+ NV-jj^RIt^RIHtHt^RIHtRRltRRltR RRllt R R lt R R lt R#)N)CallableListc<V^8dQhR\R\R\/#)init decay_ratereturn)floatr)formats"j/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/mlx/optimizers/schedulers.py __annotate__r s!EucaaVV3RlpV#)aMake an exponential decay scheduler. Args: init (float): Initial value. decay_rate (float): Multiplicative factor to decay by. Example: >>> lr_schedule = optim.exponential_decay(1e-1, 0.9) >>> optimizer = optim.SGD(learning_rate=lr_schedule) >>> optimizer.learning_rate array(0.1, dtype=float32) >>> >>> for _ in range(5): optimizer.update({}, {}) ... >>> optimizer.learning_rate array(0.06561, dtype=float32) c$<SSV,,#N)steprrs&r schedule#exponential_decay..schedulesj$&&&rr)rrrsff r exponential_decayr s&' OrcHV^8dQhR\R\R\R\/#)rrr step_sizer r intr)r s"r r r "s(U#(rcaaaVVV3RlpV#)aMake a step decay scheduler. Args: init (float): Initial value. decay_rate (float): Multiplicative factor to decay by. step_size (int): Decay every ``step_size`` steps. Example: >>> lr_schedule = optim.step_decay(1e-1, 0.9, 10) >>> optimizer = optim.SGD(learning_rate=lr_schedule) >>> optimizer.learning_rate array(0.1, dtype=float32) >>> >>> for _ in range(21): optimizer.update({}, {}) ... >>> optimizer.learning_rate array(0.081, dtype=float32) c2<SSVS,,,#rr)rrrrs&r rstep_decay..schedule7szdi&7899rr)rrrrsfff r step_decayr"s*: OrcHV^8dQhR\R\R\R\/#)rr decay_stepsendr r)r s"r r r =s(u3UXrcaaaVVV3RlpV#)aHMake a cosine decay scheduler. Args: init (float): Initial value. decay_steps (int): Number of steps to decay over. The decayed value is constant for steps beyond ``decay_steps``. end (float, optional): Final value to decay to. Default: ``0``. Example: >>> lr_schedule = optim.cosine_decay(1e-1, 1000) >>> optimizer = optim.SGD(learning_rate=lr_schedule) >>> optimizer.learning_rate array(0.1, dtype=float32) >>> >>> for _ in range(5): optimizer.update({}, {}) ... >>> optimizer.learning_rate array(0.0999961, dtype=float32) c<\P!VS4pRR\P!\PS, V,4,,pSVSS, ,,#)g?g?)mxminimumcosmathpi)rsdecayr r!rs& r rcosine_decay..scheduleSsM JJt[ )sRVVTWW{%:a$?@@AUdSj)))rr)rr r!rsfff r cosine_decayr,=s,* OrchV^8dQhR\\,R\\,R\/#)r schedules boundariesr )rrr)r s"r r r [s)%%d8n%$s)%%rcaa\S4^8Xd \R4h\S4\S4^,8wd,\R\S4 R\S4^, R24hVV3RlpV#)aJoin multiple schedules to create a new schedule. Args: schedules (list(Callable)): A list of schedules. Schedule :math:`i+1` receives a step count indicating the number of steps since the :math:`i`-th boundary. boundaries (list(int)): A list of integers of length ``len(schedules) - 1`` that indicates when to transition between schedules. Example: >>> linear = optim.linear_schedule(0, 1e-1, steps=10) >>> cosine = optim.cosine_decay(1e-1, 200) >>> lr_schedule = optim.join_schedules([linear, cosine], [10]) >>> optimizer = optim.Adam(learning_rate=lr_schedule) >>> optimizer.learning_rate array(0.0, dtype=float32) >>> for _ in range(12): optimizer.update({}, {}) ... >>> optimizer.learning_rate array(0.0999938, dtype=float32) z)Must provide at least 1 schedule to join.z Received z boundaries but expected .c <S^,!V4p\SSR,4F*wr#\P!W8W!W, 44pK, V#)r:NN)zipr$where)routputboundaryrr/r.s& r r join_schedules..schedulezsJ1d#"%j)B-"@ HXXdovx7PQF#A r)len ValueError)r.r/rsff r join_schedulesr;[sr, 9~DEE 9~Z1,,J()I*+1 .   OrcHV^8dQhR\R\R\R\/#)rrr!stepsr r)r s"r r r s(%eCHrcHaaaS^8d\RS R24hVVV3RlpV#)aMake a linear scheduler. Args: init (float): Initial value. end (float): Final value. steps (int): Number of steps to apply the schedule over. The value is ``end`` for any steps beyond ``steps``. Example: >>> lr_schedule = optim.linear_schedule(0, 1e-1, 100) >>> optimizer = optim.Adam(learning_rate=lr_schedule) >>> optimizer.learning_rate array(0.0, dtype=float32) >>> for _ in range(101): optimizer.update({}, {}) ... >>> optimizer.learning_rate array(0.1, dtype=float32) z&steps must be greater than 0, but got r1cn<\P!VS4pVSS, S, ,S,#r)r$r%)rr!rr=s&r r!linear_schedule..schedules,zz$&d e+,t33r)r:)rr!r=rsfff r linear_schedulerAs-( qyA%JKK4 Or)g) r'typingrrmlx.corecorer$rrr,r;rArrr rEs* !26<%Pr