


'''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


_SCREAMING_SNAKE_CASE							: Union[str, Any]										=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							: Optional[int]										=			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_SCREAMING_SNAKE_CASE							: Dict										=			{
    "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"
        ),
    },
}
_SCREAMING_SNAKE_CASE							: Optional[int]										=			{
    "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"
        ),
    },
}
_SCREAMING_SNAKE_CASE							: 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"
        ),
    },
}

_SCREAMING_SNAKE_CASE							: str										=			{
    "facebook/dpr-ctx_encoder-single-nq-base": 5_12,
    "facebook/dpr-ctx_encoder-multiset-base": 5_12,
}
_SCREAMING_SNAKE_CASE							: int										=			{
    "facebook/dpr-question_encoder-single-nq-base": 5_12,
    "facebook/dpr-question_encoder-multiset-base": 5_12,
}
_SCREAMING_SNAKE_CASE							: str										=			{
    "facebook/dpr-reader-single-nq-base": 5_12,
    "facebook/dpr-reader-multiset-base": 5_12,
}


_SCREAMING_SNAKE_CASE							: List[str]										=			{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
_SCREAMING_SNAKE_CASE							: Optional[int]										=			{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
_SCREAMING_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     (					__lowerCamelCase		):






						'''simple docstring'''







						__snake_case				=   VOCAB_FILES_NAMES
						__snake_case				=   CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						__snake_case				=   CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__snake_case				=   CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
						__snake_case				=   DPRContextEncoderTokenizer






class 		_snake_case     (					__lowerCamelCase		):






						'''simple docstring'''







						__snake_case				=   VOCAB_FILES_NAMES
						__snake_case				=   QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						__snake_case				=   QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__snake_case				=   QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
						__snake_case				=   DPRQuestionEncoderTokenizer






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

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


_SCREAMING_SNAKE_CASE							: List[str]										=			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(__lowerCamelCase		)
class 		_snake_case     :






						'''simple docstring'''







						def __call__(	self:						List[str]   ,				__UpperCamelCase:						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]   ,				)				->						BatchEncoding:





										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:
														__magic_name__					:       Dict    							=							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__   ,				)
										__magic_name__					:       Dict    							=							titles if not isinstance(lowerCamelCase__   ,				lowerCamelCase__  ) else [titles]
										__magic_name__					:       Dict    							=							texts if not isinstance(lowerCamelCase__   ,				lowerCamelCase__  ) else [texts]
										__magic_name__					:       str    							=							len(lowerCamelCase__  )
										__magic_name__					:       List[str]    							=							questions if not isinstance(lowerCamelCase__   ,				lowerCamelCase__  ) else [questions] * n_passages
										assert len(lowerCamelCase__  ) == len(
										    lowerCamelCase__  ), f"""There should be as many titles than texts but got {len(lowerCamelCase__  )} titles and {len(lowerCamelCase__  )} texts."""
										__magic_name__					:       Optional[int]    							=							super().__call__(lowerCamelCase__   ,				lowerCamelCase__   ,				padding=lowerCamelCase__   ,				truncation=lowerCamelCase__  )["input_ids"]
										__magic_name__					:       Optional[Any]    							=							super().__call__(lowerCamelCase__   ,				add_special_tokens=lowerCamelCase__   ,				padding=lowerCamelCase__   ,				truncation=lowerCamelCase__  )["input_ids"]
										__magic_name__					:       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(lowerCamelCase__   ,				lowerCamelCase__  )
										    ]
										}
										if return_attention_mask is not False:
														__magic_name__					:       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]  )
														__magic_name__					:       int    							=							attention_mask
										return self.pad(lowerCamelCase__   ,				padding=lowerCamelCase__   ,				max_length=lowerCamelCase__   ,				return_tensors=lowerCamelCase__  )







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

														__magic_name__					:       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=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:						Optional[Any]   ,				__UpperCamelCase:						List[int]   ,				__UpperCamelCase:						List[int]   ,				__UpperCamelCase:						int   ,				__UpperCamelCase:						int   ,				)				->						List[DPRSpanPrediction]:
										__magic_name__					:       Optional[Any]    							=							[]
										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)  )
										__magic_name__					:       Union[str, Any]    							=							sorted(lowerCamelCase__   ,				key=lambda __UpperCamelCase  : x[1]   ,				reverse=lowerCamelCase__  )
										__magic_name__					:       List[Any]    							=							[]
										for (start_index, end_index), score in scores:
														assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
														__magic_name__					:       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(lowerCamelCase__  ) == top_spans:
																		break
										return chosen_span_intervals






@add_end_docstrings(__lowerCamelCase		)
class 		_snake_case     (					__lowerCamelCase	,	__lowerCamelCase		):






						'''simple docstring'''







						__snake_case				=   VOCAB_FILES_NAMES
						__snake_case				=   READER_PRETRAINED_VOCAB_FILES_MAP
						__snake_case				=   READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__snake_case				=   READER_PRETRAINED_INIT_CONFIGURATION
						__snake_case				=   ['input_ids', 'attention_mask']
						__snake_case				=   DPRReaderTokenizer


def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE = 1000   ):
      '''simple docstring'''







      return sum(2 * a * ((a - 1) // 2) for a in range(3		,				n + 1   )   )


if __name__ == "__main__":
      print(solution())





