





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


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_1_2,
    """facebook/dpr-ctx_encoder-multiset-base""": 5_1_2,
}
snake_case 				=       {
    """facebook/dpr-question_encoder-single-nq-base""": 5_1_2,
    """facebook/dpr-question_encoder-multiset-base""": 5_1_2,
}
snake_case 				=       {
    """facebook/dpr-reader-single-nq-base""": 5_1_2,
    """facebook/dpr-reader-multiset-base""": 5_1_2,
}


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 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : int      =  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_      : Any      =  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_      : List[str]      =  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_      : List[Any]      =  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
       SCREAMING_SNAKE_CASE_      : List[str]      =  DPRContextEncoderTokenizer





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : List[Any]      =  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_      : Optional[int]      =  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_      : int      =  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_      : Optional[Any]      =  QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
       SCREAMING_SNAKE_CASE_      : List[Any]      =  DPRQuestionEncoderTokenizer
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)

    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(UpperCAmelCase	)
class 				A_				:







       """simple docstring"""





       def __call__(    self	:	int						,__A	:	Union[str, 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	:	Optional[int]						,)						->					BatchEncoding:
         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:
           _lowercase							= 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						,)
         _lowercase							= titles if not isinstance(__A						,__A  ) else [titles]
         _lowercase							= texts if not isinstance(__A						,__A  ) else [texts]
         _lowercase							= len(__A  )
         _lowercase							= 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."""
         _lowercase							= super().__call__(__A						,__A						,padding=__A						,truncation=__A  )['input_ids']
         _lowercase							= super().__call__(__A						,add_special_tokens=__A						,padding=__A						,truncation=__A  )['input_ids']
         _lowercase							= {
             '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:
           _lowercase							= []
           for input_ids in encoded_inputs["input_ids"]:
             attention_mask.append([int(input_id != self.pad_token_id  ) for input_id in input_ids]  )
           _lowercase							= attention_mask
         return self.pad(__A						,padding=__A						,max_length=__A						,return_tensors=__A  )







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

           _lowercase							= 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	:	List[Any]						,__A	:	List[int]						,__A	:	List[int]						,__A	:	int						,__A	:	int						,)						->					List[DPRSpanPrediction]:
         _lowercase							= []
         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)  )
         _lowercase							= sorted(__A						,key=lambda __A  : x[1]						,reverse=__A  )
         _lowercase							= []
         for (start_index, end_index), score in scores:
           assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
           _lowercase							= 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(UpperCAmelCase	)
class 				A_				(      UpperCAmelCase					,	UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Optional[int]      =  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_      : Union[str, Any]      =  READER_PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_      : int      =  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_      : Dict      =  READER_PRETRAINED_INIT_CONFIGURATION
       SCREAMING_SNAKE_CASE_      : Tuple      =  ['''input_ids''', '''attention_mask''']
       SCREAMING_SNAKE_CASE_      : Tuple      =  DPRReaderTokenizer






from ....utils import logging


snake_case 				=       logging.get_logger(__name__)





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       def __init__(    self	:	Union[str, Any]						,__A	:	int						,__A	:	List[Any]=None						,__A	:	List[str]=2048  )						->					List[Any]:
         _lowercase							= config.__dict__
         _lowercase							= modal_hidden_size
         if num_labels:
           _lowercase							= num_labels