
'''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


UpperCAmelCase_       :   Optional[int]   =       logging.get_logger(__name__)

UpperCAmelCase_       :   str   =       {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase_       :   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'
        ),
    },
}
UpperCAmelCase_       :   Union[str, 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'
        ),
    },
}
UpperCAmelCase_       :   List[Any]   =       {
    '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'
        ),
    },
}

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


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

class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		VOCAB_FILES_NAMES
    lowerCAmelCase_				    =		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase_				    =		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase_				    =		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
    lowerCAmelCase_				    =		DPRContextEncoderTokenizer


class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		VOCAB_FILES_NAMES
    lowerCAmelCase_				    =		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase_				    =		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase_				    =		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
    lowerCAmelCase_				    =		DPRQuestionEncoderTokenizer


UpperCAmelCase_       :   Optional[int]   =       collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

UpperCAmelCase_       :   Union[str, Any]   =       collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


UpperCAmelCase_       :   List[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(A )
class  UpperCAmelCase__						:



    def __call__(	self      :       str,__A      :       Dict,__A      :       Optional[str] = None,__A      :       Optional[str] = None,__A      :       Union[bool, str] = False,__A      :       Union[bool, str] = False,__A      :       Optional[int] = None,__A      :       Optional[Union[str, TensorType]] = None,__A      :       Optional[bool] = None,**__A      :       Optional[int],):
          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:
                _lowerCamelCase				:   str								=   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,)
          _lowerCamelCase				:   int								=   titles if not isinstance(__A,__A     ) else [titles]
          _lowerCamelCase				:   List[Any]								=   texts if not isinstance(__A,__A     ) else [texts]
          _lowerCamelCase				:   Tuple								=   len(__A     )
          _lowerCamelCase				:   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.'
          _lowerCamelCase				:   int								=   super().__call__(__A,__A,padding=__A,truncation=__A     )["input_ids"]
          _lowerCamelCase				:   int								=   super().__call__(__A,add_special_tokens=__A,padding=__A,truncation=__A     )["input_ids"]
          _lowerCamelCase				:   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(__A,__A     )
              ]
          }
          if return_attention_mask is not False:
                _lowerCamelCase				:   Dict								=   []
                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				:   int								=   attention_mask
          return self.pad(__A,padding=__A,max_length=__A,return_tensors=__A     )



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

                _lowerCamelCase				:   Dict								=   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   lowerCamelCase_  (	self      :       List[str],__A      :       List[int],__A      :       List[int],__A      :       int,__A      :       int,):
          _lowerCamelCase				:   int								=   []
          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)     )
          _lowerCamelCase				:   List[str]								=   sorted(__A,key=lambda __A     : x[1],reverse=__A     )
          _lowerCamelCase				:   List[Any]								=   []
          for (start_index, end_index), score in scores:
                assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
                _lowerCamelCase				:   Optional[Any]								=   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(A )
class  UpperCAmelCase__						(       A						,	A ):
    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']
    lowerCAmelCase_				    =		DPRReaderTokenizer

'''simple docstring'''






from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

UpperCAmelCase_       :   str   =       (3, 9, -11, 0, 7, 5, 1, -1)
UpperCAmelCase_       :   int   =       (4, 6, 2, 0, 8, 10, 3, -2)

@dataclass
class  UpperCAmelCase__						:
    lowerCAmelCase_				    =		42
    lowerCAmelCase_				    =		42


class  UpperCAmelCase__						:



    def __init__(	self      :       Optional[int],__A      :       Iterable[int]     ):
          _lowerCamelCase				:   Node | None								=   None
          for i in sorted(__A,reverse=__A     ):
                _lowerCamelCase				:   Dict								=   Node(__A,self.head     )



    def __iter__(	self      :       str     ):
          _lowerCamelCase				:   Dict								=   self.head
          while node:
                yield node.data
                _lowerCamelCase				:   Optional[Any]								=   node.next_node



    def __len__(	self      :       str     ):
          return sum(1 for _ in self     )



    def __str__(	self      :       str     ):
          return " -> ".join([str(__A     ) for node in self]     )







def 		A_       (	_lowerCAmelCase   :    SortedLinkedList				,	_lowerCAmelCase   :    SortedLinkedList							):



      """simple docstring"""




      return SortedLinkedList(list(_lowerCAmelCase							) + list(_lowerCAmelCase							)							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 UpperCAmelCase_       :   List[str]   =       SortedLinkedList
 print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))