


class   __a      :
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       List[str]        =       name
        lowercase__:       Any        =       value
        lowercase__:       str        =       weight
 def __repr__(       self			) ->    Dict:




        '''simple docstring'''
        return F'{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        return self.value
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        return self.name
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        return self.weight
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        return self.value / self.weight





def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case					)  ->    Tuple:
       lowercase__:       List[Any]        =       []
       for i in range(len(snake_case					)					):
              menu.append(Things(name[i]      ,							value[i]      ,							weight[i]					)					)
       return menu




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case					)  ->    Any:
       lowercase__:       str        =       sorted(snake_case      ,							key=snake_case      ,							reverse=snake_case					)
       lowercase__:       Dict        =       []
       lowercase__      ,						lowercase__:       List[str]        =       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        snake_case_					(  )  ->    Optional[Any]:
       pass


if __name__ == "__main__":
 import doctest

 doctest.testmod()






import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer





class   __a      (   tf.keras.layers.Layer				):
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None			) ->    int:




        '''simple docstring'''
        super().__init__()
        lowercase__:       Union[str, Any]        =       pad_token_id
        lowercase__:       List[str]        =       max_length
        lowercase__:       int        =       vocab
        lowercase__:       List[Any]        =       merges
        lowercase__:       str        =       BytePairTokenizer(lowerCAmelCase__    ,			lowerCAmelCase__    ,			sequence_length=lowerCAmelCase__			)
 @classmethod
 def 		SCREAMING_SNAKE_CASE__       (       cls    ,			lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			) ->    Any:




        '''simple docstring'''
        lowercase__:       Tuple        =       [' '.join(lowerCAmelCase__			) for m in tokenizer.bpe_ranks.keys()]
        lowercase__:       List[Any]        =       tokenizer.get_vocab()
        return cls(lowerCAmelCase__    ,			lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			)
 @classmethod
 def 		SCREAMING_SNAKE_CASE__       (       cls    ,			lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       int        =       GPTaTokenizer.from_pretrained(lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			)
        return cls.from_tokenizer(lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			)
 @classmethod
 def 		SCREAMING_SNAKE_CASE__       (       cls    ,			lowerCAmelCase__			) ->    Dict:




        '''simple docstring'''
        return cls(**lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        return {
            "vocab": self.vocab,
            "merges": self.merges,
            "max_length": self.max_length,
            "pad_token_id": self.pad_token_id,
        }
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       self.tf_tokenizer(lowerCAmelCase__			)
        lowercase__:       List[Any]        =       tf.ones_like(lowerCAmelCase__			)

        if self.pad_token_id is not None:
               # pad the tokens up to max length
               lowercase__:       int        =       max_length if max_length is not None else self.max_length

               if max_length is not None:
                      lowercase__      ,						lowercase__:       List[Any]        =       pad_model_inputs(
                          lowerCAmelCase__    ,			max_seq_length=lowerCAmelCase__    ,			pad_value=self.pad_token_id			)

        return {"attention_mask": attention_mask, "input_ids": input_ids}


