




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


SCREAMING_SNAKE_CASE:     Optional[Any]									= logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE:     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'''
        ),
    },
}
SCREAMING_SNAKE_CASE:     Dict									= {
    '''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:     Union[str, 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'''
        ),
    },
}

SCREAMING_SNAKE_CASE:     Tuple									= {
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_1_2,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_1_2,
}
SCREAMING_SNAKE_CASE:     List[str]									= {
    '''facebook/dpr-question_encoder-single-nq-base''': 5_1_2,
    '''facebook/dpr-question_encoder-multiset-base''': 5_1_2,
}
SCREAMING_SNAKE_CASE:     str									= {
    '''facebook/dpr-reader-single-nq-base''': 5_1_2,
    '''facebook/dpr-reader-multiset-base''': 5_1_2,
}


SCREAMING_SNAKE_CASE:     Optional[int]									= {
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
SCREAMING_SNAKE_CASE:     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:     Optional[Any]									= {
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      =VOCAB_FILES_NAMES
						lowerCAmelCase__			      =CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase__			      =CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase__			      =CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      =VOCAB_FILES_NAMES
						lowerCAmelCase__			      =QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase__			      =QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase__			      =QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION

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

SCREAMING_SNAKE_CASE:     List[Any]									= collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


SCREAMING_SNAKE_CASE:     Tuple									= 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)

    Returns:
        `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(SCREAMING_SNAKE_CASE__ )
class    lowercase_		:



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

								"""simple docstring"""

								if titles is None and texts is None:
										return super().__call__(
										    snake_case__					,							padding=snake_case__					,							truncation=snake_case__					,							max_length=snake_case__					,							return_tensors=snake_case__					,							return_attention_mask=snake_case__					,							**snake_case__					,							)
								elif titles is None or texts is None:
										SCREAMING_SNAKE_CASE_					      =							titles if texts is None else texts
										return super().__call__(
										    snake_case__					,							snake_case__					,							padding=snake_case__					,							truncation=snake_case__					,							max_length=snake_case__					,							return_tensors=snake_case__					,							return_attention_mask=snake_case__					,							**snake_case__					,							)
								SCREAMING_SNAKE_CASE_					      =							titles if not isinstance(snake_case__					,							snake_case__  ) else [titles]
								SCREAMING_SNAKE_CASE_					      =							texts if not isinstance(snake_case__					,							snake_case__  ) else [texts]
								SCREAMING_SNAKE_CASE_					      =							len(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							questions if not isinstance(snake_case__					,							snake_case__  ) else [questions] * n_passages
								if len(snake_case__  ) != len(snake_case__  ):
										raise ValueError(
										    f'''There should be as many titles than texts but got {len(snake_case__  )} titles and {len(snake_case__  )} texts.'''  )
								SCREAMING_SNAKE_CASE_					      =							super().__call__(snake_case__					,							snake_case__					,							padding=snake_case__					,							truncation=snake_case__  )['input_ids']
								SCREAMING_SNAKE_CASE_					      =							super().__call__(snake_case__					,							add_special_tokens=snake_case__					,							padding=snake_case__					,							truncation=snake_case__  )['input_ids']
								SCREAMING_SNAKE_CASE_					      =							{
								    '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(snake_case__					,							snake_case__  )
								    ]
								}
								if return_attention_mask is not False:
										SCREAMING_SNAKE_CASE_					      =							[]
										for input_ids in encoded_inputs["input_ids"]:
												attention_mask.append([int(input_id != self.pad_token_id  ) for input_id in input_ids]  )
										SCREAMING_SNAKE_CASE_					      =							attention_mask
								return self.pad(snake_case__					,							padding=snake_case__					,							max_length=snake_case__					,							return_tensors=snake_case__  )



						def 	__a					(			self	:   Optional[Any]					,							snake_case__	:   BatchEncoding					,							snake_case__	:   DPRReaderOutput					,							snake_case__	:   int = 16					,							snake_case__	:   int = 64					,							snake_case__	:   int = 4					,							):

								"""simple docstring"""

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

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






						def 	__a					(			self	:   Union[str, Any]					,							snake_case__	:   List[int]					,							snake_case__	:   List[int]					,							snake_case__	:   int					,							snake_case__	:   int					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							[]
								for start_index, start_score in enumerate(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)  )
								SCREAMING_SNAKE_CASE_					      =							sorted(snake_case__					,							key=lambda snake_case__  : x[1]					,							reverse=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							[]
								for (start_index, end_index), score in scores:
										if start_index > end_index:
												raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''  )
										SCREAMING_SNAKE_CASE_					      =							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(snake_case__  ) == top_spans:
												break
								return chosen_span_intervals



@add_end_docstrings(SCREAMING_SNAKE_CASE__ )
class    lowercase_		(SCREAMING_SNAKE_CASE__							,	SCREAMING_SNAKE_CASE__ ):
						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"]





SCREAMING_SNAKE_CASE:     dict[tuple[int, int, int], int]									= {}


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	int:

		# if we are absent twice, or late 3 consecutive days,
		# no further prize strings are possible
		if late == 3 or absent == 2:
				return 0

		# if we have no days left, and have not failed any other rules,
		# we have a prize string
		if days == 0:
				return 1

		# No easy solution, so now we need to do the recursive calculation

		# First, check if the combination is already in the cache, and
		# if yes, return the stored value from there since we already
		# know the number of possible prize strings from this point on
		SCREAMING_SNAKE_CASE_					      =							(days, absent, late)
		if key in cache:
				return cache[key]

		# now we calculate the three possible ways that can unfold from
		# this point on, depending on our attendance today

		# 1) if we are late (but not absent), the "absent" counter stays as
		# it is, but the "late" counter increases by one
		SCREAMING_SNAKE_CASE_					      =							_calculate(days - 1    ,						lowerCAmelCase    ,						late + 1 )

		# 2) if we are absent, the "absent" counter increases by 1, and the
		# "late" counter resets to 0
		SCREAMING_SNAKE_CASE_					      =							_calculate(days - 1    ,						absent + 1    ,						0 )

		# 3) if we are on time, this resets the "late" counter and keeps the
		# absent counter
		SCREAMING_SNAKE_CASE_					      =							_calculate(days - 1    ,						lowerCAmelCase    ,						0 )

		SCREAMING_SNAKE_CASE_					      =							state_late + state_absent + state_ontime

		SCREAMING_SNAKE_CASE_					      =							prizestrings
		return prizestrings


def 					_a							(					lowerCAmelCase = 30 )->	int:
		return _calculate(lowerCAmelCase    ,						absent=0    ,						late=0 )


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