


"""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:
   __snake_case    					=					None

__snake_case    					=					logging.get_logger(__name__)
__snake_case    					=					{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__snake_case    					=					{
    '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',
    },
}

__snake_case    					=					{
    '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,
}

__snake_case    					=					'▁'

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   _a   :       str									=    VOCAB_FILES_NAMES
   _a   :       Any									=    PRETRAINED_VOCAB_FILES_MAP
   _a   :       str									=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a   :       Optional[int]									=    AlbertTokenizer


   def __init__(						self ,     lowerCamelCase__=None ,     lowerCamelCase__=None ,     lowerCamelCase__=True ,     lowerCamelCase__=True ,     lowerCamelCase__=False ,     lowerCamelCase__="[CLS]" ,     lowerCamelCase__="[SEP]" ,     lowerCamelCase__="<unk>" ,     lowerCamelCase__="[SEP]" ,     lowerCamelCase__="<pad>" ,     lowerCamelCase__="[CLS]" ,     lowerCamelCase__="[MASK]" ,     **lowerCamelCase__ ,     )      ->							Optional[Any]:
    # 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.
    lowercase__       :       List[Any]				=						(
        AddedToken(lowerCamelCase__ ,     lstrip=lowerCamelCase__ ,     rstrip=lowerCamelCase__ ,     normalized=lowerCamelCase__						)
        if isinstance(lowerCamelCase__ ,     lowerCamelCase__						)
        else mask_token
    )

    super().__init__(
        lowerCamelCase__ ,     tokenizer_file=lowerCamelCase__ ,     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__ ,     **lowerCamelCase__ ,     )

    lowercase__       :       List[str]				=						do_lower_case
    lowercase__       :       int				=						remove_space
    lowercase__       :       int				=						keep_accents
    lowercase__       :       List[Any]				=						vocab_file
    lowercase__       :       int				=						False if not self.vocab_file else True


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							List[int]:
    lowercase__       :       str				=						[self.sep_token_id]
    lowercase__       :       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 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							List[int]:
    lowercase__       :       int				=						[self.sep_token_id]
    lowercase__       :       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						) * [0] + len(token_ids_a + sep						) * [1]







   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = 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(lowerCamelCase__						):
     logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
     return
    lowercase__       :       List[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,)



"""simple docstring"""


import random


def    _lowerCamelCase   (     lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							Dict       ,		lowerCamelCase__	:							str  ):
 lowercase__       :       List[Any]				=						a[left_index]
 lowercase__       :       List[Any]				=						left_index + 1
 for j in range(left_index + 1       ,		lowerCamelCase__  ):
  if a[j] < pivot:
   lowercase__	,       lowercase__       :       Dict				=						a[i], a[j]
   i += 1
 lowercase__	,       lowercase__       :       Any				=						a[i - 1], a[left_index]
 return i - 1


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							Optional[int]  ):
 if left < right:
  lowercase__       :       str				=						random.randint(lowerCamelCase__       ,		right - 1  )
  lowercase__	,       lowercase__       :       Any				=						(
      a[left],
      a[pivot],
  )  # switches the pivot with the left most bound
  lowercase__       :       str				=						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    _lowerCamelCase   (     ):
 lowercase__       :       List[Any]				=						input("""Enter numbers separated by a comma:\n"""  ).strip()
 lowercase__       :       int				=						[int(lowerCamelCase__  ) for item in user_input.split(""","""  )]

 quick_sort_random(lowerCamelCase__       ,		0       ,		len(lowerCamelCase__  )  )

 print(lowerCamelCase__  )


if __name__ == "__main__":
   main()