





"""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""": 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 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """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
 _lowerCamelCase				=					DPRContextEncoderTokenizer






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """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
 _lowerCamelCase				=					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(SCREAMING_SNAKE_CASE__					)
class 		__snake_case :





 """simple docstring"""


 def __call__(   self ,						__lowerCamelCase ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = False ,						__lowerCamelCase = False ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						):



     '''simple docstring'''




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

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = 16 ,						__lowerCamelCase = 64 ,						__lowerCamelCase = 4 ,						):



     '''simple docstring'''




     __A   :						List[str]									=					reader_input['''input_ids''']
     __A			,	__A			,	__A   :						int									=					reader_output[:3]
     __A   :						Optional[int]									=					len(__lowerCamelCase  )
     __A   :						List[Any]									=					sorted(range(__lowerCamelCase  ) ,						reverse=__lowerCamelCase ,						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[str]									=					sequence_ids.index(self.sep_token_id ,						2  ) + 1  # second sep id
         if sequence_ids[-1] == self.pad_token_id:
             __A   :						Optional[int]									=					sequence_ids.index(self.pad_token_id  )
         else:
             __A   :						List[Any]									=					len(__lowerCamelCase  )

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







 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						):



     '''simple docstring'''




     __A   :						Optional[Any]									=					[]
     for start_index, start_score in enumerate(__lowerCamelCase  ):
         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   :						List[Any]									=					sorted(__lowerCamelCase ,						key=lambda __lowerCamelCase  : x[1] ,						reverse=__lowerCamelCase  )
     __A   :						str									=					[]
     for (start_index, end_index), score in scores:
         assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
         __A   :						Optional[Any]									=					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(__lowerCamelCase  ) == top_spans:
             break
     return chosen_span_intervals






@add_end_docstrings(SCREAMING_SNAKE_CASE__					)
class 		__snake_case (		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__					):





 """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"""]
 _lowerCamelCase				=					DPRReaderTokenizer











"""simple docstring"""



from typing import TYPE_CHECKING

from ....utils import _LazyModule


a_			=      {"""tokenization_tapex""": ["""TapexTokenizer"""]}


if TYPE_CHECKING:
       from .tokenization_tapex import TapexTokenizer


else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure)




