fix(proxy): count contents text in the local token_counter fallback

a contents-only request that hits a provider error used to raise
ValueError -> 500 on the local fallback; translate text parts to
chat messages so the fallback counts them

litellm/proxy/proxy_server.py

@@ -13459,6 +13459,26 @@ def _system_message(system: object) -> ChatCompletionSystemMessage | None:
     return message
 
 
+def _contents_as_messages(contents: object) -> tuple[Mapping[str, object], ...] | None:
+    """Approximate gemini contents as chat messages for the local fallback
+    tokenizer; only text parts are countable locally."""
+    if not isinstance(contents, list):
+        return None
+    messages: Final = tuple(
+        {  # mutable-ok: transient chat-shaped message for the local tokenizer
+            "role": "assistant" if content.get("role") == "model" else "user",
+            "content": "\n".join(
+                part["text"]
+                for part in content.get("parts", ())
+                if isinstance(part, Mapping) and isinstance(part.get("text"), str)
+            ),
+        }
+        for content in contents
+        if isinstance(content, Mapping)
+    )
+    return tuple(message for message in messages if message["content"]) or None
+
+
 @router.post(
     "/utils/token_counter",
     tags=["llm utils"],
@@ -13561,7 +13581,8 @@ async def token_counter(request: TokenCountRequest, call_endpoint: bool = False)
     tokenizer_used: Final = str(_tokenizer_used["type"])
     system_message: Final = _system_message(system)
     typed_messages: Final = cast(  # cast-ok: request messages are raw chat-shaped dicts that token_counter normalizes
-        Sequence[AllMessageValues] | None, messages
+        Sequence[AllMessageValues] | None,
+        messages if messages is not None else _contents_as_messages(contents),
     )
     counted_messages: Final = (
         typed_messages if typed_messages is None or system_message is None else (system_message, *typed_messages)

tests/test_litellm/proxy/proxy_server/test_routes_utils.py

@@ -84,9 +84,7 @@ def test_token_counter_counts_off_the_event_loop(client, auth_as, patched_token_
     assert counted_off_loop == [True]
 
 
-def test_token_counter_missing_input_returns_400(
-    client, auth_as, patched_token_counter
-):
+def test_token_counter_missing_input_returns_400(client, auth_as, patched_token_counter):
     """Pins ``POST /utils/token_counter`` (error: missing input)."""
     with auth_as():
         response = client.post("/utils/token_counter", json={"model": "gpt-4"})
@@ -118,9 +116,7 @@ def patched_supported_params(monkeypatch):
 def test_supported_openai_params_happy_path(client, auth_as, patched_supported_params):
     """Pins ``GET /utils/supported_openai_params``."""
     with auth_as():
-        response = client.get(
-            "/utils/supported_openai_params", params={"model": "gpt-4"}
-        )
+        response = client.get("/utils/supported_openai_params", params={"model": "gpt-4"})
     assert response.status_code == 200
     assert normalize(response.json()) == {
         "supported_openai_params": ["max_tokens", "temperature", "top_p"],
@@ -359,7 +355,10 @@ def test_token_counter_fallback_counts_tools_system_and_anthropic_blocks(client,
             "content": [
                 {"type": "text", "text": "What is in this file?"},
                 {"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": "iVBORw0KGgo="}},
-                {"type": "document", "source": {"type": "base64", "media_type": "application/pdf", "data": "JVBERi0xLjQK"}},
+                {
+                    "type": "document",
+                    "source": {"type": "base64", "media_type": "application/pdf", "data": "JVBERi0xLjQK"},
+                },
             ],
         }
     ]
@@ -405,3 +404,24 @@ def test_token_counter_fallback_prompt_with_tools_does_not_500(client, auth_as,
 
     assert response.status_code == 200, response.text
     assert response.json()["total_tokens"] == litellm.token_counter(model="claude-fable-5", text=prompt)
+
+
+def test_token_counter_contents_only_request_counts_text_parts(client, auth_as, monkeypatch):
+    """Regression: a contents-only request (google countTokens shape) reaches the
+    local fallback as translated messages instead of raising ValueError -> 500."""
+    monkeypatch.setattr(proxy_server, "llm_router", None)
+    monkeypatch.setattr(litellm, "disable_token_counter", False, raising=False)
+    contents = [
+        {"role": "user", "parts": [{"text": "hello world"}, {"inline_data": {"data": "abc"}}]},
+        {"role": "model", "parts": [{"text": "hi there"}]},
+        {"role": "user", "parts": [{"inline_data": {"data": "abc"}}]},
+    ]
+
+    with auth_as():
+        response = client.post("/utils/token_counter", json={"model": "claude-fable-5", "contents": contents})
+
+    assert response.status_code == 200, response.text
+    assert response.json()["total_tokens"] == litellm.token_counter(
+        model="claude-fable-5",
+        messages=[{"role": "user", "content": "hello world"}, {"role": "assistant", "content": "hi there"}],
+    )