
"""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


_a		       =							logging.get_logger(__name__)

_a		       =							{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_a		       =							{
    """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"""
        ),
    },
}
_a		       =							{
    """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"""
        ),
    },
}
_a		       =							{
    """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"""
        ),
    },
}

_a		       =							{
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
_a		       =							{
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
_a		       =							{
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


_a		       =							{
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
_a		       =							{
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
_a		       =							{
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}


class A_	(UpperCAmelCase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Tuple       =		VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__			:						List[Any]       =		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__			:						List[Any]       =		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__			:						List[Any]       =		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE__			:						Any       =		DPRContextEncoderTokenizer




class A_	(UpperCAmelCase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__			:						str       =		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__			:						List[Any]       =		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		DPRQuestionEncoderTokenizer




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

_a		       =							collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


_a		       =							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(UpperCAmelCase__      )
class A_	:




  '''simple docstring'''







  def __call__(		self				,   lowercase_				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = False				,   lowercase_ = False				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = None				,   **lowercase_				,   ):

     """simple docstring"""


     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:
        UpperCAmelCase_					:					List[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_				,   )
     UpperCAmelCase_					:					Any			     =    titles if not isinstance(lowercase_				,   lowercase_  ) else [titles]
     UpperCAmelCase_					:					Union[str, Any]			     =    texts if not isinstance(lowercase_				,   lowercase_  ) else [texts]
     UpperCAmelCase_					:					Any			     =    len(lowercase_  )
     UpperCAmelCase_					:					Dict			     =    questions if not isinstance(lowercase_				,   lowercase_  ) else [questions] * n_passages
     assert len(lowercase_  ) == len(
         lowercase_  ), F"""There should be as many titles than texts but got {len(lowercase_  )} titles and {len(lowercase_  )} texts."""
     UpperCAmelCase_					:					List[str]			     =    super().__call__(lowercase_				,   lowercase_				,   padding=lowercase_				,   truncation=lowercase_  )['''input_ids''']
     UpperCAmelCase_					:					Tuple			     =    super().__call__(lowercase_				,   add_special_tokens=lowercase_				,   padding=lowercase_				,   truncation=lowercase_  )['''input_ids''']
     UpperCAmelCase_					:					Dict			     =    {
         '''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:
        UpperCAmelCase_					:					List[str]			     =    []
        for input_ids in encoded_inputs["input_ids"]:
           attention_mask.append([int(input_id != self.pad_token_id  ) for input_id in input_ids]  )
        UpperCAmelCase_					:					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				,   ):

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

        UpperCAmelCase_					:					int			     =    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_				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    []
     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)  )
     UpperCAmelCase_					:					List[str]			     =    sorted(lowercase_				,   key=lambda lowercase_  : x[1]				,   reverse=lowercase_  )
     UpperCAmelCase_					:					Optional[int]			     =    []
     for (start_index, end_index), score in scores:
        assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
        UpperCAmelCase_					:					List[str]			     =    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(lowercase_  ) == top_spans:
           break
     return chosen_span_intervals




@add_end_docstrings(UpperCAmelCase__      )
class A_	(UpperCAmelCase__				,UpperCAmelCase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						str       =		VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__			:						Dict       =		READER_PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__			:						int       =		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		READER_PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE__			:						int       =		["""input_ids""", """attention_mask"""]
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		DPRReaderTokenizer










"""simple docstring"""






from __future__ import annotations




class 							lowerCAmelCase__				:






      '''simple docstring'''

      def __init__(						self     :  Any						,	lowercase_     :  int = 0):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		key
      def _SCREAMING_SNAKE_CASE (						self     :  Any						,	lowercase_     :  str						,	lowercase_     :  int):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(lowercase_) ^ key) for ch in content]
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  str						,	lowercase_     :  int):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(lowercase_) ^ key) for ch in content]
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple						,	lowercase_     :  str						,	lowercase_     :  int = 0):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  int    						=		key or self.__key or 1

          # make sure key can be any size
          while key > 255:
              key -= 255

          # This will be returned
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		''''''

          for ch in content:
              ans += chr(ord(lowercase_) ^ key)

          return ans
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  str						,	lowercase_     :  int = 0):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		key or self.__key or 1

          # make sure key can be any size
          while key > 255:
              key -= 255

          # This will be returned
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		''''''

          for ch in content:
              ans += chr(ord(lowercase_) ^ key)

          return ans
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  str						,	lowercase_     :  int = 0):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          try:
              with open(lowercase_) as fin, open('''encrypt.out'''						,	'''w+''') as fout:
                  # actual encrypt-process
                  for line in fin:
                      fout.write(self.encrypt_string(lowercase_						,	lowercase_))

          except OSError:
              return False

          return True



      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]						,	lowercase_     :  str						,	lowercase_     :  int):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          try:
              with open(lowercase_) as fin, open('''decrypt.out'''						,	'''w+''') as fout:
                  # actual encrypt-process
                  for line in fin:
                      fout.write(self.decrypt_string(lowercase_						,	lowercase_))

          except OSError:
              return False

          return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")


