code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  from itertools import combinations_with_replacement
  def 				UpperCamelCase	(  snake_case = 1_00   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= [*list(range(1	,     21   )   ), 25]
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= [2 * x for x in range(1	,     21   )] + [50]
     SCREAMING_SNAKE_CASE__			:				Dict       						= [3 * x for x in range(1	,     21   )]
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= singles + doubles + triples + [0]
  
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= 0
     SCREAMING_SNAKE_CASE__			:				int       						= 42
     SCREAMING_SNAKE_CASE__			:				int       						= 42
     SCREAMING_SNAKE_CASE__			:				int       						= 42
     SCREAMING_SNAKE_CASE__			:				List[str]       						= 42
  
     for double in doubles:
        for throwa, throwa in combinations_with_replacement(snake_case	,     2   ):
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= double + throwa + throwa
           if checkout_total < limit:
              num_checkouts += 1
  
     return num_checkouts
  
  
  if __name__ == "__main__":
         print(F"{solution() = }")
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  import os
  import re
  import unicodedata
  from shutil import copyfile
  from typing import Any, Dict, List, Optional, Tuple
  
  import sentencepiece as spm
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  lowercase__			=	logging.get_logger(__name__)
  lowercase__			=	{'''vocab_file''': '''spiece.model'''}
  
  lowercase__			=	{
      '''vocab_file''': {
          '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/spiece.model''',
          '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/spiece.model''',
      },
  }
  
  lowercase__			=	{
      '''google/fnet-base''': 5_1_2,
      '''google/fnet-large''': 5_1_2,
  }
  
  lowercase__			=	'''▁'''
  
  
  
  
  
  
  class 						lowercase     (   UpperCAmelCase_):
        __lowercase 		=  VOCAB_FILES_NAMES
        __lowercase 		=  PRETRAINED_VOCAB_FILES_MAP
        __lowercase 		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
        __lowercase 		=  ['''input_ids''', '''token_type_ids''']
  
  
  
  
  
  
        def __init__(self ,  a__ ,  a__=False ,  a__=True ,  a__=True ,  a__="<unk>" ,  a__="[SEP]" ,  a__="<pad>" ,  a__="[CLS]" ,  a__="[MASK]" ,  a__ = None ,  **a__ ,  )       ->   None:
           # Mask token behave like a normal word, i.e. include the space before it and
           # is included in the raw text, there should be a match in a non-normalized sentence.
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= (
               AddedToken(a__ ,  lstrip=a__ ,  rstrip=a__ ,  normalized=a__					)
               if isinstance(a__ ,  a__					)
               else mask_token
           )
  
           SCREAMING_SNAKE_CASE__			:				Any       						= {} if sp_model_kwargs is None else sp_model_kwargs
  
           super().__init__(
               do_lower_case=a__ ,  remove_space=a__ ,  keep_accents=a__ ,  unk_token=a__ ,  sep_token=a__ ,  pad_token=a__ ,  cls_token=a__ ,  mask_token=a__ ,  sp_model_kwargs=self.sp_model_kwargs ,  **a__ ,  )
  
           SCREAMING_SNAKE_CASE__			:				Dict       						= do_lower_case
           SCREAMING_SNAKE_CASE__			:				Tuple       						= remove_space
           SCREAMING_SNAKE_CASE__			:				List[str]       						= keep_accents
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= vocab_file
  
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= spm.SentencePieceProcessor(**self.sp_model_kwargs					)
           self.sp_model.Load(a__					)
  
  
  
  
  
  
        @property
        def    A__(self					)       ->   Union[str, Any]:
           return len(self.sp_model					)
  
  
  
  
  
  
        def    A__(self					)       ->   Any:
           SCREAMING_SNAKE_CASE__			:				int       						= {self.convert_ids_to_tokens(a__					): i for i in range(self.vocab_size					)}
           vocab.update(self.added_tokens_encoder					)
           return vocab
  
  
  
  
  
  
        def __getstate__(self					)       ->   Any:
           SCREAMING_SNAKE_CASE__			:				List[str]       						= self.__dict__.copy()
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= None
           return state
  
  
  
  
  
  
        def __setstate__(self ,  a__					)       ->   int:
           SCREAMING_SNAKE_CASE__			:				Any       						= d
  
           # for backward compatibility
           if not hasattr(self ,  '''sp_model_kwargs'''					):
              SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= {}
  
           SCREAMING_SNAKE_CASE__			:				int       						= spm.SentencePieceProcessor(**self.sp_model_kwargs					)
           self.sp_model.Load(self.vocab_file					)
  
  
  
  
  
  
        def    A__(self ,  a__					)       ->   str:
           if self.remove_space:
              SCREAMING_SNAKE_CASE__			:				List[str]       						= ''' '''.join(inputs.strip().split()					)
           else:
              SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= inputs
           SCREAMING_SNAKE_CASE__			:				Dict       						= outputs.replace('''``''' ,  '''"'''					).replace('''\'\'''' ,  '''"'''					)
  
           if not self.keep_accents:
              SCREAMING_SNAKE_CASE__			:				Dict       						= unicodedata.normalize('''NFKD''' ,  a__					)
              SCREAMING_SNAKE_CASE__			:				List[Any]       						= ''''''.join([c for c in outputs if not unicodedata.combining(a__					)]					)
           if self.do_lower_case:
              SCREAMING_SNAKE_CASE__			:				str       						= outputs.lower()
  
           return outputs
  
  
  
  
  
  
        def    A__(self ,  a__					)       ->   List[str]:
           SCREAMING_SNAKE_CASE__			:				Dict       						= self.preprocess_text(a__					)
           SCREAMING_SNAKE_CASE__			:				List[str]       						= self.sp_model.encode(a__ ,  out_type=a__					)
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= []
           for piece in pieces:
              if len(a__					) > 1 and piece[-1] == str(''','''					) and piece[-2].isdigit():
                 SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self.sp_model.EncodeAsPieces(piece[:-1].replace(a__ ,  ''''''					)					)
                 if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                    if len(cur_pieces[0]					) == 1:
                       SCREAMING_SNAKE_CASE__			:				List[str]       						= cur_pieces[1:]
                    else:
                       SCREAMING_SNAKE_CASE__			:				str       						= cur_pieces[0][1:]
                 cur_pieces.append(piece[-1]					)
                 new_pieces.extend(a__					)
              else:
                 new_pieces.append(a__					)
  
           return new_pieces
  
  
  
  
  
  
        def    A__(self ,  a__					)       ->   str:
           return self.sp_model.PieceToId(a__					)
  
  
  
  
  
  
        def    A__(self ,  a__					)       ->   Optional[int]:
           return self.sp_model.IdToPiece(a__					)
  
  
  
  
  
  
        def    A__(self ,  a__					)       ->   Optional[Any]:
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= []
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= ''''''
           SCREAMING_SNAKE_CASE__			:				Any       						= False
           for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                 if not prev_is_special:
                    out_string += " "
                 out_string += self.sp_model.decode(a__					) + token
                 SCREAMING_SNAKE_CASE__			:				List[Any]       						= True
                 SCREAMING_SNAKE_CASE__			:				int       						= []
              else:
                 current_sub_tokens.append(a__					)
                 SCREAMING_SNAKE_CASE__			:				Dict       						= False
           out_string += self.sp_model.decode(a__					)
           return out_string.strip()
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = False ,  a__ = None ,  a__ = True ,  **a__ ,  )       ->   str:
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= kwargs.pop('''use_source_tokenizer''' ,  a__					)
  
           SCREAMING_SNAKE_CASE__			:				int       						= self.convert_ids_to_tokens(a__ ,  skip_special_tokens=a__					)
  
           # To avoid mixing byte-level and unicode for byte-level BPT
           # we need to build string separately for added tokens and byte-level tokens
           # cf. https://github.com/huggingface/transformers/issues/1133
           SCREAMING_SNAKE_CASE__			:				Dict       						= []
           SCREAMING_SNAKE_CASE__			:				List[str]       						= []
           for token in filtered_tokens:
              if skip_special_tokens and token in self.all_special_ids:
                 continue
              if token in self.added_tokens_encoder:
                 if current_sub_text:
                    sub_texts.append(self.convert_tokens_to_string(a__					)					)
                    SCREAMING_SNAKE_CASE__			:				List[str]       						= []
                 sub_texts.append(a__					)
              else:
                 current_sub_text.append(a__					)
           if current_sub_text:
              sub_texts.append(self.convert_tokens_to_string(a__					)					)
  
           # Mimic the behavior of the Rust tokenizer:
           # No space after <unk>
           if spaces_between_special_tokens:
              SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= re.sub(R'''(<unk>) ''' ,  R'''\1''' ,  ''' '''.join(a__					)					)
           else:
              SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= ''''''.join(a__					)
  
           SCREAMING_SNAKE_CASE__			:				int       						= (
               clean_up_tokenization_spaces
               if clean_up_tokenization_spaces is not None
               else self.clean_up_tokenization_spaces
           )
           if clean_up_tokenization_spaces:
              SCREAMING_SNAKE_CASE__			:				str       						= self.clean_up_tokenization(a__					)
              return clean_text
           else:
              return text
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = None					)       ->   List[int]:
           SCREAMING_SNAKE_CASE__			:				str       						= [self.sep_token_id]
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= [self.cls_token_id]
           if token_ids_a is None:
              return cls + token_ids_a + sep
           return cls + token_ids_a + sep + token_ids_a + sep
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = None ,  a__ = False					)       ->   List[int]:
  
           if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=a__ ,  token_ids_a=a__ ,  already_has_special_tokens=a__					)
  
           if token_ids_a is not None:
              return [1] + ([0] * len(a__					)) + [1] + ([0] * len(a__					)) + [1]
           return [1] + ([0] * len(a__					)) + [1]
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = None					)       ->   List[int]:
           SCREAMING_SNAKE_CASE__			:				List[str]       						= [self.sep_token_id]
           SCREAMING_SNAKE_CASE__			:				Tuple       						= [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    A__(self ,  a__ ,  a__ = None					)       ->   Tuple[str]:
           if not os.path.isdir(a__					):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
              return
           SCREAMING_SNAKE_CASE__			:				str       						= 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__					) and os.path.isfile(self.vocab_file					):
              copyfile(self.vocab_file ,  a__					)
           elif not os.path.isfile(self.vocab_file					):
              with open(a__ ,  '''wb'''					) as fi:
                 SCREAMING_SNAKE_CASE__			:				int       						= self.sp_model.serialized_model_proto()
                 fi.write(a__					)
  
           return (out_vocab_file,)
  
  