






'''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	:			Optional[int]         =logging.get_logger(__name__)

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

_A	:			Tuple         ={
    '''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	:			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	:			Optional[int]         ={
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
_A	:			Any         ={
    '''facebook/dpr-question_encoder-single-nq-base''': 512,
    '''facebook/dpr-question_encoder-multiset-base''': 512,
}
_A	:			Union[str, Any]         ={
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


_A	:			str         ={
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
_A	:			Any         ={
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
_A	:			Dict         ={
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}

class _lowercase	(				_lowercase		):
							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 _lowercase	(				_lowercase		):
							a				      =       VOCAB_FILES_NAMES
							a				      =       QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							a				      =       QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a				      =       QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



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

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


_A	:			str         =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 _lowercase	:




							def __call__(							self:				List[str]			,							UpperCamelCase__:				Union[str, Any]			,							UpperCamelCase__:				Optional[str] = None			,							UpperCamelCase__:				Optional[str] = None			,							UpperCamelCase__:				Union[bool, str] = False			,							UpperCamelCase__:				Union[bool, str] = False			,							UpperCamelCase__:				Optional[int] = None			,							UpperCamelCase__:				Optional[Union[str, TensorType]] = None			,							UpperCamelCase__:				Optional[bool] = None			,							**UpperCamelCase__:				int			,							):
														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__					:	List[str]         =       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__					:	Union[str, Any]         =       titles if not isinstance(UpperCamelCase__			,							UpperCamelCase__     ) else [titles]
														lowerCamelCase__					:	Any         =       texts if not isinstance(UpperCamelCase__			,							UpperCamelCase__     ) else [texts]
														lowerCamelCase__					:	str         =       len(UpperCamelCase__     )
														lowerCamelCase__					:	Any         =       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__					:	Optional[Any]         =       super().__call__(UpperCamelCase__			,							UpperCamelCase__			,							padding=UpperCamelCase__			,							truncation=UpperCamelCase__     )["""input_ids"""]
														lowerCamelCase__					:	Union[str, Any]         =       super().__call__(UpperCamelCase__			,							add_special_tokens=UpperCamelCase__			,							padding=UpperCamelCase__			,							truncation=UpperCamelCase__     )["""input_ids"""]
														lowerCamelCase__					:	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(UpperCamelCase__			,							UpperCamelCase__     )
														    ]
														}
														if return_attention_mask is not False:
																					lowerCamelCase__					:	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]     )
																					lowerCamelCase__					:	List[Any]         =       attention_mask
														return self.pad(UpperCamelCase__			,							padding=UpperCamelCase__			,							max_length=UpperCamelCase__			,							return_tensors=UpperCamelCase__     )




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

																					lowerCamelCase__					:	Union[str, 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=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 		lowerCamelCase_				(							self:				Optional[int]			,							UpperCamelCase__:				List[int]			,							UpperCamelCase__:				List[int]			,							UpperCamelCase__:				int			,							UpperCamelCase__:				int			,							):
														lowerCamelCase__					:	List[str]         =       []
														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__					:	Optional[Any]         =       sorted(UpperCamelCase__			,							key=lambda UpperCamelCase__     : x[1]			,							reverse=UpperCamelCase__     )
														lowerCamelCase__					:	List[Any]         =       []
														for (start_index, end_index), score in scores:
																					if start_index > end_index:
																												raise ValueError(F'''Wrong span indices: [{start_index}:{end_index}]'''     )
																					lowerCamelCase__					:	List[str]         =       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 _lowercase	(				_lowercase				,      _lowercase		):
							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"""]








from math import loga



def A_	(		A__				)  ->		int:
       if a < 0:
              raise ValueError('Input value must be a positive integer'				)
       elif isinstance(A__		,      A__				):
              raise TypeError('Input value must be a \'int\' type'				)
       return 0 if (a == 0) else int(loga(a & -a				)				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


