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Nlossprediction_logitsseq_relationship_logitsr) rjrkrlrmrr! FloatTensorrrrrrrr*r(rr~sd &*D%  d ")26u((4/68<U..5<59M5**+d29r*rceZdZdZdfd ZdZdZe ddejdzdejdzdejdzd ejdzd e dzd e dzd e e zfd ZxZS) FNetModelz The model can behave as an encoder, following the architecture described in [FNet: Mixing Tokens with Fourier Transforms](https://huggingface.co/papers/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon. ct||||_t||_t ||_|r t|nd|_|jy)zv add_pooling_layer (bool, *optional*, defaults to `True`): Whether to add a pooling layer N) rCrDr]r7rhrencoderrpooler post_init)r\r]add_pooling_layerr^s r(rDzFNetModel.__init__sM   (0"6* ,=j(4  r*c.|jjSr,rhrIr\s r(get_input_embeddingszFNetModel.get_input_embeddingss...r*c&||j_yr,r)r\values r(set_input_embeddingszFNetModel.set_input_embeddingss*/'r*Nrcr@r<rdrrrc |||n|jj}||n|jj}| | td||j }|\} } n&||j dd}|\} } n td|jj r)| dkr$|jj | k7r td| |jn |j} |pt|jdr4|jjddd| f} | j| | } | }n&tj|tj| }|j||||}|j||| }|d }|j |j!|nd}|s ||f|d dzSt#|||j$ S) NzDYou cannot specify both input_ids and inputs_embeds at the same timer>z5You have to specify either input_ids or inputs_embedsrxzThe `tpu_short_seq_length` in FNetConfig should be set equal to the sequence length being passed to the model when using TPU optimizations.r@r`)rcr<r@rd)rrrr)r pooler_outputr)r]rr ValueErrorrYr{rrarbrhr@rWr!rXrZr r rr)r\rcr@r<rdrrkwargsre batch_sizer'rarfrgembedding_outputencoder_outputsrrs r(rizFNetModel.forwards%9$D $++JjJj &1%cd d  "#..*K%0 "J  &',,.s3K%0 "J TU U KK 5 5d" 00J>;  &/%:!! @T@T  !t(89*.//*H*HKZK*X'3J3Q3QR\^h3i0!A!&[ SY!Z??%)' +  ,, !5#'  *!,8< 8O O4UY #]3oab6II I)-')77  r*)T)NNNNNN)rjrkrlrmrDrrr r! LongTensorrboolrrrirnros@r(rrs /0.2260426,0#'D ##d*D ((4/D &&- D ((4/ D #Tk D D[D   D D r*rz FNet Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next sentence prediction (classification)` head. c*eZdZdddZfdZdZdZe ddejdzd ejdzd ejdzd ejdzd ejdzd ejdzde dzde dzde e zfdZ xZS)FNetForPreTrainingcls.predictions.bias&fnet.embeddings.word_embeddings.weightzcls.predictions.decoder.biaszcls.predictions.decoder.weightct||t||_t ||_|j yr,)rCrDrrrclsr r[s r(rDzFNetForPreTraining.__init__ s4  f% '/ r*cB|jjjSr,r#rrrs r(get_output_embeddingsz(FNetForPreTraining.get_output_embeddingsxx##+++r*c||jj_|j|jj_yr,r#rrrr\new_embeddingss r(set_output_embeddingsz(FNetForPreTraining.set_output_embeddings,'5$$2$7$7!r*Nrcr@r<rdlabelsnext_sentence_labelrrrc  ||n|jj}|j||||||} | dd\} } |j| | \} }d}|u|st }|| j d|jj |j d}||j dd|j d}||z}|s| |f| ddz}||f|zS|St|| || jS)aH labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` next_sentence_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see `input_ids` docstring) Indices should be in `[0, 1]`: - 0 indicates sequence B is a continuation of sequence A, - 1 indicates sequence B is a random sequence. Example: ```python >>> from transformers import AutoTokenizer, FNetForPreTraining >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("google/fnet-base") >>> model = FNetForPreTraining.from_pretrained("google/fnet-base") >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> prediction_logits = outputs.prediction_logits >>> seq_relationship_logits = outputs.seq_relationship_logits ```Nr@r<rdrrrur>)rrrr) r]rrr#rviewrFrr)r\rcr@r<rdr.r/rrrrrrrr total_lossloss_fctmasked_lm_lossnext_sentence_lossrs r(rizFNetForPreTraining.forwards5L&1%rurlogitsr) r]rrr#rr2rFrr)r\rcr@r<rdr.rrrrrrr5r4rs r(rizFNetForMaskedLM.forwardws$&1%:K[b[p[pqqr*NNNNNNN)rjrkrlr8rDr&r,r r!rrrrrirnros@r(r:r:`s)?*R ,8*..2,0-1&*,0#'(r<<$&(r t+(rllT) (r ||d* (r  t# (r#Tk(rD[(r  (r(rr*r:zT FNet Model with a `next sentence prediction (classification)` head on top. ceZdZfdZe d dej dzdej dzdej dzdej dzdej dzdedzd edzd ee zfd Z xZ S) FNetForNextSentencePredictionct||t||_t ||_|j yr,)rCrDrrrr#r r[s r(rDz&FNetForNextSentencePrediction.__init__r<r*Nrcr@r<rdr.rrrc V||n|jj}|j||||||} | d} |j| } d} |2t } | | j dd|j d} |s| f| ddz}| | f|zS|St | | | jS)a labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see `input_ids` docstring). Indices should be in `[0, 1]`: - 0 indicates sequence B is a continuation of sequence A, - 1 indicates sequence B is a random sequence. Example: ```python >>> from transformers import AutoTokenizer, FNetForNextSentencePrediction >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("google/fnet-base") >>> model = FNetForNextSentencePrediction.from_pretrained("google/fnet-base") >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced." >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light." >>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt") >>> outputs = model(**encoding, labels=torch.LongTensor([1])) >>> logits = outputs.logits >>> assert logits[0, 0] < logits[0, 1] # next sentence was random ```Nr1rr>rur@)r]rrr#rr2rr)r\rcr@r<rdr.rrrrrseq_relationship_scoresr6r4rs r(riz%FNetForNextSentencePrediction.forwardsH&1% 1` a classification loss is computed (Cross-Entropy). Nr1r regressionsingle_label_classificationmulti_label_classificationr>rur@)r]rrrTrM problem_typerLrBr!rZintrsqueezerr2rrr)r\rcr@r<rdr.rrrrrrArr4rs r(riz%FNetForSequenceClassification.forwards$&1%r1rur@) r]rrr2rYrrTrMrrr)r\rcr@r<rdr.rrr num_choicesrrrAreshaped_logitsrr4rs r(rizFNetForMultipleChoice.forwardQsT&1%G>SINN2y~~b'9:Y] M[Mg,,R1D1DR1HImqGSG_|((\->->r-BCei (   r=#5#5b#9=;M;Mb;Q R  )) )%'!5#     ]3 / ++b+6  ')HOV4D%''!"+5F)-)9TGf$ Ev E(d?ZaZoZoppr*rB) rjrkrlrDr r!rrrrrirnros@r(rVrVEs*..2,0-1&*,0#'Mq<<$&Mq t+MqllT) Mq ||d* Mq  t# Mq#TkMqD[Mq * *MqMqr*rVceZdZfdZe d dej dzdej dzdej dzdej dzdej dzdedzd edzd ee zfd Z xZ S) FNetForTokenClassificationc,t|||j|_t||_t j |j|_t j|j|j|_ |jyr,rKr[s r(rDz#FNetForTokenClassification.__init__si   ++f% zz&"<"<= ))F$6$68I8IJ r*Nrcr@r<rdr.rrrc ||n|jj}|j||||||} | d} |j| } |j | } d} |rur@) r]rrrTrMrr2rLrr)r\rcr@r<rdr.rrrrrrArr4rs r(riz"FNetForTokenClassification.forwards &1%rv) ignore_indexru)r start_logits end_logitsr) r]rrrcsplitrT contiguouslenrYclamprrr)r\rcr@r<rdrdrerrrrrrArhrir3 ignored_indexr4 start_lossend_lossrs r(riz FNetForQuestionAnswering.forwards&1%rs! AA&7!9   .6*   H %M>  8RYY8v# #Lbii 299  ryy   *6a"))a8   "))" 299 !bii!&bii& 9299 9 // / /  :{:  :$c #c c L Z ,Z  Z z?r)?r?rD J $7J  J Z Gg$7Gg  GgTYq/YqYqx7d!47d7dtB 2B B J r*