


from typing import Any



class 	__lowerCamelCase			:







       def __init__(			self: int,A_: Any					):






            '''simple docstring'''




            __UpperCamelCase =			data
            __UpperCamelCase =			None







       def __repr__(			self: Any					):






            '''simple docstring'''




            return F'''Node({self.data})'''



class 	__lowerCamelCase			:







       def __init__(			self: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			None







       def __iter__(			self: int					):






            '''simple docstring'''




            __UpperCamelCase =			self.head
            while node:
                 yield node.data
                 __UpperCamelCase =			node.next







       def __len__(			self: List[str]					):






            '''simple docstring'''




            return sum(1 for _ in self					)







       def __repr__(			self: Any					):






            '''simple docstring'''




            return "->".join([str(A_					) for item in self]					)







       def __getitem__(			self: int,A_: int					):






            '''simple docstring'''




            if not 0 <= index < len(self					):
                 raise ValueError('list index out of range.'					)
            for i, node in enumerate(self					):
                 if i == index:
                      return node
            return None







       def __setitem__(			self: int,A_: int,A_: Any					):






            '''simple docstring'''




            if not 0 <= index < len(self					):
                 raise ValueError('list index out of range.'					)
            __UpperCamelCase =			self.head
            for _ in range(A_					):
                 __UpperCamelCase =			current.next
            __UpperCamelCase =			data







       def 		snake_case_	(			self: Union[str, Any],A_: Any					):






            '''simple docstring'''




            self.insert_nth(len(self					),A_					)







       def 		snake_case_	(			self: List[Any],A_: Any					):






            '''simple docstring'''




            self.insert_nth(0,A_					)







       def 		snake_case_	(			self: Optional[Any],A_: int,A_: Any					):






            '''simple docstring'''




            if not 0 <= index <= len(self					):
                 raise IndexError('list index out of range'					)
            __UpperCamelCase =			Node(A_					)
            if self.head is None:
                 __UpperCamelCase =			new_node
            elif index == 0:
                 __UpperCamelCase =			self.head  # link new_node to head
                 __UpperCamelCase =			new_node
            else:
                 __UpperCamelCase =			self.head
                 for _ in range(index - 1					):
                      __UpperCamelCase =			temp.next
                 __UpperCamelCase =			temp.next
                 __UpperCamelCase =			new_node







       def 		snake_case_	(			self: str					):  # print every node data






            '''simple docstring'''




            print(self					)







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            return self.delete_nth(0					)







       def 		snake_case_	(			self: str					):  # delete from tail






            '''simple docstring'''




            return self.delete_nth(len(self					) - 1					)







       def 		snake_case_	(			self: Any,A_: int = 0					):






            '''simple docstring'''




            if not 0 <= index <= len(self					) - 1:  # test if index is valid
                 raise IndexError('List index out of range.'					)
            __UpperCamelCase =			self.head  # default first node
            if index == 0:
                 __UpperCamelCase =			self.head.next
            else:
                 __UpperCamelCase =			self.head
                 for _ in range(index - 1					):
                      __UpperCamelCase =			temp.next
                 __UpperCamelCase =			temp.next
                 __UpperCamelCase =			temp.next.next
            return delete_node.data







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            return self.head is None







       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




            __UpperCamelCase =			None
            __UpperCamelCase =			self.head

            while current:
                 # Store the current node's next node.
                 __UpperCamelCase =			current.next
                 # Make the current node's next point backwards
                 __UpperCamelCase =			prev
                 # Make the previous node be the current node
                 __UpperCamelCase =			current
                 # Make the current node the next node (to progress iteration)
                 __UpperCamelCase =			next_node
            # Return prev in order to put the head at the end
            __UpperCamelCase =			prev






def 		_A		(		)					-> None:


     """simple docstring"""


     __UpperCamelCase =			LinkedList()
     assert linked_list.is_empty() is True
     assert str(_lowercase ) == ""

     try:
          linked_list.delete_head()
          raise AssertionError  # This should not happen.
     except IndexError:
          assert True  # This should happen.

     try:
          linked_list.delete_tail()
          raise AssertionError  # This should not happen.
     except IndexError:
          assert True  # This should happen.

     for i in range(10 ):
          assert len(_lowercase ) == i
          linked_list.insert_nth(_lowercase					,  i + 1 )
     assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(1					,  11 ) )

     linked_list.insert_head(0 )
     linked_list.insert_tail(11 )
     assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(0					,  12 ) )

     assert linked_list.delete_head() == 0
     assert linked_list.delete_nth(9 ) == 10
     assert linked_list.delete_tail() == 11
     assert len(_lowercase ) == 9
     assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(1					,  10 ) )

     assert all(linked_list[i] == i + 1 for i in range(0					,  9 ) ) is True

     for i in range(0					,  9 ):
          __UpperCamelCase =			-i
     assert all(linked_list[i] == -i for i in range(0					,  9 ) ) is True

     linked_list.reverse()
     assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(-8					,  1 ) )






def 		_A		(		)					-> None:


     """simple docstring"""


     __UpperCamelCase =			[
         -9,
         1_00,
         Node(77_34_51_12 ),
         'dlrow olleH',
         7,
         55_55,
         0,
         -1_92.5_55_55,
         'Hello, world!',
         77.9,
         Node(10 ),
         None,
         None,
         12.20,
     ]
     __UpperCamelCase =			LinkedList()

     for i in test_input:
          linked_list.insert_tail(_lowercase )

     # Check if it's empty or not
     assert linked_list.is_empty() is False
     assert (
         str(_lowercase ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
         "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
     )

     # Delete the head
     __UpperCamelCase =			linked_list.delete_head()
     assert result == -9
     assert (
         str(_lowercase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
         "Hello, world!->77.9->Node(10)->None->None->12.2"
     )

     # Delete the tail
     __UpperCamelCase =			linked_list.delete_tail()
     assert result == 12.2
     assert (
         str(_lowercase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
         "Hello, world!->77.9->Node(10)->None->None"
     )

     # Delete a node in specific location in linked list
     __UpperCamelCase =			linked_list.delete_nth(10 )
     assert result is None
     assert (
         str(_lowercase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
         "Hello, world!->77.9->Node(10)->None"
     )

     # Add a Node instance to its head
     linked_list.insert_head(Node('Hello again, world!' ) )
     assert (
         str(_lowercase )
         == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
         "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
     )

     # Add None to its tail
     linked_list.insert_tail(_lowercase )
     assert (
         str(_lowercase )
         == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
         "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
     )

     # Reverse the linked list
     linked_list.reverse()
     assert (
         str(_lowercase )
         == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
         "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
     )






def 		_A		(		)					-> List[str]:


     """simple docstring"""


     from doctest import testmod

     testmod()

     __UpperCamelCase =			LinkedList()
     linked_list.insert_head(input('Inserting 1st at head ' ).strip() )
     linked_list.insert_head(input('Inserting 2nd at head ' ).strip() )
     print('\nPrint list:' )
     linked_list.print_list()
     linked_list.insert_tail(input('\nInserting 1st at tail ' ).strip() )
     linked_list.insert_tail(input('Inserting 2nd at tail ' ).strip() )
     print('\nPrint list:' )
     linked_list.print_list()
     print('\nDelete head' )
     linked_list.delete_head()
     print('Delete tail' )
     linked_list.delete_tail()
     print('\nPrint list:' )
     linked_list.print_list()
     print('\nReverse linked list' )
     linked_list.reverse()
     print('\nPrint list:' )
     linked_list.print_list()
     print('\nString representation of linked list:' )
     print(_lowercase )
     print('\nReading/changing Node data using indexing:' )
     print(f'''Element at Position 1: {linked_list[1]}''' )
     __UpperCamelCase =			input('Enter New Value: ' ).strip()
     print('New list:' )
     print(_lowercase )
     print(f'''length of linked_list is : {len(_lowercase )}''' )


if __name__ == "__main__":
    main()










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


__a:     Tuple   					=					logging.get_logger(__name__)

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

__a:     Any   					=					{
    '''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:     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'''
        ),
    },
}
__a:     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:     Dict   					=					{
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
__a:     List[str]   					=					{
    '''facebook/dpr-question_encoder-single-nq-base''': 512,
    '''facebook/dpr-question_encoder-multiset-base''': 512,
}
__a:     Dict   					=					{
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


__a:     Optional[int]   					=					{
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
__a:     Tuple   					=					{
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
__a:     Optional[int]   					=					{
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''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



class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''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



__a:     List[Any]   					=					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'''
    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(UpperCAmelCase     )
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __call__(      self		:			int       ,				lowerCamelCase		:			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		:			Optional[int]       ,				)						->	BatchEncoding:


         """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:
                _UpperCAmelCase							=    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       ,				)
         _UpperCAmelCase							=    titles if not isinstance(lowerCamelCase       ,				lowerCamelCase							) else [titles]
         _UpperCAmelCase							=    texts if not isinstance(lowerCamelCase       ,				lowerCamelCase							) else [texts]
         _UpperCAmelCase							=    len(lowerCamelCase							)
         _UpperCAmelCase							=    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."""							)
         _UpperCAmelCase							=    super().__call__(lowerCamelCase       ,				lowerCamelCase       ,				padding=lowerCamelCase       ,				truncation=lowerCamelCase							)["""input_ids"""]
         _UpperCAmelCase							=    super().__call__(lowerCamelCase       ,				add_special_tokens=lowerCamelCase       ,				padding=lowerCamelCase       ,				truncation=lowerCamelCase							)["""input_ids"""]
         _UpperCAmelCase							=    {
             """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:
                _UpperCAmelCase							=    []
                for input_ids in encoded_inputs["input_ids"]:
                       attention_mask.append([int(input_id != self.pad_token_id							) for input_id in input_ids]							)
                _UpperCAmelCase							=    attention_mask
         return self.pad(lowerCamelCase       ,				padding=lowerCamelCase       ,				max_length=lowerCamelCase       ,				return_tensors=lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			BatchEncoding       ,				lowerCamelCase		:			DPRReaderOutput       ,				lowerCamelCase		:			int = 16       ,				lowerCamelCase		:			int = 64       ,				lowerCamelCase		:			int = 4       ,				)						->	List[DPRSpanPrediction]:


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

                _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			List[int]       ,				lowerCamelCase		:			List[int]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			int       ,				)						->	List[DPRSpanPrediction]:


         """simple docstring"""
         _UpperCAmelCase							=    []
         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)							)
         _UpperCAmelCase							=    sorted(lowerCamelCase       ,				key=lambda lowerCamelCase							: x[1]       ,				reverse=lowerCamelCase							)
         _UpperCAmelCase							=    []
         for (start_index, end_index), score in scores:
                if start_index > end_index:
                       raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]"""							)
                _UpperCAmelCase							=    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(UpperCAmelCase     )
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      UpperCAmelCase     ):

  '''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''']