code: 
  
  
  
  
  
  '''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
  
  
  a__				: Dict									=    torch.cuda.is_available()
  a__				: Dict									=    [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
  a__				: str									=    ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
  a__				: List[str]									=    {
      '''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 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->List[str]:
  	snake_case__  					=      '\n'.join(lowercase__       )
  	Path(lowercase__       ).open('w'       ).writelines(lowercase__       )
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_       )      ->int:
  
  
  
  
  
  
  
  	for split in ["train", "val", "test"]:
  		_dump_articles(os.path.join(lowercase__				,							f'''{split}.source'''       )				,							lowercase__       )
  		_dump_articles(os.path.join(lowercase__				,							f'''{split}.target'''       )				,							lowercase__       )
  	return tmp_dir
  
  
  
  
  
  
  
  class 							__snake_case    (				A_       ):
  
  
  
  
  
  	@classmethod
  	def      _snake_case      (		cls		)			->		Dict:
  		return cls
  
  
  
  
  
  	@require_torch_multi_gpu
  	def      _snake_case      (		self		)			->		Any:
  		snake_case__  					=      {
  		    'no_teacher': True,
  		    'freeze_encoder': True,
  		    'gpus': 2,
  		    'overwrite_output_dir': True,
  		    'sortish_sampler': True,
  		}
  		self._test_distiller_cli_fork(UpperCamelCase_			,					check_contents=UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_=True		)			->		Tuple:
  		snake_case__  					=      {
  		    '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(UpperCamelCase_		)
  		snake_case__  					=      CHEAP_ARGS.copy()
  		snake_case__  					=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
  		snake_case__  					=      self.get_auto_remove_tmp_dir()
  		args_d.update(data_dir=UpperCamelCase_			,					output_dir=UpperCamelCase_			,					**UpperCamelCase_		)
  
  		def convert(UpperCamelCase_			,					UpperCamelCase_		):
  			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}'''
  
  		snake_case__  					=      [x for x in (convert(UpperCamelCase_			,					UpperCamelCase_		) for k, v in args_d.items()) if len(UpperCamelCase_		)]
  		snake_case__  					=      [sys.executable, F'''{self.test_file_dir}/distillation.py'''] + cli_args
  		execute_subprocess_async(UpperCamelCase_			,					env=self.get_env()		)
  
  		snake_case__  					=      os.listdir(UpperCamelCase_		)
  		snake_case__  					=      {os.path.basename(UpperCamelCase_		) for p in contents}
  		snake_case__  					=      [p for p in contents if p.endswith('ckpt'		)]
  		assert len(UpperCamelCase_		) > 0
  
  		self.assertIn('test_generations.txt'			,					UpperCamelCase_		)
  		self.assertIn('test_results.txt'			,					UpperCamelCase_		)
  
  		# get the following from the module, (we don't have access to `model` here)
  		snake_case__  					=      os.path.join(UpperCamelCase_			,					'metrics.json'		)
  		snake_case__  					=      'rouge2'
  
  		snake_case__  					=      load_json(UpperCamelCase_		)
  		# {'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(UpperCamelCase_		)
  		snake_case__  					=      metrics['val'][-1]
  		self.assertGreaterEqual(last_step_stats['val_avg_gen_time']			,					0.0_1		)
  		self.assertIsInstance(last_step_stats[F'''val_avg_{val_metric}''']			,					UpperCamelCase_		)
  		self.assertEqual(len(metrics['test']		)			,					1		)
  		snake_case__  					=      int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1		)
  		self.assertEqual(len(metrics['val']		)			,					UpperCamelCase_		)
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_       )      ->str:
  	snake_case__  					=      len(UpperCAmelCase_       )
  	for i in range(length - 1       ):
  		snake_case__  					=      False
  		for j in range(length - 1 - i       ):
  			if collection[j] > collection[j + 1]:
  				snake_case__  					=      True
  				snake_case__					,     snake_case__  					=      collection[j + 1], collection[j]
  		if not swapped:
  			break  # Stop iteration if the collection is sorted.
  	return collection
  
  
  if __name__ == "__main__":
  			import doctest
  			import time
  
  			doctest.testmod()
  
  			a__				: Dict									=    input('''Enter numbers separated by a comma:''').strip()
  			a__				: Dict									=    [int(item) for item in user_input.split(''',''')]
  			a__				: Tuple									=    time.process_time()
  			print(*bubble_sort(unsorted), sep=''',''')
  			print(f"""Processing time: {(time.process_time() - start)%1e9 + 7}""")
  
  
  
  