


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 :       Union[str, Any]						=   None


A :       Union[str, Any]						=   logging.get_logger(__name__)

A :       int						=   {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

A :       Tuple						=   {
    "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      _lowercase					(				lowercase__):


       """simple docstring"""




       A__   		=     VOCAB_FILES_NAMES
       A__   		=     PRETRAINED_VOCAB_FILES_MAP
       A__   		=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       A__   		=     ["input_ids", "attention_mask"]
       A__   		=     XLMRobertaTokenizer





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


             '''simple docstring'''



             lowerCamelCase__						:							Union[str, Any]       =    AddedToken(__lowerCamelCase   , lstrip=__lowerCamelCase   , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase   , __lowerCamelCase			) else mask_token

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

             lowerCamelCase__						:							List[Any]       =    vocab_file
             lowerCamelCase__						:							Dict       =    False if not self.vocab_file else True





       def 		lowerCAmelCase	(  self				:     int   , __lowerCamelCase				:     List[int]   , __lowerCamelCase				:     Optional[List[int]] = None			):


             '''simple docstring'''



             if token_ids_a is None:
                   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             lowerCamelCase__						:							Optional[int]       =    [self.cls_token_id]
             lowerCamelCase__						:							Optional[Any]       =    [self.sep_token_id]
             return cls + token_ids_a + sep + sep + token_ids_a + sep





       def 		lowerCAmelCase	(  self				:     Optional[Any]   , __lowerCamelCase				:     List[int]   , __lowerCamelCase				:     Optional[List[int]] = None			):


             '''simple docstring'''



             lowerCamelCase__						:							Union[str, Any]       =    [self.sep_token_id]
             lowerCamelCase__						:							Optional[int]       =    [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 		lowerCAmelCase	(  self				:     str   , __lowerCamelCase				:     str   , __lowerCamelCase				:     Optional[str] = None			):


             '''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(__lowerCamelCase			):
                   logger.error(f"Vocabulary path ({save_directory}) should be a directory."			)
                   return
             lowerCamelCase__						:							str       =    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			):
                   copyfile(self.vocab_file   , __lowerCamelCase			)

             return (out_vocab_file,)






def  lowercase_						(						_A     :       Dict		):

      """simple docstring"""




      lowerCamelCase__						:							Optional[Any]       =    len(_A		)
      for _ in range(
          0       ,   int(((no_of_elements - 1) / 2) + 1		)		):  # we don't need to traverse to end of list as
            for j in range(0       ,   no_of_elements - 1		):
                  if (
                      lst[j + 1] < lst[j]
                  ):  # applying bubble sort algorithm from left to right (or forwards)
                        lowerCamelCase__						:							Tuple       =    lst[j + 1]
                        lowerCamelCase__						:							Optional[Any]       =    lst[j]
                        lowerCamelCase__						:							Optional[Any]       =    temp
                  if (
                      lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
                  ):  # applying bubble sort algorithm from right to left (or backwards)
                        lowerCamelCase__						:							Any       =    lst[no_of_elements - 1 - j]
                        lowerCamelCase__						:							List[str]       =    lst[no_of_elements - 2 - j]
                        lowerCamelCase__						:							str       =    temp
      return lst


if __name__ == "__main__":
    print("enter the list to be sorted")
    A :       Optional[int]						=   [int(x) for x in input().split()]  # inputing elements of the list in one line
    A :       Optional[Any]						=   double_sort(lst)
    print("the sorted list is")
    print(sorted_lst)


