text: # coding=utf-8
  # Copyright 2018 The HuggingFace Inc. team.
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  # distributed under the License is distributed on an "AS IS" BASIS,
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  """Tokenization classes for DPR."""
  
  
  import collections
  from typing import List, Optional, Union
  
  from .file_utils import add_end_docstrings, add_start_docstrings
  from .tokenization_bert import BertTokenizer
  from .tokenization_utils_base import BatchEncoding, TensorType
  from .utils import logging
  
  
  logger = logging.get_logger(__name__)
  
  VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
  
  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP = {
      "vocab_file": {
          "facebook/dpr-ctx_encoder-single-nq-base": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
      }
  }
  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP = {
      "vocab_file": {
          "facebook/dpr-question_encoder-single-nq-base": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
      }
  }
  READER_PRETRAINED_VOCAB_FILES_MAP = {
      "vocab_file": {
          "facebook/dpr-reader-single-nq-base": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
      }
  }
  
  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
      "facebook/dpr-ctx_encoder-single-nq-base": 512,
  }
  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
      "facebook/dpr-question_encoder-single-nq-base": 512,
  }
  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
      "facebook/dpr-reader-single-nq-base": 512,
  }
  
  
  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION = {
      "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
  }
  QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION = {
      "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
  }
  READER_PRETRAINED_INIT_CONFIGURATION = {
      "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
  }
  
  
  class DPRContextEncoderTokenizer(BertTokenizer):
      r"""
      Construct a DPRContextEncoder tokenizer.
  
      :class:`~transformers.DPRContextEncoderTokenizer` is identical to :class:`~transformers.BertTokenizer` and runs
      end-to-end tokenization: punctuation splitting and wordpiece.
  
      Refer to superclass :class:`~transformers.BertTokenizer` for usage examples and documentation concerning
      parameters.
      """
  
      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"""
      Constructs a DPRQuestionEncoder tokenizer.
  
      :class:`~transformers.DPRQuestionEncoderTokenizer` is identical to :class:`~transformers.BertTokenizer` and runs
      end-to-end tokenization: punctuation splitting and wordpiece.
  
      Refer to superclass :class:`~transformers.BertTokenizer` for usage examples and documentation concerning
      parameters.
      """
  
      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"""
      Return a dictionary with the token ids of the input strings and other information to give to
      :obj:`.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 :obj:`input_ids` is a matrix of size
      :obj:`(n_passages, sequence_length)` with the format:
  
          [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
  
      Args:
          questions (:obj:`str` or :obj:`List[str]`):
              The questions to be encoded.
              You can specify one question for many passages. In this case, the question will be duplicated like
              :obj:`[questions] * n_passages`.
              Otherwise you have to specify as many questions as in :obj:`titles` or :obj:`texts`.
          titles (:obj:`str` or :obj:`List[str]`):
              The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
          texts (:obj:`str` or :obj:`List[str]`):
              The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
          padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`False`):
              Activates and controls padding. Accepts the following values:
  
              * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a
                single sequence if provided).
              * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
                maximum acceptable input length for the model if that argument is not provided.
              * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
                different lengths).
          truncation (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.TruncationStrategy`, `optional`, defaults to :obj:`False`):
              Activates and controls truncation. Accepts the following values:
  
              * :obj:`True` or :obj:`'longest_first'`: Truncate to a maximum length specified with the argument
                :obj:`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.
              * :obj:`'only_first'`: Truncate to a maximum length specified with the argument :obj:`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.
              * :obj:`'only_second'`: Truncate to a maximum length specified with the argument :obj:`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.
              * :obj:`False` or :obj:`'do_not_truncate'` (default): No truncation (i.e., can output batch with
                sequence lengths greater than the model maximum admissible input size).
          max_length (:obj:`int`, `optional`):
                  Controls the maximum length to use by one of the truncation/padding parameters.
  
                  If left unset or set to :obj:`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 (:obj:`str` or :class:`~transformers.tokenization_utils_base.TensorType`, `optional`):
                  If set, will return tensors instead of list of python integers. Acceptable values are:
  
                  * :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects.
                  * :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects.
                  * :obj:`'np'`: Return Numpy :obj:`np.ndarray` objects.
          return_attention_mask (:obj:`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 :obj:`return_outputs` attribute.
  
              `What are attention masks? <../glossary.html#attention-mask>`__
  
      Return:
          :obj:`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.
          """
  
  
  @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
          assert len(titles) == len(
              texts
          ), "There should be as many titles than texts but got {} titles and {} texts.".format(len(titles), len(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 = [input_ids != self.pad_token_id for input_ids in encoded_inputs["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]:
          """
          Get the span predictions for the extractive Q&A model.
          Outputs: `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::
  
              >>> 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
  
          """
          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])
              # assuming question & title information is at the beginning of the sequence
              passage_offset = sequence_ids.index(self.sep_token_id, 2) + 1  # second sep id
              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]:
          """
          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.
          """
          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:
              assert start_index <= end_index, "Wrong span indices: [{}:{}]".format(start_index, end_index)
              length = end_index - start_index + 1
              assert length <= max_answer_length, "Span is too long: {} > {}".format(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"""
      Construct a DPRReader tokenizer.
  
      :class:`~transformers.DPRReaderTokenizer` is almost identical to :class:`~transformers.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 :class:`~transformers.DPRReader` model.
  
      Refer to superclass :class:`~transformers.BertTokenizer` for usage examples and documentation concerning
      parameters.
      """
  
      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 = ["attention_mask"]
  