i1ddlmZddlmZmZmZmZddlmZm Z m Z m Z m Z m Z mZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZm Z m!Z!ddl"m#Z$ddl%m&Z&m'Z'ddl(m)Z)m*Z*ddl+m,Z,dd l-m.Z.dd l/m0Z0d d l1m1Z1d d l2m3Z3ddl4m5Z5dZ6 d0dede7dee8deee0e*ffdZ9dZ: d0de7de8de7dee8deee0e*ff dZ; d1de7de8de7de8dee8deee0e*ff dZ< d0de7de8de7deee)e)e)fe)fdee8deee0e*ff dZ= d0deee0ee*fdeee0e*fdee8deee0e*ffd Z>defd!Z? d0d"e8d#ee@dee8deee0e*ffd$ZA d0deee0ee*fde7dee8deee0e*ffd%ZBdd&deee0ee*fd'ee*e*fd(eee*e*fdeee0e*ffd)ZCdefd*ZD d0dede7d+e7d,e7d-e7d.e7dee8deee0e*ffd/ZEy)2)partial)ListOptionalTuplecast)DropoutGelu LayerNormLinearLogisticMaxoutModelParametricAttentionParametricAttention_v2ReluSoftmax SparseLinearSparseLinear_v2chainclone concatenate list2ragged reduce_first reduce_last reduce_max reduce_mean reduce_sumresidual resizablesoftmax_activationwith_cpu)init)resize_linear_weighted resize_model)ArrayXdFloats2d)ORTH)Errors)Doc)extract_ngrams) StaticVectors)get_tok2vec_widthixNtok2vecexclusive_classesnOreturnc &t||dddd|S)a1 Build a simple CNN text classifier, given a token-to-vector model as inputs. If exclusive_classes=True, a softmax non-linearity is applied, so that the outputs sum to 1. If exclusive_classes=False, a logistic non-linearity is applied instead, so that outputs are in the range [0, 1]. FT)r0r1use_reduce_firstuse_reduce_lastuse_reduce_maxuse_reduce_meanr2)build_reduce_text_classifier)r0r1r2s h/var/www/vps2.regionflexible.com/Desarrollo/venv/lib/python3.12/site-packages/spacy/ml/models/textcat.py build_simple_cnn_text_classifierr;.s& (+  ct||}|jd|jd|jd|d|S)N output_layerrr2T)force)r$set_reflayersset_dim)modelnew_nOresizable_layers r:resize_and_set_refrFBs>"?F;O MM./"8"8";< MM$dM+ Lr< ngram_sizeno_output_layerc 6t||||t|S)Nr2r1rGrHr2 sparse_linear)_build_bow_text_classifierr)r1rGrHr2s r:build_bow_text_classifierrNIs% &+' "b)  r<lengthc |dkr)ttjj|d|dz j z}t ||||t ||S)Nr.)rOr+)r2rOrK) ValueErrorr)E1056format bit_lengthrMr)r1rGrHrOr2s r:build_bow_text_classifier_v3rUXsdz,,F,;<<6A:))+ +F %+' %F;  r<rLc<ddd}tjdti5d}|st|d<|r t n t }t |tt|}t|t|z }t||j}|r|t||jz }ddd| jdtt|j!d || |j"d <tt$ |j"d <|S#1swYmxYw) NrbW>>rX fill_defaults resize_layer)attrr2r> multi_labelrE resize_output)rdefine_operatorsr NEG_VALUEr r rrr#r,r(r!opsrBrintr@attrsrF) r1rGrHrLr2r\r>rErCs r:rMrMns!$M   u . F !*M# 3D-/(*L5>  !7}U6 z5H * XlL4D4DEEE F ~ dDbM* MM.-0%6!6EKK #*O$EKK  L) F Fs BDD linear_modelcJ|jd }tjttd5|j d}t |}t||}t|}|tz |z tz t||z tdz z }|r|dznd} |rt|| } nt|| tz } ||z| z } | j!d|ddd j#dd ur"| | j%dt't(|| j!d |j+d | j!d | j!d | j!d | | jd<t,| _| S#1swYxYw)Nr`rZ|r2r2nIrmr+r0Fr>attention_layer maxout_layer norm_layer)rgrrcrr maybe_get_dimrr r rrrrrr r r@has_dimrBrrfget_refinit_ensemble_textcatr") r0rhr2r1widthrprqrr cnn_model nO_doubler>rCs r:build_text_classifier_v2rzs)..}==   u; ? @*%%d+-e451 %( }  |  2gclBC  D !BFd "bY7L!RI6(*DL )l: i)'*( }}T%'BN dDbM* MM.,"6"6~"FG MM#_5 MM.,/ MM, +%6!6EKK &EJ L;**s B7FF"c|jd}|j|t|}|jdjd||jdjd||jdjd||jdjd||jdjd|t ||||S)Nr0rpr2rqrmrrru initializer/rB init_chainrCXYr0 tok2vec_widths r:rvrvsmmI&G q%e,M MM#$,,T=A MM.!))$ > MM.!))$ > MM,''m< MM,''m<ua Lr<rwdropoutcRtjttd5t |t z t |z tz tt||dzz t||z }|r|t|z }|tz }ddd|S#1swYSxYw)N)rZz**r+) rrcrrr-rrrrrr rr )rwrr2rCs r:build_text_classifier_lowdatars   uE : ; $ % } "5) *| UE*+q0  1 b%  !  WW--E# $ L $ Ls A1BB&c|jd}t|t||t||}t j dt i5|r t|}nt|tz }||z }dddjddur"| |jdtt||jd| |jd<|S#1swYbxYw) Nr2)rmr2)r0nonlinear_layer key_transformrZrJFr>r`)rs3_build_parametric_attention_with_residual_nonlinearr r rrcrrr r rtrBrrfr@rg)r0r1r2rwparametric_attentionr>rCs r:%build_textcat_parametric_attention_v1rs  ! !$ 'EN%E2e.   u .5 "b>L!R=HJ6L$ 4 5  }}T%'BN dDbM* MM.,/%6!6EKK  L55s ,CC')rrrctjttd5|j d}t ||}t |}|tz |z tz t||z tdz z }t|_ |jd||jd||jd||jd ||jd ||cdddS#1swYyxYw) Nrjr2)r2rrnror0rprrrr)rrcrrrsrr rrrr2_init_parametric_attention_with_residual_nonlinearr"r@)r0rrrwrprrrs r:rrs   u; ? @$%%d+0EW%( }  | :5EF  G %W!$$Y8$$%6H$$_mD$$%6H$$\:>#)$$$s CC22C;cH|jd}|j|t|}|jdjd||jdjd||jdjd||jdjd||jdjd||jdjd||jdjd|t ||||S)Nr0rpr2rrmrrrr|rs r:rrsmmI&G q%e,M MM#$,,T=A MM/"**4? MM/"**4? MM#$,,T=A MM#$,,T=A MM,''m< MM,''m<ua Lr<r5r6r7r8cddd}g}|r|jt|r|jt|r|jt|r|jt t |st tjtjdti5|tz t|z } |jd} | | t |znd} |r8t|| } t |d<t#| t%t&| } | | z }n9t)|| } t#| t%t&| } | | z t+z }|j-d | t%t.| |j0d <dddj-d || |j3dt5t6|| |j0d<|S#1swYOxYw)aBuild a model that classifies pooled `Doc` representations. Pooling is performed using reductions. Reductions are concatenated when multiple reductions are used. tok2vec (Model): the tok2vec layer to pool over. exclusive_classes (bool): Whether or not classes are mutually exclusive. use_reduce_first (bool): Pool by using the hidden representation of the first token of a `Doc`. use_reduce_last (bool): Pool by using the hidden representation of the last token of a `Doc`. use_reduce_max (bool): Pool by taking the maximum values of the hidden representations of a `Doc`. use_reduce_mean (bool): Pool by taking the mean of all hidden representations of a `Doc`. nO (Optional[int]): Number of classes. rrWrZr2NrlrXr[r]r>rarbr0r`)appendrrrrlenrQr)E1057rrcrrrrsrrdrrr#r r r@rFrgrBrrf)r0r1r5r6r7r8r2r\ reductionscnn nO_tok2vecrmr>rErCs r:r9r9"s6!$MJ,.)+-(*,'+-( z?&&   u . &+z*BB**40 -7-CZ#j/ ) "bR0L!*M# %.$*-&O ?*E!RB/L'$*-O ?*hj8E nl3'. +(  O$1 8 MM)W% ~ dDbM*%6!6EKK  LA  s 3CGG')N)iN)F functoolsrtypingrrrr thinc.apirr r r r r rrrrrrrrrrrrrrrrrrr r!thinc.layers.chainr"r~thinc.layers.resizabler#r$ thinc.typesr%r&rgr(errorsr)tokensr*r, staticvectorsr-r0r/rdboolrfr;rFrNrUrMrzrvfloatrrrrr9r<r:rs..82G)+)&  BF '+19# 49h (        49h  &       49h  6 w897BC     49h  D& 49d8n, -&S 8+,&  & 49h  &R % "?C !%.6sm 49h . 49d8n, -   49h  ::> $ 49d8n, -$8X-.$E(H"456 $  49h  $:u2I III I  I  I  I 49h Ir<