


"""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""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
__snake_case   			=    {
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
__snake_case   			=    {
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


__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 _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       Dict							  =  VOCAB_FILES_NAMES
   __UpperCAmelCase :       Any							  =  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase :       Any							  =  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase :       List[str]							  =  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       List[Any]							  =  VOCAB_FILES_NAMES
   __UpperCAmelCase :       int							  =  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase :       int							  =  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase :       Any							  =  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(snake_case_	)
class _lowerCAmelCase				:







   def __call__(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						,	UpperCamelCase__ = None						,	UpperCamelCase__ = False						,	UpperCamelCase__ = False						,	UpperCamelCase__ = None						,	UpperCamelCase__ = None						,	UpperCamelCase__ = None						,	**UpperCamelCase__						,	)	->		BatchEncoding:

       '''simple docstring'''




       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:
           snake_case    :      List[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__						,	)
       snake_case    :      List[Any]      					=    titles if not isinstance(UpperCamelCase__						,	UpperCamelCase__						) else [titles]
       snake_case    :      int      					=    texts if not isinstance(UpperCamelCase__						,	UpperCamelCase__						) else [texts]
       snake_case    :      Union[str, Any]      					=    len(UpperCamelCase__						)
       snake_case    :      Dict      					=    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.'						)
       snake_case    :      int      					=    super().__call__(UpperCamelCase__						,	UpperCamelCase__						,	padding=UpperCamelCase__						,	truncation=UpperCamelCase__						)["input_ids"]
       snake_case    :      Dict      					=    super().__call__(UpperCamelCase__						,	add_special_tokens=UpperCamelCase__						,	padding=UpperCamelCase__						,	truncation=UpperCamelCase__						)["input_ids"]
       snake_case    :      Optional[int]      					=    {
           "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:
           snake_case    :      Optional[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(UpperCamelCase__						,	padding=UpperCamelCase__						,	max_length=UpperCamelCase__						,	return_tensors=UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__ = 16						,	UpperCamelCase__ = 64						,	UpperCamelCase__ = 4						,	)	->		List[DPRSpanPrediction]:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    reader_input["input_ids"]
       snake_case					,snake_case					,snake_case    :      Optional[Any]      					=    reader_output[:3]
       snake_case    :      List[str]      					=    len(UpperCamelCase__						)
       snake_case    :      int      					=    sorted(range(UpperCamelCase__						)						,	reverse=UpperCamelCase__						,	key=relevance_logits.__getitem__						)
       snake_case    :      List[DPRReaderOutput]      					=    []
       for doc_id in sorted_docs:
           snake_case    :      List[str]      					=    list(input_ids[doc_id]						)
           # assuming question & title information is at the beginning of the sequence
           snake_case    :      Tuple      					=    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[int]      					=    len(UpperCamelCase__						)

           snake_case    :      Any      					=    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  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	)	->		List[DPRSpanPrediction]:

       '''simple docstring'''




       snake_case    :      Any      					=    []
       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)						)
       snake_case    :      Optional[Any]      					=    sorted(UpperCamelCase__						,	key=lambda UpperCamelCase__						: x[1]						,	reverse=UpperCamelCase__						)
       snake_case    :      Union[str, Any]      					=    []
       for (start_index, end_index), score in scores:
           if start_index > end_index:
               raise ValueError(F'Wrong span indices: [{start_index}:{end_index}]'						)
           snake_case    :      Union[str, Any]      					=    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(snake_case_	)
class _lowerCAmelCase				(      snake_case_    ,							snake_case_	):
   __UpperCAmelCase :       List[str]							  =  VOCAB_FILES_NAMES
   __UpperCAmelCase :       List[Any]							  =  READER_PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase :       Optional[Any]							  =  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase :       List[str]							  =  READER_PRETRAINED_INIT_CONFIGURATION
   __UpperCAmelCase :       str							  =  ['''input_ids''', '''attention_mask''']









"""simple docstring"""



from typing import Dict

from .base import GenericTensor, Pipeline







class _lowerCAmelCase				(      snake_case_	):







   def  lowerCamelCase		(	self						,	UpperCamelCase__=None						,	UpperCamelCase__=None						,	UpperCamelCase__=None						,	**UpperCamelCase__						)	->		Union[str, Any]:

       '''simple docstring'''




       if tokenize_kwargs is None:
           snake_case    :      Optional[Any]      					=    {}

       if truncation is not None:
           if "truncation" in tokenize_kwargs:
               raise ValueError(
                   "truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"						)
           snake_case    :      List[str]      					=    truncation

       snake_case    :      Union[str, Any]      					=    tokenize_kwargs

       snake_case    :      List[Any]      					=    {}
       if return_tensors is not None:
           snake_case    :      Tuple      					=    return_tensors

       return preprocess_params, {}, postprocess_params







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	**UpperCamelCase__						)	->		Dict[str, GenericTensor]:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    self.framework
       snake_case    :      str      					=    self.tokenizer(UpperCamelCase__						,	return_tensors=UpperCamelCase__						,	**UpperCamelCase__						)
       return model_inputs







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Tuple:

       '''simple docstring'''




       snake_case    :      int      					=    self.model(**UpperCamelCase__						)
       return model_outputs







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__=False						)	->		Union[str, Any]:

       '''simple docstring'''




       if return_tensors:
           return model_outputs[0]
       if self.framework == "pt":
           return model_outputs[0].tolist()
       elif self.framework == "tf":
           return model_outputs[0].numpy().tolist()







   def __call__(	self						,	*UpperCamelCase__						,	**UpperCamelCase__						)	->		Union[str, Any]:

       '''simple docstring'''




       return super().__call__(*UpperCamelCase__						,	**UpperCamelCase__						)





