






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


A__			:    Any         =				logging.get_logger(__name__)

A__			:    List[Any]         =				{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A__			:    List[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'
        ),
    },
}
A__			:    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'
        ),
    },
}
A__			:    Optional[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'
        ),
    },
}

A__			:    str         =				{
    'facebook/dpr-ctx_encoder-single-nq-base': 5_12,
    'facebook/dpr-ctx_encoder-multiset-base': 5_12,
}
A__			:    List[Any]         =				{
    'facebook/dpr-question_encoder-single-nq-base': 5_12,
    'facebook/dpr-question_encoder-multiset-base': 5_12,
}
A__			:    Union[str, Any]         =				{
    'facebook/dpr-reader-single-nq-base': 5_12,
    'facebook/dpr-reader-multiset-base': 5_12,
}


A__			:    int         =				{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
A__			:    str         =				{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
A__			:    int         =				{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= VOCAB_FILES_NAMES
				lowercase__       							= CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				lowercase__       							= CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__       							= CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION



class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= VOCAB_FILES_NAMES
				lowercase__       							= QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				lowercase__       							= QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__       							= QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



A__			:    List[Any]         =				collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

A__			:    int         =				collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


A__			:    int         =				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    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\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    Returns:\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      :

				"""simple docstring"""




				def __call__(  self		:					int,						lowerCamelCase		:					Tuple,						lowerCamelCase		:					Optional[str] = None,						lowerCamelCase		:					Optional[str] = None,						lowerCamelCase		:					Union[bool, str] = False,						lowerCamelCase		:					Union[bool, str] = False,						lowerCamelCase		:					Optional[int] = None,						lowerCamelCase		:					Optional[Union[str, TensorType]] = None,						lowerCamelCase		:					Optional[bool] = None,						**lowerCamelCase		:					List[str],						):

								'''simple docstring'''
								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:
												lowercase__     				=    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,						)
								lowercase__     				=    titles if not isinstance(lowerCamelCase,						lowerCamelCase						) else [titles]
								lowercase__     				=    texts if not isinstance(lowerCamelCase,						lowerCamelCase						) else [texts]
								lowercase__     				=    len(lowerCamelCase						)
								lowercase__     				=    questions if not isinstance(lowerCamelCase,						lowerCamelCase						) else [questions] * n_passages
								if len(lowerCamelCase						) != len(lowerCamelCase						):
												raise ValueError(
												    F"""There should be as many titles than texts but got {len(lowerCamelCase						)} titles and {len(lowerCamelCase						)} texts."""						)
								lowercase__     				=    super().__call__(lowerCamelCase,						lowerCamelCase,						padding=lowerCamelCase,						truncation=lowerCamelCase						)['''input_ids''']
								lowercase__     				=    super().__call__(lowerCamelCase,						add_special_tokens=lowerCamelCase,						padding=lowerCamelCase,						truncation=lowerCamelCase						)['''input_ids''']
								lowercase__     				=    {
								    '''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:
												lowercase__     				=    []
												for input_ids in encoded_inputs["input_ids"]:
																attention_mask.append([int(input_id != self.pad_token_id						) for input_id in input_ids]						)
												lowercase__     				=    attention_mask
								return self.pad(lowerCamelCase,						padding=lowerCamelCase,						max_length=lowerCamelCase,						return_tensors=lowerCamelCase						)




				def        lowercase__     (  self		:					Tuple,						lowerCamelCase		:					BatchEncoding,						lowerCamelCase		:					DPRReaderOutput,						lowerCamelCase		:					int = 16,						lowerCamelCase		:					int = 64,						lowerCamelCase		:					int = 4,						):

								'''simple docstring'''
								lowercase__     				=    reader_input['''input_ids''']
								lowercase__				,		lowercase__				,		lowercase__     				=    reader_output[:3]
								lowercase__     				=    len(lowerCamelCase						)
								lowercase__     				=    sorted(range(lowerCamelCase						),						reverse=lowerCamelCase,						key=relevance_logits.__getitem__						)
								lowercase__     				=    []
								for doc_id in sorted_docs:
												lowercase__     				=    list(input_ids[doc_id]						)
												# assuming question & title information is at the beginning of the sequence
												lowercase__     				=    sequence_ids.index(self.sep_token_id,						2						) + 1  # second sep id
												if sequence_ids[-1] == self.pad_token_id:
																lowercase__     				=    sequence_ids.index(self.pad_token_id						)
												else:
																lowercase__     				=    len(lowerCamelCase						)

												lowercase__     				=    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        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					List[int],						lowerCamelCase		:					List[int],						lowerCamelCase		:					int,						lowerCamelCase		:					int,						):

								'''simple docstring'''
								lowercase__     				=    []
								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)						)
								lowercase__     				=    sorted(lowerCamelCase,						key=lambda lowerCamelCase						: x[1],						reverse=lowerCamelCase						)
								lowercase__     				=    []
								for (start_index, end_index), score in scores:
												if start_index > end_index:
																raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]"""						)
												lowercase__     				=    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(lowerCamelCase						) == top_spans:
																break
								return chosen_span_intervals



@add_end_docstrings(A__			)
class    _UpperCAmelCase      ( A__	,A__			):

				"""simple docstring"""




				lowercase__       							= VOCAB_FILES_NAMES
				lowercase__       							= READER_PRETRAINED_VOCAB_FILES_MAP
				lowercase__       							= READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__       							= READER_PRETRAINED_INIT_CONFIGURATION
				lowercase__       							= ["""input_ids""", """attention_mask"""]












from functools import reduce

A__			:    Union[str, Any]         =				(
    '73167176531330624919225119674426574742355349194934'
    '96983520312774506326239578318016984801869478851843'
    '85861560789112949495459501737958331952853208805511'
    '12540698747158523863050715693290963295227443043557'
    '66896648950445244523161731856403098711121722383113'
    '62229893423380308135336276614282806444486645238749'
    '30358907296290491560440772390713810515859307960866'
    '70172427121883998797908792274921901699720888093776'
    '65727333001053367881220235421809751254540594752243'
    '52584907711670556013604839586446706324415722155397'
    '53697817977846174064955149290862569321978468622482'
    '83972241375657056057490261407972968652414535100474'
    '82166370484403199890008895243450658541227588666881'
    '16427171479924442928230863465674813919123162824586'
    '17866458359124566529476545682848912883142607690042'
    '24219022671055626321111109370544217506941658960408'
    '07198403850962455444362981230987879927244284909188'
    '84580156166097919133875499200524063689912560717606'
    '05886116467109405077541002256983155200055935729725'
    '71636269561882670428252483600823257530420752963450'
)






def 						a    (		lowerCamelCase_ = N       ):






				'''simple docstring'''
				return max(
				    # mypy cannot properly interpret reduce
				    int(reduce(lambda lowerCamelCase_	,     lowerCamelCase_       : str(int(lowerCamelCase_       ) * int(lowerCamelCase_       )       )	,     n[i : i + 13]       )       )
				    for i in range(len(lowerCamelCase_       ) - 12       )       )


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




