


'''simple docstring'''







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

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


if is_sentencepiece_available():
   from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
   A__:   List[str]    =	None


A__:   Any    =	logging.get_logger(__name__)

A__:   Any    =	{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

A__:   Union[str, Any]    =	{
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

A__:   Optional[Any]    =	{
    '''xlm-roberta-base''': 512,
    '''xlm-roberta-large''': 512,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 512,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 512,
    '''xlm-roberta-large-finetuned-conll03-english''': 512,
    '''xlm-roberta-large-finetuned-conll03-german''': 512,
}







class A__	(						UpperCAmelCase__				):
      __UpperCamelCase	:  str     	=							VOCAB_FILES_NAMES
      __UpperCamelCase	:  List[str]     	=							PRETRAINED_VOCAB_FILES_MAP
      __UpperCamelCase	:  Optional[Any]     	=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCamelCase	:  Optional[Any]     	=							["input_ids", "attention_mask"]
      __UpperCamelCase	:  Optional[Any]     	=							XLMRobertaTokenizer






      def __init__(			self      :Optional[Any]					,						SCREAMING_SNAKE_CASE      :Dict=None					,						SCREAMING_SNAKE_CASE      :Tuple=None					,						SCREAMING_SNAKE_CASE      :int="<s>"					,						SCREAMING_SNAKE_CASE      :Optional[Any]="</s>"					,						SCREAMING_SNAKE_CASE      :List[str]="</s>"					,						SCREAMING_SNAKE_CASE      :int="<s>"					,						SCREAMING_SNAKE_CASE      :int="<unk>"					,						SCREAMING_SNAKE_CASE      :Optional[int]="<pad>"					,						SCREAMING_SNAKE_CASE      :Any="<mask>"					,						**SCREAMING_SNAKE_CASE      :List[str]					,						)	-> str:




             '''simple docstring'''
             # Mask token behave like a normal word, i.e. include the space before it
             _a   :			List[str]					 =AddedToken(SCREAMING_SNAKE_CASE					,						lstrip=SCREAMING_SNAKE_CASE					,						rstrip=SCREAMING_SNAKE_CASE     ) if isinstance(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     ) else mask_token

             super().__init__(
                 SCREAMING_SNAKE_CASE					,						tokenizer_file=SCREAMING_SNAKE_CASE					,						bos_token=SCREAMING_SNAKE_CASE					,						eos_token=SCREAMING_SNAKE_CASE					,						sep_token=SCREAMING_SNAKE_CASE					,						cls_token=SCREAMING_SNAKE_CASE					,						unk_token=SCREAMING_SNAKE_CASE					,						pad_token=SCREAMING_SNAKE_CASE					,						mask_token=SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE					,						)

             _a   :			Any					 =vocab_file
             _a   :			List[str]					 =False if not self.vocab_file else True






      def __UpperCAmelCase	(			self      :str					,						SCREAMING_SNAKE_CASE      :List[int]					,						SCREAMING_SNAKE_CASE      :Optional[List[int]] = None     )	-> List[int]:




             '''simple docstring'''

             if token_ids_a is None:
                    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             _a   :			List[str]					 =[self.cls_token_id]
             _a   :			Union[str, Any]					 =[self.sep_token_id]
             return cls + token_ids_a + sep + sep + token_ids_a + sep






      def __UpperCAmelCase	(			self      :str					,						SCREAMING_SNAKE_CASE      :List[int]					,						SCREAMING_SNAKE_CASE      :Optional[List[int]] = None     )	-> List[int]:




             '''simple docstring'''
             _a   :			Union[str, Any]					 =[self.sep_token_id]
             _a   :			str					 =[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 + sep + token_ids_a + sep     ) * [0]






      def __UpperCAmelCase	(			self      :str					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :Optional[str] = None     )	-> Tuple[str]:




             '''simple docstring'''
             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(SCREAMING_SNAKE_CASE     ):
                    logger.error(f"Vocabulary path ({save_directory}) should be a directory."     )
                    return
             _a   :			Optional[Any]					 =os.path.join(
                 SCREAMING_SNAKE_CASE					,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )

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

             return (out_vocab_file,)






'''simple docstring'''







def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Union[str, Any]  )  ->      int:
       _a   :			List[str]					 =[701, 301, 132, 57, 23, 10, 4, 1]
       for gap in gaps:
              for i in range(_UpperCAmelCase   ,len(_UpperCAmelCase  )  ):
                     _a   :			Tuple					 =collection[i]
                     _a   :			Optional[Any]					 =i
                     while j >= gap and collection[j - gap] > insert_value:
                            _a   :			Union[str, Any]					 =collection[j - gap]
                            j -= gap
                     if j != i:
                            _a   :			Union[str, Any]					 =insert_value
       return collection


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   A__:   int    =	input('''Enter numbers separated by a comma:\n''').strip()
   A__:   Optional[Any]    =	[int(item) for item in user_input.split(''',''')]
   print(shell_sort(unsorted))


