


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 					_a							(				_lowerCAmelCase):
	_a  :  Optional[Any]          =							VOCAB_FILES_NAMES
	_a  :  Union[str, Any]          =							CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	_a  :  Tuple          =							CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_a  :  str          =							CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
	_a  :  List[Any]          =							DPRContextEncoderTokenizer





class 					_a							(				_lowerCAmelCase):
	_a  :  Dict          =							VOCAB_FILES_NAMES
	_a  :  List[str]          =							QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	_a  :  Optional[Any]          =							QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_a  :  Union[str, Any]          =							QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
	_a  :  Tuple          =							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(_lowerCAmelCase)
class 					_a							:




	def __call__(					self						:		Any		,					_SCREAMING_SNAKE_CASE						:		Union[str, Any]		,					_SCREAMING_SNAKE_CASE						:		Optional[str] = None		,					_SCREAMING_SNAKE_CASE						:		Optional[str] = None		,					_SCREAMING_SNAKE_CASE						:		Union[bool, str] = False		,					_SCREAMING_SNAKE_CASE						:		Union[bool, str] = False		,					_SCREAMING_SNAKE_CASE						:		Optional[int] = None		,					_SCREAMING_SNAKE_CASE						:		Optional[Union[str, TensorType]] = None		,					_SCREAMING_SNAKE_CASE						:		Optional[bool] = None		,					**_SCREAMING_SNAKE_CASE						:		List[Any]		,					)->       Optional[int]:




			if titles is None and texts is None:
					return super().__call__(
					    _SCREAMING_SNAKE_CASE		,					padding=_SCREAMING_SNAKE_CASE		,					truncation=_SCREAMING_SNAKE_CASE		,					max_length=_SCREAMING_SNAKE_CASE		,					return_tensors=_SCREAMING_SNAKE_CASE		,					return_attention_mask=_SCREAMING_SNAKE_CASE		,					**_SCREAMING_SNAKE_CASE		,					)
			elif titles is None or texts is None:
					lowerCAmelCase__   :			List[Any]         =					titles if texts is None else texts
					return super().__call__(
					    _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					padding=_SCREAMING_SNAKE_CASE		,					truncation=_SCREAMING_SNAKE_CASE		,					max_length=_SCREAMING_SNAKE_CASE		,					return_tensors=_SCREAMING_SNAKE_CASE		,					return_attention_mask=_SCREAMING_SNAKE_CASE		,					**_SCREAMING_SNAKE_CASE		,					)
			lowerCAmelCase__   :			Union[str, Any]         =					titles if not isinstance(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		) else [titles]
			lowerCAmelCase__   :			Union[str, Any]         =					texts if not isinstance(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		) else [texts]
			lowerCAmelCase__   :			Any         =					len(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__   :			Optional[Any]         =					questions if not isinstance(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		) else [questions] * n_passages
			assert len(_SCREAMING_SNAKE_CASE		) == len(
			    _SCREAMING_SNAKE_CASE		), F'There should be as many titles than texts but got {len(_SCREAMING_SNAKE_CASE		)} titles and {len(_SCREAMING_SNAKE_CASE		)} texts.'
			lowerCAmelCase__   :			int         =					super().__call__(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					padding=_SCREAMING_SNAKE_CASE		,					truncation=_SCREAMING_SNAKE_CASE		)["input_ids"]
			lowerCAmelCase__   :			Dict         =					super().__call__(_SCREAMING_SNAKE_CASE		,					add_special_tokens=_SCREAMING_SNAKE_CASE		,					padding=_SCREAMING_SNAKE_CASE		,					truncation=_SCREAMING_SNAKE_CASE		)["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(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		)
			    ]
			}
			if return_attention_mask is not False:
					lowerCAmelCase__   :			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]		)
					lowerCAmelCase__   :			List[Any]         =					attention_mask
			return self.pad(_SCREAMING_SNAKE_CASE		,					padding=_SCREAMING_SNAKE_CASE		,					max_length=_SCREAMING_SNAKE_CASE		,					return_tensors=_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__(					self						:		Tuple		,					_SCREAMING_SNAKE_CASE						:		BatchEncoding		,					_SCREAMING_SNAKE_CASE						:		DPRReaderOutput		,					_SCREAMING_SNAKE_CASE						:		int = 16		,					_SCREAMING_SNAKE_CASE						:		int = 64		,					_SCREAMING_SNAKE_CASE						:		int = 4		,					)->       Optional[int]:
			lowerCAmelCase__   :			Dict         =					reader_input["input_ids"]
			lowerCAmelCase__   :			Optional[int]         =					reader_output[:3]
			lowerCAmelCase__   :			List[str]         =					len(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__   :			List[str]         =					sorted(range(_SCREAMING_SNAKE_CASE		)		,					reverse=_SCREAMING_SNAKE_CASE		,					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__   :			Tuple         =					sequence_ids.index(self.sep_token_id		,					2		) + 1  # second sep id
					if sequence_ids[-1] == self.pad_token_id:
							lowerCAmelCase__   :			List[str]         =					sequence_ids.index(self.pad_token_id		)
					else:
							lowerCAmelCase__   :			Union[str, Any]         =					len(_SCREAMING_SNAKE_CASE		)

					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=_SCREAMING_SNAKE_CASE		,					top_spans=_SCREAMING_SNAKE_CASE		,					)
					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=_SCREAMING_SNAKE_CASE		,					start_index=_SCREAMING_SNAKE_CASE		,					end_index=_SCREAMING_SNAKE_CASE		,					text=self.decode(sequence_ids[start_index : end_index + 1]		)		,					)		)
					if len(_SCREAMING_SNAKE_CASE		) >= num_spans:
							break
			return nbest_spans_predictions[:num_spans]




	def 							UpperCAmelCase__(					self						:		List[str]		,					_SCREAMING_SNAKE_CASE						:		List[int]		,					_SCREAMING_SNAKE_CASE						:		List[int]		,					_SCREAMING_SNAKE_CASE						:		int		,					_SCREAMING_SNAKE_CASE						:		int		,					)->       List[Any]:
			lowerCAmelCase__   :			List[str]         =					[]
			for start_index, start_score in enumerate(_SCREAMING_SNAKE_CASE		):
					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__   :			Union[str, Any]         =					sorted(_SCREAMING_SNAKE_CASE		,					key=lambda _SCREAMING_SNAKE_CASE		: x[1]		,					reverse=_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__   :			Dict         =					[]
			for (start_index, end_index), score in scores:
					assert start_index <= end_index, F'Wrong span indices: [{start_index}:{end_index}]'
					lowerCAmelCase__   :			Union[str, 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(_SCREAMING_SNAKE_CASE		) == top_spans:
							break
			return chosen_span_intervals





@add_end_docstrings(_lowerCAmelCase)
class 					_a							(				_lowerCAmelCase     ,						_lowerCAmelCase):
	_a  :  Optional[int]          =							VOCAB_FILES_NAMES
	_a  :  int          =							READER_PRETRAINED_VOCAB_FILES_MAP
	_a  :  int          =							READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_a  :  Optional[int]          =							READER_PRETRAINED_INIT_CONFIGURATION
	_a  :  List[str]          =							['''input_ids''', '''attention_mask''']
	_a  :  Union[str, Any]          =							DPRReaderTokenizer











'''simple docstring'''


def 							UpperCAmelCase_  (						__lowerCamelCase					:					int = 1_00 ):
   lowercase_			:Tuple    			= n * (n + 1) * (2 * n + 1) / 6
   lowercase_			:List[str]    			= (n * (n + 1) / 2) ** 2
   return int(square_of_sum - sum_of_squares )


if __name__ == "__main__":
 print(F'''{solution() = }''')


