





import os
import re
import warnings
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_ta import TaTokenizer
else:
		__lowerCamelCase     :							Optional[int]  							=				None


__lowerCamelCase     :							int  							=				logging.get_logger(__name__)

__lowerCamelCase     :							Any  							=				{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__lowerCamelCase     :							List[str]  							=				{
    "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",
    },
    "tokenizer_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__lowerCamelCase     :							List[str]  							=				{
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}





class  __magic_name__   (							A__						):
		lowercase      :       List[str]          =VOCAB_FILES_NAMES
		lowercase      :       int          =PRETRAINED_VOCAB_FILES_MAP
		lowercase      :       Union[str, Any]          =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase      :       Union[str, Any]          =['''input_ids''', '''attention_mask''']
		lowercase      :       Any          =TaTokenizer

		lowercase      :       List[int]          =[]






		def __init__(						self      :     Optional[Any]				,		UpperCamelCase__      :     List[str]=None				,		UpperCamelCase__      :     List[str]=None				,		UpperCamelCase__      :     Any="</s>"				,		UpperCamelCase__      :     Dict="<unk>"				,		UpperCamelCase__      :     Tuple="<pad>"				,		UpperCamelCase__      :     Tuple=1_00				,		UpperCamelCase__      :     List[Any]=None				,		**UpperCamelCase__      :     int				,		)				->					Optional[Any]:
								'''simple docstring'''







								if extra_ids > 0 and additional_special_tokens is None:
														UpperCAmelCase         =     [F'<extra_id_{i}>' for i in range(UpperCamelCase__      )]
								elif extra_ids > 0 and additional_special_tokens is not None:
														# Check that we have the right number of extra special tokens
														UpperCAmelCase         =     len(set(filter(lambda UpperCamelCase__      : bool("extra_id_" in str(UpperCamelCase__      )      )				,		UpperCamelCase__      )      )      )
														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"      )

								super().__init__(
								    UpperCamelCase__				,		tokenizer_file=UpperCamelCase__				,		eos_token=UpperCamelCase__				,		unk_token=UpperCamelCase__				,		pad_token=UpperCamelCase__				,		extra_ids=UpperCamelCase__				,		additional_special_tokens=UpperCamelCase__				,		**UpperCamelCase__				,		)

								UpperCAmelCase         =     vocab_file
								UpperCAmelCase         =     False if not self.vocab_file else True
								UpperCAmelCase         =     extra_ids






		@staticmethod
		def SCREAMING_SNAKE_CASE_			(						UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Union[str, Any]      )				->					Optional[Any]:
								'''simple docstring'''







								if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
														UpperCAmelCase         =     TaTokenizerFast.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."				,		UpperCamelCase__				,		)

								return max_model_length






		def SCREAMING_SNAKE_CASE_			(						self      :     Any				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     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(UpperCamelCase__      ):
														logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
														return
								UpperCAmelCase         =     os.path.join(
								    UpperCamelCase__				,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )

								if os.path.abspath(self.vocab_file      ) != os.path.abspath(UpperCamelCase__      ):
														copyfile(self.vocab_file				,		UpperCamelCase__      )
														logger.info(F'Copy vocab file to {out_vocab_file}'      )

								return (out_vocab_file,)






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     List[int]				,		UpperCamelCase__      :     Optional[List[int]] = None      )				->					List[int]:
								'''simple docstring'''







								UpperCAmelCase         =     token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
														return self.prefix_tokens + token_ids_a
								else:
														UpperCAmelCase         =     token_ids_a + [self.eos_token_id]
														return self.prefix_tokens + token_ids_a + token_ids_a






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		UpperCamelCase__      :     List[int]				,		UpperCamelCase__      :     Optional[List[int]] = None      )				->					List[int]:
								'''simple docstring'''







								UpperCAmelCase         =     [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 SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					List[Any]:
								'''simple docstring'''







								return list(
								    set(filter(lambda UpperCamelCase__      : bool(re.search(R"<extra_id_\d+>"				,		UpperCamelCase__      )      ) is not None				,		self.additional_special_tokens      )      )      )







		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Dict:
								'''simple docstring'''







								return [self.convert_tokens_to_ids(UpperCamelCase__      ) for token in self.get_sentinel_tokens()]








def    lowerCamelCase_(lowerCamelCase_    ) ->       list[int]:
						UpperCAmelCase         =     len(lowerCamelCase_    )
						for i in range(lowerCamelCase_    ):
												for j in range(i + 1    ,   lowerCamelCase_    ):
																		if numbers[j] < numbers[i]:
																								UpperCAmelCase       ,		UpperCAmelCase         =     numbers[j], numbers[i]
						return numbers


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



