uPj(,UdZddlmZmZddlmZddlmZmZddl Z ddl m Z ee Z ee d<eiZeeeefe d<d Zee d <d Zee d <d Zee d<dZee d<edZee d<deeefdeeeffdZdeeefdeeeffdZdeeefdeedfdzfdZdededeedfdzfdZdeeefdedefdZdeeefdzdeeefdefd Zdeeefd!edeedfdzfd"Zd#eedzd$eedzdeedfdzfd%Z y)&aResolve which reasoning_effort values a deployment, and by intersection a model group, accepts. An entry that states its levels outright in reasoning_effort_levels is read first and wins whole, for a model whose set the per-level flags cannot express: Kimi K3 takes low, high and max, and no flag can drop medium because medium has none. Every other entry answers through the supports_*_reasoning_effort flags below, whose polarity mirrors how a request path reads that same flag. medium and high are unconditional for a reasoning model. minimal and low are opt-out: openai/chat/gpt_5_transformation.py refuses them only when the map says false. xhigh and max are opt-in. none is opt-out everywhere except the azure gpt-5 family, whose config raises UnsupportedParamsError without an explicit true. xhigh is gated on the request path by the openai and azure gpt-5 configs. max is not gated there at all: every entry carrying supports_max_reasoning_effort is Claude-family, and anthropic/chat/transformation.py gates max on the output_config path while its reasoning_effort path maps any level to a thinking budget. Making max opt-in is a deliberate trade, then, since an explicit flag is the only signal that the tier is a real one rather than litellm rounding the level to a budget, and a missing flag costs advisory metadata rather than a rejected request. A deployment the map describes with no effort flags at all resolves to None rather than to the opt-out defaults. 689 of the map's 854 reasoning entries carry no flag, and the o-series, xai and bedrock nova entries among them take neither none nor minimal, so composing a set out of the defaults alone would advertise levels those providers reject. The advertisement order is the REASONING_EFFORT declaration order, which is presentation only. It is not a strength scale and does not reconcile with bedrock's output_config ceiling order in llms/bedrock/common_utils.py, which ranks max below xhigh while the thinking-budget constants rank it above. )MappingSequence)MappingProxyType)Finalget_argsN)REASONING_EFFORT$REASONING_EFFORT_ADVERTISEMENT_ORDER _EMPTY_ENTRY))nonesupports_none_reasoning_effort)minimal!supports_minimal_reasoning_effort)lowsupports_low_reasoning_effort)xhighsupports_xhigh_reasoning_effort)maxsupports_max_reasoning_effort _EFFORT_FLAGSreasoning_effort_levels_DECLARED_EFFORTS_KEY)r r_OPT_OUT_EFFORTS)rr_OPT_IN_EFFORTS)mediumhigh_UNCONDITIONAL_EFFORTS model_inforeturnc0|jd}|jd}t|tr$t|tr|j|dstSt j j|j|d}||StS)aVThe unprefixed twin of a provider-prefixed map entry, which is where the flags often live: azure/gpt-5-mini carries none of them while gpt-5-mini carries all three. The request-path gates resolve through the same twin (_supports_factory, #20885), so reading it here is what keeps the advertisement and the gate on the same answer.keylitellm_provider/)get isinstancestr startswithr litellm model_cost removeprefix)rr providerentrys u/home/oem/CyberBrain26-unified/.venv/lib/python3.12/site-packages/litellm/router_utils/reasoning_effort_capability.py_bare_model_entryr-5s &C nn%78H c3 z(C'@ZbYccdWeHf070B0B0F0FsGWGW[cZddeXfGg0hE%57<7c t|}ttDcic]9\}}||j||j|n|j|;c}}Scc}}wN)r-rrr#)rbareeffortflags r,_declared_effort_flagsr4Bse#J/D !.  JNN4,@,LJNN4(RVRZRZ[_R` `   s>A .c|jt}||nt|jt}t|trt|t t frytd|DtfdtDS)aThe entry's own answer, read through the same bare twin as the flags so both spellings of one model agree. Present-and-a-list IS the answer, so a declared [] correctly empties the group and an unknown level is dropped rather than raised: the bundled map is enum-validated by validate-model-prices-json, but an operator can put this key on a config.yaml model_info block where that schema never runs, and one mistyped level must not fail every sibling on the proxy.Nc3BK|]}t|ts|ywr0)r$r%).0r2s r, z-declared_reasoning_efforts..VsT6Jvs.WsaFfX`N`a ) r#rr-r$rr%bytes frozensettupler )rownrawr;s @r,declared_reasoning_effortsrBLsm  56C->z-J-N-NOd-eC c8 $ 3e (ETSTTH a&Ja aar.modelcustom_llm_providerctjj|d|xstjj|}t|tsyt |S)ajThe levels an entry declares, resolved from the model string a provider config holds rather than from a router deployment's model_info. None means the map has no opinion, either because the entry declares nothing or because it describes no such model, so a caller keeps whatever it did before the entry was described. The entry is read straight off the map rather than through get_model_info, which raises for a model it does not know: a provider config runs on the request path for every model it serves, most of which the map never named, and a lookup miss there must not fail the call. r"N)r'r(r#r$dictrB)rCrDr+s r,$declared_reasoning_efforts_for_modelrGZsV%%))-@,A5'*JKlwOaOaOeOefkOlE eT " %e ,,r.r3c|jddk7r|duSddlm}|jd}t|tr|j |s|duS|duS)a Opt-in only where a request path refuses the level. AzureOpenAIGPT5Config raises UnsupportedParamsError on reasoning_effort='none' without an explicit true, and it is selected only for the gpt-5 family, so every other azure deployment keeps the opt-out default.r!azureFr)AzureOpenAIGPT5Configr T)r#,litellm.llms.azure.chat.gpt_5_transformationrJr$r%is_model_gpt_5_model)rr3rJr s r,_supports_none_reasoning_effortrMjs`~~()W45  R&C c3 '<'Q'QRU'V5  4<r.resolved_model_infooperator_model_infocP||jdy|jdduS)aWhether the model map described this deployment, as opposed to the operator describing it. Every deployment is registered in the cost map under its own id, so a mode the operator wrote on an off-map deployment reads back here exactly like one the catalog supplied. Excluding it is what stops such a deployment from claiming to be a known non-reasoning model and emptying the levels its mapped siblings agree on. NmodeF)r#)rNrOs r,deployment_is_catalog_mappedrRys4"&9&=&=f&E&M  " "6 *d 22r.deployment_is_mappedc |jd}|duryt| td jD}|dur|s|rdSdSt |}||St d jDryt fdtD}t fd tD}t| d r t d n t }|tz|z|ztfd tDS) aANone = nothing is known about this deployment, so it must not narrow its group; () = a known model that accepts no effort level, which correctly empties the group. Telling those apart needs provenance the flattened ModelInfo does not carry. A deployment the map does not describe arrives with supports_reasoning None, exactly like a mapped non-reasoning model: 2273 of the map's 3165 entries omit the key rather than setting it false, so reading an unset flag as () would let one custom deployment empty every level its mapped siblings agree on. deployment_is_mapped is that provenance, and an operator who wants either answer for an off-map deployment gets it by setting supports_reasoning explicitly. If supports_reasoning is unset but at least one per-level flag (e.g. supports_minimal_reasoning_effort) is present, treat it as implicitly True, since the per-level flags are evidence the model supports reasoning. An explicit False always wins: it is the operator's escape hatch and must not be overridden by inherited per-level flags. supports_reasoningFr:c3$K|]}|du ywr0r:r7values r,r8z6resolve_supported_reasoning_efforts..s#R%E$5#RTNc3$K|]}|du ywr0r:rWs r,r8z6resolve_supported_reasoning_efforts..s 5U5D= 5rYc32K|]}|dus |yw)FNr:r7r2flagss r,r8z6resolve_supported_reasoning_efforts..sc&f ]bHbvc c32K|]}|dus |yw)TNr:r\s r,r8z6resolve_supported_reasoning_efforts..s\eFmW[F[f\r^r )r c3,K|] }|vs| ywr0r:)r7r2alloweds r,r8z6resolve_supported_reasoning_efforts..s`FfX_N_`r<) r#r4anyvaluesrBallr>rrrMrr?r ) rrSrUhas_per_level_flagr;opt_outopt_in none_levelrar]s @@r,#resolve_supported_reasoning_effortsris(!+/C DU")*5E ##R5<<>#R R%.@)r3t30KG `&J` ``r.currentresolvedc|| t|SdS| t|St|t|ztfdtDS)zDeployments without metadata (None) never narrow the group; an effort survives only when every deployment with metadata accepts it, so the group offers nothing routing could reject.Nc3,K|] }|vs| ywr0r:)r7r2keeps r,r8z8intersect_supported_reasoning_efforts..s]FfX\n]r<)r?r>r )rjrkrns @r,%intersect_supported_reasoning_effortsrosT !(!4uW~>$>XG$y'::D ]&J] ]]r.)!__doc__collections.abcrrtypesrtypingrrr'litellm.types.llms.openairr __annotations__r r%objectrrrrr>rr-r4r?rBrGboolrMrRriror:r.r,rxs:.""6.67G.H$eH,