






"""simple docstring"""





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

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


if is_sentencepiece_available():
  from .tokenization_xglm import XGLMTokenizer
else:
  _SCREAMING_SNAKE_CASE :    Optional[Any]					   =						None


_SCREAMING_SNAKE_CASE :    int					   =						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE :    Union[str, Any]					   =						{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE :    Optional[Any]					   =						{
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json''',
    },
}

_SCREAMING_SNAKE_CASE :    List[Any]					   =						{
    '''facebook/xglm-564M''': 2048,
}






class 					a						( __lowerCAmelCase		):
    SCREAMING_SNAKE_CASE						:					int						=			VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE						:					Optional[int]						=			PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE						:					Optional[int]						=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE						:					Dict						=			['''input_ids''', '''attention_mask''']
    SCREAMING_SNAKE_CASE						:					Any						=			XGLMTokenizer






    def __init__(	self					:							Any	,						__SCREAMING_SNAKE_CASE					:							Tuple=None	,						__SCREAMING_SNAKE_CASE					:							Tuple=None	,						__SCREAMING_SNAKE_CASE					:							List[str]="<s>"	,						__SCREAMING_SNAKE_CASE					:							Optional[Any]="</s>"	,						__SCREAMING_SNAKE_CASE					:							List[Any]="</s>"	,						__SCREAMING_SNAKE_CASE					:							Tuple="<s>"	,						__SCREAMING_SNAKE_CASE					:							Optional[int]="<unk>"	,						__SCREAMING_SNAKE_CASE					:							Dict="<pad>"	,						**__SCREAMING_SNAKE_CASE					:							List[str]	,						)					->		Tuple:
      lowerCamelCase_   					=						7
      lowerCamelCase_   					=						[F'''<madeupword{i}>''' for i in range(self.num_madeup_words )]

      lowerCamelCase_   					=						kwargs.get('additional_special_tokens'	,						[] )
      kwargs["additional_special_tokens"] += [
          word for word in madeup_words if word not in kwargs["additional_special_tokens"]
      ]

      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	,						**__SCREAMING_SNAKE_CASE	,						)

      lowerCamelCase_   					=						vocab_file
      lowerCamelCase_   					=						False if not self.vocab_file else True






    def        UpperCamelCase	(	self					:							List[Any]	,						__SCREAMING_SNAKE_CASE					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							List[Any] = None )					->		List[int]:




      if token_ids_a is None:
        return [self.sep_token_id] + token_ids_a
      lowerCamelCase_   					=						[self.sep_token_id]
      return sep + token_ids_a + sep + sep + token_ids_a






    def        UpperCamelCase	(	self					:							Union[str, Any]	,						__SCREAMING_SNAKE_CASE					:							Dict	,						__SCREAMING_SNAKE_CASE					:							Tuple = None )					->		List[int]:
      lowerCamelCase_   					=						[self.sep_token_id]

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






    def        UpperCamelCase	(	self					:							Union[str, Any]	,						__SCREAMING_SNAKE_CASE					:							Dict	,						__SCREAMING_SNAKE_CASE					:							List[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(__SCREAMING_SNAKE_CASE ):
        logger.error(F'''Vocabulary path ({save_directory}) should be a directory.''' )
        return
      lowerCamelCase_   					=						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"""





from __future__ import annotations

from random import randrange







def 					lowerCamelCase__	(							_lowerCamelCase	:		list			)				->    list:
  if len(_lowerCamelCase			) < 2:
    return collection
  lowerCamelCase_   					=						randrange(len(_lowerCamelCase			)			)  # Use random element as pivot
  lowerCamelCase_   					=						collection[pivot_index]
  lowerCamelCase_   					=						[]  # All elements greater than pivot
  lowerCamelCase_   					=						[]  # All elements less than or equal to pivot

  for element in collection[:pivot_index]:
    (greater if element > pivot else lesser).append(_lowerCamelCase			)

  for element in collection[pivot_index + 1 :]:
    (greater if element > pivot else lesser).append(_lowerCamelCase			)

  return [*quick_sort(_lowerCamelCase			), pivot, *quick_sort(_lowerCamelCase			)]


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE :    Dict    =						input('''Enter numbers separated by a comma:\n''').strip()
  _SCREAMING_SNAKE_CASE :    List[str]    =						[int(item) for item in user_input.split(''',''')]
  print(quick_sort(unsorted))




