"""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


_a      :		str           =   logging.get_logger(__name__)

_a      :		Union[str, Any]           =   {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_a      :		int           =   {
    '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      :		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      :		List[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'
        ),
    },
}

_a      :		List[str]           =   {
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
_a      :		Optional[Any]           =   {
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
_a      :		Union[str, Any]           =   {
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


_a      :		Optional[int]           =   {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
_a      :		Any           =   {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
_a      :		Dict           =   {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Optional[int]							=		VOCAB_FILES_NAMES
     _UpperCamelCase							:			Optional[int]							=		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:			List[str]							=		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:			int							=		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
     _UpperCamelCase							:			Tuple							=		DPRContextEncoderTokenizer







class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Any							=		VOCAB_FILES_NAMES
     _UpperCamelCase							:			Optional[int]							=		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:			Tuple							=		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:			List[Any]							=		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
     _UpperCamelCase							:			Tuple							=		DPRQuestionEncoderTokenizer







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

_a      :		Tuple           =   collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


_a      :		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(SCREAMING_SNAKE_CASE_		)
class 						__A							:






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






     def     __A  (		self				,     a__				,     a__				,     a__ = 16				,     a__ = 64				,     a__ = 4				,     ):
       _lowerCAmelCase						:       Dict  			=     reader_input["""input_ids"""]
       _lowerCAmelCase			,	_lowerCAmelCase			,	_lowerCAmelCase						:       Optional[Any]  			=     reader_output[:3]
       _lowerCAmelCase						:       List[str]  			=     len(a__  )
       _lowerCAmelCase						:       Union[str, Any]  			=     sorted(range(a__  )				,     reverse=a__				,     key=relevance_logits.__getitem__  )
       _lowerCAmelCase						:       List[DPRReaderOutput]  			=     []
       for doc_id in sorted_docs:
         _lowerCAmelCase						:       Tuple  			=     list(input_ids[doc_id]  )
         # assuming question & title information is at the beginning of the sequence
         _lowerCAmelCase						:       List[str]  			=     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						:       Tuple  			=     len(a__  )

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






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     ):
       _lowerCAmelCase						:       List[str]  			=     []
       for start_index, start_score in enumerate(a__  ):
         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						:       Optional[Any]  			=     sorted(a__				,     key=lambda a__  : x[1]				,     reverse=a__  )
       _lowerCAmelCase						:       Union[str, Any]  			=     []
       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(a__  ) == top_spans:
           break
       return chosen_span_intervals







@add_end_docstrings(SCREAMING_SNAKE_CASE_		)
class 						__A							( SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Any							=		VOCAB_FILES_NAMES
     _UpperCamelCase							:			Optional[Any]							=		READER_PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:			Union[str, Any]							=		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:			Optional[int]							=		READER_PRETRAINED_INIT_CONFIGURATION
     _UpperCamelCase							:			int							=		["input_ids", "attention_mask"]
     _UpperCamelCase							:			Optional[int]							=		DPRReaderTokenizer







from __future__ import annotations


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	len(SCREAMING_SNAKE_CASE__       )
						# We need to create solution object to save path.
						snake_case_   =	[[0 for _ in range(SCREAMING_SNAKE_CASE__       )] for _ in range(SCREAMING_SNAKE_CASE__       )]
						snake_case_   =	run_maze(SCREAMING_SNAKE_CASE__  ,		0  ,		0  ,		SCREAMING_SNAKE_CASE__       )
						if solved:
												print('''\n'''.join(str(SCREAMING_SNAKE_CASE__       ) for row in solutions       )       )
						else:
												print('''No solution exists!'''       )
						return solved


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	len(SCREAMING_SNAKE_CASE__       )
						# Final check point.
						if i == j == (size - 1):
												snake_case_   =	1
												return True

						snake_case_   =	(not i < 0) and (not j < 0)  # Check lower bounds
						snake_case_   =	(i < size) and (j < size)  # Check upper bounds

						if lower_flag and upper_flag:
												# check for already visited and block points.
												snake_case_   =	(not solutions[i][j]) and (not maze[i][j])
												if block_flag:
																		# check visited
																		snake_case_   =	1

																		# check for directions
																		if (
																		    run_maze(SCREAMING_SNAKE_CASE__  ,		i + 1  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
																		    or run_maze(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		j + 1  ,		SCREAMING_SNAKE_CASE__       )
																		    or run_maze(SCREAMING_SNAKE_CASE__  ,		i - 1  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
																		    or run_maze(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		j - 1  ,		SCREAMING_SNAKE_CASE__       )
																		):
																								return True

																		snake_case_   =	0
																		return False
						return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()