




def 						lowerCamelCase__    (		_A     ,	_A     ,	_A     ,	_A							):







	'''simple docstring'''





	global f  # a global dp table for knapsack
	if f[i][j] < 0:
		if j < wt[i - 1]:
			snake_case_		    =     mf_knapsack(i - 1     ,	lowerCAmelCase__     ,	lowerCAmelCase__     ,	lowerCAmelCase__							)
		else:
			snake_case_		    =     max(
			    mf_knapsack(i - 1     ,	lowerCAmelCase__     ,	lowerCAmelCase__     ,	lowerCAmelCase__							)     ,	mf_knapsack(i - 1     ,	lowerCAmelCase__     ,	lowerCAmelCase__     ,	j - wt[i - 1]							) + val[i - 1]     ,	)
		snake_case_		    =     val
	return f[i][j]


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A     ,	_A							):







	'''simple docstring'''





	snake_case_		    =     [[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_:
				snake_case_		    =     max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]     ,	dp[i - 1][w_]							)
			else:
				snake_case_		    =     dp[i - 1][w_]

	return dp[n][w_], dp


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A							):







	'''simple docstring'''





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

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

	snake_case_		    =     knapsack(lowerCAmelCase__     ,	lowerCAmelCase__     ,	lowerCAmelCase__     ,	lowerCAmelCase__							)
	snake_case_		    =     set()
	_construct_solution(lowerCAmelCase__     ,	lowerCAmelCase__     ,	lowerCAmelCase__     ,	lowerCAmelCase__     ,	lowerCAmelCase__							)

	return optimal_val, example_optional_set


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A     ,	_A     ,	_A							):







	'''simple docstring'''





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


if __name__ == "__main__":
		lowercase__				:     Tuple 		=						[3, 2, 4, 4]
		lowercase__				:     List[str] 		=						[4, 3, 2, 3]
		lowercase__				:     Tuple 		=						4
		lowercase__				:     str 		=						6
		lowercase__				:     Union[str, Any] 		=						[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
		lowercase__		,       lowercase__				:     Any 		=						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
		lowercase__		,       lowercase__				:     Any 		=						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


lowercase__       =logging.get_logger(__name__)

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

lowercase__       ={
    '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'
        ),
    }
}

lowercase__       ={
    'junnyu/roformer_chinese_small': 1536,
    'junnyu/roformer_chinese_base': 1536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


lowercase__       ={
    '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 		UpperCamelCase__  (       __lowercase    ):
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE      :							List[Any]	       =      PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE      :							List[str]	       =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      PRETRAINED_INIT_CONFIGURATION
   _SCREAMING_SNAKE_CASE      :							Optional[int]	       =      RoFormerTokenizer
   def __init__(self :					List[str]					,				snake_case_ :					Optional[int]=None					,				snake_case_ :					str=None					,				snake_case_ :					Optional[Any]=True					,				snake_case_ :					str="[UNK]"					,				snake_case_ :					Dict="[SEP]"					,				snake_case_ :					Any="[PAD]"					,				snake_case_ :					str="[CLS]"					,				snake_case_ :					List[Any]="[MASK]"					,				snake_case_ :					Any=True					,				snake_case_ :					List[str]=None					,				**snake_case_ :					Optional[int]					,				):
    super().__init__(
        snake_case_					,				tokenizer_file=snake_case_					,				do_lower_case=snake_case_					,				unk_token=snake_case_					,				sep_token=snake_case_					,				pad_token=snake_case_					,				cls_token=snake_case_					,				mask_token=snake_case_					,				tokenize_chinese_chars=snake_case_					,				strip_accents=snake_case_					,				**snake_case_					,				)

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

    __a :  Optional[Any]						=  do_lower_case
   def __getstate__(self :					Union[str, Any]   ):
    __a :  Any						=  self.__dict__.copy()
    __a :  Union[str, Any]						=  BertPreTokenizer()
    return state
   def __setstate__(self :					Tuple					,				snake_case_ :					Optional[Any]   ):
    __a :  Dict						=  d
    __a :  str						=  self.__dict__['''_tokenizer'''].get_vocab()
    __a :  Optional[Any]						=  PreTokenizer.custom(JiebaPreTokenizer(snake_case_   )   )
   def       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					List[Any]					,				snake_case_ :					Optional[Any]=None   ):
    __a :  Optional[int]						=  [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       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					List[int]					,				snake_case_ :					Optional[List[int]] = None   ):
    __a :  int						=  [self.sep_token_id]
    __a :  List[Any]						=  [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       lowerCAmelCase			(self :					int					,				snake_case_ :					str					,				snake_case_ :					Optional[str] = None   ):
    __a :  Optional[Any]						=  self._tokenizer.model.save(snake_case_					,				name=snake_case_   )
    return tuple(snake_case_   )







   def       lowerCAmelCase			(self :					Dict					,				snake_case_ :					Dict					,				snake_case_ :					Tuple=None					,				snake_case_ :					Optional[Any]=None					,				snake_case_ :					Union[str, Any]=False					,				**snake_case_ :					Tuple					,				):
    __a :  List[str]						=  BertPreTokenizer()
    return super().save_pretrained(snake_case_					,				snake_case_					,				snake_case_					,				snake_case_					,				**snake_case_   )
