






"""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


_UpperCamelCase		:  int						=		logging.get_logger(__name__)

_UpperCamelCase		:  str						=		{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase		:  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'
        ),
    },
}
_UpperCamelCase		:  Union[str, 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'
        ),
    },
}
_UpperCamelCase		:  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'
        ),
    },
}

_UpperCamelCase		:  str						=		{
    'facebook/dpr-ctx_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-ctx_encoder-multiset-base': 5_1_2,
}
_UpperCamelCase		:  Union[str, Any]						=		{
    'facebook/dpr-question_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-question_encoder-multiset-base': 5_1_2,
}
_UpperCamelCase		:  Dict						=		{
    'facebook/dpr-reader-single-nq-base': 5_1_2,
    'facebook/dpr-reader-multiset-base': 5_1_2,
}


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

class        a     (   a_	):
    UpperCAmelCase_ :     Optional[int]	     =VOCAB_FILES_NAMES
    UpperCAmelCase_ :     Optional[Any]	     =CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase_ :     Tuple	     =CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase_ :     Any	     =CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
    UpperCAmelCase_ :     Tuple	     =DPRContextEncoderTokenizer


class        a     (   a_	):
    UpperCAmelCase_ :     Union[str, Any]	     =VOCAB_FILES_NAMES
    UpperCAmelCase_ :     Optional[int]	     =QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase_ :     List[Any]	     =QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase_ :     Union[str, Any]	     =QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
    UpperCAmelCase_ :     str	     =DPRQuestionEncoderTokenizer


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

_UpperCamelCase		:  Dict						=		collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


_UpperCamelCase		:  Any						=		R'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '

@add_start_docstrings(a_	)
class        a     :







    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:
        lowercase							    =       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							,		)
      lowercase							    =       titles if not isinstance(_lowerCamelCase							,		_lowerCamelCase       ) else [titles]
      lowercase							    =       texts if not isinstance(_lowerCamelCase							,		_lowerCamelCase       ) else [texts]
      lowercase							    =       len(_lowerCamelCase       )
      lowercase							    =       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.'
      lowercase							    =       super().__call__(_lowerCamelCase							,		_lowerCamelCase							,		padding=_lowerCamelCase							,		truncation=_lowerCamelCase       )['input_ids']
      lowercase							    =       super().__call__(_lowerCamelCase							,		add_special_tokens=_lowerCamelCase							,		padding=_lowerCamelCase							,		truncation=_lowerCamelCase       )['input_ids']
      lowercase							    =       {
          '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:
        lowercase							    =       []
        for input_ids in encoded_inputs["input_ids"]:
          attention_mask.append([int(input_id != self.pad_token_id       ) for input_id in input_ids]       )
        lowercase							    =       attention_mask
      return self.pad(_lowerCamelCase							,		padding=_lowerCamelCase							,		max_length=_lowerCamelCase							,		return_tensors=_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = 1_6							,		_lowerCamelCase = 6_4							,		_lowerCamelCase = 4							,		):
      lowercase							    =       reader_input['input_ids']
      lowercase    ,     lowercase    ,     lowercase							    =       reader_output[:3]
      lowercase							    =       len(_lowerCamelCase       )
      lowercase							    =       sorted(range(_lowerCamelCase       )							,		reverse=_lowerCamelCase							,		key=relevance_logits.__getitem__       )
      lowercase							    =       []
      for doc_id in sorted_docs:
        lowercase							    =       list(input_ids[doc_id]       )
        # assuming question & title information is at the beginning of the sequence
        lowercase							    =       sequence_ids.index(self.sep_token_id							,		2       ) + 1  # second sep id
        if sequence_ids[-1] == self.pad_token_id:
          lowercase							    =       sequence_ids.index(self.pad_token_id       )
        else:
          lowercase							    =       len(_lowerCamelCase       )

        lowercase							    =       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 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		):
      lowercase							    =       []
      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)       )
      lowercase							    =       sorted(_lowerCamelCase							,		key=lambda _lowerCamelCase       : x[1]							,		reverse=_lowerCamelCase       )
      lowercase							    =       []
      for (start_index, end_index), score in scores:
        assert start_index <= end_index, F'Wrong span indices: [{start_index}:{end_index}]'
        lowercase							    =       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(a_	)
class        a     (   a_,				a_	):
    UpperCAmelCase_ :     Tuple	     =VOCAB_FILES_NAMES
    UpperCAmelCase_ :     Union[str, Any]	     =READER_PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase_ :     Optional[Any]	     =READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase_ :     Optional[Any]	     =READER_PRETRAINED_INIT_CONFIGURATION
    UpperCAmelCase_ :     Dict	     =["input_ids", "attention_mask"]
    UpperCAmelCase_ :     List[str]	     =DPRReaderTokenizer










"""simple docstring"""



def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int    ):





  '''simple docstring'''





  return sum(i for i in range(1		,					number // 2 + 1    ) if number % i == 0    ) == number


if __name__ == "__main__":
    print('Program to check whether a number is a Perfect number or not...')
    _UpperCamelCase		:  Tuple						=		int(input('Enter number: ').strip())
    print(F'''{number} is {'' if perfect(number) else 'not '}a Perfect Number.''')


