


import functools
import operator

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


snake_case				:    Union[str, Any]       				=     logging.get_logger(__name__)

snake_case				:    Any       				=     {
    '''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       'wavlm'




 def __init__(		self					,  _lowerCamelCase=32					,  _lowerCamelCase=768					,  _lowerCamelCase=12					,  _lowerCamelCase=12					,  _lowerCamelCase=3072					,  _lowerCamelCase="gelu"					,  _lowerCamelCase=0.1					,  _lowerCamelCase=0.1					,  _lowerCamelCase=0.1					,  _lowerCamelCase=0.0					,  _lowerCamelCase=0.1					,  _lowerCamelCase=0.1					,  _lowerCamelCase=0.02					,  _lowerCamelCase=1e-5					,  _lowerCamelCase="group"					,  _lowerCamelCase="gelu"					,  _lowerCamelCase=(512, 512, 512, 512, 512, 512, 512)					,  _lowerCamelCase=(5, 2, 2, 2, 2, 2, 2)					,  _lowerCamelCase=(10, 3, 3, 3, 3, 2, 2)					,  _lowerCamelCase=False					,  _lowerCamelCase=128					,  _lowerCamelCase=16					,  _lowerCamelCase=320					,  _lowerCamelCase=800					,  _lowerCamelCase=False					,  _lowerCamelCase=True					,  _lowerCamelCase=0.05					,  _lowerCamelCase=10					,  _lowerCamelCase=2					,  _lowerCamelCase=0.0					,  _lowerCamelCase=10					,  _lowerCamelCase=320					,  _lowerCamelCase=2					,  _lowerCamelCase=0.1					,  _lowerCamelCase=100					,  _lowerCamelCase=256					,  _lowerCamelCase=256					,  _lowerCamelCase=0.1					,  _lowerCamelCase="mean"					,  _lowerCamelCase=False					,  _lowerCamelCase=False					,  _lowerCamelCase=256					,  _lowerCamelCase=(512, 512, 512, 512, 1500)					,  _lowerCamelCase=(5, 3, 3, 1, 1)					,  _lowerCamelCase=(1, 2, 3, 1, 1)					,  _lowerCamelCase=512					,  _lowerCamelCase=80					,  _lowerCamelCase=0					,  _lowerCamelCase=1					,  _lowerCamelCase=2					,  _lowerCamelCase=False					,  _lowerCamelCase=3					,  _lowerCamelCase=2					,  _lowerCamelCase=3					,  _lowerCamelCase=None					,  **_lowerCamelCase					,  ):
       super().__init__(**_lowerCamelCase					,  pad_token_id=_lowerCamelCase					,  bos_token_id=_lowerCamelCase					,  eos_token_id=_lowerCamelCase    )
       a						:Union[str, Any] 							=       hidden_size
       a						:str 							=       feat_extract_norm
       a						:Optional[Any] 							=       feat_extract_activation
       a						:int 							=       list(_lowerCamelCase    )
       a						:List[str] 							=       list(_lowerCamelCase    )
       a						:Union[str, Any] 							=       list(_lowerCamelCase    )
       a						:Dict 							=       conv_bias
       a						:List[Any] 							=       num_buckets
       a						:int 							=       max_bucket_distance
       a						:str 							=       num_conv_pos_embeddings
       a						:List[Any] 							=       num_conv_pos_embedding_groups
       a						:Optional[Any] 							=       len(self.conv_dim    )
       a						:Union[str, Any] 							=       num_hidden_layers
       a						:Dict 							=       intermediate_size
       a						:Optional[Any] 							=       hidden_act
       a						:int 							=       num_attention_heads
       a						:Optional[Any] 							=       hidden_dropout
       a						:List[Any] 							=       attention_dropout
       a						:Optional[Any] 							=       activation_dropout
       a						:Dict 							=       feat_proj_dropout
       a						:Union[str, Any] 							=       final_dropout
       a						:str 							=       layerdrop
       a						:Any 							=       layer_norm_eps
       a						:Dict 							=       initializer_range
       a						:str 							=       num_ctc_classes
       a						:Optional[Any] 							=       vocab_size
       a						:Union[str, Any] 							=       do_stable_layer_norm
       a						:List[str] 							=       use_weighted_layer_sum
       a						:Any 							=       classifier_proj_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)`, but is `len(config.conv_dim) ='''
                 F''' {len(self.conv_dim    )}`, `len(config.conv_stride) = {len(self.conv_stride    )}`,'''
                 F''' `len(config.conv_kernel) = {len(self.conv_kernel    )}`.'''    )

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

       # parameters for pretraining with codevector quantized representations
       a						:Optional[int] 							=       num_codevectors_per_group
       a						:Dict 							=       num_codevector_groups
       a						:Optional[Any] 							=       contrastive_logits_temperature
       a						:Any 							=       num_negatives
       a						:List[Any] 							=       codevector_dim
       a						:List[Any] 							=       proj_codevector_dim
       a						:List[str] 							=       diversity_loss_weight

       # ctc loss
       a						:Tuple 							=       ctc_loss_reduction
       a						:List[str] 							=       ctc_zero_infinity

       # adapter
       a						:List[Any] 							=       add_adapter
       a						:Union[str, Any] 							=       adapter_kernel_size
       a						:List[str] 							=       adapter_stride
       a						:Optional[int] 							=       num_adapter_layers
       a						:Union[str, Any] 							=       output_hidden_size or hidden_size

       # SequenceClassification-specific parameter. Feel free to ignore for other classes.
       a						:Dict 							=       classifier_proj_size

       # XVector-specific parameters. Feel free to ignore for other classes.
       a						:Dict 							=       list(_lowerCamelCase    )
       a						:Optional[int] 							=       list(_lowerCamelCase    )
       a						:List[Any] 							=       list(_lowerCamelCase    )
       a						:Any 							=       xvector_output_dim




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return functools.reduce(operator.mul					,  self.conv_stride					,  1    )









def 				__lowerCamelCase						( UpperCAmelCase_						:		list ,  UpperCAmelCase_						:		list ,  UpperCAmelCase_						:		int						):




      """simple docstring"""





      if len(UpperCAmelCase_						) != len(UpperCAmelCase_						):
            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.
      a						:Optional[int] 							=       [p / w for p, w in zip(UpperCAmelCase_ ,  UpperCAmelCase_						)]

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

      # declaring useful variables
      a						:Dict 							=       len(UpperCAmelCase_						)
      a						:Tuple 							=       0
      a						:List[Any] 							=       0
      a						:str 							=       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
            a						:List[Any] 							=       sorted_profit_by_weight[length - i - 1]
            a						:Optional[Any] 							=       profit_by_weight.index(UpperCAmelCase_						)
            a						:Optional[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.'''
     )

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

     # Function Call
     calc_profit(profit, weight, max_weight)





