
"""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__ :       int				= logging.get_logger(__name__)

A__ :       List[Any]				= {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A__ :       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'
        ),
    },
}
A__ :       List[str]				= {
    '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__ :       List[str]				= {
    '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__ :       Tuple				= {
    'facebook/dpr-ctx_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-ctx_encoder-multiset-base': 5_1_2,
}
A__ :       Tuple				= {
    'facebook/dpr-question_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-question_encoder-multiset-base': 5_1_2,
}
A__ :       List[str]				= {
    'facebook/dpr-reader-single-nq-base': 5_1_2,
    'facebook/dpr-reader-multiset-base': 5_1_2,
}


A__ :       Dict				= {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
A__ :       List[str]				= {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
A__ :       Union[str, Any]				= {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		VOCAB_FILES_NAMES
       UpperCamelCase_ =		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase_ =		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase_ =		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
       UpperCamelCase_ =		DPRContextEncoderTokenizer


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		VOCAB_FILES_NAMES
       UpperCamelCase_ =		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase_ =		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase_ =		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
       UpperCamelCase_ =		DPRQuestionEncoderTokenizer

A__ :       Tuple				= collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

A__ :       Optional[int]				= collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


A__ :       Optional[Any]				= r'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '


@add_start_docstrings(SCREAMING_SNAKE_CASE__			)
class       __magic_name__							:
       def __call__(  self			,					A_			,					A_ = None			,					A_ = None			,					A_ = False			,					A_ = False			,					A_ = None			,					A_ = None			,					A_ = None			,					**A_			,					)  ->						BatchEncoding:





         """simple docstring"""




         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:      Any   			=  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:      List[str]   			=  titles if not isinstance(A_			,					A_							) else [titles]
         _lowercase:      int   			=  texts if not isinstance(A_			,					A_							) else [texts]
         _lowercase:      List[str]   			=  len(A_							)
         _lowercase:      str   			=  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:      Any   			=  super().__call__(A_			,					A_			,					padding=A_			,					truncation=A_							)['''input_ids''']
         _lowercase:      Tuple   			=  super().__call__(A_			,					add_special_tokens=A_			,					padding=A_			,					truncation=A_							)['''input_ids''']
         _lowercase:      List[str]   			=  {
             '''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:      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]							)
           _lowercase:      List[Any]   			=  attention_mask
         return self.pad(A_			,					padding=A_			,					max_length=A_			,					return_tensors=A_							)
       def 				lowercase_				(  self			,					A_			,					A_			,					A_ = 16			,					A_ = 64			,					A_ = 4			,					)  ->						List[DPRSpanPrediction]:





         """simple docstring"""




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

           _lowercase:      List[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=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 				lowercase_				(  self			,					A_			,					A_			,					A_			,					A_			,					)  ->						List[DPRSpanPrediction]:





         """simple docstring"""




         _lowercase:      str   			=  []
         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:      Tuple   			=  sorted(A_			,					key=lambda A_							: x[1]			,					reverse=A_							)
         _lowercase:      Tuple   			=  []
         for (start_index, end_index), score in scores:
           assert start_index <= end_index, f'''Wrong span indices: [{start_index}:{end_index}]'''
           _lowercase:      Dict   			=  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(SCREAMING_SNAKE_CASE__			)
class       __magic_name__							(							SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			):
       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''']
       UpperCamelCase_ =		DPRReaderTokenizer


"""simple docstring"""




from collections.abc import Callable




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      float   			=  a
  _lowercase:      float   			=  b
  if function(_UpperCamelCase    ) == 0:  # one of the a or b is a root for the function
    return a
  elif function(_UpperCamelCase    ) == 0:
    return b
  elif (
      function(_UpperCamelCase    ) * function(_UpperCamelCase    ) > 0
  ):  # if none of these are root and they are both positive or negative,
    # then this algorithm can't find the root
    raise ValueError('''could not find root in given interval.'''    )
  else:
    _lowercase:      float   			=  start + (end - start) / 2.0
    while abs(start - mid    ) > 10**-7:  # until precisely equals to 10^-7
      if function(_UpperCamelCase    ) == 0:
        return mid
      elif function(_UpperCamelCase    ) * function(_UpperCamelCase    ) < 0:
        _lowercase:      Union[str, Any]   			=  mid
      else:
        _lowercase:      Any   			=  mid
      _lowercase:      List[Any]   			=  start + (end - start) / 2.0
    return mid




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  return x**3 - 2 * x - 5


if __name__ == "__main__":
  print(bisection(f, 1, 1_0_0_0))

  import doctest

  doctest.testmod()
