






"""simple docstring"""
import os
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


lowerCAmelCase__    						=			logging.get_logger(__name__)
lowerCAmelCase__    						=			{'vocab_file': 'spiece.model'}

lowerCAmelCase__    						=			{
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    }
}

lowerCAmelCase__    						=			{
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

lowerCAmelCase__    						=			'▁'


class _lowerCAmelCase						(    _A       ):
						SCREAMING_SNAKE_CASE_:					Tuple 					=				VOCAB_FILES_NAMES
						SCREAMING_SNAKE_CASE_:					str 					=				PRETRAINED_VOCAB_FILES_MAP
						SCREAMING_SNAKE_CASE_:					int 					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_=True   ,				lowerCAmelCase_=True   ,				lowerCAmelCase_=False   ,				lowerCAmelCase_="[CLS]"   ,				lowerCAmelCase_="[SEP]"   ,				lowerCAmelCase_="<unk>"   ,				lowerCAmelCase_="[SEP]"   ,				lowerCAmelCase_="<pad>"   ,				lowerCAmelCase_="[CLS]"   ,				lowerCAmelCase_="[MASK]"   ,				lowerCAmelCase_ = None   ,				**lowerCAmelCase_   ,				)		->							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   :		Tuple       		=    (
										    AddedToken(__lowerCAmelCase   ,				lstrip=__lowerCAmelCase   ,				rstrip=__lowerCAmelCase   ,				normalized=__lowerCAmelCase							)
										    if isinstance(__lowerCAmelCase   ,				__lowerCAmelCase							)
										    else mask_token
										)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    do_lower_case=__lowerCAmelCase   ,				remove_space=__lowerCAmelCase   ,				keep_accents=__lowerCAmelCase   ,				bos_token=__lowerCAmelCase   ,				eos_token=__lowerCAmelCase   ,				unk_token=__lowerCAmelCase   ,				sep_token=__lowerCAmelCase   ,				pad_token=__lowerCAmelCase   ,				cls_token=__lowerCAmelCase   ,				mask_token=__lowerCAmelCase   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**__lowerCAmelCase   ,				)

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    do_lower_case
										_SCREAMING_SNAKE_CASE   :		int       		=    remove_space
										_SCREAMING_SNAKE_CASE   :		int       		=    keep_accents
										_SCREAMING_SNAKE_CASE   :		Dict       		=    vocab_file

										_SCREAMING_SNAKE_CASE   :		Dict       		=    spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(__lowerCAmelCase							)







						@property
						def 			A      (    self							)		->							int:
										return len(self.sp_model							)







						def 			A      (    self							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    {self.convert_ids_to_tokens(__lowerCAmelCase							): i for i in range(self.vocab_size							)}
										vocab.update(self.added_tokens_encoder							)
										return vocab







						def __getstate__(    self							)		->							str:
										_SCREAMING_SNAKE_CASE   :		int       		=    self.__dict__.copy()
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    None
										return state







						def __setstate__(    self   ,				lowerCAmelCase_							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    d

										# for backward compatibility
										if not hasattr(self   ,				'sp_model_kwargs'							):
														_SCREAMING_SNAKE_CASE   :		Dict       		=    {}

										_SCREAMING_SNAKE_CASE   :		str       		=    spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(self.vocab_file							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							Any:
										if self.remove_space:
														_SCREAMING_SNAKE_CASE   :		Dict       		=    ' '.join(inputs.strip().split()							)
										else:
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    inputs
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    outputs.replace('``'   ,				'"'							).replace('\'\''   ,				'"'							)

										if not self.keep_accents:
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    unicodedata.normalize('NFKD'   ,				__lowerCAmelCase							)
														_SCREAMING_SNAKE_CASE   :		str       		=    ''.join([c for c in outputs if not unicodedata.combining(__lowerCAmelCase							)]							)
										if self.do_lower_case:
														_SCREAMING_SNAKE_CASE   :		Any       		=    outputs.lower()

										return outputs







						def 			A      (    self   ,				lowerCAmelCase_							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		str       		=    self.preprocess_text(__lowerCAmelCase							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    self.sp_model.encode(__lowerCAmelCase   ,				out_type=__lowerCAmelCase							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    []
										for piece in pieces:
														if len(__lowerCAmelCase							) > 1 and piece[-1] == str(','							) and piece[-2].isdigit():
																		_SCREAMING_SNAKE_CASE   :		Dict       		=    self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowerCAmelCase   ,				''							)							)
																		if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																						if len(cur_pieces[0]							) == 1:
																										_SCREAMING_SNAKE_CASE   :		Any       		=    cur_pieces[1:]
																						else:
																										_SCREAMING_SNAKE_CASE   :		Any       		=    cur_pieces[0][1:]
																		cur_pieces.append(piece[-1]							)
																		new_pieces.extend(__lowerCAmelCase							)
														else:
																		new_pieces.append(__lowerCAmelCase							)

										return new_pieces







						def 			A      (    self   ,				lowerCAmelCase_							)		->							Dict:
										return self.sp_model.PieceToId(__lowerCAmelCase							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							List[Any]:
										return self.sp_model.IdToPiece(__lowerCAmelCase							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							Optional[Any]:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    []
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    ''
										_SCREAMING_SNAKE_CASE   :		Union[str, 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(__lowerCAmelCase							) + token
																		_SCREAMING_SNAKE_CASE   :		int       		=    True
																		_SCREAMING_SNAKE_CASE   :		Dict       		=    []
														else:
																		current_sub_tokens.append(__lowerCAmelCase							)
																		_SCREAMING_SNAKE_CASE   :		List[Any]       		=    False
										out_string += self.sp_model.decode(__lowerCAmelCase							)
										return out_string.strip()







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = None							)		->							List[int]:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    [self.sep_token_id]
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    [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   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = False							)		->							List[int]:

										if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=__lowerCAmelCase   ,				token_ids_a=__lowerCAmelCase   ,				already_has_special_tokens=__lowerCAmelCase							)

										if token_ids_a is not None:
														return [1] + ([0] * len(__lowerCAmelCase							)) + [1] + ([0] * len(__lowerCAmelCase							)) + [1]
										return [1] + ([0] * len(__lowerCAmelCase							)) + [1]







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = None							)		->							List[int]:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    [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   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = None							)		->							Tuple[str]:
										if not os.path.isdir(__lowerCAmelCase							):
														logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""							)
														return
										_SCREAMING_SNAKE_CASE   :		int       		=    os.path.join(
										    __lowerCAmelCase   ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

										if os.path.abspath(self.vocab_file							) != os.path.abspath(__lowerCAmelCase							) and os.path.isfile(self.vocab_file							):
														copyfile(self.vocab_file   ,				__lowerCAmelCase							)
										elif not os.path.isfile(self.vocab_file							):
														with open(__lowerCAmelCase   ,				'wb'							) as fi:
																		_SCREAMING_SNAKE_CASE   :		Dict       		=    self.sp_model.serialized_model_proto()
																		fi.write(__lowerCAmelCase							)

										return (out_vocab_file,)






UpperCAmelCase_		 =					0  # The first color of the flag.
UpperCAmelCase_		 =					1  # The second color of the flag.
UpperCAmelCase_		 =					2  # The third color of the flag.
UpperCAmelCase_		 =					(red, white, blue)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:list				)  ->     list:
   if not sequence:
      return []
   if len(_snake_case				) == 1:
      return list(_snake_case				)
   _A      =       0
   _A      =       len(_snake_case				) - 1
   _A      =       0
   while mid <= high:
      if sequence[mid] == colors[0]:
         _A  ,    _A      =       sequence[mid], sequence[low]
         low += 1
         mid += 1
      elif sequence[mid] == colors[1]:
         mid += 1
      elif sequence[mid] == colors[2]:
         _A  ,    _A      =       sequence[high], sequence[mid]
         high -= 1
      else:
         _A      =       F'''The elements inside the sequence must contains only {colors} values'''
         raise ValueError(_snake_case				)
   return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   UpperCAmelCase_		 =					input("""Enter numbers separated by commas:\n""").strip()
   UpperCAmelCase_		 =					[int(item.strip()) for item in user_input.split(""",""")]
   print(f'{dutch_national_flag_sort(unsorted)}')



