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
  
  
  lowerCAmelCase					 =	torch.cuda.is_available()
  lowerCAmelCase					 =	[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
  lowerCAmelCase					 =	['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
  lowerCAmelCase					 =	{
      '''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-08,
      '''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(			lowercase__     ,				lowercase__			)		->			List[str]:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowercase=    '\n'.join(lowercase__			)
  		Path(lowercase__			).open('w'			).writelines(lowercase__			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__			)		->			int:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		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    A					(     A_				):
  
  
  							@classmethod
  							def     _A (cls      ):
  									return cls
  
  
  							@require_torch_multi_gpu
  							def     _A (self      ):
  									__lowercase=    {
  									    '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   ,       lowerCAmelCase   ,       lowerCAmelCase=True      ):
  									__lowercase=    {
  									    '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      )
  									__lowercase=    CHEAP_ARGS.copy()
  									__lowercase=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
  									__lowercase=    self.get_auto_remove_tmp_dir()
  									args_d.update(data_dir=lowerCAmelCase   ,       output_dir=lowerCAmelCase   ,       **lowerCAmelCase      )
  
  									def convert(lowerCAmelCase   ,       lowerCAmelCase      ):
  											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}'
  
  									__lowercase=    [x for x in (convert(lowerCAmelCase   ,       lowerCAmelCase      ) for k, v in args_d.items()) if len(lowerCAmelCase      )]
  									__lowercase=    [sys.executable, f'{self.test_file_dir}/distillation.py'] + cli_args
  									execute_subprocess_async(lowerCAmelCase   ,       env=self.get_env()      )
  
  									__lowercase=    os.listdir(lowerCAmelCase      )
  									__lowercase=    {os.path.basename(lowerCAmelCase      ) for p in contents}
  									__lowercase=    [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)
  									__lowercase=    os.path.join(lowerCAmelCase   ,       'metrics.json'      )
  									__lowercase=    'rouge2'
  
  									__lowercase=    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      )
  									__lowercase=    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      )
  									__lowercase=    int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1      )
  									self.assertEqual(len(metrics['val']      )   ,       lowerCAmelCase      )
  
  
  
  
  
  
  
style_context: 
  
  
  from typing import Dict, List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  lowerCAmelCase					 =	logging.get_logger(__name__)
  
  
  
  
  
  class    A					(     A_				):
  							UpperCamelCase_     :							Any =['''input_ids''', '''attention_mask''']
  
  
  							def __init__(self   ,       lowerCAmelCase="[PAD]"   ,       lowerCAmelCase="[BOS]"   ,       lowerCAmelCase="[EOS]"   ,       lowerCAmelCase="[MASK]"   ,       lowerCAmelCase="[CLS]"   ,       lowerCAmelCase="[SEP]"   ,       lowerCAmelCase=2_0_4_8   ,       **lowerCAmelCase   ,       ):
  									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else pad_token
  									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else bos_token
  									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else eos_token
  									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else mask_token
  									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else cls_token
  									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else sep_token
  
  									super().__init__(
  									    pad_token=lowerCAmelCase   ,       bos_token=lowerCAmelCase   ,       eos_token=lowerCAmelCase   ,       mask_token=lowerCAmelCase   ,       cls_token=lowerCAmelCase   ,       sep_token=lowerCAmelCase   ,       model_max_length=lowerCAmelCase   ,       **lowerCAmelCase   ,       )
  
  									__lowercase=    2**8  # utf is 8 bits
  
  									# define special tokens dict
  									__lowercase=    {
  									    self.pad_token: 0,
  									    self.bos_token: 1,
  									    self.eos_token: 2,
  									    self.mask_token: 3,
  									    self.cls_token: 4,
  									    self.sep_token: 5,
  									}
  									__lowercase=    len(self.special_tokens_encoder      )
  									__lowercase=    {v: k for k, v in self.special_tokens_encoder.items()}
  
  
  							def     _A (self      ):
  									__lowercase=    self.special_tokens_encoder.copy()
  									vocab.update(self.added_tokens_encoder      )
  									for i in range(self._utf_vocab_size      ):
  											__lowercase=    chr(lowerCAmelCase      )
  											__lowercase=    i + len(self.special_tokens_encoder      )
  									return vocab
  
  
  							@property
  							def     _A (self      ):
  									return self._utf_vocab_size + self._num_special_tokens
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = None   ,       lowerCAmelCase = False      ):
  									if already_has_special_tokens:
  											return super().get_special_tokens_mask(
  											    token_ids_a=lowerCAmelCase   ,       token_ids_a=lowerCAmelCase   ,       already_has_special_tokens=lowerCAmelCase      )
  
  									# normal case: some special tokens
  									if token_ids_a is None:
  											return [1] + [0] * len(lowerCAmelCase      ) + [1]
  									return [1] + ([0] * len(lowerCAmelCase      )) + [1] + ([0] * len(lowerCAmelCase      )) + [1]
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = None      ):
  									if token_ids_a is None:
  											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  									else:
  											return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]
  
  
  							def     _A (self   ,       lowerCAmelCase      ):
  									__lowercase=    [chr(lowerCAmelCase      ) for i in text.encode('utf-8'      )]
  									return tokens
  
  
  							def     _A (self   ,       lowerCAmelCase      ):
  									if token in self.special_tokens_encoder:
  											__lowercase=    self.special_tokens_encoder[token]
  									elif token in self.added_tokens_encoder:
  											__lowercase=    self.added_tokens_encoder[token]
  									elif len(lowerCAmelCase      ) != 1:
  											__lowercase=    self.unk_token_id
  									else:
  											__lowercase=    ord(lowerCAmelCase      ) + self._num_special_tokens
  									return token_id
  
  
  							def     _A (self   ,       lowerCAmelCase      ):
  									if index in self.special_tokens_decoder:
  											__lowercase=    self.special_tokens_decoder[index]
  									elif index in self.added_tokens_decoder:
  											__lowercase=    self.added_tokens_decoder[index]
  									else:
  											__lowercase=    chr(index - self._num_special_tokens      )
  									return token
  
  
  							def     _A (self   ,       lowerCAmelCase      ):
  									__lowercase=    B''
  									for token in tokens:
  											if token in self.special_tokens_decoder:
  													__lowercase=    self.special_tokens_decoder[token].encode('utf-8'      )
  											elif token in self.added_tokens_decoder:
  													__lowercase=    self.special_tokens_decoder[token].encode('utf-8'      )
  											elif token in self.special_tokens_encoder:
  													__lowercase=    token.encode('utf-8'      )
  											elif token in self.added_tokens_encoder:
  													__lowercase=    token.encode('utf-8'      )
  											else:
  													__lowercase=    bytes([ord(lowerCAmelCase      )]      )
  											bstring += tok_string
  									__lowercase=    bstring.decode('utf-8'   ,       errors='replace'      )
  									return string
  
  
  
  
  
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = None      ):
  									return ()
  
  
  
  
  
  
  