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
  
  
  __magic_name__	:   int          =							torch.cuda.is_available()
  __magic_name__	:   List[Any]          =							[' Sam ate lunch today.', 'Sams lunch ingredients.']
  __magic_name__	:   Dict          =							['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
  __magic_name__	:   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 			a_   (  lowercase__   :Optional[Any], lowercase__   :Any    ):
  							__lowerCamelCase        =			"""\n""".join(lowercase__    )
  							Path(lowercase__    ).open("""w"""    ).writelines(lowercase__    )
  
  
  
  
  
  def 			a_   (  lowercase__   :Any    ):
  
  
  
  
  
  
  
  							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      __a      (    cls:						Optional[Any]      ):
  														return cls
  
  
  
  
  							@require_torch_multi_gpu
  							def      __a      (    self:						Any      ):
  														__lowerCamelCase        =			{
  														    """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      __a      (    self:						Optional[int]    ,       A_:						Union[str, Any]    ,       A_:						List[Any]=True      ):
  														__lowerCamelCase        =			{
  														    """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_      )
  														__lowerCamelCase        =			CHEAP_ARGS.copy()
  														__lowerCamelCase        =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
  														__lowerCamelCase        =			self.get_auto_remove_tmp_dir()
  														args_d.update(data_dir=A_    ,       output_dir=A_    ,       **A_      )
  
  														def convert(A_:						Tuple    ,       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}'
  
  														__lowerCamelCase        =			[x for x in (convert(A_    ,       A_      ) for k, v in args_d.items()) if len(A_      )]
  														__lowerCamelCase        =			[sys.executable, f'{self.test_file_dir}/distillation.py'] + cli_args
  														execute_subprocess_async(A_    ,       env=self.get_env()      )
  
  														__lowerCamelCase        =			os.listdir(A_      )
  														__lowerCamelCase        =			{os.path.basename(A_      ) for p in contents}
  														__lowerCamelCase        =			[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)
  														__lowerCamelCase        =			os.path.join(A_    ,       """metrics.json"""      )
  														__lowerCamelCase        =			"""rouge2"""
  
  														__lowerCamelCase        =			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_      )
  														__lowerCamelCase        =			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      )
  														__lowerCamelCase        =			int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1      )
  														self.assertEqual(len(metrics["""val"""]      )    ,       A_      )
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import numpy as np
  
  
  
  
  
  def 			a_   (  lowercase__   :List[str]    ):
  			__lowerCamelCase        =			np.exp(lowercase__    )
  
  			# Add up the all the exponentials
  			__lowerCamelCase        =			np.sum(lowercase__    )
  
  			# Divide every exponent by the sum of all exponents
  			__lowerCamelCase        =			exponent_vector / sum_of_exponents
  
  			return softmax_vector
  
  
  if __name__ == "__main__":
  					print(softmax((0,)))
  
  
  
  
  