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ÚbertTcCs¤t|tjƒr:|jjjd|jjd|jdk r |jj  ¡nft|tj ƒrz|jjjd|jjd|j dk r |jj|j   ¡n&t|tj ƒr |jj  ¡|jj  d¡dS)zInitialize the weightsr¿)ZmeanZstdNgð?)rÞrr–r0rLZnormal_rNZinitializer_ranger2Zzero_rerYrmZfill_)rxÚmoduler)r)r,Ú _init_weightsCs      z!BertPreTrainedModel._init_weightsN) rˆr‰rŠr‹r%Ú config_classrWZload_tf_weightsZbase_model_prefixZsupports_gradient_checkpointingZ_supports_sdparr)r)r)r,r7src@sleZdZUdZdZeejed<dZ ejed<dZ ejed<dZ ee ejed<dZ ee ejed<dS)ÚBertForPreTrainingOutputa\ Output type of [`BertForPreTraining`]. Args: loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`): Total loss as the sum of the masked language modeling loss and the next sequence prediction (classification) loss. prediction_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). seq_relationship_logits (`torch.FloatTensor` of shape `(batch_size, 2)`): Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation 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. 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. NÚlossÚprediction_logitsÚseq_relationship_logitsr£rú)rˆr‰rŠr‹rrrKrŒÚ__annotations__rrr£rrúr)r)r)r,rTs ra> 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 ([`BertConfig`]): 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. a5 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) 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 `({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. 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 Bert Model transformer outputting raw hidden-states without any specific head on top.csàeZdZdZddgZd‡fdd„ Zdd„Zd d „Zd d „Ze e   d ¡ƒe e eeddeejeejeejeejeejeejeejeejeeejeeeeeeeeeeejefdœdd„ƒƒZ‡ZS)Ú BertModela The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in [Attention is all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. rXråTcsTtƒ |¡||_t|ƒ|_t|ƒ|_|r2t|ƒnd|_|j |_ |j |_ |  ¡dSrË) rcrdrNrXr†rîÚencoderrÚpoolerrÓÚattn_implementationr[Ú post_init)rxrNÚadd_pooling_layerryr)r,rdËs   zBertModel.__init__cCs|jjSrË©r†rir r)r)r,Úget_input_embeddingsÚszBertModel.get_input_embeddingscCs ||j_dSrËr%)rxr™r)r)r,Úset_input_embeddingsÝszBertModel.set_input_embeddingscCs*| ¡D]\}}|jj|j |¡qdS)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 N)Úitemsr rórèrÚ)rxZheads_to_prunerórØr)r)r,Ú _prune_headsàszBertModel._prune_headsúbatch_size, sequence_length©Ú checkpointÚ output_typerN)r{r¤r`r]r¥r|r¦r§rõr·r©rör÷r~c CsÐ| dk r | n|jj} | dk r | n|jj} | dk r4| n|jj} |jjrZ| dk rP| n|jj} nd} |dk rx|dk rxtdƒ‚n@|dk r–| ||¡| ¡}n"|dk r°| ¡dd…}ntdƒ‚|\}}|dk rÎ|j n|j }| dk rî| ddj dnd}|dkrFt |j dƒr4|j j dd…d|…f}| ||¡}|}ntj|tj|d }|j |||||d }|dkrztj|||f|d }|jd ko |jd ko |dko | }|rÔ|jjrÂt||||ƒ}nt||j|d}n | ||¡}|jjrF|dk rF| ¡\}}}||f}|dkr"tj||d }|r:t||j|d}n | |¡}nd}| ||jj¡}|j|||||| | | | | d }|d}|jdk r–| |¡nd}| s´||f|dd…St|||j|j |j!|j"dS)a  encoder_hidden_states (`torch.FloatTensor` 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 (`torch.FloatTensor` 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(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)`. 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`). NFzDYou cannot specify both input_ids and inputs_embeds at the same timer^z5You have to specify either input_ids or inputs_embedsrr4r`r)r{r]r`r|r})r€rÐr\)rÄ) r¤r¥r¦r§rõr·r©rör÷r$)rùZ pooler_outputrõr£rúrû)#rNr©röÚuse_return_dictrœr·rIZ%warn_if_padding_and_no_attention_maskrur€rHrr†r`rsrKrtrvZonesr"r[rrrbZget_extended_attention_maskZinvert_attention_maskZ get_head_maskròr r!rrõr£rúrû) rxr{r¤r`r]r¥r|r¦r§rõr·r©rör÷r‚Z batch_sizerƒr€r}r„r…Zembedding_outputZuse_sdpa_attention_masksZextended_attention_maskZencoder_batch_sizeZencoder_sequence_lengthrÅZencoder_hidden_shapeZencoder_extended_attention_maskZencoder_outputsr rr)r)r,r‡ès¸*ÿ     û  ÿþü üÿ  ÿ ö úzBertModel.forward)T) NNNNNNNNNNNNN)rˆr‰rŠr‹Z_no_split_modulesrdr&r'r)r ÚBERT_INPUTS_DOCSTRINGÚformatrÚ_CHECKPOINT_FOR_DOCrÚ_CONFIG_FOR_DOCrrKrrrŒr¹rrr‡rŽr)r)ryr,r¸sR  ýò ñrz¨ Bert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next sentence prediction (classification)` head. cs¾eZdZddgZ‡fdd„Zdd„Zdd„Zee  d ¡ƒe e e d de eje eje eje eje eje eje eje eje ee ee eeeeje fd œ d d„ƒƒZ‡ZS)ÚBertForPreTrainingúpredictions.decoder.biasúcls.predictions.decoder.weightcs,tƒ |¡t|ƒ|_t|ƒ|_| ¡dSrË)rcrdrrrÚclsr#rwryr)r,rd›s   zBertForPreTraining.__init__cCs |jjjSrË©r6r rr r)r)r,Úget_output_embeddings¤sz(BertForPreTraining.get_output_embeddingscCs||jj_|j|jj_dSrË©r6r rr2©rxZnew_embeddingsr)r)r,Úset_output_embeddings§s z(BertForPreTraining.set_output_embeddingsr*©r-rN) r{r¤r`r]r¥r|ÚlabelsÚnext_sentence_labelr©rör÷r~c  Csê| dk r | n|jj} |j||||||| | | d } | dd…\} }| | |¡\}}d}|dk r¦|dk r¦tƒ}|| d|jj¡| d¡ƒ}|| dd¡| d¡ƒ}||}| sÔ||f| dd…}|dk rÐ|f|S|St|||| j| j dS)a1 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. kwargs (`Dict[str, any]`, *optional*, defaults to `{}`): Used to hide legacy arguments that have been deprecated. Returns: Example: ```python >>> from transformers import AutoTokenizer, BertForPreTraining >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = BertForPreTraining.from_pretrained("google-bert/bert-base-uncased") >>> 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 ``` N©r¤r`r]r¥r|r©rör÷r4r^)rrrr£rú) rNr.rr6r rŸrfrr£rú)rxr{r¤r`r]r¥r|r=r>r©rör÷r¸r rrrÚ total_lossÚloss_fctÚmasked_lm_lossÚnext_sentence_lossrÔr)r)r,r‡«s<0÷ ûzBertForPreTraining.forward) NNNNNNNNNNN)rˆr‰rŠÚ_tied_weights_keysrdr8r;r r/r0r#rr2rrKrr¹rrr‡rŽr)r)ryr,r3‘s>   ôór3zFBert Model with a `language modeling` head on top for CLM fine-tuning.csìeZdZddgZ‡fdd„Zdd„Zdd„Zee  d ¡ƒe e e e d deejeejeejeejeejeejeejeejeejeeejeeeeeeeeeeeje fd œd d„ƒƒZddd„Zdd„Z‡ZS)ÚBertLMHeadModelzcls.predictions.decoder.biasr5cs@tƒ |¡|jst d¡t|dd|_t|ƒ|_|  ¡dS)NzLIf you want to use `BertLMHeadModel` as a standalone, add `is_decoder=True.`F©r$© rcrdrœr7Úwarningrrr r6r#rwryr)r,rds    zBertLMHeadModel.__init__cCs |jjjSrËr7r r)r)r,r8sz%BertLMHeadModel.get_output_embeddingscCs||jj_|j|jj_dSrËr9r:r)r)r,r;s z%BertLMHeadModel.set_output_embeddingsr*r+N)r{r¤r`r]r¥r|r¦r§r=rõr·r©rör÷r~cCs|dk r |n|jj}| dk r d} |j||||||||| | | | |d }|d}| |¡}d}| dk rº|dd…dd…dd…f ¡}| dd…dd…f ¡} tƒ}|| d|jj¡|  d¡ƒ}|sæ|f|dd…}|dk râ|f|S|St|||j |j |j |j dS) aà encoder_hidden_states (`torch.FloatTensor` 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 (`torch.FloatTensor` 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**. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the left-to-right language modeling loss (next word prediction). 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 n `[0, ..., config.vocab_size]` 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)`. 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`). NF) r¤r`r]r¥r|r¦r§rõr·r©rör÷rr^r$r4)rÚlogitsrõr£rúrû) rNr.rr6r²r rŸrfrrõr£rúrû)rxr{r¤r`r]r¥r|r¦r§r=rõr·r©rör÷r¸r rZlm_lossZshifted_prediction_scoresrArÔr)r)r,r‡sJ/ó úzBertLMHeadModel.forwardTc Ksv|j}|dkr| |¡}|dk rh|ddjd}|jd|krF|}n|jdd}|dd…|d…f}||||dœS)Nrr4r$)r{r¤rõr·)rHZnew_ones) rxr{rõr¤r·Ú model_kwargsr‚Z past_lengthZremove_prefix_lengthr)r)r,Úprepare_inputs_for_generationts üz-BertLMHeadModel.prepare_inputs_for_generationcs.d}|D] }|t‡fdd„|Dƒƒf7}q|S)Nr)c3s"|]}| dˆ |j¡¡VqdS)rN)Z index_selectr®r€)r*Z past_state©Úbeam_idxr)r,r-”sz1BertLMHeadModel._reorder_cache..)rþ)rxrõrMZreordered_pastZ layer_pastr)rLr,Ú_reorder_caches ÿzBertLMHeadModel._reorder_cache)NNNNNNNNNNNNNN)NNT)rˆr‰rŠrDrdr8r;r r/r0rr1rr2rrKrrr¹rrr‡rKrNrŽr)r)ryr,rEs\  ýñ ðVÿ rEz2Bert Model with a `language modeling` head on top.csÖeZdZddgZ‡fdd„Zdd„Zdd„Zee  d ¡ƒe e e e d d d deejeejeejeejeejeejeejeejeejeeeeeeeeeje fdœ dd„ƒƒZddd„Z‡ZS)ÚBertForMaskedLMr4r5cs@tƒ |¡|jrt d¡t|dd|_t|ƒ|_|  ¡dS)NzkIf you want to use `BertForMaskedLM` make sure `config.is_decoder=False` for bi-directional self-attention.FrFrGrwryr)r,rds ÿ zBertForMaskedLM.__init__cCs |jjjSrËr7r r)r)r,r8¬sz%BertForMaskedLM.get_output_embeddingscCs||jj_|j|jj_dSrËr9r:r)r)r,r;¯s z%BertForMaskedLM.set_output_embeddingsr*z'paris'g)\Âõ(ì?©r,r-rÚexpected_outputÚ expected_lossN) r{r¤r`r]r¥r|r¦r§r=r©rör÷r~c  Cs´| dk r | n|jj} |j||||||||| | | d } | d}| |¡}d}| dk rttƒ}|| d|jj¡|  d¡ƒ}| s |f| dd…}|dk rœ|f|S|St||| j| j dS)a¢ 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]` N) r¤r`r]r¥r|r¦r§r©rör÷rr^r4©rrIr£rú) rNr.rr6r rŸrfrr£rú)rxr{r¤r`r]r¥r|r¦r§r=r©rör÷r¸r rrBrArÔr)r)r,r‡³s:õ üzBertForMaskedLM.forwardcKs~|j}|d}|jjdkr"tdƒ‚tj|| |jddf¡gdd}tj|df|jjtj|j d}tj||gdd}||dœS)Nrz.The PAD token should be defined for generationr$r^rªr)r{r¤) rHrNrhrIrKr¬Z new_zerosÚfullrvr€)rxr{r¤rJr‚Zeffective_batch_sizeZ dummy_tokenr)r)r,rKôs "ÿz-BertForMaskedLM.prepare_inputs_for_generation) NNNNNNNNNNNN)N)rˆr‰rŠrDrdr8r;r r/r0rr1rr2rrKrr¹rrr‡rKrŽr)r)ryr,rO™sP  û óò9rOzJBert Model with a `next sentence prediction (classification)` head on top.csžeZdZ‡fdd„Zee d¡ƒeee dd e e j e e j e e j e e j e e j e e j e e j e e e e e e eee j efdœ dd„ƒƒZ‡ZS) ÚBertForNextSentencePredictioncs,tƒ |¡t|ƒ|_t|ƒ|_| ¡dSrË)rcrdrrrr6r#rwryr)r,rd s   z&BertForNextSentencePrediction.__init__r*r<N© r{r¤r`r]r¥r|r=r©rör÷r~c  KsÊd| krt dt¡|  d¡}| dk r*| n|jj} |j|||||||| | d } | d} | | ¡}d}|dk rŠtƒ}||  dd¡|  d¡ƒ}| s¶|f| dd…}|dk r²|f|S|St ||| j | j dS) aÌ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see `input_ids` docstring). Indices should be in `[0, 1]`: - 0 indicates sequence B is a continuation of sequence A, - 1 indicates sequence B is a random sequence. Returns: Example: ```python >>> from transformers import AutoTokenizer, BertForNextSentencePrediction >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = BertForNextSentencePrediction.from_pretrained("google-bert/bert-base-uncased") >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced." >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light." >>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt") >>> outputs = model(**encoding, labels=torch.LongTensor([1])) >>> logits = outputs.logits >>> assert logits[0, 0] < logits[0, 1] # next sentence was random ``` r>zoThe `next_sentence_label` argument is deprecated and will be removed in a future version, use `labels` instead.Nr?r$r^r4rS) ÚwarningsÚwarnÚ FutureWarningÚpoprNr.rr6r rŸrr£rú)rxr{r¤r`r]r¥r|r=r©rör÷Úkwargsr¸rZseq_relationship_scoresrCrArÔr)r)r,r‡sB-ý ÷  üz%BertForNextSentencePrediction.forward) NNNNNNNNNN)rˆr‰rŠrdr r/r0r#rr2rrKrr¹rrr‡rŽr)r)ryr,rUs4  õórUzœ Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. cs¤eZdZ‡fdd„Zee d¡ƒeee e e e dd e eje eje eje eje eje eje eje ee ee eeeeje fdœ dd„ƒƒZ‡ZS) ÚBertForSequenceClassificationcsdtƒ |¡|j|_||_t|ƒ|_|jdk r4|jn|j}t  |¡|_ t  |j |j¡|_ | ¡dSrË)rcrdÚ num_labelsrNrrÚclassifier_dropoutrprrorqr–rgr3r#©rxrNr^ryr)r,rdss  ÿ z&BertForSequenceClassification.__init__r*rPNrVc  Cs|| dk r | n|jj} |j|||||||| | d } | d} | | ¡} | | ¡} d}|dk r8|jjdkr®|jdkrzd|j_n4|jdkr¦|jtj ksœ|jtj kr¦d|j_nd|j_|jjdkrêt ƒ}|jdkrÞ||   ¡|  ¡ƒ}n || |ƒ}nN|jjdkrt ƒ}||  d|j¡| d¡ƒ}n|jjdkr8tƒ}|| |ƒ}| sh| f| dd…}|dk rd|f|S|St|| | j| jd 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). Nr?r$Z regressionZsingle_label_classificationZmulti_label_classificationr^r4rS)rNr.rrqr3Z problem_typer]rbrKrvrFr Úsqueezer rŸr rr£rú)rxr{r¤r`r]r¥r|r=r©rör÷r¸rrIrrArÔr)r)r,r‡‚sV÷       "     üz%BertForSequenceClassification.forward) NNNNNNNNNN)rˆr‰rŠrdr r/r0rÚ'_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATIONrr2Ú_SEQ_CLASS_EXPECTED_OUTPUTÚ_SEQ_CLASS_EXPECTED_LOSSrrKrr¹rrr‡rŽr)r)ryr,r\ks@  û õôr\z¥ Bert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. cs eZdZ‡fdd„Zee d¡ƒeee e dd e e j e e j e e j e e j e e j e e j e e j e ee ee eeee j e fdœ dd„ƒƒZ‡ZS) ÚBertForMultipleChoicecsTtƒ |¡t|ƒ|_|jdk r&|jn|j}t |¡|_t  |j d¡|_ |  ¡dS)Nr$) rcrdrrr^rprrorqr–rgr3r#r_ryr)r,rdÚs  ÿ zBertForMultipleChoice.__init__z(batch_size, num_choices, sequence_lengthr+NrVc  Cst| dk r | n|jj} |dk r&|jdn|jd} |dk rJ| d| d¡¡nd}|dk rh| d| d¡¡nd}|dk r†| d| d¡¡nd}|dk r¤| d| d¡¡nd}|dk rÊ| d| d¡| d¡¡nd}|j|||||||| | d } | d} | | ¡} | | ¡}| d| ¡}d}|dk r0tƒ}|||ƒ}| s`|f| dd…}|dk r\|f|S|St ||| j | j dS)aJ 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) Nr$r^r«r?r4rS) rNr.rHrŸrurrqr3r rr£rú)rxr{r¤r`r]r¥r|r=r©rör÷Z num_choicesr¸rrIZreshaped_logitsrrArÔr)r)r,r‡çsLÿý÷      üzBertForMultipleChoice.forward) NNNNNNNNNN)rˆr‰rŠrdr r/r0rr1rr2rrKrr¹rrr‡rŽr)r)ryr,rdÒs< ýõôrdz£ Bert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. cs¤eZdZ‡fdd„Zee d¡ƒeee e e e dd e eje eje eje eje eje eje eje ee ee eeeeje fdœ dd„ƒƒZ‡ZS) ÚBertForTokenClassificationcsbtƒ |¡|j|_t|dd|_|jdk r2|jn|j}t |¡|_ t  |j |j¡|_ |  ¡dS©NFrF)rcrdr]rrr^rprrorqr–rgr3r#r_ryr)r,rd8s ÿ z#BertForTokenClassification.__init__r*rPNrVc  Cs¸| dk r | n|jj} |j|||||||| | d } | d} | | ¡} | | ¡} d}|dk rxtƒ}||  d|j¡| d¡ƒ}| s¤| f| dd…}|dk r |f|S|St|| | j | j dS)zÛ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. Nr?rr^r4rS) rNr.rrqr3r rŸr]rr£rú)rxr{r¤r`r]r¥r|r=r©rör÷r¸r rIrrArÔr)r)r,r‡Fs8÷   üz"BertForTokenClassification.forward) NNNNNNNNNN)rˆr‰rŠrdr r/r0rÚ$_CHECKPOINT_FOR_TOKEN_CLASSIFICATIONrr2Ú_TOKEN_CLASS_EXPECTED_OUTPUTÚ_TOKEN_CLASS_EXPECTED_LOSSrrKrr¹rrr‡rŽr)r)ryr,re0s@  û õôrezÝ Bert 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`). cs°eZdZ‡fdd„Zee d¡ƒeee e e e e edd eejeejeejeejeejeejeejeejeeeeeeeeeje fdœ dd„ƒƒZ‡ZS) ÚBertForQuestionAnsweringcs@tƒ |¡|j|_t|dd|_t |j|j¡|_|  ¡dSrf) rcrdr]rrrr–rgÚ qa_outputsr#rwryr)r,rd‹s  z!BertForQuestionAnswering.__init__r*)r,r-rZqa_target_start_indexZqa_target_end_indexrQrRN) r{r¤r`r]r¥r|Ústart_positionsÚ end_positionsr©rör÷r~c  CsP| dk r | n|jj} |j||||||| | | d } | d} | | ¡}|jddd\}}| d¡ ¡}| d¡ ¡}d}|dk r|dk rt| ¡ƒdkr¢| d¡}t| ¡ƒdkr¼| d¡}| d¡}|  d|¡}|  d|¡}t |d}|||ƒ}|||ƒ}||d}| s:||f| dd…}|dk r6|f|S|St |||| j | j d 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. Nr?rr$r^rª)Z ignore_indexr4)rÚ start_logitsÚ end_logitsr£rú)rNr.rrkr?r`r²rEruÚclampr rr£rú)rxr{r¤r`r]r¥r|rlrmr©rör÷r¸r rIrnror@Z ignored_indexrAZ start_lossZend_lossrÔr)r)r,r‡•sP"÷           ûz BertForQuestionAnswering.forward) NNNNNNNNNNN)rˆr‰rŠrdr r/r0rÚ_CHECKPOINT_FOR_QArr2Ú_QA_TARGET_START_INDEXÚ_QA_TARGET_END_INDEXÚ_QA_EXPECTED_OUTPUTÚ_QA_EXPECTED_LOSSrrKrr¹rrr‡rŽr)r)ryr,rjƒsH ù ôórj)br‹r¯r9rWZ dataclassesrÚtypingrrrrrKZtorch.utils.checkpointÚ packagingrrZtorch.nnr r r Z activationsr Zmodeling_attn_mask_utilsrrZmodeling_outputsrrrrrrrrrZmodeling_utilsrZ pytorch_utilsrrrÚutilsrrrr r!r"r#Zconfiguration_bertr%Z get_loggerrˆr7r1r2rgrhrirqrtrurrrsrarbrcrWÚModulerXrrºrÆrÒrÑrÜrärårîrrrr rrrrZBERT_START_DOCSTRINGr/rr3rErOrUr\rdrerjr)r)r)r,ÚsÊ    , $  ÿI@eþ3V]   !2þVûhÿkþbû`ûWûLû