mirror of
https://github.com/BerriAI/litellm.git
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fa2c8984ba
* test: move the unit half of 126 mixed legacy files into tests/unit * test: restore litellm globals that moved tests set * test: finalize migration test cleanup * test: restore original bodies of moved legacy tests The move into tests/unit had rewritten 612 test bodies, and some of the rewrites dropped assertions. Each moved test now carries its original body from the legacy file, with only the imports, helpers, fake provider credentials and monkeypatched env it needs to run under tests/unit test_timeout_streaming goes back to tests/local_testing because it needs the fake OpenAI endpoint server. The image payload fixture moves with its only user, and two tests that leaked global state (a registered model cost entry and queued logging tasks) are now isolated * test: drop module imports shadowed by restored local imports * test: assert on LiteLLM output in no-assertion moved tests and isolate leaks Twenty no-assertion candidates get one assertion on the value LiteLLM returns, with the original lines unchanged. Four tests go back to their legacy files because they only check types or imports, write into the working directory, or cannot assert without a body change Two moved tests leaked globals into later tests in the same worker, so monkeypatch fixtures now restore the retry-after header parser and the end user cost tracking flags * test: drain queued logging tasks before the Phoenix span test The moved Phoenix test counted spans from logging tasks that earlier tests had queued, so the drain fixture moves to tests/unit/conftest.py and both it and the Datadog batch test use it. test_factory_function goes back to its legacy file because its returned wrapper calls the real Assistants API and cannot be asserted on without a body change --------- Co-authored-by: yuneng <yuneng@berri.ai>
303 lines
9 KiB
Python
303 lines
9 KiB
Python
# What is this?
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## unit tests for openai tts endpoint
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import os
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from dotenv import load_dotenv
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load_dotenv()
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from pathlib import Path
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from unittest.mock import AsyncMock, MagicMock, patch
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import pytest
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import litellm
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async def _run_audio_speech_litellm(sync_mode, model, api_base, api_key):
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litellm.turn_on_debug()
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speech_file_path = Path(__file__).parent / "speech.mp3"
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if sync_mode:
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response = litellm.speech(
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model=model,
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voice="alloy",
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input="the quick brown fox jumped over the lazy dogs",
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api_base=api_base,
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api_key=api_key,
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organization=None,
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project=None,
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max_retries=1,
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timeout=600,
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client=None,
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optional_params={},
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)
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from litellm.types.llms.openai import HttpxBinaryResponseContent
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assert isinstance(response, HttpxBinaryResponseContent)
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else:
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response = await litellm.aspeech(
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model=model,
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voice="alloy",
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input="the quick brown fox jumped over the lazy dogs",
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api_base=api_base,
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api_key=api_key,
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organization=None,
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project=None,
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max_retries=1,
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timeout=600,
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client=None,
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optional_params={},
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)
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from litellm.llms.openai.openai import HttpxBinaryResponseContent
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assert isinstance(response, HttpxBinaryResponseContent)
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@pytest.mark.parametrize("sync_mode", [True, False])
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@pytest.mark.asyncio
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@pytest.mark.flaky(retries=3, delay=1)
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async def test_audio_speech_litellm_azure(sync_mode):
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await _run_audio_speech_litellm(
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sync_mode=sync_mode,
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model="azure/tts",
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api_base=os.getenv("AZURE_TTS_API_BASE"),
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api_key=os.getenv("AZURE_TTS_API_KEY"),
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)
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@pytest.mark.parametrize("sync_mode", [True, False])
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@pytest.mark.asyncio
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@pytest.mark.flaky(retries=3, delay=1)
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async def test_audio_speech_litellm_openai(sync_mode):
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await _run_audio_speech_litellm(
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sync_mode=sync_mode,
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model="openai/tts-1",
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api_base=None,
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api_key=os.getenv("OPENAI_API_KEY"),
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)
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@pytest.mark.flaky(retries=6, delay=2)
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@pytest.mark.asyncio
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async def test_speech_litellm_vertex_async():
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# Mock the response
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mock_response = AsyncMock()
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def return_val():
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return {
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"audioContent": "dGVzdCByZXNwb25zZQ==",
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}
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mock_response.json = return_val
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mock_response.status_code = 200
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# Set up the mock for asynchronous calls
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with patch(
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"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
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new_callable=AsyncMock,
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) as mock_async_post:
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mock_async_post.return_value = mock_response
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model = "vertex_ai/test"
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try:
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response = await litellm.aspeech(
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model=model,
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input="async hello what llm guardrail do you have",
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)
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except litellm.APIConnectionError as e:
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if "Your default credentials were not found" in str(e):
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pytest.skip("skipping test, credentials not found")
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# Assert asynchronous call
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mock_async_post.assert_called_once()
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_, kwargs = mock_async_post.call_args
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print("call args", kwargs)
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assert kwargs["url"] == "https://texttospeech.googleapis.com/v1/text:synthesize"
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assert "x-goog-user-project" in kwargs["headers"]
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assert kwargs["headers"]["Authorization"] is not None
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assert kwargs["json"] == {
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"input": {"text": "async hello what llm guardrail do you have"},
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"voice": {"languageCode": "en-US", "name": "en-US-Studio-O"},
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"audioConfig": {"audioEncoding": "LINEAR16", "speakingRate": "1"},
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}
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@pytest.mark.asyncio
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async def test_speech_litellm_vertex_async_with_voice():
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# Mock the response
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mock_response = AsyncMock()
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def return_val():
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return {
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"audioContent": "dGVzdCByZXNwb25zZQ==",
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}
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mock_response.json = return_val
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mock_response.status_code = 200
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# Set up the mock for asynchronous calls
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with patch(
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"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
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new_callable=AsyncMock,
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) as mock_async_post:
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mock_async_post.return_value = mock_response
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model = "vertex_ai/test"
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try:
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response = await litellm.aspeech(
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model=model,
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input="async hello what llm guardrail do you have",
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voice={
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"languageCode": "en-UK",
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"name": "en-UK-Studio-O",
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},
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audioConfig={
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"audioEncoding": "LINEAR22",
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"speakingRate": "10",
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},
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)
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except litellm.APIConnectionError as e:
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if "Your default credentials were not found" in str(e):
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pytest.skip("skipping test, credentials not found")
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# Assert asynchronous call
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mock_async_post.assert_called_once()
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_, kwargs = mock_async_post.call_args
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print("call args", kwargs)
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assert kwargs["url"] == "https://texttospeech.googleapis.com/v1/text:synthesize"
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assert "x-goog-user-project" in kwargs["headers"]
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assert kwargs["headers"]["Authorization"] is not None
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assert kwargs["json"] == {
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"input": {"text": "async hello what llm guardrail do you have"},
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"voice": {"languageCode": "en-UK", "name": "en-UK-Studio-O"},
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"audioConfig": {"audioEncoding": "LINEAR22", "speakingRate": "10"},
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}
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@pytest.mark.asyncio
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async def test_speech_litellm_vertex_async_with_voice_ssml():
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# Mock the response
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mock_response = AsyncMock()
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def return_val():
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return {
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"audioContent": "dGVzdCByZXNwb25zZQ==",
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}
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mock_response.json = return_val
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mock_response.status_code = 200
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ssml = """
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<speak>
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<p>Hello, world!</p>
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<p>This is a test of the <break strength="medium" /> text-to-speech API.</p>
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</speak>
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"""
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# Set up the mock for asynchronous calls
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with patch(
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"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
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new_callable=AsyncMock,
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) as mock_async_post:
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mock_async_post.return_value = mock_response
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model = "vertex_ai/test"
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try:
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response = await litellm.aspeech(
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input=ssml,
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model=model,
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voice={
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"languageCode": "en-UK",
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"name": "en-UK-Studio-O",
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},
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audioConfig={
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"audioEncoding": "LINEAR22",
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"speakingRate": "10",
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},
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)
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except litellm.APIConnectionError as e:
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if "Your default credentials were not found" in str(e):
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pytest.skip("skipping test, credentials not found")
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# Assert asynchronous call
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mock_async_post.assert_called_once()
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_, kwargs = mock_async_post.call_args
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print("call args", kwargs)
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assert kwargs["url"] == "https://texttospeech.googleapis.com/v1/text:synthesize"
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assert "x-goog-user-project" in kwargs["headers"]
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assert kwargs["headers"]["Authorization"] is not None
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assert kwargs["json"] == {
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"input": {"ssml": ssml},
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"voice": {"languageCode": "en-UK", "name": "en-UK-Studio-O"},
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"audioConfig": {"audioEncoding": "LINEAR22", "speakingRate": "10"},
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}
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@pytest.mark.asyncio
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@pytest.mark.flaky(retries=3, delay=1)
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async def test_azure_ava_tts_async():
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"""
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Test Azure AVA (Cognitive Services) Text-to-Speech with real API request.
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"""
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litellm.turn_on_debug()
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api_key = os.getenv("AZURE_TTS_API_KEY")
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api_base = os.getenv("AZURE_TTS_API_BASE")
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speech_file_path = Path(__file__).parent / "azure_speech.mp3"
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try:
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response = await litellm.aspeech(
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model="azure/tts",
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voice="alloy",
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input="Hello, this is a test of Azure text to speech",
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api_base=api_base,
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api_key=api_key,
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response_format="mp3",
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speed=1.0,
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)
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# Assert the response is HttpxBinaryResponseContent
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from litellm.types.llms.openai import HttpxBinaryResponseContent
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assert isinstance(response, HttpxBinaryResponseContent)
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# Get the binary content
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binary_content = response.content
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assert len(binary_content) > 0
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# MP3 files start with these magic bytes
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# ID3 tag or MPEG sync word
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assert (
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binary_content[:3] == b"ID3"
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or binary_content[:2] == b"\xff\xfb"
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or binary_content[:2] == b"\xff\xf3"
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)
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# Write to file
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response.stream_to_file(speech_file_path)
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# Verify file was created and has content
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assert speech_file_path.exists()
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assert speech_file_path.stat().st_size > 0
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print(f"Azure TTS audio saved to: {speech_file_path}")
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except Exception as e:
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pytest.fail(f"Test failed with exception: {str(e)}")
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