




"""simple docstring"""







from __future__ import annotations

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




@total_ordering
class    lowerCAmelCase__							(      _UpperCamelCase     ):


				"""simple docstring"""





				def __lt__(  self       :						int     , lowercase__       :						Dict    ):
						return self[-1] < other[-1]



				def __eq__(  self       :						Union[str, Any]     , lowercase__       :						Union[str, Any]    ):
						return self[-1] == other[-1]





def     snake_case__							(       _lowerCamelCase					)      ->list:






		"""simple docstring"""


		__lowercase					:		list[Stack]        =					[]
		# sort into stacks
		for element in collection:
				__lowercase					:		Any        =					Stack([element]					)
				__lowercase					:		str        =					bisect_left(SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__					)
				if i != len(SCREAMING_SNAKE_CASE__					):
						stacks[i].append(SCREAMING_SNAKE_CASE__					)
				else:
						stacks.append(SCREAMING_SNAKE_CASE__					)

    # use a heap-based merge to merge stack efficiently
		__lowercase					:		Optional[Any]        =					merge(*(reversed(SCREAMING_SNAKE_CASE__					) for stack in stacks)					)
		return collection


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





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase							:   List[Any]				=				{
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase							:   int				=				[
     "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LiltForQuestionAnswering",
     "LiltForSequenceClassification",
     "LiltForTokenClassification",
     "LiltModel",
     "LiltPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_lilt import (
      LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
      LiltForQuestionAnswering,
      LiltForSequenceClassification,
      LiltForTokenClassification,
      LiltModel,
      LiltPreTrainedModel,
  )

else:
 import sys

 _lowerCAmelCase							:   Optional[int]				=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
