


"""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__		:     List[Any]    				=				logging.get_logger(__name__)

snake_case__		:     int    				=				{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case__		:     Union[str, 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'''
        ),
    },
}
snake_case__		:     Dict    				=				{
    '''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__		:     Optional[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'''
        ),
    },
}

snake_case__		:     str    				=				{
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
snake_case__		:     Union[str, Any]    				=				{
    '''facebook/dpr-question_encoder-single-nq-base''': 512,
    '''facebook/dpr-question_encoder-multiset-base''': 512,
}
snake_case__		:     Any    				=				{
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


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



class 		snake_case_(       a__			):
     __UpperCamelCase  =					VOCAB_FILES_NAMES
     __UpperCamelCase  =					CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase  =					CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase  =					CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION



class 		snake_case_(       a__			):
     __UpperCamelCase  =					VOCAB_FILES_NAMES
     __UpperCamelCase  =					QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase  =					QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase  =					QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



snake_case__		:     Union[str, Any]    				=				collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

snake_case__		:     List[Any]    				=				collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


snake_case__		:     str    				=				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 		snake_case_:






     def __call__(	self  :					Any					,				UpperCamelCase_  :					Optional[int]					,				UpperCamelCase_  :					Optional[str] = None					,				UpperCamelCase_  :					Optional[str] = None					,				UpperCamelCase_  :					Union[bool, str] = False					,				UpperCamelCase_  :					Union[bool, str] = False					,				UpperCamelCase_  :					Optional[int] = None					,				UpperCamelCase_  :					Optional[Union[str, TensorType]] = None					,				UpperCamelCase_  :					Optional[bool] = None					,				**UpperCamelCase_  :					List[Any]					,				):
           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:
                 lowerCAmelCase					:	Optional[int]       =   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_					,				)
           lowerCAmelCase					:	Dict       =   titles if not isinstance(UpperCamelCase_					,				UpperCamelCase_				) else [titles]
           lowerCAmelCase					:	Optional[int]       =   texts if not isinstance(UpperCamelCase_					,				UpperCamelCase_				) else [texts]
           lowerCAmelCase					:	str       =   len(UpperCamelCase_				)
           lowerCAmelCase					:	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.'''				)
           lowerCAmelCase					:	str       =   super().__call__(UpperCamelCase_					,				UpperCamelCase_					,				padding=UpperCamelCase_					,				truncation=UpperCamelCase_				)['''input_ids''']
           lowerCAmelCase					:	List[Any]       =   super().__call__(UpperCamelCase_					,				add_special_tokens=UpperCamelCase_					,				padding=UpperCamelCase_					,				truncation=UpperCamelCase_				)['''input_ids''']
           lowerCAmelCase					:	Union[str, Any]       =   {
               '''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:
                 lowerCAmelCase					:	List[str]       =   []
                 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					:	str       =   attention_mask
           return self.pad(UpperCamelCase_					,				padding=UpperCamelCase_					,				max_length=UpperCamelCase_					,				return_tensors=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					List[Any]					,				UpperCamelCase_  :					BatchEncoding					,				UpperCamelCase_  :					DPRReaderOutput					,				UpperCamelCase_  :					int = 1_6					,				UpperCamelCase_  :					int = 6_4					,				UpperCamelCase_  :					int = 4					,				):
           lowerCAmelCase					:	int       =   reader_input['''input_ids''']
           lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase					:	Dict       =   reader_output[:3]
           lowerCAmelCase					:	Tuple       =   len(UpperCamelCase_				)
           lowerCAmelCase					:	Dict       =   sorted(range(UpperCamelCase_				)					,				reverse=UpperCamelCase_					,				key=relevance_logits.__getitem__				)
           lowerCAmelCase					:	List[DPRReaderOutput]       =   []
           for doc_id in sorted_docs:
                 lowerCAmelCase					:	Any       =   list(input_ids[doc_id]				)
                 # assuming question & title information is at the beginning of the sequence
                 lowerCAmelCase					:	List[Any]       =   sequence_ids.index(self.sep_token_id					,				2				) + 1  # second sep id
                 if sequence_ids[-1] == self.pad_token_id:
                       lowerCAmelCase					:	int       =   sequence_ids.index(self.pad_token_id				)
                 else:
                       lowerCAmelCase					:	List[Any]       =   len(UpperCamelCase_				)

                 lowerCAmelCase					:	str       =   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  :					Any					,				UpperCamelCase_  :					List[int]					,				UpperCamelCase_  :					List[int]					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					int					,				):
           lowerCAmelCase					:	int       =   []
           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)				)
           lowerCAmelCase					:	Dict       =   sorted(UpperCamelCase_					,				key=lambda UpperCamelCase_				: x[1]					,				reverse=UpperCamelCase_				)
           lowerCAmelCase					:	List[Any]       =   []
           for (start_index, end_index), score in scores:
                 if start_index > end_index:
                       raise ValueError(F'''Wrong span indices: [{start_index}:{end_index}]'''				)
                 lowerCAmelCase					:	int       =   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(a__			)
class 		snake_case_(       a__							,					a__			):
     __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''']






"""simple docstring"""





class 		snake_case_:






     def __init__(	self  :					Union[str, Any]					,				UpperCamelCase_  :					str				):
           lowerCAmelCase					:	Dict       =   val
           lowerCAmelCase					:	str       =   None
           lowerCAmelCase					:	Dict       =   None






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					Dict				):
           if self.val:
                 if val < self.val:
                       if self.left is None:
                             lowerCAmelCase					:	int       =   Node(UpperCamelCase_				)
                       else:
                             self.left.insert(UpperCamelCase_				)
                 elif val > self.val:
                       if self.right is None:
                             lowerCAmelCase					:	Any       =   Node(UpperCamelCase_				)
                       else:
                             self.right.insert(UpperCamelCase_				)
           else:
                 lowerCAmelCase					:	Optional[Any]       =   val



def      _snake_case						(						_snake_case				:			Tuple ,   _snake_case				:			str			):
      # Recursive traversal
      if root:
            inorder(root.left ,   _snake_case			)
            res.append(root.val			)
            inorder(root.right ,   _snake_case			)


def      _snake_case						(						_snake_case				:			Optional[Any]			):
      # Build BST
      if len(_snake_case			) == 0:
            return arr
      lowerCAmelCase					:	Optional[Any]       =   Node(arr[0]			)
      for i in range(1 ,   len(_snake_case			)			):
            root.insert(arr[i]			)
      # Traverse BST in order.
      lowerCAmelCase					:	Optional[int]       =   []
      inorder(_snake_case ,   _snake_case			)
      return res


if __name__ == "__main__":
 print(tree_sort([10, 1, 3, 2, 9, 14, 13]))


