Merge pull request #42117 from BerriAI/litellm_migrate_tests_p9

test: migrate phase 9 legacy llm provider tests to tests/unit

tests/unit/llms/minimax/chat/test_transformation.py

@@ -2,14 +2,9 @@
 Test MiniMax OpenAI-compatible API support
 """
 
-import os
 from unittest.mock import MagicMock, patch
 
-import pytest
-
-
 import litellm
-from litellm import completion
 from litellm.llms.minimax.chat.transformation import MinimaxChatConfig
 
 
@@ -107,97 +102,6 @@ def test_minimax_provider_config_manager():
     assert isinstance(config, MinimaxChatConfig)
 
 
-@pytest.mark.skip(reason="Requires actual MiniMax API key")
-def test_minimax_chat_completion_basic():
-    """Test basic chat completion with MiniMax OpenAI-compatible API"""
-    response = completion(
-        model="minimax/MiniMax-M2.1",
-        messages=[
-            {"role": "system", "content": "You are a helpful assistant."},
-            {"role": "user", "content": "Hello, how are you?"},
-        ],
-        api_key=os.getenv("MINIMAX_API_KEY"),
-        api_base="https://api.minimax.io/v1",
-    )
-
-    assert response is not None
-    assert hasattr(response, "choices")
-    assert len(response.choices) > 0
-
-
-@pytest.mark.skip(reason="Requires actual MiniMax API key")
-def test_minimax_chat_completion_with_reasoning_split():
-    """Test completion with reasoning_split parameter (MiniMax M2.1 feature)"""
-    response = completion(
-        model="minimax/MiniMax-M2.1",
-        messages=[
-            {"role": "system", "content": "You are a helpful assistant."},
-            {"role": "user", "content": "Solve this problem: 2+2=?"},
-        ],
-        api_key=os.getenv("MINIMAX_API_KEY"),
-        api_base="https://api.minimax.io/v1",
-        extra_body={"reasoning_split": True},
-    )
-
-    assert response is not None
-    # Check if reasoning_details is present in response
-    if hasattr(response.choices[0].message, "reasoning_details"):
-        assert response.choices[0].message.reasoning_details is not None
-
-
-@pytest.mark.skip(reason="Requires actual MiniMax API key")
-def test_minimax_chat_completion_with_tools():
-    """Test completion with tool calling (function calling)"""
-    tools = [
-        {
-            "type": "function",
-            "function": {
-                "name": "get_weather",
-                "description": "Get the current weather in a location",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "location": {
-                            "type": "string",
-                            "description": "The city and state, e.g. San Francisco, CA",
-                        }
-                    },
-                    "required": ["location"],
-                },
-            },
-        }
-    ]
-
-    response = completion(
-        model="minimax/MiniMax-M2.1",
-        messages=[{"role": "user", "content": "What's the weather in San Francisco?"}],
-        tools=tools,
-        api_key=os.getenv("MINIMAX_API_KEY"),
-        api_base="https://api.minimax.io/v1",
-    )
-
-    assert response is not None
-    assert hasattr(response, "choices")
-
-
-@pytest.mark.skip(reason="Requires actual MiniMax API key")
-def test_minimax_chat_completion_streaming():
-    """Test streaming completion"""
-    response = completion(
-        model="minimax/MiniMax-M2.1",
-        messages=[{"role": "user", "content": "Count to 5"}],
-        stream=True,
-        api_key=os.getenv("MINIMAX_API_KEY"),
-        api_base="https://api.minimax.io/v1",
-    )
-
-    chunks = []
-    for chunk in response:
-        chunks.append(chunk)
-
-    assert len(chunks) > 0
-
-
 if __name__ == "__main__":
     # Run basic tests that don't require API key
     print("Testing MiniMax Chat Config...")

tests/unit/llms/minimax/messages/test_transformation.py

@@ -2,14 +2,9 @@
 Test MiniMax Anthropic-compatible API support
 """
 
-import os
 from unittest.mock import MagicMock, patch
 
-import pytest
-
-
 import litellm
-from litellm import completion
 from litellm.llms.minimax.messages.transformation import MinimaxMessagesConfig
 
 
@@ -58,75 +53,6 @@ def test_minimax_provider_config_manager():
     assert config.custom_llm_provider == "minimax"
 
 
-@pytest.mark.skip(reason="Requires actual MiniMax API key")
-def test_minimax_completion_basic():
-    """Test basic completion with MiniMax Anthropic-compatible API"""
-    response = completion(
-        model="minimax/MiniMax-M2.1",
-        messages=[{"role": "user", "content": "Hello, how are you?"}],
-        api_key=os.getenv("MINIMAX_API_KEY"),
-        api_base="https://api.minimax.io/anthropic/v1/messages",
-    )
-
-    assert response is not None
-    assert hasattr(response, "choices")
-    assert len(response.choices) > 0
-
-
-@pytest.mark.skip(reason="Requires actual MiniMax API key")
-def test_minimax_completion_with_thinking():
-    """Test completion with thinking parameter (MiniMax M2.1 feature)"""
-    response = completion(
-        model="minimax/MiniMax-M2.1",
-        messages=[{"role": "user", "content": "Solve this problem: 2+2=?"}],
-        api_key=os.getenv("MINIMAX_API_KEY"),
-        api_base="https://api.minimax.io/anthropic/v1/messages",
-        thinking={"type": "enabled", "budget_tokens": 1000},
-    )
-
-    assert response is not None
-    # Check if thinking content is present in response
-    for choice in response.choices:
-        if hasattr(choice.message, "content"):
-            # MiniMax returns thinking blocks similar to Anthropic
-            assert choice.message.content is not None
-
-
-@pytest.mark.skip(reason="Requires actual MiniMax API key")
-def test_minimax_completion_with_tools():
-    """Test completion with tool calling (function calling)"""
-    tools = [
-        {
-            "type": "function",
-            "function": {
-                "name": "get_weather",
-                "description": "Get the current weather in a location",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "location": {
-                            "type": "string",
-                            "description": "The city and state, e.g. San Francisco, CA",
-                        }
-                    },
-                    "required": ["location"],
-                },
-            },
-        }
-    ]
-
-    response = completion(
-        model="minimax/MiniMax-M2.1",
-        messages=[{"role": "user", "content": "What's the weather in San Francisco?"}],
-        tools=tools,
-        api_key=os.getenv("MINIMAX_API_KEY"),
-        api_base="https://api.minimax.io/anthropic/v1/messages",
-    )
-
-    assert response is not None
-    assert hasattr(response, "choices")
-
-
 if __name__ == "__main__":
     # Run basic tests that don't require API key
     print("Testing MiniMax Anthropic Config...")

tests/unit/llms/moonshot/test_moonshot_chat_transformation.py

@@ -19,31 +19,6 @@ from litellm.llms.moonshot.chat.transformation import MoonshotChatConfig
 class TestMoonshotConfig:
     """Test class for Moonshot AI functionality"""
 
-    def test_default_api_base(self):
-        """Test that default API base is used when none is provided"""
-        config = MoonshotChatConfig()
-        headers = {}
-        api_key = "fake-moonshot-key"
-
-        # Call validate_environment without specifying api_base
-        result = config.validate_environment(
-            headers=headers,
-            model="moonshot-v1-8k",
-            messages=[{"role": "user", "content": "Hey"}],
-            optional_params={},
-            litellm_params={},
-            api_key=api_key,
-            api_base=None,  # Not providing api_base
-        )
-
-        # Verify headers are still set correctly
-        assert result["Authorization"] == f"Bearer {api_key}"
-        assert result["Content-Type"] == "application/json"
-
-        # We can't directly test the api_base value here since validate_environment
-        # only returns the headers, but we can verify it doesn't raise an exception
-        # which would happen if api_base handling was incorrect
-
     def test_get_supported_openai_params(self):
         """Test that get_supported_openai_params returns correct params"""
         config = MoonshotChatConfig()

tests/unit/llms/novita/chat/test_novita_chat_transformation.py

@@ -54,12 +54,3 @@ class TestNovitaConfig:
             )
 
         assert "Missing Novita AI API Key" in str(excinfo.value)
-
-    def test_inheritance(self):
-        """Test proper inheritance from OpenAIGPTConfig"""
-        config = NovitaConfig()
-
-        from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
-
-        assert isinstance(config, OpenAIGPTConfig)
-        assert hasattr(config, "get_supported_openai_params")