


"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE :    int									=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :    Union[str, Any]									=				{"""vocab_file""": """sentencepiece.bpe.model"""}

SCREAMING_SNAKE_CASE :    int									=				{
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    }
}

SCREAMING_SNAKE_CASE :    Union[str, Any]									=				{
    """camembert-base""": 512,
}

SCREAMING_SNAKE_CASE :    List[Any]									=				"""▁"""



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				=VOCAB_FILES_NAMES
		lowerCamelCase__				=PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase__				=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase__				=['input_ids', 'attention_mask']



		def __init__(self				,       a_				,       a_="<s>"				,       a_="</s>"				,       a_="</s>"				,       a_="<s>"				,       a_="<unk>"				,       a_="<pad>"				,       a_="<mask>"				,       a_=["<s>NOTUSED", "</s>NOTUSED"]				,       a_ = None				,       **a_				,       ):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				AddedToken(a_				,       lstrip=a_				,       rstrip=a_					) if isinstance(a_				,       a_					) else mask_token

			__snake_case	:						str						=				{} if sp_model_kwargs is None else sp_model_kwargs

			super().__init__(
			    bos_token=a_				,       eos_token=a_				,       unk_token=a_				,       sep_token=a_				,       cls_token=a_				,       pad_token=a_				,       mask_token=a_				,       additional_special_tokens=a_				,       sp_model_kwargs=self.sp_model_kwargs				,       **a_				,       )
			__snake_case	:						List[str]						=				spm.SentencePieceProcessor(**self.sp_model_kwargs					)
			self.sp_model.Load(str(a_					)					)
			__snake_case	:						Optional[Any]						=				vocab_file
			# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
			# sentencepiece vocabulary (this is the case for <s> and </s>
			__snake_case	:						List[Any]						=				{'''<s>NOTUSED''': 0, '''<pad>''': 1, '''</s>NOTUSED''': 2, '''<unk>''': 3}
			__snake_case	:						str						=				len(self.fairseq_tokens_to_ids					)
			__snake_case	:						List[Any]						=				len(self.sp_model					) + len(self.fairseq_tokens_to_ids					)
			__snake_case	:						List[str]						=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None					):





			'''simple docstring'''

			if token_ids_a is None:
				return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			__snake_case	:						Optional[Any]						=				[self.cls_token_id]
			__snake_case	:						Union[str, Any]						=				[self.sep_token_id]
			return cls + token_ids_a + sep + sep + token_ids_a + sep



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None				,       a_ = False					):





			'''simple docstring'''

			if already_has_special_tokens:
				return super().get_special_tokens_mask(
				    token_ids_a=a_				,       token_ids_a=a_				,       already_has_special_tokens=a_					)

			if token_ids_a is None:
				return [1] + ([0] * len(a_					)) + [1]
			return [1] + ([0] * len(a_					)) + [1, 1] + ([0] * len(a_					)) + [1]



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None					):





			'''simple docstring'''

			__snake_case	:						Optional[int]						=				[self.sep_token_id]
			__snake_case	:						str						=				[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 + sep + token_ids_a + sep					) * [0]



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return len(self.fairseq_tokens_to_ids					) + len(self.sp_model					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				{self.convert_ids_to_tokens(a_					): i for i in range(self.vocab_size					)}
			vocab.update(self.added_tokens_encoder					)
			return vocab



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			return self.sp_model.encode(a_				,       out_type=a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			if token in self.fairseq_tokens_to_ids:
				return self.fairseq_tokens_to_ids[token]
			elif self.sp_model.PieceToId(a_					) == 0:
				# Convert sentence piece unk token to fairseq unk token index
				return self.unk_token_id
			return self.fairseq_offset + self.sp_model.PieceToId(a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			if index in self.fairseq_ids_to_tokens:
				return self.fairseq_ids_to_tokens[index]
			return self.sp_model.IdToPiece(index - self.fairseq_offset					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				[]
			__snake_case	:						Dict						=				''''''
			__snake_case	:						int						=				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(a_					) + token
					__snake_case	:						Tuple						=				True
					__snake_case	:						Any						=				[]
				else:
					current_sub_tokens.append(a_					)
					__snake_case	:						int						=				False
			out_string += self.sp_model.decode(a_					)
			return out_string.strip()



		def __getstate__(self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				self.__dict__.copy()
			__snake_case	:						Union[str, Any]						=				None
			return state



		def __setstate__(self				,       a_					):





			'''simple docstring'''

			__snake_case	:						int						=				d

			# for backward compatibility
			if not hasattr(self				,       '''sp_model_kwargs'''					):
				__snake_case	:						Optional[Any]						=				{}

			__snake_case	:						List[Any]						=				spm.SentencePieceProcessor(**self.sp_model_kwargs					)
			self.sp_model.Load(self.vocab_file					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None					):





			'''simple docstring'''

			if not os.path.isdir(a_					):
				logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
				return
			__snake_case	:						List[Any]						=				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_					) and os.path.isfile(self.vocab_file					):
				copyfile(self.vocab_file				,       a_					)
			elif not os.path.isfile(self.vocab_file					):
				with open(a_				,       '''wb'''					) as fi:
					__snake_case	:						Optional[Any]						=				self.sp_model.serialized_model_proto()
					fi.write(a_					)

			return (out_vocab_file,)












def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Tuple:
					__A    ,				__A	:						Optional[Any]       =				[], []
					while len(a  ) > 1:
										__A    ,				__A	:						Any       =				min(a  ), max(a  )
										start.append(a  )
										end.append(a  )
										collection.remove(a  )
										collection.remove(a  )
					end.reverse()
					return start + collection + end


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




