T^(h4dZddlZddlmZddlmZmZmZddlZddl Zddlm Z ddl m Z m Z mZddlmZmZdd lmZmZdd lmZdd lmZdd lmZmZmZmZmZmZd dl m!Z!ejDe#Z$dZ%dZ&eGddeZ'eGddeZ(eGddeZ)eGddeZ*eGddeZ+eGddeZ,eGddeZ-eGdd eZ.eGd!d"eZ/eGd#d$eZ0Gd%d&e jbZ2Gd'd(e jbZ3Gd)d*e jbZ4Gd+d,e jbZ5Gd-d.e jbZ6Gd/d0e jbZ7Gd1d2e jbZ8Gd3d4e jbZ9Gd5d6e jbZ:Gd7d8e jbZ;Gd9d:e jbZ<Gd;deZ>d?Z?d@Z@edAe?GdBdCe>ZAdDZBGdEdFe jbZCedGe?GdHdIe>ZDedJe?GdKdLe>ZEedMe?GdNdOe>ZFedPe?GdQdRe>ZGedSe?GdTdUe>ZHedVe?GdWdXe>ZIedYe?GdZd[e>ZJed\e?Gd]d^e>ZKgd_ZLy)`zPyTorch LUKE model.N) dataclass)OptionalTupleUnion)nn)BCEWithLogitsLossCrossEntropyLossMSELoss)ACT2FNgelu)BaseModelOutputBaseModelOutputWithPooling)PreTrainedModel)apply_chunking_to_forward) ModelOutputadd_code_sample_docstringsadd_start_docstrings%add_start_docstrings_to_model_forwardloggingreplace_return_docstrings) LukeConfigrzstudio-ousia/luke-basecleZdZUdZdZeejed<dZ ee ejdfed<y)BaseLukeModelOutputWithPoolinga Base class for outputs of the LUKE model. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. entity_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, entity_length, hidden_size)`): Sequence of entity hidden-states at the output of the last layer of the model. pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`): Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer and a Tanh activation function. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each layer plus the initial entity embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length + entity_length, sequence_length + entity_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nentity_last_hidden_state.entity_hidden_states __name__ __module__ __qualname____doc__rrtorch FloatTensor__annotations__rrd/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/luke/modeling_luke.pyrr/@2=Ahu'8'89@DH(5):):C)?#@AHr'rcleZdZUdZdZeejed<dZ ee ejdfed<y)BaseLukeModelOutputa# Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. entity_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, entity_length, hidden_size)`): Sequence of entity hidden-states at the output of the last layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each layer plus the initial entity embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nr.rrr&r'r(r+r+Nr)r'r+cleZdZUdZdZeejed<dZ eejed<dZ eejed<dZ eejed<dZ eejed<dZ eeejed<dZeeejd fed <dZeeejd fed <y) LukeMaskedLMOutputa> Base class for model's outputs, with potential hidden states and attentions. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): The sum of masked language modeling (MLM) loss and entity prediction loss. mlm_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Masked language modeling (MLM) loss. mep_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Masked entity prediction (MEP) loss. logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). entity_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the entity prediction head (scores for each entity vocabulary token before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each layer plus the initial entity embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nlossmlm_lossmep_losslogits entity_logits hidden_states.r attentions)rr r!r"r.rr#r$r%r/r0r1r2r3rrr4r&r'r(r-r-ms>)-D(5$$ %,,0Hhu(()0,0Hhu(()0*.FHU&& '.15M8E--.58Ju00#567>r'r-ceZdZUdZdZeejed<dZ eejed<dZ ee ejdfed<dZ ee ejdfed<dZ ee ejdfed<y) EntityClassificationOutputay Outputs of entity classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification loss. logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): Classification scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each layer plus the initial entity embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nr.r1.r3rr4rr r!r"r.rr#r$r%r1r3rrr4r&r'r(r6r6,)-D(5$$ %,*.FHU&& '.=AM8E%"3"3S"89:ADH(5):):C)?#@AH:>Ju00#567>r'r6ceZdZUdZdZeejed<dZ eejed<dZ ee ejdfed<dZ ee ejdfed<dZ ee ejdfed<y) EntityPairClassificationOutputa~ Outputs of entity pair classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification loss. logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): Classification scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each layer plus the initial entity embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nr.r1.r3rr4r7r&r'r(r:r:r8r'r:ceZdZUdZdZeejed<dZ eejed<dZ ee ejdfed<dZ ee ejdfed<dZ ee ejdfed<y) EntitySpanClassificationOutputa Outputs of entity span classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification loss. logits (`torch.FloatTensor` of shape `(batch_size, entity_length, config.num_labels)`): Classification scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each layer plus the initial entity embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nr.r1.r3rr4r7r&r'r(r<r<r8r'r<ceZdZUdZdZeejed<dZ eejed<dZ ee ejdfed<dZ ee ejdfed<dZ ee ejdfed<y) LukeSequenceClassifierOutputa Outputs of sentence classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each layer plus the initial entity embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nr.r1.r3rr4r7r&r'r(r>r>2)-D(5$$ %,*.FHU&& '.=AM8E%"3"3S"89:ADH(5):):C)?#@AH:>Ju00#567>r'r>ceZdZUdZdZeejed<dZ eejed<dZ ee ejdfed<dZ ee ejdfed<dZ ee ejdfed<y) LukeTokenClassifierOutputa Base class for outputs of token classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) : Classification loss. logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`): Classification scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each layer plus the initial entity embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nr.r1.r3rr4r7r&r'r(rArAr?r'rAc eZdZUdZdZeejed<dZ eejed<dZ eejed<dZ ee ejdfed<dZ ee ejdfed<dZee ejdfed <y) LukeQuestionAnsweringModelOutputay Outputs of question answering models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. start_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Span-start scores (before SoftMax). end_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Span-end scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each layer plus the initial entity embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nr. start_logits end_logits.r3rr4)rr r!r"r.rr#r$r%rDrEr3rrr4r&r'r(rCrC9s6)-D(5$$ %,04L(5,,-4.2J**+2=AM8E%"3"3S"89:ADH(5):):C)?#@AH:>Ju00#567>r'rCceZdZUdZdZeejed<dZ eejed<dZ ee ejdfed<dZ ee ejdfed<dZ ee ejdfed<y) LukeMultipleChoiceModelOutputa  Outputs of multiple choice models. Args: loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided): Classification loss. logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`): *num_choices* is the second dimension of the input tensors. (see *input_ids* above). Classification scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each layer plus the initial entity embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nr.r1.r3rr4r7r&r'r(rGrG^s6)-D(5$$ %,*.FHU&& '.=AM8E%"3"3S"89:ADH(5):):C)?#@AH:>Ju00#567>r'rGc8eZdZdZfdZ ddZdZxZS)LukeEmbeddingszV Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. ct|tj|j|j |j |_tj|j|j |_ tj|j|j |_ tj|j |j|_ tj|j|_|j |_tj|j|j |j"|_ y)N padding_idxeps)super__init__r Embedding vocab_size hidden_size pad_token_idword_embeddingsmax_position_embeddingsposition_embeddingstype_vocab_sizetoken_type_embeddings LayerNormlayer_norm_epsDropouthidden_dropout_probdropoutrLselfconfig __class__s r(rPzLukeEmbeddings.__init__s !||F,=,=v?Q?Q_e_r_rs#%<<0N0NPVPbPb#c %'\\&2H2H&J\J\%]"f&8&8f>S>STzz&"<"<= "..#%<<  * *F,>,>DL\L\$  r'c|C|0t||jj|j}n|j |}||j }n|j dd}|:t j|t j|jj}||j|}|j|}|j|}||z|z}|j|}|j|}|S)Ndtypedevice)"create_position_ids_from_input_idsrLtorg&create_position_ids_from_inputs_embedssizer#zeroslong position_idsrUrWrYrZr^) r` input_idstoken_type_idsrn inputs_embeds input_shaperWrY embeddingss r(forwardzLukeEmbeddings.forwards  $A)TM]M]^aabkbrbrs #JJ=Y  #..*K',,.s3K  !"[[EJJtO`O`OgOghN   00;M"66|D $ : :> J"%88;PP ^^J/ \\*- r'c|jdd}|d}tj|jdz||jzdztj|j }|j dj|S)z We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. Args: inputs_embeds: torch.Tensor Returns: torch.Tensor Nrdrrer)rkr#arangerLrmrg unsqueezeexpand)r`rqrrsequence_lengthrns r(rjz5LukeEmbeddings.create_position_ids_from_inputs_embedss$((*3B/ %a.||   q /D4D4D"Dq"HPUPZPZcpcwcw %%a(// <S>STzz&"<"<= r' entity_idsrnrpcR|tj|}|j|}|jj|jj k7r|j |}|j|jd}|dk7j|jd}||z}tj|d}||jdjdz }|j|}||z|z}|j|}|j|}|S)Nr)minrddimgHz>)r# zeros_likerrarrSrrWclamptype_asrwsumrYrZr^) r`rrnrprrWposition_embedding_maskrYrss r(rtzLukeEntityEmbeddings.forwards'  !"--j9N 22:> ;; & &$++*A*A A $ ; ;">?R"S"]"]^`"a14KK#ii(;D14K4O4OTV4O4W4]4]bf4]4gg $ : :> J&)<z >$6: $$&&!!1!12 r'r}c2eZdZfdZdZ ddZxZS)LukeSelfAttentionc t||j|jzdk7r2t |ds&t d|jd|jd|j|_t |j|jz |_|j|jz|_|j|_ tj|j|j|_ tj|j|j|_ tj|j|j|_|jrtj|j|j|_tj|j|j|_tj|j|j|_tj$|j&|_y)Nrembedding_sizezThe hidden size z4 is not a multiple of the number of attention heads .)rOrPrSnum_attention_headshasattr ValueErrorintattention_head_size all_head_sizeuse_entity_aware_attentionrrquerykeyvalue w2e_query e2w_query e2e_queryr\attention_probs_dropout_probr^r_s r(rPzLukeSelfAttention.__init__s     : : :a ?PVXhHi"6#5#5"67334A7  $*#=#= #&v'9'9FS"S* **,CD $1!ZiZ2B$C!  '*. '*79:q8H*I ' 02M_G12MNGr'NNF)rr r!rPrrtrzr{s@r(rrsG0% Pr'rcneZdZfdZdej dej dej fdZxZS)LukeSelfOutputc(t|tj|j|j|_tj |j|j|_tj|j|_ yNrM) rOrPrrrSdenserZr[r\r]r^r_s r(rPzLukeSelfOutput.__init__is` YYv1163E3EF f&8&8f>S>STzz&"<"<= r'r3 input_tensorreturncr|j|}|j|}|j||z}|Srrr^rZr`r3rs r(rtzLukeSelfOutput.forwardo7 =1  ]3 }|'CD r'rr r!rPr#Tensorrtrzr{s@r(rrh1> U\\RWR^R^r'rc2eZdZfdZdZ ddZxZS) LukeAttentionct|t||_t ||_t |_yr)rOrPrr`routputset pruned_headsr_s r(rPzLukeAttention.__init__ws0 %f- $V, Er'ctdNz4LUKE does not support the pruning of attention headsNotImplementedError)r`headss r( prune_headszLukeAttention.prune_heads}!"XYYr'cF|jd}|j|||||}||d}|} n3tj|ddd}tj||gd} |j || } | ddd|ddf} |d} n| dd|dddf} | | f|ddz} | S)Nrrrr)rkr`r#rr)r`rrrrrr self_outputsconcat_self_outputsrattention_outputword_attention_outputentity_attention_outputrs r(rtzLukeAttention.forwards'++A. yy       '".q/ #5 "'))L!,S>STzz&"<"<= r'r3rrcr|j|}|j|}|j||z}|Srrrs r(rtzLukeOutput.forwardrr'rr{s@r(rrrr'rc2eZdZfdZ ddZdZxZS) LukeLayerct||j|_d|_t ||_t ||_t||_ yNr) rOrPchunk_size_feed_forward seq_len_dimr attentionr intermediaterrr_s r(rPzLukeLayer.__init__sI '-'E'E$&v.,V4 ( r'cJ|jd}|j|||||}||d}ntj|ddd}|dd} t |j |j |j|} | ddd|ddf} |d} n| dd|dddf} | | f| z} | S)Nr)rrrr)rkr r#rrfeed_forward_chunkrr ) r`rrrrrrself_attention_outputsconcat_attention_outputr layer_outputword_layer_outputentity_layer_outputs r(rtzLukeLayer.forwards'++A. !%   / "0"  '&z&LukeEncoder.forward..2 = )last_hidden_stater3r4rr) enumeraterrtraining_gradient_checkpointing_func__call__tupler+)r`rrrrroutput_hidden_states return_dictall_word_hidden_statesall_entity_hidden_statesall_self_attentionsi layer_modulelayer_head_mask layer_outputss r(rtzLukeEncoder.forwardsi(<)=24 $5b4(4 POA|#)?CUBW)W&+CG[F]+](.7.CilO**t}} $ A A ))&("#% ! !-&("#% ! "/q!1 #/'4Q'7$ &9]1=MS%S "'?CWBY'Y $ '*'(,    #00*%9!9   r')NNFFTrr r!rPrtrzr{s@r(rrs,"D r'rcVeZdZfdZdej dej fdZxZS) LukePoolerct|tj|j|j|_tj |_yr)rOrPrrrSrTanh activationr_s r(rPzLukePooler.__init__Hs9 YYv1163E3EF '')r'r3rc\|dddf}|j|}|j|}|S)Nr)rr:)r`r3first_token_tensor pooled_outputs r(rtzLukePooler.forwardMs6+1a40 #56  6 r'rr{s@r(r7r7Gs#$ U\\ellr'r7c$eZdZfdZdZxZS)EntityPredictionHeadTransformcht|tj|j|j |_t|jtrt|j|_ n|j|_ tj|j |j|_ yr)rOrPrrrSrrrrrr transform_act_fnrZr[r_s r(rPz&EntityPredictionHeadTransform.__init__Ws{ YYv1163I3IJ f'' -$*6+<+<$=D !$*$5$5D !f&<&<&BWBWXr'cl|j|}|j|}|j|}|Sr)rrArZrs r(rtz%EntityPredictionHeadTransform.forward`s4 =1 --m< }5 r'r5r{s@r(r?r?VsYr'r?c$eZdZfdZdZxZS)EntityPredictionHeadc t|||_t||_t j |j|jd|_ t jtj|j|_ y)NFr)rOrPrar? transformrrrrdecoder Parameterr#rlrr_s r(rPzEntityPredictionHead.__init__hsa  6v>yy!7!79Q9QX]^ LLV-E-E!FG r'cd|j|}|j||jz}|Sr)rFrGrrs r(rtzEntityPredictionHead.forwardos-}5  ]3dii? r'r5r{s@r(rDrDgsHr'rDcDeZdZdZeZdZdZddgZde jfdZ y) LukePreTrainedModelz An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. lukeTrr}modulecjt|tjrm|jjj d|j j|j%|jjjyyt|tjr|jdk(r%|jjjn;|jjj d|j j|j2|jj|jjyyt|tjrJ|jjj|jjjdyy)zInitialize the weightsg)meanstdNr?)rrrweightdatanormal_rainitializer_rangerzero_rQ embedding_dimrLrZfill_)r`rMs r( _init_weightsz!LukePreTrainedModel._init_weightss( fbii ( MM   & &CT[[5R5R & S{{&   &&('  -##q( ""((* ""**9V9V*W!!- ""6#5#56<<>.  - KK   " " $ MM   $ $S ).r'N) rr r!r"r config_classbase_model_prefixsupports_gradient_checkpointing_no_split_modulesrModulerYr&r'r(rKrKvs6 L&*#(*@A*BII*r'rKa> 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 ([`LukeConfig`]): 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 `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` 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 (`torch.LongTensor` 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 (`torch.LongTensor` 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) entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`): Indices of entity tokens in the entity vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*): Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`: - 1 for entity tokens that are **not masked**, - 0 for entity tokens that are **masked**. entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*): Segment token indices to indicate first and second portions of the entity token inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *portion A* entity token, - 1 corresponds to a *portion B* entity token. entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*): Indices of positions of each input entity in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. inputs_embeds (`torch.FloatTensor` 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. 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**. 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. zThe bare LUKE model transformer outputting raw hidden-states for both word tokens and entities without any specific head on top.c!PeZdZddedeffd ZdZdZdZdZ dZ e e jd eee  dd eej&d eej(d eej&deej&deej&deej(deej&deej&deej(deej(deedeedeedeeeffdZdej&deej&fdZxZS) LukeModelraadd_pooling_layerct||||_t||_t ||_t||_|r t|nd|_ |jyr) rOrPrarIrsr}rrencoderr7pooler post_init)r`rararbs r(rPzLukeModel.__init__sX   (0!5f!="6* ,=j(4  r'c.|jjSrrsrUr`s r(get_input_embeddingszLukeModel.get_input_embeddingss...r'c&||j_yrrgr`rs r(set_input_embeddingszLukeModel.set_input_embeddingss*/'r'c.|jjSrrrhs r(get_entity_embeddingszLukeModel.get_entity_embeddingss%%777r'c&||j_yrrnrks r(set_entity_embeddingszLukeModel.set_entity_embeddings s380r'ctdrr)r`heads_to_prunes r( _prune_headszLukeModel._prune_heads rr'batch_size, sequence_length output_typerZrorrprnrentity_attention_maskentity_token_type_idsentity_position_idsrrqrr,r-rc | | n|jj} | | n|jj} | | n|jj} | | t d|#|j |||j }n!| | j dd}n t d|\}}| |jn | j}|tj||f|}|&tj|tj|}|V|j d}|tj||f|}|(tj||ftj|}|j| |jj} |j|||| }|j||}|d}n|j!|||}|j#|||| | | | }|d }|j$|j%|nd}| s ||f|ddzSt'|||j(|j*|j,|j. S) u Returns: Examples: ```python >>> from transformers import AutoTokenizer, LukeModel >>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-base") >>> model = LukeModel.from_pretrained("studio-ousia/luke-base") # Compute the contextualized entity representation corresponding to the entity mention "Beyoncé" >>> text = "Beyoncé lives in Los Angeles." >>> entity_spans = [(0, 7)] # character-based entity span corresponding to "Beyoncé" >>> encoding = tokenizer(text, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt") >>> outputs = model(**encoding) >>> word_last_hidden_state = outputs.last_hidden_state >>> entity_last_hidden_state = outputs.entity_last_hidden_state # Input Wikipedia entities to obtain enriched contextualized representations of word tokens >>> text = "Beyoncé lives in Los Angeles." >>> entities = [ ... "Beyoncé", ... "Los Angeles", ... ] # Wikipedia entity titles corresponding to the entity mentions "Beyoncé" and "Los Angeles" >>> entity_spans = [ ... (0, 7), ... (17, 28), ... ] # character-based entity spans corresponding to "Beyoncé" and "Los Angeles" >>> encoding = tokenizer( ... text, entities=entities, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt" ... ) >>> outputs = model(**encoding) >>> word_last_hidden_state = outputs.last_hidden_state >>> entity_last_hidden_state = outputs.entity_last_hidden_state ```NzDYou cannot specify both input_ids and inputs_embeds at the same timerdz5You have to specify either input_ids or inputs_embeds)rgrer)rornrprq)rrrr,r-r)r& pooler_outputr3r4rr)rarr,use_return_dictr%warn_if_padding_and_no_attention_maskrkrgr#onesrlrm get_head_maskrrsget_extended_attention_maskrrcrdrr3r4rr)r`rorrprnrrxryrzrrqrr,r-rr batch_size seq_lengthrgentity_seq_lengthword_embedding_outputextended_attention_maskentity_embedding_outputencoder_outputssequence_outputr=s r(rtzLukeModel.forwardsr2C1N-TXT_T_TqTq$8$D $++JjJj &1%cd d  "  6 6y. Q#..*K  &',,.s3KTU U!, J%.%:!! @T@T  !"ZZZ(@PN  !"[[EJJvVN  ! * 2 $,(- J@Q3R[a(b%$,(- ZAR4S[`[e[ent(u%&&y$++2O2OP !%%)' !0! #'"B"B>Sh"i  &* #&*&<&1559,0/3&*H E,,-H !!2!23H !!1!12 H u//0 H U--. H  ((9(9:H  ((8(89H &e&6&67H E--.H   1 12H $D>H 'tnH d^H  u44 5H ihH T'#(#3#3'LTUZUeUeLf'r'r`c|j|j}tj|dj ||z}|j |zS)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: x: torch.Tensor x: Returns: torch.Tensor rr)nerr#cumsumrrm)rorLmaskincremental_indicess r(rhrhsP << $ ( ( *D <<!4<->!?@  II r'c |j|}t|}|j|}|j|}|Sr)rr rrG)r`featureskwargsrs r(rtzLukeLMHead.forwards; JJx  G OOA  LLOr'c|jjjjdk(r|j|j_y|jj|_y)Nmeta)rGrrgtyperhs r( _tie_weightszLukeLMHead._tie_weightssC <<   # # ( (F 2 $ DLL  ))DIr')rr r!r"rPrtrrzr{s@r(rrs4&*r'rz The LUKE model with a language modeling head and entity prediction head on top for masked language modeling and masked entity prediction. c%FeZdZgdZfdZfdZdZdZee jde e e  ddeej d eej"d eej d eej d eej d eej deej deej deej deej deej"deej"deedeedeedeee ff dZxZS)LukeForMaskedLM)zlm_head.decoder.weightzlm_head.decoder.biasz!entity_predictions.decoder.weightct||t||_t ||_t ||_tj|_ |jyr) rOrPr`rLrlm_headrDentity_predictionsrr loss_fnrer_s r(rPzLukeForMaskedLM.__init__sQ  f% !&) "6v">**,  r'ct||j|jj|j j j yr)rO tie_weights_tie_or_clone_weightsrrGrLr)r`rbs r(rzLukeForMaskedLM.tie_weightss:  ""4#:#:#B#BDIID_D_DqDqrr'c.|jjSrrrGrhs r(get_output_embeddingsz%LukeForMaskedLM.get_output_embeddingss||###r'c&||j_yrr)r`new_embeddingss r(set_output_embeddingsz%LukeForMaskedLM.set_output_embeddings s- r'rurvrorrprnrrxryrzlabels entity_labelsrrqrr,r-rcJ||n|jj}|j||||||||| | | |d }d}d}|j|j}| d| j |j } |j|jd|jj| jd}||}d}d}|jl|j|j}| O|j|jd|jj| jd}||}n||z}|s8td||||||j|j|j fDSt#||||||j|j|j S)aS labels (`torch.LongTensor` 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]` entity_labels (`torch.LongTensor` of shape `(batch_size, entity_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]` Returns: NT rorrprnrrxryrzrrqrr,r-rdc3$K|]}|| ywrr&r s r(r#z*LukeForMaskedLM.forward..Ws = r%)r.r/r0r1r2r3rr4)rar}rLrr&rirgrrrRrrrr+r3rr4r-)r`rorrprnrrxryrzrrrrqrr,r-rr.r/r1r0r2s r(rtzLukeForMaskedLM.forward sD&1%-1481559,0/3&*!a E,,-a !!2!23a !!1!12 a u//0 a U--. a  ((8(89a  ((8(89a &e&6&67a ))*a   0 01a E--.a   1 12a $D>a 'tna d^!a " u(( )#a ]ha r'rz The LUKE model with a classification head on top (a linear layer on top of the hidden state of the first entity token) for entity classification tasks, such as Open Entity. c#eZdZfdZeej deee  dde e jde e jde e jde e jde e jd e e jd e e jd e e jd e e jd e e jde e jde ede ede edeeeffdZxZS)LukeForEntityClassificationc,t||t||_|j|_t j |j|_t j|j|j|_ |jyr rOrPr`rL num_labelsrr\r]r^rrS classifierrer_s r(rPz$LukeForEntityClassification.__init__zsi  f%  ++zz&"<"<= ))F$6$68I8IJ r'rurvrorrprnrrxryrzrrqrrr,r-rc||n|jj}|j||||||||| | | | d }|jdddddf}|j |}|j |}d}| | j |j} | jdk(r!tjj|| }nMtjj|jd| jdj|}|s5td|||j |j"|j$fDSt'|||j |j"|j$S) u labels (`torch.LongTensor` of shape `(batch_size,)` or `(batch_size, num_labels)`, *optional*): Labels for computing the classification loss. If the shape is `(batch_size,)`, the cross entropy loss is used for the single-label classification. In this case, labels should contain the indices that should be in `[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, num_labels)`, the binary cross entropy loss is used for the multi-label classification. In this case, labels should only contain `[0, 1]`, where 0 and 1 indicate false and true, respectively. Returns: Examples: ```python >>> from transformers import AutoTokenizer, LukeForEntityClassification >>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-open-entity") >>> model = LukeForEntityClassification.from_pretrained("studio-ousia/luke-large-finetuned-open-entity") >>> text = "Beyoncé lives in Los Angeles." >>> entity_spans = [(0, 7)] # character-based entity span corresponding to "Beyoncé" >>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt") >>> outputs = model(**inputs) >>> logits = outputs.logits >>> predicted_class_idx = logits.argmax(-1).item() >>> print("Predicted class:", model.config.id2label[predicted_class_idx]) Predicted class: person ```NTrrrrdc3$K|]}|| ywrr&r s r(r#z6LukeForEntityClassification.forward..=r%r.r1r3rr4)rar}rLrr^rrirgndimrr cross_entropy binary_cross_entropy_with_logitsrrr+r3rr4r6r`rorrprnrrxryrzrrqrrr,r-rfeature_vectorr1r.s r(rtz#LukeForEntityClassification.forwardsm\&1%1559.2,0/3&*Z E,,-Z !!2!23Z !!1!12 Z u//0 Z U--. Z  ((9(9:Z  ((8(89Z &e&6&67Z E--.Z   1 12Z **+Z $D>Z 'tnZ d^Z u00 1!Z ehZ r'rz The LUKE model with a classification head on top (a linear layer on top of the hidden states of the two entity tokens) for entity pair classification tasks, such as TACRED. c#eZdZfdZeej deee  dde e jde e jde e jde e jde e jd e e jd e e jd e e jd e e jd e e jde e jde ede ede edeeeffdZxZS)LukeForEntityPairClassificationc4t||t||_|j|_t j |j|_t j|jdz|jd|_ |jy)NrFrr_s r(rPz(LukeForEntityPairClassification.__init__sp  f%  ++zz&"<"<= ))F$6$6$:F>> from transformers import AutoTokenizer, LukeForEntityPairClassification >>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-tacred") >>> model = LukeForEntityPairClassification.from_pretrained("studio-ousia/luke-large-finetuned-tacred") >>> text = "Beyoncé lives in Los Angeles." >>> entity_spans = [ ... (0, 7), ... (17, 28), ... ] # character-based entity spans corresponding to "Beyoncé" and "Los Angeles" >>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt") >>> outputs = model(**inputs) >>> logits = outputs.logits >>> predicted_class_idx = logits.argmax(-1).item() >>> print("Predicted class:", model.config.id2label[predicted_class_idx]) Predicted class: per:cities_of_residence ```NTrrrrrdc3$K|]}|| ywrr&r s r(r#z:LukeForEntityPairClassification.forward..Nrr%r)rar}rLr#rrr^rrirgrrrrrrrr+r3rr4r:rs r(rtz'LukeForEntityPairClassification.forwardsb&1%1559-1,0/3&*_ E,,-_ !!2!23_ !!1!12 _ u//0 _ U--. _  ((9(9:_  ((8(89_ &e&6&67_ E--._   1 12_ ))*_ $D>_ 'tn_ d^_ u44 5!_ ih_ r'rz The LUKE model with a span classification head on top (a linear layer on top of the hidden states output) for tasks such as named entity recognition. c'HeZdZfdZeej deee  dde e jde e jde e jde e jde e jd e e jd e e jd e e jd e e jd e e jde e jde e jde e jde ede ede edeeeff"dZxZS)LukeForEntitySpanClassificationc2t||t||_|j|_t j |j|_t j|jdz|j|_ |jy)Nr rr_s r(rPz(LukeForEntitySpanClassification.__init__esn  f%  ++zz&"<"<= ))F$6$6$:F>> from transformers import AutoTokenizer, LukeForEntitySpanClassification >>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003") >>> model = LukeForEntitySpanClassification.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003") >>> text = "Beyoncé lives in Los Angeles" # List all possible entity spans in the text >>> word_start_positions = [0, 8, 14, 17, 21] # character-based start positions of word tokens >>> word_end_positions = [7, 13, 16, 20, 28] # character-based end positions of word tokens >>> entity_spans = [] >>> for i, start_pos in enumerate(word_start_positions): ... for end_pos in word_end_positions[i:]: ... entity_spans.append((start_pos, end_pos)) >>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt") >>> outputs = model(**inputs) >>> logits = outputs.logits >>> predicted_class_indices = logits.argmax(-1).squeeze().tolist() >>> for span, predicted_class_idx in zip(entity_spans, predicted_class_indices): ... if predicted_class_idx != 0: ... print(text[span[0] : span[1]], model.config.id2label[predicted_class_idx]) Beyoncé PER Los Angeles LOC ```NTrrdrrrc3$K|]}|| ywrr&r s r(r#z:LukeForEntitySpanClassification.forward..rr%r)rar}rLr&rkrwrxrgrir#gatherrrr^rrrrrrrrrr+r3rr4r<)r`rorrprnrrxryrzrrrrqrrr,r-rrS start_states end_statesrr1r.s r(rtz'LukeForEntitySpanClassification.forwardqsB&1%%>w?X?X?_?_%` "||G$=$=rCYZ 3==bAHHRQ\]  & &'*C*C*J*J J#7#:#:7;T;T;[;[#\ \\'";";RAUV L*g>^>^#_efgn50  YYv}}-F{{a}}226;;r4??3SU[U`U`acUde}}EEfkkRToW]WbWbceWfWnWnouWvw(=(=w?[?[]d]o]op  .!//!(!=!=))   r')NNNNNNNNNNNNNNNN)rr r!rPrrrrr<rrr#rr$rrrrtrzr{s@r(rr]s ++@+G+GHe+fg+IXgh156:593715<@<@:>=A;?1559-1,0/3&*#y E,,-y !!2!23y !!1!12 y u//0 y U--. y  ((8(89y  ((8(89y &e&6&67y !))9)9 :y 'u'7'78y E--.y   1 12y ))*y $D>y 'tn!y "d^#y $ u44 5%y ihy r'rz The LUKE 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# eZdZfdZeej deee e  dde e jde e jde e jde e jde e jd e e jd e e jd e e jd e e jd e e jde e jde ede ede edeee ffdZxZS)LukeForSequenceClassificationc\t|||j|_t||_t j |j |jn |j|_ t j|j|j|_ |jyrrOrPrr`rLrr\classifier_dropoutr]r^rrSrrer_s r(rPz&LukeForSequenceClassification.__init__s   ++f% zz)/)B)B)NF % %TZTnTn  ))F$6$68I8IJ r'ru checkpointrwrZrorrprnrrxryrzrrqrrr,r-rc||n|jj}|j||||||||| | | | d }|j}|j |}|j |}d}| | j |j} |jj|jdk(rd|j_nl|jdkDrL| jtjk(s| jtjk(rd|j_nd|j_|jjdk(rIt}|jdk(r&||j| j}n||| }n|jjdk(r=t!}||j#d|j| j#d}n,|jjdk(rt%}||| }|s5t'd |||j(|j*|j,fDSt/|||j(|j*|j, S) a labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). NTrr regressionsingle_label_classificationmulti_label_classificationrdc3$K|]}|| ywrr&r s r(r#z8LukeForSequenceClassification.forward..Qrr%r)rar}rLr|r^rrirg problem_typerrfr#rmrr squeezer rrr+r3rr4r>)r`rorrprnrrxryrzrrqrrr,r-rr=r1r.loss_fcts r(rtz%LukeForSequenceClassification.forwards":&1%rrr#rr$rrrrtrzr{s@r(rrs ++@+G+GHe+fg&0$156:593715=A<@:>1559.2,0/3&*T E,,-T !!2!23T !!1!12 T u//0 T U--. T  ((9(9:T  ((8(89T &e&6&67T E--.T   1 12T **+T $D>T 'tnT d^T u22 3!T h T r'rz The LUKE Model with a token classification head on top (a linear layer on top of the hidden-states output). To solve Named-Entity Recognition (NER) task using LUKE, `LukeForEntitySpanClassification` is more suitable than this class. c# eZdZfdZeej deee e  dde e jde e jde e jde e jde e jd e e jd e e jd e e jd e e jd e e jde e jde ede ede edeee ffdZxZS)LukeForTokenClassificationc`t|||j|_t|d|_t j |j |jn |j|_ t j|j|j|_ |jyNF)rarr_s r(rPz#LukeForTokenClassification.__init__is   ++f> zz)/)B)B)NF % %TZTnTn  ))F$6$68I8IJ r'rurrorrprnrrxryrzrrqrrr,r-rcN||n|jj}|j||||||||| | | | d }|j}|j |}|j |}d}| W| j |j} t}||jd|j| jd}|s5td|||j|j|jfDSt|||j|j|jS)J labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) NTrrdc3$K|]}|| ywrr&r s r(r#z5LukeForTokenClassification.forward..rr%r)rar}rLr&r^rrirgr rrr+r3rr4rA)r`rorrprnrrxryrzrrqrrr,r-rrr1r.rs r(rtz"LukeForTokenClassification.forwardvs0:&1%1559.2,0/3&*B E,,-B !!2!23B !!1!12 B u//0 B U--. B  ((9(9:B  ((8(89B &e&6&67B E--.B   1 12B **+B $D>B 'tnB d^B u// 0!B h B r'rz The LUKE 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%*eZdZfdZeej deee e  dde e jde e jde e jde e jde e jd e e jd e e jd e e jd e e jd e e jde e jde e jde ede ede edeee ff dZxZS)LukeForQuestionAnsweringct|||j|_t|d|_t j |j|j|_|jyr) rOrPrr`rLrrrS qa_outputsrer_s r(rPz!LukeForQuestionAnswering.__init__sU   ++f> ))F$6$68I8IJ r'rurrorrprnrrxryrzrrqstart_positions end_positionsrr,r-rc`||n|jj}|j||||||||| | | |d }|j}|j |}|j dd\}}|j d}|j d}d}| | t| jdkDr| j d} t| jdkDr| j d} |jd}| jd|| jd|t|}||| }||| }||zd z }|s6td ||||j|j|jfDSt||||j|j|j S) a start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. NTrrrdrr) ignore_indexrc3$K|]}|| ywrr&r s r(r#z3LukeForQuestionAnswering.forward..!s = r%)r.rDrEr3rr4)rar}rLr&rsplitrlenrkclamp_r r+r3rr4rC)r`rorrprnrrxryrzrrqrrrr,r-rrr1rDrE total_loss ignored_indexr start_lossend_losss r(rtz LukeForQuestionAnswering.forwardsD&1%15596:48,0/3&*![ E,,-[ !!2!23[ !!1!12 [ u001 [ U--. [  ((9(9:[  ((8(89[ &e&6&67[ E--.[   1 12[ "%"2"23[   0 01[ $D>[ 'tn[ d^![ " u66 7#[ h [ r'rz The LUKE 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. c# eZdZfdZeej deee e  dde e jde e jde e jde e jde e jd e e jd e e jd e e jd e e jd e e jde e jde ede ede edeee ffdZxZS)LukeForMultipleChoicec&t||t||_t j |j |j n |j|_t j|jd|_ |jyr) rOrPr`rLrr\rr]r^rrSrrer_s r(rPzLukeForMultipleChoice.__init__@so  f% zz)/)B)B)NF % %TZTnTn  ))F$6$6: r'z(batch_size, num_choices, sequence_lengthrrorrprnrrxryrzrrqrrr,r-rc:||n|jj}||jdn| jd}|!|jd|j dnd}|!|jd|j dnd}|!|jd|j dnd}|!|jd|j dnd}| 1| jd| j d| j dnd} |!|jd|j dnd}|!|jd|j dnd}|!|jd|j dnd}|1|jd|j d|j dnd}|j ||||||||| | | | d }|j }|j|}|j|}|jd|}d}| .| j|j} t}||| }|s5td|||j|j|jfDSt!|||j|j|jS) rNrrdrTrc3$K|]}|| ywrr&r s r(r#z0LukeForMultipleChoice.forward..r$r%r)rar}rrrkrLr|r^rrirgr r+r3rr4rG)r`rorrprnrrxryrzrrqrrr,r- num_choicesrr=r1reshaped_logitsr.rs r(rtzLukeForMultipleChoice.forwardLs:&1%G>SINN2y~~b'9:Y] M[Mg,,R1D1DR1HImqM[Mg,,R1D1DR1HImqGSG_|((\->->r-BCei (   r=#5#5b#9=;M;Mb;Q R  BLAWZ__R)<=]a %0 " & &r+@+E+Eb+I J %0 " & &r+@+E+Eb+I J #. $ $R)<)A)A")EGZG_G_`bGc d  ))))%!"7"7 3'/!5   --  ]3 / ++b+6  YY556F')HOV4D #))00&&    -"!//!(!=!=))   r'r)rr r!rPrrrrrrGrrr#rr$rrrrtrzr{s@r(rr8s ++@+G+GHr+st&1$156:593715=A<@:>1559.2,0/3&*e E,,-e !!2!23e !!1!12 e u//0 e U--. e  ((9(9:e  ((8(89e &e&6&67e E--.e   1 12e **+e $D>e 'tne d^e u33 4!e u e r'r) rrrrrrrrr`rK)Mr"r dataclassesrtypingrrrr#torch.utils.checkpointrtorch.nnrr r activationsr r modeling_outputsrrmodeling_utilsr pytorch_utilsrutilsrrrrrrconfiguration_luker get_loggerrloggerrrrr+r-r6r:r<r>rArCrGr^rIr}rrrrrrrr7r?rDrKLUKE_START_DOCSTRINGrr`rhrrrrrrrrr__all__r&r'r(rs$ !)) AA'K-6+   H %. I%?I I< I/I I< '?'? '?T ?? ?< ?[? ?< ?[? ?< ?;? ?B ? ? ?B !?{!? !?H  ?K ?  ?FF=RYYF=R(299(Vn nd RYY ,BII,` ryy   1 1hK "))K ^  BII" 299 */*: HV E'#E'  E'P 4"**> } )} } @ i "5i i X n &9n n b H &9H H V g $7g g T  V !4V V r m 2m m ` x /x x v r'