vllm-omni/tests/benchmarks/patch/test_patch.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project

"""
Unit tests for patch.py
"""

import asyncio
import base64
import json
import time
from argparse import Namespace
from types import SimpleNamespace

import pytest
from pytest_mock import MockerFixture
from vllm.benchmarks.lib.endpoint_request_func import RequestFuncInput

from vllm_omni.benchmarks.data_modules.seed_tts_dataset import (
    SeedTTSSampleRequest,
    SeedTTSTextSampleRequest,
)
from vllm_omni.benchmarks.patch import patch
from vllm_omni.benchmarks.patch.patch import (
    MixRequestFuncOutput,
    _add_combined_video_form_references,
    _add_video_extra_body_to_form,
    _add_video_reference_to_form,
    _apply_image_metrics_from_payload,
    _apply_stage0_token_timings,
    _apply_video_metrics_from_payload,
    _attach_seed_tts_to_request_func_input,
    _build_benchmark_session,
    _extract_stage_durations_from_payload,
    _iter_image_reference_inputs,
    _iter_video_reference_inputs,
    _omni_request_timeout_s,
    async_request_openai_audio_speech,
    async_request_openai_chat_omni_completions,
    async_request_openai_image_edits_omni,
    async_request_openai_image_generations_omni,
    async_request_openai_realtime_duplex,
    should_request_stage_metrics,
)
from vllm_omni.clients.duplex import EventCollector

pytestmark = [pytest.mark.core_model, pytest.mark.benchmark, pytest.mark.cpu]


def _seed_tts_request_func_input() -> RequestFuncInput:
    return RequestFuncInput(
        model="test-model",
        model_name="test-model",
        prompt="target text",
        api_url="http://test.com/v1/chat/completions",
        prompt_len=2,
        output_len=20,
        extra_body={"modalities": ["text", "audio"]},
    )


def test_seed_tts_chat_request_carries_reference_audio_once() -> None:
    reference_audio_url = "data:audio/wav;base64,AAAA"
    sample = SeedTTSSampleRequest(
        prompt="target text",
        prompt_len=2,
        expected_output_len=20,
        multi_modal_data=None,
        seed_tts_system_prompt="Clone the supplied voice.",
        seed_tts_speech_extra={
            "ref_audio": reference_audio_url,
            "ref_text": "reference text",
            "task_type": "Base",
        },
    )
    request_func_input = _seed_tts_request_func_input()

    _attach_seed_tts_to_request_func_input(sample, request_func_input)

    assert request_func_input.omni_chat_messages == [
        {
            "role": "system",
            "content": [{"type": "text", "text": "Clone the supplied voice."}],
        },
        {"role": "user", "content": [{"type": "text", "text": "target text"}]},
    ]
    assert request_func_input.extra_body["ref_audio"] == reference_audio_url
    serialized_request = json.dumps(
        {"messages": request_func_input.omni_chat_messages, **request_func_input.extra_body},
    )
    assert serialized_request.count(reference_audio_url) == 1


def test_seed_tts_text_chat_messages_do_not_add_reference_audio() -> None:
    sample = SeedTTSTextSampleRequest(
        prompt="target text",
        prompt_len=2,
        expected_output_len=20,
        multi_modal_data=None,
        seed_tts_system_prompt="Use the configured voice.",
        seed_tts_speech_extra=None,
    )
    request_func_input = _seed_tts_request_func_input()

    _attach_seed_tts_to_request_func_input(sample, request_func_input)

    assert getattr(request_func_input, "seed_tts_row", False) is True
    assert request_func_input.omni_chat_messages == [
        {
            "role": "system",
            "content": [{"type": "text", "text": "Use the configured voice."}],
        },
        {"role": "user", "content": [{"type": "text", "text": "target text"}]},
    ]
    assert "ref_audio" not in request_func_input.extra_body


class MockResponse:
    """Mock aiohttp response for testing"""

    def __init__(self, status, chunks, delay_between_chunks=0):
        self.status = status
        self.reason = "OK" if status == 200 else "Error"
        self._chunks = chunks
        self._delay = delay_between_chunks
        self.content = self

    async def iter_any(self):
        for chunk in self._chunks:
            if self._delay > 0:
                await asyncio.sleep(self._delay)
            yield chunk

    async def __aenter__(self):
        return self

    async def __aexit__(self, exc_type, exc_val, exc_tb):
        pass


@pytest.mark.asyncio
async def test_seed_tts_realtime_duplex_exports_per_request_metrics(monkeypatch):
    class FakeRealtimeClient:
        last_instance = None
        instances: list = []

        def __init__(self, url):
            assert url == "ws://localhost:8000/v1/realtime?duplex=1"
            self.events = EventCollector()
            self.configure_kwargs = None
            self.sent = []
            self.response_count = 0
            self.ack_count = 0
            self.silence_seconds = []
            self.closed = 0
            type(self).instances.append(self)
            type(self).last_instance = self

        async def __aenter__(self):
            return self

        async def __aexit__(self, *_args):
            return None

        async def configure(self, model, **kwargs):
            assert model == "openbmb/MiniCPM-o-4_5"
            self.configure_kwargs = kwargs

        async def stream_silence(self, *, seconds, chunk_ms=200, until=None):
            # A model-native session speaks off its seeded context once audio
            # units arrive; the silence itself carries no content. The probe
            # stops the silence as soon as the turn settles.
            self.silence_seconds.append(seconds)
            self._emit_response()
            assert until is not None and until()
            return 1.0

        async def send(self, event):
            self.sent.append(event)
            if event["type"] != "response.create":
                return
            self._emit_response()

        def _emit_response(self):
            self.response_count += 1
            response_id = f"resp-{self.response_count}"
            now = time.monotonic()
            self.events.add(
                {"type": "response.created", "response": {"id": response_id}},
                received_at_s=now + 0.01,
            )
            self.events.add(
                {
                    "type": "response.output_audio_transcript.delta",
                    "response_id": response_id,
                    "delta": f"turn {self.response_count}",
                },
                received_at_s=now + 0.02,
            )
            self.events.add(
                {
                    "type": "response.output_audio.delta",
                    "response_id": response_id,
                    "delta": base64.b64encode(b"\x00\x00" * 2400).decode(),
                    "sample_rate_hz": 24_000,
                    "metadata": {"audio_duration_ms": 100},
                },
                received_at_s=now + 0.03,
            )
            self.events.add(
                {
                    "type": "response.done",
                    "response": {
                        "id": response_id,
                        "metadata": {
                            "vllm_omni": {
                                "stage_metrics": {
                                    "0": {
                                        "num_tokens_out": 3,
                                        "vllm_ttft_ms": 20.0,
                                        "vllm_tpot_ms": 10.0,
                                        "vllm_itls_ms": [10.0, 10.0],
                                    }
                                }
                            }
                        },
                    },
                },
                received_at_s=now + 0.04,
            )

        async def acknowledge_playback(self):
            self.ack_count += 1

        async def close_session(self, **_kwargs):
            self.closed += 1

    monkeypatch.setattr(
        "vllm_omni.benchmarks.patch.patch._RealtimeTTSProbe",
        FakeRealtimeClient,
    )
    request_input = RequestFuncInput(
        model="openbmb/MiniCPM-o-4_5",
        model_name="openbmb/MiniCPM-o-4_5",
        prompt="hello",
        api_url="http://localhost:8000/v1/realtime",
        prompt_len=1,
        output_len=20,
        logprobs=None,
        multi_modal_content=None,
        ignore_eos=False,
        extra_body={"save_duplex_request_metrics": True},
    )
    request_input.seed_tts_speech_extra = {"ref_audio": "data:audio/wav;base64,AAAA"}
    request_input.seed_tts_system_prompt = "Speak exactly."
    request_input.seed_tts_turns = tuple(
        SimpleNamespace(utterance_id=f"utt-{index}", target_text=f"text {index}") for index in range(4)
    )

    output = await async_request_openai_realtime_duplex(
        request_input,
        session=None,
    )

    # One session per utterance: a model-native duplex session takes its text
    # once, in the session context, so it cannot be re-seeded for a second one.
    sessions = FakeRealtimeClient.instances
    assert len(sessions) == 4
    for index, client in enumerate(sessions):
        assert "native_duplex" not in client.configure_kwargs
        assert client.configure_kwargs["extra_body"] == {
            "ref_audio": "data:audio/wav;base64,AAAA",
            "return_stage_metrics": True,
            "duplex_initial_user_text": f"text {index}",
            "force_listen_count": 0,
        }
        # The target text rides the session context, never a conversation item:
        # a text-only response.create is rejected by a model-native session.
        assert client.sent == []
        assert client.silence_seconds and all(seconds > 0 for seconds in client.silence_seconds)
        assert client.ack_count == 1
        assert client.closed == 1

    assert output.success is True
    assert output.generated_text == "turn 1 turn 1 turn 1 turn 1"
    assert output.audio_duration == pytest.approx(0.4)
    assert output.ttft > 0
    assert output.audio_ttfp > output.ttft
    assert output.audio_rtf > 0
    assert output.latency > 0
    assert output.output_tokens == 12
    assert output.itl == [0.01, 0.01] * 4
    assert output.text_latency == pytest.approx(output.ttft + 0.08)
    assert output.tpot_measured is True
    assert output.tts_turn_pcm_bytes == [b"\x00\x00" * 2400] * 4
    assert output.tts_output_pcm_bytes == b"\x00\x00" * 9600
    session_id = output.duplex_request_metrics[0]["session_id"]
    assert len(output.duplex_request_metrics) == 4
    for index, metrics in enumerate(output.duplex_request_metrics):
        assert metrics == {
            "session_id": session_id,
            "request_index": index,
            "utterance_id": f"utt-{index}",
            # Response ids are per session, and each utterance now gets its own.
            "response_id": "resp-1",
            "source": "client_monotonic_receive",
            "measurement_origin": {
                "ttft": "first silence append client send to first non-empty text delta",
                "ttfp": "first silence append client send to first audio packet",
                "rtf": "request-start-to-last-audio receive time divided by emitted audio duration",
                "tpot": "Stage-0 engine mean time per output token",
            },
            "ttft_ms": pytest.approx(20.0, abs=2.0),
            "tpot_ms": 10.0,
            "ttfp_ms": pytest.approx(30.0, abs=2.0),
            "rtf": pytest.approx(0.3, abs=0.03),
            "audio_generation_ms": pytest.approx(30.0, abs=2.0),
            "audio_duration_ms": 100.0,
        }
    session = output.duplex_session_metrics
    assert session["session_id"] == session_id
    assert session["audio_turn_count"] == 4
    assert session["ttft_ms"]["count"] == 4
    assert session["ttft_ms"]["mean"] == pytest.approx(20.0, abs=2.0)
    assert session["tpot_ms"] == {"count": 4, "mean": 10.0, "p50": 10.0, "p99": 10.0}
    assert session["ttfp_ms"]["count"] == 4
    assert session["ttfp_ms"]["mean"] == pytest.approx(30.0, abs=2.0)
    assert session["rtf"]["count"] == 4
    assert session["rtf"]["mean"] == pytest.approx(0.3, abs=0.03)


def test_stage0_token_timings_use_weighted_tpot_when_itls_are_incomplete():
    output = MixRequestFuncOutput(ttft=0.1)

    measured = _apply_stage0_token_timings(
        output,
        [
            {"output_token_count": 3, "itls_ms": [], "tpot_ms": 10.0},
            {"output_token_count": 2, "itls_ms": [], "tpot_ms": 40.0},
        ],
        expected_output_tokens=5,
    )

    assert measured is True
    assert output.itl == []
    assert output.tpot_measured is True
    assert (output.text_latency - output.ttft) / 4 == pytest.approx(0.02)


def test_stage0_zero_itls_fall_back_to_positive_tpot():
    output = MixRequestFuncOutput(ttft=0.1)

    measured = _apply_stage0_token_timings(
        output,
        [{"output_token_count": 3, "itls_ms": [0.0, 0.0], "tpot_ms": 25.0}],
        expected_output_tokens=3,
    )

    assert measured is True
    assert output.itl == []
    assert output.tpot_measured is True
    assert (output.text_latency - output.ttft) / 2 == pytest.approx(0.025)


def test_stage0_token_count_without_timing_is_not_measured():
    output = MixRequestFuncOutput(ttft=0.1)

    measured = _apply_stage0_token_timings(
        output,
        [{"output_token_count": 3, "itls_ms": [], "tpot_ms": None}],
        expected_output_tokens=3,
    )

    assert measured is False
    assert output.itl == []
    assert output.text_latency == output.ttft
    assert output.tpot_measured is False


def create_sse_chunk(data_dict):
    """Helper to create SSE formatted chunk"""
    return f"data: {json.dumps(data_dict)}\n\n".encode()


def test_chat_text_timing_metrics_request_stage_metrics():
    args = Namespace(
        backend="openai-chat-omni",
        percentile_metrics="ttft,tpot,itl,e2el",
        print_stage=False,
        extra_body={},
    )

    assert should_request_stage_metrics(args) is True


@pytest.mark.asyncio
async def test_bundled_first_text_chunk_uses_stage0_token_timings(mocker: MockerFixture):
    """Engine timings recover TPOT when every text token arrives together."""
    request_input = RequestFuncInput(
        model="test-model",
        model_name="test-model",
        prompt="test prompt",
        api_url="http://test.com/v1/chat/completions",
        prompt_len=10,
        output_len=20,
    )
    chunks = [
        create_sse_chunk(
            {
                "choices": [{"delta": {"content": "ABCD"}}],
                "modality": "text",
                "usage": {"prompt_tokens": 10, "completion_tokens": 4, "total_tokens": 14},
                "metrics": {
                    "stage_metrics": {
                        "0": {
                            "num_tokens_out": 4,
                            "vllm_itls_ms": [10.0, 11.0, 12.0],
                            "vllm_tpot_ms": 11.0,
                        }
                    }
                },
            }
        ),
        b"data: [DONE]\n\n",
    ]
    mock_response = MockResponse(200, chunks)
    mock_session = mocker.AsyncMock()
    mock_session.post = mocker.MagicMock(return_value=mock_response)

    output = await async_request_openai_chat_omni_completions(request_input, mock_session)

    assert output.success is True
    assert output.output_tokens == 4
    assert output.itl == pytest.approx([0.010, 0.011, 0.012])
    assert output.text_latency - output.ttft == pytest.approx(0.033)
    assert output.tpot_measured is True


@pytest.mark.asyncio
async def test_streaming_error_chunk_marks_request_failed(mocker: MockerFixture):
    """HTTP 200 streams can still terminate with an OpenAI error event."""
    request_input = RequestFuncInput(
        model="test-model",
        model_name="test-model",
        prompt="test prompt",
        api_url="http://test.com/v1/chat/completions",
        prompt_len=10,
        output_len=20,
    )
    chunks = [
        create_sse_chunk(
            {
                "choices": [{"delta": {"content": "partial response"}}],
                "modality": "text",
            }
        ),
        create_sse_chunk(
            {
                "error": {
                    "message": "EngineCore encountered an issue",
                    "type": "BadRequestError",
                    "code": 400,
                }
            }
        ),
        b"data: [DONE]\n\n",
    ]
    mock_response = MockResponse(200, chunks)
    mock_session = mocker.AsyncMock()
    mock_session.post = mocker.MagicMock(return_value=mock_response)

    output = await async_request_openai_chat_omni_completions(request_input, mock_session)

    assert output.success is False
    assert output.error == "EngineCore encountered an issue"


@pytest.mark.asyncio
@pytest.mark.parametrize(
    ("headers", "env", "expected_duration_s"),
    [
        # Server-stated format wins over the env fallback.
        ({"X-Audio-Sample-Rate": "48000", "X-Audio-Channels": "2"}, {}, 1.0),
        ({"X-Audio-Sample-Rate": "48000", "X-Audio-Channels": "2"}, {"rate": "24000", "channels": "1"}, 1.0),
        # Older servers send no headers: the env (default 24 kHz mono) applies.
        ({}, {}, 4.0),
        ({}, {"rate": "48000", "channels": "2"}, 1.0),
    ],
)
async def test_audio_speech_stream_uses_server_pcm_format(
    mocker: MockerFixture, monkeypatch: pytest.MonkeyPatch, headers, env, expected_duration_s
):
    from vllm_omni.metrics import definitions as defs

    for key, var in (("rate", defs.AUDIO_SAMPLE_RATE_ENV), ("channels", defs.AUDIO_CHANNELS_ENV)):
        if key in env:
            monkeypatch.setenv(var, env[key])
        else:
            monkeypatch.delenv(var, raising=False)
    request_input = RequestFuncInput(
        model="test-model",
        model_name="test-model",
        prompt="test prompt",
        api_url="http://test.com/v1/audio/speech",
        prompt_len=10,
        output_len=20,
    )
    # 1 s of 48 kHz stereo s16le = 192000 bytes.
    mock_response = MockResponse(200, [b"\x00" * 96000, b"\x00" * 96000])
    mock_response.headers = headers
    mock_session = mocker.AsyncMock()
    mock_session.post = mocker.MagicMock(return_value=mock_response)

    output = await async_request_openai_audio_speech(request_input, mock_session)

    assert output.success is True
    assert output.audio_duration == pytest.approx(expected_duration_s)


@pytest.mark.asyncio
async def test_positive_client_text_timings_take_precedence_over_stage0(mocker: MockerFixture):
    request_input = RequestFuncInput(
        model="test-model",
        model_name="test-model",
        prompt="test prompt",
        api_url="http://test.com/v1/chat/completions",
        prompt_len=10,
        output_len=20,
    )
    chunks = [
        create_sse_chunk(
            {
                "choices": [{"delta": {"content": "A"}}],
                "modality": "text",
                "usage": {"prompt_tokens": 10, "completion_tokens": 1, "total_tokens": 11},
            }
        ),
        create_sse_chunk(
            {
                "choices": [{"delta": {"content": "B"}}],
                "modality": "text",
                "usage": {"prompt_tokens": 10, "completion_tokens": 2, "total_tokens": 12},
                "metrics": {
                    "stage_metrics": {
                        "0": {
                            "num_tokens_out": 2,
                            "vllm_itls_ms": [1000.0],
                            "vllm_tpot_ms": 1000.0,
                        }
                    }
                },
            }
        ),
        b"data: [DONE]\n\n",
    ]
    mock_response = MockResponse(200, chunks, delay_between_chunks=0.01)
    mock_session = mocker.AsyncMock()
    mock_session.post = mocker.MagicMock(return_value=mock_response)

    output = await async_request_openai_chat_omni_completions(request_input, mock_session)

    assert output.success is True
    assert len(output.itl) == 1
    assert 0.0 < output.itl[0] < 0.1


# ============================================================================
# output_tokens Tests
# ============================================================================


@pytest.mark.asyncio
async def test_output_tokens_assigned_with_metrics(mocker: MockerFixture):
    """Test that output.output_tokens is assigned when metrics are present"""
    # Arrange
    request_input = RequestFuncInput(
        model="test-model",
        model_name="test-model",
        prompt="test prompt",
        api_url="http://test.com/v1/chat/completions",
        prompt_len=10,
        output_len=20,
    )

    # Create response chunks with metrics
    chunks = [
        create_sse_chunk(
            {
                "choices": [{"delta": {"content": "Hello"}}],
                "modality": "text",
            }
        ),
        create_sse_chunk(
            {
                "choices": [{"delta": {"content": " world"}}],
                "modality": "text",
                "metrics": {"num_tokens_out": 42, "num_tokens_in": 10},
            }
        ),
        b"data: [DONE]\n\n",
    ]

    mock_response = MockResponse(200, chunks)
    mock_session = mocker.AsyncMock()
    mock_session.post = mocker.MagicMock(return_value=mock_response)

    # Act
    output = await async_request_openai_chat_omni_completions(request_input, mock_session)

    # Assert
    assert output.success is True
    assert output.output_tokens == 42, "output_tokens should be assigned from metrics"
    assert output.generated_text == "Hello world"


@pytest.mark.asyncio
async def test_output_tokens_not_assigned_without_metrics(mocker: MockerFixture):
    """Test that output.output_tokens defaults to 0 when no metrics present"""
    # Arrange
    request_input = RequestFuncInput(
        model="test-model",
        model_name="test-model",
        prompt="test prompt",
        api_url="http://test.com/v1/chat/completions",
        prompt_len=10,
        output_len=20,
    )

    # Create response chunks without metrics
    chunks = [
        create_sse_chunk(
            {
                "choices": [{"delta": {"content": "Hello"}}],
                "modality": "text",
            }
        ),
        create_sse_chunk(
            {
                "choices": [{"delta": {"content": " world"}}],
                "modality": "text",
            }
        ),
        b"data: [DONE]\n\n",
    ]

    mock_response = MockResponse(200, chunks)
    mock_session = mocker.AsyncMock()
    mock_session.post = mocker.MagicMock(return_value=mock_response)

    # Act
    output = await async_request_openai_chat_omni_completions(request_input, mock_session)

    # Assert
    assert output.success is True
    # Without server metrics we no longer infer count from SSE chunks because
    # one chunk may carry multiple tokens. output_tokens stays 0 here.
    assert output.output_tokens == 0, "output_tokens should be 0 when no server metrics are present"
    assert output.generated_text == "Hello world"


@pytest.mark.asyncio
async def test_output_tokens_assigned_multiple_metrics(mocker: MockerFixture):
    """Test that output.output_tokens is updated with the latest metrics value"""
    # Arrange
    request_input = RequestFuncInput(
        model="test-model",
        model_name="test-model",
        prompt="test prompt",
        api_url="http://test.com/v1/chat/completions",
        prompt_len=10,
        output_len=20,
    )

    # Create response chunks with multiple metrics updates
    chunks = [
        create_sse_chunk(
            {
                "choices": [{"delta": {"content": "Hello"}}],
                "modality": "text",
                "metrics": {"num_tokens_out": 5},
            }
        ),
        create_sse_chunk(
            {
                "choices": [{"delta": {"content": " world"}}],
                "modality": "text",
                "metrics": {"num_tokens_out": 10},
            }
        ),
        create_sse_chunk(
            {
                "choices": [{"delta": {"content": "!"}}],
                "modality": "text",
                "metrics": {"num_tokens_out": 15},
            }
        ),
        b"data: [DONE]\n\n",
    ]

    mock_response = MockResponse(200, chunks)
    mock_session = mocker.AsyncMock()
    mock_session.post = mocker.MagicMock(return_value=mock_response)

    # Act
    output = await async_request_openai_chat_omni_completions(request_input, mock_session)

    # Assert
    assert output.success is True
    assert output.output_tokens == 15, "output_tokens should be updated to the latest value"
    assert output.generated_text == "Hello world!"


@pytest.mark.asyncio
async def test_output_tokens_with_audio_and_text(mocker: MockerFixture):
    """Test output_tokens assignment in mixed audio and text response"""
    # Arrange
    request_input = RequestFuncInput(
        model="test-model",
        model_name="test-model",
        prompt="test prompt",
        api_url="http://test.com/v1/chat/completions",
        prompt_len=10,
        output_len=20,
    )

    # Create response chunks with both audio and text, with metrics
    chunks = [
        create_sse_chunk(
            {
                "choices": [{"delta": {"content": "Text response"}}],
                "modality": "text",
            }
        ),
        create_sse_chunk(
            {
                "choices": [{"delta": {"content": ""}}],
                "modality": "audio",
            }
        ),
        create_sse_chunk(
            {
                "modality": "text",
                "metrics": {"num_tokens_out": 25, "num_tokens_in": 10},
            }
        ),
        b"data: [DONE]\n\n",
    ]

    mock_response = MockResponse(200, chunks)
    mock_session = mocker.AsyncMock()
    mock_session.post = mocker.MagicMock(return_value=mock_response)

    # Act
    output = await async_request_openai_chat_omni_completions(request_input, mock_session)

    # Assert
    assert output.success is True
    assert output.output_tokens == 25, "output_tokens should be assigned even with audio modality"


@pytest.mark.asyncio
async def test_output_tokens_with_missing_num_tokens_out(mocker: MockerFixture):
    """Test that output_tokens defaults to 0 when num_tokens_out is missing in metrics"""
    # Arrange
    request_input = RequestFuncInput(
        model="test-model",
        model_name="test-model",
        prompt="test prompt",
        api_url="http://test.com/v1/chat/completions",
        prompt_len=10,
        output_len=20,
    )

    # Create response chunks with metrics but without num_tokens_out
    chunks = [
        create_sse_chunk(
            {
                "choices": [{"delta": {"content": "Hello"}}],
                "modality": "text",
            }
        ),
        create_sse_chunk(
            {
                "choices": [{"delta": {"content": " world"}}],
                "modality": "text",
                "metrics": {"num_tokens_in": 10},  # Missing num_tokens_out
            }
        ),
        b"data: [DONE]\n\n",
    ]

    mock_response = MockResponse(200, chunks)
    mock_session = mocker.AsyncMock()
    mock_session.post = mocker.MagicMock(return_value=mock_response)

    # Act
    output = await async_request_openai_chat_omni_completions(request_input, mock_session)

    # Assert
    assert output.success is True
    assert output.output_tokens == 0, "output_tokens should be 0 when num_tokens_out is missing"


@pytest.mark.asyncio
async def test_output_tokens_initialization():
    """Test that MixRequestFuncOutput initializes output_tokens correctly"""
    # Arrange & Act
    output = MixRequestFuncOutput()

    # Assert
    assert hasattr(output, "output_tokens"), "MixRequestFuncOutput should have output_tokens attribute"
    assert output.output_tokens == 0, "output_tokens should be initialized to 0"


# ============================================================================
# text_latency Tests
# ============================================================================


class TestTextLatencyAttribute:
    """Tests for text_latency attribute existence and assignment"""

    def test_mix_request_func_output_has_text_latency(self):
        """Test that MixRequestFuncOutput has text_latency attribute"""
        output = MixRequestFuncOutput()
        assert hasattr(output, "text_latency"), "MixRequestFuncOutput should have text_latency attribute"

    def test_text_latency_initial_value(self):
        """Test that text_latency initializes to a default value"""
        output = MixRequestFuncOutput()
        # Check if attribute exists and has a value (should be 0.0 or similar default)
        text_latency = getattr(output, "text_latency", None)
        assert text_latency is not None or hasattr(output, "text_latency"), "text_latency attribute should exist"

    @pytest.mark.asyncio
    async def test_text_latency_assigned_with_text_response(self, mocker: MockerFixture):
        """Test that text_latency is assigned when text response is received"""
        request_input = RequestFuncInput(
            model="test-model",
            model_name="test-model",
            prompt="test prompt",
            api_url="http://test.com/v1/chat/completions",
            prompt_len=10,
            output_len=20,
        )

        # Create response chunks with text modality
        chunks = [
            create_sse_chunk(
                {
                    "choices": [{"delta": {"content": "Hello"}}],
                    "modality": "text",
                }
            ),
            create_sse_chunk(
                {
                    "choices": [{"delta": {"content": " world"}}],
                    "modality": "text",
                }
            ),
            b"data: [DONE]\n\n",
        ]

        mock_response = MockResponse(200, chunks)
        mock_session = mocker.AsyncMock()
        mock_session.post = mocker.MagicMock(return_value=mock_response)

        # Act
        output = await async_request_openai_chat_omni_completions(request_input, mock_session)

        # Assert
        assert output.success is True
        assert hasattr(output, "text_latency"), "Output should have text_latency attribute"
        assert output.text_latency > 0, "text_latency should be greater than 0 for text response"

    @pytest.mark.asyncio
    async def test_text_latency_updated_with_multiple_text_chunks(self, mocker: MockerFixture):
        """Test that text_latency is updated as more text chunks arrive"""
        request_input = RequestFuncInput(
            model="test-model",
            model_name="test-model",
            prompt="test prompt",
            api_url="http://test.com/v1/chat/completions",
            prompt_len=10,
            output_len=20,
        )

        # Create response chunks with small delays to simulate streaming
        chunks = [
            create_sse_chunk(
                {
                    "choices": [{"delta": {"content": "First"}}],
                    "modality": "text",
                }
            ),
            create_sse_chunk(
                {
                    "choices": [{"delta": {"content": " second"}}],
                    "modality": "text",
                }
            ),
            create_sse_chunk(
                {
                    "choices": [{"delta": {"content": " third"}}],
                    "modality": "text",
                }
            ),
            b"data: [DONE]\n\n",
        ]

        mock_response = MockResponse(200, chunks)
        mock_session = mocker.AsyncMock()
        mock_session.post = mocker.MagicMock(return_value=mock_response)

        # Act
        output = await async_request_openai_chat_omni_completions(request_input, mock_session)

        # Assert
        assert output.success is True
        assert hasattr(output, "text_latency"), "Output should have text_latency attribute"
        assert output.text_latency > 0, "text_latency should accumulate"
        assert output.generated_text == "First second third"

    @pytest.mark.asyncio
    async def test_text_latency_with_only_audio_response(self, mocker: MockerFixture):
        """Test text_latency behavior when only audio is received"""
        request_input = RequestFuncInput(
            model="test-model",
            model_name="test-model",
            prompt="test prompt",
            api_url="http://test.com/v1/chat/completions",
            prompt_len=10,
            output_len=20,
        )

        # Create response chunks with only audio modality (no text)
        chunks = [
            create_sse_chunk(
                {
                    "choices": [{"delta": {"content": ""}}],
                    "modality": "audio",
                }
            ),
            b"data: [DONE]\n\n",
        ]

        mock_response = MockResponse(200, chunks)
        mock_session = mocker.AsyncMock()
        mock_session.post = mocker.MagicMock(return_value=mock_response)

        # Act
        output = await async_request_openai_chat_omni_completions(request_input, mock_session)

        # Assert
        assert output.success is True
        assert hasattr(output, "text_latency"), "Output should have text_latency attribute even with audio-only"
        # text_latency should either be 0 or the initial value since no text was processed
        assert output.text_latency >= 0, "text_latency should be non-negative"

    @pytest.mark.asyncio
    async def test_text_latency_not_affected_by_metrics(self, mocker: MockerFixture):
        """Test that text_latency is independent of metrics data"""
        request_input = RequestFuncInput(
            model="test-model",
            model_name="test-model",
            prompt="test prompt",
            api_url="http://test.com/v1/chat/completions",
            prompt_len=10,
            output_len=20,
        )

        # Create response with text and metrics
        chunks = [
            create_sse_chunk(
                {
                    "choices": [{"delta": {"content": "Response text"}}],
                    "modality": "text",
                }
            ),
            create_sse_chunk(
                {
                    "choices": [{"delta": {"content": ""}}],
                    "modality": "text",
                    "metrics": {"num_tokens_out": 100, "num_tokens_in": 20},
                }
            ),
            b"data: [DONE]\n\n",
        ]

        mock_response = MockResponse(200, chunks)
        mock_session = mocker.AsyncMock()
        mock_session.post = mocker.MagicMock(return_value=mock_response)

        # Act
        output = await async_request_openai_chat_omni_completions(request_input, mock_session)

        # Assert
        assert output.success is True
        assert hasattr(output, "text_latency"), "text_latency should exist"
        assert output.text_latency > 0, "text_latency should be set for text response"
        # Verify metrics are also present
        assert output.output_tokens == 100, "metrics should not affect text_latency"

    @pytest.mark.asyncio
    async def test_text_latency_mixed_modalities(self, mocker: MockerFixture):
        """Test text_latency with mixed text and audio modalities"""
        request_input = RequestFuncInput(
            model="test-model",
            model_name="test-model",
            prompt="test prompt",
            api_url="http://test.com/v1/chat/completions",
            prompt_len=10,
            output_len=20,
        )

        # Create response with both text and audio
        chunks = [
            create_sse_chunk(
                {
                    "choices": [{"delta": {"content": "Text"}}],
                    "modality": "text",
                }
            ),
            create_sse_chunk(
                {
                    "choices": [{"delta": {"content": ""}}],
                    "modality": "audio",
                }
            ),
            create_sse_chunk(
                {
                    "choices": [{"delta": {"content": " more text"}}],
                    "modality": "text",
                }
            ),
            b"data: [DONE]\n\n",
        ]

        mock_response = MockResponse(200, chunks)
        mock_session = mocker.AsyncMock()
        mock_session.post = mocker.MagicMock(return_value=mock_response)

        # Act
        output = await async_request_openai_chat_omni_completions(request_input, mock_session)

        # Assert
        assert output.success is True
        assert hasattr(output, "text_latency"), "text_latency should exist with mixed modalities"
        assert output.text_latency > 0, "text_latency should be set when text is present"
        assert output.generated_text == "Text more text"

    @pytest.mark.asyncio
    async def test_text_latency_value_consistency(self, mocker: MockerFixture):
        """Test that text_latency matches latency minus ttft relationship"""
        request_input = RequestFuncInput(
            model="test-model",
            model_name="test-model",
            prompt="test prompt",
            api_url="http://test.com/v1/chat/completions",
            prompt_len=10,
            output_len=20,
        )

        chunks = [
            create_sse_chunk(
                {
                    "choices": [{"delta": {"content": "Hello"}}],
                    "modality": "text",
                }
            ),
            create_sse_chunk(
                {
                    "choices": [{"delta": {"content": " world"}}],
                    "modality": "text",
                }
            ),
            b"data: [DONE]\n\n",
        ]

        mock_response = MockResponse(200, chunks)
        mock_session = mocker.AsyncMock()
        mock_session.post = mocker.MagicMock(return_value=mock_response)

        # Act
        output = await async_request_openai_chat_omni_completions(request_input, mock_session)

        # Assert
        assert output.success is True
        assert hasattr(output, "text_latency"), "text_latency should exist"
        assert hasattr(output, "ttft"), "ttft should exist"
        assert hasattr(output, "latency"), "latency should exist"
        # text_latency should be between ttft and total latency
        assert output.ttft <= output.text_latency <= output.latency, (
            "text_latency should be between ttft and total latency"
        )


# ============================================================================
# prompt_len Tests
# ============================================================================


@pytest.mark.asyncio
async def test_skips_empty_role_chunk_for_ttft_and_itl(mocker: MockerFixture):
    """Role/empty text chunks must not count toward TTFT or ITL."""
    request_input = RequestFuncInput(
        model="test-model",
        model_name="test-model",
        prompt="test prompt",
        api_url="http://test.com/v1/chat/completions",
        prompt_len=10,
        output_len=20,
    )

    chunks = [
        create_sse_chunk(
            {
                "choices": [{"delta": {"role": "assistant", "content": ""}}],
                "modality": "text",
            }
        ),
        create_sse_chunk(
            {
                "choices": [{"delta": {"content": "Hello"}}],
                "modality": "text",
                "usage": {"prompt_tokens": 10, "completion_tokens": 1, "total_tokens": 11},
            }
        ),
        create_sse_chunk(
            {
                "choices": [{"delta": {"content": " world"}}],
                "modality": "text",
                "usage": {"prompt_tokens": 10, "completion_tokens": 2, "total_tokens": 12},
            }
        ),
        b"data: [DONE]\n\n",
    ]

    mock_response = MockResponse(200, chunks, delay_between_chunks=0.01)
    mock_session = mocker.AsyncMock()
    mock_session.post = mocker.MagicMock(return_value=mock_response)

    output = await async_request_openai_chat_omni_completions(request_input, mock_session)

    assert output.success is True
    assert output.output_tokens == 2
    assert len(output.itl) == 1
    assert output.ttft > 0
    assert output.text_latency - output.ttft == pytest.approx(sum(output.itl), rel=1e-6, abs=1e-6)


@pytest.mark.asyncio
async def test_bundled_tokens_split_itl_from_usage(mocker: MockerFixture):
    """Multiple tokens in one SSE chunk should expand ITL via usage deltas."""
    request_input = RequestFuncInput(
        model="test-model",
        model_name="test-model",
        prompt="test prompt",
        api_url="http://test.com/v1/chat/completions",
        prompt_len=10,
        output_len=20,
    )

    chunks = [
        create_sse_chunk(
            {
                "choices": [{"delta": {"content": "A"}}],
                "modality": "text",
                "usage": {"prompt_tokens": 10, "completion_tokens": 1, "total_tokens": 11},
            }
        ),
        create_sse_chunk(
            {
                "choices": [{"delta": {"content": "BCD"}}],
                "modality": "text",
                "usage": {"prompt_tokens": 10, "completion_tokens": 4, "total_tokens": 14},
            }
        ),
        b"data: [DONE]\n\n",
    ]

    mock_response = MockResponse(200, chunks, delay_between_chunks=0.02)
    mock_session = mocker.AsyncMock()
    mock_session.post = mocker.MagicMock(return_value=mock_response)

    output = await async_request_openai_chat_omni_completions(request_input, mock_session)

    assert output.success is True
    assert output.output_tokens == 4
    assert len(output.itl) == 3
    assert output.text_latency - output.ttft == pytest.approx(sum(output.itl), rel=1e-6, abs=1e-6)


@pytest.mark.asyncio
async def test_output_tokens_prefers_usage_over_metrics(mocker: MockerFixture):
    request_input = RequestFuncInput(
        model="test-model",
        model_name="test-model",
        prompt="test prompt",
        api_url="http://test.com/v1/chat/completions",
        prompt_len=10,
        output_len=20,
    )

    chunks = [
        create_sse_chunk(
            {
                "choices": [{"delta": {"content": "Hello"}}],
                "modality": "text",
                "metrics": {"num_tokens_out": 5},
                "usage": {"prompt_tokens": 10, "completion_tokens": 8, "total_tokens": 18},
            }
        ),
        b"data: [DONE]\n\n",
    ]

    mock_response = MockResponse(200, chunks)
    mock_session = mocker.AsyncMock()
    mock_session.post = mocker.MagicMock(return_value=mock_response)

    output = await async_request_openai_chat_omni_completions(request_input, mock_session)

    assert output.success is True
    assert output.output_tokens == 8


@pytest.mark.asyncio
async def test_metrics_only_chunk_updates_output_tokens(mocker: MockerFixture):
    request_input = RequestFuncInput(
        model="test-model",
        model_name="test-model",
        prompt="test prompt",
        api_url="http://test.com/v1/chat/completions",
        prompt_len=10,
        output_len=20,
    )

    chunks = [
        create_sse_chunk(
            {
                "choices": [{"delta": {"content": "Text response"}}],
                "modality": "text",
            }
        ),
        create_sse_chunk(
            {
                "modality": "text",
                "metrics": {"num_tokens_out": 25, "num_tokens_in": 10},
            }
        ),
        b"data: [DONE]\n\n",
    ]

    mock_response = MockResponse(200, chunks)
    mock_session = mocker.AsyncMock()
    mock_session.post = mocker.MagicMock(return_value=mock_response)

    output = await async_request_openai_chat_omni_completions(request_input, mock_session)

    assert output.success is True
    assert output.output_tokens == 25


@pytest.mark.asyncio
async def test_prompt_len_assigned_from_usage(mocker: MockerFixture):
    # Arrange: request claims prompt_len=100, but server reports 4992 (multimodal).
    request_input = RequestFuncInput(
        model="test-model",
        model_name="test-model",
        prompt="test prompt",
        api_url="http://test.com/v1/chat/completions",
        prompt_len=100,
        output_len=20,
    )

    chunks = [
        create_sse_chunk(
            {
                "choices": [{"delta": {"content": "Hello"}}],
                "modality": "text",
            }
        ),
        create_sse_chunk(
            {
                "choices": [{"delta": {"content": " world"}}],
                "modality": "text",
            }
        ),
        # Final usage chunk emitted because stream_options.include_usage=True.
        create_sse_chunk(
            {
                "choices": [],
                "usage": {"prompt_tokens": 4992, "completion_tokens": 2, "total_tokens": 4994},
            }
        ),
        b"data: [DONE]\n\n",
    ]

    mock_response = MockResponse(200, chunks)
    mock_session = mocker.AsyncMock()
    mock_session.post = mocker.MagicMock(return_value=mock_response)

    # Act
    output = await async_request_openai_chat_omni_completions(request_input, mock_session)

    # Assert
    assert output.success is True
    assert output.prompt_len == 4992, (
        "prompt_len should be overridden by usage.prompt_tokens to reflect the true multimodal input token count"
    )


class TestOmniRequestTimeout:
    """``--omni-request-timeout-s`` precedence: explicit value > 900 s default."""

    _OVERRIDE = "vllm_omni.benchmarks.patch.patch._REQUEST_TIMEOUT_OVERRIDE_S"

    def test_default_timeout_is_900s_when_not_set(self, monkeypatch: pytest.MonkeyPatch) -> None:
        monkeypatch.setattr(self._OVERRIDE, None)
        assert _omni_request_timeout_s() == 900.0

    def test_explicit_value_wins_over_default(self, monkeypatch: pytest.MonkeyPatch) -> None:
        monkeypatch.setattr(self._OVERRIDE, 123.5)
        assert _omni_request_timeout_s() == 123.5

    def test_non_positive_restores_legacy_6h_cap(self, monkeypatch: pytest.MonkeyPatch) -> None:
        monkeypatch.setattr(self._OVERRIDE, 0)
        assert _omni_request_timeout_s() == 6 * 60 * 60.0

    async def test_benchmark_session_uses_configured_timeout(self, monkeypatch: pytest.MonkeyPatch) -> None:
        monkeypatch.setattr(self._OVERRIDE, 42.0)
        session = _build_benchmark_session(max_concurrency=8, ssl_setting=False)
        try:
            assert session.timeout.total == 42.0
            assert session.connector.limit == 8
            assert session.connector.limit_per_host == 8
        finally:
            await session.close()

    async def test_hung_server_request_times_out_as_failed(self, monkeypatch: pytest.MonkeyPatch) -> None:
        """A server that accepts the request but never responds must surface as ``failed``."""

        async def handler(reader, writer):
            # Drain the request but never respond — the original hang failure mode.
            try:
                while await reader.read(4096):
                    pass
            except ConnectionResetError:
                pass
            finally:
                # Close our side of the socket: the client aborts after the
                # timeout and only sends FIN, so without writer.close() the
                # half-closed connection keeps Server.wait_closed() (which on
                # Python 3.12+ waits for every accepted connection to drop)
                # blocked forever.
                writer.close()
                try:
                    await writer.wait_closed()
                except (ConnectionResetError, BrokenPipeError):
                    pass

        monkeypatch.setattr(self._OVERRIDE, 1.0)
        server = await asyncio.start_server(handler, "127.0.0.1", 0)
        port = server.sockets[0].getsockname()[1]
        session = _build_benchmark_session(max_concurrency=1, ssl_setting=False)
        try:
            request_input = _seed_tts_request_func_input()
            request_input.api_url = f"http://127.0.0.1:{port}/v1/chat/completions"
            output = await async_request_openai_chat_omni_completions(request_input, session)
            assert output.success is False
            assert output.error
        finally:
            await session.close()
            server.close()
            await server.wait_closed()


def test_video_rtf_prefers_generation_time_over_poll_latency():
    """RTF must not grow with client poll_interval overshoot in output.latency."""
    payload = {
        "duration_s": 2.0,
        "num_frames": 48,
        "fps": 24.0,
        "stage_durations": {"stage_0_gen_ms": 4000.0},
    }
    request_body: dict[str, object] = {}

    short_poll = MixRequestFuncOutput()
    short_poll.latency = 4.2  # ~generation + small poll overshoot
    _apply_video_metrics_from_payload(short_poll, payload, request_body)

    long_poll = MixRequestFuncOutput()
    long_poll.latency = 8.0  # same job, larger poll_interval_s overshoot
    _apply_video_metrics_from_payload(long_poll, payload, request_body)

    assert short_poll.video_generation_time_ms == pytest.approx(4000.0)
    assert long_poll.video_generation_time_ms == pytest.approx(4000.0)
    assert short_poll.video_rtf == pytest.approx(2.0)
    assert long_poll.video_rtf == pytest.approx(2.0)
    assert short_poll.video_rtf == long_poll.video_rtf


def test_video_rtf_falls_back_to_e2e_latency_without_generation_time():
    output = MixRequestFuncOutput()
    output.latency = 6.0
    _apply_video_metrics_from_payload(
        output,
        {"duration_s": 2.0, "num_frames": 48, "fps": 24.0},
        {},
    )
    assert output.video_generation_time_ms == 0.0
    assert output.video_rtf == pytest.approx(3.0)


# 1x1 PNG used as a valid b64_json image payload in edit-client tests.
_MIN_PNG_B64 = "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8z8BQDwAEhQGAhKmMIQAAAABJRU5ErkJggg=="


@pytest.mark.asyncio
async def test_image_edits_defaults_to_non_streaming_json(mocker: MockerFixture) -> None:
    """Single-stage servers reject stream=true; default path must send stream=false + JSON."""

    class MockJsonResponse:
        status = 200

        def __init__(self):
            self._payload = {
                "created": 1,
                "data": [
                    {
                        "b64_json": _MIN_PNG_B64,
                        "stage_durations": {"stage_0_gen_ms": 12.5},
                    }
                ],
            }

        async def json(self):
            return self._payload

        async def text(self):
            return json.dumps(self._payload)

        async def __aenter__(self):
            return self

        async def __aexit__(self, *_args):
            return None

    captured_stream: list[str] = []
    import aiohttp

    real_add_field = aiohttp.FormData.add_field

    def tracking_add_field(self, name, value=None, **kwargs):
        if name == "stream":
            captured_stream.append(str(value))
        return real_add_field(self, name, value, **kwargs)

    mocker.patch.object(aiohttp.FormData, "add_field", tracking_add_field)

    mock_session = mocker.AsyncMock()
    mock_session.post = mocker.MagicMock(return_value=MockJsonResponse())

    request = RequestFuncInput(
        model="single-stage-edit",
        model_name="single-stage-edit",
        prompt="make it sunny",
        api_url="http://test.com/v1/images/edits",
        prompt_len=4,
        output_len=1,
        multi_modal_content=[{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{_MIN_PNG_B64}"}}],
        extra_body={"num_inference_steps": 4},
    )
    output = await async_request_openai_image_edits_omni(request, mock_session, pbar=None)

    assert captured_stream == ["false"]
    assert output.success is True
    assert not output.error
    assert output.image_count == 1
    assert output.image_generation_time_ms == pytest.approx(12.5)


@pytest.mark.asyncio
async def test_image_edits_stream_true_uses_sse_path(mocker: MockerFixture) -> None:
    """Explicit stream=true keeps the multi-stage SSE client path."""
    sse_chunk = (
        b'data: {"type":"image","data":[{"b64_json":"' + _MIN_PNG_B64.encode() + b'"}]}\n\n' + b"data: [DONE]\n\n"
    )
    mock_response = MockResponse(200, [sse_chunk])
    captured_stream: list[str] = []

    import aiohttp

    real_add_field = aiohttp.FormData.add_field

    def tracking_add_field(self, name, value=None, **kwargs):
        if name == "stream":
            captured_stream.append(str(value))
        return real_add_field(self, name, value, **kwargs)

    mocker.patch.object(aiohttp.FormData, "add_field", tracking_add_field)

    mock_session = mocker.AsyncMock()
    mock_session.post = mocker.MagicMock(return_value=mock_response)

    request = RequestFuncInput(
        model="multi-stage-edit",
        model_name="multi-stage-edit",
        prompt="edit",
        api_url="http://test.com/v1/images/edits",
        prompt_len=2,
        output_len=1,
        multi_modal_content=[{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{_MIN_PNG_B64}"}}],
        extra_body={"stream": True},
    )
    output = await async_request_openai_image_edits_omni(request, mock_session, pbar=None)

    assert captured_stream == ["true"]
    assert output.success is True
    assert output.image_count == 1


@pytest.mark.asyncio
async def test_image_edits_stream_error_marks_request_failed(mocker: MockerFixture) -> None:
    """HTTP 200 image-edit streams can terminate with an error event."""
    sse_chunk = (
        b'data: {"object":"image.edit.chunk","type":"ar_delta","delta":"partial"}\n\n'
        b'data: {"object":"error","error":{"message":"image generation failed",'
        b'"type":"server_error","code":500}}\n\n'
        b"data: [DONE]\n\n"
    )
    mock_response = MockResponse(200, [sse_chunk])
    mock_session = mocker.AsyncMock()
    mock_session.post = mocker.MagicMock(return_value=mock_response)

    request = RequestFuncInput(
        model="multi-stage-edit",
        model_name="multi-stage-edit",
        prompt="edit",
        api_url="http://test.com/v1/images/edits",
        prompt_len=2,
        output_len=1,
        multi_modal_content=[
            {
                "type": "image_url",
                "image_url": {"url": f"data:image/png;base64,{_MIN_PNG_B64}"},
            }
        ],
        extra_body={"stream": True},
    )

    output = await async_request_openai_image_edits_omni(request, mock_session, pbar=None)

    assert output.success is False
    assert output.error == "image generation failed"
    assert output.generated_text == "partial"


@pytest.mark.asyncio
async def test_image_generations_e2el_includes_json_body_consume(mocker: MockerFixture) -> None:
    """E2EL must include body transfer/decode, not stop at HTTP headers."""

    class SlowJsonResponse:
        status = 200

        def __init__(self):
            self._payload = {
                "created": 1,
                "data": [{"b64_json": _MIN_PNG_B64, "stage_durations": {"stage_0_gen_ms": 1.0}}],
            }

        async def json(self):
            await asyncio.sleep(0.05)
            return self._payload

        async def text(self):
            return json.dumps(self._payload)

        async def __aenter__(self):
            return self

        async def __aexit__(self, *_args):
            return None

    mock_session = mocker.AsyncMock()
    mock_session.post = mocker.MagicMock(return_value=SlowJsonResponse())
    request = RequestFuncInput(
        model="img-gen",
        model_name="img-gen",
        prompt="a cat",
        api_url="http://test.com/v1/images/generations",
        prompt_len=2,
        output_len=1,
        extra_body={},
    )
    output = await async_request_openai_image_generations_omni(request, mock_session, pbar=None)
    assert output.success is True
    assert output.latency >= 0.05


def test_video_local_image_reference_not_forwarded_as_raw_extra_field(tmp_path, mocker: MockerFixture) -> None:
    """Local image_reference must only be uploaded via dedicated serializer."""
    import aiohttp

    ref_path = tmp_path / "ref.png"
    ref_path.write_bytes(base64.b64decode(_MIN_PNG_B64))

    captured: list[tuple[str, object]] = []
    real_add_field = aiohttp.FormData.add_field

    def tracking_add_field(self, name, value=None, **kwargs):
        captured.append((str(name), value))
        return real_add_field(self, name, value, **kwargs)

    mocker.patch.object(aiohttp.FormData, "add_field", tracking_add_field)

    form = aiohttp.FormData()
    extra_body = {
        "num_inference_steps": 2,
        "image_reference": str(ref_path),
    }
    request_body = {"model": "vid", "prompt": "p", **extra_body}
    _add_video_extra_body_to_form(form, extra_body, request_body)
    assert _add_video_reference_to_form(form, extra_body["image_reference"]) is True

    field_names = [name for name, _ in captured]
    assert "image_reference" not in field_names
    assert field_names.count("input_reference") == 1
    # Dedicated uploader sends file bytes, not the raw local path string.
    uploaded = next(value for name, value in captured if name == "input_reference")
    assert uploaded == base64.b64decode(_MIN_PNG_B64)


@pytest.mark.parametrize(
    "reference",
    [
        {"image_url": "https://example.com/ref.png"},
        [{"image_url": "https://example.com/a.png"}, {"image_url": "https://example.com/b.png"}],
    ],
)
def test_video_structured_image_reference_serialized_to_form(reference: object, mocker: MockerFixture) -> None:
    """Object-form image_reference must be JSON-serialized, not silently dropped."""
    import aiohttp

    captured: list[tuple[str, object]] = []
    real_add_field = aiohttp.FormData.add_field

    def tracking_add_field(self, name, value=None, **kwargs):
        captured.append((str(name), value))
        return real_add_field(self, name, value, **kwargs)

    mocker.patch.object(aiohttp.FormData, "add_field", tracking_add_field)

    form = aiohttp.FormData()
    extra_body = {"image_reference": reference}
    request_body = {"model": "vid", "prompt": "p", **extra_body}
    _add_video_extra_body_to_form(form, extra_body, request_body)
    assert _add_video_reference_to_form(form, reference) is True

    field_names = [name for name, _ in captured]
    # Reserved key must not be double-forwarded as a generic extra field;
    # exactly one dedicated image_reference field.
    assert field_names.count("image_reference") == 1
    assert "input_reference" not in field_names
    payload = next(value for name, value in captured if name == "image_reference")
    assert isinstance(payload, (str, bytes, bytearray))
    assert json.loads(payload) == reference


def test_video_file_id_reference_is_rejected() -> None:
    """file_id is unsupported on the server and must not be sent as image_reference."""
    import aiohttp

    form = aiohttp.FormData()
    with pytest.raises(ValueError, match="file_id is not supported yet"):
        _add_video_reference_to_form(form, {"file_id": "file-abc"})
    with pytest.raises(ValueError, match="file_id is not supported yet"):
        _add_video_reference_to_form(form, [{"image_url": "https://example.com/a.png"}, {"file_id": "file-xyz"}])
    with pytest.raises(ValueError, match="file_id is not supported yet"):
        _add_combined_video_form_references(form, None, {"video_reference": {"file_id": "file-vid"}})


def test_image_reference_urls_from_random_mm_content(mocker: MockerFixture) -> None:
    """random-mm image_url parts keep an explicit image_reference type."""
    import aiohttp

    content = [
        {
            "type": "image_url",
            "image_url": {"url": "https://example.com/ref.png"},
        },
        {
            "type": "image_url",
            "image_url": {"url": f"data:image/png;base64,{_MIN_PNG_B64}"},
        },
    ]
    refs = list(_iter_image_reference_inputs(content))
    assert refs == [
        {"image_url": "https://example.com/ref.png"},
        {"image_url": f"data:image/png;base64,{_MIN_PNG_B64}"},
    ]

    captured: list[tuple[str, object]] = []
    real_add_field = aiohttp.FormData.add_field

    def tracking_add_field(self, name, value=None, **kwargs):
        captured.append((str(name), value))
        return real_add_field(self, name, value, **kwargs)

    mocker.patch.object(aiohttp.FormData, "add_field", tracking_add_field)
    form = aiohttp.FormData()
    assert _add_video_reference_to_form(form, refs[0]) is True
    assert _add_video_reference_to_form(form, refs[1]) is True

    field_names = [name for name, _ in captured]
    assert field_names.count("image_reference") == 2
    assert "video_reference" not in field_names
    payloads = []
    for name, value in captured:
        if name != "image_reference":
            continue
        assert isinstance(value, (str, bytes, bytearray))
        payloads.append(json.loads(value))
    assert payloads == refs


def test_video_reference_urls_from_random_mm_content(mocker: MockerFixture) -> None:
    """random-mm data:video_url parts stay on video_reference."""
    import aiohttp

    content = [
        {
            "type": "video_url",
            "video_url": {"url": "data:video/mp4;base64,AAAA"},
        }
    ]
    refs = list(_iter_video_reference_inputs(content))
    assert refs == [{"video_url": "data:video/mp4;base64,AAAA"}]

    captured: list[tuple[str, object]] = []
    real_add_field = aiohttp.FormData.add_field

    def tracking_add_field(self, name, value=None, **kwargs):
        captured.append((str(name), value))
        return real_add_field(self, name, value, **kwargs)

    mocker.patch.object(aiohttp.FormData, "add_field", tracking_add_field)
    form = aiohttp.FormData()
    assert _add_video_reference_to_form(form, refs[0]) is True

    field_names = [name for name, _ in captured]
    assert field_names.count("video_reference") == 1
    assert "input_references" not in field_names
    assert "input_reference" not in field_names
    assert "image_reference" not in field_names
    payload = next(value for name, value in captured if name == "video_reference")
    assert isinstance(payload, (str, bytes, bytearray))
    assert json.loads(payload) == {"video_url": "data:video/mp4;base64,AAAA"}


def test_video_reference_https_url_from_random_mm_content(mocker: MockerFixture) -> None:
    """HTTP(S) video_url parts must stay on video_reference, not image_reference."""
    import aiohttp

    content = [
        {
            "type": "video_url",
            "video_url": {"url": "https://example.com/ref.mp4"},
        }
    ]
    refs = list(_iter_video_reference_inputs(content))
    assert refs == [{"video_url": "https://example.com/ref.mp4"}]

    captured: list[tuple[str, object]] = []
    real_add_field = aiohttp.FormData.add_field

    def tracking_add_field(self, name, value=None, **kwargs):
        captured.append((str(name), value))
        return real_add_field(self, name, value, **kwargs)

    mocker.patch.object(aiohttp.FormData, "add_field", tracking_add_field)
    form = aiohttp.FormData()
    assert _add_video_reference_to_form(form, refs[0]) is True

    field_names = [name for name, _ in captured]
    assert field_names.count("video_reference") == 1
    assert "image_reference" not in field_names
    assert "input_references" not in field_names
    payload = next(value for name, value in captured if name == "video_reference")
    assert isinstance(payload, (str, bytes, bytearray))
    assert json.loads(payload) == {"video_url": "https://example.com/ref.mp4"}


@pytest.mark.parametrize(
    ("reference", "field_name", "expected"),
    [
        ("data:image/png;base64,AAAA", "image_reference", {"image_url": "data:image/png;base64,AAAA"}),
        ("data:video/mp4;base64,AAAA", "video_reference", {"video_url": "data:video/mp4;base64,AAAA"}),
        ("https://example.com/ref.png", "image_reference", {"image_url": "https://example.com/ref.png"}),
        ("https://example.com/ref.mp4", "video_reference", {"video_url": "https://example.com/ref.mp4"}),
    ],
)
def test_bare_reference_string_uses_matching_json_field(
    reference: str,
    field_name: str,
    expected: dict[str, str],
    mocker: MockerFixture,
) -> None:
    """Image and video URLs use matching JSON fields; neither is a file upload."""
    import aiohttp

    captured: list[tuple[str, object]] = []
    real_add_field = aiohttp.FormData.add_field

    def tracking_add_field(self, name, value=None, **kwargs):
        captured.append((str(name), value))
        return real_add_field(self, name, value, **kwargs)

    mocker.patch.object(aiohttp.FormData, "add_field", tracking_add_field)
    assert _add_video_reference_to_form(aiohttp.FormData(), reference) is True
    assert [name for name, _ in captured] == [field_name]
    payload = captured[0][1]
    assert isinstance(payload, (str, bytes, bytearray))
    assert json.loads(payload) == expected


def test_bare_video_string_is_not_collected_as_image() -> None:
    content = ["https://example.com/ref.mp4", "data:video/mp4;base64,AAAA"]
    assert list(_iter_image_reference_inputs(content)) == []
    assert list(_iter_video_reference_inputs(content)) == [
        {"video_url": "https://example.com/ref.mp4"},
        {"video_url": "data:video/mp4;base64,AAAA"},
    ]


def test_bare_http_without_media_extension_is_rejected() -> None:
    import aiohttp

    with pytest.raises(ValueError, match="image or video extension"):
        _add_video_reference_to_form(aiohttp.FormData(), "https://example.com/ref")


def test_image_and_inline_video_use_combined_reference_fields(mocker: MockerFixture) -> None:
    """Image plus data:video must be image_reference + video_reference, not input_references."""
    import aiohttp

    content = [
        {"type": "image_url", "image_url": {"url": "https://example.com/ref.png"}},
        {"type": "video_url", "video_url": {"url": "data:video/mp4;base64,AAAA"}},
    ]
    captured: list[tuple[str, object]] = []
    real_add_field = aiohttp.FormData.add_field

    def tracking_add_field(self, name, value=None, **kwargs):
        captured.append((str(name), value))
        return real_add_field(self, name, value, **kwargs)

    mocker.patch.object(aiohttp.FormData, "add_field", tracking_add_field)
    form = aiohttp.FormData()
    _add_combined_video_form_references(form, content)

    field_names = [name for name, _ in captured]
    assert field_names.count("image_reference") == 1
    assert field_names.count("video_reference") == 1
    assert "input_references" not in field_names
    assert "input_reference" not in field_names
    image_payload = next(value for name, value in captured if name == "image_reference")
    video_payload = next(value for name, value in captured if name == "video_reference")
    assert isinstance(image_payload, (str, bytes, bytearray))
    assert isinstance(video_payload, (str, bytes, bytearray))
    assert json.loads(image_payload) == {"image_url": "https://example.com/ref.png"}
    assert json.loads(video_payload) == {"video_url": "data:video/mp4;base64,AAAA"}


def test_image_url_and_bare_data_video_string_use_json_fields(mocker: MockerFixture) -> None:
    """Image URL plus a bare data:video string must not upload input_references."""
    import aiohttp

    content = [{"type": "image_url", "image_url": {"url": "https://example.com/ref.png"}}]
    extra_body = {"video_reference": "data:video/mp4;base64,AAAA"}
    captured: list[tuple[str, object]] = []
    real_add_field = aiohttp.FormData.add_field

    def tracking_add_field(self, name, value=None, **kwargs):
        captured.append((str(name), value))
        return real_add_field(self, name, value, **kwargs)

    mocker.patch.object(aiohttp.FormData, "add_field", tracking_add_field)
    form = aiohttp.FormData()
    _add_combined_video_form_references(form, content, extra_body)

    field_names = [name for name, _ in captured]
    assert field_names.count("image_reference") == 1
    assert field_names.count("video_reference") == 1
    assert "input_references" not in field_names
    assert "input_reference" not in field_names
    image_payload = next(value for name, value in captured if name == "image_reference")
    video_payload = next(value for name, value in captured if name == "video_reference")
    assert isinstance(image_payload, (str, bytes, bytearray))
    assert isinstance(video_payload, (str, bytes, bytearray))
    assert json.loads(image_payload) == {"image_url": "https://example.com/ref.png"}
    assert json.loads(video_payload) == {"video_url": "data:video/mp4;base64,AAAA"}

    captured.clear()
    assert _add_video_reference_to_form(form, "data:video/mp4;base64,AAAA") is True
    assert [name for name, _ in captured] == ["video_reference"]


def _oversized_data_video_url() -> str:
    """data:video whose JSON text part is larger than 1MB."""
    return "data:video/mp4;base64," + ("A" * (1024 * 1024))


def test_oversized_data_video_uploads_as_input_references(mocker: MockerFixture) -> None:
    """A lone data:video over the 1MB text limit is uploaded, not sent as JSON."""
    import aiohttp

    video_url = _oversized_data_video_url()
    captured: list[tuple[str, object]] = []
    real_add_field = aiohttp.FormData.add_field

    def tracking_add_field(self, name, value=None, **kwargs):
        captured.append((str(name), value))
        return real_add_field(self, name, value, **kwargs)

    mocker.patch.object(aiohttp.FormData, "add_field", tracking_add_field)
    assert _add_video_reference_to_form(aiohttp.FormData(), {"video_url": video_url}) is True
    uploaded = next(value for name, value in captured if name == "input_references")
    assert uploaded == base64.b64decode("A" * (1024 * 1024))
    field_names = [name for name, _ in captured]
    assert "video_reference" not in field_names
    assert "image_reference" not in field_names


def test_oversized_data_video_with_image_stays_json(mocker: MockerFixture) -> None:
    """input_references cannot be combined with image_reference, even for a large video."""
    import aiohttp

    content = [{"type": "image_url", "image_url": {"url": "https://example.com/ref.png"}}]
    extra_body = {"video_reference": _oversized_data_video_url()}
    captured: list[tuple[str, object]] = []
    real_add_field = aiohttp.FormData.add_field

    def tracking_add_field(self, name, value=None, **kwargs):
        captured.append((str(name), value))
        return real_add_field(self, name, value, **kwargs)

    mocker.patch.object(aiohttp.FormData, "add_field", tracking_add_field)
    _add_combined_video_form_references(aiohttp.FormData(), content, extra_body)
    field_names = [name for name, _ in captured]
    assert "image_reference" in field_names
    assert "video_reference" in field_names
    assert "input_references" not in field_names


def test_image_upload_bytes_and_structured_video_use_json_fields(mocker: MockerFixture) -> None:
    """Image upload bytes plus a video URL must not use singular input_reference."""
    import aiohttp

    image_bytes = base64.b64decode(_MIN_PNG_B64)
    extra_body = {
        "image_reference": {"bytes": image_bytes, "content_type": "image/png"},
        "video_reference": {"video_url": "https://example.com/ref.mp4"},
    }
    captured: list[tuple[str, object]] = []
    real_add_field = aiohttp.FormData.add_field

    def tracking_add_field(self, name, value=None, **kwargs):
        captured.append((str(name), value))
        return real_add_field(self, name, value, **kwargs)

    mocker.patch.object(aiohttp.FormData, "add_field", tracking_add_field)
    form = aiohttp.FormData()
    _add_combined_video_form_references(form, None, extra_body)

    field_names = [name for name, _ in captured]
    assert field_names.count("image_reference") == 1
    assert field_names.count("video_reference") == 1
    assert "input_reference" not in field_names
    assert "input_references" not in field_names
    image_payload = next(value for name, value in captured if name == "image_reference")
    video_payload = next(value for name, value in captured if name == "video_reference")
    assert isinstance(image_payload, (str, bytes, bytearray))
    assert isinstance(video_payload, (str, bytes, bytearray))
    assert json.loads(image_payload) == {"image_url": f"data:image/png;base64,{_MIN_PNG_B64}"}
    assert json.loads(video_payload) == {"video_url": "https://example.com/ref.mp4"}


def test_video_unsupported_image_reference_raises() -> None:
    import aiohttp

    form = aiohttp.FormData()
    with pytest.raises(ValueError, match="Unsupported reference"):
        _add_video_reference_to_form(form, {"not_a_supported_key": "x"})
    with pytest.raises(ValueError, match="Unsupported reference"):
        _add_video_reference_to_form(form, "/tmp/does-not-exist-ref.png")


def test_extract_stage_durations_from_video_and_image_shapes() -> None:
    video_payload = {
        "stage_durations": {
            "diffuse": 1.5,
            "text_encoder.forward": 0.2,
            "vae.decode": 0.1,
            "stage_0_gen_ms": 4000.0,
        }
    }
    assert _extract_stage_durations_from_payload(video_payload) == video_payload["stage_durations"]

    image_stage_durations = {
        "diffuse": 2.0,
        "vae.decode": 0.3,
    }
    image_payload = {
        "metrics": {"stage_durations": image_stage_durations},
        "data": [{"b64_json": "x"}],
    }
    assert _extract_stage_durations_from_payload(image_payload) == image_stage_durations


def test_video_metrics_persist_full_stage_durations() -> None:
    output = MixRequestFuncOutput()
    output.latency = 4.2
    stage_durations = {
        "diffuse": 1.5,
        "vae.decode": 0.25,
        "stage_0_gen_ms": 4000.0,
    }
    payload = {
        "duration_s": 2.0,
        "num_frames": 48,
        "fps": 24.0,
        "stage_durations": stage_durations,
    }
    _apply_video_metrics_from_payload(output, payload, {})
    assert output.stage_durations == stage_durations
    assert output.video_generation_time_ms == pytest.approx(4000.0)


def test_image_metrics_persist_stage_durations_from_metrics() -> None:
    output = MixRequestFuncOutput()
    stage_durations = {
        "diffuse": 1.1,
        "text_encoder.forward": 0.4,
        "vae.decode": 0.2,
    }
    payload = {
        "created": 1,
        "data": [{"b64_json": _MIN_PNG_B64}],
        "metrics": {"stage_durations": stage_durations},
    }
    assert _apply_image_metrics_from_payload(output, payload) == 1
    assert output.stage_durations == stage_durations


if __name__ == "__main__":
    pytest.main([__file__, "-v", "-s"])


def test_get_samples_forwards_upstream_multimodal_backends_kwarg(mocker: MockerFixture) -> None:
    """The patched ``datasets.get_samples`` must stay call-compatible upstream.

    Upstream ``vllm.benchmarks.datasets.get_samples`` takes a keyword-only
    ``multimodal_backends`` (``vllm/benchmarks/throughput.py`` passes it) and
    ``patch.py`` rebinds that symbol module-wide, so a non-omni request must
    forward the keyword to the original implementation instead of raising
    ``TypeError`` or silently dropping it.
    """
    calls: list[tuple[Namespace, object, dict]] = []

    def fake_get_samples_old(args, tokenizer, **kwargs):
        calls.append((args, tokenizer, kwargs))
        return ["delegated"]

    mocker.patch.object(patch, "get_samples_old", fake_get_samples_old)

    args = Namespace(
        dataset_name="random",
        backend="vllm-chat",
        dataset_path=None,
        hf_name=None,
    )
    sentinel = object()
    mm_backends = ("openai-chat", "openai-audio")

    assert patch.get_samples(args, sentinel, multimodal_backends=mm_backends) == ["delegated"]
    assert calls == [(args, sentinel, {"multimodal_backends": mm_backends})]
    # No upstream kwargs: unchanged legacy delegate call.
    assert patch.get_samples(args, sentinel) == ["delegated"]
    assert calls[-1] == (args, sentinel, {})


@pytest.mark.asyncio
async def test_benchmark_preserves_stage_metrics_request_order_and_missing_snapshots(monkeypatch):
    """Persist compact formal-request snapshots in input order, excluding warmups and retaining gaps."""
    second_finished = asyncio.Event()
    completion_order = []
    # Request 1 carries the empty dict that openai-chat-omni initializes before any SSE merge.
    snapshots = [
        {"1": {"num_tokens_out": 1536, "finish_reason": "length", "vllm_itls_ms": [8.0, 9.0]}},
        {},
        {"1": {"num_tokens_out": 486, "finish_reason": "stop"}, "2": {"audio_frames": 24000, "audio_duration_s": 1.0}},
    ]
    expected = [
        {"1": {"num_tokens_out": 1536, "finish_reason": "length"}},
        None,
        {"1": {"num_tokens_out": 486, "finish_reason": "stop"}, "2": {"audio_frames": 24000, "audio_duration_s": 1.0}},
    ]

    async def request_func(request_func_input, session, pbar=None):
        request_id = request_func_input.request_id
        if not request_id:
            return MixRequestFuncOutput(success=True, stage_metrics={"1": {"num_tokens_out": 999}})
        index = int(request_id)
        if index == 0:
            await second_finished.wait()
        elif index == 1:
            second_finished.set()
        completion_order.append(index)
        return MixRequestFuncOutput(
            success=index != 1,
            stage_metrics=snapshots[index],
            prompt_len=1,
            output_tokens=900,
            ttft=0.01,
            text_latency=0.1,
            latency=0.1,
        )

    monkeypatch.setitem(patch.ASYNC_REQUEST_FUNCS, "test-stage-metrics", request_func)
    result = await patch.benchmark(
        task_type=patch.TaskType.GENERATION,
        endpoint_type="test-stage-metrics",
        api_url="http://unused/v1/chat/completions",
        base_url="http://unused",
        model_id="test-model",
        model_name="test-model",
        tokenizer=None,
        input_requests=[
            patch.SampleRequest(prompt="hello", prompt_len=1, expected_output_len=900, request_id=str(i))
            for i in range(3)
        ],
        logprobs=None,
        request_rate=float("inf"),
        burstiness=1.0,
        disable_tqdm=True,
        num_warmups=2,
        profile=False,
        selected_percentile_metrics=[],
        selected_percentiles=[],
        ignore_eos=True,
        goodput_config_dict={},
        max_concurrency=3,
        lora_modules=None,
        extra_headers=None,
        extra_body={"return_stage_metrics": True},
        ready_check_timeout_sec=0,
    )
    assert completion_order.index(1) < completion_order.index(0)
    assert result["request_stage_metrics"] == expected


@pytest.mark.parametrize("snapshot", [None, {}, "not-a-dict"])
def test_compact_request_stage_metrics_drops_empty_snapshots(snapshot):
    """Empty chat-omni snapshots must not make every result persist request_stage_metrics."""
    assert patch._compact_request_stage_metrics(snapshot) is None