sgdZddlmZddlZddlZddlmZmZmZddl Z ddl Z ddl mZddlmZmZmZmZmZmZmZddlmZmZmZmZmZmZmZmZm Z m!Z!m"Z"dd l#m$Z$m%Z%m&Z&dd l'm(Z(m)Z)m*Z*m+Z+d d l,m-Z-e+j\e/Z0d Z1dZ2GddeZ3Gdde jhjjZ6Gdde jhjjZ7Gdde jhjjZ8Gdde jhjjZ9Gdde jhjjZ:Gdde jhjjZ;Gdde jhjjZ<Gdd e jhjjZ=e!Gd!d"e jhjjZ>d#Z?d$Z@e)d%e?Gd&d'e3ZAGd(d)e jhjjZBe)d*e?Gd+d,e3eZCGd-d.e jhjjZDe)d/e?Gd0d1e3eZEe)d2e?Gd3d4e3eZFe)d5e?Gd6d7e3eZGe)d8e?Gd9d:e3eZHy);zTF 2.0 MPNet model.) annotationsN)OptionalTupleUnion)get_tf_activation)TFBaseModelOutputTFBaseModelOutputWithPoolingTFMaskedLMOutputTFMultipleChoiceModelOutputTFQuestionAnsweringModelOutputTFSequenceClassifierOutputTFTokenClassifierOutput) TFMaskedLanguageModelingLossTFModelInputTypeTFMultipleChoiceLossTFPreTrainedModelTFQuestionAnsweringLossTFSequenceClassificationLossTFTokenClassificationLossget_initializerkeraskeras_serializable unpack_inputs)check_embeddings_within_bounds shape_liststable_softmax)add_code_sample_docstringsadd_start_docstrings%add_start_docstrings_to_model_forwardlogging) MPNetConfigzmicrosoft/mpnet-baser#ceZdZdZeZdZy)TFMPNetPreTrainedModelz An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. mpnetN)__name__ __module__ __qualname____doc__r# config_classbase_model_prefix^/var/www/html/venv/lib/python3.12/site-packages/transformers/models/mpnet/modeling_tf_mpnet.pyr%r%Bs Lr.r%c8eZdZdZfdZddZdZddZxZS)TFMPNetEmbeddingsz8Construct the embeddings from word, position embeddings.c dt|di|d|_||_|j|_|j |_|j |_tjj|jd|_ tjj|j|_y)Nr" LayerNormepsilonnamerater-)super__init__ padding_idxconfig hidden_sizemax_position_embeddingsinitializer_rangerlayersLayerNormalizationlayer_norm_epsr3Dropouthidden_dropout_probdropoutselfr<kwargs __class__s r/r:zTFMPNetEmbeddings.__init__Os "6" !--'-'E'E$!'!9!988AVAV]h8i||++1K1K+L r.ctjd5|jd|jj|j gt |j|_dddtjd5|jd|j|j gt |j|_ ddd|jryd|_ t|ddetj|jj5|jjdd|jj gdddyy#1swYxYw#1swYxYw#1swYyxYw) Nword_embeddingsweight)r?r6shape initializerposition_embeddings embeddingsTr3)tf name_scope add_weightr< vocab_sizer=rr?rLr>rPbuiltgetattrr3r6buildrG input_shapes r/rXzTFMPNetEmbeddings.buildZs@ ]], - //{{--t/?/?@+d>T>TU*DK ]]0 1 '+!33T5E5EF+d>T>TU(7(D $  ::  4d + 7t~~223 L$$dD$++2I2I%JK L L 8#     L Ls%AEAE 3E,E E),E5ctjtjj||j|j }tjj |d|z}||jzS)a Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: input_ids: tf.Tensor Returns: tf.Tensor dtyper"axis)rRcastmath not_equalr;r]cumsum)rG input_idsmaskincremental_indicess r/"create_position_ids_from_input_idsz4TFMPNetEmbeddings.create_position_ids_from_input_idsps\wwrww((D4D4DEY__] ggnnTn:TA"T%5%555r.c||J|At||jjtj|j |}t |dd}|a||j|}nLtjtj|jdz|d|jzdzd}tj|j|}||z}|j| }|j|| }|S) z Applies embedding based on inputs tensor. Returns: final_embeddings (`tf.Tensor`): output embedding tensor. N)paramsindices)rdr")startlimitrr^inputsrotraining)rr<rUrRgatherrLrrg expand_dimsranger;rPr3rE)rGrd position_ids inputs_embedsrqrZposition_embedsfinal_embeddingss r/callzTFMPNetEmbeddings.call~s%-*?@@  *9dkk6L6L MIIT[[)LM /4  $#FFQZF[ !~~HH4#3#3a#7{2QUQaQa?ade?efmn  ))4+C+C\Z(?:>>1A>B<$>>!CCC#&v'9'9F(> ?+bggll2.>>  $  -   %/.@ ()9C,,,J  - 9O IIoq ) LL . JJq:r4+=+=> ? FF1I*;1o&CDr.c|jryd|_t|dddtj|jj 5|jj dd|jjgdddt|dddtj|jj 5|jj dd|jjgdddt|dddtj|jj 5|jj dd|jjgdddt|ddetj|jj 5|jj dd|jjgdddyy#1swY_xYw#1swYxYw#1swYxYw#1swYyxYw)NTrrrr) rVrWrRrSrr6rXr<r=rrrrYs r/rXzTFMPNetSelfAttention.builds ::  4d # /tvv{{+ D dD$++*A*ABC D 4d # /tvv{{+ D dD$++*A*ABC D 4d # /tvv{{+ D dD$++*A*ABC D 4d # /tvv{{+ D dD$++*A*ABC D D 0 D D D D D D D Ds0 3G<3G(-3G43HG%(G14G=H NFrz)r'r(r)r:rryrXr{r|s@r/rrs:2 #JDr.rc4eZdZfdZdZddZddZxZS)TFMPNetAttentionc t|di|t|d|_tj j |jd|_tj j|j|_ ||_ y)Nattnr6r3r4r-) r9r:rrrr@rArBr3rCrDrEr<rFs r/r:zTFMPNetAttention.__init__sf "6"(f= 88AVAV]h8i||++F,F,FG  r.ctrzNotImplementedError)rGheadss r/ prune_headszTFMPNetAttention.prune_heads s!!r.c|j||||||}|j|j|d|z}|f|ddz} | S)Nrrqrr")rr3rE) rG input_tensorrrrrrq self_outputsattention_outputrs r/ryzTFMPNetAttention.call#sayy .)5FVcnv!  >>$,,|A*G,*VW#% QR(88r.c|jryd|_t|ddMtj|jj 5|jj ddddt|ddetj|jj 5|jj dd|jjgdddyy#1swY|xYw#1swYyxYw)NTrr3) rVrWrRrSrr6rXr3r<r=rYs r/rXzTFMPNetAttention.build+s ::  4 & 2tyy~~. & % & 4d + 7t~~223 L$$dD$++2I2I%JK L L 8 & & L L C"%3C."C+.C7rrz)r'r(r)r:rryrXr{r|s@r/rrs" Lr.rc0eZdZdfd ZddZddZxZS)TFMPNetIntermediatec Tt|di|tjj |j t |jd|_t|jtr"t|j|_ ||_y|j|_ ||_y)Nrrrr6r-)r9r:rr@rintermediate_sizerr?r isinstance hidden_actstrrintermediate_act_fnr<rFs r/r:zTFMPNetIntermediate.__init__9s "6"\\''**vOgOg?hov(   f'' -'89J9J'KD $ (.'8'8D $ r.cL|j|}|j|}|S)Nrn)rr)rGrs r/ryzTFMPNetIntermediate.callFs( - 8 00? r.c(|jryd|_t|ddetj|jj 5|jj dd|jjgdddyy#1swYyxYwrrrYs r/rXzTFMPNetIntermediate.buildLrrrrrzrr|s@r/rr8s  Hr.rc2eZdZdfd ZdddZddZxZS) TFMPNetOutputc xt|di|tjj |j t |jd|_tjj|jd|_ tjj|j|_||_y)Nrrr3r4r7r-)r9r:rr@rr=rr?rrArBr3rCrDrEr<rFs r/r:zTFMPNetOutput.__init__Ws "6"\\''$$IaIa9bip(  88AVAV]h8i||++1K1K+L  r.cz|j|}|j||}|j||z}|S)Nrnrp)rrEr3)rGrrrqs r/ryzTFMPNetOutput.callas? - 8  MH M ml.JK r.c"|jryd|_t|dddtj|jj 5|jj dd|jjgdddt|ddetj|jj 5|jj dd|jjgdddyy#1swY|xYw#1swYyxYw)NTrr3) rVrWrRrSrr6rXr<rr3r=rYs r/rXzTFMPNetOutput.buildhs ::  4$ ' 3tzz/ N   $dkk.K.K!LM N 4d + 7t~~223 L$$dD$++2I2I%JK L L 8 N N L L 3C9<3D9DDrF)rrrrrqboolrrrzrr|s@r/rrVs Lr.rc.eZdZfdZddZddZxZS) TFMPNetLayerc t|di|t|d|_t |d|_t |d|_y)N attentionr intermediateoutputr-)r9r:rrrrroutrFs r/r:zTFMPNetLayer.__init__us> "6")&{C/^L h7r.c|j||||||}|d}|dd} |j|} |j| ||} | f| z} | S)Nrrr"r)rrr) rGrrrrrrqself_attention_outputsrrintermediate_output layer_outputs r/ryzTFMPNetLayer.call|sy!% >96GWdow"0" 2!4(,"//0@Axx 35EPXxY /G+r.c|jryd|_t|ddMtj|jj 5|jj ddddt|ddMtj|jj 5|jj ddddt|ddNtj|jj 5|jj ddddyy#1swYxYw#1swYqxYw#1swYyxYw)NTrrr) rVrWrRrSrr6rXrrrYs r/rXzTFMPNetLayer.builds ::  4d + 7t~~223 +$$T* + 4 . :t00556 .!!''- . 4 % 1txx}}- %t$ % % 2  + + . . % %$ D%%D1?D=%D.1D:=Errzrr|s@r/rrts8  %r.rcJeZdZfdZddZ ddZeddZddZxZ S) TFMPNetEncoderc tt|di|||_|j|_|j |_|j |_|j|_|j|_t|jDcgc]}t|d|c}|_ |j|_ycc}w)Nzlayer_._rr-) r9r:r<rn_headsroutput_hidden_statesrelative_attention_num_bucketsr?rtnum_hidden_layersrlayer)rGr<rHirIs r/r:zTFMPNetEncoder.__init__s "6" 11 !'!9!9$*$?$?!.4.S.S+!'!9!9INvOgOgIhiAl6(1#?i .4.S.S+jsB5c|jryd|_tjd5|jd|j|j gt |j|_dddt|ddK|jD];}tj|j5|jdddd=yy#1swYbxYw#1swYUxYw)NTrelative_attention_biasrQrMr) rVrRrSrTrrrr?rrWrr6rX)rGrZrs r/rXzTFMPNetEncoder.builds ::  ]]4 5 +/??!::DLLI+D,B,BC,;,D (  4$ ' 3 &]]5::.&KK%&& & 4   &&sAC0CCC# c $|j|}|rdnd} |rdnd} t|jD]/\} } |r| |fz} | |||| |||} | d}|s'| | dfz} 1|r| |fz} |std|| | fDSt || | S)Nr-rrr"c3&K|] }|| ywrzr-).0rs r/ z&TFMPNetEncoder.call..shqZ[Zghs)last_hidden_stater attentions)compute_position_bias enumeratertupler )rGrrrrr return_dictrqrall_hidden_statesall_attentionsr layer_module layer_outputss r/ryzTFMPNetEncoder.calls22=A "6BD0d(4 FOA|#$58H$H!(! !+! M*!,M !/=3C2E!E F$ 1]4D D h]4E~$Vhh h +;LYg  r.cld}| }|dz}|tjtjj|d|j|zz }tjj |}|dz}tjj||}|tjtjj ||z tj ||z z ||z z|jz}tjj||dz }|tj|||z }|S)Nrrr\r") rRr`ralessr]abslogminimumwhere)relative_position num_buckets max_distanceretn max_exactis_small val_if_larges r/_relative_position_bucketz(TFMPNetEncoder._relative_position_buckets    rwwrww||Aq)1B1H1HIKWW GGKKN 1$ 77<<9- 277 GGKKI &,2J)K K{]fOf g#))$  ww|[1_E  rxx!\22 r.ct|}|d|d}}||dddddf}|dddddf}n8tj|dddf}tj|dddf}||z }|j||j} tj |j | } tjtj| gdd} | S)z%Compute binned relative position biasr"N)r )rrr"rr^) rrRrtrrrrrrsr) rGrrurZqlenklencontext_positionmemory_positionr  rp_bucketvaluess r/rz$TFMPNetEncoder.compute_position_biass m  ^[^d  #+Aq$J7 *1dA:6O!xx~ag6  hhtnT1W5O+.>>22 ;;3 477C VY ?aH r.rzr) ) r'r(r)r:rXry staticmethodrrr{r|s@r/rrs2 T &.( T*r.rcdeZdZeZfdZddZddZdZe d dZ d dZ xZ S) TFMPNetMainLayerc Ht|di|||_|j|_|j|_|j |_|j |_|j|_t|d|_ t|d|_ t|d|_y)NencoderrpoolerrQr-)r9r:r<rr?rruse_return_dictrrr"r~r#r1rQrFs r/r:zTFMPNetMainLayer.__init__s "6" !'!9!9!'!9!9!'!9!9$*$?$?!!11%f9= #F: +FFr.c|jSrz)rQrGs r/get_input_embeddingsz%TFMPNetMainLayer.get_input_embeddingss r.c`||j_t|d|j_yNr)rQrLrrUrGvalues r/set_input_embeddingsz%TFMPNetMainLayer.set_input_embeddings#s$!&%/%6q%9"r.ct)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 r)rGheads_to_prunes r/ _prune_headszTFMPNetMainLayer._prune_heads(s "!r.c | | td| t|} n|t|dd} n td|tj| d}|j |||| } tj || ddd| df} tj | | j} tjd| j} tjd | j}tjtj| | |} |tdg|jz}|j| | ||||| }|d}|j|}|s ||f|ddzSt|||j |j" S) NzDYou cannot specify both input_ids and inputs_embeds at the same timerkz5You have to specify either input_ids or inputs_embedsr"rrg?r\g)r pooler_outputrr)rrrRfillrQrr`r]constantmultiplysubtractrrr"r#r rr)rGrdrrurrvrrrrqrZembedding_outputextended_attention_maskone_cstten_thousand_cstencoder_outputssequence_outputrs r/ryzTFMPNetMainLayer.call/s  ]%>cd d  "$Y/K  &$]3CR8KTU U  !WW[!4N??    + #%**^k!naQRT_`aTb=c"d#%''*ACSCYCY"Z++c)9)?)?@;;x7G7M7MN"$++bkk'CZ.[]m"n  % %!7!77I,,  #   ' *!, O4  #$ $ ,-')77&11   r.c|jryd|_t|ddMtj|jj 5|jj ddddt|ddMtj|jj 5|jj ddddt|ddNtj|jj 5|jj ddddyy#1swYxYw#1swYqxYw#1swYyxYw)NTr"r#rQ) rVrWrRrSr"r6rXr#rQrYs r/rXzTFMPNetMainLayer.builds ::  4D ) 5t||001 ) ""4( ) 44 ( 4t{{//0 ( !!$' ( 4t , 8t334 ,%%d+ , , 9  ) ) ( ( , ,r)rzkeras.layers.Layer)r+z tf.Variable NNNNNNNNFrz) r'r(r)r#r+r:r'r,r/rryrXr{r|s@r/r r  sVL G: "!R R h ,r.r au This model inherits from [`TFPreTrainedModel`]. 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 [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. TensorFlow models and layers in `transformers` accept two formats as input: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument: - a single Tensor with `input_ids` only and nothing else: `model(input_ids)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})` Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python function! Args: config ([`MPNetConfig`]): 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. a Args: input_ids (`Numpy array` or `tf.Tensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and [`PreTrainedTokenizer.encode`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`Numpy array` or `tf.Tensor` of shape `({0})`, *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) position_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *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 (`Numpy array` or `tf.Tensor` 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 (`tf.Tensor` of shape `({0}, 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. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. 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. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). z_The bare MPNet Model transformer outputting raw hidden-states without any specific head on top.ceZdZfdZeeejdee e e  d ddZ ddZ xZS) TFMPNetModelcPt||g|i|t|d|_y)Nr&r)r9r:r r&rGr<rorHrIs r/r:zTFMPNetModel.__init__s( 3&3F3%f7; r.batch_size, sequence_length checkpoint output_typer+c :|j|||||||||  } | S)N rdrrurrvrrrrq)r&) rGrdrrurrvrrrrqrs r/ryzTFMPNetModel.calls9&**)%'/!5#  r.c|jryd|_t|ddNtj|jj 5|jj ddddyy#1swYyxYw)NTr&)rVrWrRrSr&r6rXrYs r/rXzTFMPNetModel.buildsg ::  4$ ' 3tzz/ '   & ' ' 4 ' 's A11A:r=)rdTFModelInputType | Noner$Optional[Union[np.array, tf.Tensor]]rurJrrJrvtf.Tensor | NonerOptional[bool]rrLrrLrqrrz*Union[TFBaseModelOutput, Tuple[tf.Tensor]]rz)r'r(r)r:rr MPNET_INPUTS_DOCSTRINGformatr_CHECKPOINT_FOR_DOCr _CONFIG_FOR_DOCryrXr{r|s@r/r?r?s <*+A+H+HIf+gh&%$.2?C=A:>*.,0/3&**=;  8  ( *-$ 4 i2'r.r?cHeZdZdZfdZd dZdZdZdZdZ dZ xZ S) TFMPNetLMHeadz4MPNet head for masked and permuted language modelingc jt|di|||_|j|_tj j |jt|jd|_ tj j|jd|_ td|_||_y)Nrr layer_normr4gelur-)r9r:r<r=rr@rrr?rrArBrTractdecoder)rGr<input_embeddingsrHrIs r/r:zTFMPNetLMHead.__init__"s "6" !--\\''   ?6C[C[3\cj(   ,,99&BWBW^j9k$V,( r.c|j|jjfddd|_|jryd|_t |dddt j|jj5|jjdd|jjgdddt |ddet j|jj5|jjdd|jjgdddyy#1swY|xYw#1swYyxYw)NzerosTbias)rNrO trainabler6rrT) rTr<rUr[rVrWrRrSrr6rXr=rTrYs r/rXzTFMPNetLMHead.build1sOO4;;+A+A*CQXdhouOv ::  4$ ' 3tzz/ H   $dkk.E.E!FG H 4t , 8t334 M%%tT4;;3J3J&KL M M 9 H H M Ms:3D(+3D4(D14D=c|jSrz)rWr&s r/get_output_embeddingsz#TFMPNetLMHead.get_output_embeddings> ||r.c`||j_t|d|j_yr))rWrLrrUr*s r/set_output_embeddingsz#TFMPNetLMHead.set_output_embeddingsAs$# ",U"3A"6 r.cd|jiS)Nr[)r[r&s r/get_biaszTFMPNetLMHead.get_biasEs ""r.cX|d|_t|dd|j_y)Nr[r)r[rr<rUr*s r/set_biaszTFMPNetLMHead.set_biasHs'&M !+E&M!:1!= r.c|j|}|j|}|j|}t|d}t j |d|j g}t j||jjd}t j |d||jjg}tjj||j}|S)N)tensorr"rk)rgrNT)abr)r+r[)rrVrTrrRrr=rrWrLr<rUnnbias_addr[)rGr seq_lengths r/ryzTFMPNetLMHead.callLs =1 /  6  }5a8  -DDTDT?UV  MT\\5H5HVZ[  -JPTP[P[PfPf?gh ]K r.rz) r'r(r)r*r:rXr^rarcreryr{r|s@r/rRrRs*> ( M7#> r.rRz3MPNet Model with a `language modeling` head on top.ceZdZdgZfdZdZdZeee jde e e e d d dZd dZxZS) TFMPNetForMaskedLMr#ct||g|i|t|d|_t ||jj d|_y)Nr&rlm_head)r9r:r r&rRrQrprAs r/r:zTFMPNetForMaskedLM.__init___sB 3&3F3%f7; $VTZZ-B-BS r.c|jSrz)rpr&s r/ get_lm_headzTFMPNetForMaskedLM.get_lm_header_r.ctjdt|jdz|jjzS)NzMThe method get_prefix_bias_name is deprecated. Please use `get_bias` instead./)warningswarn FutureWarningr6rpr&s r/get_prefix_bias_namez'TFMPNetForMaskedLM.get_prefix_bias_namehs/ egtuyy3!2!222r.rBrCc |j|||||||||  } | d} |j| } | dn|j| | }|s| f| ddz}||f|zS|St|| | j| j S)a labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` rrurrvrrrrqrNrlosslogitsrr)r&rphf_compute_lossr rr)rGrdrrurrvrrrlabelsrqrr;prediction_scoresr|rs r/ryzTFMPNetForMaskedLM.callls4** )%'/!5#  "!* LL9~t4+?+?HY+Z')GABK7F)-)9TGf$ Ev E$!//))   r.c|jryd|_t|ddMtj|jj 5|jj ddddt|ddNtj|jj 5|jj ddddyy#1swYexYw#1swYyxYw)NTr&rp)rVrWrRrSr&r6rXrprYs r/rXzTFMPNetForMaskedLM.builds ::  4$ ' 3tzz/ '   & ' 4D ) 5t||001 ) ""4( ) ) 6 ' ' ) ) C %C CC  NNNNNNNNNF)rdrIrnp.ndarray | tf.Tensor | NonerurrrrvrKrrLrrLrrLrrKrqrrz)Union[TFMaskedLMOutput, Tuple[tf.Tensor]]rz)r'r(r)_keys_to_ignore_on_load_missingr:rrrxrr rMrNrrOr rPryrXr{r|s@r/rnrn[s'0k#T 3*+A+H+HIf+gh&$$.28<6:37*.,0/3&*#', *, 6, 4 , 1 , ( , *, -, $, !, ,  3, i, \ )r.rnc2eZdZdZfdZddZddZxZS)TFMPNetClassificationHeadz-Head for sentence-level classification tasks.c t|di|tjj |j t |jdd|_tjj|j|_ tjj |jt |jd|_ ||_y)Nrr)rrr6out_projrr-)r9r:rr@rr=rr?rrCrDrE num_labelsrr<rFs r/r:z"TFMPNetClassificationHead.__init__s "6"\\''   .v/G/GH (  ||++F,F,FG  **   /&BZBZ2[bl+   r.c|dddddf}|j||}|j|}|j||}|j|}|S)Nrr)rErr)rGfeaturesrqrs r/ryzTFMPNetClassificationHead.callsV Q1W  LLXL . JJqM LLXL . MM! r.c"|jryd|_t|dddtj|jj 5|jj dd|jjgdddt|ddetj|jj 5|jj dd|jjgdddyy#1swY|xYw#1swYyxYw)NTrr) rVrWrRrSrr6rXr<r=rrYs r/rXzTFMPNetClassificationHead.builds ::  4$ ' 3tzz/ H   $dkk.E.E!FG H 4T * 6t}}112 K ##T41H1H$IJ K K 7 H H K Krrrz)r'r(r)r*r:ryrXr{r|s@r/rrs7  Kr.rz MPNet Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. ceZdZdgZfdZeeejde e e e  d ddZ d dZxZS) TFMPNetForSequenceClassificationr#ct||g|i||j|_t|d|_t |d|_y)Nr&r classifier)r9r:rr r&rrrAs r/r:z)TFMPNetForSequenceClassification.__init__sC 3&3F3 ++%f7; 3FNr.rBrCc |j|||||||||  } | d} |j| | } | dn|j| | }|s| f| ddz}||f|zS|St|| | j| j S)a labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). rzrrNrr{)r&rr~rrrrGrdrrurrvrrrrrqrr;r}r|rs r/ryz%TFMPNetForSequenceClassification.calls4** )%'/!5#  "!*8D~t4+?+?+OY,F)-)9TGf$ Ev E)!//))   r.c|jryd|_t|ddMtj|jj 5|jj ddddt|ddNtj|jj 5|jj ddddyy#1swYexYw#1swYyxYwNTr&r)rVrWrRrSr&r6rXrrYs r/rXz&TFMPNetForSequenceClassification.builds ::  4$ ' 3tzz/ '   & ' 4t , 8t334 ,%%d+ , , 9 ' ' , ,rr)rdrIrrJrurJrrJrvrKrrLrrLrrLrrKrqrrz3Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]rz)r'r(r)rr:rr rMrNrrOrrPryrXr{r|s@r/rrs(1k#O*+A+H+HIf+gh&.$.2?C=A:>*.,0/3&*#'- *- =- ; - 8 - ( - *- -- $- !- -  =- i- ^ ,r.rz MPNet Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. ceZdZfdZeeejdee e e  d ddZ ddZ xZS) TFMPNetForMultipleChoicec.t||g|i|t|d|_tj j |j|_tj jdt|jd|_ ||_ y)Nr&rr"rr)r9r:r r&rr@rCrDrErrr?rr<rAs r/r:z!TFMPNetForMultipleChoice.__init__-s{ 3&3F3%f7; ||++F,F,FG ,,,, /&2J2J"KR^-  r.z(batch_size, num_choices, sequence_lengthrCc |t|d} t|d} nt|d} t|d} |tj|d| fnd} |tj|d| fnd}|tj|d| fnd}|%tj|d| t|dfnd}|j| ||||||||  }|d}|j || }|j |}tj|d| f}| dn|j | |}|s|f|ddz}||f|zS|St|||j|jS) a5 labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) Nr"rrkr)rrqrr{) rrRrr&rErr~r rr)rGrdrrurrvrrrrrq num_choicesrlflat_input_idsflat_attention_maskflat_position_idsflat_inputs_embedsrrr}reshaped_logitsr|rs r/ryzTFMPNetForMultipleChoice.call7s2  $Y/2K#I.q1J$]3A6K#M215JDMDYIJ/?@_cN\Nhbjj"j9IJnrJVJbBJJ|b*5EFhl( JJ}r:z-7PQR7S&T U  **       #     ]X F /**Vb+->?~t4+?+?+X%''!"+5F)-)9TGf$ Ev E*"!//))   r.c|jryd|_t|ddMtj|jj 5|jj ddddt|ddetj|jj 5|jj dd|jjgdddyy#1swY|xYw#1swYyxYwr rVrWrRrSr&r6rXrr<r=rYs r/rXzTFMPNetForMultipleChoice.build{ ::  4$ ' 3tzz/ '   & ' 4t , 8t334 M%%tT4;;3J3J&KL M M 9 ' ' M Mrr)rdrIrrrurrrrvrKrrLrrLrrLrrKrqrrz4Union[TFMultipleChoiceModelOutput, Tuple[tf.Tensor]]rz)r'r(r)r:rr rMrNrrOr rPryrXr{r|s@r/rr%s*+A+H+HIs+tu&/$.28<6:37*.,0/3&*#'; *; 6; 4 ; 1 ; ( ; *; -; $; !; ;  >; v; z Mr.rz MPNet Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. ceZdZdgZfdZeeejde e e e  d ddZ d dZxZS) TFMPNetForTokenClassificationr#cdt||g|i||j|_t|d|_t j j|j|_ t j j|jt|jd|_ ||_y)Nr&rrr)r9r:rr r&rr@rCrDrErrr?rr<rAs r/r:z&TFMPNetForTokenClassification.__init__s 3&3F3 ++%f7; ||++F,F,FG ,,,,   /&BZBZ2[bn-  r.rBrCc $|j|||||||||  } | d} |j| | } |j| } | dn|j| | }|s| f| ddz}||f|zS|St || | j | j S)z labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. rGrrNr"r{)r&rErr~rrrrs r/ryz"TFMPNetForTokenClassification.calls0**)%'/!5#  "!*,,,J1~t4+?+?+OY,F)-)9TGf$ Ev E&!//))   r.c|jryd|_t|ddMtj|jj 5|jj ddddt|ddetj|jj 5|jj dd|jjgdddyy#1swY|xYw#1swYyxYwrrrYs r/rXz#TFMPNetForTokenClassification.buildrrr)rdrIrrrurrrrvrKrrLrrLrrLrrKrqrrz0Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]rz)r'r(r)rr:rr rMrNrrOrrPryrXr{r|s@r/rrs(1k# *+A+H+HIf+gh&+$.28<6:37*.,0/3&*#', *, 6, 4 , 1 , ( , *, -, $, !, ,  :, i, \ Mr.rz MPNet Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). ceZdZdgZfdZeeejde e e e  d ddZ d dZxZS) TFMPNetForQuestionAnsweringr#ct||g|i||j|_t|d|_t j j|jt|jd|_ ||_ y)Nr&r qa_outputsr) r9r:rr r&rr@rrr?rr<rAs r/r:z$TFMPNetForQuestionAnswering.__init__sr 3&3F3 ++%f7; ,,,,   /&BZBZ2[bn-  r.rBrCc |j|||||||||  } | d}|j|}tj|dd\}}tj|d}tj|d}d}| | | | d}|j |||f}|s||f| ddz}||f|zS|St |||| j| jS) a start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. rzrrrkr^N)start_position end_position)r| start_logits end_logitsrr) r&rrRsplitsqueezer~r rr)rGrdrrurrvrrrstart_positions end_positionsrqrHrr;r}rrr|rrs r/ryz TFMPNetForQuestionAnswering.calls @** )%'/!5#  "!*1#%88FAB#? jzz,R8 ZZ 4   &=+D(7WF''z0JKD"J/'!"+=F)-)9TGf$ Ev E-%!!//))   r.c|jryd|_t|ddMtj|jj 5|jj ddddt|ddetj|jj 5|jj dd|jjgdddyy#1swY|xYw#1swYyxYw)NTr&r) rVrWrRrSr&r6rXrr<r=rYs r/rXz!TFMPNetForQuestionAnswering.build4rr) NNNNNNNNNNF)rdrIrrJrurJrrJrvrKrrLrrLrrLrrKrrKrqrrz7Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]rz)r'r(r)rr:rr rMrNrrOr rPryrXr{r|s@r/rrs(1k#*+A+H+HIf+gh&2$.2?C=A:>*.,0/3&*,0*.: *: =: ; : 8 : ( : *: -: $: *: (: :  A: i: x Mr.r)Ir* __future__rrarutypingrrrnumpynp tensorflowrRactivations_tfrmodeling_tf_outputsr r r r r rrmodeling_tf_utilsrrrrrrrrrrrtf_utilsrrrutilsrrr r!configuration_mpnetr# get_loggerr'loggerrOrPr%r@Layerr1r~rrrrrrr MPNET_START_DOCSTRINGrMr?rRrnrrrrrr-r.r/rs " ))/    SR -   H %, . O  **O fHELL&&H:XD5<<--XDvLu||))LBH%,,,,H<LELL&&L