


'''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


a_						:       Tuple      			=				logging.get_logger(__name__)

a_						:       List[Any]      			=				{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

a_						:       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_						:       Dict      			=				{
    """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_						:       int      			=				{
    """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""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
a_						:       List[Any]      			=				{
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
a_						:       Any      			=				{
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


a_						:       Dict      			=				{
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
a_						:       int      			=				{
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
a_						:       Optional[int]      			=				{
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						VOCAB_FILES_NAMES
  _lowerCamelCase						=						CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase						=						CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase						=						CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						VOCAB_FILES_NAMES
  _lowerCamelCase						=						QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase						=						QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase						=						QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



a_						:       Dict      			=				collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

a_						:       List[Any]      			=				collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


a_						:       int      			=				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(lowercase    )
class   snake_case					:





  """simple docstring"""




  def __call__(					self    ,						UpperCamelCase    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = False    ,						UpperCamelCase = False    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   if titles is None and texts is None:
    return super().__call__(
        UpperCamelCase    ,						padding=UpperCamelCase    ,						truncation=UpperCamelCase    ,						max_length=UpperCamelCase    ,						return_tensors=UpperCamelCase    ,						return_attention_mask=UpperCamelCase    ,						**UpperCamelCase    ,						)
   elif titles is None or texts is None:
    lowerCamelCase_  				=						titles if texts is None else texts
    return super().__call__(
        UpperCamelCase    ,						UpperCamelCase    ,						padding=UpperCamelCase    ,						truncation=UpperCamelCase    ,						max_length=UpperCamelCase    ,						return_tensors=UpperCamelCase    ,						return_attention_mask=UpperCamelCase    ,						**UpperCamelCase    ,						)
   lowerCamelCase_  				=						titles if not isinstance(UpperCamelCase    ,						UpperCamelCase  ) else [titles]
   lowerCamelCase_  				=						texts if not isinstance(UpperCamelCase    ,						UpperCamelCase  ) else [texts]
   lowerCamelCase_  				=						len(UpperCamelCase  )
   lowerCamelCase_  				=						questions if not isinstance(UpperCamelCase    ,						UpperCamelCase  ) else [questions] * n_passages
   if len(UpperCamelCase  ) != len(UpperCamelCase  ):
    raise ValueError(
        f'''There should be as many titles than texts but got {len(UpperCamelCase  )} titles and {len(UpperCamelCase  )} texts.'''  )
   lowerCamelCase_  				=						super().__call__(UpperCamelCase    ,						UpperCamelCase    ,						padding=UpperCamelCase    ,						truncation=UpperCamelCase  )["input_ids"]
   lowerCamelCase_  				=						super().__call__(UpperCamelCase    ,						add_special_tokens=UpperCamelCase    ,						padding=UpperCamelCase    ,						truncation=UpperCamelCase  )["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(UpperCamelCase    ,						UpperCamelCase  )
       ]
   }
   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(UpperCamelCase    ,						padding=UpperCamelCase    ,						max_length=UpperCamelCase    ,						return_tensors=UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase = 16    ,						UpperCamelCase = 64    ,						UpperCamelCase = 4    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						reader_input["input_ids"]
   lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=						reader_output[:3]
   lowerCamelCase_  				=						len(UpperCamelCase  )
   lowerCamelCase_  				=						sorted(range(UpperCamelCase  )    ,						reverse=UpperCamelCase    ,						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(UpperCamelCase  )

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







  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						[]
   for start_index, start_score in enumerate(UpperCamelCase  ):
    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(UpperCamelCase    ,						key=lambda UpperCamelCase  : x[1]    ,						reverse=UpperCamelCase  )
   lowerCamelCase_  				=						[]
   for (start_index, end_index), score in scores:
    if start_index > end_index:
     raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''  )
    lowerCamelCase_  				=						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(UpperCamelCase  ) == top_spans:
     break
   return chosen_span_intervals





@add_end_docstrings(lowercase    )
class   snake_case					(					lowercase    ,		lowercase    ):





  """simple docstring"""




  _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"]







'''simple docstring'''

from __future__ import annotations





def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int	):
 if b == 0:
  return (1, 0)
 ((lowerCamelCase_)				,(lowerCamelCase_))  				=						extended_euclid(UpperCAmelCase_      ,       a % b	)
 lowerCamelCase_  				=						a // b
 return (y, x - k * y)





def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int	):
 ((lowerCamelCase_)				,(lowerCamelCase_))  				=						extended_euclid(UpperCAmelCase_      ,       UpperCAmelCase_	)
 lowerCamelCase_  				=						na * na
 lowerCamelCase_  				=						ra * x * na + ra * y * na
 return (n % m + m) % m





def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int	):
 ((lowerCamelCase_)				,(lowerCamelCase_))  				=						extended_euclid(UpperCAmelCase_      ,       UpperCAmelCase_	)
 if b < 0:
  lowerCamelCase_  				=						(b % n + n) % n
 return b





def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int	):
 lowerCamelCase_				,lowerCamelCase_  				=						invert_modulo(UpperCAmelCase_      ,       UpperCAmelCase_	), invert_modulo(UpperCAmelCase_      ,       UpperCAmelCase_	)
 lowerCamelCase_  				=						na * na
 lowerCamelCase_  				=						ra * x * na + ra * y * na
 return (n % m + m) % m


if __name__ == "__main__":
     from doctest import testmod

     testmod(name="""chinese_remainder_theorem""", verbose=True)
     testmod(name="""chinese_remainder_theorem2""", verbose=True)
     testmod(name="""invert_modulo""", verbose=True)
     testmod(name="""extended_euclid""", verbose=True)



