test: migrate wave 1 phase 1 legacy tests to tests/unit

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

tests/test_litellm/completion_extras/test_responses_bridge_provider_propagation.py

@@ -1,153 +0,0 @@
-"""
-Regression test for https://github.com/BerriAI/litellm/issues/28505 -
-the Responses API bridge double-strips the provider prefix from the
-model name when a Chat Completions request has both `tools` and
-`reasoning_effort`.
-
-Root cause: the bridge handler called `litellm.responses()` /
-`litellm.aresponses()` without passing the already-resolved
-`custom_llm_provider`. The downstream call then re-invoked
-`get_llm_provider()` with `custom_llm_provider=None`, which stripped
-a second provider prefix from a `provider/provider/model` deployment
-string.
-
-This test pins both the sync and async bridge handler call sites:
-the resolved `custom_llm_provider` must be forwarded to the underlying
-`responses` / `aresponses` call so the provider isn't re-detected.
-"""
-
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-from litellm.completion_extras.litellm_responses_transformation.handler import (
-    ResponsesToCompletionBridgeHandler,
-)
-
-
-def _validated_kwargs():
-    return {
-        "model": "openai/openai/openai/gpt-5.5",
-        "messages": [{"role": "user", "content": "hi"}],
-        "optional_params": {},
-        "litellm_params": {},
-        "headers": {},
-        "model_response": MagicMock(),
-        "logging_obj": MagicMock(),
-        "custom_llm_provider": "openai",
-    }
-
-
-def test_sync_completion_forwards_custom_llm_provider():
-    handler = ResponsesToCompletionBridgeHandler()
-    handler.transformation_handler = MagicMock()
-    handler.transformation_handler.transform_request.return_value = {
-        "model": "openai/openai/openai/gpt-5.5",
-        "input": [],
-        # `_build_sanitized_litellm_params` spreads `custom_llm_provider` from
-        # `litellm_params` into request_data on the real bridge path.  Seed
-        # it here so the test exercises the overwrite (not an explicit kwarg
-        # that would TypeError against an already-present key).
-        "custom_llm_provider": "should-be-overwritten",
-    }
-    handler.transformation_handler.transform_response.return_value = (
-        _validated_kwargs()["model_response"]
-    )
-    with (
-        patch.object(
-            handler, "validate_input_kwargs", return_value=_validated_kwargs()
-        ),
-        patch(
-            "litellm.responses",
-            return_value=MagicMock(spec=[]),
-        ) as mock_responses,
-    ):
-        # The handler routes ResponsesAPIResponse through transform_response.
-        # We just want to verify the kwargs going INTO responses().
-        try:
-            handler.completion(acompletion=False)
-        except Exception:
-            # Downstream handling (transform_response, type checks) is not
-            # the subject of this test.
-            pass
-        assert mock_responses.called
-        kwargs = mock_responses.call_args.kwargs
-        assert kwargs.get("custom_llm_provider") == "openai", (
-            "sync bridge must forward custom_llm_provider to litellm.responses() "
-            "so the downstream get_llm_provider() call does not re-strip the "
-            "provider prefix on a provider/provider/model deployment string"
-        )
-
-
-@pytest.mark.asyncio
-async def test_async_completion_forwards_custom_llm_provider():
-    handler = ResponsesToCompletionBridgeHandler()
-    handler.transformation_handler = MagicMock()
-    handler.transformation_handler.transform_request.return_value = {
-        "model": "openai/openai/openai/gpt-5.5",
-        "input": [],
-        # `_build_sanitized_litellm_params` spreads `custom_llm_provider` from
-        # `litellm_params` into request_data on the real bridge path.  Seed
-        # it here so the test exercises the overwrite (not an explicit kwarg
-        # that would TypeError against an already-present key).
-        "custom_llm_provider": "should-be-overwritten",
-    }
-
-    async def _fake_aresponses(**kwargs):
-        _fake_aresponses.kwargs = kwargs
-        return MagicMock(spec=[])
-
-    _fake_aresponses.kwargs = {}
-
-    with (
-        patch.object(
-            handler, "validate_input_kwargs", return_value=_validated_kwargs()
-        ),
-        patch("litellm.aresponses", _fake_aresponses),
-    ):
-        try:
-            await handler.acompletion()
-        except Exception:
-            pass
-        assert _fake_aresponses.kwargs.get("custom_llm_provider") == "openai", (
-            "async bridge must forward custom_llm_provider to litellm.aresponses() "
-            "so the downstream get_llm_provider() call does not re-strip the "
-            "provider prefix on a provider/provider/model deployment string"
-        )
-
-
-@pytest.mark.asyncio
-async def test_async_completion_forwards_aws_region_name():
-    handler = ResponsesToCompletionBridgeHandler()
-    handler.transformation_handler = MagicMock()
-    handler.transformation_handler.transform_request.return_value = {
-        "model": "openai.gpt-5.5",
-        "input": [],
-        "aws_region_name": "us-east-2",
-        "api_base": "https://bedrock-mantle.us-east-1.api.aws/v1",
-        "custom_llm_provider": "bedrock_mantle",
-    }
-
-    async def _fake_aresponses(**kwargs):
-        _fake_aresponses.kwargs = kwargs
-        return MagicMock(spec=[])
-
-    _fake_aresponses.kwargs = {}
-
-    validated = _validated_kwargs()
-    validated["custom_llm_provider"] = "bedrock_mantle"
-    validated["litellm_params"] = {
-        "aws_region_name": "us-east-2",
-        "api_base": "https://bedrock-mantle.us-east-1.api.aws/v1",
-        "custom_llm_provider": "bedrock_mantle",
-    }
-
-    with (
-        patch.object(handler, "validate_input_kwargs", return_value=validated),
-        patch("litellm.aresponses", _fake_aresponses),
-    ):
-        try:
-            await handler.acompletion()
-        except Exception:
-            pass
-        assert _fake_aresponses.kwargs.get("aws_region_name") == "us-east-2"

tests/unit/a2a_protocol/providers/watsonx_orchestrate/test_watsonx_orchestrate_transformation.py

@@ -1,7 +1,6 @@
 import asyncio
 import json
 import time
-from pathlib import Path
 
 import httpx
 import pytest
@@ -571,20 +570,3 @@ def test_config_manager_returns_wxo_provider():
     )
     assert config is not None
     assert config.__class__.__name__ == "WatsonxOrchestrateA2AConfig"
-
-
-def test_wxo_dashboard_auth_fields():
-    fields_path = (
-        Path(__file__).resolve().parents[5]
-        / "litellm/proxy/public_endpoints/agent_create_fields.json"
-    )
-    agent_fields = json.loads(fields_path.read_text())
-    wxo_agent = next(
-        agent for agent in agent_fields if agent["agent_type"] == "watsonx_orchestrate"
-    )
-    fields_by_key = {field["key"]: field for field in wxo_agent["credential_fields"]}
-
-    assert fields_by_key["auth_mode"]["default_value"] == "cp4d"
-    # Username is CP4D-only; UI does not require it so ibm_cloud users are not blocked.
-    assert fields_by_key["username"]["required"] is False
-    assert "cp4d" in fields_by_key["username"]["tooltip"].lower()

tests/unit/batches/test_responses_batch_cost.py

@@ -12,17 +12,28 @@ Line shape decides the parse, not the batch's declared endpoint, so an output
 file mixing Responses-shaped and chat-shaped lines sums across both.
 """
 
-from typing import Literal, get_args, get_type_hints
 
 import pytest
 
 import litellm
 import litellm.batches.batch_utils as bu
-from litellm.types.llms.openai import CreateBatchRequest
 
 MODEL = "gpt-5.6"
 
 
+@pytest.fixture
+def local_model_cost_map(monkeypatch):
+    original_model_cost = litellm.model_cost
+    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
+    litellm.model_cost = litellm.get_model_cost_map(url="")
+    litellm.get_model_info.cache_clear()
+    try:
+        yield
+    finally:
+        litellm.model_cost = original_model_cost
+        litellm.get_model_info.cache_clear()
+
+
 def _responses_line(input_tokens: int, output_tokens: int) -> dict:
     return {
         "response": {
@@ -107,13 +118,3 @@ async def test_mixed_shape_batch_output_sums_across_both_line_shapes(local_model
     assert result.cost == pytest.approx(
         133 * model_info["input_cost_per_token_batches"] + 107 * model_info["output_cost_per_token_batches"]
     )
-
-
-def test_create_batch_endpoint_accepts_v1_responses():
-    """A type-checked caller can pass endpoint="/v1/responses", which the runtime
-    already forwarded correctly."""
-    endpoint_annotation = get_type_hints(CreateBatchRequest)["endpoint"]
-    assert "/v1/responses" in get_args(endpoint_annotation)
-
-    for create_fn in (litellm.create_batch, litellm.acreate_batch):
-        assert "/v1/responses" in get_args(get_type_hints(create_fn)["endpoint"])

tests/unit/chat_completions/test_dispatch.py

@@ -1,11 +1,9 @@
-import inspect
 from collections.abc import Awaitable, Callable, Mapping
-from typing import Final, cast  # noqa: TID251  # narrows legacy callable signatures for inspect
+from typing import Final, cast  # noqa: TID251  # narrows legacy callable signatures
 
 import pytest
 
 import litellm
-from litellm import main as python_chat
 from litellm.chat_completions.dispatch import (
     _ADISPATCH,  # pyright: ignore[reportPrivateUsage]  # tests configured dispatch
     _DISPATCH,  # pyright: ignore[reportPrivateUsage]  # tests configured dispatch
@@ -40,15 +38,6 @@ def acompletion_binding(native: NativeAcompletion | None) -> NativeBinding[Nativ
     return binding
 
 
-def test_public_signature_is_the_legacy_signature() -> None:
-    public_completion: Final = cast(Callable[..., object], litellm.completion)
-    legacy_completion: Final = cast(Callable[..., object], python_chat.completion)
-    public_acompletion: Final = cast(Callable[..., object], litellm.acompletion)
-    legacy_acompletion: Final = cast(Callable[..., object], python_chat.acompletion)
-    assert inspect.signature(public_completion) == inspect.signature(legacy_completion)
-    assert inspect.signature(public_acompletion) == inspect.signature(legacy_acompletion)
-
-
 def test_python_route_forwards_original_call_shape() -> None:
     metadata: Final = {"user_id": "u"}
     args: Final[tuple[object, ...]] = ("gpt-4o", MESSAGES)

tests/unit/integrations/helicone/test_helicone_gemini.py

@@ -3,7 +3,6 @@ Test HeliconeLogger Gemini/Vertex AI support.
 Fixes: https://github.com/BerriAI/litellm/issues/19093
 """
 
-import pytest
 
 
 def test_helicone_gemini_model_in_list():
@@ -36,39 +35,6 @@ def test_helicone_gemini_models_recognized():
         assert is_recognized, f"{model} should be recognized by helicone_model_list"
 
 
-def test_helicone_vertex_ai_models_recognized():
-    """
-    Test that Vertex AI models (GLM, DeepSeek, etc.) are recognized via custom_llm_provider.
-    """
-    # Test models that don't contain "gemini" but are vertex_ai
-    test_models = [
-        "vertex_ai/zai-org/glm-4.7-maas",
-        "vertex_ai/deepseek-ai/deepseek-v3",
-        "vertex_ai/meta/llama-3.1-405b",
-    ]
-    for model in test_models:
-        is_vertex_ai = model.startswith("vertex_ai/")
-        assert is_vertex_ai, f"{model} should be recognized as vertex_ai model"
-
-
-def test_helicone_vertex_ai_via_custom_llm_provider():
-    """
-    Test that vertex_ai models are recognized when custom_llm_provider is set.
-    """
-    # Models without vertex_ai/ prefix but with custom_llm_provider="vertex_ai"
-    test_cases = [
-        ("zai-org/glm-4.7-maas", "vertex_ai"),
-        ("deepseek-ai/deepseek-v3", "vertex_ai"),
-    ]
-    for model, custom_llm_provider in test_cases:
-        is_vertex_ai = custom_llm_provider == "vertex_ai" or model.startswith(
-            "vertex_ai/"
-        )
-        assert (
-            is_vertex_ai
-        ), f"{model} with custom_llm_provider={custom_llm_provider} should be recognized as vertex_ai"
-
-
 def test_helicone_vertex_gemini_gets_vertex_provider_url():
     """
     Test that vertex_ai/gemini-* models route to aiplatform.googleapis.com,