9i SrSSKrSSKrSSKrSSKJr SSKJr SSKJr SSK J r SSK J r SSK JrJr SS KJr SS KJr SS KJr SS KJr SS KJr SSKJrJrJr SSKJr \"5r Sr!"SS\RD5r#"SS\RD5r$"SS\RD5r%"SS\RD5r&"SS\RD5r'"SS\RD5r("SS\RD5r)"S S!\RD5r*"S"S#\RD5r+"S$S%\\ 5r,\RZ"\R\\ R^S&9"S'S(\,55r0g))zPyTorch BERT model. N)version)nn)ACT2FN)PreTrainedModel)Models)Model TorchModel)MODELS)AttentionBackboneModelOutput)Tasks) get_logger)parse_labels_in_order)apply_chunking_to_forward find_pruneable_heads_and_indicesprune_linear_layer BertConfigrc@^\rSrSrSrU4SjrSSjrSrU=r$)BertEmbeddings,zGConstruct the embeddings from word, position and token_type embeddings.c>[TU]5 [R"URUR UR S9Ul[R"URUR 5Ul [R"URUR 5Ul [R"UR URS9Ul [R"UR5Ul[#USS5UlUR'S[(R*"UR5R-S55 [.R0"[(R25[.R0"S5:aKUR'S[(R4"UR6R95[(R:S 9S S 9 gg) N) padding_idxepsposition_embedding_typeabsolute position_ids)rz1.6.0token_type_idsdtypeF) persistent)super__init__r Embedding vocab_size hidden_size pad_token_idword_embeddingsmax_position_embeddingsposition_embeddingstype_vocab_sizetoken_type_embeddings LayerNormlayer_norm_epsDropouthidden_dropout_probdropoutgetattrrregister_buffertorcharangeexpandrparse __version__zerosrsizelongselfconfig __class__s c/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/modelscope/models/nlp/bert/backbone.pyr%BertEmbeddings.__init__/se !||      ++ -$&<<0N0N060B0B$D %'\\&2H2H282D2D&F"    F$9$9;zz&"<"<= (/v/H/9(;$   LL77 8 ? ? H J ==** +gmmG.D D    D--224EJJG  !  EcdUbUR5nOUR5SSnUSnUcURSS2XWU-24nUcv[US5(a-URSS2SU24nUR USU5n U nO8[ R "U[ RURRS9nUcURU5nURU5n XJ-n URS:XaURU5n X- n URU 5n URU 5n U $)Nrrr rr"devicer)r<rhasattrr r8r6r;r=rGr*r.rr,r/r3) r? input_idsr r inputs_embedspast_key_values_length input_shape seq_lengthbuffered_token_type_ids buffered_token_type_ids_expandedr. embeddingsr,s rBforwardBertEmbeddings.forwardNsU  #..*K',,.s3K ^  ,,Q-C/EEF.F.FGL  !t-..*.*=*=a*n*M'3J3Q3QNJ400!A!&**,,33"5   00;M $ : :> J":  ' ': 5"&":":<"H   -J^^J/ \\*- rD)r/r3rr,r.r*)NNNNr) __name__ __module__ __qualname____firstlineno____doc__r%rQ__static_attributes__ __classcell__rAs@rBrr,s'Q@#!"'( ,,rDrcH^\rSrSrSU4SjjrSrSSjrSrU=r$)BertSelfAttention}c>[TU]5 URUR-S:wa7[ US5(d&[ SURSURS35eURUl[ URUR- 5UlURUR-Ul[R"URUR5Ul [R"URUR5Ul [R"URUR5Ul [R"UR5UlU=(d [#USS5UlUR$S:XdUR$S :XaGUR&Ul[R("S UR&-S - UR5UlUR,Ulg) Nrembedding_sizezThe hidden size (z6) is not a multiple of the number of attention heads ()rr relative_keyrelative_key_queryr)r$r%r(num_attention_headsrH ValueErrorintattention_head_size all_head_sizerLinearquerykeyvaluer1attention_probs_dropout_probr3r4rr+r&distance_embedding is_decoderr?r@rrAs rBr%BertSelfAttention.__init__s     : : :a ?(I*I*#F$6$6#78 445Q89 9$*#=#= #&v'9'9)/)C)C(D$E !558P8PPYYv1143E3EF 99V//1C1CDYYv1143E3EF zz&"E"EF '>(;' -zC;$  ' '> 9T=Y=Y]q=q+1+I+ID (&(llF222Q6(('*D #!++rDcUR5SSURUR4-nUR"U6nUR SSSS5$)Nrrrcr)r<rdrgviewpermute)r?x new_x_shapes rBtranspose_for_scores&BertSelfAttention.transpose_for_scoressSffhsmt'?'?'+'?'?'AA FFK yyAq!$$rDcURU5nUSLn U (aUbUSn USn UnGOU (aCURURU55n URURU55n UnOUbuURURU55n URURU55n [R "USU /SS9n [R "USU /SS9n O@URURU55n URURU55n URU5n UR (aX4n[R"U U RSS55n URS:XdURS:XGaDUR5Sn[R"U[RURS 9RSS5n[R"U[RURS 9RSS5nUU- nURUUR -S- 5nUR#U R$S 9nURS:Xa[R&"S U U5nU U-n OHURS:Xa8[R&"S U U5n[R&"S U U5nU U-U-n U [(R*"UR,5- n UbX-n [.R0R3U SS9nUR5U5nUbUU-n[R"UU 5nUR7SSSS 5R95nUR5SSUR:4-nUR"U6nU(aUU4OU4nUR (aUU4-nU$)NrrrcdimrrarbrFr!zbhld,lrd->bhlrzbhrd,lrd->bhlrrs)rjrxrkrlr6catromatmul transposerr<r7r=rGrtrnr+tor"einsummathsqrtrgr functionalsoftmaxr3ru contiguousrh)r? hidden_statesattention_mask head_maskencoder_hidden_statesencoder_attention_maskpast_key_valueoutput_attentionsmixed_query_layeris_cross_attention key_layer value_layer query_layerattention_scoresrMposition_ids_lposition_ids_rdistancepositional_embeddingrelative_position_scoresrelative_position_scores_queryrelative_position_scores_keyattention_probs context_layernew_context_layer_shapeoutputss rBrQBertSelfAttention.forwards!JJ}5 3$> ."<&q)I(+K3N 11./1I33 013K3N  '11$((=2IJI33DJJ}4MNK >!#4i"@aHI))^A%6 $D!LK11$((=2IJI33DJJ}4MNK//0AB ??(5N!<< (1(;(;B(CE  ' '> 9T=Y=Y]q=q&++-a0J"\\%**$++--1T"a[ #\\%**$++--1T!R[ &6H#'#:#:4777!;$= #7#:#:!''$;$) ++~=+0<<$k3G,I(#36N#N --1EE16$k3G2I./4||$i1E0G,#36T#TWs#s +dii  $ $/&&  %/@ --//0@b/I,,7  - 9O _kB %--aAq9DDF "/"4"4"6s";   ?"#"%**,CD (9!"$?L>O  ?? 22GrD) rhrgrnr3rorkr+rdrrjrlNNNNNNF) rSrTrUrVr%rxrQrXrYrZs@rBr\r\}s-,8%"#mmrDr\c.^\rSrSrU4SjrSrSrU=r$)BertSelfOutputic(>[TU]5 [R"URUR5Ul[R "URURS9Ul[R"UR5Ul gNr) r$r%rrir(denser/r0r1r2r3r>s rBr%BertSelfOutput.__init__sc YYv1163E3EF    F$9$9;zz&"<"<= rDcpURU5nURU5nURX-5nU$rrr3r/r?r input_tensors rBrQBertSelfOutput.forward5 =1  ]3 }'CD rDr/rr3rSrTrUrVr%rQrXrYrZs@rBrr>rDrcH^\rSrSrSU4SjjrSrSSjrSrU=r$) BertAttentioni!c|>[TU]5 [XS9Ul[ U5Ul[ 5Ulg)Nr)r$r%r\r?routputset pruned_headsrps rBr%BertAttention.__init__#s4 % E $V, ErDc6[U5S:Xag[XRRURRUR 5up[ URRU5URl[ URRU5URl[ URRU5URl [ URRUSS9URl URR[U5- URlURRURR-URl UR RU5Ulg)Nrrr{)lenrr?rdrgrrrjrkrlrrrhunion)r?headsindexs rB prune_headsBertAttention.prune_heads*s u:? 7 9900 II ) )4+<+<>  -TYY__eD *499==%@ ,TYY__eD .t{{/@/@%QO )- (E(E I) %"&))"?"?$))B_B_"_  --33E:rDc pURUUUUUUU5nURUSU5n U 4USS-n U $)Nrr)r?r) r?rrrrrrr self_outputsattention_outputrs rBrQBertAttention.forward=s\yy    ! "    ;;|A F##AB'(rD)rrr?rr) rSrTrUrVr%rrQrXrYrZs@rBrr!s+";,"#rDrc.^\rSrSrU4SjrSrSrU=r$)BertIntermediateiVc>[TU]5 [R"URUR 5Ul[UR[5(a[URUl gURUl gr) r$r%rrir(intermediate_sizer isinstance hidden_actstrrintermediate_act_fnr>s rBr%BertIntermediate.__init__Xs` YYv1163K3KL f'' - -'-f.?.?'@D $'-'8'8D $rDcJURU5nURU5nU$rrr)r?rs rBrQBertIntermediate.forward`s& =1 00? rDrrrZs@rBrrVs9rDrc.^\rSrSrU4SjrSrSrU=r$) BertOutputifc(>[TU]5 [R"URUR 5Ul[R"UR URS9Ul[R"UR5Ul gr) r$r%rrirr(rr/r0r1r2r3r>s rBr%BertOutput.__init__hsc YYv779K9KL    F$9$9;zz&"<"<= rDcpURU5nURU5nURX-5nU$rrrs rBrQBertOutput.forwardorrDrrrZs@rBrrfrrDrcD^\rSrSrU4SjrSSjrSrSrU=r$) BertLayerivct>[TU]5 URUlSUl[ U5UlUR UlURUlUR(a.UR (d[US35e[ USS9Ul [U5Ul [U5Ul g)Nrz> should be used as a decoder model if cross attention is addedrr)r$r%chunk_size_feed_forward seq_len_dimr attentionroadd_cross_attentionrecrossattentionr intermediaterrr>s rBr%BertLayer.__init__xs '-'E'E$&v. ++#)#=#=  # #?? fZ[#0 #[TU]5 Xl[R"[ UR 5Vs/sHn[U5PM sn5UlSUl gs snf)NF) r$r%r@r ModuleListrangenum_hidden_layersrlayergradient_checkpointing)r?r@_rAs rBr%BertEncoder.__init__sV  ]](-f.F.F(G H(G1Yv (G HJ &+# IsA&c ,^^U (aSOSn T(aSOSn T(aURR(aSOSn U(aSOSn[UR5HunnU (aX4-n UbX?OSnUbUUOSmUR(akUR (aZU(a[ RS5 SnUU4Sjn[RRRU"U5UUUUU5nOU"UUUUUTT5nUSnU(a UUS4- nT(dMU US4-n URR(dMU US4-n M U (aX4-n U (d[S UUU U U 455$[UUU U U S 9$) NzZ`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...Fc>^UUU4SjnU$)Nc>T"/UQTPTP76$rr)inputsmodulerrs rBcustom_forwardJBertEncoder.forward..create_custom_forward..custom_forwards&% 9v 9~ 9&7 99rDr)rrrrs` rBcreate_custom_forward2BertEncoder.forward..create_custom_forwards9*)rDrrrrcc30# UH nUcMUv M g7frr).0vs rB &BertEncoder.forward..*s" %q  %s )last_hidden_statepast_key_valuesr attentionscross_attentions) r@r enumeraterrtrainingloggerwarningr6utils checkpointtupler )r?rrrrrr use_cacheroutput_hidden_states return_dictall_hidden_statesall_self_attentionsall_cross_attentionsnext_decoder_cachei layer_modulelayer_head_maskr layer_outputsrs ` @rBrQBertEncoder.forwards#7BD$5b4 4;;#B#B HL $-R$(4OA|#$58I$I!.7.CilO%1-7; **t}}NNt!&I*!& 6 6 A A),7!"#)* ! !-!"#)*"%! *!,M"}R'8&;;"  &9!!$=('(#;;222+?%a(C,,,(e 5j 14E E  "!#$ %   ,+.+*1   rD)r@rr) NNNNNNFFTrrZs@rBrrs1,"#"Y Y rDrc.^\rSrSrU4SjrSrSrU=r$) BertPooleri:c>[TU]5 [R"URUR5Ul[R "5Ulgr)r$r%rrir(rTanh activationr>s rBr%BertPooler.__init__<s9 YYv1163E3EF '')rDc\USS2S4nURU5nURU5nU$)Nr)rr")r?rfirst_token_tensor pooled_outputs rBrQBertPooler.forwardAs6+1a40 #56  6 rD)r"rrrZs@rBrr:s$ rDrcd^\rSrSrSr\rSrSrS/r U4Sjr Sr S Sjr \ U4S j5rS rU=r$) BertPreTrainedModeliJzz An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. bertTrc b>[TU]"UR40UD6 [[U]U5 gr)r$r% name_or_pathr)r?r@kwargsrAs rBr%BertPreTrainedModel.__init__Us* ,,77 eT#F+rDc[U[R5(akURRR SUR RS9 URb%URRR5 gg[U[R5(axURRR SUR RS9 URb2URRURR5 gg[U[R5(aJURRR5 URRRS5 gg)zInitialize the weightsg)meanstdNg?)rrriweightdatanormal_r@initializer_rangebiaszero_r&rr/fill_)r?rs rB _init_weights!BertPreTrainedModel._init_weightsYs fbii ( ( MM   & &dkk;; ' ={{&   &&('  - - MM   & &dkk;; ' =!!- ""6#5#56<<>.  - - KK   " " $ MM   $ $S ).rDc<[U[5(aX!lggr)rrr)r?rrls rB_set_gradient_checkpointing/BertPreTrainedModel._set_gradient_checkpointingks fk * *,1 ) +rDc >URSS5nURSS5n[X#40UD6nUc[S0UD6nU"U5nO[[U]"SSU0UD6nX&lU$)a Instantiate the model. Args: kwargs: Input args. model_dir: The model dir used to load the checkpoint and the label information. num_labels: An optional arg to tell the model how many classes to initialize. Method will call utils.parse_label_mapping if num_labels not supplied. If num_labels is not found, the model will use the default setting (2 classes). Returns: The loaded model, which is initialized by transformers.PreTrainedModel.from_pretrained model_dirNcfgpretrained_model_name_or_pathr)poprrr$rfrom_pretrainedr?)clsr-r?r@ model_argsr@modelrAs rB _instantiate BertPreTrainedModel._instantiateos~JJ{D1 jj%*9DVD  -*-FKE%5G.7G;EGE# rDr)F)rSrTrUrVrWr config_classbase_model_prefixsupports_gradient_checkpointing_keys_to_ignore_on_load_missingr%r9r< classmethodrGrXrYrZs@rBr)r)JsG L&*#'6&7#,*$2rDr)) group_key module_namec^\rSrSrSrS U4Sjjr\SSj5rSrSr Sr SS\ 4S jjr S r S rS rU=r$) BertModeliagThe Bert Model transformer outputting raw hidden-states without any specific head on top. This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`BertConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in [Attention is all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. c>[TU]U5 [U5Ul[ U5UlU(a [ U5OSUlUR5 gr) r$r%rrPrencoderrpooler post_init)r?r@add_pooling_layerrAs rBr%BertModel.__init__sD  (0"6* ,=j(4  rDc ,[S0UD6nU"X25nU$)Nrr)rDr?rVr@rFs rBrGBertModel._instantiates%f%F. rDc.URR$rrPr*)r?s rBget_input_embeddingsBertModel.get_input_embeddingss...rDc$XRlgrr[)r?rls rBset_input_embeddingsBertModel.set_input_embeddingss */'rDcUR5H7up#URRURR U5 M9 g)z Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel N)itemsrSrrr)r?heads_to_prunerrs rB _prune_headsBertModel._prune_headss< +002LE LL  u % / / ; ;E B3rDreturnc U bU OURRn U bU OURRn U bU OURRn URR(aU bU OURR n OSn UbUb [ S5eUbUR5nO"UbUR5SSnO [ S5eUunnUb URO URnU bU SSRSOSnUc[R"UUU-4US9nUcs[URS 5(a4URRSS2SU24nURUU5nUnO$[R "U[R"US 9nUR%X/U5nURR(aEUbBUR5unnnUU4nUc[R"UUS9nUR'U5nOSnUR)UURR*5nURUUUUUS 9nUR-UUUUUU U U U U S 9 nUSnUR.bUR/U5OSnU (d UU4US S-$[1UUUR2UR4UR6UR8S9$)a Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`BertTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). Others (**kwargs) some additional parameters might passed in from upstream pipeline, which not influence the results. NFzDYou cannot specify both input_ids and inputs_embeds at the same timerz5You have to specify either input_ids or inputs_embedsrrc)rGr rF)rIrr rJrK) rrrrrrrrrr)r pooler_outputrrr r )r@rruse_return_dictrorrer<rGshaper6onesrHrPr r8r;r=get_extended_attention_maskinvert_attention_mask get_head_maskrrSrTr rrr r ) r?rIrr rrrJrrrrrrrr-rL batch_sizerMrGrKrNrOextended_attention_maskencoder_batch_sizeencoder_sequence_lengthrencoder_hidden_shapeencoder_extended_attention_maskembedding_outputencoder_outputssequence_outputr&s rBrQBertModel.forwardsUH2C1N-TXT_T_TqTq$8$D KK , , &1%V  "#..*K  &',,.s3KGI I"- J%.%:!! @T@T"-"1!3A!6! :  7$6$;$= %-).(*9&.2.H.H&/( +/3 +&&y'+{{'D'DF  ??%)'#9 + ,, 2"7#B+/!5#'  *!, $ 7 =A #]3oab6II I+-'+;;)77&11,==   rDc US$)Nrrr?rs rBextract_sequence_outputs"BertModel.extract_sequence_outputss*++rDc US$)Nrhrrzs rBextract_pooled_outputs BertModel.extract_pooled_outputss ''rD)rPrSrT)T)NT) NNNNNNNNNNNNN)rSrTrUrVrWr%rMrGr\r_rdr rQr{r~rXrYrZs@rBrQrQs|#J /0C##!"&*'+ $"&%) O :O b,((rDrQ)1rWrr6torch.utils.checkpoint packagingrrtransformers.activationsrtransformers.modeling_utilsrmodelscope.metainformodelscope.modelsrr modelscope.models.builderr modelscope.outputsr modelscope.utils.constantr modelscope.utils.loggerr modelscope.utils.nlp.utilsrmodelscope.utils.torch_utilsrrr configurationrr _CONFIG_FOR_DOCModulerr\rrrrrrrr)register_modulebackboner*rQrrDrBrs0  +7&/,;+.<>>& NRYYNbQ Qh RYY  2BII2j ryy    \ \~b "))b J   >*o>B%..fkkJY(#Y(KY(rD