


import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
	from ...tokenization_utils_base import TextInput
from ...utils import logging


_lowerCAmelCase:						Any   				=       logging.get_logger(__name__)

_lowerCAmelCase:						Tuple   				=       {'vocab_file': 'spiece.model'}

_lowerCAmelCase:						List[Any]   				=       {
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
_lowerCAmelCase:						str   				=       {
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}

_lowerCAmelCase:						Optional[Any]   				=       '▁'




class       lowercase_					(lowercase__   ):
				snake_case	    =VOCAB_FILES_NAMES
				snake_case	    =PRETRAINED_VOCAB_FILES_MAP
				snake_case	    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				snake_case	    =['input_ids', 'attention_mask']





				def __init__(				self       ,  lowercase_       ,  lowercase_="</s>"       ,  lowercase_="<unk>"       ,  lowercase_="<pad>"       ,  lowercase_=100       ,  lowercase_=None       ,  lowercase_ = None       ,  lowercase_=True       ,  **lowercase_       ,  )  -> None:
											# Add extra_ids to the special token list
											if extra_ids > 0 and additional_special_tokens is None:
																		a__      =[F"""<extra_id_{i}>""" for i in range(lowercase_)]
											elif extra_ids > 0 and additional_special_tokens is not None:
																		# Check that we have the right number of extra_id special tokens
																		a__      =len(set(filter(lambda lowercase_: bool('extra_id' in str(lowercase_))       ,  lowercase_)))
																		if extra_tokens != extra_ids:
																									raise ValueError(
																									    F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
																									    ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
																									    ' tokens')
											if legacy:
																		logger.warning_once(
																		    F"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
																		    ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565')

											a__      =legacy
											a__      ={} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    eos_token=lowercase_       ,  unk_token=lowercase_       ,  pad_token=lowercase_       ,  extra_ids=lowercase_       ,  additional_special_tokens=lowercase_       ,  sp_model_kwargs=self.sp_model_kwargs       ,  legacy=lowercase_       ,  **lowercase_       ,  )

											a__      =vocab_file
											a__      =extra_ids

											a__      =spm.SentencePieceProcessor(**self.sp_model_kwargs)
											self.sp_model.Load(lowercase_)





				@staticmethod
				def 						__UpperCamelCase    (				lowercase_       ,  lowercase_       ,  lowercase_)  -> Any:
											if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
																		a__      =TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
																		if init_max_model_length is not None and init_max_model_length != max_model_length:
																									return init_max_model_length
																		elif init_max_model_length is None:
																									warnings.warn(
																									    'This tokenizer was incorrectly instantiated with a model max length of'
																									    F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
																									    ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
																									    ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
																									    F""" {pretrained_model_name_or_path} automatically truncating your input to"""
																									    F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
																									    F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
																									    ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
																									    ' instantiate this tokenizer with `model_max_length` set to your preferred value.'       ,  lowercase_       ,  )

											return max_model_length





				@property
				def 						__UpperCamelCase    (				self)  -> int:
											return self.sp_model.get_piece_size() + self._extra_ids





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											a__      ={self.convert_ids_to_tokens(lowercase_): i for i in range(self.vocab_size)}
											vocab.update(self.added_tokens_encoder)
											return vocab





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_ = None       ,  lowercase_ = False)  -> List[int]:
											if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=lowercase_       ,  token_ids_a=lowercase_       ,  already_has_special_tokens=lowercase_)

											# normal case: some special tokens
											if token_ids_a is None:
																		return ([0] * len(lowercase_)) + [1]
											return ([0] * len(lowercase_)) + [1] + ([0] * len(lowercase_)) + [1]





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											return list(
											    set(filter(lambda lowercase_: bool(re.search(R'<extra_id_\d+>'       ,  lowercase_)) is not None       ,  self.additional_special_tokens)))





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											return [self._convert_token_to_id(lowercase_) for token in self.get_sentinel_tokens()]





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> List[int]:
											if len(lowercase_) > 0 and token_ids[-1] == self.eos_token_id:
																		warnings.warn(
																		    F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
																		    ' eos tokens being added.')
																		return token_ids
											else:
																		return token_ids + [self.eos_token_id]





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_ = None)  -> List[int]:
											a__      =[self.eos_token_id]

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





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_ = None)  -> List[int]:
											a__      =self._add_eos_if_not_present(lowercase_)
											if token_ids_a is None:
																		return token_ids_a
											else:
																		a__      =self._add_eos_if_not_present(lowercase_)
																		return token_ids_a + token_ids_a





				def __getstate__(				self)  -> int:
											a__      =self.__dict__.copy()
											a__      =None
											return state





				def __setstate__(				self       ,  lowercase_)  -> str:
											a__      =d

											# for backward compatibility
											if not hasattr(self       ,  'sp_model_kwargs'):
																		a__      ={}

											a__      =spm.SentencePieceProcessor(**self.sp_model_kwargs)
											self.sp_model.Load(self.vocab_file)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  **lowercase_)  -> List[str]:
											# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
											# the beginning of the text
											if not self.legacy:
																		a__      =SPIECE_UNDERLINE + text.replace(lowercase_       ,  ' ')
											return super().tokenize(lowercase_       ,  **lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  **lowercase_)  -> Tuple:
											if not self.legacy:
																		a__      =text.startswith(lowercase_)
																		if is_first:
																									a__      =text[1:]

											a__      =self.sp_model.encode(lowercase_       ,  out_type=lowercase_)

											if not self.legacy and not is_first and not text.startswith(' ') and tokens[0].startswith(lowercase_):
																		a__      =([tokens[0][1:]] if len(tokens[0]) > 1 else []) + tokens[1:]
											return tokens





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> Dict:
											if token.startswith('<extra_id_'):
																		a__      =re.match(R'<extra_id_(\d+)>'       ,  lowercase_)
																		a__      =int(match.group(1))
																		return self.vocab_size - num - 1
											return self.sp_model.piece_to_id(lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> Dict:
											if index < self.sp_model.get_piece_size():
																		a__      =self.sp_model.IdToPiece(lowercase_)
											else:
																		a__      =F"""<extra_id_{self.vocab_size - 1 - index}>"""
											return token





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> Optional[Any]:
											a__      =[]
											a__      =''
											a__      =False
											for token in tokens:
																		# make sure that special tokens are not decoded using sentencepiece model
																		if token in self.all_special_tokens:
																									if not prev_is_special:
																																out_string += " "
																									out_string += self.sp_model.decode(lowercase_) + token
																									a__      =True
																									a__      =[]
																		else:
																									current_sub_tokens.append(lowercase_)
																									a__      =False
											out_string += self.sp_model.decode(lowercase_)
											return out_string.strip()





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_ = None)  -> Tuple[str]:
											if not os.path.isdir(lowercase_):
																		logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
																		return
											a__      =os.path.join(
											    lowercase_       ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

											if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase_) and os.path.isfile(self.vocab_file):
																		copyfile(self.vocab_file       ,  lowercase_)
											elif not os.path.isfile(self.vocab_file):
																		with open(lowercase_       ,  'wb') as fi:
																									a__      =self.sp_model.serialized_model_proto()
																									fi.write(lowercase_)

											return (out_vocab_file,)





def   _lowercase(					__a				:						list[int]     ):
							a__      =len(__a     )
							for i in range(__a     ):
														for j in range(i + 1    ,     __a     ):
																					if numbers[j] < numbers[i]:
																												a__    ,     a__      =numbers[j], numbers[i]
							return numbers


if __name__ == "__main__":
	_lowerCAmelCase:						Tuple   				=       input('Enter numbers separated by a comma:\n').strip()
	_lowerCAmelCase:						int   				=       [int(item) for item in user_input.split(',')]
	print(exchange_sort(unsorted))

