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Merge remote-tracking branch 'upstream/main' into codex/pr-live-public-20260917

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4
.circleci/scripts/unit_selection.sh
View file

@ -7,6 +7,8 @@ legacy_flags=(
caching-local
enterprise-package
enterprise-routing
llm-other-providers
llm-vertex-ai
mcp-integration
misc
proxy-db-auth-checks
@ -50,6 +52,8 @@ legacy_paths() {
echo tests/unit/enterprise/proxy/test_file_deletion_blocking.py
echo tests/unit/enterprise/proxy/test_managed_files_access_check.py
echo tests/unit/enterprise/proxy/test_managed_files_hook.py ;;
llm-other-providers) find tests/unit/llms -name 'test_*.py' -not -path 'tests/unit/llms/vertex_ai/*' ;;
llm-vertex-ai) echo tests/unit/llms/vertex_ai ;;
mcp-integration)
echo tests/unit/experimental_mcp_client
echo tests/unit/proxy/_experimental/mcp_server

15
.circleci/tests.yml
View file

@ -354,6 +354,21 @@ workflows:
- proxy-db-endpoints-and-responses
base_ref: << pipeline.event.name == "pull_request" and pipeline.event.github.pull_request.base.ref or "" >>
pull_request_url: << pipeline.event.name == "pull_request" and pipeline.event.github.pull_request.url or "" >>
- unit:
name: unit-llm-vertex-ai
flag: llm-vertex-ai
shards: 2
workers: 1
reruns: 2
base_ref: << pipeline.event.name == "pull_request" and pipeline.event.github.pull_request.base.ref or "" >>
pull_request_url: << pipeline.event.name == "pull_request" and pipeline.event.github.pull_request.url or "" >>
- unit:
name: unit-llm-other-providers
flag: llm-other-providers
shards: 3
reruns: 2
base_ref: << pipeline.event.name == "pull_request" and pipeline.event.github.pull_request.base.ref or "" >>
pull_request_url: << pipeline.event.name == "pull_request" and pipeline.event.github.pull_request.url or "" >>
- unit:
name: unit-misc
flag: misc

6
.github/merge-smoke-tests.json vendored
View file

@ -1,8 +1,8 @@
{
"cases": {
"CHAT-JSON": "tests/test_litellm/llms/openai/test_openai.py::test_acompletion_returns_json_reply_over_injected_transport",
"CHAT-TEXT-STREAM": "tests/test_litellm/llms/openai/test_openai.py::test_acompletion_streams_text_deltas_over_injected_transport",
"CHAT-TOOL-STREAM": "tests/test_litellm/llms/openai/test_openai.py::test_acompletion_streams_tool_call_arguments_over_injected_transport",
"CHAT-JSON": "tests/unit/llms/openai/test_openai.py::test_acompletion_returns_json_reply_over_injected_transport",
"CHAT-TEXT-STREAM": "tests/unit/llms/openai/test_openai.py::test_acompletion_streams_text_deltas_over_injected_transport",
"CHAT-TOOL-STREAM": "tests/unit/llms/openai/test_openai.py::test_acompletion_streams_tool_call_arguments_over_injected_transport",
"MODEL-ALLOW": "tests/test_litellm/proxy/auth/test_auth_checks.py::test_can_object_call_model_allows_listed_model_for_key",
"MODEL-DENY": "tests/test_litellm/proxy/auth/test_auth_checks.py::test_can_object_call_model_denials_return_forbidden[key-key_model_access_denied]",
"COST-EXPLICIT": "tests/unit/test_cost_calculator.py::test_completion_cost_charges_explicit_per_token_rates_over_registered_ones",

2
.github/workflows/test-unit.yml vendored
View file

@ -89,6 +89,7 @@ jobs:
- shard: Vertex AI
artifact-name: llm-vertex-ai
test-path: "tests/test_litellm/llms/vertex_ai"
unit-flag: llm-vertex-ai
workers: 1
reruns: 2
timeout-minutes: 20
@ -97,6 +98,7 @@ jobs:
- shard: All Other Providers
artifact-name: llm-other-providers
test-path: "tests/test_litellm/llms --ignore=tests/test_litellm/llms/vertex_ai"
unit-flag: llm-other-providers
workers: 2
reruns: 2
timeout-minutes: 20

2
Makefile
View file

@ -314,7 +314,7 @@ test-unit: install-test-deps
# Matrix test targets (matching CI workflow groups)
test-unit-llms: install-test-deps
$(UV_RUN) pytest tests/test_litellm/llms --tb=short -vv -n 4 --durations=20
$(UV_RUN) pytest tests/unit/llms --tb=short -vv -n 4 --durations=20
test-unit-proxy-guardrails: install-test-deps
$(UV_RUN) pytest tests/test_litellm/proxy/guardrails tests/test_litellm/proxy/management_endpoints tests/test_litellm/proxy/management_helpers --tb=short -vv -n 4 --durations=20

2
tests/llm_translation/test_bedrock_gpt_oss.py
View file

@ -22,7 +22,7 @@ class TestBedrockGPTOSS(BaseLLMChatTest):
"""Bedrock GPT-OSS intermittently emits truncated toolUse.input deltas on
the live endpoint, which makes the inherited live integration test flaky.
The accumulation side is covered deterministically by
tests/test_litellm/llms/bedrock/chat/test_invoke_handler.py::test_transform_tool_calls_index;
tests/unit/llms/bedrock/chat/test_invoke_handler.py::test_transform_tool_calls_index;
the GPT-OSS-specific request-body transformation is covered by
test_function_calling_request_body_gpt_oss below.
"""

2
tests/local_testing/test_function_calling.py
View file

@ -324,7 +324,7 @@ def test_parallel_function_call_anthropic_error_msg(model, messages):
Anthropic (and Bedrock Invoke via ``AnthropicConfig.transform_request``)
inject a dummy tool so CLIs work with ``modify_params`` left off. Bedrock
Converse's no-raise behavior is covered offline in
``tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py``
``tests/unit/llms/bedrock/chat/test_converse_transformation.py``
(see #24158, #27138), which needs no live credentials.
"""
# Force modify_params off as a clean baseline: it exercises the Anthropic

2
tests/local_testing/test_handler_gc_does_not_close_client.py
View file

@ -17,7 +17,7 @@ body can still arrive, released once the caller is done with the response.
Nothing here re-tests the shapes ``_handler_may_close_client`` covers -- a
borrowed ``handler.client``, a caller-supplied client, an evicted-but-held
client. Those are pinned in ``tests/test_litellm/llms/custom_httpx/
client. Those are pinned in ``tests/unit/llms/custom_httpx/
test_http_handler.py``. What is uncovered there is the in-flight response, so no
test here may keep the client in a local: that inflates the very refcount under
test, and the test then passes on a broken handler. They hold weak references

4
tests/local_testing/test_sagemaker_nova_integration.py
View file

@ -4,7 +4,7 @@ Integration tests for SageMaker Nova provider.
These tests require a live SageMaker Nova endpoint and AWS credentials.
They are skipped by default — run manually with:
pytest tests/test_litellm/llms/sagemaker/test_sagemaker_nova_integration.py -v --no-header -rN
pytest tests/local_testing/test_sagemaker_nova_integration.py -v --no-header -rN
Prerequisites:
export AWS_PROFILE=<your-profile> # or set AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY
@ -251,7 +251,7 @@ class TestSagemakerNova2LiteIntegration:
Run with:
export SAGEMAKER_NOVA2_LITE_ENDPOINT=<your-nova-2-lite-endpoint>
pytest tests/test_litellm/llms/sagemaker/test_sagemaker_nova_integration.py::TestSagemakerNova2LiteIntegration -v
pytest tests/local_testing/test_sagemaker_nova_integration.py::TestSagemakerNova2LiteIntegration -v
"""
def test_should_accept_reasoning_effort_low(self):

2
tests/search_tests/test_bing_grounding_search.py
View file

@ -85,7 +85,7 @@ class TestBingGroundingSearch(BaseSearchTest):
class TestBingGroundingSearchTransformation:
"""
Full-stack tests through `litellm.search` / `litellm.asearch` with the HTTP layer mocked.
Transformation details are unit-tested in tests/test_litellm/llms/azure/search/.
Transformation details are unit-tested in tests/unit/llms/azure/search/.
"""
@pytest.fixture(autouse=True)

2
tests/search_tests/test_nimble_search.py
View file

@ -58,7 +58,7 @@ class TestNimbleSearch(BaseSearchTest):
class TestNimbleSearchTransformation:
"""
Full-stack tests through `litellm.search` / `litellm.asearch` with the HTTP layer mocked.
Transformation details are unit-tested in tests/test_litellm/llms/nimble/search/.
Transformation details are unit-tested in tests/unit/llms/nimble/search/.
"""
@pytest.fixture(autouse=True)

2
tests/test_litellm/integrations/test_helicone.py
View file

@ -13,7 +13,7 @@ def _claude_mapping(messages, response_obj):
def test_claude_mapping_serializes_custom_tool_calls(monkeypatch):
"""
Stub the anthropic module unconditionally: the SDK may be absent (it lives in the
proxy-runtime extra), and the tests/test_litellm/llms/anthropic test package can
proxy-runtime extra), and the tests/unit/llms/anthropic test package can
shadow it on sys.path, so an import probe proves nothing about the real SDK.
"""
stub = types.ModuleType("anthropic")

165
tests/test_litellm/llms/cometapi/chat/test_cometapi_chat_transformation.py
View file

@ -9,171 +9,6 @@ import os
import pytest
from litellm.llms.cometapi.chat.transformation import (
CometAPIChatCompletionStreamingHandler,
CometAPIConfig,
)
from litellm.llms.cometapi.common_utils import CometAPIException
class TestCometAPIChatCompletionStreamingHandler:
def test_chunk_parser_successful(self):
handler = CometAPIChatCompletionStreamingHandler(
streaming_response=None, sync_stream=True
)
# Test input chunk
chunk = {
"id": "test_id",
"created": 1234567890,
"model": "gpt-3.5-turbo",
"usage": {"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30},
"choices": [
{"delta": {"content": "test content", "reasoning": "test reasoning"}}
],
}
# Parse chunk
result = handler.chunk_parser(chunk)
# Verify response
assert result.id == "test_id"
assert result.object == "chat.completion.chunk"
assert result.created == 1234567890
assert result.model == "gpt-3.5-turbo"
assert result.usage.prompt_tokens == chunk["usage"]["prompt_tokens"]
assert result.usage.completion_tokens == chunk["usage"]["completion_tokens"]
assert result.usage.total_tokens == chunk["usage"]["total_tokens"]
assert len(result.choices) == 1
assert result.choices[0]["delta"]["reasoning_content"] == "test reasoning"
def test_chunk_parser_error_response(self):
handler = CometAPIChatCompletionStreamingHandler(
streaming_response=None, sync_stream=True
)
# Test error chunk
error_chunk = {
"error": {
"message": "test error",
"code": 400,
}
}
# Verify error handling
with pytest.raises(CometAPIException) as exc_info:
handler.chunk_parser(error_chunk)
assert "CometAPI Error: test error" in str(exc_info.value)
assert exc_info.value.status_code == 400
def test_chunk_parser_key_error(self):
handler = CometAPIChatCompletionStreamingHandler(
streaming_response=None, sync_stream=True
)
# Test invalid chunk missing required fields
invalid_chunk = {"incomplete": "data"}
# Verify KeyError handling
with pytest.raises(CometAPIException) as exc_info:
handler.chunk_parser(invalid_chunk)
assert "KeyError" in str(exc_info.value)
assert exc_info.value.status_code == 400
class TestCometAPIConfig:
def test_transform_request_basic(self):
"""Test basic request transformation"""
config = CometAPIConfig()
transformed_request = config.transform_request(
model="cometapi/gpt-3.5-turbo",
messages=[{"role": "user", "content": "Hello, world!"}],
optional_params={},
litellm_params={},
headers={},
)
assert transformed_request["model"] == "cometapi/gpt-3.5-turbo"
assert transformed_request["messages"] == [
{"role": "user", "content": "Hello, world!"}
]
def test_transform_request_with_extra_body(self):
"""Test request transformation with extra_body parameters"""
config = CometAPIConfig()
transformed_request = config.transform_request(
model="cometapi/gpt-4",
messages=[{"role": "user", "content": "Hello, world!"}],
optional_params={"extra_body": {"custom_param": "custom_value"}},
litellm_params={},
headers={},
)
# Validate that extra_body parameters are merged into the request
assert transformed_request["custom_param"] == "custom_value"
assert transformed_request["messages"] == [
{"role": "user", "content": "Hello, world!"}
]
def test_cache_control_flag_removal(self):
"""Test cache control flag removal from messages"""
config = CometAPIConfig()
transformed_request = config.transform_request(
model="cometapi/gpt-3.5-turbo",
messages=[
{
"role": "user",
"content": "Hello, world!",
"cache_control": {"type": "ephemeral"},
}
],
optional_params={},
litellm_params={},
headers={},
)
# CometAPI should remove cache_control flags by default
assert transformed_request["messages"][0].get("cache_control") is None
def test_map_openai_params(self):
"""Test OpenAI parameter mapping"""
config = CometAPIConfig()
non_default_params = {
"temperature": 0.7,
"max_tokens": 100,
"top_p": 0.9,
}
mapped_params = config.map_openai_params(
non_default_params=non_default_params,
optional_params={},
model="cometapi/gpt-3.5-turbo",
drop_params=False,
)
assert mapped_params["temperature"] == 0.7
assert mapped_params["max_tokens"] == 100
assert mapped_params["top_p"] == 0.9
def test_get_error_class(self):
"""Test error class creation"""
config = CometAPIConfig()
error = config.get_error_class(
error_message="Test error",
status_code=400,
headers={"Content-Type": "application/json"},
)
assert isinstance(error, CometAPIException)
assert error.message == "Test error"
assert error.status_code == 400
# Integration test example (requires real API key)

79
tests/test_litellm/llms/databricks/chat/test_databricks_chat_transformation.py
View file

@ -1,79 +0,0 @@
import json
from typing import Final
import httpx
import respx
import litellm
def test_completion_merges_leading_system_and_developer_messages_for_chat_template_models(
respx_mock: respx.MockRouter,
):
upstream: Final = respx_mock.post("https://example.databricks.test/serving-endpoints/chat/completions").mock(
return_value=httpx.Response(
status_code=200,
json={
"id": "chatcmpl-123",
"object": "chat.completion",
"created": 1677652288,
"model": "my-custom-model",
"choices": [{"index": 0, "message": {"role": "assistant", "content": "Answer"}, "finish_reason": "stop"}],
"usage": {"prompt_tokens": 9, "completion_tokens": 1, "total_tokens": 10},
},
)
)
response: Final = litellm.completion(
model="databricks/my-custom-model",
messages=[
{"role": "system", "content": "You are terse."},
{"role": "developer", "content": "Skills: none."},
{"role": "user", "content": "Hello"},
],
api_base="https://example.databricks.test/serving-endpoints",
api_key="fake-databricks-api-key",
num_retries=0,
)
assert upstream.call_count == 1
request_body: Final = json.loads(upstream.calls[0].request.read())
assert request_body["messages"] == [
{"role": "system", "content": "You are terse.\n\nSkills: none."},
{"role": "user", "content": "Hello"},
]
assert response.choices[0].message.content == "Answer"
def test_completion_merges_system_messages_when_one_has_empty_content(respx_mock: respx.MockRouter):
upstream: Final = respx_mock.post("https://example.databricks.test/serving-endpoints/chat/completions").mock(
return_value=httpx.Response(
status_code=200,
json={
"id": "chatcmpl-123",
"object": "chat.completion",
"created": 1677652288,
"model": "my-custom-model",
"choices": [{"index": 0, "message": {"role": "assistant", "content": "Answer"}, "finish_reason": "stop"}],
"usage": {"prompt_tokens": 9, "completion_tokens": 1, "total_tokens": 10},
},
)
)
litellm.completion(
model="databricks/my-custom-model",
messages=[
{"role": "system", "content": "You are terse."},
{"role": "system", "content": ""},
{"role": "user", "content": "Hello"},
],
api_base="https://example.databricks.test/serving-endpoints",
api_key="fake-databricks-api-key",
num_retries=0,
)
request_body: Final = json.loads(upstream.calls[0].request.read())
assert request_body["messages"] == [
{"role": "system", "content": "You are terse."},
{"role": "user", "content": "Hello"},
]

433
tests/test_litellm/llms/deepinfra/test_deepinfra_rerank_integration.py
View file

@ -1,433 +0,0 @@
"""
Integration tests for DeepInfra rerank functionality.
Tests the full rerank flow following the repository patterns.
"""
import asyncio
import json
from unittest.mock import AsyncMock, MagicMock, patch
import pytest
import litellm
def assert_response_shape(response, custom_llm_provider):
"""Helper function to validate response structure specific to DeepInfra."""
assert hasattr(response, "id")
assert hasattr(response, "results")
assert hasattr(response, "meta")
assert isinstance(response.results, list)
for result in response.results:
assert "index" in result
assert "relevance_score" in result
assert isinstance(result["index"], int)
assert isinstance(result["relevance_score"], (int, float))
# Check meta structure
assert "tokens" in response.meta
assert "billed_units" in response.meta
assert "input_tokens" in response.meta["tokens"]
assert "total_tokens" in response.meta["billed_units"]
@pytest.mark.parametrize("sync_mode", [True, False])
@patch("litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post")
@patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post")
def test_basic_rerank_deepinfra(mock_sync_post, mock_async_post, sync_mode):
"""Test basic DeepInfra rerank functionality."""
# Mock response data that matches DeepInfra API format
mock_response_data = {
"scores": [0.9, 0.1],
"input_tokens": 25,
"request_id": "deepinfra-request-123",
"inference_status": {
"status": "success",
"runtime_ms": 150,
"cost": 0.0001,
"tokens_generated": 0,
"tokens_input": 25,
},
}
def return_val():
return mock_response_data
api_key = "test_deepinfra_api_key"
api_base = "https://api.deepinfra.com"
if sync_mode:
# Create mock response object for sync
mock_response = MagicMock()
mock_response.json = return_val
mock_response.status_code = 200
mock_response.headers = {"content-type": "application/json"}
mock_response.text = json.dumps(mock_response_data)
mock_sync_post.return_value = mock_response
response = litellm.rerank(
model="deepinfra/Qwen/Qwen3-Reranker-0.6B",
query="hello",
documents=["hello", "world"],
top_n=2,
custom_llm_provider="deepinfra",
api_key=api_key,
api_base=api_base,
)
mock_sync_post.assert_called_once()
else:
# Create mock response object for async
mock_response = AsyncMock()
def return_val():
return mock_response_data
mock_response.json = return_val
mock_response.status_code = 200
mock_response.headers = {"content-type": "application/json"}
mock_response.text = json.dumps(mock_response_data)
mock_async_post.return_value = mock_response
response = asyncio.run(
litellm.arerank(
model="deepinfra/Qwen/Qwen3-Reranker-0.6B",
query="hello",
documents=["hello", "world"],
top_n=2,
custom_llm_provider="deepinfra",
api_key=api_key,
api_base=api_base,
)
)
mock_async_post.assert_called_once()
# Verify response structure
assert response.id == "deepinfra-request-123"
assert response.results is not None
assert len(response.results) == 2
assert response.results[0]["index"] == 0
assert response.results[0]["relevance_score"] == 0.9
assert response.results[1]["index"] == 1
assert response.results[1]["relevance_score"] == 0.1
# Verify metadata
assert response.meta["tokens"]["input_tokens"] == 25
assert response.meta["billed_units"]["total_tokens"] == 25
# Verify hidden params specific to DeepInfra
assert response._hidden_params["status"] == "success"
assert response._hidden_params["runtime_ms"] == 150
assert response._hidden_params["cost"] == 0.0001
# Note: The model name is processed and the 'deepinfra/' prefix is removed
assert response._hidden_params["model"] == "Qwen/Qwen3-Reranker-0.6B"
assert_response_shape(response, custom_llm_provider="deepinfra")
@pytest.mark.parametrize("sync_mode", [True, False])
@patch("litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post")
@patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post")
def test_deepinfra_rerank_with_queries_param(
mock_sync_post, mock_async_post, sync_mode
):
"""Test DeepInfra rerank with multiple queries parameter."""
mock_response_data = {
"scores": [0.8, 0.6, 0.2],
"input_tokens": 35,
"request_id": "deepinfra-multi-query-123",
"inference_status": {"status": "success", "runtime_ms": 200},
}
def return_val():
return mock_response_data
if sync_mode:
mock_response = MagicMock()
mock_response.json = return_val
mock_response.status_code = 200
mock_response.headers = {"content-type": "application/json"}
mock_response.text = json.dumps(mock_response_data)
mock_sync_post.return_value = mock_response
response = litellm.rerank(
model="deepinfra/Qwen/Qwen3-Reranker-4B",
query="hello",
documents=["hello", "world", "test"],
queries=["hello", "hi there"], # DeepInfra specific param
custom_llm_provider="deepinfra",
api_key="test_key",
api_base="https://api.deepinfra.com",
)
mock_sync_post.assert_called_once()
# Verify that queries parameter was passed in request
call_data = json.loads(mock_sync_post.call_args.kwargs["data"])
assert "queries" in call_data
assert call_data["queries"] == ["hello", "hi there"]
else:
mock_response = AsyncMock()
mock_response.json = return_val
mock_response.status_code = 200
mock_response.headers = {"content-type": "application/json"}
mock_response.text = json.dumps(mock_response_data)
mock_async_post.return_value = mock_response
response = asyncio.run(
litellm.arerank(
model="deepinfra/Qwen/Qwen3-Reranker-4B",
query="hello",
documents=["hello", "world", "test"],
queries=["hello", "hi there"],
custom_llm_provider="deepinfra",
api_key="test_key",
api_base="https://api.deepinfra.com",
)
)
mock_async_post.assert_called_once()
call_data = json.loads(mock_async_post.call_args.kwargs["data"])
assert "queries" in call_data
assert call_data["queries"] == ["hello", "hi there"]
assert response.results is not None
assert len(response.results) == 3
@patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post")
def test_deepinfra_rerank_with_service_tier(mock_post):
"""Test DeepInfra rerank with service_tier parameter."""
mock_response_data = {
"scores": [0.95, 0.75],
"input_tokens": 30,
"request_id": "deepinfra-premium-123",
}
def return_val():
return mock_response_data
mock_response = MagicMock()
mock_response.json = return_val
mock_response.status_code = 200
mock_response.headers = {"content-type": "application/json"}
mock_response.text = json.dumps(mock_response_data)
mock_post.return_value = mock_response
response = litellm.rerank(
model="deepinfra/Qwen/Qwen3-Reranker-8B",
query="premium search",
documents=["doc1", "doc2"],
service_tier="premium", # DeepInfra specific param
custom_llm_provider="deepinfra",
api_key="test_key",
api_base="https://api.deepinfra.com",
)
mock_post.assert_called_once()
# Verify URL
call_url = mock_post.call_args.kwargs["url"]
assert "api.deepinfra.com/inference/Qwen/Qwen3-Reranker-8B" in call_url
# Verify request contains service_tier
call_data = json.loads(mock_post.call_args.kwargs["data"])
assert call_data["service_tier"] == "premium"
assert response.results is not None
@patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post")
def test_deepinfra_rerank_with_env_vars(mock_post, monkeypatch):
"""Test DeepInfra rerank with environment variable configuration."""
monkeypatch.setenv("DEEPINFRA_API_KEY", "env_test_key")
monkeypatch.setenv("DEEPINFRA_API_BASE", "https://custom-deepinfra.com")
mock_response_data = {
"scores": [0.88, 0.22],
"input_tokens": 28,
"request_id": "env-test-123",
}
def return_val():
return mock_response_data
mock_response = MagicMock()
mock_response.json = return_val
mock_response.status_code = 200
mock_response.headers = {"content-type": "application/json"}
mock_response.text = json.dumps(mock_response_data)
mock_post.return_value = mock_response
response = litellm.rerank(
model="deepinfra/Qwen/Qwen3-Reranker-0.6B",
query="hello",
documents=["hello", "world"],
custom_llm_provider="deepinfra",
)
mock_post.assert_called_once()
# Verify headers contain env API key
headers = mock_post.call_args.kwargs.get("headers", {})
assert "Bearer env_test_key" in headers.get("Authorization", "")
assert response.results is not None
@patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post")
def test_deepinfra_rerank_error_handling(mock_post):
"""Test DeepInfra rerank error handling."""
error_response = {"detail": {"error": "Invalid API key"}}
def return_val():
return error_response
mock_response = MagicMock()
mock_response.status_code = 401
mock_response.json = return_val
mock_response.text = json.dumps(error_response)
mock_response.headers = {"content-type": "application/json"}
mock_post.return_value = mock_response
# The current implementation handles errors gracefully, so we expect a successful response
# with the error information in the hidden params
response = litellm.rerank(
model="deepinfra/Qwen/Qwen3-Reranker-0.6B",
query="hello",
documents=["hello", "world"],
custom_llm_provider="deepinfra",
api_key="invalid_key",
api_base="https://api.deepinfra.com",
)
# Verify that the response contains error information
assert (
response._hidden_params["status"] == "unknown"
) # Default status when error occurs
@patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post")
def test_deepinfra_rerank_defaults_api_base_when_missing(mock_post, monkeypatch):
"""With no api_base anywhere, the call still goes out against DeepInfra's own base."""
monkeypatch.delenv("DEEPINFRA_API_BASE", raising=False)
mock_response = MagicMock()
mock_response.json = lambda: {"scores": [0.9, 0.1], "input_tokens": 20}
mock_response.status_code = 200
mock_response.headers = {"content-type": "application/json"}
mock_post.return_value = mock_response
response = litellm.rerank(
model="deepinfra/Qwen/Qwen3-Reranker-0.6B",
query="hello",
documents=["hello", "world"],
custom_llm_provider="deepinfra",
api_key="test_key",
# api_base is intentionally missing
)
assert "api.deepinfra.com" in mock_post.call_args.kwargs["url"]
assert [result["relevance_score"] for result in response.results] == [0.9, 0.1]
@patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post")
def test_deepinfra_rerank_request_format(mock_post):
"""Test that the request is properly formatted for DeepInfra API."""
mock_response_data = {"scores": [0.9, 0.1], "input_tokens": 20}
def return_val():
return mock_response_data
mock_response = MagicMock()
mock_response.json = return_val
mock_response.status_code = 200
mock_response.headers = {"content-type": "application/json"}
mock_response.text = json.dumps(mock_response_data)
mock_post.return_value = mock_response
response = litellm.rerank(
model="deepinfra/Qwen/Qwen3-Reranker-0.6B",
query="test query",
documents=["doc1", "doc2"],
custom_llm_provider="deepinfra",
api_key="test_key",
api_base="https://api.deepinfra.com",
instruction="custom instruction",
webhook="https://webhook.example.com",
)
mock_post.assert_called_once()
# Verify URL format
call_url = mock_post.call_args.kwargs["url"]
assert call_url == "https://api.deepinfra.com/inference/Qwen/Qwen3-Reranker-0.6B"
# Verify headers
headers = mock_post.call_args.kwargs["headers"]
assert headers["Authorization"] == "Bearer test_key"
assert headers["accept"] == "application/json"
assert headers["content-type"] == "application/json"
# Verify request body format
request_data = json.loads(mock_post.call_args.kwargs["data"])
assert request_data["queries"] == [
"test query",
"test query",
] # DeepInfra requires queries to match documents length
assert request_data["documents"] == ["doc1", "doc2"]
assert request_data["instruction"] == "custom instruction"
assert request_data["webhook"] == "https://webhook.example.com"
assert response.results is not None
def test_deepinfra_rerank_models():
"""Test that DeepInfra Qwen rerank models are recognized."""
# These should not raise errors during model validation
models = [
"deepinfra/Qwen/Qwen3-Reranker-0.6B",
"deepinfra/Qwen/Qwen3-Reranker-4B",
"deepinfra/Qwen/Qwen3-Reranker-8B",
]
for model in models:
resolved_model, provider, _, api_base = litellm.get_llm_provider(model=model)
assert provider == "deepinfra"
assert resolved_model == model.removeprefix("deepinfra/")
assert api_base == "https://api.deepinfra.com/v1/openai"
@patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post")
def test_deepinfra_rerank_minimal_response(mock_post):
"""Test handling of minimal DeepInfra response."""
# Minimal response with just scores
mock_response_data = {"scores": [0.7, 0.3]}
def return_val():
return mock_response_data
mock_response = MagicMock()
mock_response.json = return_val
mock_response.status_code = 200
mock_response.headers = {"content-type": "application/json"}
mock_response.text = json.dumps(mock_response_data)
mock_post.return_value = mock_response
response = litellm.rerank(
model="deepinfra/Qwen/Qwen3-Reranker-0.6B",
query="hello",
documents=["hello", "world"],
custom_llm_provider="deepinfra",
api_key="test_key",
api_base="https://api.deepinfra.com",
)
# Should handle minimal response gracefully
assert response.results is not None
assert len(response.results) == 2
assert response.results[0]["relevance_score"] == 0.7
assert response.results[1]["relevance_score"] == 0.3
# Should have default values for missing fields
assert response.meta["tokens"]["input_tokens"] == 0 # Default when missing
assert response._hidden_params["status"] == "unknown" # Default when missing

1
tests/test_litellm/llms/gemini/files/__init__.py
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@ -1 +0,0 @@
"""Tests for Gemini files functionality"""

1
tests/test_litellm/llms/gemini/videos/__init__.py
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@ -1 +0,0 @@
# Gemini Video Generation Tests

1
tests/test_litellm/llms/manus/__init__.py
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@ -1 +0,0 @@
# Manus provider tests

1
tests/test_litellm/llms/manus/responses/__init__.py
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@ -1 +0,0 @@
# Manus Responses API tests

1
tests/test_litellm/llms/minimax/__init__.py
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@ -1 +0,0 @@
# MiniMax tests

1
tests/test_litellm/llms/minimax/chat/__init__.py
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@ -1 +0,0 @@
# MiniMax chat tests

1
tests/test_litellm/llms/minimax/messages/__init__.py
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@ -1 +0,0 @@
# MiniMax messages tests

191
tests/test_litellm/llms/mistral/audio_transcription/test_mistral_audio_transcription_transformation.py
View file

@ -1,19 +1,9 @@
import os
from typing import Dict
from unittest.mock import MagicMock
import httpx
import litellm
import pytest
from litellm.llms.base_llm.audio_transcription.transformation import (
BaseAudioTranscriptionConfig,
)
from litellm.llms.mistral.audio_transcription.transformation import (
MistralAudioTranscriptionConfig,
)
from litellm.types.utils import TranscriptionResponse
from litellm.utils import ProviderConfigManager
from tests.llm_translation.base_audio_transcription_unit_tests import (
BaseLLMAudioTranscriptionTest,
)
@ -37,184 +27,3 @@ class TestMistralAudioTranscription(BaseLLMAudioTranscriptionTest):
"Async audio transcription test for Mistral is skipped in this suite; "
"async test plugins (e.g. pytest-asyncio/anyio) are not configured here."
)
def test_mistral_audio_transcription_config_installed():
"""Ensure Mistral audio transcription config is registered with ProviderConfigManager."""
config = ProviderConfigManager.get_provider_audio_transcription_config(
model="mistral/voxtral-mini-latest",
provider=litellm.LlmProviders.MISTRAL,
)
assert config is not None
assert isinstance(config, BaseAudioTranscriptionConfig)
assert isinstance(config, MistralAudioTranscriptionConfig)
def test_mistral_audio_transcription_get_complete_url():
config = MistralAudioTranscriptionConfig()
url = config.get_complete_url(
api_base=None,
api_key="fake-key",
model="voxtral-mini-latest",
optional_params={},
litellm_params={},
)
assert url == "https://api.mistral.ai/v1/audio/transcriptions"
def test_mistral_audio_transcription_get_complete_url_custom_base():
config = MistralAudioTranscriptionConfig()
url = config.get_complete_url(
api_base="https://custom.api.example.com/v1/",
api_key="fake-key",
model="voxtral-mini-latest",
optional_params={},
litellm_params={},
)
assert url == "https://custom.api.example.com/v1/audio/transcriptions"
def test_mistral_audio_transcription_validate_environment():
config = MistralAudioTranscriptionConfig()
headers = config.validate_environment(
headers={},
model="voxtral-mini-latest",
messages=[],
optional_params={},
litellm_params={},
api_key="test-key-123",
)
assert headers["Authorization"] == "Bearer test-key-123"
assert headers["accept"] == "application/json"
def test_mistral_audio_transcription_supported_params():
config = MistralAudioTranscriptionConfig()
params = config.get_supported_openai_params("voxtral-mini-latest")
assert "language" in params
assert "temperature" in params
assert "response_format" in params
assert "timestamp_granularities" in params
def test_mistral_audio_transcription_request_transform():
config = MistralAudioTranscriptionConfig()
wav_path = os.path.join(
os.path.dirname(__file__),
"../../../../..",
"tests",
"llm_translation",
"gettysburg.wav",
)
audio_file = open(wav_path, "rb")
result = config.transform_audio_transcription_request(
model="voxtral-mini-latest",
audio_file=audio_file,
optional_params={"language": "en", "temperature": 0.0},
litellm_params={},
)
audio_file.close()
assert isinstance(result.data, dict)
assert result.data["model"] == "voxtral-mini-latest"
assert result.data["language"] == "en"
assert result.data["temperature"] == 0.0
assert result.files is not None
assert "file" in result.files
def test_mistral_audio_transcription_request_with_diarize():
"""Test that Mistral-specific params like diarize are passed through."""
config = MistralAudioTranscriptionConfig()
wav_path = os.path.join(
os.path.dirname(__file__),
"../../../../..",
"tests",
"llm_translation",
"gettysburg.wav",
)
audio_file = open(wav_path, "rb")
result = config.transform_audio_transcription_request(
model="voxtral-mini-latest",
audio_file=audio_file,
optional_params={"diarize": True},
litellm_params={},
)
audio_file.close()
assert isinstance(result.data, dict)
assert result.data["diarize"] == "true"
def test_mistral_audio_transcription_response_transform():
config = MistralAudioTranscriptionConfig()
mock_response = MagicMock(spec=httpx.Response)
mock_response.json.return_value = {"text": "Four score and seven years ago..."}
response = config.transform_audio_transcription_response(mock_response)
assert isinstance(response, TranscriptionResponse)
assert response.text == "Four score and seven years ago..."
def test_mistral_audio_transcription_response_transform_diarized():
"""Test that diarized responses preserve segments and language."""
config = MistralAudioTranscriptionConfig()
mock_response = MagicMock(spec=httpx.Response)
mock_response.json.return_value = {
"model": "voxtral-mini-latest",
"text": "Hello, how are you? I am fine.",
"language": None,
"segments": [
{
"text": "Hello, how are you?",
"start": 0.3,
"end": 2.1,
"speaker_id": "speaker_1",
"type": "transcription_segment",
},
{
"text": "I am fine.",
"start": 2.5,
"end": 3.8,
"speaker_id": "speaker_2",
"type": "transcription_segment",
},
],
"usage": {
"prompt_audio_seconds": 4,
"prompt_tokens": 5,
"total_tokens": 50,
"completion_tokens": 20,
},
}
response = config.transform_audio_transcription_response(mock_response)
assert isinstance(response, TranscriptionResponse)
assert response.text == "Hello, how are you? I am fine."
assert response["segments"] is not None
assert len(response["segments"]) == 2
assert response["segments"][0]["speaker_id"] == "speaker_1"
assert response["segments"][1]["speaker_id"] == "speaker_2"
assert response["language"] is None
def test_mistral_audio_transcription_response_transform_empty():
config = MistralAudioTranscriptionConfig()
mock_response = MagicMock(spec=httpx.Response)
mock_response.json.return_value = {}
response = config.transform_audio_transcription_response(mock_response)
assert isinstance(response, TranscriptionResponse)
assert response.text == ""

363
tests/test_litellm/llms/openai_like/test_json_providers.py
View file

@ -3,321 +3,12 @@ Tests for JSON-based provider configuration system.
"""
import os
import sys
from unittest.mock import patch
try:
import pytest
except ImportError:
# pytest not available, will run as standalone script
pytest = None
# Add workspace to path
workspace_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../.."))
sys.path.insert(0, workspace_path)
import pytest
import litellm
class TestJSONProviderLoader:
"""Test JSON provider loading and configuration"""
def test_load_json_providers(self):
"""Test that JSON providers load correctly"""
from litellm.llms.openai_like.json_loader import JSONProviderRegistry
# Verify publicai is loaded
assert JSONProviderRegistry.exists("publicai")
# Get publicai config
publicai = JSONProviderRegistry.get("publicai")
assert publicai is not None
assert publicai.base_url == "https://api.publicai.co/v1"
assert publicai.api_key_env == "PUBLICAI_API_KEY"
assert publicai.api_base_env == "PUBLICAI_API_BASE"
assert publicai.param_mappings.get("max_completion_tokens") == "max_tokens"
def test_dynamic_config_generation(self):
"""Test dynamic config class creation"""
from litellm.llms.openai_like.dynamic_config import create_config_class
from litellm.llms.openai_like.json_loader import JSONProviderRegistry
provider = JSONProviderRegistry.get("publicai")
config_class = create_config_class(provider)
config = config_class()
# Test API info resolution
api_base, api_key = config._get_openai_compatible_provider_info(None, None)
assert api_base == "https://api.publicai.co/v1"
# Test with custom base
api_base, api_key = config._get_openai_compatible_provider_info(
"https://custom.api.com", "test-key"
)
assert api_base == "https://custom.api.com"
assert api_key == "test-key"
def test_parameter_mapping(self):
"""Test parameter mapping works"""
from litellm.llms.openai_like.dynamic_config import create_config_class
from litellm.llms.openai_like.json_loader import JSONProviderRegistry
provider = JSONProviderRegistry.get("publicai")
config_class = create_config_class(provider)
config = config_class()
# Test parameter mapping
optional_params = {}
non_default_params = {"max_completion_tokens": 100, "temperature": 0.7}
result = config.map_openai_params(
non_default_params, optional_params, "gpt-4", False
)
# max_completion_tokens should be mapped to max_tokens
assert "max_tokens" in result
assert result["max_tokens"] == 100
assert "max_completion_tokens" not in result
# temperature should be passed through
assert result["temperature"] == 0.7
def test_supported_params(self):
"""Test that config returns supported params"""
from litellm.llms.openai_like.dynamic_config import create_config_class
from litellm.llms.openai_like.json_loader import JSONProviderRegistry
provider = JSONProviderRegistry.get("publicai")
config_class = create_config_class(provider)
config = config_class()
# Get supported params
supported = config.get_supported_openai_params("gpt-4")
# Should have standard OpenAI params
assert isinstance(supported, list)
assert len(supported) > 0
def test_tool_params_excluded_when_function_calling_not_supported(self):
"""Test that tool-related params are excluded for models that don't support
function calling. Regression test for https://github.com/BerriAI/litellm/issues/21125
"""
from litellm.llms.openai_like.dynamic_config import create_config_class
from litellm.llms.openai_like.json_loader import JSONProviderRegistry
provider = JSONProviderRegistry.get("publicai")
config_class = create_config_class(provider)
config = config_class()
# Mock supports_function_calling to return False
with patch("litellm.utils.supports_function_calling", return_value=False):
supported = config.get_supported_openai_params("some-model-without-fc")
tool_params = [
"tools",
"tool_choice",
"function_call",
"functions",
"parallel_tool_calls",
]
for param in tool_params:
assert (
param not in supported
), f"'{param}' should not be in supported params when function calling is not supported"
# Non-tool params should still be present
assert "temperature" in supported
assert "max_tokens" in supported
assert "stop" in supported
def test_tool_params_included_when_function_calling_supported(self):
"""Test that tool-related params are included for models that support function calling."""
from litellm.llms.openai_like.dynamic_config import create_config_class
from litellm.llms.openai_like.json_loader import JSONProviderRegistry
provider = JSONProviderRegistry.get("publicai")
config_class = create_config_class(provider)
config = config_class()
# Mock supports_function_calling to return True
with patch("litellm.utils.supports_function_calling", return_value=True):
supported = config.get_supported_openai_params("some-model-with-fc")
assert "tools" in supported
assert "tool_choice" in supported
def test_provider_resolution(self):
"""Test that provider resolution finds JSON providers"""
from litellm.litellm_core_utils.get_llm_provider_logic import (
get_llm_provider,
)
model, provider, api_key, api_base = get_llm_provider(
model="publicai/gpt-4",
custom_llm_provider=None,
api_base=None,
api_key=None,
)
assert model == "gpt-4"
assert provider == "publicai"
assert api_base == "https://api.publicai.co/v1"
def test_provider_config_manager(self):
"""Test that ProviderConfigManager returns JSON-based configs"""
from litellm import LlmProviders
from litellm.utils import ProviderConfigManager
config = ProviderConfigManager.get_provider_chat_config(
model="gpt-4", provider=LlmProviders.PUBLICAI
)
assert config is not None
assert config.custom_llm_provider == "publicai"
class TestPinstripes:
"""Tests for Pinstripes JSON-configured provider"""
def test_pinstripes_json_config_exists(self):
"""Test that pinstripes is configured in providers.json"""
from litellm.llms.openai_like.json_loader import JSONProviderRegistry
assert JSONProviderRegistry.exists("pinstripes")
pinstripes = JSONProviderRegistry.get("pinstripes")
assert pinstripes is not None
assert pinstripes.base_url == "https://pinstripes.io/v1"
assert pinstripes.api_key_env == "PINSTRIPES_API_KEY"
assert pinstripes.param_mappings.get("max_completion_tokens") == "max_tokens"
def test_pinstripes_provider_resolution(self):
"""Test that provider resolution finds pinstripes and returns the default base URL"""
from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
model, provider, api_key, api_base = get_llm_provider(
model="pinstripes/ps/glm-4.5-air",
custom_llm_provider=None,
api_base=None,
api_key=None,
)
assert model == "ps/glm-4.5-air"
assert provider == "pinstripes"
assert api_base == "https://pinstripes.io/v1"
def test_pinstripes_dynamic_config(self):
"""Test dynamic config class creation for pinstripes"""
from litellm.llms.openai_like.dynamic_config import create_config_class
from litellm.llms.openai_like.json_loader import JSONProviderRegistry
provider = JSONProviderRegistry.get("pinstripes")
config_class = create_config_class(provider)
config = config_class()
api_base, api_key = config._get_openai_compatible_provider_info(None, None)
assert api_base == "https://pinstripes.io/v1"
api_base, api_key = config._get_openai_compatible_provider_info(
"https://custom.pinstripes.io/v1", "test-key"
)
assert api_base == "https://custom.pinstripes.io/v1"
assert api_key == "test-key"
def test_pinstripes_parameter_mapping(self):
"""Test that max_completion_tokens is mapped to max_tokens for pinstripes"""
from litellm.llms.openai_like.dynamic_config import create_config_class
from litellm.llms.openai_like.json_loader import JSONProviderRegistry
provider = JSONProviderRegistry.get("pinstripes")
config_class = create_config_class(provider)
config = config_class()
optional_params = {}
non_default_params = {"max_completion_tokens": 100, "temperature": 0.7}
result = config.map_openai_params(
non_default_params, optional_params, "ps/glm-4.5-air", False
)
assert "max_tokens" in result
assert result["max_tokens"] == 100
assert "max_completion_tokens" not in result
assert result["temperature"] == 0.7
class TestDarkbloom:
def test_darkbloom_json_config_exists(self):
from litellm.llms.openai_like.json_loader import JSONProviderRegistry
darkbloom = JSONProviderRegistry.get("darkbloom")
assert darkbloom is not None
assert darkbloom.base_url == "https://api.darkbloom.dev/v1"
assert darkbloom.api_key_env == "DARKBLOOM_API_KEY"
assert darkbloom.api_base_env == "DARKBLOOM_API_BASE"
assert darkbloom.param_mappings.get("max_completion_tokens") == "max_tokens"
def test_darkbloom_provider_resolution(self):
from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
model, provider, api_key, api_base = get_llm_provider(
model="darkbloom/gemma-4-26b",
custom_llm_provider=None,
api_base=None,
api_key=None,
)
assert model == "gemma-4-26b"
assert provider == "darkbloom"
assert api_key is None
assert api_base == "https://api.darkbloom.dev/v1"
def test_darkbloom_dynamic_config(self):
from litellm.llms.openai_like.dynamic_config import create_config_class
from litellm.llms.openai_like.json_loader import JSONProviderRegistry
provider = JSONProviderRegistry.get("darkbloom")
config_class = create_config_class(provider)
config = config_class()
api_base, api_key = config._get_openai_compatible_provider_info(None, None)
assert api_base == "https://api.darkbloom.dev/v1"
api_base, api_key = config._get_openai_compatible_provider_info(
"https://custom.darkbloom.dev/v1", "test-key"
)
assert api_base == "https://custom.darkbloom.dev/v1"
assert api_key == "test-key"
def test_darkbloom_complete_url_appends_endpoint(self):
from litellm.llms.openai_like.dynamic_config import create_config_class
from litellm.llms.openai_like.json_loader import JSONProviderRegistry
provider = JSONProviderRegistry.get("darkbloom")
config_class = create_config_class(provider)
config = config_class()
url = config.get_complete_url(
api_base="https://api.darkbloom.dev/v1",
api_key="test-key",
model="darkbloom/gemma-4-26b",
optional_params={},
litellm_params={},
stream=True,
)
assert url == "https://api.darkbloom.dev/v1/chat/completions"
def test_darkbloom_provider_config_manager(self):
from litellm import LlmProviders
from litellm.utils import ProviderConfigManager
config = ProviderConfigManager.get_provider_chat_config(
model="gemma-4-26b", provider=LlmProviders.DARKBLOOM
)
assert config is not None
assert config.custom_llm_provider == "darkbloom"
class TestPublicAIIntegration:
"""Integration tests for PublicAI provider"""
@ -457,55 +148,3 @@ class TestPublicAIIntegration:
pytest.fail(f"Content list conversion test failed: {str(e)}")
else:
raise
if __name__ == "__main__":
# Run basic tests
print("Testing JSON Provider System...")
test_loader = TestJSONProviderLoader()
print("\n1. Testing JSON provider loading...")
test_loader.test_load_json_providers()
print(" ✓ JSON providers loaded")
print("\n2. Testing dynamic config generation...")
test_loader.test_dynamic_config_generation()
print(" ✓ Dynamic config works")
print("\n3. Testing parameter mapping...")
test_loader.test_parameter_mapping()
print(" ✓ Parameter mapping works")
print("\n4. Testing excluded params...")
test_loader.test_excluded_params()
print(" ✓ Excluded params work")
print("\n5. Testing provider resolution...")
test_loader.test_provider_resolution()
print(" ✓ Provider resolution works")
print("\n6. Testing provider config manager...")
test_loader.test_provider_config_manager()
print(" ✓ Config manager works")
print("\n" + "=" * 50)
print("PublicAI Integration Tests...")
print("=" * 50)
test_integration = TestPublicAIIntegration()
print("\n7. Testing basic completion...")
test_integration.test_publicai_completion_basic()
print("\n8. Testing streaming...")
test_integration.test_publicai_completion_with_streaming()
print("\n9. Testing parameter mapping...")
test_integration.test_publicai_parameter_mapping()
print("\n10. Testing content list conversion...")
test_integration.test_publicai_content_list_conversion()
print("\n" + "=" * 50)
print("✓ All tests passed!")
print("=" * 50)

103
tests/test_litellm/llms/openai_like/test_xiaomi_mimo.py
View file

@ -4,86 +4,12 @@ Related to issue #18794
"""
import os
import sys
from unittest.mock import MagicMock, patch
try:
import pytest
except ImportError:
pytest = None
# Add workspace to path
workspace_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../.."))
sys.path.insert(0, workspace_path)
import pytest
import litellm
class TestXiaomiMiMoProviderConfig:
"""Test Xiaomi MiMo provider configuration"""
def test_xiaomi_mimo_in_provider_list(self):
"""Test that xiaomi_mimo is in the provider list (fixes #18794)"""
from litellm import LlmProviders
# Verify xiaomi_mimo is in the enum
assert hasattr(LlmProviders, "XIAOMI_MIMO")
assert LlmProviders.XIAOMI_MIMO.value == "xiaomi_mimo"
# Verify it's in the provider list
assert "xiaomi_mimo" in litellm.provider_list
def test_xiaomi_mimo_json_config_exists(self):
"""Test that xiaomi_mimo is configured in providers.json"""
from litellm.llms.openai_like.json_loader import JSONProviderRegistry
# Verify xiaomi_mimo is loaded
assert JSONProviderRegistry.exists("xiaomi_mimo")
# Get xiaomi_mimo config
xiaomi_mimo = JSONProviderRegistry.get("xiaomi_mimo")
assert xiaomi_mimo is not None
assert xiaomi_mimo.base_url == "https://api.xiaomimimo.com/v1"
assert xiaomi_mimo.api_key_env == "XIAOMI_MIMO_API_KEY"
assert xiaomi_mimo.param_mappings.get("max_completion_tokens") == "max_tokens"
def test_xiaomi_mimo_provider_resolution(self):
"""Test that provider resolution finds xiaomi_mimo"""
from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
model, provider, api_key, api_base = get_llm_provider(
model="xiaomi_mimo/mimo-v2-flash",
custom_llm_provider=None,
api_base=None,
api_key=None,
)
assert model == "mimo-v2-flash"
assert provider == "xiaomi_mimo"
assert api_base == "https://api.xiaomimimo.com/v1"
def test_xiaomi_mimo_router_config(self):
"""Test that xiaomi_mimo can be used in Router configuration (fixes #18794)"""
from litellm import Router
# This should not raise "Unsupported provider - xiaomi_mimo"
router = Router(
model_list=[
{
"model_name": "mimo-v2-flash",
"litellm_params": {
"model": "xiaomi_mimo/mimo-v2-flash",
"api_key": "test-key",
},
}
]
)
# Verify the deployment was created successfully
assert len(router.model_list) == 1
assert router.model_list[0]["model_name"] == "mimo-v2-flash"
class TestXiaomiMiMoIntegration:
"""Integration tests for Xiaomi MiMo provider"""
@ -128,30 +54,3 @@ class TestXiaomiMiMoIntegration:
pytest.fail(f"Xiaomi MiMo completion failed: {str(e)}")
else:
raise
if __name__ == "__main__":
# Run basic tests
print("Testing Xiaomi MiMo Provider...")
test_config = TestXiaomiMiMoProviderConfig()
print("\n1. Testing provider in list...")
test_config.test_xiaomi_mimo_in_provider_list()
print(" ✓ xiaomi_mimo in provider list")
print("\n2. Testing JSON config...")
test_config.test_xiaomi_mimo_json_config_exists()
print(" ✓ xiaomi_mimo JSON config loaded")
print("\n3. Testing provider resolution...")
test_config.test_xiaomi_mimo_provider_resolution()
print(" ✓ Provider resolution works")
print("\n4. Testing router configuration...")
test_config.test_xiaomi_mimo_router_config()
print(" ✓ Router configuration works (issue #18794 fixed)")
print("\n" + "=" * 50)
print("✓ All configuration tests passed!")
print("=" * 50)

58
tests/test_litellm/llms/ovhcloud/test_ovhcloud_audio_transcription_transformation.py
View file

@ -54,61 +54,3 @@ def test_ovhcloud_audio_transcription_config_installed():
assert config is not None
assert isinstance(config, BaseAudioTranscriptionConfig)
class TestOVHCloudDurationFieldMigration:
"""Tests for OVHCloud duration -> seconds field migration."""
def test_seconds_field_mapped_to_duration(self):
"""New `seconds` field should be normalized to `duration`."""
from litellm.llms.ovhcloud.audio_transcription.transformation import (
OVHCloudAudioTranscriptionConfig,
)
from unittest.mock import MagicMock
config = OVHCloudAudioTranscriptionConfig()
mock_response = MagicMock()
mock_response.json.return_value = {
"text": "Hello world",
"seconds": 3.14,
}
result = config.transform_audio_transcription_response(mock_response)
assert result.text == "Hello world"
assert result._hidden_params["duration"] == 3.14
def test_legacy_duration_field_still_works(self):
"""Legacy `duration` field should still be accepted."""
from litellm.llms.ovhcloud.audio_transcription.transformation import (
OVHCloudAudioTranscriptionConfig,
)
from unittest.mock import MagicMock
config = OVHCloudAudioTranscriptionConfig()
mock_response = MagicMock()
mock_response.json.return_value = {
"text": "Hello world",
"duration": 2.71,
}
result = config.transform_audio_transcription_response(mock_response)
assert result.text == "Hello world"
assert result._hidden_params["duration"] == 2.71
def test_seconds_zero_mapped_to_duration(self):
"""seconds=0.0 must not be treated as falsy and lost."""
from litellm.llms.ovhcloud.audio_transcription.transformation import (
OVHCloudAudioTranscriptionConfig,
)
from unittest.mock import MagicMock
config = OVHCloudAudioTranscriptionConfig()
mock_response = MagicMock()
mock_response.json.return_value = {"text": "silence", "seconds": 0.0}
result = config.transform_audio_transcription_response(mock_response)
assert result._hidden_params["duration"] == 0.0

237
tests/test_litellm/llms/ovhcloud/test_ovhcloud_chat_transformation.py
View file

@ -6,174 +6,12 @@ import os
import pytest
from litellm.llms.ovhcloud.utils import OVHCloudException
from litellm.utils import get_optional_params
from litellm.llms.ovhcloud.chat.transformation import (
OVHCloudChatCompletionStreamingHandler,
OVHCloudChatConfig,
)
config = OVHCloudChatConfig()
model = "ovhcloud/Mistral-7B-Instruct-v0.3"
class TestOvhCloudChatCompletionStreamingHandler:
def test_chunk_parser_successful(self):
handler = OVHCloudChatCompletionStreamingHandler(
streaming_response=None, sync_stream=True
)
chunk = {
"id": "test_id",
"created": 1234567890,
"model": "gpt-oss-20b",
"usage": {"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30},
"choices": [
{"delta": {"content": "test content", "reasoning": "test reasoning"}}
],
}
result = handler.chunk_parser(chunk)
assert result.id == "test_id"
assert result.object == "chat.completion.chunk"
assert result.created == 1234567890
assert result.model == "gpt-oss-20b"
assert result.usage.prompt_tokens == chunk["usage"]["prompt_tokens"]
assert result.usage.completion_tokens == chunk["usage"]["completion_tokens"]
assert result.usage.total_tokens == chunk["usage"]["total_tokens"]
assert len(result.choices) == 1
assert result.choices[0]["delta"]["reasoning_content"] == "test reasoning"
def test_chunk_parser_error_response(self):
handler = OVHCloudChatCompletionStreamingHandler(
streaming_response=None, sync_stream=True
)
error_chunk = {
"error": {
"message": "test error",
"code": 400,
}
}
with pytest.raises(OVHCloudException) as exc_info:
handler.chunk_parser(error_chunk)
assert "OVHCloud Error: test error" in str(exc_info.value)
assert exc_info.value.status_code == 400
def test_chunk_parser_key_error(self):
handler = OVHCloudChatCompletionStreamingHandler(
streaming_response=None, sync_stream=True
)
invalid_chunk = {"incomplete": "data"}
with pytest.raises(OVHCloudException) as exc_info:
handler.chunk_parser(invalid_chunk)
assert "KeyError" in str(exc_info.value)
assert exc_info.value.status_code == 400
class TestOVHCloudConfig:
def test_transform_request_basic(self):
"""Test basic request transformation"""
transformed_request = config.transform_request(
model,
messages=[{"role": "user", "content": "Hello, world!"}],
optional_params={},
litellm_params={},
headers={},
)
assert transformed_request["model"] == model
assert transformed_request["messages"] == [
{"role": "user", "content": "Hello, world!"}
]
def test_transform_request_with_extra_body(self):
"""Test request transformation with extra_body parameters"""
transformed_request = config.transform_request(
model,
messages=[{"role": "user", "content": "Hello, world!"}],
optional_params={"extra_body": {"custom_param": "custom_value"}},
litellm_params={},
headers={},
)
assert transformed_request["custom_param"] == "custom_value"
assert transformed_request["messages"] == [
{"role": "user", "content": "Hello, world!"}
]
def test_map_openai_params(self):
"""Test OpenAI parameter mapping"""
non_default_params = {
"temperature": 0.7,
"max_tokens": 100,
"top_p": 0.9,
}
mapped_params = config.map_openai_params(
non_default_params=non_default_params,
optional_params={},
model=model,
drop_params=False,
)
assert mapped_params["temperature"] == 0.7
assert mapped_params["max_tokens"] == 100
assert mapped_params["top_p"] == 0.9
def test_get_error_class(self):
"""Test error class creation"""
error = config.get_error_class(
error_message="Test error",
status_code=400,
headers={"Content-Type": "application/json"},
)
assert isinstance(error, OVHCloudException)
assert error.message == "Test error"
assert error.status_code == 400
@pytest.mark.parametrize(
"model",
[
"Meta-Llama-3_3-70B-Instruct",
"Meta-Llama-3_1-70B-Instruct",
"Mixtral-8x7B-Instruct-v0.1",
"gpt-oss-120b",
"some-model-not-in-the-cost-map",
],
)
def test_tools_not_filtered_by_static_model_map(self, model):
"""
OVHCloud AI Endpoints are OpenAI-compatible; tools/tool_choice must pass
through for any model. The server is responsible for rejecting unsupported
tool calls — LiteLLM must not strip them based on a stale static catalog.
"""
params = get_optional_params(
model=model,
custom_llm_provider="ovhcloud",
tools=[
{
"type": "function",
"function": {"name": "x", "parameters": {}},
}
],
tool_choice="auto",
)
assert "tools" in params
assert "tool_choice" in params
def test_ovhcloud_integration():
from litellm import completion
@ -285,78 +123,3 @@ def test_ovhcloud_with_custom_base_url():
if __name__ == "__main__":
pytest.main([__file__, "-v"])
class TestOVHCloudReasoningFieldMigration:
"""Tests for OVHCloud reasoning_content -> reasoning field migration."""
def test_streaming_new_reasoning_field(self):
"""New `reasoning` field should be mapped to `reasoning_content`."""
handler = OVHCloudChatCompletionStreamingHandler(
streaming_response=iter([]),
sync_stream=True,
)
chunk = {
"id": "test-id",
"created": 1234567890,
"model": "test-model",
"choices": [
{
"delta": {
"role": "assistant",
"reasoning": "Let me think...",
},
"index": 0,
}
],
}
result = handler.chunk_parser(chunk)
assert result.choices[0]["delta"]["reasoning_content"] == "Let me think..."
def test_streaming_legacy_reasoning_content_unchanged(self):
"""Legacy `reasoning_content` field should pass through untouched."""
handler = OVHCloudChatCompletionStreamingHandler(
streaming_response=iter([]),
sync_stream=True,
)
chunk = {
"id": "test-id",
"created": 1234567890,
"model": "test-model",
"choices": [
{
"delta": {
"role": "assistant",
"reasoning_content": "Already correct field.",
},
"index": 0,
}
],
}
result = handler.chunk_parser(chunk)
assert result.choices[0]["delta"]["reasoning_content"] == "Already correct field."
def test_streaming_both_fields_legacy_wins(self):
"""When both fields present, existing `reasoning_content` is not overwritten."""
handler = OVHCloudChatCompletionStreamingHandler(
streaming_response=iter([]),
sync_stream=True,
)
chunk = {
"id": "test-id",
"created": 1234567890,
"model": "test-model",
"choices": [
{
"delta": {
"reasoning": "new field",
"reasoning_content": "legacy field",
},
"index": 0,
}
],
}
result = handler.chunk_parser(chunk)
assert result.choices[0]["delta"]["reasoning_content"] == "legacy field"

1
tests/test_litellm/llms/reducto/__init__.py
View file

@ -1 +0,0 @@

1
tests/test_litellm/llms/s3_vectors/__init__.py
View file

@ -1 +0,0 @@
# S3 Vectors tests

1
tests/test_litellm/llms/s3_vectors/vector_stores/__init__.py
View file

@ -1 +0,0 @@
# S3 Vectors vector store tests

1
tests/test_litellm/llms/soniox/__init__.py
View file

@ -1 +0,0 @@
"""Soniox provider tests."""

2733
tests/test_litellm/llms/vertex_ai/gemini/test_vertex_ai_gemini_transformation.py
View file

File diff suppressed because it is too large Load diff

1
tests/test_litellm/llms/vertex_ai/image_edit/__init__.py
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@ -1 +0,0 @@
# Vertex AI Image Edit Tests

624
tests/test_litellm/llms/vertex_ai/image_generation/test_vertex_ai_image_generation_transformation.py
View file

@ -1,13 +1,9 @@
import os
from unittest.mock import MagicMock, patch
from unittest.mock import patch
import httpx
import pytest
from litellm.llms.vertex_ai.image_generation import (
get_vertex_ai_image_generation_config,
)
from litellm.llms.vertex_ai.image_generation.vertex_gemini_transformation import (
VertexAIGeminiImageGenerationConfig,
)
@ -16,588 +12,6 @@ from litellm.llms.vertex_ai.image_generation.vertex_imagen_transformation import
)
class TestVertexAIGeminiImageGenerationConfig:
def setup_method(self):
"""Set up test fixtures"""
self.config = VertexAIGeminiImageGenerationConfig()
def test_get_supported_openai_params(self):
"""Test get_supported_openai_params returns correct params"""
supported = self.config.get_supported_openai_params("gemini-2.5-flash-image")
assert "n" in supported
assert "size" in supported
def test_map_openai_params_n(self):
"""Test mapping n parameter to candidate_count"""
non_default_params = {"n": 3}
optional_params = {}
result = self.config.map_openai_params(non_default_params, optional_params, "gemini-2.5-flash-image", False)
assert result.get("candidate_count") == 3
def test_map_openai_params_size(self):
"""Test mapping size parameter to aspectRatio"""
non_default_params = {"size": "1024x1024"}
optional_params = {}
result = self.config.map_openai_params(non_default_params, optional_params, "gemini-2.5-flash-image", False)
assert result.get("aspectRatio") == "1:1"
def test_map_openai_params_size_16_9(self):
"""Test mapping 16:9 size"""
non_default_params = {"size": "1792x1024"}
optional_params = {}
result = self.config.map_openai_params(non_default_params, optional_params, "gemini-2.5-flash-image", False)
assert result.get("aspectRatio") == "16:9"
def test_map_size_to_aspect_ratio(self):
"""Test size to aspect ratio mapping"""
assert self.config._map_size_to_aspect_ratio("1024x1024") == "1:1"
assert self.config._map_size_to_aspect_ratio("1792x1024") == "16:9"
assert self.config._map_size_to_aspect_ratio("1024x1792") == "9:16"
assert self.config._map_size_to_aspect_ratio("1280x896") == "4:3"
assert self.config._map_size_to_aspect_ratio("896x1280") == "3:4"
assert self.config._map_size_to_aspect_ratio("unknown") == "1:1" # default
def test_get_supported_openai_params_includes_native_gemini_params(self):
"""Test that native Gemini imageConfig params are supported"""
supported = self.config.get_supported_openai_params("gemini-3-pro-image-preview")
assert "aspectRatio" in supported
assert "aspect_ratio" in supported
assert "imageSize" in supported
assert "image_size" in supported
assert "imageConfig" in supported
def test_map_openai_params_aspect_ratio_camel_case(self):
"""Test mapping native aspectRatio parameter"""
result = self.config.map_openai_params({"aspectRatio": "9:16"}, {}, "gemini-3-pro-image-preview", False)
assert result["aspectRatio"] == "9:16"
def test_map_openai_params_aspect_ratio_snake_case(self):
"""Test mapping native aspect_ratio parameter"""
result = self.config.map_openai_params({"aspect_ratio": "16:9"}, {}, "gemini-3-pro-image-preview", False)
assert result["aspectRatio"] == "16:9"
def test_map_openai_params_image_size_camel_case(self):
"""Test mapping native imageSize parameter"""
result = self.config.map_openai_params({"imageSize": "4K"}, {}, "gemini-3-pro-image-preview", False)
assert result["imageSize"] == "4K"
def test_map_openai_params_image_size_snake_case(self):
"""Test mapping native image_size parameter"""
result = self.config.map_openai_params({"image_size": "2K"}, {}, "gemini-3-pro-image-preview", False)
assert result["imageSize"] == "2K"
def test_map_openai_params_image_config_dict_stored_whole(self):
"""imageConfig dict is stored as-is so all fields survive"""
result = self.config.map_openai_params(
{"imageConfig": {"aspectRatio": "16:9", "imageSize": "2K"}},
{},
"gemini-3.1-flash-image",
False,
)
assert result["imageConfig"] == {"aspectRatio": "16:9", "imageSize": "2K"}
def test_map_openai_params_image_config_all_fields(self):
"""All ImageConfig fields (personGeneration, imageOutputOptions) pass through"""
payload = {
"imageConfig": {
"aspectRatio": "9:16",
"imageSize": "4K",
"personGeneration": "DONT_ALLOW",
"imageOutputOptions": {
"mimeType": "image/jpeg",
"compressionQuality": 80,
},
}
}
result = self.config.map_openai_params(payload, {}, "gemini-3.1-flash-image", False)
assert result["imageConfig"] == payload["imageConfig"]
def test_map_openai_params_image_config_non_dict_warns_and_drops(self):
"""Non-dict imageConfig is dropped with a warning, not silently discarded"""
with patch("litellm.llms.vertex_ai.image_generation.vertex_gemini_transformation.verbose_logger") as mock_log:
result = self.config.map_openai_params(
{"imageConfig": "bad-string-value"}, {}, "gemini-3.1-flash-image", False
)
assert "imageConfig" not in result
mock_log.warning.assert_called_once()
def test_transform_image_generation_request_from_image_config(self):
"""Full imageConfig dict is forwarded verbatim into generationConfig"""
full_config = {
"aspectRatio": "16:9",
"imageSize": "2K",
"personGeneration": "DONT_ALLOW",
"imageOutputOptions": {"mimeType": "image/jpeg", "compressionQuality": 85},
}
mapped = self.config.map_openai_params(
{"imageConfig": full_config},
{},
"gemini-3.1-flash-image",
False,
)
request = self.config.transform_image_generation_request(
model="gemini-3.1-flash-image",
prompt="A nano banana on a desk",
optional_params=mapped,
litellm_params={},
headers={},
)
assert request["generationConfig"]["imageConfig"] == full_config
def test_transform_image_generation_flat_params_override_image_config(self):
"""Explicit flat params win over the same key inside imageConfig"""
request = self.config.transform_image_generation_request(
model="gemini-3.1-flash-image",
prompt="A nano banana",
optional_params={
"imageConfig": {"aspectRatio": "1:1", "personGeneration": "DONT_ALLOW"},
"aspectRatio": "16:9", # should win
},
litellm_params={},
headers={},
)
assert request["generationConfig"]["imageConfig"]["aspectRatio"] == "16:9"
assert request["generationConfig"]["imageConfig"]["personGeneration"] == "DONT_ALLOW"
def test_transform_image_generation_request_basic(self):
"""Test basic request transformation"""
request = self.config.transform_image_generation_request(
model="gemini-2.5-flash-image",
prompt="A nano banana",
optional_params={},
litellm_params={},
headers={},
)
assert "contents" in request
assert "generationConfig" in request
assert request["generationConfig"]["responseModalities"] == ["IMAGE"]
assert request["contents"][0]["parts"][0]["text"] == "A nano banana"
def test_transform_image_generation_request_with_aspect_ratio(self):
"""Test request transformation with aspectRatio"""
request = self.config.transform_image_generation_request(
model="gemini-2.5-flash-image",
prompt="A nano banana",
optional_params={"aspectRatio": "16:9"},
litellm_params={},
headers={},
)
assert request["generationConfig"]["imageConfig"]["aspectRatio"] == "16:9"
def test_transform_image_generation_request_with_image_size(self):
"""Test request transformation with imageSize (Gemini 3 Pro)"""
request = self.config.transform_image_generation_request(
model="gemini-3-pro-image-preview",
prompt="A nano banana",
optional_params={"imageSize": "4K"},
litellm_params={},
headers={},
)
assert request["generationConfig"]["imageConfig"]["imageSize"] == "4K"
def test_map_openai_params_web_search_options(self):
"""Test web_search_options maps to googleSearch tool"""
result = self.config.map_openai_params({"web_search_options": {}}, {}, "gemini-3.1-flash-image-preview", False)
assert result["tools"] == [{"googleSearch": {}}]
def test_transform_image_generation_request_with_web_search_tools(self):
"""Test request transformation includes googleSearch tools"""
request = self.config.transform_image_generation_request(
model="gemini-3.1-flash-image-preview",
prompt="Generate an image of the latest iPhone",
optional_params={"tools": [{"googleSearch": {}}]},
litellm_params={},
headers={},
)
assert request["tools"] == [{"googleSearch": {}}]
def test_transform_image_generation_request_forwards_tool_config(self):
"""Test request transformation forwards toolConfig side-effects from tool mapping"""
mapped = self.config.map_openai_params(
{"tools": [{"googleMaps": {"latitude": 37.7, "longitude": -122.4}}]},
{},
"gemini-3.1-flash-image-preview",
False,
)
request = self.config.transform_image_generation_request(
model="gemini-3.1-flash-image-preview",
prompt="Generate an image of a coffee shop nearby",
optional_params=mapped,
litellm_params={},
headers={},
)
assert request["tools"] == [{"googleMaps": {}}]
assert request["toolConfig"] == {"retrievalConfig": {"latLng": {"latitude": 37.7, "longitude": -122.4}}}
def test_transform_image_generation_request_with_candidate_count(self):
"""Test request transformation with candidate_count"""
request = self.config.transform_image_generation_request(
model="gemini-2.5-flash-image",
prompt="A nano banana",
optional_params={"candidate_count": 2},
litellm_params={},
headers={},
)
assert request["generationConfig"]["candidateCount"] == 2
def test_transform_image_generation_request_with_n(self):
"""Test request transformation with n parameter"""
request = self.config.transform_image_generation_request(
model="gemini-2.5-flash-image",
prompt="A nano banana",
optional_params={"n": 2},
litellm_params={},
headers={},
)
assert request["generationConfig"]["candidateCount"] == 2
def test_transform_image_generation_response(self):
"""Test response transformation"""
mock_response = MagicMock(spec=httpx.Response)
mock_response.status_code = 200
mock_response.json.return_value = {
"candidates": [
{
"content": {
"parts": [
{
"inlineData": {
"mimeType": "image/png",
"data": "base64_encoded_image_data",
}
}
]
}
}
],
"usageMetadata": {
"promptTokenCount": 93,
"promptTokensDetails": [
{
"modality": "TEXT",
"tokenCount": 54,
},
{
"modality": "IMAGE",
"tokenCount": 39,
},
],
"candidatesTokenCount": 17,
"totalTokenCount": 110,
},
}
mock_response.headers = {}
from litellm.types.utils import ImageResponse
model_response = ImageResponse()
result = self.config.transform_image_generation_response(
model="gemini-2.5-flash-image",
raw_response=mock_response,
model_response=model_response,
logging_obj=MagicMock(),
request_data={},
optional_params={},
litellm_params={},
encoding=None,
)
assert len(result.data) == 1
assert result.data[0].b64_json == "base64_encoded_image_data"
assert result.data[0].url is None
assert result.usage.input_tokens == 93
assert result.usage.input_tokens_details.text_tokens == 54
assert result.usage.input_tokens_details.image_tokens == 39
assert result.usage.output_tokens == 17
assert result.usage.total_tokens == 110
def test_transform_image_generation_response_multiple_images(self):
"""Test response transformation with multiple images"""
mock_response = MagicMock(spec=httpx.Response)
mock_response.status_code = 200
mock_response.json.return_value = {
"candidates": [
{
"content": {
"parts": [
{
"inlineData": {
"mimeType": "image/png",
"data": "image1",
}
},
{
"inlineData": {
"mimeType": "image/png",
"data": "image2",
}
},
]
}
}
]
}
mock_response.headers = {}
from litellm.types.utils import ImageResponse
model_response = ImageResponse()
result = self.config.transform_image_generation_response(
model="gemini-2.5-flash-image",
raw_response=mock_response,
model_response=model_response,
logging_obj=MagicMock(),
request_data={},
optional_params={},
litellm_params={},
encoding=None,
)
assert len(result.data) == 2
assert result.data[0].b64_json == "image1"
assert result.data[1].b64_json == "image2"
def test_transform_image_generation_response_signature(self):
"""Test response transformation includes thoughtSignature for Gemini 3 Pro"""
mock_response = MagicMock(spec=httpx.Response)
mock_response.status_code = 200
mock_response.json.return_value = {
"candidates": [
{
"content": {
"parts": [
{
"inlineData": {
"mimeType": "image/png",
"data": "base64_encoded_image_data",
},
"thoughtSignature": "test_signature_abc123",
}
]
}
}
]
}
mock_response.headers = {}
from litellm.types.utils import ImageResponse
model_response = ImageResponse()
result = self.config.transform_image_generation_response(
model="gemini-3-pro-image-preview",
raw_response=mock_response,
model_response=model_response,
logging_obj=MagicMock(),
request_data={},
optional_params={},
litellm_params={},
encoding=None,
)
assert len(result.data) == 1
assert result.data[0].b64_json == "base64_encoded_image_data"
assert result.data[0].provider_specific_fields["thought_signature"] == "test_signature_abc123"
def test_transform_image_generation_response_tracks_web_search_requests(self):
"""Grounding queries are carried onto usage so search spend can be billed"""
mock_response = MagicMock(spec=httpx.Response)
mock_response.status_code = 200
mock_response.json.return_value = {
"candidates": [
{
"content": {
"parts": [
{
"inlineData": {
"mimeType": "image/png",
"data": "base64_encoded_image_data",
}
}
]
},
"groundingMetadata": {"webSearchQueries": ["eiffel tower", "paris skyline"]},
}
],
"usageMetadata": {
"promptTokenCount": 93,
"candidatesTokenCount": 17,
"totalTokenCount": 110,
},
}
mock_response.headers = {}
from litellm.types.utils import ImageResponse
result = self.config.transform_image_generation_response(
model="gemini-2.5-flash-image",
raw_response=mock_response,
model_response=ImageResponse(),
logging_obj=MagicMock(),
request_data={},
optional_params={},
litellm_params={},
encoding=None,
)
assert result.usage.web_search_requests == 2
class TestVertexAIImagenImageGenerationConfig:
def setup_method(self):
"""Set up test fixtures"""
self.config = VertexAIImagenImageGenerationConfig()
def test_get_supported_openai_params(self):
"""Test get_supported_openai_params returns correct params"""
supported = self.config.get_supported_openai_params("imagegeneration@006")
assert "n" in supported
assert "size" in supported
def test_map_openai_params_n(self):
"""Test mapping n parameter to sampleCount"""
non_default_params = {"n": 3}
optional_params = {}
result = self.config.map_openai_params(non_default_params, optional_params, "imagegeneration@006", False)
assert result.get("sampleCount") == 3
def test_map_openai_params_size(self):
"""Test mapping size parameter to aspectRatio"""
non_default_params = {"size": "1024x1024"}
optional_params = {}
result = self.config.map_openai_params(non_default_params, optional_params, "imagegeneration@006", False)
assert result.get("aspectRatio") == "1:1"
def test_map_size_to_aspect_ratio(self):
"""Test size to aspect ratio mapping"""
assert self.config._map_size_to_aspect_ratio("1024x1024") == "1:1"
assert self.config._map_size_to_aspect_ratio("1792x1024") == "16:9"
assert self.config._map_size_to_aspect_ratio("unknown") == "1:1" # default
def test_transform_image_generation_request_basic(self):
"""Test basic request transformation"""
request = self.config.transform_image_generation_request(
model="imagegeneration@006",
prompt="A cat",
optional_params={},
litellm_params={},
headers={},
)
assert "instances" in request
assert "parameters" in request
assert request["instances"][0]["prompt"] == "A cat"
assert request["parameters"]["sampleCount"] == 1
def test_transform_image_generation_request_with_params(self):
"""Test request transformation with parameters"""
request = self.config.transform_image_generation_request(
model="imagegeneration@006",
prompt="A cat",
optional_params={"sampleCount": 2, "aspectRatio": "16:9"},
litellm_params={},
headers={},
)
assert request["parameters"]["sampleCount"] == 2
assert request["parameters"]["aspectRatio"] == "16:9"
def test_transform_image_generation_request_labels_from_metadata(self):
"""Billing labels from litellm_params.metadata.requester_metadata on predict body."""
request = self.config.transform_image_generation_request(
model="imagegeneration@006",
prompt="A cat",
optional_params={},
litellm_params={"metadata": {"requester_metadata": {"team": "platform", "env": "prod"}}},
headers={},
)
assert request["labels"] == {"team": "platform", "env": "prod"}
assert "labels" not in request["parameters"]
def test_transform_image_generation_response(self):
"""Test response transformation"""
mock_response = MagicMock(spec=httpx.Response)
mock_response.status_code = 200
mock_response.json.return_value = {"predictions": [{"bytesBase64Encoded": "base64_encoded_image_data"}]}
mock_response.headers = {}
from litellm.types.utils import ImageResponse
model_response = ImageResponse()
result = self.config.transform_image_generation_response(
model="imagegeneration@006",
raw_response=mock_response,
model_response=model_response,
logging_obj=MagicMock(),
request_data={},
optional_params={},
litellm_params={},
encoding=None,
)
assert len(result.data) == 1
assert result.data[0].b64_json == "base64_encoded_image_data"
assert result.data[0].url is None
def test_transform_image_generation_response_multiple_images(self):
"""Test response transformation with multiple images"""
mock_response = MagicMock(spec=httpx.Response)
mock_response.status_code = 200
mock_response.json.return_value = {
"predictions": [
{"bytesBase64Encoded": "image1"},
{"bytesBase64Encoded": "image2"},
]
}
mock_response.headers = {}
from litellm.types.utils import ImageResponse
model_response = ImageResponse()
result = self.config.transform_image_generation_response(
model="imagegeneration@006",
raw_response=mock_response,
model_response=model_response,
logging_obj=MagicMock(),
request_data={},
optional_params={},
litellm_params={},
encoding=None,
)
assert len(result.data) == 2
assert result.data[0].b64_json == "image1"
assert result.data[1].b64_json == "image2"
class TestGetVertexAIImageGenerationConfig:
"""Test the router function that selects the correct config"""
def test_get_gemini_model_config(self):
"""Test that Gemini models return Gemini config"""
config = get_vertex_ai_image_generation_config("gemini-2.5-flash-image")
assert isinstance(config, VertexAIGeminiImageGenerationConfig)
config = get_vertex_ai_image_generation_config("gemini-3-pro-image-preview")
assert isinstance(config, VertexAIGeminiImageGenerationConfig)
config = get_vertex_ai_image_generation_config("vertex_ai/gemini-2.5-flash-image")
assert isinstance(config, VertexAIGeminiImageGenerationConfig)
def test_get_imagen_model_config(self):
"""Test that Imagen models return Imagen config"""
config = get_vertex_ai_image_generation_config("imagegeneration@006")
assert isinstance(config, VertexAIImagenImageGenerationConfig)
config = get_vertex_ai_image_generation_config("imagen-4.0-generate-001")
assert isinstance(config, VertexAIImagenImageGenerationConfig)
config = get_vertex_ai_image_generation_config("vertex_ai/imagegeneration@006")
assert isinstance(config, VertexAIImagenImageGenerationConfig)
def test_get_non_gemini_model_config(self):
"""Test that non-Gemini models default to Imagen config"""
config = get_vertex_ai_image_generation_config("some-other-model")
assert isinstance(config, VertexAIImagenImageGenerationConfig)
class TestVertexAIImageGenerationIntegration:
"""Integration tests for Vertex AI image generation"""
@ -642,39 +56,3 @@ class TestVertexAIImageGenerationIntegration:
litellm_params={},
)
assert "Authorization" in headers
def test_gemini_get_complete_url(self):
"""Test Gemini config URL generation"""
config = VertexAIGeminiImageGenerationConfig()
url = config.get_complete_url(
api_base=None,
api_key=None,
model="gemini-2.5-flash-image",
optional_params={},
litellm_params={
"vertex_project": "test-project",
"vertex_location": "us-central1",
},
)
assert "test-project" in url
assert "us-central1" in url
assert "gemini-2.5-flash-image" in url
assert "generateContent" in url
def test_imagen_get_complete_url(self):
"""Test Imagen config URL generation"""
config = VertexAIImagenImageGenerationConfig()
url = config.get_complete_url(
api_base=None,
api_key=None,
model="imagegeneration@006",
optional_params={},
litellm_params={
"vertex_project": "test-project",
"vertex_location": "us-central1",
},
)
assert "test-project" in url
assert "us-central1" in url
assert "imagegeneration@006" in url
assert "predict" in url

1
tests/test_litellm/llms/vertex_ai/vertex_gemma_models/__init__.py
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@ -1 +0,0 @@
"""Tests for Vertex AI Gemma-AI models"""

3
tests/test_litellm/llms/vertex_ai/videos/__init__.py
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@ -1,3 +0,0 @@
"""
Tests for Vertex AI video generation.
"""

2
tests/test_litellm/proxy/guardrails/guardrail_hooks/test_bedrock_guardrails.py
View file

@ -30,7 +30,7 @@ from litellm.types.proxy.guardrails.guardrail_hooks.bedrock_guardrails import (
BedrockTextContent,
)
from litellm.types.utils import CallTypes, ModelResponse
from tests.test_litellm.llms.bedrock.event_loop_probe import EventLoopProbe
from tests.unit.llms.bedrock.event_loop_probe import EventLoopProbe
@pytest.mark.asyncio

2
tests/test_litellm/proxy/guardrails/guardrail_hooks/test_bedrock_invoke_guardrail_checks.py
View file

@ -23,7 +23,7 @@ from litellm.types.proxy.guardrails.guardrail_hooks.bedrock_guardrails import (
BedrockGuardrailResponse,
)
from litellm.types.utils import Choices, Message, ModelResponse
from tests.test_litellm.llms.bedrock.event_loop_probe import EventLoopProbe
from tests.unit.llms.bedrock.event_loop_probe import EventLoopProbe
CONTENT_FILTER_CHECKS = {"contentFilter": {"categories": [{"category": "VIOLENCE"}]}}

2
tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py
View file

@ -24,7 +24,7 @@ from starlette.datastructures import FormData
import litellm
from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing
from tests.test_litellm.llms.bedrock.event_loop_probe import EventLoopProbe
from tests.unit.llms.bedrock.event_loop_probe import EventLoopProbe
from litellm.constants import LITELLM_PROXY_MASTER_KEY_ALIAS
from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import (
BaseOpenAIPassThroughHandler,

50
tests/unit/conftest.py
View file

@ -1,11 +1,14 @@
import asyncio
import base64
import importlib
import os
from collections.abc import Coroutine, Iterator
from dataclasses import dataclass, field
from pathlib import Path
from typing import Final
import boto3
import httpx
import pytest
from pytest_socket import enable_socket, socket_allow_hosts
@ -15,9 +18,12 @@ import litellm # noqa: E402 # litellm reads LITELLM_LOCAL_MODEL_COST_MAP at im
import litellm.router as litellm_router_module # noqa: E402 # same import-time dependency
import litellm.utils as litellm_utils_module # noqa: E402 # same import-time dependency
from litellm._logging import ALL_LOGGERS # noqa: E402 # same import-time dependency
from litellm.anthropic_beta_headers_manager import reload_beta_headers_config # noqa: E402 # same import-time dependency
from litellm.litellm_core_utils.prompt_templates import factory as prompt_factory_module # noqa: E402 # same import-time dependency
from litellm.litellm_core_utils.prompt_templates import ( # noqa: E402 # same import-time dependency
image_handling as image_handling_module,
)
from litellm.llms.gemini.chat import transformation as gemini_chat_transformation_module # noqa: E402 # same import-time dependency
from litellm.llms.custom_httpx.async_client_cleanup import ( # noqa: E402 # same import-time dependency
close_litellm_async_clients,
)
@ -89,6 +95,9 @@ RESTORED_GLOBALS: Final = (
)
MODULE_LEVEL_CLIENTS: Final = ("module_level_client", "module_level_aclient")
SESSION_CLIENTS: Final = ("base_llm_aiohttp_handler", "httpx_client", "aclient", "client")
ONE_PIXEL_PNG: Final = base64.b64decode(
"iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNkYPhfDwAChwGA60e6kgAAAABJRU5ErkJggg=="
)
def _allow_loopback_only() -> None:
@ -236,6 +245,47 @@ def local_model_cost_map(monkeypatch: pytest.MonkeyPatch) -> Iterator[None]:
litellm.get_model_info.cache_clear()
@pytest.fixture
def local_beta_headers_config(monkeypatch: pytest.MonkeyPatch) -> Iterator[None]:
monkeypatch.setenv("LITELLM_LOCAL_ANTHROPIC_BETA_HEADERS", "True")
reload_beta_headers_config()
yield
monkeypatch.delenv("LITELLM_LOCAL_ANTHROPIC_BETA_HEADERS", raising=False)
reload_beta_headers_config()
@dataclass(slots=True)
class AsyncOnlyImageFetch:
fetched: list[str] = field(default_factory=list) # mutable-ok: tests assert on the URLs fetched, in order
base64_png: str = base64.b64encode(ONE_PIXEL_PNG).decode()
data_url: str = "data:image/png;base64," + base64.b64encode(ONE_PIXEL_PNG).decode()
@pytest.fixture
def async_only_image_fetch(monkeypatch: pytest.MonkeyPatch) -> AsyncOnlyImageFetch:
fetch: Final = AsyncOnlyImageFetch()
def forbid_sync_fetch(client: object, url: str, **kwargs: object) -> httpx.Response:
raise litellm.ImageFetchError(f"sync image fetch ran on the event loop: {url}")
async def serve_png(client: object, url: str, **kwargs: object) -> httpx.Response:
fetch.fetched.append(url)
return httpx.Response(
200, content=ONE_PIXEL_PNG, headers={"content-type": "image/png"}, request=httpx.Request("GET", url)
)
def forbid_sync_convert(url: str, *args: object, **kwargs: object) -> str:
if url.startswith(("http://", "https://")):
raise litellm.ImageFetchError(f"sync convert_url_to_base64 ran on the request path: {url}")
return url
monkeypatch.setattr(image_handling_module, "safe_get", forbid_sync_fetch)
monkeypatch.setattr(image_handling_module, "async_safe_get", serve_png)
for module in (image_handling_module, prompt_factory_module, gemini_chat_transformation_module):
monkeypatch.setattr(module, "convert_url_to_base64", forbid_sync_convert)
return fetch
@pytest.fixture
def no_ambient_azure_credentials(monkeypatch: pytest.MonkeyPatch) -> None:
for name in AMBIENT_AZURE_CREDENTIAL_ENV_VARS:

0
tests/test_litellm/llms/anthropic/__init__.py → tests/unit/expected_fine_tuning_api/__init__.py
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0
tests/test_litellm/expected_fine_tuning_api/azure_cancel_expected_output.json → tests/unit/expected_fine_tuning_api/azure_cancel_expected_output.json
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0
tests/test_litellm/expected_fine_tuning_api/azure_cancel_raw_response.json → tests/unit/expected_fine_tuning_api/azure_cancel_raw_response.json
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0
tests/test_litellm/expected_fine_tuning_api/azure_cancel_request.json → tests/unit/expected_fine_tuning_api/azure_cancel_request.json
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0
tests/test_litellm/expected_fine_tuning_api/azure_create_expected_output.json → tests/unit/expected_fine_tuning_api/azure_create_expected_output.json
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0
tests/test_litellm/expected_fine_tuning_api/azure_create_raw_response.json → tests/unit/expected_fine_tuning_api/azure_create_raw_response.json
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0
tests/test_litellm/expected_fine_tuning_api/azure_create_request.json → tests/unit/expected_fine_tuning_api/azure_create_request.json
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0
tests/test_litellm/expected_fine_tuning_api/azure_list_raw_response.json → tests/unit/expected_fine_tuning_api/azure_list_raw_response.json
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0
tests/test_litellm/expected_fine_tuning_api/azure_list_request.json → tests/unit/expected_fine_tuning_api/azure_list_request.json
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0
tests/test_litellm/llms/anthropic/batches/__init__.py → tests/unit/llms/aiml/__init__.py
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0
tests/test_litellm/llms/anthropic/experimental_pass_through/context_management/__init__.py → tests/unit/llms/aiml/image_generation/__init__.py
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0
tests/test_litellm/llms/aiml/image_generation/test_aiml_image_generation_transformation.py → tests/unit/llms/aiml/image_generation/test_aiml_image_generation_transformation.py
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2
tests/unit/llms/anthropic/batches/test_transformation.py
View file

@ -616,7 +616,7 @@ def test_transform_response_reraises_unexpected_error(config):
# automatically. See base_batches_config_test.py.
# --------------------------------------------------------------------------- #
from tests.test_litellm.llms.base_llm.batches.base_batches_config_test import ( # noqa: E402
from tests.unit.llms.base_llm.batches.base_batches_config_test import ( # noqa: E402
BatchesConfigContractTests,
)

0
tests/test_litellm/llms/anthropic/experimental_pass_through/responses_adapters/__init__.py → tests/unit/llms/anthropic/chat/__init__.py
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0
tests/test_litellm/llms/anthropic/chat/conftest.py → tests/unit/llms/anthropic/chat/conftest.py
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0
tests/test_litellm/llms/anthropic/files/__init__.py → tests/unit/llms/anthropic/chat/guardrail_translation/__init__.py
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0
tests/test_litellm/llms/anthropic/chat/guardrail_translation/test_anthropic_guardrail_handler.py → tests/unit/llms/anthropic/chat/guardrail_translation/test_anthropic_guardrail_handler.py
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0
tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py → tests/unit/llms/anthropic/chat/test_anthropic_chat_handler.py
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tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py → tests/unit/llms/anthropic/chat/test_anthropic_chat_transformation.py
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0
tests/test_litellm/llms/anthropic/chat/test_code_interpreter_results_extraction.py → tests/unit/llms/anthropic/chat/test_code_interpreter_results_extraction.py
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tests/test_litellm/llms/azure/batches/__init__.py → tests/unit/llms/anthropic/experimental_pass_through/adapters/__init__.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py → tests/unit/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_handler_output_config_passthrough.py → tests/unit/llms/anthropic/experimental_pass_through/adapters/test_handler_output_config_passthrough.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_handler_prompt_cache_key.py → tests/unit/llms/anthropic/experimental_pass_through/adapters/test_handler_prompt_cache_key.py
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0
tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_handler_reasoning_effort_normalization.py → tests/unit/llms/anthropic/experimental_pass_through/adapters/test_handler_reasoning_effort_normalization.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_streaming_iterator_combined_chunk.py → tests/unit/llms/anthropic/experimental_pass_through/adapters/test_streaming_iterator_combined_chunk.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_streaming_iterator_compaction.py → tests/unit/llms/anthropic/experimental_pass_through/adapters/test_streaming_iterator_compaction.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_streaming_iterator_empty_choices.py → tests/unit/llms/anthropic/experimental_pass_through/adapters/test_streaming_iterator_empty_choices.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_streaming_iterator_first_delta.py → tests/unit/llms/anthropic/experimental_pass_through/adapters/test_streaming_iterator_first_delta.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_streaming_iterator_message_id.py → tests/unit/llms/anthropic/experimental_pass_through/adapters/test_streaming_iterator_message_id.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_streaming_iterator_mid_stream_error.py → tests/unit/llms/anthropic/experimental_pass_through/adapters/test_streaming_iterator_mid_stream_error.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_streaming_iterator_stop_reason.py → tests/unit/llms/anthropic/experimental_pass_through/adapters/test_streaming_iterator_stop_reason.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_streaming_iterator_tool_args.py → tests/unit/llms/anthropic/experimental_pass_through/adapters/test_streaming_iterator_tool_args.py
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tests/test_litellm/llms/azure/vector_stores/__init__.py → tests/unit/llms/anthropic/experimental_pass_through/context_management/__init__.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/context_management/test_clear_tool_uses.py → tests/unit/llms/anthropic/experimental_pass_through/context_management/test_clear_tool_uses.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/context_management/test_compact.py → tests/unit/llms/anthropic/experimental_pass_through/context_management/test_compact.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/context_management/test_dispatcher.py → tests/unit/llms/anthropic/experimental_pass_through/context_management/test_dispatcher.py
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tests/test_litellm/llms/base_llm/__init__.py → tests/unit/llms/anthropic/experimental_pass_through/messages/__init__.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_advisor_integration.py → tests/unit/llms/anthropic/experimental_pass_through/messages/test_advisor_integration.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_agentic_streaming_iterator.py → tests/unit/llms/anthropic/experimental_pass_through/messages/test_agentic_streaming_iterator.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_experimental_pass_through_messages_handler.py → tests/unit/llms/anthropic/experimental_pass_through/messages/test_anthropic_experimental_pass_through_messages_handler.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_messages_effort.py → tests/unit/llms/anthropic/experimental_pass_through/messages/test_anthropic_messages_effort.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_messages_encrypted_reasoning.py → tests/unit/llms/anthropic/experimental_pass_through/messages/test_anthropic_messages_encrypted_reasoning.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_messages_per_turn_control.py → tests/unit/llms/anthropic/experimental_pass_through/messages/test_anthropic_messages_per_turn_control.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_messages_speed.py → tests/unit/llms/anthropic/experimental_pass_through/messages/test_anthropic_messages_speed.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_messages_structured_outputs.py → tests/unit/llms/anthropic/experimental_pass_through/messages/test_anthropic_messages_structured_outputs.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_content_after_stop_reason.py → tests/unit/llms/anthropic/experimental_pass_through/messages/test_content_after_stop_reason.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_mcp_handler.py → tests/unit/llms/anthropic/experimental_pass_through/messages/test_mcp_handler.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_mid_conversation_system.py → tests/unit/llms/anthropic/experimental_pass_through/messages/test_mid_conversation_system.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_parallel_tool_calls.py → tests/unit/llms/anthropic/experimental_pass_through/messages/test_parallel_tool_calls.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_reasoning_auto_summary_messages.py → tests/unit/llms/anthropic/experimental_pass_through/messages/test_reasoning_auto_summary_messages.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_reasoning_effort_translation.py → tests/unit/llms/anthropic/experimental_pass_through/messages/test_reasoning_effort_translation.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_request_optional_param_utils.py → tests/unit/llms/anthropic/experimental_pass_through/messages/test_request_optional_param_utils.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_response_cache.py → tests/unit/llms/anthropic/experimental_pass_through/messages/test_response_cache.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_sse_wrapper.py → tests/unit/llms/anthropic/experimental_pass_through/messages/test_sse_wrapper.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_streaming_iterator.py → tests/unit/llms/anthropic/experimental_pass_through/messages/test_streaming_iterator.py
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tests/test_litellm/llms/base_llm/batches/__init__.py → tests/unit/llms/anthropic/experimental_pass_through/responses_adapters/__init__.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/responses_adapters/test_responses_adapters_handler.py → tests/unit/llms/anthropic/experimental_pass_through/responses_adapters/test_responses_adapters_handler.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/responses_adapters/test_responses_adapters_streaming_iterator.py → tests/unit/llms/anthropic/experimental_pass_through/responses_adapters/test_responses_adapters_streaming_iterator.py
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tests/test_litellm/llms/anthropic/experimental_pass_through/responses_adapters/test_responses_adapters_transformation.py → tests/unit/llms/anthropic/experimental_pass_through/responses_adapters/test_responses_adapters_transformation.py
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