code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import unittest
  from functools import partial
  
  import torch
  
  from accelerate import Accelerator, debug_launcher
  from accelerate.state import AcceleratorState, GradientState
  from accelerate.test_utils import require_cpu, require_huggingface_suite
  from accelerate.utils import GradientAccumulationPlugin
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase=2      ,			lowercase=True      ,			lowercase=False			)      ->      Optional[Any]:
  			__lowerCAmelCase      		=       Accelerator(step_scheduler_with_optimizer=lowercase      ,			split_batches=lowercase			)
  			__lowerCAmelCase      		=       torch.nn.Linear(2      ,			4			)
  			__lowerCAmelCase      		=       torch.optim.AdamW(model.parameters()      ,			lr=1.0			)
  			__lowerCAmelCase      		=       torch.optim.lr_scheduler.OneCycleLR(lowercase      ,			max_lr=0.01      ,			steps_per_epoch=2      ,			epochs=1			)
  			__lowerCAmelCase		, __lowerCAmelCase		, __lowerCAmelCase      		=       accelerator.prepare(lowercase      ,			lowercase      ,			lowercase			)
  
  			# Optimizer has stepped
  			scheduler.step()
  			if step_scheduler_with_optimizer or (num_processes == 1):
  						assert (
  						    scheduler.scheduler.last_epoch == num_processes
  						), f'Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'
  			else:
  						assert (
  						    scheduler.scheduler.last_epoch != num_processes
  						), f'Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase=2      ,			lowercase=True      ,			lowercase=False			)      ->      Union[str, Any]:
  			__lowerCAmelCase      		=       Accelerator(step_scheduler_with_optimizer=lowercase      ,			split_batches=lowercase			)
  			__lowerCAmelCase      		=       torch.nn.Linear(2      ,			4			)
  			__lowerCAmelCase      		=       torch.optim.AdamW(model.parameters()      ,			lr=1.0			)
  			__lowerCAmelCase      		=       torch.optim.lr_scheduler.LambdaLR(lowercase      ,			lr_lambda=lambda lowercase			: 1 - n / 10			)
  			__lowerCAmelCase		, __lowerCAmelCase		, __lowerCAmelCase      		=       accelerator.prepare(lowercase      ,			lowercase      ,			lowercase			)
  
  			# Optimizer has stepped
  			__lowerCAmelCase      		=       False
  			scheduler.step()
  			__lowerCAmelCase      		=       1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
  			assert (
  			    scheduler.get_last_lr()[0] == expected_lr
  			), f'Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'
  
  			# Optimizer has not stepped
  			__lowerCAmelCase      		=       True
  			scheduler.step()
  			if not step_scheduler_with_optimizer:
  						__lowerCAmelCase      		=       1 - 2 / 10
  			assert (
  			    scheduler.get_last_lr()[0] == expected_lr
  			), f'Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase = 2			)      ->      Tuple:
  			from transformers import get_linear_schedule_with_warmup
  
  			__lowerCAmelCase      		=       [1, 2, 4]
  			for num_steps in steps:
  						__lowerCAmelCase      		=       GradientAccumulationPlugin(num_steps=lowercase      ,			adjust_scheduler=num_steps > 1			)
  						__lowerCAmelCase      		=       Accelerator(gradient_accumulation_plugin=lowercase			)
  						__lowerCAmelCase      		=       torch.nn.Linear(2      ,			4			)
  						__lowerCAmelCase      		=       torch.optim.AdamW(model.parameters()      ,			lr=10.0			)
  						__lowerCAmelCase      		=       get_linear_schedule_with_warmup(optimizer=lowercase      ,			num_warmup_steps=0      ,			num_training_steps=20			)
  
  						__lowerCAmelCase		, __lowerCAmelCase		, __lowerCAmelCase      		=       accelerator.prepare(lowercase      ,			lowercase      ,			lowercase			)
  
  						for i in range(10 * num_steps			):
  									with accelerator.accumulate(lowercase			):
  												optimizer.step()
  												scheduler.step()
  
  									if i == (10 * num_steps - 2):
  												assert (
  												    scheduler.get_last_lr()[0] != 0
  												), f'Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'
  						assert (
  						    scheduler.get_last_lr()[0] == 0
  						), f'Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'
  						GradientState._reset_state()
  
  
  
  
  
  
  
  @require_cpu
  class 					_UpperCAmelCase							(		unittest.TestCase     ):
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					debug_launcher(partial(__SCREAMING_SNAKE_CASE,num_processes=1    ),num_processes=1    )
  					debug_launcher(partial(__SCREAMING_SNAKE_CASE,num_processes=1,split_batches=__SCREAMING_SNAKE_CASE    ),num_processes=1    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					debug_launcher(partial(__SCREAMING_SNAKE_CASE,num_processes=1    ),num_processes=1    )
  					debug_launcher(partial(__SCREAMING_SNAKE_CASE,num_processes=1,split_batches=__SCREAMING_SNAKE_CASE    ),num_processes=1    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					debug_launcher(partial(__SCREAMING_SNAKE_CASE,num_processes=1,step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE    ),num_processes=1    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					debug_launcher(partial(__SCREAMING_SNAKE_CASE,num_processes=1,step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE    ),num_processes=1    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE    )
  					debug_launcher(__SCREAMING_SNAKE_CASE    )
  					debug_launcher(partial(__SCREAMING_SNAKE_CASE,num_processes=1,split_batches=__SCREAMING_SNAKE_CASE    ),num_processes=1    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE    )
  					debug_launcher(__SCREAMING_SNAKE_CASE    )
  					debug_launcher(partial(__SCREAMING_SNAKE_CASE,num_processes=1,split_batches=__SCREAMING_SNAKE_CASE    ),num_processes=1    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE    )
  					debug_launcher(partial(__SCREAMING_SNAKE_CASE,step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE    )    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE    )
  					debug_launcher(partial(__SCREAMING_SNAKE_CASE,step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE    )    )
  
  
  
  
  
  
  
  		@require_huggingface_suite
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE    )
  					debug_launcher(partial(__SCREAMING_SNAKE_CASE,num_processes=1    )    )
  					debug_launcher(__SCREAMING_SNAKE_CASE    )
  
  
style_context: 
  
  
  
  
  
  '''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
  
  
  _a      :				Optional[Any]											=					torch.cuda.is_available()
  _a      :				Dict											=					[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
  _a      :				str											=					["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
  _a      :				Optional[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""": 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			)      ->      Any:
  			__lowerCAmelCase      		=       """\n""".join(lowercase			)
  			Path(lowercase			).open("""w"""			).writelines(lowercase			)
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase			)      ->      Optional[int]:
  			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 					_UpperCAmelCase							(		lowerCAmelCase_     ):
  		@classmethod
  		def 						lowerCamelCase__    (						cls    ):
  
  
  
  					'''simple docstring'''
  
  					return cls
  		@require_torch_multi_gpu
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__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 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=True    ):
  
  
  
  					'''simple docstring'''
  
  					__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"""],0.01    )
  					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    )
  
  