

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      :					Dict					    =      logging.get_logger(__name__)

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

_lowerCamelCase      :					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      :					Dict					    =      {
    '''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      :					List[Any]					    =      {
    '''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      :					Dict					    =      {
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
_lowerCamelCase      :					List[str]					    =      {
    '''facebook/dpr-question_encoder-single-nq-base''': 512,
    '''facebook/dpr-question_encoder-multiset-base''': 512,
}
_lowerCamelCase      :					Union[str, Any]					    =      {
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


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





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    VOCAB_FILES_NAMES
       lowercase_						=    CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowercase_						=    CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_						=    CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    VOCAB_FILES_NAMES
       lowercase_						=    QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowercase_						=    QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_						=    QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION

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

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


_lowerCamelCase      :					Any					    =      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(__magic_name__			)
class        lowerCAmelCase__  :



       '''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:
                   __A    =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__ ,   )
             __A    =titles if not isinstance(lowercase__ ,   lowercase__     ) else [titles]
             __A    =texts if not isinstance(lowercase__ ,   lowercase__     ) else [texts]
             __A    =len(lowercase__     )
             __A    =questions if not isinstance(lowercase__ ,   lowercase__     ) else [questions] * n_passages
             if len(lowercase__     ) != len(lowercase__     ):
                   raise ValueError(
                       f'''There should be as many titles than texts but got {len(lowercase__     )} titles and {len(lowercase__     )} texts.'''     )
             __A    =super().__call__(lowercase__ ,   lowercase__ ,   padding=lowercase__ ,   truncation=lowercase__     )['''input_ids''']
             __A    =super().__call__(lowercase__ ,   add_special_tokens=lowercase__ ,   padding=lowercase__ ,   truncation=lowercase__     )['''input_ids''']
             __A    ={
                 '''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:
                   __A    =[]
                   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    =attention_mask
             return self.pad(lowercase__ ,   padding=lowercase__ ,   max_length=lowercase__ ,   return_tensors=lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ = 1_6 ,   lowercase__ = 6_4 ,   lowercase__ = 4 ,   ):







             '''simple docstring'''



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

                   __A    =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'''



             __A    =[]
             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)     )
             __A    =sorted(lowercase__ ,   key=lambda lowercase__     : x[1] ,   reverse=lowercase__     )
             __A    =[]
             for (start_index, end_index), score in scores:
                   if start_index > end_index:
                         raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''     )
                   __A    =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(lowercase__     ) == top_spans:
                         break
             return chosen_span_intervals





@add_end_docstrings(__magic_name__			)
class        lowerCAmelCase__  (					__magic_name__				,							__magic_name__			):



       '''simple docstring'''







       lowercase_						=    VOCAB_FILES_NAMES
       lowercase_						=    READER_PRETRAINED_VOCAB_FILES_MAP
       lowercase_						=    READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_						=    READER_PRETRAINED_INIT_CONFIGURATION
       lowercase_						=    ["""input_ids""", """attention_mask"""]





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCamelCase      :					Tuple					    =      {
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					List[str]					    =      ['''SqueezeBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					Tuple					    =      [
       '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SqueezeBertForMaskedLM''',
       '''SqueezeBertForMultipleChoice''',
       '''SqueezeBertForQuestionAnswering''',
       '''SqueezeBertForSequenceClassification''',
       '''SqueezeBertForTokenClassification''',
       '''SqueezeBertModel''',
       '''SqueezeBertModule''',
       '''SqueezeBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_squeezebert import (
       SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       SqueezeBertConfig,
       SqueezeBertOnnxConfig,
   )
   from .tokenization_squeezebert import SqueezeBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_squeezebert import (
          SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          SqueezeBertForMaskedLM,
          SqueezeBertForMultipleChoice,
          SqueezeBertForQuestionAnswering,
          SqueezeBertForSequenceClassification,
          SqueezeBertForTokenClassification,
          SqueezeBertModel,
          SqueezeBertModule,
          SqueezeBertPreTrainedModel,
      )

else:
   import sys

   _lowerCamelCase      :					List[str]					    =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


