


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


snake_case							:       Dict										=	logging.get_logger(__name__)

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

snake_case							:       List[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'''
        ),
    },
}
snake_case							:       Any										=	{
    '''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							:       List[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'''
        ),
    },
}

snake_case							:       Optional[Any]										=	{
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_12,
}
snake_case							:       int										=	{
    '''facebook/dpr-question_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-question_encoder-multiset-base''': 5_12,
}
snake_case							:       Union[str, Any]										=	{
    '''facebook/dpr-reader-single-nq-base''': 5_12,
    '''facebook/dpr-reader-multiset-base''': 5_12,
}


snake_case							:       List[str]										=	{
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
snake_case							:       Union[str, Any]										=	{
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
snake_case							:       Dict										=	{
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE__ =			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE__ =			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE__ =			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE__ =			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE__ =			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE__ =			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION







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

snake_case							:       Optional[Any]										=	collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


snake_case							:       Optional[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(_snake_case				)
class 						_snake_case    :




 def __call__(						self   ,    _lowerCamelCase   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = False   ,    _lowerCamelCase = False   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    **_lowerCamelCase   ,    ):
       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		:Any									=			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		:List[Any]									=			titles if not isinstance(_lowerCamelCase   ,    _lowerCamelCase       ) else [titles]
       a		:List[str]									=			texts if not isinstance(_lowerCamelCase   ,    _lowerCamelCase       ) else [texts]
       a		:int									=			len(_lowerCamelCase       )
       a		:Dict									=			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		:Tuple									=			super().__call__(_lowerCamelCase   ,    _lowerCamelCase   ,    padding=_lowerCamelCase   ,    truncation=_lowerCamelCase       )['''input_ids''']
       a		:List[str]									=			super().__call__(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase   ,    padding=_lowerCamelCase   ,    truncation=_lowerCamelCase       )['''input_ids''']
       a		:List[Any]									=			{
           '''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		:Union[str, 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]       )
             a		:List[str]									=			attention_mask
       return self.pad(_lowerCamelCase   ,    padding=_lowerCamelCase   ,    max_length=_lowerCamelCase   ,    return_tensors=_lowerCamelCase       )




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

             a		: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=_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 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    ):
       a		:Union[str, Any]									=			[]
       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		:Optional[Any]									=			sorted(_lowerCamelCase   ,    key=lambda _lowerCamelCase       : x[1]   ,    reverse=_lowerCamelCase       )
       a		:Dict									=			[]
       for (start_index, end_index), score in scores:
             if start_index > end_index:
                   raise ValueError(F'''Wrong span indices: [{start_index}:{end_index}]'''       )
             a		:Optional[Any]									=			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(_snake_case				)
class 						_snake_case    ( _snake_case		,       _snake_case				):
 SCREAMING_SNAKE_CASE__ =			VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE__ =			READER_PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE__ =			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE__ =			READER_PRETRAINED_INIT_CONFIGURATION
 SCREAMING_SNAKE_CASE__ =			['input_ids', 'attention_mask']









def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      if b == 0:
            return 1
      if (b % 2) == 0:
            return actual_power(UpperCAmelCase_					,    int(b / 2		)		) * actual_power(UpperCAmelCase_					,    int(b / 2		)		)
      else:
            return a * actual_power(UpperCAmelCase_					,    int(b / 2		)		) * actual_power(UpperCAmelCase_					,    int(b / 2		)		)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      if b < 0:
            return 1 / actual_power(UpperCAmelCase_					,    UpperCAmelCase_		)
      return actual_power(UpperCAmelCase_					,    UpperCAmelCase_		)


if __name__ == "__main__":
     print(power(-2, -3))





