokens=total or (prompt + completion),
    )


def make_mock_stream(
    content_tokens=None,
    reasoning_tokens=None,
    tool_calls=None,
    finish_reason="stop",
    usage=None,
):
    """Create an iterable of mock chunks simulating an OpenAI streaming response.

    Parameters
    ----------
    content_tokens : list[str] | None
        Content token strings, each emitted as a separate chunk.
    reasoning_tokens : list[str] | None
        Reasoning token strings, emitted before content.
    tool_calls : list[tuple[str, str, str]] | None
        Each entry is (call_id, function_name, arguments_json).
        When provided, finish_reason defaults to "tool_calls".
    finish_reason : str
        Finish reason on the last content/tool chunk.
    usage : SimpleNamespace | None
        Usage object for the final chunk.  Defaults to a sensible value.
    """
    chunks = []

    if reasoning_tokens:
        for token in reasoning_tokens:
            chunks.append(_make_chunk(reasoning_content=token))

    if content_tokens:
        for i, token in enumerate(content_tokens):
            is_last = (i == len(content_tokens) - 1) and not tool_calls
            chunks.append(
                _make_chunk(
                    content=token,
                    finish_reason=finish_reason if is_last else None,
                )
            )

    if tool_calls:
        for i, (call_id, name, arguments) in enumerate(tool_calls):
            is_last = i == len(tool_calls) - 1
            tc_deltas = _make_tool_call_deltas(call_id, name, arguments)
            chunks.append(
                _make_chunk(
                    tool_calls=tc_deltas,
                    finish_reason="tool_calls" if is_last else None,
                )
            )

    # Final usage-only chunk (no choices)
    if usage is None:
        usage = _usage()
    chunks.append(SimpleNamespace(choices=[], usage=usage))

    return iter(chunks)


def _mock_client():
    """Create a mock OpenAI client with a patchable chat.completions.create."""
    client = MagicMock(spec=OpenAI)
    client.chat = MagicMock()
    client.chat.completions = MagicMock()
    client.chat.completions.create = MagicMock()
    return client


# ---------------------------------------------------------------------------
# Tests -- Backend connectivity (live, requires running LLM)
# ---------------------------------------------------------------------------


@pytest.mark.live
class TestBackendConnectivity:
    """Verify the LLM backend is reachable and returns valid responses."""

    def test_models_endpoint(self, live_client):
        models = live_client.models.list()
        assert len(models.data) > 0

    def test_model_id_detected(self, live_model_id):
        assert isinstance(live_model_id, str)
        assert len(live_model_id) > 0

    def test_basic_completion(self, live_client, live_model_id):
        """Raw API call -- no turnstone involved."""
        resp = live_client.chat.completions.create(
            model=live_model_id,
            messages=[{"role": "user", "content": "Say 'hello'"}],
            max_completion_tokens=200,
            temperature=0.0,
            stream=False,
        )
        message = resp.choices[0].message
        # OpenAI-compatible servers use either non-standard field for parsed
        # reasoning (vLLM: ``reasoning``; llama.cpp: ``reasoning_content``).
        reasoning = getattr(message, "reasoning", None) or getattr(
            message, "reasoning_content", None
        )
        assert message.content or reasoning
        assert resp.usage.total_tokens > 0


# ---------------------------------------------------------------------------
# Tests -- Streaming session (mocked)
# ---------------------------------------------------------------------------


class TestStreamingSession:
    """Test ChatSession.send() with mocked streaming responses."""

    def test_simple_response(self, tmp_db):
        """Mock returns content tokens; verify RecordingUI captures them."""
        client = _mock_client()
        client.chat.completions.create.return_value = make_mock_stream(
            content_tokens=["Hello", " ", "world"],
        )

        session, ui = _make_session(client, "mock-model", tmp_db)
        session._title_generated = True  # prevent background title generation

        session.send("Say hello")

        assert "Hello world" in ui.full_content

    def test_reasoning_tokens_appear(self, tmp_db):
        """Mock returns reasoning tokens then content; verify both captured."""
        client = _mock_client()
        client.chat.completions.create.return_value = make_mock_stream(
            reasoning_tokens=["Let me", " think..."],
            content_tokens=["The answer", " is 56"],
        )

        session, ui = _make_session(client, "mock-model", tmp_db)
        session._title_generated = True

        session.send("What is 7 * 8?")

        assert len(ui.reasoning_tokens) > 0
        assert "think" in ui.full_reasoning.lower()
        assert "56" in ui.full_content

    def test_stream_end_event(self, tmp_db):
        """stream_end event is emitted after response."""
        client = _mock_client()
        client.chat.completions.create.return_value = make_mock_stream(
            content_tokens=["Hi"],
        )

        session, ui = _make_session(client, "mock-model", tmp_db)
        session._title_generated = True

        session.send("Say hi")

        event_types = [e[0] for e in ui.events]
        assert "stream_end" in event_types

    def test_thinking_lifecycle(self, tmp_db):
        """thinking_start and thinking_stop bracket the response."""
        client = _mock_client()
        client.chat.completions.create.return_value = make_mock_stream(
            content_tokens=["Hi", " there"],
        )

        session, ui = _make_session(client, "mock-model", tmp_db)
        session._title_generated = True

        session.send("Say hi")

        event_types = [e[0] for e in ui.events]
        assert "thinking_start" in event_types
        assert "thinking_stop" in event_types
        start_idx = event_types.index("thinking_start")
        stop_idx = event_types.index("thinking_stop")
        assert start_idx < stop_idx


# ---------------------------------------------------------------------------
# Tests -- Tool calling (mocked LLM, real tool execution)
# ---------------------------------------------------------------------------


class TestToolCalling:
    """Test that mocked tool_calls trigger real tool execution."""

    def test_bash_tool(self, tmp_db):
        """First call returns tool_call for bash, second returns content."""
        client = _mock_client()

        stream1 = make_mock_stream(
            tool_calls=[("call_bash_1", "bash", json.dumps({"command": "echo hello"}))],
        )
        stream2 = make_mock_stream(
            content_tokens=["The command printed: ", "hello"],
        )
        client.chat.completions.create.side_effect = [stream1, stream2]

        session, ui = _make_session(client, "mock-model", tmp_db)
        session._title_generated = True

        session.send("Run echo hello")

        bash_results = [r for r in ui.tool_results if r[1] == "bash"]
        assert len(bash_results) > 0
        assert "hello" in bash_results[0][2]

    def test_read_file_tool(self, tmp_db):
        """First call returns tool_call for read_file, second returns content."""
        with tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False) as f:
            f.write("SECRET_CONTENT_42\n")
            path = f.name

        try:
            client = _mock_client()

            stream1 = make_mock_stream(
                tool_calls=[("call_read_1", "read_file", json.dumps({"path": path}))],
            )
            stream2 = make_mock_stream(
                content_tokens=["The file says: SECRET_CONTENT_42"],
            )
            client.chat.completions.create.side_effect = [stream1, stream2]

            session, ui = _make_session(client, "mock-model", tmp_db)
            session._title_generated = True

            session.send(f"Read {path}")

            read_results = [r for r in ui.tool_results if r[1] == "read_file"]
            assert len(read_results) > 0

            # Model relays the content
            assert "SECRET_CONTENT_42" in ui.full_content
        finally:
            os.unlink(path)


# ---------------------------------------------------------------------------
# Tests -- Multi-turn conversation (mocked)
# ---------------------------------------------------------------------------


class TestMultiTurn:
    """Test multi-turn conversation state with mocked responses."""

    def test_context_retained(self, tmp_db):
        """Second send references context from the first."""
        client = _mock_client()

        stream1 = make_mock_stream(
            content_tokens=["I'll remember ", "Zephyr"],
        )
        stream2 = make_mock_stream(
            content_tokens=["Your name is ", "Zephyr"],
        )
        client.chat.completions.create.side_effect = [stream1, stream2]

        session, ui = _make_session(client, "mock-model", tmp_db, max_tokens=1024)
        session._title_generated = True

        session.send("My name is Zephyr. Remember it.")

        # Reset UI tracking for second turn
        ui.content_tokens.clear()
        ui.reasoning_tokens.clear()

        session.send("What is my name?")

        assert "zephyr" in ui.full_content.lower()

    def test_message_list_grows(self, tmp_db):
        """Each send adds user + assistant messages."""
        client = _mock_client()

        stream1 = make_mock_stream(content_tokens=["Hello"])
        stream2 = make_mock_stream(content_tokens=["World"])
        client.chat.completions.create.side_effect = [stream1, stream2]

        session, ui = _make_session(client, "mock-model", tmp_db, max_tokens=512)
        session._title_generated = True

        initial_count = len(session.messages)
        session.send("Hello")
        after_first = len(session.messages)
        assert after_first >= initial_count + 2

        session.send("World")
        after_second = len(session.messages)
        assert after_second >= after_first + 2


# ---------------------------------------------------------------------------
# Tests -- Session configuration (mocked)
# ---------------------------------------------------------------------------


class TestSessionConfig:
    """Test session construction and configuration with mocked responses."""

    def test_empty_toolset_persona_no_tools_on_wire(self, tmp_db):
        """Guard: an empty-toolset persona (writer/scribe) sends ZERO tool
        definitions on the wire — create() is called without a tools kwarg.
        Replaces the removed /creative fork's equivalent assertion."""
        from turnstone.core.personas import PersonaSnapshot

        client = _mock_client()
        client.chat.completions.create.return_value = make_mock_stream(
            content_tokens=["A haiku about code"],
        )

        session, ui = _make_session(
            client,
            "mock-model",
            tmp_db,
            max_tokens=256,
            persona_snapshot=PersonaSnapshot(
                name="writer",
                prompt="You are a creative writing partner.",
                tools=frozenset(),
                mcp=False,
                memory=True,
            ),
        )
        session._title_generated = True

        session.send("Write a haiku about code.")

        # Verify create() was called without 'tools' in kwargs
        call_kwargs = client.chat.completions.create.call_args
        assert "tools" not in call_kwargs.kwargs, (
            "tools should not be passed under an empty-toolset persona"
        )

        # Should get content back without tool calls
        assert len(ui.full_content) > 0
        assert len(ui.tool_results) == 0

    def test_custom_instructions(self, tmp_db):
        """Custom instructions appear in system messages."""
        client = _mock_client()
        client.chat.completions.create.return_value = make_mock_stream(
            content_tokens=["Hello. ENDMARKER"],
        )

        session, ui = _make_session(
            client,
            "mock-model",
            tmp_db,
            instructions="Always end your response with ENDMARKER.",
            max_tokens=512,
        )
        session._title_generated = True

        # Verify custom instructions appear in system messages
        dev_msg = session.system_messages[0]
        assert "ENDMARKER" in dev_msg["content"]

        session.send("Say hello briefly.")
        assert len(ui.errors) == 0


# ---------------------------------------------------------------------------
# Tests -- /health and /metrics endpoints (no live LLM required)
# ---------------------------------------------------------------------------


_TEST_JWT_SECRET = "test-jwt-secret-minimum-32-chars!"


def _server_jwt() -> str:
    from turnstone.core.auth import JWT_AUD_SERVER, create_jwt

    return create_jwt(
        user_id="test-server-live",
        scopes=frozenset({"read", "write", "approve", "service"}),
        source="test",
        secret=_TEST_JWT_SECRET,
        audience=JWT_AUD_SERVER,
    )


_SERVER_AUTH_HEADERS = {"Authorization": f"Bearer {_server_jwt()}"}


class TestServerHealthMetrics:
    """Verify /health and /metrics endpoints using a Starlette TestClient.

    These tests create a Starlette app with a mock SessionManager
    so no live LLM backend is required.  Run them independently with:

        pytest tests/test_server_live.py::TestServerHealthMetrics -v
    """

    @classmethod
    def setup_class(cls):
        from unittest.mock import MagicMock

        from starlette.testclient import TestClient

        import turnstone.server as srv_mod
        from turnstone.core.metrics import MetricsCollector
        from turnstone.core.workstream import WorkstreamState

        srv_mod._metrics = MetricsCollector()
        srv_mod._metrics.model = "test-model"

        mock_ui = MagicMock()
        mock_ui._ws_lock = threading.Lock()
        mock_ui._ws_prompt_tokens = 0
        mock_ui._ws_completion_tokens = 0
        mock_ui._ws_messages = 0
        mock_ui._ws_tool_calls = {}
        mock_ui._ws_context_ratio = 0.0

        mock_session = MagicMock()
        mock_session.ws_id = "test-session-id"

        mock_ws = MagicMock()
        mock_ws.id = "test-ws"
        mock_ws.name = "test"
        mock_ws.state = WorkstreamState.IDLE
        mock_ws.ui = mock_ui
        mock_ws.session = mock_session

        mock_mgr = MagicMock()
        mock_mgr.list_all.return_value = [mock_ws]
        mock_mgr.max_active = 10

        app = srv_mod.create_app(
            workstreams=mock_mgr,
            global_queue=queue.Queue(),
            global_listeners=[],
            global_listeners_lock=threading.Lock(),
            skip_permissions=False,
            jwt_secret=_TEST_JWT_SECRET,
        )
        cls.client = TestClient(app, raise_server_exceptions=False)

    @classmethod
    def teardown_class(cls):
        cls.client.close()

    def _get(self, path) -> tuple[int, str, str]:
        """Make a GET request; return (status, content_type, body_str)."""
        resp = self.client.get(path)
        ct = resp.headers.get("content-type", "")
        return resp.status_code, ct, resp.text

    def test_health_returns_200(self):
        status, _, _ = self._get("/health")
        assert status == 200

    def test_health_content_type_json(self):
        _, ct, _ = self._get("/health")
        assert "application/json" in ct

    def test_health_response_structure(self):
        _, _, body = self._get("/health")
        data = json.loads(body)
        assert data["status"] == "ok"
        assert "version" in data
        assert "uptime_seconds" in data
        assert "model" in data
        assert "workstreams" in data

    def test_health_model_field(self):
        _, _, body = self._get("/health")
        data = json.loads(body)
        assert data["model"] == "test-model"

    def test_health_workstream_counts(self):
        _, _, body = self._get("/health")
        data = json.loads(body)
        wss = data["workstreams"]
        assert wss["total"] == 1
        assert wss["idle"] == 1

    def test_health_uptime_positive(self):
        _, _, body = self._get("/health")
        data = json.loads(body)
        assert data["uptime_seconds"] >= 0

    def test_metrics_returns_200(self):
        status, _, _ = self._get("/metrics")
        assert status == 200

    def test_metrics_content_type_prometheus(self):
        _, ct, _ = self._get("/metrics")
        assert "text/plain" in ct
        assert "version=0.0.4" in ct

    def test_metrics_contains_uptime(self):
        _, _, body = self._get("/metrics")
        assert "turnstone_uptime_seconds" in body

    def test_metrics_contains_build_info(self):
        _, _, body = self._get("/metrics")
        assert "turnstone_build_info" in body
        assert 'model="test-model"' in body

    def test_metrics_contains_workstreams(self):
        _, _, body = self._get("/metrics")
        assert "turnstone_workstreams_active_total" in body
        assert "turnstone_workstreams_by_state" in body

    def test_metrics_contains_token_counters(self):
        _, _, body = self._get("/metrics")
        assert "turnstone_tokens_total" in body
        assert 'type="prompt"' in body
        assert 'type="completion"' in body

    def test_metrics_contains_http_requests(self):
        _, _, body = self._get("/metrics")
        assert "turnstone_http_requests_total" in body

    def test_metrics_request_counter_increments(self):
        """Hitting /health increments the HTTP request counter."""
        # Make a known request to /health
        self._get("/health")
        _, _, body = self._get("/metrics")
        # Counter should mention /health endpoint
        assert 'endpoint="/health"' in body

    def test_metrics_histogram_present(self):
        _, _, body = self._get("/metrics")
        assert "turnstone_http_request_duration_seconds" in body
        assert 'le="' in body
        assert 'le="+Inf"' in body

    def test_unknown_endpoint_returns_404(self):
        resp = self.client.get("/does-not-exist", headers=_SERVER_AUTH_HEADERS)
        assert resp.status_code == 404

    def test_health_contains_backend_field(self):
        _, _, body = self._get("/health")
        data = json.loads(body)
        assert "backend" in data
        assert data["backend"]["status"] in ("up", "down")

    def test_metrics_contains_sse_connections(self):
        _, _, body = self._get("/metrics")
        assert "turnstone_sse_connections_active" in body

    def test_metrics_contains_ratelimit_counter(self):
        _, _, body = self._get("/metrics")
        assert "turnstone_ratelimit_rejected_total" in body

    def test_metrics_contains_backend_up(self):
        _, _, body = self._get("/metrics")
        assert "turnstone_backend_up" in body

    def test_metrics_no_circuit_state(self):
        """Circuit state metric was removed (passive health tracking only)."""
        _, _, body = self._get("/metrics")
        assert "turnstone_circuit_state" not in body

    def test_metrics_contains_eviction_counter(self):
        _, _, body = self._get("/metrics")
        assert "turnstone_workstreams_evicted_total" in body


class TestServerRateLimiting:
    """Verify per-IP rate limiting returns 429 with Retry-After header.

    Creates a Starlette app with a tight rate limiter (rate=2, burst=3)
    and verifies that requests beyond the burst are rejected.
    """

    @classmethod
    def setup_class(cls):
        from unittest.mock import MagicMock

        from starlette.testclient import TestClient

        import turnstone.server as srv_mod
        from turnstone.core.metrics import MetricsCollector
        from turnstone.core.ratelimit import RateLimiter
        from turnstone.core.workstream import WorkstreamState

        srv_mod._metrics = MetricsCollector()
        srv_mod._metrics.model = "test-model"

        mock_ui = MagicMock()
        mock_ui._ws_lock = threading.Lock()
        mock_ui._ws_prompt_tokens = 0
        mock_ui._ws_completion_tokens = 0
        mock_ui._ws_messages = 0
        mock_ui._ws_tool_calls = {}
        mock_ui._ws_context_ratio = 0.0

        mock_session = MagicMock()
        mock_session.ws_id = "test-session-id"

        mock_ws = MagicMock()
        mock_ws.id = "test-ws"
        mock_ws.name = "test"
        mock_ws.state = WorkstreamState.IDLE
        mock_ws.ui = mock_ui
        mock_ws.session = mock_session

        mock_mgr = MagicMock()
        mock_mgr.list_all.return_value = [mock_ws]
        mock_mgr.max_active = 10

        app = srv_mod.create_app(
            workstreams=mock_mgr,
            global_queue=queue.Queue(),
            global_listeners=[],
            global_listeners_lock=threading.Lock(),
            skip_permissions=False,
            jwt_secret=_TEST_JWT_SECRET,
            rate_limiter=RateLimiter(enabled=True, rate=2.0, burst=3),
        )
        cls.client = TestClient(app, raise_server_exceptions=False)

    @classmethod
    def teardown_class(cls):
        cls.client.close()

    def _get(self, path) -> httpx.Response:
        return self.client.get(path)

    def test_burst_requests_succeed(self):
        """First burst requests should all succeed."""
        for _ in range(3):
            resp = self._get("/health")
            assert resp.status_code == 200

    def test_exceeded_rate_returns_429(self):
        """After exhausting burst on a non-exempt endpoint, get 429."""
        # Exhaust burst on a non-exempt endpoint
        for _ in range(5):
            self.client.get("/v1/api/workstreams", headers=_SERVER_AUTH_HEADERS)
        # At least one should be 429
        statuses = [
            self.client.get("/v1/api/workstreams", headers=_SERVER_AUTH_HEADERS).status_code
            for _ in range(3)
        ]
        assert 429 in statuses

    def test_429_includes_retry_after(self):
        """429 response includes Retry-After header."""
        # Burn through burst
        for _ in range(10):
            resp = self.client.get("/v1/api/workstreams", headers=_SERVER_AUTH_HEADERS)
            if resp.status_code == 429:
                assert "retry-after" in resp.headers
                data = resp.json()
                assert "retry_after" in data
                return
        pytest.skip("Did not hit rate limit in 10 requests")

    def test_health_exempt_from_ratelimit(self):
        """Health endpoint is always accessible regardless of rate limit."""
        # Burn through bucket on non-exempt path
        for _ in range(10):
            self.client.get("/v1/api/workstreams", headers=_SERVER_AUTH_HEADERS)
        # Health should still work
        resp = self._get("/health")
        assert resp.status_code == 200

    def test_metrics_exempt_from_ratelimit(self):
        """Metrics endpoint is always accessible regardless of rate limit."""
        for _ in range(10):
            self.client.get("/v1/api/workstreams", headers=_SERVER_AUTH_HEADERS)
        resp = self._get("/metrics")
        assert resp.status_code == 200