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
  
  
  lowerCAmelCase   :Optional[int]								=    torch.cuda.is_available()
  lowerCAmelCase   :Tuple								=    [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
  lowerCAmelCase   :Any								=    ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
  lowerCAmelCase   :Optional[int]								=    {
      '''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''': 4_2,
      '''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-0_8,
      '''warmup_steps''': 0,
      '''max_epochs''': 1,
      '''train_batch_size''': 2,
      '''eval_batch_size''': 2,
      '''max_source_length''': 1_2,
      '''max_target_length''': 1_2,
      '''val_max_target_length''': 1_2,
      '''test_max_target_length''': 1_2,
      '''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    (    lowerCAmelCase       :			Path	,	lowerCAmelCase       :			list	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Union[str, Any]  			=  '\n'.join(lowerCAmelCase	)
  	Path(lowerCAmelCase	).open('w'	).writelines(lowerCAmelCase	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Optional[int]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	for split in ["train", "val", "test"]:
  		_dump_articles(os.path.join(lowerCAmelCase	,	f'{split}.source'	)	,	lowerCAmelCase	)
  		_dump_articles(os.path.join(lowerCAmelCase	,	f'{split}.target'	)	,	lowerCAmelCase	)
  	return tmp_dir
  
  
  
  
  
  class _lowerCamelCase (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		@classmethod
  		def      __lowerCAmelCase	(      cls	:		Any    )						->				List[str]:
  			return cls
  
  
  
  
  
  
  
  		@require_torch_multi_gpu
  		def      __lowerCAmelCase	(      self	:		List[Any]    )						->				Optional[Any]:
  			__magic_name__ :					Optional[int]  			=  {
  			    '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      __lowerCAmelCase	(      self	:		Optional[int]						,  _A	:		Optional[Any]						,  _A	:		Dict=True    )						->				str:
  			__magic_name__ :					Tuple  			=  {
  			    '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    )
  			__magic_name__ :					dict  			=  CHEAP_ARGS.copy()
  			__magic_name__ :					Tuple  			=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
  			__magic_name__ :					Optional[int]  			=  self.get_auto_remove_tmp_dir()
  			args_d.update(data_dir=_A						,  output_dir=_A						,  **_A    )
  
  			def convert(_A	:		int						,  _A	:		List[Any]    ):
  				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}'
  
  			__magic_name__ :					List[Any]  			=  [x for x in (convert(_A						,  _A    ) for k, v in args_d.items()) if len(_A    )]
  			__magic_name__ :					Union[str, Any]  			=  [sys.executable, F'{self.test_file_dir}/distillation.py'] + cli_args
  			execute_subprocess_async(_A						,  env=self.get_env()    )
  
  			__magic_name__ :					Optional[Any]  			=  os.listdir(_A    )
  			__magic_name__ :					Tuple  			=  {os.path.basename(_A    ) for p in contents}
  			__magic_name__ :					Union[str, 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)
  			__magic_name__ :					str  			=  os.path.join(_A						,  'metrics.json'    )
  			__magic_name__ :					int  			=  'rouge2'
  
  			__magic_name__ :					Optional[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    )
  			__magic_name__ :					Any  			=  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    )
  			__magic_name__ :					str  			=  int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1    )
  			self.assertEqual(len(metrics['val']    )						,  _A    )
style_context: 
  
  
  '''simple docstring'''
  
  import math
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			int	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if not isinstance(lowerCAmelCase	,	lowerCAmelCase	):
  		__magic_name__ :					str  			=  f'Input value of [number={number}] must be an integer'
  		raise TypeError(lowerCAmelCase	)
  	if number < 1:
  		__magic_name__ :					Tuple  			=  f'Input value of [number={number}] must be a positive integer'
  		raise ValueError(lowerCAmelCase	)
  	__magic_name__ :					Tuple  			=  [number]
  	while number != 1:
  		if number % 2 == 0:
  			__magic_name__ :					Optional[int]  			=  math.floor(math.sqrt(lowerCAmelCase	)	)
  		else:
  			__magic_name__ :					Union[str, Any]  			=  math.floor(
  			    math.sqrt(lowerCAmelCase	) * math.sqrt(lowerCAmelCase	) * math.sqrt(lowerCAmelCase	)	)
  		sequence.append(lowerCAmelCase	)
  	return sequence
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()