'''simple docstring'''




def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     if len(lowercase__							) != len(lowercase__							):
          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.
     lowercase_					:       int     				=					[p / w for p, w in zip(lowercase__  ,       lowercase__							)]

     # Creating a copy of the list and sorting profit/weight in ascending order
     lowercase_					:       Union[str, Any]     				=					sorted(lowercase__							)

     # declaring useful variables
     lowercase_					:       Union[str, Any]     				=					len(lowercase__							)
     lowercase_					:       List[Any]     				=					0
     lowercase_					:       str     				=					0
     lowercase_					:       Optional[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
          lowercase_					:       List[Any]     				=					sorted_profit_by_weight[length - i - 1]
          lowercase_					:       str     				=					profit_by_weight.index(lowercase__							)
          lowercase_					:       int     				=					-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.'
 )

 _A								=   [int(x) for x in input('Input profits separated by spaces: ').split()]
 _A								=   [int(x) for x in input('Input weights separated by spaces: ').split()]
 _A								=   int(input('Max weight allowed: '))

 # Function Call
 calc_profit(profit, weight, max_weight)





'''simple docstring'''




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


_A								=   logging.get_logger(__name__)

_A								=   {
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       Any     =    '''mra'''
   def __init__(self       ,				_a=50_265       ,				_a=768       ,				_a=12       ,				_a=12       ,				_a=3_072       ,				_a="gelu"       ,				_a=0.1       ,				_a=0.1       ,				_a=512       ,				_a=1       ,				_a=0.02       ,				_a=1e-5       ,				_a="absolute"       ,				_a=4       ,				_a="full"       ,				_a=0       ,				_a=0       ,				_a=1       ,				_a=0       ,				_a=2       ,				**_a       ,				)    ->     int:
        super().__init__(pad_token_id=_a       ,				bos_token_id=_a       ,				eos_token_id=_a       ,				**_a				)

        lowercase_					:       Union[str, Any]     				=					vocab_size
        lowercase_					:       List[str]     				=					max_position_embeddings
        lowercase_					:       Optional[Any]     				=					hidden_size
        lowercase_					:       Optional[int]     				=					num_hidden_layers
        lowercase_					:       Dict     				=					num_attention_heads
        lowercase_					:       Optional[int]     				=					intermediate_size
        lowercase_					:       Dict     				=					hidden_act
        lowercase_					:       str     				=					hidden_dropout_prob
        lowercase_					:       int     				=					attention_probs_dropout_prob
        lowercase_					:       str     				=					initializer_range
        lowercase_					:       Union[str, Any]     				=					type_vocab_size
        lowercase_					:       Any     				=					layer_norm_eps
        lowercase_					:       Union[str, Any]     				=					position_embedding_type
        lowercase_					:       Any     				=					block_per_row
        lowercase_					:       Optional[int]     				=					approx_mode
        lowercase_					:       int     				=					initial_prior_first_n_blocks
        lowercase_					:       str     				=					initial_prior_diagonal_n_blocks




