


'''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


UpperCamelCase__    					=      logging.get_logger(__name__)

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

UpperCamelCase__    					=      {
    '''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'''
        ),
    },
}
UpperCamelCase__    					=      {
    '''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'''
        ),
    },
}
UpperCamelCase__    					=      {
    '''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'''
        ),
    },
}

UpperCamelCase__    					=      {
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_1_2,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_1_2,
}
UpperCamelCase__    					=      {
    '''facebook/dpr-question_encoder-single-nq-base''': 5_1_2,
    '''facebook/dpr-question_encoder-multiset-base''': 5_1_2,
}
UpperCamelCase__    					=      {
    '''facebook/dpr-reader-single-nq-base''': 5_1_2,
    '''facebook/dpr-reader-multiset-base''': 5_1_2,
}


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

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

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


UpperCamelCase__    					=      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				:    Any						,						_A				:    List[str]						,						_A				:    Optional[str] = None						,						_A				:    Optional[str] = None						,						_A				:    Union[bool, str] = False						,						_A				:    Union[bool, str] = False						,						_A				:    Optional[int] = None						,						_A				:    Optional[Union[str, TensorType]] = None						,						_A				:    Optional[bool] = None						,						**_A				:    Optional[Any]						,						):


          '''simple docstring'''


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






      def lowercase_							(					self				:    Dict						,						_A				:    BatchEncoding						,						_A				:    DPRReaderOutput						,						_A				:    int = 16						,						_A				:    int = 64						,						_A				:    int = 4						,						):


          '''simple docstring'''


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

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






      def lowercase_							(					self				:    Tuple						,						_A				:    List[int]						,						_A				:    List[int]						,						_A				:    int						,						_A				:    int						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				[]
          for start_index, start_score in enumerate(_A ):
              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) )
          UpperCAmelCase__			:		Tuple													=				sorted(_A						,						key=lambda _A : x[1]						,						reverse=_A )
          UpperCAmelCase__			:		int													=				[]
          for (start_index, end_index), score in scores:
              assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
              UpperCAmelCase__			:		int													=				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(_A ) == 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




'''simple docstring'''
# Algorithm for the pigeonhole sorting







def 					a__       (					lowerCAmelCase__						)    ->	Optional[Any]:
    UpperCAmelCase__			:		Any													=				min(lowerCAmelCase__						)  # min() finds the minimum value
    UpperCAmelCase__			:		Optional[int]													=				max(lowerCAmelCase__						)  # max() finds the maximum value

    UpperCAmelCase__			:		int													=				max_val - min_val + 1  # size is difference of max and min values plus one

    # list of pigeonholes of size equal to the variable size
    UpperCAmelCase__			:		Any													=				[0] * size

    # Populate the pigeonholes.
    for x in a:
        assert isinstance(lowerCAmelCase__  ,					lowerCAmelCase__						), "integers only please"
        holes[x - min_val] += 1

    # Putting the elements back into the array in an order.
    UpperCAmelCase__			:		Optional[int]													=				0
    for count in range(lowerCAmelCase__						):
        while holes[count] > 0:
            holes[count] -= 1
            UpperCAmelCase__			:		Dict													=				count + min_val
            i += 1





def 					a__       (					)    ->	Union[str, Any]:
    UpperCAmelCase__			:		List[str]													=				[8, 3, 2, 7, 4, 6, 8]
    pigeonhole_sort(lowerCAmelCase__						)
    print('''Sorted order is:'''  ,					''' '''.join(lowerCAmelCase__						)						)


if __name__ == "__main__":
  main()
