





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


_snake_case           =			logging.get_logger(__name__)

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

_snake_case           =			{
    '''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'''
        ),
    },
}
_snake_case           =			{
    '''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'''
        ),
    },
}
_snake_case           =			{
    '''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'''
        ),
    },
}

_snake_case           =			{
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
_snake_case           =			{
    '''facebook/dpr-question_encoder-single-nq-base''': 512,
    '''facebook/dpr-question_encoder-multiset-base''': 512,
}
_snake_case           =			{
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


_snake_case           =			{
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
_snake_case           =			{
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
_snake_case           =			{
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}





class  lowerCAmelCase							(       a__ ):
  __lowerCamelCase		=      VOCAB_FILES_NAMES
  __lowerCamelCase		=      CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  __lowerCamelCase		=      CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCamelCase		=      CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
  __lowerCamelCase		=      DPRContextEncoderTokenizer





class  lowerCAmelCase							(       a__ ):
  __lowerCamelCase		=      VOCAB_FILES_NAMES
  __lowerCamelCase		=      QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  __lowerCamelCase		=      QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCamelCase		=      QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
  __lowerCamelCase		=      DPRQuestionEncoderTokenizer
_snake_case           =			collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

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


_snake_case           =			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(a__ )
class  lowerCAmelCase							:







  def __call__(      self  :List[str]   ,							_lowercase  :Tuple   ,							_lowercase  :List[str] = None   ,							_lowercase  :List[Any] = None   ,							_lowercase  :int = False   ,							_lowercase  :Dict = False   ,							_lowercase  :str = None   ,							_lowercase  :List[Any] = None   ,							_lowercase  :str = None   ,							**_lowercase  :List[Any]   ,							):






     '''simple docstring'''





     if titles is None and texts is None:
        return super().__call__(
            _lowerCamelCase   ,							padding=_lowerCamelCase   ,							truncation=_lowerCamelCase   ,							max_length=_lowerCamelCase   ,							return_tensors=_lowerCamelCase   ,							return_attention_mask=_lowerCamelCase   ,							**_lowerCamelCase   ,							)
     elif titles is None or texts is None:
        lowercase__			       =  titles if texts is None else texts
        return super().__call__(
            _lowerCamelCase   ,							_lowerCamelCase   ,							padding=_lowerCamelCase   ,							truncation=_lowerCamelCase   ,							max_length=_lowerCamelCase   ,							return_tensors=_lowerCamelCase   ,							return_attention_mask=_lowerCamelCase   ,							**_lowerCamelCase   ,							)
     lowercase__			       =  titles if not isinstance(_lowerCamelCase   ,							_lowerCamelCase ) else [titles]
     lowercase__			       =  texts if not isinstance(_lowerCamelCase   ,							_lowerCamelCase ) else [texts]
     lowercase__			       =  len(_lowerCamelCase )
     lowercase__			       =  questions if not isinstance(_lowerCamelCase   ,							_lowerCamelCase ) else [questions] * n_passages
     assert len(_lowerCamelCase ) == len(
         _lowerCamelCase ), f'''There should be as many titles than texts but got {len(_lowerCamelCase )} titles and {len(_lowerCamelCase )} texts.'''
     lowercase__			       =  super().__call__(_lowerCamelCase   ,							_lowerCamelCase   ,							padding=_lowerCamelCase   ,							truncation=_lowerCamelCase )['''input_ids''']
     lowercase__			       =  super().__call__(_lowerCamelCase   ,							add_special_tokens=_lowerCamelCase   ,							padding=_lowerCamelCase   ,							truncation=_lowerCamelCase )['''input_ids''']
     lowercase__			       =  {
         '''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(_lowerCamelCase   ,							_lowerCamelCase )
         ]
     }
     if return_attention_mask is not False:
        lowercase__			       =  []
        for input_ids in encoded_inputs["input_ids"]:
           attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
        lowercase__			       =  attention_mask
     return self.pad(_lowerCamelCase   ,							padding=_lowerCamelCase   ,							max_length=_lowerCamelCase   ,							return_tensors=_lowerCamelCase )







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :List[str]   ,							_lowercase  :List[Any]   ,							_lowercase  :Tuple = 16   ,							_lowercase  :Dict = 64   ,							_lowercase  :Any = 4   ,							):






     '''simple docstring'''





     lowercase__			       =  reader_input['''input_ids''']
     lowercase__			       =  reader_output[:3]
     lowercase__			       =  len(_lowerCamelCase )
     lowercase__			       =  sorted(range(_lowerCamelCase )   ,							reverse=_lowerCamelCase   ,							key=relevance_logits.__getitem__ )
     lowercase__			       =  []
     for doc_id in sorted_docs:
        lowercase__			       =  list(input_ids[doc_id] )
        # assuming question & title information is at the beginning of the sequence
        lowercase__			       =  sequence_ids.index(self.sep_token_id   ,							2 ) + 1  # second sep id
        if sequence_ids[-1] == self.pad_token_id:
           lowercase__			       =  sequence_ids.index(self.pad_token_id )
        else:
           lowercase__			       =  len(_lowerCamelCase )

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







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :List[Any]   ,							_lowercase  :Optional[int]   ,							_lowercase  :List[str]   ,							_lowercase  :int   ,							):






     '''simple docstring'''





     lowercase__			       =  []
     for start_index, start_score in enumerate(_lowerCamelCase ):
        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) )
     lowercase__			       =  sorted(_lowerCamelCase   ,							key=lambda _lowercase : x[1]   ,							reverse=_lowerCamelCase )
     lowercase__			       =  []
     for (start_index, end_index), score in scores:
        assert start_index <= end_index, f'''Wrong span indices: [{start_index}:{end_index}]'''
        lowercase__			       =  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(_lowerCamelCase ) == top_spans:
           break
     return chosen_span_intervals





@add_end_docstrings(a__ )
class  lowerCAmelCase							(       a__							,							a__ ):
  __lowerCamelCase		=      VOCAB_FILES_NAMES
  __lowerCamelCase		=      READER_PRETRAINED_VOCAB_FILES_MAP
  __lowerCamelCase		=      READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCamelCase		=      READER_PRETRAINED_INIT_CONFIGURATION
  __lowerCamelCase		=      ['input_ids', 'attention_mask']
  __lowerCamelCase		=      DPRReaderTokenizer













from __future__ import annotations

from collections import deque





class  lowerCAmelCase							:







  def __init__(      self  :List[Any]   ,							_lowercase  :list[str] ):






     '''simple docstring'''





     lowercase__			       =  []
     self.adlist.append(
         {"value": "", "next_states": [], "fail_state": 0, "output": []} )

     for keyword in keywords:
        self.add_keyword(_lowercase )
     self.set_fail_transitions()







  def   UpperCAmelCase					(      self  :str   ,							_lowercase  :int   ,							_lowercase  :str ):






     '''simple docstring'''





     for state in self.adlist[current_state]["next_states"]:
        if char == self.adlist[state]["value"]:
           return state
     return None







  def   UpperCAmelCase					(      self  :List[str]   ,							_lowercase  :str ):






     '''simple docstring'''





     lowercase__			       =  0
     for character in keyword:
        lowercase__			       =  self.find_next_state(_lowercase   ,							_lowercase )
        if next_state is None:
           self.adlist.append(
               {
                   "value": character,
                   "next_states": [],
                   "fail_state": 0,
                   "output": [],
               } )
           self.adlist[current_state]["next_states"].append(len(self.adlist ) - 1 )
           lowercase__			       =  len(self.adlist ) - 1
        else:
           lowercase__			       =  next_state
     self.adlist[current_state]["output"].append(_lowercase )







  def   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





     lowercase__			       =  deque()
     for node in self.adlist[0]["next_states"]:
        q.append(_lowercase )
        lowercase__			       =  0
     while q:
        lowercase__			       =  q.popleft()
        for child in self.adlist[r]["next_states"]:
           q.append(_lowercase )
           lowercase__			       =  self.adlist[r]["fail_state"]
           while (
               self.find_next_state(_lowercase   ,							self.adlist[child]["value"] ) is None
               and state != 0
           ):
              lowercase__			       =  self.adlist[state]["fail_state"]
           lowercase__			       =  self.find_next_state(
               _lowercase   ,							self.adlist[child]["value"] )
           if self.adlist[child]["fail_state"] is None:
              lowercase__			       =  0
           lowercase__			       =  (
               self.adlist[child]["output"]
               + self.adlist[self.adlist[child]["fail_state"]]["output"]
           )







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :str ):






     '''simple docstring'''





     lowercase__			       =  {}  # returns a dict with keywords and list of its occurrences
     lowercase__			       =  0
     for i in range(len(_lowercase ) ):
        while (
            self.find_next_state(_lowercase   ,							string[i] ) is None
            and current_state != 0
        ):
           lowercase__			       =  self.adlist[current_state]["fail_state"]
        lowercase__			       =  self.find_next_state(_lowercase   ,							string[i] )
        if next_state is None:
           lowercase__			       =  0
        else:
           lowercase__			       =  next_state
           for key in self.adlist[current_state]["output"]:
              if key not in result:
                 lowercase__			       =  []
              result[key].append(i - len(_lowercase ) + 1 )
     return result


if __name__ == "__main__":
  import doctest

  doctest.testmod()






