ut for audio and vision encode function (#489)

## Summary

<!--
Include a short paragraph of the changes introduced in this PR.
If this PR requires additional context or rationale, explain why
the changes are necessary.
-->

pytest tests/unit/extras/test_vision.py 
pytest tests/unit/extras/test_audio.py 

## Details

<!--
Provide a detailed list of all changes introduced in this pull request.
-->
- [ ]

## Test Plan

<!--
List the steps needed to test this PR.
-->
-

## Related Issues

<!--
Link any relevant issues that this PR addresses.
-->
- Resolves #

---

- [ ] "I certify that all code in this PR is my own, except as noted
below."

## Use of AI

- [ ] Includes AI-assisted code completion
- [ ] Includes code generated by an AI application
- [ ] Includes AI-generated tests (NOTE: AI written tests should have a
docstring that includes `## WRITTEN BY AI ##`)

Author: sjmonson

.github/actions/run-tox/action.yml

@@ -17,6 +17,10 @@ runs:
       uses: pdm-project/setup-pdm@v4
       with:
         python-version: ${{ inputs.python-version }}
+    - name: Install system dependencies
+      run: |
+        sudo apt install -y ffmpeg
+      shell: bash
     - name: Install dependencies
       run: |
         pip install tox tox-pdm

tests/unit/extras/__init__.py

@@ -0,0 +1,212 @@
+import tempfile
+import wave
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+import numpy as np
+import pytest
+import torch
+
+from guidellm.extras.audio import encode_audio
+
+
+@pytest.fixture
+def sample_audio_tensor():
+    sample_rate = 16000
+    t = torch.linspace(0, 1, sample_rate)
+    return 0.3 * torch.sin(2 * np.pi * 440 * t).unsqueeze(0)
+
+
+@pytest.fixture
+def sample_wav_file(sample_audio_tensor):
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
+        f.write(b"fake_wav_content")
+        temp_path = Path(f.name)
+    yield temp_path
+
+    if temp_path.exists():
+        temp_path.unlink()
+
+
+@pytest.fixture
+def real_wav_file():
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
+        sample_rate = 16000
+        duration = 1.0
+        t = np.linspace(0, duration, int(sample_rate * duration))
+        audio_data = (np.sin(2 * np.pi * 440 * t) * 32767).astype(np.int16)
+
+        with wave.open(f.name, "wb") as wav_file:
+            wav_file.setnchannels(1)
+            wav_file.setsampwidth(2)
+            wav_file.setframerate(sample_rate)
+            wav_file.writeframes(audio_data.tobytes())
+
+        temp_path = Path(f.name)
+
+    yield temp_path
+
+    if temp_path.exists():
+        temp_path.unlink()
+
+
+def test_encode_audio_with_tensor_input(sample_audio_tensor):
+    result = encode_audio(
+        audio=sample_audio_tensor,
+        sample_rate=16000,
+        audio_format="mp3",
+        bitrate="64k",
+        b64encode=False,
+    )
+
+    assert result["type"] == "audio_file"
+    assert isinstance(result["audio"], bytes)
+    assert result["format"] == "mp3"
+    assert result["mimetype"] == "audio/mp3"
+    assert result["audio_samples"] == 16000
+    assert result["audio_seconds"] == 1.0
+    assert isinstance(result["audio_bytes"], int)
+    assert result["audio_bytes"] > 0
+
+
+def test_encode_audio_with_base64(sample_audio_tensor):
+    result = encode_audio(audio=sample_audio_tensor, sample_rate=16000, b64encode=True)
+
+    assert result["type"] == "audio_base64"
+    assert isinstance(result["audio"], str)
+    import base64
+
+    try:
+        decoded = base64.b64decode(result["audio"])
+        assert len(decoded) > 0
+    except (base64.binascii.Error, ValueError) as e:
+        pytest.fail(f"Invalid base64 encoding: {e}")
+
+
+def test_encode_audio_with_numpy_array(sample_audio_tensor):
+    numpy_audio = sample_audio_tensor.numpy()
+
+    result = encode_audio(audio=numpy_audio, sample_rate=16000)
+
+    assert result["type"] == "audio_file"
+    assert isinstance(result["audio"], bytes)
+    assert result["audio_bytes"] > 0
+
+
+def test_encode_audio_with_real_file_path(real_wav_file):
+    result = encode_audio(audio=real_wav_file, sample_rate=16000, max_duration=1.0)
+
+    assert result["type"] == "audio_file"
+    assert isinstance(result["audio"], bytes)
+    assert result["format"] == "mp3"
+    assert result["mimetype"] == "audio/mp3"
+    assert result["file_name"] == Path(real_wav_file).name
+    assert result["audio_bytes"] > 0
+    assert result["audio_seconds"] <= 1.0
+
+
+def test_encode_audio_with_dict_input_complete():
+    audio_dict = {"data": torch.randn(1, 16000), "sample_rate": 16000}
+
+    result = encode_audio(audio=audio_dict)
+
+    assert result["type"] == "audio_file"
+    assert result["audio_bytes"] > 0
+    assert result["audio_samples"] == 16000
+    assert result["audio_seconds"] == 1.0
+
+
+@patch("httpx.get")
+@patch("guidellm.extras.audio._encode_audio")
+def test_encode_audio_with_url(mock_http_get, sample_audio_tensor):
+    # mock http get response
+    mock_response = MagicMock()
+    mock_response.content = b"fake_audio_content"
+    mock_response.raise_for_status = MagicMock()
+    mock_http_get.return_value = mock_response
+
+    # mock decode - return sample audio tensor
+    with patch("guidellm.extras.audio._decode_audio") as mock_decoder:
+        mock_audio_result = MagicMock()
+        mock_audio_result.data = sample_audio_tensor
+        mock_audio_result.sample_rate = 16000
+        mock_decoder.return_value = mock_audio_result
+
+        result = encode_audio(audio="https://example.com/audio.wav", sample_rate=16000)
+        assert result["type"] == "audio_file"
+
+
+def test_encode_audio_with_max_duration(sample_audio_tensor):
+    long_audio = torch.randn(1, 32000)
+
+    result = encode_audio(audio=long_audio, sample_rate=16000, max_duration=1.0)
+
+    assert result["audio_seconds"] == 1.0
+
+
+def test_encode_audio_different_formats(sample_audio_tensor):
+    formats = ["mp3", "wav", "flac"]
+
+    for fmt in formats:
+        result = encode_audio(
+            audio=sample_audio_tensor, sample_rate=16000, audio_format=fmt
+        )
+
+        assert result["format"] == fmt
+        assert result["mimetype"] == f"audio/{fmt}"
+        assert result["audio_bytes"] > 0
+
+
+def test_encode_audio_resampling(sample_audio_tensor):
+    original_rate = 16000
+    target_rate = 8000
+
+    result = encode_audio(
+        audio=sample_audio_tensor,
+        sample_rate=original_rate,
+        encode_sample_rate=target_rate,
+    )
+
+    assert "audio_samples" in result
+
+
+def test_encode_audio_error_handling():
+    with pytest.raises(ValueError):
+        encode_audio(audio=123)
+
+    with pytest.raises(ValueError):
+        encode_audio(audio=torch.randn(1, 16000), sample_rate=None)
+
+    with pytest.raises(ValueError):
+        encode_audio(audio="/nonexistent/path/audio.wav")
+
+
+def test_audio_quality_preservation(sample_audio_tensor):
+    result = encode_audio(
+        audio=sample_audio_tensor,
+        sample_rate=16000,
+        audio_format="mp3",
+        bitrate="128k",
+    )
+
+    assert len(result["audio"]) > 1000
+
+
+def test_end_to_end_audio_processing(sample_audio_tensor):
+    original_samples = sample_audio_tensor.shape[1]
+    original_duration = original_samples / 16000
+
+    result = encode_audio(
+        audio=sample_audio_tensor,
+        sample_rate=16000,
+        audio_format="mp3",
+        bitrate="64k",
+        b64encode=True,
+        max_duration=0.5,
+    )
+
+    assert result["type"] == "audio_base64"
+    assert isinstance(result["audio"], str)
+    assert result["format"] == "mp3"
+    assert result["audio_samples"] == 16000
+    assert result["audio_seconds"] <= original_duration