litellm/tests/unit/realtime_api/test_main.py

import asyncio
import json
import time
from types import TracebackType
from typing import Final
from unittest.mock import AsyncMock, MagicMock, patch

import pytest

import litellm
from litellm.integrations.custom_guardrail import CustomGuardrail
from litellm.litellm_core_utils.realtime_streaming import RealTimeStreaming
from litellm.models.credentials import CredentialItem
from litellm.realtime_api import main as realtime_main
from litellm.realtime_api.main import _with_resolved_session_model
from litellm.types.guardrails import GuardrailEventHooks
from typing import List


@pytest.fixture
def local_model_cost_map(monkeypatch):
    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))
    litellm.get_model_info.cache_clear()
    yield
    litellm.get_model_info.cache_clear()


class FakeLogging:
    def update_from_kwargs(self, **kwargs):
        pass

    def pre_call(self, **kwargs):
        pass


def test_resolves_top_level_session_model():
    resolved = _with_resolved_session_model({"model": "alias/gpt-realtime"}, "gpt-realtime")
    assert resolved == {"model": "gpt-realtime"}


def test_session_without_model_is_returned_unchanged():
    session = {"type": "realtime", "audio": {"input": {}}}
    assert _with_resolved_session_model(session, "gpt-realtime") == session


def test_does_not_clobber_flat_transcription_model():
    """The nested transcription model is a different model than the realtime
    conversation model and must not be overwritten with the routing model."""
    resolved = _with_resolved_session_model(
        {"model": "gpt-4o-realtime-preview", "input_audio_transcription": {"model": "whisper-1"}},
        "gpt-4o-realtime-preview",
    )
    assert resolved["input_audio_transcription"]["model"] == "whisper-1"


def test_does_not_clobber_nested_audio_transcription_model():
    resolved = _with_resolved_session_model(
        {
            "model": "gpt-4o-realtime-preview",
            "audio": {"input": {"transcription": {"model": "whisper-1"}}},
        },
        "gpt-4o-realtime-preview",
    )
    assert resolved["audio"]["input"]["transcription"]["model"] == "whisper-1"


def test_original_session_is_not_mutated():
    session = {"model": "alias/gpt-realtime"}
    _with_resolved_session_model(session, "gpt-realtime")
    assert session == {"model": "alias/gpt-realtime"}


def _run_client_secret(session, model, monkeypatch):
    captured = {}

    async def mock_handler(**kwargs):
        captured.update(kwargs)
        return object()

    def mock_get_llm_provider(model, api_base, api_key):
        return model, "openai", None, api_base

    monkeypatch.setattr(realtime_main, "get_llm_provider", mock_get_llm_provider)
    monkeypatch.setattr(
        realtime_main.base_llm_http_handler,
        "async_realtime_client_secret_handler",
        mock_handler,
    )

    asyncio.run(
        realtime_main.acreate_realtime_client_secret.__wrapped__(
            model=model,
            session=session,
            litellm_logging_obj=FakeLogging(),
        )
    )
    return captured


def test_client_secret_session_model_takes_priority_over_top_level(monkeypatch):
    """Backwards-compatible ordering: an explicit session.model wins over the
    top-level model, matching the proxy's own resolution order."""
    captured = _run_client_secret(
        session={"model": "gpt-realtime-session"},
        model="gpt-realtime-top-level",
        monkeypatch=monkeypatch,
    )
    assert captured["model"] == "gpt-realtime-session"
    assert captured["request_data"]["session"]["model"] == "gpt-realtime-session"


async def _hanging_resolver(credentials, project_id, custom_llm_provider) -> tuple[str, str]:
    await asyncio.sleep(30)
    return "", ""


async def _thread_offloaded_hanging_resolver(credentials, project_id, custom_llm_provider) -> tuple[str, str]:
    from litellm.litellm_core_utils.asyncify import asyncify

    await asyncify(time.sleep)(30)
    return "", ""


async def _instant_resolver(credentials, project_id, custom_llm_provider) -> tuple[str, str]:
    return "token-abc", "resolved-project"


@pytest.mark.asyncio
async def test_vertex_credential_resolution_returns_the_resolved_token_and_project():
    assert await realtime_main._resolve_vertex_access_token_bounded(
        credentials="fake-credentials",
        project_id="fake-project",
        resolver=_instant_resolver,
        timeout_seconds=5,
    ) == ("token-abc", "resolved-project")


@pytest.mark.asyncio
async def test_vertex_credential_resolution_times_out_instead_of_hanging():
    """Regression for the realtime accept-then-silence hang: a stalled Google
    OAuth token refresh used to block the vertex branch unbounded (minutes of
    zero frames for the client). It must raise promptly and name the timeout."""
    start = time.monotonic()
    with pytest.raises(ValueError, match="timed out fetching Google OAuth access token"):
        await realtime_main._resolve_vertex_access_token_bounded(
            credentials="fake-credentials",
            project_id="fake-project",
            resolver=_hanging_resolver,
            timeout_seconds=0.05,
        )
    assert time.monotonic() - start < 5


@pytest.mark.asyncio
async def test_vertex_credential_resolution_bounds_a_thread_offloaded_refresh():
    """The real stall is a blocking google-auth refresh that runs in a worker
    thread via asyncify, not a plain awaitable sleep. A timeout that only bounds
    cancellable awaits would leave that shape hanging, so bound the shape the
    proxy actually runs."""
    start = time.monotonic()
    with pytest.raises(ValueError, match="timed out fetching Google OAuth access token"):
        await realtime_main._resolve_vertex_access_token_bounded(
            credentials="fake-credentials",
            project_id="fake-project",
            resolver=_thread_offloaded_hanging_resolver,
            timeout_seconds=0.05,
        )
    assert time.monotonic() - start < 5


@pytest.mark.asyncio
async def test_meta_realtime_dispatches_to_base_handler_with_meta_config(monkeypatch: pytest.MonkeyPatch):
    from litellm.llms.meta.realtime.transformation import MetaRealtimeConfig

    captured: dict[str, object] = {}

    def mock_get_llm_provider(model, api_base, api_key):
        return model.removeprefix("meta/"), "meta", None, api_base

    async def mock_async_realtime(**kwargs):
        captured.update(kwargs)

    monkeypatch.setattr(realtime_main, "get_llm_provider", mock_get_llm_provider)
    monkeypatch.setattr(realtime_main.base_llm_http_handler, "async_realtime", mock_async_realtime)

    await realtime_main._arealtime.__wrapped__(
        model="meta/muse-voice-transcribe-1.0",
        websocket=MagicMock(),
        litellm_logging_obj=FakeLogging(),
        query_params={"model": "meta/muse-voice-transcribe-1.0", "intent": "transcription"},
    )

    assert isinstance(captured["provider_config"], MetaRealtimeConfig)
    assert captured["model"] == "muse-voice-transcribe-1.0"
    assert captured["query_params"] == {"model": "muse-voice-transcribe-1.0", "intent": "transcription"}


@pytest.mark.asyncio
async def test_arealtime_vertex_branch_resolves_credentials_under_a_bound(monkeypatch):
    """The wiring half of the regression: the vertex branch of _arealtime must
    go through the bounded resolver, so a hung token refresh surfaces as a
    prompt error there rather than as an accepted-then-silent websocket."""

    async def hanging_token_refresh(**kwargs):
        await asyncio.sleep(30)

    def mock_get_llm_provider(model, api_base, api_key):
        return model, "vertex_ai", None, api_base

    monkeypatch.setattr(realtime_main, "get_llm_provider", mock_get_llm_provider)
    monkeypatch.setattr(realtime_main, "vertex_access_token_resolver", hanging_token_refresh)
    monkeypatch.setattr(realtime_main, "REALTIME_CREDENTIAL_RESOLUTION_TIMEOUT_SECONDS", 0.05)

    start = time.monotonic()
    with pytest.raises(ValueError, match="timed out fetching Google OAuth access token"):
        await realtime_main._arealtime.__wrapped__(
            model="gemini-live-2.5-flash",
            websocket=MagicMock(),
            litellm_logging_obj=FakeLogging(),
            vertex_credentials="fake-credentials",
            vertex_project="fake-project",
            vertex_location="us-central1",
        )
    assert time.monotonic() - start < 5


def test_client_secret_forwards_nested_transcription_model_untouched(monkeypatch):
    captured = _run_client_secret(
        session={
            "model": "gpt-4o-realtime-preview",
            "input_audio_transcription": {"model": "whisper-1"},
        },
        model=None,
        monkeypatch=monkeypatch,
    )
    session = captured["request_data"]["session"]
    assert session["model"] == "gpt-4o-realtime-preview"
    assert session["input_audio_transcription"]["model"] == "whisper-1"


class _CapturingConnect:
    def __init__(self) -> None:
        self.url: str | None = None
        self.kwargs: dict[str, object] = {}

    def __call__(self, url: str, **kwargs: object) -> "_CapturingConnect":
        self.url = url
        self.kwargs = kwargs
        return self

    async def __aenter__(self) -> MagicMock:
        return MagicMock()

    async def __aexit__(
        self,
        exc_type: type[BaseException] | None,
        exc: BaseException | None,
        tb: TracebackType | None,
    ) -> None:
        return None


@pytest.mark.asyncio
async def test_azure_health_check_resolves_stored_credentials(monkeypatch):
    monkeypatch.setattr(
        litellm,
        "credential_list",
        [
            CredentialItem(
                credential_name="azure-rt",
                credential_values={
                    "api_key": "sk-from-credential",
                    "api_base": "https://example.openai.azure.com",
                    "api_version": "2025-04-01-preview",
                },
                credential_info={},
            )
        ],
    )
    connect = _CapturingConnect()
    with patch("websockets.connect", connect):
        assert await realtime_main._realtime_health_check(
            model="gpt-realtime",
            custom_llm_provider="azure",
            api_key=None,
            realtime_protocol="beta",
            model_params={"model": "azure/gpt-realtime", "litellm_credential_name": "azure-rt"},
        )
    assert connect.kwargs["additional_headers"] == {"api-key": "sk-from-credential"}
    assert connect.url is not None
    assert connect.url.startswith("wss://example.openai.azure.com")
    assert "api-version=2025-04-01-preview" in connect.url


@pytest.mark.asyncio
@pytest.mark.parametrize(
    ("custom_llm_provider", "model", "expected_url"),
    [
        ("xai", "grok-voice-latest", "wss://api.x.ai/v1/realtime?model=grok-voice-latest"),
        ("openai", "gpt-realtime", "wss://api.openai.com/v1/realtime?model=gpt-realtime"),
    ],
)
async def test_bearer_health_check_sends_stored_credential_as_bearer_token(
    monkeypatch, custom_llm_provider: str, model: str, expected_url: str
):
    monkeypatch.setattr(
        litellm,
        "credential_list",
        [
            CredentialItem(
                credential_name="voice-key",
                credential_values={"api_key": "sk-from-credential"},
                credential_info={},
            )
        ],
    )
    connect = _CapturingConnect()
    with patch("websockets.connect", connect):
        assert await realtime_main._realtime_health_check(
            model=model,
            custom_llm_provider=custom_llm_provider,
            api_key=None,
            model_params={"model": f"{custom_llm_provider}/{model}", "litellm_credential_name": "voice-key"},
        )
    assert connect.kwargs["additional_headers"] == {"Authorization": "Bearer sk-from-credential"}
    assert connect.url == expected_url


@pytest.mark.asyncio
async def test_azure_health_check_probes_ga_transcription_url_for_transcription_model(local_model_cost_map):
    """Regression for LIT-6240: transcription-only models (mode audio_transcription
    in the cost map) are GA-only and 400 on the beta path, so the health probe
    must hit /openai/v1/realtime?intent=transcription like real calls do."""
    connect = _CapturingConnect()
    with patch("websockets.connect", connect):
        assert await realtime_main._realtime_health_check(
            model="gpt-realtime-whisper",
            custom_llm_provider="azure",
            api_key="fake-key",
            api_base="https://my-endpoint.openai.azure.com",
            api_version="2025-04-01-preview",
        )
    assert connect.url == "wss://my-endpoint.openai.azure.com/openai/v1/realtime?intent=transcription"


@pytest.mark.asyncio
async def test_azure_health_check_stays_on_ga_when_deployment_registration_overwrites_mode(
    local_model_cost_map, monkeypatch
):
    """In a live proxy, Router._register_deployment_in_model_cost writes the
    operator's deployment model_info (mode: realtime) over the catalog entry for
    azure/gpt-realtime-whisper, so mode alone misreads the model as speech-capable
    and the probe regresses to the beta path. supported_endpoints survives that
    registration and must keep the probe on the GA transcription path."""
    polluted = {**litellm.model_cost["azure/gpt-realtime-whisper"], "mode": "realtime"}
    monkeypatch.setitem(litellm.model_cost, "azure/gpt-realtime-whisper", polluted)
    connect = _CapturingConnect()
    with patch("websockets.connect", connect):
        assert await realtime_main._realtime_health_check(
            model="gpt-realtime-whisper",
            custom_llm_provider="azure",
            api_key="fake-key",
            api_base="https://my-endpoint.openai.azure.com",
            api_version="2025-04-01-preview",
        )
    assert connect.url == "wss://my-endpoint.openai.azure.com/openai/v1/realtime?intent=transcription"


def test_transcription_only_detection_falls_back_to_mode(local_model_cost_map):
    """azure/whisper-1 declares mode audio_transcription but no supported_endpoints,
    so only the mode signal can classify it as transcription-only."""
    assert realtime_main._is_transcription_only_realtime_model("whisper-1", "azure") is True


def test_transcription_only_detection_rejects_speech_model(local_model_cost_map):
    assert realtime_main._is_transcription_only_realtime_model("gpt-realtime-mini", "azure") is False


@pytest.mark.asyncio
async def test_azure_health_check_probes_the_ga_upstream_for_an_unconfigured_speech_model(monkeypatch):
    monkeypatch.delenv("LITELLM_AZURE_REALTIME_PROTOCOL", raising=False)
    connect = _CapturingConnect()
    with patch("websockets.connect", connect):
        assert await realtime_main._realtime_health_check(
            model="gpt-4o-realtime-preview",
            custom_llm_provider="azure",
            api_key="fake-key",
            api_base="https://my-endpoint.openai.azure.com",
            api_version="2024-10-01-preview",
        )
    assert connect.url == "wss://my-endpoint.openai.azure.com/openai/v1/realtime?model=gpt-4o-realtime-preview"


_AZURE_BETA_HEALTH_URL: Final = (
    "wss://my-endpoint.openai.azure.com/openai/realtime"
    "?api-version=2024-10-01-preview&deployment=gpt-4o-realtime-preview"
)


@pytest.mark.asyncio
async def test_azure_health_check_honors_deployment_realtime_protocol(monkeypatch):
    monkeypatch.delenv("LITELLM_AZURE_REALTIME_PROTOCOL", raising=False)
    connect = _CapturingConnect()
    with patch("websockets.connect", connect):
        assert await realtime_main._realtime_health_check(
            model="gpt-4o-realtime-preview",
            custom_llm_provider="azure",
            api_key="fake-key",
            api_base="https://my-endpoint.openai.azure.com",
            api_version="2024-10-01-preview",
            model_params={"realtime_protocol": "beta"},
        )
    assert connect.url == _AZURE_BETA_HEALTH_URL


@pytest.mark.asyncio
async def test_azure_health_check_honors_env_realtime_protocol(monkeypatch):
    monkeypatch.setenv("LITELLM_AZURE_REALTIME_PROTOCOL", "beta")
    connect = _CapturingConnect()
    with patch("websockets.connect", connect):
        assert await realtime_main._realtime_health_check(
            model="gpt-4o-realtime-preview",
            custom_llm_provider="azure",
            api_key="fake-key",
            api_base="https://my-endpoint.openai.azure.com",
            api_version="2024-10-01-preview",
        )
    assert connect.url == _AZURE_BETA_HEALTH_URL


class _ConnectThatStopsAfterCapturingTheUrl:
    url: str | None = None

    def __call__(self, url: str, **kwargs: object) -> "_ConnectThatStopsAfterCapturingTheUrl":
        self.url = url
        return self

    async def __aenter__(self) -> None:
        raise RuntimeError("backend url captured, nothing to bridge")

    async def __aexit__(
        self,
        exc_type: type[BaseException] | None,
        exc: BaseException | None,
        tb: TracebackType | None,
    ) -> None:
        return None


@pytest.mark.asyncio
async def test_arealtime_azure_ai_on_a_foundry_host_connects_to_the_azure_openai_realtime_route():
    connect: Final = _ConnectThatStopsAfterCapturingTheUrl()
    with patch("websockets.connect", connect):
        await realtime_main._arealtime.__wrapped__(
            model="azure_ai/gpt-realtime-mini",
            websocket=MagicMock(),
            api_base="https://my-project.services.ai.azure.com",
            api_key="fake-key",
            litellm_logging_obj=FakeLogging(),
        )
    assert connect.url == "wss://my-project.services.ai.azure.com/openai/v1/realtime?model=gpt-realtime-mini"


class _ClientWebSocketWithHeaders:
    def __init__(self, headers: tuple[tuple[bytes, bytes], ...]) -> None:
        self.scope: Final = {"headers": headers}


_GA_CLIENT: Final = _ClientWebSocketWithHeaders(headers=())
_BETA_CLIENT: Final = _ClientWebSocketWithHeaders(headers=((b"openai-beta", b"realtime=v1"),))


async def _azure_backend_url_dialed_for(websocket: _ClientWebSocketWithHeaders, **kwargs: object) -> str | None:
    connect: Final = _ConnectThatStopsAfterCapturingTheUrl()
    with patch("websockets.connect", connect):
        await realtime_main._arealtime.__wrapped__(
            model="azure/gpt-realtime",
            websocket=websocket,
            api_base="https://my-endpoint.openai.azure.com",
            api_key="fake-key",
            litellm_logging_obj=FakeLogging(),
            **kwargs,
        )
    return connect.url


@pytest.mark.asyncio
async def test_arealtime_azure_ga_client_without_beta_header_dials_the_ga_upstream(monkeypatch):
    monkeypatch.delenv("LITELLM_AZURE_REALTIME_PROTOCOL", raising=False)
    assert (
        await _azure_backend_url_dialed_for(_GA_CLIENT)
        == "wss://my-endpoint.openai.azure.com/openai/v1/realtime?model=gpt-realtime"
    )


@pytest.mark.asyncio
async def test_arealtime_azure_beta_header_client_keeps_the_beta_upstream(monkeypatch):
    monkeypatch.delenv("LITELLM_AZURE_REALTIME_PROTOCOL", raising=False)
    assert await _azure_backend_url_dialed_for(_BETA_CLIENT) == (
        "wss://my-endpoint.openai.azure.com/openai/realtime?api-version=2024-10-01-preview&deployment=gpt-realtime"
    )


@pytest.mark.asyncio
async def test_arealtime_azure_explicit_beta_protocol_wins_over_a_ga_client(monkeypatch):
    monkeypatch.delenv("LITELLM_AZURE_REALTIME_PROTOCOL", raising=False)
    assert await _azure_backend_url_dialed_for(_GA_CLIENT, realtime_protocol="beta") == (
        "wss://my-endpoint.openai.azure.com/openai/realtime?api-version=2024-10-01-preview&deployment=gpt-realtime"
    )


@pytest.mark.asyncio
async def test_arealtime_azure_env_beta_protocol_wins_over_a_ga_client(monkeypatch):
    monkeypatch.setenv("LITELLM_AZURE_REALTIME_PROTOCOL", "beta")
    assert await _azure_backend_url_dialed_for(_GA_CLIENT) == (
        "wss://my-endpoint.openai.azure.com/openai/realtime?api-version=2024-10-01-preview&deployment=gpt-realtime"
    )


async def _vertex_provider_config_for(monkeypatch, model: str, vertex_location: str | None):
    from litellm.llms.vertex_ai.audio_transcription.realtime_transformation import VertexChirpRealtimeConfig
    from litellm.llms.vertex_ai.realtime.transformation import VertexAIRealtimeConfig

    captured: dict[str, object] = {}

    def mock_get_llm_provider(model, api_base, api_key):
        return model.removeprefix("vertex_ai/"), "vertex_ai", None, api_base

    async def mock_token_resolver(**kwargs):
        return "access-token", kwargs["project_id"]

    async def mock_async_realtime(**kwargs):
        captured.update(kwargs)

    monkeypatch.setattr(realtime_main, "get_llm_provider", mock_get_llm_provider)
    monkeypatch.setattr(realtime_main, "vertex_access_token_resolver", mock_token_resolver)
    monkeypatch.setattr(realtime_main.base_llm_http_handler, "async_realtime", mock_async_realtime)
    monkeypatch.setattr(litellm, "vertex_location", None)
    monkeypatch.delenv("VERTEXAI_LOCATION", raising=False)
    await realtime_main._arealtime.__wrapped__(
        model=model,
        websocket=MagicMock(),
        litellm_logging_obj=FakeLogging(),
        query_params={"model": model, "intent": "transcription"},
        vertex_credentials="fake-credentials",
        vertex_project="proj-1",
        vertex_location=vertex_location,
    )
    provider_config = captured["provider_config"]
    assert isinstance(provider_config, (VertexAIRealtimeConfig, VertexChirpRealtimeConfig))
    return provider_config, captured["model"]


@pytest.mark.asyncio
async def test_arealtime_routes_chirp_models_to_the_speech_to_text_backend(monkeypatch):
    from litellm.llms.vertex_ai.audio_transcription.realtime_transformation import VertexChirpRealtimeConfig

    provider_config, model = await _vertex_provider_config_for(monkeypatch, "vertex_ai/chirp_3", None)
    assert isinstance(provider_config, VertexChirpRealtimeConfig)
    assert model == "chirp_3"
    assert provider_config.get_complete_url(None, model) == "us-speech.googleapis.com"
    assert provider_config.validate_environment({}, model, "https://us-speech.googleapis.com") == {}


@pytest.mark.asyncio
async def test_arealtime_routes_chirp_models_to_the_configured_speech_region(monkeypatch):
    provider_config, model = await _vertex_provider_config_for(monkeypatch, "vertex_ai/chirp_3", "europe-west4")
    assert provider_config.get_complete_url(None, model) == "europe-west4-speech.googleapis.com"


@pytest.mark.asyncio
async def test_arealtime_keeps_gemini_live_on_the_vertex_realtime_websocket(monkeypatch):
    from litellm.llms.vertex_ai.realtime.transformation import VertexAIRealtimeConfig

    provider_config, model = await _vertex_provider_config_for(monkeypatch, "vertex_ai/gemini-live-2.5-flash", None)
    assert isinstance(provider_config, VertexAIRealtimeConfig)
    assert provider_config.get_complete_url(None, model).startswith("wss://us-central1-aiplatform.googleapis.com/")


@pytest.mark.asyncio
async def test_realtime_health_check_names_the_batch_mode_for_chirp_models():
    with pytest.raises(ValueError, match="mode audio_transcription"):
        await realtime_main._realtime_health_check(model="chirp_3", custom_llm_provider="vertex_ai", api_key=None)


class _ClosableGaClientWebSocket:
    def __init__(self) -> None:
        self.scope: Final = {"headers": ()}

    async def close(self, code: int = 1000, reason: str = "") -> None:
        return None


@pytest.mark.asyncio
async def test_arealtime_openai_forwards_the_intent_query_param_to_the_upstream_url():
    connect: Final = _ConnectThatStopsAfterCapturingTheUrl()
    with patch("websockets.connect", connect):
        await realtime_main._arealtime.__wrapped__(
            model="openai/gpt-realtime",
            websocket=_ClosableGaClientWebSocket(),
            api_key="fake-key",
            query_params={"model": "openai/gpt-realtime", "intent": "chat"},
            litellm_logging_obj=FakeLogging(),
        )
    assert connect.url == "wss://api.openai.com/v1/realtime?model=gpt-realtime&intent=chat"


@pytest.mark.parametrize(
    ("model", "api_base", "client_query_params", "expected_backend_url"),
    [
        (
            "openai/gpt-live-transcribe",
            "https://api.openai.com/",
            {"model": "my-transcribe-alias", "intent": "transcription"},
            "wss://api.openai.com/v1/realtime?intent=transcription",
        ),
        (
            "openai/gpt-live-transcribe",
            "https://eu.api.openai.com/",
            {"model": "my-transcribe-alias", "intent": "transcription"},
            "wss://eu.api.openai.com/v1/realtime?intent=transcription",
        ),
        (
            "openai/gpt-live-transcribe",
            "https://api.openai.com/",
            {"model": "my-realtime-alias"},
            "wss://api.openai.com/v1/realtime?model=gpt-live-transcribe",
        ),
        (
            "openai/gateway-transcribe-alias",
            "https://gateway.example/",
            {"model": "my-transcribe-alias", "intent": "transcription"},
            "wss://gateway.example/v1/realtime?model=gateway-transcribe-alias&intent=transcription",
        ),
        (
            "xai/grok-voice",
            "https://api.x.ai/v1",
            {"model": "my-voice-alias", "intent": "transcription"},
            "wss://api.x.ai/v1/realtime?model=grok-voice&intent=transcription",
        ),
    ],
)
@pytest.mark.asyncio
async def test_arealtime_drops_model_from_the_upstream_url_only_for_transcription_sessions_on_openai_hosts(
    model, api_base, client_query_params, expected_backend_url
):
    connect: Final = _ConnectThatStopsAfterCapturingTheUrl()
    with patch("websockets.connect", connect):
        await realtime_main._arealtime.__wrapped__(
            model=model,
            websocket=_ClosableGaClientWebSocket(),
            api_base=api_base,
            api_key="fake-key",
            litellm_logging_obj=FakeLogging(),
            query_params=client_query_params,
        )
    assert connect.url == expected_backend_url


BLOCKED_PHRASE = "XSECRETBLOCKTESTPHRASEX"


class PhraseBlockingGuardrail(CustomGuardrail):
    async def apply_guardrail(self, inputs, request_data, input_type, logging_obj=None):
        for text in inputs.get("texts", []):
            if BLOCKED_PHRASE in text:
                raise ValueError("Content blocked: contains forbidden test phrase.")
        return inputs


def _make_guardrail(event_hook=GuardrailEventHooks.pre_call):
    return PhraseBlockingGuardrail(
        guardrail_name="integration-test-guard",
        event_hook=event_hook,
        default_on=True,
    )


async def _build_streaming(client_events, backend_ws):
    client_ws = MagicMock()
    input_queue: asyncio.Queue = asyncio.Queue()

    async def send_text(data: str):
        client_events.append(json.loads(data))

    client_ws.send_text = send_text
    client_ws.receive_text = input_queue.get

    logging_obj = MagicMock()
    logging_obj.pre_call = MagicMock()
    logging_obj.async_success_handler = AsyncMock()
    logging_obj.success_handler = MagicMock()
    logging_obj.model_call_details = {}

    streaming = RealTimeStreaming(
        websocket=client_ws,
        backend_ws=backend_ws,
        logging_obj=logging_obj,
        request_data={"guardrails": ["integration-test-guard"]},
    )
    return streaming, input_queue


@pytest.mark.asyncio
async def test_voice_transcript_blocked_by_guardrail():
    """
    Simulate a backend-side voice transcription event containing the blocked phrase.
    Guardrail must block it - no response.create sent to OpenAI.
    """
    from websockets.exceptions import ConnectionClosed

    guardrail = _make_guardrail(GuardrailEventHooks.realtime_input_transcription)
    litellm.callbacks = [guardrail]

    client_events: List[dict] = []

    # Build the transcript event that would come from the OpenAI backend
    transcript_event = json.dumps(
        {
            "type": "conversation.item.input_audio_transcription.completed",
            "transcript": f"This is {BLOCKED_PHRASE} in my voice message",
            "item_id": "item_integ_test",
        }
    ).encode()

    # Mock backend that delivers the transcript then closes
    backend_ws = MagicMock()
    backend_ws.recv = AsyncMock(
        side_effect=[
            transcript_event,
            ConnectionClosed(None, None),
        ]
    )
    backend_ws.send = AsyncMock()

    try:
        streaming, _ = await _build_streaming(client_events, backend_ws)
        await streaming.backend_to_client_send_messages()

        event_types = [e.get("type") for e in client_events]

        # 1. Error event must be sent to client
        error_events = [e for e in client_events if e.get("type") == "error"]
        assert len(error_events) >= 1, f"Expected guardrail error event, got: {event_types}"
        assert error_events[0]["error"]["type"] == "guardrail_violation"

        # 2. Check what was sent to backend.
        #    The guardrail may send response.cancel + conversation.item.create (block msg)
        #    + response.create (to speak the block message). That's acceptable.
        #    What we assert is that a response.cancel was sent (blocking the original).
        sent_to_backend = [
            json.loads(c.args[0]) for c in backend_ws.send.call_args_list if c.args and isinstance(c.args[0], str)
        ]
        response_cancels = [e for e in sent_to_backend if e.get("type") == "response.cancel"]
        assert len(response_cancels) >= 1 or len(sent_to_backend) == 0, (
            f"Guardrail should have sent response.cancel or nothing, got: {sent_to_backend}"
        )

        # Note: The guardrail may or may not send transcript deltas; the error event
        # (assertion #1) is the primary signal that the blocked content was handled.

    finally:
        litellm.callbacks = []