




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


__magic_name__  :  Tuple					=       logging.get_logger(__name__)

__magic_name__  :  List[str]					=       {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

__magic_name__  :  Optional[int]					=       {
    """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"""
        ),
    },
}
__magic_name__  :  Optional[Any]					=       {
    """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"""
        ),
    },
}
__magic_name__  :  int					=       {
    """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"""
        ),
    },
}

__magic_name__  :  Any					=       {
    """facebook/dpr-ctx_encoder-single-nq-base""": 5_12,
    """facebook/dpr-ctx_encoder-multiset-base""": 5_12,
}
__magic_name__  :  Tuple					=       {
    """facebook/dpr-question_encoder-single-nq-base""": 5_12,
    """facebook/dpr-question_encoder-multiset-base""": 5_12,
}
__magic_name__  :  List[Any]					=       {
    """facebook/dpr-reader-single-nq-base""": 5_12,
    """facebook/dpr-reader-multiset-base""": 5_12,
}


__magic_name__  :  int					=       {
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
__magic_name__  :  Union[str, Any]					=       {
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
__magic_name__  :  Union[str, Any]					=       {
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}





class     SCREAMING_SNAKE_CASE__		(_a			):
     lowercase_ :      List[Any]				= VOCAB_FILES_NAMES
     lowercase_ :      List[str]				= CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     lowercase_ :      List[Any]				= CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase_ :      Any				= CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class     SCREAMING_SNAKE_CASE__		(_a			):
     lowercase_ :      Dict				= VOCAB_FILES_NAMES
     lowercase_ :      Optional[int]				= QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     lowercase_ :      Dict				= QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase_ :      List[Any]				= QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



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

__magic_name__  :  int					=       collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


__magic_name__  :  Any					=       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(_a			)
class     SCREAMING_SNAKE_CASE__		:
     def __call__(						self	:		List[str]       ,						__lowerCamelCase	:		Optional[int]       ,						__lowerCamelCase	:		Optional[str] = None       ,						__lowerCamelCase	:		Optional[str] = None       ,						__lowerCamelCase	:		Union[bool, str] = False       ,						__lowerCamelCase	:		Union[bool, str] = False       ,						__lowerCamelCase	:		Optional[int] = None       ,						__lowerCamelCase	:		Optional[Union[str, TensorType]] = None       ,						__lowerCamelCase	:		Optional[bool] = None       ,						**__lowerCamelCase	:		List[str]       ,						):



       """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:
         lowerCAmelCase__										=      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       ,						)
       lowerCAmelCase__										=      titles if not isinstance(__lowerCamelCase       ,						__lowerCamelCase						) else [titles]
       lowerCAmelCase__										=      texts if not isinstance(__lowerCamelCase       ,						__lowerCamelCase						) else [texts]
       lowerCAmelCase__										=      len(__lowerCamelCase						)
       lowerCAmelCase__										=      questions if not isinstance(__lowerCamelCase       ,						__lowerCamelCase						) else [questions] * n_passages
       if len(__lowerCamelCase						) != len(__lowerCamelCase						):
         raise ValueError(
             F"""There should be as many titles than texts but got {len(__lowerCamelCase						)} titles and {len(__lowerCamelCase						)} texts."""						)
       lowerCAmelCase__										=      super().__call__(__lowerCamelCase       ,						__lowerCamelCase       ,						padding=__lowerCamelCase       ,						truncation=__lowerCamelCase						)['''input_ids''']
       lowerCAmelCase__										=      super().__call__(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase       ,						padding=__lowerCamelCase       ,						truncation=__lowerCamelCase						)['''input_ids''']
       lowerCAmelCase__										=      {
           '''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:
         lowerCAmelCase__										=      []
         for input_ids in encoded_inputs["input_ids"]:
           attention_mask.append([int(input_id != self.pad_token_id						) for input_id in input_ids]						)
         lowerCAmelCase__										=      attention_mask
       return self.pad(__lowerCamelCase       ,						padding=__lowerCamelCase       ,						max_length=__lowerCamelCase       ,						return_tensors=__lowerCamelCase						)
     def        A__      (						self	:		int       ,						__lowerCamelCase	:		BatchEncoding       ,						__lowerCamelCase	:		DPRReaderOutput       ,						__lowerCamelCase	:		int = 16       ,						__lowerCamelCase	:		int = 64       ,						__lowerCamelCase	:		int = 4       ,						):



       """simple docstring"""
       lowerCAmelCase__										=      reader_input['''input_ids''']
       lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__										=      reader_output[:3]
       lowerCAmelCase__										=      len(__lowerCamelCase						)
       lowerCAmelCase__										=      sorted(range(__lowerCamelCase						)       ,						reverse=__lowerCamelCase       ,						key=relevance_logits.__getitem__						)
       lowerCAmelCase__										=      []
       for doc_id in sorted_docs:
         lowerCAmelCase__										=      list(input_ids[doc_id]						)
         # assuming question & title information is at the beginning of the sequence
         lowerCAmelCase__										=      sequence_ids.index(self.sep_token_id       ,						2						) + 1  # second sep id
         if sequence_ids[-1] == self.pad_token_id:
           lowerCAmelCase__										=      sequence_ids.index(self.pad_token_id						)
         else:
           lowerCAmelCase__										=      len(__lowerCamelCase						)

         lowerCAmelCase__										=      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        A__      (						self	:		Tuple       ,						__lowerCamelCase	:		List[int]       ,						__lowerCamelCase	:		List[int]       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		int       ,						):



       """simple docstring"""
       lowerCAmelCase__										=      []
       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)						)
       lowerCAmelCase__										=      sorted(__lowerCamelCase       ,						key=lambda __lowerCamelCase						: x[1]       ,						reverse=__lowerCamelCase						)
       lowerCAmelCase__										=      []
       for (start_index, end_index), score in scores:
         if start_index > end_index:
           raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]"""						)
         lowerCAmelCase__										=      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(__lowerCamelCase						) == top_spans:
           break
       return chosen_span_intervals





@add_end_docstrings(_a			)
class     SCREAMING_SNAKE_CASE__		(_a    ,					_a			):
     lowercase_ :      Tuple				= VOCAB_FILES_NAMES
     lowercase_ :      Union[str, Any]				= READER_PRETRAINED_VOCAB_FILES_MAP
     lowercase_ :      int				= READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase_ :      str				= READER_PRETRAINED_INIT_CONFIGURATION
     lowercase_ :      Dict				= ["input_ids", "attention_mask"]







import numpy as np
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      int(np.ceil((x_end - xa) / h				)				)
  lowerCAmelCase__										=      np.zeros((n + 1,)				)
  lowerCAmelCase__										=      ya
  lowerCAmelCase__										=      xa

  for k in range(__lowerCAmelCase				):
    lowerCAmelCase__										=      f(__lowerCAmelCase   , y[k]				)
    lowerCAmelCase__										=      f(x + 0.5 * h   , y[k] + 0.5 * h * ka				)
    lowerCAmelCase__										=      f(x + 0.5 * h   , y[k] + 0.5 * h * ka				)
    lowerCAmelCase__										=      f(x + h   , y[k] + h * ka				)
    lowerCAmelCase__										=      y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
    x += h

  return y


if __name__ == "__main__":
      import doctest

      doctest.testmod()

