"""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


lowercase__					       =	logging.get_logger(__name__)

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

lowercase__					       =	{
    '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'
        ),
    },
}
lowercase__					       =	{
    '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'
        ),
    },
}
lowercase__					       =	{
    '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'
        ),
    },
}

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


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




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		VOCAB_FILES_NAMES
      a__        =		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      a__        =		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__        =		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
      a__        =		DPRContextEncoderTokenizer







class    __snake_case       (     __lowerCAmelCase				):
      a__        =		VOCAB_FILES_NAMES
      a__        =		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      a__        =		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__        =		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
      a__        =		DPRQuestionEncoderTokenizer







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

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


lowercase__					       =	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       :



      def __call__(						self    ,			lowercase    ,			lowercase = None    ,			lowercase = None    ,			lowercase = False    ,			lowercase = False    ,			lowercase = None    ,			lowercase = None    ,			lowercase = None    ,			**lowercase    ,			)	->			BatchEncoding:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase = 16    ,			lowercase = 64    ,			lowercase = 4    ,			)	->			List[DPRSpanPrediction]:





        '''simple docstring'''





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

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



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			)	->			List[DPRSpanPrediction]:





        '''simple docstring'''





        a__:     List[str]										=	[]
        for start_index, start_score in enumerate(lowercase):
          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))
        a__:     Dict										=	sorted(lowercase    ,			key=lambda lowercase: x[1]    ,			reverse=lowercase)
        a__:     Union[str, Any]										=	[]
        for (start_index, end_index), score in scores:
          assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
          a__:     List[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(lowercase) == top_spans:
            break
        return chosen_span_intervals







@add_end_docstrings(__lowerCAmelCase				)
class    __snake_case       (     __lowerCAmelCase       ,      __lowerCAmelCase				):
      a__        =		VOCAB_FILES_NAMES
      a__        =		READER_PRETRAINED_VOCAB_FILES_MAP
      a__        =		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__        =		READER_PRETRAINED_INIT_CONFIGURATION
      a__        =		["""input_ids""", """attention_mask"""]
      a__        =		DPRReaderTokenizer



"""simple docstring"""
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Union[str, Any]:
  if height >= 1:
    move_tower(height - 1		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
    move_disk(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
    move_tower(height - 1		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Union[str, Any]:
  print('moving disk from'		,     _SCREAMING_SNAKE_CASE		,     'to'		,     _SCREAMING_SNAKE_CASE   )







def 						__a   (	)	->List[str]:
  a__:     Dict										=	int(input('Height of hanoi: '   ).strip()   )
  move_tower(_SCREAMING_SNAKE_CASE		,     'A'		,     'B'		,     'C'   )


if __name__ == "__main__":
 main()


