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       : List[Any]      					=		logging.get_logger(__name__)

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

_lowerCamelCase       : Optional[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'''
        ),
    },
}
_lowerCamelCase       : Tuple      					=		{
    '''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       : Tuple      					=		{
    '''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       : int      					=		{
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_12,
}
_lowerCamelCase       : Optional[Any]      					=		{
    '''facebook/dpr-question_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-question_encoder-multiset-base''': 5_12,
}
_lowerCamelCase       : List[str]      					=		{
    '''facebook/dpr-reader-single-nq-base''': 5_12,
    '''facebook/dpr-reader-multiset-base''': 5_12,
}


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


class     lowercase     (  snake_case_  ):
							lowercase__    :							Tuple										=				VOCAB_FILES_NAMES
							lowercase__    :							List[str]										=				CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							lowercase__    :							Union[str, Any]										=				CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__    :							Optional[Any]										=				CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION



class     lowercase     (  snake_case_  ):
							lowercase__    :							str										=				VOCAB_FILES_NAMES
							lowercase__    :							List[str]										=				QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							lowercase__    :							Dict										=				QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__    :							Any										=				QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



_lowerCamelCase       : List[str]      					=		collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

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


_lowerCamelCase       : Dict      					=		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(snake_case_  )
class     lowercase     :




							def __call__(    self :       int						,				_UpperCamelCase :       str						,				_UpperCamelCase :       Optional[int] = None						,				_UpperCamelCase :       Union[str, Any] = None						,				_UpperCamelCase :       Union[str, Any] = False						,				_UpperCamelCase :       List[str] = False						,				_UpperCamelCase :       Dict = None						,				_UpperCamelCase :       Any = None						,				_UpperCamelCase :       List[str] = None						,				**_UpperCamelCase :       List[str]						,				)       ->					BatchEncoding:



												'''simple docstring'''

												if titles is None and texts is None:
																	return super().__call__(
																	    UpperCamelCase__						,				padding=UpperCamelCase__						,				truncation=UpperCamelCase__						,				max_length=UpperCamelCase__						,				return_tensors=UpperCamelCase__						,				return_attention_mask=UpperCamelCase__						,				**UpperCamelCase__						,				)
												elif titles is None or texts is None:
																	SCREAMING_SNAKE_CASE           =							titles if texts is None else texts
																	return super().__call__(
																	    UpperCamelCase__						,				UpperCamelCase__						,				padding=UpperCamelCase__						,				truncation=UpperCamelCase__						,				max_length=UpperCamelCase__						,				return_tensors=UpperCamelCase__						,				return_attention_mask=UpperCamelCase__						,				**UpperCamelCase__						,				)
												SCREAMING_SNAKE_CASE           =							titles if not isinstance(UpperCamelCase__						,				UpperCamelCase__   ) else [titles]
												SCREAMING_SNAKE_CASE           =							texts if not isinstance(UpperCamelCase__						,				UpperCamelCase__   ) else [texts]
												SCREAMING_SNAKE_CASE           =							len(UpperCamelCase__   )
												SCREAMING_SNAKE_CASE           =							questions if not isinstance(UpperCamelCase__						,				UpperCamelCase__   ) else [questions] * n_passages
												if len(UpperCamelCase__   ) != len(UpperCamelCase__   ):
																	raise ValueError(
																	    F"There should be as many titles than texts but got {len(UpperCamelCase__   )} titles and {len(UpperCamelCase__   )} texts."   )
												SCREAMING_SNAKE_CASE           =							super().__call__(UpperCamelCase__						,				UpperCamelCase__						,				padding=UpperCamelCase__						,				truncation=UpperCamelCase__   )["input_ids"]
												SCREAMING_SNAKE_CASE           =							super().__call__(UpperCamelCase__						,				add_special_tokens=UpperCamelCase__						,				padding=UpperCamelCase__						,				truncation=UpperCamelCase__   )["input_ids"]
												SCREAMING_SNAKE_CASE           =							{
												    "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(UpperCamelCase__						,				UpperCamelCase__   )
												    ]
												}
												if return_attention_mask is not False:
																	SCREAMING_SNAKE_CASE           =							[]
																	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           =							attention_mask
												return self.pad(UpperCamelCase__						,				padding=UpperCamelCase__						,				max_length=UpperCamelCase__						,				return_tensors=UpperCamelCase__   )




							def 						__snake_case(    self :       List[str]						,				_UpperCamelCase :       Union[str, Any]						,				_UpperCamelCase :       Any						,				_UpperCamelCase :       int = 16						,				_UpperCamelCase :       Optional[Any] = 64						,				_UpperCamelCase :       str = 4						,				)       ->					List[DPRSpanPrediction]:



												'''simple docstring'''

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

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





							def 						__snake_case(    self :       Union[str, Any]						,				_UpperCamelCase :       List[str]						,				_UpperCamelCase :       Dict						,				_UpperCamelCase :       Tuple						,				_UpperCamelCase :       int						,				)       ->					List[DPRSpanPrediction]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							[]
												for start_index, start_score in enumerate(UpperCamelCase__   ):
																	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           =							sorted(UpperCamelCase__						,				key=lambda _UpperCamelCase   : x[1]						,				reverse=UpperCamelCase__   )
												SCREAMING_SNAKE_CASE           =							[]
												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           =							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(UpperCamelCase__   ) == top_spans:
																						break
												return chosen_span_intervals



@add_end_docstrings(snake_case_  )
class     lowercase     (  snake_case_					,							snake_case_  ):
							lowercase__    :							List[str]										=				VOCAB_FILES_NAMES
							lowercase__    :							Dict										=				READER_PRETRAINED_VOCAB_FILES_MAP
							lowercase__    :							int										=				READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__    :							Optional[Any]										=				READER_PRETRAINED_INIT_CONFIGURATION
							lowercase__    :							Optional[int]										=				['''input_ids''', '''attention_mask''']








"""simple docstring"""



import string







def     __lowerCAmelCase		(		lowercase    :    str					)      ->		None:


    """simple docstring"""





    for key in range(len(string.ascii_uppercase					)					):
        snake_case       : Dict    =       ""
        for symbol in message:
            if symbol in string.ascii_uppercase:
                snake_case       : Dict    =       string.ascii_uppercase.find(lowercase					)
                snake_case       : Optional[int]    =       num - key
                if num < 0:
                    snake_case       : Optional[int]    =       num + len(string.ascii_uppercase					)
                snake_case       : Dict    =       translated + string.ascii_uppercase[num]
            else:
                snake_case       : Any    =       translated + symbol
        print(F'Decryption using Key #{key}: {translated}'					)







def     __lowerCAmelCase		(		)      ->		None:


    """simple docstring"""





    snake_case       : Dict    =       input("Encrypted message: "					)
    snake_case       : Any    =       message.upper()
    decrypt(lowercase					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()





