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
  
  
  SCREAMING_SNAKE_CASE__		:       Dict   							=							torch.cuda.is_available()
  SCREAMING_SNAKE_CASE__		:       Optional[Any]   							=							[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
  SCREAMING_SNAKE_CASE__		:       Union[str, Any]   							=							["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
  SCREAMING_SNAKE_CASE__		:       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    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[Any]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Optional[Any]										= """\n""".join(__lowerCamelCase					)
       Path(__lowerCamelCase					).open("""w"""					).writelines(__lowerCamelCase					)
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> Dict:
  
       '''simple docstring'''
       for split in ["train", "val", "test"]:
            _dump_articles(os.path.join(__lowerCamelCase   ,       F"{split}.source"					)   ,       __lowerCamelCase					)
            _dump_articles(os.path.join(__lowerCamelCase   ,       F"{split}.target"					)   ,       __lowerCamelCase					)
       return tmp_dir
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     (		__lowerCamelCase		):
  
  
  
  
       @classmethod
       def      __UpperCAmelCase     (							cls		):
            return cls
  
  
  
  
       @require_torch_multi_gpu
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Any										= {
                """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      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase=True		):
            UpperCAmelCase__			:     Optional[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(_lowerCAmelCase		)
            UpperCAmelCase__			:     dict										= CHEAP_ARGS.copy()
            UpperCAmelCase__			:     List[str]										= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
            UpperCAmelCase__			:     int										= 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}"
  
            UpperCAmelCase__			:     Optional[int]										= [x for x in (convert(_lowerCAmelCase  ,	_lowerCAmelCase		) for k, v in args_d.items()) if len(_lowerCAmelCase		)]
            UpperCAmelCase__			:     Optional[Any]										= [sys.executable, f"{self.test_file_dir}/distillation.py"] + cli_args
            execute_subprocess_async(_lowerCAmelCase  ,	env=self.get_env()		)
  
            UpperCAmelCase__			:     Optional[int]										= os.listdir(_lowerCAmelCase		)
            UpperCAmelCase__			:     Dict										= {os.path.basename(_lowerCAmelCase		) for p in contents}
            UpperCAmelCase__			:     str										= [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)
            UpperCAmelCase__			:     Any										= os.path.join(_lowerCAmelCase  ,	"""metrics.json"""		)
            UpperCAmelCase__			:     Optional[int]										= """rouge2"""
  
            UpperCAmelCase__			:     int										= 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		)
            UpperCAmelCase__			:     Dict										= 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}"]  ,	_lowerCAmelCase		)
            self.assertEqual(len(metrics["""test"""]		)  ,	1		)
            UpperCAmelCase__			:     Optional[int]										= int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1		)
            self.assertEqual(len(metrics["""val"""]		)  ,	_lowerCAmelCase		)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import collections
  
  from .utils import ExplicitEnum, is_torch_available, logging
  
  
  if is_torch_available():
        import torch
  
  
  SCREAMING_SNAKE_CASE__		:       Any   							=							logging.get_logger(__name__)
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     :
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase=21  ,	_lowerCAmelCase=[]  ,	_lowerCAmelCase=None		):
            UpperCAmelCase__			:     Union[str, Any]										= model
            UpperCAmelCase__			:     Union[str, Any]										= trace_batch_nums
            UpperCAmelCase__			:     Optional[Any]										= abort_after_batch_num
  
            # keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
            UpperCAmelCase__			:     int										= collections.deque([]  ,	_lowerCAmelCase		)
            UpperCAmelCase__			:     str										= []
            UpperCAmelCase__			:     Optional[int]										= 0
            UpperCAmelCase__			:     int										= 0
            UpperCAmelCase__			:     int										= False
            UpperCAmelCase__			:     List[str]										= """                 """
  
            self.analyse_model()
  
            self.register_forward_hook()
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase=None		):
            if frame is not None:
                 self.expand_frame(_lowerCAmelCase		)
            self.frames.append("""\n""".join(self.frame		)		)
            UpperCAmelCase__			:     str										= []  # start a new frame
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
            self.frame.append(_lowerCAmelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            print("""\n""".join(self.frames		)		)
            UpperCAmelCase__			:     List[Any]										= []
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Optional[int]										= []
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            print(f"\nDetected inf/nan during batch_number={self.batch_number}"		)
            print(f"Last {len(self.frames		)} forward frames:"		)
            print(f"{'abs min':8} {'abs max':8} metadata"		)
            print("""\n""".join(self.frames		)		)
            print("""\n\n"""		)
            UpperCAmelCase__			:     Any										= []
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            # extract the fully qualified module names, to be able to report at run time. e.g.:
            # encoder.block.2.layer.0.SelfAttention.o
            #
            # for shared weights only the first shared module name will be registered
            UpperCAmelCase__			:     str										= {m: name for name, m in self.model.named_modules()}
            # self.longest_module_name = max(len(v) for v in self.module_names.values())
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            if torch.is_tensor(_lowerCAmelCase		):
                 self.expand_frame(get_abs_min_max(_lowerCAmelCase  ,	_lowerCAmelCase		)		)
                 if detect_overflow(_lowerCAmelCase  ,	_lowerCAmelCase		):
                      UpperCAmelCase__			:     Optional[int]										= True
            elif var is None:
                 self.expand_frame(f"{'None':>17} {ctx}"		)
            else:
                 self.expand_frame(f"{'not a tensor':>17} {ctx}"		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            self.expand_frame(f"\n\n{self.prefix} *** Starting batch number={self.batch_number} ***"		)
            self.expand_frame(f"{'abs min':8} {'abs max':8} metadata"		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            self.expand_frame(f"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            self.expand_frame(f"{self.prefix} {self.module_names[module]} {module.__class__.__name__}"		)
  
            # params
            for name, p in module.named_parameters(recurse=_lowerCAmelCase		):
                 self.analyse_variable(_lowerCAmelCase  ,	_lowerCAmelCase		)
  
            # inputs
            if isinstance(_lowerCAmelCase  ,	_lowerCAmelCase		):
                 for i, x in enumerate(_lowerCAmelCase		):
                      self.analyse_variable(_lowerCAmelCase  ,	f"input[{i}]"		)
            else:
                 self.analyse_variable(_lowerCAmelCase  ,	"""input"""		)
  
            # outputs
            if isinstance(_lowerCAmelCase  ,	_lowerCAmelCase		):
                 for i, x in enumerate(_lowerCAmelCase		):
                      # possibly a tuple of tuples
                      if isinstance(_lowerCAmelCase  ,	_lowerCAmelCase		):
                           for j, y in enumerate(_lowerCAmelCase		):
                                self.analyse_variable(_lowerCAmelCase  ,	f"output[{i}][{j}]"		)
                      else:
                           self.analyse_variable(_lowerCAmelCase  ,	f"output[{i}]"		)
            else:
                 self.analyse_variable(_lowerCAmelCase  ,	"""output"""		)
  
            self.save_frame()
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            self.model.apply(self._register_forward_hook		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
            module.register_forward_hook(self.forward_hook		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            # - input is a tuple of packed inputs (could be non-Tensors)
            # - output could be a Tensor or a tuple of Tensors and non-Tensors
  
            UpperCAmelCase__			:     Union[str, Any]										= False
  
            UpperCAmelCase__			:     List[str]										= True if self.batch_number in self.trace_batch_nums else False
            if trace_mode:
                 self.reset_saved_frames()
  
            if self.total_calls == 0:
                 self.batch_start_frame()
            self.total_calls += 1
  
            # count batch numbers - the very first forward hook of the batch will be called when the
            # batch completes - i.e. it gets called very last - we know this batch has finished
            if module == self.model:
                 self.batch_number += 1
                 UpperCAmelCase__			:     Dict										= True
  
            self.create_frame(_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)
  
            # if last_frame_of_batch:
            #     self.batch_end_frame()
  
            if trace_mode:
                 self.trace_frames()
  
            if last_frame_of_batch:
                 self.batch_start_frame()
  
            if self.detected_overflow and not trace_mode:
                 self.dump_saved_frames()
  
                 # now we can abort, as it's pointless to continue running
                 raise ValueError(
                     """DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. """
                     """Please scroll up above this traceback to see the activation values prior to this event."""		)
  
            # abort after certain batch if requested to do so
            if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
                 raise ValueError(
                     f"DebugUnderflowOverflow: aborting after {self.batch_number} batches due to"
                     f" `abort_after_batch_num={self.abort_after_batch_num}` arg"		)
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[Any]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Dict										= var.abs()
       return F"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> int:
  
       '''simple docstring'''
       UpperCAmelCase__			:     str										= False
       if torch.isnan(__lowerCamelCase					).any().item():
            UpperCAmelCase__			:     List[Any]										= True
            print(F"{ctx} has nans"					)
       if torch.isinf(__lowerCamelCase					).any().item():
            UpperCAmelCase__			:     List[str]										= True
            print(F"{ctx} has infs"					)
  
       # if needed to monitor large elements can enable the following
       if 0:  # and detected:
            UpperCAmelCase__			:     Dict										= var[torch.ge(var.abs()   ,       100					)]
            if naaa.numel() > 0:
                 print(F"{ctx}:  n100={naaa.numel()}"					)
            UpperCAmelCase__			:     Tuple										= var[torch.ge(var.abs()   ,       1000					)]
            if naaaa.numel() > 0:
                 print(F"{ctx}: n1000={naaaa.numel()}"					)
            UpperCAmelCase__			:     Optional[int]										= var[torch.ge(var.abs()   ,       1_0000					)]
            if naaaaa.numel() > 0:
                 print(F"{ctx}: n10000={naaaaa.numel()}"					)
  
       if 0:
            print(F"min={var.min():9.2e} max={var.max():9.2e}"					)
  
       if 0:
            print(F"min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})"					)
  
       return detected
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     (		__lowerCamelCase		):
       __lowerCamelCase										=     'underflow_overflow'
       __lowerCamelCase										=     'tpu_metrics_debug'
  
  
  
  
  
  
  