

import functools
import operator

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


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

lowerCAmelCase     :   Any						     =  {
    'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
    # See all SEW models at https://huggingface.co/models?filter=sew
}
class  _A		(  __magic_name__):
      SCREAMING_SNAKE_CASE    :      List[str]       =  '''sew'''

      def __init__(				self ,					_SCREAMING_SNAKE_CASE=32 ,					_SCREAMING_SNAKE_CASE=768 ,					_SCREAMING_SNAKE_CASE=12 ,					_SCREAMING_SNAKE_CASE=12 ,					_SCREAMING_SNAKE_CASE=3072 ,					_SCREAMING_SNAKE_CASE=2 ,					_SCREAMING_SNAKE_CASE="gelu" ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=0.02 ,					_SCREAMING_SNAKE_CASE=1e-5 ,					_SCREAMING_SNAKE_CASE="group" ,					_SCREAMING_SNAKE_CASE="gelu" ,					_SCREAMING_SNAKE_CASE=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) ,					_SCREAMING_SNAKE_CASE=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,					_SCREAMING_SNAKE_CASE=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=128 ,					_SCREAMING_SNAKE_CASE=16 ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=0.05 ,					_SCREAMING_SNAKE_CASE=10 ,					_SCREAMING_SNAKE_CASE=2 ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=10 ,					_SCREAMING_SNAKE_CASE=0 ,					_SCREAMING_SNAKE_CASE="mean" ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=256 ,					_SCREAMING_SNAKE_CASE=0 ,					_SCREAMING_SNAKE_CASE=1 ,					_SCREAMING_SNAKE_CASE=2 ,					**_SCREAMING_SNAKE_CASE ,					):





            """simple docstring"""







            super().__init__(**_SCREAMING_SNAKE_CASE ,					pad_token_id=_SCREAMING_SNAKE_CASE ,					bos_token_id=_SCREAMING_SNAKE_CASE ,					eos_token_id=_SCREAMING_SNAKE_CASE					)
            SCREAMING_SNAKE_CASE_				:  Any           =     hidden_size
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     feat_extract_norm
            SCREAMING_SNAKE_CASE_				:  str           =     feat_extract_activation
            SCREAMING_SNAKE_CASE_				:  List[str]           =     list(_SCREAMING_SNAKE_CASE					)
            SCREAMING_SNAKE_CASE_				:  Tuple           =     list(_SCREAMING_SNAKE_CASE					)
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     list(_SCREAMING_SNAKE_CASE					)
            SCREAMING_SNAKE_CASE_				:  List[str]           =     conv_bias
            SCREAMING_SNAKE_CASE_				:  str           =     num_conv_pos_embeddings
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     num_conv_pos_embedding_groups
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     len(self.conv_dim					)
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     num_hidden_layers
            SCREAMING_SNAKE_CASE_				:  Any           =     intermediate_size
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     squeeze_factor
            SCREAMING_SNAKE_CASE_				:  str           =     hidden_act
            SCREAMING_SNAKE_CASE_				:  List[str]           =     num_attention_heads
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     hidden_dropout
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     attention_dropout
            SCREAMING_SNAKE_CASE_				:  List[str]           =     activation_dropout
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     feat_proj_dropout
            SCREAMING_SNAKE_CASE_				:  Tuple           =     final_dropout
            SCREAMING_SNAKE_CASE_				:  Any           =     layerdrop
            SCREAMING_SNAKE_CASE_				:  str           =     layer_norm_eps
            SCREAMING_SNAKE_CASE_				:  List[str]           =     initializer_range
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     vocab_size

            if (
                (len(self.conv_stride					) != self.num_feat_extract_layers)
                or (len(self.conv_kernel					) != self.num_feat_extract_layers)
                or (len(self.conv_dim					) != self.num_feat_extract_layers)
            ):
                  raise ValueError(
                      'Configuration for convolutional layers is incorrect.'
                      'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
                      f"but is `len(config.conv_dim) = {len(self.conv_dim					)}`, `len(config.conv_stride)"
                      f"= {len(self.conv_stride					)}`, `len(config.conv_kernel) = {len(self.conv_kernel					)}`."					)

            # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
            SCREAMING_SNAKE_CASE_				:  str           =     apply_spec_augment
            SCREAMING_SNAKE_CASE_				:  Dict           =     mask_time_prob
            SCREAMING_SNAKE_CASE_				:  Tuple           =     mask_time_length
            SCREAMING_SNAKE_CASE_				:  List[str]           =     mask_time_min_masks
            SCREAMING_SNAKE_CASE_				:  int           =     mask_feature_prob
            SCREAMING_SNAKE_CASE_				:  str           =     mask_feature_length
            SCREAMING_SNAKE_CASE_				:  str           =     mask_feature_min_masks

            # ctc loss
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     ctc_loss_reduction
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     ctc_zero_infinity

            # sequence classification
            SCREAMING_SNAKE_CASE_				:  Dict           =     use_weighted_layer_sum
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     classifier_proj_size



      @property
      def  UpperCAmelCase    (				self					):





            """simple docstring"""







            return functools.reduce(operator.mul ,					self.conv_stride ,					1					)









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)






