

"""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


SCREAMING_SNAKE_CASE_     :			Any	  =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_     :			Tuple	  =			{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_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'''
        ),
    },
}
SCREAMING_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'''
        ),
    },
}
SCREAMING_SNAKE_CASE_     :			Dict	  =			{
    '''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'''
        ),
    },
}

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


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


class 		_A       (			__a					):
      __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 		_A       (			__a					):
      __a								=	VOCAB_FILES_NAMES
      __a								=	QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      __a								=	QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __a								=	QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION






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

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


SCREAMING_SNAKE_CASE_     :			Optional[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(__a					)
class 		_A       :





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





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = 16    ,						SCREAMING_SNAKE_CASE__ = 64    ,						SCREAMING_SNAKE_CASE__ = 4    ,						)   ->					List[DPRSpanPrediction]:
         lowerCamelCase__						=			reader_input["input_ids"]
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			reader_output[:3]
         lowerCamelCase__						=			len(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			sorted(range(SCREAMING_SNAKE_CASE__		)    ,						reverse=SCREAMING_SNAKE_CASE__    ,						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(SCREAMING_SNAKE_CASE__		)

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





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						)   ->					List[DPRSpanPrediction]:
         lowerCamelCase__						=			[]
         for start_index, start_score in enumerate(SCREAMING_SNAKE_CASE__		):
            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(SCREAMING_SNAKE_CASE__    ,						key=lambda SCREAMING_SNAKE_CASE__		: x[1]    ,						reverse=SCREAMING_SNAKE_CASE__		)
         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(SCREAMING_SNAKE_CASE__		) == top_spans:
               break
         return chosen_span_intervals






@add_end_docstrings(__a					)
class 		_A       (			__a				,       __a					):
      __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']







"""simple docstring"""




def 	UpperCAmelCase__   (				A__   )      ->      list[int]:




   """simple docstring"""

   if num <= 0:
      raise ValueError("Input must be a positive integer"   )

   lowerCamelCase__						=			[True] * (num + 1)

   lowerCamelCase__						=			2
   while p * p <= num:
      if primes[p]:
         for i in range(p * p	,    num + 1	,    A__   ):
            lowerCamelCase__						=			False
      p += 1

   return [prime for prime in range(2	,    num + 1   ) if primes[prime]]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			int(input('''Enter a positive integer: ''').strip())
     print(prime_sieve_eratosthenes(user_num))




