






'''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_fnet import FNetTokenizer
else:
						_UpperCamelCase		:		Union[str, Any]            = None

_UpperCamelCase		:		str            = logging.get_logger(__name__)
_UpperCamelCase		:		Tuple            = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCamelCase		:		Optional[Any]            = {
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json""",
    },
}

_UpperCamelCase		:		Optional[Any]            = {
    """google/fnet-base""": 512,
    """google/fnet-large""": 512,
}

_UpperCamelCase		:		List[str]            = """▁"""


class       snake_case__	( a_):
					a_								= VOCAB_FILES_NAMES
					a_								= PRETRAINED_VOCAB_FILES_MAP
					a_								= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a_								= ["input_ids", "token_type_ids"]
					a_								= FNetTokenizer




					def __init__(   self   :						Union[str, Any]							,      _A   :						Tuple=None							,      _A   :						str=None							,      _A   :						str=False							,      _A   :						List[Any]=True							,      _A   :						List[Any]=True							,      _A   :						Optional[Any]="<unk>"							,      _A   :						Union[str, Any]="[SEP]"							,      _A   :						Tuple="<pad>"							,      _A   :						Dict="[CLS]"							,      _A   :						Union[str, Any]="[MASK]"							,      **_A   :						Optional[Any]							,      )  ->				str:
								# 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.
								UpperCAmelCase_	:    int	  =    (
								    AddedToken(_a							,      lstrip=_a							,      rstrip=_a							,      normalized=_a	)
								    if isinstance(_a							,      _a	)
								    else mask_token
								)

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

								UpperCAmelCase_	:    Dict	  =    do_lower_case
								UpperCAmelCase_	:    Union[str, Any]	  =    remove_space
								UpperCAmelCase_	:    Any	  =    keep_accents
								UpperCAmelCase_	:    List[Any]	  =    vocab_file
								UpperCAmelCase_	:    Union[str, Any]	  =    False if not self.vocab_file else True




					def 						A	(   self   :						Optional[Any]							,      _A   :						Tuple							,      _A   :						List[str] = None	)  ->				Any:
								UpperCAmelCase_	:    Optional[Any]	  =    [self.sep_token_id]
								UpperCAmelCase_	:    str	  =    [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 						A	(   self   :						int							,      _A   :						List[str]							,      _A   :						str = None	)  ->				int:
								UpperCAmelCase_	:    Any	  =    [self.sep_token_id]
								UpperCAmelCase_	:    Dict	  =    [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 						A	(   self   :						Any							,      _A   :						Tuple							,      _A   :						List[str] = None	)  ->				Tuple:
								if not os.path.isdir(_a	):
											logger.error(F"Vocabulary path ({save_directory}) should be a directory"	)
											return
								UpperCAmelCase_	:    Optional[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	):
											copyfile(self.vocab_file							,      _a	)

								return (out_vocab_file,)




"""simple docstring"""




from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge

@total_ordering
class 		a__						(      a_						):
   def __lt__(			self	,    _a				):
          return self[-1] < other[-1]

   def __eq__(			self	,    _a				):
          return self[-1] == other[-1]


def   __magic_name__	(      __snake_case				:       list  ) ->				list:
       lowercase    :				list[Stack]       		=							[]
       # sort into stacks
       for element in collection:
              lowercase    :				Union[str, Any]       		=							Stack([element]  )
              lowercase    :				Dict       		=							bisect_left(__snake_case     , __snake_case  )
              if i != len(__snake_case  ):
                     stacks[i].append(__snake_case  )
              else:
                     stacks.append(__snake_case  )

    # use a heap-based merge to merge stack efficiently
       lowercase    :				Dict       		=							merge(*(reversed(__snake_case  ) for stack in stacks)  )
       return collection


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


