



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_		:     Optional[Any]        =     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_		:     List[str]        =     {
    '''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_		:     Optional[Any]        =     {
    '''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_		:     str        =     {
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_12,
}
A_		:     Dict        =     {
    '''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_		:     Optional[Any]        =     {
    '''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_		:     int        =     {
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			VOCAB_FILES_NAMES
   _UpperCAmelCase     	=			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   _UpperCAmelCase     	=			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCAmelCase     	=			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			VOCAB_FILES_NAMES
   _UpperCAmelCase     	=			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   _UpperCAmelCase     	=			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCAmelCase     	=			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION

A_		:     Optional[int]        =     collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

A_		:     Union[str, Any]        =     collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


A_		:     List[Any]        =     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(UpperCAmelCase__	)
class 		_lowercase		:

   def __call__(      self			:       Tuple							,   __lowerCAmelCase			:       List[Any]							,   __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			:       int							,   )       ->				BatchEncoding:


    """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:
     a						=		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							,   )
    a						=		titles if not isinstance(__lowerCAmelCase							,   __lowerCAmelCase							) else [titles]
    a						=		texts if not isinstance(__lowerCAmelCase							,   __lowerCAmelCase							) else [texts]
    a						=		len(__lowerCAmelCase							)
    a						=		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."""							)
    a						=		super().__call__(__lowerCAmelCase							,   __lowerCAmelCase							,   padding=__lowerCAmelCase							,   truncation=__lowerCAmelCase							)["input_ids"]
    a						=		super().__call__(__lowerCAmelCase							,   add_special_tokens=__lowerCAmelCase							,   padding=__lowerCAmelCase							,   truncation=__lowerCAmelCase							)["input_ids"]
    a						=		{
        "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:
     a						=		[]
     for input_ids in encoded_inputs["input_ids"]:
      attention_mask.append([int(input_id != self.pad_token_id							) for input_id in input_ids]							)
     a						=		attention_mask
    return self.pad(__lowerCAmelCase							,   padding=__lowerCAmelCase							,   max_length=__lowerCAmelCase							,   return_tensors=__lowerCAmelCase							)

   def 		A							(      self			:       Optional[int]							,   __lowerCAmelCase			:       BatchEncoding							,   __lowerCAmelCase			:       DPRReaderOutput							,   __lowerCAmelCase			:       int = 16							,   __lowerCAmelCase			:       int = 64							,   __lowerCAmelCase			:       int = 4							,   )       ->				List[DPRSpanPrediction]:


    """simple docstring"""






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

     a						=		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 		A							(      self			:       List[str]							,   __lowerCAmelCase			:       List[int]							,   __lowerCAmelCase			:       List[int]							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int							,   )       ->				List[DPRSpanPrediction]:


    """simple docstring"""






    a						=		[]
    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)							)
    a						=		sorted(__lowerCAmelCase							,   key=lambda __lowerCAmelCase							: x[1]							,   reverse=__lowerCAmelCase							)
    a						=		[]
    for (start_index, end_index), score in scores:
     if start_index > end_index:
      raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]"""							)
     a						=		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(UpperCAmelCase__	)
class 		_lowercase		(       UpperCAmelCase__,   UpperCAmelCase__	):
   _UpperCAmelCase     	=			VOCAB_FILES_NAMES
   _UpperCAmelCase     	=			READER_PRETRAINED_VOCAB_FILES_MAP
   _UpperCAmelCase     	=			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCAmelCase     	=			READER_PRETRAINED_INIT_CONFIGURATION
   _UpperCAmelCase     	=			['''input_ids''', '''attention_mask''']






from __future__ import annotations


def 	UpperCAmelCase__      (      UpperCAmelCase__ :int					):






 '''simple docstring'''

 a						=		str(UpperCAmelCase__					)
 return len(UpperCAmelCase__					) == 9 and set(UpperCAmelCase__					) == set("123456789"					)


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 for base_num in range(99_99 ,					49_99 ,					-1					):
  a						=		10_00_02 * base_num
  if is_9_pandigital(UpperCAmelCase__					):
   return candidate

 for base_num in range(3_33 ,					99 ,					-1					):
  a						=		1_00_20_03 * base_num
  if is_9_pandigital(UpperCAmelCase__					):
   return candidate

 return None


if __name__ == "__main__":
     print(F"""{solution() = }""")

