
"""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 import BertTokenizer


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


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__				(						lowerCamelCase__	):
     __a						=					VOCAB_FILES_NAMES
     __a						=					CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     __a						=					CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __a						=					CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class       lowerCAmelCase__				(						lowerCamelCase__	):
     __a						=					VOCAB_FILES_NAMES
     __a						=					QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     __a						=					QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __a						=					QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION






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)

    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.
    '''

@add_start_docstrings(lowerCamelCase__	)
class       lowerCAmelCase__				:







     def __call__(							self			:			Optional[int]    ,      _lowerCamelCase			:			Dict    ,      _lowerCamelCase			:			Optional[str] = None    ,      _lowerCamelCase			:			Optional[str] = None    ,      _lowerCamelCase			:			Union[bool, str] = False    ,      _lowerCamelCase			:			Union[bool, str] = False    ,      _lowerCamelCase			:			Optional[int] = None    ,      _lowerCamelCase			:			Optional[Union[str, TensorType]] = None    ,      _lowerCamelCase			:			Optional[bool] = None    ,      **_lowerCamelCase			:			Any    ,      ):






       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:
         _snake_case					     =       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__    ,      )
       _snake_case					     =       titles if not isinstance(lowerCAmelCase__    ,      lowerCAmelCase__			) else [titles]
       _snake_case					     =       texts if not isinstance(lowerCAmelCase__    ,      lowerCAmelCase__			) else [texts]
       _snake_case					     =       len(lowerCAmelCase__			)
       _snake_case					     =       questions if not isinstance(lowerCAmelCase__    ,      lowerCAmelCase__			) else [questions] * n_passages
       if len(lowerCAmelCase__			) != len(lowerCAmelCase__			):
         raise ValueError(
             f'''There should be as many titles than texts but got {len(lowerCAmelCase__			)} titles and {len(lowerCAmelCase__			)} texts.'''			)
       _snake_case					     =       super().__call__(lowerCAmelCase__    ,      lowerCAmelCase__    ,      padding=lowerCAmelCase__    ,      truncation=lowerCAmelCase__			)["""input_ids"""]
       _snake_case					     =       super().__call__(lowerCAmelCase__    ,      add_special_tokens=lowerCAmelCase__    ,      padding=lowerCAmelCase__    ,      truncation=lowerCAmelCase__			)["""input_ids"""]
       _snake_case					     =       {
           """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:
         _snake_case					     =       []
         for input_ids in encoded_inputs["input_ids"]:
           attention_mask.append([int(input_id != self.pad_token_id			) for input_id in input_ids]			)
         _snake_case					     =       attention_mask
       return self.pad(lowerCAmelCase__    ,      padding=lowerCAmelCase__    ,      max_length=lowerCAmelCase__    ,      return_tensors=lowerCAmelCase__			)







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

         _snake_case					     =       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      lowercase	(							self			:			int    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			int    ,      ):
       _snake_case					     =       []
       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)			)
       _snake_case					     =       sorted(lowerCAmelCase__    ,      key=lambda _lowerCamelCase			: x[1]    ,      reverse=lowerCAmelCase__			)
       _snake_case					     =       []
       for (start_index, end_index), score in scores:
         if start_index > end_index:
           raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''			)
         _snake_case					     =       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(lowerCAmelCase__			) == top_spans:
           break
       return chosen_span_intervals






@add_end_docstrings(lowerCamelCase__	)
class       lowerCAmelCase__				(						lowerCamelCase__ ,	lowerCamelCase__	):
     __a						=					VOCAB_FILES_NAMES
     __a						=					READER_PRETRAINED_VOCAB_FILES_MAP
     __a						=					READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __a						=					READER_PRETRAINED_INIT_CONFIGURATION
     __a						=					['''input_ids''', '''attention_mask''']








'''simple docstring'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   int					):
		assert (
		    isinstance(_lowerCamelCase       ,      _lowerCamelCase					) and number_of_steps > 0
		), F"number_of_steps needs to be positive integer, your input {number_of_steps}"
		if number_of_steps == 1:
				return 1
		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Dict           =		1, 1
		for _ in range(number_of_steps - 1					):
				__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Tuple           =		current + previous, current
		return current


if __name__ == "__main__":
						import doctest

						doctest.testmod()