codingutf8 2018 the huggingface inc team the hugging face team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license tokenization classes for dpr import collections from typing import list optional union from tokenizationutilsbase import batchencoding from utils import tensortype addenddocstrings addstartdocstrings logging from bert tokenizationbert import berttokenizer logger logging getloggername vocabfilesnames vocabfile vocab txt tokenizerfile tokenizer json contextencoderpretrainedvocabfilesmap vocabfile facebookdprctxencodersinglenqbase https huggingface cofacebookdprctxencodersinglenqbaseresolvemainvocab txt facebookdprctxencodermultisetbase https huggingface cofacebookdprctxencodermultisetbaseresolvemainvocab txt tokenizerfile facebookdprctxencodersinglenqbase https huggingface cofacebookdprctxencodersinglenqbaseresolvemaintokenizer json facebookdprctxencodermultisetbase https huggingface cofacebookdprctxencodermultisetbaseresolvemaintokenizer json questionencoderpretrainedvocabfilesmap vocabfile facebookdprquestionencodersinglenqbase https huggingface cofacebookdprquestionencodersinglenqbaseresolvemainvocab txt facebookdprquestionencodermultisetbase https huggingface cofacebookdprquestionencodermultisetbaseresolvemainvocab txt tokenizerfile facebookdprquestionencodersinglenqbase https huggingface cofacebookdprquestionencodersinglenqbaseresolvemaintokenizer json facebookdprquestionencodermultisetbase https huggingface cofacebookdprquestionencodermultisetbaseresolvemaintokenizer json readerpretrainedvocabfilesmap vocabfile facebookdprreadersinglenqbase https huggingface cofacebookdprreadersinglenqbaseresolvemainvocab txt facebookdprreadermultisetbase https huggingface cofacebookdprreadermultisetbaseresolvemainvocab txt tokenizerfile facebookdprreadersinglenqbase https huggingface cofacebookdprreadersinglenqbaseresolvemaintokenizer json facebookdprreadermultisetbase https huggingface cofacebookdprreadermultisetbaseresolvemaintokenizer json contextencoderpretrainedpositionalembeddingssizes facebookdprctxencodersinglenqbase 512 facebookdprctxencodermultisetbase 512 questionencoderpretrainedpositionalembeddingssizes facebookdprquestionencodersinglenqbase 512 facebookdprquestionencodermultisetbase 512 readerpretrainedpositionalembeddingssizes facebookdprreadersinglenqbase 512 facebookdprreadermultisetbase 512 contextencoderpretrainedinitconfiguration facebookdprctxencodersinglenqbase dolowercase true facebookdprctxencodermultisetbase dolowercase true questionencoderpretrainedinitconfiguration facebookdprquestionencodersinglenqbase dolowercase true facebookdprquestionencodermultisetbase dolowercase true readerpretrainedinitconfiguration facebookdprreadersinglenqbase dolowercase true facebookdprreadermultisetbase dolowercase true class dprcontextencodertokenizerberttokenizer r construct a dprcontextencoder tokenizer dprcontextencodertokenizer is identical to berttokenizer and runs endtoend tokenization punctuation splitting and wordpiece refer to superclass berttokenizer for usage examples and documentation concerning parameters vocabfilesnames vocabfilesnames pretrainedvocabfilesmap questionencoderpretrainedvocabfilesmap maxmodelinputsizes questionencoderpretrainedpositionalembeddingssizes pretrainedinitconfiguration questionencoderpretrainedinitconfiguration dprspanprediction collections namedtuple dprspanprediction spanscore relevancescore docid startindex endindex text dprreaderoutput collections namedtupledprreaderoutput startlogits endlogits relevancelogits customdprreaderdocstring r return a dictionary with the token ids of the input strings and other information to give to decodebestspans it converts the strings of a question and different passages title and text in a sequence of ids integers using the tokenizer and vocabulary the resulting inputids is a matrix of size npassages sequencelength with the format cls question token ids sep titles ids sep texts ids args questions str or liststr the questions to be encoded you can specify one question for many passages in this case the question will be duplicated like questions npassages otherwise you have to specify as many questions as in titles or texts titles str or liststr the passages titles to be encoded this can be a string or a list of strings if there are several passages texts str or liststr the passages texts to be encoded this can be a string or a list of strings if there are several passages padding bool str or utils paddingstrategy optional defaults to false activates and controls padding accepts the following values true or longest pad to the longest sequence in the batch or no padding if only a single sequence if provided maxlength pad to a maximum length specified with the argument maxlength or to the maximum acceptable input length for the model if that argument is not provided false or donotpad default no padding i e can output a batch with sequences of different lengths truncation bool str or tokenizationutilsbase truncationstrategy optional defaults to false activates and controls truncation accepts the following values true or longestfirst truncate to a maximum length specified with the argument maxlength or to the maximum acceptable input length for the model if that argument is not provided this will truncate token by token removing a token from the longest sequence in the pair if a pair of sequences or a batch of pairs is provided onlyfirst truncate to a maximum length specified with the argument maxlength or to the maximum acceptable input length for the model if that argument is not provided this will only truncate the first sequence of a pair if a pair of sequences or a batch of pairs is provided onlysecond truncate to a maximum length specified with the argument maxlength or to the maximum acceptable input length for the model if that argument is not provided this will only truncate the second sequence of a pair if a pair of sequences or a batch of pairs is provided false or donottruncate default no truncation i e can output batch with sequence lengths greater than the model maximum admissible input size maxlength int optional controls the maximum length to use by one of the truncationpadding parameters if left unset or set to none this will use the predefined model maximum length if a maximum length is required by one of the truncationpadding parameters if the model has no specific maximum input length like xlnet truncationpadding to a maximum length will be deactivated returntensors str or utils tensortype optional if set will return tensors instead of list of python integers acceptable values are tf return tensorflow tf constant objects pt return pytorch torch tensor objects np return numpy np ndarray objects returnattentionmask bool optional whether or not to return the attention mask if not set will return the attention mask according to the specific tokenizer s default defined by the returnoutputs attribute what are attention masks glossaryattentionmask returns dictstr listlistint a dictionary with the following keys inputids list of token ids to be fed to a model attentionmask list of indices specifying which tokens should be attended to by the model get the span predictions for the extractive qa model returns list of dprreaderoutput sorted by descending relevancescore spanscore each dprreaderoutput is a tuple with spanscore float that corresponds to the score given by the reader for this span compared to other spans in the same passage it corresponds to the sum of the start and end logits of the span relevancescore float that corresponds to the score of the each passage to answer the question compared to all the other passages it corresponds to the output of the qa classifier of the dprreader docid int the id of the passage startindex int the start index of the span inclusive endindex int the end index of the span inclusive examples python from transformers import dprreader dprreadertokenizer tokenizer dprreadertokenizer frompretrainedfacebookdprreadersinglenqbase model dprreader frompretrainedfacebookdprreadersinglenqbase encodedinputs tokenizer questionswhat is love titleshaddaway texts what is love is a song recorded by the artist haddaway returntensorspt outputs modelencodedinputs predictedspans tokenizer decodebestspansencodedinputs outputs printpredictedspans0 text best span a song inputids readerinputinputids startlogits endlogits relevancelogits readeroutput 3 npassages lenrelevancelogits sorteddocs sortedrangenpassages reversetrue keyrelevancelogits getitem nbestspanspredictions listdprreaderoutput for docid in sorteddocs sequenceids listinputidsdocid assuming question title information is at the beginning of the sequence passageoffset sequenceids indexself septokenid 2 1 second sep id if sequenceids1 self padtokenid sequencelen sequenceids indexself padtokenid else sequencelen lensequenceids bestspans self getbestspans startlogitsstartlogitsdocidpassageoffset sequencelen endlogitsendlogitsdocidpassageoffset sequencelen maxanswerlengthmaxanswerlength topspansnumspansperpassage for startindex endindex in bestspans startindex passageoffset endindex passageoffset nbestspanspredictions append dprspanprediction spanscorestartlogitsdocidstartindex endlogitsdocidendindex relevancescorerelevancelogitsdocid dociddocid startindexstartindex endindexendindex textself decodesequenceidsstartindex endindex 1 if lennbestspanspredictions numspans break return nbestspanspredictions numspans def getbestspans self startlogits listint endlogits listint maxanswerlength int topspans int listdprspanprediction scores for startindex startscore in enumeratestartlogits for answerlength endscore in enumerateendlogitsstartindex startindex maxanswerlength scores appendstartindex startindex answerlength startscore endscore scores sortedscores keylambda x x1 reversetrue chosenspanintervals for startindex endindex score in scores if startindex endindex raise valueerrorfwrong span indices startindex endindex length endindex startindex 1 if length maxanswerlength raise valueerrorfspan is too long length maxanswerlength if any startindex prevstartindex prevendindex endindex or prevstartindex startindex endindex prevendindex for prevstartindex prevendindex in chosenspanintervals continue chosenspanintervals appendstartindex endindex if lenchosenspanintervals topspans break return chosenspanintervals addenddocstringscustomdprreaderdocstring class dprreadertokenizercustomdprreadertokenizermixin berttokenizer r construct a dprreader tokenizer dprreadertokenizer is almost identical to berttokenizer and runs endtoend tokenization punctuation splitting and wordpiece the difference is that is has three inputs strings question titles and texts that are combined to be fed to the dprreader model refer to superclass berttokenizer for usage examples and documentation concerning parameters coding utf 8 2018 the huggingface inc team the hugging face team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license tokenization classes for dpr construct a dprcontextencoder tokenizer dprcontextencodertokenizer is identical to berttokenizer and runs end to end tokenization punctuation splitting and wordpiece refer to superclass berttokenizer for usage examples and documentation concerning parameters constructs a dprquestionencoder tokenizer dprquestionencodertokenizer is identical to berttokenizer and runs end to end tokenization punctuation splitting and wordpiece refer to superclass berttokenizer for usage examples and documentation concerning parameters return a dictionary with the token ids of the input strings and other information to give to decode_best_spans it converts the strings of a question and different passages title and text in a sequence of ids integers using the tokenizer and vocabulary the resulting input_ids is a matrix of size n_passages sequence_length with the format cls question token ids sep titles ids sep texts ids args questions str or list str the questions to be encoded you can specify one question for many passages in this case the question will be duplicated like questions n_passages otherwise you have to specify as many questions as in titles or texts titles str or list str the passages titles to be encoded this can be a string or a list of strings if there are several passages texts str or list str the passages texts to be encoded this can be a string or a list of strings if there are several passages padding bool str or utils paddingstrategy optional defaults to false activates and controls padding accepts the following values true or longest pad to the longest sequence in the batch or no padding if only a single sequence if provided max_length pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided false or do_not_pad default no padding i e can output a batch with sequences of different lengths truncation bool str or tokenization_utils_base truncationstrategy optional defaults to false activates and controls truncation accepts the following values true or longest_first truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided this will truncate token by token removing a token from the longest sequence in the pair if a pair of sequences or a batch of pairs is provided only_first truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided this will only truncate the first sequence of a pair if a pair of sequences or a batch of pairs is provided only_second truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided this will only truncate the second sequence of a pair if a pair of sequences or a batch of pairs is provided false or do_not_truncate default no truncation i e can output batch with sequence lengths greater than the model maximum admissible input size max_length int optional controls the maximum length to use by one of the truncation padding parameters if left unset or set to none this will use the predefined model maximum length if a maximum length is required by one of the truncation padding parameters if the model has no specific maximum input length like xlnet truncation padding to a maximum length will be deactivated return_tensors str or utils tensortype optional if set will return tensors instead of list of python integers acceptable values are tf return tensorflow tf constant objects pt return pytorch torch tensor objects np return numpy np ndarray objects return_attention_mask bool optional whether or not to return the attention mask if not set will return the attention mask according to the specific tokenizer s default defined by the return_outputs attribute what are attention masks glossary attention mask returns dict str list list int a dictionary with the following keys input_ids list of token ids to be fed to a model attention_mask list of indices specifying which tokens should be attended to by the model get the span predictions for the extractive q a model returns list of dprreaderoutput sorted by descending relevance_score span_score each dprreaderoutput is a tuple with span_score float that corresponds to the score given by the reader for this span compared to other spans in the same passage it corresponds to the sum of the start and end logits of the span relevance_score float that corresponds to the score of the each passage to answer the question compared to all the other passages it corresponds to the output of the qa classifier of the dprreader doc_id int the id of the passage start_index int the start index of the span inclusive end_index int the end index of the span inclusive 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 predicted_spans tokenizer decode_best_spans encoded_inputs outputs print predicted_spans 0 text best span a song assuming question title information is at the beginning of the sequence second sep id finds the best answer span for the extractive q a model for one passage it returns the best span by descending span_score order and keeping max top_spans spans spans longer that max_answer_length are ignored construct a dprreader tokenizer dprreadertokenizer is almost identical to berttokenizer and runs end to end tokenization punctuation splitting and wordpiece the difference is that is has three inputs strings question titles and texts that are combined to be fed to the dprreader model refer to superclass berttokenizer for usage examples and documentation concerning parameters
import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
READER_PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}

CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION = {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION = {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
READER_PRETRAINED_INIT_CONFIGURATION = {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}


class DPRContextEncoderTokenizer(BertTokenizer):
    r

    vocab_files_names = VOCAB_FILES_NAMES
    pretrained_vocab_files_map = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    max_model_input_sizes = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    pretrained_init_configuration = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION


class DPRQuestionEncoderTokenizer(BertTokenizer):
    r

    vocab_files_names = VOCAB_FILES_NAMES
    pretrained_vocab_files_map = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    max_model_input_sizes = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    pretrained_init_configuration = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION


DPRSpanPrediction = collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

DPRReaderOutput = collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


CUSTOM_DPR_READER_DOCSTRING = r


@add_start_docstrings(CUSTOM_DPR_READER_DOCSTRING)
class CustomDPRReaderTokenizerMixin:
    def __call__(
        self,
        questions,
        titles: Optional[str] = None,
        texts: Optional[str] = None,
        padding: Union[bool, str] = False,
        truncation: Union[bool, str] = False,
        max_length: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        return_attention_mask: Optional[bool] = None,
        **kwargs,
    ) -> BatchEncoding:
        if titles is None and texts is None:
            return super().__call__(
                questions,
                padding=padding,
                truncation=truncation,
                max_length=max_length,
                return_tensors=return_tensors,
                return_attention_mask=return_attention_mask,
                **kwargs,
            )
        elif titles is None or texts is None:
            text_pair = titles if texts is None else texts
            return super().__call__(
                questions,
                text_pair,
                padding=padding,
                truncation=truncation,
                max_length=max_length,
                return_tensors=return_tensors,
                return_attention_mask=return_attention_mask,
                **kwargs,
            )
        titles = titles if not isinstance(titles, str) else [titles]
        texts = texts if not isinstance(texts, str) else [texts]
        n_passages = len(titles)
        questions = questions if not isinstance(questions, str) else [questions] * n_passages
        if len(titles) != len(texts):
            raise ValueError(
                f"There should be as many titles than texts but got {len(titles)} titles and {len(texts)} texts."
            )
        encoded_question_and_titles = super().__call__(questions, titles, padding=False, truncation=False)["input_ids"]
        encoded_texts = super().__call__(texts, add_special_tokens=False, padding=False, truncation=False)["input_ids"]
        encoded_inputs = {
            "input_ids": [
                (encoded_question_and_title + encoded_text)[:max_length]
                if max_length is not None and truncation
                else encoded_question_and_title + encoded_text
                for encoded_question_and_title, encoded_text in zip(encoded_question_and_titles, encoded_texts)
            ]
        }
        if return_attention_mask is not False:
            attention_mask = []
            for input_ids in encoded_inputs["input_ids"]:
                attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
            encoded_inputs["attention_mask"] = attention_mask
        return self.pad(encoded_inputs, padding=padding, max_length=max_length, return_tensors=return_tensors)

    def decode_best_spans(
        self,
        reader_input: BatchEncoding,
        reader_output: DPRReaderOutput,
        num_spans: int = 16,
        max_answer_length: int = 64,
        num_spans_per_passage: int = 4,
    ) -> List[DPRSpanPrediction]:
        
        input_ids = reader_input["input_ids"]
        start_logits, end_logits, relevance_logits = reader_output[:3]
        n_passages = len(relevance_logits)
        sorted_docs = sorted(range(n_passages), reverse=True, key=relevance_logits.__getitem__)
        nbest_spans_predictions: List[DPRReaderOutput] = []
        for doc_id in sorted_docs:
            sequence_ids = list(input_ids[doc_id])
            
            passage_offset = sequence_ids.index(self.sep_token_id, 2) + 1  
            if sequence_ids[-1] == self.pad_token_id:
                sequence_len = sequence_ids.index(self.pad_token_id)
            else:
                sequence_len = len(sequence_ids)

            best_spans = self._get_best_spans(
                start_logits=start_logits[doc_id][passage_offset:sequence_len],
                end_logits=end_logits[doc_id][passage_offset:sequence_len],
                max_answer_length=max_answer_length,
                top_spans=num_spans_per_passage,
            )
            for start_index, end_index in best_spans:
                start_index += passage_offset
                end_index += passage_offset
                nbest_spans_predictions.append(
                    DPRSpanPrediction(
                        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index],
                        relevance_score=relevance_logits[doc_id],
                        doc_id=doc_id,
                        start_index=start_index,
                        end_index=end_index,
                        text=self.decode(sequence_ids[start_index : end_index + 1]),
                    )
                )
            if len(nbest_spans_predictions) >= num_spans:
                break
        return nbest_spans_predictions[:num_spans]

    def _get_best_spans(
        self,
        start_logits: List[int],
        end_logits: List[int],
        max_answer_length: int,
        top_spans: int,
    ) -> List[DPRSpanPrediction]:
        
        scores = []
        for start_index, start_score in enumerate(start_logits):
            for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]):
                scores.append(((start_index, start_index + answer_length), start_score + end_score))
        scores = sorted(scores, key=lambda x: x[1], reverse=True)
        chosen_span_intervals = []
        for (start_index, end_index), score in scores:
            if start_index > end_index:
                raise ValueError(f"Wrong span indices: [{start_index}:{end_index}]")
            length = end_index - start_index + 1
            if length > max_answer_length:
                raise ValueError(f"Span is too long: {length} > {max_answer_length}")
            if any(
                start_index <= prev_start_index <= prev_end_index <= end_index
                or prev_start_index <= start_index <= end_index <= prev_end_index
                for (prev_start_index, prev_end_index) in chosen_span_intervals
            ):
                continue
            chosen_span_intervals.append((start_index, end_index))

            if len(chosen_span_intervals) == top_spans:
                break
        return chosen_span_intervals


@add_end_docstrings(CUSTOM_DPR_READER_DOCSTRING)
class DPRReaderTokenizer(CustomDPRReaderTokenizerMixin, BertTokenizer):
    r

    vocab_files_names = VOCAB_FILES_NAMES
    pretrained_vocab_files_map = READER_PRETRAINED_VOCAB_FILES_MAP
    max_model_input_sizes = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    pretrained_init_configuration = READER_PRETRAINED_INIT_CONFIGURATION
    model_input_names = ["input_ids", "attention_mask"]