


'''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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


_lowercase      :      Tuple            =					logging.get_logger(__name__)

_lowercase      :      Tuple            =					{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowercase      :      Dict            =					{
    """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"""
        ),
    },
}
_lowercase      :      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"""
        ),
    },
}
_lowercase      :      Optional[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"""
        ),
    },
}

_lowercase      :      Dict            =					{
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
_lowercase      :      str            =					{
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
_lowercase      :      Optional[int]            =					{
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


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






class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				Any  		=	VOCAB_FILES_NAMES
				__magic_name__    :				Optional[Any]  		=	CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				__magic_name__    :				Optional[int]  		=	CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__magic_name__    :				Union[str, Any]  		=	CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
				__magic_name__    :				Any  		=	DPRContextEncoderTokenizer







class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				List[str]  		=	VOCAB_FILES_NAMES
				__magic_name__    :				Union[str, Any]  		=	QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				__magic_name__    :				Optional[int]  		=	QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__magic_name__    :				Tuple  		=	QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
				__magic_name__    :				Union[str, Any]  		=	DPRQuestionEncoderTokenizer







_lowercase      :      List[Any]            =					collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

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


_lowercase      :      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)

    Return:
        `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 	UpperCamelCase__:




				def __call__(		self      :			Dict				,   lowerCAmelCase      :			Optional[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      :			Union[str, Any]				,   )->					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:
																UpperCAmelCase					    =     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				,   )
										UpperCAmelCase					    =     titles if not isinstance(lowerCAmelCase				,   lowerCAmelCase				) else [titles]
										UpperCAmelCase					    =     texts if not isinstance(lowerCAmelCase				,   lowerCAmelCase				) else [texts]
										UpperCAmelCase					    =     len(lowerCAmelCase				)
										UpperCAmelCase					    =     questions if not isinstance(lowerCAmelCase				,   lowerCAmelCase				) else [questions] * n_passages
										assert len(lowerCAmelCase				) == len(
										    lowerCAmelCase				), F"""There should be as many titles than texts but got {len(lowerCAmelCase				)} titles and {len(lowerCAmelCase				)} texts."""
										UpperCAmelCase					    =     super().__call__(lowerCAmelCase				,   lowerCAmelCase				,   padding=lowerCAmelCase				,   truncation=lowerCAmelCase				)['''input_ids''']
										UpperCAmelCase					    =     super().__call__(lowerCAmelCase				,   add_special_tokens=lowerCAmelCase				,   padding=lowerCAmelCase				,   truncation=lowerCAmelCase				)['''input_ids''']
										UpperCAmelCase					    =     {
										    '''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:
																UpperCAmelCase					    =     []
																for input_ids in encoded_inputs["input_ids"]:
																						attention_mask.append([int(input_id != self.pad_token_id				) for input_id in input_ids]				)
																UpperCAmelCase					    =     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"""




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

																UpperCAmelCase					    =     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      :			Any				,   lowerCAmelCase      :			List[int]				,   lowerCAmelCase      :			List[int]				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			int				,   )->					List[DPRSpanPrediction]:

										"""simple docstring"""




										UpperCAmelCase					    =     []
										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)				)
										UpperCAmelCase					    =     sorted(lowerCAmelCase				,   key=lambda lowerCAmelCase				: x[1]				,   reverse=lowerCAmelCase				)
										UpperCAmelCase					    =     []
										for (start_index, end_index), score in scores:
																assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
																UpperCAmelCase					    =     end_index - start_index + 1
																assert length <= max_answer_length, 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 	UpperCamelCase__(       lowerCAmelCase						,    lowerCAmelCase	):
				__magic_name__    :				Tuple  		=	VOCAB_FILES_NAMES
				__magic_name__    :				List[Any]  		=	READER_PRETRAINED_VOCAB_FILES_MAP
				__magic_name__    :				Tuple  		=	READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__magic_name__    :				Any  		=	READER_PRETRAINED_INIT_CONFIGURATION
				__magic_name__    :				List[str]  		=	["input_ids", "attention_mask"]
				__magic_name__    :				str  		=	DPRReaderTokenizer









'''simple docstring'''



_lowercase      :      Any            =					range(2, 20 + 1)
_lowercase      :      str            =					[10**k for k in range(ks[-1] + 1)]
_lowercase      :      dict[int, dict[int, list[list[int]]]]            =					{}


def 				lowerCamelCase__				(	A       :      int				,       A       :      str				,       A       :      List[Any]				,       A       :      Union[str, Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     sum(a_i[j] for j in range(A				,       len(A					)					)					)
						UpperCAmelCase					    =     sum(a_i[j] * base[j] for j in range(min(len(A					)				,       A					)					)					)

						UpperCAmelCase    ,    UpperCAmelCase					    =     0, 0
						UpperCAmelCase					    =     n - i

						UpperCAmelCase					    =     memo.get(A					)

						if sub_memo is not None:
												UpperCAmelCase					    =     sub_memo.get(A					)

												if jumps is not None and len(A					) > 0:
																		# find and make the largest jump without going over
																		UpperCAmelCase					    =     -1
																		for _k in range(len(A					) - 1				,       -1				,       -1					):
																								if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
																														UpperCAmelCase					    =     _k
																														break

																		if max_jump >= 0:
																								UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     jumps[max_jump]
																								# since the difference between jumps is cached, add c
																								UpperCAmelCase					    =     diff + c
																								for j in range(min(A				,       len(A					)					)					):
																														UpperCAmelCase    ,    UpperCAmelCase					    =     divmod(A				,       10					)
																								if new_c > 0:
																														add(A				,       A				,       A					)

												else:
																		UpperCAmelCase					    =     []
						else:
												UpperCAmelCase					    =     {c: []}
												UpperCAmelCase					    =     sub_memo

						if dn >= max_dn or c + diff >= base[k]:
												return diff, dn

						if k > ks[0]:
												while True:
																		# keep doing smaller jumps
																		UpperCAmelCase    ,    UpperCAmelCase					    =     next_term(A				,       k - 1				,       i + dn				,       A					)
																		diff += _diff
																		dn += terms_jumped

																		if dn >= max_dn or c + diff >= base[k]:
																								break
						else:
												# would be too small a jump, just compute sequential terms instead
												UpperCAmelCase    ,    UpperCAmelCase					    =     compute(A				,       A				,       i + dn				,       A					)
												diff += _diff
												dn += terms_jumped

						UpperCAmelCase					    =     sub_memo[c]

						# keep jumps sorted by # of terms skipped
						UpperCAmelCase					    =     0
						while j < len(A					):
												if jumps[j][1] > dn:
																		break
												j += 1

						# cache the jump for this value digitsum(b) and c
						sub_memo[c].insert(A				,       (diff, dn, k)					)
						return (diff, dn)


def 				lowerCamelCase__				(	A       :      Dict				,       A       :      Optional[int]				,       A       :      List[Any]				,       A       :      int					):




						'''simple docstring'''






						if i >= n:
												return 0, i
						if k > len(A					):
												a_i.extend([0 for _ in range(k - len(A					)					)]					)

						# note: a_i -> b * 10^k + c
						# ds_b -> digitsum(b)
						# ds_c -> digitsum(c)
						UpperCAmelCase					    =     i
						UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     0, 0, 0
						for j in range(len(A					)					):
												if j >= k:
																		ds_b += a_i[j]
												else:
																		ds_c += a_i[j]

						while i < n:
												i += 1
												UpperCAmelCase					    =     ds_c + ds_b
												diff += addend
												UpperCAmelCase					    =     0
												for j in range(A					):
																		UpperCAmelCase					    =     a_i[j] + addend
																		UpperCAmelCase    ,    UpperCAmelCase					    =     divmod(A				,       10					)

																		ds_c += a_i[j]

												if addend > 0:
																		break

						if addend > 0:
												add(A				,       A				,       A					)
						return diff, i - start_i


def 				lowerCamelCase__				(	A       :      List[str]				,       A       :      Optional[int]				,       A       :      Optional[Any]					):




						'''simple docstring'''






						for j in range(A				,       len(A					)					):
												UpperCAmelCase					    =     digits[j] + addend
												if s >= 10:
																		UpperCAmelCase    ,    UpperCAmelCase					    =     divmod(A				,       10					)
																		UpperCAmelCase					    =     addend // 10 + quotient
												else:
																		UpperCAmelCase					    =     s
																		UpperCAmelCase					    =     addend // 10

												if addend == 0:
																		break

						while addend > 0:
												UpperCAmelCase    ,    UpperCAmelCase					    =     divmod(A				,       10					)
												digits.append(A					)


def 				lowerCamelCase__				(	A       :      int = 10**15					):




						'''simple docstring'''






						UpperCAmelCase					    =     [1]
						UpperCAmelCase					    =     1
						UpperCAmelCase					    =     0
						while True:
												UpperCAmelCase    ,    UpperCAmelCase					    =     next_term(A				,       20				,       i + dn				,       A					)
												dn += terms_jumped
												if dn == n - i:
																		break

						UpperCAmelCase					    =     0
						for j in range(len(A					)					):
												a_n += digits[j] * 10**j
						return a_n


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





