code: 
  
  
  
  
  
  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
  
  
  _lowercase		:     List[str] 					=torch.cuda.is_available()
  _lowercase		:     List[str] 					=[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
  _lowercase		:     str 					=["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
  _lowercase		:     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 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__	):
  		lowerCamelCase_  :    Dict   	=			'\n'.join(lowerCAmelCase__	)
  		Path(lowerCAmelCase__	).open('w'	).writelines(lowerCAmelCase__	)
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
  		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 	UpperCamelCase_     (     snake_case__       ):
  
  
  
  					@classmethod
  					def      __a    (		cls    :    List[str]	):
  							return cls
  
  
  
  					@require_torch_multi_gpu
  					def      __a    (		self    :    List[Any]	):
  							lowerCamelCase_  :    Tuple   	=			{
  							    'no_teacher': True,
  							    'freeze_encoder': True,
  							    'gpus': 2,
  							    'overwrite_output_dir': True,
  							    'sortish_sampler': True,
  							}
  							self._test_distiller_cli_fork(lowerCamelCase					,			check_contents=lowerCamelCase	)
  
  
  
  
  
  					def      __a    (		self    :    Optional[int]					,			lowerCamelCase    :    Tuple					,			lowerCamelCase    :    Any=True	):
  							lowerCamelCase_  :    Any   	=			{
  							    '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(lowerCamelCase	)
  							lowerCamelCase_  :    dict   	=			CHEAP_ARGS.copy()
  							lowerCamelCase_  :    Optional[Any]   	=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
  							lowerCamelCase_  :    Union[str, Any]   	=			self.get_auto_remove_tmp_dir()
  							args_d.update(data_dir=lowerCamelCase					,			output_dir=lowerCamelCase					,			**lowerCamelCase	)
  
  							def convert(lowerCamelCase    :    Optional[Any]					,			lowerCamelCase    :    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}"
  
  							lowerCamelCase_  :    Union[str, Any]   	=			[x for x in (convert(lowerCamelCase					,			lowerCamelCase	) for k, v in args_d.items()) if len(lowerCamelCase	)]
  							lowerCamelCase_  :    List[Any]   	=			[sys.executable, F"{self.test_file_dir}/distillation.py"] + cli_args
  							execute_subprocess_async(lowerCamelCase					,			env=self.get_env()	)
  
  							lowerCamelCase_  :    Optional[int]   	=			os.listdir(lowerCamelCase	)
  							lowerCamelCase_  :    Tuple   	=			{os.path.basename(lowerCamelCase	) for p in contents}
  							lowerCamelCase_  :    int   	=			[p for p in contents if p.endswith('ckpt'	)]
  							assert len(lowerCamelCase	) > 0
  
  							self.assertIn('test_generations.txt'					,			lowerCamelCase	)
  							self.assertIn('test_results.txt'					,			lowerCamelCase	)
  
  							# get the following from the module, (we don't have access to `model` here)
  							lowerCamelCase_  :    Any   	=			os.path.join(lowerCamelCase					,			'metrics.json'	)
  							lowerCamelCase_  :    Union[str, Any]   	=			'rouge2'
  
  							lowerCamelCase_  :    int   	=			load_json(lowerCamelCase	)
  							# {'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(lowerCamelCase	)
  							lowerCamelCase_  :    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}"]					,			lowerCamelCase	)
  							self.assertEqual(len(metrics['test']	)					,			1	)
  							lowerCamelCase_  :    Dict   	=			int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1	)
  							self.assertEqual(len(metrics['val']	)					,			lowerCamelCase	)
  
style_context: 
  
  
  
  
  
  from __future__ import annotations
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
  		lowerCamelCase_  :    int
  		lowerCamelCase_  :    list[str]   	=			list(lowerCAmelCase__	)
  		lowerCamelCase_	,			lowerCamelCase_  :    List[str]   	=			_vector[:-1], int(_vector[-1]	)
  		lowerCamelCase_  :    list[int]   	=			[int(lowerCAmelCase__	) for digit in __vector]
  
  		vector.reverse()
  		for i, digit in enumerate(lowerCAmelCase__	):
  				if i & 1 == 0:
  						lowerCamelCase_  :    int   	=			digit * 2
  						if doubled > 9:
  								doubled -= 9
  						check_digit += doubled
  				else:
  						check_digit += digit
  
  		return check_digit % 10 == 0
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  		assert is_luhn("""79927398713""")
  		assert not is_luhn("""79927398714""")
  