'''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


_lowerCAmelCase  :Any  				=    logging.get_logger(__name__)

_lowerCAmelCase  :List[Any]  				=    {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowerCAmelCase  :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"""
        ),
    },
}
_lowerCAmelCase  :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"""
        ),
    },
}
_lowerCAmelCase  :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"""
        ),
    },
}

_lowerCAmelCase  :str  				=    {
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
_lowerCAmelCase  :Union[str, Any]  				=    {
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
_lowerCAmelCase  :Optional[Any]  				=    {
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


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






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Union[str, Any]							=						VOCAB_FILES_NAMES
    snake_case__							:   Tuple							=						CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    snake_case__							:   List[Any]							=						CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__							:   Tuple							=						CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   str							=						VOCAB_FILES_NAMES
    snake_case__							:   Tuple							=						QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    snake_case__							:   Tuple							=						QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__							:   int							=						QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION






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

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


_lowerCAmelCase  :Union[str, 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(_SCREAMING_SNAKE_CASE )
class 				UpperCAmelCase :




    '''simple docstring'''







    def __call__(       self    ,							lowercase__    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = False    ,							lowercase__ = False    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							**lowercase__    ,							)							-> BatchEncoding:
         if titles is None and texts is None:
              return super().__call__(
                  lowercase__    ,							padding=lowercase__    ,							truncation=lowercase__    ,							max_length=lowercase__    ,							return_tensors=lowercase__    ,							return_attention_mask=lowercase__    ,							**lowercase__    ,							)
         elif titles is None or texts is None:
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       titles if texts is None else texts
              return super().__call__(
                  lowercase__    ,							lowercase__    ,							padding=lowercase__    ,							truncation=lowercase__    ,							max_length=lowercase__    ,							return_tensors=lowercase__    ,							return_attention_mask=lowercase__    ,							**lowercase__    ,							)
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       titles if not isinstance(lowercase__    ,							lowercase__ ) else [titles]
         SCREAMING_SNAKE_CASE					:			Dict    	=       texts if not isinstance(lowercase__    ,							lowercase__ ) else [texts]
         SCREAMING_SNAKE_CASE					:			Any    	=       len(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       questions if not isinstance(lowercase__    ,							lowercase__ ) else [questions] * n_passages
         if len(lowercase__ ) != len(lowercase__ ):
              raise ValueError(
                  F"""There should be as many titles than texts but got {len(lowercase__ )} titles and {len(lowercase__ )} texts.""" )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       super().__call__(lowercase__    ,							lowercase__    ,							padding=lowercase__    ,							truncation=lowercase__ )['input_ids']
         SCREAMING_SNAKE_CASE					:			List[str]    	=       super().__call__(lowercase__    ,							add_special_tokens=lowercase__    ,							padding=lowercase__    ,							truncation=lowercase__ )['input_ids']
         SCREAMING_SNAKE_CASE					:			Union[str, 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(lowercase__    ,							lowercase__ )
             ]
         }
         if return_attention_mask is not False:
              SCREAMING_SNAKE_CASE					:			int    	=       []
              for input_ids in encoded_inputs["input_ids"]:
                   attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       attention_mask
         return self.pad(lowercase__    ,							padding=lowercase__    ,							max_length=lowercase__    ,							return_tensors=lowercase__ )






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

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






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							)							-> List[DPRSpanPrediction]:
         SCREAMING_SNAKE_CASE					:			Dict    	=       []
         for start_index, start_score in enumerate(lowercase__ ):
              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) )
         SCREAMING_SNAKE_CASE					:			int    	=       sorted(lowercase__    ,							key=lambda lowercase__ : x[1]    ,							reverse=lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       []
         for (start_index, end_index), score in scores:
              if start_index > end_index:
                   raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]""" )
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       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(lowercase__ ) == top_spans:
                   break
         return chosen_span_intervals






@add_end_docstrings(_SCREAMING_SNAKE_CASE )
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE   ,    _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   int							=						VOCAB_FILES_NAMES
    snake_case__							:   Any							=						READER_PRETRAINED_VOCAB_FILES_MAP
    snake_case__							:   List[Any]							=						READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__							:   Optional[int]							=						READER_PRETRAINED_INIT_CONFIGURATION
    snake_case__							:   int							=						["input_ids", "attention_mask"]




'''simple docstring'''





def     __lowerCAmelCase						(       a_ = 1000		)      ->	int:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       2**power
     SCREAMING_SNAKE_CASE					:			Any    	=       str(a_		)
     SCREAMING_SNAKE_CASE					:			int    	=       list(a_		)
     SCREAMING_SNAKE_CASE					:			Any    	=       0

     for i in list_num:
          sum_of_num += int(a_		)

     return sum_of_num


if __name__ == "__main__":
 _lowerCAmelCase  :Tuple  				=    int(input("""Enter the power of 2: """).strip())
 print("""2 ^ """, power, """ = """, 2**power)
 _lowerCAmelCase  :int  				=    solution(power)
 print("""Sum of the digits is: """, result)



