




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


__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(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = VOCAB_FILES_NAMES
    lowerCAmelCase         = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase         = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase         = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION


class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = VOCAB_FILES_NAMES
    lowerCAmelCase         = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase         = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase         = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION


__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'\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    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\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    Returns:\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(_a  )
class     __a:






    """simple docstring"""





    def __call__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = False  ,_SCREAMING_SNAKE_CASE = False  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = None  ,**_SCREAMING_SNAKE_CASE  ,)					->						BatchEncoding:
         if titles is None and texts is None:
              return super().__call__(
                  _SCREAMING_SNAKE_CASE  ,padding=_SCREAMING_SNAKE_CASE  ,truncation=_SCREAMING_SNAKE_CASE  ,max_length=_SCREAMING_SNAKE_CASE  ,return_tensors=_SCREAMING_SNAKE_CASE  ,return_attention_mask=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE  ,)
         elif titles is None or texts is None:
              UpperCAmelCase_     :				List[str]  =     titles if texts is None else texts
              return super().__call__(
                  _SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,padding=_SCREAMING_SNAKE_CASE  ,truncation=_SCREAMING_SNAKE_CASE  ,max_length=_SCREAMING_SNAKE_CASE  ,return_tensors=_SCREAMING_SNAKE_CASE  ,return_attention_mask=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE  ,)
         UpperCAmelCase_     :				List[Any]  =     titles if not isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ) else [titles]
         UpperCAmelCase_     :				List[str]  =     texts if not isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ) else [texts]
         UpperCAmelCase_     :				Any  =     len(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				List[Any]  =     questions if not isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ) else [questions] * n_passages
         if len(_SCREAMING_SNAKE_CASE    ) != len(_SCREAMING_SNAKE_CASE    ):
              raise ValueError(
                  f'''There should be as many titles than texts but got {len(_SCREAMING_SNAKE_CASE    )} titles and {len(_SCREAMING_SNAKE_CASE    )} texts.'''    )
         UpperCAmelCase_     :				Tuple  =     super().__call__(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,padding=_SCREAMING_SNAKE_CASE  ,truncation=_SCREAMING_SNAKE_CASE    )['''input_ids''']
         UpperCAmelCase_     :				int  =     super().__call__(_SCREAMING_SNAKE_CASE  ,add_special_tokens=_SCREAMING_SNAKE_CASE  ,padding=_SCREAMING_SNAKE_CASE  ,truncation=_SCREAMING_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(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
             ]
         }
         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_     :				Dict  =     attention_mask
         return self.pad(_SCREAMING_SNAKE_CASE  ,padding=_SCREAMING_SNAKE_CASE  ,max_length=_SCREAMING_SNAKE_CASE  ,return_tensors=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = 16  ,_SCREAMING_SNAKE_CASE = 64  ,_SCREAMING_SNAKE_CASE = 4  ,)					->						List[DPRSpanPrediction]:
         UpperCAmelCase_     :				Tuple  =     reader_input['''input_ids''']
         UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				Optional[Any]  =     reader_output[:3]
         UpperCAmelCase_     :				Optional[Any]  =     len(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				int  =     sorted(range(_SCREAMING_SNAKE_CASE    )  ,reverse=_SCREAMING_SNAKE_CASE  ,key=relevance_logits.__getitem__    )
         UpperCAmelCase_     :				List[DPRReaderOutput]  =     []
         for doc_id in sorted_docs:
              UpperCAmelCase_     :				List[Any]  =     list(input_ids[doc_id]    )
              # assuming question & title information is at the beginning of the sequence
              UpperCAmelCase_     :				str  =     sequence_ids.index(self.sep_token_id  ,2    ) + 1  # second sep id
              if sequence_ids[-1] == self.pad_token_id:
                   UpperCAmelCase_     :				List[Any]  =     sequence_ids.index(self.pad_token_id    )
              else:
                   UpperCAmelCase_     :				int  =     len(_SCREAMING_SNAKE_CASE    )

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





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,)					->						List[DPRSpanPrediction]:
         UpperCAmelCase_     :				Tuple  =     []
         for start_index, start_score in enumerate(_SCREAMING_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_     :				int  =     sorted(_SCREAMING_SNAKE_CASE  ,key=lambda _SCREAMING_SNAKE_CASE    : x[1]  ,reverse=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Union[str, Any]  =     []
         for (start_index, end_index), score in scores:
              if start_index > end_index:
                   raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''    )
              UpperCAmelCase_     :				str  =     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(_SCREAMING_SNAKE_CASE    ) == top_spans:
                   break
         return chosen_span_intervals


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






    """simple docstring"""





    lowerCAmelCase         = VOCAB_FILES_NAMES
    lowerCAmelCase         = READER_PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase         = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase         = READER_PRETRAINED_INIT_CONFIGURATION
    lowerCAmelCase         = ['''input_ids''', '''attention_mask''']

"""simple docstring"""






from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url





class 				a__					(						A__       ):
     UpperCAmelCase__         =      ''''''
     UpperCAmelCase__         =      '''hf-legacy'''  # "hf://"" is reserved for hffs



     def __init__(							self :Dict      ,  _lowerCamelCase :Optional[DatasetInfo] = None      ,  _lowerCamelCase :Optional[str] = None      ,  **_lowerCamelCase :Tuple      ,  ):







      '''simple docstring'''



      super().__init__(self      ,  **_lowerCamelCase   )
      UpperCamelCase_     :					List[str]   	=repo_info
      UpperCamelCase_     :					Any   	=token
      UpperCamelCase_     :					Tuple   	=None



     def  lowerCamelCase_						(							self :Dict   ):







      '''simple docstring'''



      if self.dir_cache is None:
       UpperCamelCase_     :					Any   	={}
       for hf_file in self.repo_info.siblings:
        # TODO(QL): add sizes
        UpperCamelCase_     :					Optional[Any]   	={
            'name': hf_file.rfilename,
            'size': None,
            'type': 'file',
        }
        self.dir_cache.update(
            {
                str(_lowerCamelCase   ): {'name': str(_lowerCamelCase   ), 'size': None, 'type': 'directory'}
                for d in list(PurePosixPath(hf_file.rfilename   ).parents   )[:-1]
            }   )



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  _lowerCamelCase :str      ,  _lowerCamelCase :str = "rb"      ,  **_lowerCamelCase :str      ,  ):







      '''simple docstring'''



      if not isinstance(self.repo_info      ,  _lowerCamelCase   ):
       raise NotImplementedError(f'''Open is only implemented for dataset repositories, but got {self.repo_info}'''   )
      UpperCamelCase_     :					List[Any]   	=hf_hub_url(self.repo_info.id      ,  _lowerCamelCase      ,  revision=self.repo_info.sha   )
      return fsspec.open(
          _lowerCamelCase      ,  mode=_lowerCamelCase      ,  headers=get_authentication_headers_for_url(_lowerCamelCase      ,  use_auth_token=self.token   )      ,  client_kwargs={'trust_env': True}      ,  ).open()



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :Tuple      ,  **_lowerCamelCase :Any   ):







      '''simple docstring'''



      self._get_dirs()
      UpperCamelCase_     :					Tuple   	=self._strip_protocol(_lowerCamelCase   )
      if path in self.dir_cache:
       return self.dir_cache[path]
      else:
       raise FileNotFoundError(_lowerCamelCase   )




     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :List[str]      ,  _lowerCamelCase :List[Any]=False      ,  **_lowerCamelCase :Any   ):







      '''simple docstring'''



      self._get_dirs()
      UpperCamelCase_     :					str   	=PurePosixPath(path.strip('/'   )   )
      UpperCamelCase_     :					List[str]   	={}
      for p, f in self.dir_cache.items():
       UpperCamelCase_     :					List[Any]   	=PurePosixPath(p.strip('/'   )   )
       UpperCamelCase_     :					Tuple   	=p.parent
       if root == path:
        UpperCamelCase_     :					int   	=f
      UpperCamelCase_     :					Optional[int]   	=list(paths.values()   )
      if detail:
       return out
      else:
       return sorted(f['name'] for f in out   )
