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
  
  
  UpperCAmelCase   =	torch.cuda.is_available()
  UpperCAmelCase   =	[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
  UpperCAmelCase   =	["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
  UpperCAmelCase   =	{
      """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-0_8,
      """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			(      __snake_case       :			int	,   __snake_case       :			List[Any]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Dict      =		'\n'.join(lowerCAmelCase__      )
     Path(lowerCAmelCase__      ).open("""w"""      ).writelines(lowerCAmelCase__      )
  
  def 							_snake_case			(      __snake_case       :			List[Any]      ):
  
  
  
  
     """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   lowercase__							(				snake_case__							):
  
  
  
  
  
     @classmethod
     def 		UpperCamelCase_     (       cls)     ->     Tuple:
        return cls
  
  
  
  
  
     @require_torch_multi_gpu
     def 		UpperCamelCase_     (       self)     ->     Optional[int]:
        _lowerCamelCase  :				Tuple      =		{
            'no_teacher': True,
            'freeze_encoder': True,
            'gpus': 2,
            'overwrite_output_dir': True,
            'sortish_sampler': True,
        }
        self._test_distiller_cli_fork(SCREAMING_SNAKE_CASE			,      check_contents=SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE=True)     ->     Any:
        _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(SCREAMING_SNAKE_CASE)
        _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=SCREAMING_SNAKE_CASE			,      output_dir=SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)
  
        def convert(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
           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(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE) for k, v in args_d.items()) if len(SCREAMING_SNAKE_CASE)]
        _lowerCamelCase  :				List[Any]      =		[sys.executable, F'{self.test_file_dir}/distillation.py'] + cli_args
        execute_subprocess_async(SCREAMING_SNAKE_CASE			,      env=self.get_env())
  
        _lowerCamelCase  :				Optional[int]      =		os.listdir(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Tuple      =		{os.path.basename(SCREAMING_SNAKE_CASE) for p in contents}
        _lowerCamelCase  :				int      =		[p for p in contents if p.endswith("""ckpt""")]
        assert len(SCREAMING_SNAKE_CASE) > 0
  
        self.assertIn("""test_generations.txt"""			,      SCREAMING_SNAKE_CASE)
        self.assertIn("""test_results.txt"""			,      SCREAMING_SNAKE_CASE)
  
        # get the following from the module, (we don't have access to `model` here)
        _lowerCamelCase  :				Any      =		os.path.join(SCREAMING_SNAKE_CASE			,      """metrics.json""")
        _lowerCamelCase  :				Union[str, Any]      =		'rouge2'
  
        _lowerCamelCase  :				int      =		load_json(SCREAMING_SNAKE_CASE)
        # {'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(SCREAMING_SNAKE_CASE)
        _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}']			,      SCREAMING_SNAKE_CASE)
        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"""])			,      SCREAMING_SNAKE_CASE)
  
  
style_context: 
  
  
  """simple docstring"""
  import os
  
  from apache_beam.io.filesystems import FileSystems
  from apache_beam.pipeline import Pipeline
  
  from .logging import get_logger
  
  
  UpperCAmelCase   =	2 << 20  # 2mb
  UpperCAmelCase   =	get_logger(__name__)
  
  
  
  
  class   lowercase__							(				A_							):
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Dict:
        _lowerCamelCase  :				Tuple      =		self._options.get_all_options().get("""runner""")
        return runner in [None, "DirectRunner", "PortableRunner"]
  
  
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			Union[str, Any]	,   __snake_case       :			List[Any]	,   __snake_case       :			Any=False      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Any      =		FileSystems
     if fs.exists(__snake_case      ):
        if force_upload:
           logger.info(F'Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'      )
        else:
           logger.info(F'Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'      )
           return
     with fs.create(__snake_case      ) as remote_file:
        with open(__snake_case	,   """rb"""      ) as local_file:
           _lowerCamelCase  :				Optional[Any]      =		local_file.read(__snake_case      )
           while chunk:
              remote_file.write(__snake_case      )
              _lowerCamelCase  :				Tuple      =		local_file.read(__snake_case      )
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			Union[str, Any]	,   __snake_case       :			List[Any]	,   __snake_case       :			Union[str, Any]=False      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				int      =		FileSystems
     if os.path.exists(__snake_case      ):
        if force_download:
           logger.info(F'Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'      )
        else:
           logger.info(F'Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'      )
           return
     with fs.open(__snake_case      ) as remote_file:
        with open(__snake_case	,   """wb"""      ) as local_file:
           _lowerCamelCase  :				str      =		remote_file.read(__snake_case      )
           while chunk:
              local_file.write(__snake_case      )
              _lowerCamelCase  :				Any      =		remote_file.read(__snake_case      )
  
  