





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

A__:  int        =     {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A__:  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'
        ),
    },
}
A__:  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'
        ),
    },
}
A__:  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'
        ),
    },
}

A__:  Optional[Any]        =     {
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
A__:  Tuple        =     {
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
A__:  List[Any]        =     {
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


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




class     _a  (  UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__   						=    VOCAB_FILES_NAMES
      UpperCamelCase__   						=    CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase__   						=    CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase__   						=    CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase__   						=    DPRContextEncoderTokenizer




class     _a  (  UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__   						=    VOCAB_FILES_NAMES
      UpperCamelCase__   						=    QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase__   						=    QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase__   						=    QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase__   						=    DPRQuestionEncoderTokenizer
A__:  Optional[Any]        =     collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

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


A__:  Tuple        =     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(UpperCamelCase__)
class     _a  :

      """simple docstring"""


      def __call__(						self:       Tuple     ,  __lowerCamelCase:       int     ,  __lowerCamelCase:       Dict = None     ,  __lowerCamelCase:       Optional[int] = None     ,  __lowerCamelCase:       Union[str, Any] = False     ,  __lowerCamelCase:       str = False     ,  __lowerCamelCase:       Dict = None     ,  __lowerCamelCase:       str = None     ,  __lowerCamelCase:       List[str] = None     ,  **__lowerCamelCase:       int     ,  ):
          '''simple docstring'''


          if titles is None and texts is None:
              return super().__call__(
                  _snake_case     ,  padding=_snake_case     ,  truncation=_snake_case     ,  max_length=_snake_case     ,  return_tensors=_snake_case     ,  return_attention_mask=_snake_case     ,  **_snake_case     ,  )
          elif titles is None or texts is None:
              UpperCamelCase__:     List[Any]     							=			titles if texts is None else texts
              return super().__call__(
                  _snake_case     ,  _snake_case     ,  padding=_snake_case     ,  truncation=_snake_case     ,  max_length=_snake_case     ,  return_tensors=_snake_case     ,  return_attention_mask=_snake_case     ,  **_snake_case     ,  )
          UpperCamelCase__:     Optional[int]     							=			titles if not isinstance(_snake_case     ,  _snake_case ) else [titles]
          UpperCamelCase__:     Optional[int]     							=			texts if not isinstance(_snake_case     ,  _snake_case ) else [texts]
          UpperCamelCase__:     List[str]     							=			len(_snake_case )
          UpperCamelCase__:     str     							=			questions if not isinstance(_snake_case     ,  _snake_case ) else [questions] * n_passages
          assert len(_snake_case ) == len(
              _snake_case ), F"There should be as many titles than texts but got {len(_snake_case )} titles and {len(_snake_case )} texts."
          UpperCamelCase__:     List[str]     							=			super().__call__(_snake_case     ,  _snake_case     ,  padding=_snake_case     ,  truncation=_snake_case )["input_ids"]
          UpperCamelCase__:     Dict     							=			super().__call__(_snake_case     ,  add_special_tokens=_snake_case     ,  padding=_snake_case     ,  truncation=_snake_case )["input_ids"]
          UpperCamelCase__:     Optional[int]     							=			{
              "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(_snake_case     ,  _snake_case )
              ]
          }
          if return_attention_mask is not False:
              UpperCamelCase__:     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__:     Tuple     							=			attention_mask
          return self.pad(_snake_case     ,  padding=_snake_case     ,  max_length=_snake_case     ,  return_tensors=_snake_case )






      def        UpperCAmelCase_    (						self:       Any     ,  __lowerCamelCase:       Any     ,  __lowerCamelCase:       List[Any]     ,  __lowerCamelCase:       Tuple = 16     ,  __lowerCamelCase:       Optional[Any] = 64     ,  __lowerCamelCase:       Optional[Any] = 4     ,  ):
          '''simple docstring'''


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

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






      def        UpperCAmelCase_    (						self:       Optional[int]     ,  __lowerCamelCase:       Optional[int]     ,  __lowerCamelCase:       List[str]     ,  __lowerCamelCase:       Optional[int]     ,  __lowerCamelCase:       int     ,  ):
          '''simple docstring'''


          UpperCamelCase__:     List[Any]     							=			[]
          for start_index, start_score in enumerate(_snake_case ):
              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__:     Any     							=			sorted(_snake_case     ,  key=lambda __lowerCamelCase : x[1]     ,  reverse=_snake_case )
          UpperCamelCase__:     Any     							=			[]
          for (start_index, end_index), score in scores:
              assert start_index <= end_index, F"Wrong span indices: [{start_index}:{end_index}]"
              UpperCamelCase__:     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(_snake_case ) == top_spans:
                  break
          return chosen_span_intervals




@add_end_docstrings(UpperCamelCase__)
class     _a  (  UpperCamelCase__		,       UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__   						=    VOCAB_FILES_NAMES
      UpperCamelCase__   						=    READER_PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase__   						=    READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase__   						=    READER_PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase__   						=    ["""input_ids""", """attention_mask"""]
      UpperCamelCase__   						=    DPRReaderTokenizer



def     __lowerCAmelCase			(			a__						,    a__  )       ->  float:

				def get_matched_characters(a__						,    a__  ) -> str:
								__a										=    []
								__a										=    min(len(_stra  )						,    len(_stra  )  ) // 2
								for i, l in enumerate(_stra  ):
												__a										=    int(max(0						,    i - limit  )  )
												__a										=    int(min(i + limit + 1						,    len(_stra  )  )  )
												if l in _stra[left:right]:
																matched.append(a__  )
																__a										=    F"""{_stra[0:_stra.index(a__  )]} {_stra[_stra.index(a__  ) + 1:]}"""

								return "".join(a__  )

				# matching characters
				__a										=    get_matched_characters(a__						,    a__  )
				__a										=    get_matched_characters(a__						,    a__  )
				__a										=    len(a__  )

				# transposition
				__a										=    (
				    len([(ca, ca) for ca, ca in zip(a__						,    a__  ) if ca != ca]  ) // 2
				)

				if not match_count:
								__a										=    0.0
				else:
								__a										=    (
								    1
								    / 3
								    * (
								        match_count / len(a__  )
								        + match_count / len(a__  )
								        + (match_count - transpositions) / match_count
								    )
								)

				# common prefix up to 4 characters
				__a										=    0
				for ca, ca in zip(stra[:4]						,    stra[:4]  ):
								if ca == ca:
												prefix_len += 1
								else:
												break

				return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(jaro_winkler('hello', 'world'))