

'''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 import BertTokenizer


_snake_case         =  logging.get_logger(__name__)

_snake_case         =  {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_snake_case         =  {
    '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'
        ),
    },
}
_snake_case         =  {
    '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'
        ),
    },
}
_snake_case         =  {
    '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'
        ),
    },
}

_snake_case         =  {
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
_snake_case         =  {
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
_snake_case         =  {
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


_snake_case         =  {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
_snake_case         =  {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
_snake_case         =  {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					Optional[Any]     	=							VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE   :					str     	=							CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE   :					Dict     	=							CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION




class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					List[Any]     	=							VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE   :					Dict     	=							QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE   :					Tuple     	=							QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE   :					str     	=							QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION




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

_snake_case         =  collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


_snake_case         =  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(lowerCamelCase_  )
class        a__  :


   def __call__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase = 16      ,    _UpperCamelCase = 64      ,    _UpperCamelCase = 4      ,    ):





      """simple docstring"""





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

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






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    ):





      """simple docstring"""





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




@add_end_docstrings(lowerCamelCase_  )
class        a__  (							lowerCamelCase_  ,				lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					Dict     	=							VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE   :					Any     	=							READER_PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE   :					Optional[Any]     	=							READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE   :					Tuple     	=							READER_PRETRAINED_INIT_CONFIGURATION
   _SCREAMING_SNAKE_CASE   :					str     	=							['input_ids', 'attention_mask']









'''simple docstring'''
def  _A		(     snake_case		,							snake_case					)      ->     int:
   return int((input_a, input_a).count(0					) != 0					)







def  _A		(     )      ->     None:
   assert nand_gate(0		,							0					) == 1
   assert nand_gate(0		,							1					) == 1
   assert nand_gate(1		,							0					) == 1
   assert nand_gate(1		,							1					) == 0


if __name__ == "__main__":
    print(nand_gate(0, 0))
    print(nand_gate(0, 1))
    print(nand_gate(1, 0))
    print(nand_gate(1, 1))






