+ TV-jRt^RIt^RIt^RIt^RIt^RIHt^RIH t ^RI t ^RI H t ^RIHtHtRRltR RltRt]R8Xd ]!4R#R#) z* Evaluate perplexity (PPL) of MLX models. N) load_dataset)get_total_parametersloadc<V^8dQhR\R\R\/#) data_path num_samplessequence_length)strint)formats"b/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/mlx_lm/perplexity.py __annotate__rs( cD\P!RVRRRR/RRR7p\W@4^,p\PP \ V44P4pV^8d W2,M \R4p.p^p \ V4V8d=VPWVV ,,4wrV ^, p VPV 4KL\P!VR \ V4V,V,4pVPR V4pV^8dVR VpV#) path train_splittrain valid_splitz train[:1]TF) hf_datasetrtestinfN)typesSimpleNamespacernprandom permutationlentolistfloatprocessextendmxarrayreshape) tokenizerrrr argsdatasetperm num_tokensdataitokens_s &&&& r load_datar/s  I 7 ;   D4+A.G 99 W . 5 5 7D2=/.uU|J D A d)j OOGG$45  Q F 88DKCI8OKL MD <<O ,DQL[! Krc.p\V4V,^, V,p\\^\V4V44FwrVWWb,pV!VRRR 13,4P\P 4p\ PPWR ,RR7p \P!V 4VPV P44V^,^,^8XgV^,V8XgK\RV^, RV R2RR 7K \4\P!V4pVP4P4p \ P"!V 4p \P$!\P&!V^R 74P4p VP(p V \ P$!V 4, pW,pW3#) z Evaluate perplexity on a dataset with standard error calculation. Args: model: The model to evaluate data: Tokenized data tensor batch_size: Batch size for evaluation Returns: tuple: (perplexity, standard_error) NNNNnone) reductionz Processed /z batches... )end)ddofr)r1:NN)r enumeraterangeastyper#float32nnlosses cross_entropyevalappendflattenprint concatenatemeanitemmathexpsqrtvarsize)modelr+ batch_size all_losses num_batchesr,sbatchlogitsr> mean_losspplstd_devr*standard_errorstandard_error_ppls&&& r eval_pplrX5suJt9z)A-*    , X   sC.M    DIINN499IINN499  |3 /0+T5K5K5KTQUVDJJ9IJE(.L N<+C0 => "  4 45 67  $$,,  D Ic$i[ )* 4T__4ES IJJut?GC j(Izz!} 1 (9: /  (O GDJJ< ! LS bX ./ i_H 56 M",,.4S9 =>  0 r~sN .3D-#`W*t zFr