





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
	import torch

	from transformers import XLMRobertaModel




@require_sentencepiece
@require_tokenizers
@require_torch
class 	UpperCamelCase_							(  unittest.TestCase     ):


							@slow
							def   _snake_case	(				self     :str		) -> Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   XLMRobertaModel.from_pretrained("""xlm-roberta-base"""		)
									SCREAMING_SNAKE_CASE__								=   torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]		)
									# The dog is cute and lives in the garden house

									SCREAMING_SNAKE_CASE__								=   torch.Size((1, 12, 768)		)  # batch_size, sequence_length, embedding_vector_dim
									SCREAMING_SNAKE_CASE__								=   torch.tensor(
									    [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]		)
									#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
									#  xlmr.eval()
									#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
									with torch.no_grad():
											SCREAMING_SNAKE_CASE__								=   model(__A		)["""last_hidden_state"""].detach()
									self.assertEqual(output.shape				,					__A		)
									# compare the actual values for a slice of last dim
									self.assertTrue(torch.allclose(output[:, :, -1]				,					__A				,					atol=1E-3		)		)


							@slow
							def   _snake_case	(				self     :Any		) -> List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   XLMRobertaModel.from_pretrained("""xlm-roberta-large"""		)
									SCREAMING_SNAKE_CASE__								=   torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]		)
									# The dog is cute and lives in the garden house

									SCREAMING_SNAKE_CASE__								=   torch.Size((1, 12, 1024)		)  # batch_size, sequence_length, embedding_vector_dim
									SCREAMING_SNAKE_CASE__								=   torch.tensor(
									    [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]		)
									#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
									#  xlmr.eval()
									#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
									with torch.no_grad():
											SCREAMING_SNAKE_CASE__								=   model(__A		)["""last_hidden_state"""].detach()
									self.assertEqual(output.shape				,					__A		)
									# compare the actual values for a slice of last dim
									self.assertTrue(torch.allclose(output[:, :, -1]				,					__A				,					atol=1E-3		)		)




'''simple docstring'''

from __future__ import annotations

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






@total_ordering
class 						a_		(     snake_case    ):


							def __lt__(				self   :						List[Any]      ,						a_   :						Optional[int]			)			->					List[str]:
										return self[-1] < other[-1]




							def __eq__(				self   :						int      ,						a_   :						Union[str, Any]			)			->					List[str]:
										return self[-1] == other[-1]







def a_     (    __UpperCAmelCase					)				->					list:





			"""simple docstring"""







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

    # use a heap-based merge to merge stack efficiently
			snake_case:    int      							=merge(*(reversed(__UpperCAmelCase					) for stack in stacks)					)
			return collection


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




