

import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

lowerCAmelCase     :   Dict						     =  {
    'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/config.json',
    'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/config.json',
}
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      str       =  '''xlnet'''
  SCREAMING_SNAKE_CASE    :      str       =  ['''mems''']
  SCREAMING_SNAKE_CASE    :      int       =  {
      '''n_token''': '''vocab_size''',  # Backward compatibility
      '''hidden_size''': '''d_model''',
      '''num_attention_heads''': '''n_head''',
      '''num_hidden_layers''': '''n_layer''',
  }

  def __init__(				self ,					_SCREAMING_SNAKE_CASE=3_2000 ,					_SCREAMING_SNAKE_CASE=1024 ,					_SCREAMING_SNAKE_CASE=24 ,					_SCREAMING_SNAKE_CASE=16 ,					_SCREAMING_SNAKE_CASE=4096 ,					_SCREAMING_SNAKE_CASE="gelu" ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE="bi" ,					_SCREAMING_SNAKE_CASE=0.02 ,					_SCREAMING_SNAKE_CASE=1e-12 ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=512 ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=-1 ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE="last" ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE="tanh" ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=5 ,					_SCREAMING_SNAKE_CASE=5 ,					_SCREAMING_SNAKE_CASE=5 ,					_SCREAMING_SNAKE_CASE=1 ,					_SCREAMING_SNAKE_CASE=2 ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     vocab_size
        SCREAMING_SNAKE_CASE_				:  int           =     d_model
        SCREAMING_SNAKE_CASE_				:  Tuple           =     n_layer
        SCREAMING_SNAKE_CASE_				:  int           =     n_head
        if d_model % n_head != 0:
              raise ValueError(f"'d_model % n_head' ({d_model % n_head}) should be equal to 0"					)
        if "d_head" in kwargs:
              if kwargs["d_head"] != d_model // n_head:
                    raise ValueError(
                        f"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})"					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     d_model // n_head
        SCREAMING_SNAKE_CASE_				:  List[str]           =     ff_activation
        SCREAMING_SNAKE_CASE_				:  List[str]           =     d_inner
        SCREAMING_SNAKE_CASE_				:  List[str]           =     untie_r
        SCREAMING_SNAKE_CASE_				:  str           =     attn_type

        SCREAMING_SNAKE_CASE_				:  Dict           =     initializer_range
        SCREAMING_SNAKE_CASE_				:  str           =     layer_norm_eps

        SCREAMING_SNAKE_CASE_				:  str           =     dropout
        SCREAMING_SNAKE_CASE_				:  Tuple           =     mem_len
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     reuse_len
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     bi_data
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     clamp_len
        SCREAMING_SNAKE_CASE_				:  Any           =     same_length

        SCREAMING_SNAKE_CASE_				:  List[str]           =     summary_type
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     summary_use_proj
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     summary_activation
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     summary_last_dropout
        SCREAMING_SNAKE_CASE_				:  str           =     start_n_top
        SCREAMING_SNAKE_CASE_				:  Any           =     end_n_top

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     bos_token_id
        SCREAMING_SNAKE_CASE_				:  str           =     pad_token_id
        SCREAMING_SNAKE_CASE_				:  Tuple           =     eos_token_id

        if "use_cache" in kwargs:
              warnings.warn(
                  'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
                  ' instead.' ,					_SCREAMING_SNAKE_CASE ,					)
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     kwargs['use_cache']

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     use_mems_eval
        SCREAMING_SNAKE_CASE_				:  str           =     use_mems_train
        super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE ,					bos_token_id=_SCREAMING_SNAKE_CASE ,					eos_token_id=_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit."					)
        return -1



  @max_position_embeddings.setter
  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        raise NotImplementedError(
            f"The model {self.model_type} is one of the few models that has no sequence length limit."					)









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)






