





'''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 import BertTokenizer


snake_case_     =				logging.get_logger(__name__)

snake_case_     =				{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case_     =				{
    """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"""
        ),
    },
}
snake_case_     =				{
    """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"""
        ),
    },
}
snake_case_     =				{
    """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"""
        ),
    },
}

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


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


class   _lowercase						(							lowercase_				):
   _UpperCamelCase       		=   VOCAB_FILES_NAMES
   _UpperCamelCase       		=   CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase       		=   CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase       		=   CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class   _lowercase						(							lowercase_				):
   _UpperCamelCase       		=   VOCAB_FILES_NAMES
   _UpperCamelCase       		=   QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase       		=   QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase       		=   QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION







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

snake_case_     =				collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


snake_case_     =				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(lowercase_				)
class   _lowercase						:


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


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = 16    ,   _UpperCAmelCase = 64    ,   _UpperCAmelCase = 4    ,   ):
       A       :       List[str]							=						reader_input['''input_ids''']
       A,	A,	A       :       List[str]							=						reader_output[:3]
       A       :       Tuple							=						len(_UpperCAmelCase					)
       A       :       Any							=						sorted(range(_UpperCAmelCase					)    ,   reverse=_UpperCAmelCase    ,   key=relevance_logits.__getitem__					)
       A       :       Optional[Any]							=						[]
       for doc_id in sorted_docs:
           A       :       Any							=						list(input_ids[doc_id]					)
           # assuming question & title information is at the beginning of the sequence
           A       :       Optional[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       :       Optional[Any]							=						len(_UpperCAmelCase					)

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


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   ):
       A       :       str							=						[]
       for start_index, start_score in enumerate(_UpperCAmelCase					):
           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       :       Optional[int]							=						sorted(_UpperCAmelCase    ,   key=lambda _UpperCAmelCase					: x[1]    ,   reverse=_UpperCAmelCase					)
       A       :       List[Any]							=						[]
       for (start_index, end_index), score in scores:
           if start_index > end_index:
               raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''					)
           A       :       int							=						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(_UpperCAmelCase					) == top_spans:
               break
       return chosen_span_intervals







@add_end_docstrings(lowercase_				)
class   _lowercase						(							lowercase_		,					lowercase_				):
   _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''']







'''simple docstring'''






from ..utils import DummyObject, requires_backends


class   _lowercase						(							metaclass=a				):
   _UpperCamelCase       		=   ["""onnx"""]


   def __init__( self    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       requires_backends(self    ,   ['''onnx''']					)


   @classmethod
   def 					snake_case				( cls    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       requires_backends(cls    ,   ['''onnx''']					)


   @classmethod
   def 					snake_case				( cls    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       requires_backends(cls    ,   ['''onnx''']					)
