





'''simple docstring'''


from __future__ import annotations

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






@total_ordering
class       UpperCamelCase__  (		__lowerCAmelCase     ):







	def __lt__(				self			:   Dict , lowerCamelCase			:   Any		):




				'''simple docstring'''
				return self[-1] < other[-1]







	def __eq__(				self			:   str , lowerCamelCase			:   List[str]		):




				'''simple docstring'''
				return self[-1] == other[-1]







def    _lowerCamelCase	(__lowerCamelCase  :     list     )   ->    list:
			a__     = []
			# sort into stacks
			for element in collection:
						a__     = Stack([element]     )
						a__     = bisect_left(__lowerCamelCase					,  __lowerCamelCase     )
						if i != len(__lowerCamelCase     ):
									stacks[i].append(__lowerCamelCase     )
						else:
									stacks.append(__lowerCamelCase     )

    # use a heap-based merge to merge stack efficiently
			a__     = merge(*(reversed(__lowerCamelCase     ) for stack in stacks)     )
			return collection


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










'''simple docstring'''


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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_   :   str       =					logging.get_logger(__name__)

lowerCAmelCase_   :   Optional[Any]       =					"▁"

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

lowerCAmelCase_   :   str       =					{
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

lowerCAmelCase_   :   Optional[int]       =					{
    "facebook/xglm-564M": 2048,
}






class       UpperCamelCase__  (		__lowerCAmelCase     ):
	lowerCAmelCase__					:			int								=						VOCAB_FILES_NAMES
	lowerCAmelCase__					:			Optional[int]								=						PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__					:			Tuple								=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__					:			Optional[int]								=						["input_ids", "attention_mask"]







	def __init__(				self			:   Optional[Any] , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   int="<s>" , lowerCamelCase			:   List[Any]="</s>" , lowerCamelCase			:   Any="</s>" , lowerCamelCase			:   Tuple="<s>" , lowerCamelCase			:   Optional[Any]="<unk>" , lowerCamelCase			:   Tuple="<pad>" , lowerCamelCase			:   Optional[Dict[str, Any]] = None , **lowerCamelCase			:   Union[str, Any] , ):




				'''simple docstring'''
				a__     = {} if sp_model_kwargs is None else sp_model_kwargs

				# Compatibility with the original tokenizer
				a__     = 7
				a__     = [F'''<madeupword{i}>''' for i in range(self.num_madeup_words		)]

				a__     = kwargs.get("additional_special_tokens" , []		)
				kwargs["additional_special_tokens"] += [
				    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
				]

				super().__init__(
				    bos_token=lowerCamelCase , eos_token=lowerCamelCase , unk_token=lowerCamelCase , sep_token=lowerCamelCase , cls_token=lowerCamelCase , pad_token=lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **lowerCamelCase , )

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

				# Original fairseq vocab and spm vocab must be "aligned":
				# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
				# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
				# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
				# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

				# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
				a__     = 1

				# Mimic fairseq token-to-id alignment for the first 4 token
				a__     = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

				a__     = len(self.sp_model		)
				a__     = {F'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words		)}
				self.fairseq_tokens_to_ids.update(lowerCamelCase		)

				a__     = {v: k for k, v in self.fairseq_tokens_to_ids.items()}







	def __getstate__(				self			:   Optional[Any]		):




				'''simple docstring'''
				a__     = self.__dict__.copy()
				a__     = None
				a__     = self.sp_model.serialized_model_proto()
				return state







	def __setstate__(				self			:   Dict , lowerCamelCase			:   List[Any]		):




				'''simple docstring'''
				a__     = d

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

				a__     = spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)







	def 						__a   (				self			:   Union[str, Any] , lowerCamelCase			:   List[int] , lowerCamelCase			:   Optional[List[int]] = None		):




				'''simple docstring'''

				if token_ids_a is None:
							return [self.sep_token_id] + token_ids_a
				a__     = [self.sep_token_id]
				return sep + token_ids_a + sep + sep + token_ids_a







	def 						__a   (				self			:   int , lowerCamelCase			:   List[int] , lowerCamelCase			:   Optional[List[int]] = None , lowerCamelCase			:   bool = False		):




				'''simple docstring'''

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

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







	def 						__a   (				self			:   Dict , lowerCamelCase			:   List[int] , lowerCamelCase			:   Optional[List[int]] = None		):




				'''simple docstring'''
				a__     = [self.sep_token_id]

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







	@property
	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				return len(self.sp_model		) + self.fairseq_offset + self.num_madeup_words







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				a__     = {self.convert_ids_to_tokens(lowerCamelCase		): i for i in range(self.vocab_size		)}
				vocab.update(self.added_tokens_encoder		)
				return vocab







	def 						__a   (				self			:   int , lowerCamelCase			:   str		):




				'''simple docstring'''
				return self.sp_model.encode(lowerCamelCase , out_type=lowerCamelCase		)







	def 						__a   (				self			:   List[str] , lowerCamelCase			:   Union[str, Any]		):




				'''simple docstring'''
				if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
				a__     = self.sp_model.PieceToId(lowerCamelCase		)

				# Need to return unknown token if the SP model returned 0
				return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







	def 						__a   (				self			:   Any , lowerCamelCase			:   List[str]		):




				'''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 						__a   (				self			:   int , lowerCamelCase			:   Optional[Any]		):




				'''simple docstring'''
				a__     = "".join(lowerCamelCase		).replace(lowerCamelCase , " "		).strip()
				return out_string







	def 						__a   (				self			:   int , lowerCamelCase			:   str , lowerCamelCase			:   Optional[str] = None		):




				'''simple docstring'''
				if not os.path.isdir(lowerCamelCase		):
							logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
							return
				a__     = os.path.join(
				    lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

				return (out_vocab_file,)



