diff --git a/litellm/constants.py b/litellm/constants.py index 012599ab6ab..385e3723bee 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -409,9 +409,6 @@ CACHED_STREAMING_CHUNK_DELAY = float(os.getenv("CACHED_STREAMING_CHUNK_DELAY", 0 AUDIO_SPEECH_CHUNK_SIZE = int( os.getenv("AUDIO_SPEECH_CHUNK_SIZE", 8192) ) # chunk_size for audio speech streaming. Balance between latency and memory usage -MAX_SIZE_PER_ITEM_IN_MEMORY_CACHE_IN_KB = int( - os.getenv("MAX_SIZE_PER_ITEM_IN_MEMORY_CACHE_IN_KB", 512) -) DEFAULT_MAX_TOKENS_FOR_TRITON = int(os.getenv("DEFAULT_MAX_TOKENS_FOR_TRITON", 2000)) #### Networking settings #### # Sentinel used when `REQUEST_TIMEOUT` is unset: `litellm.request_timeout` keeps this diff --git a/litellm/integrations/arize/_utils.py b/litellm/integrations/arize/_utils.py index 8dfaa8b1425..a1bf65141c9 100644 --- a/litellm/integrations/arize/_utils.py +++ b/litellm/integrations/arize/_utils.py @@ -220,23 +220,57 @@ def _set_structured_outputs(span: "Span", response_obj, msg_attrs, span_attrs): safe_set_attribute(span, f"{prefix}.{msg_attrs.MESSAGE_ROLE}", message_role) +def _safe_get(obj, key, default=None): + """Read ``key`` from a dict-like or Pydantic-model-like object. + + The arize/langfuse_otel logger receives ``usage`` objects from many sources: + plain dicts, litellm ``Usage`` (which exposes ``.get``), and raw OpenAI + Pydantic models (e.g. ``openai.types.completion_usage.CompletionUsage`` and + nested ``CompletionTokensDetails`` / ``OutputTokensDetails``) which do NOT + expose ``.get``. Calling ``.get`` on the latter raised ``AttributeError`` — + see https://github.com/BerriAI/litellm/issues/13672. + """ + if obj is None: + return default + getter = getattr(obj, "get", None) + if callable(getter): + try: + return getter(key, default) + except TypeError: + # Some objects expose `.get` with a different signature + pass + return getattr(obj, key, default) + + def _set_usage_outputs(span: "Span", response_obj, span_attrs): usage = response_obj and response_obj.get("usage") if not usage: return safe_set_attribute( - span, span_attrs.LLM_TOKEN_COUNT_TOTAL, usage.get("total_tokens") + span, span_attrs.LLM_TOKEN_COUNT_TOTAL, _safe_get(usage, "total_tokens") + ) + completion_tokens = _safe_get(usage, "completion_tokens") or _safe_get( + usage, "output_tokens" ) - completion_tokens = usage.get("completion_tokens") or usage.get("output_tokens") if completion_tokens: safe_set_attribute( span, span_attrs.LLM_TOKEN_COUNT_COMPLETION, completion_tokens ) - prompt_tokens = usage.get("prompt_tokens") or usage.get("input_tokens") + prompt_tokens = _safe_get(usage, "prompt_tokens") or _safe_get( + usage, "input_tokens" + ) if prompt_tokens: safe_set_attribute(span, span_attrs.LLM_TOKEN_COUNT_PROMPT, prompt_tokens) - reasoning_tokens = usage.get("output_tokens_details", {}).get("reasoning_tokens") + + # Reasoning tokens live in `completion_tokens_details` for Chat Completions + # API (Usage) and in `output_tokens_details` for Responses API + # (ResponseAPIUsage). Both nested objects may be plain Pydantic models + # without `.get`. + token_details = _safe_get(usage, "completion_tokens_details") or _safe_get( + usage, "output_tokens_details" + ) + reasoning_tokens = _safe_get(token_details, "reasoning_tokens") if reasoning_tokens: safe_set_attribute( span, diff --git a/litellm/llms/cloudflare/chat/transformation.py b/litellm/llms/cloudflare/chat/transformation.py index 9e59782bf73..d0c2e86f708 100644 --- a/litellm/llms/cloudflare/chat/transformation.py +++ b/litellm/llms/cloudflare/chat/transformation.py @@ -147,9 +147,9 @@ class CloudflareChatConfig(BaseConfig): ) -> ModelResponse: completion_response = raw_response.json() - model_response.choices[0].message.content = completion_response["result"][ # type: ignore - "response" - ] + # Support both "response" and "response_text" keys (newer models like Nemotron use "response_text") + result = completion_response["result"] + model_response.choices[0].message.content = result.get("response") if result.get("response") is not None else result.get("response_text", "") # type: ignore prompt_tokens = litellm.utils.get_token_count(messages=messages, model=model) completion_tokens = len( diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index f6de40717d1..5cccd5f00af 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -4645,6 +4645,169 @@ "supports_vision": true, "supports_web_search": true }, + "azure/gpt-5.5": { + "cache_read_input_token_cost": 5e-07, + "cache_read_input_token_cost_above_272k_tokens": 1e-06, + "cache_read_input_token_cost_priority": 1e-06, + "cache_read_input_token_cost_above_272k_tokens_priority": 2e-06, + "input_cost_per_token": 5e-06, + "input_cost_per_token_above_272k_tokens": 1e-05, + "input_cost_per_token_priority": 1e-05, + "input_cost_per_token_above_272k_tokens_priority": 2e-05, + "litellm_provider": "azure", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 3e-05, + "output_cost_per_token_above_272k_tokens": 4.5e-05, + "output_cost_per_token_priority": 6e-05, + "output_cost_per_token_above_272k_tokens_priority": 9e-05, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": false + }, + "azure/gpt-5.5-2026-04-23": { + "cache_read_input_token_cost": 5e-07, + "cache_read_input_token_cost_above_272k_tokens": 1e-06, + "cache_read_input_token_cost_priority": 1e-06, + "cache_read_input_token_cost_above_272k_tokens_priority": 2e-06, + "input_cost_per_token": 5e-06, + "input_cost_per_token_above_272k_tokens": 1e-05, + "input_cost_per_token_priority": 1e-05, + "input_cost_per_token_above_272k_tokens_priority": 2e-05, + "litellm_provider": "azure", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 3e-05, + "output_cost_per_token_above_272k_tokens": 4.5e-05, + "output_cost_per_token_priority": 6e-05, + "output_cost_per_token_above_272k_tokens_priority": 9e-05, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true + }, + "azure/gpt-5.5-pro": { + "cache_read_input_token_cost": 6e-06, + "cache_read_input_token_cost_above_272k_tokens": 1.2e-05, + "input_cost_per_token": 6e-05, + "input_cost_per_token_above_272k_tokens": 0.00012, + "litellm_provider": "azure", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "output_cost_per_token": 0.00036, + "output_cost_per_token_above_272k_tokens": 0.00054, + "supported_endpoints": [ + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": false, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": false, + "supports_low_reasoning_effort": false + }, + "azure/gpt-5.5-pro-2026-04-23": { + "cache_read_input_token_cost": 6e-06, + "cache_read_input_token_cost_above_272k_tokens": 1.2e-05, + "input_cost_per_token": 6e-05, + "input_cost_per_token_above_272k_tokens": 0.00012, + "litellm_provider": "azure", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "output_cost_per_token": 0.00036, + "output_cost_per_token_above_272k_tokens": 0.00054, + "supported_endpoints": [ + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": false, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_web_search": true + }, "azure/gpt-5.4-mini": { "cache_read_input_token_cost": 7.5e-08, "input_cost_per_token": 7.5e-07, diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index 4ca6895c2ce..4afac7173cd 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -5845,10 +5845,15 @@ async def initialize( # noqa: PLR0915 if litellm_log_setting.upper() == "INFO": import logging - from litellm._logging import verbose_proxy_logger, verbose_router_logger + from litellm._logging import ( + verbose_logger, + verbose_proxy_logger, + verbose_router_logger, + ) # this must ALWAYS remain logging.INFO, DO NOT MODIFY THIS + verbose_logger.setLevel(level=logging.INFO) # set package log to info verbose_router_logger.setLevel( level=logging.INFO ) # set router logs to info diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index c8ed58ff3ea..4d8f3a984f7 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -4659,6 +4659,169 @@ "supports_vision": true, "supports_web_search": true }, + "azure/gpt-5.5": { + "cache_read_input_token_cost": 5e-07, + "cache_read_input_token_cost_above_272k_tokens": 1e-06, + "cache_read_input_token_cost_priority": 1e-06, + "cache_read_input_token_cost_above_272k_tokens_priority": 2e-06, + "input_cost_per_token": 5e-06, + "input_cost_per_token_above_272k_tokens": 1e-05, + "input_cost_per_token_priority": 1e-05, + "input_cost_per_token_above_272k_tokens_priority": 2e-05, + "litellm_provider": "azure", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 3e-05, + "output_cost_per_token_above_272k_tokens": 4.5e-05, + "output_cost_per_token_priority": 6e-05, + "output_cost_per_token_above_272k_tokens_priority": 9e-05, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": false + }, + "azure/gpt-5.5-2026-04-23": { + "cache_read_input_token_cost": 5e-07, + "cache_read_input_token_cost_above_272k_tokens": 1e-06, + "cache_read_input_token_cost_priority": 1e-06, + "cache_read_input_token_cost_above_272k_tokens_priority": 2e-06, + "input_cost_per_token": 5e-06, + "input_cost_per_token_above_272k_tokens": 1e-05, + "input_cost_per_token_priority": 1e-05, + "input_cost_per_token_above_272k_tokens_priority": 2e-05, + "litellm_provider": "azure", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 3e-05, + "output_cost_per_token_above_272k_tokens": 4.5e-05, + "output_cost_per_token_priority": 6e-05, + "output_cost_per_token_above_272k_tokens_priority": 9e-05, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true + }, + "azure/gpt-5.5-pro": { + "cache_read_input_token_cost": 6e-06, + "cache_read_input_token_cost_above_272k_tokens": 1.2e-05, + "input_cost_per_token": 6e-05, + "input_cost_per_token_above_272k_tokens": 0.00012, + "litellm_provider": "azure", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "output_cost_per_token": 0.00036, + "output_cost_per_token_above_272k_tokens": 0.00054, + "supported_endpoints": [ + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": false, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": false, + "supports_low_reasoning_effort": false + }, + "azure/gpt-5.5-pro-2026-04-23": { + "cache_read_input_token_cost": 6e-06, + "cache_read_input_token_cost_above_272k_tokens": 1.2e-05, + "input_cost_per_token": 6e-05, + "input_cost_per_token_above_272k_tokens": 0.00012, + "litellm_provider": "azure", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "output_cost_per_token": 0.00036, + "output_cost_per_token_above_272k_tokens": 0.00054, + "supported_endpoints": [ + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": false, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_web_search": true + }, "azure/gpt-5.4-mini": { "cache_read_input_token_cost": 7.5e-08, "input_cost_per_token": 7.5e-07, diff --git a/tests/test_litellm/integrations/arize/test_arize_utils.py b/tests/test_litellm/integrations/arize/test_arize_utils.py index a87a4167899..86c5448d468 100644 --- a/tests/test_litellm/integrations/arize/test_arize_utils.py +++ b/tests/test_litellm/integrations/arize/test_arize_utils.py @@ -273,6 +273,91 @@ def test_arize_set_attributes_responses_api(): ) +def test_set_usage_outputs_pydantic_completion_usage(): + """ + Regression test for https://github.com/BerriAI/litellm/issues/13672 + + `_set_usage_outputs` previously called `usage.get(...)` which crashes when + `usage` is a plain Pydantic model (e.g. openai.types.completion_usage.CompletionUsage) + that does not implement dict-style `.get()`. Same crash for nested + `output_tokens_details` / `completion_tokens_details`. + + The function must: + 1. Read total/prompt/completion tokens from a Pydantic usage without `.get`. + 2. Read reasoning_tokens from `completion_tokens_details` (chat completions API) + OR `output_tokens_details` (responses API), even when those nested objects + are Pydantic models without `.get`. + 3. Not raise AttributeError; not call span.record_exception. + """ + from unittest.mock import MagicMock + + from openai.types.completion_usage import ( + CompletionTokensDetails, + CompletionUsage, + ) + + from litellm.integrations.arize._utils import _set_usage_outputs + + span = MagicMock() + + # Plain OpenAI Pydantic model — has no `.get()` + usage = CompletionUsage( + completion_tokens=60, + prompt_tokens=40, + total_tokens=100, + completion_tokens_details=CompletionTokensDetails(reasoning_tokens=25), + ) + assert not hasattr(usage, "get"), "precondition: CompletionUsage must lack .get" + + response_obj = {"usage": usage} + + # Must not raise + _set_usage_outputs(span, response_obj, SpanAttributes) + + span.set_attribute.assert_any_call(SpanAttributes.LLM_TOKEN_COUNT_TOTAL, 100) + span.set_attribute.assert_any_call(SpanAttributes.LLM_TOKEN_COUNT_PROMPT, 40) + span.set_attribute.assert_any_call(SpanAttributes.LLM_TOKEN_COUNT_COMPLETION, 60) + # reasoning_tokens for chat completions live in completion_tokens_details + span.set_attribute.assert_any_call( + SpanAttributes.LLM_TOKEN_COUNT_COMPLETION_DETAILS_REASONING, 25 + ) + + +def test_set_usage_outputs_pydantic_response_api_usage(): + """ + Same crash also affects Responses API with `output_tokens_details` as a + Pydantic model that lacks `.get()`. Verifies the responses-API path. + """ + from unittest.mock import MagicMock + + from litellm.integrations.arize._utils import _set_usage_outputs + from litellm.types.llms.openai import OutputTokensDetails + + # Build an object that mimics openai ResponsesAPI usage but lacks `.get` + # (uses a plain class — not BaseLiteLLMOpenAIResponseObject) + class PlainResponsesUsage: + def __init__(self): + self.total_tokens = 370 + self.input_tokens = 120 + self.output_tokens = 250 + self.output_tokens_details = OutputTokensDetails(reasoning_tokens=180) + + usage = PlainResponsesUsage() + assert not hasattr(usage, "get") + + span = MagicMock() + response_obj = {"usage": usage} + + _set_usage_outputs(span, response_obj, SpanAttributes) + + span.set_attribute.assert_any_call(SpanAttributes.LLM_TOKEN_COUNT_TOTAL, 370) + span.set_attribute.assert_any_call(SpanAttributes.LLM_TOKEN_COUNT_PROMPT, 120) + span.set_attribute.assert_any_call(SpanAttributes.LLM_TOKEN_COUNT_COMPLETION, 250) + span.set_attribute.assert_any_call( + SpanAttributes.LLM_TOKEN_COUNT_COMPLETION_DETAILS_REASONING, 180 + ) + + class TestArizeLogger(CustomLogger): """ Custom logger implementation to capture standard_callback_dynamic_params. diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py index dc344a433bc..93c61e003d9 100644 --- a/tests/test_litellm/test_utils.py +++ b/tests/test_litellm/test_utils.py @@ -769,6 +769,7 @@ def test_aaamodel_prices_and_context_window_json_is_valid(): "uses_embed_content": {"type": "boolean"}, "supports_reasoning": {"type": "boolean"}, "supports_minimal_reasoning_effort": {"type": "boolean"}, + "supports_low_reasoning_effort": {"type": "boolean"}, "supports_none_reasoning_effort": {"type": "boolean"}, "supports_xhigh_reasoning_effort": {"type": "boolean"}, "supports_max_reasoning_effort": {"type": "boolean"}, diff --git a/ui/litellm-dashboard/src/components/provider_info_helpers.test.tsx b/ui/litellm-dashboard/src/components/provider_info_helpers.test.tsx index a8021f94d84..fa014de4e62 100644 --- a/ui/litellm-dashboard/src/components/provider_info_helpers.test.tsx +++ b/ui/litellm-dashboard/src/components/provider_info_helpers.test.tsx @@ -68,6 +68,16 @@ describe("provider_info_helpers", () => { expect(result.logo).toBe(providerLogoMap[Providers.OpenAI]); }); + it("should resolve the zai (Z.AI) provider value to the Z.AI display name", () => { + // Regression test for https://github.com/BerriAI/litellm/issues/25482 — + // the backend already returns `zai` from /public/providers and the docs + // have a dedicated page, but the UI dropdown was missing an entry, so + // `getProviderLogoAndName("zai")` previously returned the raw value as + // the display name (no mapping). + const result = getProviderLogoAndName("zai"); + expect(result.displayName).toBe(Providers.ZAI); + }); + it("should return provider value as display name when no mapping exists", () => { const unknownProvider = "unknown_provider"; const result = getProviderLogoAndName(unknownProvider); @@ -156,6 +166,10 @@ describe("provider_info_helpers", () => { expect(getPlaceholder(Providers.WATSONX)).toBe("watsonx/ibm/granite-3-3-8b-instruct"); }); + it("should return zai/glm-4.5 placeholder for Z.AI provider", () => { + expect(getPlaceholder(Providers.ZAI)).toBe("zai/glm-4.5"); + }); + it("should return default gpt-3.5-turbo placeholder for unknown provider", () => { expect(getPlaceholder("UnknownProvider" as any)).toBe("gpt-3.5-turbo"); }); diff --git a/ui/litellm-dashboard/src/components/provider_info_helpers.tsx b/ui/litellm-dashboard/src/components/provider_info_helpers.tsx index e833d0eb4fb..62c0633d117 100644 --- a/ui/litellm-dashboard/src/components/provider_info_helpers.tsx +++ b/ui/litellm-dashboard/src/components/provider_info_helpers.tsx @@ -103,6 +103,7 @@ export enum Providers { WATSONX_TEXT = "Watsonx Text", xAI = "xAI", XINFERENCE = "Xinference", + ZAI = "Z.AI (Zhipu AI)", } export const provider_map: Record = { @@ -210,6 +211,7 @@ export const provider_map: Record = { WATSONX_TEXT: "watsonx_text", xAI: "xai", XINFERENCE: "xinference", + ZAI: "zai", }; const asset_logos_folder = "../ui/assets/logos/"; @@ -366,6 +368,8 @@ export const getPlaceholder = (selectedProvider: string): string => { return "watsonx/ibm/granite-3-3-8b-instruct"; } else if (selectedProvider === Providers.Cursor) { return "cursor/claude-4-sonnet"; + } else if (selectedProvider === Providers.ZAI) { + return "zai/glm-4.5"; } else { return "gpt-3.5-turbo"; }