o h@sddlmZddlZddlmmmZ zedgZ edgZ ej e e ddZ ddZ Wn ey>d dZ YnwGd d d eZdS) )divisionNg?g@trunc rounding_modecCstj||ddS)Nrr)torchdivabr T/var/www/html/env_mimamsha/lib/python3.10/site-packages/stanza/models/common/beam.pytrunc_division"r cCs||SNr rr r r r %sc@sHeZdZdddZddZddZddd Zd d Zd d ZddZ dS)BeamNcCsd||_d|_tj|tj|d|_g|_g|_tj|tj|d t j g|_ t j |j dd<g|_dS)NF)dtypedevicer)sizedonerzerosfloat32scores allScoresprevKsint64fill_constantPAD_IDnextYsSOS_IDcopy)selfrrr r r __init__)s z Beam.__init__cC |jdS)z)Get the outputs for the current timestep.)rr!r r r get_current_state; zBeam.get_current_statecCr#)z.Get the backpointers for the current timestep.r$)rr%r r r get_current_origin?r'zBeam.get_current_originc Cs|jrdS|d}t|jdkr||jd|}n|d}|d}||jddd\}}|j |j||_t ||}|j ||j ||||dur^|j |d||j ddtjkrrd|_|j |j|jS)aN Given prob over words for every last beam `wordLk` and attention `attnOut`: Compute and update the beam search. Parameters: * `wordLk`- probs of advancing from the last step (K x words) * `copy_indices` - copy indices (K x ctx_len) Returns: True if beam search is complete. Trr$N)rrlenrr unsqueeze expand_asviewtopkrappendr rr index_selectrEOS_ID) r!wordLk copy_indicesnumWordsbeamLk flatBeamLk bestScores bestScoresIdprevKr r r advanceCs&     z Beam.advancecCst|jddS)NrT)rsortrr%r r r sort_bestqrzBeam.sort_bestcCs|\}}|d|dfS)z&Get the score of the best in the beam.r))sortBest)r!ridsr r r get_bestts z Beam.get_bestcCsg}g}tt|jdddD]&}||j|d|t|jdkr.||j|||j||}q|ddd}|ddd}t|D]\}}|dkrW|d ||<qH|S)z Walk back to construct the full hypothesis. Parameters: * `k` - the position in the beam to construct. Returns: The hypothesis r)r$rN)ranger*rr/rr enumerate)r!khypcpyjicidxr r r get_hypys z Beam.get_hypr) __name__ __module__ __qualname__r"r&r(r:r<r?rHr r r r r(s  . r) __future__rr%stanza.models.common.seq2seq_constantmodelscommonseq2seq_constantrtensorr r rcr TypeErrorobjectrr r r r s