code: 
  
  
  
  """simple docstring"""
  
  
  
  
  import unittest
  
  from transformers import is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  
  
  if is_torch_available():
   import torch
  
   from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer
  
  
  
  
  
  
  @require_torch
  class 			UpperCAmelCase_						(       unittest.TestCase		):
  
  
  
        @slow
        def 		snake_case_      (  self  :			Optional[Any]		):
             _UpperCAmelCase    :			Any              =					XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"		)
             model.to(A		)
  
             # encoder-decoder outputs
             _UpperCAmelCase    :			Dict              =					torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]		).to(A		)
             _UpperCAmelCase    :			Any              =					torch.tensor(
                 [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]		).to(A		)
             _UpperCAmelCase    :			Any              =					model(
                 input_ids=A  ,			attention_mask=A  ,			encoder_outputs=A  ,			decoder_input_ids=A		)
             _UpperCAmelCase    :			Any              =					output[0]
             _UpperCAmelCase    :			Optional[Any]              =					torch.Size((1, 1_4, 2_5_0_0_1_2)		)
             self.assertEqual(output_predited_logis.shape  ,			A		)
             _UpperCAmelCase    :			int              =					torch.tensor(
                 [[[-6.3_986, -8.2_391, 12.5_189], [-6.3_289, -8.0_864, 12.6_211], [-6.2_418, -8.0_445, 12.7_968]]]		).to(A		)
             self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]  ,			A  ,			atol=1e-4		)		)
  
             # encoder outputs
             _UpperCAmelCase    :			int              =					model.prophetnet.encoder(A		)[0]
             _UpperCAmelCase    :			Optional[Any]              =					torch.tensor(
                 [[[-1.4_260, -0.7_628, 0.8_453], [-1.4_719, -0.1_391, 0.7_807], [-1.7_678, 0.0_114, 0.4_646]]]		).to(A		)
             _UpperCAmelCase    :			Optional[Any]              =					torch.Size((1, 4, 1_0_2_4)		)
             self.assertEqual(encoder_outputs.shape  ,			A		)
             self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]  ,			A  ,			atol=1e-4		)		)
  
             # decoder outputs
             _UpperCAmelCase    :			List[Any]              =					model.prophetnet.decoder(
                 A  ,			encoder_hidden_states=A  ,			)
             _UpperCAmelCase    :			str              =					decoder_outputs[1].view(1  ,			model.config.ngram  ,			1_4  ,			-1		)
             _UpperCAmelCase    :			Optional[int]              =					model.lm_head(A		)
             _UpperCAmelCase    :			Union[str, Any]              =					predicting_streams_logits[:, 0]
             self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]  ,			A  ,			atol=1e-4		)		)
  
  
  
        @slow
        def 		snake_case_      (  self  :			Any		):
             _UpperCAmelCase    :			Tuple              =					XLMProphetNetForConditionalGeneration.from_pretrained(
                 "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"		)
             model.to(A		)
  
             _UpperCAmelCase    :			int              =					torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]		).to(A		)
             _UpperCAmelCase    :			Any              =					torch.tensor(
                 [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]		).to(A		)
             _UpperCAmelCase    :			int              =					model(
                 input_ids=A  ,			attention_mask=A  ,			encoder_outputs=A  ,			decoder_input_ids=A		)
             _UpperCAmelCase    :			Dict              =					output[0]
             _UpperCAmelCase    :			Optional[Any]              =					torch.Size((1, 1_4, 2_5_0_0_1_2)		)
             self.assertEqual(output_predited_logis.shape  ,			A		)
             # compare the actual values for a slice.
             _UpperCAmelCase    :			Tuple              =					torch.tensor(
                 [[[-9.2_253, -9.7_173, -6.3_529], [-7.6_701, -9.0_145, -1.9_382], [-8.0_195, -7.0_004, -0.1_523]]]		).to(A		)
  
             self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]  ,			A  ,			atol=1e-4		)		)
  
  
  
        @slow
        def 		snake_case_      (  self  :			str		):
             _UpperCAmelCase    :			Optional[Any]              =					XLMProphetNetForConditionalGeneration.from_pretrained(
                 "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"		)
             model.to(A		)
             _UpperCAmelCase    :			int              =					5_1_2
  
             _UpperCAmelCase    :			str              =					XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"		)
  
             _UpperCAmelCase    :			str              =					(
                 "Microsoft Corporation intends to officially end free support for the Windows 7 operating system after"
                 " January 14, 2020, according to the official portal of the organization. From that day, users of this"
                 " system will not be able to receive security updates, which could make their computers vulnerable to"
                 " cyber attacks."
             )
             _UpperCAmelCase    :			Tuple              =					(
                 "орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7"
                 " после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи"
                 " этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми"
                 " к кибератакам."
             )
             _UpperCAmelCase    :			Optional[Any]              =					(
                 "根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。"
             )
  
             _UpperCAmelCase    :			Any              =					tokenizer(
                 [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]  ,			padding=A  ,			max_length=2_5_5  ,			return_tensors="pt"		).input_ids
             _UpperCAmelCase    :			Union[str, Any]              =					input_ids.to(A		)
  
             _UpperCAmelCase    :			Optional[Any]              =					model.generate(
                 A  ,			num_beams=1_0  ,			length_penalty=1.0  ,			no_repeat_ngram_size=3  ,			early_stopping=A		)
             _UpperCAmelCase    :			Optional[int]              =					[tokenizer.decode(A  ,			skip_special_tokens=A		) for g in summary_ids]
             _UpperCAmelCase    :			Dict              =					"Microsoft to end Windows 7 free support after January 14, 2020"
             _UpperCAmelCase    :			Tuple              =					"Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"
             _UpperCAmelCase    :			str              =					"微软打算终止对Windows 7操作系统的免费支持"
             self.assertListEqual(
                 [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH]  ,			A  ,			)
  
             _UpperCAmelCase    :			Union[str, Any]              =					model.generate(
                 A  ,			num_beams=1  ,			length_penalty=1.0  ,			no_repeat_ngram_size=3  ,			early_stopping=A		)
             _UpperCAmelCase    :			Optional[Any]              =					[
                 tokenizer.convert_ids_to_tokens(A  ,			skip_special_tokens=A		) for g in summary_ids_beama
             ]
             _UpperCAmelCase    :			int              =					"▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7".split(" "		)
             _UpperCAmelCase    :			Dict              =					"▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года".split(
                 " "		)
             _UpperCAmelCase    :			int              =					"微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持".split(" "		)
             self.assertListEqual(
                 [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK]  ,			A  ,			)
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		int       )						->     None:
  
  
  
  
  
       '''simple docstring'''
  
       if right <= left:
            return
       _UpperCAmelCase    :			Any              =					left
       _UpperCAmelCase    :			Tuple              =					right
       _UpperCAmelCase    :			List[Any]              =					sorting[left]
       while i <= b:
            if sorting[i] < pivot:
                 _UpperCAmelCase       , _UpperCAmelCase    :			List[Any]              =					sorting[i], sorting[a]
                 a += 1
                 i += 1
            elif sorting[i] > pivot:
                 _UpperCAmelCase       , _UpperCAmelCase    :			Any              =					sorting[i], sorting[b]
                 b -= 1
            else:
                 i += 1
       quick_sort_apartition(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						a - 1       )
       quick_sort_apartition(SCREAMING_SNAKE_CASE__		,						b + 1		,						SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		int       )						->     None:
  
  
  
  
  
       '''simple docstring'''
  
       if left < right:
            _UpperCAmelCase    :			Optional[int]              =					lomuto_partition(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
            quick_sort_lomuto_partition(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						pivot_index - 1       )
            quick_sort_lomuto_partition(SCREAMING_SNAKE_CASE__		,						pivot_index + 1		,						SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		int       )						->     int:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Dict              =					sorting[right]
       _UpperCAmelCase    :			Any              =					left
       for i in range(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ):
            if sorting[i] < pivot:
                 _UpperCAmelCase       , _UpperCAmelCase    :			Dict              =					sorting[i], sorting[store_index]
                 store_index += 1
       _UpperCAmelCase       , _UpperCAmelCase    :			List[str]              =					sorting[store_index], sorting[right]
       return store_index
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list       )						->     list:
  
  
  
  
  
       '''simple docstring'''
  
       if len(SCREAMING_SNAKE_CASE__       ) <= 1:
            return sorting
       return (
           three_way_radix_quicksort([i for i in sorting if i < sorting[0]]       )
           + [i for i in sorting if i == sorting[0]]
           + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]       )
       )
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod(verbose=True)
  
   _lowerCAmelCase       :      Union[str, Any]							=							input("Enter numbers separated by a comma:\n").strip()
   _lowerCAmelCase       :      List[str]							=							[int(item) for item in user_input.split(",")]
   quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
   print(unsorted)
  