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
  
  
  __a     :   Optional[int]   				=      torch.cuda.is_available()
  __a     :   Tuple   				=      [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
  __a     :   Any   				=      ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
  __a     :   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''': 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 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->Dict:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Union[str, Any]					   =		'\n'.join(_UpperCamelCase     )
        Path(_UpperCamelCase     ).open('''w'''     ).writelines(_UpperCamelCase     )
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->List[Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        for split in ["train", "val", "test"]:
              _dump_articles(os.path.join(_UpperCamelCase,    f"""{split}.source"""     ),    _UpperCamelCase     )
              _dump_articles(os.path.join(_UpperCamelCase,    f"""{split}.target"""     ),    _UpperCamelCase     )
        return tmp_dir
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   (			lowercase__   ):
  
  
        @classmethod
        def        __lowerCamelCase			(							cls		):
              return cls
  
  
        @require_torch_multi_gpu
        def        __lowerCamelCase			(							self		):
              lowercase			:   Optional[int]					   =		{
                  '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							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__=True		):
              lowercase			:   Tuple					   =		{
                  '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		)
              lowercase			:   dict					   =		CHEAP_ARGS.copy()
              lowercase			:   Tuple					   =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
              lowercase			:   Optional[int]					   =		self.get_auto_remove_tmp_dir()
              args_d.update(data_dir=_A							,						output_dir=_A							,						**_A		)
  
              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}"""
  
              lowercase			:   List[Any]					   =		[x for x in (convert(_A							,						_A		) for k, v in args_d.items()) if len(_A		)]
              lowercase			:   Union[str, Any]					   =		[sys.executable, f"""{self.test_file_dir}/distillation.py"""] + cli_args
              execute_subprocess_async(_A							,						env=self.get_env()		)
  
              lowercase			:   Optional[Any]					   =		os.listdir(_A		)
              lowercase			:   Tuple					   =		{os.path.basename(_A		) for p in contents}
              lowercase			:   Union[str, Any]					   =		[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)
              lowercase			:   str					   =		os.path.join(_A							,						'''metrics.json'''		)
              lowercase			:   int					   =		'rouge2'
  
              lowercase			:   Optional[Any]					   =		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		)
              lowercase			:   Any					   =		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		)
              lowercase			:   str					   =		int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1		)
              self.assertEqual(len(metrics['''val''']		)							,						_A		)
  
  
style_context: 
  
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      is_torch_available,
      is_transformers_available,
      is_transformers_version,
  )
  
  
  try:
    if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.27.0''')):
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import (
        AudioLDMPipeline,
    )
  else:
    from .pipeline_audioldm import AudioLDMPipeline
  
  