




"""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''': 5_12,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_12,
}
A_      			=       {
    '''facebook/dpr-question_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-question_encoder-multiset-base''': 5_12,
}
A_      			=       {
    '''facebook/dpr-reader-single-nq-base''': 5_12,
    '''facebook/dpr-reader-multiset-base''': 5_12,
}


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       lowercase(  __a				):







 '''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
 lowercase__				      =      DPRContextEncoderTokenizer







class       lowercase(  __a				):







 '''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
 lowercase__				      =      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(__a				)
class       lowercase:







 '''simple docstring'''





 def __call__(       self:	List[str],   a_:	Optional[Any],   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_:	Any,   ):




   '''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:
     _snake_case							:	Optional[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_,   )
   _snake_case							:	List[Any]			=						titles if not isinstance(a_,   a_	) else [titles]
   _snake_case							:	Optional[Any]			=						texts if not isinstance(a_,   a_	) else [texts]
   _snake_case							:	List[Any]			=						len(a_	)
   _snake_case							:	Union[str, 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."
   _snake_case							:	int			=						super().__call__(a_,   a_,   padding=a_,   truncation=a_	)["""input_ids"""]
   _snake_case							:	List[Any]			=						super().__call__(a_,   add_special_tokens=a_,   padding=a_,   truncation=a_	)["""input_ids"""]
   _snake_case							:	List[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:
     _snake_case							:	int			=						[]
     for input_ids in encoded_inputs["input_ids"]:
       attention_mask.append([int(input_id != self.pad_token_id	) for input_id in input_ids]	)
     _snake_case							:	str			=						attention_mask
   return self.pad(a_,   padding=a_,   max_length=a_,   return_tensors=a_	)


 def 				UpperCamelCase_     (       self:	List[str],   a_:	BatchEncoding,   a_:	DPRReaderOutput,   a_:	int = 16,   a_:	int = 64,   a_:	int = 4,   ):




   '''simple docstring'''



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

     _snake_case							:	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=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 				UpperCamelCase_     (       self:	Optional[int],   a_:	List[int],   a_:	List[int],   a_:	int,   a_:	int,   ):




   '''simple docstring'''



   _snake_case							:	int			=						[]
   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)	)
   _snake_case							:	Tuple			=						sorted(a_,   key=lambda a_	: x[1],   reverse=a_	)
   _snake_case							:	Optional[int]			=						[]
   for (start_index, end_index), score in scores:
     assert start_index <= end_index, f"Wrong span indices: [{start_index}:{end_index}]"
     _snake_case							:	int			=						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(__a				)
class       lowercase(  __a ,    __a				):







 '''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"]
 lowercase__				      =      DPRReaderTokenizer








"""simple docstring"""


from ...processing_utils import ProcessorMixin






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      ["image_processor", "feature_extractor"]
 lowercase__				      =      "TvltImageProcessor"
 lowercase__				      =      "TvltFeatureExtractor"


 def __init__(       self:	Dict,   a_:	Union[str, Any],   a_:	Union[str, Any]	):




   '''simple docstring'''



   super().__init__(image_processor=a_,   feature_extractor=a_	)

   _snake_case							:	Any			=						image_processor
   _snake_case							:	Dict			=						feature_extractor


 def __call__(       self:	int,   a_:	str=None,   a_:	Tuple=None,   a_:	Dict=None,   a_:	str=None,   a_:	Optional[int]=False,   a_:	Tuple=False,   *a_:	List[str],   **a_:	int,   ):




   '''simple docstring'''



   if images is None and audio is None:
     raise ValueError("""You need to specify either an `images` or `audio` input to process."""	)

   _snake_case							:	Optional[int]			=						None
   if images is not None:
     _snake_case							:	Tuple			=						self.image_processor(a_,   mask_pixel=a_,   *a_,   **a_	)
   if images_mixed is not None:
     _snake_case							:	Optional[int]			=						self.image_processor(a_,   is_mixed=a_,   *a_,   **a_	)
   if audio is not None:
     _snake_case							:	Any			=						self.feature_extractor(
         a_,   *a_,   sampling_rate=a_,   mask_audio=a_,   **a_	)

   _snake_case							:	List[str]			=						{}
   if audio is not None:
     output_dict.update(a_	)
   if images is not None:
     output_dict.update(a_	)
   if images_mixed_dict is not None:
     output_dict.update(a_	)
   return output_dict


 @property
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.image_processor.model_input_names
   _snake_case							:	List[str]			=						self.feature_extractor.model_input_names
   return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names	)	)


