vllm-omni/tests/entrypoints/openai_api/test_serving_speech.py

# tests/entrypoints/openai/test_serving_speech.py
import logging
from inspect import Signature, signature

import numpy as np
import pytest
import torch
from fastapi import FastAPI
from fastapi.testclient import TestClient
from pydantic import ValidationError
from pytest_mock import MockerFixture

from vllm_omni.entrypoints.openai.audio_utils_mixin import AudioMixin
from vllm_omni.entrypoints.openai.protocol.audio import CreateAudio, OpenAICreateSpeechRequest
from vllm_omni.entrypoints.openai.serving_speech import (
    OmniOpenAIServingSpeech,
)
from vllm_omni.outputs import OmniRequestOutput

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

logger = logging.getLogger(__name__)


class TestAudioMixin:
    @pytest.fixture
    def audio_mixin(self):
        return AudioMixin()

    def test_stereo_to_mono_conversion(self, audio_mixin, mocker: MockerFixture):
        stereo_tensor = np.random.rand(24000, 2).astype(np.float32)
        audio_obj = CreateAudio(audio_tensor=stereo_tensor)

        mock_speed = mocker.patch.object(
            audio_mixin, "_apply_speed_adjustment", side_effect=lambda tensor, speed, sr: (tensor, sr)
        )
        mocker.patch("soundfile.write")

        audio_mixin.create_audio(audio_obj)

        # Check that the tensor passed to speed adjustment is mono
        mock_speed.assert_called_once()
        adjusted_tensor = mock_speed.call_args[0][0]
        assert len(adjusted_tensor) == 24000

    def test_speed_adjustment(self, audio_mixin, mocker: MockerFixture):
        mock_time_stretch = mocker.patch("librosa.effects.time_stretch")
        mock_time_stretch.return_value = np.zeros(12000)
        audio_tensor = np.random.rand(24000).astype(np.float32)

        adjusted_audio, _ = audio_mixin._apply_speed_adjustment(audio_tensor, speed=2.0, sample_rate=24000)

        mock_time_stretch.assert_called_with(y=audio_tensor, rate=2.0)
        assert adjusted_audio.shape == (12000,)

    def test_unsupported_format_fallback(self, audio_mixin, caplog, mocker: MockerFixture):
        mock_write = mocker.patch("soundfile.write")
        audio_tensor = np.random.rand(24000).astype(np.float32)
        # Use a format that is not in the list of supported formats
        audio_obj = CreateAudio(audio_tensor=audio_tensor, response_format="vorbis")

        audio_mixin.create_audio(audio_obj)

        # Should fall back to 'wav'
        mock_write.assert_called_once()
        write_kwargs = mock_write.call_args.kwargs
        assert write_kwargs["format"] == "WAV"

    def test_mono_audio_preservation(self, audio_mixin, mocker: MockerFixture):
        """Test that mono (1D) audio tensors are processed correctly and passed to writer."""
        mono_tensor = np.random.rand(24000).astype(np.float32)
        audio_obj = CreateAudio(audio_tensor=mono_tensor)

        mock_write = mocker.patch("soundfile.write")
        audio_mixin.create_audio(audio_obj)

        mock_write.assert_called_once()
        # Verify the tensor passed to soundfile.write is the exact 1D tensor
        output_tensor = mock_write.call_args[0][1]
        assert output_tensor.ndim == 1
        assert output_tensor.shape == (24000,)
        assert np.array_equal(output_tensor, mono_tensor)

    def test_stereo_audio_preservation(self, audio_mixin, mocker: MockerFixture):
        """Test that stereo (2D) audio tensors are processed correctly and preserved."""
        stereo_tensor = np.random.rand(24000, 2).astype(np.float32)
        audio_obj = CreateAudio(audio_tensor=stereo_tensor)

        mock_write = mocker.patch("soundfile.write")
        audio_mixin.create_audio(audio_obj)

        mock_write.assert_called_once()
        # Verify the tensor passed to soundfile.write is the exact 2D tensor
        output_tensor = mock_write.call_args[0][1]
        assert output_tensor.ndim == 2
        assert output_tensor.shape == (24000, 2)
        assert np.array_equal(output_tensor, stereo_tensor)

    def test_speed_adjustment_bypass(self, audio_mixin, mocker: MockerFixture):
        """Test that speed=1.0 bypasses the expensive librosa time stretching."""
        audio_tensor = np.random.rand(24000).astype(np.float32)

        mock_time_stretch = mocker.patch("librosa.effects.time_stretch")
        # speed=1.0 should return immediately without calling librosa
        result, _ = audio_mixin._apply_speed_adjustment(audio_tensor, speed=1.0, sample_rate=24000)

        mock_time_stretch.assert_not_called()
        assert np.array_equal(result, audio_tensor)

    def test_speed_adjustment_stereo_handling(self, audio_mixin, mocker: MockerFixture):
        """Test that speed adjustment is attempted on stereo inputs."""
        mock_time_stretch = mocker.patch("librosa.effects.time_stretch")
        stereo_tensor = np.random.rand(24000, 2).astype(np.float32)
        # Mock return value representing a sped-up version (half length)
        mock_time_stretch.return_value = np.zeros((12000, 2), dtype=np.float32)

        result, _ = audio_mixin._apply_speed_adjustment(stereo_tensor, speed=2.0, sample_rate=24000)

        mock_time_stretch.assert_called_once()
        # Ensure the stereo tensor was passed to librosa
        call_args = mock_time_stretch.call_args
        assert np.array_equal(call_args.kwargs["y"], stereo_tensor)
        assert call_args.kwargs["rate"] == 2.0
        assert result.shape == (12000, 2)


# Helper to create mock model output for endpoint tests
def create_mock_audio_output_for_test(
    request_id: str = "speech-mock-123",
) -> OmniRequestOutput:
    class MockCompletionOutput:
        def __init__(self, index: int = 0):
            self.index = index
            self.text = ""
            self.token_ids = []
            self.finish_reason = "stop"
            self.stop_reason = None
            self.logprobs = None

    class MockRequestOutput:
        def __init__(self, request_id: str, audio_tensor: torch.Tensor):
            self.request_id = request_id
            self.outputs = [MockCompletionOutput(index=0)]
            self.multimodal_output = {"audio": audio_tensor}
            self.finished = True
            self.prompt_token_ids = None
            self.encoder_prompt_token_ids = None
            self.num_cached_tokens = None
            self.prompt_logprobs = None
            self.kv_transfer_params = None

    num_samples = 24000
    audio_tensor = torch.sin(torch.linspace(0, 440 * 2 * torch.pi, num_samples))
    mock_request_output = MockRequestOutput(request_id=request_id, audio_tensor=audio_tensor)

    return OmniRequestOutput(
        stage_id=0,
        final_output_type="audio",
        request_output=mock_request_output,
    )


@pytest.fixture
def test_app(mocker: MockerFixture):
    # Mock the engine client
    mock_engine_client = mocker.MagicMock()
    mock_engine_client.errored = False

    async def mock_generate_fn(*args, **kwargs):
        yield create_mock_audio_output_for_test(request_id=kwargs.get("request_id"))

    mock_engine_client.generate = mocker.MagicMock(side_effect=mock_generate_fn)
    mock_engine_client.default_sampling_params_list = [{}]

    # Mock models to have an is_base_model method
    mock_models = mocker.MagicMock()
    mock_models.is_base_model.return_value = True

    mock_request_logger = mocker.MagicMock()

    speech_server = OmniOpenAIServingSpeech(
        engine_client=mock_engine_client,
        models=mock_models,
        request_logger=mock_request_logger,
    )

    # Patch the signature of create_speech to remove 'raw_request' for FastAPI route introspection
    original_create_speech = speech_server.create_speech
    _ = mocker.MagicMock(side_effect=original_create_speech)

    sig = signature(original_create_speech)

    new_parameters = [param for name, param in sig.parameters.items() if name != "raw_request"]

    new_sig = Signature(parameters=new_parameters, return_annotation=sig.return_annotation)

    async def awaitable_patched_create_speech(*args, **kwargs):
        return await original_create_speech(*args, **kwargs)

    awaitable_patched_create_speech.__signature__ = new_sig
    speech_server.create_speech = awaitable_patched_create_speech

    app = FastAPI()
    app.add_api_route("/v1/audio/speech", speech_server.create_speech, methods=["POST"], response_model=None)

    # Add list_voices endpoint
    async def list_voices():
        speakers = sorted(speech_server.supported_speakers) if speech_server.supported_speakers else []
        return {"voices": speakers}

    app.add_api_route("/v1/audio/voices", list_voices, methods=["GET"])

    return app


@pytest.fixture
def client(test_app):
    return TestClient(test_app)


class TestSpeechAPI:
    def test_create_speech_success(self, client):
        payload = {
            "input": "Hello world",
            "model": "tts-model",
            "voice": "alloy",
            "response_format": "wav",
        }
        response = client.post("/v1/audio/speech", json=payload)
        assert response.status_code == 200
        assert response.headers["content-type"] == "audio/wav"
        assert len(response.content) > 0

    def test_create_speech_mp3_format(self, client):
        payload = {
            "input": "Hello world",
            "model": "tts-model",
            "voice": "alloy",
            "response_format": "mp3",
        }
        response = client.post("/v1/audio/speech", json=payload)
        assert response.status_code == 200
        assert response.headers["content-type"] == "audio/mpeg"
        assert len(response.content) > 0

    def test_create_speech_invalid_format(self, client):
        payload = {
            "input": "Hello world",
            "model": "tts-model",
            "voice": "alloy",
            "response_format": "invalid_format",
        }
        response = client.post("/v1/audio/speech", json=payload)
        assert response.status_code == 422  # Unprocessable Entity

    def test_speed_parameter_is_used(self, test_app, mocker: MockerFixture):
        mock_create_audio = mocker.patch(
            "vllm_omni.entrypoints.openai.serving_speech.OmniOpenAIServingSpeech.create_audio"
        )
        client = TestClient(test_app)

        mock_audio_response = mocker.MagicMock()
        mock_audio_response.audio_data = b"dummy_audio"
        mock_audio_response.media_type = "audio/wav"
        mock_create_audio.return_value = mock_audio_response

        payload = {
            "input": "This should be fast.",
            "model": "tts-model",
            "voice": "alloy",
            "response_format": "wav",
            "speed": 2.5,
        }
        client.post("/v1/audio/speech", json=payload)

        mock_create_audio.assert_called_once()
        call_args = mock_create_audio.call_args[0]
        audio_obj = call_args[0]
        assert isinstance(audio_obj, CreateAudio)
        assert audio_obj.speed == 2.5

    def test_list_voices_endpoint(self, client):
        response = client.get("/v1/audio/voices")
        assert response.status_code == 200
        assert "voices" in response.json()


class TestTTSMethods:
    """Unit tests for TTS validation and parameter building."""

    @pytest.fixture
    def speech_server(self, mocker: MockerFixture):
        mock_engine_client = mocker.MagicMock()
        mock_engine_client.errored = False
        mock_engine_client.stage_list = None
        mock_engine_client.tts_max_instructions_length = None
        mock_models = mocker.MagicMock()
        mock_models.is_base_model.return_value = True
        return OmniOpenAIServingSpeech(
            engine_client=mock_engine_client,
            models=mock_models,
            request_logger=mocker.MagicMock(),
        )

    def test_is_tts_detection_no_stage(self, speech_server):
        """Test TTS model detection when no TTS stage exists."""
        # Fixture creates server with stage_list = None -> _is_tts should be False
        assert speech_server._is_tts is False
        assert speech_server._tts_stage is None

    def test_is_tts_detection_with_tts_stage(self, mocker: MockerFixture):
        """Test TTS model detection when TTS stage exists."""
        mock_engine_client = mocker.MagicMock()
        mock_engine_client.errored = False
        mock_engine_client.tts_max_instructions_length = None

        # Create a TTS stage
        mock_stage = mocker.MagicMock()
        mock_stage.model_stage = "qwen3_tts"
        mock_stage.tts_args = {}
        mock_engine_client.stage_list = [mock_stage]

        mock_models = mocker.MagicMock()
        mock_models.is_base_model.return_value = True

        server = OmniOpenAIServingSpeech(
            engine_client=mock_engine_client,
            models=mock_models,
            request_logger=mocker.MagicMock(),
        )

        assert server._is_tts is True
        assert server._tts_stage is mock_stage

    def test_estimate_prompt_len_fallback(self, speech_server):
        """Test prompt length estimation falls back to 2048 when model is unavailable."""
        tts_params = {"text": ["Hello"], "task_type": ["CustomVoice"]}
        result = speech_server._estimate_prompt_len(tts_params)
        # Without a real model, it should fall back to 2048.
        assert result == 2048

    def test_validate_tts_request_basic(self, speech_server):
        """Test basic validation cases."""
        # Empty input
        req = OpenAICreateSpeechRequest(input="")
        assert speech_server._validate_tts_request(req) == "Input text cannot be empty"

        # Invalid language
        req = OpenAICreateSpeechRequest(input="Hello", language="InvalidLang")
        assert "Invalid language" in speech_server._validate_tts_request(req)

        # CustomVoice on model with no speakers -> rejected
        req = OpenAICreateSpeechRequest(input="Hello", voice="Invalid")
        assert "does not support CustomVoice" in speech_server._validate_tts_request(req)

        # CustomVoice without voice on model with no speakers -> also rejected
        req = OpenAICreateSpeechRequest(input="Hello")
        assert "does not support CustomVoice" in speech_server._validate_tts_request(req)

    def test_validate_tts_request_task_types(self, speech_server):
        """Test task-specific validation."""
        # Base task requires ref_audio
        req = OpenAICreateSpeechRequest(input="Hello", task_type="Base")
        assert "ref_audio" in speech_server._validate_tts_request(req)

        # VoiceDesign requires instructions
        req = OpenAICreateSpeechRequest(input="Hello", task_type="VoiceDesign")
        assert "instructions" in speech_server._validate_tts_request(req)

        # ref_text without task_type auto-infers Base, then fails on missing ref_audio
        req = OpenAICreateSpeechRequest(input="Hello", ref_text="text")
        assert "ref_audio" in speech_server._validate_tts_request(req)

    def test_validate_tts_request_auto_infer_base(self, speech_server):
        """Test auto-inference of Base task when ref_audio/ref_text is provided."""
        # ref_audio without task_type -> infers Base, requires non-empty ref_text
        req = OpenAICreateSpeechRequest(input="Hello", ref_audio="data:audio/wav;base64,abc")
        result = speech_server._validate_tts_request(req)
        assert "ref_text" in result
        assert req.task_type == "Base"

        # ref_text without task_type -> infers Base, requires ref_audio
        req = OpenAICreateSpeechRequest(input="Hello", ref_text="some text")
        result = speech_server._validate_tts_request(req)
        assert "ref_audio" in result
        assert req.task_type == "Base"

    def test_validate_tts_request_base_empty_ref_text(self, speech_server):
        """Empty ref_text on Base task returns 400 instead of crashing engine."""
        req = OpenAICreateSpeechRequest(
            input="Hello", task_type="Base", ref_audio="data:audio/wav;base64,abc", ref_text=""
        )
        result = speech_server._validate_tts_request(req)
        assert "non-empty 'ref_text'" in result

        # x_vector_only_mode bypasses ref_text requirement
        req = OpenAICreateSpeechRequest(
            input="Hello", task_type="Base", ref_audio="data:audio/wav;base64,abc", ref_text="", x_vector_only_mode=True
        )
        assert speech_server._validate_tts_request(req) is None

    def test_validate_tts_request_customvoice_no_speakers(self, speech_server):
        """CustomVoice on a model with no speakers returns 400 instead of crashing engine."""
        req = OpenAICreateSpeechRequest(input="Hello", task_type="CustomVoice")
        result = speech_server._validate_tts_request(req)
        assert "does not support CustomVoice" in result

    def test_build_tts_params(self, speech_server):
        """Test TTS parameter building."""
        req = OpenAICreateSpeechRequest(input="Hello", voice="Ryan", language="English")
        params = speech_server._build_tts_params(req)

        assert params["text"] == ["Hello"]
        assert params["speaker"] == ["Ryan"]
        assert params["language"] == ["English"]
        assert params["task_type"] == ["CustomVoice"]

    def test_load_supported_speakers(self, mocker: MockerFixture):
        """Test _load_supported_speakers."""
        mock_engine_client = mocker.MagicMock()
        mock_engine_client.errored = False
        mock_engine_client.stage_list = None

        # Mock talker_config with mixed-case speaker names
        mock_talker_config = mocker.MagicMock()
        mock_talker_config.spk_id = {"Ryan": 0, "Vivian": 1, "Aiden": 2}
        mock_engine_client.model_config.hf_config.talker_config = mock_talker_config

        mock_models = mocker.MagicMock()
        mock_models.is_base_model.return_value = True

        server = OmniOpenAIServingSpeech(
            engine_client=mock_engine_client,
            models=mock_models,
            request_logger=mocker.MagicMock(),
        )

        # Verify speakers are normalized to lowercase
        assert server.supported_speakers == {"ryan", "vivian", "aiden"}

    def test_max_instructions_length_default(self, speech_server):
        """Test default max instructions length (500) when no config provided."""
        # Fixture creates server with no CLI override and no TTS stage
        assert speech_server._max_instructions_length == 500

    def test_max_instructions_length_cli_override(self, mocker: MockerFixture):
        """Test CLI override (stored in engine_client) takes highest priority."""
        mock_engine_client = mocker.MagicMock()
        mock_engine_client.errored = False
        mock_engine_client.stage_list = None
        # CLI override is stored in engine_client
        mock_engine_client.tts_max_instructions_length = 1000
        mock_models = mocker.MagicMock()
        mock_models.is_base_model.return_value = True

        server = OmniOpenAIServingSpeech(
            engine_client=mock_engine_client,
            models=mock_models,
            request_logger=mocker.MagicMock(),
        )
        # Value is cached during __init__
        assert server._max_instructions_length == 1000

    def test_max_instructions_length_stage_config(self, mocker: MockerFixture):
        """Test stage config value is used when no CLI override."""
        mock_engine_client = mocker.MagicMock()
        mock_engine_client.errored = False
        mock_engine_client.tts_max_instructions_length = None  # No CLI override
        mock_models = mocker.MagicMock()
        mock_models.is_base_model.return_value = True

        # Mock stage with tts_args
        mock_stage = mocker.MagicMock()
        mock_stage.model_stage = "qwen3_tts"
        mock_stage.tts_args = {"max_instructions_length": 750}
        mock_engine_client.stage_list = [mock_stage]

        server = OmniOpenAIServingSpeech(
            engine_client=mock_engine_client,
            models=mock_models,
            request_logger=mocker.MagicMock(),
        )
        # Value is cached during __init__
        assert server._max_instructions_length == 750

    def test_max_instructions_length_cli_overrides_stage_config(self, mocker: MockerFixture):
        """Test CLI override (in engine_client) takes precedence over stage config."""
        mock_engine_client = mocker.MagicMock()
        mock_engine_client.errored = False
        # CLI override stored in engine_client
        mock_engine_client.tts_max_instructions_length = 2000
        mock_models = mocker.MagicMock()
        mock_models.is_base_model.return_value = True

        # Mock stage with tts_args
        mock_stage = mocker.MagicMock()
        mock_stage.model_stage = "qwen3_tts"
        mock_stage.tts_args = {"max_instructions_length": 750}
        mock_engine_client.stage_list = [mock_stage]

        server = OmniOpenAIServingSpeech(
            engine_client=mock_engine_client,
            models=mock_models,
            request_logger=mocker.MagicMock(),
        )
        # CLI value (2000) should override stage config (750)
        assert server._max_instructions_length == 2000

    def test_validate_instructions_length_uses_cached_value(self, mocker: MockerFixture):
        """Test instructions length validation uses cached _max_instructions_length."""
        mock_engine_client = mocker.MagicMock()
        mock_engine_client.errored = False
        mock_engine_client.stage_list = None
        # CLI override with max length of 10 characters
        mock_engine_client.tts_max_instructions_length = 10
        mock_models = mocker.MagicMock()
        mock_models.is_base_model.return_value = True

        server = OmniOpenAIServingSpeech(
            engine_client=mock_engine_client,
            models=mock_models,
            request_logger=mocker.MagicMock(),
        )

        # Verify cached value
        assert server._max_instructions_length == 10

        # Instructions within limit should pass
        req = OpenAICreateSpeechRequest(
            input="Hello",
            task_type="VoiceDesign",
            instructions="short",
        )
        assert server._validate_tts_request(req) is None

        # Instructions exceeding limit should fail
        req = OpenAICreateSpeechRequest(
            input="Hello",
            task_type="VoiceDesign",
            instructions="this is too long",
        )
        error = server._validate_tts_request(req)
        assert error is not None
        assert "max 10 characters" in error


class TestStreamingProtocolValidation:
    """Unit tests for the stream field validators in OpenAICreateSpeechRequest."""

    def test_stream_validation_errors(self):
        """stream=True requires response_format='pcm' and speed=1.0."""
        with pytest.raises(ValidationError, match="response_format='pcm'"):
            OpenAICreateSpeechRequest(input="Hello", stream=True, response_format="wav")
        with pytest.raises(ValidationError, match="Speed adjustment is not supported"):
            OpenAICreateSpeechRequest(input="Hello", stream=True, response_format="pcm", speed=2.0)

    def test_stream_valid(self):
        """stream=True + response_format='pcm' + speed=1.0 is accepted."""
        req = OpenAICreateSpeechRequest(input="Hello", stream=True, response_format="pcm")
        assert req.stream is True

    def test_sse_stream_format_is_blocked(self):
        """stream_format='sse' is blocked."""
        with pytest.raises(ValidationError, match="sse"):
            OpenAICreateSpeechRequest(input="Hello", stream_format="sse")


class TestStreamingResponse:
    """Integration tests for the streaming audio response path."""

    @pytest.fixture
    def streaming_app(self, mocker: MockerFixture):
        """Test app whose mock engine yields one intermediate chunk then a final chunk."""
        mock_engine_client = mocker.MagicMock()
        mock_engine_client.errored = False

        def _make_output(finished: bool) -> OmniRequestOutput:
            chunk = torch.sin(torch.linspace(0, 440 * 2 * torch.pi, 24000))

            class MockCompletionOutput:
                def __init__(self, index: int = 0):
                    self.index = index
                    self.text = ""
                    self.token_ids = []
                    self.finish_reason = "stop"
                    self.stop_reason = None
                    self.logprobs = None

            class MockRequestOutput:
                def __init__(self, audio_tensor: torch.Tensor):
                    self.request_id = "speech-stream-test"
                    self.outputs = [MockCompletionOutput(index=0)]
                    self.multimodal_output = {"audio": audio_tensor}
                    self.finished = finished
                    self.prompt_token_ids = None
                    self.encoder_prompt_token_ids = None
                    self.num_cached_tokens = None
                    self.prompt_logprobs = None
                    self.kv_transfer_params = None

            return OmniRequestOutput(
                stage_id=0,
                final_output_type="audio",
                request_output=MockRequestOutput(audio_tensor=chunk),
                finished=finished,
            )

        async def mock_generate_streaming(*args, **kwargs):
            yield _make_output(finished=False)
            yield _make_output(finished=True)

        mock_engine_client.generate = mocker.MagicMock(side_effect=mock_generate_streaming)
        mock_engine_client.default_sampling_params_list = [{}]
        mock_models = mocker.MagicMock()
        mock_models.is_base_model.return_value = True

        speech_server = OmniOpenAIServingSpeech(
            engine_client=mock_engine_client,
            models=mock_models,
            request_logger=mocker.MagicMock(),
        )

        original_create_speech = speech_server.create_speech
        sig = signature(original_create_speech)
        new_parameters = [p for name, p in sig.parameters.items() if name != "raw_request"]
        new_sig = Signature(parameters=new_parameters, return_annotation=sig.return_annotation)

        async def awaitable_create_speech(*args, **kwargs):
            return await original_create_speech(*args, **kwargs)

        awaitable_create_speech.__signature__ = new_sig
        speech_server.create_speech = awaitable_create_speech

        app = FastAPI()
        app.add_api_route("/v1/audio/speech", speech_server.create_speech, methods=["POST"], response_model=None)
        return app

    def test_streaming(self, streaming_app):
        """stream=True must return audio/pcm with non-empty body."""
        client = TestClient(streaming_app)
        response = client.post("/v1/audio/speech", json={"input": "Hello", "stream": True, "response_format": "pcm"})
        assert response.status_code == 200
        assert "audio/pcm" in response.headers["content-type"]
        assert len(response.content) > 0

    def test_non_streaming_unchanged(self, streaming_app):
        """Non-streaming path must still return audio/wav."""
        client = TestClient(streaming_app)
        response = client.post("/v1/audio/speech", json={"input": "Hello", "response_format": "wav"})
        assert response.status_code == 200
        assert "audio/wav" in response.headers["content-type"]