

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[Any]     ,					SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     global f  # a global dp table for knapsack
     if f[i][j] < 0:
          if j < wt[i - 1]:
               lowercase__   :       Any				=	mf_knapsack(i - 1     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
          else:
               lowercase__   :       Dict				=	max(
                   mf_knapsack(i - 1     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )     ,					mf_knapsack(i - 1     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					j - wt[i - 1]      ) + val[i - 1]     ,					)
          lowercase__   :       Union[str, Any]				=	val
     return f[i][j]

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   List[str]     ,					SCREAMING_SNAKE_CASE_		:   List[Any]     ,					SCREAMING_SNAKE_CASE_		:   Any      ):



     '''simple docstring'''





     lowercase__   :       Optional[Any]				=	[[0] * (w + 1) for _ in range(n + 1      )]

     for i in range(1     ,					n + 1      ):
          for w_ in range(1     ,					w + 1      ):
               if wt[i - 1] <= w_:
                    lowercase__   :       List[str]				=	max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]     ,					dp[i - 1][w_]      )
               else:
                    lowercase__   :       str				=	dp[i - 1][w_]

     return dp[n][w_], dp

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   list     ,					SCREAMING_SNAKE_CASE_		:   list      ):



     '''simple docstring'''





     if not (isinstance(SCREAMING_SNAKE_CASE_     ,					(list, tuple)      ) and isinstance(SCREAMING_SNAKE_CASE_     ,					(list, tuple)      )):
          raise ValueError(
              'Both the weights and values vectors must be either lists or tuples'      )

     lowercase__   :       Tuple				=	len(SCREAMING_SNAKE_CASE_      )
     if num_items != len(SCREAMING_SNAKE_CASE_      ):
          lowercase__   :       Tuple				=	(
              'The number of weights must be the same as the number of values.\n'
              f"""But got {num_items} weights and {len(SCREAMING_SNAKE_CASE_      )} values"""
          )
          raise ValueError(SCREAMING_SNAKE_CASE_      )
     for i in range(SCREAMING_SNAKE_CASE_      ):
          if not isinstance(wt[i]     ,					SCREAMING_SNAKE_CASE_      ):
               lowercase__   :       Tuple				=	(
                   'All weights must be integers but got weight of '
                   f"""type {type(wt[i]      )} at index {i}"""
               )
               raise TypeError(SCREAMING_SNAKE_CASE_      )

     lowercase__   :       int				=	knapsack(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
     lowercase__   :       set				=	set()
     _construct_solution(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )

     return optimal_val, example_optional_set

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   list     ,					SCREAMING_SNAKE_CASE_		:   list     ,					SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   set      ):



     '''simple docstring'''





     if i > 0 and j > 0:
          if dp[i - 1][j] == dp[i][j]:
               _construct_solution(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					i - 1     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
          else:
               optimal_set.add(SCREAMING_SNAKE_CASE_      )
               _construct_solution(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					i - 1     ,					j - wt[i - 1]     ,					SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
    snake_case_        =			[3, 2, 4, 4]
    snake_case_        =			[4, 3, 2, 3]
    snake_case_        =			4
    snake_case_        =			6
    snake_case_        =			[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
    snake_case_  ,		snake_case_        =			knapsack(w, wt, val, n)
    print(optimal_solution)
    print(mf_knapsack(n, wt, val, w))  # switched the n and w

    # testing the dynamic programming problem with example
    # the optimal subset for the above example are items 3 and 4
    snake_case_  ,		snake_case_        =			knapsack_with_example_solution(w, wt, val)
    assert optimal_solution == 8
    assert optimal_subset == {3, 4}
    print('''optimal_value = ''', optimal_solution)
    print('''An optimal subset corresponding to the optimal value''', optimal_subset)




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_        =			{
    '''vocab_file''': {
        '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_char_small''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_chinese_char_base''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_discriminator''': (
            '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_generator''': (
            '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'''
        ),
    }
}

snake_case_        =			{
    '''junnyu/roformer_chinese_small''': 1_536,
    '''junnyu/roformer_chinese_base''': 1_536,
    '''junnyu/roformer_chinese_char_small''': 512,
    '''junnyu/roformer_chinese_char_base''': 512,
    '''junnyu/roformer_small_discriminator''': 128,
    '''junnyu/roformer_small_generator''': 128,
}


snake_case_        =			{
    '''junnyu/roformer_chinese_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_discriminator''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_generator''': {'''do_lower_case''': True},
}


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Union[str, Any]														=					VOCAB_FILES_NAMES
    __lowerCamelCase						:	int														=					PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:	Any														=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:	int														=					PRETRAINED_INIT_CONFIGURATION
    __lowerCamelCase						:	Tuple														=					RoFormerTokenizer





    def __init__(				self						,       a=None						,       a=None						,       a=True						,       a="[UNK]"						,       a="[SEP]"						,       a="[PAD]"						,       a="[CLS]"						,       a="[MASK]"						,       a=True						,       a=None						,       **a						,       ):
         super().__init__(
             a						,       tokenizer_file=a						,       do_lower_case=a						,       unk_token=a						,       sep_token=a						,       pad_token=a						,       cls_token=a						,       mask_token=a						,       tokenize_chinese_chars=a						,       strip_accents=a						,       **a						,       )

         lowercase__   :       Optional[Any]				=	json.loads(self.backend_tokenizer.normalizer.__getstate__())
         if (
             pre_tok_state.get('lowercase'						,       a) != do_lower_case
             or pre_tok_state.get('strip_accents'						,       a) != strip_accents
         ):
              lowercase__   :       Optional[int]				=	getattr(a						,       pre_tok_state.pop('type'))
              lowercase__   :       str				=	do_lower_case
              lowercase__   :       Union[str, Any]				=	strip_accents
              lowercase__   :       int				=	pre_tok_class(**a)

         lowercase__   :       Optional[int]				=	do_lower_case





    def __getstate__(				self):
         lowercase__   :       str				=	self.__dict__.copy()
         lowercase__   :       Any				=	BertPreTokenizer()
         return state





    def __setstate__(				self						,       a):
         lowercase__   :       Union[str, Any]				=	d
         lowercase__   :       int				=	self.__dict__['_tokenizer'].get_vocab()
         lowercase__   :       List[str]				=	PreTokenizer.custom(JiebaPreTokenizer(a))





    def 				snake_case_    (				self						,       a						,       a=None):
         lowercase__   :       Union[str, Any]				=	[self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
              output += token_ids_a + [self.sep_token_id]

         return output





    def 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       Tuple				=	[self.sep_token_id]
         lowercase__   :       Optional[int]				=	[self.cls_token_id]
         if token_ids_a is None:
              return len(cls + token_ids_a + sep) * [0]
         return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]





    def 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       Optional[Any]				=	self._tokenizer.model.save(a						,       name=a)
         return tuple(a)





    def 				snake_case_    (				self						,       a						,       a=None						,       a=None						,       a=False						,       **a						,       ):
         lowercase__   :       List[str]				=	BertPreTokenizer()
         return super().save_pretrained(a						,       a						,       a						,       a						,       **a)

