



'''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


__lowerCAmelCase						      =       logging.get_logger(__name__)

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

__lowerCAmelCase						      =       {
    '''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'''
        ),
    },
}
__lowerCAmelCase						      =       {
    '''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'''
        ),
    },
}
__lowerCAmelCase						      =       {
    '''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'''
        ),
    },
}

__lowerCAmelCase						      =       {
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
__lowerCAmelCase						      =       {
    '''facebook/dpr-question_encoder-single-nq-base''': 512,
    '''facebook/dpr-question_encoder-multiset-base''': 512,
}
__lowerCAmelCase						      =       {
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


__lowerCAmelCase						      =       {
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
__lowerCAmelCase						      =       {
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
__lowerCAmelCase						      =       {
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}

class      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   Union[str, Any]           = VOCAB_FILES_NAMES
							lowerCAmelCase	:   Any           = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase	:   Tuple           = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase	:   Optional[Any]           = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
							lowerCAmelCase	:   Optional[int]           = DPRContextEncoderTokenizer

class      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   Tuple           = VOCAB_FILES_NAMES
							lowerCAmelCase	:   Optional[Any]           = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase	:   List[Any]           = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase	:   Any           = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
							lowerCAmelCase	:   Union[str, Any]           = DPRQuestionEncoderTokenizer
__lowerCAmelCase						      =       collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

__lowerCAmelCase						      =       collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


__lowerCAmelCase						      =       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)

    Return:
        `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      __magic_name__						:







							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:
											_a					:				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     ,)
									_a					:				int				=							titles if not isinstance(_UpperCAmelCase     ,_UpperCAmelCase		) else [titles]
									_a					:				Tuple				=							texts if not isinstance(_UpperCAmelCase     ,_UpperCAmelCase		) else [texts]
									_a					:				List[Any]				=							len(_UpperCAmelCase		)
									_a					:				List[Any]				=							questions if not isinstance(_UpperCAmelCase     ,_UpperCAmelCase		) else [questions] * n_passages
									assert len(_UpperCAmelCase		) == len(
									    _UpperCAmelCase		), F"""There should be as many titles than texts but got {len(_UpperCAmelCase		)} titles and {len(_UpperCAmelCase		)} texts."""
									_a					:				int				=							super().__call__(_UpperCAmelCase     ,_UpperCAmelCase     ,padding=_UpperCAmelCase     ,truncation=_UpperCAmelCase		)['input_ids']
									_a					:				Tuple				=							super().__call__(_UpperCAmelCase     ,add_special_tokens=_UpperCAmelCase     ,padding=_UpperCAmelCase     ,truncation=_UpperCAmelCase		)['input_ids']
									_a					:				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(_UpperCAmelCase     ,_UpperCAmelCase		)
									    ]
									}
									if return_attention_mask is not False:
											_a					:				Optional[Any]				=							[]
											for input_ids in encoded_inputs["input_ids"]:
													attention_mask.append([int(input_id != self.pad_token_id		) for input_id in input_ids]		)
											_a					:				int				=							attention_mask
									return self.pad(_UpperCAmelCase     ,padding=_UpperCAmelCase     ,max_length=_UpperCAmelCase     ,return_tensors=_UpperCAmelCase		)







							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	BatchEncoding     ,_UpperCAmelCase :	DPRReaderOutput     ,_UpperCAmelCase :	int = 16     ,_UpperCAmelCase :	int = 64     ,_UpperCAmelCase :	int = 4     ,):
									_a					:				Optional[Any]				=							reader_input['input_ids']
									_a	, _a	, _a					:				Optional[Any]				=							reader_output[:3]
									_a					:				Any				=							len(_UpperCAmelCase		)
									_a					:				Any				=							sorted(range(_UpperCAmelCase		)     ,reverse=_UpperCAmelCase     ,key=relevance_logits.__getitem__		)
									_a					:				List[DPRReaderOutput]				=							[]
									for doc_id in sorted_docs:
											_a					:				List[str]				=							list(input_ids[doc_id]		)
											# assuming question & title information is at the beginning of the sequence
											_a					:				List[str]				=							sequence_ids.index(self.sep_token_id     ,2		) + 1  # second sep id
											if sequence_ids[-1] == self.pad_token_id:
													_a					:				List[Any]				=							sequence_ids.index(self.pad_token_id		)
											else:
													_a					:				List[str]				=							len(_UpperCAmelCase		)

											_a					:				Optional[int]				=							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 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	List[int]     ,_UpperCAmelCase :	List[int]     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	int     ,):
									_a					:				List[str]				=							[]
									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)		)
									_a					:				str				=							sorted(_UpperCAmelCase     ,key=lambda _UpperCAmelCase		: x[1]     ,reverse=_UpperCAmelCase		)
									_a					:				Any				=							[]
									for (start_index, end_index), score in scores:
											assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
											_a					:				Optional[int]				=							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(_UpperCAmelCase		) == top_spans:
													break
									return chosen_span_intervals

@add_end_docstrings(_UpperCamelCase       )
class      __magic_name__						(   _UpperCamelCase      ,						_UpperCamelCase       ):
							lowerCAmelCase	:   Any           = VOCAB_FILES_NAMES
							lowerCAmelCase	:   str           = READER_PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase	:   Optional[Any]           = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase	:   Dict           = READER_PRETRAINED_INIT_CONFIGURATION
							lowerCAmelCase	:   Optional[int]           = ['input_ids', 'attention_mask']
							lowerCAmelCase	:   int           = DPRReaderTokenizer









'''simple docstring'''

from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class      __magic_name__						:







							def __init__(							self :	Dict     ,_UpperCAmelCase :	Any		):
									_a					:				Any				=							data
									_a					:				Node | None				=							None

class      __magic_name__						:







							def __init__(							self :	Any		):
									_a					:				int				=							None
									_a					:				Optional[int]				=							None







							def __iter__(							self :	Optional[int]		):
									_a					:				List[Any]				=							self.head
									while self.head:
											yield node.data
											_a					:				str				=							node.next
											if node == self.head:
													break







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







							def __repr__(							self :	int		):
									return "->".join(str(_UpperCAmelCase		) for item in iter(self		)		)







							def 							__lowercase      (							self :	Optional[Any]     ,_UpperCAmelCase :	Any		):
									self.insert_nth(len(self		)     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	Any		):
									self.insert_nth(0     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	List[str]     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Any		):
									if index < 0 or index > len(self		):
											raise IndexError('list index out of range.'		)
									_a					:				List[str]				=							Node(_UpperCAmelCase		)
									if self.head is None:
											_a					:				Tuple				=							new_node  # first node points itself
											_a					:				int				=							new_node
									elif index == 0:  # insert at head
											_a					:				Any				=							self.head
											_a					:				Tuple				=							new_node
									else:
											_a					:				Any				=							self.head
											for _ in range(index - 1		):
													_a					:				int				=							temp.next
											_a					:				Optional[int]				=							temp.next
											_a					:				int				=							new_node
											if index == len(self		) - 1:  # insert at tail
													_a					:				Optional[int]				=							new_node







							def 							__lowercase      (							self :	List[Any]		):
									return self.delete_nth(0		)







							def 							__lowercase      (							self :	Dict		):
									return self.delete_nth(len(self		) - 1		)







							def 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	int = 0		):
									if not 0 <= index < len(self		):
											raise IndexError('list index out of range.'		)
									_a					:				Optional[int]				=							self.head
									if self.head == self.tail:  # just one node
											_a					:				Optional[int]				=							None
									elif index == 0:  # delete head node
											_a					:				Dict				=							self.tail.next.next
											_a					:				Dict				=							self.head.next
									else:
											_a					:				List[Any]				=							self.head
											for _ in range(index - 1		):
													_a					:				Union[str, Any]				=							temp.next
											_a					:				Optional[int]				=							temp.next
											_a					:				List[str]				=							temp.next.next
											if index == len(self		) - 1:  # delete at tail
													_a					:				int				=							temp
									return delete_node.data







							def 							__lowercase      (							self :	int		):
									return len(self		) == 0



def 					__lowerCamelCase					(				)      ->				None:
		_a					:				int				=							CircularLinkedList()
		assert len(lowerCAmelCase_  ) == 0
		assert circular_linked_list.is_empty() is True
		assert str(lowerCAmelCase_  ) == ""

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

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

		try:
				circular_linked_list.delete_nth(-1  )
				raise AssertionError
		except IndexError:
				assert True

		try:
				circular_linked_list.delete_nth(0  )
				raise AssertionError
		except IndexError:
				assert True

		assert circular_linked_list.is_empty() is True
		for i in range(5  ):
				assert len(lowerCAmelCase_  ) == i
				circular_linked_list.insert_nth(lowerCAmelCase_						,      i + 1  )
		assert str(lowerCAmelCase_  ) == "->".join(str(lowerCAmelCase_  ) for i in range(1						,      6  )  )

		circular_linked_list.insert_tail(6  )
		assert str(lowerCAmelCase_  ) == "->".join(str(lowerCAmelCase_  ) for i in range(1						,      7  )  )
		circular_linked_list.insert_head(0  )
		assert str(lowerCAmelCase_  ) == "->".join(str(lowerCAmelCase_  ) for i in range(0						,      7  )  )

		assert circular_linked_list.delete_front() == 0
		assert circular_linked_list.delete_tail() == 6
		assert str(lowerCAmelCase_  ) == "->".join(str(lowerCAmelCase_  ) for i in range(1						,      6  )  )
		assert circular_linked_list.delete_nth(2  ) == 3

		circular_linked_list.insert_nth(2						,      3  )
		assert str(lowerCAmelCase_  ) == "->".join(str(lowerCAmelCase_  ) for i in range(1						,      6  )  )

		assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
							import doctest

							doctest.testmod()




