T^(h1tdZddlZddlZddlmZmZmZmZddlZddlm Z ddl m Z m Z m Z ddlmZddlmZmZmZmZdd lmZdd lmZdd lmZmZmZmZmZmZm Z dd l!m"Z"dd l#m$Z$m%Z%m&Z&m'Z'm(Z(m)Z)m*Z*m+Z+m,Z,ddl-m.Z.e(r ddl/m0Z0ddl1m2Z2e+jfe4Z5dZ6dZ7Gdde jpZ9Gdde jpZ:Gdde jpZ;Gdde jpZ<Gdde jpZ=Gdde jpZ>Gd d!e jpZ?Gd"d#e jpZ@Gd$d%e jpZAGd&d'e"ZBGd(d)eBZCd*ZDd+ZEd,ZFe&d-eDGd.d/eBZGe&d0eDGd1d2eBeZHe&d3eDGd4d5eBZIe&d6eDGd7d8eBZJe&d9eDGd:d;eBZKe&deBZLgd?ZMy)@zPyTorch UMT5 model.N)ListOptionalTupleUnion)nn)BCEWithLogitsLossCrossEntropyLossMSELoss)ACT2FN)Cache DynamicCacheEncoderDecoderCache StaticCache)GenerationMixin)AttentionMaskConverter)BaseModelOutput)BaseModelOutputWithPastAndCrossAttentionsSeq2SeqLMOutputSeq2SeqModelOutput#Seq2SeqQuestionAnsweringModelOutputSeq2SeqSequenceClassifierOutputTokenClassifierOutput)PreTrainedModel) DUMMY_INPUTS DUMMY_MASKadd_start_docstrings%add_start_docstrings_to_model_forwardis_torch_flex_attn_availableis_torch_fx_proxyis_torchdynamo_compilingloggingreplace_return_docstrings) UMT5Config) BlockMask)make_flex_block_causal_maskr%zgoogle/umt5-smallc&eZdZdfd ZdZxZS) UMT5LayerNormct|tjt j ||_||_y)ze Construct a layernorm module in the UMT5 style. 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N)super__init__r Parametertorchonesweightvariance_epsilon)self hidden_sizeeps __class__s d/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/umt5/modeling_umt5.pyr,zUMT5LayerNorm.__init__Bs1 ll5::k#:; #c|jtjjdj dd}|tj ||j zz}|jjtjtjfvr%|j|jj}|j|zS)NT)keepdim) tor.float32powmeanrsqrtr1r0dtypefloat16bfloat16)r2 hidden_statesvariances r6forwardzUMT5LayerNorm.forwardJs !##EMM266q9>>r4>P% Ht?T?T4T(UU  ;;   ? ?),,T[[->->?M{{]**r7)gư>)__name__ __module__ __qualname__r,rF __classcell__r5s@r6r)r)As $ +r7r)c*eZdZdeffd ZdZxZS)UMT5DenseActDenseconfigc^t|tj|j|j d|_tj|j |jd|_tj|j|_ t|j|_ yNFbias)r+r,rLineard_modeld_ffwiwoDropout dropout_ratedropoutr dense_act_fnactr2rNr5s r6r,zUMT5DenseActDense.__init__\sn ))FNNFKKeD))FKKeDzz&"5"56 &--.r7c|j|}|j|}|j|}t|jj t jr|j|jj jk7r`|jj jt jk7r/|j|jj j}|j |}|SN) rVr\rZ isinstancerWr0r.TensorrAint8r<r2rDs r6rFzUMT5DenseActDense.forwardcs . /  ]3 tww~~u|| 4##tww~~';';;$$ 2),,TWW^^-A-ABM . r7rGrHrIr%r,rFrJrKs@r6rMrM[s/z/ r7rMc*eZdZdeffd ZdZxZS)UMT5DenseGatedActDenserNct|tj|j|j d|_tj|j|j d|_tj|j |jd|_tj|j|_ t|j|_yrP)r+r,rrSrTrUwi_0wi_1rWrXrYrZr r[r\r]s r6r,zUMT5DenseGatedActDense.__init__ss IIfnnfkkF IIfnnfkkF ))FKKeDzz&"5"56 &--.r7c,|j|j|}|j|}||z}|j|}t |j j tjr|j|j j jk7r`|j j jtjk7r/|j|j j j}|j |}|Sr_) r\rhrirZr`rWr0r.rarArbr<)r2rD hidden_gelu hidden_linears r6rFzUMT5DenseGatedActDense.forward{shhtyy78  -0 #m3  ]3 tww~~u|| 4##tww~~';';;$$ 2),,TWW^^-A-ABM . r7rdrKs@r6rfrfrs/z/r7rfc*eZdZdeffd ZdZxZS) UMT5LayerFFrNct||jrt||_nt ||_t |j|j|_ tj|j|_ y)Nr4)r+r, is_gated_actrfDenseReluDenserMr)rTlayer_norm_epsilon layer_normrrXrYrZr]s r6r,zUMT5LayerFF.__init__s_    "8"@D "3F";D 'FeZdZdZddeeffd ZdejdejfdZ dZ ddZ dd ejd eejd ee ejd eejd eejdeejf dZ xZS) UMT5Attentionz7 T5's attention using relative_attention_bias. layer_idxct||j|_||_|j|_|j |_|j |_|j|_|j|_ |j|_ |j|jz|_ ||_|9|jr-tj!d|j"j$dt'j(|j |jd|_t'j(|j |jd|_t'j(|j |jd|_t'j(|j|j d|_|jr/t'j2|j|j|_t7|_y)NzInstantiating a decoder z without passing `layer_idx` is not recommended and will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` when creating this class.FrQ)r+r, is_decoderhas_relative_attention_biasrelative_attention_num_bucketsrelative_attention_max_distancerTd_kvkey_value_proj_dim num_headsn_headsrYrZ inner_dimrylogger warning_oncer5rGrrSqkvo Embeddingrelative_attention_biasset pruned_heads)r2rNr|ryr5s r6r,zUMT5Attention.__init__si  +++F(.4.S.S+/5/U/U,~~ "(++'' ** (?(??"     *4>>+B+B*CD,, 4<<eD4<<eD4<<eD4>>4<FFq!QPQRr7cd}|j}|j}|jsC|dz}||dkDjtj |zz }t j |}n*t j|t j| }|dz}||k}t j|j|z tj||z z }|||z z}||jtj z}t j|t j||dz }|t j|||z }|S)a Adapted from Mesh Tensorflow: https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 Translate relative position to a bucket number for relative attention. The relative position is defined as memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for small absolute relative_position and larger buckets for larger absolute relative_positions. All relative positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. This should allow for more graceful generalization to longer sequences than the model has been trained on Args: relative_position: an int32 Tensor bidirectional: a boolean - whether the attention is bidirectional num_buckets: an integer max_distance: an integer Returns: a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets) rr9r$)r}r~r{r<r.longabsmin zeros_likelogfloatmath full_likewhere) r2relative_positionrelative_buckets num_buckets max_distance max_exactis_small log_ratiorelative_position_if_larges r6_relative_position_bucketz'UMT5Attention._relative_position_bucketsA*99 ;;  A K !2Q!6 : :5:: F T T  % *; < !&+N>NO`>a!b b  1$ $y0II/557)CDtxxP\_hPhGii y!89 %.ejj1I%I"%*YY &8RT_bcTc(d& " EKK2CE_``r7c| |jjj}|.tj|tj |dddf}n |dddf}tj|tj |dddf}||z }|j |}|j|} | jgdjd} | S)z%Compute binned relative position biasN)rAdevice)r9rr$r) rr0rr.arangerrr unsqueeze) r2 query_length key_lengthrcache_positioncontext_positionmemory_positionrrelative_position_bucketvaluess r6 compute_biaszUMT5Attention.compute_biass >1188??F  !$||L SYZ[\^b[bc -ag6 ,,zFSTXZ[T[\+.>>#'#A#ABS#T --.FG *44Q7 r7rDencoder_hidden_statespast_key_valueattention_masklayer_head_maskrc|jdd\}}|du} |j|} | j|d|j|jj dd} |@|j j|j} | r |j} n |j} | r|n|} | r7|5 r3 j|j}| j|j}n|j| }|j| }|j|d|j|jj dd}|j|d|j|jj dd}|D| s|nd} j|||jd|i\}}| rd|j |j<t!j"| |j dd}|||j%zn|}|jd}|j&s;t!j(d|j||f|j*|j,}n1|j/|||j*| }|dddd| dddf}|#|ddddddd|jdf}||z}|j0rRt!j2|jd}d |t5|j0<|dd|j7f}n|}||z }t8j:j=|j?d jA|}t8j:jC||jB|jD }|||z}t!j"||}|j ddjG}|j||d}|jI|}|||fS) Nr9r:r$rTr )rrA)rrrdim)ptraining)%shaperrrr transpose is_updatedgetrycross_attention_cacheself_attention_cache key_cache value_cacherrupdater.matmulget_seq_lengthr|zerosrrArrr/listboolr functionalsoftmaxrtype_asrZr contiguousr)r2rDrrrrr batch_size seq_lengthis_cross_attention query_statesrcurr_past_key_valuecurrent_states key_states value_statesscoresreal_seq_lengthr position_bias causal_maskmaskposition_bias_masked attn_weights attn_outputs r6rFzUMT5Attention.forwards"/!4!4Ra!8 J3$>vvm, #((RtG^G^_iijkmno  %'2266t~~FJ!&4&J&J#&4&I&I#2D.- ."<,66t~~FJ.::4>>JL/J66.1L#RtG^G^_iijkmnoJ',,ZT\\4KbKbcmmnoqrsL)7It+>+E+E dnn?OQ_>`,( L&@DN--dnn=lJ,@,@A,FGKYJd*~'D'D'FFjt%%b) //!KKDLL*j9&--W]WcWcM!--FMMR`.M*!Q a*?@M  %(Aq2HJ4D4DR4H2H)HIK)K7M   ::m11!45D,-Dd''( )#0DIIK#@ #0 &&}},,V\\^,DLLVT }},,\T\\TXTaTa,b   &'/9Lll<> !++Aq1<<> !&&z:rB ff[) L.88r7)FN)NNNNNNN)rGrHrI__doc__rintr,r.rarrrrrFrJrKs@r6rxrxs"XVY]":%,, - ^ $9=8<152615Y9||Y9 ( 5Y9!u||!45 Y9 !. Y9 "%,,/ Y9!.Y9r7rxc<eZdZddeeffd Z ddZxZS)UMT5LayerSelfAttentionryct|t|d||_t |j |j |_tj|j|_ y)NTr|ryrp) r+r,rx SelfAttentionr)rTrsrtrrXrYrZr2rNryr5s r6r,zUMT5LayerSelfAttention.__init__esN *6t_hi'FeZdZddeeffd Z ddZxZS)UMT5LayerCrossAttentionryct|t|d||_t |j |j |_tj|j|_ y)NFrrp) r+r,rxEncDecAttentionr)rTrsrtrrXrYrZrs r6r,z UMT5LayerCrossAttention.__init__sO ,VQVbkl'F >Gr7r_) NNNNNNFFNrrKs@r6rrs5/(3-/"##'>r7rc`eZdZdZdeffd ZdejdejfdZxZ S)UMT5ClassificationHeadz-Head for sentence-level classification tasks.rNc(t|tj|j|j|_tj |j|_tj|j|j|_ y)N)r) r+r,rrSrTdenserXclassifier_dropoutrZ num_labelsout_projr]s r6r,zUMT5ClassificationHead.__init__sZ YYv~~v~~> zzF$=$=>  &..&2C2CD r7rDrc|j|}|j|}tj|}|j|}|j |}|Sr_)rZrr.tanhrrcs r6rFzUMT5ClassificationHead.forwardsN ]3  =1  =1  ]3  m4 r7) rGrHrIrr%r,r.rarFrJrKs@r6rrs/7EzE U\\ellr7rcLeZdZdZeZdZdZdZdZ dgZ dgZ e dZ dZdZy ) UMT5PreTrainedModelz An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. transformerTrrWcvtjt}tjt}|||d}|S)N)decoder_input_ids input_idsdecoder_attention_mask)r.tensorrr)r2r input_mask dummy_inputss r6r z UMT5PreTrainedModel.dummy_inputs s6LL. \\*- !*"&0 r7cx|jj}t|tr)|jj j |dzy t|ttttfr|jjj jd|dzt|drJ|jjs4|jjj jd|dzt|drz|j jj jd||jj"dzz|j j$j j'y y t|t(rpt|drc|j*jj jd|dz|j*j$j j'y y t|t,rM|j.jj jd||jj"dzzt|j.drD|j.j$.|j.j$j j'|j0jj jd||jj"dzzt|j0drF|j0j$/|j0j$j j'y y y t|t2rM|j4jj jd||jj"dzzt|j4drD|j4j$.|j4j$j j'|j6jj jd||jj8dzzt|j6drF|j6j$/|j6j$j j'y y y t|t:r|j<jj jd||jj"dzzt|j<drD|j<j$.|j<j$j j'|j>jj jd||jj"dzzt|j>drD|j>j$.|j>j$j j'|j6jj jd||jj8dzzt|j6drF|j6j$/|j6j$j j'y y y t|t@ri|jj"}|jjB}|jjD}|jFjj jd|||zdzz|jHjj jd||dzz|jJjj jd||dzz|jLjj jd|||zdzz|jNr8|jPjj jd||dzzy y y ) zInitialize the weights?)r?stdlm_head qa_outputs classifierrRN))rNinitializer_factorr`r)r0datafill_ UMT5ModelUMT5ForConditionalGenerationUMT5EncoderModelUMT5ForQuestionAnsweringsharednormal_hasattrtie_word_embeddingsr%r&rTrRzero_UMT5ForTokenClassificationr(rrrrMrVrWrUrfrhrirxrrrrrrr|r)r2modulefactorrTrrs r6 _init_weightsz!UMT5PreTrainedModel._init_weightss// fm , MM   $ $Vc\ 2  , (    MM % % - -3FSL - Ivy)$++2Q2Q%%**22#2Nv|,!!((--553Ft{{ObObgkNkDl5m!!&&++113- : ;v|,!!((--553FSL5Q!!&&++113- 6 7 LL   $ $ , ,#6dkkFYFY^bEb;c , dv||V,1B1B1N !!&&,,. OO " " ' ' / /SfI\I\aeHe>f / gv/FOO4H4H4T$$))//15U/  1 2 II   ! ! ) )s4;;CVCV[_B_8` ) avyy&)fiinn.H ##))+ II   ! ! ) )s4;;CSCSX\B\8] ) ^vyy&)fiinn.H ##))+/I)  6 7 KK   # # + +&T[[EXEX]aDa:b + cv{{F+ 0@0@0L   %%++- KK   # # + +&T[[EXEX]aDa:b + cv{{F+ 0@0@0L   %%++- II   ! ! ) )s4;;CSCSX\B\8] ) ^vyy&)fiinn.H ##))+/I)  .kk))G!%!1!1 kk++G HHOO ( (cv'L^B^cgAg7h ( i HHOO ( (cv$7O ( P HHOO ( (cv$7O ( P HHOO ( (cv'L^B^cgAg7h ( i11..55::BBQW\chl[lQmBn2/r7c|jj}|jj}| tdt |rGt j |jdddz|}t j||dddfgd}n>|j|j}|dddfj|dddf<||d<| td|j|d k(||S) Nzself.model.config.decoder_start_token_id has to be defined. In UMT5 it is usually set to the pad_token_id. See UMT5 docs for more information.r:)r$.rr$).rz1self.model.config.pad_token_id has to be defined.) rNdecoder_start_token_id pad_token_id ValueErrorr r.fullrcat new_zerosclone masked_fill_)r2rr;r<shifted_input_idss r6 _shift_rightz UMT5PreTrainedModel._shift_rightXs!%!C!C{{// ! )6  Y ' % 9??3B+?$+FH^ _  % +Q*RXZ [  ) 3 3IOO D )238)<)B)B)D c12g &(> f %  PQ Q&&'8D'@,O  r7N)rGrHrIrr% config_classbase_model_prefixsupports_gradient_checkpointing_supports_cache_class_supports_static_cache_no_split_modules_keep_in_fp32_modulespropertyr r8rDr7r6rrsU L%&*# !$ !F @oD!r7rc(eZdZdfd ZdZdZ ddZ ddejdejdejde d e f d Z e dejd e d e d ejdejdejde fdZxZS) UMT5Stackc t||||_|j|_t j t |jDcgc]}t||c}|_ t|j|j|_ t j|j|_d|_|j%ycc}w)NrrpF)r+r, embed_tokensr{rrrange num_layersrblockr)rTrsfinal_layer_normrXrYrZgradient_checkpointing post_init)r2rNrQir5s r6r,zUMT5Stack.__init__us  ( ++]]ERXRcRcLd#eqIf$B#ef -fnn&B[B[ \zz&"5"56 ',#  $fsCc|jSr_rQr2s r6get_input_embeddingszUMT5Stack.get_input_embeddingss   r7c||_yr_rZr2new_embeddingss r6set_input_embeddingszUMT5Stack.set_input_embeddingss *r7c8 | | n|jj} | | n|jj} | | n|jj} | | n|jj} |$|"|j rdnd}t d|d|d|&|j}|jd|d}n8||jdd}n"|j rdnd}t d|d|d |jr%|jr| rtjd d } |(|j t d |j|}|\}}| d ur|j st d|dd }d }|j r| s|t|tr't|t sd }t!|t#}njt|t s-d }tjdt!j$|}n-|+t!t#t#}n|j sd}||j'nd}| %t)j*|||z|j,} |1t/s'||z}t)j0|||j,}|j r$|j3||| | |j4nd| }n]|Y|ddddddf}|j7|j8}d|z t)j:|j8j<z}nd}|j rO|M|j\}}}||f}|!t)j0||j,}|j?|}nd}|jA||jjB}|jA||jjB}| rdnd}| rdnd}| r|j rdnd}|jE|}tG|jHD]\} }!|| }"|| }#| r||fz}|jr2|jr&|jK|!jL|||||"|#d| | | }$n|!|||||"|#|| | |  }$|$d}| r|$d}%| sw||$dfz }|j s||$dfz }|jO|}|jE|}| r||fz}| r%nd}&|r |j4}&|r|jQ}&| stSd||&|||fDStU||&|||S)Ndecoder_zYou cannot specify both zinput_ids and zinputs_embeds at the same timer:zYou have to specify either z input_ids or inputs_embedszZ`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...Fzz$UMT5Stack.forward..=s = s)last_hidden_statepast_key_valuesrD attentionscross_attentions)+rNrroutput_hidden_statesuse_return_dictr{r=rrrVrrrrQr`r rrfrom_legacy_cacherr.rrr!r/_update_causal_maskrr<rArrinvert_attention_mask get_head_maskrSrZ enumeraterT_gradient_checkpointing_funcrFrUto_legacy_cachetupler)'r2rrrrrd head_maskcross_attn_head_maskrjrrrm return_dictrerr_msg_prefix input_shaperrreturn_legacy_cachereturn_self_attention_cachepast_key_values_lengthmask_seq_lengthrencoder_batch_sizeencoder_sequence_length_encoder_hidden_shapeencoder_extended_attention_maskall_hidden_statesall_attentionsall_cross_attentionsrDrX layer_modulerr layer_outputsnext_decoder_cache next_caches' r6rFzUMT5Stack.forwards "+!6IDKK+/??ZN*>*:.HXXvw  "#..*K!r;r?;I  &',,.s3K+/??ZN:>:J-XfWggtuv v  & &4==##p"    ( !_`` --i8M!, J  ?? #LTFRg!hii$&+# ?? _-H/51*_Vi:j.2+"5o|~"V1DE&*###` #6"G"G"X ("5lnln"U#OETE`!?!?!Afg  !"\\&(>(KTaThThN  !*B*D4zAO"ZZ OML`L`aN ??228G8S44Y]! K '(D$)9:K%..}/B/B.CK, MPYj%>>*'7 MM  $**L,?,?v&,,Q/ +??AMnell;$$R(%7!; PP +')#))!,Q  KK , , 6*%%**o=%  E*..I0CCKQZ[Kr7rrrArrc ||jdk(r|}|Stj|j} tj||f| ||}|dk7rtj |d}|tj |||jddkDz}|ddddddfj|ddd}||j}|jd} |ddddddd| f|ddddddfj|jz} | dk(} |ddddddd| fj| | |ddddddd| f<|S) a Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. device (`torch.device`): The device to place the 4D attention mask on. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. N) fill_valuerArr$)diagonalrer:r)rr.rrr>triurreshapeexpandrArr<r masked_fill) rrrrArrrkwargsrr mask_length padding_masks r6rz?UMT5Stack._prepare_4d_causal_attention_mask_with_cache_positionsyD  %.*<*<*>!*C(K*' E*..I** -0Ye\bK!##jjqA 5<< fEH^H^_acdHee eK%dD!Q&67>>z1bRTUK))//1 ,2226 *1aL[L+@ANSTVZ\`bcScDdDgDg&&E   ,q0 5@Aq,;,AV5W5c5c )6 Aq!\k\12r7r_) NNNNNNNNNNNNN)F)rGrHrIr,r\r`rFr.rar rrp staticmethodrrArrrJrKs@r6rOrOts !+ "#!!G `#( D DllD D  D  DL7 777{{ 7  7  777r7rOa The UMT5 model was proposed in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. It's an encoder decoder transformer pre-trained in a text-to-text denoising generative setting. 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 ([`UMT5Config`]): 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 (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. UMT5 is a model with relative position embeddings so you should be able to pad the inputs on both the right and the left. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for detail. [What are input IDs?](../glossary#input-ids) To know more on how to prepare `input_ids` for pretraining take a look a [UMT5 Training](./umt5#training). 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) decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) UMT5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). To know more on how to prepare `decoder_input_ids` for pretraining take a look at [UMT5 Training](./umt5#training). decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` is a sequence of hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. 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)`. 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. decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be input (see `past_key_values`). This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value of `inputs_embeds`. 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`). 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 [`~utils.ModelOutput`] instead of a plain tuple. cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): Indices depicting the position of the input sequence tokens in the sequence. It is used to update the cache in the correct position and to infer the complete sequence length. a Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. UMT5 is a model with relative position embeddings so you should be able to pad the inputs on both the right and the left. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for detail. To know more on how to prepare `input_ids` for pretraining take a look a [UMT5 Training](./umt5#training). 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) 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 [`~utils.ModelOutput`] instead of a plain tuple. z^The bare UMT5 Model transformer outputting raw hidden-states without any specific head on top.c'eZdZdZdZeZddgZfdZdZ dZ dZ d Z d Z d Zeeeee  dd eej,deej.deej,deej0deej.deej.deej2deeeej.deeeej.deej2deej2deedeedeedeedeej,deeej.eff"dZxZS) r,ao Examples: ```python >>> from transformers import UMT5Model, AutoTokenizer >>> model = UMT5Model.from_pretrained("google/umt5-small") >>> tokenizer = AutoTokenizer.from_pretrained("google/umt5-small") >>> noisy_text = "UN Offizier sagt, dass weiter werden muss in Syrien." >>> label = " verhandelt" >>> inputs = tokenizer(inputs, return_tensors="pt") >>> labels = tokenizer(label=label, return_tensors="pt") >>> outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=labels["input_ids"]) >>> hidden_states = outputs.last_hidden_state ```umt5encoder.embed_tokens.weightdecoder.embed_tokens.weightct||tj|j|j |_tj|}d|_ d|_ d|_ t||j |_ tj|}d|_ d|_ |j|_t||j |_|j#yNFT)r+r,rr vocab_sizerTr0copydeepcopyr{ris_encoder_decoderrOencodernum_decoder_layersrSdecoderrWr2rNencoder_configdecoder_configr5s r6r,zUMT5Model.__init__s  ll6#4#4fnnE v.$)!#( ,1) = v.$(!,1)$*$=$=! =  r7c|jSr_r0r[s r6r\zUMT5Model.get_input_embeddings {{r7c~||_|jj||jj|yr_r0rr`rr^s r6r`zUMT5Model.set_input_embeddings-$  )).9 )).9r7c|jjra|j|jj|j |j|j j|j yyr_rNr3_tie_or_clone_weightsrrQr0rr[s r6 _tie_weightszUMT5Model._tie_weightsP ;; * *  & &t||'@'@$++ N  & &t||'@'@$++ N +r7c|jSr_rr[s r6 get_encoderzUMT5Model.get_encoder ||r7c|jSr_rr[s r6 get_decoderzUMT5Model.get_decoderrr7c|jD]7\}}|jj|jj |9y) 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)itemsrr attention prune_headsr2heads_to_prunerheadss r6 _prune_headszUMT5Model._prune_headssE +002 CLE5 LL  u % / / ; ;E B Cr7 output_typerErrrrrwdecoder_head_maskrxencoder_outputsrjrddecoder_inputs_embedsrrrmryrrcL| | n|jj} ||n|jj}||j||| || ||}nI|rGt |t s7t |dt |dkDr|dndt |dkDr|dnd}|d}|j||| | ||||| | ||| }|s||zSt|j|j|j|j|j|j|j|jS) a Returns: Example: ```python >>> from transformers import AutoTokenizer, UMT5Model >>> tokenizer = AutoTokenizer.from_pretrained("google/umt5-small") >>> model = UMT5Model.from_pretrained("google/umt5-small") >>> input_ids = tokenizer( ... "Studies have been shown that owning a dog is good for you", return_tensors="pt" ... ).input_ids # Batch size 1 >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1 >>> # preprocess: Prepend decoder_input_ids with start token which is pad token for UMT5Model. >>> # This is not needed for torch's UMT5ForConditionalGeneration as it does this internally using labels arg. >>> decoder_input_ids = model._shift_right(decoder_input_ids) >>> # forward pass >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids) >>> last_hidden_states = outputs.last_hidden_state ```Nrrrdrwrrmryrr$r9rirDrk rrrdrjrrrwrxrrrmryr)rirjdecoder_hidden_statesdecoder_attentionsrlencoder_last_hidden_staterencoder_attentions)rNrrnrr`rlenrrrirjrDrkrl)r2rrrrrwrrxrrjrdrrrrmryrrDdecoder_outputss r6rFzUMT5Model.forwards[Z"+!6IDKK_ $D>_ 'tn_ d^!_ "!!1!12#_ $ uU&&');; <%_ ]B_ r7r,z2UMT5 Model with a `language modeling` head on top.c)eZdZdZdZgdZfdZdZdZdZ dZ d Z d Z d Z eeeee  d"d eej*deej,deej*deej.deej,deej,deej0deeeej0deeeej0deej,deej,deej*deedeedeedeedeej*deeej,eff$dZdej0fd Zed!ZxZ S)#r-a Examples: ```python >>> from transformers import UMT5ForConditionalGeneration, AutoTokenizer >>> model = UMT5ForConditionalGeneration.from_pretrained("google/umt5-small") >>> tokenizer = AutoTokenizer.from_pretrained("google/umt5-small") >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien." >>> summary = "Weiter Verhandlung in Syrien." >>> inputs = tokenizer(article, text_target=summary, return_tensors="pt") >>> outputs = model(**inputs) >>> loss = outputs.loss ```r)rrzlm_head.weightcNt|||j|_t j |j |j|_tj|}d|_ d|_ d|_ t||j|_tj|}d|_ d|_ |j|_t||j|_t j$|j|j d|_|j)y)NFTrQ)r+r,rT model_dimrrrr0rrr{rrrOrrrSrrSr%rWrs r6r,z%UMT5ForConditionalGeneration.__init__5s  ll6#4#4fnnE v.$)!#( ,1) = v.$(!,1)$*$=$=! = yy1B1BO  r7c|jSr_rr[s r6r\z1UMT5ForConditionalGeneration.get_input_embeddingsMrr7c~||_|jj||jj|yr_rr^s r6r`z1UMT5ForConditionalGeneration.set_input_embeddingsQrr7c|jjra|j|jj|j |j|j j|j yyr_rr[s r6rz)UMT5ForConditionalGeneration._tie_weightsWrr7c||_yr_r%r^s r6set_output_embeddingsz2UMT5ForConditionalGeneration.set_output_embeddings]s % r7c|jSr_rr[s r6get_output_embeddingsz2UMT5ForConditionalGeneration.get_output_embeddingsarr7c|jSr_rr[s r6rz(UMT5ForConditionalGeneration.get_encodererr7c|jSr_rr[s r6rz(UMT5ForConditionalGeneration.get_decoderirr7rrrrrrwrrxrrjrdrlabelsrrrmryrrc| | n|jj} ||n|jj}||j||| ||||}nI|rGt |t s7t |dt |dkDr|dndt |dkDr|dnd}|d}| || |j| }|j||| | ||||| |||| }|d}|jjr||jdzz}|j|}d}| ^td }| j|j} ||jd |j!d | jd }|s|f|ddz|z}||f|zS|St#|||j$|j&|j(|j*|j,|j&|j( S) a labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ..., config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` Returns: Examples: ```python >>> from transformers import AutoTokenizer, UMT5ForConditionalGeneration >>> tokenizer = AutoTokenizer.from_pretrained("google/umt5-small") >>> model = UMT5ForConditionalGeneration.from_pretrained("google/umt5-small") >>> # training >>> input_ids = tokenizer("The walks in park", return_tensors="pt").input_ids >>> labels = tokenizer(" cute dog the ", return_tensors="pt").input_ids >>> outputs = model(input_ids=input_ids, labels=labels) >>> loss = outputs.loss >>> logits = outputs.logits >>> # inference >>> input_ids = tokenizer("Studies have shown that good for you", return_tensors="pt").input_ids >>> outputs = model.generate(input_ids) >>> tokenizer.decode(outputs[0], skip_special_tokens=True) ```Nrrr$r9rrr'r: ignore_indexr: losslogitsrjrrrlrrr)rNrrnrr`rrrDrr3rr%r r<rrrrrjrDrkrlri)r2rrrrrwrrxrrjrdrrrrrmryrrDrsequence_output lm_logitsrloss_fctoutputs r6rFz$UMT5ForConditionalGeneration.forwardls+d"+!6IDKKUMT5ForConditionalGeneration._reorder_cache..s.nU_j--aZ=N=N1OPns58)rv)rjr reordered_past layer_pasts ` r6_reorder_cachez+UMT5ForConditionalGeneration._reorder_caches=) J ncmnn N r7)NNNNNNNNNNNNNNNNN)!rGrHrIrrrr,r\r`rrrrrrrr#rrrr.rrrrarrrrFrrrrJrKs@r6r-r- sK Ji0: O &++@A?Y156:8<=A159=7;@D@D59=A-1$(,0/3&*59%{ E,,-{ !!2!23{ $E$4$45 { !))9)9 : { E--. { $E$5$56{ 'u||4{ "%ell(;"<={ "%ell(;"<={   1 12{  ((9(9:{ ))*{ D>{ $D>{ 'tn!{ "d^#{ $!!1!12%{ & uU&&'8 9'{ ZB{ |)ELL)r7r-zhThe bare UMT5 Model transformer outputting encoder's raw hidden-states without any specific head on top.cPeZdZdZdZdgZfdZdZdZdZ dZ d Z e e eee  dd eej&d eej(d eej(deej(deedeedeedeeej(effdZxZS)r.a Examples: ```python >>> from transformers import UMT5EncoderModel, AutoTokenizer >>> model = UMT5EncoderModel.from_pretrained("google/umt5-small") >>> tokenizer = AutoTokenizer.from_pretrained("google/umt5-small") >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien." >>> input_ids = tokenizer(article, return_tensors="pt").input_ids >>> outputs = model(input_ids) >>> hidden_state = outputs.last_hidden_state ```rrct||tj|j|j |_tj|}d|_ d|_ t||j |_ |jyNF)r+r,rrrrTr0rrrrrOrrW)r2rNrr5s r6r,zUMT5EncoderModel.__init__sh  ll6#4#4fnnE v.#( ,1) =  r7c|jSr_rr[s r6r\z%UMT5EncoderModel.get_input_embeddingsrr7cH||_|jj|yr_)r0rr`r^s r6r`z%UMT5EncoderModel.set_input_embeddings!s$  )).9r7c|jjr1|j|jj|j yyr_)rNr3rrrQr0r[s r6rzUMT5EncoderModel._tie_weights&s2 ;; * *  & &t||'@'@$++ N +r7c|jSr_rr[s r6rzUMT5EncoderModel.get_encoder+rr7c|jD]D\}}|jj|jdjj |Fy)rrN)rrrTrrrrs r6rzUMT5EncoderModel._prune_heads/sP +002 PLE5 LL  u % + +A . < < H H O Pr7rrrrwrdrrmryrc j||n|jj}|j|||||||}|S)a5 Returns: Example: ```python >>> from transformers import AutoTokenizer, UMT5EncoderModel >>> tokenizer = AutoTokenizer.from_pretrained("google/umt5-small") >>> model = UMT5EncoderModel.from_pretrained("google/umt5-small") >>> input_ids = tokenizer( ... "Studies have been shown that owning a dog is good for you", return_tensors="pt" ... ).input_ids # Batch size 1 >>> outputs = model(input_ids=input_ids) >>> last_hidden_states = outputs.last_hidden_state ```r)rNrnr) r2rrrwrdrrmryrs r6rFzUMT5EncoderModel.forward7sJ:&1% &'tn&d^& uU&&'8 9&ZJ&r7r.z UMT5 model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. c%eZdZdgZddgZdeffd Zeee e e  dde e jde e jd e e jd e e jd e e jd e e jd e e jde ee j"de e j"de e j"de e jde ede ede ede edeee ff dZxZS)UMT5ForSequenceClassificationFdecoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weightrrrNct||t||_t ||_|j d|_yr)r+r,r,rrclassification_headrWmodel_parallelr]s r6r,z&UMT5ForSequenceClassification.__init__os>  $V,#9&#A  #r7rrrrrrwrrxrrdrrrrrmryrc||n|jj}| d} |$| "td|jj| | | t d|j |}|j||||||||| | | | ||}|d}|j|jjj|j}ttj|jddkDr t d|j \}}}||ddfj#|d |ddd ddf}|j%|}d}| | j|j} |jj&|jj(dk(rd |j_nv|jj(dkDrL| j*tj,k(s| j*tj.k(rd |j_nd |j_|jj&d k(rSt1}|jj(dk(r&||j3| j3}n||| }n|jj&d k(rGt5}||j#d |jj(| j#d }n,|jj&d k(rt7}||| }|s|f|ddz}||f|zS|St9|||j:|j<|j>|j@|jB|jD|jF S)aD labels (`torch.LongTensor` 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 classification loss is computed (Cross-Entropy). Returns: NFz8Passing input embeddings is currently not supported for If no `decoder_input_ids` or `decoder_inputs_embeds` are passed, `input_ids` cannot be `None`. Please pass either `input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`.) rrrrwrrxrrdrrrrmryrr$z7All examples must have the same number of tokens.r: regressionsingle_label_classificationmulti_label_classificationr)$rNrnNotImplementedErrorr5rGr=rDreq eos_token_idr<rrr.unique_consecutivesumrrr problem_typerrArrr squeezer rrrjrrrlrrr)r2rrrrrwrrxrrdrrrrrmryrreos_maskrrr3sentence_representationrrrrs r6rFz%UMT5ForSequenceClassification.forwardysQ4&1%>KbKbJcd   $)>)F  U !% 1 1) < "" )/#9/!5+'"7/!5##  "!*<< 8 89<<_=S=ST u'' Q8 9A =VW W%4%:%:" A{"1(A+">"C"CJPRT_"`abdfhiai"j))*AB  YYv}}-F{{''/;;))Q./;DKK,[[++a/V\\UZZ5OSYS_S_chclclSl/LDKK,/KDKK,{{''<7"9;;))Q.#FNN$4fnn6FGD#FF3D))-JJ+- B 0F0F GUWY))-II,./Y,F)-)9TGf$ Ev E.#33")"?"?&99$55&-&G&G")"?"?&99  r7)NNNNNNNNNNNNNNN)rGrHrI"_keys_to_ignore_on_load_unexpectedrr%r,rrr#rrrr.rrarrrrrrFrJrKs@r6rrcs+s)s&79VW$z$++@A+JYhi15158<=A,0487;=A59=A-1$(,0/3&*!k E,,-k !.k $E$4$45 k !))9)9 : k ELL) k $ELL1k 'u||4k "$u'8'8"9:k   1 12k  ((9(9:k ))*k D>k $D>k 'tnk d^!k " u55 6#k jBk r7rz UMT5 Encoder 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. cVeZdZdgZdgZdeffd Zeee e e  dde e jde e jde e jd e e jd e e jd e ed e ed e edeee je ffdZxZS)r5rz'transformer.encoder.embed_tokens.weightrNc,t|||j|_t||_t j |j|_t j|j|j|_ |jyr_) r+r,rr.rrrXrrZrSr3r(rWr]s r6r,z#UMT5ForTokenClassification.__init__sj   +++F3zz&";";< ))F$6$68I8IJ r7rrrrwrdrrrmryrc ||n|jj}|j|||||||} | d} |j| } |j | } d} |. 'tn. d^.  uU\\"$99 :. `B. r7r5z UMT5 Model with a span classification head on top for extractive question-answering tasks like SQuAD (linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). c'beZdZddgZfdZdZdZdZdZdZ e e e e e  dd eej"d eej$d eej"d eej&deej$deej$deej(deeeej(deej"deej"deej$deej$deedeedeedeedeeej$e ff"dZxZS)r/rrclt|||j|_t j |j |j|_tj|}d|_ d|_ d|_ t||j|_tj|}d|_ d|_ |j|_t||j|_|j$|_t j&|j|j$|_|j+yr)r+r,rTrrrrr0rrr{rrrOrrrSrrrSr&rWrs r6r,z!UMT5ForQuestionAnswering.__init__>s  ll6#4#4fnnE v.$)!#( ,1) = v.$(!,1)$*$=$=! =  ++))FNNF4E4EF r7c|jSr_rr[s r6r\z-UMT5ForQuestionAnswering.get_input_embeddingsWrr7c~||_|jj||jj|yr_rr^s r6r`z-UMT5ForQuestionAnswering.set_input_embeddings[rr7c|jjra|j|jj|j |j|j j|j yyr_rr[s r6rz%UMT5ForQuestionAnswering._tie_weightsarr7c|jSr_rr[s r6rz$UMT5ForQuestionAnswering.get_encodergrr7c|jSr_rr[s r6rz$UMT5ForQuestionAnswering.get_decoderkrr7rrrrrrwrrxrstart_positions end_positionsrdrrrrmryrc||n|jj}| | n|jj} | | d} | | | td|j |}| | n|jj} ||n|jj}||j ||| ||||}nI|rGt |ts7t|dt|dkDr|dndt|dkDr|dnd}|d}|j||| d||||| ||| }|d}|j|}|jdd \}}|jd j}|jd j}d}| | t| jdkDr*| jd j|j } t| jdkDr*| jd j|j } |jd}| j#d|} | j#d|} t%| }||| }||| }||zdz }|s||f|ddz|z}||f|zS|St'||||j(|j*|j,|j.|j0|j*|j, S)a start_positions (`torch.LongTensor` 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 (`torch.LongTensor` 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. Returns: NFr rrr$r9r) rrrdrjrrrwrxrrrmryr:rr) r start_logits end_logitsrjrrrlrrr)rNrnrr=rDrr`rrrr&splitr*rrr<rrr rrjrDrkrlri)r2rrrrrwrrxrr8r9rdrrrrmryrDrrrr;r< total_loss ignored_indexr start_lossend_lossrs r6rFz UMT5ForQuestionAnswering.forwardns9@&1%)F  U !% 1 1) < !*!6IDKK} $D>} 'tn!} "d^#} $ uU&&')LL M%} nB} r7r/)r.r-r/rr5r,r)Nrrrtypingrrrrr.rtorch.nnrr r activationsr cache_utilsr rrr generationrmodeling_attn_mask_utilsrmodeling_outputsrrrrrrrmodeling_utilsrutilsrrrrrr r!r"r#configuration_umt5r%!torch.nn.attention.flex_attentionr&integrations.flex_attentionr' get_loggerrGrr_CHECKPOINT_FOR_DOCModuler)rMrfrnrxrrrrrrOUMT5_START_DOCSTRINGrrr,r-r.rr5r/__all__rMr7r6rSsj // AA!PP)>.   + !;J   H %)+BII+4 .RYY<"))$9BII9DRYY8bii