




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


__lowerCAmelCase						:							Optional[int]			     =						logging.get_logger(__name__)

__lowerCAmelCase						:							Tuple			     =						{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase						:							Dict			     =						{
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json',
    },
}




class        snake_case__				(a_  ):
				"""simple docstring"""



				def __init__( self							:	Tuple   , __lowerCamelCase							:	int=None   , __lowerCamelCase							:	int=None   , __lowerCamelCase							:	Dict=False   , __lowerCamelCase							:	Any=True   , __lowerCamelCase							:	str=False   , __lowerCamelCase							:	str="<s>"   , __lowerCamelCase							:	str="</s>"   , __lowerCamelCase							:	Optional[int]="<unk>"   , __lowerCamelCase							:	List[Any]="<sep>"   , __lowerCamelCase							:	Optional[int]="<pad>"   , __lowerCamelCase							:	Optional[Any]="<cls>"   , __lowerCamelCase							:	List[str]="<mask>"   , __lowerCamelCase							:	Tuple=["<eop>", "<eod>"]   , **__lowerCamelCase							:	Optional[Any]   , )   ->	Optional[int]:
							a       =     AddedToken(_a   , lstrip=_a   , rstrip=_a				) if isinstance(_a   , _a				) else mask_token

							super().__init__(
							    vocab_file=_a   , tokenizer_file=_a   , do_lower_case=_a   , remove_space=_a   , keep_accents=_a   , bos_token=_a   , eos_token=_a   , unk_token=_a   , sep_token=_a   , pad_token=_a   , cls_token=_a   , mask_token=_a   , additional_special_tokens=_a   , **_a   , )

							a       =     3
							a       =     do_lower_case
							a       =     remove_space
							a       =     keep_accents
							a       =     vocab_file
							a       =     False if not self.vocab_file else True

							try:
										import jieba
							except ModuleNotFoundError as error:
										raise error.__class__(
										    "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
										    "See https://pypi.org/project/jieba/ for installation."				)
							a       =     jieba
							a       =     str.maketrans(" \n"   , "\u2582\u2583"				)





				def 				__UpperCAmelCase				( self							:	List[Any]   , __lowerCamelCase							:	List[int]   , __lowerCamelCase							:	Optional[List[int]] = None				)   ->	List[int]:
							a       =     [self.sep_token_id]
							a       =     [self.cls_token_id]
							if token_ids_a is None:
										return token_ids_a + sep + cls
							return token_ids_a + sep + token_ids_a + sep + cls





				def 				__UpperCAmelCase				( self							:	int   , __lowerCamelCase							:	List[int]   , __lowerCamelCase							:	Optional[List[int]] = None				)   ->	List[int]:
							a       =     [self.sep_token_id]
							a       =     [2]

							if token_ids_a is None:
										return len(token_ids_a + sep				) * [0] + cls_segment_id
							return len(token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1] + cls_segment_id





				def 				__UpperCAmelCase				( self							:	Union[str, Any]   , __lowerCamelCase							:	str   , __lowerCamelCase							:	Optional[str] = None				)   ->	Tuple[str]:
							if not self.can_save_slow_tokenizer:
										raise ValueError(
										    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
										    "tokenizer."				)

							if not os.path.isdir(_a				):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
										return
							a       =     os.path.join(
							    _a   , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

							if os.path.abspath(self.vocab_file				) != os.path.abspath(_a				):
										copyfile(self.vocab_file   , _a				)

							return (out_vocab_file,)





				def 				__UpperCAmelCase				( self							:	Dict   , __lowerCamelCase							:	int   , *__lowerCamelCase							:	int   , **__lowerCamelCase							:	Optional[Any]				)   ->	Union[str, Any]:
							a       =     [
							    " ".join([x.translate(self.translator				) for x in self.jieba.cut(_a   , cut_all=_a				)]				)
							    for text in batch_text_or_text_pairs
							]
							return super()._batch_encode_plus(_a   , *_a   , **_a				)





				def 				__UpperCAmelCase				( self							:	Optional[Any]   , *__lowerCamelCase							:	Optional[Any]   , **__lowerCamelCase							:	List[str]				)   ->	Tuple:
							a       =     super()._decode(*_a   , **_a				)
							a       =     text.replace(" "   , ""				).replace("\u2582"   , " "				).replace("\u2583"   , "\n"				)
							return text










from __future__ import annotations







def 	A (		A__		,		A__		,		A__		,		A__			):

     # Base Case
     if counter == 0 or capacity == 0:
          return 0

     # If weight of the nth item is more than Knapsack of capacity,
     #   then this item cannot be included in the optimal solution,
     # else return the maximum of two cases:
     #   (1) nth item included
     #   (2) not included
     if weights[counter - 1] > capacity:
          return knapsack(A__		,		A__		,		A__		,		counter - 1			)
     else:
          UpperCAmelCase    :					List[str]			=      capacity - weights[counter - 1]
          UpperCAmelCase    :					Tuple			=      values[counter - 1] + knapsack(
              A__		,		A__		,		A__		,		counter - 1			)
          UpperCAmelCase    :					Optional[int]			=      knapsack(A__		,		A__		,		A__		,		counter - 1			)
          return max(A__		,		A__			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



