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
  
  
  UpperCAmelCase_       :				Optional[Any]		   =      torch.cuda.is_available()
  UpperCAmelCase_       :				List[Any]		   =      [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
  UpperCAmelCase_       :				Optional[Any]		   =      ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
  UpperCAmelCase_       :				Any		   =      {
      '''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    (      a__						:      Path ,a__						:      list					)				->							List[Any]:
  	__A   : Tuple								= """\n""".join(a__					)
  	Path(a__					).open("""w"""					).writelines(a__					)
  def        __SCREAMING_SNAKE_CASE    (      a__						:      Tuple					)				->							Union[str, Any]:
  	for split in ["train", "val", "test"]:
  		_dump_articles(os.path.join(a__ ,f"""{split}.source"""					) ,a__					)
  		_dump_articles(os.path.join(a__ ,f"""{split}.target"""					) ,a__					)
  	return tmp_dir
  
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase  ):
  
  
  
  
  	@classmethod
  	def 					lowerCAmelCase_					(    cls	:    Optional[Any]       ):
  		return cls
  
  
  
  
  	@require_torch_multi_gpu
  	def 					lowerCAmelCase_					(    self	:    Optional[Any]       ):
  		__A   : Any								= {
  		    """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	:    Tuple				,			__A	:    List[str]				,			__A	:    Optional[int]=True       ):
  		__A   : List[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(__A       )
  		__A   : dict								= CHEAP_ARGS.copy()
  		__A   : int								= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
  		__A   : int								= self.get_auto_remove_tmp_dir()
  		args_d.update(data_dir=__A				,			output_dir=__A				,			**__A       )
  
  		def convert(__A	:    int				,			__A	:    Tuple       ):
  			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}"""
  
  		__A   : List[str]								= [x for x in (convert(__A				,			__A       ) for k, v in args_d.items()) if len(__A       )]
  		__A   : str								= [sys.executable, F"""{self.test_file_dir}/distillation.py"""] + cli_args
  		execute_subprocess_async(__A				,			env=self.get_env()       )
  
  		__A   : Union[str, Any]								= os.listdir(__A       )
  		__A   : Optional[Any]								= {os.path.basename(__A       ) for p in contents}
  		__A   : Optional[int]								= [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)
  		__A   : Dict								= os.path.join(__A				,			"""metrics.json"""       )
  		__A   : Optional[int]								= """rouge2"""
  
  		__A   : Optional[int]								= 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       )
  		__A   : Optional[int]								= 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}"""]				,			__A       )
  		self.assertEqual(len(metrics["""test"""]       )				,			1       )
  		__A   : Union[str, Any]								= int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1       )
  		self.assertEqual(len(metrics["""val"""]       )				,			__A       )
  
  
  
  
  
  
style_context: 
  
  import os
  import tempfile
  import unittest
  from typing import List
  
  from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
  from transformers.testing_utils import require_tokenizers
  from transformers.tokenization_utils import PreTrainedTokenizer
  from transformers.tokenization_utils_base import PreTrainedTokenizerBase
  
  
  
  
  
  
  @require_tokenizers
  class     lowerCamelCase_			(  unittest.TestCase  ):
  	_lowercase		:      List[str]    				=							EsmTokenizer
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    List[Any]       ):
  		super().setUp()
  		__A   : Dict								= tempfile.mkdtemp()
  		# fmt: off
  		__A   : List[str]								= ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
  		# fmt: on
  		__A   : int								= os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES["""vocab_file"""]       )
  		with open(self.vocab_file				,			"""w"""				,			encoding="""utf-8"""       ) as vocab_writer:
  			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]       )       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Dict				,			**__A	:    int       ):
  		return [self.get_tokenizer(**__A       )]
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			**__A	:    List[Any]       ):
  		return self.tokenizer_class.from_pretrained(self.tmpdirname				,			**__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    List[str]       ):
  		__A   : Union[str, Any]								= self.tokenizer_class(self.vocab_file       )
  
  		__A   : int								= tokenizer.tokenize("""LAGVS"""       )
  		self.assertListEqual(__A				,			["""L""", """A""", """G""", """V""", """S"""]       )
  		self.assertListEqual(tokenizer.convert_tokens_to_ids(__A       )				,			[4, 5, 6, 7, 8]       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    str       ):
  		__A   : Union[str, Any]								= self.tokenizer_class(self.vocab_file       )
  
  		__A   : int								= """LAGVS"""
  		self.assertListEqual(tokenizer.encode(__A       )				,			[0, 4, 5, 6, 7, 8, 2]       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Dict       ):
  		__A   : str								= self.tokenizer_class(self.vocab_file       )
  
  		__A   : Tuple								= ["""LAGVS""", """WCB"""]
  		__A   : str								= tokenizer(__A				,			padding=__A       )["""input_ids"""]
  
  		self.assertListEqual(__A				,			[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    List[Any]       ):
  		__A   : Dict								= self.tokenizer_class(self.vocab_file       )
  
  		__A   : Any								= ["""LAGVS""", """WCB"""]
  		__A   : str								= tokenizer(__A				,			padding=__A       )["""input_ids"""]
  
  		self.assertListEqual(__A				,			[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    List[str]       ):
  		__A   : Optional[int]								= self.get_tokenizers(fast=__A       )
  		for tokenizer in tokenizers:
  			with self.subTest(F"""{tokenizer.__class__.__name__}"""       ):
  				__A   : Any								= """<unk>"""
  				__A   : str								= """<mask>"""
  
  				__A   : Optional[Any]								= tokenizer.tokenize(__A       )
  				__A   : str								= tokenizer.tokenize(__A       )
  
  				self.assertEqual(len(__A       )				,			1       )
  				self.assertEqual(len(__A       )				,			1       )
  				self.assertEqual(token_a[0]				,			__A       )
  				self.assertEqual(token_a[0]				,			__A       )
  
  
  
  
  
  