+ j3^RIHt^RIt^RIHtHt^RIHt!RR4t!RR]4t ]!R R ]44t !R R ]4t !R R]4t !RR] 4t R#)) dataclassN)GenerationMixinStoppingCriteria) ModelOutputc6a]tRt^toRtRtRRltRtVtR#)ParakeetRNNTDecoderCachecJWnRVnRVnRVnRVnR#)NF)configcache hidden_state cell_stateis_initialized)selfr s&&q/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/parakeet/generation_parakeet.py__init__!ParakeetRNNTDecoderCache.__init__s% *. 15/3$)cp\P!VP^,^VPPVP VP R7Vn\P!VPPVP^,VPPVP VP R7Vn \P!VPPVP^,VPPVP VP R7Vn \PPVP4\PPVP4\PPVP4RVn R#)rdevicedtypeTN)torchzerosshaper decoder_hidden_sizerrr num_decoder_layersr r _dynamomark_static_addressr)r hidden_statess&&rlazy_initialization,ParakeetRNNTDecoderCache.lazy_initializations)[[    " KK + + ''%%   "KK KK * *    " KK + + ''%%   ++ KK * *    " KK + + ''%%    ))$**5 ))$*;*;< ))$//:"rNcVP'gVPV4VfTVPPV4VPPV4VP PV4R#VP VP4pVP^,pVP^V^4pVPV^^4p\P!WqVP 4Vn\P!WbVP4Vn\P!WcVP4VnR#N) rr r copy_r r torrviewrwhere)rdecoder_outputr r mask batch_sizemask_hmask_ds&&&&& rupdateParakeetRNNTDecoderCache.update<s"""  $ $^ 4 <    # #L 1 OO ! !* - JJ  ^ ,77>001D'--a0JYYq*a0FYYz1a0FVTZZHDJ % F$BSBS TD #kk&dooNDOr)r r r r rr#) __name__ __module__ __qualname____firstlineno__rr r-__static_attributes____classdictcell__ __classdict__s@rrrs*#:OOrrc]tRt^VtRtR#)ParakeetTDTDecoderCacheN)r/r0r1r2r3r9rrr8r8Vsrr8c<a]tRt^YtoRtRtRtRtV3RltRt Vt R#)ParakeetRNNTGenerateOutputa Outputs of Parakeet transducer (RNN-T / TDT) generation. Args: sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Generated token sequences (including blank tokens). durations (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Per-step durations in frames. Combined with `sequences`, this is sufficient to reconstruct full timestamp information (frame indices are the cumulative sum of durations). attentions (`tuple(tuple(torch.FloatTensor))`, *optional*): Encoder attention weights per layer. hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*): Encoder hidden states per layer. Nc<V^8dQh/S[P;R&S[PR,;R&S[S[S[P,,R,;R&S[S[S[P,,R,;R&#) sequencesN durations attentionsr)r LongTensortuple FloatTensor)formatr6s"r __annotate__'ParakeetRNNTGenerateOutput.__annotate__Yst$%&$&-'(eE--./$6=)*u0012T9@+rr9) r/r0r1r2__doc__r?r@r__annotate_func__r3r4r5s@rr;r;Ys""*.I9=J<@M+rr;c0a]tRt^qtoRtRtRtRtVtR#)EncoderExhaustedCriteriazVStops generation when all batch elements have walked past their encoder output length.cWnR#r#model)rrMs&&rr!EncoderExhaustedCriteria.__init__ts rc VPPfC\P!VP^,\P VP R7#VPP#)Nrr)rM_encoder_finishedrrrboolr)r input_idsscoreskwargss&&&,r__call__!EncoderExhaustedCriteria.__call__wsH :: ' ' /;;yq1IL\L\] ]zz+++rrLN) r/r0r1r2rGrrVr3r4r5s@rrJrJqs`,,rrJc|aa]tRt^}toRtV3RltV3RltV3RltV3RltRt V3Rlt R V3Rllt R t Vt V;t#) ParakeetRNNTGenerationMixinaGeneration mixin for Parakeet RNN-T models, and the base for all Parakeet transducer generation. Handles the transducer machinery shared by RNN-T and TDT: encoder frame tracking, decoder cache preparation, encoder-exhaustion stopping, and output-buffer sizing. For RNN-T greedy decoding the encoder frame pointer advances by one frame on every blank emission and stays put on every non-blank emission; a ``max_symbols_per_step`` guard forces an advance after too many consecutive non-blank emissions at the same frame, mirroring NeMo's greedy RNN-T decoding. The duration-aware [`ParakeetTDTGenerationMixin`] extends this by advancing the frame pointer by a predicted duration instead. c\<\SV`!V/VBpVP\V44V#r#)super_get_stopping_criteriaappendrJ)rargsrUcriteria __class__s&*, rr\2ParakeetRNNTGenerationMixin._get_stopping_criterias.714B6B067rc<\S V`!V.VO5/VBpVPR,pVPRR7pW`PP 8HpVP f\P!V4Vn\P!V\P!VP 4VP ^,4pWP8p \P!Wy,\P!V4V4VnWy,P4p VR,V ,VR&VPPV 4VR,VR,8VnV#)NNNdimencoder_frame_idxsencoder_valid_lengthsrcrhrc)r[#_update_model_kwargs_for_generationlogitsargmaxr blank_token_id_symbols_at_framer zeros_liker'max_symbols_per_steplong_step_durationsr]rQ) routputsr^rU model_kwargsrktokens blank_masksymbols force_advanceadvancer`s &&*, rrj?ParakeetRNNTGenerationMixin._update_model_kwargs_for_generations0wB7\T\U[\ )2&{{999   ! ! )%*%5%5f%=D "++j%*:*:4;Q;Q*RTXTjTjmnTno#<#<< !&Z-GIYIYZaIbdk!l-335-9:N-ORY-Y )* ##G,!-.B!C|TkGl!lrcB<V'dVPfvVPP\P!VP ^,.VP R74P4pVPV,Vn Rp\SV`-VVVVVV4#)N)rF) max_new_tokensencoder_get_subsampling_output_lengthrtensorrritemrp max_lengthr[_prepare_generated_length) rgeneration_confighas_default_max_lengthhas_default_min_lengthmodel_input_nameinput_ids_length inputs_tensorencoder_seq_lenr`s &&&&&&& rr5ParakeetRNNTGenerationMixin._prepare_generated_lengths "&7&F&F&N"llII m11!45m>R>RSdf ,0+D+D+V  (%* "w0  " "      rc <\SV`!V/VBwr4p0R mpR pVP4UU u/uF(wrW9gK VPV4'dK&WbK* p pp VP!R RVRVP RR4RR/V Bp WR&V P eV P PR 4p MuV PP^,p \P!V 3V PP^,\PV PPR7p WR&\P!VP^,VP\PR7VR &W4V3#uup pi)input_featuresattention_maskoutput_attention_maskNTencoder_outputsrPrgrrf>rrr)decoder_ cross_attn use_cachepast_key_values cache_paramsr9rh)r[_prepare_model_inputsitems startswithget_audio_featuresgetrsumlast_hidden_staterrfullrqrr)rr^rUinputs input_namertexplicitirrelevant_prefixkeyvalueencoder_kwargsrrgr*r`s&*, rr1ParakeetRNNTGenerationMixin._prepare_model_inputss+07+H$+YRX+Y(LPf+002 2 " +.>>:K+L CJ2  11 ! '++,Q>QRS>TO/55/A:M5N*D' Z(@BTVZ(Z[+ &'rc <RVnRVn.Vn\SV`!RRVRV/VBp\ P !VP^R7p\ P!\ P!VP^,^VPVPR7V.^R7pV=V=V=\\V\4'dVPVR7#TVR7#)NrrrdrP)r>r?r9)rQrnrrr[generaterstackcatrrrrr; isinstancerr>)rrrrUrsr?r`s&&&, rr$ParakeetRNNTGenerationMixin.generates!%!%!'"`&`DU`Y_`KK 4 4!< II [[+QiooiN^N^ _aj kqr   "D$:DParakeetTDTGenerationMixin._update_model_kwargs_for_generations 'JJ4j[_jcij )3T[[33334;;;C1dkk44667>>2>F {{999 KK 1n =uy?Y[de -9:N-OR[-[ )* ##I."..B!C|TkGl!lr)rQN)r/r0r1r2rGrjr3r4r5s@rrrsrr) dataclassesrr generationrrutilsrrr8r;rJrYrr9rrrsr" ; ;O;O~=6< AA A. ,/ ,O /O d!<r