[Bug Fix] Anthropic - Token Usage Null Handling in calculate_usage (#12068)

* [Bug Fix] Anthropic - Token Usage Null Handling in calculate_usage (BerriAI/litellm#11920)

* [Fix] Missed a null check and used a cast instead by error

litellm/llms/anthropic/chat/transformation.py

@@ -708,7 +708,8 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
             _litellm_metadata
             and isinstance(_litellm_metadata, dict)
             and "user_id" in _litellm_metadata
-            and not _valid_user_id(_litellm_metadata.get("user_id", None))
+            and _litellm_metadata["user_id"] is not None
+            and not _valid_user_id(_litellm_metadata["user_id"])
         ):
             optional_params["metadata"] = {"user_id": _litellm_metadata["user_id"]}
 
@@ -805,19 +806,29 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
     def calculate_usage(
         self, usage_object: dict, reasoning_content: Optional[str]
     ) -> Usage:
-        prompt_tokens = usage_object.get("input_tokens", 0)
-        completion_tokens = usage_object.get("output_tokens", 0)
+        # NOTE: Sometimes the usage object has None set explicitly for token counts, meaning .get() & key access returns None, and we need to account for this
+        prompt_tokens = usage_object.get("input_tokens", 0) or 0
+        completion_tokens = usage_object.get("output_tokens", 0) or 0
         _usage = usage_object
         cache_creation_input_tokens: int = 0
         cache_read_input_tokens: int = 0
         web_search_requests: Optional[int] = None
-        if "cache_creation_input_tokens" in _usage:
+        if (
+            "cache_creation_input_tokens" in _usage
+            and _usage["cache_creation_input_tokens"] is not None
+        ):
             cache_creation_input_tokens = _usage["cache_creation_input_tokens"]
-        if "cache_read_input_tokens" in _usage:
+        if (
+            "cache_read_input_tokens" in _usage
+            and _usage["cache_read_input_tokens"] is not None
+        ):
             cache_read_input_tokens = _usage["cache_read_input_tokens"]
             prompt_tokens += cache_read_input_tokens
-        if "server_tool_use" in _usage:
-            if "web_search_requests" in _usage["server_tool_use"]:
+        if "server_tool_use" in _usage and _usage["server_tool_use"] is not None:
+            if (
+                "web_search_requests" in _usage["server_tool_use"]
+                and _usage["server_tool_use"]["web_search_requests"] is not None
+            ):
                 web_search_requests = cast(
                     int, _usage["server_tool_use"]["web_search_requests"]
                 )

tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py

@@ -11,6 +11,7 @@ sys.path.insert(
 from unittest.mock import MagicMock, patch
 
 from litellm.llms.anthropic.chat.transformation import AnthropicConfig
+from litellm.types.utils import PromptTokensDetailsWrapper, ServerToolUse
 
 
 def test_response_format_transformation_unit_test():
@@ -57,6 +58,83 @@ def test_calculate_usage():
     assert usage._cache_creation_input_tokens == 12304
     assert usage._cache_read_input_tokens == 0
 
+@pytest.mark.parametrize("usage_object,expected_usage", [
+    [
+        {
+            "cache_creation_input_tokens": None,
+            "cache_read_input_tokens": None,
+            "input_tokens": None,
+            "output_tokens": 43,
+            "server_tool_use": None
+        },
+        {
+            "prompt_tokens": 0,
+            "completion_tokens": 43,
+            "total_tokens": 43,
+            "_cache_creation_input_tokens": 0,
+            "_cache_read_input_tokens": 0
+        }
+    ],
+    [
+        {
+            "cache_creation_input_tokens": 100,
+            "cache_read_input_tokens": 200,
+            "input_tokens": 1,
+            "output_tokens": None,
+            "server_tool_use": None
+        },
+        {
+            "prompt_tokens": 1 + 200,
+            "completion_tokens": 0,
+            "total_tokens": 1 + 200,
+            "_cache_creation_input_tokens": 100,
+            "_cache_read_input_tokens": 200,
+        }
+    ],
+    [
+        {
+            "server_tool_use": {
+                "web_search_requests": 10
+            }
+        },
+        {
+            "server_tool_use": ServerToolUse(web_search_requests=10)
+        }
+    ]
+])
+def test_calculate_usage_nulls(usage_object, expected_usage):
+    """
+    Correctly deal with null values in usage object
+
+    Fixes https://github.com/BerriAI/litellm/issues/11920
+    """
+    config = AnthropicConfig()
+
+    usage = config.calculate_usage(usage_object=usage_object, reasoning_content=None)
+    for k, v in expected_usage.items():
+        assert hasattr(usage, k)
+        assert getattr(usage, k) == v
+
+@pytest.mark.parametrize("usage_object", [
+    {
+        "server_tool_use": {
+            "web_search_requests": None
+        }
+    },
+    {
+        "server_tool_use": None
+    }
+])
+def test_calculate_usage_server_tool_null(usage_object):
+    """
+    Correctly deal with null values in usage object
+
+    Fixes https://github.com/BerriAI/litellm/issues/11920
+    """
+    config = AnthropicConfig()
+    
+    usage = config.calculate_usage(usage_object=usage_object, reasoning_content=None)
+    assert not hasattr(usage, "server_tool_use")
 
 def test_extract_response_content_with_citations():
     config = AnthropicConfig()