





'''simple docstring'''






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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


A__    :       Optional[Any]              =logging.get_logger(__name__)

A__    :       Dict              ={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

A__    :       List[str]              ={
    '''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'''
        ),
    },
}
A__    :       Union[str, Any]              ={
    '''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'''
        ),
    },
}
A__    :       str              ={
    '''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'''
        ),
    },
}

A__    :       Tuple              ={
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_12,
}
A__    :       List[str]              ={
    '''facebook/dpr-question_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-question_encoder-multiset-base''': 5_12,
}
A__    :       int              ={
    '''facebook/dpr-reader-single-nq-base''': 5_12,
    '''facebook/dpr-reader-multiset-base''': 5_12,
}


A__    :       str              ={
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
A__    :       Dict              ={
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
A__    :       Any              ={
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}




class 			UpperCAmelCase					(      snake_case_       ):
      _lowercase:      int											=       VOCAB_FILES_NAMES
      _lowercase:      List[str]											=       CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      _lowercase:      str											=       CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase:      List[Any]											=       CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
      _lowercase:      int											=       DPRContextEncoderTokenizer




class 			UpperCAmelCase					(      snake_case_       ):
      _lowercase:      List[str]											=       VOCAB_FILES_NAMES
      _lowercase:      Any											=       QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      _lowercase:      List[Any]											=       QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase:      Tuple											=       QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
      _lowercase:      Union[str, Any]											=       DPRQuestionEncoderTokenizer




A__    :       Union[str, Any]              =collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

A__    :       Optional[Any]              =collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


A__    :       List[str]              =r'''
    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)

    Return:
        `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(snake_case_       )
class 			UpperCAmelCase					:




      def __call__(      self      :			Dict      ,      __snake_case      :			Tuple      ,      __snake_case      :			Optional[str] = None      ,      __snake_case      :			Optional[str] = None      ,      __snake_case      :			Union[bool, str] = False      ,      __snake_case      :			Union[bool, str] = False      ,      __snake_case      :			Optional[int] = None      ,      __snake_case      :			Optional[Union[str, TensorType]] = None      ,      __snake_case      :			Optional[bool] = None      ,      **__snake_case      :			Optional[Any]      ,      )    ->				BatchEncoding:
            if titles is None and texts is None:
                  return super().__call__(
                      __snake_case      ,      padding=__snake_case      ,      truncation=__snake_case      ,      max_length=__snake_case      ,      return_tensors=__snake_case      ,      return_attention_mask=__snake_case      ,      **__snake_case      ,      )
            elif titles is None or texts is None:
                  _lowerCAmelCase												=							titles if texts is None else texts
                  return super().__call__(
                      __snake_case      ,      __snake_case      ,      padding=__snake_case      ,      truncation=__snake_case      ,      max_length=__snake_case      ,      return_tensors=__snake_case      ,      return_attention_mask=__snake_case      ,      **__snake_case      ,      )
            _lowerCAmelCase												=							titles if not isinstance(__snake_case      ,      __snake_case  ) else [titles]
            _lowerCAmelCase												=							texts if not isinstance(__snake_case      ,      __snake_case  ) else [texts]
            _lowerCAmelCase												=							len(__snake_case  )
            _lowerCAmelCase												=							questions if not isinstance(__snake_case      ,      __snake_case  ) else [questions] * n_passages
            assert len(__snake_case  ) == len(
                __snake_case  ), f"There should be as many titles than texts but got {len(__snake_case  )} titles and {len(__snake_case  )} texts."
            _lowerCAmelCase												=							super().__call__(__snake_case      ,      __snake_case      ,      padding=__snake_case      ,      truncation=__snake_case  )["""input_ids"""]
            _lowerCAmelCase												=							super().__call__(__snake_case      ,      add_special_tokens=__snake_case      ,      padding=__snake_case      ,      truncation=__snake_case  )["""input_ids"""]
            _lowerCAmelCase												=							{
                """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(__snake_case      ,      __snake_case  )
                ]
            }
            if return_attention_mask is not False:
                  _lowerCAmelCase												=							[]
                  for input_ids in encoded_inputs["input_ids"]:
                        attention_mask.append([int(input_id != self.pad_token_id  ) for input_id in input_ids]  )
                  _lowerCAmelCase												=							attention_mask
            return self.pad(__snake_case      ,      padding=__snake_case      ,      max_length=__snake_case      ,      return_tensors=__snake_case  )




      def     lowercase__   (      self      :			List[str]      ,      __snake_case      :			BatchEncoding      ,      __snake_case      :			DPRReaderOutput      ,      __snake_case      :			int = 16      ,      __snake_case      :			int = 64      ,      __snake_case      :			int = 4      ,      )    ->				List[DPRSpanPrediction]:
            _lowerCAmelCase												=							reader_input["""input_ids"""]
            _lowerCAmelCase					,							_lowerCAmelCase					,							_lowerCAmelCase												=							reader_output[:3]
            _lowerCAmelCase												=							len(__snake_case  )
            _lowerCAmelCase												=							sorted(range(__snake_case  )      ,      reverse=__snake_case      ,      key=relevance_logits.__getitem__  )
            _lowerCAmelCase												=							[]
            for doc_id in sorted_docs:
                  _lowerCAmelCase												=							list(input_ids[doc_id]  )
                  # assuming question & title information is at the beginning of the sequence
                  _lowerCAmelCase												=							sequence_ids.index(self.sep_token_id      ,      2  ) + 1  # second sep id
                  if sequence_ids[-1] == self.pad_token_id:
                        _lowerCAmelCase												=							sequence_ids.index(self.pad_token_id  )
                  else:
                        _lowerCAmelCase												=							len(__snake_case  )

                  _lowerCAmelCase												=							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=__snake_case      ,      top_spans=__snake_case      ,      )
                  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=__snake_case      ,      start_index=__snake_case      ,      end_index=__snake_case      ,      text=self.decode(sequence_ids[start_index : end_index + 1]  )      ,      )  )
                  if len(__snake_case  ) >= num_spans:
                        break
            return nbest_spans_predictions[:num_spans]




      def     lowercase__   (      self      :			List[str]      ,      __snake_case      :			List[int]      ,      __snake_case      :			List[int]      ,      __snake_case      :			int      ,      __snake_case      :			int      ,      )    ->				List[DPRSpanPrediction]:
            _lowerCAmelCase												=							[]
            for start_index, start_score in enumerate(__snake_case  ):
                  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)  )
            _lowerCAmelCase												=							sorted(__snake_case      ,      key=lambda __snake_case  : x[1]      ,      reverse=__snake_case  )
            _lowerCAmelCase												=							[]
            for (start_index, end_index), score in scores:
                  assert start_index <= end_index, f"Wrong span indices: [{start_index}:{end_index}]"
                  _lowerCAmelCase												=							end_index - start_index + 1
                  assert length <= max_answer_length, 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(__snake_case  ) == top_spans:
                        break
            return chosen_span_intervals




@add_end_docstrings(snake_case_       )
class 			UpperCAmelCase					(      snake_case_							,		snake_case_       ):
      _lowercase:      Tuple											=       VOCAB_FILES_NAMES
      _lowercase:      Union[str, Any]											=       READER_PRETRAINED_VOCAB_FILES_MAP
      _lowercase:      int											=       READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase:      Optional[Any]											=       READER_PRETRAINED_INIT_CONFIGURATION
      _lowercase:      Union[str, Any]											=       ['''input_ids''', '''attention_mask''']
      _lowercase:      Any											=       DPRReaderTokenizer













'''simple docstring'''






def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      if not (isinstance(lowerCAmelCase							,     lowerCAmelCase  ) and isinstance(lowerCAmelCase							,     lowerCAmelCase  )):
            raise ValueError("""longest_common_substring() takes two strings for inputs"""  )

      _lowerCAmelCase												=							len(lowerCAmelCase  )
      _lowerCAmelCase												=							len(lowerCAmelCase  )

      _lowerCAmelCase												=							[[0] * (texta_length + 1) for _ in range(texta_length + 1  )]
      _lowerCAmelCase												=							0
      _lowerCAmelCase												=							0

      for i in range(1							,     texta_length + 1  ):
            for j in range(1							,     texta_length + 1  ):
                  if texta[i - 1] == texta[j - 1]:
                        _lowerCAmelCase												=							1 + dp[i - 1][j - 1]
                        if dp[i][j] > ans_length:
                              _lowerCAmelCase												=							i
                              _lowerCAmelCase												=							dp[i][j]

      return texta[ans_index - ans_length : ans_index]


if __name__ == "__main__":
      import doctest

      doctest.testmod()






