qiXU\dZddlmZddlZddlmZmZddlmZddlm Z ddl m Z m Z m Z d d lmZd d lmZe j$eZee d Gdde Zee d Gdde Zee d Gdde Ze Gdde ZGddeZGddeZGddeZGddeZGdd eZe d!Gd"d#eZe d$Gd%d&eZe d'Gd(d)eZ gd*Z!y)+z5PyTorch DPR model for Open Domain Question Answering.) dataclassN)Tensornn)BaseModelOutputWithPooling)PreTrainedModel) ModelOutputauto_docstringlogging) BertModel) DPRConfigz6 Class for outputs of [`DPRQuestionEncoder`]. ) custom_introceZdZUdZej ed<dZeej dfdzed<dZ eej dfdzed<y)DPRContextEncoderOutputa pooler_output (`torch.FloatTensor` of shape `(batch_size, embeddings_size)`): The DPR encoder outputs the *pooler_output* that corresponds to the context representation. Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer. This output is to be used to embed contexts for nearest neighbors queries with questions embeddings. pooler_outputN. hidden_states attentions __name__ __module__ __qualname____doc__torch FloatTensor__annotations__rtuplerV/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/dpr/modeling_dpr.pyrr(S$$$:>M5**C/047>7;Je'',-4;r rceZdZUdZej ed<dZeej dfdzed<dZ eej dfdzed<y)DPRQuestionEncoderOutputa pooler_output (`torch.FloatTensor` of shape `(batch_size, embeddings_size)`): The DPR encoder outputs the *pooler_output* that corresponds to the question representation. Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer. This output is to be used to embed questions for nearest neighbors queries with context embeddings. rN.rrrrr r!r$r$;r"r r$ceZdZUdZej ed<dZej dzed<dZej dzed<dZ e ej dfdzed<dZ e ej dfdzed<y) DPRReaderOutputa start_logits (`torch.FloatTensor` of shape `(n_passages, sequence_length)`): Logits of the start index of the span for each passage. end_logits (`torch.FloatTensor` of shape `(n_passages, sequence_length)`): Logits of the end index of the span for each passage. relevance_logits (`torch.FloatTensor` of shape `(n_passages, )`): Outputs of the QA classifier of the DPRReader that corresponds to the scores of each passage to answer the question, compared to all the other passages. start_logitsN end_logitsrelevance_logits.rr) rrrrrrrr(r)rrrrr r!r&r&Ns~###+/J!!D(/15e''$.5:>M5**C/047>7;Je'',-4;r r&ceZdZdZy)DPRPreTrainedModelTN)rrr_supports_sdparr r!r+r+fsNr r+ceZdZdZdeffd Z ddededzdedzdedzd ed ed ed ee ed fzfdZ e d e fdZ xZS) DPREncoder bert_modelconfigct||t|d|_|jjj dkr t d|j|_|jdkDrCtj|jjj |j|_ |jy)NF)add_pooling_layerrz!Encoder hidden_size can't be zero) super__init__r r/r0 hidden_size ValueErrorprojection_dimrLinear encode_proj post_initselfr0 __class__s r!r4zDPREncoder.__init__ns  #FeD ?? ! ! - - 2@A A$33    "!yy)?)?)K)KVMbMbcD  r N input_idsattention_masktoken_type_ids inputs_embedsoutput_attentionsoutput_hidden_states return_dictreturn.c |j|||||||} | d} | dddddf} |jdkDr|j| } |s | | f| ddzSt| | | j| j S)Nr>r?r@rArBrCrDrr )last_hidden_staterrr)r/r7r9rrr) r<r>r?r@rArBrCrDkwargsoutputssequence_output pooled_outputs r!forwardzDPREncoder.forwardys//))'/!5#" "!*'1a0    " ,,];M#]3gabkA A)-'!//))   r c|jdkDr|jjS|jjj S)Nr)r7r9 out_featuresr/r0r5)r<s r!embeddings_sizezDPREncoder.embeddings_sizes8    "##00 0%%111r )NNNFFF)rrrbase_model_prefixrr4rboolrrrMpropertyintrP __classcell__r=s@r!r.r.ks$ y )-(,'+"'%*!" "  "  " } "  " #" "  $eFCK&8 8" H222r r.cleZdZdZdeffd Z ddedededzded ed ed ee ed fzfd Z xZ S)DPRSpanPredictorencoderr0c t||t||_t j |jj d|_t j |jj d|_|jy)Nr r) r3r4r.rYrr8rP qa_outputs qa_classifierr:r;s r!r4zDPRSpanPredictor.__init__s_  !&) ))DLL$@$@!DYYt||'C'CQG r Nr>r?rArBrCrDrE.c V||jn|jdd\}} |j||||||} | d} |j| } | jdd\} }| j dj } |j dj }|j | dddddf}| j|| } |j|| }|j|}|s | ||f| ddzSt| ||| j| jS)Nr )r?rArBrCrDrr)dim)r'r(r)rr) sizerYr[splitsqueeze contiguousr\viewr&rr)r<r>r?rArBrCrDrI n_passagessequence_lengthrJrKlogitsr'r(r)s r!rMzDPRSpanPredictor.forwardsN;D:Oinn&6UbUgUgUijlklUm# O,, )'/!5#  "!*1#)<<r<#: j#++B/::< ''+668 --oaAg.FG$((_E __ZA +00< *.>?'!"+M M%!-!//))   r )NFFF) rrrrQrr4rrRr&rrMrUrVs@r!rXrXs~!y(,"'%*!, , , } ,  , # , ,  5- -, r rXc eZdZUdZeed<dZy)DPRPretrainedContextEncoder An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. r0 ctx_encoderNrrrrrrrQrr r!riris %r ric eZdZUdZeed<dZy)DPRPretrainedQuestionEncoderrjr0question_encoderNrlrr r!rnrns *r rnc eZdZUdZeed<dZy)DPRPretrainedReaderrjr0span_predictorNrlrr r!rqrqs (r rqzf The bare DPRContextEncoder transformer outputting pooler outputs as context representations. ceZdZdeffd Ze ddedzdedzdedzdedzdedzd edzd edzd ee ed fzfd Z xZ S)DPRContextEncoderr0crt||||_t||_|j yN)r3r4r0r.rkr:r;s r!r4zDPRContextEncoder.__init__ s.   %f- r Nr>r?r@rArBrCrDrE.c ||n|jj}||n|jj}||n|jj}| | t d||j } n!||j dd} n t d| |j n |j } |2|tj| | n||jjk7}|&tj| tj| }|j|||||||} |s| ddSt| j| j| j S) aS input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. To match pretraining, DPR input sequence should be formatted with [CLS] and [SEP] tokens as follows: (a) For sequence pairs (for a pair title+text for example): ``` tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] token_type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 ``` (b) For single sequences (for a question for example): ``` tokens: [CLS] the dog is hairy . [SEP] token_type_ids: 0 0 0 0 0 0 0 ``` DPR is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than the left. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) Examples: ```python >>> from transformers import DPRContextEncoder, DPRContextEncoderTokenizer >>> tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") >>> model = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") >>> input_ids = tokenizer("Hello, is my dog cute ?", return_tensors="pt")["input_ids"] >>> embeddings = model(input_ids).pooler_output ```NDYou cannot specify both input_ids and inputs_embeds at the same timer^5You have to specify either input_ids or inputs_embedsdevicedtyper{rGrrrr)r0rBrCuse_return_dictr6r`r{rones pad_token_idzeroslongrkrrrr r<r>r?r@rArBrCrDrI input_shaper{rJs r!rMzDPRContextEncoder.forwardspd2C1N-TXT_T_TqTq$8$D $++JjJj &1%cd d  "#..*K  &',,.s3KTU U%.%:!! @T@T  !$ ;v64;;#;#;;   !"[[EJJvVN""))'/!5## 12; &!//w?T?Tahasas  r NNNNNNN) rrrrr4r rrRrrrMrUrVs@r!rtrts y$((,(,'+)-,0#'Y D=Y  Y  Y } Y  $; Y #TkY D[Y  !5#5 5Y Y r rtzh The bare DPRQuestionEncoder transformer outputting pooler outputs as question representations. ceZdZdeffd Ze ddedzdedzdedzdedzdedzd edzd edzd ee ed fzfd Z xZ S)DPRQuestionEncoderr0crt||||_t||_|j yrv)r3r4r0r.ror:r;s r!r4zDPRQuestionEncoder.__init__vs.   *6 2 r Nr>r?r@rArBrCrDrE.c ||n|jj}||n|jj}||n|jj}| | t d|#|j |||j } n!||j dd} n t d| |jn |j} |2|tj| | n||jjk7}|&tj| tj| }|j|||||||} |s| ddSt| j| j | j" S) aj input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. To match pretraining, DPR input sequence should be formatted with [CLS] and [SEP] tokens as follows: (a) For sequence pairs (for a pair title+text for example): ``` tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] token_type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 ``` (b) For single sequences (for a question for example): ``` tokens: [CLS] the dog is hairy . [SEP] token_type_ids: 0 0 0 0 0 0 0 ``` DPR is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than the left. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) Examples: ```python >>> from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizer >>> tokenizer = DPRQuestionEncoderTokenizer.from_pretrained("facebook/dpr-question_encoder-single-nq-base") >>> model = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base") >>> input_ids = tokenizer("Hello, is my dog cute ?", return_tensors="pt")["input_ids"] >>> embeddings = model(input_ids).pooler_output ``` Nrxr^ryrzr|rGrr~)r0rBrCrr6%warn_if_padding_and_no_attention_maskr`r{rrrrrror$rrrrs r!rMzDPRQuestionEncoder.forward}sd2C1N-TXT_T_TqTq$8$D $++JjJj &1%cd d  "  6 6y. Q#..*K  &',,.s3KTU U%.%:!! @T@T  !$ ;v64;;#;#;;   !"[[EJJvVN''))'/!5#( 12; '!//w?T?Tahasas  r r) rrrrr4r rrRr$rrMrUrVs@r!rrps y$((,(,'+)-,0#'Z D=Z  Z  Z } Z  $; Z #TkZ D[Z  "E&#+$6 6Z Z r rzE The bare DPRReader transformer outputting span predictions. ceZdZdeffd Ze d dedzdedzdedzdedzdedzd edzd ee ed fzfd Z xZ S) DPRReaderr0crt||||_t||_|j yrv)r3r4r0rXrrr:r;s r!r4zDPRReader.__init__s.   .v6 r Nr>r?rArBrCrDrE.c ||n|jj}||n|jj}||n|jj}| | t d|#|j |||j }n!||j dd}n t d| |jn |j} |tj|| }|j||||||S)a input_ids (`tuple[torch.LongTensor]` of shapes `(n_passages, sequence_length)`): Indices of input sequence tokens in the vocabulary. It has to be a sequence triplet with 1) the question and 2) the passages titles and 3) the passages texts To match pretraining, DPR `input_ids` sequence should be formatted with [CLS] and [SEP] with the format: `[CLS] [SEP] [SEP] ` DPR is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than the left. Indices can be obtained using [`DPRReaderTokenizer`]. See this class documentation for more details. [What are input IDs?](../glossary#input-ids) inputs_embeds (`torch.FloatTensor` of shape `(n_passages, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. Examples: ```python >>> from transformers import DPRReader, DPRReaderTokenizer >>> tokenizer = DPRReaderTokenizer.from_pretrained("facebook/dpr-reader-single-nq-base") >>> model = DPRReader.from_pretrained("facebook/dpr-reader-single-nq-base") >>> encoded_inputs = tokenizer( ... questions=["What is love ?"], ... titles=["Haddaway"], ... texts=["'What Is Love' is a song recorded by the artist Haddaway"], ... return_tensors="pt", ... ) >>> outputs = model(**encoded_inputs) >>> start_logits = outputs.start_logits >>> end_logits = outputs.end_logits >>> relevance_logits = outputs.relevance_logits ``` Nrxr^ryrz)rArBrCrD) r0rBrCrr6rr`r{rrrr) r<r>r?rArBrCrDrIrr{s r!rMzDPRReader.forwards b2C1N-TXT_T_TqTq$8$D $++JjJj &1%cd d  "  6 6y. Q#..*K  &',,.s3KTU U%.%:!! @T@T  !"ZZ FCN""  '/!5# #  r )NNNNNN) rrrrr4r rrRr&rrMrUrVs@r!rrs y$((,'+)-,0#'L D=L  L } L  $; L #Tk L D[L  5- -L L r r)rtrir+rnrqrr)"r dataclassesrrrrmodeling_outputsrmodeling_utilsrutilsr r r bert.modeling_bertr configuration_dprr get_loggerrloggerrr$r&r+r.rXrirnrqrtrr__all__rr r!rs<! :- +(   H %