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__    :       Tuple						=			torch.cuda.is_available()
  lowercase__    :       Union[str, Any]						=			[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
  lowercase__    :       List[Any]						=			['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
  lowercase__    :       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-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      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Union[str, Any]:
  							lowerCAmelCase				=	'''\n'''.join(snake_case__							)
  							Path(snake_case__							).open('''w'''							).writelines(snake_case__							)
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			List[str]:
  							for split in ["train", "val", "test"]:
  														_dump_articles(os.path.join(snake_case__     ,						f"{split}.source"							)     ,						snake_case__							)
  														_dump_articles(os.path.join(snake_case__     ,						f"{split}.target"							)     ,						snake_case__							)
  							return tmp_dir
  
  
  
  
  
  
  class      lowercase_						(						UpperCamelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	@classmethod
  	def     SCREAMING_SNAKE_CASE_       (							cls       )		->List[str]:
  								return cls
  
  
  	@require_torch_multi_gpu
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->str:
  								lowerCAmelCase				=	{
  								    '''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     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=True       )		->str:
  								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(__SCREAMING_SNAKE_CASE       )
  								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=__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				=	[x for x in (convert(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ) for k, v in args_d.items()) if len(__SCREAMING_SNAKE_CASE       )]
  								lowerCAmelCase				=	[sys.executable, F"{self.test_file_dir}/distillation.py"] + cli_args
  								execute_subprocess_async(__SCREAMING_SNAKE_CASE	,    env=self.get_env()       )
  
  								lowerCAmelCase				=	os.listdir(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	{os.path.basename(__SCREAMING_SNAKE_CASE       ) for p in contents}
  								lowerCAmelCase				=	[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				=	os.path.join(__SCREAMING_SNAKE_CASE	,    '''metrics.json'''       )
  								lowerCAmelCase				=	'''rouge2'''
  
  								lowerCAmelCase				=	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				=	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}"]	,    __SCREAMING_SNAKE_CASE       )
  								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''']       )	,    __SCREAMING_SNAKE_CASE       )
  
  
  
  
style_context: from typing import Callable, Optional, Union
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowercase__    :       Optional[Any]						=			logging.get_logger(__name__)
  
  lowercase__    :       Union[str, Any]						=			{
      '''microsoft/prophetnet-large-uncased''': (
          '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
      ),
  }
  
  
  
  
  class      lowercase_						(						UpperCamelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      Optional[int]     =       """prophetnet"""
  	UpperCAmelCase_						:      Optional[int]     =       ["""past_key_values"""]
  	UpperCAmelCase_						:      Any     =       {
  	    """num_attention_heads""": """num_encoder_attention_heads""",
  	}
  
  
  	def __init__(							self	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = "gelu"	,    __SCREAMING_SNAKE_CASE = 30522	,    __SCREAMING_SNAKE_CASE = 1024	,    __SCREAMING_SNAKE_CASE = 4096	,    __SCREAMING_SNAKE_CASE = 12	,    __SCREAMING_SNAKE_CASE = 16	,    __SCREAMING_SNAKE_CASE = 4096	,    __SCREAMING_SNAKE_CASE = 12	,    __SCREAMING_SNAKE_CASE = 16	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = 512	,    __SCREAMING_SNAKE_CASE = 0.0_2	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = 0	,    __SCREAMING_SNAKE_CASE = 2	,    __SCREAMING_SNAKE_CASE = 32	,    __SCREAMING_SNAKE_CASE = 128	,    __SCREAMING_SNAKE_CASE = False	,    __SCREAMING_SNAKE_CASE = 0.0	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = 0	,    __SCREAMING_SNAKE_CASE = 1	,    __SCREAMING_SNAKE_CASE = 2	,    **__SCREAMING_SNAKE_CASE	,    )		->int:
  								lowerCAmelCase				=	vocab_size
  								lowerCAmelCase				=	hidden_size
  								lowerCAmelCase				=	encoder_ffn_dim
  								lowerCAmelCase				=	num_encoder_layers
  								lowerCAmelCase				=	num_encoder_attention_heads
  								lowerCAmelCase				=	decoder_ffn_dim
  								lowerCAmelCase				=	num_decoder_layers
  								lowerCAmelCase				=	num_decoder_attention_heads
  								lowerCAmelCase				=	max_position_embeddings
  								lowerCAmelCase				=	init_std  # Normal(0, this parameter)
  								lowerCAmelCase				=	activation_function
  
  								# parameters for prophetnet
  								lowerCAmelCase				=	ngram
  								lowerCAmelCase				=	num_buckets
  								lowerCAmelCase				=	relative_max_distance
  								lowerCAmelCase				=	disable_ngram_loss
  								lowerCAmelCase				=	eps
  
  								# 3 Types of Dropout
  								lowerCAmelCase				=	attention_dropout
  								lowerCAmelCase				=	activation_dropout
  								lowerCAmelCase				=	dropout
  
  								lowerCAmelCase				=	use_cache
  
  								super().__init__(
  								    pad_token_id=__SCREAMING_SNAKE_CASE	,    bos_token_id=__SCREAMING_SNAKE_CASE	,    eos_token_id=__SCREAMING_SNAKE_CASE	,    is_encoder_decoder=__SCREAMING_SNAKE_CASE	,    add_cross_attention=__SCREAMING_SNAKE_CASE	,    decoder_start_token_id=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE	,    )
  
  
  	@property
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->int:
  								return self.num_encoder_layers + self.num_decoder_layers
  
  
  
  
  	@num_hidden_layers.setter
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
  								raise NotImplementedError(
  								    '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
  								    ''' `num_decoder_layers`.'''       )
  
  
  
  