





"""simple docstring"""



class 		__snake_case					:





 """simple docstring"""


 def __init__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						str						 =     name
     __A    :						List[Any]						 =     value
     __A    :						Optional[Any]						 =     weight

 def __repr__(    self  ):



     '''simple docstring'''




     return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return self.value

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return self.name

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return self.weight







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return self.value / self.weight







def 		__lowercase     (   snake_case_  :  List[Any]				,snake_case_  :  List[str]				,snake_case_  :  List[Any]						)   ->Dict:



    '''simple docstring'''







    __A    :						Tuple						 =     []
    for i in range(len(snake_case_						)						):
        menu.append(Things(name[i]				,value[i]				,weight[i]						)						)
    return menu







def 		__lowercase     (   snake_case_  :  Optional[int]				,snake_case_  :  Tuple				,snake_case_  :  int						)   ->List[Any]:



    '''simple docstring'''







    __A    :						str						 =     sorted(snake_case_				,key=snake_case_				,reverse=snake_case_						)
    __A    :						Optional[Any]						 =     []
    __A ,       __A    :						Any						 =     0.0, 0.0
    for i in range(len(snake_case_						)						):
        if (total_cost + items_copy[i].get_weight()) <= max_cost:
            result.append(items_copy[i]						)
            total_cost += items_copy[i].get_weight()
            total_value += items_copy[i].get_value()
    return (result, total_value)







def 		__lowercase     (   )   ->int:



    '''simple docstring'''









if __name__ == "__main__":
       import doctest

       doctest.testmod()











"""simple docstring"""



import logging

from transformers.configuration_utils import PretrainedConfig


a_ =			logging.getLogger(__name__)






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """masked_bert"""

 def __init__(    self					,						__lowerCamelCase=3_0522					,						__lowerCamelCase=768					,						__lowerCamelCase=12					,						__lowerCamelCase=12					,						__lowerCamelCase=3072					,						__lowerCamelCase="gelu"					,						__lowerCamelCase=0.1					,						__lowerCamelCase=0.1					,						__lowerCamelCase=512					,						__lowerCamelCase=2					,						__lowerCamelCase=0.0_2					,						__lowerCamelCase=1e-1_2					,						__lowerCamelCase=0					,						__lowerCamelCase="topK"					,						__lowerCamelCase="constant"					,						__lowerCamelCase=0.0					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__(pad_token_id=__lowerCamelCase					,						**__lowerCamelCase  )

     __A    :						Dict						 =     vocab_size
     __A    :						Union[str, Any]						 =     hidden_size
     __A    :						Tuple						 =     num_hidden_layers
     __A    :						Tuple						 =     num_attention_heads
     __A    :						Optional[Any]						 =     hidden_act
     __A    :						List[str]						 =     intermediate_size
     __A    :						Any						 =     hidden_dropout_prob
     __A    :						Optional[Any]						 =     attention_probs_dropout_prob
     __A    :						Any						 =     max_position_embeddings
     __A    :						str						 =     type_vocab_size
     __A    :						List[Any]						 =     initializer_range
     __A    :						str						 =     layer_norm_eps
     __A    :						Optional[int]						 =     pruning_method
     __A    :						str						 =     mask_init
     __A    :						Any						 =     mask_scale




