code: 
  
  
  
  """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)
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  import os
  import sys
  from pathlib import Path
  
  import torch
  
  from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
  from utils import load_json
  
  
  _lowerCAmelCase       :      Optional[Any]							=							torch.cuda.is_available()
  _lowerCAmelCase       :      str							=							[" Sam ate lunch today.", "Sams lunch ingredients."]
  _lowerCAmelCase       :      Dict							=							["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
  _lowerCAmelCase       :      Union[str, Any]							=							{
      "max_tokens_per_batch": None,
      "supervise_forward": True,
      "normalize_hidden": True,
      "label_smoothing": 0.2,
      "eval_max_gen_length": None,
      "eval_beams": 1,
      "val_metric": "loss",
      "save_top_k": 1,
      "adafactor": True,
      "early_stopping_patience": 2,
      "logger_name": "default",
      "length_penalty": 0.5,
      "cache_dir": "",
      "task": "summarization",
      "num_workers": 2,
      "alpha_hid": 0,
      "freeze_embeds": True,
      "enc_only": False,
      "tgt_suffix": "",
      "resume_from_checkpoint": None,
      "sortish_sampler": True,
      "student_decoder_layers": 1,
      "val_check_interval": 1.0,
      "output_dir": "",
      "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
      "no_teacher": False,
      "fp16_opt_level": "O1",
      "gpus": 1 if CUDA_AVAILABLE else 0,
      "n_tpu_cores": 0,
      "max_grad_norm": 1.0,
      "do_train": True,
      "do_predict": True,
      "accumulate_grad_batches": 1,
      "server_ip": "",
      "server_port": "",
      "seed": 42,
      "model_name_or_path": "sshleifer/bart-tiny-random",
      "config_name": "",
      "tokenizer_name": "facebook/bart-large",
      "do_lower_case": False,
      "learning_rate": 0.3,
      "lr_scheduler": "linear",
      "weight_decay": 0.0,
      "adam_epsilon": 1E-08,
      "warmup_steps": 0,
      "max_epochs": 1,
      "train_batch_size": 2,
      "eval_batch_size": 2,
      "max_source_length": 12,
      "max_target_length": 12,
      "val_max_target_length": 12,
      "test_max_target_length": 12,
      "fast_dev_run": False,
      "no_cache": False,
      "n_train": -1,
      "n_val": -1,
      "n_test": -1,
      "student_encoder_layers": 1,
      "freeze_encoder": False,
      "auto_scale_batch_size": False,
      "overwrite_output_dir": False,
      "student": None,
  }
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Path		,						SCREAMING_SNAKE_CASE__			:		list       )						->     Dict:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Tuple              =					"\n".join(SCREAMING_SNAKE_CASE__       )
       Path(SCREAMING_SNAKE_CASE__       ).open("w"       ).writelines(SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[Any]       )						->     Union[str, Any]:
  
  
  
  
  
       '''simple docstring'''
  
       for split in ["train", "val", "test"]:
            _dump_articles(os.path.join(SCREAMING_SNAKE_CASE__		,						f'{split}.source'       )		,						SCREAMING_SNAKE_CASE__       )
            _dump_articles(os.path.join(SCREAMING_SNAKE_CASE__		,						f'{split}.target'       )		,						SCREAMING_SNAKE_CASE__       )
       return tmp_dir
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       _UpperCamelCase		):
  
  
  
        @classmethod
        def 		snake_case_      (  cls  :			Optional[int]		):
             return cls
  
  
  
        @require_torch_multi_gpu
        def 		snake_case_      (  self  :			Union[str, Any]		):
             _UpperCAmelCase    :			Dict              =					{
                 "no_teacher": True,
                 "freeze_encoder": True,
                 "gpus": 2,
                 "overwrite_output_dir": True,
                 "sortish_sampler": True,
             }
             self._test_distiller_cli_fork(A  ,			check_contents=A		)
  
  
  
        def 		snake_case_      (  self  :			List[str]  ,			A  :			List[Any]  ,			A  :			Optional[Any]=True		):
             _UpperCAmelCase    :			int              =					{
                 "label_smoothing": 0.0,
                 "early_stopping_patience": -1,
                 "train_batch_size": 1,
                 "eval_batch_size": 2,
                 "max_epochs": 2,
                 "alpha_mlm": 0.2,
                 "alpha_ce": 0.8,
                 "do_predict": True,
                 "model_name_or_path": "sshleifer/tinier_bart",
                 "teacher": CHEAP_ARGS["model_name_or_path"],
                 "val_check_interval": 0.5,
             }
             default_updates.update(A		)
             _UpperCAmelCase    :			dict              =					CHEAP_ARGS.copy()
             _UpperCAmelCase    :			Union[str, Any]              =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
             _UpperCAmelCase    :			List[Any]              =					self.get_auto_remove_tmp_dir()
             args_d.update(data_dir=A  ,			output_dir=A  ,			**A		)
  
             def convert(A  :			List[Any]  ,			A  :			Optional[int]		):
                  if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
                       return ""
                  if v is False or v is None:
                       return ""
                  if v is True:  # or len(str(v))==0:
                       return f'--{k}'
                  return f'--{k}={v}'
  
             _UpperCAmelCase    :			Tuple              =					[x for x in (convert(A  ,			A		) for k, v in args_d.items()) if len(A		)]
             _UpperCAmelCase    :			Union[str, Any]              =					[sys.executable, f'{self.test_file_dir}/distillation.py'] + cli_args
             execute_subprocess_async(A  ,			env=self.get_env()		)
  
             _UpperCAmelCase    :			int              =					os.listdir(A		)
             _UpperCAmelCase    :			List[Any]              =					{os.path.basename(A		) for p in contents}
             _UpperCAmelCase    :			List[Any]              =					[p for p in contents if p.endswith("ckpt"		)]
             assert len(A		) > 0
  
             self.assertIn("test_generations.txt"  ,			A		)
             self.assertIn("test_results.txt"  ,			A		)
  
             # get the following from the module, (we don't have access to `model` here)
             _UpperCAmelCase    :			List[Any]              =					os.path.join(A  ,			"metrics.json"		)
             _UpperCAmelCase    :			str              =					"rouge2"
  
             _UpperCAmelCase    :			List[Any]              =					load_json(A		)
             # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
             print(A		)
             _UpperCAmelCase    :			List[str]              =					metrics["val"][-1]
             self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]  ,			0.01		)
             self.assertIsInstance(last_step_stats[f'val_avg_{val_metric}']  ,			A		)
             self.assertEqual(len(metrics["test"]		)  ,			1		)
             _UpperCAmelCase    :			Dict              =					int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1		)
             self.assertEqual(len(metrics["val"]		)  ,			A		)
  