


'''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


__magic_name__			=      logging.get_logger(__name__)

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

__magic_name__			=      {
    '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'
        ),
    },
}
__magic_name__			=      {
    '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'
        ),
    },
}
__magic_name__			=      {
    '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'
        ),
    },
}

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


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



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							VOCAB_FILES_NAMES
  a_  					=							CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  a_  					=							CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_  					=							CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
  a_  					=							DPRContextEncoderTokenizer







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							VOCAB_FILES_NAMES
  a_  					=							QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  a_  					=							QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_  					=							QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
  a_  					=							DPRQuestionEncoderTokenizer







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

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


__magic_name__			=      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(__SCREAMING_SNAKE_CASE    )
class 	__lowerCAmelCase		:


  '''simple docstring'''







  def __call__(							self :       Any	,_a :       List[str]	,_a :       Optional[str] = None	,_a :       Optional[str] = None	,_a :       Union[bool, str] = False	,_a :       Union[bool, str] = False	,_a :       Optional[int] = None	,_a :       Optional[Union[str, TensorType]] = None	,_a :       Optional[bool] = None	,**_a :       Tuple	,):

        '''simple docstring'''




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






  def        _a							(							self :       Optional[int]	,_a :       BatchEncoding	,_a :       DPRReaderOutput	,_a :       int = 16	,_a :       int = 64	,_a :       int = 4	,):

        '''simple docstring'''




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

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






  def        _a							(							self :       Optional[int]	,_a :       List[int]	,_a :       List[int]	,_a :       int	,_a :       int	,):

        '''simple docstring'''




        A_						:  List[str]          =				[]
        for start_index, start_score in enumerate(_a ):
              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) )
        A_						:  Any          =				sorted(_a	,key=lambda _a : x[1]	,reverse=_a )
        A_						:  List[str]          =				[]
        for (start_index, end_index), score in scores:
              assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
              A_						:  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(_a ) == top_spans:
                    break
        return chosen_span_intervals







@add_end_docstrings(__SCREAMING_SNAKE_CASE    )
class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE  , __SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







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




'''simple docstring'''


from ..utils import DummyObject, requires_backends



class 	__lowerCAmelCase		(							metaclass=__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""torch""", """torchsde"""]






  def __init__(							self :       Any	,*_a :       Union[str, Any]	,**_a :       Optional[int] ):

        '''simple docstring'''




        requires_backends(self	,["""torch""", """torchsde"""] )






  @classmethod
  def        _a							(							cls :       Optional[int]	,*_a :       List[Any]	,**_a :       Any ):

        '''simple docstring'''




        requires_backends(cls	,["""torch""", """torchsde"""] )






  @classmethod
  def        _a							(							cls :       List[Any]	,*_a :       Tuple	,**_a :       Union[str, Any] ):

        '''simple docstring'''




        requires_backends(cls	,["""torch""", """torchsde"""] )
