






"""simple docstring"""







import random





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  a[left_index]
					a__          =  left_index + 1
					for j in range(left_index + 1 ,     lowerCamelCase_):
										if a[j] < pivot:
															a__       ,a__          =  a[i], a[j]
															i += 1
					a__       ,a__          =  a[i - 1], a[left_index]
					return i - 1





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					if left < right:
										a__          =  random.randint(lowerCamelCase_ ,     right - 1)
										a__       ,a__          =  (
										    a[left],
										    a[pivot],
										)  # switches the pivot with the left most bound
										a__          =  partition(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
										quick_sort_random(
										    lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)  # recursive quicksort to the left of the pivot point
										quick_sort_random(
										    lowerCamelCase_ ,     pivot_index + 1 ,     lowerCamelCase_)  # recursive quicksort to the right of the pivot point





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  input('''Enter numbers separated by a comma:\n''').strip()
					a__          =  [int(lowerCamelCase_) for item in user_input.split(''',''')]

					quick_sort_random(lowerCamelCase_ ,     0 ,     len(lowerCamelCase_))

					print(lowerCamelCase_)


if __name__ == "__main__":
	main()










"""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_albert import AlbertTokenizer
else:
	__a       :	int             = None

__a       :	List[str]             = logging.get_logger(__name__)
__a       :	Union[str, Any]             = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__a       :	str             = {
    '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',
    },
    'tokenizer_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json',
    },
}

__a       :	Optional[int]             = {
    '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,
}

__a       :	Union[str, Any]             = '▁'







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =VOCAB_FILES_NAMES
							_SCREAMING_SNAKE_CASE    =PRETRAINED_VOCAB_FILES_MAP
							_SCREAMING_SNAKE_CASE    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_SCREAMING_SNAKE_CASE    =AlbertTokenizer




							def __init__(   self:     List[Any]				,		__A:     List[Any]=None				,		__A:     Tuple=None				,		__A:     Union[str, Any]=True				,		__A:     int=True				,		__A:     Optional[int]=False				,		__A:     List[Any]="[CLS]"				,		__A:     Optional[int]="[SEP]"				,		__A:     Union[str, Any]="<unk>"				,		__A:     int="[SEP]"				,		__A:     Optional[Any]="<pad>"				,		__A:     Optional[int]="[CLS]"				,		__A:     str="[MASK]"				,		**__A:     List[Any]				,		):

												'''simple docstring'''

												a__          =  (
												    AddedToken(__A				,		lstrip=__A				,		rstrip=__A				,		normalized=__A    )
												    if isinstance(__A				,		__A    )
												    else mask_token
												)

												super().__init__(
												    __A				,		tokenizer_file=__A				,		do_lower_case=__A				,		remove_space=__A				,		keep_accents=__A				,		bos_token=__A				,		eos_token=__A				,		unk_token=__A				,		sep_token=__A				,		pad_token=__A				,		cls_token=__A				,		mask_token=__A				,		**__A				,		)

												a__          =  do_lower_case
												a__          =  remove_space
												a__          =  keep_accents
												a__          =  vocab_file
												a__          =  False if not self.vocab_file else True




							def  lowercase		(   self:     Union[str, Any]				,		__A:     List[int]				,		__A:     Optional[List[int]] = None    ):

												'''simple docstring'''

												a__          =  [self.sep_token_id]
												a__          =  [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  lowercase		(   self:     List[Any]				,		__A:     List[int]				,		__A:     Optional[List[int]] = None    ):

												'''simple docstring'''

												a__          =  [self.sep_token_id]
												a__          =  [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  lowercase		(   self:     Union[str, Any]				,		__A:     str				,		__A:     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(__A    ):
																	logger.error(F'Vocabulary path ({save_directory}) should be a directory'    )
																	return
												a__          =  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    ):
																	copyfile(self.vocab_file				,		__A    )

												return (out_vocab_file,)


