



"""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_						:    Dict				=      None

A_						:    Union[str, Any]				=      logging.get_logger(__name__)
A_						:    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_						:    List[Any]				=      {
    '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     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		List[Any]  						= VOCAB_FILES_NAMES
	_SCREAMING_SNAKE_CASE	:		int  						= PRETRAINED_VOCAB_FILES_MAP
	_SCREAMING_SNAKE_CASE	:		Tuple  						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_SCREAMING_SNAKE_CASE	:		List[str]  						= AlbertTokenizer





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     (
					    AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE     ,       normalized=_SCREAMING_SNAKE_CASE    )
					    if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
					    else mask_token
					)

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

					SCREAMING_SNAKE_CASE						:							Tuple							=     do_lower_case
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     remove_space
					SCREAMING_SNAKE_CASE						:							int							=     keep_accents
					SCREAMING_SNAKE_CASE						:							List[Any]							=     vocab_file
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     False if not self.vocab_file else True





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       List[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[List[int]] = None    )						->	List[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     [self.sep_token_id]
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     [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 			_lowerCAmelCase     (      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       List[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[List[int]] = None    )						->	List[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     [self.sep_token_id]
					SCREAMING_SNAKE_CASE						:							Tuple							=     [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 			_lowerCAmelCase     (      self				:       Dict     ,       _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
					SCREAMING_SNAKE_CASE						:							List[str]							=     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
def  __snake_case							(	__A      :						list[int]     )     ->       list[int]:






				'''simple docstring'''
				if len(__A     ) == 0:
								return array

				SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Optional[int]							=     min(__A     ), max(__A     )

				# Compute the variables
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     _max - _min + 1
				SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[str]							=     [0] * holes_range, [0] * holes_range

				# Make the sorting.
				for i in array:
								SCREAMING_SNAKE_CASE						:							List[Any]							=     i - _min
								SCREAMING_SNAKE_CASE						:							Dict							=     i
								holes_repeat[index] += 1

				# Makes the array back by replacing the numbers.
				SCREAMING_SNAKE_CASE						:							int							=     0
				for i in range(__A     ):
								while holes_repeat[i] > 0:
												SCREAMING_SNAKE_CASE						:							int							=     holes[i]
												index += 1
												holes_repeat[i] -= 1

    # Returns the sorted array.
				return array


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							A_						:    Optional[Any]				=      input('Enter numbers separated by comma:\n')
							A_						:    Union[str, Any]				=      [int(x) for x in user_input.split(',')]
							print(pigeon_sort(unsorted))





