





"""simple docstring"""





import random

class 		lowercase					:





      @staticmethod
      def     _snake_case			(       lowercase )		->     tuple[list[int], list[int]]:
            lowerCAmelCase  						=       [ord(lowercase ) for i in text]
            lowerCAmelCase  						=       []
            lowerCAmelCase  						=       []
            for i in plain:
                  lowerCAmelCase  						=       random.randint(1      ,						300 )
                  lowerCAmelCase  						=       (i + k) * k
                  cipher.append(lowercase )
                  key.append(lowercase )
            return cipher, key





      @staticmethod
      def     _snake_case			(       lowercase      ,						lowercase )		->     str:
            lowerCAmelCase  						=       []
            for i in range(len(lowercase ) ):
                  lowerCAmelCase  						=       int((cipher[i] - (key[i]) ** 2) / key[i] )
                  plain.append(chr(lowercase ) )
            return "".join(lowercase )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__					=   Onepad().encrypt("Hello")
       print(c, k)
       print(Onepad().decrypt(c, k))











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

UpperCamelCase__       :	Any    = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase__       :	Union[str, Any]    = {
    '''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__       :	Optional[int]    = {
    '''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[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'''
        ),
    },
}

UpperCamelCase__       :	Any    = {
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_12,
}
UpperCamelCase__       :	Optional[Any]    = {
    '''facebook/dpr-question_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-question_encoder-multiset-base''': 5_12,
}
UpperCamelCase__       :	Dict    = {
    '''facebook/dpr-reader-single-nq-base''': 5_12,
    '''facebook/dpr-reader-multiset-base''': 5_12,
}


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






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		str			    =		VOCAB_FILES_NAMES
			_A      :		str			    =		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			_A      :		Any			    =		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_A      :		Optional[Any]			    =		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
			_A      :		Any			    =		DPRContextEncoderTokenizer






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		Dict			    =		VOCAB_FILES_NAMES
			_A      :		Any			    =		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			_A      :		Tuple			    =		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_A      :		Union[str, Any]			    =		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
			_A      :		Dict			    =		DPRQuestionEncoderTokenizer
UpperCamelCase__       :	str    = collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

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


UpperCamelCase__       :	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)

    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  :


			'''simple docstring'''

			def __call__(							self     : str							,     lowerCAmelCase__     : Union[str, 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__     : str							,     ):





					"""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:
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		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__							,     )
					__SCREAMING_SNAKE_CASE       :		Dict           =		titles if not isinstance(lowerCAmelCase__							,     lowerCAmelCase__						) else [titles]
					__SCREAMING_SNAKE_CASE       :		Tuple           =		texts if not isinstance(lowerCAmelCase__							,     lowerCAmelCase__						) else [texts]
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		len(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Tuple           =		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."
					__SCREAMING_SNAKE_CASE       :		int           =		super().__call__(lowerCAmelCase__							,     lowerCAmelCase__							,     padding=lowerCAmelCase__							,     truncation=lowerCAmelCase__						)["""input_ids"""]
					__SCREAMING_SNAKE_CASE       :		str           =		super().__call__(lowerCAmelCase__							,     add_special_tokens=lowerCAmelCase__							,     padding=lowerCAmelCase__							,     truncation=lowerCAmelCase__						)["""input_ids"""]
					__SCREAMING_SNAKE_CASE       :		Optional[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(lowerCAmelCase__							,     lowerCAmelCase__						)
					    ]
					}
					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       :		int           =		attention_mask
					return self.pad(lowerCAmelCase__							,     padding=lowerCAmelCase__							,     max_length=lowerCAmelCase__							,     return_tensors=lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Union[str, Any]							,     lowerCAmelCase__     : BatchEncoding							,     lowerCAmelCase__     : DPRReaderOutput							,     lowerCAmelCase__     : int = 1_6							,     lowerCAmelCase__     : int = 6_4							,     lowerCAmelCase__     : int = 4							,     ):





					"""simple docstring"""






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

							__SCREAMING_SNAKE_CASE       :		Tuple           =		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     : str							,     lowerCAmelCase__     : List[int]							,     lowerCAmelCase__     : List[int]							,     lowerCAmelCase__     : int							,     lowerCAmelCase__     : int							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		[]
					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)						)
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		sorted(lowerCAmelCase__							,     key=lambda lowerCAmelCase__						: x[1]							,     reverse=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		[]
					for (start_index, end_index), score in scores:
							assert start_index <= end_index, F"Wrong span indices: [{start_index}:{end_index}]"
							__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		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__    ):


			'''simple docstring'''

			_A      :		Optional[int]			    =		VOCAB_FILES_NAMES
			_A      :		int			    =		READER_PRETRAINED_VOCAB_FILES_MAP
			_A      :		str			    =		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_A      :		str			    =		READER_PRETRAINED_INIT_CONFIGURATION
			_A      :		Dict			    =		['''input_ids''', '''attention_mask''']
			_A      :		Tuple			    =		DPRReaderTokenizer