

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


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

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

lowerCAmelCase     :   str						     =  {
    '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'
        ),
    },
}
lowerCAmelCase     :   Optional[Any]						     =  {
    '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'
        ),
    },
}
lowerCAmelCase     :   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'
        ),
    },
}

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


lowerCAmelCase     :   Optional[int]						     =  {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
lowerCAmelCase     :   Any						     =  {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
lowerCAmelCase     :   Any						     =  {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      Dict       =  VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE    :      Optional[Any]       =  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE    :      List[Any]       =  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE    :      int       =  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE    :      Any       =  DPRContextEncoderTokenizer

class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      Tuple       =  VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE    :      Optional[Any]       =  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE    :      Optional[int]       =  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE    :      int       =  QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE    :      Any       =  DPRQuestionEncoderTokenizer

lowerCAmelCase     :   Optional[int]						     =  collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

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


lowerCAmelCase     :   Union[str, Any]						     =  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(__magic_name__)
class  _A		:

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





        """simple docstring"""







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

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = 16 ,					_SCREAMING_SNAKE_CASE = 64 ,					_SCREAMING_SNAKE_CASE = 4 ,					):





        """simple docstring"""







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

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



  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     []
        for start_index, start_score in enumerate(_SCREAMING_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_				:  List[Any]           =     sorted(_SCREAMING_SNAKE_CASE ,					key=lambda _SCREAMING_SNAKE_CASE					: x[1] ,					reverse=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     []
        for (start_index, end_index), score in scores:
              assert start_index <= end_index, f"Wrong span indices: [{start_index}:{end_index}]"
              SCREAMING_SNAKE_CASE_				:  Optional[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(_SCREAMING_SNAKE_CASE					) == top_spans:
                    break
        return chosen_span_intervals

@add_end_docstrings(__magic_name__)
class  _A		(  __magic_name__   ,     __magic_name__):
  SCREAMING_SNAKE_CASE    :      Any       =  VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE    :      Tuple       =  READER_PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE    :      Union[str, Any]       =  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE    :      List[str]       =  READER_PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE    :      str       =  ['''input_ids''', '''attention_mask''']
  SCREAMING_SNAKE_CASE    :      List[Any]       =  DPRReaderTokenizer









def    A_  (							a					,   a					,   a  ):

      """simple docstring"""



      if len(a  ) != len(a  ):
            raise ValueError('The length of profit and weight must be same.'  )
      if max_weight <= 0:
            raise ValueError('max_weight must greater than zero.'  )
      if any(p < 0 for p in profit  ):
            raise ValueError('Profit can not be negative.'  )
      if any(w < 0 for w in weight  ):
            raise ValueError('Weight can not be negative.'  )

      # List created to store profit gained for the 1kg in case of each weight
      # respectively.  Calculate and append profit/weight for each element.
      SCREAMING_SNAKE_CASE_				:  Tuple           =     [p / w for p, w in zip(a					,   a  )]

      # Creating a copy of the list and sorting profit/weight in ascending order
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     sorted(a  )

      # declaring useful variables
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     len(a  )
      SCREAMING_SNAKE_CASE_				:  str           =     0
      SCREAMING_SNAKE_CASE_				:  List[str]           =     0
      SCREAMING_SNAKE_CASE_				:  Any           =     0

      # loop till the total weight do not reach max limit e.g. 15 kg and till i<length
      while limit <= max_weight and i < length:
            # flag value for encountered greatest element in sorted_profit_by_weight
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     sorted_profit_by_weight[length - i - 1]
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     profit_by_weight.index(a  )
            SCREAMING_SNAKE_CASE_				:  Dict           =     -1

            # check if the weight encountered is less than the total weight
            # encountered before.
            if max_weight - limit >= weight[index]:
                  limit += weight[index]
                  # Adding profit gained for the given weight 1 ===
                  # weight[index]/weight[index]
                  gain += 1 * profit[index]
            else:
                  # Since the weight encountered is greater than limit, therefore take the
                  # required number of remaining kgs and calculate profit for it.
                  # weight remaining / weight[index]
                  gain += (max_weight - limit) / weight[index] * profit[index]
                  break
            i += 1
      return gain


if __name__ == "__main__":
    print(
        'Input profits, weights, and then max_weight (all positive ints) separated by '
        'spaces.'
    )

    lowerCAmelCase     :   Tuple						     =  [int(x) for x in input('Input profits separated by spaces: ').split()]
    lowerCAmelCase     :   Union[str, Any]						     =  [int(x) for x in input('Input weights separated by spaces: ').split()]
    lowerCAmelCase     :   Dict						     =  int(input('Max weight allowed: '))

    # Function Call
    calc_profit(profit, weight, max_weight)






