S^(h?~dZddlmZddlZddlZddlmZmZmZddl Z ddl Z ddl mZddlmZmZmZmZmZmZmZmZddlmZmZmZmZmZmZmZm Z m!Z!m"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`e1Z2d Z3dZ4dZ5dZ6Gdde"jnjpZ9Gdde"jnjpZ:Gdde"jnjpZ;Gdde"jnjpZ<Gdde"jnjpZ=Gdde"jnjpZ>Gdde"jnjpZ?Gdd e"jnjpZ@Gd!d"e"jnjpZAe#Gd#d$e"jnjpZBGd%d&eZCe+d'e5Gd(d)eCZDGd*d+e"jnjpZEe+d,e5Gd-d.eCeZFGd/d0e"jnjpZGe+d1e5Gd2d3eCeZHe+d4e5Gd5d6eCe ZIe+d7e5Gd8d9eCeZJe+d:e5Gd;dd?eCeZLgd@ZMy)AzTF 2.0 CamemBERT model.) annotationsN)OptionalTupleUnion)get_tf_activation)+TFBaseModelOutputWithPastAndCrossAttentions.TFBaseModelOutputWithPoolingAndCrossAttentions#TFCausalLMOutputWithCrossAttentionsTFMaskedLMOutputTFMultipleChoiceModelOutputTFQuestionAnsweringModelOutputTFSequenceClassifierOutputTFTokenClassifierOutput) TFCausalLanguageModelingLossTFMaskedLanguageModelingLossTFModelInputTypeTFMultipleChoiceLossTFPreTrainedModelTFQuestionAnsweringLossTFSequenceClassificationLossTFTokenClassificationLossget_initializerkeraskeras_serializable unpack_inputs)check_embeddings_within_bounds shape_liststable_softmax)add_code_sample_docstringsadd_start_docstrings%add_start_docstrings_to_model_forwardlogging)CamembertConfigzalmanach/camembert-baser%a 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! Parameters: config ([`CamembertConfig`]): 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) token_type_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *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 (`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). cFeZdZdZfdZddZddZ ddZxZS) TFCamembertEmbeddingszV Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. c dt|di|d|_||_|j|_|j |_|j |_tjj|jd|_ tjj|j|_y)Nr$ LayerNormepsilonnamerate)super__init__ padding_idxconfig hidden_sizemax_position_embeddingsinitializer_rangerlayersLayerNormalizationlayer_norm_epsr)Dropouthidden_dropout_probdropoutselfr3kwargs __class__s q/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/camembert/modeling_tf_camembert.pyr1zTFCamembertEmbeddings.__init__s "6" !--'-'E'E$!'!9!988AVAV]h8i||++1K1K+L ctjd5|jd|jj|j gt |j|_dddtjd5|jd|jj|j gt |j|_ dddtjd5|jd|j|j gt |j|_ ddd|jryd|_ t|ddetj|jj 5|jj#dd|jj gdddyy#1swY[xYw#1swYxYw#1swYxYw#1swYyxYw) Nword_embeddingsweight)r,shape initializertoken_type_embeddings embeddingsposition_embeddingsTr))tf name_scope add_weightr3 vocab_sizer4rr6rEtype_vocab_sizerHr5rJbuiltgetattrr)r,buildr> input_shapes rArRzTFCamembertEmbeddings.builds ]], - //{{--t/?/?@+D,B,BC*DK ]]2 3 )-!{{22D4D4DE+D,B,BC*9*D & ]]0 1 '+!33T5E5EF+D,B,BC(7(D $  ::  4d + 7t~~223 L$$dD$++2I2I%JK L L 81       L Ls2A F<A G *AG?3G!<G GG!G*ctjtjj||j|j }tjj |d|z|z}||jzS)a Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: input_ids: tf.Tensor Returns: tf.Tensor dtyper$axis)rKcastmath not_equalr2rWcumsum)r> input_idspast_key_values_lengthmaskincremental_indicess rA"create_position_ids_from_input_idsz8TFCamembertEmbeddings.create_position_ids_from_input_idsscwwrww((D4D4DEY__]!ww~~d~;>TTX\\"T%5%555rBc||J|At||jjtj|j |}t |dd}|tj|d}|b||j||}nLtjtj|jdz|d|jzdzd }tj|j|}tj|j|} ||z| z} |j| } |j| | } | S) z Applies embedding based on inputs tensor. Returns: final_embeddings (`tf.Tensor`): output embedding tensor. N)paramsindicesrdimsvalue)r^r_r$)startlimitrXinputsrmtraining)rr3rNrKgatherrErfillrb expand_dimsranger2rJrHr)r<) r>r^ position_idstoken_type_ids inputs_embedsr_rorTposition_embedstoken_type_embedsfinal_embeddingss rAcallzTFCamembertEmbeddings.callsA%-*?@@  *9dkk6L6L MIIT[[)LM /4  !WW+Q?N  $#FF'@V G   "~~HH4#3#3a#7{2QUQaQa?ade?efmn  ))4+C+C\ZIIT-G-GQ_`(?:=NN>>1A>B< hidden_statesfirst_token_tensor pooled_outputs rArzzTFCamembertPooler.calls*+1a40 *< = rBc(|jryd|_t|ddetj|jj 5|jj dd|jjgdddyy#1swYyxYwNTr rPrQrKrLrr,rRr3r4rSs rArRzTFCamembertPooler.build%} ::  4$ ' 3tzz/ H   $dkk.E.E!FG H H 4 H H 3BBr3r%r tf.Tensorreturnrr{r|r}r~r1rzrRrrs@rArrs HrBrc^eZdZdfd ZddZ d ddZd dZxZS) TFCamembertSelfAttentionc t|d i||j|jzdk7r&t d|jd|jd|j|_t |j|jz |_|j|j z|_tj|j |_ tjj|jt|jd|_tjj|jt|jd|_tjj|jt|jd|_tjj'|j( |_|j,|_||_y) NrzThe hidden size (z6) is not a multiple of the number of attention heads ()queryrrr,keyrir-r/)r0r1r4num_attention_heads ValueErrorintattention_head_size all_head_sizer[sqrtsqrt_att_head_sizerr7rrr6rrrir:attention_probs_dropout_probr< is_decoderr3r=s rAr1z!TFCamembertSelfAttention.__init__0s "6"    : : :a ?#F$6$6#78''-'A'A&B!E  $*#=#= #&v'9'9Fr batch_sizes rAtranspose_for_scoresz-TFCamembertSelfAttention.transpose_for_scoresLs;6*b$BZBZ\`\t\t1uv||F66rBc t|d} |j|} |du} | r||d} |d} |}n | rG|j|j|| } |j|j || } |}n|}|j|j|| } |j|j || } t j |d| gd} t j |d| gd} nD|j|j|| } |j|j || } |j| | }|jr| | f}t j|| d}t j|j|j}t j||}|t j||}t|d }|j|| }|t j ||}t j|| }t j"|gd  }t j$|| d |j&f}|r||fn|f}|jr||fz}|S)Nrrlr$rrXT) transpose_brVrf)logitsrYrnrrr)rrrrrirKconcatrmatmulrZrrWdivideaddrr<multiplyrrr)r>rattention_mask head_maskencoder_hidden_statesencoder_attention_maskpast_key_valueoutput_attentionsrormixed_query_layeris_cross_attention key_layer value_layer query_layerattention_scoresdkattention_probsattention_outputoutputss rArzzTFCamembertSelfAttention.callSsy .q1  JJmJ< 3$> ."<&q)I(+K3N 11$((BW(2XZdeI33DJJF[J4\^hiK3N  '11$((-(2PR\]I33DJJmJ4TV`aK >!#4i"@qII))^A%6 $D1MK11$((-(2PR\]I33DJJmJ4TV`aK//0A:N ??(5N99[)N WWT,,4D4J4J K99%5r:  %!vv&6G )0@rJ,,o,Q   kk/9EO99_kB<<(8|L::-=jRTVZVhVhEij9J#_5QaPc ?? 11GrBc|jryd|_t|dddtj|jj 5|jj dd|jjgdddt|dddtj|jj 5|jj dd|jjgdddt|ddetj|jj 5|jj dd|jjgdddyy#1swYxYw#1swYxYw#1swYyxYw)NTrrri) rPrQrKrLrr,rRr3r4rrirSs rArRzTFCamembertSelfAttention.builds9 ::  4$ ' 3tzz/ H   $dkk.E.E!FG H 4 % 1txx}}- FdDKK,C,CDE F 4$ ' 3tzz/ H   $dkk.E.E!FG H H 4  H H F F H Hs$ 3E*<3E6-3F*E36E?F r)rrrrrrF)rrrrrrrrrrrTuple[tf.Tensor]rboolrorrrr{)r|r}r~r1rrzrRrrs@rArr/s87 O O"O O ) O !* O)O OO Ob HrBrc2eZdZdfd ZdddZddZxZS)TFCamembertSelfOutputc xt|di|tjj |j t |jd|_tjj|jd|_ tjj|j|_||_yNrrr)r*r-r/r0r1rr7rr4rr6rr8r9r)r:r;r<r3r=s rAr1zTFCamembertSelfOutput.__init__ "6"\\''$$IaIa9bip(  88AVAV]h8i||++1K1K+L  rBcz|j|}|j||}|j||z}|SNrlrnrr<r)r>r input_tensorros rArzzTFCamembertSelfOutput.call? - 8  MH M ml.JK rBc"|jryd|_t|dddtj|jj 5|jj dd|jjgdddt|ddetj|jj 5|jj dd|jjgdddyy#1swY|xYw#1swYyxYwNTrr)) rPrQrKrLrr,rRr3r4r)rSs rArRzTFCamembertSelfOutput.builds ::  4$ ' 3tzz/ H   $dkk.E.E!FG H 4d + 7t~~223 L$$dD$++2I2I%JK L L 8 H H L L 3C9<3D9DDrrrrrrrorrrr{rrs@rArr LrBrc\eZdZdfd ZdZ d ddZddZxZS) TFCamembertAttentionc lt|di|t|d|_t |d|_y)Nr>r,outputr/)r0r1rself_attentionr dense_outputr=s rAr1zTFCamembertAttention.__init__s1 "6"6vFK1&xHrBctr{NotImplementedError)r>headss rA prune_headsz TFCamembertAttention.prune_headss!!rBc x|j||||||||} |j| d||} | f| ddz} | S)Nrrrrrrrrorrrror$)rr) r>rrrrrrrro self_outputsrrs rArzzTFCamembertAttention.callso**&)"7#9)/+   ,,&q/ x- $% QR(88rBc|jryd|_t|ddMtj|jj 5|jj ddddt|ddNtj|jj 5|jj ddddyy#1swYexYw#1swYyxYw)NTrr)rPrQrKrLrr,rRrrSs rArRzTFCamembertAttention.builds ::  4)4 0 <t22778 0##))$/ 0 4 . :t00556 .!!''- . . ; 0 0 . . 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M*!,M"}R'8&::" !/=3C2E!E;;227L7X+?=QRCSBU+U(1 V6 1]4D D )+rTrs rArRzTFCamembertEncoder.buildsp ::  4$ ' 3 &]]5::.&KK%&& & 4&&s A..A7 rr)rrrrrrrrrrr$zTuple[Tuple[tf.Tensor]] | Noner)Optional[bool]rrr*rr+rrorrzDUnion[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]r{rrs@rArrsn"<  < "<  < 0 < !1 < 8< "<  < #< < <  N< |&rBrceZdZeZdfd ZddZd dZdZe d d dZ d dZ xZ S) TFCamembertMainLayerc rt|di|||_|j|_|j|_|j |_|j |_|j|_|j|_ t|d|_ |r t|dnd|_ t|d|_y)NencoderrpoolerrIr/)r0r1r3rrr6rr*use_return_dictr+rr7rr8r'rI)r>r3add_pooling_layerr?r@s rAr1zTFCamembertMainLayer.__init__s "6"  ++!'!9!9!'!9!9!'!9!9$*$?$?!!11)&yA BS'X>Y] /\JrBc|jSr{)rIr>s rAget_input_embeddingsz)TFCamembertMainLayer.get_input_embeddingss rBc`||j_t|d|j_yNr)rIrErrNr>ris rAset_input_embeddingsz)TFCamembertMainLayer.set_input_embeddingss$!&%/%6q%9"rBct)z Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel r)r>heads_to_prunes rA _prune_headsz!TFCamembertMainLayer._prune_headss "!rBc |jjsd} | | td| t|}n|t|dd}n td|\}}| &d}dgt |j j z} nt| ddd}|tj|||zfd}|tj|d}|j|||||| }t|}||z}|jrtj|}tjtj|ddddf||df|ddddf}tj||j }||dddddfz}t|}tj||dd|d|d f}| d3|dddd| dddf}n tj||ddd|df}tj||j }tj d |j }tj d |j }tj"tj$|||}|jrf|dtj||j }t t|}|dk(r|dddddddf}|d k(r |ddddddf}d z d z}nd}|t&dg|jj(z}|j |||||| | | | | | }|d}|j*|j+|nd}| s ||f|ddzSt-|||j.|j0|j2|j4S)NFzDYou cannot specify both input_ids and inputs_embeds at the same timerfz5You have to specify either input_ids or inputs_embedsrr r$rg)r^rtrurvr_rorVrg?gr) rrrrrr$r)rr*r+ror )r# pooler_outputr$rr%r&)r3rrrlenr7rrKrqrIrs less_equaltilerZrWrconstantrsubtractrrr8r r$rr%r&) r>r^rrurtrrvrrr$r)rr*r+rorTr seq_lengthr_embedding_outputattention_mask_shapemask_seq_lengthseq_ids causal_maskextended_attention_maskone_cstten_thousand_cstnum_dims_encoder_attention_maskencoder_extended_attention_maskencoder_outputssequence_outputrs rArzzTFCamembertMainLayer.call s&{{%%I  ]%>cd d  "$Y/K  &$]3CR8KTU U!, J  "%& "#fs4<<+=+='>>O%/0B10E%Fr%J "  !WW:zDZ7Z*[cdeN  !WW+Q?N??%)'#9 +  *.9$'== ??hh/G--dA ._a0PQa &K''+^5I5IJK&1N1dA:4N&N ##-.E#F &(jj'*>q*A1FZ[\F]_stu_v)w' #q!-*A!Q VWBW*X'&(jj!5a!8!Q@TUV@W X' ##%''*AIYI_I_"`++c)9)?)?@;;x7G7M7MN"$++bkk'CZ.[]m"n ??5A&(WW-CKbKhKh%i ".1*=S2T.U +.!32HDRSUV2W/.!32HDRVXYIY2Z/035T/TX`.` +.2 +  % %!>!>>I,,*2"7#B+/!5#'  *!,FJkkF] / Bcg  #$ $ >-'+;;)77&11,==   rBc|jryd|_t|ddMtj|jj 5|jj ddddt|ddMtj|jj 5|jj ddddt|ddNtj|jj 5|jj ddddyy#1swYxYw#1swYqxYw#1swYyxYw)NTr7r8rI) rPrQrKrLr7r,rRr8rIrSs rArRzTFCamembertMainLayer.builds ::  4D ) 5t||001 ) ""4( ) 44 ( 4t{{//0 ( !!$' ( 4t , 8t334 ,%%d+ , , 9  ) ) ( ( , ,s$ D%%D1?D=%D.1D:=E)T)rzkeras.layers.Layer)riz tf.VariableNNNNNNNNNNNNNF)r^TFModelInputType | Nonernp.ndarray | tf.Tensor | Nonerur\rtr\rr\rvr\rr\rr\r$4Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]]r)r3rr3r*r3r+r3rorrzGUnion[TFBaseModelOutputWithPoolingAndCrossAttentions, Tuple[tf.Tensor]]r{) r|r}r~r% config_classr1r=rArDrrzrRrrs@rAr5r5s #LK": ".28<8<6:377;?C@DPT$(,0/3&*[ *[ 6[ 6 [ 4 [ 1 [ 5[  =[ !>[ N[ "[ *[ -[ $[ [ Q![ [ z ,rBr5ceZdZdZeZdZy)TFCamembertPreTrainedModelz An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. robertaN)r|r}r~rr%r^base_model_prefixr/rBrAr`r`s #L!rBr`zcThe bare CamemBERT Model transformer outputting raw hidden-states without any specific head on top.ceZdZfdZeeejdee e e  d ddZ ddZ xZS) TFCamembertModelcPt||g|i|t|d|_y)Nrar)r0r1r5rar>r3rmr?r@s rAr1zTFCamembertModel.__init__s( 3&3F3+FC rBbatch_size, sequence_length checkpoint output_typer^cD|j||||||||| | | | | |}|S)a encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*, defaults to `True`): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). Set to `False` during training, `True` during generation r^rrurtrrvrrr$r)rr*r+ro)ra)r>r^rrurtrrvrrr$r)rr*r+rors rArzzTFCamembertModel.callsIX,,))%'"7#9+/!5# "rBc|jryd|_t|ddNtj|jj 5|jj ddddyy#1swYyxYw)NTra)rPrQrKrLrar,rRrSs rArRzTFCamembertModel.build!si ::  4D ) 5t||001 ) ""4( ) ) 6 ) )s A11A:rZ)r^r[rr\rur\rtr\rr\rvr\rr\rr\r$r]r)r3rr3r*r3r+r3ror3rz6N6"6*6-6$6!6 F!6 m6p)rBrdcHeZdZdZfdZd dZdZdZdZdZ dZ xZ S) TFCamembertLMHeadz,Camembert Head for masked language modeling.c jt|di|||_|j|_tj j |jt|jd|_ tj j|jd|_ td|_||_y)Nrrr, layer_normr*gelur/)r0r1r3r4rr7rrr6rr8r9rvractdecoder)r>r3input_embeddingsr?r@s rAr1zTFCamembertLMHead.__init__.s "6" !--\\''   ?6C[C[3\cj(   ,,99&BWBW^j9k$V,( rBc|j|jjfddd|_|jryd|_t |dddt j|jj5|jjdd|jjgdddt |ddet j|jj5|jjdd|jjgdddyy#1swY|xYw#1swYyxYw)NzerosTbias)rFrG trainabler,rrv) rMr3rNr}rPrQrKrLrr,rRr4rvrSs rArRzTFCamembertLMHead.build=sOO4;;+A+A*CQXdhouOv ::  4$ ' 3tzz/ H   $dkk.E.E!FG H 4t , 8t334 M%%tT4;;3J3J&KL M M 9 H H M Ms:3D(+3D4(D14D=c|jSr{)ryr<s rAget_output_embeddingsz'TFCamembertLMHead.get_output_embeddingsJ ||rBc`||j_t|d|j_yr?)ryrErrNr@s rAset_output_embeddingsz'TFCamembertLMHead.set_output_embeddingsMs$# ",U"3A"6 rBcd|jiS)Nr})r}r<s rAget_biaszTFCamembertLMHead.get_biasQs ""rBcX|d|_t|dd|j_y)Nr}r)r}rr3rNr@s rAset_biaszTFCamembertLMHead.set_biasTs'&M !+E&M!:1!= rBc|j|}|j|}|j|}t|d}t j |d|j g}t j||jjd}t j |d||jjg}tjj||j}|S)N)rr$rfrT)abr)rir})rrxrvrrKrr4rryrEr3rNnnbias_addr})r>rrLs rArzzTFCamembertLMHead.callXs =1 /  6  }5a8  -DDTDT?UV  MT\\5H5HVZ[  -JPTP[P[PfPf?gh ]K rBr{) r|r}r~rr1rRrrrrrzrrs@rArsrs+s*6 ( M7#> rBrsz7CamemBERT Model with a `language modeling` head on top.c eZdZddgZfdZdZdZeee jde e e eddd  d dd Zdd ZxZS)TFCamembertForMaskedLMr8lm_head.decoder.weightct||g|i|t|dd|_t ||jj d|_y)NFrar:r,lm_headr)r0r1r5rarsrIrrfs rAr1zTFCamembertForMaskedLM.__init__psE 3&3F3+FeR[\ (1H1HyY rBc|jSr{rr<s rA get_lm_headz"TFCamembertForMaskedLM.get_lm_headvrrBctjdt|jdz|jjzSNzMThe method get_prefix_bias_name is deprecated. Please use `get_bias` instead./warningswarn FutureWarningr,rr<s rAget_prefix_bias_namez+TFCamembertForMaskedLM.get_prefix_bias_namey/ egtuyy3!2!222rBrgzz' Paris'g?)rirjr^r`expected_output expected_lossc |j||||||||| |  } | d} |j| }| dn|j| |}| s|f| ddz}||f|zS|St||| j| j S)a labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` rrurtrrvrr*r+rorNrlossrrr%)rarhf_compute_lossr rr%)r>r^rrurtrrvrr*r+labelsrorrXprediction_scoresrrs rArzzTFCamembertForMaskedLM.call}s<,, ))%'/!5#  "!* LL9~t4+?+?HY+Z')GABK7F)-)9TGf$ Ev E$!//))   rBc|jryd|_t|ddMtj|jj 5|jj ddddt|ddNtj|jj 5|jj ddddyy#1swYexYw#1swYyxYwNTrarrPrQrKrLrar,rRrrSs rArRzTFCamembertForMaskedLM.build ::  4D ) 5t||001 ) ""4( ) 4D ) 5t||001 ) ""4( ) ) 6 ) ) ) )r NNNNNNNNNNF)r^r[rr\rur\rtr\rr\rvr\rr3r*r3r+r3rr\ror3rz)Union[TFMaskedLMOutput, Tuple[tf.Tensor]]r{)r|r}r~"_keys_to_ignore_on_load_unexpectedr1rrrr"rnror rpr rqrzrRrrs@rArrgs+45N)O&Z 3*+E+L+LMj+kl&$$ " .28<8<6:377;,0/3&*04#(/ */ 6/ 6 / 4 / 1 / 5/ */ -/ $/ ./ !/  3/ m/ b )rBrc2eZdZdZfdZddZddZxZS)TFCamembertClassificationHeadz-Head for sentence-level classification tasks.c t|di|tjj |j t |jdd|_|j |jn |j}tjj||_ tjj |jt |jd|_||_y)Nrr)rrr,out_projrur/)r0r1rr7rr4rr6rclassifier_dropoutr;r:r< num_labelsrr3)r>r3r?rr@s rAr1z&TFCamembertClassificationHead.__init__s "6"\\''   .v/G/GH (  *0)B)B)NF % %TZTnTn ||++,>?  **   /&BZBZ2[bl+   rBc|dddddf}|j||}|j|}|j||}|j|}|S)Nrro)r<rr)r>featuresroxs rArzz"TFCamembertClassificationHead.callsV Q1W  LLXL . JJqM LLXL . MM! rBc"|jryd|_t|dddtj|jj 5|jj dd|jjgdddt|ddetj|jj 5|jj dd|jjgdddyy#1swY|xYw#1swYyxYw)NTrr) rPrQrKrLrr,rRr3r4rrSs rArRz#TFCamembertClassificationHead.builds ::  4$ ' 3tzz/ H   $dkk.E.E!FG H 4T * 6t}}112 K ##T41H1H$IJ K K 7 H H K Krrr{)r|r}r~rr1rzrRrrs@rArrs7" KrBrz CamemBERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. c eZdZddgZfdZeeejde de e dd d d d Z d d Z xZS)$TFCamembertForSequenceClassificationr8rct||g|i||j|_t|dd|_t |d|_y)NFrar classifierr)r0r1rr5rarrrfs rAr1z-TFCamembertForSequenceClassification.__init__sF 3&3F3 +++FeR[\ 7\RrBrgz'cardiffnlp/twitter-roberta-base-emotionz 'optimism'g{Gz?rirjr^rrc |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). rrrNrr)rarrrrr%r>r^rrurtrrvrr*r+rrorrXrrrs rArzz)TFCamembertForSequenceClassification.calls:,, ))%'/!5#  "!*8D~t4+?+?+OY,F)-)9TGf$ Ev E)!//))   rBc|jryd|_t|ddMtj|jj 5|jj ddddt|ddNtj|jj 5|jj ddddyy#1swYexYw#1swYyxYwNTrar)rPrQrKrLrar,rRrrSs rArRz*TFCamembertForSequenceClassification.build9s ::  4D ) 5t||001 ) ""4( ) 4t , 8t334 ,%%d+ , , 9 ) ) , ,rr)r^r[rr\rur\rtr\rr\rvr\rr3r*r3r+r3rr\ror3rz3Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]r{)r|r}r~rr1rr"rnror rrqrzrRrrs@rArrs+4Z)@&S*+E+L+LMj+kl<.$$ .28<8<6:377;,0/3&*04#(. *. 6. 6 . 4 . 1 . 5. *. -. $. .. !.  =. m. ` ,rBrz CamemBERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. c eZdZddgZdgZfdZeeejde de e dd  d d d Z dd ZxZS)!TFCamembertForTokenClassificationr8rr<ct||g|i||j|_t|dd|_|j |j n |j }tjj||_ tjj|jt|jd|_||_y)NFrarrru)r0r1rr5rarr;rr7r:r<rrr6rr3)r>r3rmr?rr@s rAr1z*TFCamembertForTokenClassification.__init__Rs 3&3F3 +++FeR[\ )/)B)B)NF % %TZTnTn ||++,>? ,,,,   /&BZBZ2[bn-  rBrgz!ydshieh/roberta-large-ner-englishzF['O', 'ORG', 'ORG', 'O', 'O', 'O', 'O', 'O', 'LOC', 'O', 'LOC', 'LOC']g{Gz?rc &|j||||||||| |  } | d} |j| | } |j| }| dn|j| |}| s|f| ddz}||f|zS|St ||| j | j S)z labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. rrrNrr)rar<rrrrr%rs rArzz&TFCamembertForTokenClassification.call`s6,, ))%'/!5#  "!*,,,J1~t4+?+?+OY,F)-)9TGf$ Ev E&!//))   rBc|jryd|_t|ddMtj|jj 5|jj ddddt|ddetj|jj 5|jj dd|jjgdddyy#1swY|xYw#1swYyxYwr rPrQrKrLrar,rRrr3r4rSs rArRz'TFCamembertForTokenClassification.build ::  4D ) 5t||001 ) ""4( ) 4t , 8t334 M%%tT4;;3J3J&KL M M 9 ) ) M M C"%3C."C+.C7r)r^r[rr\rur\rtr\rr\rvr\rr3r*r3r+r3rr\ror3rz0Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]r{)r|r}r~r_keys_to_ignore_on_load_missingr1rr"rnror rrqrzrRrrs@rArrEs+4Z)@&'1l# *+E+L+LMj+kl6+$` .28<8<6:377;,0/3&*04#(. *. 6. 6 . 4 . 1 . 5. *. -. $. .. !.  :. m. ` MrBrz CamemBERT Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. ceZdZdgZdgZfdZeeejde e e e  d d dZd dZxZS) TFCamembertForMultipleChoicerr<c.t||g|i|t|d|_tj j |j|_tj jdt|jd|_ ||_ y)Nrarr$rru)r0r1r5rarr7r:r;r<rrr6rr3rfs rAr1z%TFCamembertForMultipleChoice.__init__s{ 3&3F3+FC ||++F,F,FG ,,,, /&2J2J"KR^-  rBz(batch_size, num_choices, sequence_lengthrhc |t|d} t|d} nt|d} t|d} |tj|d| fnd}|tj|d| fnd}|tj|d| fnd}|tj|d| fnd}|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$rrf)r+rorr) rrKrrar<rrr rr%)r>r^rrurtrrvrr*r+rro num_choicesrLflat_input_idsflat_attention_maskflat_token_type_idsflat_position_idsrrrreshaped_logitsrrs rArzz!TFCamembertForMultipleChoice.calls:  $Y/2K#I.q1J$]3A6K#M215JDMDYIJ/?@_cN\Nhbjj"j9IJnrN\Nhbjj"j9IJnrJVJbBJJ|b*5EFhl,,        #     ]X F /**Vb+->?~t4+?+?+X%''!"+5F)-)9TGf$ Ev E*"!//))   rBc|jryd|_t|ddMtj|jj 5|jj ddddt|ddetj|jj 5|jj dd|jjgdddyy#1swY|xYw#1swYyxYwrrrSs rArRz"TFCamembertForMultipleChoice.buildrrr)r^r[rr\rur\rtr\rr\rvr\rr3r*r3r+r3rr\ror3rz4Union[TFMultipleChoiceModelOutput, Tuple[tf.Tensor]]r{)r|r}r~rrr1rr"rnror rpr rqrzrRrrs@rArrs+5&'1l#*"))*TU &/$.28<8<6:377;,0/3&*04#(; *; 6; 6 ; 4 ; 1 ; 5; *; -; $; .; !;  >; ; z MrBrz CamemBERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). c eZdZddgZfdZeeejde de e dd d d d Z d d Z xZS)TFCamembertForQuestionAnsweringr8rc t||g|i||j|_t|dd|_t j j|jt|jd|_ ||_ y)NFrar qa_outputsru) r0r1rr5rarr7rrr6rr3rfs rAr1z(TFCamembertForQuestionAnswering.__init__su 3&3F3 +++FeR[\ ,,,,   /&BZBZ2[bn-  rBrgzydshieh/roberta-base-squad2z ' puppet'gQ?rc |j||||||||| |  } | d}|j|}tj|dd\}}tj|d}tj|d}d}| | d| i}| |d<|j |||f}| s||f| ddz}||f|zS|St |||| j| j S) a start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. rrrrfrXNstart_position end_position)r start_logits end_logitsrr%) rarrKsplitsqueezerrrr%)r>r^rrurtrrvrr*r+start_positions end_positionsrorrXrrrrrrs rArzz$TFCamembertForQuestionAnswering.call$sD,, ))%'/!5#  "!*1#%88FAB#? jzz,R8 ZZ 4   &=+D&8F%2F> "''z0JKD"J/'!"+=F)-)9TGf$ Ev E-%!!//))   rBc|jryd|_t|ddMtj|jj 5|jj ddddt|ddetj|jj 5|jj dd|jjgdddyy#1swY|xYw#1swYyxYw)NTrar) rPrQrKrLrar,rRrr3r4rSs rArRz%TFCamembertForQuestionAnswering.buildkrr) NNNNNNNNNNNF)r^r[rr\rur\rtr\rr\rvr\rr3r*r3r+r3rr\rr\ror3rz7Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]r{)r|r}r~rr1rr"rnror rrqrzrRrrs@rArrs+4Z)@&*+E+L+LMj+kl02$# .28<8<6:377;,0/3&*9=7;#(< *< 6< 6 < 4 < 1 < 5< *< -< $< 7< 5< !<  A< m< | MrBrzKCamemBERT Model with a `language modeling` head on top for CLM fine-tuning.ceZdZddgZd fd ZdZdZd dZee e jde e ee d dd Zdd ZxZS)TFCamembertForCausalLMr8rct||g|i||jstj dt |dd|_t||j jd|_ y)NzSIf you want to use `TFCamembertLMHeadModel` as a standalone, add `is_decoder=True.`Frarr)rzr,) r0r1rloggerwarningr5rarsrIrrfs rAr1zTFCamembertForCausalLM.__init__s\ 3&3F3  NNp q+FeR[\ ($,,BYBY`ij rBc|jSr{rr<s rArz"TFCamembertForCausalLM.get_lm_headrrBctjdt|jdz|jjzSrrr<s rArz+TFCamembertForCausalLM.get_prefix_bias_namerrBc n|j}|tj|}| |ddddf}|||dS)Nrf)r^rr$)rFrKones)r>r^r$r model_kwargsrTs rAprepare_inputs_for_generationz4TFCamembertForCausalLM.prepare_inputs_for_generationsEoo  !WW[1N  &!!RS&)I&.]lmmrBrgrhcf|j||||||||| | | | | |}|d}|j||}d}|)|ddddf}|ddddf}|j||}| s|f|ddz}||f|zS|St|||j|j |j |j S) a encoder_hidden_states (`tf.Tensor` 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 (`tf.Tensor` 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[tf.Tensor]]` of length `config.n_layers`) 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*, defaults to `True`): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). Set to `False` during training, `True` during generation labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the cross entropy classification loss. 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